From 8090374882f91cedbd41e1962a3dff043b54ddf7 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sat, 20 Apr 2024 16:37:59 -0400 Subject: [PATCH 001/112] Add epsilon effective calculion --- desc/compute/_field.py | 4 +- desc/compute/_stability.py | 78 ++++++++++++++++++++++++++++- desc/compute/bounce_integral.py | 21 ++++++++ devtools/dev-requirements_conda.yml | 1 + requirements.txt | 1 + requirements_conda.yml | 1 + 6 files changed, 103 insertions(+), 3 deletions(-) create mode 100644 desc/compute/bounce_integral.py diff --git a/desc/compute/_field.py b/desc/compute/_field.py index 6285fd9c92..a3ff96f586 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -3312,7 +3312,7 @@ def _kappa(params, transforms, profiles, data, **kwargs): label="\\kappa_n", units="m^{-1}", units_long="Inverse meters", - description="Normal curvature vector of magnetic field lines", + description="Normal curvature of magnetic field lines", dim=1, params=[], transforms={}, @@ -3330,7 +3330,7 @@ def _kappa_n(params, transforms, profiles, data, **kwargs): label="\\kappa_g", units="m^{-1}", units_long="Inverse meters", - description="Geodesic curvature vector of magnetic field lines", + description="Geodesic curvature of magnetic field lines", dim=1, params=[], transforms={}, diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index ea93734248..fa329838d1 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -1,4 +1,4 @@ -"""Compute functions for Mercier stability objectives. +"""Compute functions for stability objectives. Notes ----- @@ -9,10 +9,17 @@ expensive computations. """ +from orthax import legendre from scipy.constants import mu_0 from desc.backend import jnp +from .bounce_integral import ( + affine_bijection_reverse, + bounce_integral_map, + grad_affine_bijection_reverse, + pitch_of_extrema, +) from .data_index import register_compute_fun from .utils import dot, surface_integrals_map @@ -228,3 +235,72 @@ def _magnetic_well(params, transforms, profiles, data, **kwargs): 0, # coefficient of limit is V_r / V_rr, rest is finite ) return data + + +@register_compute_fun( + name="effective ripple", + label="\\epsilon_{\\text{eff}}", + units="~", + units_long="None", + description="Effective ripple modulation amplitude", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["rho", "|grad(psi)|", "kappa_g", "sqrt(g)", "psi_r", "S(r)", "R"], +) +def _effective_ripple(params, transforms, profiles, data, **kwargs): + # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + # Evaluation of 1/ν neoclassical transport in stellarators. + # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + # https://doi.org/10.1063/1.873749. + raise NotImplementedError("Effective ripple requires GitHub PR #854.") + + rho = transforms["grid"].compress(data["rho"]) + # TODO: Choose grid with a better API. + # Should be handled when resolving GitHub issue #719. + x, w = legendre.leggauss(transforms["grid"].num_theta) + alpha_max = (2 - transforms["grid"].sym) * jnp.pi + alpha = affine_bijection_reverse(x, 0, alpha_max) + w = w * grad_affine_bijection_reverse(0, alpha_max) + knots = jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta) + bounce_integral, items = bounce_integral_map(rho, alpha, knots) + + def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): + return ( + pitch + * jnp.sqrt(1 / pitch - B) + * (4 / B - pitch) + * grad_psi_norm + * kappa_g + / B + ) + + def integrand_I(B, pitch, Z): + return jnp.sqrt(1 - pitch * B) / B + + # TODO: Intersperse linearly spaced pitch between each 1/pitch for trapezoidal + # integration within each class of particles (i.e. between local min and max). + pitch = pitch_of_extrema(knots, items["B.c"], items["B_z_ra.c"]) + H = bounce_integral(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) + I = bounce_integral(integrand_I, [], pitch) + H = H.reshape(H.shape[0], rho.size, alpha.size, -1) + I = I.reshape(I.shape[0], rho.size, alpha.size, -1) + pitch = pitch.reshape(-1, rho.size, alpha.size) + + # TODO: Simple fix, composite trapezoidal integration between each + # 1/pitch value in pitch. + db = -jnp.diff(1 / pitch, prepend=0, axis=0) + E = jnp.sum(db * jnp.nansum(H**2 / I, axis=-1), axis=0) + assert E.shape == (rho.size, alpha.size) + # E = ∫ db ∑ⱼ Hⱼ² / Iⱼ + E = jnp.dot(E, w) + + surface_integrate = surface_integrals_map(transforms["grid"], expand_out=False) + s1 = surface_integrate(data["sqrt(g)"] / data["psi_r"]) + s2 = transforms["grid"].compress(data["S(r)"]) + # Nemov et al., equation 29. + epsilon = (jnp.pi * data["R"] ** 2 / (8 * 2**0.5) * s1 / s2**2 * E) ** (2 / 3) + data["effective ripple"] = transforms["grid"].expand(epsilon) + return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py new file mode 100644 index 0000000000..abc260c0b6 --- /dev/null +++ b/desc/compute/bounce_integral.py @@ -0,0 +1,21 @@ +""".""" + + +def affine_bijection_reverse(): + """.""" + pass + + +def grad_affine_bijection_reverse(): + """.""" + pass + + +def bounce_integral_map(): + """.""" + pass + + +def pitch_of_extrema(): + """.""" + pass diff --git a/devtools/dev-requirements_conda.yml b/devtools/dev-requirements_conda.yml index 331ee79904..5523a047b3 100644 --- a/devtools/dev-requirements_conda.yml +++ b/devtools/dev-requirements_conda.yml @@ -16,6 +16,7 @@ dependencies: - interpax - jax[cpu] >= 0.3.2, < 0.5.0 - nvgpu + - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 # testing and benchmarking diff --git a/requirements.txt b/requirements.txt index a667a2a2db..4844fbeb82 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ mpmath >= 1.0.0, < 2.0 netcdf4 >= 1.5.4, < 2.0 numpy >= 1.20.0, < 2.0.0 nvgpu +orthax plotly >= 5.16, < 6.0 psutil pylatexenc >= 2.0, < 3.0 diff --git a/requirements_conda.yml b/requirements_conda.yml index e458f03c9f..2551d7e2a1 100644 --- a/requirements_conda.yml +++ b/requirements_conda.yml @@ -15,5 +15,6 @@ dependencies: - interpax - jax[cpu] >= 0.3.2, < 0.5.0 - nvgpu + - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 From 6ac22519b1fddbe5d1ebf1fdce78555f10362d28 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 21 Apr 2024 02:09:58 -0400 Subject: [PATCH 002/112] Add trapezoidal integration to compute db integral --- desc/backend.py | 16 ++++++++++++++ desc/compute/_stability.py | 39 ++++++++++++++++----------------- desc/compute/bounce_integral.py | 13 +++++++---- 3 files changed, 44 insertions(+), 24 deletions(-) diff --git a/desc/backend.py b/desc/backend.py index 721920190c..65fdc6c741 100644 --- a/desc/backend.py +++ b/desc/backend.py @@ -368,6 +368,14 @@ def tangent_solve(g, y): ) return x, (jnp.linalg.norm(res), niter) + def trapezoid(y, x=None, dx=1.0, axis=-1): + """Integrate along the given axis using the composite trapezoidal rule.""" + if hasattr(jnp, "trapezoid"): + # https://github.com/google/jax/issues/20410 + return jnp.trapezoid(y, x, dx, axis) + else: + return jax.scipy.integrate.trapezoid(y, x, dx, axis) + # we can't really test the numpy backend stuff in automated testing, so we ignore it # for coverage purposes @@ -739,3 +747,11 @@ def root( """ out = scipy.optimize.root(fun, x0, args, jac=jac, tol=tol) return out.x, out + + def trapezoid(y, x=None, dx=1.0, axis=-1): + """Integrate along the given axis using the composite trapezoidal rule.""" + if hasattr(np, "trapezoid"): + # https://github.com/numpy/numpy/issues/25586 + return np.trapezoid(y, x, dx, axis) + else: + return np.trapz(y, x, dx, axis) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index fa329838d1..808f1127d9 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -12,13 +12,14 @@ from orthax import legendre from scipy.constants import mu_0 -from desc.backend import jnp +from desc.backend import jnp, trapezoid from .bounce_integral import ( affine_bijection_reverse, bounce_integral_map, grad_affine_bijection_reverse, pitch_of_extrema, + pitch_trapz, ) from .data_index import register_compute_fun from .utils import dot, surface_integrals_map @@ -257,15 +258,18 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): # https://doi.org/10.1063/1.873749. raise NotImplementedError("Effective ripple requires GitHub PR #854.") - rho = transforms["grid"].compress(data["rho"]) - # TODO: Choose grid with a better API. - # Should be handled when resolving GitHub issue #719. - x, w = legendre.leggauss(transforms["grid"].num_theta) + kwargs.setdefault( + "knots", + jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta), + ) alpha_max = (2 - transforms["grid"].sym) * jnp.pi - alpha = affine_bijection_reverse(x, 0, alpha_max) - w = w * grad_affine_bijection_reverse(0, alpha_max) - knots = jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta) - bounce_integral, items = bounce_integral_map(rho, alpha, knots) + alpha, alpha_weight = legendre.leggauss( + kwargs.get("num_alpha", transforms["grid"].num_theta) + ) + alpha = affine_bijection_reverse(alpha, 0, alpha_max) + alpha_weight = alpha_weight * grad_affine_bijection_reverse(0, alpha_max) + rho = transforms["grid"].compress(data["rho"]) + bounce_integral, items = bounce_integral_map(rho, alpha, kwargs["knots"]) def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): return ( @@ -280,27 +284,22 @@ def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): def integrand_I(B, pitch, Z): return jnp.sqrt(1 - pitch * B) / B - # TODO: Intersperse linearly spaced pitch between each 1/pitch for trapezoidal - # integration within each class of particles (i.e. between local min and max). - pitch = pitch_of_extrema(knots, items["B.c"], items["B_z_ra.c"]) + pitch = pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) + pitch = pitch_trapz(pitch, kwargs.get("pitch quad resolution", 20)) H = bounce_integral(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) I = bounce_integral(integrand_I, [], pitch) H = H.reshape(H.shape[0], rho.size, alpha.size, -1) I = I.reshape(I.shape[0], rho.size, alpha.size, -1) pitch = pitch.reshape(-1, rho.size, alpha.size) - # TODO: Simple fix, composite trapezoidal integration between each - # 1/pitch value in pitch. - db = -jnp.diff(1 / pitch, prepend=0, axis=0) - E = jnp.sum(db * jnp.nansum(H**2 / I, axis=-1), axis=0) - assert E.shape == (rho.size, alpha.size) + # E = ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across the axis enumerating alpha. + E = jnp.nansum(H**2 / I, axis=-1) # E = ∫ db ∑ⱼ Hⱼ² / Iⱼ - E = jnp.dot(E, w) - + E = jnp.dot(trapezoid(E, 1 / pitch, axis=0), alpha_weight) surface_integrate = surface_integrals_map(transforms["grid"], expand_out=False) s1 = surface_integrate(data["sqrt(g)"] / data["psi_r"]) s2 = transforms["grid"].compress(data["S(r)"]) - # Nemov et al., equation 29. + epsilon = (jnp.pi * data["R"] ** 2 / (8 * 2**0.5) * s1 / s2**2 * E) ** (2 / 3) data["effective ripple"] = transforms["grid"].expand(epsilon) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index abc260c0b6..7811c40c96 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1,21 +1,26 @@ """.""" -def affine_bijection_reverse(): +def affine_bijection_reverse(*args): """.""" pass -def grad_affine_bijection_reverse(): +def grad_affine_bijection_reverse(*args): """.""" pass -def bounce_integral_map(): +def bounce_integral_map(*args): """.""" pass -def pitch_of_extrema(): +def pitch_of_extrema(*args): + """.""" + pass + + +def pitch_trapz(*args): """.""" pass From c02306bd63d7597f1f619ca5f47ba0df20c8b185 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 21 Apr 2024 11:43:20 -0400 Subject: [PATCH 003/112] Prepare for merge with bounce --- desc/compute/bounce_integral.py | 26 -------------------------- 1 file changed, 26 deletions(-) delete mode 100644 desc/compute/bounce_integral.py diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py deleted file mode 100644 index 7811c40c96..0000000000 --- a/desc/compute/bounce_integral.py +++ /dev/null @@ -1,26 +0,0 @@ -""".""" - - -def affine_bijection_reverse(*args): - """.""" - pass - - -def grad_affine_bijection_reverse(*args): - """.""" - pass - - -def bounce_integral_map(*args): - """.""" - pass - - -def pitch_of_extrema(*args): - """.""" - pass - - -def pitch_trapz(*args): - """.""" - pass From f1560831d1a26d3c15f6d2c68c31dc8e2e8a1b49 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 21 Apr 2024 13:05:04 -0400 Subject: [PATCH 004/112] Rewrite epsilon_effective computation --- desc/compute/_stability.py | 95 ++++++++++++++++++++++------- desc/compute/bounce_integral.py | 2 +- devtools/dev-requirements_conda.yml | 1 + requirements.txt | 1 + requirements_conda.yml | 1 + 5 files changed, 78 insertions(+), 22 deletions(-) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index 808f1127d9..e5ee6a0b63 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -10,6 +10,7 @@ """ from orthax import legendre +from quadax import simpson from scipy.constants import mu_0 from desc.backend import jnp, trapezoid @@ -17,9 +18,9 @@ from .bounce_integral import ( affine_bijection_reverse, bounce_integral_map, + composite_linspace, grad_affine_bijection_reverse, pitch_of_extrema, - pitch_trapz, ) from .data_index import register_compute_fun from .utils import dot, surface_integrals_map @@ -249,15 +250,13 @@ def _magnetic_well(params, transforms, profiles, data, **kwargs): transforms={"grid": []}, profiles=[], coordinates="r", - data=["rho", "|grad(psi)|", "kappa_g", "sqrt(g)", "psi_r", "S(r)", "R"], + data=["rho", "|grad(psi)|", "kappa_g", "sqrt(g)", "psi_r", "S(r)", "V_r(r)", "R"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. # Evaluation of 1/ν neoclassical transport in stellarators. # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. # https://doi.org/10.1063/1.873749. - raise NotImplementedError("Effective ripple requires GitHub PR #854.") - kwargs.setdefault( "knots", jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta), @@ -269,7 +268,12 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): alpha = affine_bijection_reverse(alpha, 0, alpha_max) alpha_weight = alpha_weight * grad_affine_bijection_reverse(0, alpha_max) rho = transforms["grid"].compress(data["rho"]) - bounce_integral, items = bounce_integral_map(rho, alpha, kwargs["knots"]) + # TODO: (outside scope of ripple pr) + # bounce_integral_map only needs eq because map_coordinates needs it. + # Maybe modify map coordinates to work with transforms and not eq object? + bounce_integral, items = bounce_integral_map( + kwargs["eq"], rho, alpha, kwargs["knots"] + ) def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): return ( @@ -284,22 +288,71 @@ def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): def integrand_I(B, pitch, Z): return jnp.sqrt(1 - pitch * B) / B - pitch = pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) - pitch = pitch_trapz(pitch, kwargs.get("pitch quad resolution", 20)) - H = bounce_integral(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) - I = bounce_integral(integrand_I, [], pitch) - H = H.reshape(H.shape[0], rho.size, alpha.size, -1) - I = I.reshape(I.shape[0], rho.size, alpha.size, -1) - pitch = pitch.reshape(-1, rho.size, alpha.size) + def ripple_sum(b): + """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. + + Parameters + ---------- + b : Array + Multiplicative inverse of pitch angle. + + Returns + ------- + ripple : Array, shape(..., rho.size, alpha.size) + ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across axis enumerating alpha. + + """ + pitch = 1 / b + H = bounce_integral(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) + I = bounce_integral(integrand_I, [], pitch) + H = H.reshape(H.shape[0], rho.size, alpha.size, -1) + I = I.reshape(I.shape[0], rho.size, alpha.size, -1) + ripple = jnp.nansum(H**2 / I, axis=-1) + return ripple + + def db_integrate(integrand, b_knot, quad): + """Return the ripple sum integral ∫ db ∑ⱼ Hⱼ² / Iⱼ. + + Parameters + ---------- + integrand : callable + Function to integrate. + b_knot : Array + Breakpoints where the quadrature should take more care. + For simple schemes this means to include a quadrature point here. + quad : callable + Quadrature method. + See https://quadax.readthedocs.io/en/latest/index.html. + + Returns + ------- + ripple : Array, shape(rho.size, ) + ∫ db ∑ⱼ Hⱼ² / Iⱼ. + + """ + is_Newton_Cotes = quad == trapezoid or quad == simpson + if is_Newton_Cotes: + b = composite_linspace(b_knot, kwargs.get("db quad resolution", 19)) + ripple_sum = integrand(b) + ripple = quad(ripple_sum, b.reshape(ripple_sum.shape), axis=0) + else: + # Could use adaptive Clenshaw-Curtis quadrature with + # quadax.quadcc(integrand, b_knot). + raise NotImplementedError + return jnp.dot(ripple, alpha_weight) - # E = ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across the axis enumerating alpha. - E = jnp.nansum(H**2 / I, axis=-1) - # E = ∫ db ∑ⱼ Hⱼ² / Iⱼ - E = jnp.dot(trapezoid(E, 1 / pitch, axis=0), alpha_weight) - surface_integrate = surface_integrals_map(transforms["grid"], expand_out=False) - s1 = surface_integrate(data["sqrt(g)"] / data["psi_r"]) - s2 = transforms["grid"].compress(data["S(r)"]) + # TODO: Seems more natural to define pitch = lambda = b from the start... + # TODO: need to add B_max_tz. + b = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) - epsilon = (jnp.pi * data["R"] ** 2 / (8 * 2**0.5) * s1 / s2**2 * E) ** (2 / 3) - data["effective ripple"] = transforms["grid"].expand(epsilon) + ripple = db_integrate(ripple_sum, b, kwargs.get("db quad", trapezoid)) + ripple = transforms["grid"].expand(ripple) + data["effective ripple"] = ( + jnp.pi + * data["R"] ** 2 + / (8 * 2**0.5) + * (data["V_r(r)"] / data["psi_r"]) + / data["S(r)"] ** 2 + * ripple + ) ** (2 / 3) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 11e13eef75..ffad6a9f33 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -639,7 +639,7 @@ def tanh_sinh_quad(resolution, w=lambda x: 1): Parameters ---------- resolution: int - Number of quadrature points. + Number of quadrature points, preferably odd. w : callable Weight function defined, positive, and continuous on (-1, 1). diff --git a/devtools/dev-requirements_conda.yml b/devtools/dev-requirements_conda.yml index 02b784589a..518097d060 100644 --- a/devtools/dev-requirements_conda.yml +++ b/devtools/dev-requirements_conda.yml @@ -22,6 +22,7 @@ dependencies: # testing and benchmarking - qsc - qicna @ git+https://github.com/rogeriojorge/pyQIC/ + - quadax # building the docs - nbsphinx == 0.8.12 diff --git a/requirements.txt b/requirements.txt index 142be71bc4..fe2af881b9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,5 +11,6 @@ orthax plotly >= 5.16, < 6.0 psutil pylatexenc >= 2.0, < 3.0 +quadax scipy >= 1.7.0, < 2.0.0 termcolor diff --git a/requirements_conda.yml b/requirements_conda.yml index f4b9a35442..8c3fddbd7b 100644 --- a/requirements_conda.yml +++ b/requirements_conda.yml @@ -18,3 +18,4 @@ dependencies: - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 + - quadax From de9bc71fc239ff7615645ead4080c102bd54bea5 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 22 Apr 2024 01:05:41 -0400 Subject: [PATCH 005/112] Add max_tz B to b values in integral for effective ripple --- desc/compute/_stability.py | 32 ++++++++++++++++++++++---------- tests/test_stability_funs.py | 9 +++++++++ 2 files changed, 31 insertions(+), 10 deletions(-) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index ee87292276..56ac3018eb 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -16,7 +16,7 @@ from .bounce_integral import ( affine_bijection_reverse, - bounce_integral_map, + bounce_integral, composite_linspace, grad_affine_bijection_reverse, pitch_of_extrema, @@ -249,7 +249,17 @@ def _magnetic_well(params, transforms, profiles, data, **kwargs): transforms={"grid": []}, profiles=[], coordinates="r", - data=["rho", "|grad(psi)|", "kappa_g", "sqrt(g)", "psi_r", "S(r)", "V_r(r)", "R"], + data=[ + "rho", + "|grad(psi)|", + "kappa_g", + "sqrt(g)", + "psi_r", + "S(r)", + "V_r(r)", + "R", + "max_tz |B|", # Could use softmax. + ], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. @@ -267,12 +277,11 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): alpha = affine_bijection_reverse(alpha, 0, alpha_max) alpha_weight = alpha_weight * grad_affine_bijection_reverse(0, alpha_max) rho = transforms["grid"].compress(data["rho"]) + # TODO: (outside scope of ripple pr) # bounce_integral_map only needs eq because map_coordinates needs it. # Maybe modify map coordinates to work with transforms and not eq object? - bounce_integral, items = bounce_integral_map( - kwargs["eq"], rho, alpha, kwargs["knots"] - ) + bounce_integrate, items = bounce_integral(kwargs["eq"], rho, alpha, kwargs["knots"]) def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): return ( @@ -302,8 +311,8 @@ def ripple_sum(b): """ pitch = 1 / b - H = bounce_integral(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) - I = bounce_integral(integrand_I, [], pitch) + H = bounce_integrate(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) + I = bounce_integrate(integrand_I, [], pitch) H = H.reshape(H.shape[0], rho.size, alpha.size, -1) I = I.reshape(I.shape[0], rho.size, alpha.size, -1) ripple = jnp.nansum(H**2 / I, axis=-1) @@ -341,11 +350,14 @@ def db_integrate(integrand, b_knot, quad): raise NotImplementedError return jnp.dot(ripple, alpha_weight) - # TODO: Seems more natural to define pitch = lambda = b from the start... - # TODO: need to add B_max_tz. b = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) - + b = jnp.append( + b.reshape(-1, rho.size, alpha.size), + transforms["grid"].compress(data["max_tz |B|"])[jnp.newaxis, :, jnp.newaxis], + axis=0, + ).reshape(-1, rho.size * alpha.size) ripple = db_integrate(ripple_sum, b, kwargs.get("db quad", trapezoid)) + ripple = transforms["grid"].expand(ripple) data["effective ripple"] = ( jnp.pi diff --git a/tests/test_stability_funs.py b/tests/test_stability_funs.py index 54aa43e278..9adde02495 100644 --- a/tests/test_stability_funs.py +++ b/tests/test_stability_funs.py @@ -7,6 +7,7 @@ import desc.examples import desc.io from desc.equilibrium import Equilibrium +from desc.examples import get from desc.grid import LinearGrid from desc.objectives import MagneticWell, MercierStability @@ -341,3 +342,11 @@ def test_magwell_print(capsys): + "\n" ) assert out.out == corr_out + + +@pytest.mark.unit +def test_effective_ripple(): + """Compare DESC effective ripple against neo stellopt.""" + eq = get("HELIOTRON") + data = eq.compute("effective ripple") + assert np.isfinite(data["effective ripple"]).all() From 14ac6a7c61720c15e45ec42fe0b7a85ad7d0a974 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 22 Apr 2024 03:33:48 -0400 Subject: [PATCH 006/112] Note to use cvdrift0 --- desc/compute/_stability.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index 56ac3018eb..4ca6e627f2 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -289,7 +289,7 @@ def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): * jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm - * kappa_g + * kappa_g # todo: review and use cvdrift0 / B ) From cd5094e43ab8a16a23a066f17d7049b0eb003f1d Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 23 Apr 2024 21:27:23 -0400 Subject: [PATCH 007/112] Fix catostrophic floating point error in epsilon effective computation --- desc/compute/_stability.py | 94 ++++++++++++++++++++------------------ 1 file changed, 49 insertions(+), 45 deletions(-) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index 4ca6e627f2..ae9137d369 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -238,6 +238,23 @@ def _magnetic_well(params, transforms, profiles, data, **kwargs): return data +def _dH(grad_psi_norm, kappa_g, B, pitch, Z): + # used to compute effective ripple + return ( + pitch + * jnp.sqrt(1 / pitch - B) + * (4 / B - pitch) + * grad_psi_norm + * kappa_g # todo: review and use cvdrift0 + / B + ) + + +def _dI(B, pitch, Z): + # used to compute effective ripple + return jnp.sqrt(1 - pitch * B) / B + + @register_compute_fun( name="effective ripple", label="\\epsilon_{\\text{eff}}", @@ -266,16 +283,15 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): # Evaluation of 1/ν neoclassical transport in stellarators. # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. # https://doi.org/10.1063/1.873749. + kwargs.setdefault( "knots", jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta), ) alpha_max = (2 - transforms["grid"].sym) * jnp.pi - alpha, alpha_weight = legendre.leggauss( - kwargs.get("num_alpha", transforms["grid"].num_theta) - ) + alpha, w = legendre.leggauss(kwargs.get("num_alpha", transforms["grid"].num_theta)) alpha = affine_bijection_reverse(alpha, 0, alpha_max) - alpha_weight = alpha_weight * grad_affine_bijection_reverse(0, alpha_max) + w = w * grad_affine_bijection_reverse(0, alpha_max) rho = transforms["grid"].compress(data["rho"]) # TODO: (outside scope of ripple pr) @@ -283,49 +299,31 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): # Maybe modify map coordinates to work with transforms and not eq object? bounce_integrate, items = bounce_integral(kwargs["eq"], rho, alpha, kwargs["knots"]) - def integrand_H(grad_psi_norm, kappa_g, B, pitch, Z): - return ( - pitch - * jnp.sqrt(1 / pitch - B) - * (4 / B - pitch) - * grad_psi_norm - * kappa_g # todo: review and use cvdrift0 - / B - ) - - def integrand_I(B, pitch, Z): - return jnp.sqrt(1 - pitch * B) / B - def ripple_sum(b): """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. Parameters ---------- - b : Array + b : Array, shape(b.shape[0], rho.size * alpha.size) Multiplicative inverse of pitch angle. Returns ------- - ripple : Array, shape(..., rho.size, alpha.size) - ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across axis enumerating alpha. + ripple_sum : Array, shape(b.shape[0], rho.size * alpha.size) + ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha grid. """ pitch = 1 / b - H = bounce_integrate(integrand_H, [data["|grad(psi)|"], data["kappa_g"]], pitch) - I = bounce_integrate(integrand_I, [], pitch) - H = H.reshape(H.shape[0], rho.size, alpha.size, -1) - I = I.reshape(I.shape[0], rho.size, alpha.size, -1) - ripple = jnp.nansum(H**2 / I, axis=-1) - return ripple - - def db_integrate(integrand, b_knot, quad): + H = bounce_integrate(_dH, [data["|grad(psi)|"], data["kappa_g"]], pitch) + I = bounce_integrate(_dI, [], pitch) + return jnp.nansum(H**2 / I, axis=-1) + + def db_integrate(b_break, quad): """Return the ripple sum integral ∫ db ∑ⱼ Hⱼ² / Iⱼ. Parameters ---------- - integrand : callable - Function to integrate. - b_knot : Array + b_break : Array Breakpoints where the quadrature should take more care. For simple schemes this means to include a quadrature point here. quad : callable @@ -339,26 +337,32 @@ def db_integrate(integrand, b_knot, quad): """ # FIXME: dependency conflict with DESC and quadax. - is_Newton_Cotes = quad == trapezoid # or quad == quadax.simpson - if is_Newton_Cotes: - b = composite_linspace(b_knot, kwargs.get("db quad resolution", 19)) - ripple_sum = integrand(b) - ripple = quad(ripple_sum, b.reshape(ripple_sum.shape), axis=0) + # If Newton-Cotes quadrature, collect more b between breakpoints. + if quad == trapezoid: # or quad == quadax.simpson + b = composite_linspace(b_break, kwargs.get("db quad resolution", 19)) + ripple_field_line = quad(ripple_sum(b), b, axis=0) + # Otherwise use an adaptive quadrature. else: - # Could use adaptive Clenshaw-Curtis quadrature with - # quadax.quadcc(integrand, b_knot). + # Recommend using adaptive Clenshaw-Curtis quadrature. + # quadax.quadcc(ripple_sum, b_break). raise NotImplementedError - return jnp.dot(ripple, alpha_weight) + # Integrate over flux surface. + return ripple_field_line.reshape(-1, w.size) @ w - b = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) - b = jnp.append( - b.reshape(-1, rho.size, alpha.size), - transforms["grid"].compress(data["max_tz |B|"])[jnp.newaxis, :, jnp.newaxis], + max_tz_B = transforms["grid"].compress(data["max_tz |B|"]) + # Required to avoid floating point errors. + relative_shift = 1e-6 + max_tz_B = (1 - relative_shift) * max_tz_B + + b_break = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) + b_break = jnp.append( + b_break.reshape(-1, rho.size, alpha.size), + max_tz_B[jnp.newaxis, :, jnp.newaxis], axis=0, ).reshape(-1, rho.size * alpha.size) - ripple = db_integrate(ripple_sum, b, kwargs.get("db quad", trapezoid)) - + ripple = db_integrate(b_break, kwargs.get("db quad", trapezoid)) ripple = transforms["grid"].expand(ripple) + data["effective ripple"] = ( jnp.pi * data["R"] ** 2 From bebc40b22ba97b42b252c6901c6104bb78e13f04 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 24 Apr 2024 13:13:47 -0400 Subject: [PATCH 008/112] Refactor effective ripple compute fun --- desc/compute/_stability.py | 42 +++++++++++++++++--------------------- 1 file changed, 19 insertions(+), 23 deletions(-) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index ae9137d369..9812ab70d2 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -255,6 +255,10 @@ def _dI(B, pitch, Z): return jnp.sqrt(1 - pitch * B) / B +def _is_Newton_Cotes(quad): + return quad == trapezoid # or quad == quadax.simpson + + @register_compute_fun( name="effective ripple", label="\\epsilon_{\\text{eff}}", @@ -318,17 +322,17 @@ def ripple_sum(b): I = bounce_integrate(_dI, [], pitch) return jnp.nansum(H**2 / I, axis=-1) - def db_integrate(b_break, quad): + def compute_ripple(quad, breaks): """Return the ripple sum integral ∫ db ∑ⱼ Hⱼ² / Iⱼ. Parameters ---------- - b_break : Array - Breakpoints where the quadrature should take more care. - For simple schemes this means to include a quadrature point here. quad : callable Quadrature method. See https://quadax.readthedocs.io/en/latest/index.html. + breaks : Array + Breakpoints where the quadrature should take more care. + For simple schemes this means to include a quadrature point here. Returns ------- @@ -336,39 +340,31 @@ def db_integrate(b_break, quad): ∫ db ∑ⱼ Hⱼ² / Iⱼ. """ - # FIXME: dependency conflict with DESC and quadax. - # If Newton-Cotes quadrature, collect more b between breakpoints. - if quad == trapezoid: # or quad == quadax.simpson - b = composite_linspace(b_break, kwargs.get("db quad resolution", 19)) - ripple_field_line = quad(ripple_sum(b), b, axis=0) - # Otherwise use an adaptive quadrature. + if _is_Newton_Cotes(quad): + b = composite_linspace(breaks, kwargs.get("db quad resolution", 19)) + rip = quad(ripple_sum(b), b, axis=0) else: - # Recommend using adaptive Clenshaw-Curtis quadrature. - # quadax.quadcc(ripple_sum, b_break). - raise NotImplementedError - # Integrate over flux surface. - return ripple_field_line.reshape(-1, w.size) @ w + # want to use Clenshaw-Curtis with quadax.quadcc(ripple_sum, breaks) + raise NotImplementedError("Dependency conflict with quadax.") + return rip.reshape(-1, w.size) @ w # Integrate over flux surface. max_tz_B = transforms["grid"].compress(data["max_tz |B|"]) - # Required to avoid floating point errors. relative_shift = 1e-6 max_tz_B = (1 - relative_shift) * max_tz_B - - b_break = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) - b_break = jnp.append( - b_break.reshape(-1, rho.size, alpha.size), + breaks = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) + breaks = jnp.append( + breaks.reshape(-1, rho.size, alpha.size), max_tz_B[jnp.newaxis, :, jnp.newaxis], axis=0, ).reshape(-1, rho.size * alpha.size) - ripple = db_integrate(b_break, kwargs.get("db quad", trapezoid)) - ripple = transforms["grid"].expand(ripple) + ripple = compute_ripple(kwargs.get("db quad", trapezoid), breaks) data["effective ripple"] = ( jnp.pi * data["R"] ** 2 / (8 * 2**0.5) * (data["V_r(r)"] / data["psi_r"]) / data["S(r)"] ** 2 - * ripple + * transforms["grid"].expand(ripple) ) ** (2 / 3) return data From b13d7ad199e4df3b8f33de0b948a4d8623a0f340 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 24 Apr 2024 14:59:37 -0400 Subject: [PATCH 009/112] Split effective ripple compute function --- desc/compute/_stability.py | 117 ++++++++++++++++++------------------- 1 file changed, 58 insertions(+), 59 deletions(-) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index 9812ab70d2..06a5fb6611 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -260,111 +260,110 @@ def _is_Newton_Cotes(quad): @register_compute_fun( - name="effective ripple", - label="\\epsilon_{\\text{eff}}", + name="ripple", + label="∫ db ∑ⱼ Hⱼ² / Iⱼ", units="~", units_long="None", - description="Effective ripple modulation amplitude", + description="Ripple sum integral", dim=1, params=[], transforms={"grid": []}, profiles=[], coordinates="r", - data=[ - "rho", - "|grad(psi)|", - "kappa_g", - "sqrt(g)", - "psi_r", - "S(r)", - "V_r(r)", - "R", - "max_tz |B|", # Could use softmax. - ], + # Could use softmax. TODO: cvdrift0 not kappa_g + data=["rho", "|grad(psi)|", "kappa_g", "max_tz |B|"], ) -def _effective_ripple(params, transforms, profiles, data, **kwargs): +def _ripple(params, transforms, profiles, data, **kwargs): # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. # Evaluation of 1/ν neoclassical transport in stellarators. # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. # https://doi.org/10.1063/1.873749. - kwargs.setdefault( + ripple_quad = kwargs.pop("ripple quad", trapezoid) + ripple_quad_resolution = kwargs.pop("ripple quad resolution", 19) + relative_shift = kwargs.pop("relative shift", 1e-6) + knots = kwargs.pop( "knots", jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta), ) + num_alpha = kwargs.pop("num_alpha", transforms["grid"].num_theta) + alpha, w = legendre.leggauss(num_alpha) alpha_max = (2 - transforms["grid"].sym) * jnp.pi - alpha, w = legendre.leggauss(kwargs.get("num_alpha", transforms["grid"].num_theta)) alpha = affine_bijection_reverse(alpha, 0, alpha_max) w = w * grad_affine_bijection_reverse(0, alpha_max) rho = transforms["grid"].compress(data["rho"]) - # TODO: (outside scope of ripple pr) - # bounce_integral_map only needs eq because map_coordinates needs it. - # Maybe modify map coordinates to work with transforms and not eq object? - bounce_integrate, items = bounce_integral(kwargs["eq"], rho, alpha, kwargs["knots"]) + # FIXME: (outside scope of ripple pr) + # Modify map_coordinates to also work with transforms instead of eq object? + bounce_integrate, items = bounce_integral( + kwargs.pop("eq"), rho, alpha, knots, **kwargs + ) def ripple_sum(b): """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. Parameters ---------- - b : Array, shape(b.shape[0], rho.size * alpha.size) + b : Array, shape(b.shape[0], rho.size, alpha.size) Multiplicative inverse of pitch angle. Returns ------- - ripple_sum : Array, shape(b.shape[0], rho.size * alpha.size) - ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha grid. + ripple_sum : Array, shape(b.shape[0], rho.size, alpha.size) + ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha axis. """ - pitch = 1 / b + pitch = (1 / b).reshape(b.shape[0], -1) H = bounce_integrate(_dH, [data["|grad(psi)|"], data["kappa_g"]], pitch) I = bounce_integrate(_dI, [], pitch) - return jnp.nansum(H**2 / I, axis=-1) + return jnp.nansum(H**2 / I, axis=-1).reshape(-1, rho.size, alpha.size) - def compute_ripple(quad, breaks): - """Return the ripple sum integral ∫ db ∑ⱼ Hⱼ² / Iⱼ. - - Parameters - ---------- - quad : callable - Quadrature method. - See https://quadax.readthedocs.io/en/latest/index.html. - breaks : Array - Breakpoints where the quadrature should take more care. - For simple schemes this means to include a quadrature point here. - - Returns - ------- - ripple : Array, shape(rho.size, ) - ∫ db ∑ⱼ Hⱼ² / Iⱼ. - - """ - if _is_Newton_Cotes(quad): - b = composite_linspace(breaks, kwargs.get("db quad resolution", 19)) - rip = quad(ripple_sum(b), b, axis=0) - else: - # want to use Clenshaw-Curtis with quadax.quadcc(ripple_sum, breaks) - raise NotImplementedError("Dependency conflict with quadax.") - return rip.reshape(-1, w.size) @ w # Integrate over flux surface. - - max_tz_B = transforms["grid"].compress(data["max_tz |B|"]) - relative_shift = 1e-6 - max_tz_B = (1 - relative_shift) * max_tz_B - breaks = 1 / pitch_of_extrema(kwargs["knots"], items["B.c"], items["B_z_ra.c"]) + max_tz_B = (1 - relative_shift) * transforms["grid"].compress(data["max_tz |B|"]) + # Breakpoints where the quadrature should take more care. + # For simple schemes this means to include a quadrature point here. + breaks = 1 / pitch_of_extrema(items["knots"], items["B.c"], items["B_z_ra.c"]) breaks = jnp.append( breaks.reshape(-1, rho.size, alpha.size), max_tz_B[jnp.newaxis, :, jnp.newaxis], axis=0, - ).reshape(-1, rho.size * alpha.size) + ) + breaks = jnp.sort(breaks, axis=0) + if _is_Newton_Cotes(ripple_quad): + b = composite_linspace(breaks, ripple_quad_resolution, is_sorted=True) + rip = ripple_quad(ripple_sum(b), b, axis=0) + else: + # want to use Clenshaw-Curtis with quadax.quadcc(ripple_sum, breaks) + raise NotImplementedError("Dependency conflict with quadax.") + # Integrate over flux surface. + ripple = rip.reshape(-1, w.size) @ w + data["ripple"] = transforms["grid"].expand(ripple) + return data + - ripple = compute_ripple(kwargs.get("db quad", trapezoid), breaks) +@register_compute_fun( + name="effective ripple", + label="\\epsilon_{\\text{eff}}", + units="~", + units_long="None", + description="Effective ripple modulation amplitude", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["ripple" "psi_r", "S(r)", "V_r(r)", "R"], +) +def _effective_ripple(params, transforms, profiles, data, **kwargs): + # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + # Evaluation of 1/ν neoclassical transport in stellarators. + # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + # https://doi.org/10.1063/1.873749. data["effective ripple"] = ( jnp.pi * data["R"] ** 2 / (8 * 2**0.5) * (data["V_r(r)"] / data["psi_r"]) / data["S(r)"] ** 2 - * transforms["grid"].expand(ripple) + * data["ripple"] ) ** (2 / 3) return data From 169556f2eaf4ffde7d07d55a6543340e2c246793 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 25 Apr 2024 09:07:26 -0400 Subject: [PATCH 010/112] Write ripple compute fun with new bounce integral api --- desc/compute/__init__.py | 1 + desc/compute/_neoclassical.py | 141 ++++++++++++++++++++++++++++++++++ desc/compute/_stability.py | 141 +--------------------------------- tests/test_neoclassical.py | 18 +++++ tests/test_stability_funs.py | 9 --- 5 files changed, 161 insertions(+), 149 deletions(-) create mode 100644 desc/compute/_neoclassical.py create mode 100644 tests/test_neoclassical.py diff --git a/desc/compute/__init__.py b/desc/compute/__init__.py index ecc83b816c..4785bd947d 100644 --- a/desc/compute/__init__.py +++ b/desc/compute/__init__.py @@ -37,6 +37,7 @@ _field, _geometry, _metric, + _neoclassical, _omnigenity, _profiles, _stability, diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py new file mode 100644 index 0000000000..5ff09e78f8 --- /dev/null +++ b/desc/compute/_neoclassical.py @@ -0,0 +1,141 @@ +"""Compute functions for neoclassical transport objectives. + +Notes +----- +Some quantities require additional work to compute at the magnetic axis. +A Python lambda function is used to lazily compute the magnetic axis limits +of these quantities. These lambda functions are evaluated only when the +computational grid has a node on the magnetic axis to avoid potentially +expensive computations. +""" + +from orthax import legendre + +from desc.backend import jnp, trapezoid + +from .bounce_integral import ( + affine_bijection_reverse, + bounce_integral, + composite_linspace, + grad_affine_bijection_reverse, + pitch_of_extrema, +) +from .data_index import register_compute_fun + + +def _dH(grad_psi_norm, cvdrift0, B, pitch, Z): + return ( + pitch * jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * cvdrift0 / B + ) + + +def _dI(B, pitch, Z): + return jnp.sqrt(1 - pitch * B) / B + + +def _alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): + # Set resolution > 1 to see if long field line integral can be approximated + # by flux surface average of field line integrals over finite transits. + x, w = legendre.leggauss(resolution) + w = w * grad_affine_bijection_reverse(a_min, a_max) + alpha = affine_bijection_reverse(x, a_min, a_max) + return alpha, w + + +@register_compute_fun( + name="ripple", + label="∫ db ∑ⱼ Hⱼ² / Iⱼ", + units="~", + units_long="None", + description="Ripple sum integral", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["B^zeta", "|B|_z|r,a", "|B|", "max_tz |B|", "|grad(psi)|", "cvdrift0"], +) +def _ripple(params, transforms, profiles, data, **kwargs): + ripple_quad = kwargs.pop("ripple quad", trapezoid) + ripple_quad_res = kwargs.pop("ripple quad resolution", 19) + relative_shift = kwargs.pop("relative shift", 1e-6) + + grid_desc = transforms["grid"] + grid_fl = kwargs.pop("grid_fl") + num_rho = grid_fl.num_rho + alpha = grid_fl.compress(grid_fl.nodes[:, 1], surface_label="theta") + knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") + alpha_weight = kwargs.pop("alpha weight", 2 * jnp.pi / alpha.size) + bounce_integrate, spline = bounce_integral( + data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs + ) + + def ripple_sum(b): + """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. + + Parameters + ---------- + b : Array, shape(..., rho.size * alpha.size) + Multiplicative inverse of pitch angle. + + Returns + ------- + ripple_sum : Array, shape(..., rho.size * alpha.size) + ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha. + + """ + pitch = 1 / b + H = bounce_integrate(_dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch) + I = bounce_integrate(_dI, [], pitch) + return jnp.nansum(H**2 / I, axis=-1) + + # Breakpoints where the quadrature should take more care. + # For simple schemes this means to include a quadrature point here. + breaks = 1 / pitch_of_extrema( + spline["knots"], spline["B.c"], spline["B_z_ra.c"], relative_shift + ).reshape(-1, num_rho, alpha.size) + max_tz_B = (1 - relative_shift) * grid_desc.compress(data["max_tz |B|"]) + max_tz_B = jnp.broadcast_to(max_tz_B[:, jnp.newaxis], (num_rho, alpha.size)) + breaks = jnp.vstack([breaks, max_tz_B[jnp.newaxis]]) + breaks = jnp.sort(breaks, axis=0).reshape(breaks.shape[0], -1) + + if ripple_quad == trapezoid: # or quadax.simpson + b = composite_linspace(breaks, ripple_quad_res) + rip = ripple_quad(ripple_sum(b), b, axis=0) + else: + # want to use Clenshaw-Curtis with quadax.quadcc(ripple_sum, breaks) + raise NotImplementedError("Dependency conflict with quadax.") + + # Integrate over flux surface. + ripple = jnp.dot(rip.reshape(num_rho, -1), alpha_weight) + data["ripple"] = grid_desc.expand(ripple) + return data + + +@register_compute_fun( + name="effective ripple", + label="\\epsilon_{\\text{eff}}", + units="~", + units_long="None", + description="Effective ripple modulation amplitude", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["ripple", "psi_r", "S(r)", "V_r(r)", "R"], +) +def _effective_ripple(params, transforms, profiles, data, **kwargs): + # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + # Evaluation of 1/ν neoclassical transport in stellarators. + # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + # https://doi.org/10.1063/1.873749. + data["effective ripple"] = ( + jnp.pi + * data["R"] ** 2 + / (8 * 2**0.5) + * (data["V_r(r)"] / data["psi_r"]) + / data["S(r)"] ** 2 + * data["ripple"] + ) ** (2 / 3) + return data diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index 06a5fb6611..d8fc27838e 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -9,18 +9,10 @@ expensive computations. """ -from orthax import legendre from scipy.constants import mu_0 -from desc.backend import jnp, trapezoid +from desc.backend import jnp -from .bounce_integral import ( - affine_bijection_reverse, - bounce_integral, - composite_linspace, - grad_affine_bijection_reverse, - pitch_of_extrema, -) from .data_index import register_compute_fun from .utils import dot, surface_integrals_map @@ -236,134 +228,3 @@ def _magnetic_well(params, transforms, profiles, data, **kwargs): 0, # coefficient of limit is V_r / V_rr, rest is finite ) return data - - -def _dH(grad_psi_norm, kappa_g, B, pitch, Z): - # used to compute effective ripple - return ( - pitch - * jnp.sqrt(1 / pitch - B) - * (4 / B - pitch) - * grad_psi_norm - * kappa_g # todo: review and use cvdrift0 - / B - ) - - -def _dI(B, pitch, Z): - # used to compute effective ripple - return jnp.sqrt(1 - pitch * B) / B - - -def _is_Newton_Cotes(quad): - return quad == trapezoid # or quad == quadax.simpson - - -@register_compute_fun( - name="ripple", - label="∫ db ∑ⱼ Hⱼ² / Iⱼ", - units="~", - units_long="None", - description="Ripple sum integral", - dim=1, - params=[], - transforms={"grid": []}, - profiles=[], - coordinates="r", - # Could use softmax. TODO: cvdrift0 not kappa_g - data=["rho", "|grad(psi)|", "kappa_g", "max_tz |B|"], -) -def _ripple(params, transforms, profiles, data, **kwargs): - # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. - # Evaluation of 1/ν neoclassical transport in stellarators. - # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - # https://doi.org/10.1063/1.873749. - - ripple_quad = kwargs.pop("ripple quad", trapezoid) - ripple_quad_resolution = kwargs.pop("ripple quad resolution", 19) - relative_shift = kwargs.pop("relative shift", 1e-6) - knots = kwargs.pop( - "knots", - jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, transforms["grid"].num_zeta), - ) - num_alpha = kwargs.pop("num_alpha", transforms["grid"].num_theta) - alpha, w = legendre.leggauss(num_alpha) - alpha_max = (2 - transforms["grid"].sym) * jnp.pi - alpha = affine_bijection_reverse(alpha, 0, alpha_max) - w = w * grad_affine_bijection_reverse(0, alpha_max) - rho = transforms["grid"].compress(data["rho"]) - - # FIXME: (outside scope of ripple pr) - # Modify map_coordinates to also work with transforms instead of eq object? - bounce_integrate, items = bounce_integral( - kwargs.pop("eq"), rho, alpha, knots, **kwargs - ) - - def ripple_sum(b): - """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. - - Parameters - ---------- - b : Array, shape(b.shape[0], rho.size, alpha.size) - Multiplicative inverse of pitch angle. - - Returns - ------- - ripple_sum : Array, shape(b.shape[0], rho.size, alpha.size) - ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha axis. - - """ - pitch = (1 / b).reshape(b.shape[0], -1) - H = bounce_integrate(_dH, [data["|grad(psi)|"], data["kappa_g"]], pitch) - I = bounce_integrate(_dI, [], pitch) - return jnp.nansum(H**2 / I, axis=-1).reshape(-1, rho.size, alpha.size) - - max_tz_B = (1 - relative_shift) * transforms["grid"].compress(data["max_tz |B|"]) - # Breakpoints where the quadrature should take more care. - # For simple schemes this means to include a quadrature point here. - breaks = 1 / pitch_of_extrema(items["knots"], items["B.c"], items["B_z_ra.c"]) - breaks = jnp.append( - breaks.reshape(-1, rho.size, alpha.size), - max_tz_B[jnp.newaxis, :, jnp.newaxis], - axis=0, - ) - breaks = jnp.sort(breaks, axis=0) - if _is_Newton_Cotes(ripple_quad): - b = composite_linspace(breaks, ripple_quad_resolution, is_sorted=True) - rip = ripple_quad(ripple_sum(b), b, axis=0) - else: - # want to use Clenshaw-Curtis with quadax.quadcc(ripple_sum, breaks) - raise NotImplementedError("Dependency conflict with quadax.") - # Integrate over flux surface. - ripple = rip.reshape(-1, w.size) @ w - data["ripple"] = transforms["grid"].expand(ripple) - return data - - -@register_compute_fun( - name="effective ripple", - label="\\epsilon_{\\text{eff}}", - units="~", - units_long="None", - description="Effective ripple modulation amplitude", - dim=1, - params=[], - transforms={"grid": []}, - profiles=[], - coordinates="r", - data=["ripple" "psi_r", "S(r)", "V_r(r)", "R"], -) -def _effective_ripple(params, transforms, profiles, data, **kwargs): - # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. - # Evaluation of 1/ν neoclassical transport in stellarators. - # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - # https://doi.org/10.1063/1.873749. - data["effective ripple"] = ( - jnp.pi - * data["R"] ** 2 - / (8 * 2**0.5) - * (data["V_r(r)"] / data["psi_r"]) - / data["S(r)"] ** 2 - * data["ripple"] - ) ** (2 / 3) - return data diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py new file mode 100644 index 0000000000..1dbbd2915a --- /dev/null +++ b/tests/test_neoclassical.py @@ -0,0 +1,18 @@ +"""Test for neoclassical transport compute functions.""" + +import numpy as np +import pytest + +from desc.compute.bounce_integral import desc_grid_from_field_line_coords +from desc.examples import get + + +@pytest.mark.unit +def test_effective_ripple(): + """Compare DESC effective ripple against neo stellopt.""" + eq = get("HELIOTRON") + grid_desc, grid_fl = desc_grid_from_field_line_coords(eq) + # just want to pass some custom keyword arguments into compute func + data = eq.compute(["ripple", "effective ripple"], grid=grid_desc) + assert np.isfinite(data["ripple"]).all() + assert np.isfinite(data["effective ripple"]).all() diff --git a/tests/test_stability_funs.py b/tests/test_stability_funs.py index 9adde02495..54aa43e278 100644 --- a/tests/test_stability_funs.py +++ b/tests/test_stability_funs.py @@ -7,7 +7,6 @@ import desc.examples import desc.io from desc.equilibrium import Equilibrium -from desc.examples import get from desc.grid import LinearGrid from desc.objectives import MagneticWell, MercierStability @@ -342,11 +341,3 @@ def test_magwell_print(capsys): + "\n" ) assert out.out == corr_out - - -@pytest.mark.unit -def test_effective_ripple(): - """Compare DESC effective ripple against neo stellopt.""" - eq = get("HELIOTRON") - data = eq.compute("effective ripple") - assert np.isfinite(data["effective ripple"]).all() From a647856e8d382b6d5482bdcf8ae6b2e7df9dea15 Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 26 Apr 2024 07:51:34 -0400 Subject: [PATCH 011/112] Add support for quadax adaptive integration --- desc/compute/_neoclassical.py | 36 ++++++++++++++++++++++++----- devtools/dev-requirements_conda.yml | 2 +- requirements.txt | 2 +- requirements_conda.yml | 2 +- 4 files changed, 33 insertions(+), 9 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 5ff09e78f8..4d5a13986a 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -9,6 +9,7 @@ expensive computations. """ +import quadax from orthax import legendre from desc.backend import jnp, trapezoid @@ -89,6 +90,22 @@ def ripple_sum(b): I = bounce_integrate(_dI, [], pitch) return jnp.nansum(H**2 / I, axis=-1) + # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ in equation 29 of + # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + # Evaluation of 1/ν neoclassical transport in stellarators. + # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + # https://doi.org/10.1063/1.873749 + # the contribution of ∑ⱼ Hⱼ² / Iⱼ to ε is largest in the intervals such that + # b ∈ [|B|(ζ*) - db, |B|(ζ*)]. To see this, observe that Iⱼ ∼ √(1 − λ B), + # hence Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ B). For λ = 1 / |B|(ζ*), near |B|(ζ*), the + # quantity 1 / √(1 − λ B) is singular. The slower |B| tends to |B|(ζ*) the + # less integrable this singularity becomes. Therefore, a quadrature for + # ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ would do well to evaluate the integrand near + # b = 1 / λ = |B|(ζ*). + # The same should be done for the minima. For particles + # with 1 / λ = |B|(ζ*) minima, the measure of the bounce integral ∫ f(ℓ) dℓ + # ~ |ζ₂ − ζ₁| → 0, and the strength of the singularity ~ 1 / |∂|B|/∂_ζ| → ∞. + # So ∫ f(ℓ) dℓ ≈ [f(ζ₂) ζ₂ - f(ζ₁) ζ₁] / |∂|B|/∂_ζ|. # Breakpoints where the quadrature should take more care. # For simple schemes this means to include a quadrature point here. breaks = 1 / pitch_of_extrema( @@ -99,12 +116,19 @@ def ripple_sum(b): breaks = jnp.vstack([breaks, max_tz_B[jnp.newaxis]]) breaks = jnp.sort(breaks, axis=0).reshape(breaks.shape[0], -1) - if ripple_quad == trapezoid: # or quadax.simpson - b = composite_linspace(breaks, ripple_quad_res) - rip = ripple_quad(ripple_sum(b), b, axis=0) - else: - # want to use Clenshaw-Curtis with quadax.quadcc(ripple_sum, breaks) - raise NotImplementedError("Dependency conflict with quadax.") + is_Newton_Cotes = ( + ripple_quad == trapezoid + or ripple_quad == quadax.trapezoid + or ripple_quad == quadax.simpson + ) + try: + if is_Newton_Cotes: + b = composite_linspace(breaks, ripple_quad_res) + rip = ripple_quad(ripple_sum(b), b, axis=0) + else: + rip = ripple_quad(ripple_sum, breaks) + except TypeError as e: + raise NotImplementedError from e # Integrate over flux surface. ripple = jnp.dot(rip.reshape(num_rho, -1), alpha_weight) diff --git a/devtools/dev-requirements_conda.yml b/devtools/dev-requirements_conda.yml index 33b3eec246..980c50fc32 100644 --- a/devtools/dev-requirements_conda.yml +++ b/devtools/dev-requirements_conda.yml @@ -26,7 +26,7 @@ dependencies: # testing and benchmarking - qsc - qicna @ git+https://github.com/rogeriojorge/pyQIC/ - # - quadax + - quadax >= 0.2.1 # building the docs - nbsphinx == 0.8.12 diff --git a/requirements.txt b/requirements.txt index a6fac3ce82..389feaf6b7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,6 +11,6 @@ orthax plotly >= 5.16, < 6.0 psutil pylatexenc >= 2.0, < 3.0 -# quadax +quadax >= 0.2.1 scipy >= 1.7.0, < 2.0.0 termcolor diff --git a/requirements_conda.yml b/requirements_conda.yml index fdc7b6896e..48c89752d2 100644 --- a/requirements_conda.yml +++ b/requirements_conda.yml @@ -20,4 +20,4 @@ dependencies: - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 - # - quadax + - quadax >= 0.2.1 From e848f9132c93c8dbbaddab89172865f952c82d40 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 29 Apr 2024 03:08:45 -0400 Subject: [PATCH 012/112] Fix bug with recomputing quantities on incorrect grid --- desc/equilibrium/equilibrium.py | 31 +++++++++++++++++++++++++++---- 1 file changed, 27 insertions(+), 4 deletions(-) diff --git a/desc/equilibrium/equilibrium.py b/desc/equilibrium/equilibrium.py index 7e6faadc0c..570f77cfdb 100644 --- a/desc/equilibrium/equilibrium.py +++ b/desc/equilibrium/equilibrium.py @@ -883,7 +883,13 @@ def compute( params=params, transforms=get_transforms(dep0d, obj=self, grid=grid0d, **kwargs), profiles=get_profiles(dep0d, obj=self, grid=grid0d), - data=None, + # If a dependency of something is already computed, use it + # instead of recomputing it on a potentially bad grid. + data={ + key: data[key] + for key in data + if data_index[p][key]["coordinates"] == "" + }, **kwargs, ) # these should all be 0d quantities so don't need to compress/expand @@ -899,14 +905,22 @@ def compute( sym=self.sym, ) # TODO: Pass in data0d as a seed once there are 1d quantities that - # depend on 0d quantities in data_index. + # depend on 0d quantities in data_index. data1dr = compute_fun( self, dep1dr, params=params, transforms=get_transforms(dep1dr, obj=self, grid=grid1dr, **kwargs), profiles=get_profiles(dep1dr, obj=self, grid=grid1dr), - data=None, + # If a dependency of something is already computed, use it + # instead of recomputing it on a potentially bad grid. + data={ + key: grid1dr.copy_data_from_other( + data[key], grid, surface_label="rho" + ) + for key in data + if data_index[p][key]["coordinates"] == "r" + }, **kwargs, ) # need to make this data broadcast with the data on the original grid @@ -915,6 +929,7 @@ def compute( for key, val in data1dr.items() if key in dep1dr } + data.update(data1dr) if calc1dz and override_grid: @@ -933,7 +948,15 @@ def compute( params=params, transforms=get_transforms(dep1dz, obj=self, grid=grid1dz, **kwargs), profiles=get_profiles(dep1dz, obj=self, grid=grid1dz), - data=None, + # If a dependency of something is already computed, use it + # instead of recomputing it on a potentially bad grid. + data={ + key: grid1dz.copy_data_from_other( + data[key], grid, surface_label="zeta" + ) + for key in data + if data_index[p][key]["coordinates"] == "z" + }, **kwargs, ) # need to make this data broadcast with the data on the original grid From 263a15bd437efa31ebe1def58eb9301caa15933b Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 29 Apr 2024 03:09:45 -0400 Subject: [PATCH 013/112] Epsilon effective ready for accuracy testing --- desc/compute/_neoclassical.py | 109 ++++++++++++++++++++------------ desc/compute/bounce_integral.py | 4 +- tests/test_bounce_integral.py | 3 +- tests/test_neoclassical.py | 93 ++++++++++++++++++++++++++- 4 files changed, 160 insertions(+), 49 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 4d5a13986a..844c5f44aa 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -14,12 +14,13 @@ from desc.backend import jnp, trapezoid +from ..utils import errorif from .bounce_integral import ( affine_bijection_reverse, bounce_integral, composite_linspace, + get_extrema, grad_affine_bijection_reverse, - pitch_of_extrema, ) from .data_index import register_compute_fun @@ -34,9 +35,33 @@ def _dI(B, pitch, Z): return jnp.sqrt(1 - pitch * B) / B -def _alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): - # Set resolution > 1 to see if long field line integral can be approximated - # by flux surface average of field line integrals over finite transits. +def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): + """Gauss-Legendre quadrature. + + Returns quadrature points αₖ and weights wₖ for the approximate evaluation + of the integral ∫ f(α) dα ≈ ∑ₖ wₖ f(αₖ). + + For use with computing effective ripple, set resolution > 1 to see if a long + field line integral can be approximated by flux surface average of field line + integrals over finite transits. + + Parameters + ---------- + resolution: int + Number of quadrature points. + a_min: float + Min α value. + a_max: float + Max α value. + + Returns + ------- + alpha : Array + Quadrature points. + w : Array + Quadrature weights. + + """ x, w = legendre.leggauss(resolution) w = w * grad_affine_bijection_reverse(a_min, a_max) alpha = affine_bijection_reverse(x, a_min, a_max) @@ -55,18 +80,34 @@ def _alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): profiles=[], coordinates="r", data=["B^zeta", "|B|_z|r,a", "|B|", "max_tz |B|", "|grad(psi)|", "cvdrift0"], + grid_fl="Grid : Field line grid.", + alpha_weight="Array : Quadrature weight over alpha.", + b_quad="callable : Quadrature method to integrate over dB.", + b_quad_res="int : Resolution for quadrature over dB.", + shift="float : Relative amount to shift maxima down and minima up" + " to avoid floating point errors.", + quad="callable : Quadrature method to compute bounce integrals.", + automorphism="(callable, callable) : Change of variables for bounce integral.", ) def _ripple(params, transforms, profiles, data, **kwargs): - ripple_quad = kwargs.pop("ripple quad", trapezoid) - ripple_quad_res = kwargs.pop("ripple quad resolution", 19) - relative_shift = kwargs.pop("relative shift", 1e-6) - - grid_desc = transforms["grid"] + # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + # Evaluation of 1/ν neoclassical transport in stellarators. + # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + # https://doi.org/10.1063/1.873749 grid_fl = kwargs.pop("grid_fl") num_rho = grid_fl.num_rho + errorif(num_rho != transforms["grid"].num_rho) + errorif( + # TODO: Add grid labels to compute quantities so this doesn't occur. + grid_fl.num_nodes != transforms["grid"].num_nodes, + msg="Set override_grid=False.", + ) alpha = grid_fl.compress(grid_fl.nodes[:, 1], surface_label="theta") + alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 2 * jnp.pi / alpha.size)) knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") - alpha_weight = kwargs.pop("alpha weight", 2 * jnp.pi / alpha.size) + b_quad = kwargs.pop("b_quad", trapezoid) + b_quad_res = kwargs.pop("b_quad_res", 19) + shift = kwargs.pop("shift", 1e-6) bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs ) @@ -76,12 +117,12 @@ def ripple_sum(b): Parameters ---------- - b : Array, shape(..., rho.size * alpha.size) + b : Array, shape(..., num_rho * alpha.size) Multiplicative inverse of pitch angle. Returns ------- - ripple_sum : Array, shape(..., rho.size * alpha.size) + ripple_sum : Array, shape(..., num_rho * alpha.size) ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha. """ @@ -90,49 +131,35 @@ def ripple_sum(b): I = bounce_integrate(_dI, [], pitch) return jnp.nansum(H**2 / I, axis=-1) - # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ in equation 29 of - # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. - # Evaluation of 1/ν neoclassical transport in stellarators. - # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - # https://doi.org/10.1063/1.873749 - # the contribution of ∑ⱼ Hⱼ² / Iⱼ to ε is largest in the intervals such that - # b ∈ [|B|(ζ*) - db, |B|(ζ*)]. To see this, observe that Iⱼ ∼ √(1 − λ B), - # hence Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ B). For λ = 1 / |B|(ζ*), near |B|(ζ*), the - # quantity 1 / √(1 − λ B) is singular. The slower |B| tends to |B|(ζ*) the - # less integrable this singularity becomes. Therefore, a quadrature for - # ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ would do well to evaluate the integrand near - # b = 1 / λ = |B|(ζ*). - # The same should be done for the minima. For particles - # with 1 / λ = |B|(ζ*) minima, the measure of the bounce integral ∫ f(ℓ) dℓ - # ~ |ζ₂ − ζ₁| → 0, and the strength of the singularity ~ 1 / |∂|B|/∂_ζ| → ∞. - # So ∫ f(ℓ) dℓ ≈ [f(ζ₂) ζ₂ - f(ζ₁) ζ₁] / |∂|B|/∂_ζ|. + # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ is largest in the + # intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To + # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). + # For λ = 1 / |B|(ζ*), near |B|(ζ*), the quantity 1 / √(1 − λ |B|) is singular + # with strength ~ 1 / |∂|B|/∂_ζ|. Therefore, a quadrature for ε should evaluate + # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. # Breakpoints where the quadrature should take more care. # For simple schemes this means to include a quadrature point here. - breaks = 1 / pitch_of_extrema( - spline["knots"], spline["B.c"], spline["B_z_ra.c"], relative_shift + breaks = jnp.nan_to_num( + get_extrema(**spline, relative_shift=shift, sort=False) ).reshape(-1, num_rho, alpha.size) - max_tz_B = (1 - relative_shift) * grid_desc.compress(data["max_tz |B|"]) + max_tz_B = (1 - shift) * transforms["grid"].compress(data["max_tz |B|"]) max_tz_B = jnp.broadcast_to(max_tz_B[:, jnp.newaxis], (num_rho, alpha.size)) breaks = jnp.vstack([breaks, max_tz_B[jnp.newaxis]]) breaks = jnp.sort(breaks, axis=0).reshape(breaks.shape[0], -1) - is_Newton_Cotes = ( - ripple_quad == trapezoid - or ripple_quad == quadax.trapezoid - or ripple_quad == quadax.simpson - ) try: + is_Newton_Cotes = b_quad in [trapezoid, quadax.trapezoid, quadax.simpson] if is_Newton_Cotes: - b = composite_linspace(breaks, ripple_quad_res) - rip = ripple_quad(ripple_sum(b), b, axis=0) + b = composite_linspace(breaks, b_quad_res) + rip = b_quad(ripple_sum(b), b, axis=0) else: - rip = ripple_quad(ripple_sum, breaks) + rip = b_quad(ripple_sum, breaks) except TypeError as e: raise NotImplementedError from e # Integrate over flux surface. - ripple = jnp.dot(rip.reshape(num_rho, -1), alpha_weight) - data["ripple"] = grid_desc.expand(ripple) + ripple = rip.reshape(num_rho, -1) @ alpha_weight + data["ripple"] = transforms["grid"].expand(ripple) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 5a2454a46a..38e2e6d626 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1361,7 +1361,7 @@ def bounce_integrate(integrand, f, pitch, method="akima"): def desc_grid_from_field_line_coords( eq, rho=jnp.linspace(1e-7, 1, 10), - alpha=None, + alpha=0, zeta=jnp.linspace(-3 * jnp.pi, 3 * jnp.pi, 40), ): """Return DESC coordinate grid from given Clebsch-Type field-line coordinates. @@ -1389,8 +1389,6 @@ def desc_grid_from_field_line_coords( Clebsch-Type field-line coordinate grid. """ - if alpha is None: - alpha = jnp.linspace(0, (2 - eq.sym) * jnp.pi, 20) grid_fl = Grid.create_meshgrid(rho, alpha, zeta) coords_desc = eq.map_coordinates( grid_fl.nodes, diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index eedecbb36d..46744e9aa6 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -629,8 +629,7 @@ def _compute_field_line_data(eq, rho, alpha, names_field_line, names_0d_or_1dr=N Field line quantities that will be computed on the returned field line grid. Should not include 0d or 1dr quantities. names_0d_or_1dr : list - Other quantities to compute that are constant throughout volume or over - flux surface. + Things to compute that are constant throughout volume or over flux surface. Returns ------- diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 1dbbd2915a..90db764a01 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -1,18 +1,105 @@ """Test for neoclassical transport compute functions.""" +import matplotlib.pyplot as plt import numpy as np import pytest +from desc.compute import data_index, get_data_deps from desc.compute.bounce_integral import desc_grid_from_field_line_coords from desc.examples import get +from desc.grid import LinearGrid + + +def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=None): + """Compute field line quantities on correct grids. + + Parameters + ---------- + eq : Equilibrium + Equilibrium to compute on. + grid_desc : Grid + Grid on which the field line quantities should be computed. + names_field_line : list + Field line quantities that will be computed on the returned field line grid. + Should not include 0d or 1dr quantities. + names_0d_or_1dr : list + Things to compute that are constant throughout volume or over flux surface. + + Returns + ------- + data : dict + Computed quantities. + + """ + # TODO: https://github.com/PlasmaControl/DESC/issues/719 + if names_0d_or_1dr is None: + names_0d_or_1dr = [] + names_0d_or_1dr.append("iota") + p = "desc.equilibrium.equilibrium.Equilibrium" + # Gather dependencies of given quantities. + deps = ( + get_data_deps(names_field_line + names_0d_or_1dr, obj=p, has_axis=False) + + names_0d_or_1dr + ) + deps = list(set(deps)) + # Create grid with given flux surfaces. + rho = grid_desc.compress(grid_desc.nodes[:, 0]) + grid1dr = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, sym=eq.sym, NFP=eq.NFP) + # Compute dependencies on correct grids. + seed_data = eq.compute(deps, grid=grid1dr) + dep1dr = {dep for dep in deps if data_index[p][dep]["coordinates"] == "r"} + dep0d = {dep for dep in deps if data_index[p][dep]["coordinates"] == ""} + + # Collect quantities that can be used as a seed to compute the + # field line quantities over the grid mapped from field line coordinates. + # (Single field line grid won't have enough poloidal resolution to + # compute these quantities accurately). + data0d = {key: val for key, val in seed_data.items() if key in dep0d} + data1d = { + key: grid_desc.copy_data_from_other(val, grid1dr) + for key, val in seed_data.items() + if key in dep1dr + } + data = {} + data.update(data0d) + data.update(data1d) + # Compute field line quantities with precomputed dependencies. + for name in names_field_line: + if name in data: + del data[name] + data = eq.compute( + names=names_field_line, grid=grid_desc, data=data, override_grid=False + ) + return data @pytest.mark.unit def test_effective_ripple(): """Compare DESC effective ripple against neo stellopt.""" eq = get("HELIOTRON") - grid_desc, grid_fl = desc_grid_from_field_line_coords(eq) - # just want to pass some custom keyword arguments into compute func - data = eq.compute(["ripple", "effective ripple"], grid=grid_desc) + grid_desc, grid_fl = desc_grid_from_field_line_coords( + eq, rho=np.linspace(1e-7, 1, 10) + ) + data = _compute_field_line_data( + eq, + grid_desc, + ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], + ["max_tz |B|"], + ) + data = eq.compute( + "ripple", grid=grid_desc, data=data, override_grid=False, grid_fl=grid_fl + ) assert np.isfinite(data["ripple"]).all() + rho = grid_desc.compress(grid_desc.nodes[:, 0]) + ripple = grid_desc.compress(data["ripple"]) + fig, ax = plt.subplots() + ax.plot(rho, ripple, label="ripple") + plt.show() + plt.close() + data = eq.compute("effective ripple", grid=grid_desc, data=data) assert np.isfinite(data["effective ripple"]).all() + eff_ripple = grid_desc.compress(data["effective ripple"]) + fig, ax = plt.subplots() + ax.plot(rho, eff_ripple, label="Effective ripple") + plt.show() + plt.close() From 93ff35b89392214aae3bfd15f811faafaba5585d Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 29 Apr 2024 15:37:10 -0400 Subject: [PATCH 014/112] Add orthax to requiments --- desc/compute/_neoclassical.py | 4 ++-- devtools/dev-requirements_conda.yml | 3 ++- requirements.txt | 1 + requirements_conda.yml | 1 + 4 files changed, 6 insertions(+), 3 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 844c5f44aa..c2bc87efec 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -9,8 +9,8 @@ expensive computations. """ +import orthax import quadax -from orthax import legendre from desc.backend import jnp, trapezoid @@ -62,7 +62,7 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): Quadrature weights. """ - x, w = legendre.leggauss(resolution) + x, w = orthax.legendre.leggauss(resolution) w = w * grad_affine_bijection_reverse(a_min, a_max) alpha = affine_bijection_reverse(x, a_min, a_max) return alpha, w diff --git a/devtools/dev-requirements_conda.yml b/devtools/dev-requirements_conda.yml index b321444473..d6f7fb2f75 100644 --- a/devtools/dev-requirements_conda.yml +++ b/devtools/dev-requirements_conda.yml @@ -18,14 +18,15 @@ dependencies: - interpax >= 0.3.1 - jax[cpu] >= 0.3.2, < 0.5.0 - nvgpu + - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 + - quadax >= 0.2.1 # building the docs - sphinx-github-style >= 1.0, < 2.0 # testing and benchmarking - qsc - qicna @ git+https://github.com/rogeriojorge/pyQIC/ - - quadax >= 0.2.1 # building the docs - nbsphinx == 0.8.12 diff --git a/requirements.txt b/requirements.txt index fc91d5ab91..389feaf6b7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ mpmath >= 1.0.0, < 2.0 netcdf4 >= 1.5.4, < 2.0 numpy >= 1.20.0, < 2.0.0 nvgpu +orthax plotly >= 5.16, < 6.0 psutil pylatexenc >= 2.0, < 3.0 diff --git a/requirements_conda.yml b/requirements_conda.yml index 28425c052d..c75194ff20 100644 --- a/requirements_conda.yml +++ b/requirements_conda.yml @@ -17,6 +17,7 @@ dependencies: # If two pip lists are given, all but the last list is skipped. - jax[cpu] >= 0.3.2, < 0.5.0 - nvgpu + - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 - quadax >= 0.2.1 From de718387d97a8acd765eb7e86247bd64c1d545f2 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 29 Apr 2024 16:51:03 -0400 Subject: [PATCH 015/112] Use R0 in eps_eff --- desc/compute/_neoclassical.py | 4 ++-- desc/equilibrium/equilibrium.py | 1 - tests/test_neoclassical.py | 15 +++++++++++++-- 3 files changed, 15 insertions(+), 5 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index c2bc87efec..fb7734626b 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -174,7 +174,7 @@ def ripple_sum(b): transforms={"grid": []}, profiles=[], coordinates="r", - data=["ripple", "psi_r", "S(r)", "V_r(r)", "R"], + data=["ripple", "psi_r", "S(r)", "V_r(r)", "R0"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. @@ -183,7 +183,7 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): # https://doi.org/10.1063/1.873749. data["effective ripple"] = ( jnp.pi - * data["R"] ** 2 + * data["R0"] ** 2 # average major radius / (8 * 2**0.5) * (data["V_r(r)"] / data["psi_r"]) / data["S(r)"] ** 2 diff --git a/desc/equilibrium/equilibrium.py b/desc/equilibrium/equilibrium.py index cee9ca6c2c..b54337354b 100644 --- a/desc/equilibrium/equilibrium.py +++ b/desc/equilibrium/equilibrium.py @@ -932,7 +932,6 @@ def compute( for key, val in data1dr.items() if key in dep1dr } - data.update(data1dr) if calc1dz and override_grid: diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 90db764a01..e59d790450 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -84,10 +84,14 @@ def test_effective_ripple(): eq, grid_desc, ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], - ["max_tz |B|"], + ["max_tz |B|", "R0"], ) data = eq.compute( - "ripple", grid=grid_desc, data=data, override_grid=False, grid_fl=grid_fl + "ripple", + grid=grid_desc, + data=data, + override_grid=False, + grid_fl=grid_fl, ) assert np.isfinite(data["ripple"]).all() rho = grid_desc.compress(grid_desc.nodes[:, 0]) @@ -96,6 +100,13 @@ def test_effective_ripple(): ax.plot(rho, ripple, label="ripple") plt.show() plt.close() + # Workaround until eq.compute() is fixed. + data_R0 = eq.compute("R0") + for key in data_R0: + if key not in data: + # Need to add R0's dependencies which are surface functions of zeta + # aren't attempted to be recomputed on grid_desc. + data[key] = data_R0[key] data = eq.compute("effective ripple", grid=grid_desc, data=data) assert np.isfinite(data["effective ripple"]).all() eff_ripple = grid_desc.compress(data["effective ripple"]) From 0fa255d37a92aca6c174f790691528c4196378a4 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 30 Apr 2024 00:01:23 -0400 Subject: [PATCH 016/112] Add eps_eff results --- desc/compute/_neoclassical.py | 4 +- desc/compute/bounce_integral.py | 2 +- ...nk_fixed_bdry_r0.15_L_9_M_9_N_24_output.h5 | Bin 0 -> 131569 bytes tests/test_neoclassical.py | 42 ++++++++++++------ 4 files changed, 32 insertions(+), 16 deletions(-) create mode 100644 tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0.15_L_9_M_9_N_24_output.h5 diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index fb7734626b..e73131c38b 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -88,6 +88,8 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): " to avoid floating point errors.", quad="callable : Quadrature method to compute bounce integrals.", automorphism="(callable, callable) : Change of variables for bounce integral.", + check="bool : Flag for debugging.", + plot="bool : Whether to plot some things if check is true.", ) def _ripple(params, transforms, profiles, data, **kwargs): # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. @@ -106,7 +108,7 @@ def _ripple(params, transforms, profiles, data, **kwargs): alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 2 * jnp.pi / alpha.size)) knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") b_quad = kwargs.pop("b_quad", trapezoid) - b_quad_res = kwargs.pop("b_quad_res", 19) + b_quad_res = kwargs.pop("b_quad_res", 5) shift = kwargs.pop("shift", 1e-6) bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 38e2e6d626..3c4a4a3897 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1286,7 +1286,7 @@ def group_data_by_field_line(g): spline = {"knots": knots, "B_c": B_c, "B_z_ra_c": B_z_ra_c} if quad == tanh_sinh_quad: - kwargs.setdefault("resolution", 19) + kwargs.setdefault("resolution", 29) x, w = quad(**kwargs) # The gradient of the transformation is the weight function 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desc_grid_from_field_line_coords -from desc.examples import get +from desc.compute.bounce_integral import ( + desc_grid_from_field_line_coords, + tanh_sinh_quad, +) +from desc.equilibrium import Equilibrium from desc.grid import LinearGrid @@ -34,7 +38,6 @@ def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=No # TODO: https://github.com/PlasmaControl/DESC/issues/719 if names_0d_or_1dr is None: names_0d_or_1dr = [] - names_0d_or_1dr.append("iota") p = "desc.equilibrium.equilibrium.Equilibrium" # Gather dependencies of given quantities. deps = ( @@ -60,9 +63,7 @@ def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=No for key, val in seed_data.items() if key in dep1dr } - data = {} - data.update(data0d) - data.update(data1d) + data = data0d | data1d # Compute field line quantities with precomputed dependencies. for name in names_field_line: if name in data: @@ -76,9 +77,12 @@ def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=No @pytest.mark.unit def test_effective_ripple(): """Compare DESC effective ripple against neo stellopt.""" - eq = get("HELIOTRON") + eq = Equilibrium.load( + "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" + ".15_L_9_M_9_N_24_output.h5" + ) grid_desc, grid_fl = desc_grid_from_field_line_coords( - eq, rho=np.linspace(1e-7, 1, 10) + eq, rho=np.linspace(0.01, 1, 20) ) data = _compute_field_line_data( eq, @@ -92,14 +96,21 @@ def test_effective_ripple(): data=data, override_grid=False, grid_fl=grid_fl, + # an adaptive quadrature would use less memory + b_quad=trapezoid, + b_quad_res=5, + quad=lambda: tanh_sinh_quad(30), + check=False, + plot=False, ) assert np.isfinite(data["ripple"]).all() rho = grid_desc.compress(grid_desc.nodes[:, 0]) ripple = grid_desc.compress(data["ripple"]) - fig, ax = plt.subplots() - ax.plot(rho, ripple, label="ripple") - plt.show() - plt.close() + fig, ax = plt.subplots(2) + ax[0].plot(rho, ripple, marker="o", label="∫ db ∑ⱼ Hⱼ² / Iⱼ") + ax[0].set_xlabel(r"$\rho$") + ax[0].set_ylabel("ripple") + ax[0].set_title("Ripple, defined as ∫ db ∑ⱼ Hⱼ² / Iⱼ") # Workaround until eq.compute() is fixed. data_R0 = eq.compute("R0") for key in data_R0: @@ -110,7 +121,10 @@ def test_effective_ripple(): data = eq.compute("effective ripple", grid=grid_desc, data=data) assert np.isfinite(data["effective ripple"]).all() eff_ripple = grid_desc.compress(data["effective ripple"]) - fig, ax = plt.subplots() - ax.plot(rho, eff_ripple, label="Effective ripple") + ax[1].plot(rho, eff_ripple, marker="o", label=r"$\epsilon_{\text{effective}}$") + ax[1].set_xlabel(r"$\rho$") + ax[1].set_ylabel(r"$\epsilon_{\text{effective}}$") + ax[1].set_title("Effective ripple (not raised to 3/2 power)") + plt.tight_layout() plt.show() plt.close() From b955d7e524c986e4b4bccf971117cbd8716aad66 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 30 Apr 2024 12:10:44 -0400 Subject: [PATCH 017/112] Fix logic bug in algorithm for first integration width - no change in results --- desc/compute/_neoclassical.py | 40 ++++++++++++++++++++------------- desc/compute/bounce_integral.py | 29 ++++++++++++++++-------- tests/test_neoclassical.py | 9 ++++++-- 3 files changed, 52 insertions(+), 26 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index e73131c38b..1e6d98c0dc 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -79,7 +79,14 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): transforms={"grid": []}, profiles=[], coordinates="r", - data=["B^zeta", "|B|_z|r,a", "|B|", "max_tz |B|", "|grad(psi)|", "cvdrift0"], + data=[ + "B^zeta", + "|B|_z|r,a", + "|B|", + "max_tz |B|", + "|grad(psi)|", + "cvdrift0", + ], grid_fl="Grid : Field line grid.", alpha_weight="Array : Quadrature weight over alpha.", b_quad="callable : Quadrature method to integrate over dB.", @@ -92,10 +99,6 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): plot="bool : Whether to plot some things if check is true.", ) def _ripple(params, transforms, profiles, data, **kwargs): - # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. - # Evaluation of 1/ν neoclassical transport in stellarators. - # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - # https://doi.org/10.1063/1.873749 grid_fl = kwargs.pop("grid_fl") num_rho = grid_fl.num_rho errorif(num_rho != transforms["grid"].num_rho) @@ -133,6 +136,9 @@ def ripple_sum(b): I = bounce_integrate(_dI, [], pitch) return jnp.nansum(H**2 / I, axis=-1) + # could use softmax wlog + max_B = (1 - shift) * transforms["grid"].compress(data["max_tz |B|"]) + max_B = max_B[:, jnp.newaxis] # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ is largest in the # intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). @@ -141,12 +147,14 @@ def ripple_sum(b): # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. # Breakpoints where the quadrature should take more care. # For simple schemes this means to include a quadrature point here. - breaks = jnp.nan_to_num( - get_extrema(**spline, relative_shift=shift, sort=False) - ).reshape(-1, num_rho, alpha.size) - max_tz_B = (1 - shift) * transforms["grid"].compress(data["max_tz |B|"]) - max_tz_B = jnp.broadcast_to(max_tz_B[:, jnp.newaxis], (num_rho, alpha.size)) - breaks = jnp.vstack([breaks, max_tz_B[jnp.newaxis]]) + breaks = get_extrema(**spline, relative_shift=shift, sort=False).reshape( + -1, num_rho, alpha.size + ) + breaks = jnp.where(jnp.isnan(breaks), max_B, breaks) + # Need to include max_B regardless of whether there was nan. + breaks = jnp.vstack( + [breaks, jnp.broadcast_to(max_B, (num_rho, alpha.size))[jnp.newaxis]] + ) breaks = jnp.sort(breaks, axis=0).reshape(breaks.shape[0], -1) try: @@ -179,10 +187,12 @@ def ripple_sum(b): data=["ripple", "psi_r", "S(r)", "V_r(r)", "R0"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): - # V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. - # Evaluation of 1/ν neoclassical transport in stellarators. - # Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - # https://doi.org/10.1063/1.873749. + """Evaluation of 1/ν neoclassical transport in stellarators. + + V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + https://doi.org/10.1063/1.873749 + """ data["effective ripple"] = ( jnp.pi * data["R0"] ** 2 # average major radius diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 3c4a4a3897..6358ae7c94 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -405,7 +405,7 @@ def get_extrema(knots, B_c, B_z_ra_c, relative_shift=1e-6, sort=True): return jnp.sort(B_extrema, axis=0) if sort else B_extrema -def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True): +def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True, **kwargs): """Compute the bounce points given spline of |B| and pitch λ. Parameters @@ -507,11 +507,11 @@ def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True): # rotational transform to potentially capture the bounce point outside # this snapshot of the field line. if check: - _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot) + _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot, **kwargs) return bp1, bp2 -def _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot=True): +def _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot=True, **kwargs): """Check that bounce points are computed correctly. Parameters @@ -547,7 +547,7 @@ def _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot=True): ) if plot: plot_field_line_with_ripple( - B, pitch[p, s], bp1_p, bp2_p, name=f"{p},{s}" + B, pitch[p, s], bp1_p, bp2_p, name=f"{p},{s}", **kwargs ) print("bp1:", bp1_p) print("bp2:", bp2_p) @@ -561,7 +561,7 @@ def _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot=True): assert not err_3, msg_3 if plot: plot_field_line_with_ripple( - B, pitch[:, s], bp1[:, s], bp2[:, s], name=str(s) + B, pitch[:, s], bp1[:, s], bp2[:, s], name=str(s), **kwargs ) @@ -575,6 +575,7 @@ def plot_field_line_with_ripple( num=500, show=True, name=None, + plot_pitch=True, ): """Plot the field line given spline of |B| and bounce points etc. @@ -598,6 +599,8 @@ def plot_field_line_with_ripple( Whether to show the plot. name : str String to prepend to plot title. + plot_pitch: bool + Whether to plot the pitch lines. Returns ------- @@ -626,9 +629,14 @@ def add(lines): if pitch is not None: b = jnp.atleast_1d(1 / pitch) - for val in jnp.unique(b): - add(ax.axhline(val, color="tab:purple", alpha=0.75, label=r"$1 / \lambda$")) - bp1, bp2 = map(jnp.atleast_2d, (bp1, bp2)) + if plot_pitch: + for val in b: + add( + ax.axhline( + val, color="tab:purple", alpha=0.75, label=r"$1 / \lambda$" + ) + ) + bp1, bp2 = jnp.atleast_2d(bp1, bp2) for i in range(bp1.shape[0]): bp1_i, bp2_i = map(_filter_not_nan, (bp1[i], bp2[i])) add( @@ -1259,6 +1267,7 @@ def denominator(B, pitch, Z): print(np.nansum(average, axis=-1)) """ + plot_pitch = kwargs.pop("plot_pitch", True) def group_data_by_field_line(g): errorif(g.ndim > 2) @@ -1334,7 +1343,9 @@ def bounce_integrate(integrand, f, pitch, method="akima"): lines. Last axis enumerates the bounce integrals. """ - bp1, bp2 = bounce_points(pitch, knots, B_c, B_z_ra_c, check, plot) + bp1, bp2 = bounce_points( + pitch, knots, B_c, B_z_ra_c, check, plot, plot_pitch=plot_pitch + ) result = _bounce_quadrature( bp1, bp2, diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 441d0f56d6..46c4957741 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -99,9 +99,14 @@ def test_effective_ripple(): # an adaptive quadrature would use less memory b_quad=trapezoid, b_quad_res=5, + # Gauss-Legendre quadrature with sin automorph ~28 nodes. + # But the real advantage is that Gauss-Legendre with sin + # allows for composite Gauss-Legendre quadrature which + # will be able to match 30 node quadrature with maybe ~10 nodes. + # So very large memory savings. quad=lambda: tanh_sinh_quad(30), - check=False, - plot=False, + # check=True, # noqa: E800 + # plot=True, # noqa: E800 ) assert np.isfinite(data["ripple"]).all() rho = grid_desc.compress(grid_desc.nodes[:, 0]) From fa404b23b95b3cf02bed126d7ad26dd41e6be939 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 30 Apr 2024 21:44:41 -0400 Subject: [PATCH 018/112] Fix eps_eff computation --- desc/compute/_neoclassical.py | 65 ++++++++++++++++++++--------------- tests/test_neoclassical.py | 21 +++++++---- 2 files changed, 52 insertions(+), 34 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 1e6d98c0dc..349cc591fd 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -11,10 +11,11 @@ import orthax import quadax +from termcolor import colored from desc.backend import jnp, trapezoid -from ..utils import errorif +from ..utils import errorif, warnif from .bounce_integral import ( affine_bijection_reverse, bounce_integral, @@ -45,6 +46,13 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): field line integral can be approximated by flux surface average of field line integrals over finite transits. + Assuming the rotational transform is irrational, the limit where the + parameterization of the field line length tends to infinity, the + integrals in (29), (30), (31) will converge to a flux surface average. + In theory, we can compute the effective ripple via flux surface + averages. It is not tested whether such a method will converge to the + limit faster than extending the length of the field line chunk. + Parameters ---------- resolution: int @@ -79,20 +87,11 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): transforms={"grid": []}, profiles=[], coordinates="r", - data=[ - "B^zeta", - "|B|_z|r,a", - "|B|", - "max_tz |B|", - "|grad(psi)|", - "cvdrift0", - ], + data=["B^zeta", "|B|_z|r,a", "|B|", "max_tz |B|", "|grad(psi)|", "cvdrift0"], grid_fl="Grid : Field line grid.", alpha_weight="Array : Quadrature weight over alpha.", b_quad="callable : Quadrature method to integrate over dB.", b_quad_res="int : Resolution for quadrature over dB.", - shift="float : Relative amount to shift maxima down and minima up" - " to avoid floating point errors.", quad="callable : Quadrature method to compute bounce integrals.", automorphism="(callable, callable) : Change of variables for bounce integral.", check="bool : Flag for debugging.", @@ -108,11 +107,10 @@ def _ripple(params, transforms, profiles, data, **kwargs): msg="Set override_grid=False.", ) alpha = grid_fl.compress(grid_fl.nodes[:, 1], surface_label="theta") - alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 2 * jnp.pi / alpha.size)) + alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / alpha.size)) knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") b_quad = kwargs.pop("b_quad", trapezoid) b_quad_res = kwargs.pop("b_quad_res", 5) - shift = kwargs.pop("shift", 1e-6) bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs ) @@ -136,9 +134,6 @@ def ripple_sum(b): I = bounce_integrate(_dI, [], pitch) return jnp.nansum(H**2 / I, axis=-1) - # could use softmax wlog - max_B = (1 - shift) * transforms["grid"].compress(data["max_tz |B|"]) - max_B = max_B[:, jnp.newaxis] # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ is largest in the # intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). @@ -147,11 +142,11 @@ def ripple_sum(b): # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. # Breakpoints where the quadrature should take more care. # For simple schemes this means to include a quadrature point here. - breaks = get_extrema(**spline, relative_shift=shift, sort=False).reshape( - -1, num_rho, alpha.size - ) + breaks = get_extrema(**spline, sort=False).reshape(-1, num_rho, alpha.size) + # Need to remove nan and include (soft)max_B regardless of whether any nan. + max_B = (1 - 1e-6) * transforms["grid"].compress(data["max_tz |B|"]) + max_B = max_B[:, jnp.newaxis] breaks = jnp.where(jnp.isnan(breaks), max_B, breaks) - # Need to include max_B regardless of whether there was nan. breaks = jnp.vstack( [breaks, jnp.broadcast_to(max_B, (num_rho, alpha.size))[jnp.newaxis]] ) @@ -163,6 +158,7 @@ def ripple_sum(b): b = composite_linspace(breaks, b_quad_res) rip = b_quad(ripple_sum(b), b, axis=0) else: + # use adaptive quadrature from quadax rip = b_quad(ripple_sum, breaks) except TypeError as e: raise NotImplementedError from e @@ -184,7 +180,9 @@ def ripple_sum(b): transforms={"grid": []}, profiles=[], coordinates="r", - data=["ripple", "psi_r", "S(r)", "V_r(r)", "R0"], + data=["B^zeta", "|B|", "|grad(psi)|", "ripple", "R0", "V_r(r)", "psi_r", "S(r)"], + fsa="bool : Whether to surface average to approximate the limit.", + grid_fl="Grid : Field line grid.", ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """Evaluation of 1/ν neoclassical transport in stellarators. @@ -193,12 +191,25 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. https://doi.org/10.1063/1.873749 """ + g = kwargs["grid_fl"] + if kwargs.get("fsa", False): + l = g.nodes[g.unique_zeta_idx[-1], 2] - g.nodes[g.unique_zeta_idx[0], 2] + V = data["V_r(r)"] / data["psi_r"] * l + S = data["S(r)"] * l + else: + num_alpha = g.num_theta + shape = (g.num_rho, num_alpha, g.num_zeta) + z = jnp.reshape(g.nodes[:, 2], shape) + v = jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape) + V = jnp.mean(quadax.simpson(v, z, axis=-1), axis=1) + S = jnp.mean( + quadax.simpson(v * data["|grad(psi)|"].reshape(shape), z, axis=-1), + axis=1, + ) + V, S = map(transforms["grid"].expand, (V, S)) + warnif(num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) + data["effective ripple"] = ( - jnp.pi - * data["R0"] ** 2 # average major radius - / (8 * 2**0.5) - * (data["V_r(r)"] / data["psi_r"]) - / data["S(r)"] ** 2 - * data["ripple"] + jnp.pi * data["R0"] ** 2 / (8 * 2**0.5) * V / S**2 * data["ripple"] ) ** (2 / 3) return data diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 46c4957741..43bffaaaee 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -4,7 +4,6 @@ import numpy as np import pytest -from desc.backend import trapezoid from desc.compute import data_index, get_data_deps from desc.compute.bounce_integral import ( desc_grid_from_field_line_coords, @@ -82,13 +81,15 @@ def test_effective_ripple(): ".15_L_9_M_9_N_24_output.h5" ) grid_desc, grid_fl = desc_grid_from_field_line_coords( - eq, rho=np.linspace(0.01, 1, 20) + eq, + rho=np.linspace(0.01, 1, 20), + zeta=np.linspace(-3 * np.pi, 3 * np.pi, 50), ) data = _compute_field_line_data( eq, grid_desc, ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], - ["max_tz |B|", "R0"], + ["max_tz |B|", "R0", "V_r(r)", "psi_r", "S(r)"], ) data = eq.compute( "ripple", @@ -96,8 +97,6 @@ def test_effective_ripple(): data=data, override_grid=False, grid_fl=grid_fl, - # an adaptive quadrature would use less memory - b_quad=trapezoid, b_quad_res=5, # Gauss-Legendre quadrature with sin automorph ~28 nodes. # But the real advantage is that Gauss-Legendre with sin @@ -116,14 +115,22 @@ def test_effective_ripple(): ax[0].set_xlabel(r"$\rho$") ax[0].set_ylabel("ripple") ax[0].set_title("Ripple, defined as ∫ db ∑ⱼ Hⱼ² / Iⱼ") - # Workaround until eq.compute() is fixed. + # Workaround until eq.compute() is fixed to only compute dependencies + # that are needed for the requested computation. (So don't compute + # dependencies of things already in data). data_R0 = eq.compute("R0") for key in data_R0: if key not in data: # Need to add R0's dependencies which are surface functions of zeta # aren't attempted to be recomputed on grid_desc. data[key] = data_R0[key] - data = eq.compute("effective ripple", grid=grid_desc, data=data) + data = eq.compute( + "effective ripple", + grid=grid_desc, + data=data, + grid_fl=grid_fl, + override_grid=False, + ) assert np.isfinite(data["effective ripple"]).all() eff_ripple = grid_desc.compress(data["effective ripple"]) ax[1].plot(rho, eff_ripple, marker="o", label=r"$\epsilon_{\text{effective}}$") From 01f8a759fdc0ed427ea40bef851cc7c7d4f7b098 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 1 May 2024 00:04:08 -0400 Subject: [PATCH 019/112] Try going further along field line now that memory is used less --- desc/compute/_neoclassical.py | 8 ++++++-- tests/test_neoclassical.py | 5 +++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 349cc591fd..97444a396e 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -94,6 +94,7 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): b_quad_res="int : Resolution for quadrature over dB.", quad="callable : Quadrature method to compute bounce integrals.", automorphism="(callable, callable) : Change of variables for bounce integral.", + batched="bool : Whether to perform computation in a batched manner.", check="bool : Flag for debugging.", plot="bool : Whether to plot some things if check is true.", ) @@ -111,6 +112,7 @@ def _ripple(params, transforms, profiles, data, **kwargs): knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") b_quad = kwargs.pop("b_quad", trapezoid) b_quad_res = kwargs.pop("b_quad_res", 5) + batched = kwargs.pop("batched", False) bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs ) @@ -130,8 +132,10 @@ def ripple_sum(b): """ pitch = 1 / b - H = bounce_integrate(_dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch) - I = bounce_integrate(_dI, [], pitch) + H = bounce_integrate( + _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched + ) + I = bounce_integrate(_dI, [], pitch, batched=batched) return jnp.nansum(H**2 / I, axis=-1) # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ is largest in the diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 43bffaaaee..243e0e9e47 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -83,7 +83,7 @@ def test_effective_ripple(): grid_desc, grid_fl = desc_grid_from_field_line_coords( eq, rho=np.linspace(0.01, 1, 20), - zeta=np.linspace(-3 * np.pi, 3 * np.pi, 50), + zeta=np.linspace(-10 * np.pi, 10 * np.pi, 100), ) data = _compute_field_line_data( eq, @@ -103,7 +103,8 @@ def test_effective_ripple(): # allows for composite Gauss-Legendre quadrature which # will be able to match 30 node quadrature with maybe ~10 nodes. # So very large memory savings. - quad=lambda: tanh_sinh_quad(30), + quad=lambda: tanh_sinh_quad(41), + batched=False, # check=True, # noqa: E800 # plot=True, # noqa: E800 ) From 1a49329d7ece6f1a2ab1371dd07c4b58576013a4 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 1 May 2024 01:05:04 -0400 Subject: [PATCH 020/112] Simplify non batched interpolation --- desc/compute/bounce_integral.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 2fdfc037b3..83317a9e7f 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1088,9 +1088,6 @@ def group_data_by_field_line_and_pitch(g): def loop(bp): bp1, bp2 = bp - bp1 = bp1.T - bp2 = bp2.T - assert bp1.shape == bp2.shape == (pitch.shape[0], S) z = affine_bijection_reverse( x, bp1[..., jnp.newaxis], bp2[..., jnp.newaxis] ) @@ -1110,7 +1107,7 @@ def loop(bp): plot, ) - _, result = imap(loop, (bp1.T, bp2.T)) + _, result = imap(loop, (jnp.moveaxis(bp1, -1, 0), jnp.moveaxis(bp2, -1, 0))) result = jnp.moveaxis(result, source=0, destination=-1) result = result * grad_affine_bijection_reverse(bp1, bp2) From b4ca7e3a9bdf8ea57014938d62938c51af7a3828 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 1 May 2024 01:07:17 -0400 Subject: [PATCH 021/112] Always set batched to false --- desc/compute/_neoclassical.py | 7 +++---- tests/test_neoclassical.py | 1 - 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 97444a396e..806b3f5c4b 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -94,7 +94,7 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): b_quad_res="int : Resolution for quadrature over dB.", quad="callable : Quadrature method to compute bounce integrals.", automorphism="(callable, callable) : Change of variables for bounce integral.", - batched="bool : Whether to perform computation in a batched manner.", + # TODO: remove later check="bool : Flag for debugging.", plot="bool : Whether to plot some things if check is true.", ) @@ -112,7 +112,6 @@ def _ripple(params, transforms, profiles, data, **kwargs): knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") b_quad = kwargs.pop("b_quad", trapezoid) b_quad_res = kwargs.pop("b_quad_res", 5) - batched = kwargs.pop("batched", False) bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs ) @@ -133,9 +132,9 @@ def ripple_sum(b): """ pitch = 1 / b H = bounce_integrate( - _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched + _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=False ) - I = bounce_integrate(_dI, [], pitch, batched=batched) + I = bounce_integrate(_dI, [], pitch, batched=False) return jnp.nansum(H**2 / I, axis=-1) # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ is largest in the diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 243e0e9e47..4eeefe5a45 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -104,7 +104,6 @@ def test_effective_ripple(): # will be able to match 30 node quadrature with maybe ~10 nodes. # So very large memory savings. quad=lambda: tanh_sinh_quad(41), - batched=False, # check=True, # noqa: E800 # plot=True, # noqa: E800 ) From a4637ea89fb584e0341525c48c74bafa47c9eb32 Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 3 May 2024 02:10:20 -0400 Subject: [PATCH 022/112] Switch to quadrature that converges --- desc/compute/_neoclassical.py | 8 ++++++++ desc/compute/bounce_integral.py | 2 +- tests/test_neoclassical.py | 13 +++---------- 3 files changed, 12 insertions(+), 11 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 806b3f5c4b..092fbdc125 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -9,6 +9,8 @@ expensive computations. """ +from functools import partial + import orthax import quadax from termcolor import colored @@ -94,6 +96,7 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): b_quad_res="int : Resolution for quadrature over dB.", quad="callable : Quadrature method to compute bounce integrals.", automorphism="(callable, callable) : Change of variables for bounce integral.", + quad_res="int : Resolution for quadrature to compute bounce integrals.", # TODO: remove later check="bool : Flag for debugging.", plot="bool : Whether to plot some things if check is true.", @@ -112,6 +115,11 @@ def _ripple(params, transforms, profiles, data, **kwargs): knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") b_quad = kwargs.pop("b_quad", trapezoid) b_quad_res = kwargs.pop("b_quad_res", 5) + if "quad_res" in kwargs: + if "quad" in kwargs: + kwargs["quad"] = partial(kwargs["quad"], kwargs.pop("quad_res")) + else: + kwargs["deg"] = kwargs.pop("quad_res") bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs ) diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 45d4870815..5450ba8bc5 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1314,7 +1314,7 @@ def group_data_by_field_line(g): spline = {"knots": knots, "B_c": B_c, "B_z_ra_c": B_z_ra_c} if quad == leggauss: - kwargs.setdefault("deg", 19) + kwargs.setdefault("deg", 28) x, w = quad(**kwargs) # The gradient of the transformation is the weight function w(x) of the integral. auto, grad_auto = automorphism diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 322e68ebe2..b02358d9c4 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -5,7 +5,7 @@ import pytest from desc.compute import data_index, get_data_deps -from desc.compute.bounce_integral import desc_grid_from_field_line_coords, tanh_sinh +from desc.compute.bounce_integral import desc_grid_from_field_line_coords from desc.equilibrium import Equilibrium from desc.grid import LinearGrid @@ -80,7 +80,7 @@ def test_effective_ripple(): grid_desc, grid_fl = desc_grid_from_field_line_coords( eq, rho=np.linspace(0.01, 1, 20), - zeta=np.linspace(-10 * np.pi, 10 * np.pi, 100), + zeta=np.linspace(-20 * np.pi, 20 * np.pi, 200), ) data = _compute_field_line_data( eq, @@ -95,14 +95,7 @@ def test_effective_ripple(): override_grid=False, grid_fl=grid_fl, b_quad_res=5, - # Gauss-Legendre quadrature with sin automorph ~28 nodes. - # But the real advantage is that Gauss-Legendre with sin - # allows for composite Gauss-Legendre quadrature which - # will be able to match 30 node quadrature with maybe ~10 nodes. - # So very large memory savings. - quad=lambda: tanh_sinh(41), - # check=True, # noqa: E800 - # plot=True, # noqa: E800 + quad_res=28, ) assert np.isfinite(data["ripple"]).all() rho = grid_desc.compress(grid_desc.nodes[:, 0]) From 0cc5c7296139fbd9b69ed8243bb6f31fa7ebabff Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 22 May 2024 18:06:12 -0500 Subject: [PATCH 023/112] Update neoclassical compute api for changes introduced in pull request #1024 --- desc/compute/_neoclassical.py | 110 ++++++++++++++++------------------ tests/test_neoclassical.py | 31 +++++----- 2 files changed, 67 insertions(+), 74 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 092fbdc125..86d0f66464 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -1,4 +1,4 @@ -"""Compute functions for neoclassical transport objectives. +"""Compute functions for neoclassical transport. Notes ----- @@ -9,21 +9,19 @@ expensive computations. """ -from functools import partial - import orthax import quadax from termcolor import colored from desc.backend import jnp, trapezoid -from ..utils import errorif, warnif +from ..utils import warnif from .bounce_integral import ( - affine_bijection_reverse, + affine_bijection, bounce_integral, composite_linspace, get_extrema, - grad_affine_bijection_reverse, + grad_affine_bijection, ) from .data_index import register_compute_fun @@ -73,8 +71,8 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): """ x, w = orthax.legendre.leggauss(resolution) - w = w * grad_affine_bijection_reverse(a_min, a_max) - alpha = affine_bijection_reverse(x, a_min, a_max) + w = w * grad_affine_bijection(a_min, a_max) + alpha = affine_bijection(x, a_min, a_max) return alpha, w @@ -90,93 +88,88 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): profiles=[], coordinates="r", data=["B^zeta", "|B|_z|r,a", "|B|", "max_tz |B|", "|grad(psi)|", "cvdrift0"], - grid_fl="Grid : Field line grid.", + # Can use theta_PEST grids if B^zeta changed to B^theta_PEST in bounce integrals. + grid_requirement=[ + "source_grid", + lambda grid: grid.source_grid.coordinates == "raz" + and grid.source_grid.is_meshgrid, + ], + bounce_integral=( + "callable : Method to compute bounce integrals." + " (You may want to wrap desc.compute.bounce_integral.bounce_integral" + " to change optional parameters such as quadrature resolution, etc.)." + ), + batched="bool : Whether to perform computation in a batched manner.", + b_quad="callable : Quadrature method over velocity space (i.e. dB integral).", + b_quad_res="int : Resolution for quadrature over velocity space.", + # if doing a flux surface average alpha_weight="Array : Quadrature weight over alpha.", - b_quad="callable : Quadrature method to integrate over dB.", - b_quad_res="int : Resolution for quadrature over dB.", - quad="callable : Quadrature method to compute bounce integrals.", - automorphism="(callable, callable) : Change of variables for bounce integral.", - quad_res="int : Resolution for quadrature to compute bounce integrals.", - # TODO: remove later - check="bool : Flag for debugging.", - plot="bool : Whether to plot some things if check is true.", ) def _ripple(params, transforms, profiles, data, **kwargs): - grid_fl = kwargs.pop("grid_fl") - num_rho = grid_fl.num_rho - errorif(num_rho != transforms["grid"].num_rho) - errorif( - # TODO: Add grid labels to compute quantities so this doesn't occur. - grid_fl.num_nodes != transforms["grid"].num_nodes, - msg="Set override_grid=False.", - ) - alpha = grid_fl.compress(grid_fl.nodes[:, 1], surface_label="theta") - alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / alpha.size)) - knots = grid_fl.compress(grid_fl.nodes[:, 2], surface_label="zeta") - b_quad = kwargs.pop("b_quad", trapezoid) - b_quad_res = kwargs.pop("b_quad_res", 5) - if "quad_res" in kwargs: - if "quad" in kwargs: - kwargs["quad"] = partial(kwargs["quad"], kwargs.pop("quad_res")) - else: - kwargs["deg"] = kwargs.pop("quad_res") - bounce_integrate, spline = bounce_integral( - data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, **kwargs + g = transforms["grid"].source_grid + knots = g.compress(g.nodes[:, 2], surface_label="zeta") + bounce_integrate, spline = kwargs.pop("bounce_integral", bounce_integral)( + data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) + batched = kwargs.pop("batched", False) def ripple_sum(b): """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. Parameters ---------- - b : Array, shape(..., num_rho * alpha.size) + b : Array, shape(..., g.num_rho * g.num_alpha) Multiplicative inverse of pitch angle. Returns ------- - ripple_sum : Array, shape(..., num_rho * alpha.size) + ripple_sum : Array, shape(..., num_rho * num_alpha) ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha. """ pitch = 1 / b H = bounce_integrate( - _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=False + _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched ) - I = bounce_integrate(_dI, [], pitch, batched=False) + I = bounce_integrate(_dI, [], pitch, batched=batched) return jnp.nansum(H**2 / I, axis=-1) - # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ is largest in the - # intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To + # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ may be largest in + # the intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). # For λ = 1 / |B|(ζ*), near |B|(ζ*), the quantity 1 / √(1 − λ |B|) is singular # with strength ~ 1 / |∂|B|/∂_ζ|. Therefore, a quadrature for ε should evaluate # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. + # Breakpoints where the quadrature should take more care. # For simple schemes this means to include a quadrature point here. - breaks = get_extrema(**spline, sort=False).reshape(-1, num_rho, alpha.size) + breaks = get_extrema(**spline, sort=False).reshape(-1, g.num_rho, g.num_alpha) # Need to remove nan and include (soft)max_B regardless of whether any nan. - max_B = (1 - 1e-6) * transforms["grid"].compress(data["max_tz |B|"]) + max_B = (1 - 1e-6) * g.compress(data["max_tz |B|"]) max_B = max_B[:, jnp.newaxis] breaks = jnp.where(jnp.isnan(breaks), max_B, breaks) breaks = jnp.vstack( - [breaks, jnp.broadcast_to(max_B, (num_rho, alpha.size))[jnp.newaxis]] + [breaks, jnp.broadcast_to(max_B, (g.num_rho, g.num_alpha))[jnp.newaxis]] ) breaks = jnp.sort(breaks, axis=0).reshape(breaks.shape[0], -1) + b_quad = kwargs.pop("b_quad", trapezoid) try: is_Newton_Cotes = b_quad in [trapezoid, quadax.trapezoid, quadax.simpson] if is_Newton_Cotes: - b = composite_linspace(breaks, b_quad_res) - rip = b_quad(ripple_sum(b), b, axis=0) + # TODO: maybe it's better to just use a uniform quadrature in |B| + b = composite_linspace(breaks, kwargs.pop("b_quad_res", 5)) + ripple = b_quad(ripple_sum(b), b, axis=0) else: # use adaptive quadrature from quadax - rip = b_quad(ripple_sum, breaks) + ripple = b_quad(ripple_sum, breaks) except TypeError as e: raise NotImplementedError from e # Integrate over flux surface. - ripple = rip.reshape(num_rho, -1) @ alpha_weight - data["ripple"] = transforms["grid"].expand(ripple) + alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) + ripple = ripple.reshape(g.num_rho, -1) @ alpha_weight + data["ripple"] = g.expand(ripple) return data @@ -192,8 +185,12 @@ def ripple_sum(b): profiles=[], coordinates="r", data=["B^zeta", "|B|", "|grad(psi)|", "ripple", "R0", "V_r(r)", "psi_r", "S(r)"], + grid_requirement=[ + "source_grid", + lambda grid: grid.source_grid.coordinates == "raz" + and grid.source_grid.is_meshgrid, + ], fsa="bool : Whether to surface average to approximate the limit.", - grid_fl="Grid : Field line grid.", ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """Evaluation of 1/ν neoclassical transport in stellarators. @@ -202,14 +199,13 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. https://doi.org/10.1063/1.873749 """ - g = kwargs["grid_fl"] + g = transforms["grid"].source_grid if kwargs.get("fsa", False): l = g.nodes[g.unique_zeta_idx[-1], 2] - g.nodes[g.unique_zeta_idx[0], 2] V = data["V_r(r)"] / data["psi_r"] * l S = data["S(r)"] * l else: - num_alpha = g.num_theta - shape = (g.num_rho, num_alpha, g.num_zeta) + shape = (g.num_rho, g.num_alpha, g.num_zeta) z = jnp.reshape(g.nodes[:, 2], shape) v = jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape) V = jnp.mean(quadax.simpson(v, z, axis=-1), axis=1) @@ -217,8 +213,8 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): quadax.simpson(v * data["|grad(psi)|"].reshape(shape), z, axis=-1), axis=1, ) - V, S = map(transforms["grid"].expand, (V, S)) - warnif(num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) + V, S = map(g.expand, (V, S)) + warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) data["effective ripple"] = ( jnp.pi * data["R0"] ** 2 / (8 * 2**0.5) * V / S**2 * data["ripple"] diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index b02358d9c4..abcf477297 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -1,12 +1,15 @@ """Test for neoclassical transport compute functions.""" +from functools import partial + import matplotlib.pyplot as plt import numpy as np import pytest from desc.compute import data_index, get_data_deps -from desc.compute.bounce_integral import desc_grid_from_field_line_coords +from desc.compute.bounce_integral import bounce_integral from desc.equilibrium import Equilibrium +from desc.equilibrium.coords import desc_grid_from_field_line_coords from desc.grid import LinearGrid @@ -77,29 +80,29 @@ def test_effective_ripple(): "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) - grid_desc, grid_fl = desc_grid_from_field_line_coords( + grid = desc_grid_from_field_line_coords( eq, rho=np.linspace(0.01, 1, 20), - zeta=np.linspace(-20 * np.pi, 20 * np.pi, 200), + alpha=np.array([0]), + zeta=np.linspace(-2 * np.pi, 2 * np.pi, 20), ) data = _compute_field_line_data( eq, - grid_desc, + grid, ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], ["max_tz |B|", "R0", "V_r(r)", "psi_r", "S(r)"], ) data = eq.compute( "ripple", - grid=grid_desc, + grid=grid, data=data, override_grid=False, - grid_fl=grid_fl, + bounce_integral=partial(bounce_integral, deg=28), b_quad_res=5, - quad_res=28, ) assert np.isfinite(data["ripple"]).all() - rho = grid_desc.compress(grid_desc.nodes[:, 0]) - ripple = grid_desc.compress(data["ripple"]) + rho = grid.compress(grid.nodes[:, 0]) + ripple = grid.compress(data["ripple"]) fig, ax = plt.subplots(2) ax[0].plot(rho, ripple, marker="o", label="∫ db ∑ⱼ Hⱼ² / Iⱼ") ax[0].set_xlabel(r"$\rho$") @@ -114,15 +117,9 @@ def test_effective_ripple(): # Need to add R0's dependencies which are surface functions of zeta # aren't attempted to be recomputed on grid_desc. data[key] = data_R0[key] - data = eq.compute( - "effective ripple", - grid=grid_desc, - data=data, - grid_fl=grid_fl, - override_grid=False, - ) + data = eq.compute("effective ripple", grid=grid, data=data, override_grid=False) assert np.isfinite(data["effective ripple"]).all() - eff_ripple = grid_desc.compress(data["effective ripple"]) + eff_ripple = grid.compress(data["effective ripple"]) ax[1].plot(rho, eff_ripple, marker="o", label=r"$\epsilon_{\text{effective}}$") ax[1].set_xlabel(r"$\rho$") ax[1].set_ylabel(r"$\epsilon_{\text{effective}}$") From 0901d9608ce5cd5101951604855e5b821a762256 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 26 May 2024 22:30:37 -0500 Subject: [PATCH 024/112] Adaptive quadrature working now. Also reduced memory and increase speed. --- desc/compute/_neoclassical.py | 187 ++++++++++++++++++++++------------ tests/test_axis_limits.py | 3 +- tests/test_neoclassical.py | 22 +++- 3 files changed, 141 insertions(+), 71 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 86d0f66464..412d74c5a0 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -13,7 +13,7 @@ import quadax from termcolor import colored -from desc.backend import jnp, trapezoid +from desc.backend import jit, jnp, trapezoid from ..utils import warnif from .bounce_integral import ( @@ -26,14 +26,28 @@ from .data_index import register_compute_fun -def _dH(grad_psi_norm, cvdrift0, B, pitch, Z): - return ( - pitch * jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * cvdrift0 / B - ) +def vec_quadax(quad): + """Vectorize an adaptive quadrature method from quadax to compute ripple. + Parameters + ---------- + quad : callable + Adaptive quadrature method matching API from quadax. -def _dI(B, pitch, Z): - return jnp.sqrt(1 - pitch * B) / B + Returns + ------- + vec_quad : callable + Vectorized adaptive quadrature method. + + """ + + def vec_quad(fun, interval, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): + return quad(fun, interval, args=(B_sup_z, B, B_z_ra, grad_psi, cvdrift0))[0] + + vec_quad = jnp.vectorize( + vec_quad, signature="(2),(m),(m),(m),(m),(m)->()", excluded={0} + ) + return vec_quad def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): @@ -76,6 +90,18 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): return alpha, w +def _dH(grad_psi_norm, cvdrift0, B, pitch, Z): + # Used to compute "ripple". + return ( + pitch * jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * cvdrift0 / B + ) + + +def _dI(B, pitch, Z): + # Used to compute "ripple". + return jnp.sqrt(1 - pitch * B) / B + + @register_compute_fun( name="ripple", label="∫ db ∑ⱼ Hⱼ² / Iⱼ", @@ -87,8 +113,15 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): transforms={"grid": []}, profiles=[], coordinates="r", - data=["B^zeta", "|B|_z|r,a", "|B|", "max_tz |B|", "|grad(psi)|", "cvdrift0"], - # Can use theta_PEST grids if B^zeta changed to B^theta_PEST in bounce integrals. + data=[ + "min_tz |B|", + "max_tz |B|", + "B^zeta", + "|B|_z|r,a", + "|B|", + "|grad(psi)|", + "cvdrift0", + ], grid_requirement=[ "source_grid", lambda grid: grid.source_grid.coordinates == "raz" @@ -100,75 +133,99 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): " to change optional parameters such as quadrature resolution, etc.)." ), batched="bool : Whether to perform computation in a batched manner.", - b_quad="callable : Quadrature method over velocity space (i.e. dB integral).", - b_quad_res="int : Resolution for quadrature over velocity space.", + quad="callable : Quadrature method over velocity space (i.e. pitch integral)." + "Adaptive quadrature method from quadax must be wrapped with vec_quadax.", + quad_res="int : Resolution for quadrature over velocity space.", # if doing a flux surface average alpha_weight="Array : Quadrature weight over alpha.", ) def _ripple(params, transforms, profiles, data, **kwargs): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") - bounce_integrate, spline = kwargs.pop("bounce_integral", bounce_integral)( - data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots - ) - batched = kwargs.pop("batched", False) - def ripple_sum(b): - """Return the ripple sum ∑ⱼ Hⱼ² / Iⱼ evaluated at b. + _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) + batched = kwargs.pop("batched", True) + quad = kwargs.pop("quad", trapezoid) + quad_res = kwargs.pop("quad_res", 21) + alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) + + # Get boundary of integral over pitch for each field line. + min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) + min_B = (1 - 1e-6) * min_B + max_B = (1 - 1e-6) * max_B + min_B = jnp.broadcast_to(min_B[:, jnp.newaxis], (g.num_rho, g.num_alpha)) + max_B = jnp.broadcast_to(max_B[:, jnp.newaxis], (g.num_rho, g.num_alpha)) + boundary = jnp.stack([min_B, max_B]) + + is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] + if is_Newton_Cotes: + bounce_integrate, spline = _bounce_integral( + data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots + ) + + @jit + def rs(b): + """Return ∑ⱼ Hⱼ² / Iⱼ evaluated at b. + + Parameters + ---------- + b : Array, shape(*b.shape[:-1], g.num_rho * g.num_alpha) + Multiplicative inverse of pitch angle. - Parameters - ---------- - b : Array, shape(..., g.num_rho * g.num_alpha) - Multiplicative inverse of pitch angle. + Returns + ------- + rs : Array, shape(b.shape) + ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha. - Returns - ------- - ripple_sum : Array, shape(..., num_rho * num_alpha) - ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha. + """ + pitch = 1 / b + H = bounce_integrate( + _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched + ) + I = bounce_integrate(_dI, [], pitch, batched=batched) + return jnp.nansum(H**2 / I, axis=-1) - """ - pitch = 1 / b - H = bounce_integrate( - _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched + # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ may be largest in + # the intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To + # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). + # For λ = 1 / |B|(ζ*), near |B|(ζ*), the quantity 1 / √(1 − λ |B|) is singular + # with strength ~ 1 / |∂|B|/∂_ζ|. Therefore, a quadrature for ε should evaluate + # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. + breaks = get_extrema(**spline, sort=False).reshape(-1, g.num_rho, g.num_alpha) + # Need to remove nan and include max_B regardless of whether there are any nan. + breaks = jnp.where(jnp.isnan(breaks), max_B, breaks) + # Gather quadrature points at breaks and linearly spaced within boundary. + b = jnp.vstack([composite_linspace(boundary, quad_res), breaks]) + b = jnp.sort(b, axis=0).reshape(-1, g.num_rho * g.num_alpha) + ripple = quad(rs(b), b, axis=0) + else: + # Use adaptive quadrature. + @jit + def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): + # Quadax requires scalar integration interval, so we need scalar integrand. + bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) + pitch = 1 / b + H = bounce_integrate(_dH, [grad_psi, cvdrift0], pitch, batched=batched) + I = bounce_integrate(_dI, [], pitch, batched=batched) + return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) + + boundary = boundary.reshape(2, -1).T + args = list( + map( + lambda f: f.reshape(g.num_rho * g.num_alpha, g.num_zeta), + ( + data["B^zeta"], + data["|B|"], + data["|B|_z|r,a"], + data["|grad(psi)|"], + data["cvdrift0"], + ), + ) ) - I = bounce_integrate(_dI, [], pitch, batched=batched) - return jnp.nansum(H**2 / I, axis=-1) - - # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ may be largest in - # the intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To - # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). - # For λ = 1 / |B|(ζ*), near |B|(ζ*), the quantity 1 / √(1 − λ |B|) is singular - # with strength ~ 1 / |∂|B|/∂_ζ|. Therefore, a quadrature for ε should evaluate - # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. - - # Breakpoints where the quadrature should take more care. - # For simple schemes this means to include a quadrature point here. - breaks = get_extrema(**spline, sort=False).reshape(-1, g.num_rho, g.num_alpha) - # Need to remove nan and include (soft)max_B regardless of whether any nan. - max_B = (1 - 1e-6) * g.compress(data["max_tz |B|"]) - max_B = max_B[:, jnp.newaxis] - breaks = jnp.where(jnp.isnan(breaks), max_B, breaks) - breaks = jnp.vstack( - [breaks, jnp.broadcast_to(max_B, (g.num_rho, g.num_alpha))[jnp.newaxis]] - ) - breaks = jnp.sort(breaks, axis=0).reshape(breaks.shape[0], -1) - - b_quad = kwargs.pop("b_quad", trapezoid) - try: - is_Newton_Cotes = b_quad in [trapezoid, quadax.trapezoid, quadax.simpson] - if is_Newton_Cotes: - # TODO: maybe it's better to just use a uniform quadrature in |B| - b = composite_linspace(breaks, kwargs.pop("b_quad_res", 5)) - ripple = b_quad(ripple_sum(b), b, axis=0) - else: - # use adaptive quadrature from quadax - ripple = b_quad(ripple_sum, breaks) - except TypeError as e: - raise NotImplementedError from e + ripple = quad(rs, boundary, *args) # Integrate over flux surface. - alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) - ripple = ripple.reshape(g.num_rho, -1) @ alpha_weight + ripple = ripple.reshape(g.num_rho, g.num_alpha) @ alpha_weight data["ripple"] = g.expand(ripple) return data diff --git a/tests/test_axis_limits.py b/tests/test_axis_limits.py index bb403b76e0..d613440928 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -61,7 +61,6 @@ "gbdrift", "cvdrift", "grad(alpha)", - "cvdrift0", "|e^helical|", "|grad(theta)|", " Redl", # may not exist for all configurations @@ -93,7 +92,7 @@ "K_vc", # only defined on surface "iota_num_rrr", "iota_den_rrr", - "cvdrift0", + "ripple", } diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index abcf477297..4d8322ee19 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -5,12 +5,16 @@ import matplotlib.pyplot as plt import numpy as np import pytest +import quadax +from orthax.legendre import leggauss from desc.compute import data_index, get_data_deps +from desc.compute._neoclassical import vec_quadax from desc.compute.bounce_integral import bounce_integral from desc.equilibrium import Equilibrium from desc.equilibrium.coords import desc_grid_from_field_line_coords from desc.grid import LinearGrid +from desc.utils import Timer def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=None): @@ -76,30 +80,40 @@ def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=No @pytest.mark.unit def test_effective_ripple(): """Compare DESC effective ripple against neo stellopt.""" + timer = Timer() eq = Equilibrium.load( "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) + timer.start("map") grid = desc_grid_from_field_line_coords( eq, rho=np.linspace(0.01, 1, 20), alpha=np.array([0]), - zeta=np.linspace(-2 * np.pi, 2 * np.pi, 20), + zeta=np.linspace(-10 * np.pi, 10 * np.pi, 100), ) + timer.stop("map") + timer.disp("map") + timer.start("dependency compute") data = _compute_field_line_data( eq, grid, ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], - ["max_tz |B|", "R0", "V_r(r)", "psi_r", "S(r)"], + ["min_tz |B|", "max_tz |B|", "R0", "V_r(r)", "psi_r", "S(r)"], ) + timer.stop("dependency compute") + timer.disp("dependency compute") + timer.start("ripple compute") data = eq.compute( "ripple", grid=grid, data=data, override_grid=False, - bounce_integral=partial(bounce_integral, deg=28), - b_quad_res=5, + bounce_integral=partial(bounce_integral, quad=leggauss(28)), + quad=vec_quadax(quadax.quadgk), ) + timer.stop("ripple compute") + timer.disp("ripple compute") assert np.isfinite(data["ripple"]).all() rho = grid.compress(grid.nodes[:, 0]) ripple = grid.compress(data["ripple"]) From 95ce834f69aa3881ca66924d09478bf1c47dad71 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 26 May 2024 22:39:03 -0500 Subject: [PATCH 025/112] Wrap jit at outer level --- desc/compute/_neoclassical.py | 6 ++++-- tests/test_neoclassical.py | 4 +--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 412d74c5a0..00de34ba2b 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -9,6 +9,8 @@ expensive computations. """ +from functools import partial + import orthax import quadax from termcolor import colored @@ -139,6 +141,7 @@ def _dI(B, pitch, Z): # if doing a flux surface average alpha_weight="Array : Quadrature weight over alpha.", ) +@partial(jit, static_argnames=["bounce_integral"]) def _ripple(params, transforms, profiles, data, **kwargs): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") @@ -163,7 +166,6 @@ def _ripple(params, transforms, profiles, data, **kwargs): data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) - @jit def rs(b): """Return ∑ⱼ Hⱼ² / Iⱼ evaluated at b. @@ -200,7 +202,7 @@ def rs(b): ripple = quad(rs(b), b, axis=0) else: # Use adaptive quadrature. - @jit + def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): # Quadax requires scalar integration interval, so we need scalar integrand. bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 4d8322ee19..3e12e75502 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -5,11 +5,9 @@ import matplotlib.pyplot as plt import numpy as np import pytest -import quadax from orthax.legendre import leggauss from desc.compute import data_index, get_data_deps -from desc.compute._neoclassical import vec_quadax from desc.compute.bounce_integral import bounce_integral from desc.equilibrium import Equilibrium from desc.equilibrium.coords import desc_grid_from_field_line_coords @@ -110,7 +108,7 @@ def test_effective_ripple(): data=data, override_grid=False, bounce_integral=partial(bounce_integral, quad=leggauss(28)), - quad=vec_quadax(quadax.quadgk), + # quad=vec_quadax(quadax.quadgk), # noqa: E800 ) timer.stop("ripple compute") timer.disp("ripple compute") From fe0b0991a6652cb68c827e52332d6001d91467ad Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 27 May 2024 00:46:10 -0500 Subject: [PATCH 026/112] Avoid unnecessary intermediate computation in computing ripple --- desc/compute/_neoclassical.py | 49 +++++++++++++++++++-------------- desc/compute/bounce_integral.py | 10 ++----- tests/test_axis_limits.py | 2 +- tests/test_neoclassical.py | 13 +-------- 4 files changed, 34 insertions(+), 40 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 00de34ba2b..0d07f451a3 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -141,7 +141,7 @@ def _dI(B, pitch, Z): # if doing a flux surface average alpha_weight="Array : Quadrature weight over alpha.", ) -@partial(jit, static_argnames=["bounce_integral"]) +@partial(jit, static_argnames=["bounce_integral", "batched", "quad"]) def _ripple(params, transforms, profiles, data, **kwargs): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") @@ -154,10 +154,10 @@ def _ripple(params, transforms, profiles, data, **kwargs): # Get boundary of integral over pitch for each field line. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) - min_B = (1 - 1e-6) * min_B + # Floating point error impedes consistent detection of bounce points riding + # extrema. Shift values slightly to resolve this issue. + min_B = (1 + 1e-6) * min_B max_B = (1 - 1e-6) * max_B - min_B = jnp.broadcast_to(min_B[:, jnp.newaxis], (g.num_rho, g.num_alpha)) - max_B = jnp.broadcast_to(max_B[:, jnp.newaxis], (g.num_rho, g.num_alpha)) boundary = jnp.stack([min_B, max_B]) is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] @@ -195,9 +195,19 @@ def rs(b): # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. breaks = get_extrema(**spline, sort=False).reshape(-1, g.num_rho, g.num_alpha) # Need to remove nan and include max_B regardless of whether there are any nan. - breaks = jnp.where(jnp.isnan(breaks), max_B, breaks) - # Gather quadrature points at breaks and linearly spaced within boundary. - b = jnp.vstack([composite_linspace(boundary, quad_res), breaks]) + breaks = jnp.where(jnp.isnan(breaks), max_B[:, jnp.newaxis], breaks) + # Gather quadrature points at breaks and uniformly spaced within boundary. + # TODO: might be better to just do uniformly spaced + uniform = composite_linspace(boundary, quad_res) + b = jnp.vstack( + [ + jnp.broadcast_to( + uniform[..., jnp.newaxis], + (uniform.shape[0], g.num_rho, g.num_alpha), + ), + breaks, + ] + ) b = jnp.sort(b, axis=0).reshape(-1, g.num_rho * g.num_alpha) ripple = quad(rs(b), b, axis=0) else: @@ -211,19 +221,18 @@ def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): I = bounce_integrate(_dI, [], pitch, batched=batched) return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) - boundary = boundary.reshape(2, -1).T - args = list( - map( - lambda f: f.reshape(g.num_rho * g.num_alpha, g.num_zeta), - ( - data["B^zeta"], - data["|B|"], - data["|B|_z|r,a"], - data["|grad(psi)|"], - data["cvdrift0"], - ), - ) - ) + boundary = boundary.T[:, jnp.newaxis] + assert boundary.shape == (g.num_rho, 1, 2) + args = [ + f.reshape(g.num_rho, g.num_alpha, g.num_zeta) + for f in [ + data["B^zeta"], + data["|B|"], + data["|B|_z|r,a"], + data["|grad(psi)|"], + data["cvdrift0"], + ] + ] ripple = quad(rs, boundary, *args) # Integrate over flux surface. diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index da298c20f9..cf098a9273 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -278,7 +278,7 @@ def composite_linspace(breaks, resolution): breaks : Array First axis has values to return linearly spaced values between. The remaining axes are batch axes. - Assumes input is sorted. + Assumes input is sorted along first axis. resolution : int Number of points between each break. @@ -1285,15 +1285,11 @@ def denominator(B, pitch, Z): print(np.nansum(average, axis=-1)) """ - - def group_data_by_field_line(g): - errorif(g.ndim > 2) - return g.reshape(-1, knots.size) - B_sup_z = B_sup_z * L_ref / B_ref B = B / B_ref B_z_ra = B_z_ra / B_ref - B_sup_z, B, B_z_ra = map(group_data_by_field_line, (B_sup_z, B, B_z_ra)) + # group data by field line + B_sup_z, B, B_z_ra = (g.reshape(-1, knots.size) for g in [B_sup_z, B, B_z_ra]) errorif(not (B_sup_z.shape == B.shape == B_z_ra.shape)) # Compute splines. diff --git a/tests/test_axis_limits.py b/tests/test_axis_limits.py index d613440928..5563a419a2 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -92,7 +92,7 @@ "K_vc", # only defined on surface "iota_num_rrr", "iota_den_rrr", - "ripple", + "ripple", # implemented but requires source grid } diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 3e12e75502..f2329faa11 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -12,7 +12,6 @@ from desc.equilibrium import Equilibrium from desc.equilibrium.coords import desc_grid_from_field_line_coords from desc.grid import LinearGrid -from desc.utils import Timer def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=None): @@ -78,40 +77,30 @@ def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=No @pytest.mark.unit def test_effective_ripple(): """Compare DESC effective ripple against neo stellopt.""" - timer = Timer() eq = Equilibrium.load( "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) - timer.start("map") grid = desc_grid_from_field_line_coords( eq, rho=np.linspace(0.01, 1, 20), alpha=np.array([0]), zeta=np.linspace(-10 * np.pi, 10 * np.pi, 100), ) - timer.stop("map") - timer.disp("map") - timer.start("dependency compute") data = _compute_field_line_data( eq, grid, ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], ["min_tz |B|", "max_tz |B|", "R0", "V_r(r)", "psi_r", "S(r)"], ) - timer.stop("dependency compute") - timer.disp("dependency compute") - timer.start("ripple compute") data = eq.compute( "ripple", grid=grid, data=data, override_grid=False, bounce_integral=partial(bounce_integral, quad=leggauss(28)), - # quad=vec_quadax(quadax.quadgk), # noqa: E800 + # quad=vec_quadax(quadax.quadgk), # noqa: E800 ) - timer.stop("ripple compute") - timer.disp("ripple compute") assert np.isfinite(data["ripple"]).all() rho = grid.compress(grid.nodes[:, 0]) ripple = grid.compress(data["ripple"]) From 34ff7545298e4d0ac28f249c11d80c9c6432a1cc Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 27 May 2024 01:02:09 -0500 Subject: [PATCH 027/112] Use uniform quadrature over pitch integral because fat banana orbits don't matter for effective ripple and we save memory and speed, and makes differentiating easier --- desc/compute/_neoclassical.py | 36 ++++++++--------------------------- 1 file changed, 8 insertions(+), 28 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 0d07f451a3..d25e2d3f4f 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -22,7 +22,6 @@ affine_bijection, bounce_integral, composite_linspace, - get_extrema, grad_affine_bijection, ) from .data_index import register_compute_fun @@ -138,7 +137,6 @@ def _dI(B, pitch, Z): quad="callable : Quadrature method over velocity space (i.e. pitch integral)." "Adaptive quadrature method from quadax must be wrapped with vec_quadax.", quad_res="int : Resolution for quadrature over velocity space.", - # if doing a flux surface average alpha_weight="Array : Quadrature weight over alpha.", ) @partial(jit, static_argnames=["bounce_integral", "batched", "quad"]) @@ -148,8 +146,8 @@ def _ripple(params, transforms, profiles, data, **kwargs): _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) batched = kwargs.pop("batched", True) - quad = kwargs.pop("quad", trapezoid) - quad_res = kwargs.pop("quad_res", 21) + quad = kwargs.pop("quad", quadax.simpson) + quad_res = kwargs.pop("quad_res", 50) alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) # Get boundary of integral over pitch for each field line. @@ -162,7 +160,7 @@ def _ripple(params, transforms, profiles, data, **kwargs): is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] if is_Newton_Cotes: - bounce_integrate, spline = _bounce_integral( + bounce_integrate, _ = _bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) @@ -187,28 +185,10 @@ def rs(b): I = bounce_integrate(_dI, [], pitch, batched=batched) return jnp.nansum(H**2 / I, axis=-1) - # For ε ∼ ∫ db ∑ⱼ Hⱼ² / Iⱼ, the contribution of ∑ⱼ Hⱼ² / Iⱼ may be largest in - # the intervals such that b ∈ [|B|(ζ*) - db, |B|(ζ*)] where ζ* is a maxima. To - # see this, observe that Iⱼ ∼ √(1 − λ |B|), so Hⱼ² / Iⱼ ∼ Hⱼ² / √(1 − λ |B|). - # For λ = 1 / |B|(ζ*), near |B|(ζ*), the quantity 1 / √(1 − λ |B|) is singular - # with strength ~ 1 / |∂|B|/∂_ζ|. Therefore, a quadrature for ε should evaluate - # the integrand near b = 1 / λ = |B|(ζ*) to capture the fat banana orbits. - breaks = get_extrema(**spline, sort=False).reshape(-1, g.num_rho, g.num_alpha) - # Need to remove nan and include max_B regardless of whether there are any nan. - breaks = jnp.where(jnp.isnan(breaks), max_B[:, jnp.newaxis], breaks) - # Gather quadrature points at breaks and uniformly spaced within boundary. - # TODO: might be better to just do uniformly spaced - uniform = composite_linspace(boundary, quad_res) - b = jnp.vstack( - [ - jnp.broadcast_to( - uniform[..., jnp.newaxis], - (uniform.shape[0], g.num_rho, g.num_alpha), - ), - breaks, - ] - ) - b = jnp.sort(b, axis=0).reshape(-1, g.num_rho * g.num_alpha) + b = composite_linspace(boundary, quad_res) + b = jnp.broadcast_to( + b[..., jnp.newaxis], (b.shape[0], g.num_rho, g.num_alpha) + ).reshape(b.shape[0], g.num_rho * g.num_alpha) ripple = quad(rs(b), b, axis=0) else: # Use adaptive quadrature. @@ -236,7 +216,7 @@ def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): ripple = quad(rs, boundary, *args) # Integrate over flux surface. - ripple = ripple.reshape(g.num_rho, g.num_alpha) @ alpha_weight + ripple = jnp.reshape(ripple, (g.num_rho, g.num_alpha)) @ alpha_weight data["ripple"] = g.expand(ripple) return data From e6183c57fa5ca2643f6c83fbe6733af342e6d1a6 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 28 May 2024 03:53:43 -0500 Subject: [PATCH 028/112] Add neemov gamma_c --- desc/compute/_neoclassical.py | 307 ++++++++++++++++++++++++++++------ tests/test_neoclassical.py | 8 +- 2 files changed, 260 insertions(+), 55 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index d25e2d3f4f..1a7a77a1c0 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -42,8 +42,8 @@ def vec_quadax(quad): """ - def vec_quad(fun, interval, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): - return quad(fun, interval, args=(B_sup_z, B, B_z_ra, grad_psi, cvdrift0))[0] + def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): + return quad(fun, interval, args=(B_sup_z, B, B_z_ra, arg1, arg2))[0] vec_quad = jnp.vectorize( vec_quad, signature="(2),(m),(m),(m),(m),(m)->()", excluded={0} @@ -91,16 +91,65 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): return alpha, w -def _dH(grad_psi_norm, cvdrift0, B, pitch, Z): - # Used to compute "ripple". - return ( - pitch * jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * cvdrift0 / B - ) +@register_compute_fun( + name="V_psi(r)_line", + label="\\int d \\ell / \\vert B \\vert", + units="m^3 / Wb", + units_long="Cubic meters per Weber", + description="Volume enclosed by flux surfaces, derivative with respect to" + " toroidal flux, computed along field line", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["B^zeta", "|B|"], + grid_requirement=[ + "source_grid", + lambda grid: grid.source_grid.coordinates == "raz" + and grid.source_grid.is_meshgrid, + ], +) +def _V_psi_line(data, transforms, profiles, **kwargs): + g = transforms["grid"].source_grid + shape = (g.num_rho, g.num_alpha, g.num_zeta) + z = jnp.reshape(g.nodes[:, 2], shape) + V = jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape) + V = jnp.mean(quadax.simpson(V, z, axis=-1), axis=1) + warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) + # same as V_r / psi_r * (max zeta - min zeta) + data["V_psi(r)_line"] = g.expand(V) + return data -def _dI(B, pitch, Z): - # Used to compute "ripple". - return jnp.sqrt(1 - pitch * B) / B +@register_compute_fun( + name="S(r)_line", + label="\\int d \\ell \\vert \\nabla \\psi \\vert / \\vert B \\vert", + units="m^2", + units_long="Square meters", + description="Surface area of flux surfaces, computed along field line", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["B^zeta", "|B|", "|grad(psi)|"], + grid_requirement=[ + "source_grid", + lambda grid: grid.source_grid.coordinates == "raz" + and grid.source_grid.is_meshgrid, + ], +) +def _S_line(data, transforms, profiles, **kwargs): + g = transforms["grid"].source_grid + shape = (g.num_rho, g.num_alpha, g.num_zeta) + z = jnp.reshape(g.nodes[:, 2], shape) + S = jnp.reshape(data["|grad(psi)|"] / (data["B^zeta"] * data["|B|"]), shape) + S = jnp.mean(quadax.simpson(S, z, axis=-1), axis=1) + warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) + # same as S(r) * (max zeta - min zeta) + data["S(r)_line"] = g.expand(S) + return data @register_compute_fun( @@ -108,7 +157,7 @@ def _dI(B, pitch, Z): label="∫ db ∑ⱼ Hⱼ² / Iⱼ", units="~", units_long="None", - description="Ripple sum integral", + description="Effective ripple, not normalized", dim=1, params=[], transforms={"grid": []}, @@ -129,27 +178,40 @@ def _dI(B, pitch, Z): and grid.source_grid.is_meshgrid, ], bounce_integral=( - "callable : Method to compute bounce integrals." - " (You may want to wrap desc.compute.bounce_integral.bounce_integral" - " to change optional parameters such as quadrature resolution, etc.)." + "callable : Method to compute bounce integrals.\n" + "(You may want to wrap desc.compute.bounce_integral.bounce_integral " + "to change optional parameters such as quadrature resolution, etc.)." ), batched="bool : Whether to perform computation in a batched manner.", - quad="callable : Quadrature method over velocity space (i.e. pitch integral)." - "Adaptive quadrature method from quadax must be wrapped with vec_quadax.", + quad=( + "callable : Quadrature method over velocity space.\n" + "Adaptive quadrature method from quadax must be wrapped with vec_quadax." + ), quad_res="int : Resolution for quadrature over velocity space.", alpha_weight="Array : Quadrature weight over alpha.", ) -@partial(jit, static_argnames=["bounce_integral", "batched", "quad"]) +@partial(jit, static_argnames=["bounce_integral", "batched", "quad", "quad_res"]) def _ripple(params, transforms, profiles, data, **kwargs): + def dH(grad_psi_norm, cvdrift0, B, pitch, Z): + return ( + pitch + * jnp.sqrt(1 / pitch - B) + * (4 / B - pitch) + * grad_psi_norm + * cvdrift0 + / B + ) + + def dI(B, pitch, Z): + return jnp.sqrt(1 - pitch * B) / B + g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") - _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) batched = kwargs.pop("batched", True) quad = kwargs.pop("quad", quadax.simpson) quad_res = kwargs.pop("quad_res", 50) alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) - # Get boundary of integral over pitch for each field line. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) # Floating point error impedes consistent detection of bounce points riding @@ -175,14 +237,14 @@ def rs(b): Returns ------- rs : Array, shape(b.shape) - ∑ⱼ Hⱼ² / Iⱼ except the sum over j is split across alpha. + ∑ⱼ Hⱼ² / Iⱼ """ pitch = 1 / b H = bounce_integrate( - _dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched + dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched ) - I = bounce_integrate(_dI, [], pitch, batched=batched) + I = bounce_integrate(dI, [], pitch, batched=batched) return jnp.nansum(H**2 / I, axis=-1) b = composite_linspace(boundary, quad_res) @@ -197,8 +259,8 @@ def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): # Quadax requires scalar integration interval, so we need scalar integrand. bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) pitch = 1 / b - H = bounce_integrate(_dH, [grad_psi, cvdrift0], pitch, batched=batched) - I = bounce_integrate(_dI, [], pitch, batched=batched) + H = bounce_integrate(dH, [grad_psi, cvdrift0], pitch, batched=batched) + I = bounce_integrate(dI, [], pitch, batched=batched) return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) boundary = boundary.T[:, jnp.newaxis] @@ -229,16 +291,10 @@ def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): description="Effective ripple modulation amplitude", dim=1, params=[], - transforms={"grid": []}, + transforms={}, profiles=[], coordinates="r", - data=["B^zeta", "|B|", "|grad(psi)|", "ripple", "R0", "V_r(r)", "psi_r", "S(r)"], - grid_requirement=[ - "source_grid", - lambda grid: grid.source_grid.coordinates == "raz" - and grid.source_grid.is_meshgrid, - ], - fsa="bool : Whether to surface average to approximate the limit.", + data=["ripple", "R0", "V_psi(r)_line", "S(r)_line"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """Evaluation of 1/ν neoclassical transport in stellarators. @@ -247,24 +303,177 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. https://doi.org/10.1063/1.873749 """ + data["effective ripple"] = ( + jnp.pi + * data["R0"] ** 2 + / (8 * 2**0.5) + * data["V_psi(r)_line"] + / data["S(r)_line"] ** 2 + * data["ripple"] + ) ** (2 / 3) + return data + + +@register_compute_fun( + name="Gamma_c_raw", + label="∫ db ∑ⱼ (γ_c² * (∂I/∂b)ⱼ", + units="~", + units_long="None", + description="Energetic ion confinement, not normalized", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=[ + "min_tz |B|", + "max_tz |B|", + "B^zeta", + "|B|_z|r,a", + "|B|", + "cvdrift0", + "gbdrift", + ], + grid_requirement=[ + "source_grid", + lambda grid: grid.source_grid.coordinates == "raz" + and grid.source_grid.is_meshgrid, + ], + bounce_integral=( + "callable : Method to compute bounce integrals.\n" + "(You may want to wrap desc.compute.bounce_integral.bounce_integral " + "to change optional parameters such as quadrature resolution, etc.)." + ), + batched="bool : Whether to perform computation in a batched manner.", + quad=( + "callable : Quadrature method over velocity space.\n" + "Adaptive quadrature method from quadax must be wrapped with vec_quadax." + ), + quad_res="int : Resolution for quadrature over velocity space.", + alpha_weight="Array : Quadrature weight over alpha.", +) +def _Gamma_c_raw(params, transforms, profiles, data, **kwargs): + def d_gamma_c(f, B, pitch, Z): + return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) + + def d_dI_db(B, pitch, Z): + return pitch**2 / (2 * jnp.sqrt(1 - pitch * B)) + g = transforms["grid"].source_grid - if kwargs.get("fsa", False): - l = g.nodes[g.unique_zeta_idx[-1], 2] - g.nodes[g.unique_zeta_idx[0], 2] - V = data["V_r(r)"] / data["psi_r"] * l - S = data["S(r)"] * l - else: - shape = (g.num_rho, g.num_alpha, g.num_zeta) - z = jnp.reshape(g.nodes[:, 2], shape) - v = jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape) - V = jnp.mean(quadax.simpson(v, z, axis=-1), axis=1) - S = jnp.mean( - quadax.simpson(v * data["|grad(psi)|"].reshape(shape), z, axis=-1), - axis=1, + knots = g.compress(g.nodes[:, 2], surface_label="zeta") + _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) + batched = kwargs.pop("batched", True) + quad = kwargs.pop("quad", quadax.simpson) + quad_res = kwargs.pop("quad_res", 50) + alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) + # Get boundary of integral over pitch for each field line. + min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) + # Floating point error impedes consistent detection of bounce points riding + # extrema. Shift values slightly to resolve this issue. + min_B = (1 + 1e-6) * min_B + max_B = (1 - 1e-6) * max_B + boundary = jnp.stack([min_B, max_B]) + + is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] + if is_Newton_Cotes: + bounce_integrate, _ = _bounce_integral( + data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) - V, S = map(g.expand, (V, S)) - warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) - data["effective ripple"] = ( - jnp.pi * data["R0"] ** 2 / (8 * 2**0.5) * V / S**2 * data["ripple"] - ) ** (2 / 3) + def d_Gamma_c_raw(b): + """Return ∑ⱼ (γ_c² * (∂I/∂b)ⱼ evaluated at b. + + Parameters + ---------- + b : Array, shape(*b.shape[:-1], g.num_rho * g.num_alpha) + Multiplicative inverse of pitch angle. + + Returns + ------- + rs : Array, shape(b.shape) + ∑ⱼ (γ_c² * (∂I/∂b)ⱼ + + """ + pitch = 1 / b + # todo: add Nemov's |grad(psi)| + gamma_c = ( + 2 + / jnp.pi + * jnp.arctan( + bounce_integrate( + d_gamma_c, data["cvdrift0"], pitch, batched=batched + ) + / bounce_integrate( + d_gamma_c, data["gbdrift"], pitch, batched=batched + ) + ) + ) + dI_db = bounce_integrate(d_dI_db, [], pitch, batched=batched) + return jnp.nansum(gamma_c**2 * dI_db, axis=-1) + + b = composite_linspace(boundary, quad_res) + b = jnp.broadcast_to( + b[..., jnp.newaxis], (b.shape[0], g.num_rho, g.num_alpha) + ).reshape(b.shape[0], g.num_rho * g.num_alpha) + Gamma_c_raw = quad(d_Gamma_c_raw(b), b, axis=0) + else: + # Use adaptive quadrature. + + def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): + # Quadax requires scalar integration interval, so we need scalar integrand. + bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) + pitch = 1 / b + # todo: add Nemov's |grad(psi)| + gamma_c = ( + 2 + / jnp.pi + * jnp.arctan( + bounce_integrate(d_gamma_c, cvdrift0, pitch, batched=batched) + / bounce_integrate(d_gamma_c, gbdrift, pitch, batched=batched) + ) + ) + dI_db = bounce_integrate(d_dI_db, [], pitch, batched=batched) + return jnp.squeeze(jnp.nansum(gamma_c**2 * dI_db, axis=-1)) + + boundary = boundary.T[:, jnp.newaxis] + assert boundary.shape == (g.num_rho, 1, 2) + args = [ + f.reshape(g.num_rho, g.num_alpha, g.num_zeta) + for f in [ + data["B^zeta"], + data["|B|"], + data["|B|_z|r,a"], + data["cvdrift0"], + data["gbdrift"], + ] + ] + Gamma_c_raw = quad(d_Gamma_c_raw, boundary, *args) + + # Integrate over flux surface. + Gamma_c_raw = jnp.reshape(Gamma_c_raw, (g.num_rho, g.num_alpha)) @ alpha_weight + data["Gamma_c_raw"] = g.expand(Gamma_c_raw) + return data + + +@register_compute_fun( + name="Gamma_c", + label="\\Gamma_{c}", + units="~", + units_long="None", + description="Energetic ion confinement", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="r", + data=["Gamma_c_raw", "V_psi(r)_line"], +) +def _Gamma_c(params, transforms, profiles, data, **kwargs): + """Poloidal motion of trapped particle orbits in real-space coordinates. + + V. V. Nemov, S. V. Kasilov, W. Kernbichler, G. O. Leitold. + Phys. Plasmas 1 May 2008; 15 (5): 052501. + https://doi.org/10.1063/1.2912456 + """ + data["Gamma_c"] = jnp.pi / (2**1.5) * data["Gamma_c_raw"] / data["V_psi(r)_line"] return data diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index f2329faa11..a206b1ac42 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -1,14 +1,10 @@ """Test for neoclassical transport compute functions.""" -from functools import partial - import matplotlib.pyplot as plt import numpy as np import pytest -from orthax.legendre import leggauss from desc.compute import data_index, get_data_deps -from desc.compute.bounce_integral import bounce_integral from desc.equilibrium import Equilibrium from desc.equilibrium.coords import desc_grid_from_field_line_coords from desc.grid import LinearGrid @@ -85,7 +81,7 @@ def test_effective_ripple(): eq, rho=np.linspace(0.01, 1, 20), alpha=np.array([0]), - zeta=np.linspace(-10 * np.pi, 10 * np.pi, 100), + zeta=np.linspace(-100 * np.pi, 100 * np.pi, 1000), ) data = _compute_field_line_data( eq, @@ -98,7 +94,7 @@ def test_effective_ripple(): grid=grid, data=data, override_grid=False, - bounce_integral=partial(bounce_integral, quad=leggauss(28)), + # batched=False, # noqa: E800 # quad=vec_quadax(quadax.quadgk), # noqa: E800 ) assert np.isfinite(data["ripple"]).all() From c5b5d69b5418cc702a51f5b576e6c935a6177d16 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 28 May 2024 17:29:51 -0500 Subject: [PATCH 029/112] Fix units and naming schemes, remove jit around compute fun --- desc/compute/_neoclassical.py | 144 +++++++++++++++++--------------- desc/compute/bounce_integral.py | 10 +-- tests/test_bounce_integral.py | 8 +- tests/test_neoclassical.py | 29 ++++--- 4 files changed, 101 insertions(+), 90 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 1a7a77a1c0..79627b141d 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -9,13 +9,11 @@ expensive computations. """ -from functools import partial - import orthax import quadax from termcolor import colored -from desc.backend import jit, jnp, trapezoid +from desc.backend import jnp, trapezoid from ..utils import warnif from .bounce_integral import ( @@ -62,11 +60,19 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): integrals over finite transits. Assuming the rotational transform is irrational, the limit where the - parameterization of the field line length tends to infinity, the - integrals in (29), (30), (31) will converge to a flux surface average. - In theory, we can compute the effective ripple via flux surface - averages. It is not tested whether such a method will converge to the - limit faster than extending the length of the field line chunk. + parameterization of the field line length tends to infinity of an average + along the field line will converge to a flux surface average. + In theory, we can compute such quantities with averages over finite lengths + of the field line, e.g. one toroidal transit, for many values of the poloidal + field line label and then average this over the poloidal domain. + + This should also work for integrands which are bounce integrals; + Since everything is continuous, as the number of nodes tend to infinity both + approaches should converge to the same result, assuming irrational surface. + However, the order at which all the bounce integrals detected over the surface + are summed differs, so the convergence rate will differ. It is not tested whether + such a method will converge to the limit faster than extending the length of the + field line chunk. Parameters ---------- @@ -92,12 +98,14 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): @register_compute_fun( - name="V_psi(r)_line", + name="V_psi(r)*range(z)", label="\\int d \\ell / \\vert B \\vert", units="m^3 / Wb", units_long="Cubic meters per Weber", - description="Volume enclosed by flux surfaces, derivative with respect to" - " toroidal flux, computed along field line", + description=( + "Volume enclosed by flux surfaces, derivative with respect to toroidal flux, " + "computed along field line, scaled by dimensionless length of field line" + ), dim=1, params=[], transforms={"grid": []}, @@ -110,24 +118,26 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): and grid.source_grid.is_meshgrid, ], ) -def _V_psi_line(data, transforms, profiles, **kwargs): +def _V_psi_range_z(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) z = jnp.reshape(g.nodes[:, 2], shape) V = jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape) V = jnp.mean(quadax.simpson(V, z, axis=-1), axis=1) warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) - # same as V_r / psi_r * (max zeta - min zeta) - data["V_psi(r)_line"] = g.expand(V) + data["V_psi(r)*range(z)"] = g.expand(V) return data @register_compute_fun( - name="S(r)_line", + name="S(r)*range(z)", label="\\int d \\ell \\vert \\nabla \\psi \\vert / \\vert B \\vert", units="m^2", units_long="Square meters", - description="Surface area of flux surfaces, computed along field line", + description=( + "Surface area of flux surfaces, computed along field line, " + "scaled by dimensionless length of field line" + ), dim=1, params=[], transforms={"grid": []}, @@ -140,24 +150,23 @@ def _V_psi_line(data, transforms, profiles, **kwargs): and grid.source_grid.is_meshgrid, ], ) -def _S_line(data, transforms, profiles, **kwargs): +def _S_range_z(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) z = jnp.reshape(g.nodes[:, 2], shape) S = jnp.reshape(data["|grad(psi)|"] / (data["B^zeta"] * data["|B|"]), shape) S = jnp.mean(quadax.simpson(S, z, axis=-1), axis=1) warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) - # same as S(r) * (max zeta - min zeta) - data["S(r)_line"] = g.expand(S) + data["S(r)*range(z)"] = g.expand(S) return data @register_compute_fun( - name="ripple", + name="effective ripple raw", label="∫ db ∑ⱼ Hⱼ² / Iⱼ", - units="~", - units_long="None", - description="Effective ripple, not normalized", + units="Wb / m", + units_long="Webers per meter", + description="Effective ripple modulation amplitude, not normalized", dim=1, params=[], transforms={"grid": []}, @@ -178,20 +187,19 @@ def _S_line(data, transforms, profiles, **kwargs): and grid.source_grid.is_meshgrid, ], bounce_integral=( - "callable : Method to compute bounce integrals.\n" + "callable : Method to compute bounce integrals. " "(You may want to wrap desc.compute.bounce_integral.bounce_integral " "to change optional parameters such as quadrature resolution, etc.)." ), - batched="bool : Whether to perform computation in a batched manner.", + batch="bool : Whether to perform computation in a batched manner.", quad=( - "callable : Quadrature method over velocity space.\n" + "callable : Quadrature method over velocity space. " "Adaptive quadrature method from quadax must be wrapped with vec_quadax." ), quad_res="int : Resolution for quadrature over velocity space.", alpha_weight="Array : Quadrature weight over alpha.", ) -@partial(jit, static_argnames=["bounce_integral", "batched", "quad", "quad_res"]) -def _ripple(params, transforms, profiles, data, **kwargs): +def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): def dH(grad_psi_norm, cvdrift0, B, pitch, Z): return ( pitch @@ -208,7 +216,7 @@ def dI(B, pitch, Z): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) - batched = kwargs.pop("batched", True) + batch = kwargs.pop("batch", True) quad = kwargs.pop("quad", quadax.simpson) quad_res = kwargs.pop("quad_res", 50) alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) @@ -226,7 +234,7 @@ def dI(B, pitch, Z): data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) - def rs(b): + def d_ripple(b): """Return ∑ⱼ Hⱼ² / Iⱼ evaluated at b. Parameters @@ -242,25 +250,25 @@ def rs(b): """ pitch = 1 / b H = bounce_integrate( - dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batched=batched + dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batch=batch ) - I = bounce_integrate(dI, [], pitch, batched=batched) + I = bounce_integrate(dI, [], pitch, batch=batch) return jnp.nansum(H**2 / I, axis=-1) b = composite_linspace(boundary, quad_res) b = jnp.broadcast_to( b[..., jnp.newaxis], (b.shape[0], g.num_rho, g.num_alpha) ).reshape(b.shape[0], g.num_rho * g.num_alpha) - ripple = quad(rs(b), b, axis=0) + ripple = quad(d_ripple(b), b, axis=0) else: # Use adaptive quadrature. - def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): - # Quadax requires scalar integration interval, so we need scalar integrand. + def d_ripple(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): + # Quadax requires scalar integration interval, so we need to return scalar. bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) pitch = 1 / b - H = bounce_integrate(dH, [grad_psi, cvdrift0], pitch, batched=batched) - I = bounce_integrate(dI, [], pitch, batched=batched) + H = bounce_integrate(dH, [grad_psi, cvdrift0], pitch, batch=batch) + I = bounce_integrate(dI, [], pitch, batch=batch) return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) boundary = boundary.T[:, jnp.newaxis] @@ -275,11 +283,11 @@ def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): data["cvdrift0"], ] ] - ripple = quad(rs, boundary, *args) + ripple = quad(d_ripple, boundary, *args) # Integrate over flux surface. ripple = jnp.reshape(ripple, (g.num_rho, g.num_alpha)) @ alpha_weight - data["ripple"] = g.expand(ripple) + data["effective ripple raw"] = g.expand(ripple) return data @@ -294,31 +302,31 @@ def rs(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): transforms={}, profiles=[], coordinates="r", - data=["ripple", "R0", "V_psi(r)_line", "S(r)_line"], + data=["effective ripple raw", "R0", "V_psi(r)*range(z)", "S(r)*range(z)"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """Evaluation of 1/ν neoclassical transport in stellarators. V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - https://doi.org/10.1063/1.873749 + https://doi.org/10.1063/1.873749. """ data["effective ripple"] = ( jnp.pi * data["R0"] ** 2 / (8 * 2**0.5) - * data["V_psi(r)_line"] - / data["S(r)_line"] ** 2 - * data["ripple"] + * data["V_psi(r)*range(z)"] + / data["S(r)*range(z)"] ** 2 + * data["effective ripple raw"] ) ** (2 / 3) return data @register_compute_fun( - name="Gamma_c_raw", - label="∫ db ∑ⱼ (γ_c² * (∂I/∂b)ⱼ", - units="~", - units_long="None", + name="Gamma_c raw", + label="∫ db ∑ⱼ (γ_c² * ∂I/∂b)ⱼ", + units="m^3 / Wb", + units_long="Cubic meters per Weber", description="Energetic ion confinement, not normalized", dim=1, params=[], @@ -340,13 +348,13 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): and grid.source_grid.is_meshgrid, ], bounce_integral=( - "callable : Method to compute bounce integrals.\n" + "callable : Method to compute bounce integrals. " "(You may want to wrap desc.compute.bounce_integral.bounce_integral " "to change optional parameters such as quadrature resolution, etc.)." ), - batched="bool : Whether to perform computation in a batched manner.", + batch="bool : Whether to perform computation in a batched manner.", quad=( - "callable : Quadrature method over velocity space.\n" + "callable : Quadrature method over velocity space. " "Adaptive quadrature method from quadax must be wrapped with vec_quadax." ), quad_res="int : Resolution for quadrature over velocity space.", @@ -362,7 +370,7 @@ def d_dI_db(B, pitch, Z): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) - batched = kwargs.pop("batched", True) + batch = kwargs.pop("batch", True) quad = kwargs.pop("quad", quadax.simpson) quad_res = kwargs.pop("quad_res", 50) alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) @@ -381,7 +389,7 @@ def d_dI_db(B, pitch, Z): ) def d_Gamma_c_raw(b): - """Return ∑ⱼ (γ_c² * (∂I/∂b)ⱼ evaluated at b. + """Return ∑ⱼ (γ_c² * ∂I/∂b)ⱼ evaluated at b. Parameters ---------- @@ -391,7 +399,7 @@ def d_Gamma_c_raw(b): Returns ------- rs : Array, shape(b.shape) - ∑ⱼ (γ_c² * (∂I/∂b)ⱼ + ∑ⱼ (γ_c² * ∂I/∂b)ⱼ """ pitch = 1 / b @@ -400,15 +408,11 @@ def d_Gamma_c_raw(b): 2 / jnp.pi * jnp.arctan( - bounce_integrate( - d_gamma_c, data["cvdrift0"], pitch, batched=batched - ) - / bounce_integrate( - d_gamma_c, data["gbdrift"], pitch, batched=batched - ) + bounce_integrate(d_gamma_c, data["cvdrift0"], pitch, batch=batch) + / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) ) ) - dI_db = bounce_integrate(d_dI_db, [], pitch, batched=batched) + dI_db = bounce_integrate(d_dI_db, [], pitch, batch=batch) return jnp.nansum(gamma_c**2 * dI_db, axis=-1) b = composite_linspace(boundary, quad_res) @@ -420,7 +424,7 @@ def d_Gamma_c_raw(b): # Use adaptive quadrature. def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): - # Quadax requires scalar integration interval, so we need scalar integrand. + # Quadax requires scalar integration interval, so we need to return scalar. bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) pitch = 1 / b # todo: add Nemov's |grad(psi)| @@ -428,11 +432,11 @@ def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): 2 / jnp.pi * jnp.arctan( - bounce_integrate(d_gamma_c, cvdrift0, pitch, batched=batched) - / bounce_integrate(d_gamma_c, gbdrift, pitch, batched=batched) + bounce_integrate(d_gamma_c, cvdrift0, pitch, batch=batch) + / bounce_integrate(d_gamma_c, gbdrift, pitch, batch=batch) ) ) - dI_db = bounce_integrate(d_dI_db, [], pitch, batched=batched) + dI_db = bounce_integrate(d_dI_db, [], pitch, batch=batch) return jnp.squeeze(jnp.nansum(gamma_c**2 * dI_db, axis=-1)) boundary = boundary.T[:, jnp.newaxis] @@ -451,7 +455,7 @@ def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): # Integrate over flux surface. Gamma_c_raw = jnp.reshape(Gamma_c_raw, (g.num_rho, g.num_alpha)) @ alpha_weight - data["Gamma_c_raw"] = g.expand(Gamma_c_raw) + data["Gamma_c raw"] = g.expand(Gamma_c_raw) return data @@ -466,14 +470,16 @@ def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): transforms={}, profiles=[], coordinates="r", - data=["Gamma_c_raw", "V_psi(r)_line"], + data=["Gamma_c raw", "V_psi(r)*range(z)"], ) def _Gamma_c(params, transforms, profiles, data, **kwargs): """Poloidal motion of trapped particle orbits in real-space coordinates. V. V. Nemov, S. V. Kasilov, W. Kernbichler, G. O. Leitold. Phys. Plasmas 1 May 2008; 15 (5): 052501. - https://doi.org/10.1063/1.2912456 + https://doi.org/10.1063/1.2912456. """ - data["Gamma_c"] = jnp.pi / (2**1.5) * data["Gamma_c_raw"] / data["V_psi(r)_line"] + data["Gamma_c"] = ( + jnp.pi / (2**1.5) * data["Gamma_c raw"] / data["V_psi(r)*range(z)"] + ) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index cf098a9273..b3a7aa43bb 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1027,7 +1027,7 @@ def _bounce_quadrature( knots, method="akima", method_B="cubic", - batched=True, + batch=True, check=False, plot=False, ): @@ -1069,7 +1069,7 @@ def group_data_by_field_line_and_pitch(g): f = map(group_data_by_field_line_and_pitch, f) # Integrate and complete the change of variable. - if batched: + if batch: Z = affine_bijection(x, bp1[..., jnp.newaxis], bp2[..., jnp.newaxis]) result = _interpolatory_quadrature( Z, @@ -1315,7 +1315,7 @@ def denominator(B, pitch, Z): # Recall affine_bijection(auto(x), ζ_b₁, ζ_b₂) = ζ. x = auto(x) - def bounce_integrate(integrand, f, pitch, method="akima", batched=True): + def bounce_integrate(integrand, f, pitch, method="akima", batch=True): """Bounce integrate ∫ f(ℓ) dℓ. Parameters @@ -1346,7 +1346,7 @@ def bounce_integrate(integrand, f, pitch, method="akima", batched=True): Method of interpolation for functions contained in ``f``. Defaults to akima spline to suppress oscillation. See https://interpax.readthedocs.io/en/latest/_api/interpax.interp1d.html. - batched : bool + batch : bool Whether to perform computation in a batched manner. If you can afford the memory expense, batched is more efficient. @@ -1372,7 +1372,7 @@ def bounce_integrate(integrand, f, pitch, method="akima", batched=True): knots, method, method_B="monotonic" if monotonic else "cubic", - batched=batched, + batch=batch, check=check, plot=plot, ) diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index db0ed035f6..739a15e0b9 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -495,7 +495,7 @@ def denominator(B, pitch, Z): pitch = 1 / get_extrema(**spline) num = bounce_integrate(numerator, data["g_zz"], pitch) - den = bounce_integrate(denominator, [], pitch, batched=False) + den = bounce_integrate(denominator, [], pitch, batch=False) average = num / den assert np.isfinite(average).any() @@ -532,8 +532,6 @@ def _elliptic_incomplete(k2): np.testing.assert_allclose(K, _fixed_elliptic(K_integrand, k, 10)) np.testing.assert_allclose(E, _fixed_elliptic(E_integrand, k, 10)) - # Here are the notes that explain these integrals. - # https://github.com/PlasmaControl/DESC/files/15010927/bavg.pdf. I_0 = 4 / k * K I_1 = 4 * k * E I_2 = 16 * k * E @@ -635,9 +633,7 @@ def _get_data(eq, rho, alpha, names_field_line, names_0d_or_1dr=None): zeta = np.linspace(-np.pi / iota, np.pi / iota, (2 * eq.M_grid) * 4 + 1) # Make grid that can separate into field lines via a reshape operation, # as expected by bounce_integral(). - grid_desc = desc_grid_from_field_line_coords( - eq, rho, alpha=np.array([0]), zeta=zeta - ) + grid_desc = desc_grid_from_field_line_coords(eq, rho, alpha=alpha, zeta=zeta) # Collect quantities that can be used as a seed to compute the # field line quantities over the grid mapped from field line coordinates. diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index a206b1ac42..585a65ed5d 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -81,30 +81,39 @@ def test_effective_ripple(): eq, rho=np.linspace(0.01, 1, 20), alpha=np.array([0]), - zeta=np.linspace(-100 * np.pi, 100 * np.pi, 1000), + zeta=np.linspace(-100 * np.pi, 100 * np.pi, 500), ) data = _compute_field_line_data( eq, grid, - ["B^zeta", "|B|_z|r,a", "|B|", "|grad(psi)|", "cvdrift0"], - ["min_tz |B|", "max_tz |B|", "R0", "V_r(r)", "psi_r", "S(r)"], + [ + "B^zeta", + "|B|_z|r,a", + "|B|", + "|grad(psi)|", + "cvdrift0", + "V_psi(r)*range(z)", + "S(r)*range(z)", + ], + ["min_tz |B|", "max_tz |B|", "R0"], ) data = eq.compute( - "ripple", + "effective ripple raw", grid=grid, data=data, override_grid=False, - # batched=False, # noqa: E800 + # batch=False, # noqa: E800 # quad=vec_quadax(quadax.quadgk), # noqa: E800 ) - assert np.isfinite(data["ripple"]).all() + assert np.isfinite(data["effective ripple raw"]).all() rho = grid.compress(grid.nodes[:, 0]) - ripple = grid.compress(data["ripple"]) + ripple = grid.compress(data["effective ripple raw"]) fig, ax = plt.subplots(2) ax[0].plot(rho, ripple, marker="o", label="∫ db ∑ⱼ Hⱼ² / Iⱼ") ax[0].set_xlabel(r"$\rho$") - ax[0].set_ylabel("ripple") - ax[0].set_title("Ripple, defined as ∫ db ∑ⱼ Hⱼ² / Iⱼ") + ax[0].set_ylabel("effective ripple raw") + ax[0].set_title("effective ripple raw, defined as ∫ db ∑ⱼ Hⱼ² / Iⱼ") + # Workaround until eq.compute() is fixed to only compute dependencies # that are needed for the requested computation. (So don't compute # dependencies of things already in data). @@ -114,7 +123,7 @@ def test_effective_ripple(): # Need to add R0's dependencies which are surface functions of zeta # aren't attempted to be recomputed on grid_desc. data[key] = data_R0[key] - data = eq.compute("effective ripple", grid=grid, data=data, override_grid=False) + data = eq.compute("effective ripple", grid=grid, data=data) assert np.isfinite(data["effective ripple"]).all() eff_ripple = grid.compress(data["effective ripple"]) ax[1].plot(rho, eff_ripple, marker="o", label=r"$\epsilon_{\text{effective}}$") From c264cfff79cd961a136a8d35574bfe89ec37f2cf Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 28 May 2024 19:51:57 -0500 Subject: [PATCH 030/112] =?UTF-8?q?Integrate=20over=20=CE=BB=20instead=20o?= =?UTF-8?q?f=20b.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- desc/compute/_neoclassical.py | 187 +++++++++++++++++++--------------- desc/grid.py | 26 +++++ 2 files changed, 129 insertions(+), 84 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 79627b141d..a9fc75c4a7 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -49,15 +49,14 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return vec_quad -def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): +def poloidal_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): """Gauss-Legendre quadrature. Returns quadrature points αₖ and weights wₖ for the approximate evaluation of the integral ∫ f(α) dα ≈ ∑ₖ wₖ f(αₖ). - For use with computing effective ripple, set resolution > 1 to see if a long - field line integral can be approximated by flux surface average of field line - integrals over finite transits. + Set resolution > 1 to see if a long field line integral can be approximated + by flux surface average of shorter field line integrals with finite transits. Assuming the rotational transform is irrational, the limit where the parameterization of the field line length tends to infinity of an average @@ -97,6 +96,19 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): return alpha, w +def _poloidal_integrate(grid, f, name=""): + # assumes f has shape (grid.num_radial, grid.num_poloidal) + if grid.poloidal_weight is None: + warnif( + grid.num_poloidal != 1, + msg=colored(f"{name} reduced via poloidal mean.", "yellow"), + ) + f = jnp.mean(f, axis=-1) + else: + f = f @ grid.poloidal_weight + return f + + @register_compute_fun( name="V_psi(r)*range(z)", label="\\int d \\ell / \\vert B \\vert", @@ -121,10 +133,15 @@ def alpha_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): def _V_psi_range_z(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - z = jnp.reshape(g.nodes[:, 2], shape) - V = jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape) - V = jnp.mean(quadax.simpson(V, z, axis=-1), axis=1) - warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) + V = _poloidal_integrate( + g, + quadax.simpson( + jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape), + jnp.reshape(g.nodes[:, 2], shape), + axis=-1, + ), + name="V_psi(r)*range(z)", + ) data["V_psi(r)*range(z)"] = g.expand(V) return data @@ -153,17 +170,22 @@ def _V_psi_range_z(data, transforms, profiles, **kwargs): def _S_range_z(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - z = jnp.reshape(g.nodes[:, 2], shape) - S = jnp.reshape(data["|grad(psi)|"] / (data["B^zeta"] * data["|B|"]), shape) - S = jnp.mean(quadax.simpson(S, z, axis=-1), axis=1) - warnif(g.num_alpha != 1, msg=colored("Reduced via mean over alpha.", "yellow")) + S = _poloidal_integrate( + g, + quadax.simpson( + jnp.reshape(data["|grad(psi)|"] / (data["B^zeta"] * data["|B|"]), shape), + jnp.reshape(g.nodes[:, 2], shape), + axis=-1, + ), + name="S(r)*range(z)", + ) data["S(r)*range(z)"] = g.expand(S) return data @register_compute_fun( name="effective ripple raw", - label="∫ db ∑ⱼ Hⱼ² / Iⱼ", + label="-∫ dλ λ⁻² ∑ⱼ Hⱼ² / Iⱼ", units="Wb / m", units_long="Webers per meter", description="Effective ripple modulation amplitude, not normalized", @@ -193,16 +215,16 @@ def _S_range_z(data, transforms, profiles, **kwargs): ), batch="bool : Whether to perform computation in a batched manner.", quad=( - "callable : Quadrature method over velocity space. " + "callable : Quadrature method over velocity coordinate. " "Adaptive quadrature method from quadax must be wrapped with vec_quadax." ), - quad_res="int : Resolution for quadrature over velocity space.", - alpha_weight="Array : Quadrature weight over alpha.", + quad_res="int : Resolution for quadrature over velocity coordinate.", ) def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): def dH(grad_psi_norm, cvdrift0, B, pitch, Z): return ( - pitch + 1 + / pitch * jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm @@ -219,60 +241,58 @@ def dI(B, pitch, Z): batch = kwargs.pop("batch", True) quad = kwargs.pop("quad", quadax.simpson) quad_res = kwargs.pop("quad_res", 50) - alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) - # Get boundary of integral over pitch for each field line. + # Get endpoints of integral over pitch for each field line. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) # Floating point error impedes consistent detection of bounce points riding # extrema. Shift values slightly to resolve this issue. min_B = (1 + 1e-6) * min_B max_B = (1 - 1e-6) * max_B - boundary = jnp.stack([min_B, max_B]) + pitch_endpoint = 1 / jnp.stack([max_B, min_B]) is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] if is_Newton_Cotes: - bounce_integrate, _ = _bounce_integral( + bounce_int, _ = _bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) - def d_ripple(b): - """Return ∑ⱼ Hⱼ² / Iⱼ evaluated at b. + def d_ripple(pitch): + """Return λ⁻² ∑ⱼ Hⱼ² / Iⱼ evaluated at pitch. Parameters ---------- - b : Array, shape(*b.shape[:-1], g.num_rho * g.num_alpha) - Multiplicative inverse of pitch angle. + pitch : Array, shape(*pitch.shape[:-1], g.num_rho * g.num_alpha) + Pitch angle. Returns ------- - rs : Array, shape(b.shape) - ∑ⱼ Hⱼ² / Iⱼ + d_ripple : Array, shape(pitch.shape) + λ⁻² ∑ⱼ Hⱼ² / Iⱼ """ - pitch = 1 / b - H = bounce_integrate( + # absorbed λ⁻² into H + H = bounce_int( dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batch=batch ) - I = bounce_integrate(dI, [], pitch, batch=batch) + I = bounce_int(dI, [], pitch, batch=batch) return jnp.nansum(H**2 / I, axis=-1) - b = composite_linspace(boundary, quad_res) - b = jnp.broadcast_to( - b[..., jnp.newaxis], (b.shape[0], g.num_rho, g.num_alpha) - ).reshape(b.shape[0], g.num_rho * g.num_alpha) - ripple = quad(d_ripple(b), b, axis=0) + pitch = composite_linspace(pitch_endpoint, quad_res) + pitch = jnp.broadcast_to( + pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) + ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) + ripple = quad(d_ripple(pitch), pitch, axis=0) else: # Use adaptive quadrature. - def d_ripple(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): + def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): # Quadax requires scalar integration interval, so we need to return scalar. - bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) - pitch = 1 / b - H = bounce_integrate(dH, [grad_psi, cvdrift0], pitch, batch=batch) - I = bounce_integrate(dI, [], pitch, batch=batch) + bounce_int, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) + H = bounce_int(dH, [grad_psi, cvdrift0], pitch, batch=batch) + I = bounce_int(dI, [], pitch, batch=batch) return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) - boundary = boundary.T[:, jnp.newaxis] - assert boundary.shape == (g.num_rho, 1, 2) + pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] + assert pitch_endpoint.shape == (g.num_rho, 1, 2) args = [ f.reshape(g.num_rho, g.num_alpha, g.num_zeta) for f in [ @@ -283,10 +303,11 @@ def d_ripple(b, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): data["cvdrift0"], ] ] - ripple = quad(d_ripple, boundary, *args) + ripple = quad(d_ripple, pitch_endpoint, *args) - # Integrate over flux surface. - ripple = jnp.reshape(ripple, (g.num_rho, g.num_alpha)) @ alpha_weight + ripple = _poloidal_integrate( + g, ripple.reshape(g.num_rho, g.num_alpha), name="effective ripple raw" + ) data["effective ripple raw"] = g.expand(ripple) return data @@ -324,7 +345,7 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="Gamma_c raw", - label="∫ db ∑ⱼ (γ_c² * ∂I/∂b)ⱼ", + label="-∫ dλ ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ", units="m^3 / Wb", units_long="Cubic meters per Weber", description="Energetic ion confinement, not normalized", @@ -354,18 +375,17 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): ), batch="bool : Whether to perform computation in a batched manner.", quad=( - "callable : Quadrature method over velocity space. " + "callable : Quadrature method over velocity coordinate. " "Adaptive quadrature method from quadax must be wrapped with vec_quadax." ), - quad_res="int : Resolution for quadrature over velocity space.", - alpha_weight="Array : Quadrature weight over alpha.", + quad_res="int : Resolution for quadrature over velocity coordinate.", ) def _Gamma_c_raw(params, transforms, profiles, data, **kwargs): def d_gamma_c(f, B, pitch, Z): return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) - def d_dI_db(B, pitch, Z): - return pitch**2 / (2 * jnp.sqrt(1 - pitch * B)) + def dK(B, pitch, Z): + return 1 / (2 * jnp.sqrt(1 - pitch * B)) g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") @@ -373,74 +393,72 @@ def d_dI_db(B, pitch, Z): batch = kwargs.pop("batch", True) quad = kwargs.pop("quad", quadax.simpson) quad_res = kwargs.pop("quad_res", 50) - alpha_weight = jnp.atleast_1d(kwargs.pop("alpha_weight", 1 / g.num_alpha)) - # Get boundary of integral over pitch for each field line. + # Get endpoints of integral over pitch for each field line. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) # Floating point error impedes consistent detection of bounce points riding # extrema. Shift values slightly to resolve this issue. min_B = (1 + 1e-6) * min_B max_B = (1 - 1e-6) * max_B - boundary = jnp.stack([min_B, max_B]) + pitch_endpoint = 1 / jnp.stack([max_B, min_B]) is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] if is_Newton_Cotes: - bounce_integrate, _ = _bounce_integral( + bounce_int, _ = _bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) - def d_Gamma_c_raw(b): - """Return ∑ⱼ (γ_c² * ∂I/∂b)ⱼ evaluated at b. + def d_Gamma_c_raw(pitch): + """Return ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ evaluated at pitch. Parameters ---------- - b : Array, shape(*b.shape[:-1], g.num_rho * g.num_alpha) - Multiplicative inverse of pitch angle. + pitch : Array, shape(*pitch.shape[:-1], g.num_rho * g.num_alpha) + Pitch angle. Returns ------- - rs : Array, shape(b.shape) - ∑ⱼ (γ_c² * ∂I/∂b)ⱼ + d_Gamma_c_raw : Array, shape(pitch.shape) + ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ """ - pitch = 1 / b # todo: add Nemov's |grad(psi)| gamma_c = ( 2 / jnp.pi * jnp.arctan( - bounce_integrate(d_gamma_c, data["cvdrift0"], pitch, batch=batch) - / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) + bounce_int(d_gamma_c, data["cvdrift0"], pitch, batch=batch) + / bounce_int(d_gamma_c, data["gbdrift"], pitch, batch=batch) ) ) - dI_db = bounce_integrate(d_dI_db, [], pitch, batch=batch) - return jnp.nansum(gamma_c**2 * dI_db, axis=-1) - - b = composite_linspace(boundary, quad_res) - b = jnp.broadcast_to( - b[..., jnp.newaxis], (b.shape[0], g.num_rho, g.num_alpha) - ).reshape(b.shape[0], g.num_rho * g.num_alpha) - Gamma_c_raw = quad(d_Gamma_c_raw(b), b, axis=0) + # K = ∂I/∂(λ⁻¹) λ⁻² + K = bounce_int(dK, [], pitch, batch=batch) + return jnp.nansum(gamma_c**2 * K, axis=-1) + + pitch = composite_linspace(pitch_endpoint, quad_res) + pitch = jnp.broadcast_to( + pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) + ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) + Gamma_c_raw = quad(d_Gamma_c_raw(pitch), pitch, axis=0) else: # Use adaptive quadrature. - def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): + def d_Gamma_c_raw(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): # Quadax requires scalar integration interval, so we need to return scalar. - bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) - pitch = 1 / b + bounce_int, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) # todo: add Nemov's |grad(psi)| gamma_c = ( 2 / jnp.pi * jnp.arctan( - bounce_integrate(d_gamma_c, cvdrift0, pitch, batch=batch) - / bounce_integrate(d_gamma_c, gbdrift, pitch, batch=batch) + bounce_int(d_gamma_c, cvdrift0, pitch, batch=batch) + / bounce_int(d_gamma_c, gbdrift, pitch, batch=batch) ) ) - dI_db = bounce_integrate(d_dI_db, [], pitch, batch=batch) - return jnp.squeeze(jnp.nansum(gamma_c**2 * dI_db, axis=-1)) + K = bounce_int(dK, [], pitch, batch=batch) + return jnp.squeeze(jnp.nansum(gamma_c**2 * K, axis=-1)) - boundary = boundary.T[:, jnp.newaxis] - assert boundary.shape == (g.num_rho, 1, 2) + pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] + assert pitch_endpoint.shape == (g.num_rho, 1, 2) args = [ f.reshape(g.num_rho, g.num_alpha, g.num_zeta) for f in [ @@ -451,10 +469,11 @@ def d_Gamma_c_raw(b, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): data["gbdrift"], ] ] - Gamma_c_raw = quad(d_Gamma_c_raw, boundary, *args) + Gamma_c_raw = quad(d_Gamma_c_raw, pitch_endpoint, *args) - # Integrate over flux surface. - Gamma_c_raw = jnp.reshape(Gamma_c_raw, (g.num_rho, g.num_alpha)) @ alpha_weight + Gamma_c_raw = _poloidal_integrate( + g, Gamma_c_raw.reshape(g.num_rho, g.num_alpha), name="Gamma_c raw" + ) data["Gamma_c raw"] = g.expand(Gamma_c_raw) return data diff --git a/desc/grid.py b/desc/grid.py index 9bd3a10123..5fef119621 100644 --- a/desc/grid.py +++ b/desc/grid.py @@ -43,6 +43,7 @@ class _Grid(IOAble, ABC): "_inverse_poloidal_idx", "_inverse_zeta_idx", "is_meshgrid", + "_poloidal_weight", ] @abstractmethod @@ -659,6 +660,10 @@ def __init__( # can be iterated over along the relevant axis of the reshaped grid: # nodes.reshape(num_radial, num_poloidal, num_toroidal, 3). self.is_meshgrid = kwargs.pop("is_meshgrid", False) + if "poloidal_weight" in kwargs: + self._poloidal_weight = jnp.atleast_1d(kwargs.pop("poloidal_weight")) + else: + self._poloidal_weight = None self._nodes = self._create_nodes(nodes) if spacing is not None: @@ -750,6 +755,27 @@ def source_grid(self): """Coordinates from which this grid was mapped from.""" return self._source_grid + @property + def poloidal_weight(self): + """Quadrature weight over poloidal domain. + + Returns + ------- + poloidal_weight : Array, shape(self.num_poloidal) + quadrature weight over poloidal domain + + """ + if hasattr(self, "_poloidal_weight"): + return self._poloidal_weight + if hasattr(self, "_unique_poloidal_idx"): + self._poloidal_weight = jnp.array([2 * jnp.pi / self.num_poloidal]) + return self.__dict__.setdefault("_poloidal_weight", None) + + @poloidal_weight.setter + def poloidal_weight(self, value): + """Set quadrature weight over poloidal domain.""" + self._poloidal_weight = jnp.atleast_1d(value) + class LinearGrid(_Grid): """Grid in which the nodes are linearly spaced in each coordinate. From 599f895422adfe118f6384ef8d7508f440c09d39 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 29 May 2024 03:32:23 -0500 Subject: [PATCH 031/112] making progress on neoclassical stuff --- desc/compute/_equil.py | 4 +- desc/compute/_field.py | 4 +- desc/compute/_metric.py | 14 +-- desc/compute/_neoclassical.py | 177 ++++++++++++++++---------------- desc/compute/_omnigenity.py | 4 +- desc/compute/bounce_integral.py | 62 +---------- desc/equilibrium/coords.py | 50 +++++---- desc/equilibrium/equilibrium.py | 81 +++++++++++++++ desc/grid.py | 4 - tests/test_bounce_integral.py | 165 +++++++++++------------------ tests/test_neoclassical.py | 113 +++++++++----------- 11 files changed, 320 insertions(+), 358 deletions(-) diff --git a/desc/compute/_equil.py b/desc/compute/_equil.py index 8baf22e0a5..796ed7e74d 100644 --- a/desc/compute/_equil.py +++ b/desc/compute/_equil.py @@ -654,11 +654,11 @@ def _F_anisotropic(params, transforms, profiles, data, **kwargs): transforms={"grid": []}, profiles=[], coordinates="", - data=["|B|", "sqrt(g)"], + data=["|B|^2", "sqrt(g)"], ) def _W_B(params, transforms, profiles, data, **kwargs): data["W_B"] = jnp.sum( - data["|B|"] ** 2 * data["sqrt(g)"] * transforms["grid"].weights + data["|B|^2"] * data["sqrt(g)"] * transforms["grid"].weights ) / (2 * mu_0) return data diff --git a/desc/compute/_field.py b/desc/compute/_field.py index 5e2b463b5d..42b18ccf8d 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -2674,11 +2674,11 @@ def _B_vol(params, transforms, profiles, data, **kwargs): transforms={"grid": []}, profiles=[], coordinates="", - data=["sqrt(g)", "|B|", "V"], + data=["sqrt(g)", "|B|^2", "V"], ) def _B_rms(params, transforms, profiles, data, **kwargs): data["<|B|>_rms"] = jnp.sqrt( - jnp.sum(data["|B|"] ** 2 * data["sqrt(g)"] * transforms["grid"].weights) + jnp.sum(data["|B|^2"] * data["sqrt(g)"] * transforms["grid"].weights) / data["V"] ) return data diff --git a/desc/compute/_metric.py b/desc/compute/_metric.py index 42dd5c8218..cae02e2ec3 100644 --- a/desc/compute/_metric.py +++ b/desc/compute/_metric.py @@ -1843,12 +1843,12 @@ def _gradzeta(params, transforms, profiles, data, **kwargs): transforms={}, profiles=[], coordinates="rtz", - data=["|B|", "b", "grad(alpha)", "grad(|B|)"], + data=["|B|^2", "b", "grad(alpha)", "grad(|B|)"], ) def _gbdrift(params, transforms, profiles, data, **kwargs): data["gbdrift"] = ( 1 - / data["|B|"] ** 2 + / data["|B|^2"] * dot(data["b"], cross(data["grad(|B|)"], data["grad(alpha)"])) ) return data @@ -1870,11 +1870,11 @@ def _gbdrift(params, transforms, profiles, data, **kwargs): transforms={}, profiles=[], coordinates="rtz", - data=["p_r", "psi_r", "|B|", "gbdrift"], + data=["p_r", "psi_r", "|B|^2", "gbdrift"], ) def _cvdrift(params, transforms, profiles, data, **kwargs): dp_dpsi = mu_0 * data["p_r"] / data["psi_r"] - data["cvdrift"] = 1 / data["|B|"] ** 2 * dp_dpsi + data["gbdrift"] + data["cvdrift"] = 1 / data["|B|^2"] * dp_dpsi + data["gbdrift"] return data @@ -1888,16 +1888,16 @@ def _cvdrift(params, transforms, profiles, data, **kwargs): units="1/(T-m^{2})", units_long="inverse Tesla meters^2", description="Radial component of the geometric part of the curvature drift" - + " used for local stability analyses, Gamma_c, epsilon_eff etc.", + + " used for local stability analyses for Gamma_c.", dim=1, params=[], transforms={}, profiles=[], coordinates="rtz", - data=["|B|", "b", "e^rho", "grad(|B|)"], + data=["|B|^2", "b", "e^rho", "grad(|B|)"], ) def _cvdrift0(params, transforms, profiles, data, **kwargs): data["cvdrift0"] = ( - 1 / data["|B|"] ** 2 * (dot(data["b"], cross(data["grad(|B|)"], data["e^rho"]))) + 1 / data["|B|^2"] * (dot(data["b"], cross(data["grad(|B|)"], data["e^rho"]))) ) return data diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index a9fc75c4a7..e1c2bfe0a2 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -9,11 +9,13 @@ expensive computations. """ +from functools import partial + import orthax import quadax from termcolor import colored -from desc.backend import jnp, trapezoid +from desc.backend import jit, jnp, trapezoid from ..utils import warnif from .bounce_integral import ( @@ -25,6 +27,10 @@ from .data_index import register_compute_fun +def _is_Newton_Cotes(quad): + return quad in [trapezoid, quadax.trapezoid, quadax.simpson] + + def vec_quadax(quad): """Vectorize an adaptive quadrature method from quadax to compute ripple. @@ -39,6 +45,8 @@ def vec_quadax(quad): Vectorized adaptive quadrature method. """ + if _is_Newton_Cotes(quad): + return quad def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return quad(fun, interval, args=(B_sup_z, B, B_z_ra, arg1, arg2))[0] @@ -49,7 +57,7 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return vec_quad -def poloidal_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): +def poloidal_leggauss(deg, a_min=0, a_max=2 * jnp.pi): """Gauss-Legendre quadrature. Returns quadrature points αₖ and weights wₖ for the approximate evaluation @@ -69,13 +77,11 @@ def poloidal_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): Since everything is continuous, as the number of nodes tend to infinity both approaches should converge to the same result, assuming irrational surface. However, the order at which all the bounce integrals detected over the surface - are summed differs, so the convergence rate will differ. It is not tested whether - such a method will converge to the limit faster than extending the length of the - field line chunk. + are summed differs, so the convergence rate will differ. Parameters ---------- - resolution: int + deg: int Number of quadrature points. a_min: float Min α value. @@ -90,28 +96,28 @@ def poloidal_leggauss(resolution, a_min=0, a_max=2 * jnp.pi): Quadrature weights. """ - x, w = orthax.legendre.leggauss(resolution) + x, w = orthax.legendre.leggauss(deg) w = w * grad_affine_bijection(a_min, a_max) alpha = affine_bijection(x, a_min, a_max) return alpha, w -def _poloidal_integrate(grid, f, name=""): - # assumes f has shape (grid.num_radial, grid.num_poloidal) +def _poloidal_average(grid, f, name=""): + assert f.shape[-1] == grid.num_poloidal if grid.poloidal_weight is None: warnif( grid.num_poloidal != 1, - msg=colored(f"{name} reduced via poloidal mean.", "yellow"), + msg=colored(f"{name} reduced via uniform poloidal mean.", "yellow"), ) - f = jnp.mean(f, axis=-1) + avg = jnp.mean(f, axis=-1) else: - f = f @ grid.poloidal_weight - return f + avg = f @ grid.poloidal_weight / jnp.sum(grid.poloidal_weight) + return avg @register_compute_fun( - name="V_psi(r)*range(z)", - label="\\int d \\ell / \\vert B \\vert", + name="V_psi(r)*L", + label="\\int_{0}^{L} d \\ell / \\vert B \\vert", units="m^3 / Wb", units_long="Cubic meters per Weber", description=( @@ -130,30 +136,30 @@ def _poloidal_integrate(grid, f, name=""): and grid.source_grid.is_meshgrid, ], ) -def _V_psi_range_z(data, transforms, profiles, **kwargs): +def _V_psi_L(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - V = _poloidal_integrate( + V_psi_L = _poloidal_average( g, quadax.simpson( jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ), - name="V_psi(r)*range(z)", + name="V_psi(r)*L", ) - data["V_psi(r)*range(z)"] = g.expand(V) + data["V_psi(r)*L"] = g.expand(V_psi_L) return data @register_compute_fun( - name="S(r)*range(z)", - label="\\int d \\ell \\vert \\nabla \\psi \\vert / \\vert B \\vert", + name="S(r)*L", + label="\\int_{0}^{L} d \\ell \\vert \\nabla \\psi \\vert / \\vert B \\vert", units="m^2", units_long="Square meters", description=( "Surface area of flux surfaces, computed along field line, " - "scaled by dimensionless length of field line" + "scaled by dimensionless length of field line." ), dim=1, params=[], @@ -167,19 +173,19 @@ def _V_psi_range_z(data, transforms, profiles, **kwargs): and grid.source_grid.is_meshgrid, ], ) -def _S_range_z(data, transforms, profiles, **kwargs): +def _S_L(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - S = _poloidal_integrate( + S_L = _poloidal_average( g, quadax.simpson( jnp.reshape(data["|grad(psi)|"] / (data["B^zeta"] * data["|B|"]), shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ), - name="S(r)*range(z)", + name="S(r)*L", ) - data["S(r)*range(z)"] = g.expand(S) + data["S(r)*L"] = g.expand(S_L) return data @@ -201,7 +207,7 @@ def _S_range_z(data, transforms, profiles, **kwargs): "|B|_z|r,a", "|B|", "|grad(psi)|", - "cvdrift0", + "kappa_g", ], grid_requirement=[ "source_grid", @@ -220,27 +226,14 @@ def _S_range_z(data, transforms, profiles, **kwargs): ), quad_res="int : Resolution for quadrature over velocity coordinate.", ) +@partial(jit, static_argnames=["bounce_integral", "batch", "quad", "quad_res"]) def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): - def dH(grad_psi_norm, cvdrift0, B, pitch, Z): - return ( - 1 - / pitch - * jnp.sqrt(1 / pitch - B) - * (4 / B - pitch) - * grad_psi_norm - * cvdrift0 - / B - ) - - def dI(B, pitch, Z): - return jnp.sqrt(1 - pitch * B) / B - g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") - _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) - batch = kwargs.pop("batch", True) - quad = kwargs.pop("quad", quadax.simpson) - quad_res = kwargs.pop("quad_res", 50) + _bounce_integral = kwargs.get("bounce_integral", bounce_integral) + batch = kwargs.get("batch", True) + quad = kwargs.get("quad", quadax.simpson) + quad_res = kwargs.get("quad_res", 100) # Get endpoints of integral over pitch for each field line. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) # Floating point error impedes consistent detection of bounce points riding @@ -249,9 +242,14 @@ def dI(B, pitch, Z): max_B = (1 - 1e-6) * max_B pitch_endpoint = 1 / jnp.stack([max_B, min_B]) - is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] - if is_Newton_Cotes: - bounce_int, _ = _bounce_integral( + def dH(grad_psi_norm, kappa_g, B, pitch, Z): + return jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * kappa_g / B + + def dI(B, pitch, Z): + return jnp.sqrt(1 - pitch * B) / B + + if _is_Newton_Cotes(quad): + bounce_integrate, _ = _bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) @@ -269,11 +267,11 @@ def d_ripple(pitch): λ⁻² ∑ⱼ Hⱼ² / Iⱼ """ - # absorbed λ⁻² into H - H = bounce_int( - dH, [data["|grad(psi)|"], data["cvdrift0"]], pitch, batch=batch + # absorbed 1/λ into H + H = bounce_integrate( + dH, [data["|grad(psi)|"], data["kappa_g"]], pitch, batch=batch ) - I = bounce_int(dI, [], pitch, batch=batch) + I = bounce_integrate(dI, [], pitch, batch=batch) return jnp.nansum(H**2 / I, axis=-1) pitch = composite_linspace(pitch_endpoint, quad_res) @@ -284,11 +282,11 @@ def d_ripple(pitch): else: # Use adaptive quadrature. - def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): + def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): # Quadax requires scalar integration interval, so we need to return scalar. - bounce_int, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) - H = bounce_int(dH, [grad_psi, cvdrift0], pitch, batch=batch) - I = bounce_int(dI, [], pitch, batch=batch) + bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) + H = bounce_integrate(dH, [grad_psi_norm, kappa_g], pitch, batch=batch) + I = bounce_integrate(dI, [], pitch, batch=batch) return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] @@ -300,12 +298,12 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): data["|B|"], data["|B|_z|r,a"], data["|grad(psi)|"], - data["cvdrift0"], + data["kappa_g"], ] ] ripple = quad(d_ripple, pitch_endpoint, *args) - ripple = _poloidal_integrate( + ripple = _poloidal_average( g, ripple.reshape(g.num_rho, g.num_alpha), name="effective ripple raw" ) data["effective ripple raw"] = g.expand(ripple) @@ -323,7 +321,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi, cvdrift0): transforms={}, profiles=[], coordinates="r", - data=["effective ripple raw", "R0", "V_psi(r)*range(z)", "S(r)*range(z)"], + data=["effective ripple raw", "R0", "V_psi(r)*L", "S(r)*L"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """Evaluation of 1/ν neoclassical transport in stellarators. @@ -336,8 +334,8 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): jnp.pi * data["R0"] ** 2 / (8 * 2**0.5) - * data["V_psi(r)*range(z)"] - / data["S(r)*range(z)"] ** 2 + * data["V_psi(r)*L"] + / data["S(r)*L"] ** 2 * data["effective ripple raw"] ) ** (2 / 3) return data @@ -348,7 +346,7 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): label="-∫ dλ ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ", units="m^3 / Wb", units_long="Cubic meters per Weber", - description="Energetic ion confinement, not normalized", + description="Energetic ion confinement proxy, not normalized", dim=1, params=[], transforms={"grid": []}, @@ -381,18 +379,12 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): quad_res="int : Resolution for quadrature over velocity coordinate.", ) def _Gamma_c_raw(params, transforms, profiles, data, **kwargs): - def d_gamma_c(f, B, pitch, Z): - return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) - - def dK(B, pitch, Z): - return 1 / (2 * jnp.sqrt(1 - pitch * B)) - g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") - _bounce_integral = kwargs.pop("bounce_integral", bounce_integral) - batch = kwargs.pop("batch", True) - quad = kwargs.pop("quad", quadax.simpson) - quad_res = kwargs.pop("quad_res", 50) + _bounce_integral = kwargs.get("bounce_integral", bounce_integral) + batch = kwargs.get("batch", True) + quad = kwargs.get("quad", quadax.simpson) + quad_res = kwargs.get("quad_res", 100) # Get endpoints of integral over pitch for each field line. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) # Floating point error impedes consistent detection of bounce points riding @@ -401,9 +393,14 @@ def dK(B, pitch, Z): max_B = (1 - 1e-6) * max_B pitch_endpoint = 1 / jnp.stack([max_B, min_B]) - is_Newton_Cotes = quad in [trapezoid, quadax.trapezoid, quadax.simpson] - if is_Newton_Cotes: - bounce_int, _ = _bounce_integral( + def d_gamma_c(f, B, pitch, Z): + return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) + + def dK(B, pitch, Z): + return 0.5 / jnp.sqrt(1 - pitch * B) + + if _is_Newton_Cotes(quad): + bounce_integrate, _ = _bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) @@ -421,17 +418,20 @@ def d_Gamma_c_raw(pitch): ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ """ - # todo: add Nemov's |grad(psi)| + # TODO: Currently we have implemented Velasco's Gamma_c. + # If we add a 1/|grad(psi)| into the arctan of little + # gamma_c, we implement Nemov's Gamma_c. + # This will affect the gamma_c profile since |grad(psi)| + # depends on alpha. gamma_c = ( 2 / jnp.pi * jnp.arctan( - bounce_int(d_gamma_c, data["cvdrift0"], pitch, batch=batch) - / bounce_int(d_gamma_c, data["gbdrift"], pitch, batch=batch) + bounce_integrate(d_gamma_c, data["cvdrift0"], pitch, batch=batch) + / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) ) ) - # K = ∂I/∂(λ⁻¹) λ⁻² - K = bounce_int(dK, [], pitch, batch=batch) + K = bounce_integrate(dK, [], pitch, batch=batch) # ∂I/∂(λ⁻¹) λ⁻² return jnp.nansum(gamma_c**2 * K, axis=-1) pitch = composite_linspace(pitch_endpoint, quad_res) @@ -445,16 +445,15 @@ def d_Gamma_c_raw(pitch): def d_Gamma_c_raw(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): # Quadax requires scalar integration interval, so we need to return scalar. bounce_int, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) - # todo: add Nemov's |grad(psi)| gamma_c = ( 2 / jnp.pi * jnp.arctan( - bounce_int(d_gamma_c, cvdrift0, pitch, batch=batch) - / bounce_int(d_gamma_c, gbdrift, pitch, batch=batch) + bounce_integrate(d_gamma_c, cvdrift0, pitch, batch=batch) + / bounce_integrate(d_gamma_c, gbdrift, pitch, batch=batch) ) ) - K = bounce_int(dK, [], pitch, batch=batch) + K = bounce_integrate(dK, [], pitch, batch=batch) return jnp.squeeze(jnp.nansum(gamma_c**2 * K, axis=-1)) pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] @@ -471,7 +470,7 @@ def d_Gamma_c_raw(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): ] Gamma_c_raw = quad(d_Gamma_c_raw, pitch_endpoint, *args) - Gamma_c_raw = _poloidal_integrate( + Gamma_c_raw = _poloidal_average( g, Gamma_c_raw.reshape(g.num_rho, g.num_alpha), name="Gamma_c raw" ) data["Gamma_c raw"] = g.expand(Gamma_c_raw) @@ -483,13 +482,13 @@ def d_Gamma_c_raw(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): label="\\Gamma_{c}", units="~", units_long="None", - description="Energetic ion confinement", + description="Energetic ion confinement proxy", dim=1, params=[], transforms={}, profiles=[], coordinates="r", - data=["Gamma_c raw", "V_psi(r)*range(z)"], + data=["Gamma_c raw", "V_psi(r)*L"], ) def _Gamma_c(params, transforms, profiles, data, **kwargs): """Poloidal motion of trapped particle orbits in real-space coordinates. @@ -498,7 +497,5 @@ def _Gamma_c(params, transforms, profiles, data, **kwargs): Phys. Plasmas 1 May 2008; 15 (5): 052501. https://doi.org/10.1063/1.2912456. """ - data["Gamma_c"] = ( - jnp.pi / (2**1.5) * data["Gamma_c raw"] / data["V_psi(r)*range(z)"] - ) + data["Gamma_c"] = jnp.pi * data["Gamma_c raw"] / (2**1.5 * data["V_psi(r)*L"]) return data diff --git a/desc/compute/_omnigenity.py b/desc/compute/_omnigenity.py index 4deea33213..dca7e951f4 100644 --- a/desc/compute/_omnigenity.py +++ b/desc/compute/_omnigenity.py @@ -503,10 +503,10 @@ def _B_omni(params, transforms, profiles, data, **kwargs): transforms={}, profiles=[], coordinates="rtz", - data=["b", "grad(|B|)", "|B|", "grad(psi)"], + data=["b", "grad(|B|)", "|B|^2", "grad(psi)"], ) def _isodynamicity(params, transforms, profiles, data, **kwargs): data["isodynamicity"] = ( - dot(cross(data["b"], data["grad(|B|)"]), data["grad(psi)"]) / data["|B|"] ** 2 + dot(cross(data["b"], data["grad(|B|)"]), data["grad(psi)"]) / data["|B|^2"] ) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 71816a947a..d2393b89e6 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1130,7 +1130,7 @@ def bounce_integral( B, B_z_ra, knots, - quad=leggauss(19), + quad=leggauss(21), automorphism=(automorphism_sin, grad_automorphism_sin), B_ref=1, L_ref=1, @@ -1161,7 +1161,8 @@ def bounce_integral( and the quantities in ``f`` passed to the returned method can be separated into field lines via ``.reshape(S, knots.size)``. One way to satisfy this is to pass in quantities computed on the grid - returned from the method ``desc_grid_from_field_line_coords``. + returned from the method ``desc.equilibrium.coords.rtz_grid``. + See ``tests.test_bounce_integral.test_bounce_integral_checks`` for example use. Parameters ---------- @@ -1234,63 +1235,6 @@ def bounce_integral( Last axis enumerates the polynomials of the spline along a particular field line. - Examples - -------- - Suppose we want to compute a bounce average of the function - f(ℓ) = (1 − λ |B|) * g_zz, where g_zz is the squared norm of the - toroidal basis vector on some set of field lines specified by (ρ, α) - coordinates. This is defined as - (∫ f(ℓ) / √(1 − λ |B|) dℓ) / (∫ 1 / √(1 − λ |B|) dℓ) - - - .. code-block:: python - - eq = get("HELIOTRON") - rho = np.linspace(1e-12, 1, 6) - alpha = np.linspace(0, (2 - eq.sym) * np.pi, 5) - knots = np.linspace(-2 * np.pi, 2 * np.pi, 20) - grid_desc = desc_grid_from_field_line_coords(eq, rho, alpha, knots) - grid_fsa = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, sym=eq.sym, NFP=eq.NFP) - data = eq.compute(["iota"], grid=grid_fsa) - data = {"iota": grid_desc.copy_data_from_other(data["iota"], grid_fsa)} - data = eq.compute( - ["B^zeta", "|B|", "|B|_z|r,a", "g_zz"], - grid=grid_desc, - override_grid=False, - ) - bounce_integrate, spline = bounce_integral( - data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots, check=True - ) - - def numerator(g_zz, B, pitch, Z): - f = (1 - pitch * B) * g_zz - return safediv(f, jnp.sqrt(1 - pitch * B)) - - def denominator(B, pitch, Z): - return safediv(1, jnp.sqrt(1 - pitch * B)) - - pitch = 1 / get_extrema(**spline) - num = bounce_integrate(numerator, data["g_zz"], pitch) - den = bounce_integrate(denominator, [], pitch) - average = num / den - - # Now we can group the data by field line. - average = average.reshape(pitch.shape[0], rho.size, alpha.size, -1) - # The bounce averages stored at index i, j - i, j = 0, 0 - print(average[:, i, j]) - # are the bounce averages along the field line with nodes - # given in Clebsch-Type field-line coordinates ρ, α, ζ - grid_fl = grid_desc.source_grid - nodes = grid_fl.nodes.reshape(rho.size, alpha.size, -1, 3) - print(nodes[i, j]) - # for the pitch values stored in - pitch = pitch.reshape(pitch.shape[0], rho.size, alpha.size) - print(pitch[:, i, j]) - # Some of these bounce averages will evaluate as nan. - # You should filter out these nan values when computing stuff. - print(np.nansum(average, axis=-1)) - """ B_sup_z = B_sup_z * L_ref / B_ref B = B / B_ref diff --git a/desc/equilibrium/coords.py b/desc/equilibrium/coords.py index 5dbc710cec..939839e748 100644 --- a/desc/equilibrium/coords.py +++ b/desc/equilibrium/coords.py @@ -506,43 +506,49 @@ def to_sfl( return eq_sfl -def desc_grid_from_field_line_coords(eq, rho, alpha, zeta): - """Return DESC coordinate grid from given Clebsch-Type field-line coordinates. +def rtz_grid(eq, radial, poloidal, toroidal, coordinates): + """Return DESC coordinate grid from given coordinates. - Create a meshgrid from the given field line coordinates, - and return the equivalent DESC coordinate grid. + Create a meshgrid from the given coordinates, and return the + paired DESC coordinate grid. Parameters ---------- eq : Equilibrium Equilibrium on which to perform coordinate mapping. - rho : ndarray - Sorted unique flux surface label coordinates. - alpha : ndarray - Sorted unique field line label coordinates over a constant rho surface. - zeta : ndarray - Sorted unique field line-following ζ coordinates. + radial : ndarray + Sorted unique radial coordinates. + poloidal : ndarray + Sorted unique poloidal coordinates. + toroidal : ndarray + Sorted unique toroidal coordinates. + coordinates : str + Input coordinates that are specified by the arguments, respectively. + raz : rho, alpha, zeta + rpz : rho, theta_PEST, zeta + rtz : rho, theta, zeta Returns ------- - grid_desc : Grid - DESC coordinate grid for the given field line coordinates. + desc_grid : Grid + DESC coordinate grid for the given coordinates. """ - grid_fl = Grid.create_meshgrid(rho, alpha, zeta, coordinates="raz") - coords_desc = eq.map_coordinates( - grid_fl.nodes, - inbasis=("rho", "alpha", "zeta"), + grid = Grid.create_meshgrid(radial, poloidal, toroidal, coordinates) + inbasis = {"r": "rho", "t": "theta", "p": "theta_PEST", "a": "alpha", "z": "zeta"} + rtz_nodes = eq.map_coordinates( + grid.nodes, + inbasis=[inbasis[char] for char in coordinates], outbasis=("rho", "theta", "zeta"), period=(jnp.inf, 2 * jnp.pi, jnp.inf), ) - grid_desc = Grid( - nodes=coords_desc, + desc_grid = Grid( + nodes=rtz_nodes, coordinates="rtz", - source_grid=grid_fl, + source_grid=grid, sort=False, jitable=True, - _unique_rho_idx=grid_fl.unique_rho_idx, - _inverse_rho_idx=grid_fl.inverse_rho_idx, + _unique_rho_idx=grid.unique_rho_idx, + _inverse_rho_idx=grid.inverse_rho_idx, ) - return grid_desc + return desc_grid diff --git a/desc/equilibrium/equilibrium.py b/desc/equilibrium/equilibrium.py index a6acc0a6e0..08a62bd9af 100644 --- a/desc/equilibrium/equilibrium.py +++ b/desc/equilibrium/equilibrium.py @@ -2482,3 +2482,84 @@ def insert(self, i, new_item): "Members of EquilibriaFamily should be of type Equilibrium or subclass." ) self._equilibria.insert(i, new_item) + + +# TODO: in GitHub issue #1035. +# Move logic to eq.compute and store spline of rotational transform used to +# compute coordinate mapping. +def compute_raz_data( + eq, grid, names_field_line, names_0d=None, names_1dr=None, data=None +): + """Compute field line quantities and their dependencies on the correct grids. + + Parameters + ---------- + eq : Equilibrium + Equilibrium to compute on. + grid : Grid + Grid on which the field line quantities should be computed. + names_field_line : iterable + Field line quantities that will be computed on the returned field line grid. + These quantities should require grid.source_grid in grid_requirement parameter + or the register compute function decorator. + names_0d : iterable + Additional things to compute constant over volume. + names_1dr : iterable + Additional things to compute constant over flux surface. + data : dict of Array + Data computed so far, generally output from other compute functions. + + Returns + ------- + data : dict + Computed quantities. + + """ + assert grid.source_grid is not None, "Why are you using this function?" + if data is None: + data = {} + if names_0d is None: + names_0d = {} + if names_1dr is None: + names_1dr = {} + names_field_line = set(names_field_line) + names_0d = set(names_0d) - names_field_line + names_1dr = set(names_1dr) - names_field_line + p = "desc.equilibrium.equilibrium.Equilibrium" + deps = ( + names_0d + | names_1dr + | set(get_data_deps(names_field_line, obj=p, has_axis=grid.axis.size > 0)) + ) - data.keys() + dep0d = {dep for dep in deps if data_index[p][dep]["coordinates"] == ""} + dep1dr = {dep for dep in deps if data_index[p][dep]["coordinates"] == "r"} + # Create grid with given flux surfaces. + grid1dr = LinearGrid( + rho=grid.compress(grid.nodes[:, 0]), + M=eq.M_grid, + N=eq.N_grid, + sym=eq.sym, + NFP=eq.NFP, + ) + # Compute dependencies on correct grids, overriding whenever necessary. + # (The given grid may not have enough radial or poloidal resolution to compute + # dependencies of names_field_line accurately. For flux functions or scalars, + # this is not an issue since the interpolation to the given grid is trivial). + seed = eq.compute(list(dep0d | dep1dr), grid=grid1dr) + + # Remove dependencies from data_seed that cannot be interpolated trivially. + seed0d = {key: val for key, val in seed.items() if key in dep0d} + seed1dr = { + key: grid.copy_data_from_other(val, grid1dr) + for key, val in seed.items() + if key in dep1dr + } + seed = seed0d | seed1dr + # Now, compute names_field_line on grid with correct dependencies. + # Can't override grid here because computing stuff in names_field_line + # requires grid.source_grid. + data = seed | data # values of shared keys should default to data + data = eq.compute( + names=list(names_field_line), grid=grid, data=data, override_grid=False + ) + return data diff --git a/desc/grid.py b/desc/grid.py index 5fef119621..4ac70ca64c 100644 --- a/desc/grid.py +++ b/desc/grid.py @@ -765,10 +765,6 @@ def poloidal_weight(self): quadrature weight over poloidal domain """ - if hasattr(self, "_poloidal_weight"): - return self._poloidal_weight - if hasattr(self, "_unique_poloidal_idx"): - self._poloidal_weight = jnp.array([2 * jnp.pi / self.num_poloidal]) return self.__dict__.setdefault("_poloidal_weight", None) @poloidal_weight.setter diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index 29a234f351..6604227686 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -13,7 +13,6 @@ from tests.test_plotting import tol_1d from desc.backend import flatnonzero, jnp -from desc.compute import data_index from desc.compute.bounce_integral import ( _filter_not_nan, _poly_der, @@ -33,12 +32,13 @@ take_mask, tanh_sinh, ) -from desc.compute.utils import dot, get_data_deps, safediv +from desc.compute.utils import dot, safediv from desc.equilibrium import Equilibrium -from desc.equilibrium.coords import desc_grid_from_field_line_coords +from desc.equilibrium.coords import rtz_grid +from desc.equilibrium.equilibrium import compute_raz_data from desc.examples import get from desc.grid import Grid, LinearGrid -from desc.utils import errorif, only1 +from desc.utils import only1 def _affine_bijection_forward(x, a, b): @@ -85,7 +85,7 @@ def test_reshape_convention(): rho = np.linspace(0, 1, 3) alpha = np.linspace(0, 2 * np.pi, 4) zeta = np.linspace(0, 6 * np.pi, 5) - grid = Grid.create_meshgrid(rho, alpha, zeta) + grid = Grid.create_meshgrid(rho, alpha, zeta, coordinates="raz") r, a, z = grid.nodes.T # functions of zeta should separate along first two axes # since those are contiguous, this should work @@ -96,7 +96,7 @@ def test_reshape_convention(): f = r.reshape(rho.size, -1) for i in range(1, f.shape[-1]): np.testing.assert_allclose(f[:, i - 1], f[:, i]) - # test final reshape of bounce integral result won't mix data + # test reshape=ing result won't mix data f = (a**2 + z).reshape(rho.size, alpha.size, zeta.size) for i in range(1, f.shape[0]): np.testing.assert_allclose(f[i - 1], f[i]) @@ -109,9 +109,10 @@ def test_reshape_convention(): err_msg = "The ordering conventions are required for correctness." assert "P, S, N" in inspect.getsource(bounce_points), err_msg - src = inspect.getsource(bounce_integral) - assert "S, knots.size" in src, err_msg - assert "pitch.shape[0], rho.size, alpha.size" in src, err_msg + assert "S, knots.size" in inspect.getsource(bounce_integral), err_msg + assert 'meshgrid(a, b, c, indexing="ij")' in inspect.getsource( + Grid.create_meshgrid + ), err_msg @pytest.mark.unit @@ -463,17 +464,22 @@ def integrand(B, pitch, Z): @pytest.mark.unit def test_bounce_integral_checks(): """Test that all the internal correctness checks pass for real example.""" + # Suppose we want to compute a bounce average of the function + # f(ℓ) = (1 − λ |B|) * g_zz, where g_zz is the squared norm of the + # toroidal basis vector on some set of field lines specified by (ρ, α) + # coordinates. This is defined as + # (∫ f(ℓ) / √(1 − λ |B|) dℓ) / (∫ 1 / √(1 − λ |B|) dℓ) eq = get("HELIOTRON") rho = np.linspace(1e-12, 1, 6) alpha = np.linspace(0, (2 - eq.sym) * np.pi, 5) knots = np.linspace(-2 * np.pi, 2 * np.pi, 20) - grid_desc = desc_grid_from_field_line_coords(eq, rho, alpha, knots) + grid = rtz_grid(eq, rho, alpha, knots, coordinates="raz") grid_fsa = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, sym=eq.sym, NFP=eq.NFP) data = eq.compute(["iota"], grid=grid_fsa) - data = {"iota": grid_desc.copy_data_from_other(data["iota"], grid_fsa)} + data = {"iota": grid.copy_data_from_other(data["iota"], grid_fsa)} data = eq.compute( ["B^zeta", "|B|", "|B|_z|r,a", "g_zz"], - grid=grid_desc, + grid=grid, override_grid=False, data=data, ) @@ -495,9 +501,26 @@ def denominator(B, pitch, Z): pitch = 1 / get_extrema(**spline) num = bounce_integrate(numerator, data["g_zz"], pitch) + # Can reduce memory usage by specifying by not batching. den = bounce_integrate(denominator, [], pitch, batch=False) - average = num / den - assert np.isfinite(average).any() + avg = num / den + assert np.isfinite(avg).any() + + # Sum all bounce integrals across field line + avg = np.nansum(avg, axis=-1) + # Group the data by field line. + avg = avg.reshape(pitch.shape[0], rho.size, alpha.size) + # The bounce averages stored at index i, j + i, j = 0, 0 + print(avg[:, i, j]) + # are the bounce averages along the field line with nodes + # given in Clebsch-Type field-line coordinates ρ, α, ζ + raz_grid = grid.source_grid + nodes = raz_grid.nodes.reshape(rho.size, alpha.size, -1, 3) + print(nodes[i, j]) + # for the pitch values stored in + pitch = pitch.reshape(pitch.shape[0], rho.size, alpha.size) + print(pitch[:, i, j]) @partial(np.vectorize, excluded={0}) @@ -583,79 +606,6 @@ def _elliptic_incomplete(k2): return I_0, I_1, I_2, I_3, I_4, I_5, I_6, I_7 -# kludge until GitHub issue #719 is resolved. -def _get_data(eq, rho, alpha, names_field_line, names_0d_or_1dr=None): - """Compute field line quantities on correct grid for test_drift(). - - Parameters - ---------- - eq : Equilibrium - Equilibrium to compute on. - rho : Array - Field line radial label. - alpha : Array - Field line poloidal label. - names_field_line : list - Field line quantities that will be computed on the returned field line grid. - Should not include 0d or 1dr quantities. - names_0d_or_1dr : list - Things to compute that are constant throughout volume or over flux surface. - - Returns - ------- - data : dict - Computed quantities. - grid_desc : Grid - Grid on which the returned quantities can be broadcast on. - zeta : Array - Zeta values along field line. - - """ - if names_0d_or_1dr is None: - names_0d_or_1dr = [] - p = "desc.equilibrium.equilibrium.Equilibrium" - # Gather dependencies of given quantities. - deps = ( - get_data_deps(names_field_line + names_0d_or_1dr, obj=p, has_axis=False) - + names_0d_or_1dr - ) - deps = list(set(deps)) - # Create grid with given flux surfaces. - grid1dr = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, sym=eq.sym, NFP=eq.NFP) - # Compute dependencies on correct grids. - seed_data = eq.compute(deps, grid=grid1dr) - dep1dr = {dep for dep in deps if data_index[p][dep]["coordinates"] == "r"} - dep0d = {dep for dep in deps if data_index[p][dep]["coordinates"] == ""} - - # Make a set of nodes along a single fieldline. - iota = grid1dr.compress(seed_data["iota"]).item() - errorif(alpha != 0, NotImplementedError) - zeta = np.linspace(-np.pi / iota, np.pi / iota, (2 * eq.M_grid) * 4 + 1) - # Make grid that can separate into field lines via a reshape operation, - # as expected by bounce_integral(). - grid_desc = desc_grid_from_field_line_coords(eq, rho, alpha=alpha, zeta=zeta) - - # Collect quantities that can be used as a seed to compute the - # field line quantities over the grid mapped from field line coordinates. - # (Single field line grid won't have enough poloidal resolution to - # compute these quantities accurately). - data0d = {key: val for key, val in seed_data.items() if key in dep0d} - data1d = { - key: grid_desc.copy_data_from_other(val, grid1dr) - for key, val in seed_data.items() - if key in dep1dr - } - data = data0d | data1d - # Compute field line quantities with precomputed dependencies. - for name in names_field_line: - if name in data: - del data[name] - data = eq.compute( - names=names_field_line, grid=grid_desc, data=data, override_grid=False - ) - return data, grid_desc, zeta - - @pytest.mark.unit @pytest.mark.mpl_image_compare(remove_text=True, tolerance=tol_1d) def test_drift(): @@ -664,12 +614,19 @@ def test_drift(): psi_boundary = eq.Psi / (2 * np.pi) psi = 0.25 * psi_boundary rho = np.sqrt(psi / psi_boundary) - assert np.isclose(rho, 0.5) + np.testing.assert_allclose(rho, 0.5) + + # Make a set of nodes along a single fieldline. + grid_fsa = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, sym=eq.sym, NFP=eq.NFP) + data = eq.compute(["iota"], grid=grid_fsa) + iota = grid_fsa.compress(data["iota"]).item() alpha = 0 - data, grid, zeta = _get_data( + zeta = np.linspace(-np.pi / iota, np.pi / iota, (2 * eq.M_grid) * 4 + 1) + grid = rtz_grid(eq, rho, alpha, zeta, coordinates="raz") + + data = compute_raz_data( eq, - rho, - alpha, + grid, [ "B^zeta", "|B|", @@ -680,9 +637,11 @@ def test_drift(): "grad(psi)", "|grad(psi)|", ], - ["shear", "a", "psi", "iota"], + names_0d=["a"], + names_1dr=["shear", "psi", "iota"], ) - assert np.allclose(data["psi"], psi) + np.testing.assert_allclose(data["psi"], psi) + np.testing.assert_allclose(data["iota"], iota) L_ref = data["a"] B_ref = 2 * np.abs(psi_boundary) / L_ref**2 @@ -703,7 +662,6 @@ def test_drift(): # is independent of normalization length scales, like "effective r/R0". epsilon = L_ref * rho # Aspect ratio of the flux surface. np.testing.assert_allclose(epsilon, 0.05) - iota = grid.compress(data["iota"]).item() theta_PEST = alpha + iota * zeta # same as 1 / (1 + epsilon cos(theta)) assuming epsilon << 1 B_analytic = B0 * (1 - epsilon * np.cos(theta_PEST)) @@ -765,6 +723,7 @@ def test_drift(): y = np.sqrt(2 * epsilon * pitch * B0) I_0, I_2, I_4, I_6 = map(lambda I: I / y, (I_0, I_2, I_4, I_6)) I_1, I_3, I_5, I_7 = map(lambda I: I * y, (I_1, I_3, I_5, I_7)) + drift_analytic_num = ( fudge_2 * alpha_MHD / B0**2 * I_1 - 0.5 @@ -775,16 +734,14 @@ def test_drift(): - (I_6 + I_7) ) ) / G0 - - drift_analytic_denom = I_0 / G0 - - drift_analytic = drift_analytic_num / drift_analytic_denom + drift_analytic_den = I_0 / G0 + drift_analytic = drift_analytic_num / drift_analytic_den def integrand_num(cvdrift, gbdrift, B, pitch, Z): g = jnp.sqrt(1 - pitch * B) return (cvdrift * g) - (0.5 * g * gbdrift) + (0.5 * gbdrift / g) - def integrand_denom(B, pitch, Z): + def integrand_den(B, pitch, Z): g = jnp.sqrt(1 - pitch * B) return 1 / g @@ -793,17 +750,15 @@ def integrand_denom(B, pitch, Z): f=[cvdrift, gbdrift], pitch=pitch[:, np.newaxis], ) - - drift_numerical_denom = bounce_integrate( - integrand=integrand_denom, + drift_numerical_den = bounce_integrate( + integrand=integrand_den, f=[], pitch=pitch[:, np.newaxis], ) drift_numerical_num = np.squeeze(_filter_not_nan(drift_numerical_num)) - drift_numerical_denom = np.squeeze(_filter_not_nan(drift_numerical_denom)) - - drift_numerical = drift_numerical_num / drift_numerical_denom + drift_numerical_den = np.squeeze(_filter_not_nan(drift_numerical_den)) + drift_numerical = drift_numerical_num / drift_numerical_den msg = "There should be one bounce integral per pitch in this example." assert drift_numerical.size == drift_analytic.size, msg np.testing.assert_allclose(drift_numerical, drift_analytic, atol=5e-3, rtol=5e-2) diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 585a65ed5d..620d73a1af 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -4,70 +4,51 @@ import numpy as np import pytest -from desc.compute import data_index, get_data_deps +from desc.compute._neoclassical import poloidal_leggauss from desc.equilibrium import Equilibrium -from desc.equilibrium.coords import desc_grid_from_field_line_coords -from desc.grid import LinearGrid +from desc.equilibrium.coords import rtz_grid +from desc.equilibrium.equilibrium import compute_raz_data -def _compute_field_line_data(eq, grid_desc, names_field_line, names_0d_or_1dr=None): - """Compute field line quantities on correct grids. - - Parameters - ---------- - eq : Equilibrium - Equilibrium to compute on. - grid_desc : Grid - Grid on which the field line quantities should be computed. - names_field_line : list - Field line quantities that will be computed on the returned field line grid. - Should not include 0d or 1dr quantities. - names_0d_or_1dr : list - Things to compute that are constant throughout volume or over flux surface. - - Returns - ------- - data : dict - Computed quantities. +@pytest.mark.unit +def test_integration_on_field_line(): + """Test that V_psi(r)*L / S(r)*L = V_psi(r) / S(r) as L → ∞.""" + eq = Equilibrium.load( + "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" + ".15_L_9_M_9_N_24_output.h5" + ) + resolution = 10 + rho = np.linspace(0, 1, resolution) - """ - # TODO: https://github.com/PlasmaControl/DESC/issues/719 - if names_0d_or_1dr is None: - names_0d_or_1dr = [] - p = "desc.equilibrium.equilibrium.Equilibrium" - # Gather dependencies of given quantities. - deps = ( - get_data_deps(names_field_line + names_0d_or_1dr, obj=p, has_axis=False) - + names_0d_or_1dr + # Surface average field lines truncated at 1 toroidal transit. + alpha, w = poloidal_leggauss(resolution) + L = 2 * np.pi + zeta = np.linspace(0, L, resolution) + grid = rtz_grid(eq, rho, alpha, zeta, coordinates="raz") + grid.source_grid.poloidal_weight = w + data = compute_raz_data( + eq, grid, ["V_psi(r)*L", "S(r)*L"], names_1dr=["psi_r", "V_r(r)", "S(r)"] + ) + np.testing.assert_allclose( + data["V_psi(r)*L"] / data["S(r)*L"], + data["V_r(r)"] / data["psi_r"] / data["S(r)"], + rtol=0.01, ) - deps = list(set(deps)) - # Create grid with given flux surfaces. - rho = grid_desc.compress(grid_desc.nodes[:, 0]) - grid1dr = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, sym=eq.sym, NFP=eq.NFP) - # Compute dependencies on correct grids. - seed_data = eq.compute(deps, grid=grid1dr) - dep1dr = {dep for dep in deps if data_index[p][dep]["coordinates"] == "r"} - dep0d = {dep for dep in deps if data_index[p][dep]["coordinates"] == ""} - # Collect quantities that can be used as a seed to compute the - # field line quantities over the grid mapped from field line coordinates. - # (Single field line grid won't have enough poloidal resolution to - # compute these quantities accurately). - data0d = {key: val for key, val in seed_data.items() if key in dep0d} - data1d = { - key: grid_desc.copy_data_from_other(val, grid1dr) - for key, val in seed_data.items() - if key in dep1dr - } - data = data0d | data1d - # Compute field line quantities with precomputed dependencies. - for name in names_field_line: - if name in data: - del data[name] - data = eq.compute( - names=names_field_line, grid=grid_desc, data=data, override_grid=False + # Now for field line with large L. + # The drawback of this approach is that convergence can regress + # unless resolution is increased linearly with L. + L = 100 * np.pi + zeta = np.linspace(0, L, resolution**2) + grid = rtz_grid(eq, rho, 0, zeta, coordinates="raz") + data = compute_raz_data( + eq, grid, ["V_psi(r)*L", "S(r)*L"], names_1dr=["psi_r", "V_r(r)", "S(r)"] + ) + np.testing.assert_allclose( + data["V_psi(r)*L"] / data["S(r)*L"], + data["V_r(r)"] / data["psi_r"] / data["S(r)"], + rtol=0.01, ) - return data @pytest.mark.unit @@ -77,13 +58,14 @@ def test_effective_ripple(): "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) - grid = desc_grid_from_field_line_coords( + grid = rtz_grid( eq, - rho=np.linspace(0.01, 1, 20), - alpha=np.array([0]), - zeta=np.linspace(-100 * np.pi, 100 * np.pi, 500), + radial=np.linspace(0, 1, 20), + poloidal=np.array([0]), + toroidal=np.linspace(0, 100 * np.pi, 500), + coordinates="raz", ) - data = _compute_field_line_data( + data = compute_raz_data( eq, grid, [ @@ -92,10 +74,11 @@ def test_effective_ripple(): "|B|", "|grad(psi)|", "cvdrift0", - "V_psi(r)*range(z)", - "S(r)*range(z)", + "V_psi(r)*L", + "S(r)*L", ], - ["min_tz |B|", "max_tz |B|", "R0"], + names_0d=["R0"], + names_1dr=["min_tz |B|", "max_tz |B|"], ) data = eq.compute( "effective ripple raw", From bc6b98253a667b6305ca93ec53fd4efa67676c63 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 30 May 2024 02:31:37 -0500 Subject: [PATCH 032/112] Fix math, results look kinda good now --- desc/compute/_neoclassical.py | 193 ++++++++++++++++------------------ tests/test_neoclassical.py | 58 ++++------ 2 files changed, 112 insertions(+), 139 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index e1c2bfe0a2..7d3c256724 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -116,19 +116,17 @@ def _poloidal_average(grid, f, name=""): @register_compute_fun( - name="V_psi(r)*L", - label="\\int_{0}^{L} d \\ell / \\vert B \\vert", - units="m^3 / Wb", - units_long="Cubic meters per Weber", - description=( - "Volume enclosed by flux surfaces, derivative with respect to toroidal flux, " - "computed along field line, scaled by dimensionless length of field line" - ), - dim=1, + name="L|r,a", + label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" + " \\frac{d\\zeta}{B^{\\zeta} \\vert B \\vert}", + units="m", + units_long="meters", + description="Length along field line", + dim=2, params=[], transforms={"grid": []}, profiles=[], - coordinates="r", + coordinates="ra", data=["B^zeta", "|B|"], grid_requirement=[ "source_grid", @@ -136,65 +134,53 @@ def _poloidal_average(grid, f, name=""): and grid.source_grid.is_meshgrid, ], ) -def _V_psi_L(data, transforms, profiles, **kwargs): +def _L_ra(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - V_psi_L = _poloidal_average( - g, - quadax.simpson( - jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape), - jnp.reshape(g.nodes[:, 2], shape), - axis=-1, - ), - name="V_psi(r)*L", + data["L|r,a"] = quadax.simpson( + jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape), + jnp.reshape(g.nodes[:, 2], shape), + axis=-1, ) - data["V_psi(r)*L"] = g.expand(V_psi_L) return data @register_compute_fun( - name="S(r)*L", - label="\\int_{0}^{L} d \\ell \\vert \\nabla \\psi \\vert / \\vert B \\vert", - units="m^2", - units_long="Square meters", - description=( - "Surface area of flux surfaces, computed along field line, " - "scaled by dimensionless length of field line." - ), - dim=1, + name="G|r,a", + label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" + " \\frac{d\\zeta}{B^{\\zeta} \\vert B \\vert \\sqrt g}", + units="m^{-2}", + units_long="inverse meters squared", + description="Length over volume along field line", + dim=2, params=[], transforms={"grid": []}, profiles=[], - coordinates="r", - data=["B^zeta", "|B|", "|grad(psi)|"], + coordinates="ra", + data=["B^zeta", "|B|", "sqrt(g)"], grid_requirement=[ "source_grid", lambda grid: grid.source_grid.coordinates == "raz" and grid.source_grid.is_meshgrid, ], ) -def _S_L(data, transforms, profiles, **kwargs): +def _G_ra(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - S_L = _poloidal_average( - g, - quadax.simpson( - jnp.reshape(data["|grad(psi)|"] / (data["B^zeta"] * data["|B|"]), shape), - jnp.reshape(g.nodes[:, 2], shape), - axis=-1, - ), - name="S(r)*L", - ) - data["S(r)*L"] = g.expand(S_L) + data["G|r,a"] = quadax.simpson( + jnp.reshape(1 / (data["B^zeta"] * data["|B|"] * data["sqrt(g)"]), shape), + jnp.reshape(g.nodes[:, 2], shape), + axis=-1, + ) / (4 * jnp.pi**2) return data @register_compute_fun( name="effective ripple raw", - label="-∫ dλ λ⁻² ∑ⱼ Hⱼ² / Iⱼ", - units="Wb / m", - units_long="Webers per meter", - description="Effective ripple modulation amplitude, not normalized", + label="∫ dλ λ⁻² \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + units="T^2", + units_long="Tesla squared", + description="Effective ripple modulation amplitude, not dimensionless", dim=1, params=[], transforms={"grid": []}, @@ -204,10 +190,11 @@ def _S_L(data, transforms, profiles, **kwargs): "min_tz |B|", "max_tz |B|", "B^zeta", - "|B|_z|r,a", "|B|", + "|B|_z|r,a", "|grad(psi)|", "kappa_g", + "L|r,a", ], grid_requirement=[ "source_grid", @@ -243,6 +230,7 @@ def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): pitch_endpoint = 1 / jnp.stack([max_B, min_B]) def dH(grad_psi_norm, kappa_g, B, pitch, Z): + # absorbed 1/λ into H return jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * kappa_g / B def dI(B, pitch, Z): @@ -254,7 +242,7 @@ def dI(B, pitch, Z): ) def d_ripple(pitch): - """Return λ⁻² ∑ⱼ Hⱼ² / Iⱼ evaluated at pitch. + """Return λ⁻² ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. Parameters ---------- @@ -264,10 +252,9 @@ def d_ripple(pitch): Returns ------- d_ripple : Array, shape(pitch.shape) - λ⁻² ∑ⱼ Hⱼ² / Iⱼ + λ⁻² ∑ⱼ Hⱼ²/Iⱼ """ - # absorbed 1/λ into H H = bounce_integrate( dH, [data["|grad(psi)|"], data["kappa_g"]], pitch, batch=batch ) @@ -283,7 +270,6 @@ def d_ripple(pitch): # Use adaptive quadrature. def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): - # Quadax requires scalar integration interval, so we need to return scalar. bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) H = bounce_integrate(dH, [grad_psi_norm, kappa_g], pitch, batch=batch) I = bounce_integrate(dI, [], pitch, batch=batch) @@ -304,15 +290,15 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): ripple = quad(d_ripple, pitch_endpoint, *args) ripple = _poloidal_average( - g, ripple.reshape(g.num_rho, g.num_alpha), name="effective ripple raw" + g, ripple.reshape(g.num_rho, g.num_alpha) / data["L|r,a"] ) data["effective ripple raw"] = g.expand(ripple) return data @register_compute_fun( - name="effective ripple", - label="\\epsilon_{\\text{eff}}", + name="effective ripple", # this is ε¹ᐧ⁵ + label="π/(8√2) (R₀(∂_ψ V)/S)² ∫ dλ λ⁻² \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", units="~", units_long="None", description="Effective ripple modulation amplitude", @@ -321,7 +307,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): transforms={}, profiles=[], coordinates="r", - data=["effective ripple raw", "R0", "V_psi(r)*L", "S(r)*L"], + data=["R0", "V_r(r)", "psi_r", "S(r)", "effective ripple raw"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """Evaluation of 1/ν neoclassical transport in stellarators. @@ -332,21 +318,38 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): """ data["effective ripple"] = ( jnp.pi - * data["R0"] ** 2 / (8 * 2**0.5) - * data["V_psi(r)*L"] - / data["S(r)*L"] ** 2 + * (data["R0"] * data["V_r(r)"] / data["psi_r"] / data["S(r)"]) ** 2 * data["effective ripple raw"] - ) ** (2 / 3) + ) return data @register_compute_fun( - name="Gamma_c raw", - label="-∫ dλ ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ", - units="m^3 / Wb", - units_long="Cubic meters per Weber", - description="Energetic ion confinement proxy, not normalized", + name="Gamma_c", + # When comparing Velasco's Γ_c: https://doi.org/10.1088/1741-4326/ac2994 + # with Nemov's Γ_c: https://doi.org/10.1063/1.2912456, + # note that + # dλ v τ_b = 8 (∂I/∂b) db = -8 ∂I/∂(λ⁻¹) dλ λ⁻² + # and + # 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) + # where the integrals are along an irrational field line with + # ds given by dζ / B^ζ + # √g the (Ψ, θ, ζ)-coordinate Jacobian + # There is also the dimensionless difference between Nemov's and Velascos's γ_c, + # mentioned in Velasco's footnote 4. If this difference is γ_c ignored, and the + # (missing?) √g factor is pushed into the integral over alpha in Velasco eq. 18 + # then we have that + # Velasco Γ_c = Nemov Γ_c * lim{L → ∞} ∫₀ᴸ ds/(B √g) / (2π). + # In particular, Velasco Γ_c grows with the length of the field line + # which isn't what we want. + # TODO: + # We currently implement Nemov Γ_c with Velasco γ_c. + # Switch to Nemov γ_c too. + label="π/(2√2) ∫ dλ λ⁻² \\langle ∑ⱼ [γ_c² ∂I/∂(λ⁻¹)]ⱼ \\rangle", + units="~", + units_long="None", + description="Nemov's energetic ion confinement proxy", dim=1, params=[], transforms={"grid": []}, @@ -356,10 +359,11 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): "min_tz |B|", "max_tz |B|", "B^zeta", - "|B|_z|r,a", "|B|", + "|B|_z|r,a", "cvdrift0", "gbdrift", + "L|r,a", ], grid_requirement=[ "source_grid", @@ -378,7 +382,13 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): ), quad_res="int : Resolution for quadrature over velocity coordinate.", ) -def _Gamma_c_raw(params, transforms, profiles, data, **kwargs): +def _Gamma_c(params, transforms, profiles, data, **kwargs): + """Poloidal motion of trapped particle orbits in real-space coordinates. + + V. V. Nemov, S. V. Kasilov, W. Kernbichler, G. O. Leitold. + Phys. Plasmas 1 May 2008; 15 (5): 052501. + https://doi.org/10.1063/1.2912456. + """ g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") _bounce_integral = kwargs.get("bounce_integral", bounce_integral) @@ -404,8 +414,8 @@ def dK(B, pitch, Z): data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) - def d_Gamma_c_raw(pitch): - """Return ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ evaluated at pitch. + def d_Gamma_c(pitch): + """Return ∑ⱼ [γ_c² ∂I/∂(λ⁻¹)]ⱼ evaluated at λ = pitch. Parameters ---------- @@ -414,13 +424,13 @@ def d_Gamma_c_raw(pitch): Returns ------- - d_Gamma_c_raw : Array, shape(pitch.shape) - ∑ⱼ (γ_c² ∂I/∂(λ⁻¹) λ⁻²)ⱼ + d_Gamma_c : Array, shape(pitch.shape) + ∑ⱼ [γ_c² ∂I/∂(λ⁻¹)]ⱼ """ # TODO: Currently we have implemented Velasco's Gamma_c. # If we add a 1/|grad(psi)| into the arctan of little - # gamma_c, we implement Nemov's Gamma_c. + # gamma_c, we implement Nemov's Gamma_c. (Check this again). # This will affect the gamma_c profile since |grad(psi)| # depends on alpha. gamma_c = ( @@ -431,20 +441,19 @@ def d_Gamma_c_raw(pitch): / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) ) ) - K = bounce_integrate(dK, [], pitch, batch=batch) # ∂I/∂(λ⁻¹) λ⁻² + K = bounce_integrate(dK, [], pitch, batch=batch) # λ⁻² ∂I/∂(λ⁻¹) return jnp.nansum(gamma_c**2 * K, axis=-1) pitch = composite_linspace(pitch_endpoint, quad_res) pitch = jnp.broadcast_to( pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) - Gamma_c_raw = quad(d_Gamma_c_raw(pitch), pitch, axis=0) + Gamma_c = quad(d_Gamma_c(pitch), pitch, axis=0) else: # Use adaptive quadrature. - def d_Gamma_c_raw(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): - # Quadax requires scalar integration interval, so we need to return scalar. - bounce_int, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) + def d_Gamma_c(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): + bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) gamma_c = ( 2 / jnp.pi @@ -468,34 +477,10 @@ def d_Gamma_c_raw(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): data["gbdrift"], ] ] - Gamma_c_raw = quad(d_Gamma_c_raw, pitch_endpoint, *args) + Gamma_c = quad(d_Gamma_c, pitch_endpoint, *args) - Gamma_c_raw = _poloidal_average( - g, Gamma_c_raw.reshape(g.num_rho, g.num_alpha), name="Gamma_c raw" + Gamma_c = _poloidal_average( + g, Gamma_c.reshape(g.num_rho, g.num_alpha) / data["L|r,a"] ) - data["Gamma_c raw"] = g.expand(Gamma_c_raw) - return data - - -@register_compute_fun( - name="Gamma_c", - label="\\Gamma_{c}", - units="~", - units_long="None", - description="Energetic ion confinement proxy", - dim=1, - params=[], - transforms={}, - profiles=[], - coordinates="r", - data=["Gamma_c raw", "V_psi(r)*L"], -) -def _Gamma_c(params, transforms, profiles, data, **kwargs): - """Poloidal motion of trapped particle orbits in real-space coordinates. - - V. V. Nemov, S. V. Kasilov, W. Kernbichler, G. O. Leitold. - Phys. Plasmas 1 May 2008; 15 (5): 052501. - https://doi.org/10.1063/1.2912456. - """ - data["Gamma_c"] = jnp.pi * data["Gamma_c raw"] / (2**1.5 * data["V_psi(r)*L"]) + data["Gamma_c"] = g.expand(jnp.pi / 2**1.5 * Gamma_c) return data diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 620d73a1af..21f76f4043 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -4,15 +4,15 @@ import numpy as np import pytest -from desc.compute._neoclassical import poloidal_leggauss +from desc.compute._neoclassical import _poloidal_average, poloidal_leggauss from desc.equilibrium import Equilibrium from desc.equilibrium.coords import rtz_grid from desc.equilibrium.equilibrium import compute_raz_data @pytest.mark.unit -def test_integration_on_field_line(): - """Test that V_psi(r)*L / S(r)*L = V_psi(r) / S(r) as L → ∞.""" +def test_field_line_average(): + """Test that field line average converges to surface average.""" eq = Equilibrium.load( "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" @@ -26,28 +26,22 @@ def test_integration_on_field_line(): zeta = np.linspace(0, L, resolution) grid = rtz_grid(eq, rho, alpha, zeta, coordinates="raz") grid.source_grid.poloidal_weight = w - data = compute_raz_data( - eq, grid, ["V_psi(r)*L", "S(r)*L"], names_1dr=["psi_r", "V_r(r)", "S(r)"] - ) + data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) np.testing.assert_allclose( - data["V_psi(r)*L"] / data["S(r)*L"], - data["V_r(r)"] / data["psi_r"] / data["S(r)"], - rtol=0.01, + _poloidal_average(grid.source_grid, data["L|r,a"] / data["G|r,a"]), + grid.compress(data["V_r(r)"]), + rtol=3e-2, ) # Now for field line with large L. - # The drawback of this approach is that convergence can regress - # unless resolution is increased linearly with L. - L = 100 * np.pi - zeta = np.linspace(0, L, resolution**2) + L = 20 * np.pi + zeta = np.linspace(0, L, resolution * 2) grid = rtz_grid(eq, rho, 0, zeta, coordinates="raz") - data = compute_raz_data( - eq, grid, ["V_psi(r)*L", "S(r)*L"], names_1dr=["psi_r", "V_r(r)", "S(r)"] - ) + data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) np.testing.assert_allclose( - data["V_psi(r)*L"] / data["S(r)*L"], - data["V_r(r)"] / data["psi_r"] / data["S(r)"], - rtol=0.01, + np.squeeze(data["L|r,a"] / data["G|r,a"]), + grid.compress(data["V_r(r)"]), + rtol=3e-2, ) @@ -68,34 +62,26 @@ def test_effective_ripple(): data = compute_raz_data( eq, grid, - [ - "B^zeta", - "|B|_z|r,a", - "|B|", - "|grad(psi)|", - "cvdrift0", - "V_psi(r)*L", - "S(r)*L", - ], + ["B^zeta", "|B|", "|B|_z|r,a", "|grad(psi)|", "kappa_g", "L|r,a"], names_0d=["R0"], - names_1dr=["min_tz |B|", "max_tz |B|"], + names_1dr=["min_tz |B|", "max_tz |B|", "V_r(r)", "psi_r", "S(r)"], ) data = eq.compute( "effective ripple raw", grid=grid, data=data, override_grid=False, - # batch=False, # noqa: E800 + batch=False, # noqa: E800 # quad=vec_quadax(quadax.quadgk), # noqa: E800 ) assert np.isfinite(data["effective ripple raw"]).all() rho = grid.compress(grid.nodes[:, 0]) ripple = grid.compress(data["effective ripple raw"]) fig, ax = plt.subplots(2) - ax[0].plot(rho, ripple, marker="o", label="∫ db ∑ⱼ Hⱼ² / Iⱼ") + ax[0].plot(rho, ripple, marker="o") ax[0].set_xlabel(r"$\rho$") ax[0].set_ylabel("effective ripple raw") - ax[0].set_title("effective ripple raw, defined as ∫ db ∑ⱼ Hⱼ² / Iⱼ") + ax[0].set_title(r"∫ dλ λ⁻² $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$") # Workaround until eq.compute() is fixed to only compute dependencies # that are needed for the requested computation. (So don't compute @@ -109,10 +95,12 @@ def test_effective_ripple(): data = eq.compute("effective ripple", grid=grid, data=data) assert np.isfinite(data["effective ripple"]).all() eff_ripple = grid.compress(data["effective ripple"]) - ax[1].plot(rho, eff_ripple, marker="o", label=r"$\epsilon_{\text{effective}}$") + ax[1].plot(rho, eff_ripple, marker="o") ax[1].set_xlabel(r"$\rho$") - ax[1].set_ylabel(r"$\epsilon_{\text{effective}}$") - ax[1].set_title("Effective ripple (not raised to 3/2 power)") + ax[1].set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") + ax[1].set_title( + r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂_ψ V)/S)² ∫ dλ λ⁻² $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" + ) plt.tight_layout() plt.show() plt.close() From 808806c26bd7e664124eb42b75fa3256afc1322b Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 31 May 2024 00:28:41 -0500 Subject: [PATCH 033/112] Fix units after change of variables --- desc/compute/_neoclassical.py | 110 +++++++++++++++++++--------------- tests/test_neoclassical.py | 14 ++--- 2 files changed, 70 insertions(+), 54 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 7d3c256724..fc75138ff0 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -118,16 +118,16 @@ def _poloidal_average(grid, f, name=""): @register_compute_fun( name="L|r,a", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" - " \\frac{d\\zeta}{B^{\\zeta} \\vert B \\vert}", - units="m", - units_long="meters", + " \\frac{d\\zeta}{B^{\\zeta}}", + units="m / T", + units_long="Meter / Tesla", description="Length along field line", dim=2, params=[], transforms={"grid": []}, profiles=[], coordinates="ra", - data=["B^zeta", "|B|"], + data=["B^zeta"], grid_requirement=[ "source_grid", lambda grid: grid.source_grid.coordinates == "raz" @@ -138,7 +138,7 @@ def _L_ra(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) data["L|r,a"] = quadax.simpson( - jnp.reshape(1 / (data["B^zeta"] * data["|B|"]), shape), + jnp.reshape(1 / data["B^zeta"], shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) @@ -148,16 +148,16 @@ def _L_ra(data, transforms, profiles, **kwargs): @register_compute_fun( name="G|r,a", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" - " \\frac{d\\zeta}{B^{\\zeta} \\vert B \\vert \\sqrt g}", - units="m^{-2}", - units_long="inverse meters squared", + " \\frac{d\\zeta}{B^{\\zeta} \\sqrt g}", + units="1 / Wb", + units_long="Inverse webers", description="Length over volume along field line", dim=2, params=[], transforms={"grid": []}, profiles=[], coordinates="ra", - data=["B^zeta", "|B|", "sqrt(g)"], + data=["B^zeta", "sqrt(g)"], grid_requirement=[ "source_grid", lambda grid: grid.source_grid.coordinates == "raz" @@ -168,16 +168,16 @@ def _G_ra(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) data["G|r,a"] = quadax.simpson( - jnp.reshape(1 / (data["B^zeta"] * data["|B|"] * data["sqrt(g)"]), shape), + jnp.reshape(1 / (data["B^zeta"] * data["sqrt(g)"]), shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, - ) / (4 * jnp.pi**2) + ) return data @register_compute_fun( name="effective ripple raw", - label="∫ dλ λ⁻² \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + label="∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", units="T^2", units_long="Tesla squared", description="Effective ripple modulation amplitude, not dimensionless", @@ -221,19 +221,28 @@ def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): batch = kwargs.get("batch", True) quad = kwargs.get("quad", quadax.simpson) quad_res = kwargs.get("quad_res", 100) - # Get endpoints of integral over pitch for each field line. + # Get endpoints of integral over pitch for each flux surface. min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) # Floating point error impedes consistent detection of bounce points riding # extrema. Shift values slightly to resolve this issue. min_B = (1 + 1e-6) * min_B max_B = (1 - 1e-6) * max_B + # λ is the pitch angle. Note Nemov dimensionless integration variable b = (λB₀)⁻¹. pitch_endpoint = 1 / jnp.stack([max_B, min_B]) - def dH(grad_psi_norm, kappa_g, B, pitch, Z): - # absorbed 1/λ into H - return jnp.sqrt(1 / pitch - B) * (4 / B - pitch) * grad_psi_norm * kappa_g / B + def dH(grad_psi_norm, kappa_g, B, pitch): + # Pulled out dimensionless factor of (λB₀)¹ᐧ⁵ from integrand of + # Nemov equation 30. Multiplied back in at end. + return ( + jnp.sqrt(1 - pitch * B) + * (4 / (pitch * B) - 1) + * grad_psi_norm + * kappa_g + / B + ) - def dI(B, pitch, Z): + def dI(B, pitch): + # Integrand of Nemov equation 31. return jnp.sqrt(1 - pitch * B) / B if _is_Newton_Cotes(quad): @@ -242,7 +251,7 @@ def dI(B, pitch, Z): ) def d_ripple(pitch): - """Return λ⁻² ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. + """Return λ⁻² B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. Parameters ---------- @@ -252,19 +261,23 @@ def d_ripple(pitch): Returns ------- d_ripple : Array, shape(pitch.shape) - λ⁻² ∑ⱼ Hⱼ²/Iⱼ + λ⁻² B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ """ H = bounce_integrate( dH, [data["|grad(psi)|"], data["kappa_g"]], pitch, batch=batch ) I = bounce_integrate(dI, [], pitch, batch=batch) - return jnp.nansum(H**2 / I, axis=-1) + # (λB₀)³ db = (λB₀)³ B₀⁻¹ λ⁻² (-dλ) = B₀² λ (-dλ) + # We chose B₀ = 1 (inverse units of λ). + # The minus sign is accounted for with the integration order. + return pitch * jnp.nansum(H**2 / I, axis=-1) pitch = composite_linspace(pitch_endpoint, quad_res) pitch = jnp.broadcast_to( pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) + # This has units of Tesla meters. ripple = quad(d_ripple(pitch), pitch, axis=0) else: # Use adaptive quadrature. @@ -273,7 +286,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) H = bounce_integrate(dH, [grad_psi_norm, kappa_g], pitch, batch=batch) I = bounce_integrate(dI, [], pitch, batch=batch) - return jnp.squeeze(jnp.nansum(H**2 / I, axis=-1)) + return jnp.squeeze(pitch * jnp.nansum(H**2 / I, axis=-1)) pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] assert pitch_endpoint.shape == (g.num_rho, 1, 2) @@ -298,7 +311,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - label="π/(8√2) (R₀(∂_ψ V)/S)² ∫ dλ λ⁻² \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + label="π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", units="~", units_long="None", description="Effective ripple modulation amplitude", @@ -325,28 +338,28 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): return data +# When comparing Velasco's Γ_c: https://doi.org/10.1088/1741-4326/ac2994 +# with Nemov's Γ_c: https://doi.org/10.1063/1.2912456, +# note that +# dλ v τ_b = - 4 (∂I/∂b) db = 4 ∂I/∂((λB₀)⁻¹) B₀⁻¹ λ⁻² dλ +# and +# 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) +# where the integrals are along an irrational field line with +# ds / B given by dζ / B^ζ +# √g the (Ψ, α, ζ)-coordinate Jacobian +# There is also the dimensionless difference between Nemov's and Velascos's γ_c, +# mentioned in Velasco's footnote 4. If this difference is γ_c ignored, and the +# (missing?) √g factor is pushed into the integral over alpha in Velasco eq. 18 +# then we have that +# Velasco Γ_c ∼ Nemov Γ_c * lim{L → ∞} ∫₀ᴸ ds/(B √g). +# In particular, Velasco Γ_c grows with the length of the field line, +# which isn't what we want. +# TODO: +# We currently implement Nemov Γ_c with Velasco γ_c. +# Switch to Nemov γ_c too. @register_compute_fun( name="Gamma_c", - # When comparing Velasco's Γ_c: https://doi.org/10.1088/1741-4326/ac2994 - # with Nemov's Γ_c: https://doi.org/10.1063/1.2912456, - # note that - # dλ v τ_b = 8 (∂I/∂b) db = -8 ∂I/∂(λ⁻¹) dλ λ⁻² - # and - # 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) - # where the integrals are along an irrational field line with - # ds given by dζ / B^ζ - # √g the (Ψ, θ, ζ)-coordinate Jacobian - # There is also the dimensionless difference between Nemov's and Velascos's γ_c, - # mentioned in Velasco's footnote 4. If this difference is γ_c ignored, and the - # (missing?) √g factor is pushed into the integral over alpha in Velasco eq. 18 - # then we have that - # Velasco Γ_c = Nemov Γ_c * lim{L → ∞} ∫₀ᴸ ds/(B √g) / (2π). - # In particular, Velasco Γ_c grows with the length of the field line - # which isn't what we want. - # TODO: - # We currently implement Nemov Γ_c with Velasco γ_c. - # Switch to Nemov γ_c too. - label="π/(2√2) ∫ dλ λ⁻² \\langle ∑ⱼ [γ_c² ∂I/∂(λ⁻¹)]ⱼ \\rangle", + label="π/(2√2) ∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ \\rangle", units="~", units_long="None", description="Nemov's energetic ion confinement proxy", @@ -401,12 +414,13 @@ def _Gamma_c(params, transforms, profiles, data, **kwargs): # extrema. Shift values slightly to resolve this issue. min_B = (1 + 1e-6) * min_B max_B = (1 - 1e-6) * max_B + # λ is the pitch angle. Note Nemov dimensionless integration variable b = (λB₀)⁻¹. pitch_endpoint = 1 / jnp.stack([max_B, min_B]) - def d_gamma_c(f, B, pitch, Z): + def d_gamma_c(f, B, pitch): return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) - def dK(B, pitch, Z): + def dK(B, pitch): return 0.5 / jnp.sqrt(1 - pitch * B) if _is_Newton_Cotes(quad): @@ -415,7 +429,7 @@ def dK(B, pitch, Z): ) def d_Gamma_c(pitch): - """Return ∑ⱼ [γ_c² ∂I/∂(λ⁻¹)]ⱼ evaluated at λ = pitch. + """Return λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ evaluated at λ = pitch. Parameters ---------- @@ -425,7 +439,7 @@ def d_Gamma_c(pitch): Returns ------- d_Gamma_c : Array, shape(pitch.shape) - ∑ⱼ [γ_c² ∂I/∂(λ⁻¹)]ⱼ + λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ """ # TODO: Currently we have implemented Velasco's Gamma_c. @@ -441,13 +455,15 @@ def d_Gamma_c(pitch): / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) ) ) - K = bounce_integrate(dK, [], pitch, batch=batch) # λ⁻² ∂I/∂(λ⁻¹) + # K = λ⁻² B₀⁻¹ ∂I/∂((λB₀)⁻¹) where I is given in Nemov equation 36. + K = bounce_integrate(dK, [], pitch, batch=batch) return jnp.nansum(gamma_c**2 * K, axis=-1) pitch = composite_linspace(pitch_endpoint, quad_res) pitch = jnp.broadcast_to( pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) + # This has units of meters / tesla. Gamma_c = quad(d_Gamma_c(pitch), pitch, axis=0) else: # Use adaptive quadrature. diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 21f76f4043..e1e0ff6f29 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -29,19 +29,19 @@ def test_field_line_average(): data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) np.testing.assert_allclose( _poloidal_average(grid.source_grid, data["L|r,a"] / data["G|r,a"]), - grid.compress(data["V_r(r)"]), - rtol=3e-2, + grid.compress(data["V_r(r)"]) / (4 * np.pi**2), + rtol=2e-2, ) # Now for field line with large L. - L = 20 * np.pi + L = 10 * np.pi zeta = np.linspace(0, L, resolution * 2) grid = rtz_grid(eq, rho, 0, zeta, coordinates="raz") data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) np.testing.assert_allclose( np.squeeze(data["L|r,a"] / data["G|r,a"]), - grid.compress(data["V_r(r)"]), - rtol=3e-2, + grid.compress(data["V_r(r)"]) / (4 * np.pi**2), + rtol=2e-2, ) @@ -81,7 +81,7 @@ def test_effective_ripple(): ax[0].plot(rho, ripple, marker="o") ax[0].set_xlabel(r"$\rho$") ax[0].set_ylabel("effective ripple raw") - ax[0].set_title(r"∫ dλ λ⁻² $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$") + ax[0].set_title(r"∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$") # Workaround until eq.compute() is fixed to only compute dependencies # that are needed for the requested computation. (So don't compute @@ -99,7 +99,7 @@ def test_effective_ripple(): ax[1].set_xlabel(r"$\rho$") ax[1].set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") ax[1].set_title( - r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂_ψ V)/S)² ∫ dλ λ⁻² $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" + r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" ) plt.tight_layout() plt.show() From b87ca1a89e66d9a960bb8b6e99b5caa5567e8725 Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 31 May 2024 14:10:08 -0500 Subject: [PATCH 034/112] Fix comment discussing Nemov and Velasco Gamma_c --- desc/compute/_neoclassical.py | 67 ++++++++++++++++++----------------- 1 file changed, 34 insertions(+), 33 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index fc75138ff0..26ba24809d 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -338,31 +338,12 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): return data -# When comparing Velasco's Γ_c: https://doi.org/10.1088/1741-4326/ac2994 -# with Nemov's Γ_c: https://doi.org/10.1063/1.2912456, -# note that -# dλ v τ_b = - 4 (∂I/∂b) db = 4 ∂I/∂((λB₀)⁻¹) B₀⁻¹ λ⁻² dλ -# and -# 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) -# where the integrals are along an irrational field line with -# ds / B given by dζ / B^ζ -# √g the (Ψ, α, ζ)-coordinate Jacobian -# There is also the dimensionless difference between Nemov's and Velascos's γ_c, -# mentioned in Velasco's footnote 4. If this difference is γ_c ignored, and the -# (missing?) √g factor is pushed into the integral over alpha in Velasco eq. 18 -# then we have that -# Velasco Γ_c ∼ Nemov Γ_c * lim{L → ∞} ∫₀ᴸ ds/(B √g). -# In particular, Velasco Γ_c grows with the length of the field line, -# which isn't what we want. -# TODO: -# We currently implement Nemov Γ_c with Velasco γ_c. -# Switch to Nemov γ_c too. @register_compute_fun( name="Gamma_c", label="π/(2√2) ∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ \\rangle", units="~", units_long="None", - description="Nemov's energetic ion confinement proxy", + description="Energetic ion confinement proxy", dim=1, params=[], transforms={"grid": []}, @@ -396,11 +377,32 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): quad_res="int : Resolution for quadrature over velocity coordinate.", ) def _Gamma_c(params, transforms, profiles, data, **kwargs): - """Poloidal motion of trapped particle orbits in real-space coordinates. + """Energetic ion confinement proxy. + A model for the fast evaluation of prompt losses of energetic ions in stellarators. + J.L. Velasco et al 2021 Nucl. Fusion 61 116059. + https://doi.org/10.1088/1741-4326/ac2994. + Equation 16. (Not equation 18). + + Poloidal motion of trapped particle orbits in real-space coordinates. V. V. Nemov, S. V. Kasilov, W. Kernbichler, G. O. Leitold. Phys. Plasmas 1 May 2008; 15 (5): 052501. https://doi.org/10.1063/1.2912456. + Equation 61, using Velasco's γ_c from equation 15 of the above paper. + + Besides the difference in γ_c mentioned above, Nemov's Γ_c and Velasco Γ_c + as defined in equation 16 are identical, although Nemov's expression is more + explicit while Velasco's requires some interpretation. However, Velasco Γ_c + as defined in equation 18 does not seem to match equation 16. + Note that + dλ v τ_b = - 4 (∂I/∂b) db = 4 ∂I/∂((λB₀)⁻¹) B₀⁻¹ λ⁻² dλ + 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) + where the integrals are along an irrational field line with + ds / B given by dζ / B^ζ + √g the (Ψ, α, ζ)-coordinate Jacobian + If the (missing?) √g factor in Velasco Γ_c equation 18 is pushed into the + integral over alpha in Velasco equation 18 then we have that + eq. 18 Velasco Γ_c ∼ eq. 16 Velasco Γ_c * lim{L → ∞} ∫₀ᴸ ds/(B √g). """ g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") @@ -421,7 +423,7 @@ def d_gamma_c(f, B, pitch): return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) def dK(B, pitch): - return 0.5 / jnp.sqrt(1 - pitch * B) + return 1 / jnp.sqrt(1 - pitch * B) if _is_Newton_Cotes(quad): bounce_integrate, _ = _bounce_integral( @@ -429,7 +431,7 @@ def dK(B, pitch): ) def d_Gamma_c(pitch): - """Return λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ evaluated at λ = pitch. + """Return 2 λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ evaluated at λ = pitch. Parameters ---------- @@ -439,14 +441,9 @@ def d_Gamma_c(pitch): Returns ------- d_Gamma_c : Array, shape(pitch.shape) - λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ + 2 λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ """ - # TODO: Currently we have implemented Velasco's Gamma_c. - # If we add a 1/|grad(psi)| into the arctan of little - # gamma_c, we implement Nemov's Gamma_c. (Check this again). - # This will affect the gamma_c profile since |grad(psi)| - # depends on alpha. gamma_c = ( 2 / jnp.pi @@ -455,7 +452,9 @@ def d_Gamma_c(pitch): / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) ) ) - # K = λ⁻² B₀⁻¹ ∂I/∂((λB₀)⁻¹) where I is given in Nemov equation 36. + # K = 2 λ⁻² B₀⁻¹ ∂I/∂((λB₀)⁻¹) where I is given in Nemov equation 36. + # So factors of B₀ cancel, making this quantity independent of the + # chosen reference magnetic field strength. K = bounce_integrate(dK, [], pitch, batch=batch) return jnp.nansum(gamma_c**2 * K, axis=-1) @@ -495,8 +494,10 @@ def d_Gamma_c(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): ] Gamma_c = quad(d_Gamma_c, pitch_endpoint, *args) - Gamma_c = _poloidal_average( - g, Gamma_c.reshape(g.num_rho, g.num_alpha) / data["L|r,a"] + Gamma_c = ( + jnp.pi + / (4 * 2**0.5) + * _poloidal_average(g, Gamma_c.reshape(g.num_rho, g.num_alpha) / data["L|r,a"]) ) - data["Gamma_c"] = g.expand(jnp.pi / 2**1.5 * Gamma_c) + data["Gamma_c"] = g.expand(Gamma_c) return data From 169af3a59cc43c69444a8612b0a9bf7c364c776f Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 31 May 2024 16:10:55 -0500 Subject: [PATCH 035/112] Add neo comparison utitilies --- desc/compute/_neoclassical.py | 20 +- ....QI_plunk_fixed_surf_r0.15_N_24_hires_ns99 | 98 ++++++++++ tests/test_neoclassical.py | 176 ++++++++++++++++-- 3 files changed, 268 insertions(+), 26 deletions(-) create mode 100644 tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99 diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 26ba24809d..4597d9e860 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -120,7 +120,7 @@ def _poloidal_average(grid, f, name=""): label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" " \\frac{d\\zeta}{B^{\\zeta}}", units="m / T", - units_long="Meter / Tesla", + units_long="Meter / tesla", description="Length along field line", dim=2, params=[], @@ -218,7 +218,7 @@ def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") _bounce_integral = kwargs.get("bounce_integral", bounce_integral) - batch = kwargs.get("batch", True) + batch = kwargs.get("batch", False) quad = kwargs.get("quad", quadax.simpson) quad_res = kwargs.get("quad_res", 100) # Get endpoints of integral over pitch for each flux surface. @@ -277,7 +277,7 @@ def d_ripple(pitch): pitch = jnp.broadcast_to( pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) - # This has units of Tesla meters. + # This has units of tesla meters. ripple = quad(d_ripple(pitch), pitch, axis=0) else: # Use adaptive quadrature. @@ -391,10 +391,12 @@ def _Gamma_c(params, transforms, profiles, data, **kwargs): Equation 61, using Velasco's γ_c from equation 15 of the above paper. Besides the difference in γ_c mentioned above, Nemov's Γ_c and Velasco Γ_c - as defined in equation 16 are identical, although Nemov's expression is more - explicit while Velasco's requires some interpretation. However, Velasco Γ_c - as defined in equation 18 does not seem to match equation 16. - Note that + as defined in equation 16 are identical, although Nemov's expression is + precise while Velasco's requires a flexible interpretation for the expression + to be well-defined. + + Also, Velasco Γ_c as defined in equation 18 does not seem to match + equation 16. Note that dλ v τ_b = - 4 (∂I/∂b) db = 4 ∂I/∂((λB₀)⁻¹) B₀⁻¹ λ⁻² dλ 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) where the integrals are along an irrational field line with @@ -407,7 +409,7 @@ def _Gamma_c(params, transforms, profiles, data, **kwargs): g = transforms["grid"].source_grid knots = g.compress(g.nodes[:, 2], surface_label="zeta") _bounce_integral = kwargs.get("bounce_integral", bounce_integral) - batch = kwargs.get("batch", True) + batch = kwargs.get("batch", False) quad = kwargs.get("quad", quadax.simpson) quad_res = kwargs.get("quad_res", 100) # Get endpoints of integral over pitch for each field line. @@ -453,7 +455,7 @@ def d_Gamma_c(pitch): ) ) # K = 2 λ⁻² B₀⁻¹ ∂I/∂((λB₀)⁻¹) where I is given in Nemov equation 36. - # So factors of B₀ cancel, making this quantity independent of the + # The factor of B₀ cancels, making this quantity independent of the # chosen reference magnetic field strength. K = bounce_integrate(dK, [], pitch, batch=batch) return jnp.nansum(gamma_c**2 * K, axis=-1) diff --git a/tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99 b/tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99 new file mode 100644 index 0000000000..844967f0b0 --- /dev/null +++ b/tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99 @@ -0,0 +1,98 @@ + 2 0.8461201253E-03 0.3394379165E+00 -0.7055289723E+00 0.1209357718E+01 0.1009257284E+01 + 3 0.8961274543E-03 0.7315225853E+00 -0.7055496401E+00 0.1219826918E+01 0.1009257284E+01 + 4 0.9554876720E-03 0.1035346285E+01 -0.7055688078E+00 0.1226453474E+01 0.1009257284E+01 + 5 0.1024174835E-02 0.1292226518E+01 -0.7055868077E+00 0.1231531120E+01 0.1009257284E+01 + 6 0.1101017837E-02 0.1518864120E+01 -0.7056039752E+00 0.1235735256E+01 0.1009257284E+01 + 7 0.1185587630E-02 0.1723952046E+01 -0.7056206482E+00 0.1239375055E+01 0.1009257284E+01 + 8 0.1275305900E-02 0.1912685181E+01 -0.7056371665E+00 0.1242620512E+01 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0.1009257284E+01 diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index e1e0ff6f29..2b4871dec3 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -3,11 +3,13 @@ import matplotlib.pyplot as plt import numpy as np import pytest +import quadax -from desc.compute._neoclassical import _poloidal_average, poloidal_leggauss +from desc.compute._neoclassical import _poloidal_average, poloidal_leggauss, vec_quadax from desc.equilibrium import Equilibrium from desc.equilibrium.coords import rtz_grid from desc.equilibrium.equilibrium import compute_raz_data +from desc.vmec import VMECIO @pytest.mark.unit @@ -34,7 +36,7 @@ def test_field_line_average(): ) # Now for field line with large L. - L = 10 * np.pi + L = 10 * np.pi # Large enough to pass the test, apparently. zeta = np.linspace(0, L, resolution * 2) grid = rtz_grid(eq, rho, 0, zeta, coordinates="raz") data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) @@ -47,16 +49,16 @@ def test_field_line_average(): @pytest.mark.unit def test_effective_ripple(): - """Compare DESC effective ripple against neo stellopt.""" + """Compare DESC effective ripple against NEO STELLOPT.""" eq = Equilibrium.load( "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) grid = rtz_grid( eq, - radial=np.linspace(0, 1, 20), + radial=np.linspace(0, 1, 40), poloidal=np.array([0]), - toroidal=np.linspace(0, 100 * np.pi, 500), + toroidal=np.linspace(0, 100 * np.pi, 1000), coordinates="raz", ) data = compute_raz_data( @@ -66,22 +68,18 @@ def test_effective_ripple(): names_0d=["R0"], names_1dr=["min_tz |B|", "max_tz |B|", "V_r(r)", "psi_r", "S(r)"], ) + quad = vec_quadax(quadax.quadgk) # noqa: F841 data = eq.compute( "effective ripple raw", grid=grid, data=data, override_grid=False, - batch=False, # noqa: E800 - # quad=vec_quadax(quadax.quadgk), # noqa: E800 + # batch=True, # noqa: E800 + # quad=quad, # noqa: E800 ) - assert np.isfinite(data["effective ripple raw"]).all() + assert np.all(np.isfinite(data["effective ripple raw"])) rho = grid.compress(grid.nodes[:, 0]) - ripple = grid.compress(data["effective ripple raw"]) - fig, ax = plt.subplots(2) - ax[0].plot(rho, ripple, marker="o") - ax[0].set_xlabel(r"$\rho$") - ax[0].set_ylabel("effective ripple raw") - ax[0].set_title(r"∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$") + raw = grid.compress(data["effective ripple raw"]) # Workaround until eq.compute() is fixed to only compute dependencies # that are needed for the requested computation. (So don't compute @@ -93,14 +91,158 @@ def test_effective_ripple(): # aren't attempted to be recomputed on grid_desc. data[key] = data_R0[key] data = eq.compute("effective ripple", grid=grid, data=data) - assert np.isfinite(data["effective ripple"]).all() - eff_ripple = grid.compress(data["effective ripple"]) - ax[1].plot(rho, eff_ripple, marker="o") + assert np.all(np.isfinite(data["effective ripple"])) + eps_eff = grid.compress(data["effective ripple"]) + + # Plot DESC effective ripple. + fig, ax = plt.subplots(2) + ax[0].plot(rho, raw, marker="o") + ax[0].set_xlabel(r"$\rho$") + ax[0].set_ylabel("effective ripple raw") + ax[0].set_title(r"∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$") + ax[1].plot(rho, eps_eff, marker="o") ax[1].set_xlabel(r"$\rho$") ax[1].set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") ax[1].set_title( r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" ) + fig.suptitle("DESC effective ripple") + plt.tight_layout() + plt.show() + plt.close() + + # Plot neo effective ripple. What are its units? + # This should be ε¹ᐧ⁵, but need to check if it's just ε. + neo_eps = np.array( + read_neo_out("tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99") + ) + assert neo_eps.ndim == 1 + neo_rho = np.sqrt(np.linspace(1 / (neo_eps.size + 1), 1, neo_eps.size)) + fig, ax = plt.subplots() + ax.plot(neo_rho, neo_eps, marker="o", label="NEO high resolution") + ax.legend() + ax.set_xlabel(r"$\rho$") + ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") + ax.set_title("NEO effective ripple") + plt.tight_layout() + plt.show() + plt.close() + + # Plot DESC vs NEO effective ripple. + fig, ax = plt.subplots() + ax.plot(rho, eps_eff, marker="o", label="ε¹ᐧ⁵ DESC (low?) resolution") + ax.plot(neo_rho, neo_eps, marker="o", label="(ε¹ᐧ⁵?) NEO high resolution") + ax.legend() + ax.set_xlabel(r"$\rho$") + ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") + ax.set_title( + r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" + ) + fig.suptitle("DESC vs. NEO effective ripple") plt.tight_layout() plt.show() plt.close() + + +class NEOWrapper: + """Class to easily make NEO and BOOZxform inputs from DESC equilibria.""" + + def __init__(self, basename, eq=None, ns=None, M_booz=None, N_booz=None): + + self.basename = basename + self.M_booz = M_booz + self.N_booz = N_booz + + if eq: + self.build(eq, basename, ns=ns) + + def build(self, eq, basename, **kwargs): + """Pass as input an already-solved Equilibrium from DESC.""" + # equilibrium parameters + self.eq = eq + self.sym = eq.sym + self.L = eq.L + self.M = eq.M + self.N = eq.N + self.NFP = eq.NFP + self.spectral_indexing = eq.spectral_indexing + self.pressure = eq.pressure + self.iota = eq.iota + self.current = eq.current + + # wout parameters + self.ns = kwargs.get("ns", 256) + + # booz parameters + if self.M_booz is None: + self.M_booz = 3 * eq.M + 1 + if self.N_booz is None: + self.N_booz = 3 * eq.N + + # basename for files + self.basename = basename + + def save_VMEC(self): + """Save VMEC file.""" + self.eq.solved = True # must set this for NEO to run correctly + print(f"Saving VMEC wout file to wout_{self.basename}.nc") + VMECIO.save(self.eq, f"wout_{self.basename}.nc", surfs=self.ns, verbose=0) + + def write_booz(self): + """Write BOOZ_XFORM input file.""" + print(f"Writing BOOZ_XFORM input file to in_booz.{self.basename}") + with open(f"in_booz.{self.basename}", "w+") as f: + f.write("{} {}\n".format(self.M_booz, self.N_booz)) + f.write(f"'{self.basename}'\n") + f.write( + "\n".join([str(x) for x in range(2, self.ns + 1)]) + ) # surface indices + + def write_neo(self, N_particles=150): + """Write NEO input file.""" + print(f"Writing NEO input file neo_in.{self.basename}") + with open(f"neo_in.{self.basename}", "w+") as f: + f.write("'#'\n'#'\n'#'\n") + f.write(f" boozmn_{self.basename}.nc\n") # booz out file + f.write(f" neo_out.{self.basename}\n") # desired NEO out file + f.write(f" {self.ns-1}\n") # number of surfaces + f.write( + " ".join([str(x) for x in range(2, self.ns + 1)]) + "\n" + ) # surface indices + f.write( + " 300 ! number of theta points\n " + "300 ! number of zeta points\n " + + f"\n 0\n 0\n {N_particles} ! number of test particles\n " + f"1 ! 1 = singly trapped particles\n " + + "0.001 ! integration accuracy\n 100 ! number of poloidal bins\n " + "10 ! integration steps per field period\n " + + "500 ! min number of field periods\n " + "5000 ! max number of field periods\n" + ) # default values + f.write( + " 0\n 1\n 0\n 0\n 2 ! 2 = reference |B| used is max on each surface" + " \n 0\n 0\n 0\n 0\n 0\n" + ) + f.write("'#'\n'#'\n'#'\n") + f.write(" 0\n") + f.write(f"neo_cur_{self.basename}\n") + f.write(" 200\n 2\n 0\n") + + +def read_neo_out(fname): + """Read ripple from text file.""" + + def nan_helper(y): + return np.isnan(y), lambda z: z.nonzero()[0] + + # import all data from text file as an array + with open(f"{fname}") as f: + array = np.array([[float(x) for x in line.split()] for line in f]) + + eps_eff = array[:, 1] # epsilon_eff^(3/2) is the second column + nans, x = nan_helper(eps_eff) # find NaN values + + # replace NaN values with linear interpolation + eps_eff[nans] = np.interp(x(nans), x(~nans), eps_eff[~nans]) + + return eps_eff From a9feffd6bc62b188f0ab5cbdb3b228eb9c9a0f60 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sat, 1 Jun 2024 21:25:31 -0500 Subject: [PATCH 036/112] Detach Gamma_c --- desc/compute/_neoclassical.py | 245 +++++++--------------------------- tests/test_neoclassical.py | 85 ++++-------- 2 files changed, 70 insertions(+), 260 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 4597d9e860..56d8c2a0aa 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -21,14 +21,17 @@ from .bounce_integral import ( affine_bijection, bounce_integral, - composite_linspace, + get_pitch, grad_affine_bijection, ) from .data_index import register_compute_fun def _is_Newton_Cotes(quad): - return quad in [trapezoid, quadax.trapezoid, quadax.simpson] + if hasattr(quad, "is_Newton_Cotes"): + return quad.is_Newton_Cotes + else: + return quad in [trapezoid, quadax.trapezoid, quadax.simpson] def vec_quadax(quad): @@ -115,6 +118,22 @@ def _poloidal_average(grid, f, name=""): return avg +def _get_pitch(grid, data, quad, num=75): + # Get endpoints of integral over pitch for each flux surface. + # with num values uniformly spaced in between. + min_B = grid.compress(data["min_tz |B|"]) + max_B = grid.compress(data["max_tz |B|"]) + if _is_Newton_Cotes(quad): + pitch = get_pitch(min_B, max_B, num) + pitch = jnp.broadcast_to( + pitch[..., jnp.newaxis], (pitch.shape[0], grid.num_rho, grid.num_alpha) + ).reshape(pitch.shape[0], grid.num_rho * grid.num_alpha) + else: + pitch = 1 / jnp.stack([max_B, min_B], axis=-1)[:, jnp.newaxis] + assert pitch.shape == (grid.num_rho, 1, 2) + return pitch + + @register_compute_fun( name="L|r,a", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" @@ -177,7 +196,7 @@ def _G_ra(data, transforms, profiles, **kwargs): @register_compute_fun( name="effective ripple raw", - label="∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + label="∫dλ λ⁻²B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", units="T^2", units_long="Tesla squared", description="Effective ripple modulation amplitude, not dimensionless", @@ -209,26 +228,27 @@ def _G_ra(data, transforms, profiles, **kwargs): batch="bool : Whether to perform computation in a batched manner.", quad=( "callable : Quadrature method over velocity coordinate. " - "Adaptive quadrature method from quadax must be wrapped with vec_quadax." + "Default is composite Simpson's rule. " + "Accepts any callable with signature matching quad(f(λ), λ, ..., axis). " + "Accepts adaptive quadrature methods from quadax wrapped with vec_quadax. " + "If using an adaptive method, it is highly recommended to set batch=True." + ), + num_pitch=( + "int : Resolution for quadrature over velocity coordinate. " + "Default is 75. This setting is ignored for adaptive quadrature." ), - quad_res="int : Resolution for quadrature over velocity coordinate.", ) -@partial(jit, static_argnames=["bounce_integral", "batch", "quad", "quad_res"]) +# temporary +@partial(jit, static_argnames=["bounce_integral", "batch", "quad", "num_pitch"]) def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): - g = transforms["grid"].source_grid - knots = g.compress(g.nodes[:, 2], surface_label="zeta") - _bounce_integral = kwargs.get("bounce_integral", bounce_integral) + bounce = kwargs.get("bounce_integral", bounce_integral) batch = kwargs.get("batch", False) quad = kwargs.get("quad", quadax.simpson) - quad_res = kwargs.get("quad_res", 100) - # Get endpoints of integral over pitch for each flux surface. - min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) - # Floating point error impedes consistent detection of bounce points riding - # extrema. Shift values slightly to resolve this issue. - min_B = (1 + 1e-6) * min_B - max_B = (1 - 1e-6) * max_B - # λ is the pitch angle. Note Nemov dimensionless integration variable b = (λB₀)⁻¹. - pitch_endpoint = 1 / jnp.stack([max_B, min_B]) + num_pitch = kwargs.get("num_pitch", 75) + + g = transforms["grid"].source_grid + knots = g.compress(g.nodes[:, 2], surface_label="zeta") + pitch = _get_pitch(g, data, quad, num_pitch) def dH(grad_psi_norm, kappa_g, B, pitch): # Pulled out dimensionless factor of (λB₀)¹ᐧ⁵ from integrand of @@ -246,12 +266,12 @@ def dI(B, pitch): return jnp.sqrt(1 - pitch * B) / B if _is_Newton_Cotes(quad): - bounce_integrate, _ = _bounce_integral( + bounce_integrate, _ = bounce( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) def d_ripple(pitch): - """Return λ⁻² B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. + """Return λ⁻²B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. Parameters ---------- @@ -261,35 +281,30 @@ def d_ripple(pitch): Returns ------- d_ripple : Array, shape(pitch.shape) - λ⁻² B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ + λ⁻²B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ """ H = bounce_integrate( dH, [data["|grad(psi)|"], data["kappa_g"]], pitch, batch=batch ) I = bounce_integrate(dI, [], pitch, batch=batch) - # (λB₀)³ db = (λB₀)³ B₀⁻¹ λ⁻² (-dλ) = B₀² λ (-dλ) + # (λB₀)³ db = (λB₀)³ B₀⁻¹λ⁻² (-dλ) = B₀²λ (-dλ) # We chose B₀ = 1 (inverse units of λ). + # TODO: Think Neo chooses B₀ = "max_tz |B|". # The minus sign is accounted for with the integration order. return pitch * jnp.nansum(H**2 / I, axis=-1) - pitch = composite_linspace(pitch_endpoint, quad_res) - pitch = jnp.broadcast_to( - pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) - ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) # This has units of tesla meters. ripple = quad(d_ripple(pitch), pitch, axis=0) else: # Use adaptive quadrature. def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): - bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) + bounce_integrate, _ = bounce(B_sup_z, B, B_z_ra, knots) H = bounce_integrate(dH, [grad_psi_norm, kappa_g], pitch, batch=batch) I = bounce_integrate(dI, [], pitch, batch=batch) return jnp.squeeze(pitch * jnp.nansum(H**2 / I, axis=-1)) - pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] - assert pitch_endpoint.shape == (g.num_rho, 1, 2) args = [ f.reshape(g.num_rho, g.num_alpha, g.num_zeta) for f in [ @@ -300,7 +315,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): data["kappa_g"], ] ] - ripple = quad(d_ripple, pitch_endpoint, *args) + ripple = quad(d_ripple, pitch, *args) ripple = _poloidal_average( g, ripple.reshape(g.num_rho, g.num_alpha) / data["L|r,a"] @@ -311,7 +326,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - label="π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + label="π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", units="~", units_long="None", description="Effective ripple modulation amplitude", @@ -328,6 +343,7 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. https://doi.org/10.1063/1.873749. + """ data["effective ripple"] = ( jnp.pi @@ -336,170 +352,3 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): * data["effective ripple raw"] ) return data - - -@register_compute_fun( - name="Gamma_c", - label="π/(2√2) ∫ dλ λ⁻² B₀⁻¹ \\langle ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ \\rangle", - units="~", - units_long="None", - description="Energetic ion confinement proxy", - dim=1, - params=[], - transforms={"grid": []}, - profiles=[], - coordinates="r", - data=[ - "min_tz |B|", - "max_tz |B|", - "B^zeta", - "|B|", - "|B|_z|r,a", - "cvdrift0", - "gbdrift", - "L|r,a", - ], - grid_requirement=[ - "source_grid", - lambda grid: grid.source_grid.coordinates == "raz" - and grid.source_grid.is_meshgrid, - ], - bounce_integral=( - "callable : Method to compute bounce integrals. " - "(You may want to wrap desc.compute.bounce_integral.bounce_integral " - "to change optional parameters such as quadrature resolution, etc.)." - ), - batch="bool : Whether to perform computation in a batched manner.", - quad=( - "callable : Quadrature method over velocity coordinate. " - "Adaptive quadrature method from quadax must be wrapped with vec_quadax." - ), - quad_res="int : Resolution for quadrature over velocity coordinate.", -) -def _Gamma_c(params, transforms, profiles, data, **kwargs): - """Energetic ion confinement proxy. - - A model for the fast evaluation of prompt losses of energetic ions in stellarators. - J.L. Velasco et al 2021 Nucl. Fusion 61 116059. - https://doi.org/10.1088/1741-4326/ac2994. - Equation 16. (Not equation 18). - - Poloidal motion of trapped particle orbits in real-space coordinates. - V. V. Nemov, S. V. Kasilov, W. Kernbichler, G. O. Leitold. - Phys. Plasmas 1 May 2008; 15 (5): 052501. - https://doi.org/10.1063/1.2912456. - Equation 61, using Velasco's γ_c from equation 15 of the above paper. - - Besides the difference in γ_c mentioned above, Nemov's Γ_c and Velasco Γ_c - as defined in equation 16 are identical, although Nemov's expression is - precise while Velasco's requires a flexible interpretation for the expression - to be well-defined. - - Also, Velasco Γ_c as defined in equation 18 does not seem to match - equation 16. Note that - dλ v τ_b = - 4 (∂I/∂b) db = 4 ∂I/∂((λB₀)⁻¹) B₀⁻¹ λ⁻² dλ - 4π² ∂Ψₜ/∂V = lim{L → ∞} ( [∫₀ᴸ ds/(B √g)] / [∫₀ᴸ ds/B] ) - where the integrals are along an irrational field line with - ds / B given by dζ / B^ζ - √g the (Ψ, α, ζ)-coordinate Jacobian - If the (missing?) √g factor in Velasco Γ_c equation 18 is pushed into the - integral over alpha in Velasco equation 18 then we have that - eq. 18 Velasco Γ_c ∼ eq. 16 Velasco Γ_c * lim{L → ∞} ∫₀ᴸ ds/(B √g). - """ - g = transforms["grid"].source_grid - knots = g.compress(g.nodes[:, 2], surface_label="zeta") - _bounce_integral = kwargs.get("bounce_integral", bounce_integral) - batch = kwargs.get("batch", False) - quad = kwargs.get("quad", quadax.simpson) - quad_res = kwargs.get("quad_res", 100) - # Get endpoints of integral over pitch for each field line. - min_B, max_B = map(g.compress, (data["min_tz |B|"], data["max_tz |B|"])) - # Floating point error impedes consistent detection of bounce points riding - # extrema. Shift values slightly to resolve this issue. - min_B = (1 + 1e-6) * min_B - max_B = (1 - 1e-6) * max_B - # λ is the pitch angle. Note Nemov dimensionless integration variable b = (λB₀)⁻¹. - pitch_endpoint = 1 / jnp.stack([max_B, min_B]) - - def d_gamma_c(f, B, pitch): - return f * (1 - pitch * B / 2) / jnp.sqrt(1 - pitch * B) - - def dK(B, pitch): - return 1 / jnp.sqrt(1 - pitch * B) - - if _is_Newton_Cotes(quad): - bounce_integrate, _ = _bounce_integral( - data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots - ) - - def d_Gamma_c(pitch): - """Return 2 λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ evaluated at λ = pitch. - - Parameters - ---------- - pitch : Array, shape(*pitch.shape[:-1], g.num_rho * g.num_alpha) - Pitch angle. - - Returns - ------- - d_Gamma_c : Array, shape(pitch.shape) - 2 λ⁻² B₀⁻¹ ∑ⱼ [γ_c² ∂I/∂((λB₀)⁻¹)]ⱼ - - """ - gamma_c = ( - 2 - / jnp.pi - * jnp.arctan( - bounce_integrate(d_gamma_c, data["cvdrift0"], pitch, batch=batch) - / bounce_integrate(d_gamma_c, data["gbdrift"], pitch, batch=batch) - ) - ) - # K = 2 λ⁻² B₀⁻¹ ∂I/∂((λB₀)⁻¹) where I is given in Nemov equation 36. - # The factor of B₀ cancels, making this quantity independent of the - # chosen reference magnetic field strength. - K = bounce_integrate(dK, [], pitch, batch=batch) - return jnp.nansum(gamma_c**2 * K, axis=-1) - - pitch = composite_linspace(pitch_endpoint, quad_res) - pitch = jnp.broadcast_to( - pitch[..., jnp.newaxis], (pitch.shape[0], g.num_rho, g.num_alpha) - ).reshape(pitch.shape[0], g.num_rho * g.num_alpha) - # This has units of meters / tesla. - Gamma_c = quad(d_Gamma_c(pitch), pitch, axis=0) - else: - # Use adaptive quadrature. - - def d_Gamma_c(pitch, B_sup_z, B, B_z_ra, cvdrift0, gbdrift): - bounce_integrate, _ = _bounce_integral(B_sup_z, B, B_z_ra, knots) - gamma_c = ( - 2 - / jnp.pi - * jnp.arctan( - bounce_integrate(d_gamma_c, cvdrift0, pitch, batch=batch) - / bounce_integrate(d_gamma_c, gbdrift, pitch, batch=batch) - ) - ) - K = bounce_integrate(dK, [], pitch, batch=batch) - return jnp.squeeze(jnp.nansum(gamma_c**2 * K, axis=-1)) - - pitch_endpoint = pitch_endpoint.T[:, jnp.newaxis] - assert pitch_endpoint.shape == (g.num_rho, 1, 2) - args = [ - f.reshape(g.num_rho, g.num_alpha, g.num_zeta) - for f in [ - data["B^zeta"], - data["|B|"], - data["|B|_z|r,a"], - data["cvdrift0"], - data["gbdrift"], - ] - ] - Gamma_c = quad(d_Gamma_c, pitch_endpoint, *args) - - Gamma_c = ( - jnp.pi - / (4 * 2**0.5) - * _poloidal_average(g, Gamma_c.reshape(g.num_rho, g.num_alpha) / data["L|r,a"]) - ) - data["Gamma_c"] = g.expand(Gamma_c) - return data diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 2b4871dec3..18441e4af8 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -3,12 +3,10 @@ import matplotlib.pyplot as plt import numpy as np import pytest -import quadax -from desc.compute._neoclassical import _poloidal_average, poloidal_leggauss, vec_quadax +from desc.compute._neoclassical import _poloidal_average, poloidal_leggauss from desc.equilibrium import Equilibrium from desc.equilibrium.coords import rtz_grid -from desc.equilibrium.equilibrium import compute_raz_data from desc.vmec import VMECIO @@ -28,7 +26,7 @@ def test_field_line_average(): zeta = np.linspace(0, L, resolution) grid = rtz_grid(eq, rho, alpha, zeta, coordinates="raz") grid.source_grid.poloidal_weight = w - data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) + data = eq.compute(["L|r,a", "G|r,a", "V_r(r)"], grid=grid) np.testing.assert_allclose( _poloidal_average(grid.source_grid, data["L|r,a"] / data["G|r,a"]), grid.compress(data["V_r(r)"]) / (4 * np.pi**2), @@ -39,7 +37,7 @@ def test_field_line_average(): L = 10 * np.pi # Large enough to pass the test, apparently. zeta = np.linspace(0, L, resolution * 2) grid = rtz_grid(eq, rho, 0, zeta, coordinates="raz") - data = compute_raz_data(eq, grid, ["L|r,a", "G|r,a"], names_1dr=["V_r(r)"]) + data = eq.compute(["L|r,a", "G|r,a", "V_r(r)"], grid=grid) np.testing.assert_allclose( np.squeeze(data["L|r,a"] / data["G|r,a"]), grid.compress(data["V_r(r)"]) / (4 * np.pi**2), @@ -54,64 +52,30 @@ def test_effective_ripple(): "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) - grid = rtz_grid( - eq, - radial=np.linspace(0, 1, 40), - poloidal=np.array([0]), - toroidal=np.linspace(0, 100 * np.pi, 1000), - coordinates="raz", - ) - data = compute_raz_data( - eq, - grid, - ["B^zeta", "|B|", "|B|_z|r,a", "|grad(psi)|", "kappa_g", "L|r,a"], - names_0d=["R0"], - names_1dr=["min_tz |B|", "max_tz |B|", "V_r(r)", "psi_r", "S(r)"], - ) - quad = vec_quadax(quadax.quadgk) # noqa: F841 + rho = np.linspace(0, 1, 40) + # TODO: Here's a potential issue, resolve with 2d spline. + knots = np.linspace(0, 100 * np.pi, 1000) + grid = rtz_grid(eq, rho, np.array([0]), knots, coordinates="raz") data = eq.compute( - "effective ripple raw", + "effective ripple", grid=grid, - data=data, - override_grid=False, - # batch=True, # noqa: E800 - # quad=quad, # noqa: E800 + batch=False, ) - assert np.all(np.isfinite(data["effective ripple raw"])) - rho = grid.compress(grid.nodes[:, 0]) - raw = grid.compress(data["effective ripple raw"]) - - # Workaround until eq.compute() is fixed to only compute dependencies - # that are needed for the requested computation. (So don't compute - # dependencies of things already in data). - data_R0 = eq.compute("R0") - for key in data_R0: - if key not in data: - # Need to add R0's dependencies which are surface functions of zeta - # aren't attempted to be recomputed on grid_desc. - data[key] = data_R0[key] - data = eq.compute("effective ripple", grid=grid, data=data) - assert np.all(np.isfinite(data["effective ripple"])) + assert np.isfinite(data["effective ripple"]).all() eps_eff = grid.compress(data["effective ripple"]) # Plot DESC effective ripple. - fig, ax = plt.subplots(2) - ax[0].plot(rho, raw, marker="o") - ax[0].set_xlabel(r"$\rho$") - ax[0].set_ylabel("effective ripple raw") - ax[0].set_title(r"∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$") - ax[1].plot(rho, eps_eff, marker="o") - ax[1].set_xlabel(r"$\rho$") - ax[1].set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") - ax[1].set_title( - r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" - ) - fig.suptitle("DESC effective ripple") + fig, ax = plt.subplots() + rho = grid.compress(grid.nodes[:, 0]) + ax.plot(rho, eps_eff, marker="o") + ax.set_xlabel(r"$\rho$") + ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") + ax.set_title("DESC effective ripple ε¹ᐧ⁵") plt.tight_layout() plt.show() plt.close() - # Plot neo effective ripple. What are its units? + # Plot NEO effective ripple. # This should be ε¹ᐧ⁵, but need to check if it's just ε. neo_eps = np.array( read_neo_out("tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99") @@ -119,26 +83,23 @@ def test_effective_ripple(): assert neo_eps.ndim == 1 neo_rho = np.sqrt(np.linspace(1 / (neo_eps.size + 1), 1, neo_eps.size)) fig, ax = plt.subplots() - ax.plot(neo_rho, neo_eps, marker="o", label="NEO high resolution") - ax.legend() + ax.plot(neo_rho, neo_eps, marker="o") ax.set_xlabel(r"$\rho$") ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") - ax.set_title("NEO effective ripple") + ax.set_title("NEO effective ripple ε¹ᐧ⁵?") plt.tight_layout() plt.show() plt.close() # Plot DESC vs NEO effective ripple. fig, ax = plt.subplots() - ax.plot(rho, eps_eff, marker="o", label="ε¹ᐧ⁵ DESC (low?) resolution") - ax.plot(neo_rho, neo_eps, marker="o", label="(ε¹ᐧ⁵?) NEO high resolution") + ax.plot(rho, eps_eff, marker="o", label="ε¹ᐧ⁵ DESC") + # Looks more similar when neo_eps -> neo_eps**1.5... + ax.plot(neo_rho, neo_eps, marker="o", label="ε¹ᐧ⁵? NEO") ax.legend() ax.set_xlabel(r"$\rho$") ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") - ax.set_title( - r"ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫ dλ λ⁻² B₀⁻¹ $\langle$ ∑ⱼ Hⱼ²/Iⱼ $\rangle$" - ) - fig.suptitle("DESC vs. NEO effective ripple") + ax.set_title("DESC vs. NEO effective ripple") plt.tight_layout() plt.show() plt.close() From b45c0dc08ee56a69109a7351d8f925c9f30132ea Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 3 Jun 2024 01:59:41 -0500 Subject: [PATCH 037/112] Update things after merge --- desc/compute/_neoclassical.py | 103 +++++++--------------------------- desc/compute/_profiles.py | 8 ++- desc/compute/utils.py | 76 ------------------------- desc/grid.py | 22 -------- tests/test_neoclassical.py | 35 +++++------- 5 files changed, 40 insertions(+), 204 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 56d8c2a0aa..399c5dc474 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -11,27 +11,21 @@ from functools import partial -import orthax import quadax from termcolor import colored from desc.backend import jit, jnp, trapezoid from ..utils import warnif -from .bounce_integral import ( - affine_bijection, - bounce_integral, - get_pitch, - grad_affine_bijection, -) +from .bounce_integral import bounce_integral, get_pitch from .data_index import register_compute_fun -def _is_Newton_Cotes(quad): - if hasattr(quad, "is_Newton_Cotes"): - return quad.is_Newton_Cotes +def _is_adaptive(quad): + if hasattr(quad, "is_adaptive"): + return quad.is_adaptive else: - return quad in [trapezoid, quadax.trapezoid, quadax.simpson] + return quad not in [trapezoid, quadax.trapezoid, quadax.simpson] def vec_quadax(quad): @@ -48,7 +42,7 @@ def vec_quadax(quad): Vectorized adaptive quadrature method. """ - if _is_Newton_Cotes(quad): + if not _is_adaptive(quad): return quad def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): @@ -60,62 +54,18 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return vec_quad -def poloidal_leggauss(deg, a_min=0, a_max=2 * jnp.pi): - """Gauss-Legendre quadrature. - - Returns quadrature points αₖ and weights wₖ for the approximate evaluation - of the integral ∫ f(α) dα ≈ ∑ₖ wₖ f(αₖ). - - Set resolution > 1 to see if a long field line integral can be approximated - by flux surface average of shorter field line integrals with finite transits. - - Assuming the rotational transform is irrational, the limit where the - parameterization of the field line length tends to infinity of an average - along the field line will converge to a flux surface average. - In theory, we can compute such quantities with averages over finite lengths - of the field line, e.g. one toroidal transit, for many values of the poloidal - field line label and then average this over the poloidal domain. - - This should also work for integrands which are bounce integrals; - Since everything is continuous, as the number of nodes tend to infinity both - approaches should converge to the same result, assuming irrational surface. - However, the order at which all the bounce integrals detected over the surface - are summed differs, so the convergence rate will differ. - - Parameters - ---------- - deg: int - Number of quadrature points. - a_min: float - Min α value. - a_max: float - Max α value. - - Returns - ------- - alpha : Array - Quadrature points. - w : Array - Quadrature weights. - - """ - x, w = orthax.legendre.leggauss(deg) - w = w * grad_affine_bijection(a_min, a_max) - alpha = affine_bijection(x, a_min, a_max) - return alpha, w - - -def _poloidal_average(grid, f, name=""): +def _poloidal_mean(grid, f): assert f.shape[-1] == grid.num_poloidal - if grid.poloidal_weight is None: + if grid.spacing is None: warnif( grid.num_poloidal != 1, - msg=colored(f"{name} reduced via uniform poloidal mean.", "yellow"), + msg=colored("Reduced via uniform poloidal mean.", "yellow"), ) - avg = jnp.mean(f, axis=-1) + return jnp.mean(f, axis=-1) else: - avg = f @ grid.poloidal_weight / jnp.sum(grid.poloidal_weight) - return avg + assert grid.is_meshgrid + dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") + return f @ dp / jnp.sum(dp) def _get_pitch(grid, data, quad, num=75): @@ -123,7 +73,7 @@ def _get_pitch(grid, data, quad, num=75): # with num values uniformly spaced in between. min_B = grid.compress(data["min_tz |B|"]) max_B = grid.compress(data["max_tz |B|"]) - if _is_Newton_Cotes(quad): + if not _is_adaptive(quad): pitch = get_pitch(min_B, max_B, num) pitch = jnp.broadcast_to( pitch[..., jnp.newaxis], (pitch.shape[0], grid.num_rho, grid.num_alpha) @@ -147,11 +97,7 @@ def _get_pitch(grid, data, quad, num=75): profiles=[], coordinates="ra", data=["B^zeta"], - grid_requirement=[ - "source_grid", - lambda grid: grid.source_grid.coordinates == "raz" - and grid.source_grid.is_meshgrid, - ], + source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) def _L_ra(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid @@ -177,11 +123,7 @@ def _L_ra(data, transforms, profiles, **kwargs): profiles=[], coordinates="ra", data=["B^zeta", "sqrt(g)"], - grid_requirement=[ - "source_grid", - lambda grid: grid.source_grid.coordinates == "raz" - and grid.source_grid.is_meshgrid, - ], + source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) def _G_ra(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid @@ -215,17 +157,14 @@ def _G_ra(data, transforms, profiles, **kwargs): "kappa_g", "L|r,a", ], - grid_requirement=[ - "source_grid", - lambda grid: grid.source_grid.coordinates == "raz" - and grid.source_grid.is_meshgrid, - ], + source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, bounce_integral=( "callable : Method to compute bounce integrals. " "(You may want to wrap desc.compute.bounce_integral.bounce_integral " "to change optional parameters such as quadrature resolution, etc.)." ), batch="bool : Whether to perform computation in a batched manner.", + # Composite quadrature should perform better than higher order methods. quad=( "callable : Quadrature method over velocity coordinate. " "Default is composite Simpson's rule. " @@ -265,7 +204,7 @@ def dI(B, pitch): # Integrand of Nemov equation 31. return jnp.sqrt(1 - pitch * B) / B - if _is_Newton_Cotes(quad): + if not _is_adaptive(quad): bounce_integrate, _ = bounce( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots ) @@ -317,9 +256,7 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): ] ripple = quad(d_ripple, pitch, *args) - ripple = _poloidal_average( - g, ripple.reshape(g.num_rho, g.num_alpha) / data["L|r,a"] - ) + ripple = _poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha) / data["L|r,a"]) data["effective ripple raw"] = g.expand(ripple) return data diff --git a/desc/compute/_profiles.py b/desc/compute/_profiles.py index 859028dbf0..46e1c2d054 100644 --- a/desc/compute/_profiles.py +++ b/desc/compute/_profiles.py @@ -9,12 +9,13 @@ expensive computations. """ +from quadax import cumulative_trapezoid from scipy.constants import elementary_charge, mu_0 from desc.backend import cond, jnp from .data_index import register_compute_fun -from .utils import cumtrapz, dot, safediv, surface_averages, surface_integrals +from .utils import dot, safediv, surface_averages, surface_integrals @register_compute_fun( @@ -142,7 +143,10 @@ def _chi_r(params, transforms, profiles, data, **kwargs): ) def _chi(params, transforms, profiles, data, **kwargs): chi_r = transforms["grid"].compress(data["chi_r"]) - chi = cumtrapz(chi_r, transforms["grid"].compress(data["rho"]), initial=0) + # TODO: switch to cumulative_simpson once added to quadax. + chi = cumulative_trapezoid( + chi_r, transforms["grid"].compress(data["rho"]), initial=0 + ) data["chi"] = transforms["grid"].expand(chi) return data diff --git a/desc/compute/utils.py b/desc/compute/utils.py index a7f03c4478..3ce42628a2 100644 --- a/desc/compute/utils.py +++ b/desc/compute/utils.py @@ -642,82 +642,6 @@ def safediv(a, b, fill=0, threshold=0): return num / den -def cumtrapz(y, x=None, dx=1.0, axis=-1, initial=None): - """Cumulatively integrate y(x) using the composite trapezoidal rule. - - Taken from SciPy, but changed NumPy references to JAX.NumPy: - https://github.com/scipy/scipy/blob/v1.10.1/scipy/integrate/_quadrature.py - - Parameters - ---------- - y : array_like - Values to integrate. - x : array_like, optional - The coordinate to integrate along. If None (default), use spacing `dx` - between consecutive elements in `y`. - dx : float, optional - Spacing between elements of `y`. Only used if `x` is None. - axis : int, optional - Specifies the axis to cumulate. Default is -1 (last axis). - initial : scalar, optional - If given, insert this value at the beginning of the returned result. - Typically, this value should be 0. Default is None, which means no - value at ``x[0]`` is returned and `res` has one element less than `y` - along the axis of integration. - - Returns - ------- - res : ndarray - The result of cumulative integration of `y` along `axis`. - If `initial` is None, the shape is such that the axis of integration - has one less value than `y`. If `initial` is given, the shape is equal - to that of `y`. - - """ - y = jnp.asarray(y) - if x is None: - d = dx - else: - x = jnp.asarray(x) - if x.ndim == 1: - d = jnp.diff(x) - # reshape to correct shape - shape = [1] * y.ndim - shape[axis] = -1 - d = d.reshape(shape) - elif len(x.shape) != len(y.shape): - raise ValueError("If given, shape of x must be 1-D or the " "same as y.") - else: - d = jnp.diff(x, axis=axis) - - if d.shape[axis] != y.shape[axis] - 1: - raise ValueError( - "If given, length of x along axis must be the " "same as y." - ) - - def tupleset(t, i, value): - l = list(t) - l[i] = value - return tuple(l) - - nd = len(y.shape) - slice1 = tupleset((slice(None),) * nd, axis, slice(1, None)) - slice2 = tupleset((slice(None),) * nd, axis, slice(None, -1)) - res = jnp.cumsum(d * (y[slice1] + y[slice2]) / 2.0, axis=axis) - - if initial is not None: - if not jnp.isscalar(initial): - raise ValueError("`initial` parameter should be a scalar.") - - shape = list(res.shape) - shape[axis] = 1 - res = jnp.concatenate( - [jnp.full(shape, initial, dtype=res.dtype), res], axis=axis - ) - - return res - - def _get_grid_surface(grid, surface_label): """Return grid quantities associated with the given surface label. diff --git a/desc/grid.py b/desc/grid.py index caadfe0b2b..aa6d66897c 100644 --- a/desc/grid.py +++ b/desc/grid.py @@ -44,7 +44,6 @@ class _Grid(IOAble, ABC): "_inverse_poloidal_idx", "_inverse_zeta_idx", "_is_meshgrid", - "_poloidal_weight", ] @abstractmethod @@ -706,10 +705,6 @@ def __init__( self._period = period self._source_grid = source_grid self._is_meshgrid = bool(is_meshgrid) - if "poloidal_weight" in kwargs: - self._poloidal_weight = jnp.atleast_1d(kwargs.pop("poloidal_weight")) - else: - self._poloidal_weight = None self._nodes = self._create_nodes(nodes) if spacing is not None: @@ -801,23 +796,6 @@ def source_grid(self): """Coordinates from which this grid was mapped from.""" return self.__dict__.setdefault("_source_grid", None) - @property - def poloidal_weight(self): - """Quadrature weight over poloidal domain. - - Returns - ------- - poloidal_weight : Array, shape(self.num_poloidal) - quadrature weight over poloidal domain - - """ - return self.__dict__.setdefault("_poloidal_weight", None) - - @poloidal_weight.setter - def poloidal_weight(self, value): - """Set quadrature weight over poloidal domain.""" - self._poloidal_weight = jnp.atleast_1d(value) - class LinearGrid(_Grid): """Grid in which the nodes are linearly spaced in each coordinate. diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 18441e4af8..b591b894d4 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -4,7 +4,6 @@ import numpy as np import pytest -from desc.compute._neoclassical import _poloidal_average, poloidal_leggauss from desc.equilibrium import Equilibrium from desc.equilibrium.coords import rtz_grid from desc.vmec import VMECIO @@ -17,26 +16,13 @@ def test_field_line_average(): "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" ".15_L_9_M_9_N_24_output.h5" ) - resolution = 10 - rho = np.linspace(0, 1, resolution) - - # Surface average field lines truncated at 1 toroidal transit. - alpha, w = poloidal_leggauss(resolution) - L = 2 * np.pi - zeta = np.linspace(0, L, resolution) - grid = rtz_grid(eq, rho, alpha, zeta, coordinates="raz") - grid.source_grid.poloidal_weight = w - data = eq.compute(["L|r,a", "G|r,a", "V_r(r)"], grid=grid) - np.testing.assert_allclose( - _poloidal_average(grid.source_grid, data["L|r,a"] / data["G|r,a"]), - grid.compress(data["V_r(r)"]) / (4 * np.pi**2), - rtol=2e-2, - ) - - # Now for field line with large L. + rho = np.linspace(0, 1, 5) + alpha = np.array([0]) L = 10 * np.pi # Large enough to pass the test, apparently. - zeta = np.linspace(0, L, resolution * 2) - grid = rtz_grid(eq, rho, 0, zeta, coordinates="raz") + zeta = np.linspace(0, L, 20) + grid = rtz_grid( + eq, rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) + ) data = eq.compute(["L|r,a", "G|r,a", "V_r(r)"], grid=grid) np.testing.assert_allclose( np.squeeze(data["L|r,a"] / data["G|r,a"]), @@ -55,7 +41,14 @@ def test_effective_ripple(): rho = np.linspace(0, 1, 40) # TODO: Here's a potential issue, resolve with 2d spline. knots = np.linspace(0, 100 * np.pi, 1000) - grid = rtz_grid(eq, rho, np.array([0]), knots, coordinates="raz") + grid = rtz_grid( + eq, + rho, + np.array([0]), + knots, + coordinates="raz", + period=(np.inf, 2 * np.pi, np.inf), + ) data = eq.compute( "effective ripple", grid=grid, From a2903cb66e76274fd5e890ed8cbb8b0545b77763 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sat, 8 Jun 2024 18:14:29 -0500 Subject: [PATCH 038/112] Normalize effective ripple by B_0 = max_tz |B| instead of B_0 = 1. --- desc/compute/_neoclassical.py | 48 +++++++++++++++++++---------------- tests/test_neoclassical.py | 6 ++--- 2 files changed, 28 insertions(+), 26 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 399c5dc474..28207ad620 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -55,6 +55,7 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): def _poloidal_mean(grid, f): + # Integrate f over poloidal angle and divide by 2π. assert f.shape[-1] == grid.num_poloidal if grid.spacing is None: warnif( @@ -85,54 +86,56 @@ def _get_pitch(grid, data, quad, num=75): @register_compute_fun( - name="L|r,a", + name="", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" " \\frac{d\\zeta}{B^{\\zeta}}", units="m / T", units_long="Meter / tesla", - description="Length along field line", - dim=2, + description="(Mean) length along field line(s)", + dim=1, params=[], transforms={"grid": []}, profiles=[], - coordinates="ra", + coordinates="r", data=["B^zeta"], source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) -def _L_ra(data, transforms, profiles, **kwargs): +def _L_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - data["L|r,a"] = quadax.simpson( + L_ra = quadax.simpson( jnp.reshape(1 / data["B^zeta"], shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) + data[""] = g.expand(_poloidal_mean(g, L_ra)) return data @register_compute_fun( - name="G|r,a", + name="", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" " \\frac{d\\zeta}{B^{\\zeta} \\sqrt g}", units="1 / Wb", units_long="Inverse webers", - description="Length over volume along field line", - dim=2, + description="(Mean) length over volume along field line(s)", + dim=1, params=[], transforms={"grid": []}, profiles=[], - coordinates="ra", + coordinates="r", data=["B^zeta", "sqrt(g)"], source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) -def _G_ra(data, transforms, profiles, **kwargs): +def _G_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - data["G|r,a"] = quadax.simpson( + G_ra = quadax.simpson( jnp.reshape(1 / (data["B^zeta"] * data["sqrt(g)"]), shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) + data[""] = g.expand(_poloidal_mean(g, G_ra)) return data @@ -155,7 +158,7 @@ def _G_ra(data, transforms, profiles, **kwargs): "|B|_z|r,a", "|grad(psi)|", "kappa_g", - "L|r,a", + "", ], source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, bounce_integral=( @@ -210,7 +213,7 @@ def dI(B, pitch): ) def d_ripple(pitch): - """Return λ⁻²B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. + """Return λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. Parameters ---------- @@ -220,20 +223,19 @@ def d_ripple(pitch): Returns ------- d_ripple : Array, shape(pitch.shape) - λ⁻²B₀⁻¹ ∑ⱼ Hⱼ²/Iⱼ + λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ """ H = bounce_integrate( dH, [data["|grad(psi)|"], data["kappa_g"]], pitch, batch=batch ) I = bounce_integrate(dI, [], pitch, batch=batch) - # (λB₀)³ db = (λB₀)³ B₀⁻¹λ⁻² (-dλ) = B₀²λ (-dλ) - # We chose B₀ = 1 (inverse units of λ). - # TODO: Think Neo chooses B₀ = "max_tz |B|". - # The minus sign is accounted for with the integration order. return pitch * jnp.nansum(H**2 / I, axis=-1) + # Note that (λB₀)³ db = (λB₀)³ λ⁻² B₀⁻¹ (-dλ) = λ B₀² (-dλ). + # We choose B₀ = max |B| on the flux surface. We multiply by B₀² at + # the end and account for the minus sign with the integration order. - # This has units of tesla meters. + # This has units of tesla meters / B₀² where B₀ has units of λ⁻¹. ripple = quad(d_ripple(pitch), pitch, axis=0) else: # Use adaptive quadrature. @@ -256,8 +258,10 @@ def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): ] ripple = quad(d_ripple, pitch, *args) - ripple = _poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha) / data["L|r,a"]) - data["effective ripple raw"] = g.expand(ripple) + ripple = _poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha)) + data["effective ripple raw"] = ( + g.expand(ripple) * data["max_tz |B|"] ** 2 / data[""] + ) return data diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index b591b894d4..4b3a9f1e04 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -23,11 +23,9 @@ def test_field_line_average(): grid = rtz_grid( eq, rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) - data = eq.compute(["L|r,a", "G|r,a", "V_r(r)"], grid=grid) + data = eq.compute(["", "", "V_r(r)"], grid=grid) np.testing.assert_allclose( - np.squeeze(data["L|r,a"] / data["G|r,a"]), - grid.compress(data["V_r(r)"]) / (4 * np.pi**2), - rtol=2e-2, + data[""] / data[""], data["V_r(r)"] / (4 * np.pi**2), rtol=2e-2 ) From 57d6f7ffca23fff9cb88dfd2bf00dd53317d8894 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 11 Jun 2024 15:49:21 -0500 Subject: [PATCH 039/112] Add test and notes --- desc/compute/_neoclassical.py | 160 +++++++++++++--------------- tests/test_axis_limits.py | 9 +- tests/test_neoclassical.py | 189 ++++------------------------------ 3 files changed, 97 insertions(+), 261 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 28207ad620..ebf82a95fd 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -12,9 +12,10 @@ from functools import partial import quadax +from orthax.legendre import leggauss from termcolor import colored -from desc.backend import jit, jnp, trapezoid +from desc.backend import imap, jit, jnp, trapezoid from ..utils import warnif from .bounce_integral import bounce_integral, get_pitch @@ -29,7 +30,7 @@ def _is_adaptive(quad): def vec_quadax(quad): - """Vectorize an adaptive quadrature method from quadax to compute ripple. + """Vectorize an adaptive quadrature method from quadax. Parameters ---------- @@ -56,7 +57,6 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): def _poloidal_mean(grid, f): # Integrate f over poloidal angle and divide by 2π. - assert f.shape[-1] == grid.num_poloidal if grid.spacing is None: warnif( grid.num_poloidal != 1, @@ -69,19 +69,19 @@ def _poloidal_mean(grid, f): return f @ dp / jnp.sum(dp) -def _get_pitch(grid, data, quad, num=75): +def _get_pitch(grid, data, quad, num=73): # Get endpoints of integral over pitch for each flux surface. # with num values uniformly spaced in between. min_B = grid.compress(data["min_tz |B|"]) max_B = grid.compress(data["max_tz |B|"]) - if not _is_adaptive(quad): + if _is_adaptive(quad): + pitch = 1 / jnp.stack([max_B, min_B], axis=-1)[:, jnp.newaxis] + assert pitch.shape == (grid.num_rho, 1, 2) + else: pitch = get_pitch(min_B, max_B, num) pitch = jnp.broadcast_to( pitch[..., jnp.newaxis], (pitch.shape[0], grid.num_rho, grid.num_alpha) ).reshape(pitch.shape[0], grid.num_rho * grid.num_alpha) - else: - pitch = 1 / jnp.stack([max_B, min_B], axis=-1)[:, jnp.newaxis] - assert pitch.shape == (grid.num_rho, 1, 2) return pitch @@ -161,40 +161,50 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "", ], source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, - bounce_integral=( - "callable : Method to compute bounce integrals. " - "(You may want to wrap desc.compute.bounce_integral.bounce_integral " - "to change optional parameters such as quadrature resolution, etc.)." - ), - batch="bool : Whether to perform computation in a batched manner.", - # Composite quadrature should perform better than higher order methods. - quad=( - "callable : Quadrature method over velocity coordinate. " - "Default is composite Simpson's rule. " - "Accepts any callable with signature matching quad(f(λ), λ, ..., axis). " - "Accepts adaptive quadrature methods from quadax wrapped with vec_quadax. " - "If using an adaptive method, it is highly recommended to set batch=True." + num_quad=( + "int : Resolution for quadrature of bounce integrals. Default is 31, " + "which gets sufficient convergence, so higher values are unnecessary." ), num_pitch=( - "int : Resolution for quadrature over velocity coordinate. " - "Default is 75. This setting is ignored for adaptive quadrature." + "int : Resolution for quadrature over velocity coordinate, preferably odd. " + "Default is 125. Effective ripple will look smoother at high values." + "(If computed on many flux surfaces and micro oscillation is seen " + "between neighboring surfaces, increasing num_pitch will smooth the profile)." ), + # Some notes on choosing the resolution hyperparameters: + # The default settings above were chosen such that the effective ripple profile on + # the W7-X stellarator looks similar to the profile computed at higher resolution, + # indicating convergence. The final resolution parameter to keep in mind is that + # the supplied grid should sufficiently cover the flux surfaces. At/above the + # num_quad and num_pitch parameters chosen above, the grid coverage should be the + # parameter that has the strongest effect on the profile. + # As a reference for W7-X, when computing the effective ripple by tracing a single + # field line on each flux surface, a density of 100 knots per toroidal transit + # accurately reconstructs the ripples along the field line. Truncating the field + # line to [0, 20π] offers good convergence (after [0, 30π] the returns diminish). + # Note that when further truncating the field line to [0, 10π], a dip/cusp appears + # between the rho=0.7 and rho=0.8 surfaces, indicating that more coverage is + # required to resolve the effective ripple in this region. + # TODO: Improve performance... related to GitHub issue #1045. + # The difficulty is computing the magnetic field is expensive: + # the ripples along field lines are fine compared to the length of the field line + # required for sufficient coverage of the surface. This requires many knots to + # for the spline of the magnetic field to capture fine ripples in a large interval. ) -# temporary -@partial(jit, static_argnames=["bounce_integral", "batch", "quad", "num_pitch"]) +@partial(jit, static_argnames=["num_quad", "num_pitch"]) def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): - bounce = kwargs.get("bounce_integral", bounce_integral) - batch = kwargs.get("batch", False) - quad = kwargs.get("quad", quadax.simpson) - num_pitch = kwargs.get("num_pitch", 75) - g = transforms["grid"].source_grid - knots = g.compress(g.nodes[:, 2], surface_label="zeta") - pitch = _get_pitch(g, data, quad, num_pitch) + bounce_integrate, _ = bounce_integral( + data["B^zeta"], + data["|B|"], + data["|B|_z|r,a"], + knots=g.compress(g.nodes[:, 2], surface_label="zeta"), + quad=leggauss(kwargs.get("num_quad", 31)), + ) def dH(grad_psi_norm, kappa_g, B, pitch): - # Pulled out dimensionless factor of (λB₀)¹ᐧ⁵ from integrand of - # Nemov equation 30. Multiplied back in at end. + # Removed dimensionless (λB₀)¹ᐧ⁵ from integrand of Nemov equation 30. + # Reintroduced later. return ( jnp.sqrt(1 - pitch * B) * (4 / (pitch * B) - 1) @@ -203,64 +213,36 @@ def dH(grad_psi_norm, kappa_g, B, pitch): / B ) - def dI(B, pitch): - # Integrand of Nemov equation 31. + def dI(B, pitch): # Integrand of Nemov equation 31. return jnp.sqrt(1 - pitch * B) / B - if not _is_adaptive(quad): - bounce_integrate, _ = bounce( - data["B^zeta"], data["|B|"], data["|B|_z|r,a"], knots - ) - - def d_ripple(pitch): - """Return λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. - - Parameters - ---------- - pitch : Array, shape(*pitch.shape[:-1], g.num_rho * g.num_alpha) - Pitch angle. - - Returns - ------- - d_ripple : Array, shape(pitch.shape) - λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ - - """ - H = bounce_integrate( - dH, [data["|grad(psi)|"], data["kappa_g"]], pitch, batch=batch - ) - I = bounce_integrate(dI, [], pitch, batch=batch) - return pitch * jnp.nansum(H**2 / I, axis=-1) - # Note that (λB₀)³ db = (λB₀)³ λ⁻² B₀⁻¹ (-dλ) = λ B₀² (-dλ). - # We choose B₀ = max |B| on the flux surface. We multiply by B₀² at - # the end and account for the minus sign with the integration order. - - # This has units of tesla meters / B₀² where B₀ has units of λ⁻¹. - ripple = quad(d_ripple(pitch), pitch, axis=0) - else: - # Use adaptive quadrature. - - def d_ripple(pitch, B_sup_z, B, B_z_ra, grad_psi_norm, kappa_g): - bounce_integrate, _ = bounce(B_sup_z, B, B_z_ra, knots) - H = bounce_integrate(dH, [grad_psi_norm, kappa_g], pitch, batch=batch) - I = bounce_integrate(dI, [], pitch, batch=batch) - return jnp.squeeze(pitch * jnp.nansum(H**2 / I, axis=-1)) - - args = [ - f.reshape(g.num_rho, g.num_alpha, g.num_zeta) - for f in [ - data["B^zeta"], - data["|B|"], - data["|B|_z|r,a"], - data["|grad(psi)|"], - data["kappa_g"], - ] - ] - ripple = quad(d_ripple, pitch, *args) - - ripple = _poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha)) + def d_ripple(pitch): + """Return λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. + + Parameters + ---------- + pitch : Array, shape(*pitch.shape[:-1], g.num_rho * g.num_alpha) + Pitch angle. + + Returns + ------- + d_ripple : Array, shape(pitch.shape) + λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ + + """ + H = bounce_integrate(dH, [data["|grad(psi)|"], data["kappa_g"]], pitch) + I = bounce_integrate(dI, [], pitch) + return pitch * jnp.nansum(H**2 / I, axis=-1) + # (λB₀)³ db = (λB₀)³ λ⁻² B₀⁻¹ (-dλ) = λ B₀² (-dλ) where B₀ has units of λ⁻¹. + + # The integrand is continuous and likely poorly approximated by a polynomial, + # so composite quadrature should perform better than higher order methods. + pitch = _get_pitch(g, data, quadax.simpson, kwargs.get("num_pitch", 125)) + ripple = quadax.simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) data["effective ripple raw"] = ( - g.expand(ripple) * data["max_tz |B|"] ** 2 / data[""] + g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) + * data["max_tz |B|"] ** 2 + / data[""] ) return data diff --git a/tests/test_axis_limits.py b/tests/test_axis_limits.py index 3757527d9a..bd367e3a1d 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -92,7 +92,6 @@ "K_vc", # only defined on surface "iota_num_rrr", "iota_den_rrr", - "ripple", # implemented but requires source grid } @@ -131,6 +130,14 @@ def _skip_this(eq, name): or (eq.anisotropy is None and "beta_a" in name) or (eq.pressure is not None and " Redl" in name) or (eq.current is None and "iota_num" in name) + # These quantities require a coordinate mapping to compute and special grids, so + # it's not economical to test their axis limits here. Instead, a grid that + # includes the axis should be used in existing unit tests for these quantities. + or bool( + data_index["desc.equilibrium.equilibrium.Equilibrium"][name][ + "source_grid_requirement" + ] + ) ) diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 4b3a9f1e04..d2d23f180c 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -3,198 +3,45 @@ import matplotlib.pyplot as plt import numpy as np import pytest +from tests.test_plotting import tol_1d -from desc.equilibrium import Equilibrium +from desc import examples from desc.equilibrium.coords import rtz_grid -from desc.vmec import VMECIO @pytest.mark.unit def test_field_line_average(): """Test that field line average converges to surface average.""" - eq = Equilibrium.load( - "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" - ".15_L_9_M_9_N_24_output.h5" - ) - rho = np.linspace(0, 1, 5) - alpha = np.array([0]) - L = 10 * np.pi # Large enough to pass the test, apparently. - zeta = np.linspace(0, L, 20) + eq = examples.get("W7-X") grid = rtz_grid( - eq, rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) + eq, + radial=np.linspace(0, 1, 5), + poloidal=np.array([0]), + toroidal=np.linspace(0, 60 * np.pi, 900), + coordinates="raz", + period=(np.inf, 2 * np.pi, np.inf), ) data = eq.compute(["", "", "V_r(r)"], grid=grid) np.testing.assert_allclose( - data[""] / data[""], data["V_r(r)"] / (4 * np.pi**2), rtol=2e-2 + data[""] / data[""], data["V_r(r)"] / (4 * np.pi**2), rtol=1e-3 ) @pytest.mark.unit +@pytest.mark.mpl_image_compare(remove_text=True, tolerance=tol_1d) def test_effective_ripple(): - """Compare DESC effective ripple against NEO STELLOPT.""" - eq = Equilibrium.load( - "tests/inputs/DESC_from_NAE_O_r1_precise_QI_plunk_fixed_bdry_r0" - ".15_L_9_M_9_N_24_output.h5" - ) - rho = np.linspace(0, 1, 40) - # TODO: Here's a potential issue, resolve with 2d spline. - knots = np.linspace(0, 100 * np.pi, 1000) + """Test effective ripple with W7-X.""" + eq = examples.get("W7-X") + rho = np.linspace(0, 1, 10) grid = rtz_grid( eq, rho, np.array([0]), - knots, + np.linspace(0, 20 * np.pi, 1000), coordinates="raz", period=(np.inf, 2 * np.pi, np.inf), ) - data = eq.compute( - "effective ripple", - grid=grid, - batch=False, - ) - assert np.isfinite(data["effective ripple"]).all() - eps_eff = grid.compress(data["effective ripple"]) - - # Plot DESC effective ripple. - fig, ax = plt.subplots() - rho = grid.compress(grid.nodes[:, 0]) - ax.plot(rho, eps_eff, marker="o") - ax.set_xlabel(r"$\rho$") - ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") - ax.set_title("DESC effective ripple ε¹ᐧ⁵") - plt.tight_layout() - plt.show() - plt.close() - - # Plot NEO effective ripple. - # This should be ε¹ᐧ⁵, but need to check if it's just ε. - neo_eps = np.array( - read_neo_out("tests/inputs/neo_out.QI_plunk_fixed_surf_r0.15_N_24_hires_ns99") - ) - assert neo_eps.ndim == 1 - neo_rho = np.sqrt(np.linspace(1 / (neo_eps.size + 1), 1, neo_eps.size)) - fig, ax = plt.subplots() - ax.plot(neo_rho, neo_eps, marker="o") - ax.set_xlabel(r"$\rho$") - ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") - ax.set_title("NEO effective ripple ε¹ᐧ⁵?") - plt.tight_layout() - plt.show() - plt.close() - - # Plot DESC vs NEO effective ripple. + data = eq.compute("effective ripple", grid=grid) fig, ax = plt.subplots() - ax.plot(rho, eps_eff, marker="o", label="ε¹ᐧ⁵ DESC") - # Looks more similar when neo_eps -> neo_eps**1.5... - ax.plot(neo_rho, neo_eps, marker="o", label="ε¹ᐧ⁵? NEO") - ax.legend() - ax.set_xlabel(r"$\rho$") - ax.set_ylabel(r"$\epsilon_{\text{eff}}^{3/2}$") - ax.set_title("DESC vs. NEO effective ripple") - plt.tight_layout() - plt.show() - plt.close() - - -class NEOWrapper: - """Class to easily make NEO and BOOZxform inputs from DESC equilibria.""" - - def __init__(self, basename, eq=None, ns=None, M_booz=None, N_booz=None): - - self.basename = basename - self.M_booz = M_booz - self.N_booz = N_booz - - if eq: - self.build(eq, basename, ns=ns) - - def build(self, eq, basename, **kwargs): - """Pass as input an already-solved Equilibrium from DESC.""" - # equilibrium parameters - self.eq = eq - self.sym = eq.sym - self.L = eq.L - self.M = eq.M - self.N = eq.N - self.NFP = eq.NFP - self.spectral_indexing = eq.spectral_indexing - self.pressure = eq.pressure - self.iota = eq.iota - self.current = eq.current - - # wout parameters - self.ns = kwargs.get("ns", 256) - - # booz parameters - if self.M_booz is None: - self.M_booz = 3 * eq.M + 1 - if self.N_booz is None: - self.N_booz = 3 * eq.N - - # basename for files - self.basename = basename - - def save_VMEC(self): - """Save VMEC file.""" - self.eq.solved = True # must set this for NEO to run correctly - print(f"Saving VMEC wout file to wout_{self.basename}.nc") - VMECIO.save(self.eq, f"wout_{self.basename}.nc", surfs=self.ns, verbose=0) - - def write_booz(self): - """Write BOOZ_XFORM input file.""" - print(f"Writing BOOZ_XFORM input file to in_booz.{self.basename}") - with open(f"in_booz.{self.basename}", "w+") as f: - f.write("{} {}\n".format(self.M_booz, self.N_booz)) - f.write(f"'{self.basename}'\n") - f.write( - "\n".join([str(x) for x in range(2, self.ns + 1)]) - ) # surface indices - - def write_neo(self, N_particles=150): - """Write NEO input file.""" - print(f"Writing NEO input file neo_in.{self.basename}") - with open(f"neo_in.{self.basename}", "w+") as f: - f.write("'#'\n'#'\n'#'\n") - f.write(f" boozmn_{self.basename}.nc\n") # booz out file - f.write(f" neo_out.{self.basename}\n") # desired NEO out file - f.write(f" {self.ns-1}\n") # number of surfaces - f.write( - " ".join([str(x) for x in range(2, self.ns + 1)]) + "\n" - ) # surface indices - f.write( - " 300 ! number of theta points\n " - "300 ! number of zeta points\n " - + f"\n 0\n 0\n {N_particles} ! number of test particles\n " - f"1 ! 1 = singly trapped particles\n " - + "0.001 ! integration accuracy\n 100 ! number of poloidal bins\n " - "10 ! integration steps per field period\n " - + "500 ! min number of field periods\n " - "5000 ! max number of field periods\n" - ) # default values - f.write( - " 0\n 1\n 0\n 0\n 2 ! 2 = reference |B| used is max on each surface" - " \n 0\n 0\n 0\n 0\n 0\n" - ) - f.write("'#'\n'#'\n'#'\n") - f.write(" 0\n") - f.write(f"neo_cur_{self.basename}\n") - f.write(" 200\n 2\n 0\n") - - -def read_neo_out(fname): - """Read ripple from text file.""" - - def nan_helper(y): - return np.isnan(y), lambda z: z.nonzero()[0] - - # import all data from text file as an array - with open(f"{fname}") as f: - array = np.array([[float(x) for x in line.split()] for line in f]) - - eps_eff = array[:, 1] # epsilon_eff^(3/2) is the second column - nans, x = nan_helper(eps_eff) # find NaN values - - # replace NaN values with linear interpolation - eps_eff[nans] = np.interp(x(nans), x(~nans), eps_eff[~nans]) - - return eps_eff + ax.plot(rho, grid.compress(data["effective ripple"]), marker="o") + return fig From 2737194b70043d293114fa634087ef783fabd220 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 11 Jun 2024 15:52:16 -0500 Subject: [PATCH 040/112] Add baseline image test for W7-X effective ripple --- tests/baseline/test_effective_ripple.png | Bin 0 -> 13223 bytes ...nk_fixed_bdry_r0.15_L_9_M_9_N_24_output.h5 | Bin 131569 -> 0 bytes ....QI_plunk_fixed_surf_r0.15_N_24_hires_ns99 | 98 ------------------ 3 files changed, 98 deletions(-) create mode 100644 tests/baseline/test_effective_ripple.png delete mode 100644 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NaN -0.7223391527E+00 0.1349757039E+01 0.1009257284E+01 - 81 0.2165758679E-01 NaN -0.7228509802E+00 0.1350889787E+01 0.1009257284E+01 - 82 0.2238886486E-01 NaN -0.7233670880E+00 0.1352019438E+01 0.1009257284E+01 - 83 0.2307992406E-01 NaN -0.7238871220E+00 0.1353146083E+01 0.1009257284E+01 - 84 NaN NaN -0.7244107164E+00 0.1354269806E+01 0.1009257284E+01 - 85 0.2452316260E-01 NaN -0.7249374930E+00 0.1355390688E+01 0.1009257284E+01 - 86 0.2522557595E-01 NaN -0.7254670617E+00 0.1356508801E+01 0.1009257284E+01 - 87 0.2606183827E-01 NaN -0.7259990201E+00 0.1357624216E+01 0.1009257284E+01 - 88 0.2687512129E-01 NaN -0.7265329533E+00 0.1358736994E+01 0.1009257284E+01 - 89 0.2772172477E-01 NaN -0.7270684338E+00 0.1359847198E+01 0.1009257284E+01 - 90 0.2856864588E-01 NaN -0.7276050212E+00 0.1360954883E+01 0.1009257284E+01 - 91 0.2959799567E-01 NaN -0.7281422620E+00 0.1362060101E+01 0.1009257284E+01 - 92 0.3045385139E-01 NaN -0.7286796896E+00 0.1363162899E+01 0.1009257284E+01 - 93 0.3143314477E-01 NaN -0.7292168234E+00 0.1364263323E+01 0.1009257284E+01 - 94 0.3233212683E-01 NaN -0.7297531695E+00 0.1365361416E+01 0.1009257284E+01 - 95 0.3351922398E-01 NaN -0.7302882194E+00 0.1366457221E+01 0.1009257284E+01 - 96 0.3441879233E-01 NaN -0.7308214502E+00 0.1367550780E+01 0.1009257284E+01 - 97 0.3576105370E-01 NaN -0.7313523243E+00 0.1368642137E+01 0.1009257284E+01 - 98 0.3701525621E-01 NaN -0.7318802888E+00 0.1369731340E+01 0.1009257284E+01 - 99 0.3822220655E-01 NaN -0.7324047753E+00 0.1370818441E+01 0.1009257284E+01 From 104b3b4c3afbe26f3c060cfd220109fc365fde03 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 11 Jun 2024 16:39:25 -0500 Subject: [PATCH 041/112] Reduce memory usage in tests_neoclassical --- desc/compute/_neoclassical.py | 25 ++++++++++++------------- tests/test_neoclassical.py | 6 +++--- 2 files changed, 15 insertions(+), 16 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index ebf82a95fd..f78953fca4 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -56,20 +56,19 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): def _poloidal_mean(grid, f): - # Integrate f over poloidal angle and divide by 2π. - if grid.spacing is None: - warnif( - grid.num_poloidal != 1, - msg=colored("Reduced via uniform poloidal mean.", "yellow"), - ) + """Integrate f over poloidal angle and divide by 2π.""" + assert f.shape[-1] == grid.num_poloidal + if grid.num_poloidal == 1: + return jnp.squeeze(f, axis=-1) + if not hasattr(grid, "spacing"): + warnif(True, msg=colored("Reduced via uniform poloidal mean.", "yellow")) return jnp.mean(f, axis=-1) - else: - assert grid.is_meshgrid - dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") - return f @ dp / jnp.sum(dp) + assert grid.is_meshgrid + dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") + return f @ dp / jnp.sum(dp) -def _get_pitch(grid, data, quad, num=73): +def _get_pitch(grid, data, quad, num): # Get endpoints of integral over pitch for each flux surface. # with num values uniformly spaced in between. min_B = grid.compress(data["min_tz |B|"]) @@ -163,11 +162,11 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, num_quad=( "int : Resolution for quadrature of bounce integrals. Default is 31, " - "which gets sufficient convergence, so higher values are unnecessary." + "which gets sufficient convergence, so higher values are likely unnecessary." ), num_pitch=( "int : Resolution for quadrature over velocity coordinate, preferably odd. " - "Default is 125. Effective ripple will look smoother at high values." + "Default is 125. Effective ripple will look smoother at high values. " "(If computed on many flux surfaces and micro oscillation is seen " "between neighboring surfaces, increasing num_pitch will smooth the profile)." ), diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index d2d23f180c..1eeb741972 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -15,9 +15,9 @@ def test_field_line_average(): eq = examples.get("W7-X") grid = rtz_grid( eq, - radial=np.linspace(0, 1, 5), - poloidal=np.array([0]), - toroidal=np.linspace(0, 60 * np.pi, 900), + np.array([0, 0.5]), + np.array([0]), + np.linspace(0, 40 * np.pi, 200), coordinates="raz", period=(np.inf, 2 * np.pi, np.inf), ) From 4454500e8d29a23c260ec72649a636fa2073dd7f Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 12 Jun 2024 02:06:46 -0500 Subject: [PATCH 042/112] =?UTF-8?q?Interpolate=20|=E2=88=87=CF=88|=20?= =?UTF-8?q?=CE=BA=5Fg=20together=20since=20it=20is=20smoother=20than=20?= =?UTF-8?q?=CE=BA=5Fg=20alone.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Saves memory / speed --- desc/compute/_neoclassical.py | 74 ++++++++++++++++++--------------- desc/compute/bounce_integral.py | 3 -- 2 files changed, 40 insertions(+), 37 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index f78953fca4..184993747b 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -11,25 +11,18 @@ from functools import partial -import quadax from orthax.legendre import leggauss +from quadax import simpson from termcolor import colored -from desc.backend import imap, jit, jnp, trapezoid +from desc.backend import imap, jit, jnp from ..utils import warnif from .bounce_integral import bounce_integral, get_pitch from .data_index import register_compute_fun -def _is_adaptive(quad): - if hasattr(quad, "is_adaptive"): - return quad.is_adaptive - else: - return quad not in [trapezoid, quadax.trapezoid, quadax.simpson] - - -def vec_quadax(quad): +def _vec_quadax(quad, **kwargs): """Vectorize an adaptive quadrature method from quadax. Parameters @@ -43,11 +36,9 @@ def vec_quadax(quad): Vectorized adaptive quadrature method. """ - if not _is_adaptive(quad): - return quad def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): - return quad(fun, interval, args=(B_sup_z, B, B_z_ra, arg1, arg2))[0] + return quad(fun, interval, args=(B_sup_z, B, B_z_ra, arg1, arg2), **kwargs)[0] vec_quad = jnp.vectorize( vec_quad, signature="(2),(m),(m),(m),(m),(m)->()", excluded={0} @@ -68,13 +59,30 @@ def _poloidal_mean(grid, f): return f @ dp / jnp.sum(dp) -def _get_pitch(grid, data, quad, num): - # Get endpoints of integral over pitch for each flux surface. - # with num values uniformly spaced in between. +def _get_pitch(grid, data, num, for_adaptive=False): + """Get points for quadrature over velocity coordinate. + + Parameters + ---------- + grid : Grid + The grid on which data is computed. + data : dict + Dictionary containing min and max |B| over each flux surface. + num : int + Number of values to uniformly space in between. + for_adaptive : bool + Whether to return just the points useful for an adaptive quadrature. + + Returns + ------- + pitch : Array + Pitch values in the desired shape to use in compute methods. + + """ min_B = grid.compress(data["min_tz |B|"]) max_B = grid.compress(data["max_tz |B|"]) - if _is_adaptive(quad): - pitch = 1 / jnp.stack([max_B, min_B], axis=-1)[:, jnp.newaxis] + if for_adaptive: + pitch = jnp.expand_dims(1 / jnp.stack([max_B, min_B], axis=-1), axis=1) assert pitch.shape == (grid.num_rho, 1, 2) else: pitch = get_pitch(min_B, max_B, num) @@ -102,7 +110,7 @@ def _get_pitch(grid, data, quad, num): def _L_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - L_ra = quadax.simpson( + L_ra = simpson( jnp.reshape(1 / data["B^zeta"], shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, @@ -129,7 +137,7 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): def _G_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) - G_ra = quadax.simpson( + G_ra = simpson( jnp.reshape(1 / (data["B^zeta"] * data["sqrt(g)"]), shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, @@ -201,15 +209,11 @@ def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): quad=leggauss(kwargs.get("num_quad", 31)), ) - def dH(grad_psi_norm, kappa_g, B, pitch): + def dH(grad_psi_norm_kappa_g, B, pitch): # Removed dimensionless (λB₀)¹ᐧ⁵ from integrand of Nemov equation 30. # Reintroduced later. return ( - jnp.sqrt(1 - pitch * B) - * (4 / (pitch * B) - 1) - * grad_psi_norm - * kappa_g - / B + jnp.sqrt(1 - pitch * B) * (4 / (pitch * B) - 1) * grad_psi_norm_kappa_g / B ) def dI(B, pitch): # Integrand of Nemov equation 31. @@ -225,19 +229,20 @@ def d_ripple(pitch): Returns ------- - d_ripple : Array, shape(pitch.shape) - λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ + d_ripple : Array + Returned array has shape atleast_2d(pitch.shape). """ - H = bounce_integrate(dH, [data["|grad(psi)|"], data["kappa_g"]], pitch) + # Interpolate |∇ψ| κ_g together since it is smoother than κ_g alone. + H = bounce_integrate(dH, data["|grad(psi)|"] * data["kappa_g"], pitch) I = bounce_integrate(dI, [], pitch) return pitch * jnp.nansum(H**2 / I, axis=-1) - # (λB₀)³ db = (λB₀)³ λ⁻² B₀⁻¹ (-dλ) = λ B₀² (-dλ) where B₀ has units of λ⁻¹. + # (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) where B₀ has units of λ⁻¹. + pitch = _get_pitch(g, data, kwargs.get("num_pitch", 125)) # The integrand is continuous and likely poorly approximated by a polynomial, # so composite quadrature should perform better than higher order methods. - pitch = _get_pitch(g, data, quadax.simpson, kwargs.get("num_pitch", 125)) - ripple = quadax.simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) + ripple = simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) data["effective ripple raw"] = ( g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) * data["max_tz |B|"] ** 2 @@ -248,7 +253,7 @@ def d_ripple(pitch): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - label="π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + label="ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", units="~", units_long="None", description="Effective ripple modulation amplitude", @@ -260,8 +265,9 @@ def d_ripple(pitch): data=["R0", "V_r(r)", "psi_r", "S(r)", "effective ripple raw"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): - """Evaluation of 1/ν neoclassical transport in stellarators. + """ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉. + Evaluation of 1/ν neoclassical transport in stellarators. V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. https://doi.org/10.1063/1.873749. diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index b3ec5f35fe..e2f272ccac 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -335,9 +335,6 @@ def get_pitch(min_B, max_B, num, relative_shift=1e-6): # extrema. Shift values slightly to resolve this issue. min_B = (1 + relative_shift) * min_B max_B = (1 - relative_shift) * max_B - # λ is the pitch angle. Note Nemov dimensionless integration variable b = (λB₀)⁻¹. - # Uniformly space in pitch (as opposed to 1/pitch) to get faster convergence in - # an integration over pitch. pitch = composite_linspace(1 / jnp.stack([max_B, min_B]), num) assert pitch.shape == (num + 2, *pitch.shape[1:]) return pitch From 66b24a5462b6ffd25abcab3c69fca075a438d695 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 13 Jun 2024 11:40:43 -0500 Subject: [PATCH 043/112] Add effective ripple objective function --- desc/compute/_neoclassical.py | 86 ++++----- desc/compute/bounce_integral.py | 4 +- desc/equilibrium/coords.py | 23 ++- desc/equilibrium/equilibrium.py | 49 ++--- desc/objectives/_bootstrap.py | 6 +- desc/objectives/_coils.py | 36 ++-- desc/objectives/_equilibrium.py | 32 ++-- desc/objectives/_free_boundary.py | 12 +- desc/objectives/_generic.py | 14 +- desc/objectives/_geometry.py | 48 ++--- desc/objectives/_neoclassical.py | 257 +++++++++++++++++++++++++++ desc/objectives/_omnigenity.py | 30 ++-- desc/objectives/_profiles.py | 24 +-- desc/objectives/_stability.py | 14 +- desc/objectives/linear_objectives.py | 88 ++++----- desc/objectives/objective_funs.py | 6 +- desc/profiles.py | 30 +++- docs/adding_objectives.rst | 6 +- tests/test_axis_limits.py | 14 ++ tests/test_bounce_integral.py | 6 +- tests/test_neoclassical.py | 1 + 21 files changed, 539 insertions(+), 247 deletions(-) create mode 100644 desc/objectives/_neoclassical.py diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 184993747b..7621266cf6 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -13,11 +13,9 @@ from orthax.legendre import leggauss from quadax import simpson -from termcolor import colored from desc.backend import imap, jit, jnp -from ..utils import warnif from .bounce_integral import bounce_integral, get_pitch from .data_index import register_compute_fun @@ -46,19 +44,6 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return vec_quad -def _poloidal_mean(grid, f): - """Integrate f over poloidal angle and divide by 2π.""" - assert f.shape[-1] == grid.num_poloidal - if grid.num_poloidal == 1: - return jnp.squeeze(f, axis=-1) - if not hasattr(grid, "spacing"): - warnif(True, msg=colored("Reduced via uniform poloidal mean.", "yellow")) - return jnp.mean(f, axis=-1) - assert grid.is_meshgrid - dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") - return f @ dp / jnp.sum(dp) - - def _get_pitch(grid, data, num, for_adaptive=False): """Get points for quadrature over velocity coordinate. @@ -92,6 +77,18 @@ def _get_pitch(grid, data, num, for_adaptive=False): return pitch +def _poloidal_mean(grid, f): + """Integrate f over poloidal angle and divide by 2π.""" + assert f.shape[-1] == grid.num_poloidal + if grid.num_poloidal == 1: + return jnp.squeeze(f, axis=-1) + if not hasattr(grid, "spacing"): + return jnp.mean(f, axis=-1) + assert grid.is_meshgrid + dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") + return f @ dp / jnp.sum(dp) + + @register_compute_fun( name="", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" @@ -148,9 +145,9 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): @register_compute_fun( name="effective ripple raw", - label="∫dλ λ⁻²B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", - units="T^2", - units_long="Tesla squared", + label="(∂ψ/∂ρ)⁻² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", + units="m^{-4}", + units_long="Inverse meters quarted", description="Effective ripple modulation amplitude, not dimensionless", dim=1, params=[], @@ -163,7 +160,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "B^zeta", "|B|", "|B|_z|r,a", - "|grad(psi)|", + "|grad(rho)|", "kappa_g", "", ], @@ -178,6 +175,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "(If computed on many flux surfaces and micro oscillation is seen " "between neighboring surfaces, increasing num_pitch will smooth the profile)." ), + batch="bool : Whether to vectorize part of the computation. Default is true.", # Some notes on choosing the resolution hyperparameters: # The default settings above were chosen such that the effective ripple profile on # the W7-X stellarator looks similar to the profile computed at higher resolution, @@ -198,8 +196,9 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): # required for sufficient coverage of the surface. This requires many knots to # for the spline of the magnetic field to capture fine ripples in a large interval. ) -@partial(jit, static_argnames=["num_quad", "num_pitch"]) +@partial(jit, static_argnames=["num_quad", "num_pitch", "batch"]) def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): + batch = kwargs.get("batch", True) g = transforms["grid"].source_grid bounce_integrate, _ = bounce_integral( data["B^zeta"], @@ -209,39 +208,28 @@ def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): quad=leggauss(kwargs.get("num_quad", 31)), ) - def dH(grad_psi_norm_kappa_g, B, pitch): - # Removed dimensionless (λB₀)¹ᐧ⁵ from integrand of Nemov equation 30. - # Reintroduced later. + def dH(grad_rho_norm_kappa_g, B, pitch): + # Removed |∂ψ/∂ρ| (λB₀)¹ᐧ⁵ from integrand of Nemov eq. 30. Reintroduced later. return ( - jnp.sqrt(1 - pitch * B) * (4 / (pitch * B) - 1) * grad_psi_norm_kappa_g / B + jnp.sqrt(1 - pitch * B) * (4 / (pitch * B) - 1) * grad_rho_norm_kappa_g / B ) - def dI(B, pitch): # Integrand of Nemov equation 31. + def dI(B, pitch): # Integrand of Nemov eq. 31. return jnp.sqrt(1 - pitch * B) / B def d_ripple(pitch): - """Return λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. - - Parameters - ---------- - pitch : Array, shape(*pitch.shape[:-1], g.num_rho * g.num_alpha) - Pitch angle. - - Returns - ------- - d_ripple : Array - Returned array has shape atleast_2d(pitch.shape). - - """ - # Interpolate |∇ψ| κ_g together since it is smoother than κ_g alone. - H = bounce_integrate(dH, data["|grad(psi)|"] * data["kappa_g"], pitch) - I = bounce_integrate(dI, [], pitch) + # Return (∂ψ/∂ρ)⁻² λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. + # Note (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) where B₀ has units of λ⁻¹. + # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. + H = bounce_integrate( + dH, data["|grad(rho)|"] * data["kappa_g"], pitch, batch=batch + ) + I = bounce_integrate(dI, [], pitch, batch=batch) return pitch * jnp.nansum(H**2 / I, axis=-1) - # (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) where B₀ has units of λ⁻¹. + # The integrand is continuous and likely poorly approximated by a polynomial. + # Composite quadrature should perform better than higher order methods. pitch = _get_pitch(g, data, kwargs.get("num_pitch", 125)) - # The integrand is continuous and likely poorly approximated by a polynomial, - # so composite quadrature should perform better than higher order methods. ripple = simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) data["effective ripple raw"] = ( g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) @@ -253,7 +241,7 @@ def d_ripple(pitch): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - label="ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ \\langle ∑ⱼ Hⱼ²/Iⱼ \\rangle", + label="ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", units="~", units_long="None", description="Effective ripple modulation amplitude", @@ -262,21 +250,19 @@ def d_ripple(pitch): transforms={}, profiles=[], coordinates="r", - data=["R0", "V_r(r)", "psi_r", "S(r)", "effective ripple raw"], + data=["R0", "V_r(r)", "S(r)", "effective ripple raw"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): - """ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉. + """https://doi.org/10.1063/1.873749. Evaluation of 1/ν neoclassical transport in stellarators. V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - https://doi.org/10.1063/1.873749. - """ data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) - * (data["R0"] * data["V_r(r)"] / data["psi_r"] / data["S(r)"]) ** 2 + * (data["R0"] * data["V_r(r)"] / data["S(r)"]) ** 2 * data["effective ripple raw"] ) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index d871ee72a6..52b868ea94 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1197,12 +1197,12 @@ def bounce_integral( First axis enumerates the coefficients of power series. Second axis enumerates the splines along the field lines. Last axis enumerates the polynomials that compose the spline along a particular field line. - B_z_ra.c : jnp.ndarray + B_z_ra_c : jnp.ndarray Shape (3, S, knots.size - 1). Polynomial coefficients of the spline of ∂|B|/∂_ζ in local power basis. First axis enumerates the coefficients of power series. Second axis enumerates the splines along the field lines. Last axis enumerates the - polynomials that compose spline along a particular field line. + polynomials that compose the spline along a particular field line. """ B_sup_z = B_sup_z * L_ref / B_ref diff --git a/desc/equilibrium/coords.py b/desc/equilibrium/coords.py index 1ec6857874..969866b139 100644 --- a/desc/equilibrium/coords.py +++ b/desc/equilibrium/coords.py @@ -111,7 +111,11 @@ def periodic(x): # do surface average to get iota once if "iota" in profiles and profiles["iota"] is None: - profiles["iota"] = eq.get_profile("iota", params=params) + profiles["iota"] = ( + kwargs.pop("iota") + if "iota" in kwargs + else eq.get_profile("iota", params=params) + ) params["i_l"] = profiles["iota"].params @functools.partial(jit, static_argnums=1) @@ -119,11 +123,15 @@ def compute(y, basis): grid = Grid(y, sort=False, jitable=True) data = {} if "iota" in deps: - data["iota"] = profiles["iota"](grid, params=params["i_l"]) + data["iota"] = profiles["iota"](grid, params=params["i_l"], jitable=True) if "iota_r" in deps: - data["iota_r"] = profiles["iota"](grid, dr=1, params=params["i_l"]) + data["iota_r"] = profiles["iota"]( + grid, dr=1, params=params["i_l"], jitable=True + ) if "iota_rr" in deps: - data["iota_rr"] = profiles["iota"](grid, dr=2, params=params["i_l"]) + data["iota_rr"] = profiles["iota"]( + grid, dr=2, params=params["i_l"], jitable=True + ) transforms = get_transforms(basis, eq, grid, jitable=True) data = compute_fun(eq, basis, params, transforms, profiles, data) x = jnp.array([data[k] for k in basis]).T @@ -202,7 +210,7 @@ def _initial_guess_heuristic(yk, coords, inbasis, eq, profiles): theta = coords[:, inbasis.index(poloidal)] elif poloidal == "alpha": alpha = coords[:, inbasis.index("alpha")] - iota = profiles["iota"](rho) + iota = profiles["iota"](rho, jitable=True) theta = (alpha + iota * zeta) % (2 * jnp.pi) yk = jnp.array([rho, theta, zeta]).T @@ -505,7 +513,9 @@ def to_sfl( return eq_sfl -def rtz_grid(eq, radial, poloidal, toroidal, coordinates, period, jitable=True): +def rtz_grid( + eq, radial, poloidal, toroidal, coordinates, period, jitable=True, **kwargs +): """Return DESC coordinate grid from given coordinates. Create a meshgrid from the given coordinates, and return the @@ -554,6 +564,7 @@ def rtz_grid(eq, radial, poloidal, toroidal, coordinates, period, jitable=True): inbasis=[inbasis[char] for char in coordinates], outbasis=("rho", "theta", "zeta"), period=period, + **kwargs, ) desc_grid = Grid( nodes=rtz_nodes, diff --git a/desc/equilibrium/equilibrium.py b/desc/equilibrium/equilibrium.py index da30f64c41..2df8ca0f6b 100644 --- a/desc/equilibrium/equilibrium.py +++ b/desc/equilibrium/equilibrium.py @@ -785,6 +785,28 @@ def get_axis(self): axis = FourierRZCurve(R_n, Z_n, modes_R, modes_Z, NFP=self.NFP, sym=self.sym) return axis + @staticmethod + def is_0d(name): + """Is name constant throughout the plasma volume?.""" + # Should compute on a grid that samples entire plasma volume. + # In particular, a QuadratureGrid for accurate radial integration. + p = "desc.equilibrium.equilibrium.Equilibrium" + return data_index[p][name]["coordinates"] == "" + + @staticmethod + def is_1dr(name): + """Is name constant over flux surfaces?.""" + # Should compute on a grid that samples entire radial surfaces. + p = "desc.equilibrium.equilibrium.Equilibrium" + return data_index[p][name]["coordinates"] == "r" + + @staticmethod + def is_1dz(name): + """Is name constant over toroidal surfaces?.""" + # Should compute on a grid that samples entire toroidal surfaces. + p = "desc.equilibrium.equilibrium.Equilibrium" + return data_index[p][name]["coordinates"] == "z" + def compute( # noqa: C901 self, names, @@ -888,20 +910,7 @@ def need_src(name): need_src_deps = _grow_seeds(set(filter(need_src, deps)), deps) - def is_0d(name): - # Should compute on a grid that samples entire plasma volume. - # In particular, a QuadratureGrid for accurate radial integration. - return data_index[p][name]["coordinates"] == "" - - def is_1dr(name): - # Should compute on a grid that samples entire radial surfaces. - return data_index[p][name]["coordinates"] == "r" - - def is_1dz(name): - # Should compute on a grid that samples entire toroidal surfaces. - return data_index[p][name]["coordinates"] == "z" - - dep0d = {dep for dep in deps if is_0d(dep) and dep not in need_src_deps} + dep0d = {dep for dep in deps if self.is_0d(dep) and dep not in need_src_deps} # Unless user asks, don't try to recompute stuff which are only dependencies # of dep0d. Example, need R0. R0 <- A <- A(z) computable on dep0d grid. # But A(z) in dep1dz and attempt to recompute on dep1dz grid will error @@ -913,12 +922,12 @@ def is_1dz(name): dep1dr = { dep for dep in deps - if is_1dr(dep) and not just_dep0d_dep(dep) and dep not in need_src_deps + if self.is_1dr(dep) and not just_dep0d_dep(dep) and dep not in need_src_deps } dep1dz = { dep for dep in deps - if is_1dz(dep) and not just_dep0d_dep(dep) and dep not in need_src_deps + if self.is_1dz(dep) and not just_dep0d_dep(dep) and dep not in need_src_deps # These don't need a special grid, since the transforms are always # built on the (rho, theta, zeta) coordinate grid. and dep not in ["phi", "zeta"] @@ -970,7 +979,7 @@ def is_1dz(name): if calc0d and override_grid: grid0d = QuadratureGrid(self.L_grid, self.M_grid, self.N_grid, self.NFP) - data0d_seed = {key: data[key] for key in data if is_0d(key)} + data0d_seed = {key: data[key] for key in data if self.is_0d(key)} data0d = compute_fun( self, list(dep0d), @@ -989,7 +998,7 @@ def is_1dz(name): data.update(data0d) if (calc1dr or calc1dz) and override_grid: - data0d_seed = {key: data[key] for key in data if is_0d(key)} + data0d_seed = {key: data[key] for key in data if self.is_0d(key)} else: data0d_seed = {} if calc1dr and override_grid: @@ -1003,7 +1012,7 @@ def is_1dz(name): data1dr_seed = { key: grid1dr.copy_data_from_other(data[key], grid, surface_label="rho") for key in data - if is_1dr(key) + if self.is_1dr(key) } data1dr = compute_fun( self, @@ -1037,7 +1046,7 @@ def is_1dz(name): data1dz_seed = { key: grid1dz.copy_data_from_other(data[key], grid, surface_label="zeta") for key in data - if is_1dz(key) + if self.is_1dz(key) } data1dz = compute_fun( self, diff --git a/desc/objectives/_bootstrap.py b/desc/objectives/_bootstrap.py index 365a9882c2..42d34b4954 100644 --- a/desc/objectives/_bootstrap.py +++ b/desc/objectives/_bootstrap.py @@ -36,11 +36,11 @@ class BootstrapRedlConsistency(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -52,7 +52,7 @@ class BootstrapRedlConsistency(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_coils.py b/desc/objectives/_coils.py index 6042085bee..a8dea90ad9 100644 --- a/desc/objectives/_coils.py +++ b/desc/objectives/_coils.py @@ -27,10 +27,10 @@ class _CoilObjective(_Objective): Must be broadcastable to Objective.dim_f. bounds : tuple of float, ndarray, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -43,7 +43,7 @@ class _CoilObjective(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. Operates over all coils, not each individual coil. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -239,11 +239,11 @@ class CoilLength(_CoilObjective): be flattened according to the number of inputs. Defaults to ``target=2*np.pi``. bounds : tuple of float, ndarray, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=2*np.pi``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -256,7 +256,7 @@ class CoilLength(_CoilObjective): is called on the raw compute value, before any shifting, scaling, or normalization. Operates over all coils, not each individual coil. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -361,11 +361,11 @@ class CoilCurvature(_CoilObjective): be flattened according to the number of inputs. Defaults to ``bounds=(0,1)``. bounds : tuple of float, ndarray, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``bounds=(0,1)``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -378,7 +378,7 @@ class CoilCurvature(_CoilObjective): is called on the raw compute value, before any shifting, scaling, or normalization. Operates over all coils, not each individual coil. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -480,11 +480,11 @@ class CoilTorsion(_CoilObjective): be flattened according to the number of inputs. Defaults to ``target=0``. bounds : tuple of float, ndarray, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -497,7 +497,7 @@ class CoilTorsion(_CoilObjective): is called on the raw compute value, before any shifting, scaling, or normalization. Operates over all coils, not each individual coil. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -598,11 +598,11 @@ class CoilCurrentLength(CoilLength): be flattened according to the number of inputs. Defaults to ``target=0``. bounds : tuple of float, ndarray, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -615,7 +615,7 @@ class CoilCurrentLength(CoilLength): is called on the raw compute value, before any shifting, scaling, or normalization. Operates over all coils, not each individual coil. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -930,10 +930,10 @@ class ToroidalFlux(_Objective): Defaults to eq.Psi. Must be broadcastable to Objective.dim_f. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool @@ -945,7 +945,7 @@ class ToroidalFlux(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. Note: has no effect for this objective deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_equilibrium.py b/desc/objectives/_equilibrium.py index d859e996b4..a92927ec23 100644 --- a/desc/objectives/_equilibrium.py +++ b/desc/objectives/_equilibrium.py @@ -38,11 +38,11 @@ class ForceBalance(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -54,7 +54,7 @@ class ForceBalance(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -214,7 +214,7 @@ class ForceBalanceAnisotropic(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. @@ -368,11 +368,11 @@ class RadialForceBalance(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -384,7 +384,7 @@ class RadialForceBalance(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -526,11 +526,11 @@ class HelicalForceBalance(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -542,7 +542,7 @@ class HelicalForceBalance(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -680,11 +680,11 @@ class Energy(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -696,7 +696,7 @@ class Energy(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -849,11 +849,11 @@ class CurrentDensity(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -865,7 +865,7 @@ class CurrentDensity(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_free_boundary.py b/desc/objectives/_free_boundary.py index a87e836603..88bd416fec 100644 --- a/desc/objectives/_free_boundary.py +++ b/desc/objectives/_free_boundary.py @@ -47,7 +47,7 @@ class VacuumBoundaryError(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. @@ -63,7 +63,7 @@ class VacuumBoundaryError(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -357,7 +357,7 @@ class BoundaryError(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. @@ -373,7 +373,7 @@ class BoundaryError(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -807,7 +807,7 @@ class BoundaryErrorNESTOR(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. @@ -829,7 +829,7 @@ class BoundaryErrorNESTOR(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_generic.py b/desc/objectives/_generic.py index 278e043b38..b6d6734ca4 100644 --- a/desc/objectives/_generic.py +++ b/desc/objectives/_generic.py @@ -29,11 +29,11 @@ class GenericObjective(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f. + Must be broadcastable to Objective.dim_f. normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. Has no effect for this objective @@ -46,7 +46,7 @@ class GenericObjective(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -186,7 +186,7 @@ class LinearObjectiveFromUser(_FixedObjective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of dict {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : dict of {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. @@ -309,11 +309,11 @@ class ObjectiveFromUser(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. Has no effect for this objective. @@ -326,7 +326,7 @@ class ObjectiveFromUser(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_geometry.py b/desc/objectives/_geometry.py index 943c9e5f92..4528f16836 100644 --- a/desc/objectives/_geometry.py +++ b/desc/objectives/_geometry.py @@ -29,11 +29,11 @@ class AspectRatio(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=2``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=2``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. Has no effect for this objective. @@ -46,7 +46,7 @@ class AspectRatio(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. Note: Has no effect for this objective. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -191,11 +191,11 @@ class Elongation(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=1``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=1``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. Has no effect for this objective. @@ -208,7 +208,7 @@ class Elongation(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. Note: Has no effect for this objective. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -352,11 +352,11 @@ class Volume(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=1``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=1``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -369,7 +369,7 @@ class Volume(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. Note: Has no effect for this objective. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -539,11 +539,11 @@ class PlasmaVesselDistance(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``bounds=(1,np.inf)``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``bounds=(1,np.inf)``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool @@ -555,7 +555,7 @@ class PlasmaVesselDistance(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -789,11 +789,11 @@ class MeanCurvature(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``bounds=(-np.inf, 0)``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``bounds=(-np.inf, 0)``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool @@ -805,7 +805,7 @@ class MeanCurvature(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -949,11 +949,11 @@ class PrincipalCurvature(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=1``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=1``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool @@ -965,7 +965,7 @@ class PrincipalCurvature(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -1105,11 +1105,11 @@ class BScaleLength(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``bounds=(1,np.inf)``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``bounds=(1,np.inf)``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool @@ -1121,7 +1121,7 @@ class BScaleLength(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -1257,11 +1257,11 @@ class GoodCoordinates(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1273,7 +1273,7 @@ class GoodCoordinates(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py new file mode 100644 index 0000000000..572e313ee9 --- /dev/null +++ b/desc/objectives/_neoclassical.py @@ -0,0 +1,257 @@ +"""Objectives for targeting neoclassical transport.""" + +import numpy as np + +from desc.compute import get_profiles, get_transforms +from desc.compute.utils import _compute as compute_fun +from desc.grid import QuadratureGrid +from desc.utils import Timer + +from ..backend import jnp +from ..equilibrium.coords import rtz_grid +from ..profiles import SplineProfile +from .objective_funs import _Objective +from .utils import _parse_callable_target_bounds + + +class EffectiveRipple(_Objective): + """The effective ripple is a proxy for neoclassical transport. + + Evaluation of 1/ν neoclassical transport in stellarators. + V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + https://doi.org/10.1063/1.873749. + + Parameters + ---------- + eq : Equilibrium + Equilibrium that will be optimized to satisfy the Objective. + target : {float, ndarray, callable}, optional + Target value(s) of the objective. Only used if bounds is None. + Must be broadcastable to Objective.dim_f. If a callable, should take a + single argument `rho` and return the desired value of the profile at those + locations. Defaults to ``bounds=(0,np.inf)`` + bounds : tuple of {float, ndarray, callable}, optional + Lower and upper bounds on the objective. Overrides target. + Both bounds must be broadcastable to Objective.dim_f + If a callable, each should take a single argument `rho` and return the + desired bound (lower or upper) of the profile at those locations. + Defaults to ``bounds=(0, np.inf)`` + weight : {float, ndarray}, optional + Weighting to apply to the Objective, relative to other Objectives. + Must be broadcastable to Objective.dim_f + normalize : bool, optional + Whether to compute the error in physical units or non-dimensionalize. + normalize_target : bool, optional + Whether target and bounds should be normalized before comparing to computed + values. If `normalize` is `True` and the target is in physical units, + this should also be set to True. + loss_function : {None, 'mean', 'min', 'max'}, optional + Loss function to apply to the objective values once computed. This loss function + is called on the raw compute value, before any shifting, scaling, or + normalization. + deriv_mode : {"auto", "fwd", "rev"} + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. + "auto" selects forward or reverse mode based on the size of the input and output + of the objective. Has no effect on self.grad or self.hess which always use + reverse mode and forward over reverse mode respectively. + grid : Grid, optional + Collocation grid to evaluate flux surface averages. + Should have poloidal and toroidal resolution. + alpha, zeta : ndarray, ndarray + Unique coordinate values for field line label alpha, and field line following + coordinate zeta. + num_quad : int + Resolution for quadrature of bounce integrals. Default is 31, + which gets sufficient convergence, so higher values are likely unnecessary. + num_pitch : int + Resolution for quadrature over velocity coordinate, preferably odd. + Default is 99. Effective ripple will look smoother at high values. + (If computed on many flux surfaces and micro oscillation is seen + between neighboring surfaces, increasing num_pitch will smooth the profile). + batch : bool + Whether to vectorize part of the computation. Default is true. + name : str, optional + Name of the objective function. + + """ + + _coordinates = "r" + _units = "~" + _print_value_fmt = "Effective ripple ε¹ᐧ⁵: {:10.3e} " + + def __init__( + self, + eq, + target=None, + bounds=None, + weight=1, + normalize=True, + normalize_target=True, + loss_function=None, + deriv_mode="auto", + grid=None, + alpha=np.array([0]), + zeta=np.linspace(0, 15 * np.pi, 750), + num_quad=31, + num_pitch=99, + batch=True, + name="Effective ripple", + ): + if target is None and bounds is None: + bounds = (0, np.inf) + + # Assign in build. + self._grid_1dr = grid + self._grid_0d = grid if isinstance(grid, QuadratureGrid) else None + self._constants = {"quad_weights": 1} + self._dim_f = 1 + self._rho = np.array([1.0]) + # Assign here. + self._alpha = alpha + self._zeta = zeta + self._keys_1dr = [ + "iota", + "iota_r", + "S(r)", + "V_r(r)", + "min_tz |B|", + "max_tz |B|", + ] + self._keys_0d = ["R0"] + self._keys = ["effective ripple"] + self._hyperparameters = { + "num_quad": num_quad, + "num_pitch": num_pitch, + "batch": batch, + } + + super().__init__( + things=eq, + target=target, + bounds=bounds, + weight=weight, + normalize=normalize, + normalize_target=normalize_target, + loss_function=loss_function, + deriv_mode=deriv_mode, + name=name, + ) + + def build(self, use_jit=True, verbose=1): + """Build constant arrays. + + Parameters + ---------- + use_jit : bool, optional + Whether to just-in-time compile the objective and derivatives. + verbose : int, optional + Level of output. + + """ + eq = self.things[0] + if self._grid_0d is None: + self._grid_0d = QuadratureGrid( + L=eq.L_grid, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP + ) + if self._grid_1dr is None: + self._grid_1dr = self._grid_0d + self._dim_f = self._grid_1dr.num_rho + self._rho = self._grid_1dr.compress(self._grid_1dr.nodes[:, 0]) + self._target, self._bounds = _parse_callable_target_bounds( + self._target, self._bounds, self._rho + ) + + timer = Timer() + if verbose > 0: + print("Precomputing transforms") + timer.start("Precomputing transforms") + + if self._grid_1dr == self._grid_0d: + self._constants["transforms_0d"] = get_transforms( + self._keys_0d + self._keys_1dr, eq, self._grid_0d, use_jit + ) + self._constants["profiles_0d"] = get_profiles( + self._keys_0d + self._keys_1dr, eq, self._grid_0d, use_jit + ) + self._constants["transforms_1dr"] = self._constants["transforms_0d"] + self._constants["profiles_1dr"] = self._constants["profiles_0d"] + else: + self._constants["transforms_0d"] = get_transforms( + self._keys_0d, eq, self._grid_0d, use_jit + ) + self._constants["profiles_0d"] = get_profiles( + self._keys_0d, eq, self._grid_0d, use_jit + ) + self._constants["transforms_1dr"] = get_transforms( + self._keys_1dr, eq, self._grid_1dr, use_jit + ) + self._constants["profiles_1dr"] = get_profiles( + self._keys_1dr, eq, self._grid_1dr, use_jit + ) + + timer.stop("Precomputing transforms") + if verbose > 1: + timer.disp("Precomputing transforms") + + super().build(use_jit=use_jit, verbose=verbose) + + def compute(self, params, constants=None): + """Compute the effective ripple. + + Parameters + ---------- + params : dict + Dictionary of equilibrium degrees of freedom, eg Equilibrium.params_dict + constants : dict + Dictionary of constant data, eg transforms, profiles etc. Defaults to + self.constants + + Returns + ------- + effective_ripple : ndarray + + """ + if constants is None: + constants = self.constants + eq = self.things[0] + data = compute_fun( + eq, + self._keys_0d, + params, + constants["transforms_0d"], + constants["profiles_0d"], + ) + data = compute_fun( + eq, + self._keys_1dr, + params, + constants["transforms_1dr"], + constants["profiles_1dr"], + data={key: data[key] for key in data if eq.is_0d(key)}, + ) + iota = self._grid_1dr.compress(data["iota"]) + iota_r = self._grid_1dr.compress(data["iota_r"]) + grid = rtz_grid( + eq, + self._rho, + self._alpha, + self._zeta, + coordinates="raz", + period=(np.inf, 2 * np.pi, np.inf), + iota=SplineProfile(iota, df=iota_r, knots=self._rho, name="iota", jnp=jnp), + ) + data = {key: data[key] for key in self._keys_0d} | { + key: grid.copy_data_from_other(data[key], self._grid_1dr) + for key in self._keys_1dr + } + data = compute_fun( + eq, + self._keys, + params, + get_transforms(self._keys, eq, grid, jitable=True), + get_profiles(self._keys, eq, grid, jitable=True), + data=data, + **self._hyperparameters, + ) + return grid.compress(data["effective ripple"]) diff --git a/desc/objectives/_omnigenity.py b/desc/objectives/_omnigenity.py index a1acb54056..4fded346df 100644 --- a/desc/objectives/_omnigenity.py +++ b/desc/objectives/_omnigenity.py @@ -25,11 +25,11 @@ class QuasisymmetryBoozer(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -41,7 +41,7 @@ class QuasisymmetryBoozer(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -252,11 +252,11 @@ class QuasisymmetryTwoTerm(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -268,7 +268,7 @@ class QuasisymmetryTwoTerm(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -446,11 +446,11 @@ class QuasisymmetryTripleProduct(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -462,7 +462,7 @@ class QuasisymmetryTripleProduct(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -597,11 +597,11 @@ class Omnigenity(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool @@ -613,7 +613,7 @@ class Omnigenity(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -950,11 +950,11 @@ class Isodynamicity(_Objective): Must be broadcastable to Objective.dim_f. Defaults to ``target=0``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -967,7 +967,7 @@ class Isodynamicity(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_profiles.py b/desc/objectives/_profiles.py index 34dc9ee29d..90239683fd 100644 --- a/desc/objectives/_profiles.py +++ b/desc/objectives/_profiles.py @@ -26,13 +26,13 @@ class Pressure(_Objective): locations. Defaults to ``target=0``. bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f If a callable, each should take a single argument `rho` and return the desired bound (lower or upper) of the profile at those locations. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -44,7 +44,7 @@ class Pressure(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -182,13 +182,13 @@ class RotationalTransform(_Objective): locations. Defaults to ``target=0``. bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f If a callable, each should take a single argument `rho` and return the desired bound (lower or upper) of the profile at those locations. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -200,7 +200,7 @@ class RotationalTransform(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -353,13 +353,13 @@ class Shear(_Objective): locations. Defaults to ``target=0``. bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f If a callable, each should take a single argument `rho` and return the desired bound (lower or upper) of the profile at those locations. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -371,7 +371,7 @@ class Shear(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -520,13 +520,13 @@ class ToroidalCurrent(_Objective): locations. Defaults to ``target=0``. bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f If a callable, each should take a single argument `rho` and return the desired bound (lower or upper) of the profile at those locations. Defaults to ``target=0``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -538,7 +538,7 @@ class ToroidalCurrent(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/_stability.py b/desc/objectives/_stability.py index 0806375794..1f17f633b1 100644 --- a/desc/objectives/_stability.py +++ b/desc/objectives/_stability.py @@ -31,16 +31,16 @@ class MercierStability(_Objective): Target value(s) of the objective. Only used if bounds is None. Must be broadcastable to Objective.dim_f. If a callable, should take a single argument `rho` and return the desired value of the profile at those - locations. Defaults to ``bounds=(0, np.inf)`` + locations. Defaults to ``bounds=(0,np.inf)`` bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f If a callable, each should take a single argument `rho` and return the desired bound (lower or upper) of the profile at those locations. Defaults to ``bounds=(0, np.inf)`` weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -52,7 +52,7 @@ class MercierStability(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. @@ -215,13 +215,13 @@ class MagneticWell(_Objective): locations. Defaults to ``bounds=(0, np.inf)`` bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f If a callable, each should take a single argument `rho` and return the desired bound (lower or upper) of the profile at those locations. Defaults to ``bounds=(0, np.inf)`` weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -233,7 +233,7 @@ class MagneticWell(_Objective): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/objectives/linear_objectives.py b/desc/objectives/linear_objectives.py index b20317e3aa..6d9b749803 100644 --- a/desc/objectives/linear_objectives.py +++ b/desc/objectives/linear_objectives.py @@ -617,11 +617,11 @@ class FixBoundaryR(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Rb_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Rb_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -698,11 +698,11 @@ class FixBoundaryZ(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Zb_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Zb_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -878,7 +878,7 @@ class FixThetaSFL(FixParameters): Equilibrium that will be optimized to satisfy the Objective. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Has no effect for this objective. normalize_target : bool, optional @@ -923,11 +923,11 @@ class FixAxisR(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Ra_n``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Ra_n``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1004,11 +1004,11 @@ class FixAxisZ(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Za_n``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Za_n``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1085,11 +1085,11 @@ class FixModeR(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.R_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.R_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1166,11 +1166,11 @@ class FixModeZ(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Z_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Z_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1248,7 +1248,7 @@ class FixModeLambda(FixParameters): Defaults to ``target=eq.L_lmn``. bounds : tuple, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.L_lmn``. weight : float, ndarray, optional Weighting to apply to the Objective, relative to other Objectives. @@ -1310,11 +1310,11 @@ class FixSumModesR(_FixedObjective): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.R_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.R_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1476,11 +1476,11 @@ class FixSumModesZ(_FixedObjective): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Z_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Z_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1642,11 +1642,11 @@ class FixSumModesLambda(_FixedObjective): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.L_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.L_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f. + Must be broadcastable to Objective.dim_f. normalize : bool, optional Has no effect for this objective. normalize_target : bool, optional @@ -1810,11 +1810,11 @@ class FixPressure(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.p_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.p_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -1892,11 +1892,11 @@ class FixAnisotropy(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.a_lmn``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.a_lmn``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Has no effect for this objective. normalize_target : bool, optional @@ -1969,11 +1969,11 @@ class FixIota(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.i_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.i_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Has no effect for this objective. normalize_target : bool, optional @@ -2046,11 +2046,11 @@ class FixCurrent(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.c_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.c_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -2128,11 +2128,11 @@ class FixElectronTemperature(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Te_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Te_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -2210,11 +2210,11 @@ class FixElectronDensity(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.ne_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.ne_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -2294,11 +2294,11 @@ class FixIonTemperature(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Ti_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Ti_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -2376,11 +2376,11 @@ class FixAtomicNumber(FixParameters): Must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Zeff_l``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Defaults to ``target=eq.Zeff_l``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Has no effect for this objective. normalize_target : bool, optional @@ -2453,11 +2453,11 @@ class FixPsi(FixParameters): Must be broadcastable to Objective.dim_f. Default is ``target=eq.Psi``. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Default is ``target=eq.Psi``. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -2523,10 +2523,10 @@ class FixCurveShift(FixParameters): Must be broadcastable to Objective.dim_f. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -2576,10 +2576,10 @@ class FixCurveRotation(FixParameters): Must be broadcastable to Objective.dim_f. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Has no effect for this objective. normalize_target : bool, optional @@ -2953,11 +2953,11 @@ class FixSheetCurrent(FixParameters): Defaults to the equilibrium sheet current parameters. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f. + Both bounds must be broadcastable to Objective.dim_f. Default is to use target. weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional diff --git a/desc/objectives/objective_funs.py b/desc/objectives/objective_funs.py index 0a98bb0a28..a5b0553cd2 100644 --- a/desc/objectives/objective_funs.py +++ b/desc/objectives/objective_funs.py @@ -732,10 +732,10 @@ class _Objective(IOAble, ABC): Must be broadcastable to Objective.dim_f. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -747,7 +747,7 @@ class _Objective(IOAble, ABC): is called on the raw compute value, before any shifting, scaling, or normalization. deriv_mode : {"auto", "fwd", "rev"} - Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + Specify how to compute Jacobian matrix, either forward mode or reverse mode AD. "auto" selects forward or reverse mode based on the size of the input and output of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. diff --git a/desc/profiles.py b/desc/profiles.py index 359ffefb42..ec67897e28 100644 --- a/desc/profiles.py +++ b/desc/profiles.py @@ -197,13 +197,13 @@ def to_mtanh( **kwargs, ) - def __call__(self, grid, params=None, dr=0, dt=0, dz=0): + def __call__(self, grid, params=None, dr=0, dt=0, dz=0, jitable=False): """Evaluate the profile at a given set of points.""" if not isinstance(grid, _Grid): grid = jnp.atleast_1d(jnp.asarray(grid)) if grid.ndim == 1: grid = jnp.array([grid, jnp.zeros_like(grid), jnp.zeros_like(grid)]).T - grid = Grid(grid, sort=False) + grid = Grid(grid, sort=False, jitable=jitable) return self.compute(grid, params, dr, dt, dz) def __repr__(self): @@ -810,23 +810,28 @@ class SplineProfile(_Profile): - `'catmull-rom'`: C1 cubic centripetal "tension" splines name : str name of the profile + df : array-like + Optional. Values of the function derivative at knot locations. """ _io_attrs_ = _Profile._io_attrs_ + ["_knots", "_method"] - def __init__(self, values=None, knots=None, method="cubic2", name=""): + def __init__( + self, values=None, knots=None, method="cubic2", name="", df=None, jnp=np + ): super().__init__(name) if values is None: values = [0, 0, 0] - values = np.atleast_1d(values) + values = jnp.atleast_1d(values) if knots is None: - knots = np.linspace(0, 1, values.size) + knots = jnp.linspace(0, 1, values.size) else: - knots = np.atleast_1d(knots) + knots = jnp.atleast_1d(knots) self._knots = knots self._params = values + self._params_derivative = df self._method = method def __repr__(self): @@ -850,13 +855,14 @@ def params(self): def params(self, new): if len(new) == len(self._knots): self._params = jnp.asarray(new) + self._params_derivative = None else: raise ValueError( "params should have the same size as the knots, " + f"got {len(new)} values for {len(self._knots)} knots" ) - def compute(self, grid, params=None, dr=0, dt=0, dz=0): + def compute(self, grid, params=None, dr=0, dt=0, dz=0, params_derivative=None): """Compute values of profile at specified nodes. Parameters @@ -868,6 +874,9 @@ def compute(self, grid, params=None, dr=0, dt=0, dz=0): values given by the params attribute dr, dt, dz : int derivative order in rho, theta, zeta + params_derivative : array-like + spline derivative values to use. If not given, uses the + values given by the params_derivative attribute Returns ------- @@ -877,12 +886,17 @@ def compute(self, grid, params=None, dr=0, dt=0, dz=0): """ if params is None: params = self.params + if params_derivative: + params_derivative = self._params_derivative if dt != 0 or dz != 0: return jnp.zeros_like(grid.nodes[:, 0]) x = self.knots f = params + fx = {} + if params_derivative is not None: + fx["fx"] = params_derivative xq = grid.nodes[:, 0] - fq = interp1d(xq, x, f, method=self._method, derivative=dr, extrap=True) + fq = interp1d(xq, x, f, method=self._method, derivative=dr, extrap=True, **fx) return fq diff --git a/docs/adding_objectives.rst b/docs/adding_objectives.rst index 275d3eb2c7..f5bc863d57 100644 --- a/docs/adding_objectives.rst +++ b/docs/adding_objectives.rst @@ -54,10 +54,10 @@ A full example objective with comments describing key points is given below: Must be broadcastable to Objective.dim_f. bounds : tuple of {float, ndarray}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. - Must be broadcastable to to Objective.dim_f + Must be broadcastable to Objective.dim_f normalize : bool, optional Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional @@ -207,7 +207,7 @@ A full example objective with comments describing key points is given below: # and to make the objective value independent of grid resolution. return f -Adapting Existing Objectives with Different Loss Funtions +Adapting Existing Objectives with Different Loss Functions --------------------------------------------------------- If your desired objective is already implemented in DESC, but not in the correct form, a few different diff --git a/tests/test_axis_limits.py b/tests/test_axis_limits.py index bd367e3a1d..3ce4aa6975 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -17,6 +17,7 @@ from desc.examples import get from desc.grid import LinearGrid from desc.objectives import GenericObjective, ObjectiveFunction +from desc.objectives._neoclassical import EffectiveRipple # Unless mentioned in the source code of the compute function, the assumptions # made to compute the magnetic axis limit can be reduced to assuming that these @@ -386,3 +387,16 @@ def test_reverse_mode_ad_axis(name): obj.build(verbose=0) g = obj.grad(obj.x()) assert not np.any(np.isnan(g)) + + +@pytest.mark.unit +def test_reverse_mode_ad_ripple(): + """Asserts that the rho=0 axis limits are reverse mode differentiable.""" + eq = get("ESTELL") + rho = np.array([0, 0.5, 1]) + grid = LinearGrid(rho=rho, M=2, N=2, NFP=eq.NFP, sym=eq.sym) + eq.change_resolution(2, 2, 2, 4, 4, 4) + obj = ObjectiveFunction(EffectiveRipple(eq, grid=grid)) + obj.build(verbose=0) + g = obj.grad(obj.x()) + assert not np.any(np.isnan(g)) diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index 39107a7af5..211ece1c97 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -484,7 +484,7 @@ def denominator(B, pitch): pitch = 1 / get_extrema(**spline) num = bounce_integrate(numerator, data["g_zz"], pitch) - # Can reduce memory usage by specifying by not batching. + # Can reduce memory usage by not batching. den = bounce_integrate(denominator, [], pitch, batch=False) avg = num / den assert np.isfinite(avg).any() @@ -493,10 +493,10 @@ def denominator(B, pitch): avg = np.nansum(avg, axis=-1) # Group the data by field line. avg = avg.reshape(pitch.shape[0], rho.size, alpha.size) - # The bounce averages stored at index i, j + # The mean bounce average stored at index i, j i, j = 0, 0 print(avg[:, i, j]) - # are the bounce averages along the field line with nodes + # is the mean bounce average among wells along the field line with nodes # given in Clebsch-Type field-line coordinates ρ, α, ζ raz_grid = grid.source_grid nodes = raz_grid.nodes.reshape(rho.size, alpha.size, -1, 3) diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 1eeb741972..579c9ab607 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -42,6 +42,7 @@ def test_effective_ripple(): period=(np.inf, 2 * np.pi, np.inf), ) data = eq.compute("effective ripple", grid=grid) + assert np.isfinite(data["effective ripple"]).all() fig, ax = plt.subplots() ax.plot(rho, grid.compress(data["effective ripple"]), marker="o") return fig From bf981dd35ca3f8d589780410bc3e269275f21f78 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 13 Jun 2024 12:40:54 -0500 Subject: [PATCH 044/112] Add effective ripple magnetic axis limit --- desc/compute/_neoclassical.py | 11 ++++++++--- desc/grid.py | 21 ++++++++++++--------- tests/test_axis_limits.py | 14 -------------- tests/test_neoclassical.py | 22 +++++++++++++++++++--- 4 files changed, 39 insertions(+), 29 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 7621266cf6..73745c071c 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -241,16 +241,18 @@ def d_ripple(pitch): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - label="ε¹ᐧ⁵ = π/(8√2) (R₀(∂V/∂ψ)/S)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", + # 〈 ∇ψ 〉= S/(∂V/∂ψ) = 〈 ∇ρ 〉 ∂ψ/∂ρ + label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈 ∇ψ 〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", units="~", units_long="None", description="Effective ripple modulation amplitude", dim=1, params=[], - transforms={}, + transforms={"grid": []}, profiles=[], coordinates="r", data=["R0", "V_r(r)", "S(r)", "effective ripple raw"], + axis_limit_data=["V_rr(r)", "S_r(r)"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """https://doi.org/10.1063/1.873749. @@ -259,10 +261,13 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ + grad_rho_norm_fsa = transforms["grid"].replace_at_axis( + data["S(r)"] / data["V_r(r)"], lambda: data["S_r(r)"] / data["V_rr(r)"] + ) data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) - * (data["R0"] * data["V_r(r)"] / data["S(r)"]) ** 2 + * (data["R0"] / grad_rho_norm_fsa) ** 2 * data["effective ripple raw"] ) return data diff --git a/desc/grid.py b/desc/grid.py index ee77781d83..c637f65329 100644 --- a/desc/grid.py +++ b/desc/grid.py @@ -723,25 +723,28 @@ def __init__( self._weights = weights if sort: self._sort_nodes() + setable_attr = [ + "_unique_rho_idx", + "_unique_poloidal_idx", + "_unique_zeta_idx", + "_inverse_rho_idx", + "_inverse_poloidal_idx", + "_inverse_zeta_idx", + ] if jitable: # Don't do anything with symmetry since that changes # of nodes # avoid point at the axis, for now. r, t, z = self._nodes.T r = jnp.where(r == 0, 1e-12, r) - self._nodes = jnp.array([r, t, z]).T + self._nodes = jnp.column_stack([r, t, z]) self._axis = np.array([], dtype=int) # allow for user supplied indices/inverse indices for special cases - for attr in [ - "_unique_rho_idx", - "_unique_poloidal_idx", - "_unique_zeta_idx", - "_inverse_rho_idx", - "_inverse_poloidal_idx", - "_inverse_zeta_idx", - ]: + for attr in setable_attr: if attr in kwargs: setattr(self, attr, jnp.asarray(kwargs.pop(attr))) else: + for attr in setable_attr: + kwargs.pop(attr, None) self._axis = self._find_axis() ( self._unique_rho_idx, diff --git a/tests/test_axis_limits.py b/tests/test_axis_limits.py index 3ce4aa6975..bd367e3a1d 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -17,7 +17,6 @@ from desc.examples import get from desc.grid import LinearGrid from desc.objectives import GenericObjective, ObjectiveFunction -from desc.objectives._neoclassical import EffectiveRipple # Unless mentioned in the source code of the compute function, the assumptions # made to compute the magnetic axis limit can be reduced to assuming that these @@ -387,16 +386,3 @@ def test_reverse_mode_ad_axis(name): obj.build(verbose=0) g = obj.grad(obj.x()) assert not np.any(np.isnan(g)) - - -@pytest.mark.unit -def test_reverse_mode_ad_ripple(): - """Asserts that the rho=0 axis limits are reverse mode differentiable.""" - eq = get("ESTELL") - rho = np.array([0, 0.5, 1]) - grid = LinearGrid(rho=rho, M=2, N=2, NFP=eq.NFP, sym=eq.sym) - eq.change_resolution(2, 2, 2, 4, 4, 4) - obj = ObjectiveFunction(EffectiveRipple(eq, grid=grid)) - obj.build(verbose=0) - g = obj.grad(obj.x()) - assert not np.any(np.isnan(g)) diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 579c9ab607..99f06f2c2f 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -5,14 +5,17 @@ import pytest from tests.test_plotting import tol_1d -from desc import examples from desc.equilibrium.coords import rtz_grid +from desc.examples import get +from desc.grid import LinearGrid +from desc.objectives import ObjectiveFunction +from desc.objectives._neoclassical import EffectiveRipple @pytest.mark.unit def test_field_line_average(): """Test that field line average converges to surface average.""" - eq = examples.get("W7-X") + eq = get("W7-X") grid = rtz_grid( eq, np.array([0, 0.5]), @@ -31,7 +34,7 @@ def test_field_line_average(): @pytest.mark.mpl_image_compare(remove_text=True, tolerance=tol_1d) def test_effective_ripple(): """Test effective ripple with W7-X.""" - eq = examples.get("W7-X") + eq = get("W7-X") rho = np.linspace(0, 1, 10) grid = rtz_grid( eq, @@ -46,3 +49,16 @@ def test_effective_ripple(): fig, ax = plt.subplots() ax.plot(rho, grid.compress(data["effective ripple"]), marker="o") return fig + + +@pytest.mark.unit +def test_ad_ripple(): + """Make sure we can differentiate.""" + eq = get("ESTELL") + grid = LinearGrid(L=1, M=2, N=2, NFP=eq.NFP, sym=eq.sym, axis=False) + eq.change_resolution(2, 2, 2, 4, 4, 4) + + obj = ObjectiveFunction(EffectiveRipple(eq, grid=grid)) + obj.build(verbose=0) + g = obj.grad(obj.x()) + assert not np.any(np.isnan(g)) # FIXME From 3c2aabd072b972a1170fff3e460968a29ac91b07 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 13 Jun 2024 12:43:28 -0500 Subject: [PATCH 045/112] Fix effective ripple label --- desc/compute/_neoclassical.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 73745c071c..df0b04aafe 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -241,8 +241,8 @@ def d_ripple(pitch): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - # 〈 ∇ψ 〉= S/(∂V/∂ψ) = 〈 ∇ρ 〉 ∂ψ/∂ρ - label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈 ∇ψ 〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", + # 〈 |∇ψ| 〉= S/(∂V/∂ψ) = 〈 |∇ρ| 〉 ∂ψ/∂ρ + label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈 |∇ψ| 〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", units="~", units_long="None", description="Effective ripple modulation amplitude", From 4db2468854c27b735e1c5a517047fad6783fc756 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 13 Jun 2024 16:42:46 -0500 Subject: [PATCH 046/112] Fix doc build, and reduce first order dependency amount for effective ripple --- desc/compute/_metric.py | 27 ++++++++++++++++++++++++++- desc/compute/_neoclassical.py | 13 ++++--------- desc/objectives/_neoclassical.py | 9 +-------- docs/adding_objectives.rst | 2 +- 4 files changed, 32 insertions(+), 19 deletions(-) diff --git a/desc/compute/_metric.py b/desc/compute/_metric.py index 2eecb559cc..c033bd9196 100644 --- a/desc/compute/_metric.py +++ b/desc/compute/_metric.py @@ -14,7 +14,7 @@ from desc.backend import jnp from .data_index import register_compute_fun -from .utils import cross, dot, safediv, safenorm +from .utils import cross, dot, safediv, safenorm, surface_averages @register_compute_fun( @@ -1779,6 +1779,31 @@ def _gradrho(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="<|grad(rho)|>", # same as S(r) / V_r(r) + label="\\langle \\vert \\nabla \\rho \\vert \\rangle|", + units="m^{-1}", + units_long="inverse meters", + description="Magnitude of contravariant radial basis vector, flux surface average", + dim=1, + params=[], + transforms={"grid": []}, + profiles=[], + coordinates="r", + data=["|grad(rho)|", "sqrt(g)"], + axis_limit_data=["sqrt(g)_r"], +) +def _gradrho_norm_fsa(params, transforms, profiles, data, **kwargs): + data["<|grad(rho)|>"] = surface_averages( + transforms["grid"], + data["|grad(rho)|"], + transforms["grid"].replace_at_axis( + data["sqrt(g)"], lambda: data["sqrt(g)_r"], copy=True + ), + ) + return data + + @register_compute_fun( name="|grad(psi)|", label="|\\nabla\\psi|", diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index df0b04aafe..e240140902 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -241,18 +241,16 @@ def d_ripple(pitch): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - # 〈 |∇ψ| 〉= S/(∂V/∂ψ) = 〈 |∇ρ| 〉 ∂ψ/∂ρ - label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈 |∇ψ| 〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", + label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", units="~", units_long="None", description="Effective ripple modulation amplitude", dim=1, params=[], - transforms={"grid": []}, + transforms={}, profiles=[], coordinates="r", - data=["R0", "V_r(r)", "S(r)", "effective ripple raw"], - axis_limit_data=["V_rr(r)", "S_r(r)"], + data=["R0", "<|grad(rho)|>", "effective ripple raw"], ) def _effective_ripple(params, transforms, profiles, data, **kwargs): """https://doi.org/10.1063/1.873749. @@ -261,13 +259,10 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ - grad_rho_norm_fsa = transforms["grid"].replace_at_axis( - data["S(r)"] / data["V_r(r)"], lambda: data["S_r(r)"] / data["V_rr(r)"] - ) data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) - * (data["R0"] / grad_rho_norm_fsa) ** 2 + * (data["R0"] / data["<|grad(rho)|>"]) ** 2 * data["effective ripple raw"] ) return data diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 572e313ee9..1b2313ebc7 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -110,14 +110,7 @@ def __init__( # Assign here. self._alpha = alpha self._zeta = zeta - self._keys_1dr = [ - "iota", - "iota_r", - "S(r)", - "V_r(r)", - "min_tz |B|", - "max_tz |B|", - ] + self._keys_1dr = ["iota", "iota_r", "<|grad(rho)|>", "min_tz |B|", "max_tz |B|"] self._keys_0d = ["R0"] self._keys = ["effective ripple"] self._hyperparameters = { diff --git a/docs/adding_objectives.rst b/docs/adding_objectives.rst index f5bc863d57..cee2cfc0c8 100644 --- a/docs/adding_objectives.rst +++ b/docs/adding_objectives.rst @@ -208,7 +208,7 @@ A full example objective with comments describing key points is given below: return f Adapting Existing Objectives with Different Loss Functions ---------------------------------------------------------- +---------------------------------------------------------- If your desired objective is already implemented in DESC, but not in the correct form, a few different loss functions are available through the the `loss_function` kwarg when instantiating an Objective objective to From 7b5b7c08a31a78385f7dd6f0c40b43f9d1a4901f Mon Sep 17 00:00:00 2001 From: unalmis Date: Sat, 15 Jun 2024 14:24:16 -0500 Subject: [PATCH 047/112] Fix wrong alpha_t calculation and add grad(|B|)*b test --- desc/compute/_core.py | 2 +- desc/compute/_field.py | 2 +- desc/compute/_metric.py | 2 +- desc/compute/_neoclassical.py | 6 +++--- desc/compute/bounce_integral.py | 7 ++++--- desc/profiles.py | 2 +- tests/baseline/test_effective_ripple.png | Bin 13223 -> 14173 bytes tests/test_bounce_integral.py | 11 +++++------ tests/test_compute_funs.py | 14 +++++++++++++- 9 files changed, 29 insertions(+), 17 deletions(-) diff --git a/desc/compute/_core.py b/desc/compute/_core.py index a157d91412..6689e99334 100644 --- a/desc/compute/_core.py +++ b/desc/compute/_core.py @@ -1518,7 +1518,7 @@ def _alpha_r(params, transforms, profiles, data, **kwargs): data=["theta_PEST_t", "phi_t", "iota"], ) def _alpha_t(params, transforms, profiles, data, **kwargs): - data["alpha_t"] = data["theta_PEST_t"] + data["iota"] * data["phi_t"] + data["alpha_t"] = data["theta_PEST_t"] - data["iota"] * data["phi_t"] return data diff --git a/desc/compute/_field.py b/desc/compute/_field.py index 42b18ccf8d..c14ddeab0f 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -2328,7 +2328,7 @@ def _B_mag_z(params, transforms, profiles, data, **kwargs): ) def _B_mag_alpha(params, transforms, profiles, data, **kwargs): # constant ρ and ζ - data["|B|_alpha"] = safediv(data["|B|_t"], data["alpha_t"]) + data["|B|_alpha"] = data["|B|_t"] / data["alpha_t"] return data diff --git a/desc/compute/_metric.py b/desc/compute/_metric.py index c033bd9196..a93ebb3e6b 100644 --- a/desc/compute/_metric.py +++ b/desc/compute/_metric.py @@ -1797,7 +1797,7 @@ def _gradrho_norm_fsa(params, transforms, profiles, data, **kwargs): data["<|grad(rho)|>"] = surface_averages( transforms["grid"], data["|grad(rho)|"], - transforms["grid"].replace_at_axis( + sqrt_g=transforms["grid"].replace_at_axis( data["sqrt(g)"], lambda: data["sqrt(g)_r"], copy=True ), ) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index e240140902..e4ae831abe 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -67,7 +67,7 @@ def _get_pitch(grid, data, num, for_adaptive=False): min_B = grid.compress(data["min_tz |B|"]) max_B = grid.compress(data["max_tz |B|"]) if for_adaptive: - pitch = jnp.expand_dims(1 / jnp.stack([max_B, min_B], axis=-1), axis=1) + pitch = jnp.reciprocal(jnp.stack([max_B, min_B], axis=-1))[:, jnp.newaxis] assert pitch.shape == (grid.num_rho, 1, 2) else: pitch = get_pitch(min_B, max_B, num) @@ -108,7 +108,7 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) L_ra = simpson( - jnp.reshape(1 / data["B^zeta"], shape), + jnp.reciprocal(data["B^zeta"]).reshape(shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) @@ -135,7 +135,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) G_ra = simpson( - jnp.reshape(1 / (data["B^zeta"] * data["sqrt(g)"]), shape), + jnp.reciprocal(data["B^zeta"] * data["sqrt(g)"]).reshape(shape), jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 52b868ea94..a851eae460 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -354,7 +354,7 @@ def add(lines): add(ax.plot(z, B(z), label=r"$\vert B \vert (\zeta)$")) if pitch is not None: - b = 1 / jnp.atleast_1d(pitch) + b = jnp.reciprocal(pitch) for val in b: add( ax.axhline( @@ -527,7 +527,7 @@ def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=False, **kwargs P, S, N, degree = pitch.shape[0], B_c.shape[1], knots.size - 1, B_c.shape[0] - 1 intersect = _poly_root( c=B_c, - k=1 / pitch[..., jnp.newaxis], + k=jnp.reciprocal(pitch)[..., jnp.newaxis], a_min=jnp.array([0]), a_max=jnp.diff(knots), sort=True, @@ -624,7 +624,7 @@ def get_pitch(min_B, max_B, num, relative_shift=1e-6): # extrema. Shift values slightly to resolve this issue. min_B = (1 + relative_shift) * min_B max_B = (1 - relative_shift) * max_B - pitch = composite_linspace(1 / jnp.stack([max_B, min_B]), num) + pitch = composite_linspace(jnp.reciprocal(jnp.stack([max_B, min_B])), num) assert pitch.shape == (num + 2, *pitch.shape[1:]) return pitch @@ -949,6 +949,7 @@ def _interpolate_and_integrate( shape = Z.shape Z = Z.reshape(Z.shape[0], Z.shape[1], -1) f = [_interp1d_vec(Z, knots, f_i, method=method).reshape(shape) for f_i in f] + # TODO: Pass in derivative; add (∂e^ζ/∂_ζ) at constant ρ, α; use method_B. b_sup_z = _interp1d_vec(Z, knots, B_sup_z / B, method=method).reshape(shape) B = _interp1d_vec_with_df(Z, knots, B, B_z_ra, method=method_B).reshape(shape) pitch = jnp.expand_dims(pitch, axis=(2, 3) if len(shape) == 4 else 2) diff --git a/desc/profiles.py b/desc/profiles.py index ec67897e28..2606851133 100644 --- a/desc/profiles.py +++ b/desc/profiles.py @@ -886,7 +886,7 @@ def compute(self, grid, 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z9-hoZ6K%KV3^OnaCiKi5<`el6K11e2{{6Sd9sX?YiEv3x5B*o+ZF+ui2V6@y?@3_}=$(MOCT}ZQHGkfT2mhS~-iUD4|`)iaT_P;1(^okR3O)ZKtb918^CG+{3T+_vI2Z1C2K!yZVzpIh7~CHRr`f z$rez4*vbt*IZ`;0=!Z7fH9f2X|0d$tRp$TsD~bPie-N@vXLyO16jJ=`@fuhgrJ<_( Kd)`&6NB;|@AE5{U diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index 211ece1c97..1d846f43dc 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -33,7 +33,6 @@ take_mask, tanh_sinh, ) -from desc.compute.utils import safediv from desc.equilibrium import Equilibrium from desc.equilibrium.coords import rtz_grid from desc.examples import get @@ -426,7 +425,7 @@ def test_bounce_quadrature(): rtol = 1e-4 def integrand(B, pitch): - return 1 / jnp.sqrt(1 - pitch * m * B) + return jnp.reciprocal(jnp.sqrt(1 - pitch * m * B)) bp1 = -np.pi / 2 * v bp2 = -bp1 @@ -477,11 +476,12 @@ def test_bounce_integral_checks(): def numerator(g_zz, B, pitch): f = (1 - pitch * B) * g_zz - return safediv(f, jnp.sqrt(1 - pitch * B)) + return f / jnp.sqrt(1 - pitch * B) def denominator(B, pitch): - return safediv(1, jnp.sqrt(1 - pitch * B)) + return jnp.reciprocal(jnp.sqrt(1 - pitch * B)) + # Usually it's better to get values with get_pitch instead of get_extrema. pitch = 1 / get_extrema(**spline) num = bounce_integrate(numerator, data["g_zz"], pitch) # Can reduce memory usage by not batching. @@ -723,8 +723,7 @@ def integrand_num(cvdrift, gbdrift, B, pitch): return (cvdrift * g) - (0.5 * g * gbdrift) + (0.5 * gbdrift / g) def integrand_den(B, pitch): - g = jnp.sqrt(1 - pitch * B) - return 1 / g + return jnp.reciprocal(jnp.sqrt(1 - pitch * B)) drift_numerical_num = bounce_integrate( integrand=integrand_num, diff --git a/tests/test_compute_funs.py b/tests/test_compute_funs.py index 17949cf5fe..75496a236b 100644 --- a/tests/test_compute_funs.py +++ b/tests/test_compute_funs.py @@ -8,7 +8,8 @@ from scipy.signal import convolve2d from desc.coils import FourierPlanarCoil, FourierRZCoil, FourierXYZCoil, SplineXYZCoil -from desc.compute import data_index, rpz2xyz_vec +from desc.compute import data_index, get_data_deps, rpz2xyz_vec +from desc.compute.utils import dot from desc.equilibrium import Equilibrium from desc.examples import get from desc.geometry import ( @@ -1688,3 +1689,14 @@ def test_surface_equilibrium_geometry(): rtol=3e-13, atol=1e-13, ) + + +@pytest.mark.unit +def test_parallel_gradient(): + """Test different ways of computing this partial derivative agree.""" + eq = get("W7-X") + assert {"B_r", "iota_r"}.isdisjoint(get_data_deps("|B|_z|r,a", eq)) + data = eq.compute(["|B|_z|r,a", "grad(|B|)", "b"]) + # df = ∇f . dR + # ∂f/∂ζ (constant ρ and α) = ∇f . ∂R/∂ζ (constant ρ and α) + np.testing.assert_allclose(data["|B|_z|r,a"], dot(data["grad(|B|)"], data["b"])) From 2dbfeb0002827ca4b39cc63441438b4d199f7d78 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 17 Jun 2024 01:18:55 -0500 Subject: [PATCH 048/112] Add quantities in attempt to debug length derivative along field line test --- desc/compute/_basis_vectors.py | 232 ++++++++++++++++++++++++++++++++- desc/compute/_core.py | 111 ++++++++++++++++ desc/compute/_field.py | 51 +++++++- desc/compute/_metric.py | 41 ++++++ desc/compute/_neoclassical.py | 18 +-- tests/test_compute_funs.py | 37 +++++- tests/test_data_index.py | 15 ++- 7 files changed, 481 insertions(+), 24 deletions(-) diff --git a/desc/compute/_basis_vectors.py b/desc/compute/_basis_vectors.py index c404f468b2..cf1efae36c 100644 --- a/desc/compute/_basis_vectors.py +++ b/desc/compute/_basis_vectors.py @@ -13,7 +13,7 @@ from .data_index import register_compute_fun from .geom_utils import rpz2xyz_vec -from .utils import cross, safediv +from .utils import cross, dot, safediv @register_compute_fun( @@ -38,7 +38,7 @@ def _b(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="e^rho", + name="e^rho", # ∇ρ is the same in DESC and field line coordinates. label="\\mathbf{e}^{\\rho}", units="m^{-1}", units_long="inverse meters", @@ -1027,7 +1027,7 @@ def _e_sup_theta_zz(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="e^zeta", + name="e^zeta", # ∇ζ is the same in DESC and field line coordinates. label="\\mathbf{e}^{\\zeta}", units="m^{-1}", units_long="inverse meters", @@ -4978,3 +4978,229 @@ def _n_zeta(params, transforms, profiles, data, **kwargs): ).T, ) return data + + +@register_compute_fun( + name="e_rho|a,z", + label="\\mathbf{e}_{\\rho}_{\\alpha, \\zeta}", + units="m", + units_long="meters", + description="Tangent vector along radial field line label", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_rho", "e_alpha", "alpha_r"], +) +def _e_rho_az(params, transforms, profiles, data, **kwargs): + # constant α and ζ + data["e_rho|a,z"] = data["e_rho"] - (data["e_alpha"].T * data["alpha_r"]).T + return data + + +@register_compute_fun( + name="e_alpha", + label="\\mathbf{e}_{\\alpha}", + units="m", + units_long="meters", + description="Tangent vector along poloidal field line label", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_theta", "alpha_t"], +) +def _e_alpha(params, transforms, profiles, data, **kwargs): + # constant ρ and ζ + data["e_alpha"] = (data["e_theta"].T / data["alpha_t"]).T + return data + + +@register_compute_fun( + name="e_alpha_t", + label="\\partial_{\\theta} \\mathbf{e}_{\\alpha}", + units="m", + units_long="meters", + description="Tangent vector along poloidal field line label, derivative wrt" + " DESC poloidal angle", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_theta", "alpha_t", "e_theta_t", "alpha_tt"], +) +def _e_alpha_t(params, transforms, profiles, data, **kwargs): + data["e_alpha_t"] = ( + (data["e_theta_t"].T * data["alpha_t"] + data["e_theta"].T * data["alpha_tt"]) + / data["alpha_t"] ** 2 + ).T + return data + + +@register_compute_fun( + name="e_alpha_z", + label="\\partial_{\\zeta} \\mathbf{e}_{\\alpha}", + units="m", + units_long="meters", + description="Tangent vector along poloidal field line label, toroidal derivative", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_theta", "alpha_t", "e_theta_z", "alpha_tz"], +) +def _e_alpha_z(params, transforms, profiles, data, **kwargs): + data["e_alpha_z"] = ( + (data["e_theta_z"].T * data["alpha_t"] - data["e_theta"].T * data["alpha_tz"]) + / data["alpha_t"] ** 2 + ).T + return data + + +@register_compute_fun( + name="e_zeta|r,a", # Same as B/(B⋅∇ζ). + label="(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha} " + "= \\frac{\\mathbf{B}}{\\mathbf{B} \\cdot \\nabla \\zeta}", + units="m", + units_long="meters", + description="Tangent vector along (collinear to) field line", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_zeta", "e_alpha", "alpha_z"], +) +def _e_zeta_ra(params, transforms, profiles, data, **kwargs): + # ∂ℓ/∂ζ (constant ρ and α) = ∂ℓ/∂ζ (constant ρ and θ) + # - ∂ℓ/∂α (constant ρ and ζ) * ∂α/∂ζ (constant ρ and θ) + data["e_zeta|r,a"] = data["e_zeta"] - (data["e_alpha"].T * data["alpha_z"]).T + return data + + +@register_compute_fun( + name="(e_zeta|r,a)_t", + label="\\partial_{\\theta} [(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}]", + units="m", + units_long="meters", + description="Tangent vector along (collinear to) field line, poloidal derivative", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_zeta_t", "e_alpha", "alpha_z", "e_alpha_t", "alpha_tz"], +) +def _e_zeta_ra_t(params, transforms, profiles, data, **kwargs): + data["(e_zeta|r,a)_t"] = ( + data["e_zeta_t"] + - ( + data["e_alpha_t"].T * data["alpha_z"] + data["e_alpha"].T * data["alpha_tz"] + ).T + ) + return data + + +@register_compute_fun( + name="(e_zeta|r,a)_a", + label="\\partial_{\\alpha} [(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}]", + units="m", + units_long="meters", + description="Tangent vector along (collinear to) field line, derivative " + "wrt field line angle", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["(e_zeta|r,a)_t", "alpha_t"], +) +def _e_zeta_ra_a(params, transforms, profiles, data, **kwargs): + data["(e_zeta|r,a)_a"] = (data["(e_zeta|r,a)_t"].T / data["alpha_t"]).T + return data + + +@register_compute_fun( + name="(e_zeta|r,a)_z", + label="\\partial_{\\zeta} [(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}]", + units="m", + units_long="meters", + description="Tangent vector along (collinear to) field line, toroidal derivative", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_zeta_z", "e_alpha", "alpha_z", "e_alpha_z", "alpha_zz"], +) +def _e_zeta_ra_z(params, transforms, profiles, data, **kwargs): + data["(e_zeta|r,a)_z"] = ( + data["e_zeta_z"] + - ( + data["e_alpha_z"].T * data["alpha_z"] + data["e_alpha"].T * data["alpha_zz"] + ).T + ) + return data + + +@register_compute_fun( + name="(e_zeta_z)|r,a", + label="(\\partial_{\\zeta} \\mathbf{e}_{\\zeta})_{\\rho, \\alpha}", + units="m", + units_long="meters", + description="Curvature vector along field line", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["(e_zeta|r,a)_z", "(e_zeta|r,a)_a", "alpha_z"], +) +def _e_zeta_z_ra(params, transforms, profiles, data, **kwargs): + data["(e_zeta_z)|r,a"] = ( + data["(e_zeta|r,a)_z"] - (data["(e_zeta|r,a)_a"].T * data["alpha_z"]).T + ) + return data + + +@register_compute_fun( + name="|e_zeta|r,a|", # Often written as dℓ/dζ = |B/(B⋅∇ζ)|. + label="|(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}| " + "= \\frac{|\\mathbf{B}|}{|\\mathbf{B} \\cdot \\nabla \\zeta|}", + units="m", + units_long="meters", + description="Length along field line", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["e_zeta|r,a"], +) +def _d_ell_d_zeta(params, transforms, profiles, data, **kwargs): + data["|e_zeta|r,a|"] = jnp.linalg.norm(data["e_zeta|r,a"], axis=-1) + return data + + +@register_compute_fun( + name="(|e_zeta|_z)|r,a", + label="(\\partial_{\\zeta} |\\mathbf{e}_{\\zeta}|)_{\\rho, \\alpha}|", + units="m", + units_long="meters", + description="Length along field line, derivative along field line", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["|e_zeta|r,a|", "(e_zeta_z)|r,a", "e_zeta|r,a"], +) +def _d_ell_d_zeta_z(params, transforms, profiles, data, **kwargs): + data["(|e_zeta|_z)|r,a"] = ( + dot(data["(e_zeta_z)|r,a"], data["e_zeta|r,a"]) / data["|e_zeta|r,a|"] + ) + return data diff --git a/desc/compute/_core.py b/desc/compute/_core.py index 6689e99334..2947e244a2 100644 --- a/desc/compute/_core.py +++ b/desc/compute/_core.py @@ -1522,6 +1522,42 @@ def _alpha_t(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="alpha_tt", + label="\\partial_{\\theta \\theta} \\alpha", + units="~", + units_long="None", + description="Field line label, second derivative wrt poloidal coordinate", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["theta_PEST_tt", "phi_tt", "iota"], +) +def _alpha_tt(params, transforms, profiles, data, **kwargs): + data["alpha_tt"] = data["theta_PEST_tt"] - data["iota"] * data["phi_tt"] + return data + + +@register_compute_fun( + name="alpha_tz", + label="\\partial_{\\theta \\zeta} \\alpha", + units="~", + units_long="None", + description="Field line label, derivative wrt poloidal and toroidal coordinates", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["theta_PEST_tz", "phi_tz", "iota"], +) +def _alpha_tz(params, transforms, profiles, data, **kwargs): + data["alpha_tz"] = data["theta_PEST_tz"] - data["iota"] * data["phi_tz"] + return data + + @register_compute_fun( name="alpha_z", label="\\partial_\\zeta \\alpha", @@ -1540,6 +1576,24 @@ def _alpha_z(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="alpha_zz", + label="\\partial_{\\zeta \\zeta} \\alpha", + units="~", + units_long="None", + description="Field line label, second derivative wrt toroidal coordinate", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["theta_PEST_zz", "phi_zz", "iota"], +) +def _alpha_zz(params, transforms, profiles, data, **kwargs): + data["alpha_zz"] = data["theta_PEST_zz"] - data["iota"] * data["phi_zz"] + return data + + @register_compute_fun( name="lambda", label="\\lambda", @@ -2983,6 +3037,44 @@ def _theta_PEST_t(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="theta_PEST_tt", + label="\\partial_{\\theta \\theta} \\vartheta", + units="rad", + units_long="radians", + description="PEST straight field line poloidal angular coordinate, second " + "derivative wrt poloidal coordinate", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["lambda_tt"], +) +def _theta_PEST_tt(params, transforms, profiles, data, **kwargs): + data["theta_PEST_tt"] = data["lambda_tt"] + return data + + +@register_compute_fun( + name="theta_PEST_tz", + label="\\partial_{\\theta \\zeta} \\vartheta", + units="rad", + units_long="radians", + description="PEST straight field line poloidal angular coordinate, derivative wrt " + "poloidal and toroidal coordinates", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["lambda_tz"], +) +def _theta_PEST_tz(params, transforms, profiles, data, **kwargs): + data["theta_PEST_tz"] = data["lambda_tz"] + return data + + @register_compute_fun( name="theta_PEST_z", label="\\partial_{\\zeta} \\vartheta", @@ -3002,6 +3094,25 @@ def _theta_PEST_z(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="theta_PEST_zz", + label="\\partial_{\\zeta \\zeta} \\vartheta", + units="rad", + units_long="radians", + description="PEST straight field line poloidal angular coordinate, second " + "derivative wrt toroidal coordinate", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["lambda_zz"], +) +def _theta_PEST_zz(params, transforms, profiles, data, **kwargs): + data["theta_PEST_zz"] = data["lambda_zz"] + return data + + @register_compute_fun( name="theta_r", label="\\partial_{\\rho} \\theta", diff --git a/desc/compute/_field.py b/desc/compute/_field.py index c14ddeab0f..6791827487 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -501,6 +501,49 @@ def _B_sup_zeta_z(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="B^zeta_a", + label="\\partial_{\\alpha} B^{\\zeta}", + units="T \\cdot m^{-1}", + units_long="Tesla / meter", + description=( + "Contravariant toroidal component of magnetic field, derivative wrt field" + " line angle" + ), + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["B^zeta_t", "alpha_t"], +) +def _B_sup_zeta_a(params, transforms, profiles, data, **kwargs): + # constant ρ and ζ + data["B^zeta_a"] = data["B^zeta_t"] / data["alpha_t"] + return data + + +@register_compute_fun( + name="B^zeta_z|r,a", + label="(\\partial_{\\zeta} B^{\\zeta})_{\\rho, \\alpha}", + units="T \\cdot m^{-1}", + units_long="Tesla / meter", + description=( + "Contravariant toroidal component of magnetic field, derivative along field" + " line" + ), + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["B^zeta_z", "B^zeta_a", "alpha_z"], +) +def _B_sup_zeta_z_ra(params, transforms, profiles, data, **kwargs): + data["B^zeta_z|r,a"] = data["B^zeta_z"] - data["B^zeta_a"] * data["alpha_z"] + return data + + @register_compute_fun( name="B_z", label="\\partial_{\\zeta} \\mathbf{B}", @@ -2314,7 +2357,7 @@ def _B_mag_z(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="|B|_alpha", + name="|B|_a", label="\\partial_{\\alpha} |\\mathbf{B}|", units="T", units_long="Tesla", @@ -2328,7 +2371,7 @@ def _B_mag_z(params, transforms, profiles, data, **kwargs): ) def _B_mag_alpha(params, transforms, profiles, data, **kwargs): # constant ρ and ζ - data["|B|_alpha"] = data["|B|_t"] / data["alpha_t"] + data["|B|_a"] = data["|B|_t"] / data["alpha_t"] return data @@ -2343,12 +2386,12 @@ def _B_mag_alpha(params, transforms, profiles, data, **kwargs): transforms={}, profiles=[], coordinates="rtz", - data=["|B|_z", "|B|_alpha", "alpha_z"], + data=["|B|_z", "|B|_a", "alpha_z"], ) def _B_mag_z_constant_rho_alpha(params, transforms, profiles, data, **kwargs): # ∂|B|/∂ζ (constant ρ and α) = ∂|B|/∂ζ (constant ρ and θ) # - ∂|B|/∂α (constant ρ and ζ) * ∂α/∂ζ (constant ρ and θ) - data["|B|_z|r,a"] = data["|B|_z"] - data["|B|_alpha"] * data["alpha_z"] + data["|B|_z|r,a"] = data["|B|_z"] - data["|B|_a"] * data["alpha_z"] return data diff --git a/desc/compute/_metric.py b/desc/compute/_metric.py index a93ebb3e6b..14003840d4 100644 --- a/desc/compute/_metric.py +++ b/desc/compute/_metric.py @@ -55,6 +55,26 @@ def _sqrtg_pest(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="sqrt(g)_Clebsch", + label="\\sqrt{g}_{\\text{Clebsch}}", + units="m^{3}", + units_long="cubic meters", + description="Jacobian determinant of Clebsch field line coordinate system", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["sqrt(g)", "alpha_t"], +) +def _sqrtg_clebsch(params, transforms, profiles, data, **kwargs): + # Same as dot(data["e_rho|a,z"], cross(data["e_alpha"], data["e_zeta|r,a"])), but + # more efficient as it avoids computing radial derivative of alpha and hence iota. + data["sqrt(g)_Clebsch"] = data["sqrt(g)"] / data["alpha_t"] + return data + + @register_compute_fun( name="|e_theta x e_zeta|", label="|\\mathbf{e}_{\\theta} \\times \\mathbf{e}_{\\zeta}|", @@ -225,6 +245,27 @@ def _e_rho_x_e_theta(params, transforms, profiles, data, **kwargs): return data +@register_compute_fun( + name="|e_rho x e_alpha|", + label="|\\mathbf{e}_{\\rho} \\times \\mathbf{e}_{\\alpha}|", + units="m^{2}", + units_long="square meters", + description="2D Jacobian determinant for constant zeta surface in Clebsch " + "field line coordinates", + dim=1, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["|e_rho x e_theta|", "alpha_t"], +) +def _e_rho_x_e_alpha(params, transforms, profiles, data, **kwargs): + # Same as safenorm(cross(data["e_rho|a,z"], data["e_alpha"]), axis=-1), but more + # efficient as it avoids computing radial derivative of alpha and hence iota. + data["|e_rho x e_alpha|"] = data["|e_rho x e_theta|"] / jnp.abs(data["alpha_t"]) + return data + + @register_compute_fun( name="|e_rho x e_theta|_r", label="\\partial_{\\rho} |\\mathbf{e}_{\\rho} \\times \\mathbf{e}_{\\theta}|", diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index e4ae831abe..6422e919db 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -44,15 +44,15 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return vec_quad -def _get_pitch(grid, data, num, for_adaptive=False): +def _get_pitch(grid, min_B, max_B, num, for_adaptive=False): """Get points for quadrature over velocity coordinate. Parameters ---------- grid : Grid The grid on which data is computed. - data : dict - Dictionary containing min and max |B| over each flux surface. + min_B, max_B : jnp.ndarray, jnp.ndarray + Minimum and maximum |B| values. num : int Number of values to uniformly space in between. for_adaptive : bool @@ -64,8 +64,8 @@ def _get_pitch(grid, data, num, for_adaptive=False): Pitch values in the desired shape to use in compute methods. """ - min_B = grid.compress(data["min_tz |B|"]) - max_B = grid.compress(data["max_tz |B|"]) + min_B = grid.compress(min_B) + max_B = grid.compress(max_B) if for_adaptive: pitch = jnp.reciprocal(jnp.stack([max_B, min_B], axis=-1))[:, jnp.newaxis] assert pitch.shape == (grid.num_rho, 1, 2) @@ -95,7 +95,7 @@ def _poloidal_mean(grid, f): " \\frac{d\\zeta}{B^{\\zeta}}", units="m / T", units_long="Meter / tesla", - description="(Mean) length along field line(s)", + description="(Mean) proper length of field line(s)", dim=1, params=[], transforms={"grid": []}, @@ -122,7 +122,7 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): " \\frac{d\\zeta}{B^{\\zeta} \\sqrt g}", units="1 / Wb", units_long="Inverse webers", - description="(Mean) length over volume along field line(s)", + description="(Mean) proper length over volume of field line(s)", dim=1, params=[], transforms={"grid": []}, @@ -229,7 +229,9 @@ def d_ripple(pitch): # The integrand is continuous and likely poorly approximated by a polynomial. # Composite quadrature should perform better than higher order methods. - pitch = _get_pitch(g, data, kwargs.get("num_pitch", 125)) + pitch = _get_pitch( + g, data["min_tz |B|"], data["max_tz |B|"], kwargs.get("num_pitch", 125) + ) ripple = simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) data["effective ripple raw"] = ( g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) diff --git a/tests/test_compute_funs.py b/tests/test_compute_funs.py index 75496a236b..79a77e519d 100644 --- a/tests/test_compute_funs.py +++ b/tests/test_compute_funs.py @@ -8,7 +8,7 @@ from scipy.signal import convolve2d from desc.coils import FourierPlanarCoil, FourierRZCoil, FourierXYZCoil, SplineXYZCoil -from desc.compute import data_index, get_data_deps, rpz2xyz_vec +from desc.compute import data_index, rpz2xyz_vec from desc.compute.utils import dot from desc.equilibrium import Equilibrium from desc.examples import get @@ -1693,10 +1693,33 @@ def test_surface_equilibrium_geometry(): @pytest.mark.unit def test_parallel_gradient(): - """Test different ways of computing this partial derivative agree.""" + """Test geometric and physical methods of computing parallel gradients agree.""" eq = get("W7-X") - assert {"B_r", "iota_r"}.isdisjoint(get_data_deps("|B|_z|r,a", eq)) - data = eq.compute(["|B|_z|r,a", "grad(|B|)", "b"]) - # df = ∇f . dR - # ∂f/∂ζ (constant ρ and α) = ∇f . ∂R/∂ζ (constant ρ and α) - np.testing.assert_allclose(data["|B|_z|r,a"], dot(data["grad(|B|)"], data["b"])) + eq.change_resolution(2, 2, 2, 4, 4, 4) + data = eq.compute( + [ + "e_zeta|r,a", + "B", + "B^zeta", + "|B|_z|r,a", + "grad(|B|)", + "(|e_zeta|_z)|r,a", + "B^zeta_z|r,a", + "|B|", + ] + ) + np.testing.assert_allclose(data["e_zeta|r,a"], (data["B"].T / data["B^zeta"]).T) + np.testing.assert_allclose( + # df = ∇f⋅dℓ ⟹ ∂f/∂ζ (constant ρ and α) = ∇f⋅∂ℓ/∂ζ (constant ρ and α) + data["|B|_z|r,a"], + dot(data["grad(|B|)"], data["e_zeta|r,a"]), + ) + # FIXME: Not sure why, but below test fails. + np.testing.assert_allclose( + data["(|e_zeta|_z)|r,a"], + data["|B|_z|r,a"] / np.abs(data["B^zeta"]) + - data["|B|"] + * data["B^zeta_z|r,a"] + * np.sign(data["B^zeta"]) + / data["B^zeta"] ** 2, + ) diff --git a/tests/test_data_index.py b/tests/test_data_index.py index e3fd65a495..33ecaaafb7 100644 --- a/tests/test_data_index.py +++ b/tests/test_data_index.py @@ -8,6 +8,7 @@ import desc.compute from desc.compute import data_index from desc.compute.data_index import _class_inheritance +from desc.utils import errorif class TestDataIndex: @@ -39,6 +40,11 @@ def get_parameterization(fun, default="desc.equilibrium.equilibrium.Equilibrium" matches.discard("") return matches if matches else {default} + @staticmethod + def _is_function(func): + # JITed functions are not functions according to inspect. + return inspect.isfunction(func) or callable(func) + @pytest.mark.unit def test_data_index_deps(self): """Ensure developers do not add extra (or forget needed) dependencies. @@ -74,7 +80,7 @@ def test_data_index_deps(self): pattern_params = re.compile(r"params\[(.*?)]") for module_name, module in inspect.getmembers(desc.compute, inspect.ismodule): if module_name[0] == "_": - for _, fun in inspect.getmembers(module, inspect.isfunction): + for _, fun in inspect.getmembers(module, self._is_function): # quantities that this function computes names = self.get_matches(fun, pattern_names) # dependencies queried in source code of this function @@ -97,7 +103,6 @@ def test_data_index_deps(self): for p in data_index: for name, val in data_index[p].items(): - print(name) err_msg = f"Parameterization: {p}. Name: {name}." deps = val["dependencies"] data = set(deps["data"]) @@ -111,6 +116,12 @@ def test_data_index_deps(self): assert len(profiles) == len(deps["profiles"]), err_msg assert len(params) == len(deps["params"]), err_msg # assert correct dependencies are queried + errorif( + name not in queried_deps[p], + AssertionError, + "Did you reuse the function name (i.e. def_...) for" + f" '{name}' for some other quantity?", + ) assert queried_deps[p][name]["data"] == data | axis_limit_data, err_msg assert queried_deps[p][name]["profiles"] == profiles, err_msg assert queried_deps[p][name]["params"] == params, err_msg From 2d01ef4ec15abc1448335f2656baf31c73d9d108 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 17 Jun 2024 12:45:55 -0500 Subject: [PATCH 049/112] Add finite difference test or parallel gradient --- tests/test_compute_funs.py | 68 ++++++++++++++++++++++++++++++++------ 1 file changed, 57 insertions(+), 11 deletions(-) diff --git a/tests/test_compute_funs.py b/tests/test_compute_funs.py index 79a77e519d..76a0819f4b 100644 --- a/tests/test_compute_funs.py +++ b/tests/test_compute_funs.py @@ -11,6 +11,7 @@ from desc.compute import data_index, rpz2xyz_vec from desc.compute.utils import dot from desc.equilibrium import Equilibrium +from desc.equilibrium.coords import rtz_grid from desc.examples import get from desc.geometry import ( FourierPlanarCurve, @@ -1051,8 +1052,8 @@ def test_magnetic_pressure_gradient(DummyStellarator): num_rho = 110 grid = LinearGrid(NFP=eq.NFP, rho=num_rho) drho = grid.nodes[1, 0] - data = eq.compute(["|B|", "grad(|B|^2)_rho"], grid=grid) - B2_r = np.convolve(data["|B|"] ** 2, FD_COEF_1_4, "same") / drho + data = eq.compute(["|B|^2", "grad(|B|^2)_rho"], grid=grid) + B2_r = np.convolve(data["|B|^2"], FD_COEF_1_4, "same") / drho np.testing.assert_allclose( data["grad(|B|^2)_rho"][3:-2], B2_r[3:-2], @@ -1064,8 +1065,8 @@ def test_magnetic_pressure_gradient(DummyStellarator): num_theta = 90 grid = LinearGrid(NFP=eq.NFP, theta=num_theta) dtheta = grid.nodes[1, 1] - data = eq.compute(["|B|", "grad(|B|^2)_theta"], grid=grid) - B2_t = np.convolve(data["|B|"] ** 2, FD_COEF_1_4, "same") / dtheta + data = eq.compute(["|B|^2", "grad(|B|^2)_theta"], grid=grid) + B2_t = np.convolve(data["|B|^2"], FD_COEF_1_4, "same") / dtheta np.testing.assert_allclose( data["grad(|B|^2)_theta"][2:-2], B2_t[2:-2], @@ -1077,8 +1078,8 @@ def test_magnetic_pressure_gradient(DummyStellarator): num_zeta = 90 grid = LinearGrid(NFP=eq.NFP, zeta=num_zeta) dzeta = grid.nodes[1, 2] - data = eq.compute(["|B|", "grad(|B|^2)_zeta"], grid=grid) - B2_z = np.convolve(data["|B|"] ** 2, FD_COEF_1_4, "same") / dzeta + data = eq.compute(["|B|^2", "grad(|B|^2)_zeta"], grid=grid) + B2_z = np.convolve(data["|B|^2"], FD_COEF_1_4, "same") / dzeta np.testing.assert_allclose( data["grad(|B|^2)_zeta"][2:-2], B2_z[2:-2], @@ -1692,7 +1693,7 @@ def test_surface_equilibrium_geometry(): @pytest.mark.unit -def test_parallel_gradient(): +def test_parallel_grad(): """Test geometric and physical methods of computing parallel gradients agree.""" eq = get("W7-X") eq.change_resolution(2, 2, 2, 4, 4, 4) @@ -1710,11 +1711,9 @@ def test_parallel_gradient(): ) np.testing.assert_allclose(data["e_zeta|r,a"], (data["B"].T / data["B^zeta"]).T) np.testing.assert_allclose( - # df = ∇f⋅dℓ ⟹ ∂f/∂ζ (constant ρ and α) = ∇f⋅∂ℓ/∂ζ (constant ρ and α) - data["|B|_z|r,a"], - dot(data["grad(|B|)"], data["e_zeta|r,a"]), + data["|B|_z|r,a"], dot(data["grad(|B|)"], data["e_zeta|r,a"]) ) - # FIXME: Not sure why, but below test fails. + # FIXME: Don't know why below test fails. np.testing.assert_allclose( data["(|e_zeta|_z)|r,a"], data["|B|_z|r,a"] / np.abs(data["B^zeta"]) @@ -1723,3 +1722,50 @@ def test_parallel_gradient(): * np.sign(data["B^zeta"]) / data["B^zeta"] ** 2, ) + + +@pytest.mark.unit +def test_parallel_grad_fd(DummyStellarator): + """Test that the parallel gradients match with numerical gradients.""" + eq = load(load_from=str(DummyStellarator["output_path"]), file_format="hdf5") + grid = rtz_grid( + eq, + 0.5, + 0, + np.linspace(0, 2 * np.pi, 50), + coordinates="raz", + period=(np.inf, 2 * np.pi, np.inf), + ) + data = eq.compute( + [ + "|B|", + "|B|_z|r,a", + "|e_zeta|r,a|", + "(|e_zeta|_z)|r,a", + "B^zeta", + "B^zeta_z|r,a", + ], + grid=grid, + ) + dz = grid.source_grid.spacing[:, 2] + fd = np.convolve(data["|B|"], FD_COEF_1_4, "same") / dz + np.testing.assert_allclose( + data["|B|_z|r,a"][2:-2], + fd[2:-2], + rtol=1e-2, + atol=1e-2 * np.mean(np.abs(data["|B|_z|r,a"])), + ) + fd = np.convolve(data["|e_zeta|r,a|"], FD_COEF_1_4, "same") / dz + np.testing.assert_allclose( + data["(|e_zeta|_z)|r,a"][2:-2], + fd[2:-2], + rtol=1e-2, + atol=1e-2 * np.mean(np.abs(data["(|e_zeta|_z)|r,a"])), + ) + fd = np.convolve(data["B^zeta"], FD_COEF_1_4, "same") / dz + np.testing.assert_allclose( + data["B^zeta_z|r,a"][2:-2], + fd[2:-2], + rtol=1e-2, + atol=1e-2 * np.mean(np.abs(data["B^zeta_z|r,a"])), + ) From 12b39c964bb9565c92c3c3ac793514efcfeea686 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 17 Jun 2024 21:27:59 -0500 Subject: [PATCH 050/112] Fix bug with sign of derivative in effective ripple... MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Interpax's strictly increasing knots requirement enforces dζ > 0 in an bounce integral, which constraints the signs of B^ζ and ∂/∂ζ. The signs of B^ζ and (∂|B|/∂ζ)|ρ,a are now automatically corrected to match this requirement. This fixes a bug where interpax's spline was fed the wrong sign for the derivative --- desc/compute/_neoclassical.py | 8 ++++---- desc/compute/bounce_integral.py | 11 +++++------ tests/baseline/test_effective_ripple.png | Bin 14173 -> 14276 bytes tests/test_neoclassical.py | 2 ++ 4 files changed, 11 insertions(+), 10 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 6422e919db..a035ff37f1 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -92,7 +92,7 @@ def _poloidal_mean(grid, f): @register_compute_fun( name="", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" - " \\frac{d\\zeta}{B^{\\zeta}}", + " \\frac{d\\zeta}{|B^{\\zeta}|}", units="m / T", units_long="Meter / tesla", description="(Mean) proper length of field line(s)", @@ -112,14 +112,14 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) - data[""] = g.expand(_poloidal_mean(g, L_ra)) + data[""] = g.expand(jnp.abs(_poloidal_mean(g, L_ra))) return data @register_compute_fun( name="", label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" - " \\frac{d\\zeta}{B^{\\zeta} \\sqrt g}", + " \\frac{d\\zeta}{|B^{\\zeta} \\sqrt g|}", units="1 / Wb", units_long="Inverse webers", description="(Mean) proper length over volume of field line(s)", @@ -139,7 +139,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) - data[""] = g.expand(_poloidal_mean(g, G_ra)) + data[""] = g.expand(jnp.abs(_poloidal_mean(g, G_ra))) return data diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 2e129cc5f6..8c2dd5dd75 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1178,18 +1178,17 @@ def bounce_integral( Notes ----- - The strictly increasing knots requirement enforces dζ > 0, which constraints the - signs of B^ζ and ∂/∂ζ. The signs of B^ζ and (∂|B|/∂ζ)|ρ,α will automatically be - corrected to match this requirement, but this correction cannot be automated for - arbitrary f(ℓ) in the integrand. Pass in ``check=True`` to be notified if the signs - for B^ζ and (∂|B|/∂ζ)|ρ,α required correction. - The quantities ``B_sup_z``, ``B``, ``B_z_ra``, and those in ``f`` supplied to the returned method must be separable into data evaluated along particular field lines via ``.reshape(S,knots.size)``. One way to satisfy this is to compute stuff on the grid returned from the method ``desc.equilibrium.coords.rtz_grid``. See ``tests.test_bounce_integral.test_bounce_integral_checks`` for example use. + The strictly increasing knots requirement enforces dζ > 0, which constraints the + signs of B^ζ and ∂/∂ζ. The signs of B^ζ and (∂|B|/∂ζ)|ρ,α will automatically be + corrected to match this requirement. Pass in ``check=True`` to be notified if the + signs for B^ζ and (∂|B|/∂ζ)|ρ,α required correction. + Parameters ---------- B_sup_z : jnp.ndarray diff --git a/tests/baseline/test_effective_ripple.png b/tests/baseline/test_effective_ripple.png index 8cdaea34eb64893ae70589b019d736f0d2b0175d..71c4a8446b079be4be8a1f827592b1baeb764aa8 100644 GIT binary patch literal 14276 zcmeHuXH-+!+iwuXE-C^l3XC*CiUNWt0c;3TB1&&TK#DZ!y*Mh4R81751r!7YrS}#r z0VC3-w-G|`p$15D_dd?df9_rPz3bj@?}zJJqm#4G-uvm#?|Jq)NE))v2 zS6%JOO%#fiABEatxPuKmiTC>b9{5MWGyq^M(XrLw=jUViuH z`U4%)b(L+Lh2IXQ)-LCZy>7W_yv^i^p-Ga=ll^~4Y`K_qFlNWerwIG9$7BWE{oFUBL2LRwAx2}(_trjlm&%4dC{Pq6_A#6IRyOu<^VqmrDDHz2MTrL zD9aWU>bdIwfBgR@lOyKKD95EgAF$i}{PZa9^zBQk5r=h_&!JF?B&%q2%t(Fg7zK}M z?8>vs;>{4+`BXUn-p@~ouC+(>qFGQia;Cl`FBIrYZCSjja8Ua8r-y_--m1pYojZNEHBCKE(btXC zb|dtRRjp6Xg+Ep+3Vetc!dEL64JC<-j+eHhP}c=iP^ggB9s5zJ6xl6X#bg~i-e+D* zxiT=fRXcL{I5A3W(SX$uiMQ={CuW0W)l(;RdP3oo4=>MH z8JCxH>7!M9_w%Drxt!eC0B2#Xa>b&al413)*&N72G#YJGxi+adrXX+q<&P3ww01o* 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assert np.all(np.sign(data[""]) > 0) @pytest.mark.unit From 2e730bd6c94ec9a1cb24019c3c5490ba53a1c0a1 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 18 Jun 2024 00:08:10 -0500 Subject: [PATCH 051/112] Partially undo previous commit -- The sign of B_z_ra was fine before. It was B_sup_z that needed changing. --- desc/backend.py | 34 -------- desc/compute/_neoclassical.py | 6 +- desc/compute/bounce_integral.py | 97 +++++++++-------------- desc/objectives/__init__.py | 1 + tests/baseline/test_effective_ripple.png | Bin 14276 -> 14173 bytes tests/test_bounce_integral.py | 55 ++++++++----- tests/test_neoclassical.py | 31 ++++++-- 7 files changed, 99 insertions(+), 125 deletions(-) diff --git a/desc/backend.py b/desc/backend.py index 800c2fb2a2..e5cbb04179 100644 --- a/desc/backend.py +++ b/desc/backend.py @@ -116,38 +116,6 @@ def put(arr, inds, vals): return arr return jnp.asarray(arr).at[inds].set(vals) - def put_along_axis(arr, indices, values, axis): - """Put values into the destination array by matching 1d index and data slices. - - This iterates over matching 1d slices oriented along the specified axis in - the index and data arrays, and uses the former to place values into the - latter. - - Parameters - ---------- - arr : ndarray (Ni..., M, Nk...) - Destination array. - indices : ndarray (Ni..., J, Nk...) - Indices to change along each 1d slice of `arr`. This must match the - dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast - against `arr`. - values : array_like (Ni..., J, Nk...) - values to insert at those indices. Its shape and dimension are - broadcast to match that of `indices`. - axis : int - The axis to take 1d slices along. If axis is None, the destination - array is treated as if a flattened 1d view had been created of it. - - """ - if not (axis == -1 or axis == arr.ndim - 1): - raise NotImplementedError( - f"put_along_axis for axis={axis} not implemented yet." - ) - if isinstance(arr, np.ndarray): - arr[..., indices] = values - return arr - return jnp.asarray(arr).at[..., indices].set(values) - def sign(x): """Sign function, but returns 1 for x==0. @@ -429,8 +397,6 @@ def trapezoid(y, x=None, dx=1.0, axis=-1): ) from scipy.special import gammaln, logsumexp # noqa: F401 - put_along_axis = np.put_along_axis - def imap(f, xs, in_axes=0, out_axes=0): """Generalizes jax.lax.map; uses numpy.""" if not isinstance(xs, np.ndarray): diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index a035ff37f1..f2b57f5b6d 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -51,8 +51,10 @@ def _get_pitch(grid, min_B, max_B, num, for_adaptive=False): ---------- grid : Grid The grid on which data is computed. - min_B, max_B : jnp.ndarray, jnp.ndarray - Minimum and maximum |B| values. + min_B : jnp.ndarray + Minimum |B| value. + max_B : jnp.ndarray + Maximum |B| value. num : int Number of values to uniformly space in between. for_adaptive : bool diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 8c2dd5dd75..f4c0995330 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -6,9 +6,9 @@ from matplotlib import pyplot as plt from orthax.legendre import leggauss -from desc.backend import flatnonzero, imap, jnp, put_along_axis, take +from desc.backend import flatnonzero, imap, jnp, put, take from desc.compute.utils import safediv -from desc.utils import errorif, warnif +from desc.utils import Index, errorif, warnif @partial(jnp.vectorize, signature="(m),(m)->(n)", excluded={2, 3}) @@ -457,26 +457,34 @@ def _check_shape(knots, B_c, B_z_ra_c, pitch=None): λ values to evaluate the bounce integral at each field line. """ - errorif(knots.ndim != 1) + errorif(knots.ndim != 1, msg=f"knots should be 1d; got shape {knots.shape}.") if B_c.ndim == 2 and B_z_ra_c.ndim == 2: # Add axis which enumerates field lines. B_c = B_c[:, jnp.newaxis] B_z_ra_c = B_z_ra_c[:, jnp.newaxis] - msg = "Supplied invalid shape for splines." + msg = ( + "Invalid shape for spline arrays. " + f"B_c.shape={B_c.shape}. B_z_ra_c.shape={B_z_ra_c.shape}." + ) errorif(not (B_c.ndim == B_z_ra_c.ndim == 3), msg=msg) errorif(B_c.shape[0] - 1 != B_z_ra_c.shape[0], msg=msg) errorif(B_c.shape[1:] != B_z_ra_c.shape[1:], msg=msg) - msg = "Last axis fails to enumerate spline polynomials." - errorif(B_c.shape[-1] != knots.size - 1, msg=msg) + errorif( + B_c.shape[-1] != knots.size - 1, + msg=( + "Last axis does not enumerate polynomials of spline. " + f"B_c.shape={B_c.shape}. knots.shape={knots.shape}." + ), + ) if pitch is not None: pitch = jnp.atleast_2d(pitch) - msg = "Supplied invalid shape for pitch angles." + msg = f"Invalid shape {pitch.shape} for pitch angles." errorif(pitch.ndim != 2, msg=msg) errorif(pitch.shape[-1] != 1 and pitch.shape[-1] != B_c.shape[1], msg=msg) return B_c, B_z_ra_c, pitch -def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=False, **kwargs): +def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True, **kwargs): """Compute the bounce points given spline of |B| and pitch λ. Parameters @@ -561,7 +569,7 @@ def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=False, **kwargs # At each step, the likelihood that an intersection has already been lost # due to floating point errors grows, so the real solution is to pick a less # degenerate pitch value - one that does not ride the global extrema of |B|. - is_bp2 = put_along_axis(is_bp2, jnp.array(0), edge_case, axis=-1) + is_bp2 = put(is_bp2, Index[..., 0], edge_case) # Get ζ values of bounce points from the masks. bp1 = take_mask(intersect, is_bp1) bp2 = take_mask(intersect, is_bp2) @@ -606,8 +614,10 @@ def get_pitch(min_B, max_B, num, relative_shift=1e-6): Parameters ---------- - min_B, max_B : jnp.ndarray, jnp.ndarray - Minimum and maximum |B| values. + min_B : jnp.ndarray + Minimum |B| value. + max_B : jnp.ndarray + Maximum |B| value. num : int Number of values, not including endpoints. relative_shift : float @@ -1106,48 +1116,6 @@ def loop(bp): return result -def _fix_sign_and_normalize(B_sup_z, B, B_z_ra, B_ref=1, L_ref=1, check=False): - """Correct signs for consistency with strictly increasing zeta requirement. - - Parameters - ---------- - B_sup_z : jnp.ndarray - Contravariant field-line following toroidal component of magnetic field. - B : jnp.ndarray - Norm of magnetic field. - B_z_ra : jnp.ndarray - Norm of magnetic field, derivative with respect to field-line following - coordinate. - B_ref : float - Optional. Reference magnetic field strength for normalization. - L_ref : float - Optional. Reference length scale for normalization. - check : bool - Flag for debugging. Must be false for jax transformations. - - Returns - ------- - B_sup_z, B, B_z_ra : (jnp.ndarray, jnp.ndarray, jnp.ndarray) - Same as inputs but with corrected sign and normalized by length scales. - - """ - warnif( - check and jnp.any(jnp.sign(B_sup_z) <= 0), - msg="(∂ℓ/∂ζ)|ρ,a > 0 is required. Correcting signs of B^ζ and (∂|B|/∂ζ)|ρ,α.", - ) - # Strictly increasing zeta knots enforces dζ > 0. - # To retain dℓ = (|B|/B^ζ) dζ > 0 after fixing dζ > 0, we require B^ζ = B⋅∇ζ > 0. - # This is equivalent to changing the sign of ∇ζ (or [∂/∂ζ]|ρ,a). - # Recall dζ = ∇ζ⋅dR, implying 1 = ∇ζ⋅(e_ζ|ρ,a). Hence, a sign change in ∇ζ - # induces the same sign change in e_ζ|ρ,a to retain the metric identity. For any - # quantity f, we may write df = ∇f⋅dR, implying ∂f/∂ζ|ρ,α = ∇f ⋅ e_ζ|ρ,a. Therefore, - # a sign change in e_ζ|ρ,a induces the same sign change in ∂f/∂ζ|ρ,α. - B_z_ra = B_z_ra / B_ref * jnp.sign(B_sup_z) - B_sup_z = jnp.abs(B_sup_z) * L_ref / B_ref - B = B / B_ref - return B_sup_z, B, B_z_ra - - def bounce_integral( B_sup_z, B, @@ -1184,11 +1152,6 @@ def bounce_integral( grid returned from the method ``desc.equilibrium.coords.rtz_grid``. See ``tests.test_bounce_integral.test_bounce_integral_checks`` for example use. - The strictly increasing knots requirement enforces dζ > 0, which constraints the - signs of B^ζ and ∂/∂ζ. The signs of B^ζ and (∂|B|/∂ζ)|ρ,α will automatically be - corrected to match this requirement. Pass in ``check=True`` to be notified if the - signs for B^ζ and (∂|B|/∂ζ)|ρ,α required correction. - Parameters ---------- B_sup_z : jnp.ndarray @@ -1254,10 +1217,22 @@ def bounce_integral( polynomials that compose the spline along a particular field line. """ - B_sup_z, B, B_z_ra = ( - f.reshape(-1, knots.size) # group data by field line - for f in _fix_sign_and_normalize(B_sup_z, B, B_z_ra, B_ref, L_ref, check) + warnif( + check and kwargs.pop("warn", True) and jnp.any(jnp.sign(B_sup_z) <= 0), + msg="(∂ℓ/∂ζ)|ρ,a > 0 is required. Enforcing positive B^ζ.", ) + # Strictly increasing zeta knots enforces dζ > 0. + # To retain dℓ = (|B|/B^ζ) dζ > 0 after fixing dζ > 0, we require B^ζ = B⋅∇ζ > 0. + # This is equivalent to changing the sign of ∇ζ (or [∂/∂ζ]|ρ,a). + # Recall dζ = ∇ζ⋅dR, implying 1 = ∇ζ⋅(e_ζ|ρ,a). Hence, a sign change in ∇ζ + # requires the same sign change in e_ζ|ρ,a to retain the metric identity. For any + # quantity f, we may write df = ∇f⋅dR, implying ∂f/∂ζ|ρ,α = ∇f ⋅ e_ζ|ρ,a. Hence, + # a sign change in e_ζ|ρ,a requires the same sign change in ∂f/∂ζ|ρ,α. + B_sup_z = jnp.abs(B_sup_z) * L_ref / B_ref + B = B / B_ref + B_z_ra = B_z_ra / B_ref # This is already the correct sign. + # group data by field line + B_sup_z, B, B_z_ra = (f.reshape(-1, knots.size) for f in [B_sup_z, B, B_z_ra]) # Compute splines. monotonic = kwargs.pop("monotonic", False) diff --git a/desc/objectives/__init__.py b/desc/objectives/__init__.py index 29d875b7ed..bf830f3890 100644 --- a/desc/objectives/__init__.py +++ b/desc/objectives/__init__.py @@ -29,6 +29,7 @@ PrincipalCurvature, Volume, ) +from ._neoclassical import EffectiveRipple from ._omnigenity import ( Isodynamicity, Omnigenity, diff --git a/tests/baseline/test_effective_ripple.png b/tests/baseline/test_effective_ripple.png index 71c4a8446b079be4be8a1f827592b1baeb764aa8..8cdaea34eb64893ae70589b019d736f0d2b0175d 100644 GIT binary patch literal 14173 zcmeHuXH-;K*JdFt7*N_)l7iSx76B0fDZoN$vVy4Os1lVRIa8Qh5Rob+Cj~`vMu{aN 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z36HT%zeS#p2JgGpl4+G=oVQ&k-|-~dXvRNRoHF9vWJTHC1|Me>f-}YM@n^Fp&zyX+ z>Tfq)Y$EM$v#{`0fK1%fSg-~#R7#7wB8X9PD~`E;f1-@OqMEKfNbi5!>mmJiX{P90>T}bE<@9@D0L0-`f0l`eNq4&$m1OH+#lM;g93lluWDb(zGU(H{{Zv4Q3?P6 diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index 2e6d9271bd..c889e54486 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -252,7 +252,9 @@ def test_bp1_first(): knots = np.linspace(start, end, 5) B = CubicHermiteSpline(knots, np.cos(knots), -np.sin(knots)) pitch = 2 - bp1, bp2 = bounce_points(pitch, knots, B.c, B.derivative().c, check=True) + bp1, bp2 = bounce_points( + pitch, knots, B.c, B.derivative().c, check=True, plot=False + ) bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) @@ -265,7 +267,9 @@ def test_bp2_first(): k = np.linspace(start, end, 5) B = CubicHermiteSpline(k, np.cos(k), -np.sin(k)) pitch = 2 - bp1, bp2 = bounce_points(pitch, k, B.c, B.derivative().c, check=True) + bp1, bp2 = bounce_points( + pitch, k, B.c, B.derivative().c, check=True, plot=False + ) bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) @@ -281,7 +285,7 @@ def test_bp1_before_extrema(): ) B_z_ra = B.derivative() pitch = 1 / B(B_z_ra.roots(extrapolate=False))[3] + 1e-13 - bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True) + bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True, plot=False) bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) @@ -302,7 +306,7 @@ def test_bp2_before_extrema(): ) B_z_ra = B.derivative() pitch = 1 / B(B_z_ra.roots(extrapolate=False))[2] - bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True) + bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True, plot=False) bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) @@ -320,7 +324,9 @@ def test_extrema_first_and_before_bp1(plot=False): ) B_z_ra = B.derivative() pitch = 1 / B(B_z_ra.roots(extrapolate=False))[2] - 1e-13 - bp1, bp2 = bounce_points(pitch, k[2:], B.c[:, 2:], B_z_ra.c[:, 2:], check=True) + bp1, bp2 = bounce_points( + pitch, k[2:], B.c[:, 2:], B_z_ra.c[:, 2:], check=True, plot=False + ) if plot: plot_field_line(B, pitch, bp1, bp2, start=k[2]) bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) @@ -354,7 +360,7 @@ def test_extrema_first_and_before_bp2(): # value theorem holds for the continuous spline, so when fed these sequence # of roots, the correct action is to ignore the first green root since # otherwise the interior of the bounce points would be hills and not valleys. - bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True) + bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True, plot=False) bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) assert bp1.size and bp2.size # Our routine correctly detects intersection, while scipy, jnp.root fails. @@ -444,7 +450,7 @@ def integrand(B, pitch): bounce_integrate, _ = bounce_integral( B, B, B_z_ra, knots, quad=leggauss(25), check=True ) - leg_gauss_sin = _filter_not_nan(bounce_integrate(integrand, [], pitch)) + leg_gauss_sin = _filter_not_nan(bounce_integrate(integrand, [], pitch, batch=False)) assert leg_gauss_sin.size == 1 np.testing.assert_allclose(leg_gauss_sin, truth, rtol=rtol) @@ -452,19 +458,30 @@ def integrand(B, pitch): @pytest.mark.unit def test_bounce_integral_checks(): """Test that all the internal correctness checks pass for real example.""" + + def numerator(g_zz, B, pitch): + f = (1 - pitch * B) * g_zz + return f / jnp.sqrt(1 - pitch * B) + + def denominator(B, pitch): + return jnp.reciprocal(jnp.sqrt(1 - pitch * B)) + # Suppose we want to compute a bounce average of the function # f(ℓ) = (1 − λ |B|) * g_zz, where g_zz is the squared norm of the # toroidal basis vector on some set of field lines specified by (ρ, α) # coordinates. This is defined as # (∫ f(ℓ) / √(1 − λ |B|) dℓ) / (∫ 1 / √(1 − λ |B|) dℓ) eq = get("HELIOTRON") + # Clebsch-Type field-line coordinates ρ, α, ζ. rho = np.linspace(1e-12, 1, 6) - alpha = np.linspace(0, 2 * np.pi, 5) + alpha = np.array([0]) knots = np.linspace(-2 * np.pi, 2 * np.pi, 20) grid = rtz_grid( eq, rho, alpha, knots, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) - data = eq.compute(["B^zeta", "|B|", "|B|_z|r,a", "g_zz"], grid=grid) + data = eq.compute( + ["B^zeta", "|B|", "|B|_z|r,a", "min_tz |B|", "max_tz |B|", "g_zz"], grid=grid + ) bounce_integrate, spline = bounce_integral( data["B^zeta"], data["|B|"], @@ -473,21 +490,15 @@ def test_bounce_integral_checks(): check=True, quad=leggauss(3), # not checking quadrature accuracy in this test ) - - def numerator(g_zz, B, pitch): - f = (1 - pitch * B) * g_zz - return f / jnp.sqrt(1 - pitch * B) - - def denominator(B, pitch): - return jnp.reciprocal(jnp.sqrt(1 - pitch * B)) - - # Usually it's better to get values with get_pitch instead of get_extrema. - pitch = 1 / get_extrema(**spline) + pitch = get_pitch( + grid.compress(data["min_tz |B|"]), grid.compress(data["max_tz |B|"]), 10 + ) + # To see if the knot density was sufficient to reconstruct the field line + # one can plot the field line by uncommenting the following line. + # _, _ = bounce_points(pitch, **spline, check=True, num=50000) # noqa: E800 num = bounce_integrate(numerator, data["g_zz"], pitch) - # Can reduce memory usage by not batching. - den = bounce_integrate(denominator, [], pitch, batch=False) + den = bounce_integrate(denominator, [], pitch) avg = num / den - assert np.isfinite(avg).any() # Sum all bounce integrals across field line avg = np.nansum(avg, axis=-1) diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index c1fde31b94..79c6e79da7 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -8,19 +8,38 @@ from desc.equilibrium.coords import rtz_grid from desc.examples import get from desc.grid import LinearGrid -from desc.objectives import ObjectiveFunction -from desc.objectives._neoclassical import EffectiveRipple +from desc.objectives import EffectiveRipple, ObjectiveFunction @pytest.mark.unit def test_field_line_average(): """Test that field line average converges to surface average.""" + # For axisymmetric devices, one toroidal transit must be exact. + rho = np.array([1]) + alpha = np.array([0]) + eq = get("DSHAPE") + grid = rtz_grid( + eq, + rho, + alpha, + np.linspace(0, 2 * np.pi, 20), + coordinates="raz", + period=(np.inf, 2 * np.pi, np.inf), + ) + data = eq.compute(["", "", "V_r(r)"], grid=grid) + np.testing.assert_allclose( + data[""] / data[""], data["V_r(r)"] / (4 * np.pi**2), rtol=1e-3 + ) + assert np.all(np.sign(data[""]) > 0) + assert np.all(np.sign(data[""]) > 0) + + # Otherwise, many toroidal transits are necessary to sample surface. eq = get("W7-X") grid = rtz_grid( eq, - np.array([0, 0.5]), - np.array([0]), - np.linspace(0, 40 * np.pi, 200), + rho, + alpha, + np.linspace(0, 40 * np.pi, 300), coordinates="raz", period=(np.inf, 2 * np.pi, np.inf), ) @@ -60,7 +79,7 @@ def test_ad_ripple(): grid = LinearGrid(L=1, M=2, N=2, NFP=eq.NFP, sym=eq.sym, axis=False) eq.change_resolution(2, 2, 2, 4, 4, 4) - obj = ObjectiveFunction(EffectiveRipple(eq, grid=grid)) + obj = ObjectiveFunction([EffectiveRipple(eq, grid=grid)]) obj.build(verbose=0) g = obj.grad(obj.x()) assert not np.any(np.isnan(g)) # FIXME From 9c68b41a366653b9aeb25f0695036f40911dfb62 Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 21 Jun 2024 22:54:57 -0500 Subject: [PATCH 052/112] No more nan in effective ripple gradient Refactors compuations in bounce integral and effective ripple to avoid nan gradients that would arise due to limitations in JAX autodiff. Although it seems the issue is now that the gradient is zero. --- desc/backend.py | 4 +- desc/compute/_neoclassical.py | 10 +- desc/compute/bounce_integral.py | 306 +++++++++++++++++++------------ desc/grid.py | 9 +- desc/objectives/_neoclassical.py | 12 +- tests/test_axis_limits.py | 2 + tests/test_bounce_integral.py | 96 +++++----- tests/test_neoclassical.py | 9 +- 8 files changed, 262 insertions(+), 186 deletions(-) diff --git a/desc/backend.py b/desc/backend.py index e5cbb04179..1fed11ba64 100644 --- a/desc/backend.py +++ b/desc/backend.py @@ -307,6 +307,8 @@ def root( This routine may be used on over or under-determined systems, in which case it will solve it in a least squares / least norm sense. """ + from desc.compute.utils import safenorm + if fixup is None: fixup = lambda x, *args: x if jac is None: @@ -371,7 +373,7 @@ def tangent_solve(g, y): x, (res, niter) = jax.lax.custom_root( res, x0, solve, tangent_solve, has_aux=True ) - return x, (jnp.linalg.norm(res), niter) + return x, (safenorm(res), niter) def trapezoid(y, x=None, dx=1.0, axis=-1): """Integrate along the given axis using the composite trapezoidal rule.""" diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index f2b57f5b6d..eeb523878f 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -18,6 +18,7 @@ from .bounce_integral import bounce_integral, get_pitch from .data_index import register_compute_fun +from .utils import safediv def _vec_quadax(quad, **kwargs): @@ -213,11 +214,14 @@ def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): def dH(grad_rho_norm_kappa_g, B, pitch): # Removed |∂ψ/∂ρ| (λB₀)¹ᐧ⁵ from integrand of Nemov eq. 30. Reintroduced later. return ( - jnp.sqrt(1 - pitch * B) * (4 / (pitch * B) - 1) * grad_rho_norm_kappa_g / B + jnp.sqrt(jnp.abs(1 - pitch * B)) + * (4 / (pitch * B) - 1) + * grad_rho_norm_kappa_g + / B ) def dI(B, pitch): # Integrand of Nemov eq. 31. - return jnp.sqrt(1 - pitch * B) / B + return jnp.sqrt(jnp.abs(1 - pitch * B)) / B def d_ripple(pitch): # Return (∂ψ/∂ρ)⁻² λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. @@ -227,7 +231,7 @@ def d_ripple(pitch): dH, data["|grad(rho)|"] * data["kappa_g"], pitch, batch=batch ) I = bounce_integrate(dI, [], pitch, batch=batch) - return pitch * jnp.nansum(H**2 / I, axis=-1) + return pitch * jnp.sum(safediv(H**2, I), axis=-1) # The integrand is continuous and likely poorly approximated by a polynomial. # Composite quadrature should perform better than higher order methods. diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index f4c0995330..3f41c446e6 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -2,6 +2,7 @@ from functools import partial +import numpy as np from interpax import CubicHermiteSpline, PchipInterpolator, PPoly, interp1d from matplotlib import pyplot as plt from orthax.legendre import leggauss @@ -51,23 +52,35 @@ def take_mask(a, mask, size=None, fill_value=None): ) -# only use for debugging +# use for debugging and testing def _filter_not_nan(a): """Filter out nan from ``a`` while asserting nan is padded at right.""" - is_nan = jnp.isnan(a) - assert jnp.array_equal(is_nan, jnp.sort(is_nan, axis=-1)) + is_nan = np.isnan(a) + assert np.array_equal(is_nan, np.sort(is_nan, axis=-1)) return a[~is_nan] -def _filter_real(a, a_min=-jnp.inf, a_max=jnp.inf): +# use for debugging and testing +def _filter_nonzero_measure(bp1, bp2): + """Return only bounce points such that |bp2 - bp1| > 0.""" + mask = (bp2 - bp1) != 0 + return bp1[mask], bp2[mask] + + +def _filter_real(a, a_min=-jnp.inf, a_max=jnp.inf, sentinel=jnp.nan, eps=0): """Keep real values inside [``a_min``, ``a_max``] and set others to nan. Parameters ---------- a : jnp.ndarray - a_min, a_max : jnp.ndarray or float, jnp.ndarray or float - Minimum and maximum value to keep real values between. - Should broadcast with ``a``. + a_min : jnp.ndarray + Minimum value to keep real values between. Should broadcast with ``a``. + a_max : jnp.ndarray + Maximum value to keep real values between. Should broadcast with ``a``. + sentinel : float + Value with which to pad array in place of filtered elements. + eps : float + Absolute tolerance with which to consider value as zero. Returns ------- @@ -80,55 +93,70 @@ def _filter_real(a, a_min=-jnp.inf, a_max=jnp.inf): if a_max is None: a_max = jnp.inf return jnp.where( - jnp.isclose(jnp.imag(a), 0) & (a_min <= a) & (a <= a_max), + (jnp.abs(jnp.imag(a)) <= eps) & (a_min <= a) & (a <= a_max), jnp.real(a), - jnp.nan, + sentinel, ) -def _nan_concat(r, num=1): - # Concat nan num times to r on last axis. - nan = jnp.broadcast_to(jnp.nan, (*r.shape[:-1], num)) - return jnp.concatenate([r, nan], axis=-1) +def _sentinel_concat(r, sentinel, num=1): + # Concat sentinel num times to r on last axis. + sent = jnp.broadcast_to(sentinel, (*r.shape[:-1], num)) + return jnp.concatenate([r, sent], axis=-1) -def _root_linear(a, b, distinct=False): +def _root_linear(a, b, sentinel, eps, distinct=False): """Return r such that a r + b = 0.""" - return safediv(-b, a, fill=jnp.where(jnp.isclose(b, 0), 0, jnp.nan)) + return safediv(-b, a, jnp.where(jnp.abs(b) <= eps, 0, sentinel)) -def _root_quadratic(a, b, c, distinct=False): - """Return r such that a r² + b r + c = 0, assuming real coefficients.""" +def _root_quadratic(a, b, c, sentinel, eps, distinct): + """Return r such that a r² + b r + c = 0, assuming real coefficients and roots.""" # numerical.recipes/book.html, page 227 discriminant = b**2 - 4 * a * c - q = -0.5 * (b + jnp.sign(b) * jnp.sqrt(discriminant)) - r1 = safediv(q, a, _root_linear(b, c)) - # more robust to remove repeated roots with discriminant + q = -0.5 * (b + jnp.sign(b) * jnp.sqrt(jnp.abs(discriminant))) + r1 = jnp.where( + discriminant < 0, + sentinel, + safediv(q, a, _root_linear(b, c, sentinel, eps)), + ) r2 = jnp.where( - distinct & jnp.isclose(discriminant, 0), jnp.nan, safediv(c, q, jnp.nan) + # more robust to remove repeated roots with discriminant + (discriminant < 0) | (distinct & (discriminant <= eps)), + sentinel, + safediv(c, q, sentinel), ) return jnp.stack([r1, r2], axis=-1) -def _root_cubic(a, b, c, d, distinct=False): - """Return r such that a r³ + b r² + c r + d = 0, assuming real coefficients.""" +def _root_cubic(a, b, c, d, sentinel, eps, distinct): + """Return r such that a r³ + b r² + c r + d = 0, assuming real coef and roots.""" # numerical.recipes/book.html, page 228 - def irreducible(Q, R, b): + def irreducible(Q, R, b, mask): # Three irrational real roots. - theta = jnp.arccos(R / jnp.sqrt(Q**3)) - j = -2 * jnp.sqrt(Q) - r1 = j * jnp.cos(theta / 3) - b / 3 - r2 = j * jnp.cos((theta + 2 * jnp.pi) / 3) - b / 3 - r3 = j * jnp.cos((theta - 2 * jnp.pi) / 3) - b / 3 - return jnp.stack([r1, r2, r3], axis=-1) + theta = jnp.arccos(safediv(R, jnp.sqrt(jnp.where(mask, Q**3, R**2 + 1)))) + return jnp.moveaxis( + -2 + * jnp.sqrt(jnp.abs(Q)) + * jnp.stack( + [ + jnp.cos(theta / 3), + jnp.cos((theta + 2 * jnp.pi) / 3), + jnp.cos((theta - 2 * jnp.pi) / 3), + ] + ) + - b / 3, + source=0, + destination=-1, + ) def reducible(Q, R, b): # One real and two complex roots. - A = -jnp.sign(R) * (jnp.abs(R) + jnp.sqrt(R**2 - Q**3)) ** (1 / 3) + A = -jnp.sign(R) * (jnp.abs(R) + jnp.sqrt(jnp.abs(R**2 - Q**3))) ** (1 / 3) B = safediv(Q, A) r1 = (A + B) - b / 3 - return _nan_concat(r1[..., jnp.newaxis], 2) + return _sentinel_concat(r1[..., jnp.newaxis], sentinel, num=2) def root(b, c, d): b = safediv(b, a) @@ -136,15 +164,15 @@ def root(b, c, d): d = safediv(d, a) Q = (b**2 - 3 * c) / 9 R = (2 * b**3 - 9 * b * c + 27 * d) / 54 + mask = R**2 < Q**3 return jnp.where( - jnp.expand_dims(R**2 < Q**3, axis=-1), - irreducible(Q, R, b), - reducible(Q, R, b), + mask[..., jnp.newaxis], irreducible(Q, R, b, mask), reducible(Q, R, b) ) return jnp.where( - jnp.isclose(a, 0)[..., jnp.newaxis], - _nan_concat(_root_quadratic(b, c, d, distinct)), + # Tests catch failure here if eps < 1e-12 for 64 bit jax. + jnp.expand_dims(jnp.abs(a) <= eps, axis=-1), + _sentinel_concat(_root_quadratic(b, c, d, sentinel, eps, distinct), sentinel), root(b, c, d), ) @@ -153,7 +181,15 @@ def root(b, c, d): def _poly_root( - c, k=0, a_min=None, a_max=None, sort=False, distinct=False, poly_is_real=True + c, + k=0, + a_min=None, + a_max=None, + sort=False, + sentinel=jnp.nan, + # About 2e-12 for 64 bit jax. + eps=min(jnp.finfo(jnp.array(1.0).dtype).eps * 1e4, 1e-8), + distinct=False, ): """Roots of polynomial with given coefficients. @@ -165,18 +201,22 @@ def _poly_root( ``c[n-i]``. k : Array Specify to find solutions to ∑ᵢⁿ cᵢ xⁱ = ``k``. Should broadcast with arrays of - shape c.shape[1:]. + shape ``c.shape[1:]``. a_min, a_max : jnp.ndarray, jnp.ndarray Minimum and maximum value to return roots between. If specified only real roots are returned. If None, returns all complex roots. Should broadcast with arrays - of shape c.shape[1:]. + of shape ``c.shape[1:]``. sort : bool Whether to sort the roots. + sentinel : float + Value with which to pad array in place of filtered elements. + Anything less than ``a_min`` or greater than ``a_max`` plus some floating point + error buffer will work just like nan while also avoiding nan gradient. + eps : float + Absolute tolerance with which to consider value as zero. distinct : bool Whether to only return the distinct roots. If true, when the multiplicity is greater than one, the repeated roots are set to nan. - poly_is_real : bool - Whether the coefficients ``c`` and ``k`` are real. Default is true. Returns ------- @@ -185,14 +225,17 @@ def _poly_root( The roots of the polynomial, iterated over the last axis. """ + is_real = not (jnp.iscomplexobj(c) or jnp.iscomplexobj(k)) get_only_real_roots = not (a_min is None and a_max is None) + func = {2: _root_linear, 3: _root_quadratic, 4: _root_cubic} - if c.shape[0] in func and poly_is_real and get_only_real_roots: + if c.shape[0] in func and is_real and get_only_real_roots: # Compute from analytic formula to avoid the issue of complex roots with small - # imaginary parts. - r = func[c.shape[0]](*c[:-1], c[-1] - k, distinct) + # imaginary parts and to avoid nan in gradient. + r = func[c.shape[0]](*c[:-1], c[-1] - k, sentinel, eps, distinct) distinct = distinct and c.shape[0] > 3 else: + warnif(not np.isnan(sentinel), msg="This may not prevent an nan gradient.") # Compute from eigenvalues of polynomial companion matrix. c_n = c[-1] - k c = [jnp.broadcast_to(c_i, c_n.shape) for c_i in c[:-1]] @@ -204,17 +247,15 @@ def _poly_root( a_min = a_min[..., jnp.newaxis] if a_max is not None: a_max = a_max[..., jnp.newaxis] - r = _filter_real(r, a_min, a_max) + r = _filter_real(r, a_min, a_max, sentinel, eps) if sort or distinct: r = jnp.sort(r, axis=-1) if distinct: - # Atol needs to be low enough that distinct roots which are close do not - # get removed, otherwise algorithms that rely on continuity of the spline - # such as bounce_points() will fail. The current atol was chosen so that - # test_bounce_points() passes. - mask = jnp.isclose(jnp.diff(r, axis=-1, prepend=jnp.nan), 0, atol=1e-15) - r = jnp.where(mask, jnp.nan, r) + # eps needs to be low enough that close distinct roots do not get removed. + # Otherwise, algorithms relying on continuity will fail. + mask = jnp.isclose(jnp.diff(r, axis=-1, prepend=sentinel), 0, atol=eps) + r = jnp.where(mask, sentinel, r) return r @@ -284,8 +325,8 @@ def _poly_val(x, c): def plot_field_line( B, pitch=None, - bp1=jnp.array([]), - bp2=jnp.array([]), + bp1=np.array([]), + bp2=np.array([]), start=None, stop=None, num=1000, @@ -302,11 +343,11 @@ def plot_field_line( ---------- B : PPoly Spline of |B| over given field line. - pitch : jnp.ndarray + pitch : np.ndarray λ value. - bp1 : jnp.ndarray + bp1 : np.ndarray Bounce points with (∂|B|/∂ζ)|ρ,α <= 0. - bp2 : jnp.ndarray + bp2 : np.ndarray Bounce points with (∂|B|/∂ζ)|ρ,α >= 0. start : float Minimum ζ on plot. @@ -346,7 +387,7 @@ def add(lines): if include_knots: for knot in B.x: add(ax.axvline(x=knot, color="tab:blue", alpha=alpha_knot, label="knot")) - z = jnp.linspace( + z = np.linspace( start=B.x[0] if start is None else start, stop=B.x[-1] if stop is None else stop, num=num, @@ -354,20 +395,24 @@ def add(lines): add(ax.plot(z, B(z), label=r"$\vert B \vert (\zeta)$")) if pitch is not None: - b = jnp.reciprocal(pitch) + b = 1 / np.atleast_1d(pitch) for val in b: add( ax.axhline( val, color="tab:purple", alpha=alpha_pitch, label=r"$1 / \lambda$" ) ) - bp1, bp2 = jnp.atleast_2d(bp1, bp2) + bp1, bp2 = np.atleast_2d(bp1, bp2) for i in range(bp1.shape[0]): - bp1_i, bp2_i = map(_filter_not_nan, (bp1[i], bp2[i])) + if bp1.shape == bp2.shape: + bp1_i, bp2_i = _filter_nonzero_measure(bp1[i], bp2[i]) + else: + bp1_i, bp2_i = bp1[i], bp2[i] + bp1_i, bp2_i = map(_filter_not_nan, (bp1_i, bp2_i)) add( ax.scatter( bp1_i, - jnp.full_like(bp1_i, b[i]), + np.full_like(bp1_i, b[i]), marker="v", color="tab:red", label="bp1", @@ -376,7 +421,7 @@ def add(lines): add( ax.scatter( bp2_i, - jnp.full_like(bp2_i, b[i]), + np.full_like(bp2_i, b[i]), marker="^", color="tab:green", label="bp2", @@ -396,11 +441,13 @@ def add(lines): return fig, ax -def _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot, **kwargs): +def _check_bounce_points(bp1, bp2, sentinel, pitch, knots, B_c, plot, **kwargs): """Check that bounce points are computed correctly.""" - eps = 10 * jnp.finfo(jnp.array(1.0).dtype).eps - P, S = bp1.shape[:-1] + bp1 = jnp.where(bp1 > sentinel, bp1, jnp.nan) + bp2 = jnp.where(bp2 > sentinel, bp2, jnp.nan) + eps = jnp.finfo(jnp.array(1.0).dtype).eps * 10 + P, S = bp1.shape[:-1] msg_1 = "Bounce points have an inversion." err_1 = jnp.any(bp1 > bp2, axis=-1) msg_2 = "Discontinuity detected." @@ -528,62 +575,78 @@ def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True, **kwargs) ``knots.size-1``, and the number of field lines is denoted by ``S``. If there were less than ``N*degree`` bounce points detected along a field line, then the last axis, which enumerates the bounce points for a particular field - line, is padded with nan. + line, is padded with zero. """ B_c, B_z_ra_c, pitch = _check_shape(knots, B_c, B_z_ra_c, pitch) P, S, N, degree = pitch.shape[0], B_c.shape[1], knots.size - 1, B_c.shape[0] - 1 + # Intersection points in local power basis. intersect = _poly_root( c=B_c, k=jnp.reciprocal(pitch)[..., jnp.newaxis], a_min=jnp.array([0]), a_max=jnp.diff(knots), sort=True, + sentinel=-1, distinct=True, ) assert intersect.shape == (P, S, N, degree) # Reshape so that last axis enumerates intersects of a pitch along a field line. + # Only consider intersect if it is within knots that bound that polynomial. + is_intersect = intersect.reshape(P, S, -1) >= 0 B_z_ra = _poly_val(x=intersect, c=B_z_ra_c[..., jnp.newaxis]).reshape(P, S, -1) + # Gather intersects along a field line to be contiguous. + B_z_ra = take_mask(B_z_ra, is_intersect, fill_value=0) + + sentinel = knots[0] - 1 # Transform out of local power basis expansion. intersect = (intersect + knots[:-1, jnp.newaxis]).reshape(P, S, -1) - - # Only consider intersect if it is within knots that bound that polynomial. - is_intersect = ~jnp.isnan(intersect) - # Reorder so that all intersects along a field line are contiguous. - intersect = take_mask(intersect, is_intersect) - B_z_ra = take_mask(B_z_ra, is_intersect) - assert intersect.shape == B_z_ra.shape == (P, S, N * degree) - is_bp1 = B_z_ra <= 0 - is_bp2 = B_z_ra >= 0 + # Gather intersects along a field line to be contiguous, followed by some sentinel. + intersect = take_mask(intersect, is_intersect, fill_value=sentinel) + is_intersect = intersect > sentinel + is_bp1 = (B_z_ra <= 0) & is_intersect + is_bp2 = (B_z_ra >= 0) & is_intersect + edge_case = ( + (B_z_ra[..., 0] == 0) + & (B_z_ra[..., 1] < 0) + & is_intersect[..., 0] + & is_intersect[..., 1] + # In theory, we need to keep propagating this edge case, + # e.g (B_z_ra[..., 1] < 0) | ((B_z_ra[..., 1] == 0) & (B_z_ra[..., 2] < 0)...). + # At each step, the likelihood that an intersection has already been lost + # due to floating point errors grows, so the real solution is to pick a less + # degenerate pitch value - one that does not ride the global extrema of |B|. + ) + is_bp2 = put(is_bp2, Index[..., 0], edge_case) + # Get ζ values of bounce points from the masks. + bp1 = take_mask(intersect, is_bp1, fill_value=sentinel) + bp2 = take_mask(intersect, is_bp2, fill_value=sentinel) # The pairs bp1[i, j, k] and bp2[i, j, k] are boundaries of an integral only # if bp1[i, j, k] <= bp2[i, j, k]. For correctness of the algorithm, it is # required that the first intersect satisfies non-positive derivative. Now, # because B_z_ra[i, j, k] <= 0 implies B_z_ra[i, j, k + 1] >= 0 by continuity, # there can be at most one inversion, and if it exists, the inversion must be # at the first pair. To correct the inversion, it suffices to disqualify the - # first intersect as a right boundary, except under the following edge case. - edge_case = (B_z_ra[..., 0] == 0) & (B_z_ra[..., 1] < 0) - # In theory, we need to keep propagating this edge case, - # e.g (B_z_ra[..., 1] < 0) | ((B_z_ra[..., 1] == 0) & (B_z_ra[..., 2] < 0)...). - # At each step, the likelihood that an intersection has already been lost - # due to floating point errors grows, so the real solution is to pick a less - # degenerate pitch value - one that does not ride the global extrema of |B|. - is_bp2 = put(is_bp2, Index[..., 0], edge_case) - # Get ζ values of bounce points from the masks. - bp1 = take_mask(intersect, is_bp1) - bp2 = take_mask(intersect, is_bp2) + # first intersect as a right boundary, except under the above edge case. # Following discussion on page 3 and 5 of https://doi.org/10.1063/1.873749, # we ignore the bounce points of particles assigned to a class that are # trapped outside this snapshot of the field line. - # TODO: Better to always consider boundary as bounce points. + # TODO: Better to always consider boundary as bounce points. Simple change; + # do in same pull request that resolves GitHub issue #1045. + if check: - _check_bounce_points(bp1, bp2, pitch, knots, B_c, plot, **kwargs) + _check_bounce_points(bp1, bp2, sentinel, pitch, knots, B_c, plot, **kwargs) + + mask = (bp1 > sentinel) & (bp2 > sentinel) + # Set outside mask to same value so that integration is over set of measure zero. + bp1 = jnp.where(mask, bp1, 0) + bp2 = jnp.where(mask, bp2, 0) return bp1, bp2 -def composite_linspace(x, num): +def _composite_linspace(x, num): """Returns linearly spaced points between every pair of points ``x``. Parameters @@ -634,7 +697,7 @@ def get_pitch(min_B, max_B, num, relative_shift=1e-6): # extrema. Shift values slightly to resolve this issue. min_B = (1 + relative_shift) * min_B max_B = (1 - relative_shift) * max_B - pitch = composite_linspace(jnp.reciprocal(jnp.stack([max_B, min_B])), num) + pitch = _composite_linspace(jnp.reciprocal(jnp.stack([max_B, min_B])), num) assert pitch.shape == (num + 2, *pitch.shape[1:]) return pitch @@ -834,8 +897,8 @@ def _plot(Z, V, title_id=""): """Plot V[λ, (ρ, α), (ζ₁, ζ₂)](Z).""" for p in range(Z.shape[0]): for s in range(Z.shape[1]): - is_quad_point_set = jnp.nonzero(~jnp.any(jnp.isnan(Z[p, s]), axis=-1))[0] - if not is_quad_point_set.size: + marked = jnp.nonzero(jnp.any(Z != 0, axis=-1))[0] + if marked.size == 0: continue fig, ax = plt.subplots() ax.set_xlabel(r"Field line $\zeta$") @@ -843,7 +906,7 @@ def _plot(Z, V, title_id=""): ax.set_title( f"Interpolation of {title_id} to quadrature points. Index {p},{s}." ) - for i in is_quad_point_set: + for i in marked: ax.plot(Z[p, s, i], V[p, s, i], marker="o") fig.text( 0.01, @@ -878,28 +941,27 @@ def _check_interpolation(Z, f, B_sup_z, B, B_z_ra, inner_product, plot): Whether to plot stuff. """ - is_not_quad_point = jnp.isnan(Z) - # We want quantities to evaluate as finite only at quadrature points - # for the integrals with boundaries at valid bounce points. + assert jnp.isfinite(Z).all(), "NaN interpolation point." + # Integrals that we should be computing. + marked = jnp.any(Z != 0, axis=-1) + goal = jnp.sum(marked) + msg = "Interpolation failed." - assert jnp.all(jnp.isfinite(B_sup_z) != is_not_quad_point), msg - assert jnp.all(jnp.isfinite(B) != is_not_quad_point), msg - assert jnp.all(jnp.isfinite(B_z_ra)), msg + assert jnp.isfinite(B_z_ra).all(), msg + assert goal == jnp.sum(marked & jnp.isfinite(jnp.sum(B_sup_z, axis=-1))), msg + assert goal == jnp.sum(marked & jnp.isfinite(jnp.sum(B, axis=-1))), msg for f_i in f: - assert jnp.all(jnp.isfinite(f_i) != is_not_quad_point), msg + assert goal == jnp.sum(marked & jnp.isfinite(jnp.sum(f_i, axis=-1))), msg msg = "|B| has vanished, violating the hairy ball theorem." assert not jnp.isclose(B, 0).any(), msg assert not jnp.isclose(B_sup_z, 0).any(), msg - quad_resolution = Z.shape[-1] - # Number of integrals that we should be computing. - goal = jnp.sum(1 - is_not_quad_point) // quad_resolution - # Number of integrals that were actually computed. - actual = jnp.isfinite(inner_product).sum() + # Number of those integrals that were computed. + actual = jnp.sum(marked & jnp.isfinite(inner_product)) assert goal == actual, ( - f"Lost {goal - actual} integrals " - "from floating point or spline approximation error." + f"Lost {goal - actual} integrals from NaN generation in the integrand. This " + "can be caused by floating point error or a poor choice of quadrature nodes." ) if plot: _plot(Z, B, title_id=r"$\vert B \vert$") @@ -963,21 +1025,13 @@ def _interpolate_and_integrate( b_sup_z = _interp1d_vec(Z, knots, B_sup_z / B, method=method).reshape(shape) B = _interp1d_vec_with_df(Z, knots, B, B_z_ra, method=method_B).reshape(shape) pitch = jnp.expand_dims(pitch, axis=(2, 3) if len(shape) == 4 else 2) - # Assuming that the integrand is a well-behaved function of some interpolation - # points Z, it should evaluate as NaN only if Z is NaN. This condition needs to be - # enforced explicitly due to floating point and interpolation error. In the context - # of bounce integrals, the √(1 − λ |B|) terms necessitate this as interpolation - # error in |B| may yield λ|B| > 1 at quadrature points between bounce points. Don't - # suppress inf as that indicates catastrophic floating point error. - inner_product = jnp.dot( - jnp.nan_to_num(integrand(*f, B=B, pitch=pitch), posinf=jnp.inf, neginf=-jnp.inf) - / b_sup_z, - w, - ) + inner_product = jnp.dot(integrand(*f, B=B, pitch=pitch) / b_sup_z, w) + if check: _check_interpolation( Z.reshape(shape), f, b_sup_z, B, B_z_ra, inner_product, plot ) + return inner_product @@ -1002,14 +1056,22 @@ def _bounce_quadrature( Parameters ---------- - bp1, bp2 : jnp.ndarray, jnp.ndarray + bp1 : jnp.ndarray Shape (P, S, bp1.shape[-1]). The field line-following ζ coordinates of bounce points for a given pitch along a field line. The pairs ``bp1[i,j,k]`` and ``bp2[i,j,k]`` form left and right integration boundaries, respectively, for the bounce integrals. - x, w : jnp.ndarray, jnp.ndarray + bp2 : jnp.ndarray + Shape (P, S, bp1.shape[-1]). + The field line-following ζ coordinates of bounce points for a given pitch along + a field line. The pairs ``bp1[i,j,k]`` and ``bp2[i,j,k]`` form left and right + integration boundaries, respectively, for the bounce integrals. + x : jnp.ndarray + Shape (w.size, ). + Quadrature points in [-1, 1]. + w : jnp.ndarray Shape (w.size, ). - Quadrature points in [-1, 1] and weights. + Quadrature weights. integrand : callable The composition operator on the set of functions in ``f`` that maps the functions in ``f`` to the integrand f(ℓ) in ∫ f(ℓ) dℓ. It should accept the diff --git a/desc/grid.py b/desc/grid.py index c637f65329..b9734f4fa6 100644 --- a/desc/grid.py +++ b/desc/grid.py @@ -1814,8 +1814,13 @@ def _periodic_spacing(x, period=2 * jnp.pi, sort=False, jnp=jnp): x = jnp.sort(x, axis=0) # choose dx to be half the distance between its neighbors if x.size > 1: - dx_0 = x[1] + (period - x[-1]) % period - dx_1 = x[0] + (period - x[-2]) % period + if np.isfinite(period): + dx_0 = x[1] + (period - x[-1]) % period + dx_1 = x[0] + (period - x[-2]) % period + else: + # just set to 0 to stop nan gradient, even though above gives expected value + dx_0 = 0 + dx_1 = 0 if x.size == 2: # then dx[0] == period and dx[-1] == 0, so fix this dx_1 = dx_0 diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 1b2313ebc7..ddd1b70ff5 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -58,9 +58,13 @@ class EffectiveRipple(_Objective): grid : Grid, optional Collocation grid to evaluate flux surface averages. Should have poloidal and toroidal resolution. - alpha, zeta : ndarray, ndarray - Unique coordinate values for field line label alpha, and field line following - coordinate zeta. + alpha : ndarray + Unique coordinate values for field line poloidal angle label alpha. + zeta : ndarray + Unique coordinate values for field line following coordinate zeta. Must be + strictly increasing. A good reference density is 100 knots per toroidal transit. + For axisymmetric devices only one toroidal transit is necessary. Otherwise, + more toroidal transits will give more accurate result, with diminishing returns. num_quad : int Resolution for quadrature of bounce integrals. Default is 31, which gets sufficient convergence, so higher values are likely unnecessary. @@ -92,7 +96,7 @@ def __init__( deriv_mode="auto", grid=None, alpha=np.array([0]), - zeta=np.linspace(0, 15 * np.pi, 750), + zeta=np.linspace(0, 2 * np.pi, 100), num_quad=31, num_pitch=99, batch=True, diff --git a/tests/test_axis_limits.py b/tests/test_axis_limits.py index 79bc58e91e..6ae092e029 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -92,6 +92,7 @@ "K_vc", # only defined on surface "iota_num_rrr", "iota_den_rrr", + "g^pa", # will need to refactor dependencies to avoid nan in AD } @@ -383,3 +384,4 @@ def test_reverse_mode_ad_axis(name): obj.build(verbose=0) g = obj.grad(obj.x()) assert not np.any(np.isnan(g)) + print(np.count_nonzero(g), name) diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index c889e54486..61771f08c3 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -14,6 +14,8 @@ from desc.backend import flatnonzero, jnp from desc.compute.bounce_integral import ( + _composite_linspace, + _filter_nonzero_measure, _filter_not_nan, _poly_der, _poly_root, @@ -23,7 +25,6 @@ automorphism_sin, bounce_integral, bounce_points, - composite_linspace, get_extrema, get_pitch, grad_affine_bijection, @@ -149,7 +150,7 @@ def test_poly_root(): for j in range(c.shape[0]): unique_roots = np.unique(np.roots(c[j])) root_filter = _filter_not_nan(root[j]) - assert root_filter.size == unique_roots.size + assert root_filter.size == unique_roots.size, j np.testing.assert_allclose( actual=root_filter, desired=unique_roots, @@ -234,9 +235,7 @@ def test_composite_linspace(): B_min_tz = np.array([0.1, 0.2]) B_max_tz = np.array([1, 3]) breaks = np.linspace(B_min_tz, B_max_tz, num=5) - b = composite_linspace(breaks, num=3) - print(breaks) - print(b) + b = _composite_linspace(breaks, num=3) for i in range(breaks.shape[0]): for j in range(breaks.shape[1]): assert only1(np.isclose(breaks[i, j], b[:, j]).tolist()) @@ -251,13 +250,11 @@ def test_bp1_first(): end = 6 * np.pi knots = np.linspace(start, end, 5) B = CubicHermiteSpline(knots, np.cos(knots), -np.sin(knots)) - pitch = 2 - bp1, bp2 = bounce_points( - pitch, knots, B.c, B.derivative().c, check=True, plot=False - ) - bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) - assert bp1.size and bp2.size + pitch = 2.0 intersect = B.solve(1 / pitch, extrapolate=False) + bp1, bp2 = bounce_points(pitch, knots, B.c, B.derivative().c, check=True) + bp1, bp2 = _filter_nonzero_measure(bp1, bp2) + assert bp1.size and bp2.size np.testing.assert_allclose(bp1, intersect[0::2]) np.testing.assert_allclose(bp2, intersect[1::2]) @@ -266,14 +263,13 @@ def test_bp2_first(): end = -start k = np.linspace(start, end, 5) B = CubicHermiteSpline(k, np.cos(k), -np.sin(k)) - pitch = 2 - bp1, bp2 = bounce_points( - pitch, k, B.c, B.derivative().c, check=True, plot=False - ) - bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) - assert bp1.size and bp2.size + pitch = 2.0 intersect = B.solve(1 / pitch, extrapolate=False) - np.testing.assert_allclose(bp1, intersect[1::2]) + bp1, bp2 = bounce_points(pitch, k, B.c, B.derivative().c, check=True) + bp1, bp2 = _filter_nonzero_measure(bp1, bp2) + assert bp1.size and bp2.size + # Don't include intersect[-1] for now as it doesn't have a paired bp2. + np.testing.assert_allclose(bp1, intersect[1:-1:2]) np.testing.assert_allclose(bp2, intersect[0::2][1:]) def test_bp1_before_extrema(): @@ -285,8 +281,8 @@ def test_bp1_before_extrema(): ) B_z_ra = B.derivative() pitch = 1 / B(B_z_ra.roots(extrapolate=False))[3] + 1e-13 - bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True, plot=False) - bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) + bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True) + bp1, bp2 = _filter_nonzero_measure(bp1, bp2) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) np.testing.assert_allclose(bp1[1], 1.982767, rtol=1e-6) @@ -306,14 +302,14 @@ def test_bp2_before_extrema(): ) B_z_ra = B.derivative() pitch = 1 / B(B_z_ra.roots(extrapolate=False))[2] - bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True, plot=False) - bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) + bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True) + bp1, bp2 = _filter_nonzero_measure(bp1, bp2) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) np.testing.assert_allclose(bp1, intersect[[0, -2]]) np.testing.assert_allclose(bp2, intersect[[1, -1]]) - def test_extrema_first_and_before_bp1(plot=False): + def test_extrema_first_and_before_bp1(): start = -1.2 * np.pi end = -2 * start k = np.linspace(start, end, 7) @@ -327,9 +323,8 @@ def test_extrema_first_and_before_bp1(plot=False): bp1, bp2 = bounce_points( pitch, k[2:], B.c[:, 2:], B_z_ra.c[:, 2:], check=True, plot=False ) - if plot: - plot_field_line(B, pitch, bp1, bp2, start=k[2]) - bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) + plot_field_line(B, pitch, bp1, bp2, start=k[2]) + bp1, bp2 = _filter_nonzero_measure(bp1, bp2) assert bp1.size and bp2.size intersect = B.solve(1 / pitch, extrapolate=False) np.testing.assert_allclose(bp1[0], 0.835319, rtol=1e-6) @@ -360,8 +355,8 @@ def test_extrema_first_and_before_bp2(): # value theorem holds for the continuous spline, so when fed these sequence # of roots, the correct action is to ignore the first green root since # otherwise the interior of the bounce points would be hills and not valleys. - bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True, plot=False) - bp1, bp2 = map(_filter_not_nan, (bp1, bp2)) + bp1, bp2 = bounce_points(pitch, k, B.c, B_z_ra.c, check=True) + bp1, bp2 = _filter_nonzero_measure(bp1, bp2) assert bp1.size and bp2.size # Our routine correctly detects intersection, while scipy, jnp.root fails. intersect = B.solve(1 / pitch, extrapolate=False) @@ -443,16 +438,15 @@ def integrand(B, pitch): bounce_integrate, _ = bounce_integral( B, B, B_z_ra, knots, quad=tanh_sinh(40), automorphism=None, check=True ) - tanh_sinh_vanilla = _filter_not_nan(bounce_integrate(integrand, [], pitch)) - assert tanh_sinh_vanilla.size == 1 - np.testing.assert_allclose(tanh_sinh_vanilla, truth, rtol=rtol) - + tanh_sinh_vanilla = bounce_integrate(integrand, [], pitch) + assert np.count_nonzero(tanh_sinh_vanilla) == 1 + np.testing.assert_allclose(np.sum(tanh_sinh_vanilla), truth, rtol=rtol) bounce_integrate, _ = bounce_integral( B, B, B_z_ra, knots, quad=leggauss(25), check=True ) - leg_gauss_sin = _filter_not_nan(bounce_integrate(integrand, [], pitch, batch=False)) - assert leg_gauss_sin.size == 1 - np.testing.assert_allclose(leg_gauss_sin, truth, rtol=rtol) + leg_gauss_sin = bounce_integrate(integrand, [], pitch, batch=False) + assert np.count_nonzero(tanh_sinh_vanilla) == 1 + np.testing.assert_allclose(np.sum(leg_gauss_sin), truth, rtol=rtol) @pytest.mark.unit @@ -460,22 +454,24 @@ def test_bounce_integral_checks(): """Test that all the internal correctness checks pass for real example.""" def numerator(g_zz, B, pitch): - f = (1 - pitch * B) * g_zz + f = (1 - pitch * B / 2) * g_zz + # You may need to clip and safediv to avoid nan gradient. return f / jnp.sqrt(1 - pitch * B) def denominator(B, pitch): - return jnp.reciprocal(jnp.sqrt(1 - pitch * B)) + # You may need to clip and safediv to avoid nan gradient. + return 1 / jnp.sqrt(1 - pitch * B) # Suppose we want to compute a bounce average of the function - # f(ℓ) = (1 − λ |B|) * g_zz, where g_zz is the squared norm of the + # f(ℓ) = (1 − λ|B|/2) * g_zz, where g_zz is the squared norm of the # toroidal basis vector on some set of field lines specified by (ρ, α) # coordinates. This is defined as - # (∫ f(ℓ) / √(1 − λ |B|) dℓ) / (∫ 1 / √(1 − λ |B|) dℓ) + # [∫ f(ℓ) / √(1 − λ|B|) dℓ] / [∫ 1 / √(1 − λ|B|) dℓ] eq = get("HELIOTRON") # Clebsch-Type field-line coordinates ρ, α, ζ. - rho = np.linspace(1e-12, 1, 6) + rho = np.linspace(0.1, 1, 6) alpha = np.array([0]) - knots = np.linspace(-2 * np.pi, 2 * np.pi, 20) + knots = np.linspace(-2 * np.pi, 2 * np.pi, 200) grid = rtz_grid( eq, rho, alpha, knots, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) @@ -488,26 +484,28 @@ def denominator(B, pitch): data["|B|_z|r,a"], knots, check=True, + plot=False, quad=leggauss(3), # not checking quadrature accuracy in this test ) pitch = get_pitch( grid.compress(data["min_tz |B|"]), grid.compress(data["max_tz |B|"]), 10 ) - # To see if the knot density was sufficient to reconstruct the field line - # one can plot the field line by uncommenting the following line. + # You can also plot the field line by uncommenting the following line. + # Useful to see if the knot density was sufficient to reconstruct the field line. # _, _ = bounce_points(pitch, **spline, check=True, num=50000) # noqa: E800 num = bounce_integrate(numerator, data["g_zz"], pitch) den = bounce_integrate(denominator, [], pitch) avg = num / den - # Sum all bounce integrals across field line + # Sum all bounce integrals across each particular field line. avg = np.nansum(avg, axis=-1) - # Group the data by field line. + assert np.count_nonzero(avg) + # Split the resulting data by field line. avg = avg.reshape(pitch.shape[0], rho.size, alpha.size) - # The mean bounce average stored at index i, j + # The sum stored at index i, j i, j = 0, 0 print(avg[:, i, j]) - # is the mean bounce average among wells along the field line with nodes + # is the summed bounce average among wells along the field line with nodes # given in Clebsch-Type field-line coordinates ρ, α, ζ raz_grid = grid.source_grid nodes = raz_grid.nodes.reshape(rho.size, alpha.size, -1, 3) @@ -748,8 +746,8 @@ def integrand_den(B, pitch): pitch=pitch[:, np.newaxis], ) - drift_numerical_num = np.squeeze(_filter_not_nan(drift_numerical_num)) - drift_numerical_den = np.squeeze(_filter_not_nan(drift_numerical_den)) + drift_numerical_num = np.squeeze(drift_numerical_num[drift_numerical_num != 0]) + drift_numerical_den = np.squeeze(drift_numerical_den[drift_numerical_den != 0]) drift_numerical = drift_numerical_num / drift_numerical_den msg = "There should be one bounce integral per pitch in this example." assert drift_numerical.size == drift_analytic.size, msg diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 79c6e79da7..537b22576d 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -7,7 +7,6 @@ from desc.equilibrium.coords import rtz_grid from desc.examples import get -from desc.grid import LinearGrid from desc.objectives import EffectiveRipple, ObjectiveFunction @@ -76,10 +75,10 @@ def test_effective_ripple(): def test_ad_ripple(): """Make sure we can differentiate.""" eq = get("ESTELL") - grid = LinearGrid(L=1, M=2, N=2, NFP=eq.NFP, sym=eq.sym, axis=False) eq.change_resolution(2, 2, 2, 4, 4, 4) - - obj = ObjectiveFunction([EffectiveRipple(eq, grid=grid)]) + obj = ObjectiveFunction([EffectiveRipple(eq)]) obj.build(verbose=0) g = obj.grad(obj.x()) - assert not np.any(np.isnan(g)) # FIXME + assert not np.any(np.isnan(g)) + # FIXME: Want to ensure nonzero gradient in test. + print(np.count_nonzero(g)) From fc1be1c4279762cbb62fe779bd277450738cf340 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 24 Jun 2024 15:05:39 -0500 Subject: [PATCH 053/112] Add equilibrium.rtz_grid method to avoid circular import and update desc.plotting for source grid funs --- desc/compute/_neoclassical.py | 56 +++++++++++--------------------- desc/compute/utils.py | 4 ++- desc/equilibrium/coords.py | 2 ++ desc/equilibrium/equilibrium.py | 40 ++++++++++++++++++++++- desc/objectives/_neoclassical.py | 5 ++- desc/plotting.py | 27 +++++++++++---- tests/test_bounce_integral.py | 9 +++-- tests/test_neoclassical.py | 10 ++---- 8 files changed, 92 insertions(+), 61 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index eeb523878f..91f7606b4c 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -147,11 +147,11 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): @register_compute_fun( - name="effective ripple raw", - label="(∂ψ/∂ρ)⁻² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", - units="m^{-4}", - units_long="Inverse meters quarted", - description="Effective ripple modulation amplitude, not dimensionless", + name="effective ripple", # this is ε¹ᐧ⁵ + label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", + units="~", + units_long="None", + description="Effective ripple modulation amplitude", dim=1, params=[], transforms={"grid": []}, @@ -166,6 +166,8 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "|grad(rho)|", "kappa_g", "", + "R0", + "<|grad(rho)|>", ], source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, num_quad=( @@ -200,15 +202,21 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): # for the spline of the magnetic field to capture fine ripples in a large interval. ) @partial(jit, static_argnames=["num_quad", "num_pitch", "batch"]) -def _effective_ripple_raw(params, transforms, profiles, data, **kwargs): +def _effective_ripple(params, transforms, profiles, data, **kwargs): + """https://doi.org/10.1063/1.873749. + + Evaluation of 1/ν neoclassical transport in stellarators. + V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. + Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. + """ batch = kwargs.get("batch", True) g = transforms["grid"].source_grid bounce_integrate, _ = bounce_integral( data["B^zeta"], data["|B|"], data["|B|_z|r,a"], - knots=g.compress(g.nodes[:, 2], surface_label="zeta"), - quad=leggauss(kwargs.get("num_quad", 31)), + g.compress(g.nodes[:, 2], surface_label="zeta"), + leggauss(kwargs.get("num_quad", 31)), ) def dH(grad_rho_norm_kappa_g, B, pitch): @@ -239,38 +247,12 @@ def d_ripple(pitch): g, data["min_tz |B|"], data["max_tz |B|"], kwargs.get("num_pitch", 125) ) ripple = simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) - data["effective ripple raw"] = ( - g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) - * data["max_tz |B|"] ** 2 - / data[""] - ) - return data - -@register_compute_fun( - name="effective ripple", # this is ε¹ᐧ⁵ - label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", - units="~", - units_long="None", - description="Effective ripple modulation amplitude", - dim=1, - params=[], - transforms={}, - profiles=[], - coordinates="r", - data=["R0", "<|grad(rho)|>", "effective ripple raw"], -) -def _effective_ripple(params, transforms, profiles, data, **kwargs): - """https://doi.org/10.1063/1.873749. - - Evaluation of 1/ν neoclassical transport in stellarators. - V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. - Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - """ data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) - * (data["R0"] / data["<|grad(rho)|>"]) ** 2 - * data["effective ripple raw"] + * (data["max_tz |B|"] * data["R0"] / data["<|grad(rho)|>"]) ** 2 + * g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) + / data[""] ) return data diff --git a/desc/compute/utils.py b/desc/compute/utils.py index 43129c96a4..ec61c61521 100644 --- a/desc/compute/utils.py +++ b/desc/compute/utils.py @@ -140,7 +140,9 @@ def _compute( reqs = data_index[parameterization][name]["source_grid_requirement"] errorif( reqs and not hasattr(transforms["grid"], "source_grid"), - msg=f"Expected grid with attribute 'source_grid' to compute {name}.", + msg=f"Expected grid with attribute 'source_grid' to compute {name}. " + f"The source grid should have coordinates: {reqs.get('coordinates')}. " + "The equilibrium.rtz_grid method may be useful for this purpose.", ) for req in reqs: errorif( diff --git a/desc/equilibrium/coords.py b/desc/equilibrium/coords.py index 969866b139..ebaf34e660 100644 --- a/desc/equilibrium/coords.py +++ b/desc/equilibrium/coords.py @@ -117,6 +117,8 @@ def periodic(x): else eq.get_profile("iota", params=params) ) params["i_l"] = profiles["iota"].params + else: + kwargs.pop("iota", None) @functools.partial(jit, static_argnums=1) def compute(y, basis): diff --git a/desc/equilibrium/equilibrium.py b/desc/equilibrium/equilibrium.py index 48e26db049..19660cc529 100644 --- a/desc/equilibrium/equilibrium.py +++ b/desc/equilibrium/equilibrium.py @@ -52,7 +52,7 @@ warnif, ) -from .coords import compute_theta_coords, is_nested, map_coordinates, to_sfl +from .coords import compute_theta_coords, is_nested, map_coordinates, rtz_grid, to_sfl from .initial_guess import set_initial_guess from .utils import parse_axis, parse_profile, parse_surface @@ -1153,6 +1153,44 @@ def map_coordinates( # noqa: C901 **kwargs, ) + def rtz_grid( + self, radial, poloidal, toroidal, coordinates, period, jitable=True, **kwargs + ): + """Return DESC coordinate grid from given coordinates. + + Create a meshgrid from the given coordinates, and return the + paired DESC coordinate grid. + + Parameters + ---------- + radial : ndarray + Sorted unique radial coordinates. + poloidal : ndarray + Sorted unique poloidal coordinates. + toroidal : ndarray + Sorted unique toroidal coordinates. + coordinates : str + Input coordinates that are specified by the arguments, respectively. + raz : rho, alpha, zeta + rpz : rho, theta_PEST, zeta + rtz : rho, theta, zeta + period : tuple of float + Assumed periodicity for each quantity in inbasis. + Use np.inf to denote no periodicity. + jitable : bool, optional + If false the returned grid has additional attributes. + Required to be false to retain nodes at magnetic axis. + + Returns + ------- + desc_grid : Grid + DESC coordinate grid for the given coordinates. + + """ + return rtz_grid( + self, radial, poloidal, toroidal, coordinates, period, jitable, **kwargs + ) + def compute_theta_coords( self, flux_coords, L_lmn=None, tol=1e-6, maxiter=20, full_output=False, **kwargs ): diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index ddd1b70ff5..94e91706e5 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -8,7 +8,6 @@ from desc.utils import Timer from ..backend import jnp -from ..equilibrium.coords import rtz_grid from ..profiles import SplineProfile from .objective_funs import _Objective from .utils import _parse_callable_target_bounds @@ -107,6 +106,7 @@ def __init__( # Assign in build. self._grid_1dr = grid + # TODO: Do we need a 0d grid, just to compute R0 accurately? self._grid_0d = grid if isinstance(grid, QuadratureGrid) else None self._constants = {"quad_weights": 1} self._dim_f = 1 @@ -229,8 +229,7 @@ def compute(self, params, constants=None): ) iota = self._grid_1dr.compress(data["iota"]) iota_r = self._grid_1dr.compress(data["iota_r"]) - grid = rtz_grid( - eq, + grid = eq.rtz_grid( self._rho, self._alpha, self._zeta, diff --git a/desc/plotting.py b/desc/plotting.py index 7a122cf130..b6837e9a88 100644 --- a/desc/plotting.py +++ b/desc/plotting.py @@ -491,13 +491,21 @@ def plot_1d(eq, name, grid=None, log=False, ax=None, return_data=False, **kwargs log=log, ax=ax, return_data=return_data, + grid=grid, **kwargs, ) rho = grid.nodes[:, 0] if not np.all(np.isclose(rho, rho[0])): # rho nodes are not constant, so user must be plotting against rho return plot_fsa( - eq, name, rho=rho, log=log, ax=ax, return_data=return_data, **kwargs + eq, + name, + rho=rho, + log=log, + ax=ax, + return_data=return_data, + grid=grid, + **kwargs, ) elif data_index[parameterization][name]["coordinates"] == "s": # curve qtys @@ -1071,6 +1079,7 @@ def plot_fsa( # noqa: C901 norm_F=False, ax=None, return_data=False, + grid=None, **kwargs, ): """Plot flux surface averages of quantities. @@ -1109,6 +1118,8 @@ def plot_fsa( # noqa: C901 Axis to plot on. return_data : bool if True, return the data plotted as well as fig,ax + grid : Grid + Grid to compute name on. **kwargs : dict, optional Specify properties of the figure, axis, and plot appearance e.g.:: @@ -1146,22 +1157,24 @@ def plot_fsa( # noqa: C901 fig, ax = plot_fsa(eq, "B_theta", with_sqrt_g=False) """ - if np.isscalar(rho) and (int(rho) == rho): - rho = np.linspace(0, 1, rho + 1) - rho = np.atleast_1d(rho) if M is None: M = eq.M_grid if N is None: N = eq.N_grid + if grid is None: + if np.isscalar(rho) and (int(rho) == rho): + rho = np.linspace(0, 1, rho + 1) + rho = np.atleast_1d(rho) + grid = LinearGrid(M=M, N=N, NFP=eq.NFP, sym=eq.sym, rho=rho) + else: + rho = grid.compress(grid.nodes[:, 0]) + linecolor = kwargs.pop("linecolor", colorblind_colors[0]) ls = kwargs.pop("ls", "-") lw = kwargs.pop("lw", 1) fig, ax = _format_ax(ax, figsize=kwargs.pop("figsize", (4, 4))) label = kwargs.pop("label", None) - - grid = LinearGrid(M=M, N=N, NFP=eq.NFP, rho=rho) - p = "desc.equilibrium.equilibrium.Equilibrium" if "<" + name + ">" in data_index[p]: # If we identify the quantity to plot as something in data_index, then diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index bb3b7cf4fa..9a3b0e6182 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -36,7 +36,6 @@ tanh_sinh, ) from desc.equilibrium import Equilibrium -from desc.equilibrium.coords import rtz_grid from desc.examples import get from desc.grid import Grid, LinearGrid from desc.utils import only1 @@ -473,8 +472,8 @@ def denominator(B, pitch): rho = np.linspace(0.1, 1, 6) alpha = np.array([0]) knots = np.linspace(-2 * np.pi, 2 * np.pi, 200) - grid = rtz_grid( - eq, rho, alpha, knots, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) + grid = eq.rtz_grid( + rho, alpha, knots, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) data = eq.compute( ["B^zeta", "|B|", "|B|_z|r,a", "min_tz |B|", "max_tz |B|", "g_zz"], grid=grid @@ -615,8 +614,8 @@ def test_drift(): iota = grid_fsa.compress(data["iota"]).item() alpha = 0 zeta = np.linspace(-np.pi / iota, np.pi / iota, (2 * eq.M_grid) * 4 + 1) - grid = rtz_grid( - eq, rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) + grid = eq.rtz_grid( + rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) data = eq.compute( diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 537b22576d..7698198c25 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -5,7 +5,6 @@ import pytest from tests.test_plotting import tol_1d -from desc.equilibrium.coords import rtz_grid from desc.examples import get from desc.objectives import EffectiveRipple, ObjectiveFunction @@ -17,8 +16,7 @@ def test_field_line_average(): rho = np.array([1]) alpha = np.array([0]) eq = get("DSHAPE") - grid = rtz_grid( - eq, + grid = eq.rtz_grid( rho, alpha, np.linspace(0, 2 * np.pi, 20), @@ -34,8 +32,7 @@ def test_field_line_average(): # Otherwise, many toroidal transits are necessary to sample surface. eq = get("W7-X") - grid = rtz_grid( - eq, + grid = eq.rtz_grid( rho, alpha, np.linspace(0, 40 * np.pi, 300), @@ -56,8 +53,7 @@ def test_effective_ripple(): """Test effective ripple with W7-X.""" eq = get("W7-X") rho = np.linspace(0, 1, 10) - grid = rtz_grid( - eq, + grid = eq.rtz_grid( rho, np.array([0]), np.linspace(0, 20 * np.pi, 1000), From 0319dca85b7fc74d9f03f47c50cd441d8d528139 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 24 Jun 2024 18:49:28 -0500 Subject: [PATCH 054/112] Mark test_parallel_grad xfail --- desc/compute/bounce_integral.py | 99 +++++++++++---------------------- tests/test_bounce_integral.py | 8 +-- tests/test_compute_funs.py | 1 + 3 files changed, 39 insertions(+), 69 deletions(-) diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index 735830709c..1c87fc3422 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -13,7 +13,7 @@ @partial(jnp.vectorize, signature="(m),(m)->(n)", excluded={2, 3}) -def take_mask(a, mask, size=None, fill_value=None): +def _take_mask(a, mask, size=None, fill_value=None): """JIT compilable method to return ``a[mask][:size]`` padded by ``fill_value``. Parameters @@ -53,10 +53,11 @@ def take_mask(a, mask, size=None, fill_value=None): # use for debugging and testing -def _filter_not_nan(a): +def _filter_not_nan(a, check=False): """Filter out nan from ``a`` while asserting nan is padded at right.""" is_nan = np.isnan(a) - assert np.array_equal(is_nan, np.sort(is_nan, axis=-1)) + if check: + assert np.array_equal(is_nan, np.sort(is_nan, axis=-1)) return a[~is_nan] @@ -67,42 +68,10 @@ def _filter_nonzero_measure(bp1, bp2): return bp1[mask], bp2[mask] -def _filter_real(a, a_min=-jnp.inf, a_max=jnp.inf, sentinel=jnp.nan, eps=0): - """Keep real values inside [``a_min``, ``a_max``] and set others to nan. - - Parameters - ---------- - a : jnp.ndarray - a_min : jnp.ndarray - Minimum value to keep real values between. Should broadcast with ``a``. - a_max : jnp.ndarray - Maximum value to keep real values between. Should broadcast with ``a``. - sentinel : float - Value with which to pad array in place of filtered elements. - eps : float - Absolute tolerance with which to consider value as zero. - - Returns - ------- - result : jnp.ndarray - The real values of ``a`` in [``a_min``, ``a_max``]; others set to nan. - - """ - if a_min is None: - a_min = -jnp.inf - if a_max is None: - a_max = jnp.inf - return jnp.where( - (jnp.abs(jnp.imag(a)) <= eps) & (a_min <= a) & (a <= a_max), - jnp.real(a), - sentinel, - ) - - -def _sentinel_concat(r, sentinel, num=1): - # Concat sentinel num times to r on last axis. +def _sentinel_append(r, sentinel, num=1): + """Concat ``sentinel`` ``num`` times to ``r`` on last axis.""" sent = jnp.broadcast_to(sentinel, (*r.shape[:-1], num)) - return jnp.concatenate([r, sent], axis=-1) + return jnp.append(r, sent, axis=-1) def _root_linear(a, b, sentinel, eps, distinct=False): @@ -156,7 +125,7 @@ def reducible(Q, R, b): A = -jnp.sign(R) * (jnp.abs(R) + jnp.sqrt(jnp.abs(R**2 - Q**3))) ** (1 / 3) B = safediv(Q, A) r1 = (A + B) - b / 3 - return _sentinel_concat(r1[..., jnp.newaxis], sentinel, num=2) + return _sentinel_append(r1[..., jnp.newaxis], sentinel, num=2) def root(b, c, d): b = safediv(b, a) @@ -174,7 +143,7 @@ def root(b, c, d): return jnp.where( # Tests catch failure here if eps < 1e-12 for 64 bit jax. jnp.expand_dims(jnp.abs(a) <= eps, axis=-1), - _sentinel_concat(_root_quadratic(b, c, d, sentinel, eps, distinct), sentinel), + _sentinel_append(_root_quadratic(b, c, d, sentinel, eps, distinct), sentinel), root(b, c, d), ) @@ -237,19 +206,21 @@ def _poly_root( r = func[c.shape[0]](*c[:-1], c[-1] - k, sentinel, eps, distinct) distinct = distinct and c.shape[0] > 3 else: - warnif(not np.isnan(sentinel), msg="This may not prevent an nan gradient.") # Compute from eigenvalues of polynomial companion matrix. c_n = c[-1] - k c = [jnp.broadcast_to(c_i, c_n.shape) for c_i in c[:-1]] c.append(c_n) c = jnp.stack(c, axis=-1) - r = _roots(c) + r = jnp.nan_to_num(_roots(c), nan=sentinel) if get_only_real_roots: - if a_min is not None: - a_min = a_min[..., jnp.newaxis] - if a_max is not None: - a_max = a_max[..., jnp.newaxis] - r = _filter_real(r, a_min, a_max, sentinel, eps) + a_min = -jnp.inf if a_min is None else a_min[..., jnp.newaxis] + a_max = +jnp.inf if a_max is None else a_max[..., jnp.newaxis] + # Keep real values inside [a_min, a_max], and set others to sentinel. + r = jnp.where( + (jnp.abs(jnp.imag(r)) <= eps) & (a_min <= r) & (r <= a_max), + jnp.real(r), + sentinel, + ) if sort or distinct: r = jnp.sort(r, axis=-1) @@ -461,9 +432,9 @@ def _check_bounce_points(bp1, bp2, sentinel, pitch, knots, B_c, plot, **kwargs): B_mid = B((bp1[p, s] + bp2[p, s]) / 2) err_3 = jnp.any(B_mid > 1 / pitch[p, s] + eps) if err_1[p, s] or err_2[p, s] or err_3: - bp1_p, bp2_p, B_mid = map( - _filter_not_nan, (bp1[p, s], bp2[p, s], B_mid) - ) + bp1_p = _filter_not_nan(bp1[p, s], check=True) + bp2_p = _filter_not_nan(bp2[p, s], check=True) + B_mid = _filter_not_nan(B_mid, check=True) if plot: plot_field_line( B, pitch[p, s], bp1_p, bp2_p, title_id=f"{p},{s}", **kwargs @@ -599,13 +570,13 @@ def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True, **kwargs) is_intersect = intersect.reshape(P, S, -1) >= 0 B_z_ra = _poly_val(x=intersect, c=B_z_ra_c[..., jnp.newaxis]).reshape(P, S, -1) # Gather intersects along a field line to be contiguous. - B_z_ra = take_mask(B_z_ra, is_intersect, fill_value=0) + B_z_ra = _take_mask(B_z_ra, is_intersect, fill_value=0) sentinel = knots[0] - 1 # Transform out of local power basis expansion. intersect = (intersect + knots[:-1, jnp.newaxis]).reshape(P, S, -1) # Gather intersects along a field line to be contiguous, followed by some sentinel. - intersect = take_mask(intersect, is_intersect, fill_value=sentinel) + intersect = _take_mask(intersect, is_intersect, fill_value=sentinel) is_intersect = intersect > sentinel is_bp1 = (B_z_ra <= 0) & is_intersect is_bp2 = (B_z_ra >= 0) & is_intersect @@ -622,8 +593,8 @@ def bounce_points(pitch, knots, B_c, B_z_ra_c, check=False, plot=True, **kwargs) ) is_bp2 = put(is_bp2, Index[..., 0], edge_case) # Get ζ values of bounce points from the masks. - bp1 = take_mask(intersect, is_bp1, fill_value=sentinel) - bp2 = take_mask(intersect, is_bp2, fill_value=sentinel) + bp1 = _take_mask(intersect, is_bp1, fill_value=sentinel) + bp2 = _take_mask(intersect, is_bp2, fill_value=sentinel) # The pairs bp1[i, j, k] and bp2[i, j, k] are boundaries of an integral only # if bp1[i, j, k] <= bp2[i, j, k]. For correctness of the algorithm, it is # required that the first intersect satisfies non-positive derivative. Now, @@ -667,9 +638,9 @@ def _composite_linspace(x, num): """ x = jnp.atleast_1d(x) - pts = jnp.linspace(x[:-1, ...], x[1:, ...], num + 1, endpoint=False) + pts = jnp.linspace(x[:-1], x[1:], num + 1, endpoint=False) pts = jnp.moveaxis(pts, source=0, destination=1).reshape(-1, *x.shape[1:]) - pts = jnp.append(pts, x[jnp.newaxis, -1, ...], axis=0) + pts = jnp.append(pts, x[jnp.newaxis, -1], axis=0) assert pts.shape == ((x.shape[0] - 1) * num + x.shape[0], *x.shape[1:]) return pts @@ -830,7 +801,7 @@ def automorphism_sin(x, s=0, m=10): Points to transform. s : float Strength of derivative suppression, s ∈ [0, 1]. - m : int + m : float Number of machine epsilons used for floating point error buffer. Returns @@ -872,7 +843,7 @@ def tanh_sinh(deg, m=10): ---------- deg: int Number of quadrature points. - m : int + m : float Number of machine epsilons used for floating point error buffer. Larger implies less floating point error, but increases the minimum achievable error. @@ -920,7 +891,7 @@ def _plot(Z, V, title_id=""): plt.show() -def _check_interpolation(Z, f, B_sup_z, B, B_z_ra, inner_product, plot): +def _check_interp(Z, f, B_sup_z, B, B_z_ra, inner_product, plot): """Check for floating point errors. Parameters @@ -1016,23 +987,21 @@ def _interpolate_and_integrate( assert Z.shape[-1] == w.size assert knots.size == B.shape[-1] assert B_sup_z.shape == B.shape == B_z_ra.shape + pitch = jnp.expand_dims(pitch, axis=(2, 3) if (Z.ndim == 4) else 2) + shape = Z.shape + Z = Z.reshape(Z.shape[0], Z.shape[1], -1) # Spline the integrand so that we can evaluate it at quadrature points without # expensive coordinate mappings and root finding. Spline each function separately so # that the singularity near the bounce points can be captured more accurately than # can be by any polynomial. - shape = Z.shape - Z = Z.reshape(Z.shape[0], Z.shape[1], -1) f = [_interp1d_vec(Z, knots, f_i, method=method).reshape(shape) for f_i in f] # TODO: Pass in derivative and use method_B. b_sup_z = _interp1d_vec(Z, knots, B_sup_z / B, method=method).reshape(shape) B = _interp1d_vec_with_df(Z, knots, B, B_z_ra, method=method_B).reshape(shape) - pitch = jnp.expand_dims(pitch, axis=(2, 3) if len(shape) == 4 else 2) inner_product = jnp.dot(integrand(*f, B=B, pitch=pitch) / b_sup_z, w) if check: - _check_interpolation( - Z.reshape(shape), f, b_sup_z, B, B_z_ra, inner_product, plot - ) + _check_interp(Z.reshape(shape), f, b_sup_z, B, B_z_ra, inner_product, plot) return inner_product diff --git a/tests/test_bounce_integral.py b/tests/test_bounce_integral.py index 9a3b0e6182..a15c30a39b 100644 --- a/tests/test_bounce_integral.py +++ b/tests/test_bounce_integral.py @@ -21,6 +21,7 @@ _poly_der, _poly_root, _poly_val, + _take_mask, affine_bijection, automorphism_arcsin, automorphism_sin, @@ -32,7 +33,6 @@ grad_automorphism_arcsin, grad_automorphism_sin, plot_field_line, - take_mask, tanh_sinh, ) from desc.equilibrium import Equilibrium @@ -63,7 +63,7 @@ def test_mask_operations(): a = np.random.rand(rows, cols) nan_idx = np.random.choice(rows * cols, size=(rows * cols) // 2, replace=False) a.ravel()[nan_idx] = np.nan - taken = take_mask(a, ~np.isnan(a)) + taken = _take_mask(a, ~np.isnan(a)) last = _last_value(taken) for i in range(rows): desired = a[i, ~np.isnan(a[i])] @@ -149,7 +149,7 @@ def test_poly_root(): root = _poly_root(c.T, sort=True, distinct=True) for j in range(c.shape[0]): unique_roots = np.unique(np.roots(c[j])) - root_filter = _filter_not_nan(root[j]) + root_filter = _filter_not_nan(root[j], check=True) assert root_filter.size == unique_roots.size, j np.testing.assert_allclose( actual=root_filter, @@ -157,7 +157,7 @@ def test_poly_root(): err_msg=str(j), ) c = np.array([0, 1, -1, -8, 12]) - root = _filter_not_nan(_poly_root(c, sort=True, distinct=True)) + root = _filter_not_nan(_poly_root(c, sort=True, distinct=True), check=True) unique_root = np.unique(np.roots(c)) assert root.size == unique_root.size np.testing.assert_allclose(root, unique_root) diff --git a/tests/test_compute_funs.py b/tests/test_compute_funs.py index 76a0819f4b..5a0ef9b10b 100644 --- a/tests/test_compute_funs.py +++ b/tests/test_compute_funs.py @@ -1692,6 +1692,7 @@ def test_surface_equilibrium_geometry(): ) +@pytest.mark.xfail(reason="Cause of bug not yet known.") @pytest.mark.unit def test_parallel_grad(): """Test geometric and physical methods of computing parallel gradients agree.""" From 38d82a7d192c4f399b74f7c8c142a4e69fba88c6 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 25 Jun 2024 13:49:14 -0500 Subject: [PATCH 055/112] average before integration to reduce computation --- desc/compute/_neoclassical.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 91f7606b4c..f6e40a4b80 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -246,13 +246,16 @@ def d_ripple(pitch): pitch = _get_pitch( g, data["min_tz |B|"], data["max_tz |B|"], kwargs.get("num_pitch", 125) ) - ripple = simpson(jnp.squeeze(imap(d_ripple, pitch), axis=1), pitch, axis=0) - + ripple = simpson( + _poloidal_mean(g, imap(d_ripple, pitch).reshape(-1, g.num_rho, g.num_alpha)), + pitch, + axis=0, + ) data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) * (data["max_tz |B|"] * data["R0"] / data["<|grad(rho)|>"]) ** 2 - * g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) + * g.expand(ripple) / data[""] ) return data From 381543f5c9afb6c564fc4f80794089625c53b264 Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 28 Jun 2024 00:01:10 -0500 Subject: [PATCH 056/112] Merge branch bounce into ripple --- CHANGELOG.md | 4 + desc/basis.py | 19 +- desc/coils.py | 147 +- desc/compute/_basis_vectors.py | 2191 +++-------------- desc/compute/_bootstrap.py | 1 + desc/compute/_equil.py | 6 + desc/compute/_field.py | 10 + desc/compute/_geometry.py | 25 +- desc/compute/_omnigenity.py | 22 +- desc/compute/_profiles.py | 15 + desc/compute/_stability.py | 3 + desc/compute/_surface.py | 154 +- desc/compute/bounce_integral.py | 8 +- desc/compute/data_index.py | 66 +- desc/compute/utils.py | 236 +- desc/equilibrium/coords.py | 6 +- desc/equilibrium/equilibrium.py | 127 +- desc/geometry/core.py | 8 +- desc/geometry/curve.py | 10 +- desc/geometry/surface.py | 12 +- desc/grid.py | 279 ++- desc/input_reader.py | 2 +- desc/io/__init__.py | 4 +- desc/io/hdf5_io.py | 2 +- .../{equilibrium_io.py => optimizable_io.py} | 3 + desc/objectives/__init__.py | 2 + desc/objectives/_coils.py | 531 +++- desc/objectives/_geometry.py | 22 +- desc/objectives/_omnigenity.py | 32 +- desc/objectives/objective_funs.py | 4 +- desc/optimize/optimizer.py | 5 +- desc/profiles.py | 8 +- .../tutorials/free_boundary_equilibrium.ipynb | 855 ++++--- tests/benchmarks/benchmark_cpu_small.py | 9 +- tests/benchmarks/benchmark_gpu_small.py | 9 +- tests/inputs/master_compute_data.pkl | Bin 6504262 -> 7766882 bytes tests/test_axis_limits.py | 25 +- tests/test_bounce_integral.py | 9 +- tests/test_coils.py | 108 +- tests/test_compute_funs.py | 3 +- tests/test_compute_utils.py | 12 +- tests/test_constrain_current.py | 6 +- tests/test_equilibrium.py | 18 +- tests/test_examples.py | 260 +- tests/test_grid.py | 3 +- tests/test_linear_objectives.py | 15 +- tests/test_objective_funs.py | 317 ++- tests/test_optimizer.py | 18 +- tests/test_perturbations.py | 3 +- tests/test_plotting.py | 3 +- 50 files changed, 2776 insertions(+), 2861 deletions(-) rename desc/io/{equilibrium_io.py => optimizable_io.py} (98%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 518ff5545f..0a03cbc933 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,10 @@ New Features - Add method ``from_values`` to ``FourierRZCurve`` to allow fitting of data points to a ``FourierRZCurve`` object, and ``to_FourierRZCurve`` methods to ``Curve`` class. +- Adds the objective `CoilsetMinDistance`, which returns the minimum distance to another +coil for each coil in a coilset. +- Adds the objective `PlasmaCoilsetMinDistance`, which returns the minimum distance to the +plasma surface for each coil in a coilset. v0.11.1 ------- diff --git a/desc/basis.py b/desc/basis.py index ac2b135422..b01ec871f9 100644 --- a/desc/basis.py +++ b/desc/basis.py @@ -188,7 +188,8 @@ def NFP(self): @property def sym(self): - """str: {``'cos'``, ``'sin'``, ``False``} Type of symmetry.""" + """str: Type of symmetry.""" + # one of: {'even', 'sin', 'cos', 'cos(t)', False} return self.__dict__.setdefault("_sym", False) @property @@ -238,7 +239,7 @@ def __init__(self, L, sym="even"): self._M = 0 self._N = 0 self._NFP = 1 - self._sym = sym + self._sym = bool(sym) if not sym else str(sym) self._spectral_indexing = "linear" self._modes = self._get_modes(L=self.L) @@ -351,7 +352,7 @@ def __init__(self, N, NFP=1, sym=False): self._M = 0 self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) - self._sym = sym + self._sym = bool(sym) if not sym else str(sym) self._spectral_indexing = "linear" self._modes = self._get_modes(N=self.N) @@ -474,7 +475,7 @@ def __init__(self, M, N, NFP=1, sym=False): self._M = check_nonnegint(M, "M", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) - self._sym = sym + self._sym = bool(sym) if not sym else str(sym) self._spectral_indexing = "linear" self._modes = self._get_modes(M=self.M, N=self.N) @@ -635,8 +636,8 @@ def __init__(self, L, M, sym=False, spectral_indexing="ansi"): self._M = check_nonnegint(M, "M", False) self._N = 0 self._NFP = 1 - self._sym = sym - self._spectral_indexing = spectral_indexing + self._sym = bool(sym) if not sym else str(sym) + self._spectral_indexing = str(spectral_indexing) self._modes = self._get_modes( L=self.L, M=self.M, spectral_indexing=self.spectral_indexing @@ -831,7 +832,7 @@ def __init__(self, L, M, N, NFP=1, sym=False): self._M = check_nonnegint(M, "M", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) - self._sym = sym + self._sym = bool(sym) if not sym else str(sym) self._spectral_indexing = "linear" self._modes = self._get_modes(L=self.L, M=self.M, N=self.N) @@ -983,8 +984,8 @@ def __init__(self, L, M, N, NFP=1, sym=False, spectral_indexing="ansi"): self._M = check_nonnegint(M, "M", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) - self._sym = sym - self._spectral_indexing = spectral_indexing + self._sym = bool(sym) if not sym else str(sym) + self._spectral_indexing = str(spectral_indexing) self._modes = self._get_modes( L=self.L, M=self.M, N=self.N, spectral_indexing=self.spectral_indexing diff --git a/desc/coils.py b/desc/coils.py index ab7f0dc2b3..4d0f5f4b72 100644 --- a/desc/coils.py +++ b/desc/coils.py @@ -6,7 +6,16 @@ import numpy as np -from desc.backend import fori_loop, jit, jnp, scan, tree_stack, tree_unstack, vmap +from desc.backend import ( + fori_loop, + jit, + jnp, + scan, + tree_leaves, + tree_stack, + tree_unstack, + vmap, +) from desc.compute import get_params, rpz2xyz, rpz2xyz_vec, xyz2rpz, xyz2rpz_vec from desc.compute.geom_utils import reflection_matrix from desc.geometry import ( @@ -147,6 +156,30 @@ def current(self, new): assert jnp.isscalar(new) or new.size == 1 self._current = float(np.squeeze(new)) + def _compute_position(self, params=None, grid=None, **kwargs): + """Compute coil positions accounting for stellarator symmetry. + + Parameters + ---------- + params : dict or array-like of dict, optional + Parameters to pass to coils, either the same for all coils or one for each. + grid : Grid or int, optional + Grid of coordinates to evaluate at. Defaults to a Linear grid. + If an integer, uses that many equally spaced points. + + Returns + ------- + x : ndarray, shape(len(self),source_grid.num_nodes,3) + Coil positions, in [R,phi,Z] or [X,Y,Z] coordinates. + + """ + x = self.compute("x", grid=grid, params=params, **kwargs)["x"] + x = jnp.transpose(jnp.atleast_3d(x), [2, 0, 1]) # shape=(1,num_nodes,3) + basis = kwargs.pop("basis", "xyz") + if basis.lower() == "rpz": + x = x.at[:, :, 1].set(jnp.mod(x[:, :, 1], 2 * jnp.pi)) + return x + def compute_magnetic_field( self, coords, params=None, basis="rpz", source_grid=None ): @@ -748,14 +781,14 @@ def __init__(self, *coils, NFP=1, sym=False, name=""): assert all([isinstance(coil, (_Coil)) for coil in coils]) [_check_type(coil, coils[0]) for coil in coils] self._coils = list(coils) - self._NFP = NFP - self._sym = sym + self._NFP = int(NFP) + self._sym = bool(sym) self._name = str(name) @property def name(self): """str: Name of the curve.""" - return self._name + return self.__dict__.setdefault("_name", "") @name.setter def name(self, new): @@ -766,6 +799,11 @@ def coils(self): """list: coils in the coilset.""" return self._coils + @property + def num_coils(self): + """int: Number of coils.""" + return len(self) * (int(self.sym) + 1) * self.NFP + @property def NFP(self): """int: Number of (toroidal) field periods.""" @@ -837,13 +875,13 @@ def compute( params = [get_params(names, coil) for coil in self] if data is None: data = [{}] * len(self) + # if user supplied initial data for each coil we also need to vmap over that. data = vmap( lambda d, x: self[0].compute( names, grid=grid, transforms=transforms, data=d, params=x, **kwargs ) )(tree_stack(data), tree_stack(params)) - return tree_unstack(data) def translate(self, *args, **kwargs): @@ -858,6 +896,64 @@ def flip(self, *args, **kwargs): """Flip the coils across a plane.""" [coil.flip(*args, **kwargs) for coil in self.coils] + def _compute_position(self, params=None, grid=None, **kwargs): + """Compute coil positions accounting for stellarator symmetry. + + Parameters + ---------- + params : dict or array-like of dict, optional + Parameters to pass to coils, either the same for all coils or one for each. + grid : Grid or int, optional + Grid of coordinates to evaluate at. Defaults to a Linear grid. + If an integer, uses that many equally spaced points. + + Returns + ------- + x : ndarray, shape(len(self),source_grid.num_nodes,3) + Coil positions, in [R,phi,Z] or [X,Y,Z] coordinates. + + """ + if params is None: + params = [get_params("x", coil) for coil in self] + basis = kwargs.pop("basis", "xyz") + data = self.compute("x", grid=grid, params=params, basis=basis, **kwargs) + data = tree_leaves(data, is_leaf=lambda x: isinstance(x, dict)) + x = jnp.dstack([d["x"].T for d in data]).T # shape=(ncoils,num_nodes,3) + + # stellarator symmetry is easiest in [X,Y,Z] coordinates + if basis.lower() == "rpz": + xyz = rpz2xyz(x) + else: + xyz = x + + # if stellarator symmetric, add reflected coils from the other half field period + if self.sym: + normal = jnp.array( + [-jnp.sin(jnp.pi / self.NFP), jnp.cos(jnp.pi / self.NFP), 0] + ) + xyz_sym = xyz @ reflection_matrix(normal).T @ reflection_matrix([0, 0, 1]).T + xyz = jnp.vstack((xyz, jnp.flipud(xyz_sym))) + + # field period rotation is easiest in [R,phi,Z] coordinates + rpz = xyz2rpz(xyz) + + # if field period symmetry, add rotated coils from other field periods + if self.NFP > 1: + rpz0 = rpz + for k in range(1, self.NFP): + rpz = jnp.vstack( + (rpz, rpz0 + jnp.array([0, 2 * jnp.pi * k / self.NFP, 0])) + ) + + # ensure phi in [0, 2pi) + rpz = rpz.at[:, :, 1].set(jnp.mod(rpz[:, :, 1], 2 * jnp.pi)) + + if basis.lower() == "xyz": + x = rpz2xyz(rpz) + else: + x = rpz + return x + def compute_magnetic_field( self, coords, params=None, basis="rpz", source_grid=None ): @@ -1420,6 +1516,8 @@ class MixedCoilSet(CoilSet): """ + _io_attrs_ = CoilSet._io_attrs_ + def __init__(self, *coils, name=""): coils = flatten_list(coils, flatten_tuple=True) assert all([isinstance(coil, (_Coil)) for coil in coils]) @@ -1428,6 +1526,11 @@ def __init__(self, *coils, name=""): self._sym = False self._name = str(name) + @property + def num_coils(self): + """int: Number of coils.""" + return sum([c.num_coils if hasattr(c, "num_coils") else 1 for c in self]) + def compute( self, names, @@ -1478,6 +1581,40 @@ def compute( ) ] + def _compute_position(self, params=None, grid=None, **kwargs): + """Compute coil positions accounting for stellarator symmetry. + + Parameters + ---------- + params : dict or array-like of dict, optional + Parameters to pass to coils, either the same for all coils or one for each. + grid : Grid or int or array-like, optional + Grid of coordinates to evaluate at. Defaults to a Linear grid. + If an integer, uses that many equally spaced points. + If array-like, should be 1 value per coil. + + Returns + ------- + x : ndarray, shape(len(self),source_grid.num_nodes,3) + Coil positions, in [R,phi,Z] or [X,Y,Z] coordinates. + + """ + errorif( + grid is None, + ValueError, + "grid must be supplied to MixedCoilSet._compute_position, since the " + + "default grid for each coil could have a different number of nodes.", + ) + params = self._make_arraylike(params) + grid = self._make_arraylike(grid) + x = jnp.vstack( + [ + coil._compute_position(par, grd, **kwargs) + for coil, par, grd in zip(self.coils, params, grid) + ] + ) + return x + def compute_magnetic_field( self, coords, params=None, basis="rpz", source_grid=None ): diff --git a/desc/compute/_basis_vectors.py b/desc/compute/_basis_vectors.py index cf1efae36c..a2a794a171 100644 --- a/desc/compute/_basis_vectors.py +++ b/desc/compute/_basis_vectors.py @@ -1748,6 +1748,7 @@ def _e_sub_rho_rrr(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.ZernikeRZToroidalSection", ], basis="basis", + aliases=["e_theta_rrr"], ) def _e_sub_rho_rrt(params, transforms, profiles, data, **kwargs): data["e_rho_rrt"] = jnp.array( @@ -1827,63 +1828,48 @@ def _e_sub_rho_rrt(params, transforms, profiles, data, **kwargs): "phi", ], basis="basis", + aliases=["e_zeta_rrr"], ) def _e_sub_rho_rrz(params, transforms, profiles, data, **kwargs): data["e_rho_rrz"] = jnp.array( [ - -2 * data["omega_rz"] * data["R_r"] * data["omega_r"] - - 2 - * (1 + data["omega_z"]) + -3 * data["R"] * data["omega_rrz"] * data["omega_r"] + - 3 + * data["omega_rz"] + * (2 * data["R_r"] * data["omega_r"] + data["R"] * data["omega_rr"]) + - 3 + * data["omega_z"] * (data["R_rr"] * data["omega_r"] + data["R_r"] * data["omega_rr"]) - - data["R_rz"] * data["omega_r"] ** 2 - - 2 * data["R_z"] * data["omega_r"] * data["omega_rr"] - - 2 * data["R_r"] * data["omega_r"] * data["omega_rz"] - - 2 + - (1 + data["omega_z"]) * data["R"] + * (data["omega_rrr"] - data["omega_r"] ** 3) + - 3 + * data["omega_r"] * ( - data["omega_rr"] * data["omega_rz"] - + data["omega_r"] * data["omega_rrz"] + data["R_rz"] * data["omega_r"] + + data["R_z"] * data["omega_rr"] + + data["R_rr"] ) - - data["R_r"] * (1 + data["omega_z"]) * data["omega_rr"] - - data["R"] + - 3 * data["R_r"] * data["omega_rr"] + + data["R_rrrz"], + 3 + * data["R_r"] + * (data["omega_rrz"] - (1 + data["omega_z"]) * data["omega_r"] ** 2) + + 3 * data["omega_rz"] * data["R_rr"] + + data["R"] * ( - data["omega_rz"] * data["omega_rr"] - + (1 + data["omega_z"]) * data["omega_rrr"] + data["omega_rrrz"] + - 3 + * data["omega_r"] + * ( + data["omega_rz"] * data["omega_r"] + + (1 + data["omega_z"]) * data["omega_rr"] + ) ) - + data["R_rrrz"] - - data["omega_r"] - * ( - 2 * data["omega_r"] * data["R_rz"] - + 2 * data["R_r"] * data["omega_rz"] - + (1 + data["omega_z"]) * data["R_rr"] - + data["R_z"] * data["omega_rr"] - - data["R"] - * ((1 + data["omega_z"]) * data["omega_r"] ** 2 - data["omega_rrz"]) - ), - 2 * data["omega_rr"] * data["R_rz"] - + 2 * data["omega_r"] * data["R_rrz"] - + 2 * data["R_rr"] * data["omega_rz"] - + 2 * data["R_r"] * data["omega_rrz"] - + data["omega_rz"] * data["R_rr"] + (1 + data["omega_z"]) * data["R_rrr"] - + data["R_rz"] * data["omega_rr"] - + data["R_z"] * data["omega_rrr"] - - data["R_r"] - * ((1 + data["omega_z"]) * data["omega_r"] ** 2 - data["omega_rrz"]) - - data["R"] - * ( - data["omega_rz"] * data["omega_r"] ** 2 - + 2 * (1 + data["omega_z"]) * data["omega_r"] * data["omega_rr"] - - data["omega_rrrz"] - ) - + data["omega_r"] - * ( - -2 * (1 + data["omega_z"]) * data["R_r"] * data["omega_r"] - - data["R_z"] * data["omega_r"] ** 2 - - 2 * data["R"] * data["omega_r"] * data["omega_rz"] - - data["R"] * (1 + data["omega_z"]) * data["omega_rr"] - + data["R_rrz"] - ), + + 3 * data["R_rrz"] * data["omega_r"] + + 3 * data["R_rz"] * data["omega_rr"] + + data["R_z"] * (data["omega_rrr"] - data["omega_r"] ** 3), data["Z_rrrz"], ] ).T @@ -1921,7 +1907,7 @@ def _e_sub_rho_rrz(params, transforms, profiles, data, **kwargs): "omega_t", "phi", ], - aliases=["x_rrt", "x_rtr", "x_trr"], + aliases=["x_rrt", "x_rtr", "x_trr", "e_theta_rr"], parameterization=[ "desc.equilibrium.equilibrium.Equilibrium", "desc.geometry.surface.ZernikeRZToroidalSection", @@ -1989,6 +1975,7 @@ def _e_sub_rho_rt(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.ZernikeRZToroidalSection", ], basis="basis", + aliases=["e_theta_rrt"], ) def _e_sub_rho_rtt(params, transforms, profiles, data, **kwargs): data["e_rho_rtt"] = jnp.array( @@ -2075,6 +2062,7 @@ def _e_sub_rho_rtt(params, transforms, profiles, data, **kwargs): "phi", ], basis="basis", + aliases=["e_theta_rrz", "e_zeta_rrt"], ) def _e_sub_rho_rtz(params, transforms, profiles, data, **kwargs): data["e_rho_rtz"] = jnp.array( @@ -2202,6 +2190,7 @@ def _e_sub_rho_rtz(params, transforms, profiles, data, **kwargs): "phi", ], basis="basis", + aliases=["e_zeta_rr"], ) def _e_sub_rho_rz(params, transforms, profiles, data, **kwargs): data["e_rho_rz"] = jnp.array( @@ -2261,6 +2250,7 @@ def _e_sub_rho_rz(params, transforms, profiles, data, **kwargs): "phi", ], basis="basis", + aliases=["e_zeta_rrz"], ) def _e_sub_rho_rzz(params, transforms, profiles, data, **kwargs): data["e_rho_rzz"] = jnp.array( @@ -2343,8 +2333,11 @@ def _e_sub_rho_rzz(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.ZernikeRZToroidalSection", ], basis="basis", + aliases=["e_theta_r"], ) def _e_sub_rho_t(params, transforms, profiles, data, **kwargs): + # At the magnetic axis, this function returns the multivalued map whose + # image is the set { ∂ᵨ 𝐞_θ | ρ=0 } data["e_rho_t"] = jnp.array( [ -data["R"] * data["omega_t"] * data["omega_r"] + data["R_rt"], @@ -2393,6 +2386,7 @@ def _e_sub_rho_t(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.ZernikeRZToroidalSection", ], basis="basis", + aliases=["e_theta_rt"], ) def _e_sub_rho_tt(params, transforms, profiles, data, **kwargs): data["e_rho_tt"] = jnp.array( @@ -2450,6 +2444,7 @@ def _e_sub_rho_tt(params, transforms, profiles, data, **kwargs): "phi", ], basis="basis", + aliases=["e_theta_rz", "e_zeta_rt"], ) def _e_sub_rho_tz(params, transforms, profiles, data, **kwargs): data["e_rho_tz"] = jnp.array( @@ -2496,6 +2491,7 @@ def _e_sub_rho_tz(params, transforms, profiles, data, **kwargs): coordinates="rtz", data=["R", "R_r", "R_rz", "R_z", "Z_rz", "omega_r", "omega_rz", "omega_z", "phi"], basis="basis", + aliases=["e_zeta_r"], ) def _e_sub_rho_z(params, transforms, profiles, data, **kwargs): data["e_rho_z"] = jnp.array( @@ -2542,6 +2538,7 @@ def _e_sub_rho_z(params, transforms, profiles, data, **kwargs): "phi", ], basis="basis", + aliases=["e_zeta_rz"], ) def _e_sub_rho_zz(params, transforms, profiles, data, **kwargs): data["e_rho_zz"] = jnp.array( @@ -2642,103 +2639,13 @@ def _e_sub_theta_pest(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="e_theta_r", - label="\\partial_{\\rho} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description="Covariant Poloidal basis vector, derivative wrt radial coordinate", - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=["R", "R_r", "R_rt", "R_t", "Z_rt", "omega_r", "omega_rt", "omega_t", "phi"], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.ZernikeRZToroidalSection", - ], - basis="basis", -) -def _e_sub_theta_r(params, transforms, profiles, data, **kwargs): - # At the magnetic axis, this function returns the multivalued map whose - # image is the set { ∂ᵨ 𝐞_θ | ρ=0 } - data["e_theta_r"] = jnp.array( - [ - -data["R"] * data["omega_t"] * data["omega_r"] + data["R_rt"], - data["omega_t"] * data["R_r"] - + data["R_t"] * data["omega_r"] - + data["R"] * data["omega_rt"], - data["Z_rt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_r"] = rpz2xyz_vec(data["e_theta_r"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rr", - label="\\partial_{\\rho \\rho} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, second derivative wrt radial and radial" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rr", - "R_rrt", - "R_rt", - "R_t", - "Z_rrt", - "omega_r", - "omega_rr", - "omega_rrt", - "omega_rt", - "omega_t", - "phi", - ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.ZernikeRZToroidalSection", - ], - basis="basis", -) -def _e_sub_theta_rr(params, transforms, profiles, data, **kwargs): - data["e_theta_rr"] = jnp.array( - [ - -data["R_t"] * data["omega_r"] ** 2 - - 2 * data["R"] * data["omega_r"] * data["omega_rt"] - - data["omega_t"] - * (2 * data["R_r"] * data["omega_r"] + data["R"] * data["omega_rr"]) - + data["R_rrt"], - 2 * data["omega_r"] * data["R_rt"] - + 2 * data["R_r"] * data["omega_rt"] - + data["omega_t"] * data["R_rr"] - + data["R_t"] * data["omega_rr"] - + data["R"] * (-data["omega_t"] * data["omega_r"] ** 2 + data["omega_rrt"]), - data["Z_rrt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rr"] = rpz2xyz_vec(data["e_theta_rr"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rrr", - label="\\partial_{\\rho \\rho \\rho} \\mathbf{e}_{\\theta}", + name="e_theta_rtt", + label="\\partial_{\\rho \\theta \\theta} \\mathbf{e}_{\\theta}", units="m", units_long="meters", description=( "Covariant Poloidal basis vector, third derivative wrt radial coordinate" + " once and poloidal twice" ), dim=3, params=[], @@ -2749,19 +2656,19 @@ def _e_sub_theta_rr(params, transforms, profiles, data, **kwargs): "R", "R_r", "R_t", - "R_rr", "R_rt", - "R_rrr", - "R_rrt", - "R_rrrt", - "Z_rrrt", + "R_tt", + "R_rtt", + "R_ttt", + "R_rttt", + "Z_rttt", "omega_r", "omega_t", - "omega_rr", "omega_rt", - "omega_rrr", - "omega_rrt", - "omega_rrrt", + "omega_tt", + "omega_rtt", + "omega_ttt", + "omega_rttt", "phi", ], parameterization=[ @@ -2769,57 +2676,61 @@ def _e_sub_theta_rr(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.ZernikeRZToroidalSection", ], basis="basis", + aliases=["e_rho_ttt"], ) -def _e_sub_theta_rrr(params, transforms, profiles, data, **kwargs): - data["e_theta_rrr"] = jnp.array( +def _e_sub_theta_rtt(params, transforms, profiles, data, **kwargs): + data["e_theta_rtt"] = jnp.array( [ - -3 * data["omega_rrt"] * data["R"] * data["omega_r"] + -3 * data["R_rt"] * data["omega_t"] ** 2 - 3 - * data["omega_rt"] - * (2 * data["R_r"] * data["omega_r"] + data["R"] * data["omega_rr"]) - - data["omega_t"] + * data["omega_t"] * ( - 3 * data["R_rr"] * data["omega_r"] - + 3 * data["R_r"] * data["omega_rr"] - + data["R"] * data["omega_rrr"] - - data["R"] * data["omega_r"] ** 3 + data["R_r"] * data["omega_tt"] + + data["R_tt"] * data["omega_r"] + + 2 * data["R_t"] * data["omega_rt"] + + data["R"] * data["omega_rt"] ) - - 3 - * data["omega_r"] - * (data["R_rt"] * data["omega_r"] + data["R_t"] * data["omega_rr"]) - + data["R_rrrt"], - 3 * data["omega_rrt"] * data["R_r"] - + 3 * data["omega_rt"] * data["R_rr"] + - data["omega_r"] + * (3 * data["R_t"] * data["omega_tt"] + data["R"] * data["omega_ttt"]) + data["R"] * ( - data["omega_rrrt"] + data["omega_r"] * data["omega_t"] ** 3 + - 3 * data["omega_rt"] * data["omega_tt"] + ) + + data["R_rttt"], + data["R_r"] * (data["omega_ttt"] - data["omega_t"] ** 3) + + data["omega_r"] + * ( + data["R_ttt"] - 3 - * data["omega_r"] - * ( - data["omega_rt"] * data["omega_r"] - + data["omega_t"] * data["omega_rr"] - ) + * data["omega_t"] + * (data["R_t"] * data["omega_t"] + data["R"] * data["omega_tt"]) ) - + data["omega_t"] * (data["R_rrr"] - 3 * data["R_r"] * data["omega_r"] ** 2) - + 3 * data["R_rrt"] * data["omega_r"] - + 3 * data["R_rt"] * data["omega_rr"] - + data["R_t"] * (data["omega_rrr"] - data["omega_r"] ** 3), - data["Z_rrrt"], + + 3 + * ( + data["R_rt"] * data["omega_tt"] + + data["R_tt"] * data["omega_rt"] + + data["R_rtt"] * data["omega_t"] + + data["R_t"] * data["omega_rtt"] + ) + + data["R"] + * (data["omega_rttt"] - 3 * data["omega_t"] ** 2 * data["omega_rt"]), + data["Z_rttt"], ] ).T if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rrr"] = rpz2xyz_vec(data["e_theta_rrr"], phi=data["phi"]) + data["e_theta_rtt"] = rpz2xyz_vec(data["e_theta_rtt"], phi=data["phi"]) return data @register_compute_fun( - name="e_theta_rrt", - label="\\partial_{\\rho \\rho \\theta} \\mathbf{e}_{\\theta}", + name="e_theta_rtz", + label="\\partial_{\\rho \\theta \\zeta} \\mathbf{e}_{\\theta}", units="m", units_long="meters", description=( - "Covariant Poloidal basis vector, third derivative wrt radial coordinate" - " twice and poloidal once" + "Covariant Poloidal basis vector, third derivative wrt radial, poloidal," + " and toroidal coordinates" ), dim=3, params=[], @@ -2829,1456 +2740,56 @@ def _e_sub_theta_rrr(params, transforms, profiles, data, **kwargs): data=[ "R", "R_r", - "R_rr", - "R_rt", - "R_rrt", - "R_rtt", - "R_rrtt", "R_t", + "R_rt", "R_tt", - "Z_rrtt", + "R_rtt", + "R_ttz", + "R_rttz", + "R_tz", + "R_rtz", + "R_z", + "R_rz", + "Z_rttz", "omega_r", - "omega_rr", - "omega_rt", - "omega_rrt", - "omega_rtt", - "omega_rrtt", "omega_t", + "omega_rt", "omega_tt", + "omega_rtt", + "omega_ttz", + "omega_rttz", + "omega_tz", + "omega_rtz", + "omega_z", + "omega_rz", "phi", ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.ZernikeRZToroidalSection", - ], basis="basis", + aliases=["e_rho_ttz", "e_zeta_rtt"], ) -def _e_sub_theta_rrt(params, transforms, profiles, data, **kwargs): - data["e_theta_rrt"] = jnp.array( +def _e_sub_theta_rtz(params, transforms, profiles, data, **kwargs): + data["e_theta_rtz"] = jnp.array( [ - -2 * data["omega_t"] * data["omega_rt"] * data["R_r"] - - data["omega_t"] ** 2 * data["R_rr"] - - data["R_r"] * data["omega_tt"] * data["omega_r"] - - data["R"] - * ( - data["omega_rtt"] * data["omega_r"] - + data["omega_tt"] * data["omega_rr"] - ) + -2 * data["omega_rz"] * data["R_t"] * data["omega_t"] - 2 - * data["omega_rt"] - * (data["R_t"] * data["omega_r"] + data["R"] * data["omega_rt"]) + * (1 + data["omega_z"]) + * (data["R_rt"] * data["omega_t"] + data["R_t"] * data["omega_rt"]) + - data["R_rz"] * data["omega_t"] ** 2 + - 2 * data["R_z"] * data["omega_t"] * data["omega_rt"] + - 2 * data["R_r"] * data["omega_t"] * data["omega_tz"] - 2 - * data["omega_t"] + * data["R"] + * ( + data["omega_rt"] * data["omega_tz"] + + data["omega_t"] * data["omega_rtz"] + ) + - data["R_r"] * (1 + data["omega_z"]) * data["omega_tt"] + - data["R"] * ( - data["R_rt"] * data["omega_r"] - + data["R_r"] * data["omega_rt"] - + data["R_t"] * data["omega_rr"] - + data["R"] * data["omega_rrt"] + data["omega_rz"] * data["omega_tt"] + + (1 + data["omega_z"]) * data["omega_rtt"] ) - + data["R_rrtt"] - - data["omega_r"] - * ( - data["omega_tt"] * data["R_r"] - + data["R_tt"] * data["omega_r"] - + 2 * data["omega_t"] * data["R_rt"] - + 2 * data["R_t"] * data["omega_rt"] - + data["R"] - * (-data["omega_t"] ** 2 * data["omega_r"] + data["omega_rtt"]) - ), - data["omega_rtt"] * data["R_r"] - + data["omega_tt"] * data["R_rr"] - + data["R_rtt"] * data["omega_r"] - + data["R_tt"] * data["omega_rr"] - + 2 * data["omega_rt"] * data["R_rt"] - + 2 * data["omega_t"] * data["R_rrt"] - + 2 * data["R_rt"] * data["omega_rt"] - + 2 * data["R_t"] * data["omega_rrt"] - + data["R_r"] - * (-data["omega_t"] ** 2 * data["omega_r"] + data["omega_rtt"]) - + data["R"] - * ( - -2 * data["omega_t"] * data["omega_rt"] * data["omega_r"] - - data["omega_t"] ** 2 * data["omega_rr"] - + data["omega_rrtt"] - ) - + data["omega_r"] - * ( - -data["omega_t"] ** 2 * data["R_r"] - - data["R"] * data["omega_tt"] * data["omega_r"] - - 2 - * data["omega_t"] - * (data["R_t"] * data["omega_r"] + data["R"] * data["omega_rt"]) - + data["R_rtt"] - ), - data["Z_rrtt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rrt"] = rpz2xyz_vec(data["e_theta_rrt"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rrz", - label="\\partial_{\\rho \\rho \\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, third derivative wrt radial coordinate" - " twice and toroidal once" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rr", - "R_rt", - "R_rrt", - "R_rtz", - "R_rrtz", - "R_rz", - "R_rrz", - "R_t", - "R_tz", - "R_z", - "Z_rrtz", - "omega_r", - "omega_rr", - "omega_rt", - "omega_rrt", - "omega_rtz", - "omega_rrtz", - "omega_rz", - "omega_rrz", - "omega_t", - "omega_tz", - "omega_z", - "phi", - ], - basis="basis", -) -def _e_sub_theta_rrz(params, transforms, profiles, data, **kwargs): - data["e_theta_rrz"] = jnp.array( - [ - -data["omega_rz"] * data["R_t"] * data["omega_r"] - - (1 + data["omega_z"]) - * (data["R_rt"] * data["omega_r"] + data["R_t"] * data["omega_rr"]) - - data["R_r"] * data["omega_tz"] * data["omega_r"] - - data["R"] - * ( - data["omega_rtz"] * data["omega_r"] - + data["omega_tz"] * data["omega_rr"] - ) - - data["omega_rt"] - * ( - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"] - ) - - data["omega_t"] - * ( - data["omega_rz"] * data["R_r"] - + (1 + data["omega_z"]) * data["R_rr"] - + data["R_rz"] * data["omega_r"] - + data["R_z"] * data["omega_rr"] - + data["R_r"] * data["omega_rz"] - + data["R"] * data["omega_rrz"] - ) - - data["R_r"] * (1 + data["omega_z"]) * data["omega_rt"] - - data["R"] - * ( - data["omega_rz"] * data["omega_rt"] - + (1 + data["omega_z"]) * data["omega_rrt"] - ) - + data["R_rrtz"] - - data["omega_r"] - * ( - data["omega_tz"] * data["R_r"] - + data["R_tz"] * data["omega_r"] - + data["omega_t"] * data["R_rz"] - + data["R_t"] * data["omega_rz"] - + data["R_rt"] - + data["omega_z"] * data["R_rt"] - + data["R_z"] * data["omega_rt"] - + data["R"] - * ( - -(1 + data["omega_z"]) * data["omega_t"] * data["omega_r"] - + data["omega_rtz"] - ) - ), - data["omega_rtz"] * data["R_r"] - + data["omega_tz"] * data["R_rr"] - + data["R_rtz"] * data["omega_r"] - + data["R_tz"] * data["omega_rr"] - + data["omega_rt"] * data["R_rz"] - + data["omega_t"] * data["R_rrz"] - + data["R_rt"] * data["omega_rz"] - + data["R_t"] * data["omega_rrz"] - + data["R_rrt"] - + data["omega_rz"] * data["R_rt"] - + data["omega_z"] * data["R_rrt"] - + data["R_rz"] * data["omega_rt"] - + data["R_z"] * data["omega_rrt"] - + data["R_r"] - * ( - -(1 + data["omega_z"]) * data["omega_t"] * data["omega_r"] - + data["omega_rtz"] - ) - + data["R"] - * ( - -data["omega_rz"] * data["omega_t"] * data["omega_r"] - - (1 + data["omega_z"]) - * ( - data["omega_rt"] * data["omega_r"] - + data["omega_t"] * data["omega_rr"] - ) - + data["omega_rrtz"] - ) - + data["omega_r"] - * ( - -(1 + data["omega_z"]) * data["R_t"] * data["omega_r"] - - data["R"] * data["omega_tz"] * data["omega_r"] - - data["omega_t"] - * ( - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"] - ) - - data["R"] * (1 + data["omega_z"]) * data["omega_rt"] - + data["R_rtz"] - ), - data["Z_rrtz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rrz"] = rpz2xyz_vec(data["e_theta_rrz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rt", - label="\\partial_{\\rho \\theta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, second derivative wrt radial and poloidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rt", - "R_rtt", - "R_t", - "R_tt", - "Z_rtt", - "omega_r", - "omega_rt", - "omega_rtt", - "omega_t", - "omega_tt", - "phi", - ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.ZernikeRZToroidalSection", - ], - basis="basis", -) -def _e_sub_theta_rt(params, transforms, profiles, data, **kwargs): - data["e_theta_rt"] = jnp.array( - [ - -data["omega_t"] ** 2 * data["R_r"] - - data["R"] * data["omega_tt"] * data["omega_r"] - - 2 - * data["omega_t"] - * (data["R_t"] * data["omega_r"] + data["R"] * data["omega_rt"]) - + data["R_rtt"], - data["omega_tt"] * data["R_r"] - + data["R_tt"] * data["omega_r"] - + 2 * data["omega_t"] * data["R_rt"] - + 2 * data["R_t"] * data["omega_rt"] - + data["R"] * (-data["omega_t"] ** 2 * data["omega_r"] + data["omega_rtt"]), - data["Z_rtt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rt"] = rpz2xyz_vec(data["e_theta_rt"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rtt", - label="\\partial_{\\rho \\theta \\theta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, third derivative wrt radial coordinate" - " once and poloidal twice" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_t", - "R_rt", - "R_tt", - "R_rtt", - "R_ttt", - "R_rttt", - "Z_rttt", - "omega_r", - "omega_t", - "omega_rt", - "omega_tt", - "omega_rtt", - "omega_ttt", - "omega_rttt", - "phi", - ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.ZernikeRZToroidalSection", - ], - basis="basis", -) -def _e_sub_theta_rtt(params, transforms, profiles, data, **kwargs): - data["e_theta_rtt"] = jnp.array( - [ - -3 * data["R_rt"] * data["omega_t"] ** 2 - - 3 - * data["omega_t"] - * ( - data["R_r"] * data["omega_tt"] - + data["R_tt"] * data["omega_r"] - + 2 * data["R_t"] * data["omega_rt"] - + data["R"] * data["omega_rt"] - ) - - data["omega_r"] - * (3 * data["R_t"] * data["omega_tt"] + data["R"] * data["omega_ttt"]) - + data["R"] - * ( - data["omega_r"] * data["omega_t"] ** 3 - - 3 * data["omega_rt"] * data["omega_tt"] - ) - + data["R_rttt"], - data["R_r"] * (data["omega_ttt"] - data["omega_t"] ** 3) - + data["omega_r"] - * ( - data["R_ttt"] - - 3 - * data["omega_t"] - * (data["R_t"] * data["omega_t"] + data["R"] * data["omega_tt"]) - ) - + 3 - * ( - data["R_rt"] * data["omega_tt"] - + data["R_tt"] * data["omega_rt"] - + data["R_rtt"] * data["omega_t"] - + data["R_t"] * data["omega_rtt"] - ) - + data["R"] - * (data["omega_rttt"] - 3 * data["omega_t"] ** 2 * data["omega_rt"]), - data["Z_rttt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rtt"] = rpz2xyz_vec(data["e_theta_rtt"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rtz", - label="\\partial_{\\rho \\theta \\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, third derivative wrt radial, poloidal," - " and toroidal coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_t", - "R_rt", - "R_tt", - "R_rtt", - "R_ttz", - "R_rttz", - "R_tz", - "R_rtz", - "R_z", - "R_rz", - "Z_rttz", - "omega_r", - "omega_t", - "omega_rt", - "omega_tt", - "omega_rtt", - "omega_ttz", - "omega_rttz", - "omega_tz", - "omega_rtz", - "omega_z", - "omega_rz", - "phi", - ], - basis="basis", -) -def _e_sub_theta_rtz(params, transforms, profiles, data, **kwargs): - data["e_theta_rtz"] = jnp.array( - [ - -2 * data["omega_rz"] * data["R_t"] * data["omega_t"] - - 2 - * (1 + data["omega_z"]) - * (data["R_rt"] * data["omega_t"] + data["R_t"] * data["omega_rt"]) - - data["R_rz"] * data["omega_t"] ** 2 - - 2 * data["R_z"] * data["omega_t"] * data["omega_rt"] - - 2 * data["R_r"] * data["omega_t"] * data["omega_tz"] - - 2 - * data["R"] - * ( - data["omega_rt"] * data["omega_tz"] - + data["omega_t"] * data["omega_rtz"] - ) - - data["R_r"] * (1 + data["omega_z"]) * data["omega_tt"] - - data["R"] - * ( - data["omega_rz"] * data["omega_tt"] - + (1 + data["omega_z"]) * data["omega_rtt"] - ) - + data["R_rttz"] - - data["omega_r"] - * ( - 2 * data["omega_t"] * data["R_tz"] - + 2 * data["R_t"] * data["omega_tz"] - + (1 + data["omega_z"]) * data["R_tt"] - + data["R_z"] * data["omega_tt"] - - data["R"] - * ((1 + data["omega_z"]) * data["omega_t"] ** 2 - data["omega_ttz"]) - ), - 2 * data["omega_rt"] * data["R_tz"] - + 2 * data["omega_t"] * data["R_rtz"] - + 2 * data["R_rt"] * data["omega_tz"] - + 2 * data["R_t"] * data["omega_rtz"] - + data["omega_rz"] * data["R_tt"] - + (1 + data["omega_z"]) * data["R_rtt"] - + data["R_rz"] * data["omega_tt"] - + data["R_z"] * data["omega_rtt"] - - data["R_r"] - * ((1 + data["omega_z"]) * data["omega_t"] ** 2 - data["omega_ttz"]) - - data["R"] - * ( - data["omega_rz"] * data["omega_t"] ** 2 - + (1 + data["omega_z"]) * 2 * data["omega_t"] * data["omega_rt"] - - data["omega_rttz"] - ) - + data["omega_r"] - * ( - -2 * (1 + data["omega_z"]) * data["R_t"] * data["omega_t"] - - data["R_z"] * data["omega_t"] ** 2 - - 2 * data["R"] * data["omega_t"] * data["omega_tz"] - - data["R"] * (1 + data["omega_z"]) * data["omega_tt"] - + data["R_ttz"] - ), - data["Z_rttz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rtz"] = rpz2xyz_vec(data["e_theta_rtz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rz", - label="\\partial_{\\rho \\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, second derivative wrt radial and toroidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rt", - "R_rtz", - "R_rz", - "R_t", - "R_tz", - "R_z", - "Z_rtz", - "omega_r", - "omega_rt", - "omega_rtz", - "omega_rz", - "omega_t", - "omega_tz", - "omega_z", - "phi", - ], - basis="basis", -) -def _e_sub_theta_rz(params, transforms, profiles, data, **kwargs): - data["e_theta_rz"] = jnp.array( - [ - -(1 + data["omega_z"]) * data["R_t"] * data["omega_r"] - - data["R"] * data["omega_tz"] * data["omega_r"] - - data["omega_t"] - * ( - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"] - ) - - data["R"] * (1 + data["omega_z"]) * data["omega_rt"] - + data["R_rtz"], - data["omega_tz"] * data["R_r"] - + data["R_tz"] * data["omega_r"] - + data["omega_t"] * data["R_rz"] - + data["R_t"] * data["omega_rz"] - + data["R_rt"] - + data["omega_z"] * data["R_rt"] - + data["R_z"] * data["omega_rt"] - + data["R"] - * ( - -(1 + data["omega_z"]) * data["omega_t"] * data["omega_r"] - + data["omega_rtz"] - ), - data["Z_rtz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rz"] = rpz2xyz_vec(data["e_theta_rz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_rzz", - label="\\partial_{\\rho \\zeta \\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, third derivative wrt radial coordinate" - " once and toroidal twice" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_t", - "R_rt", - "R_tz", - "R_rtz", - "R_tzz", - "R_rtzz", - "R_z", - "R_rz", - "R_zz", - "R_rzz", - "Z_rtzz", - "omega_t", - "omega_rt", - "omega_tz", - "omega_rtz", - "omega_tzz", - "omega_rtzz", - "omega_r", - "omega_z", - "omega_zz", - "omega_rz", - "omega_rzz", - "phi", - ], - basis="basis", -) -def _e_sub_theta_rzz(params, transforms, profiles, data, **kwargs): - data["e_theta_rzz"] = jnp.array( - [ - -2 * (1 + data["omega_z"]) * data["omega_rz"] * data["R_t"] - - (1 + data["omega_z"]) ** 2 * data["R_rt"] - - 2 * data["R_rz"] * (1 + data["omega_z"]) * data["omega_t"] - - 2 - * data["R_z"] - * ( - data["omega_rz"] * data["omega_t"] - + (1 + data["omega_z"]) * data["omega_rt"] - ) - - data["R_r"] * data["omega_zz"] * data["omega_t"] - - data["R"] - * ( - data["omega_rzz"] * data["omega_t"] - + data["omega_zz"] * data["omega_rt"] - ) - - 2 * data["R_r"] * (1 + data["omega_z"]) * data["omega_tz"] - - 2 - * data["R"] - * ( - data["omega_rz"] * data["omega_tz"] - + (1 + data["omega_z"]) * data["omega_rtz"] - ) - + data["R_rtzz"] - - data["omega_r"] - * ( - data["omega_zz"] * data["R_t"] - + data["R_zz"] * data["omega_t"] - + 2 * (1 + data["omega_z"]) * data["R_tz"] - + 2 * data["R_z"] * data["omega_tz"] - - data["R"] - * ((1 + data["omega_z"]) ** 2 * data["omega_t"] - data["omega_tzz"]) - ), - data["omega_rzz"] * data["R_t"] - + data["omega_zz"] * data["R_rt"] - + data["R_rzz"] * data["omega_t"] - + data["R_zz"] * data["omega_rt"] - + 2 * data["omega_rz"] * data["R_tz"] - + 2 * (1 + data["omega_z"]) * data["R_rtz"] - + 2 * data["R_rz"] * data["omega_tz"] - + 2 * data["R_z"] * data["omega_rtz"] - - data["R_r"] - * ((1 + data["omega_z"]) ** 2 * data["omega_t"] - data["omega_tzz"]) - - data["R"] - * ( - 2 * (1 + data["omega_z"]) * data["omega_rz"] * data["omega_t"] - + (1 + data["omega_z"]) ** 2 * data["omega_rt"] - - data["omega_rtzz"] - ) - + data["omega_r"] - * ( - -((1 + data["omega_z"]) ** 2) * data["R_t"] - - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_t"] - - data["R"] * data["omega_zz"] * data["omega_t"] - - 2 * data["R"] * (1 + data["omega_z"]) * data["omega_tz"] - + data["R_tzz"] - ), - data["Z_rtzz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_rzz"] = rpz2xyz_vec(data["e_theta_rzz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_t", - label="\\partial_{\\theta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description="Covariant Poloidal basis vector, derivative wrt poloidal coordinate", - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=["R", "R_t", "R_tt", "Z_tt", "omega_t", "omega_tt", "phi"], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.core.Surface", - ], - basis="basis", -) -def _e_sub_theta_t(params, transforms, profiles, data, **kwargs): - data["e_theta_t"] = jnp.array( - [ - -data["R"] * data["omega_t"] ** 2 + data["R_tt"], - 2 * data["R_t"] * data["omega_t"] + data["R"] * data["omega_tt"], - data["Z_tt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_t"] = rpz2xyz_vec(data["e_theta_t"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_tt", - label="\\partial_{\\theta \\theta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, second derivative wrt poloidal and poloidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_t", - "R_tt", - "R_ttt", - "Z_ttt", - "omega_t", - "omega_tt", - "omega_ttt", - "phi", - ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.core.Surface", - ], - basis="basis", -) -def _e_sub_theta_tt(params, transforms, profiles, data, **kwargs): - data["e_theta_tt"] = jnp.array( - [ - -3 * data["R_t"] * data["omega_t"] ** 2 - - 3 * data["R"] * data["omega_t"] * data["omega_tt"] - + data["R_ttt"], - 3 * (data["omega_t"] * data["R_tt"] + data["R_t"] * data["omega_tt"]) - + data["R"] * (-data["omega_t"] ** 3 + data["omega_ttt"]), - data["Z_ttt"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_tt"] = rpz2xyz_vec(data["e_theta_tt"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_tz", - label="\\partial_{\\theta \\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, second derivative wrt poloidal and toroidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_t", - "R_tt", - "R_ttz", - "R_tz", - "R_z", - "Z_ttz", - "omega_t", - "omega_tt", - "omega_ttz", - "omega_tz", - "omega_z", - "phi", - ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.FourierRZToroidalSurface", - ], - basis="basis", -) -def _e_sub_theta_tz(params, transforms, profiles, data, **kwargs): - data["e_theta_tz"] = jnp.array( - [ - -2 * (1 + data["omega_z"]) * data["R_t"] * data["omega_t"] - - data["R_z"] * data["omega_t"] ** 2 - - 2 * data["R"] * data["omega_t"] * data["omega_tz"] - - data["R"] * (1 + data["omega_z"]) * data["omega_tt"] - + data["R_ttz"], - 2 * data["omega_t"] * data["R_tz"] - + 2 * data["R_t"] * data["omega_tz"] - + (1 + data["omega_z"]) * data["R_tt"] - + data["R_z"] * data["omega_tt"] - - data["R"] - * ((1 + data["omega_z"]) * data["omega_t"] ** 2 - data["omega_ttz"]), - data["Z_ttz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_tz"] = rpz2xyz_vec(data["e_theta_tz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_z", - label="\\partial_{\\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description="Covariant Poloidal basis vector, derivative wrt toroidal coordinate", - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=["R", "R_t", "R_tz", "R_z", "Z_tz", "omega_t", "omega_tz", "omega_z", "phi"], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.FourierRZToroidalSurface", - ], - basis="basis", -) -def _e_sub_theta_z(params, transforms, profiles, data, **kwargs): - data["e_theta_z"] = jnp.array( - [ - -data["R"] * (1 + data["omega_z"]) * data["omega_t"] + data["R_tz"], - (1 + data["omega_z"]) * data["R_t"] - + data["R_z"] * data["omega_t"] - + data["R"] * data["omega_tz"], - data["Z_tz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_z"] = rpz2xyz_vec(data["e_theta_z"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_theta_zz", - label="\\partial_{\\zeta \\zeta} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description=( - "Covariant Poloidal basis vector, second derivative wrt toroidal and toroidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_t", - "R_tz", - "R_tzz", - "R_z", - "R_zz", - "Z_tzz", - "omega_t", - "omega_tz", - "omega_tzz", - "omega_z", - "omega_zz", - "phi", - ], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.FourierRZToroidalSurface", - ], - basis="basis", -) -def _e_sub_theta_zz(params, transforms, profiles, data, **kwargs): - data["e_theta_zz"] = jnp.array( - [ - -((1 + data["omega_z"]) ** 2) * data["R_t"] - - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_t"] - - data["R"] * data["omega_zz"] * data["omega_t"] - - 2 * data["R"] * (1 + data["omega_z"]) * data["omega_tz"] - + data["R_tzz"], - data["omega_zz"] * data["R_t"] - + data["R_zz"] * data["omega_t"] - + 2 * (1 + data["omega_z"]) * data["R_tz"] - + 2 * data["R_z"] * data["omega_tz"] - - data["R"] - * ((1 + data["omega_z"]) ** 2 * data["omega_t"] - data["omega_tzz"]), - data["Z_tzz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_theta_zz"] = rpz2xyz_vec(data["e_theta_zz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta", - label="\\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description="Covariant Toroidal basis vector", - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=["R", "R_z", "Z_z", "omega_z", "phi"], - parameterization=[ - "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.FourierRZToroidalSurface", - ], - basis="basis", -) -def _e_sub_zeta(params, transforms, profiles, data, **kwargs): - data["e_zeta"] = jnp.array( - [data["R_z"], data["R"] * (1 + data["omega_z"]), data["Z_z"]] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta"] = rpz2xyz_vec(data["e_zeta"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_r", - label="\\partial_{\\rho} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description="Covariant Toroidal basis vector, derivative wrt radial coordinate", - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=["R", "R_r", "R_rz", "R_z", "Z_rz", "omega_r", "omega_rz", "omega_z", "phi"], - basis="basis", -) -def _e_sub_zeta_r(params, transforms, profiles, data, **kwargs): - data["e_zeta_r"] = jnp.array( - [ - -data["R"] * (1 + data["omega_z"]) * data["omega_r"] + data["R_rz"], - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"], - data["Z_rz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_r"] = rpz2xyz_vec(data["e_zeta_r"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rr", - label="\\partial_{\\rho \\rho} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, second derivative wrt radial and radial" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rr", - "R_rrz", - "R_rz", - "R_z", - "Z_rrz", - "omega_r", - "omega_rr", - "omega_rrz", - "omega_rz", - "omega_z", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rr(params, transforms, profiles, data, **kwargs): - data["e_zeta_rr"] = jnp.array( - [ - -2 * (1 + data["omega_z"]) * data["R_r"] * data["omega_r"] - - data["R_z"] * data["omega_r"] ** 2 - - 2 * data["R"] * data["omega_r"] * data["omega_rz"] - - data["R"] * data["omega_rr"] * (1 + data["omega_z"]) - + data["R_rrz"], - 2 * data["omega_r"] * data["R_rz"] - + 2 * data["R_r"] * data["omega_rz"] - + data["R_rr"] * (1 + data["omega_z"]) - + data["R_z"] * data["omega_rr"] - - data["R"] - * ((1 + data["omega_z"]) * data["omega_r"] ** 2 - data["omega_rrz"]), - data["Z_rrz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rr"] = rpz2xyz_vec(data["e_zeta_rr"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rrr", - label="\\partial_{\\rho \\rho \\rho} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, third derivative wrt radial coordinate" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_z", - "R_rr", - "R_rz", - "R_rrr", - "R_rrz", - "R_rrrz", - "Z_rrrz", - "omega_r", - "omega_z", - "omega_rr", - "omega_rz", - "omega_rrr", - "omega_rrz", - "omega_rrrz", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rrr(params, transforms, profiles, data, **kwargs): - data["e_zeta_rrr"] = jnp.array( - [ - -3 * data["R"] * data["omega_rrz"] * data["omega_r"] - - 3 - * data["omega_rz"] - * (2 * data["R_r"] * data["omega_r"] + data["R"] * data["omega_rr"]) - - 3 - * data["omega_z"] - * (data["R_rr"] * data["omega_r"] + data["R_r"] * data["omega_rr"]) - - (1 + data["omega_z"]) - * data["R"] - * (data["omega_rrr"] - data["omega_r"] ** 3) - - 3 - * data["omega_r"] - * ( - data["R_rz"] * data["omega_r"] - + data["R_z"] * data["omega_rr"] - + data["R_rr"] - ) - - 3 * data["R_r"] * data["omega_rr"] - + data["R_rrrz"], - 3 - * data["R_r"] - * (data["omega_rrz"] - (1 + data["omega_z"]) * data["omega_r"] ** 2) - + 3 * data["omega_rz"] * data["R_rr"] - + data["R"] - * ( - data["omega_rrrz"] - - 3 - * data["omega_r"] - * ( - data["omega_rz"] * data["omega_r"] - + (1 + data["omega_z"]) * data["omega_rr"] - ) - ) - + (1 + data["omega_z"]) * data["R_rrr"] - + 3 * data["R_rrz"] * data["omega_r"] - + 3 * data["R_rz"] * data["omega_rr"] - + data["R_z"] * (data["omega_rrr"] - data["omega_r"] ** 3), - data["Z_rrrz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rrr"] = rpz2xyz_vec(data["e_zeta_rrr"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rrt", - label="\\partial_{\\rho \\theta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, third derivative wrt radial coordinate" - " twice and poloidal once" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rr", - "R_rt", - "R_rrt", - "R_rtz", - "R_rrtz", - "R_rz", - "R_rrz", - "R_t", - "R_tz", - "R_z", - "Z_rrtz", - "omega_r", - "omega_rr", - "omega_rt", - "omega_rrt", - "omega_rtz", - "omega_rrtz", - "omega_rz", - "omega_rrz", - "omega_t", - "omega_tz", - "omega_z", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rrt(params, transforms, profiles, data, **kwargs): - data["e_zeta_rrt"] = jnp.array( - [ - -(data["omega_rz"] * data["R_t"] * data["omega_r"]) - - (1 + data["omega_z"]) - * (data["R_rt"] * data["omega_r"] + data["R_t"] * data["omega_rr"]) - - data["R_r"] * data["omega_tz"] * data["omega_r"] - - data["R"] - * ( - data["omega_rtz"] * data["omega_r"] - + data["omega_tz"] * data["omega_rr"] - ) - - data["omega_rt"] - * ( - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"] - ) - - data["omega_t"] - * ( - data["omega_rz"] * data["R_r"] - + (1 + data["omega_z"]) * data["R_rr"] - + data["R_rz"] * data["omega_r"] - + data["R_z"] * data["omega_rr"] - + data["R_r"] * data["omega_rz"] - + data["R"] * data["omega_rrz"] - ) - - data["R_r"] * (1 + data["omega_z"]) * data["omega_rt"] - - data["R"] - * ( - data["omega_rz"] * data["omega_rt"] - + (1 + data["omega_z"]) * data["omega_rrt"] - ) - + data["R_rrtz"] - - data["omega_r"] - * ( - data["omega_tz"] * data["R_r"] - + data["R_tz"] * data["omega_r"] - + data["omega_t"] * data["R_rz"] - + data["R_t"] * data["omega_rz"] - + data["R_rt"] - + data["omega_z"] * data["R_rt"] - + data["R_z"] * data["omega_rt"] - + data["R"] - * ( - -(1 + data["omega_z"]) * data["omega_t"] * data["omega_r"] - + data["omega_rtz"] - ) - ), - data["omega_rtz"] * data["R_r"] - + data["omega_tz"] * data["R_rr"] - + data["R_rtz"] * data["omega_r"] - + data["R_tz"] * data["omega_rr"] - + data["omega_rt"] * data["R_rz"] - + data["omega_t"] * data["R_rrz"] - + data["R_rt"] * data["omega_rz"] - + data["R_t"] * data["omega_rrz"] - + data["R_rrt"] - + data["omega_rz"] * data["R_rt"] - + data["omega_z"] * data["R_rrt"] - + data["R_rz"] * data["omega_rt"] - + data["R_z"] * data["omega_rrt"] - + data["R_r"] - * ( - -(1 + data["omega_z"]) * data["omega_t"] * data["omega_r"] - + data["omega_rtz"] - ) - + data["R"] - * ( - -data["omega_rz"] * data["omega_t"] * data["omega_r"] - - (1 + data["omega_z"]) - * ( - data["omega_rt"] * data["omega_r"] - + data["omega_t"] * data["omega_rr"] - ) - + data["omega_rrtz"] - ) - + data["omega_r"] - * ( - -(1 + data["omega_z"]) * data["R_t"] * data["omega_r"] - - data["R"] * data["omega_tz"] * data["omega_r"] - - data["omega_t"] - * ( - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"] - ) - - data["R"] * (1 + data["omega_z"]) * data["omega_rt"] - + data["R_rtz"] - ), - data["Z_rrtz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rrt"] = rpz2xyz_vec(data["e_zeta_rrt"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rrz", - label="\\partial_{\\rho \\rho \\zeta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, third derivative wrt radial coordinate" - " twice and toroidal once" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rr", - "R_rz", - "R_rrz", - "R_rzz", - "R_rrzz", - "R_z", - "R_zz", - "Z_rrzz", - "omega_r", - "omega_rr", - "omega_rz", - "omega_rrz", - "omega_rzz", - "omega_rrzz", - "omega_z", - "omega_zz", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rrz(params, transforms, profiles, data, **kwargs): - data["e_zeta_rrz"] = jnp.array( - [ - -2 * ((1 + data["omega_z"]) * data["omega_rz"]) * data["R_r"] - - ((1 + data["omega_z"]) ** 2) * data["R_rr"] - - 2 * data["R_rz"] * (1 + data["omega_z"]) * data["omega_r"] - - 2 - * data["R_z"] - * ( - data["omega_rz"] * data["omega_r"] - + (1 + data["omega_z"]) * data["omega_rr"] - ) - - data["R_r"] * data["omega_zz"] * data["omega_r"] - - data["R"] - * ( - data["omega_rzz"] - * data["omega_r"] - * data["omega_zz"] - * data["omega_rr"] - ) - - 2 * data["R_r"] * (1 + data["omega_z"]) * data["omega_rz"] - - 2 - * data["R"] - * (data["omega_rz"] ** 2 + (1 + data["omega_z"]) * data["omega_rrz"]) - + data["R_rrzz"] - - data["omega_r"] - * ( - data["omega_zz"] * data["R_r"] - + data["R_zz"] * data["omega_r"] - + 2 * (1 + data["omega_z"]) * data["R_rz"] - + 2 * data["R_z"] * data["omega_rz"] - - data["R"] - * ((1 + data["omega_z"]) ** 2 * data["omega_r"] - data["omega_rzz"]) - ), - data["omega_rzz"] * data["R_r"] - + data["omega_zz"] * data["R_rr"] - + data["R_rzz"] * data["omega_r"] - + data["R_zz"] * data["omega_rr"] - + 2 * data["omega_rz"] * data["R_rz"] - + 2 * (1 + data["omega_z"]) * data["R_rrz"] - + 2 * data["R_rz"] * data["omega_rz"] - + 2 * data["R_z"] * data["omega_rrz"] - - data["R_r"] - * ((1 + data["omega_z"]) ** 2 * data["omega_r"] - data["omega_rzz"]) - - data["R"] - * ( - 2 * (1 + data["omega_z"]) * data["omega_rz"] * data["omega_r"] - + (1 + data["omega_z"]) ** 2 * data["omega_rr"] - - data["omega_rrzz"] - ) - + data["omega_r"] - * ( - -((1 + data["omega_z"]) ** 2) * data["R_r"] - - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_r"] - - data["R"] * data["omega_zz"] * data["omega_r"] - - 2 * data["R"] * (1 + data["omega_z"]) * data["omega_rz"] - + data["R_rzz"] - ), - data["Z_rrzz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rrz"] = rpz2xyz_vec(data["e_zeta_rrz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rt", - label="\\partial_{\\rho \\theta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, second derivative wrt radial and poloidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rt", - "R_rtz", - "R_rz", - "R_t", - "R_tz", - "R_z", - "Z_rtz", - "omega_r", - "omega_rt", - "omega_rtz", - "omega_rz", - "omega_t", - "omega_tz", - "omega_z", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rt(params, transforms, profiles, data, **kwargs): - data["e_zeta_rt"] = jnp.array( - [ - -(1 + data["omega_z"]) * data["R_t"] * data["omega_r"] - - data["R"] * data["omega_tz"] * data["omega_r"] - - data["omega_t"] - * ( - (1 + data["omega_z"]) * data["R_r"] - + data["R_z"] * data["omega_r"] - + data["R"] * data["omega_rz"] - ) - - data["R"] * (1 + data["omega_z"]) * data["omega_rt"] - + data["R_rtz"], - data["omega_tz"] * data["R_r"] - + data["R_tz"] * data["omega_r"] - + data["omega_t"] * data["R_rz"] - + data["R_t"] * data["omega_rz"] - + data["R_rt"] - + data["omega_z"] * data["R_rt"] - + data["R_z"] * data["omega_rt"] - + data["R"] - * ( - -(1 + data["omega_z"]) * data["omega_t"] * data["omega_r"] - + data["omega_rtz"] - ), - data["Z_rtz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rt"] = rpz2xyz_vec(data["e_zeta_rt"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rtt", - label="\\partial_{\\rho \\theta \\theta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, third derivative wrt radial coordinate" - " once and poloidal twice" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_t", - "R_rt", - "R_tt", - "R_rtt", - "R_ttz", - "R_rttz", - "R_tz", - "R_rtz", - "R_z", - "R_rz", - "Z_rttz", - "omega_r", - "omega_t", - "omega_rt", - "omega_tt", - "omega_rtt", - "omega_ttz", - "omega_rttz", - "omega_tz", - "omega_rtz", - "omega_z", - "omega_rz", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rtt(params, transforms, profiles, data, **kwargs): - data["e_zeta_rtt"] = jnp.array( - [ - -2 * data["omega_rz"] * data["R_t"] * data["omega_t"] - - 2 - * (1 + data["omega_z"]) - * (data["R_rt"] * data["omega_t"] + data["R_t"] * data["omega_rt"]) - - data["R_rz"] * data["omega_t"] ** 2 - - data["R_z"] * 2 * data["omega_t"] * data["omega_rt"] - - 2 * data["R_r"] * data["omega_t"] * data["omega_tz"] - - 2 - * data["R"] - * ( - data["omega_rt"] * data["omega_tz"] - + data["omega_t"] * data["omega_rtz"] - ) - - data["R_r"] * (1 + data["omega_z"]) * data["omega_tt"] - - data["R"] - * ( - data["omega_rz"] * data["omega_tt"] - + (1 + data["omega_z"]) * data["omega_rtt"] - ) - + data["R_rttz"] + + data["R_rttz"] - data["omega_r"] * ( 2 * data["omega_t"] * data["R_tz"] @@ -4316,18 +2827,18 @@ def _e_sub_zeta_rtt(params, transforms, profiles, data, **kwargs): ] ).T if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rtt"] = rpz2xyz_vec(data["e_zeta_rtt"], phi=data["phi"]) + data["e_theta_rtz"] = rpz2xyz_vec(data["e_theta_rtz"], phi=data["phi"]) return data @register_compute_fun( - name="e_zeta_rtz", - label="\\partial_{\\rho \\theta \\zeta} \\mathbf{e}_{\\zeta}", + name="e_theta_rzz", + label="\\partial_{\\rho \\zeta \\zeta} \\mathbf{e}_{\\theta}", units="m", units_long="meters", description=( - "Covariant Toroidal basis vector, third derivative wrt radial, poloidal," - " and toroidal coordinates" + "Covariant Poloidal basis vector, third derivative wrt radial coordinate" + " once and toroidal twice" ), dim=3, params=[], @@ -4348,23 +2859,24 @@ def _e_sub_zeta_rtt(params, transforms, profiles, data, **kwargs): "R_zz", "R_rzz", "Z_rtzz", - "omega_r", "omega_t", "omega_rt", "omega_tz", "omega_rtz", "omega_tzz", "omega_rtzz", + "omega_r", "omega_z", - "omega_rz", "omega_zz", + "omega_rz", "omega_rzz", "phi", ], basis="basis", + aliases=["e_rho_tzz", "e_zeta_rtz"], ) -def _e_sub_zeta_rtz(params, transforms, profiles, data, **kwargs): - data["e_zeta_rtz"] = jnp.array( +def _e_sub_theta_rzz(params, transforms, profiles, data, **kwargs): + data["e_theta_rzz"] = jnp.array( [ -2 * (1 + data["omega_z"]) * data["omega_rz"] * data["R_t"] - (1 + data["omega_z"]) ** 2 * data["R_rt"] @@ -4378,10 +2890,8 @@ def _e_sub_zeta_rtz(params, transforms, profiles, data, **kwargs): - data["R_r"] * data["omega_zz"] * data["omega_t"] - data["R"] * ( - data["omega_rzz"] - * data["omega_t"] - * data["omega_zz"] - * data["omega_rt"] + data["omega_rzz"] * data["omega_t"] + + data["omega_zz"] * data["omega_rt"] ) - 2 * data["R_r"] * (1 + data["omega_z"]) * data["omega_tz"] - 2 @@ -4406,217 +2916,117 @@ def _e_sub_zeta_rtz(params, transforms, profiles, data, **kwargs): + data["R_zz"] * data["omega_rt"] + 2 * data["omega_rz"] * data["R_tz"] + 2 * (1 + data["omega_z"]) * data["R_rtz"] - + 2 * data["R_rz"] * data["omega_tz"] - + 2 * data["R_z"] * data["omega_rtz"] - - data["R_r"] - * ((1 + data["omega_z"]) ** 2 * data["omega_t"] - data["omega_tzz"]) - - data["R"] - * ( - 2 * (1 + data["omega_z"]) * data["omega_rz"] * data["omega_t"] - + (1 + data["omega_z"]) ** 2 * data["omega_rt"] - - data["omega_rtzz"] - ) - + data["omega_r"] - * ( - -((1 + data["omega_z"]) ** 2) * data["R_t"] - - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_t"] - - data["R"] * data["omega_zz"] * data["omega_t"] - - 2 * data["R"] * (1 + data["omega_z"]) * data["omega_tz"] - + data["R_tzz"] - ), - data["Z_rtzz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rtz"] = rpz2xyz_vec(data["e_zeta_rtz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rz", - label="\\partial_{\\rho \\zeta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, second derivative wrt radial and toroidal" - " coordinates" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_rz", - "R_rzz", - "R_z", - "R_zz", - "Z_rzz", - "omega_r", - "omega_rz", - "omega_rzz", - "omega_z", - "omega_zz", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rz(params, transforms, profiles, data, **kwargs): - data["e_zeta_rz"] = jnp.array( - [ - -((1 + data["omega_z"]) ** 2) * data["R_r"] - - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_r"] - - data["R"] * data["omega_zz"] * data["omega_r"] - - 2 * data["R"] * (1 + data["omega_z"]) * data["omega_rz"] - + data["R_rzz"], - data["omega_zz"] * data["R_r"] - + data["R_zz"] * data["omega_r"] - + 2 * (1 + data["omega_z"]) * data["R_rz"] - + 2 * data["R_z"] * data["omega_rz"] - - data["R"] - * ((1 + data["omega_z"]) ** 2 * data["omega_r"] - data["omega_rzz"]), - data["Z_rzz"], - ] - ).T - if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rz"] = rpz2xyz_vec(data["e_zeta_rz"], phi=data["phi"]) - return data - - -@register_compute_fun( - name="e_zeta_rzz", - label="\\partial_{\\rho \\zeta \\zeta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description=( - "Covariant Toroidal basis vector, third derivative wrt radial coordinate" - " once and toroidal twice" - ), - dim=3, - params=[], - transforms={}, - profiles=[], - coordinates="rtz", - data=[ - "R", - "R_r", - "R_z", - "R_rz", - "R_zz", - "R_rzz", - "R_zzz", - "R_rzzz", - "Z_rzzz", - "omega_r", - "omega_z", - "omega_rz", - "omega_zz", - "omega_rzz", - "omega_zzz", - "omega_rzzz", - "phi", - ], - basis="basis", -) -def _e_sub_zeta_rzz(params, transforms, profiles, data, **kwargs): - data["e_zeta_rzz"] = jnp.array( - [ - -3 * data["R_rz"] * (1 + data["omega_z"]) ** 2 - - 6 * data["R_z"] * (1 + data["omega_z"]) * data["omega_rz"] - - 3 * data["R_r"] * (1 + data["omega_z"]) * data["omega_zz"] - - 3 - * data["R"] - * ( - data["omega_rz"] * data["omega_zz"] - + (1 + data["omega_z"]) * data["omega_rzz"] - ) - + data["R_rzzz"] - - data["omega_r"] - * ( - 3 * (1 + data["omega_z"]) * data["R_zz"] - + 3 * data["R_z"] * data["omega_zz"] - - data["R"] - * ( - 1 - + 3 * data["omega_z"] - + 3 * data["omega_z"] ** 2 - + data["omega_z"] ** 3 - - data["omega_zzz"] - ) - ), - 3 * data["omega_rz"] * data["R_zz"] - + 3 * (1 + data["omega_z"]) * data["R_rzz"] - + 3 * data["R_rz"] * data["omega_zz"] - + 3 * data["R_z"] * data["omega_rzz"] + + 2 * data["R_rz"] * data["omega_tz"] + + 2 * data["R_z"] * data["omega_rtz"] - data["R_r"] - * ( - 1 - + 3 * data["omega_z"] - + 3 * data["omega_z"] ** 2 - + data["omega_z"] ** 3 - - data["omega_zzz"] - ) + * ((1 + data["omega_z"]) ** 2 * data["omega_t"] - data["omega_tzz"]) - data["R"] * ( - 3 * data["omega_rz"] * (1 + data["omega_z"] * (1 + data["omega_z"])) - - data["omega_rzzz"] + 2 * (1 + data["omega_z"]) * data["omega_rz"] * data["omega_t"] + + (1 + data["omega_z"]) ** 2 * data["omega_rt"] + - data["omega_rtzz"] ) + data["omega_r"] * ( - -3 * data["R_z"] * (1 + data["omega_z"]) ** 2 - - 3 * data["R"] * (1 + data["omega_z"]) * data["omega_zz"] - + data["R_zzz"] + -((1 + data["omega_z"]) ** 2) * data["R_t"] + - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_t"] + - data["R"] * data["omega_zz"] * data["omega_t"] + - 2 * data["R"] * (1 + data["omega_z"]) * data["omega_tz"] + + data["R_tzz"] ), - data["Z_rzzz"], + data["Z_rtzz"], ] ).T if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_rzz"] = rpz2xyz_vec(data["e_zeta_rzz"], phi=data["phi"]) + data["e_theta_rzz"] = rpz2xyz_vec(data["e_theta_rzz"], phi=data["phi"]) return data @register_compute_fun( - name="e_zeta_t", - label="\\partial_{\\theta} \\mathbf{e}_{\\zeta}", + name="e_theta_t", + label="\\partial_{\\theta} \\mathbf{e}_{\\theta}", units="m", units_long="meters", - description="Covariant Toroidal basis vector, derivative wrt poloidal coordinate", + description="Covariant Poloidal basis vector, derivative wrt poloidal coordinate", dim=3, params=[], transforms={}, profiles=[], coordinates="rtz", - data=["R", "R_t", "R_tz", "R_z", "Z_tz", "omega_t", "omega_tz", "omega_z", "phi"], + data=["R", "R_t", "R_tt", "Z_tt", "omega_t", "omega_tt", "phi"], parameterization=[ "desc.equilibrium.equilibrium.Equilibrium", - "desc.geometry.surface.FourierRZToroidalSurface", + "desc.geometry.core.Surface", ], basis="basis", ) -def _e_sub_zeta_t(params, transforms, profiles, data, **kwargs): - data["e_zeta_t"] = jnp.array( +def _e_sub_theta_t(params, transforms, profiles, data, **kwargs): + data["e_theta_t"] = jnp.array( [ - -data["R"] * (1 + data["omega_z"]) * data["omega_t"] + data["R_tz"], - (1 + data["omega_z"]) * data["R_t"] - + data["R_z"] * data["omega_t"] - + data["R"] * data["omega_tz"], - data["Z_tz"], + -data["R"] * data["omega_t"] ** 2 + data["R_tt"], + 2 * data["R_t"] * data["omega_t"] + data["R"] * data["omega_tt"], + data["Z_tt"], + ] + ).T + if kwargs.get("basis", "rpz").lower() == "xyz": + data["e_theta_t"] = rpz2xyz_vec(data["e_theta_t"], phi=data["phi"]) + return data + + +@register_compute_fun( + name="e_theta_tt", + label="\\partial_{\\theta \\theta} \\mathbf{e}_{\\theta}", + units="m", + units_long="meters", + description=( + "Covariant Poloidal basis vector, second derivative wrt poloidal and poloidal" + " coordinates" + ), + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=[ + "R", + "R_t", + "R_tt", + "R_ttt", + "Z_ttt", + "omega_t", + "omega_tt", + "omega_ttt", + "phi", + ], + parameterization=[ + "desc.equilibrium.equilibrium.Equilibrium", + "desc.geometry.core.Surface", + ], + basis="basis", +) +def _e_sub_theta_tt(params, transforms, profiles, data, **kwargs): + data["e_theta_tt"] = jnp.array( + [ + -3 * data["R_t"] * data["omega_t"] ** 2 + - 3 * data["R"] * data["omega_t"] * data["omega_tt"] + + data["R_ttt"], + 3 * (data["omega_t"] * data["R_tt"] + data["R_t"] * data["omega_tt"]) + + data["R"] * (-data["omega_t"] ** 3 + data["omega_ttt"]), + data["Z_ttt"], ] ).T if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_t"] = rpz2xyz_vec(data["e_zeta_t"], phi=data["phi"]) + data["e_theta_tt"] = rpz2xyz_vec(data["e_theta_tt"], phi=data["phi"]) return data @register_compute_fun( - name="e_zeta_tt", - label="\\partial_{\\theta \\theta} \\mathbf{e}_{\\zeta}", + name="e_theta_tz", + label="\\partial_{\\theta \\zeta} \\mathbf{e}_{\\theta}", units="m", units_long="meters", description=( - "Covariant Toroidal basis vector, second derivative wrt poloidal and poloidal" + "Covariant Poloidal basis vector, second derivative wrt poloidal and toroidal" " coordinates" ), dim=3, @@ -4644,9 +3054,10 @@ def _e_sub_zeta_t(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.FourierRZToroidalSurface", ], basis="basis", + aliases=["e_zeta_tt"], ) -def _e_sub_zeta_tt(params, transforms, profiles, data, **kwargs): - data["e_zeta_tt"] = jnp.array( +def _e_sub_theta_tz(params, transforms, profiles, data, **kwargs): + data["e_theta_tz"] = jnp.array( [ -2 * (1 + data["omega_z"]) * data["R_t"] * data["omega_t"] - data["R_z"] * data["omega_t"] ** 2 @@ -4663,17 +3074,51 @@ def _e_sub_zeta_tt(params, transforms, profiles, data, **kwargs): ] ).T if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_tt"] = rpz2xyz_vec(data["e_zeta_tt"], phi=data["phi"]) + data["e_theta_tz"] = rpz2xyz_vec(data["e_theta_tz"], phi=data["phi"]) + return data + + +@register_compute_fun( + name="e_theta_z", + label="\\partial_{\\zeta} \\mathbf{e}_{\\theta}", + units="m", + units_long="meters", + description="Covariant Poloidal basis vector, derivative wrt toroidal coordinate", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["R", "R_t", "R_tz", "R_z", "Z_tz", "omega_t", "omega_tz", "omega_z", "phi"], + parameterization=[ + "desc.equilibrium.equilibrium.Equilibrium", + "desc.geometry.surface.FourierRZToroidalSurface", + ], + basis="basis", + aliases=["e_zeta_t"], +) +def _e_sub_theta_z(params, transforms, profiles, data, **kwargs): + data["e_theta_z"] = jnp.array( + [ + -data["R"] * (1 + data["omega_z"]) * data["omega_t"] + data["R_tz"], + (1 + data["omega_z"]) * data["R_t"] + + data["R_z"] * data["omega_t"] + + data["R"] * data["omega_tz"], + data["Z_tz"], + ] + ).T + if kwargs.get("basis", "rpz").lower() == "xyz": + data["e_theta_z"] = rpz2xyz_vec(data["e_theta_z"], phi=data["phi"]) return data @register_compute_fun( - name="e_zeta_tz", - label="\\partial_{\\theta \\zeta} \\mathbf{e}_{\\zeta}", + name="e_theta_zz", + label="\\partial_{\\zeta \\zeta} \\mathbf{e}_{\\theta}", units="m", units_long="meters", description=( - "Covariant Toroidal basis vector, second derivative wrt poloidal and toroidal" + "Covariant Poloidal basis vector, second derivative wrt toroidal and toroidal" " coordinates" ), dim=3, @@ -4701,9 +3146,10 @@ def _e_sub_zeta_tt(params, transforms, profiles, data, **kwargs): "desc.geometry.surface.FourierRZToroidalSurface", ], basis="basis", + aliases=["e_zeta_tz"], ) -def _e_sub_zeta_tz(params, transforms, profiles, data, **kwargs): - data["e_zeta_tz"] = jnp.array( +def _e_sub_theta_zz(params, transforms, profiles, data, **kwargs): + data["e_theta_zz"] = jnp.array( [ -((1 + data["omega_z"]) ** 2) * data["R_t"] - 2 * data["R_z"] * (1 + data["omega_z"]) * data["omega_t"] @@ -4720,7 +3166,126 @@ def _e_sub_zeta_tz(params, transforms, profiles, data, **kwargs): ] ).T if kwargs.get("basis", "rpz").lower() == "xyz": - data["e_zeta_tz"] = rpz2xyz_vec(data["e_zeta_tz"], phi=data["phi"]) + data["e_theta_zz"] = rpz2xyz_vec(data["e_theta_zz"], phi=data["phi"]) + return data + + +@register_compute_fun( + name="e_zeta", + label="\\mathbf{e}_{\\zeta}", + units="m", + units_long="meters", + description="Covariant Toroidal basis vector", + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=["R", "R_z", "Z_z", "omega_z", "phi"], + parameterization=[ + "desc.equilibrium.equilibrium.Equilibrium", + "desc.geometry.surface.FourierRZToroidalSurface", + ], + basis="basis", +) +def _e_sub_zeta(params, transforms, profiles, data, **kwargs): + data["e_zeta"] = jnp.array( + [data["R_z"], data["R"] * (1 + data["omega_z"]), data["Z_z"]] + ).T + if kwargs.get("basis", "rpz").lower() == "xyz": + data["e_zeta"] = rpz2xyz_vec(data["e_zeta"], phi=data["phi"]) + return data + + +@register_compute_fun( + name="e_zeta_rzz", + label="\\partial_{\\rho \\zeta \\zeta} \\mathbf{e}_{\\zeta}", + units="m", + units_long="meters", + description=( + "Covariant Toroidal basis vector, third derivative wrt radial coordinate" + " once and toroidal twice" + ), + dim=3, + params=[], + transforms={}, + profiles=[], + coordinates="rtz", + data=[ + "R", + "R_r", + "R_z", + "R_rz", + "R_zz", + "R_rzz", + "R_zzz", + "R_rzzz", + "Z_rzzz", + "omega_r", + "omega_z", + "omega_rz", + "omega_zz", + "omega_rzz", + "omega_zzz", + "omega_rzzz", + "phi", + ], + basis="basis", +) +def _e_sub_zeta_rzz(params, transforms, profiles, data, **kwargs): + data["e_zeta_rzz"] = jnp.array( + [ + -3 * data["R_rz"] * (1 + data["omega_z"]) ** 2 + - 6 * data["R_z"] * (1 + data["omega_z"]) * data["omega_rz"] + - 3 * data["R_r"] * (1 + data["omega_z"]) * data["omega_zz"] + - 3 + * data["R"] + * ( + data["omega_rz"] * data["omega_zz"] + + (1 + data["omega_z"]) * data["omega_rzz"] + ) + + data["R_rzzz"] + - data["omega_r"] + * ( + 3 * (1 + data["omega_z"]) * data["R_zz"] + + 3 * data["R_z"] * data["omega_zz"] + - data["R"] + * ( + 1 + + 3 * data["omega_z"] + + 3 * data["omega_z"] ** 2 + + data["omega_z"] ** 3 + - data["omega_zzz"] + ) + ), + 3 * data["omega_rz"] * data["R_zz"] + + 3 * (1 + data["omega_z"]) * data["R_rzz"] + + 3 * data["R_rz"] * data["omega_zz"] + + 3 * data["R_z"] * data["omega_rzz"] + - data["R_r"] + * ( + 1 + + 3 * data["omega_z"] + + 3 * data["omega_z"] ** 2 + + data["omega_z"] ** 3 + - data["omega_zzz"] + ) + - data["R"] + * ( + 3 * data["omega_rz"] * (1 + data["omega_z"] * (1 + data["omega_z"])) + - data["omega_rzzz"] + ) + + data["omega_r"] + * ( + -3 * data["R_z"] * (1 + data["omega_z"]) ** 2 + - 3 * data["R"] * (1 + data["omega_z"]) * data["omega_zz"] + + data["R_zzz"] + ), + data["Z_rzzz"], + ] + ).T + if kwargs.get("basis", "rpz").lower() == "xyz": + data["e_zeta_rzz"] = rpz2xyz_vec(data["e_zeta_rzz"], phi=data["phi"]) return data diff --git a/desc/compute/_bootstrap.py b/desc/compute/_bootstrap.py index 33de0d188b..9bfd532775 100644 --- a/desc/compute/_bootstrap.py +++ b/desc/compute/_bootstrap.py @@ -31,6 +31,7 @@ coordinates="r", data=["sqrt(g)", "V_r(r)", "|B|", "<|B|^2>", "max_tz |B|"], axis_limit_data=["sqrt(g)_r", "V_rr(r)"], + resolution_requirement="tz", n_gauss="int: Number of quadrature points to use for estimating trapped fraction. " + "Default 20.", ) diff --git a/desc/compute/_equil.py b/desc/compute/_equil.py index 796ed7e74d..a2f363a710 100644 --- a/desc/compute/_equil.py +++ b/desc/compute/_equil.py @@ -376,6 +376,7 @@ def _J_dot_B(params, transforms, profiles, data, **kwargs): coordinates="r", data=["J*sqrt(g)", "B", "V_r(r)"], axis_limit_data=["(J*sqrt(g))_r", "V_rr(r)"], + resolution_requirement="tz", ) def _J_dot_B_fsa(params, transforms, profiles, data, **kwargs): J = transforms["grid"].replace_at_axis( @@ -534,6 +535,7 @@ def _Fmag(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["|F|", "sqrt(g)", "V"], + resolution_requirement="rtz", ) def _Fmag_vol(params, transforms, profiles, data, **kwargs): data["<|F|>_vol"] = ( @@ -655,6 +657,7 @@ def _F_anisotropic(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["|B|^2", "sqrt(g)"], + resolution_requirement="rtz", ) def _W_B(params, transforms, profiles, data, **kwargs): data["W_B"] = jnp.sum( @@ -675,6 +678,7 @@ def _W_B(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["B", "sqrt(g)"], + resolution_requirement="rtz", ) def _W_Bpol(params, transforms, profiles, data, **kwargs): data["W_Bpol"] = jnp.sum( @@ -697,6 +701,7 @@ def _W_Bpol(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["B", "sqrt(g)"], + resolution_requirement="rtz", ) def _W_Btor(params, transforms, profiles, data, **kwargs): data["W_Btor"] = jnp.sum( @@ -718,6 +723,7 @@ def _W_Btor(params, transforms, profiles, data, **kwargs): coordinates="", data=["p", "sqrt(g)"], gamma="float: Adiabatic index. Default 0", + resolution_requirement="rtz", ) def _W_p(params, transforms, profiles, data, **kwargs): data["W_p"] = jnp.sum(data["p"] * data["sqrt(g)"] * transforms["grid"].weights) / ( diff --git a/desc/compute/_field.py b/desc/compute/_field.py index 6791827487..db84f6818f 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -2698,6 +2698,7 @@ def _grad_B(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["sqrt(g)", "|B|", "V"], + resolution_requirement="rtz", ) def _B_vol(params, transforms, profiles, data, **kwargs): data["<|B|>_vol"] = ( @@ -2718,6 +2719,7 @@ def _B_vol(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["sqrt(g)", "|B|^2", "V"], + resolution_requirement="rtz", ) def _B_rms(params, transforms, profiles, data, **kwargs): data["<|B|>_rms"] = jnp.sqrt( @@ -2740,6 +2742,7 @@ def _B_rms(params, transforms, profiles, data, **kwargs): coordinates="r", data=["sqrt(g)", "|B|"], axis_limit_data=["sqrt(g)_r"], + resolution_requirement="tz", ) def _B_fsa(params, transforms, profiles, data, **kwargs): data["<|B|>"] = surface_averages( @@ -2765,6 +2768,7 @@ def _B_fsa(params, transforms, profiles, data, **kwargs): coordinates="r", data=["sqrt(g)", "|B|^2"], axis_limit_data=["sqrt(g)_r"], + resolution_requirement="tz", ) def _B2_fsa(params, transforms, profiles, data, **kwargs): data["<|B|^2>"] = surface_averages( @@ -2790,6 +2794,7 @@ def _B2_fsa(params, transforms, profiles, data, **kwargs): coordinates="r", data=["sqrt(g)", "|B|"], axis_limit_data=["sqrt(g)_r"], + resolution_requirement="tz", ) def _1_over_B_fsa(params, transforms, profiles, data, **kwargs): data["<1/|B|>"] = surface_averages( @@ -2815,6 +2820,7 @@ def _1_over_B_fsa(params, transforms, profiles, data, **kwargs): coordinates="r", data=["sqrt(g)", "sqrt(g)_r", "B", "B_r", "|B|^2", "V_r(r)", "V_rr(r)"], axis_limit_data=["sqrt(g)_rr", "V_rrr(r)"], + resolution_requirement="tz", ) def _B2_fsa_r(params, transforms, profiles, data, **kwargs): integrate = surface_integrals_map(transforms["grid"]) @@ -2977,6 +2983,7 @@ def _gradB2mag(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["|grad(|B|^2)|/2mu0", "sqrt(g)", "V"], + resolution_requirement="rtz", ) def _gradB2mag_vol(params, transforms, profiles, data, **kwargs): data["<|grad(|B|^2)|/2mu0>_vol"] = ( @@ -3177,6 +3184,7 @@ def _B_dot_grad_B_mag(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["|(B*grad)B|", "sqrt(g)", "V"], + resolution_requirement="rtz", ) def _B_dot_grad_B_mag_vol(params, transforms, profiles, data, **kwargs): data["<|(B*grad)B|>_vol"] = ( @@ -3314,6 +3322,7 @@ def _B_dot_gradB_z(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["|B|"], + resolution_requirement="tz", ) def _min_tz_modB(params, transforms, profiles, data, **kwargs): data["min_tz |B|"] = surface_min(transforms["grid"], data["|B|"]) @@ -3332,6 +3341,7 @@ def _min_tz_modB(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["|B|"], + resolution_requirement="tz", ) def _max_tz_modB(params, transforms, profiles, data, **kwargs): data["max_tz |B|"] = surface_max(transforms["grid"], data["|B|"]) diff --git a/desc/compute/_geometry.py b/desc/compute/_geometry.py index 0dc16a6310..80d8da3738 100644 --- a/desc/compute/_geometry.py +++ b/desc/compute/_geometry.py @@ -27,6 +27,7 @@ profiles=[], coordinates="", data=["sqrt(g)"], + resolution_requirement="rtz", ) def _V(params, transforms, profiles, data, **kwargs): data["V"] = jnp.sum(data["sqrt(g)"] * transforms["grid"].weights) @@ -46,6 +47,7 @@ def _V(params, transforms, profiles, data, **kwargs): coordinates="", data=["e_theta", "e_zeta", "x"], parameterization="desc.geometry.surface.FourierRZToroidalSurface", + resolution_requirement="tz", ) def _V_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): # divergence theorem: integral(dV div [0, 0, Z]) = integral(dS dot [0, 0, Z]) @@ -73,6 +75,7 @@ def _V_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["e_theta", "e_zeta", "Z"], + resolution_requirement="tz", ) def _V_of_r(params, transforms, profiles, data, **kwargs): # divergence theorem: integral(dV div [0, 0, Z]) = integral(dS dot [0, 0, Z]) @@ -97,6 +100,7 @@ def _V_of_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["sqrt(g)"], + resolution_requirement="tz", ) def _V_r_of_r(params, transforms, profiles, data, **kwargs): # eq. 4.9.10 in W.D. D'haeseleer et al. (1991) doi:10.1007/978-3-642-75595-8. @@ -117,6 +121,7 @@ def _V_r_of_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["sqrt(g)_r"], + resolution_requirement="tz", ) def _V_rr_of_r(params, transforms, profiles, data, **kwargs): # The sign of sqrt(g) is enforced to be non-negative. @@ -137,6 +142,7 @@ def _V_rr_of_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["sqrt(g)_rr"], + resolution_requirement="tz", ) def _V_rrr_of_r(params, transforms, profiles, data, **kwargs): # The sign of sqrt(g) is enforced to be non-negative. @@ -160,11 +166,12 @@ def _V_rrr_of_r(params, transforms, profiles, data, **kwargs): "desc.equilibrium.equilibrium.Equilibrium", "desc.geometry.surface.ZernikeRZToroidalSection", ], + resolution_requirement="rt", ) def _A_of_z(params, transforms, profiles, data, **kwargs): data["A(z)"] = surface_integrals( transforms["grid"], - jnp.abs(data["|e_rho x e_theta|"]), + data["|e_rho x e_theta|"], surface_label="zeta", expand_out=True, ) @@ -176,7 +183,7 @@ def _A_of_z(params, transforms, profiles, data, **kwargs): label="A(\\zeta)", units="m^{2}", units_long="square meters", - description="Cross-sectional area as function of zeta", + description="Enclosed cross-sectional area as function of zeta", dim=1, params=[], transforms={"grid": []}, @@ -184,6 +191,7 @@ def _A_of_z(params, transforms, profiles, data, **kwargs): coordinates="z", data=["Z", "n_rho", "g_tt"], parameterization=["desc.geometry.surface.FourierRZToroidalSurface"], + resolution_requirement="rt", ) def _A_of_z_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): # divergence theorem: integral(dA div [0, 0, Z]) = integral(ds n dot [0, 0, Z]) @@ -196,8 +204,7 @@ def _A_of_z_FourierRZToroidalSurface(params, transforms, profiles, data, **kwarg transforms["grid"], data["Z"] * n[:, 2] * jnp.sqrt(data["g_tt"]), line_label="theta", - # This will only work if the grid has rho=1 surface. - fix_surface=("rho", 1.0), + fix_surface=("rho", jnp.max(transforms["grid"].nodes[:, 0])), expand_out=True, ) ) @@ -220,6 +227,7 @@ def _A_of_z_FourierRZToroidalSurface(params, transforms, profiles, data, **kwarg "desc.equilibrium.equilibrium.Equilibrium", "desc.geometry.core.Surface", ], + resolution_requirement="z", ) def _A(params, transforms, profiles, data, **kwargs): data["A"] = jnp.mean( @@ -262,13 +270,12 @@ def _A_of_r(params, transforms, profiles, data, **kwargs): "desc.equilibrium.equilibrium.Equilibrium", "desc.geometry.surface.FourierRZToroidalSurface", ], + resolution_requirement="tz", ) def _S(params, transforms, profiles, data, **kwargs): data["S"] = jnp.max( surface_integrals( - transforms["grid"], - data["|e_theta x e_zeta|"], - expand_out=False, + transforms["grid"], data["|e_theta x e_zeta|"], expand_out=False ) ) return data @@ -286,6 +293,7 @@ def _S(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["|e_theta x e_zeta|"], + resolution_requirement="tz", ) def _S_of_r(params, transforms, profiles, data, **kwargs): data["S(r)"] = surface_integrals(transforms["grid"], data["|e_theta x e_zeta|"]) @@ -304,6 +312,7 @@ def _S_of_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["|e_theta x e_zeta|_r"], + resolution_requirement="tz", ) def _S_r_of_r(params, transforms, profiles, data, **kwargs): data["S_r(r)"] = surface_integrals(transforms["grid"], data["|e_theta x e_zeta|_r"]) @@ -323,6 +332,7 @@ def _S_r_of_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["|e_theta x e_zeta|_rr"], + resolution_requirement="tz", ) def _S_rr_of_r(params, transforms, profiles, data, **kwargs): data["S_rr(r)"] = surface_integrals( @@ -413,6 +423,7 @@ def _R0_over_a(params, transforms, profiles, data, **kwargs): "desc.equilibrium.equilibrium.Equilibrium", "desc.geometry.core.Surface", ], + resolution_requirement="rt", # just need r near lcfs ) def _perimeter_of_z(params, transforms, profiles, data, **kwargs): max_rho = jnp.max(data["rho"]) diff --git a/desc/compute/_omnigenity.py b/desc/compute/_omnigenity.py index dca7e951f4..45de08fe06 100644 --- a/desc/compute/_omnigenity.py +++ b/desc/compute/_omnigenity.py @@ -112,6 +112,7 @@ def _w_mn(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="rtz", data=["w_Boozer_mn"], + resolution_requirement="tz", M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M", N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N", ) @@ -133,6 +134,7 @@ def _w(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="rtz", data=["w_Boozer_mn"], + resolution_requirement="tz", M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M", N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N", ) @@ -154,6 +156,7 @@ def _w_t(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="rtz", data=["w_Boozer_mn"], + resolution_requirement="tz", M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M", N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N", ) @@ -416,6 +419,7 @@ def _alpha(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="rtz", data=["eta"], + resolution_requirement="tz", parameterization="desc.magnetic_fields._core.OmnigenousField", ) def _omni_angle(params, transforms, profiles, data, **kwargs): @@ -445,15 +449,25 @@ def _omni_map(params, transforms, profiles, data, **kwargs): iota = kwargs.get("iota", 1) # coordinate mapping matrix from (alpha,h) to (theta_B,zeta_B) + # need a bunch of wheres to avoid division by zero causing NaN in backward pass + # this is fine since the incorrect values get ignored later, except in OT or OH + # where fieldlines are exactly parallel to |B| contours, but this is a degenerate + # case of measure 0 so this kludge shouldn't affect things too much. + mat_OP = jnp.array( + [[N, iota / jnp.where(N == 0, 1, N)], [0, 1 / jnp.where(N == 0, 1, N)]] + ) + mat_OT = jnp.array([[0, -1], [M, -1 / jnp.where(iota == 0, 1.0, iota)]]) + den = jnp.where((N - M * iota) == 0, 1.0, (N - M * iota)) + mat_OH = jnp.array([[N, M * iota / den], [M, M / den]]) matrix = jnp.where( M == 0, - jnp.array([N, iota / N, 0, 1 / N]), # OP + mat_OP, jnp.where( N == 0, - jnp.array([0, -1, M, -1 / iota]), # OT - jnp.array([N, M * iota / (N - M * iota), M, M / (N - M * iota)]), # OH + mat_OT, + mat_OH, ), - ).reshape((2, 2)) + ) # solve for (theta_B,zeta_B) corresponding to (eta,alpha) booz = matrix @ jnp.vstack((data["alpha"], data["h"])) diff --git a/desc/compute/_profiles.py b/desc/compute/_profiles.py index 46e1c2d054..a962fefb49 100644 --- a/desc/compute/_profiles.py +++ b/desc/compute/_profiles.py @@ -566,6 +566,7 @@ def _gradp_mag(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="", data=["|grad(p)|", "sqrt(g)", "V"], + resolution_requirement="rtz", ) def _gradp_mag_vol(params, transforms, profiles, data, **kwargs): data["<|grad(p)|>_vol"] = ( @@ -894,6 +895,7 @@ def _iota_num_current(params, transforms, profiles, data, **kwargs): coordinates="r", data=["lambda_z", "g_tt", "lambda_t", "g_tz", "sqrt(g)"], axis_limit_data=["g_tz_r", "sqrt(g)_r"], + resolution_requirement="tz", ) def _iota_num_vacuum(params, transforms, profiles, data, **kwargs): """Vacuum contribution to the numerator of rotational transform formula.""" @@ -975,6 +977,7 @@ def _iota_num_r_current(params, transforms, profiles, data, **kwargs): "sqrt(g)_r", ], axis_limit_data=["g_tt_rr", "g_tz_rr", "sqrt(g)_rr"], + resolution_requirement="tz", ) def _iota_num_r_vacuum(params, transforms, profiles, data, **kwargs): iota_num_vacuum = safediv( @@ -1088,6 +1091,7 @@ def _iota_num_r(params, transforms, profiles, data, **kwargs): "psi_rrr", ], axis_limit_data=["sqrt(g)_rrr", "g_tt_rrr", "g_tz_rrr"], + resolution_requirement="tz", ) def _iota_num_rr(params, transforms, profiles, data, **kwargs): """Numerator of rotational transform formula, second radial derivative. @@ -1216,6 +1220,7 @@ def _iota_num_rr(params, transforms, profiles, data, **kwargs): "psi_rr", "psi_rrr", ], + resolution_requirement="tz", ) def _iota_num_rrr(params, transforms, profiles, data, **kwargs): """Numerator of rotational transform formula, third radial derivative. @@ -1336,6 +1341,7 @@ def _iota_num_rrr(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["g_tt", "g_tz", "sqrt(g)", "omega_t", "omega_z"], + resolution_requirement="tz", ) def _iota_den(params, transforms, profiles, data, **kwargs): """Denominator of rotational transform formula. @@ -1380,6 +1386,7 @@ def _iota_den(params, transforms, profiles, data, **kwargs): "omega_rz", ], axis_limit_data=["sqrt(g)_rr", "g_tt_rr", "g_tz_rr"], + resolution_requirement="tz", ) def _iota_den_r(params, transforms, profiles, data, **kwargs): """Denominator of rotational transform formula, first radial derivative. @@ -1446,6 +1453,7 @@ def _iota_den_r(params, transforms, profiles, data, **kwargs): "omega_rrz", ], axis_limit_data=["sqrt(g)_rrr", "g_tt_rrr", "g_tz_rrr"], + resolution_requirement="tz", ) def _iota_den_rr(params, transforms, profiles, data, **kwargs): """Denominator of rotational transform formula, second radial derivative. @@ -1540,6 +1548,7 @@ def _iota_den_rr(params, transforms, profiles, data, **kwargs): "omega_rrz", "omega_rrrz", ], + resolution_requirement="tz", ) def _iota_den_rrr(params, transforms, profiles, data, **kwargs): """Denominator of rotational transform formula, third radial derivative. @@ -1653,6 +1662,7 @@ def _q(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["B_theta"], + resolution_requirement="tz", ) def _I(params, transforms, profiles, data, **kwargs): data["I"] = surface_averages(transforms["grid"], data["B_theta"]) @@ -1672,6 +1682,7 @@ def _I(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["B_theta_r"], + resolution_requirement="tz", ) def _I_r(params, transforms, profiles, data, **kwargs): data["I_r"] = surface_averages(transforms["grid"], data["B_theta_r"]) @@ -1691,6 +1702,7 @@ def _I_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["B_theta_rr"], + resolution_requirement="tz", ) def _I_rr(params, transforms, profiles, data, **kwargs): data["I_rr"] = surface_averages(transforms["grid"], data["B_theta_rr"]) @@ -1710,6 +1722,7 @@ def _I_rr(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["B_zeta"], + resolution_requirement="tz", ) def _G(params, transforms, profiles, data, **kwargs): data["G"] = surface_averages(transforms["grid"], data["B_zeta"]) @@ -1729,6 +1742,7 @@ def _G(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["B_zeta_r"], + resolution_requirement="tz", ) def _G_r(params, transforms, profiles, data, **kwargs): data["G_r"] = surface_averages(transforms["grid"], data["B_zeta_r"]) @@ -1748,6 +1762,7 @@ def _G_r(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["B_zeta_rr"], + resolution_requirement="tz", ) def _G_rr(params, transforms, profiles, data, **kwargs): data["G_rr"] = surface_averages(transforms["grid"], data["B_zeta_rr"]) diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index d8fc27838e..0876579e0a 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -58,6 +58,7 @@ def _D_shear(params, transforms, profiles, data, **kwargs): "|grad(psi)|", "|e_theta x e_zeta|", ], + resolution_requirement="tz", ) def _D_current(params, transforms, profiles, data, **kwargs): # Implements equation 4.17 in M. Landreman & R. Jorge (2020) @@ -103,6 +104,7 @@ def _D_current(params, transforms, profiles, data, **kwargs): "|grad(psi)|", "|e_theta x e_zeta|", ], + resolution_requirement="tz", ) def _D_well(params, transforms, profiles, data, **kwargs): # Implements equation 4.18 in M. Landreman & R. Jorge (2020) @@ -143,6 +145,7 @@ def _D_well(params, transforms, profiles, data, **kwargs): profiles=[], coordinates="r", data=["|grad(psi)|", "J*B", "|B|^2", "|e_theta x e_zeta|"], + resolution_requirement="tz", ) def _D_geodesic(params, transforms, profiles, data, **kwargs): # Implements equation 4.19 in M. Landreman & R. Jorge (2020) diff --git a/desc/compute/_surface.py b/desc/compute/_surface.py index 1ccb989da1..2368deab44 100644 --- a/desc/compute/_surface.py +++ b/desc/compute/_surface.py @@ -307,6 +307,7 @@ def _e_rho_rr_FourierRZToroidalSurface(params, transforms, profiles, data, **kwa data=[], parameterization="desc.geometry.surface.FourierRZToroidalSurface", basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", + aliases=["e_theta_r"], ) def _e_rho_t_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): coords = jnp.zeros((transforms["grid"].num_nodes, 3)) @@ -328,8 +329,12 @@ def _e_rho_t_FourierRZToroidalSurface(params, transforms, profiles, data, **kwar profiles=[], coordinates="tz", data=[], - parameterization="desc.geometry.surface.FourierRZToroidalSurface", + parameterization=[ + "desc.geometry.surface.FourierRZToroidalSurface", + "desc.geometry.surface.ZernikeRZToroidalSection", + ], basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", + aliases=["e_zeta_r"], ) def _e_rho_z_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): coords = jnp.zeros((transforms["grid"].num_nodes, 3)) @@ -337,29 +342,6 @@ def _e_rho_z_FourierRZToroidalSurface(params, transforms, profiles, data, **kwar return data -@register_compute_fun( - name="e_theta_r", - label="\\partial_{\\rho} \\mathbf{e}_{\\theta}", - units="m", - units_long="meters", - description="Covariant poloidal basis vector, derivative wrt radial coordinate", - dim=3, - params=[], - transforms={ - "grid": [], - }, - profiles=[], - coordinates="tz", - data=[], - parameterization="desc.geometry.surface.FourierRZToroidalSurface", - basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", -) -def _e_theta_r_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): - coords = jnp.zeros((transforms["grid"].num_nodes, 3)) - data["e_theta_r"] = coords - return data - - @register_compute_fun( name="e_theta_rr", label="\\partial_{\\rho \\rho} \\mathbf{e}_{\\theta}", @@ -377,6 +359,7 @@ def _e_theta_r_FourierRZToroidalSurface(params, transforms, profiles, data, **kw data=[], parameterization="desc.geometry.surface.FourierRZToroidalSurface", basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", + aliases=["e_rho_rt"], ) def _e_theta_rr_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): coords = jnp.zeros((transforms["grid"].num_nodes, 3)) @@ -384,29 +367,6 @@ def _e_theta_rr_FourierRZToroidalSurface(params, transforms, profiles, data, **k return data -@register_compute_fun( - name="e_zeta_r", - label="\\partial_{\\rho} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description="Covariant toroidal basis vector, derivative wrt radial coordinate", - dim=3, - params=[], - transforms={ - "grid": [], - }, - profiles=[], - coordinates="tz", - data=[], - parameterization="desc.geometry.surface.FourierRZToroidalSurface", - basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", -) -def _e_zeta_r_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): - coords = jnp.zeros((transforms["grid"].num_nodes, 3)) - data["e_zeta_r"] = coords - return data - - @register_compute_fun( name="e_zeta_rr", label="\\partial_{\\rho \\rho} \\mathbf{e}_{\\zeta}", @@ -422,8 +382,12 @@ def _e_zeta_r_FourierRZToroidalSurface(params, transforms, profiles, data, **kwa profiles=[], coordinates="tz", data=[], - parameterization="desc.geometry.surface.FourierRZToroidalSurface", + parameterization=[ + "desc.geometry.surface.FourierRZToroidalSurface", + "desc.geometry.surface.ZernikeRZToroidalSection", + ], basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", + aliases=["e_rho_rz"], ) def _e_zeta_rr_FourierRZToroidalSurface(params, transforms, profiles, data, **kwargs): coords = jnp.zeros((transforms["grid"].num_nodes, 3)) @@ -649,29 +613,6 @@ def _e_zeta_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwarg return data -@register_compute_fun( - name="e_rho_z", - label="\\partial_{\\zeta} \\mathbf{e}_{\\rho}", - units="m", - units_long="meters", - description="Covariant radial basis vector, derivative wrt toroidal angle", - dim=3, - params=[], - transforms={ - "grid": [], - }, - profiles=[], - coordinates="rt", - data=[], - parameterization="desc.geometry.surface.ZernikeRZToroidalSection", - basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", -) -def _e_rho_z_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwargs): - coords = jnp.zeros((transforms["grid"].num_nodes, 3)) - data["e_rho_z"] = coords - return data - - @register_compute_fun( name="e_theta_z", label="\\partial_{\\zeta} \\mathbf{e}_{\\theta}", @@ -688,6 +629,7 @@ def _e_rho_z_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwar data=[], parameterization="desc.geometry.surface.ZernikeRZToroidalSection", basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", + aliases=["e_zeta_t"], ) def _e_theta_z_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwargs): coords = jnp.zeros((transforms["grid"].num_nodes, 3)) @@ -695,76 +637,6 @@ def _e_theta_z_ZernikeRZToroidalSection(params, transforms, profiles, data, **kw return data -@register_compute_fun( - name="e_zeta_r", - label="\\partial_{\\rho} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description="Covariant toroidal basis vector, derivative wrt radial coordinate", - dim=3, - params=[], - transforms={ - "grid": [], - }, - profiles=[], - coordinates="rt", - data=[], - parameterization="desc.geometry.surface.ZernikeRZToroidalSection", - basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", -) -def _e_zeta_r_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwargs): - coords = jnp.zeros((transforms["grid"].num_nodes, 3)) - data["e_zeta_r"] = coords - return data - - -@register_compute_fun( - name="e_zeta_rr", - label="\\partial_{\\rho \\rho} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description="Covariant toroidal basis vector," - " second derivative wrt radial coordinate", - dim=3, - params=[], - transforms={ - "grid": [], - }, - profiles=[], - coordinates="rt", - data=[], - parameterization="desc.geometry.surface.ZernikeRZToroidalSection", - basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", -) -def _e_zeta_rr_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwargs): - coords = jnp.zeros((transforms["grid"].num_nodes, 3)) - data["e_zeta_rr"] = coords - return data - - -@register_compute_fun( - name="e_zeta_t", - label="\\partial_{\\theta} \\mathbf{e}_{\\zeta}", - units="m", - units_long="meters", - description="Covariant toroidal basis vector, derivative wrt poloidal angle", - dim=3, - params=[], - transforms={ - "grid": [], - }, - profiles=[], - coordinates="rt", - data=[], - parameterization="desc.geometry.surface.ZernikeRZToroidalSection", - basis="{'rpz', 'xyz'}: Basis for returned vectors, Default 'rpz'", -) -def _e_zeta_t_ZernikeRZToroidalSection(params, transforms, profiles, data, **kwargs): - coords = jnp.zeros((transforms["grid"].num_nodes, 3)) - data["e_zeta_t"] = coords - return data - - @register_compute_fun( name="e_zeta_z", label="\\partial_{\\zeta} \\mathbf{e}_{\\zeta}", diff --git a/desc/compute/bounce_integral.py b/desc/compute/bounce_integral.py index d4e39f3844..d1e764bf5d 100644 --- a/desc/compute/bounce_integral.py +++ b/desc/compute/bounce_integral.py @@ -1181,7 +1181,7 @@ def bounce_integral( The quantities ``B_sup_z``, ``B``, ``B_z_ra``, and those in ``f`` supplied to the returned method must be separable into data evaluated along particular field lines via ``.reshape(S,knots.size)``. One way to satisfy this is to compute stuff on the - grid returned from the method ``desc.equilibrium.coords.rtz_grid``. See + grid returned from the method ``desc.equilibrium.coords.get_rtz_grid``. See ``tests.test_bounce_integral.test_bounce_integral_checks`` for example use. Parameters @@ -1255,11 +1255,9 @@ def bounce_integral( ) # Strictly increasing zeta knots enforces dζ > 0. # To retain dℓ = (|B|/B^ζ) dζ > 0 after fixing dζ > 0, we require B^ζ = B⋅∇ζ > 0. - # This is equivalent to changing the sign of ∇ζ (or [∂/∂ζ]|ρ,a). + # This is equivalent to changing the sign of ∇ζ (or [∂ℓ/∂ζ]|ρ,a). # Recall dζ = ∇ζ⋅dR, implying 1 = ∇ζ⋅(e_ζ|ρ,a). Hence, a sign change in ∇ζ - # requires the same sign change in e_ζ|ρ,a to retain the metric identity. For any - # quantity f, we may write df = ∇f⋅dR, implying ∂f/∂ζ|ρ,α = ∇f ⋅ e_ζ|ρ,a. Hence, - # a sign change in e_ζ|ρ,a requires the same sign change in ∂f/∂ζ|ρ,α. + # requires the same sign change in e_ζ|ρ,a to retain the metric identity. B_sup_z = jnp.abs(B_sup_z) * L_ref / B_ref B = B / B_ref B_z_ra = B_z_ra / B_ref # This is already the correct sign. diff --git a/desc/compute/data_index.py b/desc/compute/data_index.py index 8d1260d761..7e387dc47a 100644 --- a/desc/compute/data_index.py +++ b/desc/compute/data_index.py @@ -8,8 +8,7 @@ def find_permutations(primary, separator="_"): """Finds permutations of quantity names for aliases.""" - split_name = primary.split(separator) - primary_permutation = split_name[-1] + prefix, primary_permutation = primary.rsplit(separator, 1) primary_permutation = deque(primary_permutation) new_permutations = [] @@ -18,10 +17,7 @@ def find_permutations(primary, separator="_"): new_permutations.append(list(primary_permutation)) # join new permutation to form alias keys - aliases = [ - "".join(split_name[:-1]) + separator + "".join(perm) - for perm in new_permutations - ] + aliases = [prefix + separator + "".join(perm) for perm in new_permutations] aliases = np.unique(aliases) aliases = np.delete(aliases, np.where(aliases == primary)) @@ -51,7 +47,7 @@ def assign_alias_data( return data -def register_compute_fun( +def register_compute_fun( # noqa: C901 name, label, units, @@ -63,11 +59,11 @@ def register_compute_fun( profiles, coordinates, data, + axis_limit_data=None, aliases=None, parameterization="desc.equilibrium.equilibrium.Equilibrium", resolution_requirement="", source_grid_requirement=None, - axis_limit_data=None, **kwargs, ): """Decorator to wrap a function and add it to the list of things we can compute. @@ -99,7 +95,9 @@ def register_compute_fun( a flux function, etc. data : list of str Names of other items in the data index needed to compute qty. - aliases : list + axis_limit_data : list of str + Names of other items in the data index needed to compute axis limit of qty. + aliases : list of str Aliases of `name`. Will be stored in the data dictionary as a copy of `name`s data. parameterization : str or list of str @@ -107,12 +105,17 @@ def register_compute_fun( or `'desc.equilibrium.Equilibrium'`. resolution_requirement : str Resolution requirements in coordinates. I.e. "r" expects radial resolution - in the grid, "rtz" expects grid to radial, poloidal, and toroidal resolution. + in the grid, "rtz" expects a grid with radial, poloidal, and toroidal resolution. source_grid_requirement : dict Attributes of the source grid that the compute function requires. Also assumes dependencies were computed on such a grid. - axis_limit_data : list of str - Names of other items in the data index needed to compute axis limit of qty. + By default, the source grid is assumed to be ``transforms["grid"]`` and + no requirements are expected of it. As an example, quantities that require + integration along field lines may specify + ``source_grid_requirement={"coordinates": "raz"}``. + which will allow accessing the Clebsch-Type rho, alpha, zeta coordinates in + ``transforms["grid"].source_grid``` that correspond to the DESC rho, theta, + zeta coordinates in ``transforms["grid"]``. Notes ----- @@ -136,9 +139,16 @@ def register_compute_fun( } for kw in kwargs: allowed_kwargs.add(kw) - permutable_names = ["R_", "Z_", "phi_", "lambda_", "omega_"] - if not aliases and "".join(name.split("_")[:-1]) + "_" in permutable_names: - aliases = find_permutations(name) + splits = name.rsplit("_", 1) + if ( + len(splits) > 1 + # Only look for permutations of partial derivatives of same coordinate system. + and {"r", "t", "z"}.issuperset(splits[-1]) + ): + aliases_temp = np.append(np.array(aliases), find_permutations(name)) + for alias in aliases: + aliases_temp = np.append(aliases_temp, find_permutations(alias)) + aliases = np.unique(aliases_temp) def _decorator(func): d = { @@ -251,3 +261,29 @@ def _decorator(func): data_index = {p: {} for p in _class_inheritance.keys()} all_kwargs = {p: {} for p in _class_inheritance.keys()} allowed_kwargs = set() + + +def is_0d_vol_grid(name, p="desc.equilibrium.equilibrium.Equilibrium"): + """Is name constant throughout plasma volume and needs full volume to compute?.""" + # Should compute on a grid that samples entire plasma volume. + # In particular, a QuadratureGrid for accurate radial integration. + return ( + data_index[p][name]["coordinates"] == "" + and data_index[p][name]["resolution_requirement"] != "" + ) + + +def is_1dr_rad_grid(name, p="desc.equilibrium.equilibrium.Equilibrium"): + """Is name constant over radial surfaces and needs full surface to compute?.""" + return ( + data_index[p][name]["coordinates"] == "r" + and data_index[p][name]["resolution_requirement"] == "tz" + ) + + +def is_1dz_tor_grid(name, p="desc.equilibrium.equilibrium.Equilibrium"): + """Is name constant over toroidal surfaces and needs full surface to compute?.""" + return ( + data_index[p][name]["coordinates"] == "z" + and data_index[p][name]["resolution_requirement"] == "rt" + ) diff --git a/desc/compute/utils.py b/desc/compute/utils.py index ec61c61521..1ceeba9975 100644 --- a/desc/compute/utils.py +++ b/desc/compute/utils.py @@ -79,6 +79,28 @@ def compute(parameterization, names, params, transforms, profiles, data=None, ** name, transforms, p ), f"Don't have transforms to compute {name}" + if "grid" in transforms: + + def check_fun(name): + reqs = data_index[p][name]["source_grid_requirement"] + errorif( + reqs and not hasattr(transforms["grid"], "source_grid"), + AttributeError, + f"Expected grid with attribute 'source_grid' to compute {name}. " + f"Source grid should have coordinates: {reqs.get('coordinates')}.", + ) + for req in reqs: + errorif( + not hasattr(transforms["grid"].source_grid, req) + or reqs[req] != getattr(transforms["grid"].source_grid, req), + AttributeError, + f"Expected grid with '{req}:{reqs[req]}' to compute {name}.", + ) + + _ = _get_deps( + p, names, set(), data, transforms["grid"].axis.size, check_fun=check_fun + ) + if data is None: data = {} @@ -136,20 +158,6 @@ def _compute( data=data, **kwargs, ) - if "grid" in transforms: - reqs = data_index[parameterization][name]["source_grid_requirement"] - errorif( - reqs and not hasattr(transforms["grid"], "source_grid"), - msg=f"Expected grid with attribute 'source_grid' to compute {name}. " - f"The source grid should have coordinates: {reqs.get('coordinates')}. " - "The equilibrium.rtz_grid method may be useful for this purpose.", - ) - for req in reqs: - errorif( - not hasattr(transforms["grid"].source_grid, req) - or reqs[req] != getattr(transforms["grid"].source_grid, req), - msg=f"Expected grid with '{req}:{reqs[req]}' to compute {name}.", - ) # now compute the quantity data = data_index[parameterization][name]["fun"]( params=params, transforms=transforms, profiles=profiles, data=data, **kwargs @@ -158,8 +166,8 @@ def _compute( return data -def get_data_deps(keys, obj, has_axis=False): - """Get list of data keys needed to compute a given quantity. +def get_data_deps(keys, obj, has_axis=False, data=None): + """Get list of keys needed to compute ``keys`` given already computed data. Parameters ---------- @@ -169,6 +177,8 @@ def get_data_deps(keys, obj, has_axis=False): Object to compute quantity for. has_axis : bool Whether the grid to compute on has a node on the magnetic axis. + data : dict of ndarray + Data computed so far, generally output from other compute functions Returns ------- @@ -177,13 +187,36 @@ def get_data_deps(keys, obj, has_axis=False): """ p = _parse_parameterization(obj) keys = [keys] if isinstance(keys, str) else keys - out = [] - for key in keys: - out += _get_deps_1_key(key, p, has_axis) - return sorted(set(out)) + if not data: + out = [] + for key in keys: + out += _get_deps_1_key(p, key, has_axis) + out = set(out) + else: + out = _get_deps(p, keys, deps=set(), data=data, has_axis=has_axis) + out.difference_update(keys) + return sorted(out) + + +def _get_deps_1_key(p, key, has_axis): + """Gather all quantities required to compute ``key``. + + Parameters + ---------- + p : str + Type of object to compute for, eg Equilibrium, Curve, etc. + key : str + Name of the quantity to compute. + has_axis : bool + Whether the grid to compute on has a node on the magnetic axis. + + Returns + ------- + deps_1_key : list of str + Dependencies required to compute ``key``. -def _get_deps_1_key(key, p, has_axis): + """ if has_axis: if "full_with_axis_dependencies" in data_index[p][key]: return data_index[p][key]["full_with_axis_dependencies"]["data"] @@ -195,39 +228,87 @@ def _get_deps_1_key(key, p, has_axis): return deps out = deps.copy() # to avoid modifying the data_index for dep in deps: - out += _get_deps_1_key(dep, p, has_axis) + out += _get_deps_1_key(p, dep, has_axis) if has_axis: axis_limit_deps = data_index[p][key]["dependencies"]["axis_limit_data"] out += axis_limit_deps.copy() # to be safe for dep in axis_limit_deps: - out += _get_deps_1_key(dep, p, has_axis) + out += _get_deps_1_key(p, dep, has_axis) return sorted(set(out)) -def _grow_seeds( - seeds, search_space, p="desc.equilibrium.equilibrium.Equilibrium", has_axis=False -): +def _get_deps(parameterization, names, deps, data=None, has_axis=False, check_fun=None): + """Gather all quantities required to compute ``names`` given already computed data. + + Parameters + ---------- + parameterization : str, class, or instance + Type of object to compute for, eg Equilibrium, Curve, etc. + names : str or array-like of str + Name(s) of the quantity(s) to compute. + deps : set[str] + Dependencies gathered so far. + data : dict of ndarray or None + Data computed so far, generally output from other compute functions. + has_axis : bool + Whether the grid to compute on has a node on the magnetic axis. + check_fun : callable + If provided, ``check_fun(name)`` is called before adding name to ``deps``. + + Returns + ------- + deps : set[str] + All additional quantities required to compute ``names``. + + """ + p = _parse_parameterization(parameterization) + for name in names: + if name not in deps and (data is None or name not in data): + if check_fun is not None: + check_fun(name) + deps.add(name) + deps = _get_deps( + p, + data_index[p][name]["dependencies"]["data"], + deps, + data, + has_axis, + check_fun, + ) + if has_axis: + deps = _get_deps( + p, + data_index[p][name]["dependencies"]["axis_limit_data"], + deps, + data, + has_axis, + check_fun, + ) + return deps + + +def _grow_seeds(parameterization, seeds, search_space, has_axis=False): """Traverse the dependency DAG for keys in search space dependent on seeds. Parameters ---------- - seeds : Set + parameterization : str, class, or instance + Type of object to compute for, eg Equilibrium, Curve, etc. + seeds : set[str] Keys to find paths toward. - search_space : iterable + search_space : iterable of str Additional keys to consider returning. - p: str - Name of desc types the method is valid for. eg 'desc.geometry.FourierXYZCurve' - or `desc.equilibrium.Equilibrium`. has_axis : bool Whether the grid to compute on has a node on the magnetic axis. Returns ------- - out : Set + out : set[str] All keys in search space with any path in the dependency DAG to any seed. """ + p = _parse_parameterization(parameterization) out = seeds.copy() for key in search_space: deps = data_index[p][key][ @@ -392,16 +473,25 @@ def get_transforms(keys, obj, grid, jitable=False, **kwargs): if hasattr(obj, c + "_basis"): # regular stuff like R, Z, lambda etc. basis = getattr(obj, c + "_basis") # first check if we already have a transform with a compatible basis - for transform in transforms.values(): - if basis.equiv(getattr(transform, "basis", None)): - ders = np.unique( - np.vstack([derivs[c], transform.derivatives]), axis=0 - ).astype(int) - # don't build until we know all the derivs we need - transform.change_derivatives(ders, build=False) - c_transform = transform - break - else: # if we didn't exit the loop early + if not jitable: + for transform in transforms.values(): + if basis.equiv(getattr(transform, "basis", None)): + ders = np.unique( + np.vstack([derivs[c], transform.derivatives]), axis=0 + ).astype(int) + # don't build until we know all the derivs we need + transform.change_derivatives(ders, build=False) + c_transform = transform + break + else: # if we didn't exit the loop early + c_transform = Transform( + grid, + basis, + derivs=derivs[c], + build=False, + method=method, + ) + else: # don't perform checks if jitable=True as they are not jit-safe c_transform = Transform( grid, basis, @@ -714,6 +804,8 @@ def line_integrals( label is rho and length 2π when the line label is theta or zeta. You may want to multiply the input by the line length Jacobian. + The grid must have nodes on the specified surface in ``fix_surface``. + Correctness is not guaranteed on grids with duplicate nodes. An attempt to print a warning is made if the given grid has duplicate nodes and is one of the predefined grid types @@ -894,7 +986,15 @@ def surface_integrals_map(grid, surface_label="rho", expand_out=True, tol=1e-14) operand=None, ) else: - expand_out = False + # If we don't have the idx attributes, we are forced to expand out. + errorif( + not has_idx and not expand_out, + msg=f"Grid lacks attributes 'num_{surface_label}' and " + f"'inverse_{surface_label}_idx', so this method " + f"can't satisfy the request expand_out={expand_out}.", + ) + # don't try to expand if already expanded + expand_out = expand_out and has_idx axis = {"rho": 0, "poloidal": 1, "zeta": 2}[surface_label] # Converting nodes from numpy.ndarray to jaxlib.xla_extension.ArrayImpl # reduces memory usage by > 400% for the forward computation and Jacobian. @@ -1022,13 +1122,21 @@ def surface_averages_map(grid, surface_label="rho", expand_out=True, tol=1e-14): """ surface_label = grid.get_label(surface_label) - expand_out = ( - expand_out - # don't try to expand already expanded output - and hasattr(grid, f"num_{surface_label}") - and hasattr(grid, f"_inverse_{surface_label}_idx") + has_idx = hasattr(grid, f"num_{surface_label}") and hasattr( + grid, f"_inverse_{surface_label}_idx" ) - integrate = surface_integrals_map(grid, surface_label, expand_out=False, tol=tol) + # If we don't have the idx attributes, we are forced to expand out. + errorif( + not has_idx and not expand_out, + msg=f"Grid lacks attributes 'num_{surface_label}' and " + f"'inverse_{surface_label}_idx', so this method " + f"can't satisfy the request expand_out={expand_out}.", + ) + integrate = surface_integrals_map( + grid, surface_label, expand_out=not has_idx, tol=tol + ) + # don't try to expand if already expanded + expand_out = expand_out and has_idx def _surface_averages(q, sqrt_g=jnp.array([1.0]), denominator=None): """Compute a surface average for each surface in the grid. @@ -1155,9 +1263,13 @@ def surface_integrals_transform(grid, surface_label="rho"): # discretizes f over the codomain will typically have size grid.num_nodes # to broadcast with quantities in data_index. surface_label = grid.get_label(surface_label) + has_idx = hasattr(grid, f"num_{surface_label}") and hasattr( + grid, f"_inverse_{surface_label}_idx" + ) errorif( - not hasattr(grid, f"num_{surface_label}") - or not hasattr(grid, f"_inverse_{surface_label}_idx") + not has_idx, + msg=f"Grid lacks attributes 'num_{surface_label}' and " + f"'inverse_{surface_label}_idx', which are required for this function.", ) return surface_integrals_map(grid, surface_label, expand_out=False) @@ -1244,7 +1356,16 @@ def surface_variance( """ surface_label = grid.get_label(surface_label) _, _, spacing, _, has_idx = _get_grid_surface(grid, surface_label) - integrate = surface_integrals_map(grid, surface_label, expand_out=False, tol=tol) + # If we don't have the idx attributes, we are forced to expand out. + errorif( + not has_idx and not expand_out, + msg=f"Grid lacks attributes 'num_{surface_label}' and " + f"'inverse_{surface_label}_idx', so this method " + f"can't satisfy the request expand_out={expand_out}.", + ) + integrate = surface_integrals_map( + grid, surface_label, expand_out=not has_idx, tol=tol + ) v1 = integrate(weights) v2 = integrate(weights**2 * jnp.prod(spacing, axis=-1)) @@ -1256,10 +1377,10 @@ def surface_variance( q = jnp.atleast_1d(q) # compute variance in two passes to avoid catastrophic round off error mean = (integrate((weights * q.T).T).T / v1).T - if has_idx: + if has_idx: # guard so that we don't try to expand when already expanded mean = grid.expand(mean, surface_label) variance = (correction * integrate((weights * ((q - mean) ** 2).T).T).T / v1).T - if has_idx and expand_out: + if expand_out and has_idx: return grid.expand(variance, surface_label) else: return variance @@ -1310,7 +1431,12 @@ def surface_min(grid, x, surface_label="rho"): """ surface_label = grid.get_label(surface_label) unique_size, inverse_idx, _, _, has_idx = _get_grid_surface(grid, surface_label) - errorif(not has_idx, NotImplementedError, msg="Missing unique and inverse idx.") + errorif( + not has_idx, + NotImplementedError, + msg=f"Grid lacks attributes 'num_{surface_label}' and " + f"'inverse_{surface_label}_idx', which are required for this function.", + ) inverse_idx = jnp.asarray(inverse_idx) x = jnp.asarray(x) mins = jnp.full(unique_size, jnp.inf) diff --git a/desc/equilibrium/coords.py b/desc/equilibrium/coords.py index ebaf34e660..0e04832cfd 100644 --- a/desc/equilibrium/coords.py +++ b/desc/equilibrium/coords.py @@ -515,7 +515,7 @@ def to_sfl( return eq_sfl -def rtz_grid( +def get_rtz_grid( eq, radial, poloidal, toroidal, coordinates, period, jitable=True, **kwargs ): """Return DESC coordinate grid from given coordinates. @@ -536,7 +536,7 @@ def rtz_grid( coordinates : str Input coordinates that are specified by the arguments, respectively. raz : rho, alpha, zeta - rpz : rho, theta_PEST, zeta + rvz : rho, theta_PEST, zeta rtz : rho, theta, zeta period : tuple of float Assumed periodicity for each quantity in inbasis. @@ -557,7 +557,7 @@ def rtz_grid( inbasis = { "r": "rho", "t": "theta", - "p": "theta_PEST", + "v": "theta_PEST", "a": "alpha", "z": "zeta", } diff --git a/desc/equilibrium/equilibrium.py b/desc/equilibrium/equilibrium.py index 19660cc529..e558564495 100644 --- a/desc/equilibrium/equilibrium.py +++ b/desc/equilibrium/equilibrium.py @@ -52,7 +52,14 @@ warnif, ) -from .coords import compute_theta_coords, is_nested, map_coordinates, rtz_grid, to_sfl +from ..compute.data_index import is_0d_vol_grid, is_1dr_rad_grid, is_1dz_tor_grid +from .coords import ( + compute_theta_coords, + get_rtz_grid, + is_nested, + map_coordinates, + to_sfl, +) from .initial_guess import set_initial_guess from .utils import parse_axis, parse_profile, parse_surface @@ -227,7 +234,7 @@ def __init__( ValueError, f"sym should be one of True, False, None, got {sym}", ) - self._sym = setdefault(sym, getattr(surface, "sym", False)) + self._sym = bool(setdefault(sym, getattr(surface, "sym", False))) self._R_sym = "cos" if self.sym else False self._Z_sym = "sin" if self.sym else False @@ -565,6 +572,12 @@ def change_resolution( Whether to enforce stellarator symmetry. """ + warnif( + L is not None and L < self.L, + UserWarning, + "Reducing radial (L) resolution can make plasma boundary inconsistent. " + + "Recommend calling `eq.surface = eq.get_surface_at(rho=1.0)`", + ) self._L = int(setdefault(L, self.L)) self._M = int(setdefault(M, self.M)) self._N = int(setdefault(N, self.N)) @@ -572,7 +585,7 @@ def change_resolution( self._M_grid = int(setdefault(M_grid, self.M_grid)) self._N_grid = int(setdefault(N_grid, self.N_grid)) self._NFP = int(setdefault(NFP, self.NFP)) - self._sym = setdefault(sym, self.sym) + self._sym = bool(setdefault(sym, self.sym)) old_modes_R = self.R_basis.modes old_modes_Z = self.Z_basis.modes @@ -784,28 +797,6 @@ def get_axis(self): axis = FourierRZCurve(R_n, Z_n, modes_R, modes_Z, NFP=self.NFP, sym=self.sym) return axis - @staticmethod - def is_0d(name): - """Is name constant throughout the plasma volume?.""" - # Should compute on a grid that samples entire plasma volume. - # In particular, a QuadratureGrid for accurate radial integration. - p = "desc.equilibrium.equilibrium.Equilibrium" - return data_index[p][name]["coordinates"] == "" - - @staticmethod - def is_1dr(name): - """Is name constant over flux surfaces?.""" - # Should compute on a grid that samples entire radial surfaces. - p = "desc.equilibrium.equilibrium.Equilibrium" - return data_index[p][name]["coordinates"] == "r" - - @staticmethod - def is_1dz(name): - """Is name constant over toroidal surfaces?.""" - # Should compute on a grid that samples entire toroidal surfaces. - p = "desc.equilibrium.equilibrium.Equilibrium" - return data_index[p][name]["coordinates"] == "z" - def compute( # noqa: C901 self, names, @@ -861,7 +852,7 @@ def compute( # noqa: C901 inbasis = { "r": "rho", "t": "theta", - "p": "theta_PEST", + "v": "theta_PEST", "a": "alpha", "z": "zeta", } @@ -889,15 +880,8 @@ def compute( # noqa: C901 data = {} p = "desc.equilibrium.equilibrium.Equilibrium" - # TODO: - # If the user wants to compute x which depends on y which in turn depends on z, - # and they pass in y already in data, then we shouldn't need to compute z at - # all. To resolve this we need to compute dependencies recursively, so that - # priming data with just the first order dependencies will avoid computing - # unnecessary dependencies. For now just subtract out y. - deps = ( - set(get_data_deps(names, obj=p, has_axis=grid.axis.size) + names) - - data.keys() + deps = set( + get_data_deps(names, obj=p, has_axis=grid.axis.size, data=data) + names ) def need_src(name): @@ -905,28 +889,54 @@ def need_src(name): # the compute logic assume input data is evaluated on those coordinates. # We exclude these from the depXdx sets below since the grids we will # use to compute those dependencies are coordinate-blind. + # Example, "" has coordinates="r", but requires computing on + # field line following source grid. return bool(data_index[p][name]["source_grid_requirement"]) - need_src_deps = _grow_seeds(set(filter(need_src, deps)), deps) + # Need to call _grow_seeds so that some other quantity like K = 2 * , + # which does not need a source grid to evaluate, does not compute on a + # grid that does not follow field lines. + need_src_deps = _grow_seeds(p, set(filter(need_src, deps)), deps) - dep0d = {dep for dep in deps if self.is_0d(dep) and dep not in need_src_deps} + dep0d = { + dep for dep in deps if is_0d_vol_grid(dep) and dep not in need_src_deps + } # Unless user asks, don't try to recompute stuff which are only dependencies - # of dep0d. Example, need R0. R0 <- A <- A(z) computable on dep0d grid. - # But A(z) in dep1dz and attempt to recompute on dep1dz grid will error - # without unique zeta idx defined, which mapped coordinate grids lack. - dep0d_deps = set(get_data_deps(dep0d, obj=p, has_axis=grid.axis.size)) + # of dep0d. Example, suppose the user supplied grid is a field-line following + # grid, and the user would like to compute the effective ripple, which requires + # the scalar R0 as a dependency. The scalar R0 has the following dependencies: + # R0 <- A <- A(z). Each of these are computable on the quadrature grid, and + # since R0 is a scalar we can trivially interpolate it back to the user-supplied + # grid. We don't need to additionally compute A(z) and interpolate it back; + # it was only needed to compute R0, so we should remove it from the dep1dz list. + # If we don't remove it from the dep1dz list, then the code would try to create + # a linear grid with cross-sections at all the unique zeta values in the + # user-supplied grids. Typically, the user-supplied grid lacks unique_zeta_idx + # attribute, so this would cause an error. + dep0d_deps = set( + get_data_deps(dep0d, obj=p, has_axis=grid.axis.size, data=data) + ) # This filter is stronger than the name implies, but the false positives - # will still be computed correctly with the logic in compute.utils.compute. + # that are filtered out will still get computed with the logic in + # compute.utils.compute. just_dep0d_dep = lambda name: name in dep0d_deps and name not in names dep1dr = { dep for dep in deps - if self.is_1dr(dep) and not just_dep0d_dep(dep) and dep not in need_src_deps + if is_1dr_rad_grid(dep) + and not just_dep0d_dep(dep) + and dep not in need_src_deps } dep1dz = { dep for dep in deps - if self.is_1dz(dep) and not just_dep0d_dep(dep) and dep not in need_src_deps + # By including the additional requirement that dep is not just a dependency + # of some scalar (0d) quantity, we are ensuring that we do not unnecessarily + # compute things like A(z) when it was only needed to compute R0, as in the + # example above. + if is_1dz_tor_grid(dep) + and not just_dep0d_dep(dep) + and dep not in need_src_deps # These don't need a special grid, since the transforms are always # built on the (rho, theta, zeta) coordinate grid. and dep not in ["phi", "zeta"] @@ -949,21 +959,30 @@ def need_src(name): coords = data_index[p][dep]["coordinates"] msg = lambda direction: colored( f"Dependency {dep} may require more {direction}" - f" resolution to compute.", + " resolution to compute accurately.", "yellow", ) warnif( - "r" in req and "r" in coords and grid.L < self.L_grid, + # if need more radial resolution + "r" in req and grid.L < self.L_grid + # and won't override grid to one with more radial resolution + and not (override_grid and coords in {"z", ""}), ResolutionWarning, msg("radial"), ) warnif( - "t" in req and "t" in coords and grid.M < self.M_grid, + # if need more poloidal resolution + "t" in req and grid.M < self.M_grid + # and won't override grid to one with more poloidal resolution + and not (override_grid and coords in {"r", "z", ""}), ResolutionWarning, msg("poloidal"), ) warnif( - "z" in req and "z" in coords and grid.N < self.N_grid, + # if need more toroidal resolution + "z" in req and grid.N < self.N_grid + # and won't override grid to one with more toroidal resolution + and not (override_grid and coords in {"r", ""}), ResolutionWarning, msg("toroidal"), ) @@ -978,7 +997,7 @@ def need_src(name): if calc0d and override_grid: grid0d = QuadratureGrid(self.L_grid, self.M_grid, self.N_grid, self.NFP) - data0d_seed = {key: data[key] for key in data if self.is_0d(key)} + data0d_seed = {key: data[key] for key in data if is_0d_vol_grid(key)} data0d = compute_fun( self, list(dep0d), @@ -997,7 +1016,7 @@ def need_src(name): data.update(data0d) if (calc1dr or calc1dz) and override_grid: - data0d_seed = {key: data[key] for key in data if self.is_0d(key)} + data0d_seed = {key: data[key] for key in data if is_0d_vol_grid(key)} else: data0d_seed = {} if calc1dr and override_grid: @@ -1011,7 +1030,7 @@ def need_src(name): data1dr_seed = { key: grid1dr.copy_data_from_other(data[key], grid, surface_label="rho") for key in data - if self.is_1dr(key) + if is_1dr_rad_grid(key) } data1dr = compute_fun( self, @@ -1045,7 +1064,7 @@ def need_src(name): data1dz_seed = { key: grid1dz.copy_data_from_other(data[key], grid, surface_label="zeta") for key in data - if self.is_1dz(key) + if is_1dz_tor_grid(key) } data1dz = compute_fun( self, @@ -1153,7 +1172,7 @@ def map_coordinates( # noqa: C901 **kwargs, ) - def rtz_grid( + def get_rtz_grid( self, radial, poloidal, toroidal, coordinates, period, jitable=True, **kwargs ): """Return DESC coordinate grid from given coordinates. @@ -1187,7 +1206,7 @@ def rtz_grid( DESC coordinate grid for the given coordinates. """ - return rtz_grid( + return get_rtz_grid( self, radial, poloidal, toroidal, coordinates, period, jitable, **kwargs ) diff --git a/desc/geometry/core.py b/desc/geometry/core.py index a637ff529e..7b76a80725 100644 --- a/desc/geometry/core.py +++ b/desc/geometry/core.py @@ -68,11 +68,11 @@ def rotmat(self, new): @property def name(self): """Name of the curve.""" - return self._name + return self.__dict__.setdefault("_name", "") @name.setter def name(self, new): - self._name = new + self._name = str(new) def compute( self, @@ -323,11 +323,11 @@ def _set_up(self): @property def name(self): """str: Name of the surface.""" - return self._name + return self.__dict__.setdefault("_name", "") @name.setter def name(self, new): - self._name = new + self._name = str(new) @property def L(self): diff --git a/desc/geometry/curve.py b/desc/geometry/curve.py index 3f670656e7..b928818cdc 100644 --- a/desc/geometry/curve.py +++ b/desc/geometry/curve.py @@ -1,5 +1,7 @@ """Classes for parameterized 3D space curves.""" +import warnings + import numpy as np from desc.backend import jnp, put @@ -94,7 +96,7 @@ def __init__( @property def sym(self): - """Whether this curve has stellarator symmetry.""" + """bool: Whether or not the curve is stellarator symmetric.""" return self._sym @property @@ -128,7 +130,7 @@ def change_resolution(self, N=None, NFP=None, sym=None): and (sym != self.sym) ): self._NFP = int(NFP if NFP is not None else self.NFP) - self._sym = sym if sym is not None else self.sym + self._sym = bool(sym) if sym is not None else self.sym N = int(N if N is not None else self.N) R_modes_old = self.R_basis.modes Z_modes_old = self.Z_basis.modes @@ -214,7 +216,9 @@ def from_input_file(cls, path): Axis with given Fourier coefficients. """ - inputs = InputReader().parse_inputs(path)[-1] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + inputs = InputReader().parse_inputs(path)[-1] curve = FourierRZCurve( inputs["axis"][:, 1], inputs["axis"][:, 2], diff --git a/desc/geometry/surface.py b/desc/geometry/surface.py index 3a4cf0c136..48747efd9c 100644 --- a/desc/geometry/surface.py +++ b/desc/geometry/surface.py @@ -124,7 +124,7 @@ def __init__( self._R_lmn = copy_coeffs(R_lmn, modes_R, self.R_basis.modes[:, 1:]) self._Z_lmn = copy_coeffs(Z_lmn, modes_Z, self.Z_basis.modes[:, 1:]) - self._sym = sym + self._sym = bool(sym) self._rho = rho if check_orientation and self._compute_orientation() == -1: @@ -245,7 +245,7 @@ def Z_lmn(self, new): else: raise ValueError( f"Z_lmn should have the same size as the basis, got {len(new)} for " - + f"basis with {self.R_basis.num_modes} modes." + + f"basis with {self.Z_basis.num_modes} modes." ) def get_coeffs(self, m, n=0): @@ -301,7 +301,9 @@ def from_input_file(cls, path): Surface with given Fourier coefficients. """ - inputs = InputReader().parse_inputs(path)[-1] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + inputs = InputReader().parse_inputs(path)[-1] if (inputs["bdry_ratio"] is not None) and (inputs["bdry_ratio"] != 1): warnings.warn( "boundary_ratio = {} != 1, surface may not be as expected".format( @@ -870,8 +872,8 @@ def __init__( self._R_lmn = copy_coeffs(R_lmn, modes_R, self.R_basis.modes[:, :2]) self._Z_lmn = copy_coeffs(Z_lmn, modes_Z, self.Z_basis.modes[:, :2]) - self._sym = sym - self._spectral_indexing = spectral_indexing + self._sym = bool(sym) + self._spectral_indexing = str(spectral_indexing) self._zeta = zeta diff --git a/desc/grid.py b/desc/grid.py index b9734f4fa6..de24c376d5 100644 --- a/desc/grid.py +++ b/desc/grid.py @@ -58,6 +58,12 @@ def _set_up(self): self._M = int(self._M) self._N = int(self._N) self._NFP = int(self._NFP) + if hasattr(self, "_inverse_theta_idx"): + self._inverse_poloidal_idx = self._inverse_theta_idx + del self._inverse_theta_idx + if hasattr(self, "_unique_theta_idx"): + self._unique_poloidal_idx = self._unique_theta_idx + del self._unique_theta_idx def _enforce_symmetry(self): """Enforce stellarator symmetry. @@ -205,7 +211,7 @@ def sym(self): @property def is_meshgrid(self): - """bool: Whether this grid is separable into coordinate chunks. + """bool: Whether this grid is a tensor-product grid. Let the tuple (r, p, t) ∈ R³ denote a radial, poloidal, and toroidal coordinate value. The is_meshgrid flag denotes whether any coordinate @@ -221,6 +227,7 @@ def coordinates(self): @property def period(self): + """Periodicity of coordinates.""" return self.__dict__.setdefault( "_period", (np.inf, 2 * np.pi, 2 * np.pi / self.NFP) ) @@ -255,7 +262,7 @@ def num_theta(self): @property def num_theta_PEST(self): """ndarray: Number of unique straight field line poloidal angles.""" - errorif(self.coordinates[1] != "p", AttributeError) + errorif(self.coordinates[1] != "v", AttributeError) return self.num_poloidal @property @@ -300,7 +307,7 @@ def unique_theta_idx(self): @property def unique_theta_PEST_idx(self): """ndarray: Indices of unique straight field line poloidal angles.""" - errorif(self.coordinates[1] != "p", AttributeError) + errorif(self.coordinates[1] != "v", AttributeError) return self.unique_poloidal_idx @property @@ -351,7 +358,7 @@ def inverse_theta_idx(self): @property def inverse_theta_PEST_idx(self): """ndarray: Indices that recover unique straight field line poloidal angles.""" - errorif(self.coordinates[1] != "p", AttributeError) + errorif(self.coordinates[1] != "v", AttributeError) return self.inverse_poloidal_idx @property @@ -382,7 +389,27 @@ def nodes(self): @property def spacing(self): - """ndarray: Node spacing, in (rho,theta,zeta).""" + """Quadrature weights for integration over surfaces. + + This is typically the distance between nodes when ``NFP=1``, as the quadrature + weight is by default a midpoint rule. The returned matrix has three columns, + corresponding to the radial, poloidal, and toroidal coordinate, respectively. + Each element of the matrix specifies the quadrature area associated with a + particular node for each coordinate. I.e. on a grid with coordinates + of "rtz", the columns specify dρ, dθ, dζ, respectively. An integration + over a ρ flux surface will assign quadrature weight dθ*dζ to each node. + Note that dζ is the distance between toroidal surfaces multiplied by ``NFP``. + + On a LinearGrid with duplicate nodes, the columns of spacing no longer + specify dρ, dθ, dζ. Rather, the product of each adjacent column specifies + dρ*dθ, dθ*dζ, dζ*dρ, respectively. + + Returns + ------- + spacing : ndarray + Quadrature weights for integration over surface. + + """ errorif( not hasattr(self, "_spacing"), AttributeError, @@ -426,7 +453,7 @@ def get_label(self, label): if label in {"rho", "poloidal", "zeta"}: return label rad = {"r": "rho"}[self.coordinates[0]] - pol = {"a": "alpha", "t": "theta", "p": "theta_PEST"}[self.coordinates[1]] + pol = {"a": "alpha", "t": "theta", "v": "theta_PEST"}[self.coordinates[1]] tor = {"z": "zeta"}[self.coordinates[2]] return {rad: "rho", pol: "poloidal", tor: "zeta"}[label] @@ -582,17 +609,19 @@ class Grid(_Grid): coordinates : str Coordinates that are specified by the nodes. raz : rho, alpha, zeta - rpz : rho, theta_PEST, zeta + rvz : rho, theta_PEST, zeta rtz : rho, theta, zeta period : tuple of float Assumed periodicity for each coordinate. Use np.inf to denote no periodicity. + NFP : int + Number of field periods (Default = 1). source_grid : Grid Grid from which coordinates were mapped from. sort : bool Whether to sort the nodes for use with FFT method. is_meshgrid : bool - Whether this grid is separable into coordinate chunks. + Whether this grid is a tensor-product grid. Let the tuple (r, p, t) ∈ R³ denote a radial, poloidal, and toroidal coordinate value. The is_meshgrid flag denotes whether any coordinate can be iterated over along the relevant axis of the reshaped grid: @@ -603,85 +632,6 @@ class Grid(_Grid): etc. may be wrong if grid contains duplicate nodes. """ - @classmethod - def create_meshgrid( - cls, - nodes, - spacing=None, - coordinates="rtz", - period=(np.inf, 2 * np.pi, 2 * np.pi), - **kwargs, - ): - """Create a meshgrid from the given coordinates in a jitable manner. - - Parameters - ---------- - nodes : Array, Array, Array - Sorted unique values of each coordinate. - spacing : Array, Array, Array - Weights for integration. Defaults to a midpoint rule. - coordinates : str - Coordinates that are specified by the nodes a, b, c, respectively. - raz : rho, alpha, zeta - rpz : rho, theta_PEST, zeta - rtz : rho, theta, zeta - period : tuple of float - Assumed periodicity for each coordinate. - Use np.inf to denote no periodicity. - - Returns - ------- - grid : Grid - Meshgrid. - - """ - a, b, c = jnp.atleast_1d(*nodes) - if spacing is None: - errorif(coordinates[0] != "r", NotImplementedError) - da = _midpoint_spacing(a) - db = _periodic_spacing(b, period[1])[1] - dc = _periodic_spacing(c, period[2])[1] - else: - da, db, dc = spacing - nodes = jnp.column_stack( - list(map(jnp.ravel, jnp.meshgrid(a, b, c, indexing="ij"))) - ) - spacing = jnp.column_stack( - list(map(jnp.ravel, jnp.meshgrid(da, db, dc, indexing="ij"))) - ) - weights = spacing.prod(axis=1) if np.prod(period) == 4 * jnp.pi**2 else None - - unique_a_idx = jnp.arange(a.size) * b.size * c.size - unique_b_idx = jnp.arange(b.size) * c.size - unique_c_idx = jnp.arange(c.size) - inverse_a_idx = repeat( - unique_a_idx // (b.size * c.size), - b.size * c.size, - total_repeat_length=a.size * b.size * c.size, - ) - inverse_b_idx = jnp.tile( - repeat(unique_b_idx // c.size, c.size, total_repeat_length=b.size * c.size), - a.size, - ) - inverse_c_idx = jnp.tile(unique_c_idx, a.size * b.size) - return cls( - nodes=nodes, - spacing=spacing, - weights=weights, - coordinates=coordinates, - period=period, - sort=False, - is_meshgrid=True, - jitable=True, - _unique_rho_idx=unique_a_idx, - _unique_poloidal_idx=unique_b_idx, - _unique_zeta_idx=unique_c_idx, - _inverse_rho_idx=inverse_a_idx, - _inverse_poloidal_idx=inverse_b_idx, - _inverse_zeta_idx=inverse_c_idx, - **kwargs, - ) - def __init__( self, nodes, @@ -689,6 +639,7 @@ def __init__( weights=None, coordinates="rtz", period=(np.inf, 2 * np.pi, 2 * np.pi), + NFP=1, source_grid=None, sort=False, is_meshgrid=False, @@ -698,7 +649,7 @@ def __init__( # Python 3.3 (PEP 412) introduced key-sharing dictionaries. # This change measurably reduces memory usage of objects that # define all attributes in their __init__ method. - self._NFP = 1 + self._NFP = check_posint(NFP, "NFP", False) self._sym = False self._node_pattern = "custom" self._coordinates = coordinates @@ -760,6 +711,100 @@ def __init__( self._N = self.num_nodes errorif(len(kwargs), ValueError, f"Got unexpected kwargs {kwargs.keys()}") + @classmethod + def create_meshgrid( + cls, + nodes, + spacing=None, + coordinates="rtz", + period=(np.inf, 2 * np.pi, 2 * np.pi), + NFP=1, + **kwargs, + ): + """Create a tensor-product grid from the given coordinates in a jitable manner. + + Parameters + ---------- + nodes : list of ndarray + Three arrays, one for each coordinate. + Sorted unique values of each coordinate. + spacing : list of ndarray + Three arrays, one for each coordinate. + Weights for integration. Defaults to a midpoint rule. + coordinates : str + Coordinates that are specified by the ``nodes[0]``, ``nodes[1]``, + and ``nodes[2]``, respectively. + raz : rho, alpha, zeta + rvz : rho, theta_PEST, zeta + rtz : rho, theta, zeta + period : tuple of float + Assumed periodicity for each coordinate. + Use np.inf to denote no periodicity. + NFP : int + Number of field periods (Default = 1). + Only makes sense to change from 1 if ``period[2]==2π``. + + Returns + ------- + grid : Grid + Meshgrid. + + """ + NFP = check_posint(NFP, "NFP", False) + a, b, c = jnp.atleast_1d(*nodes) + if spacing is None: + errorif(coordinates[0] != "r", NotImplementedError) + da = _midpoint_spacing(a) + db = _periodic_spacing(b, period[1])[1] + dc = _periodic_spacing(c, period[2])[1] * NFP + else: + da, db, dc = spacing + nodes = jnp.column_stack( + list(map(jnp.ravel, jnp.meshgrid(a, b, c, indexing="ij"))) + ) + spacing = jnp.column_stack( + list(map(jnp.ravel, jnp.meshgrid(da, db, dc, indexing="ij"))) + ) + weights = ( + spacing.prod(axis=1) + if period[1] * period[2] == 4 * np.pi**2 / NFP + # Doesn't make sense to assign weights if the coordinates aren't periodic + # since it's not clear how to form a surface and hence its enclosed volume. + else None + ) + + unique_a_idx = jnp.arange(a.size) * b.size * c.size + unique_b_idx = jnp.arange(b.size) * c.size + unique_c_idx = jnp.arange(c.size) + inverse_a_idx = repeat( + unique_a_idx // (b.size * c.size), + b.size * c.size, + total_repeat_length=a.size * b.size * c.size, + ) + inverse_b_idx = jnp.tile( + repeat(unique_b_idx // c.size, c.size, total_repeat_length=b.size * c.size), + a.size, + ) + inverse_c_idx = jnp.tile(unique_c_idx, a.size * b.size) + return cls( + nodes=nodes, + spacing=spacing, + weights=weights, + coordinates=coordinates, + period=period, + NFP=NFP, + sort=False, + is_meshgrid=True, + jitable=True, + _unique_rho_idx=unique_a_idx, + _unique_poloidal_idx=unique_b_idx, + _unique_zeta_idx=unique_c_idx, + _inverse_rho_idx=inverse_a_idx, + _inverse_poloidal_idx=inverse_b_idx, + _inverse_zeta_idx=inverse_c_idx, + **kwargs, + ) + def _sort_nodes(self): """Sort nodes for use with FFT.""" sort_idx = np.lexsort((self.nodes[:, 1], self.nodes[:, 0], self.nodes[:, 2])) @@ -834,14 +879,6 @@ class LinearGrid(_Grid): Toroidal coordinates (Default = 0.0). Alternatively, the number of toroidal coordinates (if an integer). Note that if supplied the values may be reordered in the resulting grid. - coordinates : str - Coordinates that are specified by the nodes. - raz : rho, alpha, zeta - rpz : rho, theta_PEST, zeta - rtz : rho, theta, zeta - period : tuple of float - Assumed periodicity for each coordinate. - Use np.inf to denote no periodicity. """ def __init__( @@ -856,8 +893,6 @@ def __init__( rho=np.array(1.0), theta=np.array(0.0), zeta=np.array(0.0), - coordinates="rtz", - period=None, ): self._L = check_nonnegint(L, "L") self._M = check_nonnegint(M, "M") @@ -868,10 +903,8 @@ def __init__( self._poloidal_endpoint = False self._toroidal_endpoint = False self._node_pattern = "linear" - self._coordinates = coordinates - self._period = ( - (np.inf, 2 * np.pi, 2 * np.pi / NFP) if period is None else period - ) + self._coordinates = "rtz" + self._period = (np.inf, 2 * np.pi, 2 * np.pi / self._NFP) self._nodes, self._spacing = self._create_nodes( L=L, M=M, @@ -907,7 +940,6 @@ def _create_nodes( # noqa: C901 rho=1.0, theta=0.0, zeta=0.0, - period=None, ): """Create grid nodes and weights. @@ -936,9 +968,6 @@ def _create_nodes( # noqa: C901 zeta : int or ndarray of float, optional Toroidal coordinates (Default = 0.0). Alternatively, the number of toroidal coordinates (if an integer). - period : tuple of float - Assumed periodicity for each coordinate. - Use np.inf to denote no periodicity. Returns ------- @@ -949,9 +978,7 @@ def _create_nodes( # noqa: C901 """ self._NFP = check_posint(NFP, "NFP", False) - self._period = ( - (np.inf, 2 * np.pi, 2 * np.pi / NFP) if period is None else period - ) + self._period = (np.inf, 2 * np.pi, 2 * np.pi / self._NFP) axis = bool(axis) endpoint = bool(endpoint) theta_period = self.period[1] @@ -1158,22 +1185,18 @@ class QuadratureGrid(_Grid): toroidal grid resolution (exactly integrates toroidal modes up to order N) NFP : int number of field periods (Default = 1) - coordinates : str - Coordinates that are specified by the nodes. - raz : rho, alpha, zeta - rpz : rho, theta_PEST, zeta - rtz : rho, theta, zeta """ - def __init__(self, L, M, N, NFP=1, coordinates="rtz"): + def __init__(self, L, M, N, NFP=1): self._L = check_nonnegint(L, "L", False) self._M = check_nonnegint(M, "N", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) self._sym = False self._node_pattern = "quad" - self._coordinates = coordinates + self._coordinates = "rtz" + self._period = (np.inf, 2 * np.pi, 2 * np.pi / self._NFP) self._nodes, self._spacing = self._create_nodes(L=L, M=M, N=N, NFP=NFP) # symmetry is never enforced for Quadrature Grid self._sort_nodes() @@ -1215,6 +1238,7 @@ def _create_nodes(self, L=1, M=1, N=1, NFP=1): self._M = check_nonnegint(M, "M", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) + self._period = (np.inf, 2 * np.pi, 2 * np.pi / self._NFP) L = L + 1 M = 2 * M + 1 N = 2 * N + 1 @@ -1302,32 +1326,18 @@ class ConcentricGrid(_Grid): * ``'ocs'``: optimal concentric sampling to minimize the condition number of the resulting transform matrix, for doing inverse transform. * ``linear`` : linear spacing in r=[0,1] - coordinates : str - Coordinates that are specified by the nodes. - raz : rho, alpha, zeta - rpz : rho, theta_PEST, zeta - rtz : rho, theta, zeta """ - def __init__( - self, - L, - M, - N, - NFP=1, - sym=False, - axis=False, - node_pattern="jacobi", - coordinates="rtz", - ): + def __init__(self, L, M, N, NFP=1, sym=False, axis=False, node_pattern="jacobi"): self._L = check_nonnegint(L, "L", False) self._M = check_nonnegint(M, "M", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) self._sym = sym self._node_pattern = node_pattern - self._coordinates = coordinates + self._coordinates = "rtz" + self._period = (np.inf, 2 * np.pi, 2 * np.pi / self._NFP) self._nodes, self._spacing = self._create_nodes( L=L, M=M, N=N, NFP=NFP, axis=axis, node_pattern=node_pattern ) @@ -1382,6 +1392,7 @@ def _create_nodes(self, L, M, N, NFP=1, axis=False, node_pattern="jacobi"): self._M = check_nonnegint(M, "M", False) self._N = check_nonnegint(N, "N", False) self._NFP = check_posint(NFP, "NFP", False) + self._period = (np.inf, 2 * np.pi, 2 * np.pi / self._NFP) def ocs(L): # Ramos-Lopez, et al. “Optimal Sampling Patterns for Zernike Polynomials.” @@ -1798,7 +1809,7 @@ def _periodic_spacing(x, period=2 * jnp.pi, sort=False, jnp=jnp): Points, assumed sorted in the cyclic domain [0, period], unless specified otherwise. period : float - Number such that x + period = x. + Number such that f(x + period) = f(x) for any function f on this domain. sort : bool Set to true if x is not sorted in the cyclic domain [0, period]. diff --git a/desc/input_reader.py b/desc/input_reader.py index b41bf8d685..62d1da61e3 100644 --- a/desc/input_reader.py +++ b/desc/input_reader.py @@ -826,7 +826,7 @@ def desc_output_to_input( # noqa: C901 - fxn too complex Fourier coefficients below this value will be set to 0. """ from desc.grid import LinearGrid - from desc.io.equilibrium_io import load + from desc.io.optimizable_io import load from desc.profiles import PowerSeriesProfile from desc.utils import copy_coeffs diff --git a/desc/io/__init__.py b/desc/io/__init__.py index a2158d229e..ecf8a0c268 100644 --- a/desc/io/__init__.py +++ b/desc/io/__init__.py @@ -1,14 +1,14 @@ """Functions and classes for reading and writing DESC data.""" # InputReader lives outside this module for import ordering reasons, so we can -# import InputReader in __main__ without importing equilibrium_io which imports JAX +# import InputReader in __main__ without importing optimizable_io which imports JAX # stuff potentially before we've set the GPU correctly. # We include a link to it here for backwards compatibility from desc.input_reader import InputReader from .ascii_io import read_ascii, write_ascii -from .equilibrium_io import IOAble, load from .hdf5_io import hdf5Reader, hdf5Writer +from .optimizable_io import IOAble, load from .pickle_io import PickleReader, PickleWriter __all__ = ["InputReader", "load"] diff --git a/desc/io/hdf5_io.py b/desc/io/hdf5_io.py index 3cda307baa..f8c196f05b 100644 --- a/desc/io/hdf5_io.py +++ b/desc/io/hdf5_io.py @@ -332,7 +332,7 @@ def isarray(x): group = loc.create_group(attr) self.write_list(data, where=group) else: - from .equilibrium_io import IOAble + from .optimizable_io import IOAble if isinstance(data, IOAble): group = loc.create_group(attr) diff --git a/desc/io/equilibrium_io.py b/desc/io/optimizable_io.py similarity index 98% rename from desc/io/equilibrium_io.py rename to desc/io/optimizable_io.py index 5aa606c1d5..5a11d39245 100644 --- a/desc/io/equilibrium_io.py +++ b/desc/io/optimizable_io.py @@ -32,6 +32,7 @@ def load(load_from, file_format=None): ------- obj : The object saved in the file + """ if file_format is None and isinstance(load_from, (str, os.PathLike)): name = str(load_from) @@ -83,6 +84,8 @@ def _unjittable(x): return any([_unjittable(y) for y in x]) if isinstance(x, dict): return any([_unjittable(y) for y in x.values()]) + if hasattr(x, "dtype") and np.ndim(x) == 0: + return np.issubdtype(x.dtype, np.bool_) or np.issubdtype(x.dtype, np.int_) return isinstance(x, (str, types.FunctionType, bool, int, np.int_)) diff --git a/desc/objectives/__init__.py b/desc/objectives/__init__.py index bf830f3890..02ddcc71d5 100644 --- a/desc/objectives/__init__.py +++ b/desc/objectives/__init__.py @@ -5,7 +5,9 @@ CoilCurrentLength, CoilCurvature, CoilLength, + CoilsetMinDistance, CoilTorsion, + PlasmaCoilsetMinDistance, QuadraticFlux, ToroidalFlux, ) diff --git a/desc/objectives/_coils.py b/desc/objectives/_coils.py index a8dea90ad9..977c47d82a 100644 --- a/desc/objectives/_coils.py +++ b/desc/objectives/_coils.py @@ -2,9 +2,17 @@ import numpy as np -from desc.backend import jnp, tree_flatten, tree_leaves, tree_map, tree_unflatten -from desc.compute import compute as compute_fun -from desc.compute import get_profiles, get_transforms +from desc.backend import ( + fori_loop, + jnp, + tree_flatten, + tree_leaves, + tree_map, + tree_unflatten, +) +from desc.compute import get_profiles, get_transforms, rpz2xyz +from desc.compute.utils import _compute as compute_fun +from desc.compute.utils import safenorm from desc.grid import LinearGrid, _Grid from desc.singularities import compute_B_plasma from desc.utils import Timer, errorif, warnif @@ -48,8 +56,8 @@ class _CoilObjective(_Objective): of the objective. Has no effect on self.grad or self.hess which always use reverse mode and forward over reverse mode respectively. grid : Grid, list, optional - Collocation grid containing the nodes to evaluate at. If list, has to adhere to - Objective.dim_f + Collocation grid containing the nodes to evaluate at. + If a list, must have the same structure as coil. name : str, optional Name of the objective function. @@ -96,102 +104,78 @@ def build(self, use_jit=True, verbose=1): # noqa:C901 """ # local import to avoid circular import - from desc.coils import CoilSet, MixedCoilSet + from desc.coils import CoilSet, MixedCoilSet, _Coil - self._dim_f = 0 - self._quad_weights = jnp.array([]) + def _is_single_coil(c): + return isinstance(c, _Coil) and not isinstance(c, CoilSet) - def to_list(coilset): - """Turn a MixedCoilSet container into a list of what it's containing.""" - if isinstance(coilset, list): - return [to_list(x) for x in coilset] - elif isinstance(coilset, MixedCoilSet): - return [to_list(x) for x in coilset] + def _prune_coilset_tree(coilset): + """Remove extra members from CoilSets (but not MixedCoilSets).""" + if isinstance(coilset, list) or isinstance(coilset, MixedCoilSet): + return [_prune_coilset_tree(c) for c in coilset] elif isinstance(coilset, CoilSet): - # use the same grid/transform for CoilSet - return to_list(coilset.coils[0]) + # CoilSet only uses a single grid/transform for all coils + return _prune_coilset_tree(coilset.coils[0]) else: - return [coilset] + return coilset # single coil - # gives structure of coils, e.g. MixedCoilSet(coils, coils) would give a - # a structure of [[*, *], [*, *]] if n = 2 coils - coil_leaves, coil_structure = tree_flatten( - self.things[0], is_leaf=lambda x: not hasattr(x, "__len__") - ) - self._num_coils = len(coil_leaves) - - # check type - if isinstance(self._grid, numbers.Integral): - self._grid = LinearGrid(N=self._grid, endpoint=False) - # all of these cases return a container MixedCoilSet that contains - # LinearGrids. i.e. MixedCoilSet.coils = list of LinearGrid - if self._grid is None: - # map default grid to structure of inputted coils - self._grid = tree_map( - lambda x: LinearGrid( - N=2 * x.N + 5, NFP=getattr(x, "NFP", 1), endpoint=False - ), - self.things[0], - is_leaf=lambda x: not hasattr(x, "__len__"), - ) - elif isinstance(self._grid, _Grid): - # map inputted single LinearGrid to structure of inputted coils - self._grid = [self._grid] * self._num_coils - self._grid = tree_unflatten(coil_structure, self._grid) - else: - # this case covers an inputted list of grids that matches the size - # of the inputted coils. Can be a 1D list or nested list. - flattened_grid = tree_leaves( - self._grid, is_leaf=lambda x: isinstance(x, _Grid) - ) - self._grid = tree_unflatten(coil_structure, flattened_grid) + coil = self.things[0] + grid = self._grid - timer = Timer() - if verbose > 0: - print("Precomputing transforms") - timer.start("Precomputing transforms") - - transforms = tree_map( - lambda x, y: get_transforms(self._data_keys, obj=x, grid=y), - self.things[0], - self._grid, - is_leaf=lambda x: not hasattr(x, "__len__"), - ) + # get individual coils from coilset + coils, structure = tree_flatten(coil, is_leaf=_is_single_coil) + self._num_coils = len(coils) - grids = tree_leaves(self._grid, is_leaf=lambda x: hasattr(x, "num_nodes")) - self._dim_f = np.sum([grid.num_nodes for grid in grids]) - self._quad_weights = np.concatenate([grid.spacing[:, 2] for grid in grids]) + # map grid to list of length coils + if grid is None: + grid = [LinearGrid(N=2 * c.N + 5, endpoint=False) for c in coils] + if isinstance(grid, numbers.Integral): + grid = LinearGrid(N=self._grid, endpoint=False) + if isinstance(grid, _Grid): + grid = [grid] * self._num_coils + if isinstance(grid, list): + grid = tree_leaves(grid, is_leaf=lambda g: isinstance(g, _Grid)) - # get only needed grids (1 per CoilSet) and flatten that list - self._grid = tree_leaves( - to_list(self._grid), is_leaf=lambda x: isinstance(x, _Grid) - ) - transforms = tree_leaves( - to_list(transforms), is_leaf=lambda x: isinstance(x, dict) + errorif( + len(grid) != len(coils), + ValueError, + "grid input must be broadcastable to the coil structure.", ) - errorif( - np.any([grid.num_rho > 1 or grid.num_theta > 1 for grid in self._grid]), + np.any([g.num_rho > 1 or g.num_theta > 1 for g in grid]), ValueError, "Only use toroidal resolution for coil grids.", ) - # CoilSet and _Coil have one grid/transform - if not isinstance(self.things[0], MixedCoilSet): - self._grid = self._grid[0] - transforms = transforms[0] + self._dim_f = np.sum([g.num_nodes for g in grid]) + quad_weights = np.concatenate([g.spacing[:, 2] for g in grid]) - self._constants = { - "transforms": transforms, - "quad_weights": self._quad_weights, - } + # map grid to the same structure as coil and then remove unnecessary members + grid = tree_unflatten(structure, grid) + grid = _prune_coilset_tree(grid) + coil = _prune_coilset_tree(coil) + + timer = Timer() + if verbose > 0: + print("Precomputing transforms") + timer.start("Precomputing transforms") + + transforms = tree_map( + lambda c, g: get_transforms(self._data_keys, obj=c, grid=g), + coil, + grid, + is_leaf=lambda x: _is_single_coil(x) or isinstance(x, _Grid), + ) + + self._grid = grid + self._constants = {"transforms": transforms, "quad_weights": quad_weights} timer.stop("Precomputing transforms") if verbose > 1: timer.disp("Precomputing transforms") if self._normalize: - self._scales = [compute_scaling_factors(coil) for coil in coil_leaves] + self._scales = [compute_scaling_factors(coil) for coil in coils] super().build(use_jit=use_jit, verbose=verbose) @@ -708,6 +692,393 @@ def compute(self, params, constants=None): return out +class CoilsetMinDistance(_Objective): + """Target the minimum distance between coils in a coilset. + + Will yield one value per coil in the coilset, which is the minimumm distance to + another coil in that coilset. + + Parameters + ---------- + coils : CoilSet + Coils that are to be optimized. + target : float, ndarray, optional + Target value(s) of the objective. Only used if bounds is None. + Must be broadcastable to Objective.dim_f. If array, it has to + be flattened according to the number of inputs. + bounds : tuple of float, ndarray, optional + Lower and upper bounds on the objective. Overrides target. + Both bounds must be broadcastable to to Objective.dim_f + weight : float, ndarray, optional + Weighting to apply to the Objective, relative to other Objectives. + Must be broadcastable to to Objective.dim_f + normalize : bool, optional + Whether to compute the error in physical units or non-dimensionalize. + normalize_target : bool, optional + Whether target and bounds should be normalized before comparing to computed + values. If `normalize` is `True` and the target is in physical units, + this should also be set to True. + be set to True. + loss_function : {None, 'mean', 'min', 'max'}, optional + Loss function to apply to the objective values once computed. This loss function + is called on the raw compute value, before any shifting, scaling, or + normalization. Operates over all coils, not each individial coil. + deriv_mode : {"auto", "fwd", "rev"} + Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + "auto" selects forward or reverse mode based on the size of the input and output + of the objective. Has no effect on self.grad or self.hess which always use + reverse mode and forward over reverse mode respectively. + grid : Grid, optional + Collocation grid used to discritize each coil. Default = LinearGrid(N=16) + name : str, optional + Name of the objective function. + + """ + + _scalar = False + _units = "(m)" + _print_value_fmt = "Minimum coil-coil distance: {:10.3e} " + + def __init__( + self, + coils, + target=None, + bounds=None, + weight=1, + normalize=True, + normalize_target=True, + loss_function=None, + deriv_mode="auto", + grid=None, + name="coil-coil minimum distance", + ): + if target is None and bounds is None: + bounds = (1, np.inf) + self._grid = grid + super().__init__( + things=coils, + target=target, + bounds=bounds, + weight=weight, + normalize=normalize, + normalize_target=normalize_target, + loss_function=loss_function, + deriv_mode=deriv_mode, + name=name, + ) + + def build(self, use_jit=True, verbose=1): + """Build constant arrays. + + Parameters + ---------- + use_jit : bool, optional + Whether to just-in-time compile the objective and derivatives. + verbose : int, optional + Level of output. + + """ + coilset = self.things[0] + grid = self._grid or LinearGrid(N=16) + + self._dim_f = coilset.num_coils + self._constants = {"coilset": coilset, "grid": grid, "quad_weights": 1.0} + + if self._normalize: + coils = tree_leaves(coilset, is_leaf=lambda x: not hasattr(x, "__len__")) + scales = [compute_scaling_factors(coil)["a"] for coil in coils] + self._normalization = np.mean(scales) # mean length of coils + + super().build(use_jit=use_jit, verbose=verbose) + + def compute(self, params, constants=None): + """Compute minimum distances between coils. + + Parameters + ---------- + params : dict + Dictionary of coilset degrees of freedom, eg CoilSet.params_dict + constants : dict + Dictionary of constant data, eg transforms, profiles etc. + Defaults to self._constants. + + Returns + ------- + f : array of floats + Minimum distance to another coil for each coil in the coilset. + + """ + if constants is None: + constants = self.constants + pts = constants["coilset"]._compute_position( + params=params, grid=constants["grid"] + ) + + def body(k): + # dist btwn all pts; shape(ncoils,num_nodes,num_nodes) + dist = safenorm(pts[k][None, :, None] - pts[:, None, :], axis=-1) + # exclude distances between points on the same coil + mask = jnp.ones(self.dim_f).at[k].set(0)[:, None, None] + return jnp.min(dist, where=mask, initial=jnp.inf) + + min_dist_per_coil = fori_loop( + 0, + self.dim_f, + lambda k, min_dist: min_dist.at[k].set(body(k)), + jnp.zeros(self.dim_f), + ) + return min_dist_per_coil + + +class PlasmaCoilsetMinDistance(_Objective): + """Target the minimum distance between the plasma and coilset. + + Will yield one value per coil in the coilset, which is the minimumm distance from + that coil to the plasma boundary surface. + + NOTE: By default, assumes the plasma boundary is not fixed and its coordinates are + computed at every iteration, for example if the equilibrium is changing in a + single-stage optimization. + If the plasma boundary is fixed, set eq_fixed=True to precompute the last closed + flux surface coordinates and improve the efficiency of the calculation. + + Parameters + ---------- + eq : Equilibrium or FourierRZToroidalSurface + Equilibrium (or FourierRZToroidalSurface) that will be optimized + to satisfy the Objective. + coils : CoilSet + Coils that are to be optimized. + target : float, ndarray, optional + Target value(s) of the objective. Only used if bounds is None. + Must be broadcastable to Objective.dim_f. If array, it has to + be flattened according to the number of inputs. + bounds : tuple of float, ndarray, optional + Lower and upper bounds on the objective. Overrides target. + Both bounds must be broadcastable to to Objective.dim_f + weight : float, ndarray, optional + Weighting to apply to the Objective, relative to other Objectives. + Must be broadcastable to to Objective.dim_f + normalize : bool, optional + Whether to compute the error in physical units or non-dimensionalize. + normalize_target : bool, optional + Whether target and bounds should be normalized before comparing to computed + values. If `normalize` is `True` and the target is in physical units, + this should also be set to True. + be set to True. + loss_function : {None, 'mean', 'min', 'max'}, optional + Loss function to apply to the objective values once computed. This loss function + is called on the raw compute value, before any shifting, scaling, or + normalization. Operates over all coils, not each individial coil. + deriv_mode : {"auto", "fwd", "rev"} + Specify how to compute jacobian matrix, either forward mode or reverse mode AD. + "auto" selects forward or reverse mode based on the size of the input and output + of the objective. Has no effect on self.grad or self.hess which always use + reverse mode and forward over reverse mode respectively. + plasma_grid : Grid, optional + Collocation grid containing the nodes to evaluate plasma geometry at. + Defaults to ``LinearGrid(M=eq.M_grid, N=eq.N_grid)``. + coil_grid : Grid, optional + Collocation grid containing the nodes to evaluate coilset geometry at. + Defaults to ``LinearGrid(N=16)``. + eq_fixed: bool, optional + Whether the equilibrium is fixed or not. If True, the last closed flux surface + is fixed and its coordinates are precomputed, which saves on computation time + during optimization, and self.things = [coils] only. + If False, the surface coordinates are computed at every iteration. + False by default, so that self.things = [coils, eq]. + coils_fixed: bool, optional + Whether the coils are fixed or not. If True, the coils + are fixed and their coordinates are precomputed, which saves on computation time + during optimization, and self.things = [eq] only. + If False, the coil coordinates are computed at every iteration. + False by default, so that self.things = [coils, eq]. + name : str, optional + Name of the objective function. + + """ + + _scalar = False + _units = "(m)" + _print_value_fmt = "Minimum plasma-coil distance: {:10.3e} " + + def __init__( + self, + eq, + coils, + target=None, + bounds=None, + weight=1, + normalize=True, + normalize_target=True, + loss_function=None, + deriv_mode="auto", + plasma_grid=None, + coil_grid=None, + eq_fixed=False, + coils_fixed=False, + name="plasma-coil minimum distance", + ): + if target is None and bounds is None: + bounds = (1, np.inf) + self._eq = eq + self._coils = coils + self._plasma_grid = plasma_grid + self._coil_grid = coil_grid + self._eq_fixed = eq_fixed + self._coils_fixed = coils_fixed + errorif(eq_fixed and coils_fixed, ValueError, "Cannot fix both eq and coils") + things = [] + if not eq_fixed: + things.append(eq) + if not coils_fixed: + things.append(coils) + super().__init__( + things=things, + target=target, + bounds=bounds, + weight=weight, + normalize=normalize, + normalize_target=normalize_target, + loss_function=loss_function, + deriv_mode=deriv_mode, + name=name, + ) + + def build(self, use_jit=True, verbose=1): + """Build constant arrays. + + Parameters + ---------- + use_jit : bool, optional + Whether to just-in-time compile the objective and derivatives. + verbose : int, optional + Level of output. + + """ + if self._eq_fixed: + eq = self._eq + coils = self.things[0] + elif self._coils_fixed: + eq = self.things[0] + coils = self._coils + else: + eq = self.things[0] + coils = self.things[1] + plasma_grid = self._plasma_grid or LinearGrid(M=eq.M_grid, N=eq.N_grid) + coil_grid = self._coil_grid or LinearGrid(N=16) + warnif( + not np.allclose(plasma_grid.nodes[:, 0], 1), + UserWarning, + "Plasma/Surface grid includes interior points, should be rho=1.", + ) + + self._dim_f = coils.num_coils + self._eq_data_keys = ["R", "phi", "Z"] + + eq_profiles = get_profiles(self._eq_data_keys, obj=eq, grid=plasma_grid) + eq_transforms = get_transforms(self._eq_data_keys, obj=eq, grid=plasma_grid) + + self._constants = { + "eq": eq, + "coils": coils, + "coil_grid": coil_grid, + "eq_profiles": eq_profiles, + "eq_transforms": eq_transforms, + "quad_weights": 1.0, + } + + if self._eq_fixed: + # precompute the equilibrium surface coordinates + data = compute_fun( + eq, + self._eq_data_keys, + params=eq.params_dict, + transforms=eq_transforms, + profiles=eq_profiles, + ) + plasma_pts = rpz2xyz(jnp.array([data["R"], data["phi"], data["Z"]]).T) + self._constants["plasma_coords"] = plasma_pts + if self._coils_fixed: + coils_pts = coils._compute_position( + params=coils.params_dict, grid=coil_grid + ) + self._constants["coil_coords"] = coils_pts + + if self._normalize: + scales = compute_scaling_factors(eq) + self._normalization = scales["a"] + + super().build(use_jit=use_jit, verbose=verbose) + + def compute(self, params_1, params_2=None, constants=None): + """Compute minimum distance between coils and the plasma/surface. + + Parameters + ---------- + params_1 : dict + Dictionary of coilset degrees of freedom, eg ``CoilSet.params_dict`` if + self._coils_fixed is False, else is the equilibrium or surface degrees of + freedom + params_2 : dict + Dictionary of equilibrium or surface degrees of freedom, + eg ``Equilibrium.params_dict`` + Only required if ``self._eq_fixed = False``. + constants : dict + Dictionary of constant data, eg transforms, profiles etc. + Defaults to self._constants. + + Returns + ------- + f : array of floats + Minimum distance from coil to surface for each coil in the coilset. + + """ + if constants is None: + constants = self.constants + if self._eq_fixed: + coils_params = params_1 + elif self._coils_fixed: + eq_params = params_1 + else: + eq_params = params_1 + coils_params = params_2 + + # coil pts; shape(ncoils,coils_grid.num_nodes,3) + if self._coils_fixed: + coils_pts = constants["coil_coords"] + else: + coils_pts = constants["coils"]._compute_position( + params=coils_params, grid=constants["coil_grid"] + ) + + # plasma pts; shape(plasma_grid.num_nodes,3) + if self._eq_fixed: + plasma_pts = constants["plasma_coords"] + else: + data = compute_fun( + constants["eq"], + self._eq_data_keys, + params=eq_params, + transforms=constants["eq_transforms"], + profiles=constants["eq_profiles"], + ) + plasma_pts = rpz2xyz(jnp.array([data["R"], data["phi"], data["Z"]]).T) + + def body(k): + # dist btwn all pts; shape(ncoils,plasma_grid.num_nodes,coil_grid.num_nodes) + dist = safenorm(coils_pts[k][None, :, :] - plasma_pts[:, None, :], axis=-1) + return jnp.min(dist, initial=jnp.inf) + + min_dist_per_coil = fori_loop( + 0, + self.dim_f, + lambda k, min_dist: min_dist.at[k].set(body(k)), + jnp.zeros(self.dim_f), + ) + return min_dist_per_coil + + class QuadraticFlux(_Objective): """Target B*n = 0 on LCFS. diff --git a/desc/objectives/_geometry.py b/desc/objectives/_geometry.py index 4528f16836..6c75fb8a12 100644 --- a/desc/objectives/_geometry.py +++ b/desc/objectives/_geometry.py @@ -1,7 +1,5 @@ """Objectives for targeting geometrical quantities.""" -import warnings - import numpy as np from desc.backend import jnp @@ -9,7 +7,7 @@ from desc.compute.utils import _compute as compute_fun from desc.compute.utils import safenorm from desc.grid import LinearGrid, QuadratureGrid -from desc.utils import Timer +from desc.utils import Timer, warnif from .normalization import compute_scaling_factors from .objective_funs import _Objective @@ -514,7 +512,7 @@ class PlasmaVesselDistance(_Objective): at every iteration, for example if the winding surface you compare to is part of the optimization and thus changing. If the bounding surface is fixed, set surface_fixed=True to precompute the surface - coordinates and improve the efficiency of the calculation + coordinates and improve the efficiency of the calculation. NOTE: for best results, use this objective in combination with either MeanCurvature or PrincipalCurvature, to penalize the tendency for the optimizer to only move the @@ -530,7 +528,7 @@ class PlasmaVesselDistance(_Objective): Parameters ---------- - eq : Equilibrium, optional + eq : Equilibrium Equilibrium that will be optimized to satisfy the Objective. surface : Surface Bounding surface to penalize distance to. @@ -651,10 +649,16 @@ def build(self, use_jit=True, verbose=1): plasma_grid = LinearGrid(M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP) else: plasma_grid = self._plasma_grid - if not np.allclose(surface_grid.nodes[:, 0], 1): - warnings.warn("Surface grid includes off-surface pts, should be rho=1") - if not np.allclose(plasma_grid.nodes[:, 0], 1): - warnings.warn("Plasma grid includes interior points, should be rho=1") + warnif( + not np.allclose(surface_grid.nodes[:, 0], 1), + UserWarning, + "Surface grid includes off-surface pts, should be rho=1.", + ) + warnif( + not np.allclose(plasma_grid.nodes[:, 0], 1), + UserWarning, + "Plasma grid includes interior points, should be rho=1.", + ) self._dim_f = surface_grid.num_nodes self._equil_data_keys = ["R", "phi", "Z"] diff --git a/desc/objectives/_omnigenity.py b/desc/objectives/_omnigenity.py index 4fded346df..980fc47bea 100644 --- a/desc/objectives/_omnigenity.py +++ b/desc/objectives/_omnigenity.py @@ -666,7 +666,7 @@ def __init__( normalize=True, normalize_target=True, loss_function=None, - deriv_mode="fwd", # FIXME: get it working with rev mode (see GH issue #943) + deriv_mode="auto", eq_grid=None, field_grid=None, M_booz=None, @@ -891,20 +891,26 @@ def compute(self, params_1=None, params_2=None, constants=None): # update theta_B and zeta_B with new iota from the equilibrium M, N = constants["helicity"] iota = jnp.mean(eq_data["iota"]) + # see comment in desc.compute._omnigenity for the explanation of these + # wheres + mat_OP = jnp.array( + [[N, iota / jnp.where(N == 0, 1, N)], [0, 1 / jnp.where(N == 0, 1, N)]] + ) + mat_OT = jnp.array([[0, -1], [M, -1 / jnp.where(iota == 0, 1.0, iota)]]) + den = jnp.where((N - M * iota) == 0, 1.0, (N - M * iota)) + mat_OH = jnp.array([[N, M * iota / den], [M, M / den]]) matrix = jnp.where( M == 0, - jnp.array([N, iota / N, 0, 1 / N]), # OP + mat_OP, jnp.where( N == 0, - jnp.array([0, -1, M, -1 / iota]), # OT - jnp.array( - [N, M * iota / (N - M * iota), M, M / (N - M * iota)] # OH - ), + mat_OT, + mat_OH, ), - ).reshape((2, 2)) + ) booz = matrix @ jnp.vstack((field_data["alpha"], field_data["h"])) - field_data["theta_B"] = booz[0, :] - field_data["zeta_B"] = booz[1, :] + theta_B = booz[0, :] + zeta_B = booz[1, :] else: field_data = compute_fun( "desc.magnetic_fields._core.OmnigenousField", @@ -915,13 +921,15 @@ def compute(self, params_1=None, params_2=None, constants=None): helicity=constants["helicity"], iota=jnp.mean(eq_data["iota"]), ) + theta_B = field_data["theta_B"] + zeta_B = field_data["zeta_B"] # additional computations that cannot be part of the regular compute API nodes = jnp.vstack( ( - jnp.zeros_like(field_data["theta_B"]), - field_data["theta_B"], - field_data["zeta_B"], + jnp.zeros_like(theta_B), + theta_B, + zeta_B, ) ).T B_eta_alpha = jnp.matmul( diff --git a/desc/objectives/objective_funs.py b/desc/objectives/objective_funs.py index a5b0553cd2..e6abfd47a9 100644 --- a/desc/objectives/objective_funs.py +++ b/desc/objectives/objective_funs.py @@ -669,7 +669,7 @@ def dim_f(self): @property def name(self): """Name of objective function (str).""" - return self._name + return self.__dict__.setdefault("_name", "") @property def target_scaled(self): @@ -1226,7 +1226,7 @@ def fixed(self): @property def name(self): """Name of objective (str).""" - return self._name + return self.__dict__.setdefault("_name", "") @property def things(self): diff --git a/desc/optimize/optimizer.py b/desc/optimize/optimizer.py index 70dc7fa36e..acc69c0030 100644 --- a/desc/optimize/optimizer.py +++ b/desc/optimize/optimizer.py @@ -207,8 +207,9 @@ def optimize( # noqa: C901 - FIXME: simplify this objective, nonlinear_constraints = _maybe_wrap_nonlinear_constraints( eq, objective, nonlinear_constraints, self.method, options ) - if not isinstance(objective, ProximalProjection) and eq is not None: - linear_constraints = maybe_add_self_consistency(eq, linear_constraints) + if not isinstance(objective, ProximalProjection): + for t in things: + linear_constraints = maybe_add_self_consistency(t, linear_constraints) linear_constraint = _combine_constraints(linear_constraints) nonlinear_constraint = _combine_constraints(nonlinear_constraints) diff --git a/desc/profiles.py b/desc/profiles.py index 2606851133..c31c490891 100644 --- a/desc/profiles.py +++ b/desc/profiles.py @@ -42,11 +42,11 @@ def __init__(self, name=""): @property def name(self): """str: Name of the profile.""" - return self._name + return self.__dict__.setdefault("_name", "") @name.setter def name(self, new): - self._name = new + self._name = str(new) @property @abstractmethod @@ -536,6 +536,7 @@ class PowerSeriesProfile(_Profile): Whether the basis should only contain even powers (True) or all powers (False). name : str Name of the profile. + """ _io_attrs_ = _Profile._io_attrs_ + ["_basis"] @@ -1190,8 +1191,7 @@ def __init__(self, params=None, modes=None, sym="auto", NFP=1, name=""): else: sym = False - self._basis = FourierZernikeBasis(L=L, M=M, N=N, NFP=NFP, sym=sym) - + self._basis = FourierZernikeBasis(L=L, M=M, N=N, NFP=int(NFP), sym=sym) self._params = copy_coeffs(params, modes, self.basis.modes) def __repr__(self): diff --git a/docs/notebooks/tutorials/free_boundary_equilibrium.ipynb b/docs/notebooks/tutorials/free_boundary_equilibrium.ipynb index 9c7929f24a..a75eab750f 100644 --- a/docs/notebooks/tutorials/free_boundary_equilibrium.ipynb +++ b/docs/notebooks/tutorials/free_boundary_equilibrium.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 30, "id": "e65f42a5-a1a4-4ba3-ac73-08533175e57d", "metadata": { "pycharm": { @@ -37,21 +37,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 31, "id": "a9fbc9ca-2ac1-494e-85d7-dcbd7dfae590", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DESC version 0.11.0+15.gb84ffffd.dirty,using JAX backend, jax version=0.4.25, jaxlib version=0.4.25, dtype=float64\n", - "Using device: CPU, with 3.50 GB available memory\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -102,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 32, "id": "f4dcf86d-c80c-4647-82c5-e36e3f077fd2", "metadata": { "tags": [] @@ -140,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 33, "id": "f00cb594-2c76-492d-ae19-1fff1ca00484", "metadata": { "tags": [] @@ -154,9 +145,10 @@ "Precomputing transforms\n", "Building objective: lcfs R\n", "Building objective: lcfs Z\n", - "Building objective: fixed-Psi\n", - "Building objective: fixed-pressure\n", - "Building objective: fixed-iota\n", + "Building objective: fixed Psi\n", + "Building objective: fixed pressure\n", + "Building objective: fixed iota\n", + "Building objective: fixed sheet current\n", "Building objective: self_consistency R\n", "Building objective: self_consistency Z\n", "Building objective: lambda gauge\n", @@ -168,11 +160,11 @@ "Using method: lsq-exact\n", "Optimization terminated successfully.\n", "`ftol` condition satisfied.\n", - " Current function value: 5.892e-12\n", - " Total delta_x: 3.553e-01\n", - " Iterations: 13\n", - " Function evaluations: 20\n", - " Jacobian evaluations: 14\n", + " Current function value: 9.452e-13\n", + " Total delta_x: 3.499e-01\n", + " Iterations: 39\n", + " Function evaluations: 47\n", + " Jacobian evaluations: 40\n", "Start of solver\n", "Total (sum of squares): 1.110e-01, \n", "Maximum absolute Force error: 8.028e+04 (N)\n", @@ -183,22 +175,24 @@ "Average absolute Force error: 6.995e-03 (normalized)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed sheet current error: 0.000e+00 (~)\n", "End of solver\n", - "Total (sum of squares): 5.892e-12, \n", - "Maximum absolute Force error: 5.591e+00 (N)\n", - "Minimum absolute Force error: 5.482e-04 (N)\n", - "Average absolute Force error: 1.515e-01 (N)\n", - "Maximum absolute Force error: 1.527e-06 (normalized)\n", - "Minimum absolute Force error: 1.497e-10 (normalized)\n", - "Average absolute Force error: 4.137e-08 (normalized)\n", + "Total (sum of squares): 9.452e-13, \n", + "Maximum absolute Force error: 1.357e+00 (N)\n", + "Minimum absolute Force error: 6.628e-05 (N)\n", + "Average absolute Force error: 6.681e-02 (N)\n", + "Maximum absolute Force error: 3.708e-07 (normalized)\n", + "Minimum absolute Force error: 1.811e-11 (normalized)\n", + "Average absolute Force error: 1.825e-08 (normalized)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-iota profile error: 0.000e+00 (dimensionless)\n" + "Fixed Psi error: 0.000e+00 (Wb)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed sheet current error: 0.000e+00 (~)\n" ] } ], @@ -219,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 50, "id": "24676a79-697c-4ace-8d36-34a9f91470a3", "metadata": { "tags": [] @@ -239,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 52, "id": "836fcd9e-e8ec-4ca1-a49a-ab5e338bd828", "metadata": { "tags": [] @@ -265,9 +259,223 @@ "\n", "$\\mathbf{B} \\cdot \\mathbf{n} = 0$\n", "\n", - "$B^2_{in} + p - B^2_{out} = 0$\n", - "\n", - "If a sheet current is known to exist on the plasma surface (such as if the pressure at the edge is nonzero), this can be modelled by passing a `FourierCurrentPotentialField` to the `BoundaryError` objective, in which case a third equation must be satisfied:\n", + "$B^2_{in} + p - B^2_{out} = 0$" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "52ea143b-84dc-42dd-8dd3-3bbeffc23159", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# For a standard free boundary solve, we set field_fixed=True. For single stage optimization, we would set to False\n", + "objective = ObjectiveFunction(BoundaryError(eq=eq2, field=ext_field, field_fixed=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "4f074a65-191e-47ef-abdb-a5e21b213898", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building objective: Boundary error\n", + "Precomputing transforms\n", + "Timer: Precomputing transforms = 69.3 ms\n", + "Timer: Objective build = 262 ms\n", + "Building objective: force\n", + "Precomputing transforms\n", + "Timer: Precomputing transforms = 97.2 ms\n", + "Timer: Objective build = 276 ms\n", + "Timer: Proximal projection build = 2.88 sec\n", + "Building objective: fixed iota\n", + "Building objective: fixed pressure\n", + "Building objective: fixed Psi\n", + "Building objective: lcfs R\n", + "Building objective: lcfs Z\n", + "Timer: Objective build = 853 ms\n", + "Timer: Linear constraint projection build = 1.76 sec\n", + "Number of parameters: 5\n", + "Number of objectives: 82\n", + "Starting optimization\n", + "Using method: proximal-lsq-exact\n", + " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", + " 0 1 5.805e+00 3.235e+01 \n", + " 1 2 5.547e-01 5.250e+00 4.782e-01 7.006e+00 \n", + " 2 3 1.808e-02 5.366e-01 4.231e-01 9.222e-01 \n", + " 3 4 8.857e-03 9.228e-03 2.740e-01 7.800e-01 \n", + " 4 5 4.001e-05 8.817e-03 1.082e-01 4.923e-02 \n", + " 5 6 1.432e-06 3.858e-05 3.032e-02 1.192e-03 \n", + " 6 8 1.379e-06 5.299e-08 2.680e-03 1.312e-04 \n", + " 7 10 1.376e-06 2.980e-09 1.347e-03 4.919e-05 \n", + " 8 11 1.376e-06 3.696e-10 3.628e-04 1.122e-05 \n", + " 9 12 1.376e-06 1.504e-10 9.282e-05 9.791e-06 \n", + " 10 32 1.376e-06 0.000e+00 0.000e+00 9.791e-06 \n", + "Warning: A bad approximation caused failure to predict improvement.\n", + " Current function value: 1.376e-06\n", + " Total delta_x: 5.907e-01\n", + " Iterations: 10\n", + " Function evaluations: 32\n", + " Jacobian evaluations: 10\n", + "Timer: Solution time = 3.58 min\n", + "Timer: Avg time per step = 19.5 sec\n", + "Start of solver\n", + "Total (sum of squares): 5.805e+00, \n", + "Maximum absolute Boundary normal field error: 1.426e-02 (T*m^2)\n", + "Minimum absolute Boundary normal field error: 3.630e-08 (T*m^2)\n", + "Average absolute Boundary normal field error: 8.363e-03 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 1.044e-02 (normalized)\n", + "Minimum absolute Boundary normal field error: 2.659e-08 (normalized)\n", + "Average absolute Boundary normal field error: 6.125e-03 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 3.201e-01 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 1.924e-01 (T^2*m^2)\n", + "Average absolute Boundary magnetic pressure error: 2.487e-01 (T^2*m^2)\n", + "Maximum absolute Boundary magnetic pressure error: 6.868e-01 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 4.128e-01 (normalized)\n", + "Average absolute Boundary magnetic pressure error: 5.336e-01 (normalized)\n", + "Maximum absolute Force error: 1.357e+00 (N)\n", + "Minimum absolute Force error: 6.628e-05 (N)\n", + "Average absolute Force error: 6.681e-02 (N)\n", + "Maximum absolute Force error: 3.708e-07 (normalized)\n", + "Minimum absolute Force error: 1.811e-11 (normalized)\n", + "Average absolute Force error: 1.825e-08 (normalized)\n", + "Fixed iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", + "R boundary error: 0.000e+00 (m)\n", + "Z boundary error: 0.000e+00 (m)\n", + "End of solver\n", + "Total (sum of squares): 1.376e-06, \n", + "Maximum absolute Boundary normal field error: 1.172e-04 (T*m^2)\n", + "Minimum absolute Boundary normal field error: 5.431e-08 (T*m^2)\n", + "Average absolute Boundary normal field error: 6.485e-05 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 8.582e-05 (normalized)\n", + "Minimum absolute Boundary normal field error: 3.977e-08 (normalized)\n", + "Average absolute Boundary normal field error: 4.750e-05 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 2.053e-04 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 5.632e-06 (T^2*m^2)\n", + "Average absolute Boundary magnetic pressure error: 1.043e-04 (T^2*m^2)\n", + "Maximum absolute Boundary magnetic pressure error: 4.404e-04 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 1.208e-05 (normalized)\n", + "Average absolute Boundary magnetic pressure error: 2.237e-04 (normalized)\n", + "Maximum absolute Force error: 1.999e+01 (N)\n", + "Minimum absolute Force error: 7.770e-03 (N)\n", + "Average absolute Force error: 6.325e-01 (N)\n", + "Maximum absolute Force error: 5.460e-06 (normalized)\n", + "Minimum absolute Force error: 2.123e-09 (normalized)\n", + "Average absolute Force error: 1.728e-07 (normalized)\n", + "Fixed iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", + "R boundary error: 0.000e+00 (m)\n", + "Z boundary error: 0.000e+00 (m)\n" + ] + } + ], + "source": [ + "# we know this is a pretty simple shape so we'll only use |m| <= 2\n", + "R_modes = eq2.surface.R_basis.modes[np.max(np.abs(eq2.surface.R_basis.modes), 1) > 2, :]\n", + "Z_modes = eq2.surface.Z_basis.modes[np.max(np.abs(eq2.surface.Z_basis.modes), 1) > 2, :]\n", + "bdry_constraints = (\n", + " FixBoundaryR(eq=eq2, modes=R_modes),\n", + " FixBoundaryZ(eq=eq2, modes=Z_modes),\n", + ")\n", + "eq2, out = eq2.optimize(\n", + " objective,\n", + " constraints + bdry_constraints,\n", + " optimizer=\"proximal-lsq-exact\",\n", + " verbose=3,\n", + " options={},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2bbe84ff-cb71-4666-987a-c11e89df42e6", + "metadata": { + "tags": [] + }, + "source": [ + "To check our solution, we can compare to a high resolution free boundary VMEC run, and we see we get extremely good agreement:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "96467bac-a250-4d3e-b060-9344999116df", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "VMECIO.plot_vmec_comparison(eq2, \"../../../tests/inputs/wout_solovev_freeb.nc\");" + ] + }, + { + "cell_type": "markdown", + "id": "527087b7-8124-415d-a94c-7ab33070d685", + "metadata": {}, + "source": [ + "We can plot the normal magnetic field error (the plotting function automatically will add the plasma current's contribution), and we can see that the normal field is very small for the final solution." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "025dade5-cc41-4ec3-976e-caad5abcc963", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "desc.plotting.plot_2d(eq2, \"B*n\", field=ext_field)" + ] + }, + { + "cell_type": "markdown", + "id": "28ef9343-fa92-4a4f-808e-348aa9cbe1e6", + "metadata": {}, + "source": [ + "If a sheet current is known (or suspected) to exist on the plasma surface (such as if the pressure at the edge is nonzero), this can be modelled by making the equilibrium `surface` into a `FourierCurrentPotentialField`.\n", "\n", "$\\mu_0 \\nabla \\Phi = \\mathbf{n} \\times (\\mathbf{B}_{out} - \\mathbf{B}_{in})$\n", "\n", @@ -284,32 +492,38 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 57, "id": "aca9234e-f099-4fca-be88-149984d26ac2", "metadata": { "tags": [] }, "outputs": [], "source": [ - "eq2.surface = FourierCurrentPotentialField.from_surface(eq2.surface, M_Phi=4)" + "eq3 = eq2.copy()\n", + "eq3.surface = FourierCurrentPotentialField.from_surface(eq3.surface, M_Phi=4)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 58, "id": "7dcacd9d-6d61-49a3-900a-890d99ed10a6", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# For a standard free boundary solve, we set field_fixed=True. For single stage optimization, we would set to False\n", - "objective = ObjectiveFunction(BoundaryError(eq=eq2, field=ext_field, field_fixed=True))" + "constraints = (\n", + " ForceBalance(eq=eq3),\n", + " FixIota(eq=eq3),\n", + " FixPressure(eq=eq3),\n", + " FixPsi(eq=eq3),\n", + ")\n", + "objective = ObjectiveFunction(BoundaryError(eq=eq3, field=ext_field, field_fixed=True))" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 59, "id": "e7f94a20-9fdd-457e-8023-cc3e301cae39", "metadata": { "tags": [] @@ -319,140 +533,114 @@ "name": "stdout", "output_type": "stream", "text": [ - "Building objective: Boundary error\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Building objective: Boundary error\n", "Precomputing transforms\n", - "Timer: Precomputing transforms = 523 ms\n", - "Timer: Objective build = 921 ms\n", + "Timer: Precomputing transforms = 113 ms\n", + "Timer: Objective build = 314 ms\n", "Building objective: force\n", "Precomputing transforms\n", - "Timer: Precomputing transforms = 36.5 ms\n", - "Timer: Objective build = 129 ms\n", - "Building objective: lcfs R\n", - "Building objective: lcfs Z\n", - "Building objective: fixed-Psi\n", - "Building objective: fixed-pressure\n", - "Building objective: fixed-iota\n", - "Building objective: self_consistency R\n", - "Building objective: self_consistency Z\n", - "Building objective: lambda gauge\n", - "Building objective: axis R self consistency\n", - "Building objective: axis Z self consistency\n", - "Timer: Proximal projection build = 3.33 sec\n", - "Building objective: fixed-iota\n", - "Building objective: fixed-pressure\n", - "Building objective: fixed-Psi\n", + "Timer: Precomputing transforms = 79.3 ms\n", + "Timer: Objective build = 239 ms\n", + "Timer: Proximal projection build = 5.62 sec\n", + "Building objective: fixed iota\n", + "Building objective: fixed pressure\n", + "Building objective: fixed Psi\n", "Building objective: lcfs R\n", "Building objective: lcfs Z\n", - "Timer: Objective build = 442 ms\n", - "Timer: Linear constraint projection build = 935 ms\n", + "Timer: Objective build = 775 ms\n", + "Timer: Linear constraint projection build = 1.20 sec\n", "Number of parameters: 11\n", "Number of objectives: 123\n", "Starting optimization\n", "Using method: proximal-lsq-exact\n", " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", - " 0 1 8.196e+00 4.298e+01 \n", - " 1 2 4.468e-01 7.749e+00 4.189e+05 7.023e+00 \n", - " 2 3 4.007e-02 4.068e-01 2.163e+05 1.015e+00 \n", - " 3 4 2.704e-02 1.303e-02 1.194e+05 7.623e-01 \n", - " 4 5 2.825e-03 2.421e-02 2.450e+05 2.390e-02 \n", - " 5 6 1.416e-03 1.409e-03 5.143e+04 3.464e-01 \n", - " 6 7 3.975e-05 1.376e-03 8.907e+03 6.368e-02 \n", - " 7 9 1.180e-05 2.795e-05 9.331e+02 4.660e-03 \n", - " 8 10 5.046e-06 6.753e-06 1.882e+03 7.956e-03 \n", - " 9 12 4.372e-06 6.741e-07 2.097e+03 6.915e-04 \n", - " 10 14 2.833e-06 1.539e-06 4.014e+02 2.733e-03 \n", - " 11 15 2.832e-06 1.077e-09 2.952e+02 1.520e-04 \n", - " 12 16 2.746e-06 8.516e-08 4.066e+01 1.476e-04 \n", - " 13 17 2.722e-06 2.397e-08 1.094e+02 2.932e-05 \n", + " 0 1 8.049e-06 4.399e-03 \n", + " 1 7 7.957e-06 9.173e-08 1.070e+02 1.261e-03 \n", + " 2 8 7.795e-06 1.621e-07 5.332e+01 1.052e-03 \n", + " 3 9 7.764e-06 3.114e-08 1.150e+02 3.609e-04 \n", + " 4 10 7.722e-06 4.226e-08 2.055e+01 1.518e-04 \n", "Optimization terminated successfully.\n", "`ftol` condition satisfied.\n", - " Current function value: 2.722e-06\n", - " Total delta_x: 5.171e+02\n", - " Iterations: 13\n", - " Function evaluations: 17\n", - " Jacobian evaluations: 14\n", - "Timer: Solution time = 1.31 min\n", - "Timer: Avg time per step = 5.63 sec\n", + " Current function value: 7.722e-06\n", + " Total delta_x: 1.184e+02\n", + " Iterations: 4\n", + " Function evaluations: 10\n", + " Jacobian evaluations: 5\n", + "Timer: Solution time = 1.40 min\n", + "Timer: Avg time per step = 16.8 sec\n", "Start of solver\n", - "Total (sum of squares): 8.196e+00, \n", - "Maximum absolute Boundary normal field error: 1.420e-02 (T*m^2)\n", - "Minimum absolute Boundary normal field error: 3.630e-08 (T*m^2)\n", - "Average absolute Boundary normal field error: 8.348e-03 (T*m^2)\n", - "Maximum absolute Boundary normal field error: 1.040e-02 (normalized)\n", - "Minimum absolute Boundary normal field error: 2.659e-08 (normalized)\n", - "Average absolute Boundary normal field error: 6.114e-03 (normalized)\n", - "Maximum absolute Boundary magnetic pressure error: 3.201e-01 (T^2*m^2)\n", - "Minimum absolute Boundary magnetic pressure error: 1.924e-01 (T^2*m^2)\n", - "Average absolute Boundary magnetic pressure error: 2.487e-01 (T^2*m^2)\n", - "Maximum absolute Boundary magnetic pressure error: 6.868e-01 (normalized)\n", - "Minimum absolute Boundary magnetic pressure error: 4.128e-01 (normalized)\n", - "Average absolute Boundary magnetic pressure error: 5.336e-01 (normalized)\n", - "Maximum absolute Boundary field jump error: 4.757e-01 (T*m^2)\n", - "Minimum absolute Boundary field jump error: 4.749e-01 (T*m^2)\n", - "Average absolute Boundary field jump error: 4.753e-01 (T*m^2)\n", - "Maximum absolute Boundary field jump error: 3.484e-01 (normalized)\n", - "Minimum absolute Boundary field jump error: 3.478e-01 (normalized)\n", - "Average absolute Boundary field jump error: 3.481e-01 (normalized)\n", - "Maximum absolute Force error: 5.591e+00 (N)\n", - "Minimum absolute Force error: 5.482e-04 (N)\n", - "Average absolute Force error: 1.515e-01 (N)\n", - "Maximum absolute Force error: 1.527e-06 (normalized)\n", - "Minimum absolute Force error: 1.497e-10 (normalized)\n", - "Average absolute Force error: 4.137e-08 (normalized)\n", - "Fixed-iota profile error: 0.000e+00 (dimensionless)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", + "Total (sum of squares): 8.049e-06, \n", + "Maximum absolute Boundary normal field error: 1.172e-04 (T*m^2)\n", + "Minimum absolute Boundary normal field error: 5.431e-08 (T*m^2)\n", + "Average absolute Boundary normal field error: 6.485e-05 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 1.092e-04 (normalized)\n", + "Minimum absolute Boundary normal field error: 5.060e-08 (normalized)\n", + "Average absolute Boundary normal field error: 6.042e-05 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 2.053e-04 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 5.632e-06 (T^2*m^2)\n", + "Average absolute Boundary magnetic pressure error: 1.043e-04 (T^2*m^2)\n", + "Maximum absolute Boundary magnetic pressure error: 9.115e-04 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 2.501e-05 (normalized)\n", + "Average absolute Boundary magnetic pressure error: 4.630e-04 (normalized)\n", + "Maximum absolute Boundary field jump error: 7.273e-04 (T*m^2)\n", + "Minimum absolute Boundary field jump error: 1.548e-05 (T*m^2)\n", + "Average absolute Boundary field jump error: 3.056e-04 (T*m^2)\n", + "Maximum absolute Boundary field jump error: 6.776e-04 (normalized)\n", + "Minimum absolute Boundary field jump error: 1.442e-05 (normalized)\n", + "Average absolute Boundary field jump error: 2.847e-04 (normalized)\n", + "Maximum absolute Force error: 1.999e+01 (N)\n", + "Minimum absolute Force error: 7.770e-03 (N)\n", + "Average absolute Force error: 6.325e-01 (N)\n", + "Maximum absolute Force error: 1.130e-05 (normalized)\n", + "Minimum absolute Force error: 4.393e-09 (normalized)\n", + "Average absolute Force error: 3.576e-07 (normalized)\n", + "Fixed iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", "End of solver\n", - "Total (sum of squares): 2.722e-06, \n", - "Maximum absolute Boundary normal field error: 2.246e-04 (T*m^2)\n", - "Minimum absolute Boundary normal field error: 5.507e-08 (T*m^2)\n", - "Average absolute Boundary normal field error: 1.036e-04 (T*m^2)\n", - "Maximum absolute Boundary normal field error: 1.645e-04 (normalized)\n", - "Minimum absolute Boundary normal field error: 4.033e-08 (normalized)\n", - "Average absolute Boundary normal field error: 7.585e-05 (normalized)\n", - "Maximum absolute Boundary magnetic pressure error: 2.053e-04 (T^2*m^2)\n", - "Minimum absolute Boundary magnetic pressure error: 8.168e-06 (T^2*m^2)\n", - "Average absolute Boundary magnetic pressure error: 1.018e-04 (T^2*m^2)\n", - "Maximum absolute Boundary magnetic pressure error: 4.404e-04 (normalized)\n", - "Minimum absolute Boundary magnetic pressure error: 1.752e-05 (normalized)\n", - "Average absolute Boundary magnetic pressure error: 2.183e-04 (normalized)\n", - "Maximum absolute Boundary field jump error: 5.668e-04 (T*m^2)\n", - "Minimum absolute Boundary field jump error: 5.253e-05 (T*m^2)\n", - "Average absolute Boundary field jump error: 3.155e-04 (T*m^2)\n", - "Maximum absolute Boundary field jump error: 4.151e-04 (normalized)\n", - "Minimum absolute Boundary field jump error: 3.847e-05 (normalized)\n", - "Average absolute Boundary field jump error: 2.310e-04 (normalized)\n", - "Maximum absolute Force error: 2.052e+01 (N)\n", - "Minimum absolute Force error: 8.049e-04 (N)\n", - "Average absolute Force error: 6.218e-01 (N)\n", - "Maximum absolute Force error: 5.607e-06 (normalized)\n", - "Minimum absolute Force error: 2.199e-10 (normalized)\n", - "Average absolute Force error: 1.698e-07 (normalized)\n", - "Fixed-iota profile error: 0.000e+00 (dimensionless)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", + "Total (sum of squares): 7.722e-06, \n", + "Maximum absolute Boundary normal field error: 8.633e-05 (T*m^2)\n", + "Minimum absolute Boundary normal field error: 5.433e-08 (T*m^2)\n", + "Average absolute Boundary normal field error: 4.600e-05 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 8.042e-05 (normalized)\n", + "Minimum absolute Boundary normal field error: 5.061e-08 (normalized)\n", + "Average absolute Boundary normal field error: 4.285e-05 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 1.989e-04 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 7.498e-06 (T^2*m^2)\n", + "Average absolute Boundary magnetic pressure error: 1.051e-04 (T^2*m^2)\n", + "Maximum absolute Boundary magnetic pressure error: 8.832e-04 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 3.330e-05 (normalized)\n", + "Average absolute Boundary magnetic pressure error: 4.666e-04 (normalized)\n", + "Maximum absolute Boundary field jump error: 6.503e-04 (T*m^2)\n", + "Minimum absolute Boundary field jump error: 3.890e-05 (T*m^2)\n", + "Average absolute Boundary field jump error: 2.912e-04 (T*m^2)\n", + "Maximum absolute Boundary field jump error: 6.058e-04 (normalized)\n", + "Minimum absolute Boundary field jump error: 3.624e-05 (normalized)\n", + "Average absolute Boundary field jump error: 2.713e-04 (normalized)\n", + "Maximum absolute Force error: 1.997e+01 (N)\n", + "Minimum absolute Force error: 4.521e-03 (N)\n", + "Average absolute Force error: 6.315e-01 (N)\n", + "Maximum absolute Force error: 1.129e-05 (normalized)\n", + "Minimum absolute Force error: 2.556e-09 (normalized)\n", + "Average absolute Force error: 3.570e-07 (normalized)\n", + "Fixed iota profile error: 0.000e+00 (dimensionless)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n" ] } ], "source": [ - "# we know this is a pretty simple shape so we'll only use |m| <= 2\n", - "R_modes = eq2.surface.R_basis.modes[np.max(np.abs(eq2.surface.R_basis.modes), 1) > 2, :]\n", - "Z_modes = eq2.surface.Z_basis.modes[np.max(np.abs(eq2.surface.Z_basis.modes), 1) > 2, :]\n", + "R_modes = eq3.surface.R_basis.modes[np.max(np.abs(eq3.surface.R_basis.modes), 1) > 2, :]\n", + "Z_modes = eq3.surface.Z_basis.modes[np.max(np.abs(eq3.surface.Z_basis.modes), 1) > 2, :]\n", "bdry_constraints = (\n", - " FixBoundaryR(eq=eq2, modes=R_modes),\n", - " FixBoundaryZ(eq=eq2, modes=Z_modes),\n", + " FixBoundaryR(eq=eq3, modes=R_modes),\n", + " FixBoundaryZ(eq=eq3, modes=Z_modes),\n", ")\n", - "eq2, out = eq2.optimize(\n", + "eq3, out = eq3.optimize(\n", " objective,\n", " constraints + bdry_constraints,\n", " optimizer=\"proximal-lsq-exact\",\n", @@ -468,12 +656,12 @@ "tags": [] }, "source": [ - "To check our solution, we can compare to a high resolution free boundary VMEC run, and we see we get extremely good agreement:" + "We can see that including the sheet current makes very little difference in the final result:" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 60, "id": "e4b1df9d-4fc9-4dae-8147-8266280f098d", "metadata": { "tags": [] @@ -481,9 +669,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -491,7 +679,7 @@ } ], "source": [ - "VMECIO.plot_vmec_comparison(eq2, \"../../../tests/inputs/wout_solovev_freeb.nc\");" + "VMECIO.plot_vmec_comparison(eq3, \"../../../tests/inputs/wout_solovev_freeb.nc\");" ] }, { @@ -499,31 +687,31 @@ "id": "2b3cda6e", "metadata": {}, "source": [ - "We can plot the normal magnetic field error (the plotting function automatically will add the plasma current's contribution), and we can see that the normal field is very small for the final solution." + "We can see that the normal field error decreased slighlty, though not by much:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 61, "id": "ff2b9bbd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(
,\n", + "(
,\n", " )" ] }, - "execution_count": 11, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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j72m5A1ZXAQBAHfFYXYDZjAs2rgOSK8nqKgAAqCMdrS7AbMYFGx1Q5O8zAgAQLUIFmyRJyRH+HPMSQFjM+1ondXYQFQAAwPeYF2wOSHJaXQQAAIhG5gWbEyLYAACAkMwMNuZVDQAA6gERAQCAaFcXg4cdEb5elDAv2JTKxKoBAEA9sOmEygAAIBaZ1/uokOS3uggAABCNzAs2AADEmkSdHWcTSTa9Z2NesCmVbQc8AQCAy2PTvAYAAGKRZR2bXr16KSnpbF/tpptu0rRp08I7sUx0bAAAQEiWBZuBAwdq8uTJF3+iV/SZAABASJYFm6+++kovvviiSkpKdP/996tTp05B71dWVqqqqqpm2+fznf1DqQg2AIDY4hare4fJsq81ceJEde/eXSdPnlTv3r21efPmmltTkjR9+nRNmTKl9onlItgAAICQLAs23bt3lyQ1btxYDRs21O7du3XDDTfUvJ+Tk6OsrKyabZ/Pp6ZNm57t2MTVd7UAAMAElgSbnTt36ssvv9TIkSPl9/t15MgRtWrVKugYp9Mpp7P2Mt6V5VICwQYAYFO1f/PhYlgSbBo0aKCPPvpI3333nQ4cOKCpU6eqcePGYZ17plSqItgAAGyqSaidTkmuCH+QTYd1WBJsWrZsqQ8//NCKjwYAADZm3Jjo1GTJTccGAIB6s2PHDo0fP14DBw5UdnZ20Ht79uzRpEmTlJ6erq+//lpDhgzRwIEDJZ0dL1tdXa2SkhJdc801+vWvfy2/369HHnlErVq1UkFBgQYMGKDhw4dHrFbjgo3TJTkJNgAA1Jvt27erT58+Id87ceKERowYodtuu03Hjh1TRkaG9u3bp927d2v58uXaunWrqqur1bx5c/3iF7/Q//7v/6qwsFC///3vVVxcrI4dO8Z2sFGybHtfEACA+uT3+/8xT9zfJSQk1Hp457777tM333wT8hoZGRk1f66urlZy8tkJd1JTU+Xz+eT3+1VaWqo2bdooKSlJaWlpOn78uCTp2LFjuvHGGyP5lQwMNi4RbAAAsSVJkZ+gzyEtW7ZMo0ePDto9adKkS1sZQNJrr72m3NxcSdIVV1yhX/ziFxoxYoROnTqlhx56SPHx8UpPT1fPnj318MMP6//+7/80fvz4y/0mQcwLNnRsAACIiMzMTOXl5QXtS0i4tGjw/vvvq2XLlsrMzJQkbdy4UR999JFWrVqlqqoqdezYUbfddpv+9re/qaioSPPmzdOpU6fUvn173X777TWdnstlXrDxiEUwAQCIAIfDIbfbfUnn7t+/X61bt5YkLVq0SCdPntSjjz6qTz/9VH379tXhw4eVlpYm6WxYaty4saqqqnTo0CE1b95c0tnbVaHmrLsc5gWbJBFsAACoR0uWLNHatWvldDrVqVMn3XrrrerXr592796tLVu2aOzYsUpPT9fixYu1f/9+bd68WQMHDtTSpUv19NNPq7y8XHfeeac6duyoFi1a6OGHH9bUqVNVWFioZ555JmLdGkmKCwQCgYhdrQ75fD55PB55b5HcBBsAgF39KfjXcpcuXTS743b1bXWe4y/RoGXS3c+8q5EjR0b2whZjtAoAALANgg0AALANgg0AALANgg0AALAN856KKhNPRQEAYkuizj4VHEk2bW3Y9GsBAIBYRLABAAC2Yd6tKK+IYwAAICTzgg0AALGmLhbBtGkCoPcBAABsw7y8ViriGAAACImIAAAAbMO8jk25iGMAACAk84JNqaQ4q4sAAKAeOSW5InxNmzYJjAs2leVSAsEGAGBTTqsLMJxxwebQ6cjPKg0AQLS4xuoCDGdcsPFJClhdBAAAiErGBZujivxtRgAAokWnUDvdivwEfTZdUNq4YHNGUoXVRQAAgKhkXLDxSfJbXQQAAIhKxgWbQ2LEOAAACM2mT7EDAIBYZFzHBgCAmJOoyM91YtPBw3RsAACAbRBsAACAbRBsAACAbRg3xsYnqcrqIgAAqE9JYoK+MNGxAQAAtkGwAQAAtkGwAQAAtkGwAQAAtmHc4GEAAGIOg4fDRscGAADYBsEGAADYBsEGAADYBmNsAACIdomSXBG+pk1bG8YFG7ckp9VFAAAQYxYtWqQNGzbI6/Vq6NCh6tu3b817fr9fjzzyiFq1aqWCggINGDBAw4cPlyQtXLhQBQUFOnbsmEpLS/Xmm29q9erVmjBhgho1aiRJmjZtmm666aaI1GlcsAEAAPXr9OnTys3N1aZNm1RWVqaMjAxt27ZN8fFn2z6ff/65CgsL9fvf/17FxcXq2LGjhg8frm+//VarVq3S22+/LUn66quvaq45a9Ys9e/fP+K1GhdsUnW2IwcAAC6P3++Xz+cL2peQkCCnM/jeyPr169WhQwfFxcXJ7XYrOTlZe/bsUfv27SVJaWlpOn78uCTp2LFjuvHGGyVJH3zwgRITEzVz5kwdP35cY8aMqbnme++9p40bN8rv9+uXv/ylkpKSIvKdCDYAAES7upjHJkFatmyZRo8eHbR70qRJmjx5ctC+oqIipaSk1GynpqaqqKioJtikp6erZ8+eevjhh/V///d/Gj9+vCTp4MGDys/P1+uvv678/Hzdfffd2rJlizp37qxnn31WrVu31h/+8Ac99dRTmjlzZqS+llncivz4KQAAYlFmZqby8vKC9iUk1I4GaWlpKikpqdk+c+aM0tLSaraXLFmioqIizZs3T6dOnVL79u11++23KzU1Vd26dZMktW3bVkeOHNGZM2fUvHnzmnN79+6tN954I2LfyaZjogEAwIU4HA653e6g1/dvQ0lSz549tWvXLgUCAfl8PpWWluraa6/VgQMHJEmHDh2qCSupqak11+jTp48KCgokSaWlpUpMTFRqaqpyc3NVXFwsSdq7d6/atm0bse9kXMcmVXRsAACoTw0aNFB2drYef/xxeb1ezZ49W/n5+Ro+fLjWr1+vESNG6OGHH9bUqVNVWFioZ555RsnJybrrrru0Zs0aTZo0SYWFhZo7d64k6ZprrtGECRN03XXXaceOHXrhhRciVmtcIBAIROxqdcjn88nj8Wizzt5qBADAjjp979dyly5dNPvh7eqbHtnPGZQt3f3Quxo5cmRkL2wx4zo2bhFsAAAxJlGR/+Vn08EoxgWblg0kd5zVVQAAgGhkXLBxNpGcNk2ZAADg8hgXbOSSbdtnAADg8pgXbJpJclhdBAAA9aiOJuizI/O+VlMRbAAAQEjmBZtEmVg1AACoB+ZFhOYysWoAAFAPzIsITSTVnu0ZAADAwGDTWCzvDQCILW5FfvCwTcermhdsmopgAwAAQjIv2DQWq2ACAICQzAs2TcRiUQAAICTzgk1zEWwAALGlLhbBZIxNdChvIsW7ra4CAIC6wWiLy2NcsDnpaaAylvcGANjUlVYXYDjjgs0pNVQ5q2ACAGyKYHN5jAs2R9RCLrveGAQAxLyOVhdgOMuCTX5+vm688UatWLFCvXr1Cvu8k2pEsAEAxJSKJKk8whP0Vdv0V6klwaa6ulovvviirr/++os+95QaKdG8RhMAAKgHliSEV155RePGjdOECRPOe0xlZaWqqqpqtn0+nyTpsK6Uk8WiAABACPU+Cnfr1q2SpPT09B88bvr06fJ4PDWvpk2b1n1xAADAaPXesVm+fLkkKTc3V/v379eCBQtUWVmpPn36BB2Xk5OjrKysmm2fz0e4AQDEpEpngspdke1FVMdXRvR60aLeg01OTk7Nn//0pz/pgQceCDl42Ol0yunklhMAAAifZRPCvP322yooKNC8efP07bffWlUGAACwEcseLxo7dqzGjh1r1ccDAAAbYgpfAABgG0wIAwBAlCtzuOR1RPZXtj+uOqLXixZ0bAAAgG0Y17EpViMm6AMAACEZF2zK5FEVwQYAAIRgXLApV6L8SrS6DAAA6k2VnKqI8O++avkier1oYVywOaVGchBsAABACMYFG6+S5JDL6jIAAEAUMi7Y+OQh2AAAgJCMCzan1FDxSrK6DAAAEIWMCzZX6jAdGwBATCmTS94I/6PerzMRvV60MC7YNNIpJTB4GACAerVo0SJt2LBBXq9XQ4cOVd++fWsd8+WXX6pPnz46cOCArrzyyvPuP3DggCZMmKBevXppy5YtGjZsmH76059GpE5Dgw3z2AAAUF9Onz6t3Nxcbdq0SWVlZcrIyNC2bdsUH/+PBQxKS0v13nvvqVWrVkHnhtpfUVGh0aNH64477tCuXbt03333xW6wuVKHmHkYAIAI8Pv98vmC57NJSEiQ0xn8e3b9+vXq0KGD4uLi5Ha7lZycrD179qh9+/Y1x0ybNk0TJ07Uxx9/HHRuqP3t2rVTu3btJEl79+5Vhw4dIvadjAs2jXRKieaVDQDAJStXknzyRPSafjm0bNkyjR49Omj/pEmTNHny5KB9RUVFSklJqdlOTU1VUVFRTbBZvny5unbtWqtbc7795+Tm5urjjz/WO++8E4FvdJZxCeFKHZZLDqvLAADAeJmZmcrLywval5BQOxqkpaWppKSkZvvMmTNKS0ur2V61apWaNWum3NxcnTp1Sq+//rqGDBly3v1du3aVJGVnZ2vUqFHq3bu3vvnmm1qdokthXLBprGKCDQAAEeBwOOR2uy94XM+ePZWVlaVAIKCysjKVlpbq2muv1YEDB3T11Vfr5Zdfrjn2rbfe0qOPPqorr7zyvPvXrFmjNm3a6JprrlGzZs1UUlIin88XkWATf+FDAABALGvQoIGys7P1+OOPa8KECZo9e7by8/P1s5/9LOi4F198UadOndKsWbNUWFh43v0ul0tTpkxRbm6uHn74YeXk5KhBgwYRqTUuEAgEInKlOubz+eTxeLTU20cuNx0bAIA9DdTnQdtdunTRL2ef0Y/7RnaMza8GHdDIu1/TyJEjI3pdqxl3K8ort/zcigIAxJBKOVUe4clpq21608a4YHNKjXgqCgAAhGRcQvDJLb95ZQMAgHpgXEKokEsB88oGAAD1wLiEUKyGzDwMAIgp5XLJpws/ln0x7Dpe1Z4jhwAAQEwyrmOTqHI5VW11GQAAIAoZF2w8KpNTVVaXAQAAopBxwaahinncGwAAhGRcQnCpQonyW10GAAD1hgn6wmdcsEmSl0UwAQBASMYFG1b3BgAA52NcsElSmZJs2j4DAACXx7hg41K5XAQbAEAMKZNLXiboCwsJAQAA2AbBBgAA2AbBBgAA2IZxY2zc8jJ4GAAAhGRcsAEAINZUyCWfPBG9JoOHAQAAopxxHZsW/iNy++OsLgMAgLphz0ZKvTEu2DTYXSl3ktVVAABQRzpaXYDZjAs22iYp0eoiAACoIyGCTYUSWQQzTOYFm79JclpdBAAAdWSo1QWYzbxgs0fcfwQAACGZF2z+Jp7lAgAAIRkXbHbukxg7DACwq05WF2A444LNHinCw6cAAIgeoYJNhRLlY3XvsBgXbG5rILmZxgYAAIRgXLBx/qvktGfIBAAAl8m4YKN+Yh4bAAAQknnBpocYPQwAiClM0Bc+44LNgW7NlMQgGwCATbW2ugDDGRdsNulf5LLpSG4AAAg2l8e4YPOlesnJmgoAAJu6x+oCDGdcsCmTR1UEGwBADCmXS17msQmLPUcOAQCAmGRcxwYAANS/RYsWacOGDfJ6vRo6dKj69u0b9P6rr76qEydO6NChQ/rVr36lzp07a/ny5frjH/+o9u3ba8uWLZo2bZquvfZaSdLTTz8tt9ut/Px8TZ06VS1btoxIncYFmyR5GWMDAEA9On36tHJzc7Vp0yaVlZUpIyND27ZtU3z82Rs/e/bs0fLly7VixQoVFBTowQcf1OrVq3Xw4EHNmDFDaWlp+uCDDzR16lTNmzdPq1atUmFhoebMmaM1a9Zo4sSJmj9/fkRq5VYUAAAxyu/3y+fzBb0qKytrHbd+/Xp16NBBcXFxcrvdSk5O1p49e2reX7VqlX784x9Lktq0aaOdO3eqoqJC48aNU1pamiSpurpaycnJkqSVK1eqe/fukqQePXrof/7nfyL2nS462JSVlUXswwEAwIVVyqkKuSL6qla8li1bJo/HE/SaPn16rc8vKipSSkpKzXZqaqqKiorO+35KSoqOHz9es11dXa28vDxlZWXVOt7tdqu4uDhiP6uwb0W9/fbbeuutt5Sfn1/ThhoxYoTuv//+mgRWHzzyyamqevs8AADsKjMzU3l5eUH7EhJqR4O0tDSVlJTUbJ85c6amE3Pu/b1799Zsl5SUqGnTpjXbWVlZys7OVuvWrWtdz+fzqVGjRhH5PtJFdGwqKyu1efNmnTx5Ujt37lSLFi20cuVKde7cWR988EHECgIAAPXD4XDI7XYHvZzO2uNYe/bsqV27dikQCMjn86m0tFTXXnutDhw4IEkaMGCANm3aJEkqKChQx44dlZh4dmHHiRMnatCgQerVq5eWLl0qSbr55pu1ceNGSdKGDRt0yy23ROw7hd2xKSkp0YkTJ9SkSRO1adNG3bp1U1ZWlo4fP65p06bJ6/XqwQcfjFhhAAAgOjRo0EDZ2dl6/PHH5fV6NXv2bOXn52v48OFav3692rVrp7vuuks5OTk6cuSI3njjDUnSrFmztGDBAn3xxReSpPj4eA0ePFgDBgzQypUrNWXKFBUUFOj555+PWK1hB5uxY8dq+PDhat26tR566CHFxZ1dr6lp06Z65ZVX9Oqrr0asqB+SqHI5VV0vnwUAQDSIhgn6hgwZoiFDhgTtW79+fc2ff/nLX9Y6Z8KECZowYULI64UayxMJYQebxo0ba9myZZo9e7buvfdelZaW6ptvvlF6erqSk5P13Xff1UmBAAAA4bqop6IcDocee+wx7du3T5988ol69uypsrIyNWrUSDNmzKirGgEAAMJySRP0xcfHq0ePHurRo0ek67kgj8p4KgoAAITEBH0AAMA2jFtSAQCAWFMul3zyRPSadl3d25Jg4/P5dP/99+snP/mJdu3apd69e+uhhx4K69zN+hclKLGOKwQAACayJNhUV1dr0KBBGjVqlHw+n1q1ahV2sPmbblS8kuq4QgAAYCJLgk1ycrJGjRolSdq3b5/at29f65jKykpVVf1jkLDP55Mk7foqXUqK7LP8AABEja5WF2A2S8fYzJ07V/Pnz9fMmTNrvTd9+nRNmTKl9klrJe5EAQBsK0SwqVCiyuWK6MdU2/T5IUuDzejRozVs2DD9+Mc/1urVq3XFFVfUvJeTk1OzCqh0tmPTtGlT6TPJpuOdAACQHrO6ALNZEmy+/vprlZaWKiMjQx6PRykpKTp69GhQsHE6nSEX4tJaSXH1VysAADCHJcHG5XJpxowZuuGGG3TkyBHdeuut6tKlS3gnl22QGDwMALCtnlYXYDRLgk27du303nvvWfHRAADAxgycoK+HFOEVTgEAiGYVSpTP4tW9TWFesGkjFoIAAAAhmRdsuomnogAAQEjmBZsbJYV4WAoAAMC8YNNZivAcRQAARDUm6AufecGmW7nktudfBgAA/Ov98hgXbK5uuU/xbuaxAQDYVUerCzAarQ8AAGAbBBsAAGAbxt2K8sineAWsLgMAgHpTrkR5maAvLHRsAACAbRBsAACAbRBsAACAbRg3xsalcsUrzuoyAACoN+VKkk+eiF7TrmNsjAs2SSqTg8HDAAAgBOOCjUvlNs2YAADgchkXbNzyyiG/1WUAAIAoZFywaaRTSlCi1WUAAIAoZFywccunBDo2AIAYUiknq3uHybhg41KFEhg8DAAAQjAu2DTRcTnltLoMAAAQhezZhwIAADHJuI6NV246NgCAmFIul3wsghkW44LNKTXiqSgAABCSccHGJzfBBgCAKPL000/L7XYrPz9fU6dOVcuWLUMe99JLL2nu3LnauXOnJKmyslJPPPGErrzySu3bt0+//e1v1aBBAz3//PM6deqUUlJStHfvXr355ptyucJ7Ksy4YFMhl/wEGwAAosKqVatUWFioOXPmaM2aNZo4caLmz59f67ht27Zp//79QfsWLFig5s2b15zz8ssva8qUKaqurlZubq4kafz48crLy9PIkSPDqse4YHNcTeSI8LP8AADEIr/fL5/PF7QvISFBTmf4Y1lXrlyp7t27S5J69OihYcOG1TqmoqJCb7zxhn7zm99oxYoVQeeOGDGi5ty3335bU6ZMUU5OTs0x1dXVSk5ODrse44JNuVwEGwBATKmrCfqWLVum0aNHB+2fNGmSJk+eHPZ1ioqK1LlzZ0mS2+1WcXFxrWNmzJihJ554Qg6Ho9a5KSkpkqTU1FQVFRUFvV9YWKj9+/fr7rvvDrse44JNmZIUrySrywAAwHiZmZnKy8sL2peQUDsa+P1+9evXr9b+rl27Ki0tTSUlJZIkn8+nRo0aBR1TVlamPXv2aPHixTp58qROnDih3NxcPfzww0HnnjlzRmlpaTXnnT59Wk8++aTmzZsXsqbzMS7YNNQpOVRmdRkAABjP4XDI7b7wY+QOh0Pr1q0L+d6qVauUl5encePGacOGDbrlllskSV6vV16vV2lpaVqwYIEkad++ffrjH/+o7OxsSdLNN9+sjRs36vbbbw8698yZM5owYYJeeOEFNWvWTEuXLtXgwYPD+k7GBZsWOsxTUQAARIkBAwZo5cqVmjJligoKCvT8889LkhYvXqwtW7bolVdekSQdPnxYr7/+uo4fP66XXnpJTzzxhB544AH9+te/1nPPPacDBw7opZdekiQ98MAD2r17t4YOHSpJ6tatW9jBJi4QCBix8JLP55PH49FYb64S3EzQBwCwp9l6PGi7S5cuOjb7SSX1zYjo5xwdNF5v3P1A2E8bmcK4js2VOsTMwwAAICTjgk0jnVKieWUDAIB6YFxCuFKH5bLp+hYAAODyGBdsGquYYAMAAEIyLtgAABBrypWkgDwRvSare0eJszMv2vMvAwAAXB7jgs0JNVUiwQYAAIRgXLApV6IC5pUNAADqgXEJ4ZQayWle2QAAXLLKygQFKiI76351dXxErxctjEsIXiUxQR8AAAjJuGBTIZcCBBsAABCCccHGKzcdGwAAEJI9b7ABAICYZFzHBgCAWFPuS1JlSYQn6Kuy59QpdGwAAIBtEGwAAIBtEGwAAIBtMMYGAIAoV1XhlMpckb2oTSfos+e3AgAAMcm4js0pNVKCIjutNAAAsAcjg42DYAMAAEIwLtgcUgs5FOH7jAAARDNfolQS4d99VfYcjWJcsDmlhopXktVlAACAKGRcsDmhpooj2AAAgBCMCzbFJxoqzu22ugwAAOpGE6sLMJtxwabyuwZSEsEGAGBTBJvLYlywUaHE2GEAgG11DbGvQlJZhD+nOsLXixLmBZvjEk97AwCAUMwLNmWybcoEAACXx7xgUyjJaXURAAAgGpkXbI7LxKoBALh0PkklEb5mVYSvFyXMiwhFMrFqAABQD8yLCMclOawuAgAARCPzgk2RJHsubwEAAC6TecHmuAg2AAAgJPOCTYmkOKuLAACgHjF4OGzmBZvjItgAABBFnn76abndbuXn52vq1Klq2bJl0PuHDx/WhAkT1K1bN23atElPPfWU0tPT9fzzz+vUqVNKSUnR3r179eabb8rlcmnevHl66623lJR0dtHruXPn6kc/+lFYtZgXbCQpYHUBAABAklatWqXCwkLNmTNHa9as0cSJEzV//vygY2bPnq2f/OQnmjBhgpYuXarc3FwtXLhQ1dXVys3NlSSNHz9eeXl5GjlypCRp4cKFuuaaay66HkuCzZ49ezRp0iSlp6fr66+/1pAhQzRw4MAwzz4hiUUwAQB2VX+rYPr9fvl8vqB9CQkJcjrDnwl35cqV6t69uySpR48eGjZsWK1jmjVrpmPHjkmSjh07phtvvFGSlJOTU3NMdXW1kpOTa7Znz56t5s2bKykpSY888ogcjvAeibYk2Jw4cUIjRozQbbfdpmPHjikjI0P79u0L8+yjkpLqsDoAAKwUItjU0SKYy5Yt0+jRo4N2T5o0SZMnTw77MkVFRercubMkye12q7i4uNYxY8eO1T333KOJEyfqz3/+s+bOnRv0fmFhofbv36+7775bktSvXz9lZmaqadOmevbZZzV79mw99thjYdVjSbDJyMio+fP3E9o5lZWVqqr6x8imfyTK0zr7NwwAAC5HZmam8vLygvYlJNSOBn6/X/369au1v2vXrkpLS1NJydmRzT6fT40aNap13JNPPqn77rtPI0eO1KpVqzRmzBj9+c9/liSdPn1aTz75pObNm1fz2W3btq05t3fv3pozZ050B5t/9tprr9XcX/tn06dP15QpU0KcUSYG2QAAcPkcDofc7gsP73A4HFq3bl3I91atWqW8vDyNGzdOGzZs0C233CJJ8nq98nq9SktL06FDh3THHXdIkq644gpVVlZKks6cOaMJEybohRdeULNmzbR06VINHjxYTz31lJ577jk5HA7t3bs3KOhciKXB5v3331fLli2VmZlZ672cnBxlZWXVbPt8PjVt2lTSGdGxAQAgOgwYMEArV67UlClTVFBQoOeff16StHjxYm3ZskWvvPKKnnnmGc2YMUNbt27VN998o5dfflmS9MADD2j37t0aOnSoJKlbt24aPHiwmjdvrkcffVStWrXSrl27NGvWrLDriQsEApa0PxYtWqSjR4/q0Ucf1aeffqq+ffv+YGr0+XzyeDyS/l1SYr3VCQBAfQoE7gva7tKli7YPmy1d3zeyHzR9kN79xd01TyHZhSUdm40bN2rs2LFKT0/X4sWLtX//fm3evDmsdtjZjg3BBgAQQ7xigr4wWRJsunfvHnLUNAAAwOWwfPDwxTsjKfzn6wEAQOwwMNj4ZNv+GQAAuCwGBhsAAGJMHU3QZ0fxVhcAAAAQKQQbAABgGwQbAABgGwQbAABgGwweBgAg2vnEBH1homMDAABsg2ADAABsg2ADAABsw8AxNm6xpAIAIKYwxiZsdGwAAIBtEGwAAIBtEGwAAIBtEGwAAIBtGDh4GACAGMPq3mEzMNikSkq0uggAABCFDAw2zSW5rC4CAABEIQODTaoINgAAIBQDg03S318AAMQIJugLm4HB5goRbAAAQCgGBptUnV1WAQAAIJiBwaaJCDYAACAUA4ONpDirCwAAANHIzGADAEAsYYK+sJkXbJJExwYAAITEWlEAAMA2zOvYJIuODQAACMm8YAMAQKxhgr6wmRdsUsQNNAAAEBIRAQAA2AbBBgAA2IZ5t6KSRBwDAAAhmRdsAACINeU6O4A4kvwRvl6UoPcBAAAuy7Jly9SuXTt9+eWXId9fvny5xo4dq9/+9rcaNmyY9u7dG/T+4sWL5Xb/Yx3IyZMnq2/fvurfv7/69++v9u3bh10LHRsAAHDJiouLlZCQoKuvvvq8xxw8eFAzZsxQWlqaPvjgA02dOlXz5s2TJB0+fFibNm1SIBCoOb579+56+umnlZCQoG+++Ubvv/9+2PXQsQEAIEb5/X75fL6gV2Vl5UVdo1GjRrrjjjt+8Jhx48YpLS1NklRdXa3k5OSa96ZNm6acnJyg43/6058qIeFs72XOnDkaM2ZM2PUQbAAAiHZlkkoj/PKfvYXk8XiCXtOnT6+zr1FdXa28vDxlZWVJkt566y3df//9SklJCXl8WVmZjh49qjZt2oT9GdyKAgAgRmVmZiovLy9o37lOyT/z+/3q169frf1du3bVG2+8EfbnZWVlKTs7W61bt5Ykbdy4UcXFxVq3bp38fr9yc3N1//331wSZRYsWaciQIRfzlQwMNmWizwQAQAQ4HI6gQbs/dNy6desu6tpVVVU6duyYWrRoIUmaOHGiBg0apF69emnp0qUaPHiw5s6dW3P85MmTlZ2dHXSN5cuX6z/+4z8u6nPNCzYlYhFMAACiyMyZM1VQUKB58+YpKSlJ6enpWrdunV577TUtWbJEs2bN0oIFC/TFF19IkuLj4zV48GBJks/n08svvyy/369p06bpN7/5jVwul7Zv367OnTvL4XBcVC1xgX8ehhzFfD6fPB6PlOSV4i6cLgEAMFHAG7zdpUsXbU+eLTXpG9kP2jRI775wt0aOHBnZ61rMvI5NmejYAABiC6t7h828YCNJRvSYAABAfTMw2JyQxK0oAIBdNbG6AKPxfBEAALANAzs2qaJjAwCIKXWxCGZ1hK8XJQwMNs6/vwAAAIIZGGzEU1EAACAk84JNkgg2AAAgJPOCTbIINgAAICTzgk2KeJYLABBb6mKdRCboixJJItgAAICQiAgAAMA2zOvYJIs4BgAAQjIv2AAAEGvKFfl/1DNBX5RIkuSwuggAABCNuKkDAABsw7yOTYro2AAAgJDMCzYAAMQanyI/7wzz2ESJZNGxAQAAIZkXbK6SiVUDAIB6YF5EaCrJaXURAAAgGpkXbNpISrS6CAAAEI3MCzZNJbmsLgIAgHrkk1QR4WsyeDhKtNHZSfoAAAC+x7hg42x6WnHuSqvLAACgjjSwugCjGRdsrmxyWPFuWjYAALsi2FwOS5ZU2LFjh/r376/c3FwrPh4AALNUSCqL8Mumi2BaEmy2b9+uPn36WPHRAADAxiwJNvfdd58cjh+ePriyslI+ny/oBQAA8EOidozN9OnTNWXKlFr7PfIpXgELKgIAANHOko5NOHJycuT1emtex48ft7okAAAQ5aK2Y+N0OuV0snYCAACSxM2K8FgSbJYsWaK1a9fK6XSqU6dOGjRokBVlAAAAm7Ek2Nx777269957L+lcl8oVr7gIVwQAAOwgam9FnU+SyuSgHwcAAEIwLti4VK4fflAcAAC7qZIU6eWE7DlDn3HBxi2vHPJbXQYAAIhCxgUbj8rksGnKBADARMuWLdOECRP0/vvvq1evXiGPWbhwoQoKCnTs2DGVlpbqzTff1NixY/Xtt99KkqqrqxUXF6c1a9bo9OnTevLJJ9WmTRsdPnxYL7/8cthPShsXbBJVrgTG2AAAEBWKi4uVkJCgq6+++rzHfPvtt1q1apXefvttSdJXX30lSbrzzjtr9n3yySc6ePCgJOmll15Snz59NHz4cE2ZMkXvvfeeRo0aFVY9xgUbt3xK4FYUAACXze/311qyKCEh4aLmkWvUqJHuuOMOvfDCC+c95oMPPlBiYqJmzpyp48ePa8yYMZKkwYMH1xyTl5enN998U5K0cuVKDR8+XJLUo0cPewcblyro2AAAYoxX0pkIX7NKy5Yt0+jRo4P2Tpo0SZMnT47oJx08eFD5+fl6/fXXlZ+fr7vvvltbtmypef+7775To0aNlJycLEkqKipSSkqKJCk1NVVFRUVhf5ZxwSZJXjnFjMQAAFyuzMxM5eXlBe1LSKgdDfx+v/r161drf9euXfXGG29c8HNSU1PVrVs3SVLbtm115MgRnTlzRqmpqZKkd955JyhgpaWlqaSkRJJ05swZpaWlhf2djAs2AAAgMhwOh9xud1jHrVu37qKuXVVVpWPHjqlFixbq06ePFi5cKEkqLS1VYmJiTajx+/3asWOHnn322Zpzb775Zm3cuFEdOnTQhg0bdMstt4T9uVG7CCYAADDDzJkzVVBQoHnz5mnr1q2SpHXr1unRRx+VJN11111q0aKFJk2apF//+teaO3duzbmffPKJ7rzzzqDrPfHEE1q7dq2ee+45FRUV6ec//3nYtcQFAgEjBqz4fD55PB5le38jp5tbUQAAe5qq6UHbXbp00fbtT0vqGeFPGqN33x2ukSNHRvi61qJjAwAAbINgAwAAbMO4wcOJKpeTmYcBAEAIdGwAAIBtGNexqZBLAeaxAQDElApJvgsedXHsOYs/HRsAAGAbBBsAAGAbxt2K8srNkgoAACAk44INAACxp1TS6QhfsyrC14sO3IoCAAC2YVzHpkweVXErCgAAhGBcsEmSlzE2AAAgJG5FAQAA2zCuYwMAQOwpFxP0hYeODQAAsA3jOjYe+eS06SNqAADg8tCxAQAAtmFcxwYAgNjjlXQmwte0590P44LNCTVVAo97AwCAEIwLNsVqqAQlWl0GAACIQkYGG4dcVpcBAACikIHBpjHBBgAAhGRcsDmlhopXktVlAABQj8p1dgBxJNlzgj7jgs0JNVUcwQYAAIRgXLApPtFQcW631WUAAFA3mlhdgNmYoA8AANiGcR0bAABiDxP0hcu4YONO8SrOHbC6DAAA6kgDqwswmnHBxpVYqbhEh9VlAACAKMQYGwAAYBvGdWyS5FW8qq0uAwAARCHjgg0AALGnVNLpCF+TwcNRwSOf4sXgYQAAUJtxwcYrN0sqAACAkIwLNmXysKQCAAAIybhgU17hVJwj0eoyAACoGyF/xZVL8kX4g1gEEwAAIMjy5cv1xz/+Ue3bt9eWLVs0bdo0XXvttbWOe/rpp+V2u5Wfn6+pU6eqZcuWOnbsmLKzs9WhQwft2rVLzz33nFq2bKl3331XW7ZsUatWrbRt2za99tpratIkvEW0jAs2vhKP4vwsggkAsCnDFsE8ePCgZsyYobS0NH3wwQeaOnWq5s2bF3TMqlWrVFhYqDlz5mjNmjWaOHGi5s+fr+eee04DBgzQ8OHD9d///d81+48ePaqZM2cqISFBv/3tb/W73/1OkydPDqse44JNZZlLinNZXQYAAJA0bty4mj9XV1crOTm51jErV65U9+7dJUk9evTQsGHDJEl79uzRz372M0lSmzZttHr1aklSVlbWBa95PsYFG5W6JD/BBgAQG7ZvL1Lkx9dIkldxcXHy+YKvnZCQIKfTedFXq66uVl5enn73u9/Veq+oqEidO3eWJLndbhUXF0uSevXqpQ0bNqhv377atGmTzpwJXujT6/Xq888/16JFi8KugyUVAACIaumS1kmq1NlJ9SLx2ifpqHbu3CmPxxP0mj59eq0K/H6/brrpplqvRx55pOaYrKwsZWdnq3Xr1rXOT0tLU0lJiSTJ5/OpUaNGkqQnn3xSlZWVeuWVV1RZWamrrrqq5pzKykpNmDBBr7/+uho0CH9h0LhAIGDEbHc+n08ej0da75WSGGMDALCnQNfg7bO//5pLukdS7UG5l+Y9Sa1VUfEnVVUFz0B8KR2biRMn6q677tJNN92kpUuXavDgwaqqqtKxY8fUokULrVq1Snl5eTVjbP7whz9o/vz5KigoUMOGDdWoUSOtXLlSX375pXJyclRVVaXHHntMjz32mDp27FhzzXAYeCtKdp0FGgCAWtxut6SbJK2W1FZS3GVe8aCkgzp1apOcTucl3Xb6Z7NmzdKCBQv0xRdfSJLi4+M1ePBgrVu3Tq+99pqWLFmiAQMGaOXKlZoyZYoKCgr0/PPPS5K2bdumd955R71791ZJSYmeeuopSdITTzyhFStW6Ouvv5YktWzZMuxgY17H5iOv5KJjAwCwp8BttfdFtmtztlsTCKy8zOtEJ8bYAAAQ5YK7NpfTjzjXrfmvSJQVlcy7FVWmy/s7BQDAQF7vh3/v2uTr0rs2qyX95KIG45rGvGBTqrMDwwEAiCFnuza9deljbc52a4qLN0a4suhiXrBJlsQ0NgCAGOT1/tdldG3WSPqJGjZsGPnCooh5wSZNBBsAQEy69K7NQUkHVFz81zqqLHqYF2ySJSVZXQQAANa4tK5NbHRrJBODTdNyyc3DXAAAu/rh2xIX37WJnW6NZGCwaZB2UnHuMqvLAACgjlx5wSMurmsTO90aycBg0zDxlOITy60uAwCAOnLhYBN+1ya2ujWSgcGmsYoVzyAbAECMC69rE1vdGsnAYNNQp+QQt6IAALHtwl2b2OvWSAYGG7e8cshvdRkAAFjuh7s2sdetkQwMNh6VyaFqq8sAAMBy5+/axGa3RjIw2CSqXAksFgUAgCSptPRDJSd/v2sTm90aycBg45ZPCdyKAgBAkuTxePSPlb/bSvpOsdqtkSwMNosWLdKGDRvk9Xo1dOhQ9e3bN6zzXKqgYwMAwD8J7tp8oVjt1kgWBZvTp08rNzdXmzZtUllZmTIyMrRt2zbFx194RuEkeeWUsx6qBADADP/o2iyT5IvZbo1kUbBZv369OnTooLi4OLndbiUnJ2vPnj1q3759zTGVlZWqqqqq2fZ6vZIkp+80wQYAYFs++ZSUlKS4uHAXuDzrbNemhWK5WyNZFGyKioqUkpJSs52amqqioqKgYDN9+nRNmTKl1rkvN51VHyUCAGCJXP1WXq/37088hc/j8SgQOFVHVZnDkmCTlpamkpKSmu0zZ84oLS0t6JicnBxlZWXVbJeWlqpZs2YqKir6e8sN5/h8PjVt2lTHjx+/6P8Q7Iyfy/nxswmNn8v58bMJra5+LklJzLB/qSwJNj179lRWVpYCgYDKyspUWlqqdu3aBR3jdDrldNa+5eTxePiP6jzcbjc/mxD4uZwfP5vQ+LmcHz+b0Pi5RA9Lgk2DBg2UnZ2txx9/XF6vV7Nnzw5r4DAAAMAPsexx7yFDhmjIkCFWfTwAALAhY9okCQkJmjRpkhISjJtTsM7xswmNn8v58bMJjZ/L+fGzCY2fS/SJCwQCzHYHAABswZiODQAAwIUQbAAAgG0Yc1PwUteWsrsdO3Zo/PjxGjhwoLKzs60uJ2rs2bNHkyZNUnp6ur7++msNGTJEAwcOtLosy/l8Pt1///36yU9+ol27dql379566KGHrC4rauTn5+vGG2/UihUr1KtXL6vLiRq9evWqmVflpptu0rRp0yyuKHps3bpVixcvlsvl0meffaa1a9daXVLMMyLYXM7aUna3fft29enTx+oyos6JEyc0YsQI3XbbbTp27JgyMjK0b98+q8uyXHV1tQYNGqRRo0bJ5/OpVatWBJu/q66u1osvvqjrr7/e6lKizsCBAzV58mSry4g6VVVV+s1vfqMVK1YoLi5OgwcPtrokyJBbUedbWwrSfffdJ4fDYXUZUScjI0O33XabpLO/sJKTky2uKDokJydr1KhRkqR9+/YFLWMS61555RWNGzdOLpfL6lKizldffaUXX3xRzz77rL755hury4ka69evV3x8vF599VVNnjxZRUVFVpcEGdKxCWdtKeB8XnvtNeXm5lpdRlSZO3eu5s+fr5kzZ1pdSlTYunWrJCk9Pd3SOqLVxIkT1b17d508eVK9e/fW5s2bmfJf0sGDB7Vx40Z9+OGHSkxMVEZGhpYvX65WrVpZXVpMM6JjE87aUkAo77//vlq2bKnMzEyrS4kqo0eP1qeffqrRo0ersLDQ6nIst3z5cpWVlSk3N1f79+/XggUL9Oc//9nqsqJG9+7dJUmNGzdWw4YNtXv3bosrig6pqanq0KGDkpOT5XQ61alTJ23evNnqsmKeEcGmZ8+e2rVrlwKBgHw+X8i1pYDvW7RokU6ePKlHHnlEn376qXw+n9UlWe7rr7/WX//6V0ln111LSUnR0aNHLa7Kejk5OcrJyVF2drZat26tBx54gLFrf7dz507NmzdPkuT3+3XkyBE6En+XkZGho0eP6tx0cN999x2/m6KAEbeiWFvq/JYsWaK1a9fW/Gth0KBBVpcUFTZu3KixY8cqPT1dixcv1v79+7V58+aYX6TO5XJpxowZuuGGG3TkyBHdeuut6tKli9VlRY23335bBQUFmjdvnpo0aaLrrrvO6pIs16BBA3300Uf67rvvdODAAU2dOlWNGze2uqyo0KxZMz377LN67LHHlJKSokGDBqlz585WlxXzmHkYAADYBm0PAABgGwQbAABgGwQbAABgGwQbAABgGwQbAABgGwQbAABgGwQbAABgGwQbAABgGwQbAABgGwQbAOcVCAT0n//5n1aXAQBhI9gAOK8dO3YoLi5OS5cuVUlJidXlAMAFsVYUgJA++eQTXXvtterQoYPVpQBA2OjYAKjl22+/1bFjxwg1AIxDsAFQy+zZszV06FCrywCAi0awARDk0KFDqqqqUmJiotWlAMBFI9gACLJ+/Xpdc801VpcBAJeEYAOglp07d1pdAgBcEp6KAhDk9OnT6tSpk/7t3/5NDz30kLp06WJ1SQAQNjo2AII0aNBAX3zxhfx+v2677Tbddddd8vv9VpcFAGGhYwPgvCoqKtS3b18tX75cTZs2tbocALigBKsLABCd/vKXv+jQoUMaPHgwoQaAMejYAAAA22CMDQAAsA2CDQAAsA2CDQAAsA2CDQAAsA2CDQAAsA2CDQAAsA2CDQAAsA2CDQAAsA2CDQAAsA2CDQAAsI3/D5b3L/NeE3J9AAAAAElFTkSuQmCC", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -531,7 +719,7 @@ } ], "source": [ - "desc.plotting.plot_2d(eq2, \"B*n\", field=ext_field)" + "desc.plotting.plot_2d(eq3, \"B*n\", field=ext_field)" ] }, { @@ -544,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 62, "id": "bc80bfcb-f3f9-46b2-bcc1-9027fcad050f", "metadata": { "tags": [] @@ -552,9 +740,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -562,12 +750,12 @@ } ], "source": [ - "desc.plotting.plot_2d(eq2.surface, \"K\");" + "desc.plotting.plot_2d(eq3.surface, \"K\");" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 63, "id": "540ce472-3b07-460b-b905-bd311ed80214", "metadata": { "tags": [] @@ -575,9 +763,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -585,7 +773,7 @@ } ], "source": [ - "desc.plotting.plot_1d(eq2, \"current\");" + "desc.plotting.plot_1d(eq3, \"current\");" ] }, { @@ -606,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "df20a314-be5e-4b1a-95bb-b09129d75428", "metadata": { "tags": [] @@ -629,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "33c9cea6-2114-4aad-a8cd-5ec234a1833d", "metadata": { "tags": [] @@ -641,17 +829,27 @@ "text": [ "Building objective: force\n", "Precomputing transforms\n", - "Compiling objective function and derivatives: ['force']\n", + "Building objective: lcfs R\n", + "Building objective: lcfs Z\n", + "Building objective: fixed Psi\n", + "Building objective: fixed pressure\n", + "Building objective: fixed current\n", + "Building objective: fixed sheet current\n", + "Building objective: self_consistency R\n", + "Building objective: self_consistency Z\n", + "Building objective: lambda gauge\n", + "Building objective: axis R self consistency\n", + "Building objective: axis Z self consistency\n", "Number of parameters: 375\n", "Number of objectives: 2450\n", "Starting optimization\n", "Using method: lsq-exact\n", "`gtol` condition satisfied.\n", - " Current function value: 1.304e-18\n", - " Total delta_x: 2.127e-01\n", - " Iterations: 65\n", - " Function evaluations: 86\n", - " Jacobian evaluations: 66\n", + " Current function value: 1.514e-18\n", + " Total delta_x: 2.119e-01\n", + " Iterations: 66\n", + " Function evaluations: 72\n", + " Jacobian evaluations: 67\n", "Start of solver\n", "Total (sum of squares): 1.158e-02, \n", "Maximum absolute Force error: 9.655e+03 (N)\n", @@ -662,22 +860,24 @@ "Average absolute Force error: 2.293e-03 (normalized)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-current profile error: 0.000e+00 (A)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed current profile error: 0.000e+00 (A)\n", + "Fixed sheet current error: 0.000e+00 (~)\n", "End of solver\n", - "Total (sum of squares): 1.304e-18, \n", - "Maximum absolute Force error: 4.408e-04 (N)\n", - "Minimum absolute Force error: 1.875e-08 (N)\n", - "Average absolute Force error: 2.920e-05 (N)\n", - "Maximum absolute Force error: 3.230e-10 (normalized)\n", - "Minimum absolute Force error: 1.374e-14 (normalized)\n", - "Average absolute Force error: 2.140e-11 (normalized)\n", + "Total (sum of squares): 1.514e-18, \n", + "Maximum absolute Force error: 4.722e-04 (N)\n", + "Minimum absolute Force error: 2.098e-07 (N)\n", + "Average absolute Force error: 3.149e-05 (N)\n", + "Maximum absolute Force error: 3.460e-10 (normalized)\n", + "Minimum absolute Force error: 1.538e-13 (normalized)\n", + "Average absolute Force error: 2.307e-11 (normalized)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-current profile error: 0.000e+00 (A)\n" + "Fixed Psi error: 0.000e+00 (Wb)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed current profile error: 0.000e+00 (A)\n", + "Fixed sheet current error: 0.000e+00 (~)\n" ] } ], @@ -696,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "id": "6b55df80-bcab-4ef6-bcec-e19f2cdafdc7", "metadata": { "tags": [] @@ -716,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "ecde303f-32a4-4ef6-8622-2cc23b676d30", "metadata": { "tags": [] @@ -741,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "id": "fe3274e8-f62a-437a-b05b-2da626bea153", "metadata": { "tags": [] @@ -763,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "id": "343a1145-fb48-4fe0-a079-4cf15ac08bbe", "metadata": { "tags": [] @@ -775,36 +975,34 @@ "text": [ "Building objective: Vacuum boundary error\n", "Precomputing transforms\n", - "Timer: Precomputing transforms = 237 ms\n", - "Timer: Objective build = 1.00 sec\n", + "Timer: Precomputing transforms = 304 ms\n", + "Timer: Objective build = 921 ms\n", "Building objective: force\n", "Precomputing transforms\n", - "Timer: Precomputing transforms = 226 ms\n", - "Timer: Objective build = 535 ms\n", - "Timer: Proximal projection build = 3.86 sec\n", - "Timer: Linear constraint projection build = 2.78 sec\n", - "Compiling objective function and derivatives: ['Vacuum boundary error']\n", - "Timer: Objective compilation time = 5.46 sec\n", - "Timer: Jacobian compilation time = 7.49 sec\n", - "Timer: Total compilation time = 12.9 sec\n", - "Compiling objective function and derivatives: ['force']\n", - "Timer: Objective compilation time = 2.65 sec\n", - "Timer: Jacobian compilation time = 6.61 sec\n", - "Timer: Total compilation time = 9.27 sec\n", + "Timer: Precomputing transforms = 90.9 ms\n", + "Timer: Objective build = 318 ms\n", + "Timer: Proximal projection build = 6.64 sec\n", + "Building objective: fixed current\n", + "Building objective: fixed pressure\n", + "Building objective: fixed Psi\n", + "Building objective: lcfs R\n", + "Building objective: lcfs Z\n", + "Timer: Objective build = 1.24 sec\n", + "Timer: Linear constraint projection build = 1.78 sec\n", "Number of parameters: 25\n", "Number of objectives: 1250\n", "Starting optimization\n", "Using method: proximal-lsq-exact\n", " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", " 0 1 2.181e+00 1.085e+02 \n", - " 1 2 3.555e-01 1.825e+00 7.803e-02 2.755e+01 \n", - " 2 3 3.467e-02 3.209e-01 8.711e-02 4.919e+00 \n", - " 3 4 1.655e-03 3.302e-02 3.865e-02 7.491e-01 \n", - " 4 5 9.987e-04 6.558e-04 3.826e-02 9.365e-01 \n", - " 5 6 3.014e-05 9.686e-04 1.011e-02 7.756e-02 \n", - " 6 7 5.440e-06 2.470e-05 2.929e-03 1.115e-02 \n", - " 7 8 5.231e-06 2.090e-07 1.264e-03 2.378e-03 \n", - " 8 9 5.225e-06 6.178e-09 1.345e-04 6.218e-05 \n", + " 1 2 3.556e-01 1.825e+00 7.801e-02 2.755e+01 \n", + " 2 3 3.469e-02 3.209e-01 8.714e-02 4.919e+00 \n", + " 3 4 1.656e-03 3.303e-02 3.863e-02 7.457e-01 \n", + " 4 5 9.891e-04 6.670e-04 3.820e-02 9.394e-01 \n", + " 5 6 2.787e-05 9.612e-04 9.914e-03 7.601e-02 \n", + " 6 7 5.401e-06 2.247e-05 2.768e-03 9.852e-03 \n", + " 7 8 5.231e-06 1.694e-07 1.276e-03 2.415e-03 \n", + " 8 9 5.225e-06 6.348e-09 1.319e-04 5.564e-05 \n", "Optimization terminated successfully.\n", "`ftol` condition satisfied.\n", " Current function value: 5.225e-06\n", @@ -812,8 +1010,8 @@ " Iterations: 8\n", " Function evaluations: 9\n", " Jacobian evaluations: 9\n", - "Timer: Solution time = 3.57 min\n", - "Timer: Avg time per step = 23.8 sec\n", + "Timer: Solution time = 4.10 min\n", + "Timer: Avg time per step = 27.3 sec\n", "Start of solver\n", "Total (sum of squares): 2.181e+00, \n", "Maximum absolute Boundary normal field error: 1.121e-02 (T*m^2)\n", @@ -828,136 +1026,129 @@ "Maximum absolute Boundary magnetic pressure error: 3.817e-01 (normalized)\n", "Minimum absolute Boundary magnetic pressure error: 2.223e-01 (normalized)\n", "Average absolute Boundary magnetic pressure error: 3.256e-01 (normalized)\n", - "Maximum absolute Force error: 4.408e-04 (N)\n", - "Minimum absolute Force error: 1.875e-08 (N)\n", - "Average absolute Force error: 2.920e-05 (N)\n", - "Maximum absolute Force error: 3.230e-10 (normalized)\n", - "Minimum absolute Force error: 1.374e-14 (normalized)\n", - "Average absolute Force error: 2.140e-11 (normalized)\n", - "Fixed-current profile error: 0.000e+00 (A)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", + "Maximum absolute Force error: 4.722e-04 (N)\n", + "Minimum absolute Force error: 2.098e-07 (N)\n", + "Average absolute Force error: 3.149e-05 (N)\n", + "Maximum absolute Force error: 3.460e-10 (normalized)\n", + "Minimum absolute Force error: 1.538e-13 (normalized)\n", + "Average absolute Force error: 2.307e-11 (normalized)\n", + "Fixed current profile error: 0.000e+00 (A)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", "End of solver\n", "Total (sum of squares): 5.225e-06, \n", - "Maximum absolute Boundary normal field error: 1.401e-04 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 1.400e-04 (T*m^2)\n", "Minimum absolute Boundary normal field error: 4.884e-18 (T*m^2)\n", "Average absolute Boundary normal field error: 4.658e-05 (T*m^2)\n", "Maximum absolute Boundary normal field error: 1.270e-03 (normalized)\n", "Minimum absolute Boundary normal field error: 4.428e-17 (normalized)\n", - "Average absolute Boundary normal field error: 4.224e-04 (normalized)\n", - "Maximum absolute Boundary magnetic pressure error: 5.364e-05 (T^2*m^2)\n", - "Minimum absolute Boundary magnetic pressure error: 4.749e-08 (T^2*m^2)\n", + "Average absolute Boundary normal field error: 4.223e-04 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 5.365e-05 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 5.022e-08 (T^2*m^2)\n", "Average absolute Boundary magnetic pressure error: 1.130e-05 (T^2*m^2)\n", - "Maximum absolute Boundary magnetic pressure error: 3.087e-04 (normalized)\n", - "Minimum absolute Boundary magnetic pressure error: 2.733e-07 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 3.088e-04 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 2.890e-07 (normalized)\n", "Average absolute Boundary magnetic pressure error: 6.502e-05 (normalized)\n", - "Maximum absolute Force error: 1.271e+01 (N)\n", - "Minimum absolute Force error: 9.713e-05 (N)\n", - "Average absolute Force error: 6.251e-01 (N)\n", - "Maximum absolute Force error: 9.316e-06 (normalized)\n", - "Minimum absolute Force error: 7.117e-11 (normalized)\n", - "Average absolute Force error: 4.580e-07 (normalized)\n", - "Fixed-current profile error: 0.000e+00 (A)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", + "Maximum absolute Force error: 1.270e+01 (N)\n", + "Minimum absolute Force error: 2.579e-04 (N)\n", + "Average absolute Force error: 6.249e-01 (N)\n", + "Maximum absolute Force error: 9.309e-06 (normalized)\n", + "Minimum absolute Force error: 1.890e-10 (normalized)\n", + "Average absolute Force error: 4.579e-07 (normalized)\n", + "Fixed current profile error: 0.000e+00 (A)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", - "Building objective: force\n", - "Precomputing transforms\n", - "Timer: Precomputing transforms = 227 ms\n", - "Timer: Objective build = 544 ms\n", - "Timer: Proximal projection build = 2.80 sec\n", - "Timer: Linear constraint projection build = 1.80 sec\n", - "Compiling objective function and derivatives: ['Vacuum boundary error']\n", - "Timer: Objective compilation time = 32.8 ms\n", - "Timer: Jacobian compilation time = 242 ms\n", - "Timer: Total compilation time = 277 ms\n", - "Compiling objective function and derivatives: ['force']\n", - "Timer: Objective compilation time = 1.95 sec\n", - "Timer: Jacobian compilation time = 5.75 sec\n", - "Timer: Total compilation time = 7.70 sec\n", + "Timer: Objective build = 22.0 ms\n", + "Timer: Proximal projection build = 3.31 sec\n", + "Building objective: lcfs R\n", + "Building objective: lcfs Z\n", + "Timer: Objective build = 608 ms\n", + "Timer: Linear constraint projection build = 3.07 sec\n", "Number of parameters: 81\n", "Number of objectives: 1250\n", "Starting optimization\n", "Using method: proximal-lsq-exact\n", " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", - " 0 1 5.225e-06 1.268e-02 \n", - " 1 4 2.795e-07 4.945e-06 2.374e-03 7.767e-03 \n", - " 2 6 1.979e-07 8.159e-08 5.633e-04 9.221e-04 \n", - " 3 7 1.674e-07 3.048e-08 1.183e-03 2.527e-03 \n", - " 4 8 1.375e-07 2.990e-08 1.005e-03 2.034e-03 \n", - " 5 9 1.182e-07 1.932e-08 9.773e-04 1.963e-03 \n", - " 6 11 1.044e-07 1.384e-08 4.943e-04 4.162e-04 \n", - " 7 12 9.596e-08 8.409e-09 9.903e-04 1.652e-03 \n", - " 8 13 8.477e-08 1.119e-08 9.955e-04 2.082e-03 \n", - " 9 14 7.476e-08 1.001e-08 1.022e-03 1.965e-03 \n", - " 10 15 6.883e-08 5.929e-09 1.076e-03 2.280e-03 \n", - " 11 16 6.335e-08 5.472e-09 1.105e-03 2.296e-03 \n", - " 12 17 5.837e-08 4.981e-09 1.093e-03 1.924e-03 \n", - " 13 18 5.560e-08 2.776e-09 1.063e-03 1.652e-03 \n", - " 14 19 5.440e-08 1.199e-09 1.044e-03 1.276e-03 \n", - " 15 20 5.070e-08 3.702e-09 2.622e-04 3.939e-04 \n", - " 16 21 4.985e-08 8.483e-10 5.142e-04 3.355e-04 \n", - " 17 23 4.946e-08 3.884e-10 2.651e-04 4.193e-04 \n", + " 0 1 5.225e-06 1.269e-02 \n", + " 1 4 2.895e-07 4.935e-06 2.339e-03 8.111e-03 \n", + " 2 6 2.144e-07 7.510e-08 5.449e-04 5.586e-04 \n", + " 3 7 1.965e-07 1.798e-08 1.200e-03 2.779e-03 \n", + " 4 8 1.684e-07 2.812e-08 1.103e-03 2.737e-03 \n", + " 5 9 1.464e-07 2.191e-08 1.049e-03 2.575e-03 \n", + " 6 10 1.240e-07 2.243e-08 1.046e-03 2.456e-03 \n", + " 7 12 1.069e-07 1.707e-08 5.082e-04 5.178e-04 \n", + " 8 13 1.014e-07 5.531e-09 9.959e-04 1.956e-03 \n", + " 9 14 9.185e-08 9.564e-09 9.848e-04 1.787e-03 \n", + " 10 15 8.490e-08 6.949e-09 9.919e-04 1.724e-03 \n", + " 11 16 7.945e-08 5.452e-09 1.003e-03 1.676e-03 \n", + " 12 17 7.517e-08 4.279e-09 1.018e-03 1.642e-03 \n", + " 13 18 7.218e-08 2.991e-09 1.037e-03 1.625e-03 \n", + " 14 19 6.997e-08 2.210e-09 1.055e-03 1.609e-03 \n", + " 15 20 6.899e-08 9.848e-10 1.059e-03 1.581e-03 \n", + " 16 21 6.392e-08 5.064e-09 2.679e-04 1.106e-04 \n", + " 17 22 6.369e-08 2.372e-10 5.300e-04 4.139e-04 \n", + " 18 23 6.309e-08 5.974e-10 1.310e-04 2.840e-05 \n", "Optimization terminated successfully.\n", "`ftol` condition satisfied.\n", - " Current function value: 4.946e-08\n", - " Total delta_x: 1.214e-02\n", - " Iterations: 17\n", + " Current function value: 6.309e-08\n", + " Total delta_x: 1.353e-02\n", + " Iterations: 18\n", " Function evaluations: 23\n", - " Jacobian evaluations: 18\n", - "Timer: Solution time = 3.60 min\n", - "Timer: Avg time per step = 12.0 sec\n", + " Jacobian evaluations: 19\n", + "Timer: Solution time = 5.18 min\n", + "Timer: Avg time per step = 16.3 sec\n", "Start of solver\n", "Total (sum of squares): 5.225e-06, \n", - "Maximum absolute Boundary normal field error: 1.401e-04 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 1.400e-04 (T*m^2)\n", "Minimum absolute Boundary normal field error: 4.884e-18 (T*m^2)\n", "Average absolute Boundary normal field error: 4.658e-05 (T*m^2)\n", "Maximum absolute Boundary normal field error: 1.270e-03 (normalized)\n", "Minimum absolute Boundary normal field error: 4.428e-17 (normalized)\n", - "Average absolute Boundary normal field error: 4.224e-04 (normalized)\n", - "Maximum absolute Boundary magnetic pressure error: 5.364e-05 (T^2*m^2)\n", - "Minimum absolute Boundary magnetic pressure error: 4.749e-08 (T^2*m^2)\n", + "Average absolute Boundary normal field error: 4.223e-04 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 5.365e-05 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 5.022e-08 (T^2*m^2)\n", "Average absolute Boundary magnetic pressure error: 1.130e-05 (T^2*m^2)\n", - "Maximum absolute Boundary magnetic pressure error: 3.087e-04 (normalized)\n", - "Minimum absolute Boundary magnetic pressure error: 2.733e-07 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 3.088e-04 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 2.890e-07 (normalized)\n", "Average absolute Boundary magnetic pressure error: 6.502e-05 (normalized)\n", - "Maximum absolute Force error: 1.271e+01 (N)\n", - "Minimum absolute Force error: 9.713e-05 (N)\n", - "Average absolute Force error: 6.251e-01 (N)\n", - "Maximum absolute Force error: 4.529e-05 (normalized)\n", - "Minimum absolute Force error: 3.460e-10 (normalized)\n", - "Average absolute Force error: 2.227e-06 (normalized)\n", - "Fixed-current profile error: 0.000e+00 (A)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", + "Maximum absolute Force error: 1.270e+01 (N)\n", + "Minimum absolute Force error: 2.579e-04 (N)\n", + "Average absolute Force error: 6.249e-01 (N)\n", + "Maximum absolute Force error: 9.309e-06 (normalized)\n", + "Minimum absolute Force error: 1.890e-10 (normalized)\n", + "Average absolute Force error: 4.579e-07 (normalized)\n", + "Fixed current profile error: 0.000e+00 (A)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n", "End of solver\n", - "Total (sum of squares): 4.946e-08, \n", - "Maximum absolute Boundary normal field error: 2.936e-05 (T*m^2)\n", - "Minimum absolute Boundary normal field error: 4.537e-18 (T*m^2)\n", - "Average absolute Boundary normal field error: 2.703e-06 (T*m^2)\n", - "Maximum absolute Boundary normal field error: 2.662e-04 (normalized)\n", - "Minimum absolute Boundary normal field error: 4.114e-17 (normalized)\n", - "Average absolute Boundary normal field error: 2.451e-05 (normalized)\n", - "Maximum absolute Boundary magnetic pressure error: 3.467e-05 (T^2*m^2)\n", - "Minimum absolute Boundary magnetic pressure error: 5.448e-09 (T^2*m^2)\n", - "Average absolute Boundary magnetic pressure error: 4.034e-06 (T^2*m^2)\n", - "Maximum absolute Boundary magnetic pressure error: 1.995e-04 (normalized)\n", - "Minimum absolute Boundary magnetic pressure error: 3.135e-08 (normalized)\n", - "Average absolute Boundary magnetic pressure error: 2.321e-05 (normalized)\n", - "Maximum absolute Force error: 2.695e+01 (N)\n", - "Minimum absolute Force error: 1.346e-05 (N)\n", - "Average absolute Force error: 1.437e+00 (N)\n", - "Maximum absolute Force error: 9.601e-05 (normalized)\n", - "Minimum absolute Force error: 4.794e-11 (normalized)\n", - "Average absolute Force error: 5.118e-06 (normalized)\n", - "Fixed-current profile error: 0.000e+00 (A)\n", - "Fixed-pressure profile error: 0.000e+00 (Pa)\n", - "Fixed-Psi error: 0.000e+00 (Wb)\n", + "Total (sum of squares): 6.309e-08, \n", + "Maximum absolute Boundary normal field error: 3.109e-05 (T*m^2)\n", + "Minimum absolute Boundary normal field error: 4.705e-18 (T*m^2)\n", + "Average absolute Boundary normal field error: 3.024e-06 (T*m^2)\n", + "Maximum absolute Boundary normal field error: 2.819e-04 (normalized)\n", + "Minimum absolute Boundary normal field error: 4.266e-17 (normalized)\n", + "Average absolute Boundary normal field error: 2.742e-05 (normalized)\n", + "Maximum absolute Boundary magnetic pressure error: 3.712e-05 (T^2*m^2)\n", + "Minimum absolute Boundary magnetic pressure error: 3.760e-08 (T^2*m^2)\n", + "Average absolute Boundary magnetic pressure error: 5.133e-06 (T^2*m^2)\n", + "Maximum absolute Boundary magnetic pressure error: 2.136e-04 (normalized)\n", + "Minimum absolute Boundary magnetic pressure error: 2.164e-07 (normalized)\n", + "Average absolute Boundary magnetic pressure error: 2.954e-05 (normalized)\n", + "Maximum absolute Force error: 1.445e+01 (N)\n", + "Minimum absolute Force error: 2.773e-04 (N)\n", + "Average absolute Force error: 9.437e-01 (N)\n", + "Maximum absolute Force error: 1.059e-05 (normalized)\n", + "Minimum absolute Force error: 2.032e-10 (normalized)\n", + "Average absolute Force error: 6.915e-07 (normalized)\n", + "Fixed current profile error: 0.000e+00 (A)\n", + "Fixed pressure profile error: 0.000e+00 (Pa)\n", + "Fixed Psi error: 0.000e+00 (Wb)\n", "R boundary error: 0.000e+00 (m)\n", "Z boundary error: 0.000e+00 (m)\n" ] @@ -999,7 +1190,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "id": "a4d7d0b9-0717-473f-866b-5551a1567f5d", "metadata": { "tags": [] @@ -1007,7 +1198,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1030,7 +1221,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "id": "58eba11c-74b1-48a8-992c-655a4167878d", "metadata": { "tags": [] @@ -1053,7 +1244,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "ab377ca1-da64-4b8c-9a0c-8692136c4468", "metadata": { "tags": [] @@ -1065,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "id": "daaeda7f-3b42-4870-a08e-de716302ecfb", "metadata": { "tags": [] @@ -1082,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "id": "4cc5dfbf-9f85-4b38-bc3c-2e2d85f01faf", "metadata": { "tags": [] @@ -1090,7 +1281,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1124,7 +1315,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/tests/benchmarks/benchmark_cpu_small.py b/tests/benchmarks/benchmark_cpu_small.py index 7c508d92ee..a9e29be420 100644 --- a/tests/benchmarks/benchmark_cpu_small.py +++ b/tests/benchmarks/benchmark_cpu_small.py @@ -367,7 +367,8 @@ def test_proximal_freeb_compute(benchmark): """Benchmark computing free boundary objective with proximal constraint.""" jax.clear_caches() eq = desc.examples.get("ESTELL") - eq.change_resolution(6, 6, 6, 12, 12, 12) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(6, 6, 6, 12, 12, 12) field = ToroidalMagneticField(1.0, 1.0) # just a dummy field for benchmarking objective = ObjectiveFunction(BoundaryError(eq, field=field)) constraint = ObjectiveFunction(ForceBalance(eq)) @@ -391,7 +392,8 @@ def test_proximal_freeb_jac(benchmark): """Benchmark computing free boundary jacobian with proximal constraint.""" jax.clear_caches() eq = desc.examples.get("ESTELL") - eq.change_resolution(6, 6, 6, 12, 12, 12) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(6, 6, 6, 12, 12, 12) field = ToroidalMagneticField(1.0, 1.0) # just a dummy field for benchmarking objective = ObjectiveFunction(BoundaryError(eq, field=field)) constraint = ObjectiveFunction(ForceBalance(eq)) @@ -415,7 +417,8 @@ def test_solve_fixed_iter(benchmark): """Benchmark running eq.solve for fixed iteration count.""" jax.clear_caches() eq = desc.examples.get("ESTELL") - eq.change_resolution(6, 6, 6, 12, 12, 12) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(6, 6, 6, 12, 12, 12) def run(eq): eq.solve(maxiter=20, ftol=0, xtol=0, gtol=0) diff --git a/tests/benchmarks/benchmark_gpu_small.py b/tests/benchmarks/benchmark_gpu_small.py index be22fc5866..921a4e4451 100644 --- a/tests/benchmarks/benchmark_gpu_small.py +++ b/tests/benchmarks/benchmark_gpu_small.py @@ -367,7 +367,8 @@ def test_proximal_freeb_compute(benchmark): """Benchmark computing free boundary objective with proximal constraint.""" jax.clear_caches() eq = desc.examples.get("ESTELL") - eq.change_resolution(6, 6, 6, 12, 12, 12) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(6, 6, 6, 12, 12, 12) field = ToroidalMagneticField(1.0, 1.0) # just a dummy field for benchmarking objective = ObjectiveFunction(BoundaryError(eq, field=field)) constraint = ObjectiveFunction(ForceBalance(eq)) @@ -391,7 +392,8 @@ def test_proximal_freeb_jac(benchmark): """Benchmark computing free boundary jacobian with proximal constraint.""" jax.clear_caches() eq = desc.examples.get("ESTELL") - eq.change_resolution(6, 6, 6, 12, 12, 12) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(6, 6, 6, 12, 12, 12) field = ToroidalMagneticField(1.0, 1.0) # just a dummy field for benchmarking objective = ObjectiveFunction(BoundaryError(eq, field=field)) constraint = ObjectiveFunction(ForceBalance(eq)) @@ -415,7 +417,8 @@ def test_solve_fixed_iter(benchmark): """Benchmark running eq.solve for fixed iteration count.""" jax.clear_caches() eq = desc.examples.get("ESTELL") - eq.change_resolution(6, 6, 6, 12, 12, 12) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(6, 6, 6, 12, 12, 12) def run(eq): eq.solve(maxiter=20, ftol=0, xtol=0, gtol=0) diff --git a/tests/inputs/master_compute_data.pkl b/tests/inputs/master_compute_data.pkl index 68d6fc91469a2f408acee86b7246b0f0b8da8040..64947aae9249cfa96c4e201cfcfcca65629e31fd 100644 GIT binary patch delta 44007 zcmeHw2Y8i5*08rE_a?dNZ+g#7NFkjT2)QYg&~8Wwp+iElfe=~(xGEh{c9kH(5XTPK zl^`I>*aa6WE(Aon1T5%ES9aC4@SmA^`+e^Xw*TM%+i#yI%X_DsIc?6IIdf)w`{!oE zC##Md+?E^LA_CXB<(k~bRuwnSowcsuDf3gU{~lMhWpdHFODTF?MQirCz}8s3d+S?< zh}LMgo*t1mE?7TmeY0+iP-EJ&{-bE_SRTsW>dCdvH1=*S)tOsQ8wRz00RN8B73hYv 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zHdYblAotJz>;omCw_kLu78?C4j@2UV8y>3#tY34i7HIs}9jmF}^jJ+xvSYRIK#9UT zJ519`1&VSP~ zJt2r3%rAT~yRRN{Ka#(k{dE_;)t~$;_hdBk%h`hvWD!4%eg8Upo>~7=7G7j@{l=m{ z7eNqy!d5@m{bv1z2n=7dE&4rK2>x+z4BBvIwiWk|bq%$U{}kqjP!CrO`E8d{r|UM7 zb>AwzNzw~H#I$FW%Zygu>RKWGNDn~P(BEJh=L$ytIW8*>PjQ{p(O;Vy=ZZudY|tVy z&Gjt#wJ9K-h`va3Ew@_w2~xsscmR*MS_X!nZ=u-hZgqvC-=(`AkkvsR0j(R+OF?N7 z{1TDKMQ8@$g8XQU2nxB)bx3FJCH72yxkaq(nyIWLKi?u&hG!9CA1V?f zKi>kC7*z0uPEUSJi3k-uz_ngaem#kNbdV3>mvmx&8tjSI<+`$o%6Arl^7a+Fd{EH> zYqN&lbMirpf5>y)E$SU0zae^urg8Z5eAis6a*5D`1EC&NE;-(c4)o%MaOKhQRXCk$1>LD3Zc z-Y_Bq=$L-hjv!cP#nzx+3OAJyEKo0eQI(m{)1|IQh>isaI7b=Cu2A&1WiGqYw-*;x z`*@2(P3_;lD0!6YaZ%asPL4(s^WP%!M}BRL*m&s}m)wiN02Ev^Rsm){zf%iX#G Date: Mon, 1 Jul 2024 20:24:24 -0500 Subject: [PATCH 057/112] Do review suggestions, remove Quadrature grid from ripple objective which was used just to compute R0. probably not worth the memory --- desc/compute/_basis_vectors.py | 28 +++++---- desc/compute/_field.py | 4 +- desc/compute/_metric.py | 10 +-- desc/compute/_neoclassical.py | 11 +++- desc/equilibrium/coords.py | 18 +++--- desc/objectives/_neoclassical.py | 104 +++++++++++++++---------------- desc/plotting.py | 5 +- desc/profiles.py | 4 +- tests/test_neoclassical.py | 20 +----- tests/test_objective_funs.py | 14 +++++ 10 files changed, 115 insertions(+), 103 deletions(-) diff --git a/desc/compute/_basis_vectors.py b/desc/compute/_basis_vectors.py index 2e406a90f1..ae32030a75 100644 --- a/desc/compute/_basis_vectors.py +++ b/desc/compute/_basis_vectors.py @@ -3527,7 +3527,7 @@ def _n_zeta(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="e_rho|a,z", - label="\\mathbf{e}_{\\rho}_{\\alpha, \\zeta}", + label="\\mathbf{e}_{\\rho} |_{\\alpha, \\zeta}", units="m", units_long="meters", description="Tangent vector along radial field line label", @@ -3608,7 +3608,7 @@ def _e_alpha_z(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="e_zeta|r,a", # Same as B/(B⋅∇ζ). - label="(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha} " + label="\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha} " "= \\frac{\\mathbf{B}}{\\mathbf{B} \\cdot \\nabla \\zeta}", units="m", units_long="meters", @@ -3629,7 +3629,7 @@ def _e_zeta_ra(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="(e_zeta|r,a)_t", - label="\\partial_{\\theta} [(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}]", + label="\\partial_{\\theta} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha})", units="m", units_long="meters", description="Tangent vector along (collinear to) field line, poloidal derivative", @@ -3652,11 +3652,11 @@ def _e_zeta_ra_t(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="(e_zeta|r,a)_a", - label="\\partial_{\\alpha} [(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}]", + label="\\partial_{\\alpha} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha})", units="m", units_long="meters", description="Tangent vector along (collinear to) field line, derivative " - "wrt field line angle", + "wrt field line poloidal label", dim=3, params=[], transforms={}, @@ -3671,7 +3671,7 @@ def _e_zeta_ra_a(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="(e_zeta|r,a)_z", - label="\\partial_{\\zeta} [(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}]", + label="\\partial_{\\zeta} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha})", units="m", units_long="meters", description="Tangent vector along (collinear to) field line, toroidal derivative", @@ -3694,7 +3694,8 @@ def _e_zeta_ra_z(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="(e_zeta_z)|r,a", - label="(\\partial_{\\zeta} \\mathbf{e}_{\\zeta})_{\\rho, \\alpha}", + label="\\partial_{\\zeta} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha}) " + "|_{\\rho, \\alpha}", units="m", units_long="meters", description="Curvature vector along field line", @@ -3713,12 +3714,12 @@ def _e_zeta_z_ra(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="|e_zeta|r,a|", # Often written as dℓ/dζ = |B/(B⋅∇ζ)|. - label="|(\\mathbf{e}_{\\zeta})_{\\rho, \\alpha}| " - "= \\frac{|\\mathbf{B}|}{|\\mathbf{B} \\cdot \\nabla \\zeta|}", + name="|e_zeta|r,a|", # Often written as |dℓ/dζ| = |B/(B⋅∇ζ)|. + label="|\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha}|" + " = \\frac{|\\mathbf{B}|}{|\\mathbf{B} \\cdot \\nabla \\zeta|}", units="m", units_long="meters", - description="Length along field line", + description="Differential length along field line", dim=3, params=[], transforms={}, @@ -3733,10 +3734,11 @@ def _d_ell_d_zeta(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="(|e_zeta|_z)|r,a", - label="(\\partial_{\\zeta} |\\mathbf{e}_{\\zeta}|)_{\\rho, \\alpha}|", + label="\\partial_{\\zeta} |\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha}| " + "|_{\\rho, \\alpha}", units="m", units_long="meters", - description="Length along field line, derivative along field line", + description="Differential length along field line, derivative along field line", dim=3, params=[], transforms={}, diff --git a/desc/compute/_field.py b/desc/compute/_field.py index fbcf27efc6..81938f7e8c 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -490,7 +490,7 @@ def _B_sup_zeta_z(params, transforms, profiles, data, **kwargs): units_long="Tesla / meter", description=( "Contravariant toroidal component of magnetic field, derivative wrt field" - " line angle" + " line poloidal label" ), dim=1, params=[], @@ -507,7 +507,7 @@ def _B_sup_zeta_a(params, transforms, profiles, data, **kwargs): @register_compute_fun( name="B^zeta_z|r,a", - label="(\\partial_{\\zeta} B^{\\zeta})_{\\rho, \\alpha}", + label="\\partial_{\\zeta} B^{\\zeta} |_{\\rho, \\alpha}", units="T \\cdot m^{-1}", units_long="Tesla / meter", description=( diff --git a/desc/compute/_metric.py b/desc/compute/_metric.py index 14003840d4..cd03412278 100644 --- a/desc/compute/_metric.py +++ b/desc/compute/_metric.py @@ -60,7 +60,8 @@ def _sqrtg_pest(params, transforms, profiles, data, **kwargs): label="\\sqrt{g}_{\\text{Clebsch}}", units="m^{3}", units_long="cubic meters", - description="Jacobian determinant of Clebsch field line coordinate system", + description="Jacobian determinant of Clebsch field line coordinate system (ρ,α,ζ)" + " where ζ is the DESC toroidal coordinate.", dim=1, params=[], transforms={}, @@ -251,7 +252,7 @@ def _e_rho_x_e_theta(params, transforms, profiles, data, **kwargs): units="m^{2}", units_long="square meters", description="2D Jacobian determinant for constant zeta surface in Clebsch " - "field line coordinates", + "field line coordinates (ρ,α,ζ) where ζ is the DESC toroidal coordinate.", dim=1, params=[], transforms={}, @@ -261,7 +262,7 @@ def _e_rho_x_e_theta(params, transforms, profiles, data, **kwargs): ) def _e_rho_x_e_alpha(params, transforms, profiles, data, **kwargs): # Same as safenorm(cross(data["e_rho|a,z"], data["e_alpha"]), axis=-1), but more - # efficient as it avoids computing radial derivative of alpha and hence iota. + # efficient as it avoids computing radial derivative of iota and stream functions. data["|e_rho x e_alpha|"] = data["|e_rho x e_theta|"] / jnp.abs(data["alpha_t"]) return data @@ -1833,6 +1834,7 @@ def _gradrho(params, transforms, profiles, data, **kwargs): coordinates="r", data=["|grad(rho)|", "sqrt(g)"], axis_limit_data=["sqrt(g)_r"], + resolution_requirement="tz", ) def _gradrho_norm_fsa(params, transforms, profiles, data, **kwargs): data["<|grad(rho)|>"] = surface_averages( @@ -1978,7 +1980,7 @@ def _cvdrift(params, transforms, profiles, data, **kwargs): units="1/(T-m^{2})", units_long="inverse Tesla meters^2", description="Radial component of the geometric part of the curvature drift" - + " used for local stability analyses for Gamma_c.", + + " used for local stability analyses, gyrokinetics, Gamma_c.", dim=1, params=[], transforms={}, diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index f6e40a4b80..f1b61e6d99 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -105,6 +105,7 @@ def _poloidal_mean(grid, f): profiles=[], coordinates="r", data=["B^zeta"], + resolution_requirement="z", # and poloidal if near rational surfaces source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) def _L_ra_fsa(data, transforms, profiles, **kwargs): @@ -132,6 +133,7 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): profiles=[], coordinates="r", data=["B^zeta", "sqrt(g)"], + resolution_requirement="z", # and poloidal if near rational surfaces source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) def _G_ra_fsa(data, transforms, profiles, **kwargs): @@ -148,7 +150,13 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ - label="ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉", + label=( + # ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉 + "\\epsilon^{3/2} = \\frac{\\pi}{8 \\sqrt{2}} " + "(\\frac{R_0}{\\langle \\vert\\nabla \\psi\\vert \\rangle})^2 " + "\\int d\\lambda \\lambda^{-2}B_0^{-1} " + "\\langle \\sum_j \\frac{H_j^2}{I_j} \\rangle" + ), units="~", units_long="None", description="Effective ripple modulation amplitude", @@ -169,6 +177,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "R0", "<|grad(rho)|>", ], + resolution_requirement="z", # and poloidal if near rational surfaces source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, num_quad=( "int : Resolution for quadrature of bounce integrals. Default is 31, " diff --git a/desc/equilibrium/coords.py b/desc/equilibrium/coords.py index 0e04832cfd..501ea16784 100644 --- a/desc/equilibrium/coords.py +++ b/desc/equilibrium/coords.py @@ -125,15 +125,11 @@ def compute(y, basis): grid = Grid(y, sort=False, jitable=True) data = {} if "iota" in deps: - data["iota"] = profiles["iota"](grid, params=params["i_l"], jitable=True) + data["iota"] = profiles["iota"].compute(grid, params=params["i_l"]) if "iota_r" in deps: - data["iota_r"] = profiles["iota"]( - grid, dr=1, params=params["i_l"], jitable=True - ) + data["iota_r"] = profiles["iota"].compute(grid, dr=1, params=params["i_l"]) if "iota_rr" in deps: - data["iota_rr"] = profiles["iota"]( - grid, dr=2, params=params["i_l"], jitable=True - ) + data["iota_rr"] = profiles["iota"].compute(grid, dr=2, params=params["i_l"]) transforms = get_transforms(basis, eq, grid, jitable=True) data = compute_fun(eq, basis, params, transforms, profiles, data) x = jnp.array([data[k] for k in basis]).T @@ -212,7 +208,13 @@ def _initial_guess_heuristic(yk, coords, inbasis, eq, profiles): theta = coords[:, inbasis.index(poloidal)] elif poloidal == "alpha": alpha = coords[:, inbasis.index("alpha")] - iota = profiles["iota"](rho, jitable=True) + rho = jnp.atleast_1d(rho) + grid = Grid( + nodes=jnp.column_stack([rho, jnp.zeros_like(rho), jnp.zeros_like(rho)]), + sort=False, + jitable=True, + ) + iota = profiles["iota"].compute(grid) theta = (alpha + iota * zeta) % (2 * jnp.pi) yk = jnp.array([rho, theta, zeta]).T diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 94e91706e5..117332918c 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -4,7 +4,7 @@ from desc.compute import get_profiles, get_transforms from desc.compute.utils import _compute as compute_fun -from desc.grid import QuadratureGrid +from desc.grid import LinearGrid from desc.utils import Timer from ..backend import jnp @@ -16,10 +16,20 @@ class EffectiveRipple(_Objective): """The effective ripple is a proxy for neoclassical transport. + The 3D geometry of the magnetic field in stellarators produces local magnetic + wells that lead to bad confinement properties with enhanced radial drift, + especially for trapped particles. Neoclassical (thermal) transport can become the + dominant transport channel in stellarators which are not optimized to reduce it. + The effective ripple is a proxy, measuring the effective modulation amplitude of the + magnetic field averaged along a magnetic surface, which can be used to optimize for + stellarators with improved confinement. + + References + ---------- + https://doi.org/10.1063/1.873749. Evaluation of 1/ν neoclassical transport in stellarators. V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. - https://doi.org/10.1063/1.873749. Parameters ---------- @@ -28,14 +38,13 @@ class EffectiveRipple(_Objective): target : {float, ndarray, callable}, optional Target value(s) of the objective. Only used if bounds is None. Must be broadcastable to Objective.dim_f. If a callable, should take a - single argument `rho` and return the desired value of the profile at those - locations. Defaults to ``bounds=(0,np.inf)`` + single argument ``rho`` and return the desired value of the profile at those + locations. Defaults to 0. bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. Both bounds must be broadcastable to Objective.dim_f - If a callable, each should take a single argument `rho` and return the + If a callable, each should take a single argument ``rho`` and return the desired bound (lower or upper) of the profile at those locations. - Defaults to ``bounds=(0, np.inf)`` weight : {float, ndarray}, optional Weighting to apply to the Objective, relative to other Objectives. Must be broadcastable to Objective.dim_f @@ -43,7 +52,7 @@ class EffectiveRipple(_Objective): Whether to compute the error in physical units or non-dimensionalize. normalize_target : bool, optional Whether target and bounds should be normalized before comparing to computed - values. If `normalize` is `True` and the target is in physical units, + values. If `normalize` is ``True`` and the target is in physical units, this should also be set to True. loss_function : {None, 'mean', 'min', 'max'}, optional Loss function to apply to the objective values once computed. This loss function @@ -86,7 +95,7 @@ class EffectiveRipple(_Objective): def __init__( self, eq, - target=None, + target=0, bounds=None, weight=1, normalize=True, @@ -101,21 +110,27 @@ def __init__( batch=True, name="Effective ripple", ): - if target is None and bounds is None: - bounds = (0, np.inf) + if bounds is not None: + target = None # Assign in build. self._grid_1dr = grid - # TODO: Do we need a 0d grid, just to compute R0 accurately? - self._grid_0d = grid if isinstance(grid, QuadratureGrid) else None self._constants = {"quad_weights": 1} self._dim_f = 1 self._rho = np.array([1.0]) # Assign here. self._alpha = alpha self._zeta = zeta - self._keys_1dr = ["iota", "iota_r", "<|grad(rho)|>", "min_tz |B|", "max_tz |B|"] - self._keys_0d = ["R0"] + # R0 should be evaluated on Quadrature grid, but it's just a constant + # factor, so it's not worth the memory of building the transforms. + self._keys_1dr = [ + "iota", + "iota_r", + "<|grad(rho)|>", + "min_tz |B|", + "max_tz |B|", + "R0", + ] self._keys = ["effective ripple"] self._hyperparameters = { "num_quad": num_quad, @@ -147,14 +162,14 @@ def build(self, use_jit=True, verbose=1): """ eq = self.things[0] - if self._grid_0d is None: - self._grid_0d = QuadratureGrid( - L=eq.L_grid, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP - ) if self._grid_1dr is None: - self._grid_1dr = self._grid_0d - self._dim_f = self._grid_1dr.num_rho - self._rho = self._grid_1dr.compress(self._grid_1dr.nodes[:, 0]) + self._rho = np.linspace(0.1, 1, 5) + self._grid_1dr = LinearGrid( + rho=self._rho, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP, sym=eq.sym + ) + else: + self._rho = self._grid_1dr.compress(self._grid_1dr.nodes[:, 0]) + self._dim_f = self._rho.size self._target, self._bounds = _parse_callable_target_bounds( self._target, self._bounds, self._rho ) @@ -164,28 +179,12 @@ def build(self, use_jit=True, verbose=1): print("Precomputing transforms") timer.start("Precomputing transforms") - if self._grid_1dr == self._grid_0d: - self._constants["transforms_0d"] = get_transforms( - self._keys_0d + self._keys_1dr, eq, self._grid_0d, use_jit - ) - self._constants["profiles_0d"] = get_profiles( - self._keys_0d + self._keys_1dr, eq, self._grid_0d, use_jit - ) - self._constants["transforms_1dr"] = self._constants["transforms_0d"] - self._constants["profiles_1dr"] = self._constants["profiles_0d"] - else: - self._constants["transforms_0d"] = get_transforms( - self._keys_0d, eq, self._grid_0d, use_jit - ) - self._constants["profiles_0d"] = get_profiles( - self._keys_0d, eq, self._grid_0d, use_jit - ) - self._constants["transforms_1dr"] = get_transforms( - self._keys_1dr, eq, self._grid_1dr, use_jit - ) - self._constants["profiles_1dr"] = get_profiles( - self._keys_1dr, eq, self._grid_1dr, use_jit - ) + self._constants["transforms_1dr"] = get_transforms( + self._keys_1dr, eq, self._grid_1dr + ) + self._constants["profiles_1dr"] = get_profiles( + self._keys_1dr, eq, self._grid_1dr + ) timer.stop("Precomputing transforms") if verbose > 1: @@ -212,33 +211,30 @@ def compute(self, params, constants=None): if constants is None: constants = self.constants eq = self.things[0] - data = compute_fun( - eq, - self._keys_0d, - params, - constants["transforms_0d"], - constants["profiles_0d"], - ) data = compute_fun( eq, self._keys_1dr, params, constants["transforms_1dr"], constants["profiles_1dr"], - data={key: data[key] for key in data if eq.is_0d(key)}, ) iota = self._grid_1dr.compress(data["iota"]) iota_r = self._grid_1dr.compress(data["iota_r"]) - grid = eq.rtz_grid( + grid = eq.get_rtz_grid( self._rho, self._alpha, self._zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf), iota=SplineProfile(iota, df=iota_r, knots=self._rho, name="iota", jnp=jnp), + params=params, ) - data = {key: data[key] for key in self._keys_0d} | { - key: grid.copy_data_from_other(data[key], self._grid_1dr) + data = { + key: ( + grid.copy_data_from_other(data[key], self._grid_1dr) + if key != "R0" + else data[key] + ) for key in self._keys_1dr } data = compute_fun( diff --git a/desc/plotting.py b/desc/plotting.py index b6837e9a88..5971c4992e 100644 --- a/desc/plotting.py +++ b/desc/plotting.py @@ -1118,8 +1118,9 @@ def plot_fsa( # noqa: C901 Axis to plot on. return_data : bool if True, return the data plotted as well as fig,ax - grid : Grid - Grid to compute name on. + grid : _Grid + Grid to compute name on. If provided, the parameters + ``rho``, ``M``, and ``N`` are ignored. **kwargs : dict, optional Specify properties of the figure, axis, and plot appearance e.g.:: diff --git a/desc/profiles.py b/desc/profiles.py index c31c490891..8fa62c7b2a 100644 --- a/desc/profiles.py +++ b/desc/profiles.py @@ -197,13 +197,13 @@ def to_mtanh( **kwargs, ) - def __call__(self, grid, params=None, dr=0, dt=0, dz=0, jitable=False): + def __call__(self, grid, params=None, dr=0, dt=0, dz=0): """Evaluate the profile at a given set of points.""" if not isinstance(grid, _Grid): grid = jnp.atleast_1d(jnp.asarray(grid)) if grid.ndim == 1: grid = jnp.array([grid, jnp.zeros_like(grid), jnp.zeros_like(grid)]).T - grid = Grid(grid, sort=False, jitable=jitable) + grid = Grid(grid, sort=False) return self.compute(grid, params, dr, dt, dz) def __repr__(self): diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 7698198c25..9ad325b736 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -6,7 +6,6 @@ from tests.test_plotting import tol_1d from desc.examples import get -from desc.objectives import EffectiveRipple, ObjectiveFunction @pytest.mark.unit @@ -16,7 +15,7 @@ def test_field_line_average(): rho = np.array([1]) alpha = np.array([0]) eq = get("DSHAPE") - grid = eq.rtz_grid( + grid = eq.get_rtz_grid( rho, alpha, np.linspace(0, 2 * np.pi, 20), @@ -32,7 +31,7 @@ def test_field_line_average(): # Otherwise, many toroidal transits are necessary to sample surface. eq = get("W7-X") - grid = eq.rtz_grid( + grid = eq.get_rtz_grid( rho, alpha, np.linspace(0, 40 * np.pi, 300), @@ -53,7 +52,7 @@ def test_effective_ripple(): """Test effective ripple with W7-X.""" eq = get("W7-X") rho = np.linspace(0, 1, 10) - grid = eq.rtz_grid( + grid = eq.get_rtz_grid( rho, np.array([0]), np.linspace(0, 20 * np.pi, 1000), @@ -65,16 +64,3 @@ def test_effective_ripple(): fig, ax = plt.subplots() ax.plot(rho, grid.compress(data["effective ripple"]), marker="o") return fig - - -@pytest.mark.unit -def test_ad_ripple(): - """Make sure we can differentiate.""" - eq = get("ESTELL") - eq.change_resolution(2, 2, 2, 4, 4, 4) - obj = ObjectiveFunction([EffectiveRipple(eq)]) - obj.build(verbose=0) - g = obj.grad(obj.x()) - assert not np.any(np.isnan(g)) - # FIXME: Want to ensure nonzero gradient in test. - print(np.count_nonzero(g)) diff --git a/tests/test_objective_funs.py b/tests/test_objective_funs.py index b315be5d18..8d7e1aa452 100644 --- a/tests/test_objective_funs.py +++ b/tests/test_objective_funs.py @@ -44,6 +44,7 @@ CoilLength, CoilsetMinDistance, CoilTorsion, + EffectiveRipple, Elongation, Energy, ForceBalance, @@ -2447,6 +2448,19 @@ def test_objective_no_nangrad_omnigenity(self, helicity): g = obj.grad(obj.x()) assert not np.any(np.isnan(g)), str(helicity) + @pytest.mark.unit + def test_objective_no_nangrad_effective_ripple(self): + """Make sure we can differentiate.""" + eq = get("ESTELL") + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(2, 2, 2, 4, 4, 4) + obj = ObjectiveFunction([EffectiveRipple(eq)]) + obj.build(verbose=0) + g = obj.grad(obj.x()) + assert not np.any(np.isnan(g)) + # FIXME: Want to ensure nonzero gradient in test. + print(np.count_nonzero(g)) + @pytest.mark.unit def test_asymmetric_normalization(): From 6827fcd84d479369d5378056a6c115759f1c34ce Mon Sep 17 00:00:00 2001 From: unalmis Date: Fri, 5 Jul 2024 14:47:16 -0500 Subject: [PATCH 058/112] Fix comment --- desc/compute/_neoclassical.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index f1b61e6d99..087b450aba 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -94,7 +94,7 @@ def _poloidal_mean(grid, f): @register_compute_fun( name="", - label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" + label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}}" " \\frac{d\\zeta}{|B^{\\zeta}|}", units="m / T", units_long="Meter / tesla", @@ -122,7 +122,7 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): @register_compute_fun( name="", - label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}" + label="\\int_{\\zeta_{\\mathrm{min}}}^{\\zeta_{\\mathrm{max}}}" " \\frac{d\\zeta}{|B^{\\zeta} \\sqrt g|}", units="1 / Wb", units_long="Inverse webers", @@ -153,9 +153,9 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): label=( # ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉 "\\epsilon^{3/2} = \\frac{\\pi}{8 \\sqrt{2}} " - "(\\frac{R_0}{\\langle \\vert\\nabla \\psi\\vert \\rangle})^2 " - "\\int d\\lambda \\lambda^{-2}B_0^{-1} " - "\\langle \\sum_j \\frac{H_j^2}{I_j} \\rangle" + "(R_0 / \\langle \\vert\\nabla \\psi\\vert \\rangle)^2 " + "\\int d\\lambda \\lambda^{-2} B_0^{-1} " + "\\langle \\sum_j H_j^2 / I_j \\rangle" ), units="~", units_long="None", From 0c995d1d2f04bdd44bd53d434a2da9de525634bc Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 7 Jul 2024 21:41:23 -0500 Subject: [PATCH 059/112] Merge with Clebsch branch --- desc/compute/_basis_vectors.py | 70 ++++++++++++++------------- desc/compute/_field.py | 2 - tests/inputs/master_compute_data.pkl | Bin 7778442 -> 7986008 bytes tests/test_compute_funs.py | 35 ++++++++------ 4 files changed, 55 insertions(+), 52 deletions(-) diff --git a/desc/compute/_basis_vectors.py b/desc/compute/_basis_vectors.py index 0ff9eb5022..016055cf08 100644 --- a/desc/compute/_basis_vectors.py +++ b/desc/compute/_basis_vectors.py @@ -38,7 +38,7 @@ def _b(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="e^rho", # ∇ρ is the same in DESC and field line coordinates. + name="e^rho", # ∇ρ is the same in (ρ,θ,ζ) and (ρ,α,ζ) coordinates. label="\\mathbf{e}^{\\rho}", units="m^{-1}", units_long="inverse meters", @@ -1007,7 +1007,7 @@ def _e_sup_theta_zz(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="e^zeta", # ∇ζ is the same in DESC and field line coordinates. + name="e^zeta", # ∇ζ is the same in (ρ,θ,ζ) and (ρ,α,ζ) coordinates. label="\\mathbf{e}^{\\zeta}", units="m^{-1}", units_long="inverse meters", @@ -3568,7 +3568,9 @@ def _e_sub_theta_rp(params, transforms, profiles, data, **kwargs): ) def _e_rho_az(params, transforms, profiles, data, **kwargs): # constant α and ζ - data["e_rho|a,z"] = data["e_rho"] - (data["e_alpha"].T * data["alpha_r"]).T + data["e_rho|a,z"] = ( + data["e_rho"] - data["e_alpha"] * data["alpha_r"][:, jnp.newaxis] + ) return data @@ -3587,7 +3589,7 @@ def _e_rho_az(params, transforms, profiles, data, **kwargs): ) def _e_alpha(params, transforms, profiles, data, **kwargs): # constant ρ and ζ - data["e_alpha"] = (data["e_theta"].T / data["alpha_t"]).T + data["e_alpha"] = data["e_theta"] / data["alpha_t"][:, jnp.newaxis] return data @@ -3607,9 +3609,9 @@ def _e_alpha(params, transforms, profiles, data, **kwargs): ) def _e_alpha_t(params, transforms, profiles, data, **kwargs): data["e_alpha_t"] = ( - (data["e_theta_t"].T * data["alpha_t"] + data["e_theta"].T * data["alpha_tt"]) - / data["alpha_t"] ** 2 - ).T + data["e_theta_t"] / data["alpha_t"][:, jnp.newaxis] + - data["e_theta"] * (data["alpha_tt"] / data["alpha_t"] ** 2)[:, jnp.newaxis] + ) return data @@ -3618,7 +3620,8 @@ def _e_alpha_t(params, transforms, profiles, data, **kwargs): label="\\partial_{\\zeta} \\mathbf{e}_{\\alpha}", units="m", units_long="meters", - description="Tangent vector along poloidal field line label, toroidal derivative", + description="Tangent vector along poloidal field line label, " + "derivative wrt DESC toroidal angle", dim=3, params=[], transforms={}, @@ -3628,9 +3631,9 @@ def _e_alpha_t(params, transforms, profiles, data, **kwargs): ) def _e_alpha_z(params, transforms, profiles, data, **kwargs): data["e_alpha_z"] = ( - (data["e_theta_z"].T * data["alpha_t"] - data["e_theta"].T * data["alpha_tz"]) - / data["alpha_t"] ** 2 - ).T + data["e_theta_z"] / data["alpha_t"][:, jnp.newaxis] + - data["e_theta"] * (data["alpha_tz"] / data["alpha_t"] ** 2)[:, jnp.newaxis] + ) return data @@ -3649,9 +3652,9 @@ def _e_alpha_z(params, transforms, profiles, data, **kwargs): data=["e_zeta", "e_alpha", "alpha_z"], ) def _e_zeta_ra(params, transforms, profiles, data, **kwargs): - # ∂ℓ/∂ζ (constant ρ and α) = ∂ℓ/∂ζ (constant ρ and θ) - # - ∂ℓ/∂α (constant ρ and ζ) * ∂α/∂ζ (constant ρ and θ) - data["e_zeta|r,a"] = data["e_zeta"] - (data["e_alpha"].T * data["alpha_z"]).T + data["e_zeta|r,a"] = ( + data["e_zeta"] - data["e_alpha"] * data["alpha_z"][:, jnp.newaxis] + ) return data @@ -3660,7 +3663,8 @@ def _e_zeta_ra(params, transforms, profiles, data, **kwargs): label="\\partial_{\\theta} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha})", units="m", units_long="meters", - description="Tangent vector along (collinear to) field line, poloidal derivative", + description="Tangent vector along (collinear to) field line, " + "derivative wrt DESC poloidal angle", dim=3, params=[], transforms={}, @@ -3669,11 +3673,9 @@ def _e_zeta_ra(params, transforms, profiles, data, **kwargs): data=["e_zeta_t", "e_alpha", "alpha_z", "e_alpha_t", "alpha_tz"], ) def _e_zeta_ra_t(params, transforms, profiles, data, **kwargs): - data["(e_zeta|r,a)_t"] = ( - data["e_zeta_t"] - - ( - data["e_alpha_t"].T * data["alpha_z"] + data["e_alpha"].T * data["alpha_tz"] - ).T + data["(e_zeta|r,a)_t"] = data["e_zeta_t"] - ( + data["e_alpha_t"] * data["alpha_z"][:, jnp.newaxis] + + data["e_alpha"] * data["alpha_tz"][:, jnp.newaxis] ) return data @@ -3693,7 +3695,7 @@ def _e_zeta_ra_t(params, transforms, profiles, data, **kwargs): data=["(e_zeta|r,a)_t", "alpha_t"], ) def _e_zeta_ra_a(params, transforms, profiles, data, **kwargs): - data["(e_zeta|r,a)_a"] = (data["(e_zeta|r,a)_t"].T / data["alpha_t"]).T + data["(e_zeta|r,a)_a"] = data["(e_zeta|r,a)_t"] / data["alpha_t"][:, jnp.newaxis] return data @@ -3702,7 +3704,8 @@ def _e_zeta_ra_a(params, transforms, profiles, data, **kwargs): label="\\partial_{\\zeta} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha})", units="m", units_long="meters", - description="Tangent vector along (collinear to) field line, toroidal derivative", + description="Tangent vector along (collinear to) field line, " + "derivative wrt DESC toroidal angle", dim=3, params=[], transforms={}, @@ -3711,17 +3714,15 @@ def _e_zeta_ra_a(params, transforms, profiles, data, **kwargs): data=["e_zeta_z", "e_alpha", "alpha_z", "e_alpha_z", "alpha_zz"], ) def _e_zeta_ra_z(params, transforms, profiles, data, **kwargs): - data["(e_zeta|r,a)_z"] = ( - data["e_zeta_z"] - - ( - data["e_alpha_z"].T * data["alpha_z"] + data["e_alpha"].T * data["alpha_zz"] - ).T + data["(e_zeta|r,a)_z"] = data["e_zeta_z"] - ( + data["e_alpha_z"] * data["alpha_z"][:, jnp.newaxis] + + data["e_alpha"] * data["alpha_zz"][:, jnp.newaxis] ) return data @register_compute_fun( - name="(e_zeta_z)|r,a", + name="(e_zeta|r,a)_z|r,a", label="\\partial_{\\zeta} (\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha}) " "|_{\\rho, \\alpha}", units="m", @@ -3735,8 +3736,9 @@ def _e_zeta_ra_z(params, transforms, profiles, data, **kwargs): data=["(e_zeta|r,a)_z", "(e_zeta|r,a)_a", "alpha_z"], ) def _e_zeta_z_ra(params, transforms, profiles, data, **kwargs): - data["(e_zeta_z)|r,a"] = ( - data["(e_zeta|r,a)_z"] - (data["(e_zeta|r,a)_a"].T * data["alpha_z"]).T + data["(e_zeta|r,a)_z|r,a"] = ( + data["(e_zeta|r,a)_z"] + - data["(e_zeta|r,a)_a"] * data["alpha_z"][:, jnp.newaxis] ) return data @@ -3761,7 +3763,7 @@ def _d_ell_d_zeta(params, transforms, profiles, data, **kwargs): @register_compute_fun( - name="(|e_zeta|_z)|r,a", + name="|e_zeta|r,a|_z|r,a", label="\\partial_{\\zeta} |\\mathbf{e}_{\\zeta} |_{\\rho, \\alpha}| " "|_{\\rho, \\alpha}", units="m", @@ -3772,10 +3774,10 @@ def _d_ell_d_zeta(params, transforms, profiles, data, **kwargs): transforms={}, profiles=[], coordinates="rtz", - data=["|e_zeta|r,a|", "(e_zeta_z)|r,a", "e_zeta|r,a"], + data=["|e_zeta|r,a|", "(e_zeta|r,a)_z|r,a", "e_zeta|r,a"], ) def _d_ell_d_zeta_z(params, transforms, profiles, data, **kwargs): - data["(|e_zeta|_z)|r,a"] = ( - dot(data["(e_zeta_z)|r,a"], data["e_zeta|r,a"]) / data["|e_zeta|r,a|"] + data["|e_zeta|r,a|_z|r,a"] = ( + dot(data["(e_zeta|r,a)_z|r,a"], data["e_zeta|r,a"]) / data["|e_zeta|r,a|"] ) return data diff --git a/desc/compute/_field.py b/desc/compute/_field.py index ca148b1f98..eef1354e3d 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -2371,8 +2371,6 @@ def _B_mag_alpha(params, transforms, profiles, data, **kwargs): data=["|B|_z", "|B|_a", "alpha_z"], ) def _B_mag_z_constant_rho_alpha(params, transforms, profiles, data, **kwargs): - # ∂|B|/∂ζ (constant ρ and α) = ∂|B|/∂ζ (constant ρ and θ) - # - ∂|B|/∂α (constant ρ and ζ) * ∂α/∂ζ (constant ρ and θ) data["|B|_z|r,a"] = data["|B|_z"] - data["|B|_a"] * data["alpha_z"] return data diff --git a/tests/inputs/master_compute_data.pkl b/tests/inputs/master_compute_data.pkl index 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zwbli`IevF~vd|qfd%E{w!ymL3hRu1C#~-#{i5LUj8q!q@Q#xgxXG+Vv%8>Q^lb$T8 z8)W@{gC|Ss23dC16SFM-D>JhgYuwQSrw#lm^JF+9Y=?Cpum<|nprX}>^b5@Ca=_YD- z+itz=EFlm!?XjLSzI3OY)D^Ne@APCzT_NkfS3Fr#SIFA&SL-=bF?7}u`qSYBtAEtK zoyfjEE{lO)`;s*vY8DRlts^@#`3zN}dF$pniwRY4+v~C<6$#Nf(Mzd9sawHWN2qXk dAB~CFyD&`DW{m&< diff --git a/tests/test_compute_funs.py b/tests/test_compute_funs.py index fd84579d79..b16dfb4752 100644 --- a/tests/test_compute_funs.py +++ b/tests/test_compute_funs.py @@ -11,7 +11,7 @@ from desc.compute import data_index, rpz2xyz_vec from desc.compute.utils import dot from desc.equilibrium import Equilibrium -from desc.equilibrium.coords import rtz_grid +from desc.equilibrium.coords import get_rtz_grid from desc.examples import get from desc.geometry import ( FourierPlanarCurve, @@ -1271,13 +1271,17 @@ def test_compute_everything(): # Max resolution of master_compute_data.pkl limited by GitHub file # size cap at 100 mb, so can't hit suggested resolution for some things. warnings.filterwarnings("ignore", category=ResolutionWarning) - this_branch_data[p] = things[p].compute( - list(data_index[p].keys()), **grid.get(p, {}) - ) + names = { + name + for name in data_index[p] + # Skip these quantities as they should be covered in other tests. + if not data_index[p][name]["source_grid_requirement"] + } + this_branch_data[p] = things[p].compute(list(names), **grid.get(p, {})) # make sure we can compute everything - assert this_branch_data[p].keys() == data_index[p].keys(), ( + assert this_branch_data[p].keys() == names, ( f"Parameterization: {p}." - f" Can't compute {data_index[p].keys() - this_branch_data[p].keys()}." + f" Can't compute {names - this_branch_data[p].keys()}." ) # compare against master branch for name in this_branch_data[p]: @@ -1693,12 +1697,12 @@ def test_surface_equilibrium_geometry(): ) -@pytest.mark.xfail(reason="Cause of bug not yet known.") @pytest.mark.unit def test_parallel_grad(): """Test geometric and physical methods of computing parallel gradients agree.""" eq = get("W7-X") - eq.change_resolution(2, 2, 2, 4, 4, 4) + with pytest.warns(UserWarning, match="Reducing radial"): + eq.change_resolution(2, 2, 2, 4, 4, 4) data = eq.compute( [ "e_zeta|r,a", @@ -1706,18 +1710,17 @@ def test_parallel_grad(): "B^zeta", "|B|_z|r,a", "grad(|B|)", - "(|e_zeta|_z)|r,a", + "|e_zeta|r,a|_z|r,a", "B^zeta_z|r,a", "|B|", - ] + ], ) np.testing.assert_allclose(data["e_zeta|r,a"], (data["B"].T / data["B^zeta"]).T) np.testing.assert_allclose( data["|B|_z|r,a"], dot(data["grad(|B|)"], data["e_zeta|r,a"]) ) - # FIXME: Don't know why below test fails. np.testing.assert_allclose( - data["(|e_zeta|_z)|r,a"], + data["|e_zeta|r,a|_z|r,a"], data["|B|_z|r,a"] / np.abs(data["B^zeta"]) - data["|B|"] * data["B^zeta_z|r,a"] @@ -1730,7 +1733,7 @@ def test_parallel_grad(): def test_parallel_grad_fd(DummyStellarator): """Test that the parallel gradients match with numerical gradients.""" eq = load(load_from=str(DummyStellarator["output_path"]), file_format="hdf5") - grid = rtz_grid( + grid = get_rtz_grid( eq, 0.5, 0, @@ -1743,7 +1746,7 @@ def test_parallel_grad_fd(DummyStellarator): "|B|", "|B|_z|r,a", "|e_zeta|r,a|", - "(|e_zeta|_z)|r,a", + "|e_zeta|r,a|_z|r,a", "B^zeta", "B^zeta_z|r,a", ], @@ -1759,10 +1762,10 @@ def test_parallel_grad_fd(DummyStellarator): ) fd = np.convolve(data["|e_zeta|r,a|"], FD_COEF_1_4, "same") / dz np.testing.assert_allclose( - data["(|e_zeta|_z)|r,a"][2:-2], + data["|e_zeta|r,a|_z|r,a"][2:-2], fd[2:-2], rtol=1e-2, - atol=1e-2 * np.mean(np.abs(data["(|e_zeta|_z)|r,a"])), + atol=1e-2 * np.mean(np.abs(data["|e_zeta|r,a|_z|r,a"])), ) fd = np.convolve(data["B^zeta"], FD_COEF_1_4, "same") / dz np.testing.assert_allclose( From 97e081ff371308a07efd31f325f54eb70824ed0e Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 11 Jul 2024 19:08:36 -0400 Subject: [PATCH 060/112] Leftover merge conflicts --- desc/compute/_basis_vectors.py | 4 ++-- docs/adding_compute_funs.rst | 2 +- tests/inputs/master_compute_data_xyz.pkl | Bin 7703174 -> 7916070 bytes 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/desc/compute/_basis_vectors.py b/desc/compute/_basis_vectors.py index 366c106541..d4acb6270e 100644 --- a/desc/compute/_basis_vectors.py +++ b/desc/compute/_basis_vectors.py @@ -3556,7 +3556,7 @@ def _e_zeta_z_ra(params, transforms, profiles, data, **kwargs): units="m", units_long="meters", description="Differential length along field line", - dim=3, + dim=1, params=[], transforms={}, profiles=[], @@ -3575,7 +3575,7 @@ def _d_ell_d_zeta(params, transforms, profiles, data, **kwargs): units="m", units_long="meters", description="Differential length along field line, derivative along field line", - dim=3, + dim=1, params=[], transforms={}, profiles=[], diff --git a/docs/adding_compute_funs.rst b/docs/adding_compute_funs.rst index 6cf3cd982a..1243d8fbaf 100644 --- a/docs/adding_compute_funs.rst +++ b/docs/adding_compute_funs.rst @@ -149,7 +149,7 @@ if ``False`` is printed, then the limit of the quantity does not evaluate as fin The second step is to run the ``test_compute_everything`` test located in the ``tests/test_compute_everything.py`` file. -This can be done with the command :console:`pytest -k test_compute_everything tests/test_compute_everything.py`. +This can be done with the command :console:`pytest tests/test_compute_everything.py`. This test is a regression test to ensure that compute quantities in each new update of DESC do not differ significantly from previous versions of DESC. 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zo+q5~VBSAg>UqK$5023BM(2qn;r9v&y_W+)@sqS^y)%F;yUWQ#9o-Fn$jUdvfIoki z)9C7G_T7-~#r0(oNDHf!Rrf>pjeDHso>Jy*b}l3bnw=G@t2P4E4R)P39848nJI@m5 zPD|zqr9h2Tvg7AWA2Ltq1KYT&MXM(CfdXB7FB3@U11TSDa2k@VEuH{e9~AOdt7M@H ztnx;aUPY(^^OkSY^SCN#pI_+ggch(XKiT5k>uLezAJYWGsA$1x0WBPBafSp|$~HT; z-OgeRPx%7m6HCzV|Aq_Z=yanfSP#r>>sTU+H=3 z>Y4WxNI%w_IC353k~KS>4c0_2OVQ=FxV< yGAbjVo_Jm-qaO0`O3cIPik7b{`Ow;U09<8DPZYqy7&PgseLN From a28daded104ab56ee11240101de843553050a9f8 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sat, 20 Jul 2024 02:11:00 -0400 Subject: [PATCH 061/112] Remove no longer needed jitable argument in get_profiles --- desc/objectives/_neoclassical.py | 2 +- tests/test_objective_funs.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 117332918c..8cec73efcf 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -242,7 +242,7 @@ def compute(self, params, constants=None): self._keys, params, get_transforms(self._keys, eq, grid, jitable=True), - get_profiles(self._keys, eq, grid, jitable=True), + get_profiles(self._keys, eq, grid), data=data, **self._hyperparameters, ) diff --git a/tests/test_objective_funs.py b/tests/test_objective_funs.py index e5dac84e61..0a647f0ea2 100644 --- a/tests/test_objective_funs.py +++ b/tests/test_objective_funs.py @@ -2284,8 +2284,8 @@ class TestObjectiveNaNGrad: GenericObjective, LinearObjectiveFromUser, ObjectiveFromUser, - # TODO: add Omnigenity objective (see GH issue #943) Omnigenity, + EffectiveRipple, ] other_objectives = list(set(objectives) - set(specials)) From 10ac679a440c0a0017d0b1006c0c1a052b3b3103 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 24 Jul 2024 21:14:56 -0400 Subject: [PATCH 062/112] Update master compute data --- tests/inputs/master_compute_data_rpz.pkl | Bin 7991326 -> 8080723 bytes tests/inputs/master_compute_data_xyz.pkl | Bin 8000149 -> 8005479 bytes tests/test_compute_everything.py | 2 +- 3 files 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zH6i2`M~$5Z*lT)97H?T3j41c5c~|BST<|3W{cGNmzgvtW?hC4U)7GfGT4GIRLX8hy zCctDeYw*gf=o~Z=1sf02jCA-B#Nwv)T~Hk4TYI|^LvoEf)hQIRP&C)8eBqF09(R8lpRZ~ W!O`m@fVh%zyBc2;Zd$Y` Date: Wed, 24 Jul 2024 21:39:39 -0400 Subject: [PATCH 063/112] Update master compute data again --- tests/inputs/master_compute_data_rpz.pkl | Bin 8080723 -> 8080732 bytes tests/inputs/master_compute_data_xyz.pkl | Bin 8005479 -> 8005479 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/tests/inputs/master_compute_data_rpz.pkl b/tests/inputs/master_compute_data_rpz.pkl index ac5a8d7328a769ad4a9b145097597422dec34610..169f45ed268ba1b5bccf52139a373062df8901f7 100644 GIT binary patch delta 38618 zcmdsg2Y8fK*1uDdnaNB@d((SQ2qhs%fRM}(LJtsnizx$vRFcri2nlvHDhT0P5J42# zr3DqP9Z=ClQ4$fQgeHQ32tif_5%`~b@7ojnP}yCd|MxuKGI`4_r=Q!ju@PCI`Yo*1D$$KkbCG+%T0t6Aish%9Y0hc`V6@8Xe* z$&?;(nq8c>J_$#7X)bd3u@|&Qxz|>G33Hp@(wyL48-0Txwd2b$s#8g4SOnfNTyvDe zy?1M2p~bJ0fu|krv!5eG!lU}>_H!OC73GCf3-YExEr$RJ*O3wh4|88#!&Y!5eoQAjm 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zM}8}ZU!MQ5_Hqw)Ifxv(=CY78H(Za1JYRajwUDtKCWFYTCZUd*cf8{zOb2{G>9{KCvUF#)Iru1O2s~A$yut{bNa3Y#)uY1)RM?~=Z)-;v zwH4U%yuwc(V-4dczEEVYIchLWBvGCqyKx~aSxgJyVKJX82ehR7yA3q8t_*Q;+O|DU98a!$xLw#xvaHioCJ$>1@ z_&FN!9jl|JjnQW6q2io8UPX}h9?aj5LIEM#|JZ8wrCMO+C2PBMDC>)a?I%e}QO& From 5d61f58c67cf3c5fb0bde7e514e59aa9fbe78c42 Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 24 Jul 2024 22:18:56 -0400 Subject: [PATCH 064/112] Only get profiles in build of objective --- desc/compute/_neoclassical.py | 5 +---- desc/objectives/_neoclassical.py | 11 +++++------ 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 087b450aba..3b5464edf6 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -179,10 +179,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): ], resolution_requirement="z", # and poloidal if near rational surfaces source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, - num_quad=( - "int : Resolution for quadrature of bounce integrals. Default is 31, " - "which gets sufficient convergence, so higher values are likely unnecessary." - ), + num_quad="int : Resolution for quadrature of bounce integrals. Default is 31.", num_pitch=( "int : Resolution for quadrature over velocity coordinate, preferably odd. " "Default is 125. Effective ripple will look smoother at high values. " diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 8cec73efcf..bdea5ab143 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -74,8 +74,7 @@ class EffectiveRipple(_Objective): For axisymmetric devices only one toroidal transit is necessary. Otherwise, more toroidal transits will give more accurate result, with diminishing returns. num_quad : int - Resolution for quadrature of bounce integrals. Default is 31, - which gets sufficient convergence, so higher values are likely unnecessary. + Resolution for quadrature of bounce integrals. Default is 31. num_pitch : int Resolution for quadrature over velocity coordinate, preferably odd. Default is 99. Effective ripple will look smoother at high values. @@ -182,8 +181,8 @@ def build(self, use_jit=True, verbose=1): self._constants["transforms_1dr"] = get_transforms( self._keys_1dr, eq, self._grid_1dr ) - self._constants["profiles_1dr"] = get_profiles( - self._keys_1dr, eq, self._grid_1dr + self._constants["profiles"] = get_profiles( + self._keys_1dr + self._keys, eq, self._grid_1dr ) timer.stop("Precomputing transforms") @@ -216,7 +215,7 @@ def compute(self, params, constants=None): self._keys_1dr, params, constants["transforms_1dr"], - constants["profiles_1dr"], + constants["profiles"], ) iota = self._grid_1dr.compress(data["iota"]) iota_r = self._grid_1dr.compress(data["iota_r"]) @@ -242,7 +241,7 @@ def compute(self, params, constants=None): self._keys, params, get_transforms(self._keys, eq, grid, jitable=True), - get_profiles(self._keys, eq, grid), + constants["profiles"], data=data, **self._hyperparameters, ) From 68efde6c3a8c1870476d0d95527c875fc49fc538 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 25 Jul 2024 15:43:49 -0400 Subject: [PATCH 065/112] Add num_wells parameter to increase performance --- desc/compute/_neoclassical.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 3b5464edf6..7aeae2d50c 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -187,6 +187,13 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "between neighboring surfaces, increasing num_pitch will smooth the profile)." ), batch="bool : Whether to vectorize part of the computation. Default is true.", + num_wells=( + "int : Maximum number of wells to detect for each pitch and field line. " + "Default is to detect all wells, but due to limitations in JAX this option " + "may consume more memory. Specifying a number that tightly upper bounds " + "the number of wells will increase performance. " + "As a reference, there are typically <= 5 wells per toroidal transit." + ), # Some notes on choosing the resolution hyperparameters: # The default settings above were chosen such that the effective ripple profile on # the W7-X stellarator looks similar to the profile computed at higher resolution, @@ -216,6 +223,7 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ batch = kwargs.get("batch", True) + num_wells = kwargs.get("size", None) g = transforms["grid"].source_grid bounce_integrate, _ = bounce_integral( data["B^zeta"], @@ -242,9 +250,13 @@ def d_ripple(pitch): # Note (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) where B₀ has units of λ⁻¹. # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. H = bounce_integrate( - dH, data["|grad(rho)|"] * data["kappa_g"], pitch, batch=batch + dH, + data["|grad(rho)|"] * data["kappa_g"], + pitch, + batch=batch, + num_wells=num_wells, ) - I = bounce_integrate(dI, [], pitch, batch=batch) + I = bounce_integrate(dI, [], pitch, batch=batch, num_wells=num_wells) return pitch * jnp.sum(safediv(H**2, I), axis=-1) # The integrand is continuous and likely poorly approximated by a polynomial. From 5fb62bfb406e424bf643ba5a4b68aa8f2458c167 Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 25 Jul 2024 18:11:26 -0400 Subject: [PATCH 066/112] Add num_wells to objective --- desc/objectives/_neoclassical.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index bdea5ab143..5312f27b00 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -82,6 +82,14 @@ class EffectiveRipple(_Objective): between neighboring surfaces, increasing num_pitch will smooth the profile). batch : bool Whether to vectorize part of the computation. Default is true. + num_wells : int + Maximum number of wells to detect for each pitch and field line. + Default is to detect all wells, but due to limitations in JAX this option + may consume more memory. Specifying a number that tightly upper bounds + the number of wells will increase performance. + As a reference, there are typically <= 5 wells per toroidal transit. + There exist utilities to plot the field line with the bounce points + to see how many wells there are. name : str, optional Name of the objective function. @@ -107,6 +115,7 @@ def __init__( num_quad=31, num_pitch=99, batch=True, + num_wells=None, name="Effective ripple", ): if bounds is not None: @@ -135,6 +144,7 @@ def __init__( "num_quad": num_quad, "num_pitch": num_pitch, "batch": batch, + "num_wells": num_wells, } super().__init__( From a603df2411491b4434b1dccf44fe614d3b3f43df Mon Sep 17 00:00:00 2001 From: unalmis Date: Thu, 25 Jul 2024 21:13:05 -0400 Subject: [PATCH 067/112] Add num_wells as static variables to compute fun --- desc/compute/_neoclassical.py | 4 ++-- desc/compute/_profiles.py | 10 +++++----- devtools/dev-requirements_conda.yml | 2 +- docs/notebooks/tutorials/nae_constraint.ipynb | 2 +- requirements.txt | 2 +- requirements_conda.yml | 2 +- tests/inputs/master_compute_data_rpz.pkl | Bin 8080732 -> 8075396 bytes tests/inputs/master_compute_data_xyz.pkl | Bin 8005479 -> 8000161 bytes 8 files changed, 11 insertions(+), 11 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 7aeae2d50c..56ed761a11 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -186,7 +186,6 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "(If computed on many flux surfaces and micro oscillation is seen " "between neighboring surfaces, increasing num_pitch will smooth the profile)." ), - batch="bool : Whether to vectorize part of the computation. Default is true.", num_wells=( "int : Maximum number of wells to detect for each pitch and field line. " "Default is to detect all wells, but due to limitations in JAX this option " @@ -194,6 +193,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "the number of wells will increase performance. " "As a reference, there are typically <= 5 wells per toroidal transit." ), + batch="bool : Whether to vectorize part of the computation. Default is true.", # Some notes on choosing the resolution hyperparameters: # The default settings above were chosen such that the effective ripple profile on # the W7-X stellarator looks similar to the profile computed at higher resolution, @@ -214,7 +214,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): # required for sufficient coverage of the surface. This requires many knots to # for the spline of the magnetic field to capture fine ripples in a large interval. ) -@partial(jit, static_argnames=["num_quad", "num_pitch", "batch"]) +@partial(jit, static_argnames=["num_quad", "num_pitch", "num_wells", "batch"]) def _effective_ripple(params, transforms, profiles, data, **kwargs): """https://doi.org/10.1063/1.873749. diff --git a/desc/compute/_profiles.py b/desc/compute/_profiles.py index a962fefb49..db20298f57 100644 --- a/desc/compute/_profiles.py +++ b/desc/compute/_profiles.py @@ -9,7 +9,7 @@ expensive computations. """ -from quadax import cumulative_trapezoid +from quadax import cumulative_simpson from scipy.constants import elementary_charge, mu_0 from desc.backend import cond, jnp @@ -142,10 +142,10 @@ def _chi_r(params, transforms, profiles, data, **kwargs): resolution_requirement="r", ) def _chi(params, transforms, profiles, data, **kwargs): - chi_r = transforms["grid"].compress(data["chi_r"]) - # TODO: switch to cumulative_simpson once added to quadax. - chi = cumulative_trapezoid( - chi_r, transforms["grid"].compress(data["rho"]), initial=0 + chi = cumulative_simpson( + y=transforms["grid"].compress(data["chi_r"]), + x=transforms["grid"].compress(data["rho"]), + initial=0, ) data["chi"] = transforms["grid"].expand(chi) return data diff --git a/devtools/dev-requirements_conda.yml b/devtools/dev-requirements_conda.yml index 4549d80347..1c1d97a2dd 100644 --- a/devtools/dev-requirements_conda.yml +++ b/devtools/dev-requirements_conda.yml @@ -21,7 +21,7 @@ dependencies: - orthax - plotly >= 5.16, < 6.0 - pylatexenc >= 2.0, < 3.0 - - quadax >= 0.2.1 + - quadax >= 0.2.2 # building the docs - sphinx-github-style >= 1.0, < 2.0 # testing and benchmarking diff --git a/docs/notebooks/tutorials/nae_constraint.ipynb b/docs/notebooks/tutorials/nae_constraint.ipynb index 3527ed6a79..f526a14f1e 100644 --- a/docs/notebooks/tutorials/nae_constraint.ipynb +++ b/docs/notebooks/tutorials/nae_constraint.ipynb @@ -471,7 +471,7 @@ " FixPsi(eq_NAE),\n", ")\n", "\n", - "# Alternatively, we can use the util function: \n", + "# Alternatively, we can use the util function:\n", "constraints = get_NAE_constraints(eq_NAE, qsc_eq, order=1)\n", "for c in constraints:\n", " print(c)\n", diff --git a/requirements.txt b/requirements.txt index 714cde3ab0..8602765446 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,6 +11,6 @@ orthax plotly >= 5.16, < 6.0 psutil pylatexenc >= 2.0, < 3.0 -quadax >= 0.2.1 +quadax >= 0.2.2 scipy >= 1.7.0, < 2.0.0 termcolor diff --git a/requirements_conda.yml 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zgurDh>zC8|w&0pz65YAzIRd8;%Uf&ZeeyWN4Nf>7Vs&86Hm5cvAU#a=q(i$hkL Date: Thu, 25 Jul 2024 22:03:59 -0400 Subject: [PATCH 068/112] Use kwargs in simpson integral to match API change in quadax from last commit --- desc/compute/_neoclassical.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 56ed761a11..57aad39c84 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -112,8 +112,8 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) L_ra = simpson( - jnp.reciprocal(data["B^zeta"]).reshape(shape), - jnp.reshape(g.nodes[:, 2], shape), + y=jnp.reciprocal(data["B^zeta"]).reshape(shape), + x=jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) data[""] = g.expand(jnp.abs(_poloidal_mean(g, L_ra))) @@ -140,8 +140,8 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): g = transforms["grid"].source_grid shape = (g.num_rho, g.num_alpha, g.num_zeta) G_ra = simpson( - jnp.reciprocal(data["B^zeta"] * data["sqrt(g)"]).reshape(shape), - jnp.reshape(g.nodes[:, 2], shape), + y=jnp.reciprocal(data["B^zeta"] * data["sqrt(g)"]).reshape(shape), + x=jnp.reshape(g.nodes[:, 2], shape), axis=-1, ) data[""] = g.expand(jnp.abs(_poloidal_mean(g, G_ra))) @@ -265,8 +265,8 @@ def d_ripple(pitch): g, data["min_tz |B|"], data["max_tz |B|"], kwargs.get("num_pitch", 125) ) ripple = simpson( - _poloidal_mean(g, imap(d_ripple, pitch).reshape(-1, g.num_rho, g.num_alpha)), - pitch, + y=_poloidal_mean(g, imap(d_ripple, pitch).reshape(-1, g.num_rho, g.num_alpha)), + x=pitch, axis=0, ) data["effective ripple"] = ( From a3e1ba4a3a3549af83750df8d2635b92074993f6 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sat, 17 Aug 2024 22:57:21 -0400 Subject: [PATCH 069/112] Take mean later since doesn't matter for speed and easier to do multiple alpha in general --- desc/compute/_neoclassical.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 57aad39c84..d9736116b0 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -264,16 +264,12 @@ def d_ripple(pitch): pitch = _get_pitch( g, data["min_tz |B|"], data["max_tz |B|"], kwargs.get("num_pitch", 125) ) - ripple = simpson( - y=_poloidal_mean(g, imap(d_ripple, pitch).reshape(-1, g.num_rho, g.num_alpha)), - x=pitch, - axis=0, - ) + ripple = simpson(y=imap(d_ripple, pitch).squeeze(axis=1), x=pitch, axis=0) data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) * (data["max_tz |B|"] * data["R0"] / data["<|grad(rho)|>"]) ** 2 - * g.expand(ripple) + * g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) / data[""] ) return data From 4e9ebb9f242a6713225bdd5f628a4411af4c0dd5 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 2 Sep 2024 04:07:22 -0400 Subject: [PATCH 070/112] Update effective ripple computation for all changes in upstream branches --- desc/backend.py | 4 +- desc/coils.py | 3 +- desc/compute/_basis_vectors.py | 2 +- desc/compute/_curve.py | 2 +- desc/compute/_equil.py | 2 +- desc/compute/_field.py | 2 +- desc/compute/_geometry.py | 2 +- desc/compute/_metric.py | 2 +- desc/compute/_neoclassical.py | 178 ++++++++++------------- desc/compute/_omnigenity.py | 2 +- desc/compute/_profiles.py | 2 +- desc/compute/_stability.py | 2 +- desc/compute/geom_utils.py | 2 +- desc/compute/utils.py | 108 -------------- desc/integrals/bounce_integral.py | 69 ++++++--- desc/integrals/bounce_utils.py | 4 +- desc/integrals/interp_utils.py | 2 +- desc/integrals/singularities.py | 3 +- desc/objectives/_coils.py | 3 +- desc/objectives/_geometry.py | 3 +- desc/optimize/bound_utils.py | 3 +- desc/optimize/tr_subproblems.py | 5 +- desc/plotting.py | 6 +- desc/utils.py | 119 ++++++++++++++- tests/baseline/test_effective_ripple.png | Bin 14173 -> 13922 bytes tests/test_axis_limits.py | 3 +- tests/test_compute_funs.py | 2 +- tests/test_derivatives.py | 3 +- tests/test_integrals.py | 33 ++++- tests/test_neoclassical.py | 22 +-- tests/test_optimizer.py | 7 +- 31 files changed, 310 insertions(+), 290 deletions(-) diff --git a/desc/backend.py b/desc/backend.py index 5538c79a8c..5aab199a2a 100644 --- a/desc/backend.py +++ b/desc/backend.py @@ -336,7 +336,7 @@ def root( This routine may be used on over or under-determined systems, in which case it will solve it in a least squares / least norm sense. """ - from desc.compute.utils import safenorm + from desc.utils import safenorm if fixup is None: fixup = lambda x, *args: x @@ -427,7 +427,7 @@ def tangent_solve(g, y): trapezoid = np.trapezoid if hasattr(np, "trapezoid") else np.trapz - def imap(f, xs, batch_size=None, in_axes=0, out_axes=0): + def imap(f, xs, *, batch_size=None, in_axes=0, out_axes=0): """Generalizes jax.lax.map; uses numpy.""" if not isinstance(xs, np.ndarray): raise NotImplementedError( diff --git a/desc/coils.py b/desc/coils.py index f184365918..5c1f4d222a 100644 --- a/desc/coils.py +++ b/desc/coils.py @@ -19,7 +19,6 @@ from desc.compute import get_params, rpz2xyz, rpz2xyz_vec, xyz2rpz, xyz2rpz_vec from desc.compute.geom_utils import reflection_matrix from desc.compute.utils import _compute as compute_fun -from desc.compute.utils import safenorm from desc.geometry import ( FourierPlanarCurve, FourierRZCurve, @@ -29,7 +28,7 @@ from desc.grid import LinearGrid from desc.magnetic_fields import _MagneticField from desc.optimizable import Optimizable, OptimizableCollection, optimizable_parameter -from desc.utils import equals, errorif, flatten_list, warnif +from desc.utils import equals, errorif, flatten_list, safenorm, warnif @jit diff --git a/desc/compute/_basis_vectors.py b/desc/compute/_basis_vectors.py index 8fca1346d2..72803a1d7a 100644 --- a/desc/compute/_basis_vectors.py +++ b/desc/compute/_basis_vectors.py @@ -11,8 +11,8 @@ from desc.backend import jnp +from ..utils import cross, dot, safediv from .data_index import register_compute_fun -from .utils import cross, dot, safediv @register_compute_fun( diff --git a/desc/compute/_curve.py b/desc/compute/_curve.py index 2e96e7a767..f8e4bbeb8c 100644 --- a/desc/compute/_curve.py +++ b/desc/compute/_curve.py @@ -2,9 +2,9 @@ from desc.backend import jnp, sign +from ..utils import cross, dot, safenormalize from .data_index import register_compute_fun from .geom_utils import rotation_matrix, rpz2xyz, rpz2xyz_vec, xyz2rpz, xyz2rpz_vec -from .utils import cross, dot, safenormalize @register_compute_fun( diff --git a/desc/compute/_equil.py b/desc/compute/_equil.py index e1cda119c8..b1e9142253 100644 --- a/desc/compute/_equil.py +++ b/desc/compute/_equil.py @@ -15,8 +15,8 @@ from desc.backend import jnp from ..integrals.surface_integral import surface_averages +from ..utils import cross, dot, safediv, safenorm from .data_index import register_compute_fun -from .utils import cross, dot, safediv, safenorm @register_compute_fun( diff --git a/desc/compute/_field.py b/desc/compute/_field.py index a5728d17ef..97cb44515f 100644 --- a/desc/compute/_field.py +++ b/desc/compute/_field.py @@ -19,8 +19,8 @@ surface_max, surface_min, ) +from ..utils import cross, dot, safediv, safenorm from .data_index import register_compute_fun -from .utils import cross, dot, safediv, safenorm @register_compute_fun( diff --git a/desc/compute/_geometry.py b/desc/compute/_geometry.py index 139f91f537..662413501b 100644 --- a/desc/compute/_geometry.py +++ b/desc/compute/_geometry.py @@ -12,8 +12,8 @@ from desc.backend import jnp from ..integrals.surface_integral import line_integrals, surface_integrals +from ..utils import cross, dot, safenorm from .data_index import register_compute_fun -from .utils import cross, dot, safenorm @register_compute_fun( diff --git a/desc/compute/_metric.py b/desc/compute/_metric.py index ceb6703386..ed4ea48145 100644 --- a/desc/compute/_metric.py +++ b/desc/compute/_metric.py @@ -14,8 +14,8 @@ from desc.backend import jnp from ..integrals.surface_integral import surface_averages +from ..utils import cross, dot, safediv, safenorm from .data_index import register_compute_fun -from .utils import cross, dot, safediv, safenorm @register_compute_fun( diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index ec74f11d16..beaed9593e 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -11,14 +11,15 @@ from functools import partial -from orthax.legendre import leggauss from quadax import simpson -from desc.backend import imap, jit, jnp +from desc.backend import jit, jnp +from ..integrals.bounce_integral import Bounce1D from ..integrals.bounce_utils import get_pitch_inv +from ..integrals.quad_utils import leggauss_lob +from ..utils import map2, safediv from .data_index import register_compute_fun -from .utils import safediv def _vec_quadax(quad, **kwargs): @@ -45,51 +46,24 @@ def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): return vec_quad -def _get_pitch(grid, min_B, max_B, num, for_adaptive=False): - """Get points for quadrature over velocity coordinate. - - Parameters - ---------- - grid : Grid - The grid on which data is computed. - min_B : jnp.ndarray - Minimum |B| value. - max_B : jnp.ndarray - Maximum |B| value. - num : int - Number of values to uniformly space in between. - for_adaptive : bool - Whether to return just the points useful for an adaptive quadrature. - - Returns - ------- - pitch : Array - Pitch values in the desired shape to use in compute methods. - - """ - min_B = grid.compress(min_B) - max_B = grid.compress(max_B) - if for_adaptive: - pitch = jnp.reciprocal(jnp.stack([max_B, min_B], axis=-1))[:, jnp.newaxis] - assert pitch.shape == (grid.num_rho, 1, 2) - else: - pitch = get_pitch_inv(min_B, max_B, num) - pitch = jnp.broadcast_to( - pitch[..., jnp.newaxis], (pitch.shape[0], grid.num_rho, grid.num_alpha) - ).reshape(pitch.shape[0], grid.num_rho * grid.num_alpha) - return pitch - - def _poloidal_mean(grid, f): """Integrate f over poloidal angle and divide by 2π.""" - assert f.shape[-1] == grid.num_poloidal + assert f.shape[0] == grid.num_poloidal if grid.num_poloidal == 1: - return jnp.squeeze(f, axis=-1) + return f.squeeze(axis=0) if not hasattr(grid, "spacing"): - return jnp.mean(f, axis=-1) + return f.mean(axis=0) assert grid.is_meshgrid dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") - return f @ dp / jnp.sum(dp) + return jnp.dot(f.T, dp) / jnp.sum(dp) + + +def _get_pitch_inv(grid, data, num_pitch): + # TODO: Try Gauss-Chebyshev quadrature after automorphism arcsin to + # make nodes more evenly spaced for effective ripple. + return get_pitch_inv( + grid.compress(data["min_tz |B|"]), grid.compress(data["max_tz |B|"]), num_pitch + )[jnp.newaxis] @register_compute_fun( @@ -105,18 +79,17 @@ def _poloidal_mean(grid, f): profiles=[], coordinates="r", data=["B^zeta"], - resolution_requirement="z", # and poloidal if near rational surfaces + resolution_requirement="z", source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) def _L_ra_fsa(data, transforms, profiles, **kwargs): - g = transforms["grid"].source_grid - shape = (g.num_rho, g.num_alpha, g.num_zeta) + grid = transforms["grid"].source_grid L_ra = simpson( - y=jnp.reciprocal(data["B^zeta"]).reshape(shape), - x=jnp.reshape(g.nodes[:, 2], shape), + y=grid.meshgrid_reshape(1 / data["B^zeta"], "arz"), + x=grid.compress(grid.nodes[:, 2], surface_label="zeta"), axis=-1, ) - data[""] = g.expand(jnp.abs(_poloidal_mean(g, L_ra))) + data[""] = grid.expand(jnp.abs(_poloidal_mean(grid, L_ra))) return data @@ -133,21 +106,35 @@ def _L_ra_fsa(data, transforms, profiles, **kwargs): profiles=[], coordinates="r", data=["B^zeta", "sqrt(g)"], - resolution_requirement="z", # and poloidal if near rational surfaces + resolution_requirement="z", source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, ) def _G_ra_fsa(data, transforms, profiles, **kwargs): - g = transforms["grid"].source_grid - shape = (g.num_rho, g.num_alpha, g.num_zeta) + grid = transforms["grid"].source_grid G_ra = simpson( - y=jnp.reciprocal(data["B^zeta"] * data["sqrt(g)"]).reshape(shape), - x=jnp.reshape(g.nodes[:, 2], shape), + y=grid.meshgrid_reshape(1 / (data["B^zeta"] * data["sqrt(g)"]), "arz"), + x=grid.compress(grid.nodes[:, 2], surface_label="zeta"), axis=-1, ) - data[""] = g.expand(jnp.abs(_poloidal_mean(g, G_ra))) + data[""] = grid.expand(jnp.abs(_poloidal_mean(grid, G_ra))) return data +def dH(grad_rho_norm_kappa_g, B, pitch): + """Removed |∂ψ/∂ρ| (λB₀)¹ᐧ⁵ from integrand of Nemov eq. 30.""" + return ( + jnp.sqrt(jnp.abs(1 - pitch * B)) + * (4 / (pitch * B) - 1) + * grad_rho_norm_kappa_g + / B + ) + + +def dI(B, pitch): + """Integrand of Nemov eq. 31.""" + return jnp.sqrt(jnp.abs(1 - pitch * B)) / B + + @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ label=( @@ -168,30 +155,27 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): data=[ "min_tz |B|", "max_tz |B|", - "B^zeta", - "|B|", - "|B|_z|r,a", "|grad(rho)|", "kappa_g", "", "R0", "<|grad(rho)|>", - ], - resolution_requirement="z", # and poloidal if near rational surfaces + ] + + Bounce1D.required_names, + resolution_requirement="z", source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, - num_quad="int : Resolution for quadrature of bounce integrals. Default is 31.", + num_quad="int : Resolution for quadrature of bounce integrals. Default is 32.", num_pitch=( "int : Resolution for quadrature over velocity coordinate, preferably odd. " - "Default is 125. Effective ripple will look smoother at high values. " - "(If computed on many flux surfaces and micro oscillation is seen " - "between neighboring surfaces, increasing num_pitch will smooth the profile)." + "Default is 125. Profile will look smoother at high values. " + "(If computed on many flux surfaces and small oscillations is seen " + "between neighboring surfaces, increasing this will smooth the profile)." ), - num_wells=( + num_well=( "int : Maximum number of wells to detect for each pitch and field line. " "Default is to detect all wells, but due to limitations in JAX this option " "may consume more memory. Specifying a number that tightly upper bounds " "the number of wells will increase performance. " - "As a reference, there are typically <= 5 wells per toroidal transit." ), batch="bool : Whether to vectorize part of the computation. Default is true.", # Some notes on choosing the resolution hyperparameters: @@ -222,54 +206,44 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ + # noqa: unused dependency + quad = leggauss_lob(kwargs.get("num_quad", 32)) + num_well = kwargs.get("num_well", None) batch = kwargs.get("batch", True) - num_wells = kwargs.get("size", None) - g = transforms["grid"].source_grid - bounce_integrate, _ = bounce_integral( # noqa: F821 - data["B^zeta"], - data["|B|"], - data["|B|_z|r,a"], - g.compress(g.nodes[:, 2], surface_label="zeta"), - leggauss(kwargs.get("num_quad", 31)), + grid = transforms["grid"].source_grid + _data = { + name: Bounce1D.reshape_data(grid, data[name]) + for name in Bounce1D.required_names + } + _data["|grad(rho)|*kappa_g"] = Bounce1D.reshape_data( + grid, data["|grad(rho)|"] * data["kappa_g"] ) + _data["pitch_inv"] = _get_pitch_inv(grid, data, kwargs.get("num_pitch", 125)) - def dH(grad_rho_norm_kappa_g, B, pitch): - # Removed |∂ψ/∂ρ| (λB₀)¹ᐧ⁵ from integrand of Nemov eq. 30. Reintroduced later. - return ( - jnp.sqrt(jnp.abs(1 - pitch * B)) - * (4 / (pitch * B) - 1) - * grad_rho_norm_kappa_g - / B - ) - - def dI(B, pitch): # Integrand of Nemov eq. 31. - return jnp.sqrt(jnp.abs(1 - pitch * B)) / B - - def d_ripple(pitch): - # Return (∂ψ/∂ρ)⁻² λ⁻²B₀⁻³ ∑ⱼ Hⱼ²/Iⱼ evaluated at λ = pitch. - # Note (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) where B₀ has units of λ⁻¹. + def compute(data): + """(∂ψ/∂ρ)⁻² B₀⁻² ∫ dλ ∑ⱼ Hⱼ²/Iⱼ.""" + bounce = Bounce1D(grid, data, quad, is_reshaped=True) # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. - H = bounce_integrate( + H = bounce.integrate( + data["pitch_inv"], dH, - data["|grad(rho)|"] * data["kappa_g"], - pitch, + data["|grad(rho)|*kappa_g"], + num_well=num_well, batch=batch, - num_wells=num_wells, ) - I = bounce_integrate(dI, [], pitch, batch=batch, num_wells=num_wells) - return pitch * jnp.sum(safediv(H**2, I), axis=-1) - - # The integrand is continuous and likely poorly approximated by a polynomial. - # Composite quadrature should perform better than higher order methods. - pitch = _get_pitch( - g, data["min_tz |B|"], data["max_tz |B|"], kwargs.get("num_pitch", 125) - ) - ripple = simpson(y=imap(d_ripple, pitch).squeeze(axis=1), x=pitch, axis=0) + I = bounce.integrate(data["pitch_inv"], dI, num_well=num_well, batch=batch) + # Note B₀ has units of λ⁻¹. + # Nemov's ∑ⱼ Hⱼ²/Iⱼ = (∂ψ/∂ρ)² (λB₀)³ ``(H**2 / I).sum(axis=-1)``. + # (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) = λ³B₀² d(λ⁻¹). + y = data["pitch_inv"] ** (-3) * safediv(H**2, I).sum(axis=-1) + return simpson(y=y, x=data["pitch_inv"]) + + out = _poloidal_mean(grid, map2(compute, _data)) data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) * (data["max_tz |B|"] * data["R0"] / data["<|grad(rho)|>"]) ** 2 - * g.expand(_poloidal_mean(g, ripple.reshape(g.num_rho, g.num_alpha))) + * grid.expand(out) / data[""] ) return data diff --git a/desc/compute/_omnigenity.py b/desc/compute/_omnigenity.py index 3359754745..90b031a0cf 100644 --- a/desc/compute/_omnigenity.py +++ b/desc/compute/_omnigenity.py @@ -13,8 +13,8 @@ from desc.backend import jnp, sign, vmap +from ..utils import cross, dot, safediv from .data_index import register_compute_fun -from .utils import cross, dot, safediv @register_compute_fun( diff --git a/desc/compute/_profiles.py b/desc/compute/_profiles.py index ad19376ec9..17d8c5711c 100644 --- a/desc/compute/_profiles.py +++ b/desc/compute/_profiles.py @@ -15,8 +15,8 @@ from desc.backend import cond, jnp from ..integrals.surface_integral import surface_averages, surface_integrals +from ..utils import dot, safediv from .data_index import register_compute_fun -from .utils import dot, safediv @register_compute_fun( diff --git a/desc/compute/_stability.py b/desc/compute/_stability.py index d30de69619..13ac01be60 100644 --- a/desc/compute/_stability.py +++ b/desc/compute/_stability.py @@ -14,8 +14,8 @@ from desc.backend import jnp from ..integrals.surface_integral import surface_integrals_map +from ..utils import dot from .data_index import register_compute_fun -from .utils import dot @register_compute_fun( diff --git a/desc/compute/geom_utils.py b/desc/compute/geom_utils.py index fc5e1dab83..eeda658b61 100644 --- a/desc/compute/geom_utils.py +++ b/desc/compute/geom_utils.py @@ -4,7 +4,7 @@ from desc.backend import jnp -from .utils import safenorm, safenormalize +from ..utils import safenorm, safenormalize def reflection_matrix(normal): diff --git a/desc/compute/utils.py b/desc/compute/utils.py index 91c93a5df3..290bb5d0d5 100644 --- a/desc/compute/utils.py +++ b/desc/compute/utils.py @@ -685,111 +685,3 @@ def _has_transforms(qty, transforms, parameterization): [d in transforms[key].derivatives.tolist() for d in derivs[key]] ).all() return all(flags.values()) - - -def dot(a, b, axis=-1): - """Batched vector dot product. - - Parameters - ---------- - a : array-like - First array of vectors. - b : array-like - Second array of vectors. - axis : int - Axis along which vectors are stored. - - Returns - ------- - y : array-like - y = sum(a*b, axis=axis) - - """ - return jnp.sum(a * b, axis=axis, keepdims=False) - - -def cross(a, b, axis=-1): - """Batched vector cross product. - - Parameters - ---------- - a : array-like - First array of vectors. - b : array-like - Second array of vectors. - axis : int - Axis along which vectors are stored. - - Returns - ------- - y : array-like - y = a x b - - """ - return jnp.cross(a, b, axis=axis) - - -def safenorm(x, ord=None, axis=None, fill=0, threshold=0): - """Like jnp.linalg.norm, but without nan gradient at x=0. - - Parameters - ---------- - x : ndarray - Vector or array to norm. - ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional - Order of norm. - axis : {None, int, 2-tuple of ints}, optional - Axis to take norm along. - fill : float, ndarray, optional - Value to return where x is zero. - threshold : float >= 0 - How small is x allowed to be. - - """ - is_zero = (jnp.abs(x) <= threshold).all(axis=axis, keepdims=True) - y = jnp.where(is_zero, jnp.ones_like(x), x) # replace x with ones if is_zero - n = jnp.linalg.norm(y, ord=ord, axis=axis) - n = jnp.where(is_zero.squeeze(), fill, n) # replace norm with zero if is_zero - return n - - -def safenormalize(x, ord=None, axis=None, fill=0, threshold=0): - """Normalize a vector to unit length, but without nan gradient at x=0. - - Parameters - ---------- - x : ndarray - Vector or array to norm. - ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional - Order of norm. - axis : {None, int, 2-tuple of ints}, optional - Axis to take norm along. - fill : float, ndarray, optional - Value to return where x is zero. - threshold : float >= 0 - How small is x allowed to be. - - """ - is_zero = (jnp.abs(x) <= threshold).all(axis=axis, keepdims=True) - y = jnp.where(is_zero, jnp.ones_like(x), x) # replace x with ones if is_zero - n = safenorm(x, ord, axis, fill, threshold) * jnp.ones_like(x) - # return unit vector with equal components if norm <= threshold - return jnp.where(n <= threshold, jnp.ones_like(y) / jnp.sqrt(y.size), y / n) - - -def safediv(a, b, fill=0, threshold=0): - """Divide a/b with guards for division by zero. - - Parameters - ---------- - a, b : ndarray - Numerator and denominator. - fill : float, ndarray, optional - Value to return where b is zero. - threshold : float >= 0 - How small is b allowed to be. - """ - mask = jnp.abs(b) <= threshold - num = jnp.where(mask, fill, a) - den = jnp.where(mask, 1, b) - return num / den diff --git a/desc/integrals/bounce_integral.py b/desc/integrals/bounce_integral.py index afe7e8493c..ced4788c75 100644 --- a/desc/integrals/bounce_integral.py +++ b/desc/integrals/bounce_integral.py @@ -1,6 +1,5 @@ """Methods for computing bounce integrals (singular or otherwise).""" -import numpy as np from interpax import CubicHermiteSpline, PPoly from orthax.legendre import leggauss @@ -108,6 +107,8 @@ def __init__( automorphism=(automorphism_sin, grad_automorphism_sin), Bref=1.0, Lref=1.0, + *, + is_reshaped=False, check=False, **kwargs, ): @@ -137,6 +138,13 @@ def __init__( Optional. Reference magnetic field strength for normalization. Lref : float Optional. Reference length scale for normalization. + is_reshaped : bool + Whether the arrays in ``data`` are already reshaped to the expected form. + (That is shape (..., N) or (..., L, N) or (M, L, N).) + This option can be used to iteratively compute bounce integrals either one + field line or one flux surface at a time, respectively, potentially reducing + memory usage. To do so, set to true and provide only those axes of the + reshaped data. Default is false. check : bool Flag for debugging. Must be false for JAX transformations. @@ -159,7 +167,12 @@ def __init__( "|B|": data["|B|"] / Bref, "|B|_z|r,a": data["|B|_z|r,a"] / Bref, # This is already the correct sign. } - self._data = dict(zip(data.keys(), Bounce1D.reshape_data(grid, *data.values()))) + if is_reshaped: + self._data = data + else: + self._data = dict( + zip(data.keys(), Bounce1D.reshape_data(grid, *data.values())) + ) self._x, self._w = get_quadrature(quad, automorphism) # Compute local splines. @@ -167,8 +180,8 @@ def __init__( self.B = jnp.moveaxis( CubicHermiteSpline( x=self._zeta, - y=self._data["|B|"].squeeze(axis=-2), - dydx=self._data["|B|_z|r,a"].squeeze(axis=-2), + y=self._data["|B|"], + dydx=self._data["|B|_z|r,a"], axis=-1, check=check, ).c, @@ -176,8 +189,10 @@ def __init__( destination=(-1, -2), ) self._dB_dz = polyder_vec(self.B) - assert self.B.shape == (grid.num_alpha, grid.num_rho, grid.num_zeta - 1, 4) - assert self._dB_dz.shape == (grid.num_alpha, grid.num_rho, grid.num_zeta - 1, 3) + + # Add axis here instead of in ``_bounce_quadrature``. + for name in self._data: + self._data[name] = self._data[name][..., jnp.newaxis, :] @staticmethod def reshape_data(grid, *arys): @@ -192,14 +207,15 @@ def reshape_data(grid, *arys): Returns ------- - f : list[jnp.ndarray] - List of reshaped data which may be given to ``integrate``. + f : jnp.ndarray + Shape (M, L, N). + Reshaped data which may be given to ``integrate``. """ - f = [grid.meshgrid_reshape(d, "arz")[..., jnp.newaxis, :] for d in arys] - return f + f = [grid.meshgrid_reshape(d, "arz") for d in arys] + return f if len(f) > 1 else f[0] - def points(self, pitch_inv, num_well=None): + def points(self, pitch_inv, /, *, num_well=None): """Compute bounce points. Parameters @@ -235,7 +251,7 @@ def points(self, pitch_inv, num_well=None): """ return bounce_points(pitch_inv, self._zeta, self.B, self._dB_dz, num_well) - def check_points(self, z1, z2, pitch_inv, plot=True, **kwargs): + def check_points(self, z1, z2, pitch_inv, /, *, plot=True, **kwargs): """Check that bounce points are computed correctly. Parameters @@ -274,9 +290,11 @@ def check_points(self, z1, z2, pitch_inv, plot=True, **kwargs): def integrate( self, pitch_inv, + /, integrand, f=None, weight=None, + *, num_well=None, method="cubic", batch=True, @@ -301,13 +319,13 @@ def integrate( ``B`` and ``pitch``. A quadrature will be performed to approximate the bounce integral of ``integrand(*f,B=B,pitch=pitch)``. f : list[jnp.ndarray] - Shape (M, L, 1, N). + Shape (M, L, N). Real scalar-valued functions evaluated on the ``grid`` supplied to construct this object. These functions should be arguments to the callable ``integrand``. Use the method ``self.reshape_data`` to reshape the data into the expected shape. weight : jnp.ndarray - Shape (M, L, 1, N). + Shape (M, L, N). If supplied, the bounce integral labeled by well j is weighted such that the returned value is w(j) ∫ f(ℓ) dℓ, where w(j) is ``weight`` interpolated to the deepest point in that magnetic well. Use the method @@ -342,7 +360,7 @@ def integrate( flux surface, and pitch value. """ - z1, z2 = self.points(pitch_inv, num_well) + z1, z2 = self.points(pitch_inv, num_well=num_well) result = _bounce_quadrature( x=self._x, w=self._w, @@ -360,7 +378,7 @@ def integrate( ) if weight is not None: result *= interp_to_argmin( - weight.squeeze(axis=-2), + weight, z1, z2, self._zeta, @@ -368,10 +386,10 @@ def integrate( self._dB_dz, method, ) - assert result.shape[-1] == setdefault(num_well, np.prod(self._dB_dz.shape[-2:])) + assert result.shape == z1.shape return result - def plot(self, m, l, pitch_inv=None, **kwargs): + def plot(self, m, l, pitch_inv=None, /, **kwargs): """Plot the field line and bounce points of the given pitch angles. Parameters @@ -392,18 +410,21 @@ def plot(self, m, l, pitch_inv=None, **kwargs): Matplotlib (fig, ax) tuple. """ + B, dB_dz = self.B, self._dB_dz + if B.ndim == 4: + B = B[m, l] + dB_dz = dB_dz[m, l] + elif B.ndim == 3: + B = B[l] + dB_dz = dB_dz[l] if pitch_inv is not None: errorif( pitch_inv.ndim > 1, msg=f"Got pitch_inv.ndim={pitch_inv.ndim}, but expected 1.", ) - z1, z2 = bounce_points( - pitch_inv, self._zeta, self.B[m, l], self._dB_dz[m, l] - ) + z1, z2 = bounce_points(pitch_inv, self._zeta, B, dB_dz) kwargs["z1"] = z1 kwargs["z2"] = z2 kwargs["k"] = pitch_inv - fig, ax = plot_ppoly( - PPoly(self.B[m, l].T, self._zeta), **_set_default_plot_kwargs(kwargs) - ) + fig, ax = plot_ppoly(PPoly(B.T, self._zeta), **_set_default_plot_kwargs(kwargs)) return fig, ax diff --git a/desc/integrals/bounce_utils.py b/desc/integrals/bounce_utils.py index 304a443961..6164abf9a4 100644 --- a/desc/integrals/bounce_utils.py +++ b/desc/integrals/bounce_utils.py @@ -320,7 +320,7 @@ def _bounce_quadrature( ``B`` and ``pitch``. A quadrature will be performed to approximate the bounce integral of ``integrand(*f,B=B,pitch=pitch)``. f : list[jnp.ndarray] - Shape (..., 1, N). + Shape (..., N). Real scalar-valued functions evaluated on the ``knots``. These functions should be arguments to the callable ``integrand``. data : dict[str, jnp.ndarray] @@ -448,7 +448,7 @@ def _interpolate_and_integrate( B = interp1d_Hermite_vec(Q, knots, data["|B|"], data["|B|_z|r,a"]) # Spline each function separately so that operations in the integrand # that do not preserve smoothness can be captured. - f = [interp1d_vec(Q, knots, f_i, method=method) for f_i in f] + f = [interp1d_vec(Q, knots, f_i[..., jnp.newaxis, :], method=method) for f_i in f] result = jnp.dot( ( integrand( diff --git a/desc/integrals/interp_utils.py b/desc/integrals/interp_utils.py index 4943be509c..42f34271e1 100644 --- a/desc/integrals/interp_utils.py +++ b/desc/integrals/interp_utils.py @@ -12,7 +12,7 @@ from interpax import interp1d from desc.backend import jnp -from desc.compute.utils import safediv +from desc.utils import safediv # Warning: method must be specified as keyword argument. interp1d_vec = jnp.vectorize( diff --git a/desc/integrals/singularities.py b/desc/integrals/singularities.py index 3730c172af..ab2371a839 100644 --- a/desc/integrals/singularities.py +++ b/desc/integrals/singularities.py @@ -9,10 +9,9 @@ from desc.backend import fori_loop, jnp, put, vmap from desc.basis import DoubleFourierSeries from desc.compute.geom_utils import rpz2xyz, rpz2xyz_vec, xyz2rpz_vec -from desc.compute.utils import safediv, safenorm from desc.grid import LinearGrid from desc.io import IOAble -from desc.utils import isalmostequal, islinspaced +from desc.utils import isalmostequal, islinspaced, safediv, safenorm def _get_quadrature_nodes(q): diff --git a/desc/objectives/_coils.py b/desc/objectives/_coils.py index 931c27aeeb..7c0335468c 100644 --- a/desc/objectives/_coils.py +++ b/desc/objectives/_coils.py @@ -12,10 +12,9 @@ ) from desc.compute import get_profiles, get_transforms, rpz2xyz from desc.compute.utils import _compute as compute_fun -from desc.compute.utils import safenorm from desc.grid import LinearGrid, _Grid from desc.integrals import compute_B_plasma -from desc.utils import Timer, errorif, warnif +from desc.utils import Timer, errorif, safenorm, warnif from .normalization import compute_scaling_factors from .objective_funs import _Objective diff --git a/desc/objectives/_geometry.py b/desc/objectives/_geometry.py index 2baf4d2c77..46c542fcf7 100644 --- a/desc/objectives/_geometry.py +++ b/desc/objectives/_geometry.py @@ -5,9 +5,8 @@ from desc.backend import jnp, vmap from desc.compute import get_profiles, get_transforms, rpz2xyz, xyz2rpz from desc.compute.utils import _compute as compute_fun -from desc.compute.utils import safenorm from desc.grid import LinearGrid, QuadratureGrid -from desc.utils import Timer, errorif, parse_argname_change, warnif +from desc.utils import Timer, errorif, parse_argname_change, safenorm, warnif from .normalization import compute_scaling_factors from .objective_funs import _Objective diff --git a/desc/optimize/bound_utils.py b/desc/optimize/bound_utils.py index b919a943cf..c111925870 100644 --- a/desc/optimize/bound_utils.py +++ b/desc/optimize/bound_utils.py @@ -2,6 +2,7 @@ import functools +import desc.utils from desc.backend import cond, jit, jnp from .utils import evaluate_quadratic_form_hess, evaluate_quadratic_form_jac @@ -361,7 +362,7 @@ def build_quadratic_1d_hess(H, g, s, diag=None, s0=None): c : float Free term. Returned only if `s0` is provided. """ - a = H.dot(s).dot(s) + a = desc.utils.dot(s) if diag is not None: a += jnp.dot(s * diag, s) a *= 0.5 diff --git a/desc/optimize/tr_subproblems.py b/desc/optimize/tr_subproblems.py index 8c39e82295..787e934695 100644 --- a/desc/optimize/tr_subproblems.py +++ b/desc/optimize/tr_subproblems.py @@ -2,6 +2,7 @@ import numpy as np +import desc.utils from desc.backend import ( cho_factor, cho_solve, @@ -122,7 +123,7 @@ def solve_trust_region_2d_subspace(g, H, trust_radius, initial_alpha=None, **kwa R, lower = cho_factor(B) q1 = -cho_solve((R, lower), g) - p1 = S.dot(q1) + p1 = desc.utils.dot(q1) a = B[0, 0] * trust_radius**2 b = B[0, 1] * trust_radius**2 @@ -139,7 +140,7 @@ def solve_trust_region_2d_subspace(g, H, trust_radius, initial_alpha=None, **kwa value = 0.5 * jnp.sum(q2 * B.dot(q2), axis=0) + jnp.dot(g, q2) i = jnp.argmin(jnp.where(jnp.isnan(value), jnp.inf, value)) q2 = q2[:, i] - p2 = S.dot(q2) + p2 = desc.utils.dot(q2) out = cond( jnp.dot(q1, q1) <= trust_radius**2, diff --git a/desc/plotting.py b/desc/plotting.py index 7e53c98bb2..b64971e564 100644 --- a/desc/plotting.py +++ b/desc/plotting.py @@ -14,6 +14,7 @@ from pylatexenc.latex2text import LatexNodes2Text from termcolor import colored +import desc.utils from desc.backend import sign from desc.basis import fourier, zernike_radial_poly from desc.coils import CoilSet, _Coil @@ -3680,12 +3681,11 @@ def plot_logo(save_path=None, **kwargs): radial = zernike_radial_poly(r[:, np.newaxis], ls, ms) poloidal = fourier(t[:, np.newaxis], ms) zern = radial * poloidal - bdry = poloidal R = zern.dot(cR).reshape((Nr, Nt)) Z = zern.dot(cZ).reshape((Nr, Nt)) - bdryR = bdry.dot(cR) - bdryZ = bdry.dot(cZ) + bdryR = desc.utils.dot(cR) + bdryZ = desc.utils.dot(cZ) R = (R - R0) / (R.max() - R.min()) * Dw + DX Z = (Z - Z0) / (Z.max() - Z.min()) * Dh + DY diff --git a/desc/utils.py b/desc/utils.py index 6ead7a5078..07123c825e 100644 --- a/desc/utils.py +++ b/desc/utils.py @@ -9,7 +9,7 @@ from scipy.special import factorial from termcolor import colored -from desc.backend import flatnonzero, fori_loop, jit, jnp, take +from desc.backend import flatnonzero, fori_loop, imap, jit, jnp, take class Timer: @@ -754,4 +754,121 @@ def atleast_2d_end(ary): return ary[:, jnp.newaxis] if ary.ndim == 1 else ary +def map2(fun, xs, *, batch_size=None): + """Map over leading two axes iteratively.""" + # Can't pass in batch_size to imap yet because only new version jax allow that. + return imap( + lambda x: imap(fun, x), + xs, + ) + + PRINT_WIDTH = 60 # current longest name is BootstrapRedlConsistency with pre-text + + +def dot(a, b, axis=-1): + """Batched vector dot product. + + Parameters + ---------- + a : array-like + First array of vectors. + b : array-like + Second array of vectors. + axis : int + Axis along which vectors are stored. + + Returns + ------- + y : array-like + y = sum(a*b, axis=axis) + + """ + return jnp.sum(a * b, axis=axis, keepdims=False) + + +def cross(a, b, axis=-1): + """Batched vector cross product. + + Parameters + ---------- + a : array-like + First array of vectors. + b : array-like + Second array of vectors. + axis : int + Axis along which vectors are stored. + + Returns + ------- + y : array-like + y = a x b + + """ + return jnp.cross(a, b, axis=axis) + + +def safenorm(x, ord=None, axis=None, fill=0, threshold=0): + """Like jnp.linalg.norm, but without nan gradient at x=0. + + Parameters + ---------- + x : ndarray + Vector or array to norm. + ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional + Order of norm. + axis : {None, int, 2-tuple of ints}, optional + Axis to take norm along. + fill : float, ndarray, optional + Value to return where x is zero. + threshold : float >= 0 + How small is x allowed to be. + + """ + is_zero = (jnp.abs(x) <= threshold).all(axis=axis, keepdims=True) + y = jnp.where(is_zero, jnp.ones_like(x), x) # replace x with ones if is_zero + n = jnp.linalg.norm(y, ord=ord, axis=axis) + n = jnp.where(is_zero.squeeze(), fill, n) # replace norm with zero if is_zero + return n + + +def safenormalize(x, ord=None, axis=None, fill=0, threshold=0): + """Normalize a vector to unit length, but without nan gradient at x=0. + + Parameters + ---------- + x : ndarray + Vector or array to norm. + ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional + Order of norm. + axis : {None, int, 2-tuple of ints}, optional + Axis to take norm along. + fill : float, ndarray, optional + Value to return where x is zero. + threshold : float >= 0 + How small is x allowed to be. + + """ + is_zero = (jnp.abs(x) <= threshold).all(axis=axis, keepdims=True) + y = jnp.where(is_zero, jnp.ones_like(x), x) # replace x with ones if is_zero + n = safenorm(x, ord, axis, fill, threshold) * jnp.ones_like(x) + # return unit vector with equal components if norm <= threshold + return jnp.where(n <= threshold, jnp.ones_like(y) / jnp.sqrt(y.size), y / n) + + +def safediv(a, b, fill=0, threshold=0): + """Divide a/b with guards for division by zero. + + Parameters + ---------- + a, b : ndarray + Numerator and denominator. + fill : float, ndarray, optional + Value to return where b is zero. + threshold : float >= 0 + How small is b allowed to be. + """ + mask = jnp.abs(b) <= threshold + num = jnp.where(mask, fill, a) + den = jnp.where(mask, 1, b) + return num / den diff --git a/tests/baseline/test_effective_ripple.png b/tests/baseline/test_effective_ripple.png index 8cdaea34eb64893ae70589b019d736f0d2b0175d..28239234f5d15f16d8872b7bc0d48a0a9a405733 100644 GIT binary patch literal 13922 zcmeHuXH-;4*KPv?sDRFhWDy;K7Rf=7CXRt9AR;*mNRl8~GUgEkv|Dmil9DB7L~OCi zNX}!^1c}mQnsBR*zVChSz2EwN-TUv(TCb&x5<*N7n1Ak(ZvEotN(|kJ~7%TV8ja-MpL~txx;h_V9Fc zbCnd46%iLY?cn8g*ApWu>hkXfB5oe`qCFf-XMm8ych!tNQK)0L;Qv%_m9iaCs0*Fy zSN^(rFKKbiFD^+xS#c>F!^QjP^l7=cLx-sTP&@tLpFsWJuT!ygy;oJ?c2E4H`;hD9 z6J8Hm4_)cKm_8{{kgwE8d-KahVx?71=#?vChu;33b_*?aMj2Ny!6WLT_9bO$Gk>bS zFt^~(G4*?w_%c!yXFRp@hn%HpP^jw)Kx~ILcg9njY26q zp7Ee8m zHsdj)OHBgC+bK&c(ZHRvMa=OlZ=pBaioY*gp|3Ca;Tf5dNjc;7e=aY}+~Q(BhMJH3 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fc3eebeb5d..e204dc423d 100644 --- a/tests/test_axis_limits.py +++ b/tests/test_axis_limits.py @@ -12,12 +12,13 @@ import pytest from desc.compute import data_index -from desc.compute.utils import _grow_seeds, dot +from desc.compute.utils import _grow_seeds from desc.equilibrium import Equilibrium from desc.examples import get from desc.grid import LinearGrid from desc.integrals import surface_integrals_map from desc.objectives import GenericObjective, ObjectiveFunction +from desc.utils import dot # Unless mentioned in the source code of the compute function, the assumptions # made to compute the magnetic axis limit can be reduced to assuming that these diff --git a/tests/test_compute_funs.py b/tests/test_compute_funs.py index b0e5932a74..83eed6ee1f 100644 --- a/tests/test_compute_funs.py +++ b/tests/test_compute_funs.py @@ -5,13 +5,13 @@ from scipy.signal import convolve2d from desc.compute import rpz2xyz_vec -from desc.compute.utils import dot from desc.equilibrium import Equilibrium from desc.equilibrium.coords import get_rtz_grid from desc.examples import get from desc.geometry import FourierRZToroidalSurface from desc.grid import LinearGrid from desc.io import load +from desc.utils import dot # convolve kernel is reverse of FD coeffs FD_COEF_1_2 = np.array([-1 / 2, 0, 1 / 2])[::-1] diff --git a/tests/test_derivatives.py b/tests/test_derivatives.py index 8cb4ed9bf5..ecc74dd1b2 100644 --- a/tests/test_derivatives.py +++ b/tests/test_derivatives.py @@ -4,6 +4,7 @@ import pytest from numpy.random import default_rng +import desc.utils from desc.backend import jnp from desc.derivatives import AutoDiffDerivative, FiniteDiffDerivative @@ -96,7 +97,7 @@ def test_fd_hessian(self): g = rando.random(n) def f(x): - return 5 + g.dot(x) + x.dot(1 / 2 * A.dot(x)) + return 5 + g.dot(x) + desc.utils.dot(1 / 2 * A.dot(x)) hess = FiniteDiffDerivative(f, argnum=0, mode="hess") diff --git a/tests/test_integrals.py b/tests/test_integrals.py index 10dbdb96e3..c345a3a139 100644 --- a/tests/test_integrals.py +++ b/tests/test_integrals.py @@ -15,7 +15,6 @@ from desc.backend import jnp from desc.basis import FourierZernikeBasis -from desc.compute.utils import dot, safediv from desc.equilibrium import Equilibrium from desc.equilibrium.coords import get_rtz_grid from desc.examples import get @@ -53,6 +52,7 @@ from desc.integrals.singularities import _get_quadrature_nodes from desc.integrals.surface_integral import _get_grid_surface from desc.transform import Transform +from desc.utils import dot, safediv class TestSurfaceIntegral: @@ -1179,6 +1179,29 @@ def dg_dz(z): assert result.shape == z1.shape np.testing.assert_allclose(h_min, result, rtol=1e-3) + @pytest.mark.unit + def test_single_fieldline(self): + """Test that the API works when given full grid but single field line data.""" + zeta = np.linspace(1, 2, 5) + grid = Grid.create_meshgrid([1, 0, zeta], coordinates="raz") + data = {"B^zeta": zeta, "B^zeta_z|r,a": zeta, "|B|": zeta, "|B|_z|r,a": zeta} + bounce = Bounce1D(grid, data, is_reshaped=True, check=True) + assert bounce.B.shape == (zeta.size - 1, 4) + assert bounce._dB_dz.shape == (zeta.size - 1, 3) + pitch_inv = np.array([1, 2]) + z1, z2 = bounce.points(pitch_inv) + assert z1.shape == z2.shape and z1.ndim == 2 and z1.shape[0] == pitch_inv.size + bounce.check_points(z1, z2, pitch_inv) + result = bounce.integrate( + pitch_inv, + integrand=TestBounce1D._example_numerator, + f=data["B^zeta"], + weight=data["B^zeta"], + check=True, + ) + assert result.shape == z1.shape + bounce.plot(0, 0, pitch_inv) + @staticmethod def drift_analytic(data): """Compute analytic approximation for bounce-averaged binormal drift. @@ -1359,17 +1382,17 @@ def test_binormal_drift_bounce1d(self): f = Bounce1D.reshape_data(grid.source_grid, cvdrift, gbdrift) drift_numerical_num = bounce.integrate( - pitch_inv=pitch_inv, + pitch_inv, integrand=TestBounce1D.drift_num_integrand, f=f, num_well=1, check=True, ) drift_numerical_den = bounce.integrate( - pitch_inv=pitch_inv, + pitch_inv, integrand=TestBounce1D.drift_den_integrand, num_well=1, - weight=np.ones(zeta.size)[np.newaxis], + weight=np.ones(zeta.size), check=True, ) drift_numerical = np.squeeze(drift_numerical_num / drift_numerical_den) @@ -1383,7 +1406,7 @@ def test_binormal_drift_bounce1d(self): bounce, TestBounce1D.drift_num_integrand, f=f, - weight=np.ones(zeta.size)[np.newaxis], + weight=np.ones(zeta.size), ) fig, ax = plt.subplots() diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index 9ad325b736..cdab755359 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -14,13 +14,10 @@ def test_field_line_average(): # For axisymmetric devices, one toroidal transit must be exact. rho = np.array([1]) alpha = np.array([0]) + zeta = np.linspace(0, 2 * np.pi, 20) eq = get("DSHAPE") grid = eq.get_rtz_grid( - rho, - alpha, - np.linspace(0, 2 * np.pi, 20), - coordinates="raz", - period=(np.inf, 2 * np.pi, np.inf), + rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) data = eq.compute(["", "", "V_r(r)"], grid=grid) np.testing.assert_allclose( @@ -31,12 +28,9 @@ def test_field_line_average(): # Otherwise, many toroidal transits are necessary to sample surface. eq = get("W7-X") + zeta = np.linspace(0, 40 * np.pi, 300) grid = eq.get_rtz_grid( - rho, - alpha, - np.linspace(0, 40 * np.pi, 300), - coordinates="raz", - period=(np.inf, 2 * np.pi, np.inf), + rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) data = eq.compute(["", "", "V_r(r)"], grid=grid) np.testing.assert_allclose( @@ -52,12 +46,10 @@ def test_effective_ripple(): """Test effective ripple with W7-X.""" eq = get("W7-X") rho = np.linspace(0, 1, 10) + alpha = np.array([0]) + zeta = np.linspace(0, 20 * np.pi, 1000) grid = eq.get_rtz_grid( - rho, - np.array([0]), - np.linspace(0, 20 * np.pi, 1000), - coordinates="raz", - period=(np.inf, 2 * np.pi, np.inf), + rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) data = eq.compute("effective ripple", grid=grid) assert np.isfinite(data["effective ripple"]).all() diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py index 24e4cd028a..c7769e9995 100644 --- a/tests/test_optimizer.py +++ b/tests/test_optimizer.py @@ -16,6 +16,7 @@ ) import desc.examples +import desc.utils from desc.backend import jit, jnp from desc.derivatives import Derivative from desc.equilibrium import Equilibrium @@ -729,12 +730,12 @@ def fun(p): def sfun(x): f = fun(x) - return 1 / 2 * f.dot(f) + return 1 / 2 * desc.utils.dot(f) def grad(x): - f = fun(x) + f = fun(x) # noqa: F841 J = jac(x) - return f.dot(J) + return desc.utils.dot(J) def hess(x): J = jac(x) From 04a0d5266b4a9b3d58b3f358dff7f6a2ccd07b2a Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 2 Sep 2024 18:27:09 -0400 Subject: [PATCH 071/112] Remove test that is now redue to test with effective ripple --- desc/compute/_neoclassical.py | 12 +++++---- desc/integrals/bounce_integral.py | 42 +++++++++++++++---------------- desc/integrals/bounce_utils.py | 25 +++++++----------- tests/test_integrals.py | 37 ++++++--------------------- 4 files changed, 43 insertions(+), 73 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index beaed9593e..830e1f1f6e 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -55,7 +55,7 @@ def _poloidal_mean(grid, f): return f.mean(axis=0) assert grid.is_meshgrid dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") - return jnp.dot(f.T, dp) / jnp.sum(dp) + return f.T.dot(dp) / jnp.sum(dp) def _get_pitch_inv(grid, data, num_pitch): @@ -206,32 +206,34 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ - # noqa: unused dependency quad = leggauss_lob(kwargs.get("num_quad", 32)) num_well = kwargs.get("num_well", None) + num_pitch = kwargs.get("num_pitch", 125) batch = kwargs.get("batch", True) + grid = transforms["grid"].source_grid _data = { + # noqa: unused dependency name: Bounce1D.reshape_data(grid, data[name]) for name in Bounce1D.required_names } _data["|grad(rho)|*kappa_g"] = Bounce1D.reshape_data( grid, data["|grad(rho)|"] * data["kappa_g"] ) - _data["pitch_inv"] = _get_pitch_inv(grid, data, kwargs.get("num_pitch", 125)) + _data["pitch_inv"] = _get_pitch_inv(grid, data, num_pitch) def compute(data): """(∂ψ/∂ρ)⁻² B₀⁻² ∫ dλ ∑ⱼ Hⱼ²/Iⱼ.""" bounce = Bounce1D(grid, data, quad, is_reshaped=True) # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. H = bounce.integrate( - data["pitch_inv"], dH, + data["pitch_inv"], data["|grad(rho)|*kappa_g"], num_well=num_well, batch=batch, ) - I = bounce.integrate(data["pitch_inv"], dI, num_well=num_well, batch=batch) + I = bounce.integrate(dI, data["pitch_inv"], num_well=num_well, batch=batch) # Note B₀ has units of λ⁻¹. # Nemov's ∑ⱼ Hⱼ²/Iⱼ = (∂ψ/∂ρ)² (λB₀)³ ``(H**2 / I).sum(axis=-1)``. # (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) = λ³B₀² d(λ⁻¹). diff --git a/desc/integrals/bounce_integral.py b/desc/integrals/bounce_integral.py index ced4788c75..0c5c8e8dc2 100644 --- a/desc/integrals/bounce_integral.py +++ b/desc/integrals/bounce_integral.py @@ -139,12 +139,12 @@ def __init__( Lref : float Optional. Reference length scale for normalization. is_reshaped : bool - Whether the arrays in ``data`` are already reshaped to the expected form. - (That is shape (..., N) or (..., L, N) or (M, L, N).) - This option can be used to iteratively compute bounce integrals either one - field line or one flux surface at a time, respectively, potentially reducing - memory usage. To do so, set to true and provide only those axes of the - reshaped data. Default is false. + Whether the arrays in ``data`` are already reshaped to the expected form of + shape (..., N) or (..., L, N) or (M, L, N). This option can be used to + iteratively compute bounce integrals one field line or one flux surface + at a time, respectively, potentially reducing memory usage. To do so, + set to true and provide only those axes of the reshaped data. + Default is false. check : bool Flag for debugging. Must be false for JAX transformations. @@ -167,12 +167,11 @@ def __init__( "|B|": data["|B|"] / Bref, "|B|_z|r,a": data["|B|_z|r,a"] / Bref, # This is already the correct sign. } - if is_reshaped: - self._data = data - else: - self._data = dict( - zip(data.keys(), Bounce1D.reshape_data(grid, *data.values())) - ) + self._data = ( + data + if is_reshaped + else dict(zip(data.keys(), Bounce1D.reshape_data(grid, *data.values()))) + ) self._x, self._w = get_quadrature(quad, automorphism) # Compute local splines. @@ -215,7 +214,7 @@ def reshape_data(grid, *arys): f = [grid.meshgrid_reshape(d, "arz") for d in arys] return f if len(f) > 1 else f[0] - def points(self, pitch_inv, /, *, num_well=None): + def points(self, pitch_inv, *, num_well=None): """Compute bounce points. Parameters @@ -289,9 +288,8 @@ def check_points(self, z1, z2, pitch_inv, /, *, plot=True, **kwargs): def integrate( self, - pitch_inv, - /, integrand, + pitch_inv, f=None, weight=None, *, @@ -307,18 +305,18 @@ def integrate( Parameters ---------- - pitch_inv : jnp.ndarray - Shape (M, L, P). - 1/λ values to compute the bounce integrals. 1/λ(α,ρ) is specified by - ``pitch_inv[α,ρ]`` where in the latter the labels are interpreted - as the indices that correspond to that field line. integrand : callable The composition operator on the set of functions in ``f`` that maps the functions in ``f`` to the integrand f(ℓ) in ∫ f(ℓ) dℓ. It should accept the arrays in ``f`` as arguments as well as the additional keyword arguments: ``B`` and ``pitch``. A quadrature will be performed to approximate the bounce integral of ``integrand(*f,B=B,pitch=pitch)``. - f : list[jnp.ndarray] + pitch_inv : jnp.ndarray + Shape (M, L, P). + 1/λ values to compute the bounce integrals. 1/λ(α,ρ) is specified by + ``pitch_inv[α,ρ]`` where in the latter the labels are interpreted + as the indices that correspond to that field line. + f : list[jnp.ndarray] or jnp.ndarray Shape (M, L, N). Real scalar-valued functions evaluated on the ``grid`` supplied to construct this object. These functions should be arguments to the callable @@ -366,8 +364,8 @@ def integrate( w=self._w, z1=z1, z2=z2, - pitch_inv=pitch_inv, integrand=integrand, + pitch_inv=pitch_inv, f=setdefault(f, []), data=self._data, knots=self._zeta, diff --git a/desc/integrals/bounce_utils.py b/desc/integrals/bounce_utils.py index 6164abf9a4..e6fe1f9657 100644 --- a/desc/integrals/bounce_utils.py +++ b/desc/integrals/bounce_utils.py @@ -285,8 +285,8 @@ def _bounce_quadrature( w, z1, z2, - pitch_inv, integrand, + pitch_inv, f, data, knots, @@ -310,15 +310,15 @@ def _bounce_quadrature( ζ coordinates of bounce points. The points are ordered and grouped such that the straight line path between ``z1`` and ``z2`` resides in the epigraph of |B|. - pitch_inv : jnp.ndarray - Shape (..., P). - 1/λ values to compute the bounce integrals. integrand : callable The composition operator on the set of functions in ``f`` that maps the functions in ``f`` to the integrand f(ℓ) in ∫ f(ℓ) dℓ. It should accept the arrays in ``f`` as arguments as well as the additional keyword arguments: ``B`` and ``pitch``. A quadrature will be performed to approximate the bounce integral of ``integrand(*f,B=B,pitch=pitch)``. + pitch_inv : jnp.ndarray + Shape (..., P). + 1/λ values to compute the bounce integrals. f : list[jnp.ndarray] Shape (..., N). Real scalar-valued functions evaluated on the ``knots``. @@ -396,8 +396,7 @@ def loop(z): # over num well axis destination=-1, ) - result = result * grad_bijection_from_disc(z1, z2) - return result + return result * grad_bijection_from_disc(z1, z2) def _interpolate_and_integrate( @@ -449,16 +448,10 @@ def _interpolate_and_integrate( # Spline each function separately so that operations in the integrand # that do not preserve smoothness can be captured. f = [interp1d_vec(Q, knots, f_i[..., jnp.newaxis, :], method=method) for f_i in f] - result = jnp.dot( - ( - integrand( - *f, - B=B, - pitch=1 / pitch_inv[..., jnp.newaxis], - ) - / b_sup_z - ).reshape(shape), - w, + result = ( + (integrand(*f, B=B, pitch=1 / pitch_inv[..., jnp.newaxis]) / b_sup_z) + .reshape(shape) + .dot(w) ) if check: _check_interp(shape, Q, f, b_sup_z, B, result, plot) diff --git a/tests/test_integrals.py b/tests/test_integrals.py index c345a3a139..85ccd4709e 100644 --- a/tests/test_integrals.py +++ b/tests/test_integrals.py @@ -952,7 +952,7 @@ def test_bounce_quadrature(self, is_strong, quad, automorphism): check=True, **kwargs, ) - result = bounce.integrate(pitch_inv, integrand, check=True, plot=True) + result = bounce.integrate(integrand, pitch_inv, check=True, plot=True) assert np.count_nonzero(result) == 1 np.testing.assert_allclose(result.sum(), truth, rtol=1e-4) @@ -1099,14 +1099,14 @@ def test_bounce1d_checks(self): grid.compress(data["min_tz |B|"]), grid.compress(data["max_tz |B|"]), 10 ) num = bounce.integrate( - pitch_inv, integrand=TestBounce1D._example_numerator, + pitch_inv=pitch_inv, f=Bounce1D.reshape_data(grid.source_grid, data["g_zz"]), check=True, ) den = bounce.integrate( - pitch_inv, integrand=TestBounce1D._example_denominator, + pitch_inv=pitch_inv, check=True, batch=False, ) @@ -1179,29 +1179,6 @@ def dg_dz(z): assert result.shape == z1.shape np.testing.assert_allclose(h_min, result, rtol=1e-3) - @pytest.mark.unit - def test_single_fieldline(self): - """Test that the API works when given full grid but single field line data.""" - zeta = np.linspace(1, 2, 5) - grid = Grid.create_meshgrid([1, 0, zeta], coordinates="raz") - data = {"B^zeta": zeta, "B^zeta_z|r,a": zeta, "|B|": zeta, "|B|_z|r,a": zeta} - bounce = Bounce1D(grid, data, is_reshaped=True, check=True) - assert bounce.B.shape == (zeta.size - 1, 4) - assert bounce._dB_dz.shape == (zeta.size - 1, 3) - pitch_inv = np.array([1, 2]) - z1, z2 = bounce.points(pitch_inv) - assert z1.shape == z2.shape and z1.ndim == 2 and z1.shape[0] == pitch_inv.size - bounce.check_points(z1, z2, pitch_inv) - result = bounce.integrate( - pitch_inv, - integrand=TestBounce1D._example_numerator, - f=data["B^zeta"], - weight=data["B^zeta"], - check=True, - ) - assert result.shape == z1.shape - bounce.plot(0, 0, pitch_inv) - @staticmethod def drift_analytic(data): """Compute analytic approximation for bounce-averaged binormal drift. @@ -1382,15 +1359,15 @@ def test_binormal_drift_bounce1d(self): f = Bounce1D.reshape_data(grid.source_grid, cvdrift, gbdrift) drift_numerical_num = bounce.integrate( - pitch_inv, integrand=TestBounce1D.drift_num_integrand, + pitch_inv=pitch_inv, f=f, num_well=1, check=True, ) drift_numerical_den = bounce.integrate( - pitch_inv, integrand=TestBounce1D.drift_den_integrand, + pitch_inv=pitch_inv, num_well=1, weight=np.ones(zeta.size), check=True, @@ -1463,11 +1440,11 @@ def integrand_grad(*args, **kwargs2): return grad_fun(*args, *kwargs2.values()) def fun1(pitch): - return bounce.integrate(1 / pitch, integrand, check=False, **kwargs).sum() + return bounce.integrate(integrand, 1 / pitch, check=False, **kwargs).sum() def fun2(pitch): return bounce.integrate( - 1 / pitch, integrand_grad, check=True, **kwargs + integrand_grad, 1 / pitch, check=True, **kwargs ).sum() pitch = 1.0 From 654a5ff194c8fb0aba472acd756d6b72d04dfeb4 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 3 Sep 2024 00:39:36 -0400 Subject: [PATCH 072/112] Same as commit f596dc6 and d382df3 --- desc/compute/_neoclassical.py | 2 +- desc/optimize/bound_utils.py | 3 +-- desc/optimize/tr_subproblems.py | 5 ++--- tests/test_derivatives.py | 3 +-- 4 files changed, 5 insertions(+), 8 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 830e1f1f6e..5e3daf02c3 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -236,7 +236,7 @@ def compute(data): I = bounce.integrate(dI, data["pitch_inv"], num_well=num_well, batch=batch) # Note B₀ has units of λ⁻¹. # Nemov's ∑ⱼ Hⱼ²/Iⱼ = (∂ψ/∂ρ)² (λB₀)³ ``(H**2 / I).sum(axis=-1)``. - # (λB₀)³ db = (λB₀)³ λ⁻²B₀⁻¹ (-dλ) = λB₀² (-dλ) = λ³B₀² d(λ⁻¹). + # (λB₀)³ db = λ³B₀² d(λ⁻¹) = λB₀² (-dλ). y = data["pitch_inv"] ** (-3) * safediv(H**2, I).sum(axis=-1) return simpson(y=y, x=data["pitch_inv"]) diff --git a/desc/optimize/bound_utils.py b/desc/optimize/bound_utils.py index c111925870..b919a943cf 100644 --- a/desc/optimize/bound_utils.py +++ b/desc/optimize/bound_utils.py @@ -2,7 +2,6 @@ import functools -import desc.utils from desc.backend import cond, jit, jnp from .utils import evaluate_quadratic_form_hess, evaluate_quadratic_form_jac @@ -362,7 +361,7 @@ def build_quadratic_1d_hess(H, g, s, diag=None, s0=None): c : float Free term. Returned only if `s0` is provided. """ - a = desc.utils.dot(s) + a = H.dot(s).dot(s) if diag is not None: a += jnp.dot(s * diag, s) a *= 0.5 diff --git a/desc/optimize/tr_subproblems.py b/desc/optimize/tr_subproblems.py index 787e934695..8c39e82295 100644 --- a/desc/optimize/tr_subproblems.py +++ b/desc/optimize/tr_subproblems.py @@ -2,7 +2,6 @@ import numpy as np -import desc.utils from desc.backend import ( cho_factor, cho_solve, @@ -123,7 +122,7 @@ def solve_trust_region_2d_subspace(g, H, trust_radius, initial_alpha=None, **kwa R, lower = cho_factor(B) q1 = -cho_solve((R, lower), g) - p1 = desc.utils.dot(q1) + p1 = S.dot(q1) a = B[0, 0] * trust_radius**2 b = B[0, 1] * trust_radius**2 @@ -140,7 +139,7 @@ def solve_trust_region_2d_subspace(g, H, trust_radius, initial_alpha=None, **kwa value = 0.5 * jnp.sum(q2 * B.dot(q2), axis=0) + jnp.dot(g, q2) i = jnp.argmin(jnp.where(jnp.isnan(value), jnp.inf, value)) q2 = q2[:, i] - p2 = desc.utils.dot(q2) + p2 = S.dot(q2) out = cond( jnp.dot(q1, q1) <= trust_radius**2, diff --git a/tests/test_derivatives.py b/tests/test_derivatives.py index ecc74dd1b2..8cb4ed9bf5 100644 --- a/tests/test_derivatives.py +++ b/tests/test_derivatives.py @@ -4,7 +4,6 @@ import pytest from numpy.random import default_rng -import desc.utils from desc.backend import jnp from desc.derivatives import AutoDiffDerivative, FiniteDiffDerivative @@ -97,7 +96,7 @@ def test_fd_hessian(self): g = rando.random(n) def f(x): - return 5 + g.dot(x) + desc.utils.dot(1 / 2 * A.dot(x)) + return 5 + g.dot(x) + x.dot(1 / 2 * A.dot(x)) hess = FiniteDiffDerivative(f, argnum=0, mode="hess") From 9e80037881832798b56828bcb263e78021d641df Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 3 Sep 2024 00:44:02 -0400 Subject: [PATCH 073/112] Fixing Pycharm's automated refactor --- tests/test_optimizer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py index c7769e9995..ced3b31d82 100644 --- a/tests/test_optimizer.py +++ b/tests/test_optimizer.py @@ -730,12 +730,12 @@ def fun(p): def sfun(x): f = fun(x) - return 1 / 2 * desc.utils.dot(f) + return 1 / 2 * f.dot(f) def grad(x): f = fun(x) # noqa: F841 J = jac(x) - return desc.utils.dot(J) + return J.dot(J) def hess(x): J = jac(x) From 8d5f3d398d2a141959770dfcf1c1b17b0be61ee8 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 3 Sep 2024 19:56:35 -0400 Subject: [PATCH 074/112] Remove adaptive quadrature for now --- desc/compute/_neoclassical.py | 98 ++++++++++------------------------- desc/compute/utils.py | 26 ++++++++++ 2 files changed, 52 insertions(+), 72 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 5e3daf02c3..ec736e520c 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -16,54 +16,10 @@ from desc.backend import jit, jnp from ..integrals.bounce_integral import Bounce1D -from ..integrals.bounce_utils import get_pitch_inv from ..integrals.quad_utils import leggauss_lob from ..utils import map2, safediv from .data_index import register_compute_fun - - -def _vec_quadax(quad, **kwargs): - """Vectorize an adaptive quadrature method from quadax. - - Parameters - ---------- - quad : callable - Adaptive quadrature method matching API from quadax. - - Returns - ------- - vec_quad : callable - Vectorized adaptive quadrature method. - - """ - - def vec_quad(fun, interval, B_sup_z, B, B_z_ra, arg1, arg2): - return quad(fun, interval, args=(B_sup_z, B, B_z_ra, arg1, arg2), **kwargs)[0] - - vec_quad = jnp.vectorize( - vec_quad, signature="(2),(m),(m),(m),(m),(m)->()", excluded={0} - ) - return vec_quad - - -def _poloidal_mean(grid, f): - """Integrate f over poloidal angle and divide by 2π.""" - assert f.shape[0] == grid.num_poloidal - if grid.num_poloidal == 1: - return f.squeeze(axis=0) - if not hasattr(grid, "spacing"): - return f.mean(axis=0) - assert grid.is_meshgrid - dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") - return f.T.dot(dp) / jnp.sum(dp) - - -def _get_pitch_inv(grid, data, num_pitch): - # TODO: Try Gauss-Chebyshev quadrature after automorphism arcsin to - # make nodes more evenly spaced for effective ripple. - return get_pitch_inv( - grid.compress(data["min_tz |B|"]), grid.compress(data["max_tz |B|"]), num_pitch - )[jnp.newaxis] +from .utils import _get_pitch_inv, _poloidal_mean @register_compute_fun( @@ -120,21 +76,6 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): return data -def dH(grad_rho_norm_kappa_g, B, pitch): - """Removed |∂ψ/∂ρ| (λB₀)¹ᐧ⁵ from integrand of Nemov eq. 30.""" - return ( - jnp.sqrt(jnp.abs(1 - pitch * B)) - * (4 / (pitch * B) - 1) - * grad_rho_norm_kappa_g - / B - ) - - -def dI(B, pitch): - """Integrand of Nemov eq. 31.""" - return jnp.sqrt(jnp.abs(1 - pitch * B)) / B - - @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ label=( @@ -198,7 +139,7 @@ def dI(B, pitch): # required for sufficient coverage of the surface. This requires many knots to # for the spline of the magnetic field to capture fine ripples in a large interval. ) -@partial(jit, static_argnames=["num_quad", "num_pitch", "num_wells", "batch"]) +@partial(jit, static_argnames=["num_quad", "num_pitch", "num_well", "batch"]) def _effective_ripple(params, transforms, profiles, data, **kwargs): """https://doi.org/10.1063/1.873749. @@ -207,20 +148,23 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ quad = leggauss_lob(kwargs.get("num_quad", 32)) - num_well = kwargs.get("num_well", None) num_pitch = kwargs.get("num_pitch", 125) + num_well = kwargs.get("num_well", None) batch = kwargs.get("batch", True) - grid = transforms["grid"].source_grid - _data = { - # noqa: unused dependency - name: Bounce1D.reshape_data(grid, data[name]) - for name in Bounce1D.required_names - } - _data["|grad(rho)|*kappa_g"] = Bounce1D.reshape_data( - grid, data["|grad(rho)|"] * data["kappa_g"] - ) - _data["pitch_inv"] = _get_pitch_inv(grid, data, num_pitch) + + def dH(grad_rho_norm_kappa_g, B, pitch): + # Integrand of Nemov eq. 30 with |∂ψ/∂ρ| (λB₀)¹ᐧ⁵ removed. + return ( + jnp.sqrt(jnp.abs(1 - pitch * B)) + * (4 / (pitch * B) - 1) + * grad_rho_norm_kappa_g + / B + ) + + def dI(B, pitch): + # Integrand of Nemov eq. 31. + return jnp.sqrt(jnp.abs(1 - pitch * B)) / B def compute(data): """(∂ψ/∂ρ)⁻² B₀⁻² ∫ dλ ∑ⱼ Hⱼ²/Iⱼ.""" @@ -239,7 +183,17 @@ def compute(data): # (λB₀)³ db = λ³B₀² d(λ⁻¹) = λB₀² (-dλ). y = data["pitch_inv"] ** (-3) * safediv(H**2, I).sum(axis=-1) return simpson(y=y, x=data["pitch_inv"]) + # TODO: Try Gauss-Chebyshev quadrature after automorphism arcsin to + # make nodes more evenly spaced. + _data = { # noqa: unused dependency + name: Bounce1D.reshape_data(grid, data[name]) + for name in Bounce1D.required_names + } + _data["|grad(rho)|*kappa_g"] = Bounce1D.reshape_data( + grid, data["|grad(rho)|"] * data["kappa_g"] + ) + _data["pitch_inv"] = _get_pitch_inv(grid, data, num_pitch) out = _poloidal_mean(grid, map2(compute, _data)) data["effective ripple"] = ( jnp.pi diff --git a/desc/compute/utils.py b/desc/compute/utils.py index b5bbe8cbbc..26ed64c081 100644 --- a/desc/compute/utils.py +++ b/desc/compute/utils.py @@ -8,6 +8,7 @@ from desc.backend import execute_on_cpu, jnp from desc.grid import Grid +from ..integrals.bounce_utils import get_pitch_inv from ..utils import errorif from .data_index import allowed_kwargs, data_index @@ -711,3 +712,28 @@ def _has_transforms(qty, transforms, parameterization): [d in transforms[key].derivatives.tolist() for d in derivs[key]] ).all() return all(flags.values()) + + +# TODO: Replace with surface integrals once they are switched to OOP and default to +# more efficient methods on tensor product grids. +def _poloidal_mean(grid, f): + """Integrate f over poloidal angle and divide by 2π.""" + assert f.shape[0] == grid.num_poloidal + if grid.num_poloidal == 1: + return f.squeeze(axis=0) + if not hasattr(grid, "spacing"): + return f.mean(axis=0) + assert grid.is_meshgrid + dp = grid.compress(grid.spacing[:, 1], surface_label="poloidal") + return f.T.dot(dp) / jnp.sum(dp) + + +def _get_pitch_inv(grid, data, num_pitch): + return jnp.broadcast_to( + get_pitch_inv( + grid.compress(data["min_tz |B|"]), + grid.compress(data["max_tz |B|"]), + num_pitch, + )[jnp.newaxis], + (grid.num_alpha, grid.num_rho, num_pitch + 2), + ) From 3b5441f0dc259c2fd5934d764270ced05c05c5a4 Mon Sep 17 00:00:00 2001 From: unalmis Date: Tue, 3 Sep 2024 20:00:08 -0400 Subject: [PATCH 075/112] Allow kwargs to bounce.plot --- desc/integrals/bounce_integral.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/desc/integrals/bounce_integral.py b/desc/integrals/bounce_integral.py index dff4db396c..fb2894e9cf 100644 --- a/desc/integrals/bounce_integral.py +++ b/desc/integrals/bounce_integral.py @@ -387,7 +387,7 @@ def integrate( assert result.shape == z1.shape return result - def plot(self, m, l, pitch_inv=None, /, **kwargs): + def plot(self, m, l, pitch_inv=None, **kwargs): """Plot the field line and bounce points of the given pitch angles. Parameters From 9d981ff57b3bc246357a6de014f73b36d780a93d Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 4 Sep 2024 00:19:38 -0400 Subject: [PATCH 076/112] Update effective ripple objective to work with recent changes to master --- desc/compute/_neoclassical.py | 2 +- desc/objectives/_neoclassical.py | 25 ++++++++++--------------- 2 files changed, 11 insertions(+), 16 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index ec736e520c..eae73e2d97 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -116,7 +116,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): "int : Maximum number of wells to detect for each pitch and field line. " "Default is to detect all wells, but due to limitations in JAX this option " "may consume more memory. Specifying a number that tightly upper bounds " - "the number of wells will increase performance. " + "the number of wells will increase performance." ), batch="bool : Whether to vectorize part of the computation. Default is true.", # Some notes on choosing the resolution hyperparameters: diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 297a2cd5e1..ff5a50a842 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -72,22 +72,19 @@ class EffectiveRipple(_Objective): For axisymmetric devices only one toroidal transit is necessary. Otherwise, more toroidal transits will give more accurate result, with diminishing returns. num_quad : int - Resolution for quadrature of bounce integrals. Default is 31. + Resolution for quadrature of bounce integrals. Default is 32. num_pitch : int Resolution for quadrature over velocity coordinate, preferably odd. - Default is 99. Effective ripple will look smoother at high values. - (If computed on many flux surfaces and micro oscillation is seen - between neighboring surfaces, increasing num_pitch will smooth the profile). + Default is 99. Profile will look smoother at high values. + (If computed on many flux surfaces and small oscillations is seen + between neighboring surfaces, increasing this will smooth the profile). batch : bool Whether to vectorize part of the computation. Default is true. - num_wells : int + num_well : int Maximum number of wells to detect for each pitch and field line. Default is to detect all wells, but due to limitations in JAX this option may consume more memory. Specifying a number that tightly upper bounds the number of wells will increase performance. - As a reference, there are typically <= 5 wells per toroidal transit. - There exist utilities to plot the field line with the bounce points - to see how many wells there are. name : str, optional Name of the objective function. @@ -95,7 +92,7 @@ class EffectiveRipple(_Objective): _coordinates = "r" _units = "~" - _print_value_fmt = "Effective ripple ε¹ᐧ⁵: {:10.3e} " + _print_value_fmt = "Effective ripple ε¹ᐧ⁵: " def __init__( self, @@ -110,10 +107,10 @@ def __init__( grid=None, alpha=np.array([0]), zeta=np.linspace(0, 2 * np.pi, 100), - num_quad=31, + num_quad=32, num_pitch=99, batch=True, - num_wells=None, + num_well=None, name="Effective ripple", ): if bounds is not None: @@ -127,22 +124,20 @@ def __init__( # Assign here. self._alpha = alpha self._zeta = zeta - # R0 should be evaluated on Quadrature grid, but it's just a constant - # factor, so it's not worth the memory of building the transforms. self._keys_1dr = [ "iota", "iota_r", "<|grad(rho)|>", "min_tz |B|", "max_tz |B|", - "R0", + "R0", # TODO: GitHub PR #1094 ] self._keys = ["effective ripple"] self._hyperparameters = { "num_quad": num_quad, "num_pitch": num_pitch, "batch": batch, - "num_wells": num_wells, + "num_well": num_well, } super().__init__( From 456d1a864c06379cb56e90738ede407ac340e94a Mon Sep 17 00:00:00 2001 From: unalmis Date: Wed, 4 Sep 2024 16:33:24 -0400 Subject: [PATCH 077/112] atone for pycharm's bad automated refactor commit number 3 --- tests/test_optimizer.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py index ced3b31d82..d7145bb27e 100644 --- a/tests/test_optimizer.py +++ b/tests/test_optimizer.py @@ -16,7 +16,6 @@ ) import desc.examples -import desc.utils from desc.backend import jit, jnp from desc.derivatives import Derivative from desc.equilibrium import Equilibrium @@ -735,7 +734,7 @@ def sfun(x): def grad(x): f = fun(x) # noqa: F841 J = jac(x) - return J.dot(J) + return f.dot(J) def hess(x): J = jac(x) From 9a968e633e0238b45544d2b2f561fbe37efbc4d9 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 15 Sep 2024 11:24:17 -0400 Subject: [PATCH 078/112] Compute quadrature nodes once outside objective.compute --- desc/compute/_neoclassical.py | 22 +++++--- desc/integrals/bounce_integral.py | 3 +- desc/integrals/bounce_utils.py | 11 ++-- desc/integrals/quad_utils.py | 3 +- desc/objectives/_neoclassical.py | 68 +++++++++++++---------- desc/plotting.py | 6 +- tests/baseline/test_effective_ripple.png | Bin 13922 -> 13812 bytes tests/test_neoclassical.py | 7 ++- 8 files changed, 70 insertions(+), 50 deletions(-) diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index eae73e2d97..3eb1c143ee 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -16,7 +16,7 @@ from desc.backend import jit, jnp from ..integrals.bounce_integral import Bounce1D -from ..integrals.quad_utils import leggauss_lob +from ..integrals.quad_utils import get_quadrature, leggauss_lob from ..utils import map2, safediv from .data_index import register_compute_fun from .utils import _get_pitch_inv, _poloidal_mean @@ -105,12 +105,12 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): + Bounce1D.required_names, resolution_requirement="z", source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, - num_quad="int : Resolution for quadrature of bounce integrals. Default is 32.", + quad="jnp.ndarray : Optional, quadrature points and weights for bounce integrals.", num_pitch=( "int : Resolution for quadrature over velocity coordinate, preferably odd. " - "Default is 125. Profile will look smoother at high values. " - "(If computed on many flux surfaces and small oscillations is seen " - "between neighboring surfaces, increasing this will smooth the profile)." + "Default is 75. Profile will look smoother at high values." + # If computed on many flux surfaces and small oscillations are seen + # between neighboring surfaces, increasing this will smooth the profile. ), num_well=( "int : Maximum number of wells to detect for each pitch and field line. " @@ -139,7 +139,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): # required for sufficient coverage of the surface. This requires many knots to # for the spline of the magnetic field to capture fine ripples in a large interval. ) -@partial(jit, static_argnames=["num_quad", "num_pitch", "num_well", "batch"]) +@partial(jit, static_argnames=["num_pitch", "num_well", "batch"]) def _effective_ripple(params, transforms, profiles, data, **kwargs): """https://doi.org/10.1063/1.873749. @@ -147,8 +147,12 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): V. V. Nemov, S. V. Kasilov, W. Kernbichler, M. F. Heyn. Phys. Plasmas 1 December 1999; 6 (12): 4622–4632. """ - quad = leggauss_lob(kwargs.get("num_quad", 32)) - num_pitch = kwargs.get("num_pitch", 125) + quad = ( + kwargs["quad"] + if "quad" in kwargs + else get_quadrature(leggauss_lob(32), Bounce1D._default_automorphism) + ) + num_pitch = kwargs.get("num_pitch", 75) num_well = kwargs.get("num_well", None) batch = kwargs.get("batch", True) grid = transforms["grid"].source_grid @@ -168,7 +172,7 @@ def dI(B, pitch): def compute(data): """(∂ψ/∂ρ)⁻² B₀⁻² ∫ dλ ∑ⱼ Hⱼ²/Iⱼ.""" - bounce = Bounce1D(grid, data, quad, is_reshaped=True) + bounce = Bounce1D(grid, data, quad, automorphism=None, is_reshaped=True) # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. H = bounce.integrate( dH, diff --git a/desc/integrals/bounce_integral.py b/desc/integrals/bounce_integral.py index fb2894e9cf..15dd39c19c 100644 --- a/desc/integrals/bounce_integral.py +++ b/desc/integrals/bounce_integral.py @@ -98,13 +98,14 @@ class Bounce1D(IOAble): required_names = ["B^zeta", "B^zeta_z|r,a", "|B|", "|B|_z|r,a"] get_pitch_inv = staticmethod(get_pitch_inv) + _default_automorphism = (automorphism_sin, grad_automorphism_sin) def __init__( self, grid, data, quad=leggauss(32), - automorphism=(automorphism_sin, grad_automorphism_sin), + automorphism=_default_automorphism, Bref=1.0, Lref=1.0, *, diff --git a/desc/integrals/bounce_utils.py b/desc/integrals/bounce_utils.py index c63477c0cc..4d4263e849 100644 --- a/desc/integrals/bounce_utils.py +++ b/desc/integrals/bounce_utils.py @@ -372,7 +372,7 @@ def _bounce_quadrature( plot=plot, ) else: - # TODO: Use batched vmap. + def loop(z): # over num well axis z1, z2 = z # Need to return tuple because input was tuple; artifact of JAX map. @@ -387,10 +387,11 @@ def loop(z): # over num well axis method=method, check=False, plot=False, - batch=True, + batch=False, ) result = jnp.moveaxis( + # TODO: Use batch_size arg of imap after increasing JAX version requirement. imap(loop, (jnp.moveaxis(z1, -1, 0), jnp.moveaxis(z2, -1, 0)))[1], source=0, destination=-1, @@ -410,7 +411,7 @@ def _interpolate_and_integrate( method, check, plot, - batch=False, + batch=True, ): """Interpolate given functions to points ``Q`` and perform quadrature. @@ -431,11 +432,11 @@ def _interpolate_and_integrate( """ assert w.ndim == 1 and Q.shape[-1] == w.size - assert Q.shape[-3 + batch] == pitch_inv.shape[-1] + assert Q.shape[-3 + (not batch)] == pitch_inv.shape[-1] assert data["|B|"].shape[-1] == knots.size shape = Q.shape - if not batch: + if batch: Q = flatten_matrix(Q) b_sup_z = interp1d_Hermite_vec( Q, diff --git a/desc/integrals/quad_utils.py b/desc/integrals/quad_utils.py index 692149e84e..5f7c5994bf 100644 --- a/desc/integrals/quad_utils.py +++ b/desc/integrals/quad_utils.py @@ -2,7 +2,7 @@ from orthax.legendre import legder, legval -from desc.backend import eigh_tridiagonal, jnp, put +from desc.backend import eigh_tridiagonal, execute_on_cpu, jnp, put from desc.utils import errorif @@ -139,6 +139,7 @@ def tanh_sinh(deg, m=10): return x, w +@execute_on_cpu # JAX implementation of eigh_tridiagonal is not differentiable on gpu. def leggauss_lob(deg, interior_only=False): """Lobatto-Gauss-Legendre quadrature. diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index ff5a50a842..be82ab6c88 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -7,6 +7,8 @@ from desc.grid import LinearGrid from desc.utils import Timer +from ..integrals import Bounce1D +from ..integrals.quad_utils import get_quadrature, leggauss_lob from .objective_funs import _Objective from .utils import _parse_callable_target_bounds @@ -66,18 +68,17 @@ class EffectiveRipple(_Objective): Should have poloidal and toroidal resolution. alpha : ndarray Unique coordinate values for field line poloidal angle label alpha. - zeta : ndarray - Unique coordinate values for field line following coordinate zeta. Must be - strictly increasing. A good reference density is 100 knots per toroidal transit. + num_transit : int + Number of toroidal transits to follow field line. For axisymmetric devices only one toroidal transit is necessary. Otherwise, more toroidal transits will give more accurate result, with diminishing returns. + knots_per_transit : int + Number of points per toroidal transit to sample data. Default is 100. num_quad : int Resolution for quadrature of bounce integrals. Default is 32. num_pitch : int Resolution for quadrature over velocity coordinate, preferably odd. - Default is 99. Profile will look smoother at high values. - (If computed on many flux surfaces and small oscillations is seen - between neighboring surfaces, increasing this will smooth the profile). + Default is 75. Profile will look smoother at high values. batch : bool Whether to vectorize part of the computation. Default is true. num_well : int @@ -97,7 +98,7 @@ class EffectiveRipple(_Objective): def __init__( self, eq, - target=0, + target=0.0, bounds=None, weight=1, normalize=True, @@ -106,9 +107,10 @@ def __init__( deriv_mode="auto", grid=None, alpha=np.array([0]), - zeta=np.linspace(0, 2 * np.pi, 100), + num_transit=10, + knots_per_transit=100, num_quad=32, - num_pitch=99, + num_pitch=75, batch=True, num_well=None, name="Effective ripple", @@ -116,14 +118,6 @@ def __init__( if bounds is not None: target = None - # Assign in build. - self._grid_1dr = grid - self._constants = {"quad_weights": 1} - self._dim_f = 1 - self._rho = np.array([1.0]) - # Assign here. - self._alpha = alpha - self._zeta = zeta self._keys_1dr = [ "iota", "iota_r", @@ -132,13 +126,20 @@ def __init__( "max_tz |B|", "R0", # TODO: GitHub PR #1094 ] - self._keys = ["effective ripple"] + self._constants = { + "quad_weights": 1, + "alpha": alpha, + "zeta": np.linspace( + 0, 2 * np.pi * num_transit, knots_per_transit * num_transit + ), + } self._hyperparameters = { "num_quad": num_quad, "num_pitch": num_pitch, "batch": batch, "num_well": num_well, } + self._grid_1dr = grid super().__init__( things=eq, @@ -165,15 +166,21 @@ def build(self, use_jit=True, verbose=1): """ eq = self.things[0] if self._grid_1dr is None: - self._rho = np.linspace(0.1, 1, 5) + rho = np.linspace(0.1, 1, 5) self._grid_1dr = LinearGrid( - rho=self._rho, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP, sym=eq.sym + rho=rho, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP, sym=eq.sym ) else: - self._rho = self._grid_1dr.compress(self._grid_1dr.nodes[:, 0]) - self._dim_f = self._rho.size + rho = self._grid_1dr.compress(self._grid_1dr.nodes[:, 0]) + self.constants["rho"] = rho + self.constants["quad"] = get_quadrature( + leggauss_lob(self._hyperparameters.pop("num_quad")), + Bounce1D._default_automorphism, + ) + + self._dim_f = rho.size self._target, self._bounds = _parse_callable_target_bounds( - self._target, self._bounds, self._rho + self._target, self._bounds, rho ) timer = Timer() @@ -185,7 +192,7 @@ def build(self, use_jit=True, verbose=1): self._keys_1dr, eq, self._grid_1dr ) self._constants["profiles"] = get_profiles( - self._keys_1dr + self._keys, eq, self._grid_1dr + self._keys_1dr + ["effective ripple"], eq, self._grid_1dr ) timer.stop("Precomputing transforms") @@ -215,6 +222,7 @@ def compute(self, params, constants=None): if constants is None: constants = self.constants eq = self.things[0] + # TODO: compute all deps of effective ripple here data = compute_fun( eq, self._keys_1dr, @@ -222,10 +230,11 @@ def compute(self, params, constants=None): constants["transforms_1dr"], constants["profiles"], ) + # TODO: interpolate all deps to this grid with fft utilities from fourier bounce grid = eq.get_rtz_grid( - self._rho, - self._alpha, - self._zeta, + constants["rho"], + constants["alpha"], + constants["zeta"], coordinates="raz", period=(np.inf, 2 * np.pi, np.inf), iota=self._grid_1dr.compress(data["iota"]), @@ -241,11 +250,12 @@ def compute(self, params, constants=None): } data = compute_fun( eq, - self._keys, + "effective ripple", params, - get_transforms(self._keys, eq, grid, jitable=True), + get_transforms("effective ripple", eq, grid, jitable=True), constants["profiles"], data=data, + quad=constants["quad"], **self._hyperparameters, ) return grid.compress(data["effective ripple"]) diff --git a/desc/plotting.py b/desc/plotting.py index 711919a32c..0fe93402a3 100644 --- a/desc/plotting.py +++ b/desc/plotting.py @@ -14,7 +14,6 @@ from pylatexenc.latex2text import LatexNodes2Text from termcolor import colored -import desc.utils from desc.backend import sign from desc.basis import fourier, zernike_radial_poly from desc.coils import CoilSet, _Coil @@ -3654,11 +3653,12 @@ def plot_logo(save_path=None, **kwargs): radial = zernike_radial_poly(r[:, np.newaxis], ls, ms) poloidal = fourier(t[:, np.newaxis], ms) zern = radial * poloidal + bdry = poloidal R = zern.dot(cR).reshape((Nr, Nt)) Z = 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zuOQ(wE(=P>#P=Xbj4iX?Cb_uDp$T$WdH0&`H&@%is58^A^g1{vMwnX;{_MJ~tUorC`U6q3oJ+uK&J03T2w^%mxj2QKY5K@`pydRxYZv4AgIwC3$^S*KY<_^SMDqu5env&|kk#d>Bjp z_0m%Hd_h~Hj3^GR(=mP2f*$1{+ZrR>+76sodXyq&kLcLy!0!9AAGTC@NEANoK; iz^f|%7yb#*Qcp>Bf diff --git a/tests/test_neoclassical.py b/tests/test_neoclassical.py index cdab755359..4874fee34d 100644 --- a/tests/test_neoclassical.py +++ b/tests/test_neoclassical.py @@ -6,16 +6,19 @@ from tests.test_plotting import tol_1d from desc.examples import get +from desc.grid import LinearGrid @pytest.mark.unit def test_field_line_average(): """Test that field line average converges to surface average.""" - # For axisymmetric devices, one toroidal transit must be exact. rho = np.array([1]) alpha = np.array([0]) - zeta = np.linspace(0, 2 * np.pi, 20) eq = get("DSHAPE") + iota_grid = LinearGrid(rho=rho, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP, sym=eq.sym) + iota = iota_grid.compress(eq.compute("iota", grid=iota_grid)["iota"]).item() + # For axisymmetric devices, one poloidal transit must be exact. + zeta = np.linspace(0, 2 * np.pi / iota, 25) grid = eq.get_rtz_grid( rho, alpha, zeta, coordinates="raz", period=(np.inf, 2 * np.pi, np.inf) ) From b4151d9ea4d332e30eba8f0629db62f63c74b143 Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 15 Sep 2024 13:48:15 -0400 Subject: [PATCH 079/112] Fix nan leak in reverse mode ad for bounce integral --- desc/integrals/interp_utils.py | 8 +++++++- desc/objectives/_neoclassical.py | 5 +++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/desc/integrals/interp_utils.py b/desc/integrals/interp_utils.py index 42f34271e1..a2c85f7df0 100644 --- a/desc/integrals/interp_utils.py +++ b/desc/integrals/interp_utils.py @@ -203,7 +203,13 @@ def _root_cubic(C, sentinel, eps, distinct): def irreducible(Q, R, b, mask): # Three irrational real roots. - theta = jnp.arccos(R / jnp.sqrt(jnp.where(mask, Q**3, R**2 + 1))) + theta = jnp.arccos( + jnp.clip( + R / jnp.sqrt(jnp.where(mask, Q**3, 1.0)), + -1.0 + 0.01 * eps, + +1.0 - 0.01 * eps, + ) + ) return jnp.moveaxis( -2 * jnp.sqrt(Q) diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index be82ab6c88..db03bea23e 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -73,7 +73,8 @@ class EffectiveRipple(_Objective): For axisymmetric devices only one toroidal transit is necessary. Otherwise, more toroidal transits will give more accurate result, with diminishing returns. knots_per_transit : int - Number of points per toroidal transit to sample data. Default is 100. + Number of points per toroidal transit at which to sample data along field + line. Default is 100. num_quad : int Resolution for quadrature of bounce integrals. Default is 32. num_pitch : int @@ -166,7 +167,7 @@ def build(self, use_jit=True, verbose=1): """ eq = self.things[0] if self._grid_1dr is None: - rho = np.linspace(0.1, 1, 5) + rho = np.linspace(0.5, 1, 3) self._grid_1dr = LinearGrid( rho=rho, M=eq.M_grid, N=eq.N_grid, NFP=eq.NFP, sym=eq.sym ) From 8b656f291c2919bef8c6726e2bb21b701ff5d2dc Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 15 Sep 2024 14:03:16 -0400 Subject: [PATCH 080/112] Fix comment that one poloidal transit is sufficient if axissymetric --- desc/objectives/_neoclassical.py | 4 ++-- tests/test_optimizer.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index db03bea23e..0306401c38 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -70,8 +70,8 @@ class EffectiveRipple(_Objective): Unique coordinate values for field line poloidal angle label alpha. num_transit : int Number of toroidal transits to follow field line. - For axisymmetric devices only one toroidal transit is necessary. Otherwise, - more toroidal transits will give more accurate result, with diminishing returns. + For axisymmetric devices, one poloidal transit is sufficient. Otherwise, + more transits will give more accurate result, with diminishing returns. knots_per_transit : int Number of points per toroidal transit at which to sample data along field line. Default is 100. diff --git a/tests/test_optimizer.py b/tests/test_optimizer.py index d7145bb27e..24e4cd028a 100644 --- a/tests/test_optimizer.py +++ b/tests/test_optimizer.py @@ -732,7 +732,7 @@ def sfun(x): return 1 / 2 * f.dot(f) def grad(x): - f = fun(x) # noqa: F841 + f = fun(x) J = jac(x) return f.dot(J) From 3a93117e15156d3e0f0f81f9a7fa2a980b12410e Mon Sep 17 00:00:00 2001 From: unalmis Date: Sun, 15 Sep 2024 14:24:39 -0400 Subject: [PATCH 081/112] Use _constants instead of constants --- desc/objectives/_neoclassical.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 0306401c38..2936fe6ebb 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -173,12 +173,11 @@ def build(self, use_jit=True, verbose=1): ) else: rho = self._grid_1dr.compress(self._grid_1dr.nodes[:, 0]) - self.constants["rho"] = rho - self.constants["quad"] = get_quadrature( + self._constants["rho"] = rho + self._constants["quad"] = get_quadrature( leggauss_lob(self._hyperparameters.pop("num_quad")), Bounce1D._default_automorphism, ) - self._dim_f = rho.size self._target, self._bounds = _parse_callable_target_bounds( self._target, self._bounds, rho From 52adba9c12aed607b417f917744e6b95917c5c05 Mon Sep 17 00:00:00 2001 From: unalmis Date: Mon, 16 Sep 2024 01:19:46 -0400 Subject: [PATCH 082/112] Improve quadrature over velocity coordiante for effective ripple --- desc/backend.py | 56 +++++++++++------------ desc/compute/_neoclassical.py | 46 +++++++++---------- desc/compute/utils.py | 24 ++++++++-- desc/integrals/bounce_utils.py | 46 ++++++++++++++++--- desc/integrals/quad_utils.py | 29 +++++++++++- desc/objectives/_neoclassical.py | 22 ++++----- tests/baseline/test_effective_ripple.png | Bin 13812 -> 13483 bytes tests/test_objective_funs.py | 17 +++++-- tests/test_quad_utils.py | 20 ++++++++ 9 files changed, 181 insertions(+), 79 deletions(-) diff --git a/desc/backend.py b/desc/backend.py index 5aab199a2a..ecbc915cbb 100644 --- a/desc/backend.py +++ b/desc/backend.py @@ -75,14 +75,7 @@ from jax.numpy import bincount, flatnonzero, repeat, take from jax.numpy.fft import irfft, rfft, rfft2 from jax.scipy.fft import dct, idct - from jax.scipy.linalg import ( - block_diag, - cho_factor, - cho_solve, - eigh_tridiagonal, - qr, - solve_triangular, - ) + from jax.scipy.linalg import block_diag, cho_factor, cho_solve, qr, solve_triangular from jax.scipy.special import gammaln, logsumexp from jax.tree_util import ( register_pytree_node, @@ -98,6 +91,31 @@ jnp.trapezoid if hasattr(jnp, "trapezoid") else jax.scipy.integrate.trapezoid ) + def execute_on_cpu(func): + """Decorator to set default device to CPU for a function. + + Parameters + ---------- + func : callable + Function to decorate + + Returns + ------- + wrapper : callable + Decorated function that will always run on CPU even if + there are available GPUs. + """ + + @functools.wraps(func) + def wrapper(*args, **kwargs): + with jax.default_device(jax.devices("cpu")[0]): + return func(*args, **kwargs) + + return wrapper + + # JAX implementation is not differentiable on gpu. + eigh_tridiagonal = execute_on_cpu(jax.scipy.linalg.eigh_tridiagonal) + def put(arr, inds, vals): """Functional interface for array "fancy indexing". @@ -123,28 +141,6 @@ def put(arr, inds, vals): return arr return jnp.asarray(arr).at[inds].set(vals) - def execute_on_cpu(func): - """Decorator to set default device to CPU for a function. - - Parameters - ---------- - func : callable - Function to decorate - - Returns - ------- - wrapper : callable - Decorated function that will run always on CPU even if - there are available GPUs. - """ - - @functools.wraps(func) - def wrapper(*args, **kwargs): - with jax.default_device(jax.devices("cpu")[0]): - return func(*args, **kwargs) - - return wrapper - def sign(x): """Sign function, but returns 1 for x==0. diff --git a/desc/compute/_neoclassical.py b/desc/compute/_neoclassical.py index 3eb1c143ee..96a0c054f0 100644 --- a/desc/compute/_neoclassical.py +++ b/desc/compute/_neoclassical.py @@ -19,7 +19,7 @@ from ..integrals.quad_utils import get_quadrature, leggauss_lob from ..utils import map2, safediv from .data_index import register_compute_fun -from .utils import _get_pitch_inv, _poloidal_mean +from .utils import _get_pitch_inv_chebgauss, _poloidal_mean @register_compute_fun( @@ -79,10 +79,10 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): @register_compute_fun( name="effective ripple", # this is ε¹ᐧ⁵ label=( - # ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² ∫dλ λ⁻²B₀⁻¹ 〈 ∑ⱼ Hⱼ²/Iⱼ 〉 + # ε¹ᐧ⁵ = π/(8√2) (R₀/〈|∇ψ|〉)² B₀⁻¹ ∫dλ λ⁻² 〈 ∑ⱼ Hⱼ²/Iⱼ 〉 "\\epsilon^{3/2} = \\frac{\\pi}{8 \\sqrt{2}} " "(R_0 / \\langle \\vert\\nabla \\psi\\vert \\rangle)^2 " - "\\int d\\lambda \\lambda^{-2} B_0^{-1} " + "B_0^{-1} \\int d\\lambda \\lambda^{-2} " "\\langle \\sum_j H_j^2 / I_j \\rangle" ), units="~", @@ -106,12 +106,7 @@ def _G_ra_fsa(data, transforms, profiles, **kwargs): resolution_requirement="z", source_grid_requirement={"coordinates": "raz", "is_meshgrid": True}, quad="jnp.ndarray : Optional, quadrature points and weights for bounce integrals.", - num_pitch=( - "int : Resolution for quadrature over velocity coordinate, preferably odd. " - "Default is 75. Profile will look smoother at high values." - # If computed on many flux surfaces and small oscillations are seen - # between neighboring surfaces, increasing this will smooth the profile. - ), + num_pitch="int : Resolution for quadrature over velocity coordinate. Default 50.", num_well=( "int : Maximum number of wells to detect for each pitch and field line. " "Default is to detect all wells, but due to limitations in JAX this option " @@ -152,7 +147,7 @@ def _effective_ripple(params, transforms, profiles, data, **kwargs): if "quad" in kwargs else get_quadrature(leggauss_lob(32), Bounce1D._default_automorphism) ) - num_pitch = kwargs.get("num_pitch", 75) + num_pitch = kwargs.get("num_pitch", 50) num_well = kwargs.get("num_well", None) batch = kwargs.get("batch", True) grid = transforms["grid"].source_grid @@ -171,24 +166,29 @@ def dI(B, pitch): return jnp.sqrt(jnp.abs(1 - pitch * B)) / B def compute(data): - """(∂ψ/∂ρ)⁻² B₀⁻² ∫ dλ ∑ⱼ Hⱼ²/Iⱼ.""" + """Return (∂ψ/∂ρ)⁻² B₀⁻² ∫ dλ λ⁻² ∑ⱼ Hⱼ²/Iⱼ. + + Notes + ----- + B₀ has units of λ⁻¹. + Nemov's ∑ⱼ Hⱼ²/Iⱼ = (∂ψ/∂ρ)² (λB₀)³ ``(H**2 / I).sum(axis=-1)``. + (λB₀)³ d(λB₀)⁻¹ = B₀² λ³ d(λ⁻¹) = -B₀² λ dλ. + """ bounce = Bounce1D(grid, data, quad, automorphism=None, is_reshaped=True) - # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. H = bounce.integrate( dH, data["pitch_inv"], + # Interpolate |∇ρ| κ_g since it is smoother than κ_g alone. data["|grad(rho)|*kappa_g"], num_well=num_well, batch=batch, ) I = bounce.integrate(dI, data["pitch_inv"], num_well=num_well, batch=batch) - # Note B₀ has units of λ⁻¹. - # Nemov's ∑ⱼ Hⱼ²/Iⱼ = (∂ψ/∂ρ)² (λB₀)³ ``(H**2 / I).sum(axis=-1)``. - # (λB₀)³ db = λ³B₀² d(λ⁻¹) = λB₀² (-dλ). - y = data["pitch_inv"] ** (-3) * safediv(H**2, I).sum(axis=-1) - return simpson(y=y, x=data["pitch_inv"]) - # TODO: Try Gauss-Chebyshev quadrature after automorphism arcsin to - # make nodes more evenly spaced. + return ( + safediv(H**2, I).sum(axis=-1) + * data["pitch_inv"] ** (-3) + * data["pitch_inv weight"] + ).sum(axis=-1) _data = { # noqa: unused dependency name: Bounce1D.reshape_data(grid, data[name]) @@ -197,13 +197,13 @@ def compute(data): _data["|grad(rho)|*kappa_g"] = Bounce1D.reshape_data( grid, data["|grad(rho)|"] * data["kappa_g"] ) - _data["pitch_inv"] = _get_pitch_inv(grid, data, num_pitch) - out = _poloidal_mean(grid, map2(compute, _data)) + _data = _get_pitch_inv_chebgauss(grid, data, num_pitch, _data) + B0 = data["max_tz |B|"] data["effective ripple"] = ( jnp.pi / (8 * 2**0.5) - * (data["max_tz |B|"] * data["R0"] / data["<|grad(rho)|>"]) ** 2 - * grid.expand(out) + * (B0 * data["R0"] / data["<|grad(rho)|>"]) ** 2 + * grid.expand(_poloidal_mean(grid, map2(compute, _data))) / data[""] ) return data diff --git a/desc/compute/utils.py b/desc/compute/utils.py index 26ed64c081..e7d496a045 100644 --- a/desc/compute/utils.py +++ b/desc/compute/utils.py @@ -8,7 +8,7 @@ from desc.backend import execute_on_cpu, jnp from desc.grid import Grid -from ..integrals.bounce_utils import get_pitch_inv +from ..integrals.bounce_utils import get_pitch_inv, get_pitch_inv_chebgauss from ..utils import errorif from .data_index import allowed_kwargs, data_index @@ -728,12 +728,28 @@ def _poloidal_mean(grid, f): return f.T.dot(dp) / jnp.sum(dp) -def _get_pitch_inv(grid, data, num_pitch): - return jnp.broadcast_to( +def _get_pitch_inv(grid, data, num_pitch, _data): + _data["pitch_inv"] = jnp.broadcast_to( get_pitch_inv( grid.compress(data["min_tz |B|"]), grid.compress(data["max_tz |B|"]), num_pitch, )[jnp.newaxis], - (grid.num_alpha, grid.num_rho, num_pitch + 2), + (grid.num_alpha, grid.num_rho, num_pitch), ) + return _data + + +def _get_pitch_inv_chebgauss(grid, data, num_pitch, _data): + p, w = get_pitch_inv_chebgauss( + grid.compress(data["min_tz |B|"]), + grid.compress(data["max_tz |B|"]), + num_pitch, + ) + _data["pitch_inv"] = jnp.broadcast_to( + p[jnp.newaxis], (grid.num_alpha, grid.num_rho, num_pitch) + ) + _data["pitch_inv weight"] = jnp.broadcast_to( + w[jnp.newaxis], (grid.num_alpha, grid.num_rho, num_pitch) + ) + return _data diff --git a/desc/integrals/bounce_utils.py b/desc/integrals/bounce_utils.py index 4d4263e849..9b784d334c 100644 --- a/desc/integrals/bounce_utils.py +++ b/desc/integrals/bounce_utils.py @@ -14,6 +14,7 @@ ) from desc.integrals.quad_utils import ( bijection_from_disc, + chebgauss_uniform, composite_linspace, grad_bijection_from_disc, ) @@ -37,7 +38,7 @@ def get_pitch_inv(min_B, max_B, num, relative_shift=1e-6): max_B : jnp.ndarray Maximum |B| value. num : int - Number of values, not including endpoints. + Number of values. relative_shift : float Relative amount to shift maxima down and minima up to avoid floating point errors in downstream routines. @@ -45,20 +46,53 @@ def get_pitch_inv(min_B, max_B, num, relative_shift=1e-6): Returns ------- pitch_inv : jnp.ndarray - Shape (*min_B.shape, num + 2). + Shape (*min_B.shape, num). 1/λ values. """ # Floating point error impedes consistent detection of bounce points riding # extrema. Shift values slightly to resolve this issue. - min_B = (1 + relative_shift) * min_B - max_B = (1 - relative_shift) * max_B + min_B = (1.0 + relative_shift) * min_B + max_B = (1.0 - relative_shift) * max_B # Samples should be uniformly spaced in |B| and not λ (GitHub issue #1228). - pitch_inv = jnp.moveaxis(composite_linspace(jnp.stack([min_B, max_B]), num), 0, -1) - assert pitch_inv.shape == (*min_B.shape, num + 2) + pitch_inv = jnp.moveaxis( + composite_linspace(jnp.stack([min_B, max_B]), num - 2), 0, -1 + ) + assert pitch_inv.shape == (*min_B.shape, num) return pitch_inv +def get_pitch_inv_chebgauss(min_B, max_B, num, relative_shift=1e-6): + """Return Chebyshev quadrature with 1/λ uniform in ``min_B`` and ``max_B``. + + Parameters + ---------- + min_B : jnp.ndarray + Minimum |B| value. + max_B : jnp.ndarray + Maximum |B| value. + num : int + Number of values. + relative_shift : float + Relative amount to shift maxima down and minima up to avoid floating point + errors in downstream routines. + + Returns + ------- + pitch_inv, weight : (jnp.ndarray, jnp.ndarray) + Shape (*min_B.shape, num). + 1/λ values and weights. + + """ + min_B = (1.0 + relative_shift) * min_B + max_B = (1.0 - relative_shift) * max_B + # Samples should be uniformly spaced in |B| (GitHub issue #1228). + x, w = chebgauss_uniform(num) + pitch_inv = bijection_from_disc(x, min_B[..., jnp.newaxis], max_B[..., jnp.newaxis]) + w = w * grad_bijection_from_disc(min_B, max_B)[..., jnp.newaxis] + return pitch_inv, w + + def _check_spline_shape(knots, g, dg_dz, pitch_inv=None): """Ensure inputs have compatible shape. diff --git a/desc/integrals/quad_utils.py b/desc/integrals/quad_utils.py index 5f7c5994bf..2f85f64058 100644 --- a/desc/integrals/quad_utils.py +++ b/desc/integrals/quad_utils.py @@ -1,8 +1,9 @@ """Utilities for quadratures.""" +from orthax.chebyshev import chebgauss from orthax.legendre import legder, legval -from desc.backend import eigh_tridiagonal, execute_on_cpu, jnp, put +from desc.backend import eigh_tridiagonal, jnp, put from desc.utils import errorif @@ -139,7 +140,6 @@ def tanh_sinh(deg, m=10): return x, w -@execute_on_cpu # JAX implementation of eigh_tridiagonal is not differentiable on gpu. def leggauss_lob(deg, interior_only=False): """Lobatto-Gauss-Legendre quadrature. @@ -191,6 +191,31 @@ def leggauss_lob(deg, interior_only=False): return x, w +def chebgauss_uniform(deg): + """Gauss-Chebyshev quadrature with uniformly spaced nodes. + + Returns quadrature points xₖ and weights wₖ for the approximate evaluation of the + integral ∫₋₁¹ f(x) dx ≈ ∑ₖ wₖ f(xₖ). + + Parameters + ---------- + deg : int + Number of quadrature points. + + Returns + ------- + x, w : (jnp.ndarray, jnp.ndarray) + Shape (deg, ). + Quadrature points and weights. + """ + # Define x = 2/π arcsin y and g : y ↦ f(x(y)). + # ∫₋₁¹ f(x) dx = 2/π ∫₋₁¹ (1−y²)⁻⁰ᐧ⁵ g(y) dy + # ∑ₖ wₖ f(x(yₖ)) = 2/π ∑ₖ ωₖ g(yₖ) + # Given roots yₖ of Chebyshev polynomial, x(yₖ) is uniform in (-1, 1). + y, w = chebgauss(deg) + return automorphism_arcsin(y), 2 * w / jnp.pi + + def get_quadrature(quad, automorphism): """Apply automorphism to given quadrature. diff --git a/desc/objectives/_neoclassical.py b/desc/objectives/_neoclassical.py index 2936fe6ebb..302db58cfa 100644 --- a/desc/objectives/_neoclassical.py +++ b/desc/objectives/_neoclassical.py @@ -42,7 +42,7 @@ class EffectiveRipple(_Objective): locations. Defaults to 0. bounds : tuple of {float, ndarray, callable}, optional Lower and upper bounds on the objective. Overrides target. - Both bounds must be broadcastable to Objective.dim_f + Both bounds must be broadcastable to Objective.dim_f. If a callable, each should take a single argument ``rho`` and return the desired bound (lower or upper) of the profile at those locations. weight : {float, ndarray}, optional @@ -68,18 +68,17 @@ class EffectiveRipple(_Objective): Should have poloidal and toroidal resolution. alpha : ndarray Unique coordinate values for field line poloidal angle label alpha. + knots_per_transit : int + Number of points per toroidal transit at which to sample data along field + line. Default is 100. num_transit : int Number of toroidal transits to follow field line. For axisymmetric devices, one poloidal transit is sufficient. Otherwise, more transits will give more accurate result, with diminishing returns. - knots_per_transit : int - Number of points per toroidal transit at which to sample data along field - line. Default is 100. num_quad : int Resolution for quadrature of bounce integrals. Default is 32. num_pitch : int - Resolution for quadrature over velocity coordinate, preferably odd. - Default is 75. Profile will look smoother at high values. + Resolution for quadrature over velocity coordinate. Default 50. batch : bool Whether to vectorize part of the computation. Default is true. num_well : int @@ -99,7 +98,7 @@ class EffectiveRipple(_Objective): def __init__( self, eq, - target=0.0, + target=None, bounds=None, weight=1, normalize=True, @@ -108,16 +107,17 @@ def __init__( deriv_mode="auto", grid=None, alpha=np.array([0]), - num_transit=10, + *, knots_per_transit=100, + num_transit=10, num_quad=32, - num_pitch=75, + num_pitch=50, batch=True, num_well=None, name="Effective ripple", ): - if bounds is not None: - target = None + if target is None and bounds is None: + target = 0.0 self._keys_1dr = [ "iota", diff --git a/tests/baseline/test_effective_ripple.png b/tests/baseline/test_effective_ripple.png index 38bbd3846b2e518fa83d9fec4a4530892612c89d..0a19da2c30efa05e5a51d6cfe429b479cdb4c1b6 100644 GIT binary patch literal 13483 zcmeHucUV(dv~K{hpML{|aU7FHsf@O3>iX`-|sGyV}JrIgI ziUBEt(u+zk)F7dR010pJqt4uS-~HZu_y6nnDV}q7S$+N1+M9dlO!T&I-M1BmLTxwD z|NR0A#U+kHabDiM5xhwYym<}$Q1#cj>~H4d;vaOy*BNDe#s8Y8kH4q;AL4<|zJBgL 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z63JBkhz1Gd__Kmk{YoJZKYe{IPcFyy(*3QV6e$>+(B-4iIam!!OCz(mG4ti)_ikB_euk;vUQmjZDcgU3+a!;b9fmc9W_^uXBsU|&eQ zS84xqtnlexS8M?EGYsep8#i%-#oH**>S%j`jVqTka%{>?G Date: Mon, 16 Sep 2024 01:47:11 -0400 Subject: [PATCH 083/112] Cleaner solution to b4151d9 --- desc/integrals/interp_utils.py | 9 ++------- tests/baseline/test_bounce1d_checks.png | Bin 66853 -> 62744 bytes 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/desc/integrals/interp_utils.py b/desc/integrals/interp_utils.py index a2c85f7df0..c00506fce8 100644 --- a/desc/integrals/interp_utils.py +++ b/desc/integrals/interp_utils.py @@ -203,13 +203,8 @@ def _root_cubic(C, sentinel, eps, distinct): def irreducible(Q, R, b, mask): # Three irrational real roots. - theta = jnp.arccos( - jnp.clip( - R / jnp.sqrt(jnp.where(mask, Q**3, 1.0)), - -1.0 + 0.01 * eps, - +1.0 - 0.01 * eps, - ) - ) + theta = R / jnp.sqrt(jnp.where(mask, Q**3, 1.0)) + theta = jnp.arccos(jnp.where(jnp.abs(theta) < 1.0, theta, 0.0)) return jnp.moveaxis( -2 * 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