Target accuracy for the models is 5 digits of precision for SANS (R=20 nm at q=1/Å) and USANS (R=20 μm at q=0.002/Å). For USANS slit smearing, which pushes q out to 0.1/Å, the target accuracy 0.2. With regard to performance, I'm trying to limit it to 2 s to evaluate 200 q points on my mac M2 chip.
I recently revised all the models to use a simple adaptive integration scheme (#658) based on qr max for the shape along the c-axis and in the a-b plane. For the outer loop I use the max of these two, but only the a-b cross section for the inner loop. I'm limiting the (θ,φ) grid to 100 000 evaluation points, with no more than 76 points in the outer loop.
Here's an example from triaxial ellipsoid:
const double qr_max_inner = fmax(q*radius_equat_minor, q*radius_equat_major);
const double qr_max = fmax(qr_max_inner, q*radius_polar);
constant double *z_outer, *w_outer;
constant double *z_inner, *w_inner;
int n_outer = gauss_weights(qr_max, ADAPTIVE_MAX_OUTER, &w_outer, &z_outer);
int n_inner = gauss_weights(qr_max_inner, n_outer, &w_inner, &z_inner);For the tetrapod, qr_max = q*sqrt(length**2 + (outer radius)**2) is probably all you need. Whether outer radius is radius or core radius + thickness depends on your parameterization.
Code in explore/check_adaptive.py compares the adaptive grid to a 5000x5000 grid on a few points and reports those that are out of tolerance.
This is not needed for this PR, but please convert this comment into a new ticket if you choose to do it later.
Originally posted by @pkienzle in #705 (comment)
Target accuracy for the models is 5 digits of precision for SANS (R=20 nm at q=1/Å) and USANS (R=20 μm at q=0.002/Å). For USANS slit smearing, which pushes q out to 0.1/Å, the target accuracy 0.2. With regard to performance, I'm trying to limit it to 2 s to evaluate 200 q points on my mac M2 chip.
I recently revised all the models to use a simple adaptive integration scheme (#658) based on qr max for the shape along the c-axis and in the a-b plane. For the outer loop I use the max of these two, but only the a-b cross section for the inner loop. I'm limiting the (θ,φ) grid to 100 000 evaluation points, with no more than 76 points in the outer loop.
Here's an example from triaxial ellipsoid:
For the tetrapod,
qr_max = q*sqrt(length**2 + (outer radius)**2)is probably all you need. Whether outer radius is radius or core radius + thickness depends on your parameterization.Code in
explore/check_adaptive.pycompares the adaptive grid to a 5000x5000 grid on a few points and reports those that are out of tolerance.This is not needed for this PR, but please convert this comment into a new ticket if you choose to do it later.
Originally posted by @pkienzle in #705 (comment)