Generalize ablastr::coarsen::average to any staggering
#4458
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… coarsen-average-Interp-any-staggering
… coarsen-average-Interp-any-staggering
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… coarsen-average-Interp-any-staggering
… coarsen-average-Interp-any-staggering
… coarsen-average-Interp-any-staggering
for more information, see https://pre-commit.ci
| return [iimin, iimax] | ||
| ii_min = 0 | ||
| ii_max = 4 * cr | ||
| return [ii_min, ii_max] |
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Potentially uninitialized local variable Error
| return [iimin, iimax] | ||
| ii_min = 0 | ||
| ii_max = 4 * cr | ||
| return [ii_min, ii_max] |
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Potentially uninitialized local variable Error
| elif sf == 1: # nodal | ||
| iimin = 0 | ||
| iimax = 8 | ||
| return [iimin, iimax] |
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Potentially uninitialized local variable Error
| elif sf == 1: # nodal | ||
| iimin = 0 | ||
| iimax = 8 | ||
| return [iimin, iimax] |
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Potentially uninitialized local variable Error
| elif cr >= 2: | ||
| numpts = cr * (1 - sf) * (1 - sc) + (2 * (cr - 1) + 1) * sf * sc | ||
| idxmin = i * cr * (1 - sf) * (1 - sc) + (i * cr - cr + 1) * sf * sc | ||
| weights = np.zeros(numpts) |
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| weights[ir] = (1 / cr) * (1 - sf) * (1 - sc) + ( | ||
| (abs(cr - abs(ii - i * cr))) / (cr * cr) | ||
| ) * sf * sc | ||
| return [numpts, idxmin, weights] |
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| elif ii % cr != 0: | ||
| numpts = (1 - sf) * (1 - sc) + 2 * sf * sc | ||
| idxmin = (ii // cr) * (1 - sf) * (1 - sc) + (ii // cr) * sf * sc | ||
| weights = np.zeros(numpts) |
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| idxmin = (ii // cr) * (1 - sf) * (1 - sc) + (ii // cr) * sf * sc | ||
| weights = np.zeros(numpts) | ||
| for ir in range(numpts): | ||
| i = idxmin + ir |
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| weights[ir] = (1 - sf) * (1 - sc) + ( | ||
| (abs(cr - abs(ii - i * cr))) / (cr) | ||
| ) * sf * sc | ||
| return [numpts, idxmin, weights] |
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| # def coarsening_points_and_weights_for_IO( i, sf, sc, cr ): | ||
| # if ( cr==1 ): | ||
| # numpts = 1+abs(sf-sc) | ||
| # idxmin = i-sc*(1-sf) | ||
| # elif ( cr>=2 ): | ||
| # numpts = 2-sf | ||
| # idxmin = i*cr+cr//2*(1-sc)-(1-sf) | ||
| # weights = np.zeros( numpts ) | ||
| # for ir in range( numpts ): | ||
| # weights[ir] = (1/numpts)*(1-sf)*(1-sc)+(1/numpts)*sf*sc | ||
| # return [ numpts, idxmin, weights ] |
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Commented-out code Note
Summary
This PR rewrites
ablastr::coarsen::averageto handle any staggering while maintaining the desired property of charge conservation. The results are the same for cell-centered for any coarsening ratiorand for nodal-fields whenr=1,2, but differs for largerr. The new weights reduce the magnitude of the highest-order error.Additional Information
The
Coarsenfunction is rewritten so thatInterpavoids calculations that are independent of the staggering. The Python filecheck_interp_points_and_weights.pyis rewritten to match the new formula. The functions ofablastr::coarsen::averagenow give the same results asablastr::coarsen::sampleforr=1, perhaps making the latter obsolete.A rough draft of the derivation of the weights can be found here:
coarsen-average-generalization-derivation.pdf
Prior Docs
2020 PDF by @EZoni : https://github.com/ECP-WarpX/WarpX/files/9904632/Slides-Interpolation-IO-MR.pdf
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