molstar
Version:
A comprehensive macromolecular library.
114 lines (113 loc) • 4.09 kB
JavaScript
;
/**
* Copyright (c) 2020-2022 mol* contributors, licensed under MIT, See LICENSE file for more info.
*
* Inspired by https://github.com/dgasmith/gau2grid.
*
* @author David Sehnal <david.sehnal@gmail.com>
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.createSphericalCollocationGrid = createSphericalCollocationGrid;
exports.computeOrbitalIsocontourValues = computeOrbitalIsocontourValues;
const util_1 = require("../../mol-data/util");
const grid3d_1 = require("../../mol-gl/compute/grid3d");
const mol_task_1 = require("../../mol-task");
const debug_1 = require("../../mol-util/debug");
const collocation_1 = require("./collocation");
const data_model_1 = require("./data-model");
const compute_1 = require("./gpu/compute");
function createSphericalCollocationGrid(params, orbital, webgl) {
return mol_task_1.Task.create('Spherical Collocation Grid', async (ctx) => {
const cubeGrid = (0, data_model_1.initCubeGrid)(params);
let matrix;
if ((0, grid3d_1.canComputeGrid3dOnGPU)(webgl)) {
if (debug_1.isTimingMode)
webgl.timer.mark('createSphericalCollocationGrid');
matrix = await (0, compute_1.gpuComputeAlphaOrbitalsGridValues)(ctx, webgl, cubeGrid, orbital);
if (debug_1.isTimingMode)
webgl.timer.markEnd('createSphericalCollocationGrid');
}
else {
// console.time('cpu');
matrix = await (0, collocation_1.sphericalCollocation)(cubeGrid, orbital, ctx);
// console.timeEnd('cpu');
}
const grid = (0, data_model_1.createGrid)(cubeGrid, matrix, [0, 1, 2]);
let isovalues;
if (!params.doNotComputeIsovalues) {
isovalues = computeOrbitalIsocontourValues(matrix, 0.85);
}
return { grid, isovalues };
});
}
function computeOrbitalIsocontourValues(input, cumulativeThreshold) {
let weightSum = 0;
for (let i = 0, _i = input.length; i < _i; i++) {
const v = input[i];
const w = v * v;
weightSum += w;
}
const avgWeight = weightSum / input.length;
let minWeight = 3 * avgWeight;
// do not try to identify isovalues for degenerate data
// e.g. all values are almost same
if (Math.abs(avgWeight - input[0] * input[0]) < 1e-5) {
return { negative: void 0, positive: void 0 };
}
let size = 0;
while (true) {
let csum = 0;
size = 0;
for (let i = 0, _i = input.length; i < _i; i++) {
const v = input[i];
const w = v * v;
if (w >= minWeight) {
csum += w;
size++;
}
}
if (csum / weightSum > cumulativeThreshold) {
break;
}
minWeight -= avgWeight;
}
const values = new Float32Array(size);
const weights = new Float32Array(size);
const indices = new Int32Array(size);
let o = 0;
for (let i = 0, _i = input.length; i < _i; i++) {
const v = input[i];
const w = v * v;
if (w >= minWeight) {
values[o] = v;
weights[o] = w;
indices[o] = o;
o++;
}
}
(0, util_1.sortArray)(indices, (indices, i, j) => weights[indices[j]] - weights[indices[i]]);
let cweight = 0, cutoffIndex = 0;
for (let i = 0; i < size; i++) {
cweight += weights[indices[i]];
if (cweight / weightSum >= cumulativeThreshold) {
cutoffIndex = i;
break;
}
}
let positive = Number.POSITIVE_INFINITY, negative = Number.NEGATIVE_INFINITY;
for (let i = 0; i < cutoffIndex; i++) {
const v = values[indices[i]];
if (v > 0) {
if (v < positive)
positive = v;
}
else if (v < 0) {
if (v > negative)
negative = v;
}
}
return {
negative: negative !== Number.NEGATIVE_INFINITY ? negative : void 0,
positive: positive !== Number.POSITIVE_INFINITY ? positive : void 0,
};
}