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Make frequencies tend to 0 in absence of data #1325

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merged 6 commits into from
Apr 10, 2021
Merged

Make frequencies tend to 0 in absence of data #1325

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7b9495b
make frequencies tend to 0 in absense of data
rneher Mar 30, 2021
15c76af
increase padding value for frequencies
rneher Apr 6, 2021
edeb19a
lint appeasment
rneher Apr 6, 2021
a98451e
set frequencies explicity to 0 if total is too low
rneher Apr 7, 2021
17a4aba
reduce frequency normalizaton threshold to single constant
rneher Apr 7, 2021
eb7f7b4
remove unassigned variable
rneher Apr 7, 2021
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56 changes: 37 additions & 19 deletions src/util/processFrequencies.js
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Original file line number Diff line number Diff line change
Expand Up @@ -46,25 +46,37 @@ export const checkIfNormalizableFromRawData = (data, pivots, nodes, visibility)
}
}
});
const minFrequency = Math.min(...pivotTotals);
const allowNormalization = minFrequency > 0.001;
// const minFrequency = Math.min(...pivotTotals);
const allowNormalization = true;
return allowNormalization;
};

export const computeMatrixFromRawData = (data, pivots, nodes, visibility, colorScale, colorBy, normalizeFrequencies) => {
export const computeMatrixFromRawData = (
data,
pivots,
nodes,
visibility,
colorScale,
colorBy,
normalizeFrequencies
) => {
/* color scale domain forms the categories in the stream graph */
const categories = colorScale.legendValues.filter((d) => d !== undefined);
categories.push(unassigned_label); /* for tips without a colorBy */
const isGenotype = isColorByGenotype(colorBy);
const matrix = {}; /* SHAPE: rows: categories (colorBys), columns: pivots */
const pivotsLen = pivots.length;
categories.forEach((x) => {matrix[x] = new Array(pivotsLen).fill(0);});
categories.forEach((x) => {
matrix[x] = new Array(pivotsLen).fill(0);
});
// let debugTipsSeen = 0;
const debugPivotTotals = new Array(pivotsLen).fill(0);
data.forEach((d) => {
if (visibility[d.idx] === NODE_VISIBLE) {
// debugTipsSeen++;
const category = assignCategory(colorScale, categories, nodes[d.idx], colorBy, isGenotype) || unassigned_label;
const category =
assignCategory(colorScale, categories, nodes[d.idx], colorBy, isGenotype) ||
unassigned_label;
// if (category === unassigned_label) return;
for (let i = 0; i < pivotsLen; i++) {
matrix[category][i] += d.values[i];
Expand All @@ -77,11 +89,14 @@ export const computeMatrixFromRawData = (data, pivots, nodes, visibility, colorS
});

if (normalizeFrequencies) {
const nCategories = Object.keys(matrix).length;
const minVal = 1e-10;
const minVal = 1e-7;
Object.keys(matrix).forEach((cat) => {
debugPivotTotals.forEach((norm, i) => {
matrix[cat][i] = (matrix[cat][i] + minVal) / (nCategories * minVal + norm);
if (norm > minVal) {
matrix[cat][i] /= norm;
} else {
matrix[cat][i] = 0.0;
}
});
});
}
Expand All @@ -104,19 +119,22 @@ export const processFrequenciesJSON = (rawJSON, tree, controls) => {
throw new Error("tree not loaded");
}
const data = [];
tree.nodes.filter((d) => !d.hasChildren).forEach((n) => {
if (!rawJSON[n.name]) {
console.warn(`No tip frequency information for ${n.name}`);
return;
}
data.push({
idx: n.arrayIdx,
values: rawJSON[n.name].frequencies,
weight: rawJSON[n.name].weight
tree.nodes
.filter((d) => !d.hasChildren)
.forEach((n) => {
if (!rawJSON[n.name]) {
console.warn(`No tip frequency information for ${n.name}`);
return;
}
data.push({
idx: n.arrayIdx,
values: rawJSON[n.name].frequencies,
weight: rawJSON[n.name].weight
});
});
});

const normalizeFrequencies = controls.normalizeFrequencies &&
const normalizeFrequencies =
controls.normalizeFrequencies &&
checkIfNormalizableFromRawData(data, pivots, tree.nodes, tree.visibility);

const matrix = computeMatrixFromRawData(
Expand Down