Html2pdf.js | -types

const html2pdf = require('html2pdf.js');

| Library | Type Support | Complexity | Use Case | | :--- | :--- | :--- | :--- | | | Excellent @types/jspdf | High | Full control | | React-PDF | Official types | Medium | React apps only | | Puppeteer (server-side) | Perfect | High | High-fidelity PDFs | | html2pdf.js + skipLibCheck | Poor | Low | Quick prototypes |

const html2pdf = require('html2pdf.js'); -types html2pdf.js

// Additional validation: ensure that any newly added content through user edits is preserved. // Since all major elements are contenteditable, the PDF will render exactly what user edits. // For better math representation, we also added inline .math class but no extra rendering. console.log("Academic paper studio ready — edit any field, then export to PDF via html2pdf.js"); )(); </script>

<h2 contenteditable="true">2. Methods & Topological Framework</h2> <h3 contenteditable="true">2.1 Neural Manifold Reconstruction</h3> <p contenteditable="true">Let <span class="math">\( \mathbfX = \\mathbfx_i\_i=1^N \subset \mathbbR^D \)</span> be the recorded neural activity after spike smoothing and dimensionality reduction via UMAP or PCA. We construct a Vietoris–Rips complex at varying scales <span class="math">\( \epsilon \)</span>. The persistence diagram tracks the birth and death of homological features. For each dimension <span class="math">\( k \)</span>, the Betti number <span class="math">\( \beta_k \)</span> is defined as the number of persistent <span class="math">\( k \)</span>-dimensional holes. We computed these using the GUDHI library. To quantify significance, we compare against null distributions generated by shuffling neural activity across trials.</p> const html2pdf = require('html2pdf

<h2 contenteditable="true">3. Experimental Results</h2> <p contenteditable="true">We tested our pipeline on two datasets: (A) simulated firing rates from a 1000-neuron balanced network performing a delayed match-to-sample task, and (B) CA1 calcium imaging data from mice navigating a virtual reality maze (N=482 neurons, 6 sessions). In both cases, we observed significant persistent <span class="math">\( H_1 \)</span> (loops) and <span class="math">\( H_2 \)</span> (voids) that emerged during the delay period. These topological features vanished when animals made errors, suggesting a direct link between manifold topology and task performance.</p>

// src/types/html2pdf.js.d.ts

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function exportToPDF() // Show a temporary loading effect (optional) const originalBtnText = generateBtn.innerHTML; generateBtn.innerHTML = '<i class="fas fa-spinner fa-pulse"></i> Generating PDF...'; generateBtn.disabled = true; console

.tool-group justify-content: center;