31:Tba8,function generate_initial_cells_and_collagen(options={}) { let { cell_seed, collagen_seed, polar_seed, n_collagen_points, } = {...default_initial_cells_options, ...options}; // Generate random cells using a fixed seed, and apply vornoi tesselation. let center_random = seedrandom(`${cell_seed}`); // TODO: maybe can pass a metric to the generator? Otherwise if we scale min // distance by xscale, then they'll be much further apart on the y scale. But // we want to keep y scale constant... let scaled_env = {width: env_size.width * xscale, height: env_size.height}; let scaled_border = {width: border.width * xscale, height: border.height}; let cell_centers = generate_poison_disc_cell_centers({ env_size: scaled_env, border: scaled_border, min_nucleus_separation, n_cells, random: center_random, }); let all_polygons = compute_voronoi_polygons({cell_centers, env_size: scaled_env}); // Un-apply the scaling. for (let poly of all_polygons) { poly.data[0] /= xscale; for (let i = 0; i < poly.length; i++){ // The polygons share vertex points so we need to modify a copy of each point. let p = poly[i].slice(); poly[i] = [p[0] / xscale, p[1]]; } } // Keep the cells within a disc at the center. let env_center = [env_size.width / 2, env_size.height / 2]; let polygons = all_polygons.filter(poly => distance(poly.data, env_center) <= circle_radius); // Generate collagen points in an extended environment. let collagen_random = seedrandom(`${collagen_seed}`); let extended_env = {width: env_size.width * 2, height: env_size.height * 2}; let collagen_points = generate_random_cell_centers({ env_size: extended_env, border, min_nucleus_separation: 24.0, n_cells: n_collagen_points, random: collagen_random, }); // We'll need offset the points in this environment so their centers coincide. let extended_env_center = [extended_env.width / 2, extended_env.height / 2]; let extended_env_offset = sub2d(env_center, extended_env_center); for (let p of collagen_points) inplace_add2d(p, extended_env_offset); // Generate collagen polygons. let all_collagen = make_collagen_circles(collagen_points); // Keep only collagen circles that don't intersect cells. let collagen = keep_nonoverlapping_polys(all_collagen, polygons); // Separate cell polygons to prevent infinity forces. let shrunk_polygons = polygons.map(poly => shrink_poly(poly, shrink_distance)); // Create cells from polygons. let initial_cells = shrunk_polygons.map(poly => perimeter_points(poly, n_points)); // Assign polarization directions. let polarization_random = seedrandom(`${polar_seed}`); // let polarized_cells = generate_random_polarization(initial_cells, polarization_random); let polarized_cells = generate_motility_angle(initial_cells, polarization_random); return [polarized_cells, collagen]; }32:Tb4e,viewof multi_simulation_results = { //delete this next row to get the thing running; it's put in so it doesn't start everytime cause it takes very long to finish and takes up a lot of memory return null; let div = d3.select(DOM.element("div")); div.append(() => md`Simulating...`); (async()=>{ let results = []; let fixed_options = { boundary_function: no_boundaries, polarization_factor : 0.0, relative_color: true, velocity_decay: 1*1e-4, relax_compression: 1.0, press_compression: 1.0, lenard_jones_strength: 0.75, osmosis_constant: 0.001, }; // for (let cs = 0; cs < 5; cs += 1) { // for (let ps = 0; ps < 1; ps += 1) { let cs = 24; let ps = 17; for (let mc = 1.06; mc <= 1.2; mc += 0.2) { for (let ncp = 220; ncp <= 250; ncp += 20) { let options = { ...fixed_options, motility_constant: mc, n_collagen_points: ncp, collagen_seed: cs, polar_seed: ps, } // Run the simulation in the background and return the final result. let running = true; let result = undefined; function ondone(sim_result){ result = sim_result; running = false; } let {setup, update} = simulation_loop(ondone, options); // Initialize the state. let state = setup(); let tick = 0; // Manually run the loop using requestAnimationFrame and observablehq's visibility promise. while(running){ // Track ticks/second. state = await update(state, {fps: 0.0}); tick += 1; // Stop if null state. if (!state) running = false; if (tick % 10 == 0) { div.select("p").remove(); div.append(() => md`Simulating ${results.length}; tick ${tick}.`); //await new Promise(requestAnimationFrame); await new Promise(resolve => setTimeout(resolve, 0)) } } results.push({ ...options, max_radius: compute_cell_circle_radius(result)[result.length-1].max_distance, shape_indexes: result[result.length-1].cell_shapeindex, radial_distances: compute_radial_distance(result[result.length-1]), }); div.node().value = results; div.node().dispatchEvent(new CustomEvent("input")); } } // } // } div.select("p").remove(); div.append(() => md`Done; ${results.length} simulations.`); div.node().value = results; div.node().dispatchEvent(new CustomEvent("input")); })(); return div.node(); }33:T1ccf,function simulation_loop(ondone, options={}, view=null){ // Merge the options parameter with the defaults so we can access them. options = {...default_simulate_options, ...options}; // Extract the few options we use in this cell, we'll pass the others to the membrane/intercell forces. let {lennard_jones_max_distance, polarization_factor, equilibrium_area, equilibrium_distance} = options; return { setup: function () { // Initialize physics for all cells. let [initial_cells, collagen] = generate_initial_cells_and_collagen(options); let cells = init_physics(initial_cells); // Initialize time, phase, and an empty set of measures. let time = 0.0; // Start in relax mode. let phase = "relax"; let measures = []; // Fix a random generator for a repeatable simulation. Mainly used for motility. let random = seedrandom("this is my seed, my seed is amazing"); // Return the state. return {cells, collagen, time, phase, measures, random}; }, update: async function({cells, collagen, time, phase, measures, random}, {fps}) { // Measures let nuclei = cells.map(cell => cell.data.slice()); let velocities = cells.map(cell => cell.data_v.slice()) let cell_areas = []; let areas = []; let sum_perimeter = 0; let sum_area = 0; let cell_shapeindex=[]; let sum_excess_p = 0; for (let cell of cells) { sum_perimeter += d3.polygonLength(cell) sum_area += d3.polygonArea(cell) cell_areas.push(d3.polygonArea(cell)) cell_shapeindex.push(d3.polygonLength(cell) / (Math.sqrt( options.relax_compression) * Math.sqrt(d3.polygonArea(cell)))); sum_excess_p += (d3.polygonLength(cell) - d3.polygonLength(d3.polygonHull(cell))) } // cell_areas.push({areas}); let average_area = sum_area / cells.length; let average_perimeter = sum_perimeter / cells.length; let average_shape_index = average_perimeter / Math.sqrt(average_area); let asphericity = Math.pow(average_perimeter,2) / (4*Math.PI*average_area) let excess_p = sum_excess_p / cells.length; let phi = sum_area / (env_size.width*env_size.height); measures.push({ time, phase, cell_areas, average_area, average_perimeter, average_shape_index, cell_shapeindex, excess_p, asphericity, phi, nuclei, velocities }); // Boundaries // ---------- // Get all current cells + cell images on the boundaries; we shouldn't apply any // forces on these cells though. We can show them on screen though. // We also move cells that pass the boundaries to wrap around the other side. let all_cells = options.boundary_function(cells, lennard_jones_max_distance); //take_over_collagen(collagen, cells); all_cells = all_cells.concat(collagen) // Phase Settings // -------------- let sim_polarization_factor = { "relax": options.relax_polarization_factor, "press": options.polarization_factor, }[phase]; let compression = { "relax": options.relax_compression, "press": options.press_compression, }[phase]; // let sim_equilibrium_area = equilibrium_area * compression; let sim_equilibrium_area = compression*(env_size.width * env_size.height) / cells.length let equilibrium_area = (env_size.width * env_size.height) / cells.length // Display // ------- function paint(view) { let {display, transform} = view; let {context, canvas} = display; // Apply zoomies. context.save(); context.fillStyle = "white"; context.fillRect(0, 0, canvas.width, canvas.height); context.translate(transform.x, transform.y); context.scale(transform.k, transform.k); //Set the color of the cells if (options.relative_color){ // let threshold_area = sim_equilibrium_area - 0.4 * options.lennard_jones_distance * 2 * Math.PI * options.nucleus_equilibrium_distance; let threshold_area = 1000; for (let i=0; i 250) { phase = "press"; } if (phase === "press" && time >251) { phase = "done"; ondone(measures); } // Return the new state. return {cells, collagen, time, phase, measures, random, paint}; }, } }34:T1352, function simulate(options={}) { // Display // ------- // Setup the main bits of the display. let div = d3.select(DOM.element("div")); let display = setup_display(env_size); let canvas = div.append(() => display.canvas); // User interface // ============== const button_style = "margin: 0 0.75em 0 0;"; const info_style = "font-size: 0.85rem; font-style: italic;"; let reset_form = div.append("form"); let reset_button = reset_form.append("input") .attr("type", "button") .attr("style", button_style) .attr("value", "Restart"); let reset_record_button = reset_form.append("input") .attr("type", "button") .attr("style", button_style) .attr("value", "Restart & Record"); let reset_zoom_button = reset_form.append("input") .attr("type", "button") .attr("style", button_style) .attr("value", "Reset Zoom"); reset_form.append("div") .attr("style", info_style) .text("Restart the simulation; or reset to initial loop."); // Video Recording // --------------- let record_text = "Save the simulation to video."; let record_default_filename = "cell-simulation"; let record_form = div.append("form"); let record_button = record_form.append("input") .attr("type", "button") .attr("style", button_style) .attr("value", "Record"); let record_save = record_form.append("input") .attr("type", "button") .attr("style", button_style) .attr("value", "Stop & Save") .property("disabled", true); record_form.append("span").attr("style", info_style).text("Save as:"); let record_filename = record_form.append("input") .attr("type", "text") .attr("placeholder", "Recording filename") .attr("value", record_default_filename); let record_info = record_form.append("div") .attr("style", info_style) .text(record_text); // Recorder logic // -------------- let recorder = null; function reset_recorder() { record_info.text(record_text).style("color", null).style("font-weight", "normal"); if (recorder !== null) recorder.discard(); recorder = null; record_save.property("disabled", true); } function recorder_error(error) { record_info.text(error).style("color", "red").style("font-weight", "bold"); recorder = null; } function complete_recording() { let typed_name = record_filename.node().value; let name = (typed_name && typed_name.length > 0) ? typed_name : record_default_filename; recorder.complete(name); reset_recorder(); } record_save.on("click", complete_recording); //Added this here let transform; // Display let view = {display, recorder, transform: null}; // Zoomies // ------- let zoom = d3.zoom() .scaleExtent([0.05, 20]) .on("zoom", (event) => {view.transform = event.transform;}); // This was updated for d3@6. canvas.call(zoom).call(zoom.transform, d3.zoomIdentity); // Simulation Loop // --------------- let loop; function ondone(measures){ if (recorder !== null) complete_recording(); // Set the value of the input for the viewof operator. div.node().value = measures; div.node().dispatchEvent(new CustomEvent("input")); } loop = run_loop(simulation_loop(ondone, options, view)); // SVG Button // ---------- let svg_form = div.append("form"); let svg_button = d3.select(DOM.download(() => { let svg_context = new C2S(display.canvas.width, display.canvas.height); let tmp_display = {canvas: display.canvas, context: svg_context, xscale: display.xscale, yscale: display.yscale}; let tmp_view = {display: tmp_display, transform: view.transform}; loop.get().paint(tmp_view); let svg_data = svg_context.getSerializedSvg(); let blob = new Blob([svg_data], {type: "image/svg+xml"}); return blob; }, "cells", "Save SVG")); svg_button.attr("style", button_style); svg_form.append(() => svg_button.node()); // Loop Control // ------------ invalidation.then(() => { loop.stop(); if (recorder !== null) reset_recorder(); }); function start_recording() { record_save.property("disabled", false); recorder = record_canvas(display.canvas, recorder_error); if (recorder) { recorder.start(); record_info.text("Recording...").style("color", null).style("font-weight", "bold"); } } function reset_zoom() { canvas.call(zoom.transform, d3.zoomIdentity); } function reset() { // Reset the state. loop.reset(); // Stop recorder. if (recorder !== null) reset_recorder(); reset_zoom(); } reset_button.on("click", reset); reset_record_button.on("click", () => { reset(); start_recording(); }); reset_zoom_button.on("click", reset_zoom); record_button.on("click", start_recording); return div.node(); }35:[] 2f:{"title":"Measurements of Cell Settling Simulation","cells":[{"id":0,"value":"md`# Measurements of Cell Settling Simulation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4380,"value":"md`## Setup environment\n\nWe will simulate a drop of cells surrounded by collagen fibers in an un-bounded \nenvironment. As a simplification we treat the collagen fibers as small fixed \ncircles in our simulation. The intuition is that fibers in 3D would project as\ncircles in the 2D plane we are simulating.\n\nTo produce the inital conditions we follow the steps:\n1. We generate cells as vornoi polygons that fill the environment rectangle.\n2. Keep only the cells whose centers are inside a disc at the center.\n3. Generate collagen points and keep those that don't intersect our cells.\n4. Separate cell membranes by an ${tex`\\epsilon`} distance to avoid infinte forces.\n5. Assign a random polarization direction for each cell.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2774,"value":"n_cells=200","pinned":false,"hidden":false,"mode":"ojs"},{"id":2615,"value":"n_points=25","pinned":false,"hidden":false,"mode":"ojs"},{"id":2780,"value":"env_size = {\n let ratio = 1.0;\n let env_area = n_cells * default_simulate_options.equilibrium_area;\n \n // To compute the environment size we want:\n // width * height == env_area;\n // width == height * ratio;\n\n let height = Math.sqrt(env_area / ratio);\n let width = height * ratio;\n \n return {width, height};\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2908,"value":"circle_radius = env_size.height/1.95","pinned":false,"hidden":false,"mode":"ojs"},{"id":4376,"value":"xscale =1.0","pinned":false,"hidden":false,"mode":"ojs"},{"id":4532,"value":"default_initial_cells_options = ({\n cell_seed: 1, collagen_seed: 11, polar_seed: 13, n_collagen_points\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":774,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":777,"value":"default_simulate_options = ({\n ...default_membrane_options,\n ...default_nucleus_options,\n ...default_intercell_options,\n ...default_motility_options,\n nucleus_elastic_constant: 0.0005,\n nucleus_damping_constant: 0.58,\n velocity_decay:1e-2,\n lenard_jones_strength: 0.2,\n nucleus_equilibrium_distance: 6.72,\n relax_polarization_factor: 0.0,\n relax_compression: 1.5,\n press_compression: 1.1,\n boundary_function: periodic_boundaries,\n relative_color:false\n \n \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":836,"value":"function fixed_boundaries(cells, max_interaction_distance){\n let d = default_simulate_options.lennard_jones_distance;\n let {width, height} = env_size;\n let left = [[-d,-d],[-d,(height+d)]];\n let top = [[-d,(height+d)],[(width+d), (height+d)]];\n let right =[ [(width+d),(height+d)], [(width+d),-d]];\n let bottom = [[(width+d),-d], [-d,-d]];\n left.data = [-d, height/2];\n top.data = [width/2, height+d];\n right.data = [width+d, height/2];\n bottom.data = [width/2, -d];\n \n let boundary_cells = init_physics([ bottom]);\n \n \n \n // Concatenate the original cells with the boundary images.\n return cells.concat(boundary_cells);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3541,"value":"function no_boundaries(cells, max_interaction_distance){\n \n \n return cells;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2858,"value":"viewof motility_constant = {\n return slider({\n min: 0.00,\n max: 3.5,\n value: 0.20,\n precision: 2,\n title: \"Motility constant\",\n description: \"How fast the cells move.\",\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2886,"value":"viewof angular_noise = {\n return slider({\n min: 0.001,\n max: 0.5,\n value: 0.1,\n precision: 3,\n title: \"Angular Noise\",\n description: \"Random difussion of the motility direction.\",\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2883,"value":"default_motility_options = ({\n motility_constant,\n angular_noise,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2863,"value":"function generate_motility_angle (cells, random) {\n for (let cell of cells) {\n cell.theta = random() * 2 * Math.PI;\n }\n return cells;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2870,"value":"function get_motility_vector(cell, motility_constant){\n let u = unit(cell.data, cell[0]);\n let v = rot2d(u, cell.theta);\n return mul2d(v, motility_constant);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2869,"value":"function advance_motility_angle(cells, tick_duration, options={}, random=Math.random) {\n let {angular_noise} = {...default_motility_options, ...options};\n \n for (let cell of cells) {\n cell.theta += randn_bm(random) * Math.sqrt(2 * angular_noise * tick_duration);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2902,"value":"function apply_motility_force(cells, options={}){\n let {motility_constant} = {...default_motility_options, ...options};\n for (let cell of cells){\n let force = get_motility_vector(cell, motility_constant);\n let perpoint_force = mul2d(force, 1.0 / cell.length);\n \n for (let i = 0; i < cell.length; i++){\n inplace_add2d(cell.a[i], perpoint_force);\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2878,"value":"function paint_motility_direction(cells, motility_constant, display) {\n let {context, xscale: x, yscale: y} = display;\n \n if (!motility_constant) return;\n \n context.save();\n \n cells.forEach((cell, i) => {\n // If the cells have an index assigned, set it to that.\n if (cell.i !== undefined) i = cell.i;\n \n let color = cell.color;\n \n let edge_color = set_opacity(color, 1.0);\n let inner_color = set_opacity(color, 0.5);\n \n // Draw the polarization direction.\n if (cell.theta !== undefined) {\n context.fillStyle = inner_color;\n context.strokeStyle = edge_color;\n context.lineWidth = 1.5;\n \n let motility_vector = get_motility_vector(cell, motility_constant);\n let motility_arrow = [\n x(cell.data[0]), y(cell.data[1]), \n x(cell.data[0] + motility_vector[0]), y(cell.data[1] + motility_vector[1]),\n ];\n \n stroke_fill_arrow(context, motility_arrow);\n }\n });\n \n context.restore();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3367,"value":"function is_inside(point,polygon){\n let x = point[0], y = point[1];\n\n let is_inside = false;\n for (let i = 0, j = polygon.length - 1; i < polygon.length; j = i++) {\n let xi = polygon[i][0], yi = polygon[i][1];\n let xj = polygon[j][0], yj = polygon[j][1];\n let intersect = ((yi > y) != (yj > y))\n && (x < (xj - xi) * (y - yi) / (yj - yi) + xi);\n //let on_edge = isOnLine(xi,yi,xj,yj,x,y);\n if (intersect ){\n is_inside = !is_inside;\n }\n }\n return is_inside\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3527,"value":"n_collagen_points = 430","pinned":false,"hidden":false,"mode":"ojs"},{"id":4357,"value":"collagen_radius = 7.0","pinned":false,"hidden":false,"mode":"ojs"},{"id":3971,"value":"function make_collagen_circles(collagen_points, points=12){\n \n let collagen = collagen_points.map(p => circle_poly(p, collagen_radius , points));\n \n // Make em green.\n for (let c of collagen) c.color = \"green\";\n \n return collagen;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3931,"value":"function add_elastic_force(young_modulus,cells,equilibrium_distance, store_forces) {\n \n for (let c of cells) {\n let cell_area = d3.polygonArea(c);\n\n for (let i = 0; i < c.length; ++i) {\n let j = (i + 1) % c.length;\n let k = (i + 2) % c.length;\n \n // Apply elastic force on j from both neighbours.\n let elastic_0 = elastic_force(c[i], c[j], young_modulus, equilibrium_distance);\n let elastic_1 = elastic_force(c[k], c[j], young_modulus, equilibrium_distance);\n inplace_add2d(c.a[j], elastic_0);\n inplace_add2d(c.a[j], elastic_1);\n \n \n // For speed we don't store forces for each node by default.\n if (store_forces) {\n c.f[j].push({kind: \"elastic\", force: elastic_0});\n c.f[j].push({kind: \"elastic\", force: elastic_1});\n \n }\n }\n \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4444,"value":"closest_neighbor = function nearestNeighbor(i, cells) {\n const {points} = cells.data;\n let minDistance = Infinity;\n let minNeighbor;\n const x0 = points[i * 2];\n const y0 = points[i * 2 + 1];\n for (const neighbor of cells.neighbors(i)) {\n const x1 = points[neighbor * 2];\n const y1 = points[neighbor * 2 + 1];\n const distance = (x0 - x1) ** 2 + (y0 - y1) ** 2;\n if (distance < minDistance) {\n minDistance = distance;\n minNeighbor = neighbor;\n }\n }\n return minNeighbor;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3859,"value":"function take_over_collagen(p, cells){\n for (let i=0; i _.pick(row, \"n_collagen_points\", \"motility_constant\", \"max_radius\", \"collagen_seed\", \"polar_seed\", \"shape_indexes\", \"radial_distances\"));\n let csv = d3.tsvFormat(picked_data);\n let blob = new Blob([csv], {type: \"text/csv\"})\n return DOM.download(blob, undefined, \"Download multisim results.\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5284,"value":"{\n let picked_data = multi_simulation_results.map(row => {\n let selected_row = _.pick(row, \"n_collagen_points\", \"motility_constant\", \"max_radius\", \"collagen_seed\", \"polar_seed\");\n selected_row[\"n_cells\"] = row.shape_indexes.length;\n for (let i = 0; i < 160; i += 1) {\n selected_row[`shape_index_${i}`] = i < row.shape_indexes.length ? row.shape_indexes[i] : \"\";\n }\n for (let i = 0; i < 160; i += 1) {\n selected_row[`radial_distance_${i}`] = i < row.radial_distances.length ? row.radial_distances[i] : \"\";\n }\n return selected_row;\n });\n let csv = d3.tsvFormat(picked_data);\n let blob = new Blob([csv], {type: \"text/csv\"})\n return DOM.download(blob, undefined, \"Download multisim results with per cell info.\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4596,"value":"function find_max_and_position(multi_simulation_results){\nlet maximum=0\nlet position=0;\nfor (let i=0; i maximum){\n maximum = multi_simulation_results[i].farthest;\n position = i;\n }\n}\n return [maximum, position];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4606,"value":"find_max_and_position(multi_simulation_results)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4609,"value":"vegalite({\n\n data: {values: multi_simulation_results},\n autosize: \"fit\",\n width,\n height: 360,\n mark: \"rect\",\n width: 600,\n height: 300,\n encoding: {\n x: {\n bin: {\"step\":0.01},\n field: \"motility_constant\",\n type: \"quantitative\"\n },\n y: {\n bin: {\"step\": 5},\n field: \"n_collagen_points\",\n type: \"quantitative\"\n },\n color: {\n field: \"max_radius\",\n type: \"quantitative\",\n scale: {\n //domain: [250, 350],\n scheme: \"viridis\",\n \n },\n }\n \n },\n config:{\n // background:\"#053061\"\n }\n \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4497,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":779,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":791,"value":"p0=default_simulate_options.equilibrium_distance*n_points\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":820,"value":"a0=default_simulate_options.equilibrium_area","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"viewof simulation = simulate({\n boundary_function: no_boundaries,\n polarization_factor : 0.0, \n \n //equilibrium_distance: q*Math.sqrt(default_simulate_options.equilibrium_area)/n_points,\n relative_color: true, \n velocity_decay: 1e-4, \n relax_compression: 1.0,\n press_compression: 1.0,\n \n lenard_jones_strength: 0.75,\n osmosis_constant: 0.001,\n \n motility_constant: 1.2 ,\n n_collagen_points: 50.0,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"simulation","pinned":false,"hidden":false,"mode":"ojs"},{"id":4408,"value":"env_size_big =( {width: env_size.width *2 , \n height: env_size.height * 2 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":4057,"value":"function compute_cell_circle_radius(simulation) {\n let x = env_size_big.width/2; \n let y = env_size_big.height/2;\n\n let env_center = [x,y];\n let circle_radius = [];\n let max_distance = 0;\n \n for (let i=0; i simulation.nuclei.slice());\n \n return nuclei_positions\n }\n\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1801,"value":"nuclei_velocities_absolute={\n let magnitudes = [];\n let nuclei_velocities= simulation.map(simulation => simulation.velocities.slice());\n \n for (let i=0; i100){\n for (let j=0; j window:mousemove!\",\n encodings: [\"x\", \"y\"],\n translate: \"[mousedown, window:mouseup] > window:mousemove!\",\n zoom: \"wheel!\",\n mark: {\"fill\": \"#333\", \"fillOpacity\": 0.125, \"stroke\": \"white\"},\n resolve: \"global\"\n },\n },\n mark: \"rect\",\n autosize: \"fit\",\n width,\n height: 360,\n encoding: {\n y: { bin: {\"maxbins\":15}, field: `velocity`, type: \"quantitative\"},\n x: {\n bin: {\"maxbins\":20},\n field: `shape`, \n type: \"quantitative\", \n scale: {\n // domain: cell_shapeindex_extent,\n zero: false,\n },\n }, \n color: {\n aggregate: \"count\",\n type: \"quantitative\",\n scale: { scheme: \"purpleblue\"}\n \n }\n \n }\n \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":857,"value":"list={\n let list=[];\n for (let i=1000; i {\n // Keep if it doesn't intesect with any of the test_polys.\n return !test_polys.some(test_poly => {\n // Our poly intersect the test poly if any of its points is inside.\n return poly.some(p => d3.polygonContains(test_poly, p));\n });\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4351,"value":"md`### Misc math`","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"function distance(p1, p2) {\n return Math.sqrt( (p1[0] - p2[0])**2 + (p1[1] - p2[1])**2 );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2872,"value":"// Standard Normal variate using Box-Muller transform.\n// Copied from https://stackoverflow.com/a/36481059/103054\nfunction randn_bm(random) {\n var u = 0, v = 0;\n while(u === 0) u = random(); //Converting [0,1) to (0,1)\n while(v === 0) v = random();\n return Math.sqrt( -2.0 * Math.log( u ) ) * Math.cos( 2.0 * Math.PI * v );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4348,"value":"md`### Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":772,"value":"seedrandom = require(\"seedrandom@2.4.3/seedrandom.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"import {\n setup_display,\n //env_size, \n run_loop, \n periodic_boundaries, \n paint_cells, \n paint_polarization, \n paint_details, \n apply_membrane_forces, \n apply_intercell_forces, \n advance_physics,\n tick_duration,\n record_canvas,\n default_membrane_options,\n default_intercell_options,\n generate_random_cell_centers,\n generate_random_polarization,\n perimeter_points,\n shrink_poly,\n shrink_distance,\n compute_voronoi_polygons,\n min_nucleus_separation,\n border,\n // n_cells,\n //n_points,\n default_nucleus_options,\n apply_nucleus_forces,\n apply_membrane_stiffness,\n generate_poison_disc_cell_centers,\n paint_actins,\n poissonDiscSampler,\n elastic_force,\n init_physics,\n \n \n // Geometry\n rot2d,\n mul2d,\n unit,\n inplace_add2d,\n move_cell_to,\n sub2d,\n circle_poly,\n \n // Display\n set_opacity,\n stroke_fill_arrow,\n pretty_float,\n}\nfrom '@csiz/cell-settling' ","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"import {checkbox, slider, radio, button, text} from \"@csiz/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4415,"value":"import { cell_cell_adhesion_force,\n apply_cell_cell_adhesion_forces,\n closest_point_on_segment,\n paint_cell_cell_junctions,\n \n \n }\nfrom '@spatac/cell-migration-simulation_version-3'","pinned":false,"hidden":false,"mode":"ojs"},{"id":453,"value":"import {serialize} from \"@mbostock/saving-svg\"\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":41,"value":"import {vegalite, vega} from \"@csiz/vega\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4796,"value":"_ = require(\"lodash\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":55,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":4983,"value":"C2S = {\n const response = await fetch(\"https://raw.githubusercontent.com/gliffy/canvas2svg/master/canvas2svg.js\");\n const blob = await response.blob();\n return require(URL.createObjectURL(blob)).catch(() => window.C2S);\n}","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}