31:T48d,viewof s2 = Inputs.form({ ax0: Inputs.range(p2[ax_vars2[0]]['minmax'], {step: p2[ax_vars2[0]]['s'], label: `${ax_vars2[0]} (Spreading rate)`, value: .16}), ax1: Inputs.range(p2[ax_vars2[1]]['minmax'], {step: p2[ax_vars2[1]]['s'], label: `${ax_vars2[1]} (Between group spread)`}), ax2: Inputs.range(p2[ax_vars2[2]]['minmax'], {step: p2[ax_vars2[2]]['s'], label: `${ax_vars2[2]} (Copying rate)`}), fp0: Inputs.range(p2[fp2[0]]['minmax'], {step: p2[fp2[0]]['s'], label: `${fp2[0]} (Simple-complex)`, value: p2[fp2[0]]['first_val']}), fp1: Inputs.range(p2[fy2]['minmax'], {step: p2[fy2]['s'], label: `${fy2} (Neg. benefits)`, value: p2[fy2]['first_val']}), fp2: Inputs.range(p2[fp2[2]]['minmax'], {step: p2[fp2[2]]['s'], label: `${fp2[2]} (Recovery rate)`, value: p2[fp2[2]]['first_val']}), fp3: Inputs.range(p2[fp2[3]]['minmax'], {step: p2[fp2[3]]['s'], label: `${fp2[3]} (Group benefits)`, value: -1}), fp4: Inputs.range(p2[fp2[4]]['minmax'], {step: p2[fp2[4]]['s'], label: `${fp2[4]} (Inst. Cost)`, value: p2[fp2[4]]['first_val']}), fp5: Inputs.range(p2[fp2[5]]['minmax'], {step: p2[fp2[5]]['s'], label: fp2[5], value: p2[fp2[5]]['first_val']}), })32:T4a1,viewof s2b = Inputs.form({ ax0: Inputs.range(p2[ax_vars2[0]]['minmax'], {step: p2[ax_vars2[0]]['s'], label: `${ax_vars2[0]} (Spreading rate)`, disabled: select == "ρ" ? false : true}), ax1: Inputs.range(p2[ax_vars2[1]]['minmax'], {step: p2[ax_vars2[1]]['s'], label: `${ax_vars2[1]}`, disabled: select == "ρ" ? true : false}), ax2: Inputs.range(p2[ax_vars2[2]]['minmax'], {step: p2[ax_vars2[2]]['s'], label: ax_vars2[2], disabled: true}), fp0: Inputs.range(p2[fp2[0]]['minmax'], {step: p2[fp2[0]]['s'], label: fp2[0], value: p2[fp2[0]]['first_val'], disabled: true}), fp1: Inputs.range(p2[fy2]['minmax'], {step: p2[fy2]['s'], label: fy2, value: p2[fy2]['first_val'], disabled: true}), fp2: Inputs.range(p2[fp2[2]]['minmax'], {step: p2[fp2[2]]['s'], label: fp2[2], value: p2[fp2[2]]['first_val'], disabled: true}), fp3: Inputs.range(p2[fp2[3]]['minmax'], {step: p2[fp2[3]]['s'], label: fp2[3], value: -1}), fp4: Inputs.range(p2[fp2[4]]['minmax'], {step: p2[fp2[4]]['s'], label: fp2[4], value: p2[fp2[4]]['first_val'], disabled: true}), fp5: Inputs.range(p2[fp2[5]]['minmax'], {step: p2[fp2[5]]['s'], label: fp2[5], value: p2[fp2[5]]['first_val'], disabled: true}), })33:T420,- The upper left quadrant represents the time evolution of the average number of people infected by institution level. The dotted line is the global average, here converging to about 41% of people being infected. - The upper right quadrant is the proportion of institutions of that strength. We see that 43.6% of institutions converged onto level two. Institutions weren't willing to pay the cost and invest stronger institutions than level 4 in this case. - The bottom left figure is basically the same plot than upper right, but this is a phase diagram to know how institutional proportional change as a function of relevant parameters in the model, here rho and beta. We can see the phenomenon of what we call **parameter localization**, where some institutional regimes take over part of the parameter space. - Finally, bottom right figure is the equivalent for the upper left figure, i.e. the global average of infected people over all regimes. This figure let us see how did the institutions perform for any combination of the parameter on the axes. 34:T464,viewof s2c = Inputs.form({ ax0: Inputs.range(p2[ax_vars2[0]]['minmax'], {step: p2[ax_vars2[0]]['s'], label: `${ax_vars2[0]} (Spreading rate)`}), ax1: Inputs.range(p2[ax_vars2[1]]['minmax'], {step: p2[ax_vars2[1]]['s'], label: `${ax_vars2[1]}`}), ax2: Inputs.range(p2[ax_vars2[2]]['minmax'], {step: p2[ax_vars2[2]]['s'], label: ax_vars2[2], disabled: true}), fp0: Inputs.range(p2[fp2[0]]['minmax'], {step: p2[fp2[0]]['s'], label: fp2[0], value: p2[fp2[0]]['first_val'], disabled: true}), fp1: Inputs.range(p2[fy2]['minmax'], {step: p2[fy2]['s'], label: fy2, value: p2[fy2]['first_val'], disabled: true}), fp2: Inputs.range(p2[fp2[2]]['minmax'], {step: p2[fp2[2]]['s'], label: fp2[2], value: p2[fp2[2]]['first_val'], disabled: true}), fp3: Inputs.range(p2[fp2[3]]['minmax'], {step: p2[fp2[3]]['s'], label: fp2[3], value: p2[fp2[3]]['first_val']}), fp4: Inputs.range(p2[fp2[4]]['minmax'], {step: p2[fp2[4]]['s'], label: fp2[4], value: p2[fp2[4]]['first_val'], disabled: true}), fp5: Inputs.range(p2[fp2[5]]['minmax'], {step: p2[fp2[5]]['s'], label: fp2[5], value: p2[fp2[5]]['first_val'], disabled: true}), })35:T875,// To get heatmap data, we need // data: data from resdb join `name` and `L` // ax_vars: variables we want as x,y,z // fx: variable to facet // fp: other vars // sliders: set of sliders function get_data_heatmap(data, lookup, fp, ax_vars, radios, sliders, fx) { const dat_hm = []; for (let i=0; i < data.length; i++) { const p_split = data[i].name.split('_') const vs = {} // dictionary containing all the values of selected parameters // Grab the chosen axis0/x, axis1/y, axis2/z const [ax0, ax1, ax2] = ax_vars vs[ax0] = parseFloat(p_split[lookup['name2idx'][ax0]]) vs[ax1] = parseFloat(p_split[lookup['name2idx'][ax1]]) vs[ax2] = parseFloat(p_split[lookup['name2idx'][ax2]]) // Grab the Fixed parameters for (let i=0; i < fp.length; i++) { vs[fp[i]] = parseFloat(p_split[lookup['name2idx'][fp[i]]]) } // Grab the radios, which will be 2 of the 3 axis vars. // The other other one we'll be the value of our heatmap. // This is where we need to know the actual param_name. const p1 = parseFloat(p_split[lookup['name2idx'][radios['x']]]) const p2 = parseFloat(p_split[lookup['name2idx'][radios['y']]]) const hm_vals_i = { 'L': data[i].L, 'fx' : typeof fx !== undefined ? null : p_split[lookup['name2idx'][fx]], 'param1': p1, 'param2': p2, 'param_str': `${p1}/${p2}`, // We need a way to groupby (p1,p2) for `global_hm()` 'value': data[i].value, 'value_prop': data[i].value_prop } if (vs[fp[0]] === sliders['fp0'] && vs[fp[1]] === sliders['fp1'] && vs[fp[2]] === sliders['fp2'] && vs[fp[3]] == sliders['fp3'] && vs[fp[4]] == sliders['fp4']) { // if ax1 == x && ax2 ==y, then ax0 == z if (radios['x'] == ax1 && radios['y'] == ax2 && vs[ax0] == sliders['ax0']) { dat_hm.push(hm_vals_i) } else if (radios['x'] == ax0 && radios['y'] == ax2 && vs[ax1] == sliders['ax1']) { dat_hm.push(hm_vals_i) } else if (radios['x'] == ax0 && radios['y'] == ax1 && vs[ax2] == sliders['ax2']) { dat_hm.push(hm_vals_i) } } } return dat_hm }36:[{"id":"e8905ca1f2da5c7d0e8cf6b3d631b7047c3236e3fb213860a35cdfd15ff659be6e203861734c57ac8834b49ab6ddb9118d394a71722137c0889ad24be35657a4","href":"https://static.observableusercontent.com/files/e8905ca1f2da5c7d0e8cf6b3d631b7047c3236e3fb213860a35cdfd15ff659be6e203861734c57ac8834b49ab6ddb9118d394a71722137c0889ad24be35657a4","name":"sourcesink2_lookup@2.parquet","type":"application/octet-stream","lastModified":1686191091873.937,"size":155364},{"id":"224642e279a82731e2c5d56256267c6cf0783d71fa7f44f3cfb847e5dc5ad075274595f3bc21dc2405eb96daebf95c7ba2b09bb59b8d7bc608e3f6ceb6646c02","href":"https://static.observableusercontent.com/files/224642e279a82731e2c5d56256267c6cf0783d71fa7f44f3cfb847e5dc5ad075274595f3bc21dc2405eb96daebf95c7ba2b09bb59b8d7bc608e3f6ceb6646c02","name":"sourcesink2_sparse.parquet","type":"application/octet-stream","lastModified":1686191139451.857,"size":26146569},{"id":"63a3a30d183e56906ce2b6a7464fc647754cb3dc646d081d0adabad4c86568125f888061464ac1d1015ce7aabe88947997fa2f6593b1ccecde378dda5d2011d4","href":"https://static.observableusercontent.com/files/63a3a30d183e56906ce2b6a7464fc647754cb3dc646d081d0adabad4c86568125f888061464ac1d1015ce7aabe88947997fa2f6593b1ccecde378dda5d2011d4","name":"image.png","type":"image/png","lastModified":1686101893339.009,"size":80552}] 2f:{"title":"Call for action [draft]","cells":[{"id":0,"value":"# Call for action [draft]","pinned":false,"hidden":false,"mode":"md"},{"id":1513,"value":"Control the axis of the phase diagrams (see below for parameter definition):","pinned":false,"hidden":false,"mode":"md"},{"id":395,"value":"viewof r2 = Inputs.form({\n x: Inputs.radio(ax_vars2, {label: \"x\", value: ax_vars2[0]}),\n y: Inputs.radio(ax_vars2, {label: \"y\", value: ax_vars2[1]})\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1849,"value":"html`\n

${[plot_time_evo(false)]}

${[plot_time_evo(true)]}

${[phase_diagram_facetted()]}

${[phase_diagram()]}

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":393,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":3137,"value":"_p.s. if the range input is disabled or a parameter is missing, is that we didn't run the parameter sweep with this set of values_","pinned":false,"hidden":false,"mode":"md"},{"id":2759,"value":"#### Decomposing the call for action","pinned":false,"hidden":false,"mode":"md"},{"id":2556,"value":"viewof select = Inputs.select([\"ρ\", \"β\"], {label: \"x-axis\", value: \"β\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2726,"value":"html`\n

${[extra2()]}

${[extra1(0.005, \"purples\")]}

${[extra1(0.05, \"blues\")]}

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2548,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2497,"value":"r2b = { return { x: select, y: \"η\" } }","pinned":false,"hidden":false,"mode":"ojs"},{"id":2707,"value":"function extra1(eta, scheme) {\n return Plot.plot({\n height: 350,\n width: 450,\n color: { type: \"ordinal\", scheme: scheme, range: [0.4, 1]},\n marginBottom: 35,\n marginLeft: 50,\n marks: [\n Plot.axisY({ labelAnchor: \"center\", label: \"equilibrium prevalence\", tickSpacing: 80, labelArrow: \"none\" }),\n Plot.axisX({ labelAnchor: \"center\", label: select, tickSpacing: 80, labelArrow: \"none\" }),\n Plot.lineY(data_hm2b.filter(d => d.param2 == eta), {\n x: \"param1\", y: \"value\", sort: \"param1\", stroke: \"L\", \n tip: true \n }),\n Plot.lineY(global_hm(data_hm2b).filter(d => d.param2 == eta), {\n x: \"param1\", y: \"value\", sort: \"param1\", stroke: \"black\", \n strokeDasharray: \"5,3\"\n }),\n Plot.frame()\n ]\n})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2449,"value":"function extra2() {\n return Plot.plot({\n height: 350,\n width: 450,\n marginBottom: 35,\n marginLeft: 50,\n marks: [\n Plot.ruleY([0]),\n Plot.axisY({ labelAnchor: \"center\", label: \"equilibrium prevalence\", tickSpacing: 80, labelArrow: \"none\" }),\n Plot.axisX({ labelAnchor: \"center\", label: select, tickSpacing: 80, labelArrow: \"none\" }),\n Plot.lineY(global_hm(data_hm2b).filter(d => d.param2 == 0.005), {\n x: \"param1\", y: \"value\", sort: \"param1\", stroke: \"purple\", \n title: d => `η = 0.005`, \n tip: true \n }),\n Plot.lineY(global_hm(data_hm2b).filter(d => d.param2 == 0.05), { \n x: \"param1\", y: \"value\", sort: \"param1\", stroke: \"blue\", \n title: d => `η = 0.05`, tip: true \n }),\n Plot.frame()\n ]\n}) \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2525,"value":"data_hm2b = get_data_heatmap(data2b, lookup2, fp2, ax_vars2, r2b, s2b, fy2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2017,"value":"## How do I interpret the quadrant?\n\nConsider the following","pinned":false,"hidden":false,"mode":"md"},{"id":2020,"value":"image = FileAttachment(\"image.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2024,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":2445,"value":"### Extra plots","pinned":false,"hidden":false,"mode":"md"},{"id":2835,"value":"Plot.plot({\n color: {legend: true, type: \"ordinal\", scheme: \"reds\", range: [0.3, 1]},\n y: {label: \"equilibrium prevalence\"},\n x: {label: select},\n marks: [\n Plot.dot(data_hm2bb, {\n x: \"param1\", y: d => d.value, \n stroke: \"L\", sort: \"param1\", tip: true\n }),\n Plot.line(data_hm2bb, {\n x: \"param1\", y: d => d.value, \n stroke: \"L\", sort: \"param1\", tip: true\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2863,"value":"WITH tmp as ( \n SELECT MEAN(value_prop) as mean_I, L FROM data_hm2b WHERE param2 = 0.05 GROUP BY L\n )\n\nSELECT * \n FROM data_hm2b d \n LEFT JOIN tmp\n ON tmp.L = d.L\n WHERE d.param2 = 0.05 ","pinned":false,"hidden":false,"mode":"sql","output":"data_hm2bb","database":"var:data_hm2b"},{"id":2774,"value":"#### _Looking at eta_","pinned":false,"hidden":false,"mode":"md"},{"id":2793,"value":"Plot.plot({\n height: 350,\n width: 450,\n color: { type: \"ordinal\", scheme: \"greys\", range: [0.4, 1]},\n marginBottom: 35,\n marginLeft: 50,\n marks: [\n Plot.ruleY([0]),\n Plot.axisY({ labelAnchor: \"center\", label: \"equilibrium prevalence\", tickSpacing: 80, labelArrow: \"none\" }),\n Plot.axisX({ labelAnchor: \"center\", label: \"η\", tickSpacing: 80, labelArrow: \"none\" }),\n Plot.lineY(data_hm2c.filter(d => d.param1 == s2c['ax0']), {\n x: \"param2\", y: \"value\", sort: \"param2\", stroke: \"L\", \n tip: true \n }),\n Plot.lineY(global_hm(data_hm2c).filter(d => d.param1 == s2c['ax0']), {\n x: \"param2\", y: \"value\", sort: \"param2\", stroke: \"black\", \n strokeDasharray: \"5,3\"\n }),\n Plot.frame()\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2783,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2785,"value":"r2c = {return {x:\"β\", y: \"η\"}}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2778,"value":"data_hm2c = get_data_heatmap(data2b, lookup2, fp2, ax_vars2, r2c, s2c, fy2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2117,"value":"#### Where are the regimes?\n\nBy focusing on the averages of average performance, we can see the potential regimes:","pinned":false,"hidden":false,"mode":"md"},{"id":2121,"value":"Plot.plot({\n width: 1000, height: 400, marginRight: 200, x: {type:\"log\"}, y: {label: \"% infected\", percent: true, grid: true}, \n marks: [\n Plot.lineY(d1a, {\n x: d => d[0], y: d => d[1], strokeDasharray: \"5,3\", opacity: 1., stroke: \"gold\"\n }),\n Plot.text(d1a, Plot.selectLast({\n x: d => d[0], y: d => d[1], text: d => \"low transmission\\nbut high imitation rate\", textAnchor: \"start\", dx: 7\n })),\n Plot.lineY(d3.flatRollup(d2, v => d3.sum(v, d => d.value * d.value_prop), d => d.timestep), {\n x: d => d[0], y: d => d[1], strokeDasharray: \"5,3\", opacity: 1., stroke: \"purple\"\n }),\n Plot.lineY(d3.flatRollup(d4, v => d3.sum(v, d => d.value * d.value_prop), d => d.timestep), {\n x: d => d[0], y: d => d[1], strokeDasharray: \"5,3\", opacity: 1., stroke: \"forestgreen\"\n }),\n // Plot.frame()\n ]\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":3133,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":1271,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":2051,"value":"d1 = sql_data_a('sourcesink', 4219)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2185,"value":"d1a = d3.flatRollup(d1, v => d3.sum(v, d => d.value * d.value_prop), d => d.timestep)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2154,"value":"d2 = sql_data_a('sourcesink', 2219)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2157,"value":"d4 = sql_data_a('sourcesink', 441)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2256,"value":"#### data","pinned":false,"hidden":false,"mode":"md"},{"id":10,"value":"resdb = DuckDBClient.of({\n sourcesink: FileAttachment(\"sourcesink2_sparse.parquet\"),\n sourcesink_lookup: FileAttachment(\"sourcesink2_lookup@2.parquet\"),\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1826,"value":"sourcesink2_lookup = resdb.query(\"SELECT param_str::STRING as name, row_id FROM sourcesink_lookup\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1828,"value":"// TODO: Ideally we would like to have resdb return a lookup\nsourcesink2_lookup_map = sourcesink2_lookup.reduce(function(map, obj) {\n map[obj.name] = obj.row_id;\n return map;\n}, {})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1843,"value":"data2 = sql_data_a('sourcesink', chosen_row_id)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1845,"value":"data2b = sql_data_b(\"sourcesink\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1847,"value":"data_hm2 = get_data_heatmap(data2b, lookup2, fp2, ax_vars2, r2, s2, fy2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1962,"value":"#### Data wrangling","pinned":false,"hidden":false,"mode":"md"},{"id":1830,"value":"lookup2 = {\n const out = {}\n out['idx2name'] = {0: 'β', 1: 'ξ', 2: 'α', 3: 'γ', 4: 'ρ', 5: 'η', 6: 'b', 7: 'c', 8:'μ'}\n out['name2idx'] = {'β': 0, 'ξ': 1, 'α': 2, 'γ': 3, 'ρ': 4, 'η': 5, 'b': 6, 'c': 7, 'μ': 8}\n return out\n}\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":1834,"value":"p2 = get_param_table(sourcesink2_lookup_map, lookup2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1836,"value":"ax_vars2 = [\"β\", \"ρ\", \"η\"] // choose the x,y,z axis, i.e. params to vary","pinned":true,"hidden":false,"mode":"ojs"},{"id":1839,"value":"fy2 = \"α\" // choose the facet variable","pinned":true,"hidden":false,"mode":"ojs"},{"id":1841,"value":"fp2 = [\"ξ\", \"α\", \"γ\", \"b\", \"c\", \"μ\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":2229,"value":"chosen_row_id = sourcesink2_lookup_map[`${f(s2['ax0'])}_${f(s2['fp0'])}_${f(s2['fp1'])}_${f(s2['fp2'])}_${f(s2['ax1'])}_${f(s2['ax2'])}_${f(s2['fp3'])}_${f(s2['fp4'])}_${f(s2['fp5'])}`]","pinned":true,"hidden":false,"mode":"ojs"},{"id":1880,"value":"// we sum for each parameter string the produc of the frequency value of infected and proportion of institution.\nfunction global_hm(d){\n return d3.flatRollup(d, v => d3.sum(v, d => d.value * d.value_prop), d => d.param_str)\n .map(currentElement => ({\n 'param1': parseFloat(currentElement[0].split('/')[0]),\n 'param2': parseFloat(currentElement[0].split('/')[1]),\n 'value': currentElement[1],\n 'L': 1\n }))\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1101,"value":"function sql_data_a(model, name) {\n return resdb.query(`\n SELECT timestep::INT as timestep, L::INT as L, value, value_prop\n FROM ${model}\n WHERE\n row_id = '${name}'\n `)\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1898,"value":"function sql_data_b(model) {\n return resdb.query(`\n WITH tmp as (\n SELECT row_id, L, MAX(timestep::INT) as timestep\n FROM ${model}\n GROUP BY row_id, L\n )\n SELECT s.value, s.L::INT as L, s.value_prop, ss.param_str::STRING as name\n FROM ${model} s\n JOIN tmp\n ON s.row_id = tmp.row_id AND s.L = tmp.L AND s.timestep = tmp.timestep\n JOIN ${model}_lookup ss\n ON s.row_id = ss.row_id\n ORDER BY (s.row_id, s.L)\n `)\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1902,"value":"function get_fixed_params(param_table, ax_vars, fx) {\n const all_params = [...Object.keys(param_table)]\n const varying_params = typeof fx !== undefined ? new Set(ax_vars) : new Set(ax_vars.concat(fx))\n const diff_params = new Set(all_params.filter((x) => !varying_params.has(x)))\n return Array.from(diff_params)\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2223,"value":"#### real helpers","pinned":false,"hidden":false,"mode":"md"},{"id":1895,"value":"f = (x) => Number.isInteger(x) ? x.toPrecision(2) : x","pinned":false,"hidden":false,"mode":"ojs"},{"id":1892,"value":"// Extract the step from a list of values for a parameter\ns = (p,i) => { \n const unique_vals = Array.from(new Set(p.map(d => parseFloat(d[i])))).sort((a,b) => a - b)\n const out = []\n for (let i=1; i < unique_vals.length; i++ ) {\n out.push(+(unique_vals[i]-unique_vals[i-1]).toPrecision(2))\n } // return whatev if length is zero\n return out.length === 0 ? 0.1 : out[0]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1889,"value":"minmax = (p, i) => d3.extent(p.map(d => parseFloat(d[i])))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1886,"value":"// Param table where each key is a parameter, and values \n// are list of values relevant to \n// model: resdb output for a specific model\n// lookup: { [0: param1, 1: param2, ...] }\nfunction get_param_table(model_lookup, param_lookup) {\n \n const p = Object.keys(model_lookup).map(d => d.split(\"_\")) \n \n const param_table = {}\n const first_line_param = p[0]\n for ( let i=0; i < first_line_param.length; i++ ) {\n param_table[param_lookup['idx2name'][i]] = { \n 's': s(p,i), 'first_val': first_line_param[i], 'minmax': minmax(p,i) \n }\n }\n return param_table\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1883,"value":"$35","pinned":true,"hidden":false,"mode":"ojs"},{"id":1679,"value":"## Plotting functions","pinned":false,"hidden":false,"mode":"md"},{"id":1876,"value":"function plot_time_evo(is_prop) {\n const global_mean = d3.flatRollup(data2, v => d3.sum(v, d => d.value * d.value_prop), d => d.timestep)\n\n return Plot.plot({\n width: 600,\n height: 300,\n x: {type: \"log\"},\n y: { percent: true, grid: true}, \n color: { \n scheme: is_prop ? \"Blues\" : \"Reds\", \n type: \"ordinal\", \n range: [0.3, 1],\n legend: true \n },\n marks: [\n Plot.axisY({ \n labelAnchor: \"center\", \n label: is_prop ? \"% of institutions of that strength\" : \"% infected\", \n tickSpacing: 80, labelArrow: \"none\" \n }),\n Plot.axisX({ \n labelAnchor: \"center\", tickSpacing: 80, labelArrow: \"none\" \n }),\n Plot.lineY(global_mean, {\n x: d => d[0], y: d => d[1],\n strokeDasharray: \"5,3\", opacity: is_prop ? 0. : 1.\n }),\n Plot.lineY(\n data2, {\n x: 'timestep', y: is_prop ? \"value_prop\" : \"value\", stroke: \"L\", tip: true\n })\n ]\n })\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1684,"value":"toSVG(plot_time_evo())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1686,"value":"toSVG(plot_time_evo(true))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1943,"value":"function phase_diagram_facetted() {\n return Plot.plot({\n width: 800,\n height: 313,\n marginLeft: 60,\n color: {\n type: \"linear\",\n scheme: \"Greens\"\n },\n fy: { label: \"\" },\n facet: { data: data_hm2, x: \"L\", y: 'fx' },\n marks: [\n Plot.axisY({ \n labelAnchor: \"center\", \n label: r2['y'], \n tickSpacing: 80, labelArrow: \"none\" \n }),\n Plot.axisX({ \n label: r2['x'],\n labelAnchor: \"center\", tickSpacing: 80, labelArrow: \"none\" \n }),\n Plot.raster(data_hm2, {\n x: \"param1\",\n y: \"param2\",\n fill: 'value_prop',\n interpolate: \"nearest\"\n }),\n Plot.tip(data_hm2, Plot.pointer({\n x: \"param1\",\n y: \"param2\",\n title: d => `ρ: ${d.param2}\\nβ: ${d.param1}\\nInst. of that\\nstrength: ${(d.value_prop*100).toFixed(2)}%`\n }))\n ]\n })\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1691,"value":"toSVG(phase_diagram_facetted())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1762,"value":"Plot.legend({color: {type: \"linear\", scheme: 'Greens', domain: d3.extent(data_hm2.map(d => d.value_prop))}})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3062,"value":"function phase_diagram(x, y, z, pal, w, h) {\n return Plot.plot({\n width: 350,\n height: 303,\n marginTop: 42,\n marginLeft: 60,\n color: {\n type: \"linear\",\n scheme: 'viridis'\n },\n marks: [\n Plot.axisY({ \n labelAnchor: \"center\", \n label: r2['y'], \n tickSpacing: 80, labelArrow: \"none\" \n }),\n Plot.axisX({ \n label: r2['x'],\n labelAnchor: \"center\", tickSpacing: 80, labelArrow: \"none\" \n }),\n Plot.raster(global_hm(data_hm2), {\n x: \"param1\",\n y: \"param2\",\n fill: 'value',\n interpolate: \"nearest\",\n tip: true,\n title: d => `ρ: ${d.param2}\\nβ: ${d.param1}\\nGlobal infection rate: ${(d.value*100).toFixed(2)}%`\n })\n ]\n })\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1716,"value":"toSVG(phase_diagram())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1727,"value":"Plot.legend({color: {type: \"linear\", scheme: 'viridis', domain: d3.extent(global_hm(data_hm2).map(d => d.value))}})","pinned":true,"hidden":false,"mode":"ojs"},{"id":2038,"value":"// function plot_time_evo_avg(d) {\n// return Plot.plot({\n// width: 600, height: 200, x: {type:\"log\"}, y: {label: \"% infected\", percent: true, grid: true}, \n// marks: [\n// Plot.lineY(d3.flatRollup(d, v => d3.sum(v, d => d.value * d.value_prop), d => d.timestep), {\n// x: d => d[0], y: d => d[1],\n// strokeDasharray: \"5,3\", opacity: 1.\n// }),\n// Plot.frame()\n// ]\n// })\n// }","pinned":false,"hidden":false,"mode":"ojs"},{"id":3087,"value":"toSVG(extra2())","pinned":true,"hidden":false,"mode":"ojs"},{"id":2429,"value":"import { toSVG } from \"@fil/plot-figure-to-svg\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}