31:T881,// 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]]]) } // vs => {β: 0.02, b: 0.02, c: 0.05, γ: 0.9, ρ: 0.1, μ: 0.0001} // 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 } const ax_z_name = Array.from(d3.difference(ax_vars, [radios['x'], radios['y']]))[0] const ax_z_idx = ax_vars.findIndex(d => d == ax_z_name) // fixed the non-axis parameter to their slider values. 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 (vs[ax_z_name] === sliders[`ax${ax_z_idx}`]) { dat_hm.push(hm_vals_i) } } } return dat_hm }32:[{"id":"c4e3900c5d7fa717d70376cd99cb864cf3eb5a9f215cbc1b5f8def4e40584a3abf319a754ac577d92d8a91e24355922d71c60b06c255c9b561e3e8107c793524","href":"https://static.observableusercontent.com/files/c4e3900c5d7fa717d70376cd99cb864cf3eb5a9f215cbc1b5f8def4e40584a3abf319a754ac577d92d8a91e24355922d71c60b06c255c9b561e3e8107c793524","name":"sourcesink1_sparse.parquet","type":"application/octet-stream","lastModified":1686591550888.27,"size":6528400},{"id":"b549e2c3f490d7eb1d52ddd2e18f55d085aa8a417d7da4be17af08e32e0d29e169f28f1db94c2432d5a47c08628354b5b6c607d858d0a5b13427f956dfc46208","href":"https://static.observableusercontent.com/files/b549e2c3f490d7eb1d52ddd2e18f55d085aa8a417d7da4be17af08e32e0d29e169f28f1db94c2432d5a47c08628354b5b6c607d858d0a5b13427f956dfc46208","name":"sourcesink1@3.parquet","type":"application/octet-stream","lastModified":1686189217823.721,"size":33739960},{"id":"eb0fd34b6e24f08b2124338cdc1355d91a4a6dd739d465170c5391bcc276e8f09cd466cfd50b11650f791d9ccaeb3866404f24e88311b01b26982a8d60d4ff8f","href":"https://static.observableusercontent.com/files/eb0fd34b6e24f08b2124338cdc1355d91a4a6dd739d465170c5391bcc276e8f09cd466cfd50b11650f791d9ccaeb3866404f24e88311b01b26982a8d60d4ff8f","name":"sourcesink1_lookup@6.parquet","type":"application/octet-stream","lastModified":1686189286276.42,"size":104937},{"id":"0a687224d3086fa0ff38e54bd89bd036e0f3ea326a7c348e469bef13b2f452b468552501d6c6ddab149bd9600136bf88f13c006153174a58afa0780bd700a0ba","href":"https://static.observableusercontent.com/files/0a687224d3086fa0ff38e54bd89bd036e0f3ea326a7c348e469bef13b2f452b468552501d6c6ddab149bd9600136bf88f13c006153174a58afa0780bd700a0ba","name":"Untitled presentation.png","type":"image/png","lastModified":1685537337206.332,"size":34856},{"id":"4fae5b7905bd9b03c275b3435f37679f6b29f339a3d875755215e0a424161548c383e9eae86143102eba6c550c9006a29f4dd6ad108a16d38c28a2add5d0d7f4","href":"https://static.observableusercontent.com/files/4fae5b7905bd9b03c275b3435f37679f6b29f339a3d875755215e0a424161548c383e9eae86143102eba6c550c9006a29f4dd6ad108a16d38c28a2add5d0d7f4","name":"image@1.png","type":"image/png","lastModified":1685537683160.193,"size":155392}] 2f:{"title":"A group-based approach to model behavior and institution co-evolution","cells":[{"id":0,"value":"# A group-based approach to model behavior and institution co-evolution\n## [Source-sink behavioural dynamics limit institutional evolution in a group-structured society (Hébert-Dufresne, Waring, et al 2022)](https://royalsocietypublishing.org/doi/10.1098/rsos.211743)","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 r1 = Inputs.form({\n x: Inputs.radio(av1, {label: \"x\", value: av1[0]}),\n y: Inputs.radio(av1, {label: \"y\", value: av1[1]})\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1318,"value":"miniPlots = html`\n

${[plot_time_evo(false)]}

${[plot_time_evo(true)]}

${[phase_diagram_facetted()]}

${[phase_diagram()]}

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":393,"value":"viewof s1 = Inputs.form({\n ax0: Inputs.range(p1[av1[0]]['minmax'], {step: p1[av1[0]]['s'], label: `${av1[0]} (Spreading rate)`}),\n ax1: Inputs.range(p1[av1[1]]['minmax'], {step: p1[av1[1]]['s'], label: `${av1[1]} (Group benefits)`}),\n ax2: Inputs.range(p1[av1[2]]['minmax'], {step: p1[av1[2]]['s'], label: `${av1[2]} (Institutional cost)`}),\n fp0: Inputs.range(p1[fp1[0]]['minmax'], {step: p1[fp1[0]]['s'], label: `${fp1[0]} (Recovery rate)`, value: p1[fp1[0]]['first_val']}),\n fp1: Inputs.range(p1[fp1[1]]['minmax'], {step: p1[fp1[1]]['s'], label: `${fp1[1]} (Behav. diffusion)`, value: p1[fp1[1]]['first_val']}),\n fp2: Inputs.range(p1[fp1[2]]['minmax'], {step: p1[fp1[2]]['s'], label: fp1[2], value: p1[fp1[2]]['first_val']})\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1562,"value":"#### Things to try\n - Try maxing out behavioral transmission rate within group while reducing institutional cost. This is the only time it is worth investing in very strong institutions!","pinned":false,"hidden":false,"mode":"md"},{"id":1605,"value":"#### Extra plots","pinned":false,"hidden":false,"mode":"md"},{"id":1778,"value":"viewof b = Inputs.range(p1[av1[1]]['minmax'], {step: p1[av1[1]]['s'], label: `${av1[1]} (group benefits)`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1643,"value":"viewof cost = Inputs.range(p1[av1[2]]['minmax'], {step: p1[av1[2]]['s'], label: `${av1[2]} (cost for left Fig)`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1638,"value":"viewof beta = Inputs.range(p1[av1[0]]['minmax'], {step: p1[av1[0]]['s'], label: `${av1[0]} (transmission rate for rigth Fig)`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1624,"value":"miniPlots2 = html`\n

${[figure4a()]}

${[figure4b()]}

\nFigure 4. Institutional frequency among groups.\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1531,"value":"#### Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":1496,"value":"_Institutional localization_: In some regime, we can see that some institutional levels dominate phase space. The faceted figure on the bottom corresponds to **Figure 5** in the paper. ","pinned":false,"hidden":false,"mode":"md"},{"id":1494,"value":"untitledPresentation = FileAttachment(\"Untitled presentation.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1526,"value":"image1 = FileAttachment(\"image@1.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1271,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":1130,"value":"function plot_time_evo(is_prop) {\n const global_mean = d3.rollup(data, v => d3.sum(v, d => d.value * d.value_prop), d => d.timestep)\n\n return Plot.plot({\n x: {type:\"log\"},\n y: {label: is_prop ? \"% of institutions of that strength\" : \"% cooperators\", percent: true}, \n width: 600,\n height: 300,\n color: { \n scheme: is_prop ? \"Blues\" : \"Reds\", \n type: \"ordinal\", \n range: [0.3, 1],\n legend: true \n },\n marks: [\n Plot.lineY(Array.from(global_mean.values()), {\n strokeDasharray: \"5,3\", opacity: is_prop ? 0. : 1.\n }),\n Plot.lineY(\n data, {\n x: 'timestep', y: is_prop ? \"value_prop\" : \"value\", stroke: \"L\", tip: true\n }),\n Plot.dot(\n data, {\n x: 'timestep', y: is_prop ? \"value_prop\" : \"value\", stroke: \"L\"\n })\n ]\n })\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1408,"value":"function phase_diagram(x, y, z, pal, w, h) {\n return Plot.plot({\n width: 350,\n height: 300,\n color: {\n type: \"linear\",\n scheme: 'viridis'\n },\n x: { label: r1['x'] },\n y: { label: r1['y'] },\n marks: [\n Plot.raster(global_hm(data_hm), {\n x: \"param1\",\n y: \"param2\",\n fill: 'value',\n interpolate: \"nearest\",\n tip: true\n }),\n ]\n })\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1384,"value":"function phase_diagram_facetted() {\n return Plot.plot({\n width: 800,\n height: 313,\n color: {\n type: \"linear\",\n scheme: \"Greens\"\n },\n x: { label: r1['x'] },\n y: { label: r1['y'] },\n fy: { label: \"\" },\n facet: { data: data_hm, x: \"L\", y: 'fx' },\n marks: [\n Plot.raster(data_hm, {\n x: \"param1\",\n y: \"param2\",\n fill: 'value_prop',\n interpolate: \"nearest\",\n tip: true\n }),\n ]\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1573,"value":"function figure4a() {\n return Plot.plot({\n width: 500,\n height: 300,\n x: {label: r1[\"x\"]},\n y: {label: \"↑ % of institutions of that strength\"},\n color: { \n scheme: \"Blues\", \n type: \"ordinal\", \n range: [0.3, 1],\n legend: true \n },\n marks: [\n Plot.lineY(\n data_hm.filter(d => d.param2 == (r1[\"y\"] == \"c\" ? cost : b)), {\n x: 'param1', y: \"value_prop\", stroke: \"L\", sort: \"param1\", tip: true\n }),\n Plot.dot(\n data_hm.filter(d => d.param2 == (r1[\"y\"] == \"c\" ? cost : b)), {\n x: 'param1', y: \"value_prop\", stroke: \"L\"\n })\n ]\n }) \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1618,"value":"function figure4b() {\n return Plot.plot({\n width: 500,\n height: 300,\n x: {label: r1[\"y\"]},\n y: {label: \"↑ % of institutions of that strength\"},\n color: { \n scheme: \"Blues\", \n type: \"ordinal\", \n range: [0.3, 1],\n legend: true \n },\n marks: [\n Plot.lineY(\n data_hm.filter(d => d.param1 == beta), {\n x: 'param2', y: \"value_prop\", stroke: \"L\", sort: \"param2\", tip: true\n }),\n Plot.dot(\n data_hm.filter(d => d.param1 == beta), {\n x: 'param2', y: \"value_prop\", stroke: \"L\"\n })\n ]\n }) \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1847,"value":"#### Data","pinned":false,"hidden":false,"mode":"md"},{"id":10,"value":"resdb = DuckDBClient.of({\n sourcesink1: FileAttachment(\"sourcesink1@3.parquet\"),\n sourcesink1_lookup: FileAttachment(\"sourcesink1_lookup@6.parquet\"),\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":786,"value":"sourcesink1_lookup = resdb.query(\"SELECT param_str::STRING as name, row_id FROM sourcesink1_lookup\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1889,"value":"sourcesink1_lookup_map = sourcesink1_lookup.reduce(function(map, obj) {\n map[obj.name] = obj.row_id;\n return map;\n}, {})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1921,"value":"lookup1 = {\n const out = {}\n out['idx2name'] = {0: \"β\", 1: 'γ', 2: 'ρ', 3: 'b', 4: 'c', 5: 'μ'}\n out['name2idx'] = {\"β\": 0, 'γ': 1, 'ρ': 2, 'b': 3, 'c': 4, 'μ': 5}\n return out\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1925,"value":"p1 = get_param_table(sourcesink1_lookup_map, lookup1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2226,"value":"p1","pinned":true,"hidden":false,"mode":"ojs"},{"id":1946,"value":"chosen_row_id = sourcesink1_lookup_map[`${f(s1['ax0'])}_${f(s1['fp0'])}_${f(s1['fp1'])}_${f(s1['ax1'])}_${f(s1['ax2'])}_${f(s1['fp2'])}`]","pinned":true,"hidden":false,"mode":"ojs"},{"id":1136,"value":"data = sql_data_a('sourcesink1', chosen_row_id)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1894,"value":"datab = sql_data_b('sourcesink1')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1185,"value":"data_hm = get_data_heatmap(datab, lookup1, fp1, av1, r1, s1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1635,"value":"### Data wrangling","pinned":false,"hidden":false,"mode":"md"},{"id":389,"value":"av1 = [\"β\", \"b\", \"c\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":391,"value":"fp1 = [\"γ\", \"ρ\", \"μ\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":1106,"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":645,"value":"function sql_data_b(model) {\n // querying the steady state value for all rows grouped by row_id and L.\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":1153,"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":1101,"value":"f = (x) => Number.isInteger(x) ? x.toPrecision(2) : x","pinned":true,"hidden":false,"mode":"ojs"},{"id":404,"value":"minmax = (p, param) => d3.extent(p.map(d => d[param]))","pinned":true,"hidden":false,"mode":"ojs"},{"id":807,"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":true,"hidden":false,"mode":"ojs"},{"id":813,"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":1935,"value":"function 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":true,"hidden":false,"mode":"ojs"},{"id":1907,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":1959,"value":"get_data_heatmap(datab, lookup1, fp1, av1, r1, s1) // all configuration for chosen axis at some fixed value for all other parameters.","pinned":true,"hidden":false,"mode":"ojs"},{"id":1679,"value":"## export plots","pinned":false,"hidden":false,"mode":"md"},{"id":1684,"value":"plot_time_evo()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1686,"value":"plot_time_evo(true)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1691,"value":"phase_diagram_facetted()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1762,"value":"Plot.legend({color: {type: \"linear\", scheme: 'Greens', domain: d3.extent(data_hm.map(d => d.value_prop))}})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1716,"value":"phase_diagram()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1727,"value":"Plot.legend({color: {type: \"linear\", scheme: 'viridis', domain: d3.extent(global_hm(data_hm).map(d => d.value))}})","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}