2c:null 2d:["$","title",null,{"children":"Visualizing Cashflows (Life Insurance) gradient/canvas bug on highlight | Declan Naughton | Observable"}] 32:T8875,function cashflows_spec({ premiums, highlighting = true, width }) { return { // todo width, params: [ { name: "marker", value: -1000 }, { name: "x_range", expr: 'range("concat_0_concat_0_x")' }, { name: "x_one", expr: 'scale("concat_0_concat_0_x",1)' }, { name: "x_domain", expr: 'domain("concat_0_concat_0_x")' }, { name: "y_domain", expr: 'domain("concat_0_concat_0_y")' }, { name: "x_diff", expr: 'scale("concat_0_concat_0_x",1)-scale("concat_0_concat_0_x",0)' }, { name: "x_one2", expr: "unit" }, { name: "highlighting", value: false, [highlighting ? "bindOFF" : "off"]: { input: "checkbox" } }, { name: "past", //expr: "indata('data_0','point_id_in',19)" expr: "extent(pluck(data('data_0'), 't_in'))[0]<0" //bind: { input: "checkbox" } } ], data: { values: [] }, height: 300, transform: [ // { calculate: "0.3", as: "sww" }, { calculate: 'datum.formula == "pv_premiums" ? "Premiums" : (datum.formula == "pv_claims" ? "Claims" : (datum.formula == "pv_expenses" ? "Expenses" : (datum.formula == "pv_commissions" ? "Commissions" : "N/A")))', as: "formula" }, //{calculate: 'datum.value_n', as: 'value'}, { filter: "abs(datum.value)>0.0001" }, // or 1 or some responsive tol? /*{ calculate: "datum.partition == 'subtract' ? -datum.value : datum.value", as: "value" }, { calculate: "(datum.partition == 'subtract') ? -datum.value : datum.value", as: "value" },*/ /*{ calculate: 'datum.t_in+'+bin_size, as: 'bin_end' },*/ /*{ calculate: 'datum.value/' + bin_size, as: 'value' // versus use an aggregate mean? },*/ { calculate: "(datum.A_keep_gte_0 == false && datum.partition == 'subtract') ? -datum.value : datum.value", as: "value" } ], hconcat: [ { vconcat: [ { //title: 'amounts are AVERAGES PER MONTH', width: width - 70, encoding: {}, layer: [ // RECTS BREAKING ZOOM ON Y { mark: { type: "rect", clip: true }, // TODO find extents, or clip? (extents = SUM of CFs) data: { values: [{}] }, transform: [{}], encoding: { y: { datum: 0 }, // can't use datum: what scale? x: { value: 0 }, //y2:{datum:10}, //y2: {}, y2: { value: -1000 }, //y2: {value: {expr: "scale('concat_0_concat_0_y', 1000)"}}, x2: { value: "width" }, fill: { value: "lightgreen" }, opacity: { value: 0.2 } } }, { mark: { type: "rect", clip: true }, data: { values: [{}] }, encoding: { y: { datum: 0 }, // can't use datum: what scale? x: { value: 0 }, //y2: {value: {expr: "scale('concat_0_concat_0_y', -1000)"}}, y2: { value: 1000 }, x2: { value: "width" }, fill: { value: "orange" }, opacity: { value: 0.2 } } }, { mark: { type: "rule" }, data: { values: [{}] }, encoding: { y: { datum: 0 }, // for Q set to 0 (not string) size: { value: 0.2 }, // remove, unnecessary? color: { value: "gray" } //x: {datum:'51', type:'quantitative', scale:null} } }, { mark: { type: "rule", strokeDash: [10, 5] }, data: { values: [{}] }, encoding: { opacity: { value: { expr: "past ? 1 : 0" } }, x: { datum: 0, scale: { zero: false, padding: 10, domain: /*[-10*12,23*12]}*/ { param: "brush", encoding: "x" }, nice: false } }, // for Q set to 0 (not string) size: { value: 1 } //x: {datum:'51', type:'quantitative', scale:null} } }, { transform: [ /* {"filter": {"param": "brush"}},*/ { filter: "datum.formula != 'profit' && if (highlighting, (datum.partition == 'keep' || datum.partition == 'add' || datum.partition == 'subtract'), datum.partition == 'keep' || (datum.partition == 'add' && datum.value > 0) || (datum.partition == 'subtract' && datum.value < 0) )" } ], params: [ { name: "grid", select: "interval", bind: "scales" }, { name: "cashflow", select: { type: "point", fields: ["formula"] }, bind: "legend" } ], mark: { type: "area", tooltip: false, interpolate: "step-after" }, encoding: { x: { field: "t_in", title: "months from now", type: "quantitative", scale: { zero: false, padding: 10, domain: [-36, 18 * 12], nice: false }, axis: { values: [0, 60, 120, -60, -120, 180, 240, -180, -240] } }, y: { field: "value", type: "quantitative", scale: { domain: [-premiums * 1.5, premiums * 1.5 /*-10000,10000*/] }, axis: { grid: false, title: "amount", labelAngle: -90, values: [0, 100, -100] } /*, stack:'normalize'*/ }, color: { field: "formula", type: "nominal", scale: { domain: ["Premiums", "Claims", "Commissions", "Expenses"], range: [ "steelblue", "darkorange", "brown", "mediumvioletred" ] }, legend: { direction: "horizontal", orient: "top", strokeColor: "lightgray", rows: 1, title: "cashflows", padding: 3 } }, order: { field: "formula" /* also partition */, type: "nominal" }, // I want expenses @ bottom detail: { field: "partition" }, opacity: { value: { expr: 'highlighting ? (datum.partition == "add" || datum.partition == "subtract" ? 1 : 0.2) : 1' } }, strokeWidth: { // MAKE THIS RESPONSIVE TO ZOOM? value: 0 //{ // Needs to be sized for SUM OVER t_in // But even that won't work? because only one stroke over many marks? //expr: '(highlighting && (datum.partition == "add" || datum.partition == "subtract")) ? 0.7 : 0' //expr: "(warn('hello'+(datum.sw)+(datum.formula),'')+datum.partition == 'add' || datum.partition=='subtract') ? (datum.formula == 'Claims' ? 2 : datum.sww) : 0" //} /*condition: { //test: 'datum.partition == "add" || datum.partition == "subtract"', param: "cashflow", empty: false, value: 0.5 * 2 * (bin_size <= 2 ? 0.5 : bin_size == 12 ? 1 : 0.5) // Scale down for bin size 1 month }*/ }, order: { field: "sww" }, stroke: { value: "darkgreen", condition: { test: 'datum.partition == "subtract"', value: "red" } } } }, // BAD GUYS: { transform: [ /* {"filter": {"param": "brush"}},*/ { filter: "datum.formula != 'profit' && highlighting && (datum.partition == 'keep' || datum.partition == 'subtract')" } ], mark: { type: "area", tooltip: false, interpolate: "step-after", fill: { x1: 0, y1: 0, x2: 1, y2, gradient: "linear", stops: stops(50, "red") } }, encoding: { x: { field: "t_in", title: "months from now", type: "quantitative", scale: { zero: false, padding: 10, domain: [-36, 18 * 12], nice: false }, axis: { values: [0, 60, 120, -60, -120, 180, 240, -180, -240] } }, y: { field: "value", type: "quantitative", scale: { domain: [-premiums * 1.5, premiums * 1.5 /*-10000,10000*/] }, axis: { grid: false, title: "amount", labelAngle: -90, values: [0, 100, -100] } /*, stack:'normalize'*/ }, order: { field: "formula" /* also partition */, type: "nominal" }, // I want expenses @ bottom detail: { field: "partition" }, opacity: { value: { expr: 'highlighting ? (datum.partition == "add" || datum.partition == "subtract" ? 1 : 0) : 1' } }, strokeWidth: { // MAKE THIS RESPONSIVE TO ZOOM? value: 0 //{ // Needs to be sized for SUM OVER t_in // But even that won't work? because only one stroke over many marks? //expr: '(highlighting && (datum.partition == "add" || datum.partition == "subtract")) ? 0.7 : 0' //expr: "(warn('hello'+(datum.sw)+(datum.formula),'')+datum.partition == 'add' || datum.partition=='subtract') ? (datum.formula == 'Claims' ? 2 : datum.sww) : 0" //} /*condition: { //test: 'datum.partition == "add" || datum.partition == "subtract"', param: "cashflow", empty: false, value: 0.5 * 2 * (bin_size <= 2 ? 0.5 : bin_size == 12 ? 1 : 0.5) // Scale down for bin size 1 month }*/ }, order: { field: "sww" }, color: { field: "formula", type: "nominal" }, stroke: { value: "darkgreen", condition: { test: 'datum.partition == "subtract"', value: "red" } } } }, // GOOD GUYS { transform: [ /* {"filter": {"param": "brush"}},*/ { filter: "datum.formula != 'profit' && highlighting && (datum.partition == 'keep' || datum.partition == 'add')" } ], mark: { type: "area", tooltip: false, interpolate: "step-after", fill: { x1: 0, y1: 0, x2: 1, y2: 1, gradient: "linear", stops: stops(50, "green") } }, encoding: { x: { field: "t_in", title: "months from now", type: "quantitative", scale: { zero: false, padding: 10, domain: [-36, 18 * 12], nice: false }, axis: { values: [0, 60, 120, -60, -120, 180, 240, -180, -240] } }, y: { field: "value", type: "quantitative", scale: { domain: [-premiums * 1.5, premiums * 1.5 /*-10000,10000*/] }, axis: { grid: false, title: "amount", labelAngle: -90, values: [0, 100, -100] } /*, stack:'normalize'*/ }, order: { field: "formula" /* also partition */, type: "nominal" }, // I want expenses @ bottom detail: { field: "partition" }, opacity: { value: { expr: 'highlighting ? (datum.partition == "add" || datum.partition == "subtract" ? 1 : 0) : 1' } }, strokeWidth: { // MAKE THIS RESPONSIVE TO ZOOM? value: 0 //{ // Needs to be sized for SUM OVER t_in // But even that won't work? because only one stroke over many marks? //expr: '(highlighting && (datum.partition == "add" || datum.partition == "subtract")) ? 0.7 : 0' //expr: "(warn('hello'+(datum.sw)+(datum.formula),'')+datum.partition == 'add' || datum.partition=='subtract') ? (datum.formula == 'Claims' ? 2 : datum.sww) : 0" //} /*condition: { //test: 'datum.partition == "add" || datum.partition == "subtract"', param: "cashflow", empty: false, value: 0.5 * 2 * (bin_size <= 2 ? 0.5 : bin_size == 12 ? 1 : 0.5) // Scale down for bin size 1 month }*/ }, order: { field: "sww" }, color: { field: "formula", type: "nominal" }, stroke: { value: "darkgreen", condition: { test: 'datum.partition == "subtract"', value: "red" } } } }, { mark: { type: "rect", clip: true }, data: { values: [{}] }, encoding: { x: { datum: -10000 }, x2: { datum: 0 }, fill: { value: "purple" }, y: { value: -100 }, y2: { value: 1000 }, opacity: { value: { expr: "past ? 0.1 : 0" } } } }, // layers for summary dots: line, point, text (some are B only) { transform: [ { calculate: "datum.t_in_mean", as: "t_in" }, /* {"filter": {"param": "brush"}*/ /* "slow" per docs vs scale-domain-param but also does extent fix */ { filter: "datum.partition == 'B'" }, // filter out unselected cashflows, for better y range? {filter: "datum.t_in "}, //sum?{calculate: 'datum.val'} //{"extent": "sum_value", "param": "b_extent"}// not using, can use in order to make a greedy selection? (pick one at edge of interval) (change above also) { calculate: "((datum.t_in_step_end-datum.t_in)*4)", as: "step_size" }, { aggregate: [ { // de-duplicate and use value_n op: "min", field: "value_n", as: "value_n" } ], groupby: [ /*"duration_n",*/ "t_in" /* has already become mean */, "partition", "formula" ] }, { aggregate: [ { op: "sum", field: "value_n", as: "sum_value" } ], groupby: [/*"duration_n",*/ "t_in", "partition"] }, { aggregate: [ { op: "mean", field: "sum_value", as: "sum_value" } ], groupby: [/*"duration_n",*/ "t_in", "partition"] } ], mark: { type: "line", tooltip: true, //point: true, interpolate: "monotone" // I also like natural }, encoding: { //opacity: {value: 0.8}, //size: { field: "1", scale: null }, // make dept on step size? //fill: {value:'green', condition: {test: 'datum.sum_value<0', value: 'red'}}, strokeWidth: { value: 2.5 }, // remember: no grouping so many appeareances!! opacity: { value: { expr: "if(highlighting, 0, 0.8)" } }, color: { value: "black" }, x: { field: "t_in", type: "quantitative", scale: { zero: false, padding: 10, nice: false } }, y: { field: "sum_value", type: "quantitative" /*, scale: {domain:{param:'b_extent'}}*/ /*, stack:'normalize'*/ }, detail: { field: "partition" } //color: {field: 'formula', type:'nominal'}, } }, { transform: [ { calculate: "datum.t_in_mean", as: "t_in" }, /* {"filter": {"param": "brush"}*/ /* "slow" per docs vs scale-domain-param but also does extent fix */ { filter: "datum.partition == 'B' ||datum.partition == 'A' " }, // filter out unselected cashflows, for better y range? {filter: "datum.t_in "}, //sum?{calculate: 'datum.val'} //{"extent": "sum_value", "param": "b_extent"}// not using, can use in order to make a greedy selection? (pick one at edge of interval) (change above also) { calculate: "((datum.t_in_step_end-datum.t_in)*4)", as: "step_size" }, { aggregate: [ { // de-duplicate and use value_n op: "min", field: "value_n", as: "value_n" } ], groupby: [ /*"duration_n",*/ "t_in" /* has already become mean */, "partition", "formula" ] }, { aggregate: [ { op: "sum", field: "value_n", as: "sum_value" } ], groupby: [/*"duration_n",*/ "t_in", "partition"] }, { aggregate: [ { op: "mean", field: "sum_value", as: "sum_value" } ], groupby: [/*"duration_n",*/ "t_in", "partition"] } ], mark: { type: "point", tooltip: false }, encoding: { //opacity: {value: 0.8}, size: { field: "step_size", scale: null }, // make dept on step size? fill: { value: { expr: 'datum.partition == "A" ? "transparent" : (datum.sum_value >= 0 ? "green" : "red")' } //value: "green", //condition: { test: "datum.sum_value<0", value: "red" } }, strokeWidth: { value: 2 }, // remember: no grouping so many appeareances!! opacity: { value: { expr: 'if(highlighting, datum.partition == "B" ? 0.9 : 0.2, datum.partition == "B" ? 1 : 0.3)' } /*field: "partition", type: "nominal", scale: { domain: ["A", "B"], range: [0.3, 0.8] }, legend: null*/ }, color: { value: "black" }, x: { field: "t_in", type: "quantitative", scale: { zero: false, padding: 10, domain: [-36, 18 * 12], nice: false } }, y: { field: "sum_value", type: "quantitative" /*, scale: {domain:{param:'b_extent'}}*/ /*, stack:'normalize'*/ }, detail: { field: "partition" } //color: {field: 'formula', type:'nominal'}, } }, { transform: [ { calculate: "datum.t_in_mean", as: "t_in" }, /* {"filter": {"param": "brush"}*/ /* "slow" per docs vs scale-domain-param but also does extent fix */ { filter: "datum.partition == 'B'" }, // filter out unselected cashflows, for better y range? {filter: "datum.t_in "}, //sum?{calculate: 'datum.val'} //{"extent": "sum_value", "param": "b_extent"}// not using, can use in order to make a greedy selection? (pick one at edge of interval) (change above also) { calculate: "((datum.t_in_step_end-datum.t_in)*4)", as: "step_size" }, { aggregate: [ { // de-duplicate and use value_n op: "min", field: "value_n", as: "value_n" } ], groupby: [ "duration_n", "t_in" /* has already become mean */, "partition", "formula" ] }, { aggregate: [ { op: "sum", field: "value_n", as: "sum_value" } ], groupby: ["duration_n", "t_in", "partition"] }, { aggregate: [ { op: "mean", field: "sum_value", as: "sum_value" } ], groupby: ["duration_n", "t_in", "partition"] }, { filter: "(datum.duration_n+1) % 5 == 0 || datum.duration_n == 0" } ], mark: { type: "text", tooltip: false, point: true, fontWeight: "bold" }, encoding: { opacity: { value: { expr: "if(highlighting, 0, 1)" } }, size: { value: 9 }, // make dept on step size? //fill: {value:'green', condition: {test: 'datum.sum_value<0', value: 'red'}}, //strokeWidth: {value:0.1}, // remember: no grouping so many appeareances!! //opacity: {field:'partition', type:'nominal', scale: {domain:['A','B'],range:[0.1,0.1]}, legend:null}, color: { value: "black" }, text: { value: { expr: "datum.duration_n+1" } }, x: { field: "t_in", type: "quantitative", scale: { zero: false, padding: 10, domain: [-36, 18 * 12], nice: false } }, y: { field: "sum_value", type: "quantitative" /*, scale: {domain:{param:'b_extent'}}*/ /*, stack:'normalize'*/ }, yOffset: { value: 9 }, detail: { field: "partition" } //color: {field: 'formula', type:'nominal'}, } }, { mark: { type: "rule", strokeDash: [3, 3] }, data: { values: [{}] }, encoding: { color: { value: "orange" }, x: { value: { expr: "scale('concat_0_concat_0_x',marker)" } /*scale: { zero: false, padding: 10, domain: { param: "brush", encoding: "x" }, nice: false }*/ } // for Q set to 0 (not string) //x: {datum:'51', type:'quantitative', scale:null} } }, { mark: { type: "text", interactive: false }, // why do these break params? data: { values: [{}] }, encoding: { y: { datum: premiums * 1.2 }, // can't use datum: what scale? x: { value: width - 130 }, text: { value: "➕" }, size: { value: 20 }, opacity: { value: 0.6 } } }, { mark: { type: "text", interactive: false }, // why do these break params? data: { values: [{}] }, encoding: { y: { datum: -premiums * 1.5 }, // can't use datum: what scale? x: { value: width - 130 }, text: { value: "➖" }, size: { value: 20 }, opacity: { value: 0.6 } } }, { mark: { type: "text", align: "right", font: "monospace" }, data: { values: [{}] }, encoding: { x: { datum: -1 }, y: { value: 180 }, text: { value: { expr: "past ? 'past ↤' : ''" } } } }, { mark: { type: "text", align: "left", font: "monospace" }, data: { values: [{}] }, encoding: { x: { datum: 1 }, y: { value: 180 }, text: { value: "↦ future" } } } /*, {mark: {type:'text'}, data: {values:[{}]}, encoding: { y: {datum:-40}, // can't use datum: what scale? x: {datum:100}, text:{value:''}, size: {value:20} //opacity: {value: 0.2} } } */ ] } //,{ // title: '', // ex "zoom" // width:width-50, // height:100, // transform: [ { // filter: // "datum.formula != 'profit' && (datum.partition == 'keep' || datum.partition == 'add' || datum.partition == 'subtract')" // }], // encoding: { x: {field: 't_in', type:'quantitative',nice:false, axis:null}, // y: {field: 'value', type:'quantitative', /*scale: {domain:bin_size <=12 ? [-100,60] : null},*/ axis:null}, // }, // layer:[ // { // params: [{ // name: 'brush', // value: {x: [-50,21*12], y:[-40,40]},// // select: {type:'interval', encodings:['x','y']} // },/*{ // name: 'brush2', // select: {type:'interval', encodings:['y']} // }*/], // mark: {type:'bar', clip:false, tooltip: false}, // can I add rules? In Altair something about interactive, non-interactive layers? // encoding: { // color: {value:'gray'}, // //stroke: {} // //color: {field: 'formula', type:'nominal', legend: {orient: 'top', title: 'cashflow', titleFontSize:20}}, // //order: {field: 'formula', type:'nominal'}, // opacity: { // value: 0.6, // condition: { // test: 'datum.partition == "add" || datum.partition == "subtract"', // value: 1 // } // }, // strokeWidth: { // value: 0, // condition: { // test: 'datum.partition == "add" || datum.partition == "subtract"', // value: 0.5*(bin_size<= 2 ? .2 : bin_size == 12 ? 1 : 0.5) // Scale down for bin size 1 month // } // }, // stroke: { // value: "darkgreen", // condition: { test: 'datum.partition == "subtract"', value: "red" } // }, // }},{ // "mark": {type:"rule", strokeDash: [10,10]}, // "encoding": { // "x": {"datum": 0}, // color: {value:'black'}, // size: {value:2} // }}, // { // mark: {type :'rule'}, // why can't I get rules here ?! // //interactive:false, // data: {values:[{}]}, // encoding: { // x: {value:{expr:"0"}, scale:{padding:0, domain:[-10*12,23*12]}} // for Q set to 0 (not string) // ,color: {value:'red'}, // //size:{value:10} // //x: {datum:'51', type:'quantitative', scale:null} // } // }]} ] } /*{title:'Σ fut. cfs',layer: [{ transform: [ { filter: "datum.t_in>=0"}, { filter: "datum.formula != 'profit' && (datum.partition == 'keep' || datum.partition == 'add' || datum.partition == 'subtract')" },{ aggregate: [{op:'sum',field:'value',as:'value2'}], groupby: ['formula','partition'] }], mark: {type:'bar', tooltip: true}, encoding: { y: {field: 'value2', type:'quantitative'}, color: {field: 'formula', type:'nominal', legend: {orient: 'top', title: 'cashflow', titleFontSize:20}}, //order: {field: 'formula', type:'nominal'}, opacity: { //value: 0.6, condition: { test: 'datum.partition == "add" || datum.partition == "subtract"', value: 1 } }, strokeWidth: { value: 0, condition: { test: 'datum.partition == "add" || datum.partition == "subtract"', value: 2 } }, stroke: { value: "darkgreen", condition: { test: 'datum.partition == "subtract"', value: "red" } }, }}, { transform: [ { filter: "1 && (datum.partition == 'B' ||datum.partition == 'A' )" },], mark: {type:'point', tooltip: true}, encoding: { opacity: {value: 0.8}, size: {value: 190}, strokeWidth: {value:4}, color: {field:'partition', type:'nominal'}, y: {field: 'value', aggregate:'sum', type:'quantitative', axis:{orient:'right', title: null, format:'s'}}, detail: {field:'partition'}, //color: {field: 'formula', type:'nominal'}, }} ]}*/ ], config: { scale: { padding: 0, paddingInner: 0, paddingOuter: 0 } } }; }33:T880,function cashflows_spec_patch() { return (spec) => { //debugger spec.legends[0].encode.symbols.update.shape = { value: "square" }; spec.legends[0].encode.symbols.update.stroke = null; spec.legends[0].encode.symbols.update.opacity = { value: 1 }; //const grid = spec.signals.findIndex((d) => d.name == "grid_t_in"); spec.signals.push({ name: "panOffset", value: 0 }); spec.marks[0].marks[0].signals; const a = spec.marks[0].marks[0].signals.findIndex( (d) => d.name == "grid_t_in" ); const b = spec.marks[0].marks[0].signals.findIndex( (d) => d.name == "grid_translate_anchor" ); const c = spec.marks[0].marks[0].signals.findIndex( (d) => d.name == "grid_zoom_anchor" ); const d = spec.scales.findIndex((d) => d.name == "concat_0_concat_0_x"); spec.scales[d].domainRaw.signal = 'grid["t_in"] ? [grid["t_in"][0]+panOffset,grid["t_in"][1]+panOffset] : null'; spec.marks[0].marks[0].signals[a].on[2] /*should paramaterise*/.update = 'zoomLinear([domain("concat_0_concat_0_x")[0]-panOffset,domain("concat_0_concat_0_x")[1]-panOffset], grid_zoom_anchor.x, grid_zoom_delta)'; spec.marks[0].marks[0].signals[b].on[0] /*should paramaterise*/.update = '{x: x(unit), y: y(unit), extent_x: [domain("concat_0_concat_0_x")[0]-panOffset,domain("concat_0_concat_0_x")[1]-panOffset], extent_y: domain("concat_0_concat_0_y")}'; spec.marks[0].marks[0].signals[c].on[0] /*should paramaterise*/.update = '{x: invert("concat_0_concat_0_x", x(unit))-panOffset, y: invert("concat_0_concat_0_y", y(unit))}'; /*spec.scales[s].domainRaw = { signal: 'grid["t_in"] ? [grid["t_in"][0]+panOffset,grid["t_in"][1]+panOffset] : null' };*/ /*spec.signals[grid].on = [ { events: { signal: "panOffset" }, update: "grid_t_in ? [grid_t_in[0]+panOffset, grid_t_in[1]+panOffset] : [x_domain[0]+panOffset,x_domain[1]+panOffset]" } ];*/ //'{"t_in": grid_t_in ? [grid_t_in[0]+panOffset,grid_t_in[1]+panOffset] : null, "value": grid_value}'; //'{"t_in": [grid_t_in[0],grid_t_in[1]], "value": grid_value}'; return spec; }; }34:T71b,function cashflows_summary_and_keep_add_subtract({ cfs, offset, singular }) { return ( aq .from(cfs) .params({ duration_mth_in: offset }) .params({ singular }) //.filter(d => d.t_in < 0) // bin: //.groupby(["formula", { // t_in: aq.bin('t_in', {step:bin_size,/*offset:0,*/ nice:false}) //}, 'input_cursor_id']) //.rollup({value: d => aq.op.sum(d.value)}) // nobin: .groupby(["t_in", "formula"]) .orderby(["formula", "t_in", "input_cursor_id"]) .derive({ A: (d) => (singular ? d.value : aq.op.lag(d.value, 1, 0)), B: (d) => d.value, t_in_step_end: (d) => Math.min(d.t_in + 12, 0) // EDITED DONT COPY }) .filter((d) => (d.input_cursor_id == singular ? 0 : 1)) // CHANGED HERE .derive({ keep: ( d // =IF(C6<0,MIN(0,MAX(D6,C6)),IF(C6>0,MAX(0,MIN(D6,C6)))) ) => { if (d.A < 0) return Math.min(0, Math.max(d.A, d.B)); return Math.max(0, Math.min(d.A, d.B)); }, add: (d) => Math.max(0, d.B - d.A) * (d.A < 0 ? -1 : 1), subtract: (d) => Math.max(0, d.A - d.B) * (d.A < 0 ? 1 : 1), // todo edge case for flips A_keep_gte_0: (d) => d.A >= 0 }) .fold(["A", "B", "keep", "add", "subtract"], { as: ["partition", "value"] }) //.derive({'value': d => d.value}) .derive({ //duration_mth: (d) => Math.floor((d.t_in + duration_mth_in) / 1), duration_n: (d) => Math.floor((d.t_in + duration_mth_in) / 12) }) .groupby("partition", "formula", "input_cursor_id", "duration_n") .derive({ value_n: aq.rolling(aq.op.mean("value"), [-Infinity, Infinity], false), t_in_mean: aq.rolling(aq.op.mean("t_in"), [-Infinity, Infinity], false) }) ); }35:[{"id":"88d2ca942b5398b831064887cbcef9a30e5ef52ad491f971800232b2c5f6e770345d7d6a015f224526e9015d8094a2b14b5e0dc2e25ce4093d9c4f3534286d91","href":"https://static.observableusercontent.com/files/88d2ca942b5398b831064887cbcef9a30e5ef52ad491f971800232b2c5f6e770345d7d6a015f224526e9015d8094a2b14b5e0dc2e25ce4093d9c4f3534286d91","name":"basicterm@3.cul.js","type":"application/javascript","lastModified":1726322247805.598,"size":38199},{"id":"fbb31f5fbd5a416316cc5d923415b80aef119f77da0bffac195ad044baa32a02dba53049fee035a2de7ebdba402d34a9c1bb0b8e6e6ad8d01bb7d705a4328050","href":"https://static.observableusercontent.com/files/fbb31f5fbd5a416316cc5d923415b80aef119f77da0bffac195ad044baa32a02dba53049fee035a2de7ebdba402d34a9c1bb0b8e6e6ad8d01bb7d705a4328050","name":"mps-basicterm@2.cul.js","type":"application/javascript","lastModified":1726322247805.598,"size":5612}] 30:{"id":"c78aae6e014a6b6d","slug":null,"title":"Visualizing Cashflows (Life Insurance) gradient/canvas bug on highlight","thumbnail":"4be9b820b765832f18751e2d6c458f3c85163ed1f0224e5dbde3b6d63919d250","access_level":"public","create_time":"$D2024-09-14T13:57:27.805Z","update_time":"$D2024-09-14T15:19:58.902Z","view_time":null,"first_public_time":"$D2024-09-14T13:57:27.805Z","has_unpublished_edits":null,"fork":{"id":"3a26a6e95deb8afb","slug":null,"title":"Visualizing Cashflows (Life Insurance) new highlighting dev","owner":{"id":"fa28dcfb9c2f53ce","login":"declann","name":"Declan Naughton"}},"stars":0,"starred":false,"owner":{"type":"team","id":"fa28dcfb9c2f53ce","login":"declann","name":"Declan Naughton","avatar_url":"https://avatars-next.observableusercontent.com/b36edfd0f90184ac4c0a8d8726969fe861a1d1fe9417e998413c8a47e6163983","tier":"starter_2024"},"version":1239,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"fca367047a53a31f","login":"declann","name":"Declan Naughton","avatar_url":"https://avatars-next.observableusercontent.com/b36edfd0f90184ac4c0a8d8726969fe861a1d1fe9417e998413c8a47e6163983","tier":null}],"members":[{"type":"team","id":"fa28dcfb9c2f53ce","login":"declann","name":"Declan Naughton","avatar_url":"https://avatars-next.observableusercontent.com/b36edfd0f90184ac4c0a8d8726969fe861a1d1fe9417e998413c8a47e6163983","tier":"starter_2024","role":"editor"},{"type":"individual","id":"fca367047a53a31f","login":"declann","name":"Declan Naughton","avatar_url":"https://avatars-next.observableusercontent.com/b36edfd0f90184ac4c0a8d8726969fe861a1d1fe9417e998413c8a47e6163983","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Visualizing Cashflows (Life Insurance) gradient/canvas bug on highlight","cells":[{"id":0,"value":"# Visualizing Cashflows (Life Insurance) gradient/canvas bug on highlight","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":551,"value":"v = \"new-highlighting-working-rough\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1082,"value":"## Visual","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1201,"value":"viewof renderer = Inputs.select([\"canvas\", \"svg\"], { label: \"renderer\" })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"viewof point_id_in = Inputs.range([1, 30], {\n step: 1,\n value: 1,\n label: \"point_id_in\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":420,"value":"viewof mort_rate_factor_in = Inputs.range([0, 3], {\n step: 0.01,\n label: \"mort_rate_factor_in\",\n value: 3\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1234,"value":"viewof y2 = Inputs.range([0, 1], { label: \"y2\", step: 0.1 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1204,"value":"viewof hightlighting = Inputs.toggle({ label: \"highlighting\", value: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"viewof viz = embed(cashflows_spec({ premiums, width: 600 }), {\n renderer,\n actions: true,\n patch: cashflows_spec_patch()\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1207,"value":"viz.signal(\"highlighting\", hightlighting)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"This doesn't support cashflows flipping sides, but that shouldn't happen for exercises?\n\ntodo:\n\n- axis offset only works after a manual pan - init properly","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":789,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1134,"value":"function stops(n, color) {\n return _.range(0, n).map((d) => ({\n offset: d / n,\n color: !(d % 3) ? color : \"transparent\"\n }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":765,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":742,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"duration_mth_in = model.js.duration_mth({ ...cursor, t_in: 0 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":991,"value":"viz\n .signal(\n \"panOffset\",\n x\n //\"grid_t_in\",\n //[viz.signal(\"x_domain\")].map(([a, b]) => [a + 1, b + 1])[0] // better to add an offset in patch?\n )\n .run()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1025,"value":"new update handler in grid_t_in ?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":912,"value":"viewof x = Inputs.range([-100, 100], {\n value: 0,\n step: 1,\n label: \"axis offset (pan manually first)\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1092,"value":"viz.signal(\"highlighting\", true).run()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":869,"value":"viz.getState()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":853,"value":"{\n viz.signal(\"grid_translate_anchor\", {\n x: 55.8125,\n y: 12,\n extent_x: [75, 150],\n extent_y: [20, 40]\n });\n //viz.signal(\"grid\", { t_in: [10], value: [10] }).run();\n //viz.signal(\"grid_t_in\", [10]);\n //viz.signal(\"grid_value\", [10]).run();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":855,"value":"viz.signal(\"marker\", 20).run()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":279,"value":"# Data\n\n## calculang -> Arquero -> Arrow using `.toArrow()`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":414,"value":"cursor","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"cfs = await calcudata({\n models: [model.js],\n input_cursors: [cursor, { ...cursor, mort_rate_factor_in }],\n //input_cursors: [{...cursor, stress_in: 'none',duration_mth_in:0},{...cursor, stress_in: 'none'}],\n input_domains: {\n t_in: _.range(\n -model.js.duration_mth({ ...cursor, t_in: 0 }),\n model.js.proj_len(cursor) + 0.1,\n 1\n )\n },\n outputs: [\"pv_premiums\", \"pv_claims\", \"pv_commissions\", \"pv_expenses\"]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"cfs_impacts = cashflows_summary_and_keep_add_subtract({\n cfs,\n offset: duration_mth_in,\n singular: false\n}).toArrow()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":286,"value":"# How I populate data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":152,"value":"viz.data(\"source_0\", cfs_impacts.toArray()).run()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1227,"value":"JSON.stringify([...cfs_impacts])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"## This versus Observable default is different but neither works","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":350,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"basictermCul = FileAttachment(\"basicterm@3.cul.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"mpsBasictermCul = FileAttachment(\"mps-basicterm@2.cul.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"calculang = import(\n \"https://cdn.jsdelivr.net/gh/calculang/calculang@dev/packages/standalone/index.js\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"model = calculang.compile({\n entrypoint: \"entrypoint.cul.js\",\n fs: {\n \"entrypoint.cul.js\": await mpsBasictermCul.text(),\n \"./basicterm.cul.js\": await basictermCul.text()\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"helpers = import(\n \"https://cdn.jsdelivr.net/gh/declann/calculang-js-browser-dev@main/helpers.js\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"calcudata = helpers.calcudata","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":62,"value":"cursor = ({\n point_id_in,\n t_in: 0,\n zero_duration_in: false,\n mort_rate_factor_in: 1\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"model.js.pv_fut_net_cf(cursor)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"bin_size = 12","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"premiums = 94 //model.js.premium_pp({ ...cursor, point_id_in: 1 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"stress = ({ formula: \"\", t_in: 0 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":173,"value":"embed = require(\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 31:"$30:body"