KAYA model - table, selected values (old)

carbon emissions projection using KAYA identity (old approach)

Author

input values ⚙️

stepp = 0

mutable playing = Mutable {}

playing = 0

Code

mutable spec_now = spec_post_process(c_spec1)

x = vega_interactive(spec_now, { renderer: 'svg'})

OJS Runtime Error (line 54, column 20)

spec_post_process is not defined

OJS Runtime Error (line 54, column 20)

spec_post_process is not defined

OJS Runtime Error

spec_post_process is not defined

I need sep./addl. UIs for this.

Code

domains11 = ({...domains, formula: Object.values(introspection.cul_functions).filter(d => d.reason == 'definition' && inputs.indexOf(d.name+'_in') == -1).map(d => d.name) /*domains.formula??domains.formulae*/})

p = projection_fn ({ mapped:['formula'], // no 'value'
  domains: domains11, cursor:ui // problems when something missing from cursor, in this case an input mapped in the main story
                 })

xx = vega_interactive({ // modd from https://observablehq.com/@declann/some-cashflows?collection=@declann/calculang v~908
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": {"name": "projection"},
  "height": 480,
  "width": 550,
  "transform": [
    {"calculate": "round(100*datum.value)/100", "as": "amount2"}
  ],
  "mark": {"type": "text", "tooltip": true},
  "encoding": {
    "y": {"field": "formula", "axis": {"orient": "left", "labelAngle": 30}},
    /*"y": {
      "field": "month_in",
      "type": "quantitative",
      "sort": "descending",
      "axis": {"tickOffset": 2, "tickCount": 20, "grid": 0}
    },*/
    "color": {"field": "formula", "type": "nominal"},
    "text": {"field": "amount2", "format": ",.2f"}
  },
  "config": {"legend": {"disable": true}},
  "datasets": {
    "projection": p
  }
}, { renderer: 'svg'})

p

domains11 = Object {year_in: Array(6), formulae: Array(5), formula: Array(11)}

p = Array(11) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object]

OJS Runtime Error

ui is not defined

Array(11) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object]

population = () => {
  if (year() <= 2023) return population_2023();
  else if (year() <= 2050)
    return (
      population_2023() +
      ((population_2050() - population_2023()) * (year() - 2023)) /
        (2050 - 2023)
    );
  else
    return (
      population_2050() +
      ((population_2100() - population_2050()) * (year() - 2050)) /
        (2100 - 2050)
    );
};

population_factor = () => population() / population_2023();

per_capita_gdp_factor = () => {
  if (year() <= 2023) return 1;
  else
    return (
      per_capita_gdp_factor({ year_in: year() - 1 }) *
      (1 + per_capita_gdp_rate())
    );
};

per_capita_gdp = () =>
  per_capita_gdp_2023() * per_capita_gdp_factor();

energy_intensity_factor = () => {
  // energy intensity of economy

  if (year() <= 2023) return 1;
  else
    return (
      energy_intensity_factor({ year_in: year() - 1 }) *
      (1 + energy_intensity_rate())
    );
};

energy_intensity = () =>
  energy_intensity_2023() * energy_intensity_factor();

carbon_intensity_factor = () => {
  // carbon intensity of energy sources

  if (year() <= 2023) return 1;
  else
    return (
      carbon_intensity_factor({ year_in: year() - 1 }) *
      (1 + carbon_intensity_rate())
    );
};

carbon_intensity = () =>
  carbon_intensity_2023() * carbon_intensity_factor();

technology_factor = () =>
  energy_intensity_factor() * carbon_intensity_factor();

income_factor = () => per_capita_gdp_factor();

carbon_emissions = () =>
  population() * per_capita_gdp() * energy_intensity() * carbon_intensity();

formula-input dependence map (todo pre-pop all values?):

Code

md`
formula | ${inputs/*.map(d => d.slice(0, -3))*/.join(' | ')}
-------- | ${inputs.map(d => ':--------:').join(' | ')}
 | ${inputs.map(d => '<img width=80/>').join(' | ')}
${formulae_objs.map(f => `${f.name} | ${inputs.map(d => /*this will only work if I populate negs in cul_functions OR if I use cul_links. f.negs.includes(d) ? 'NEG' : */ (f.inputs.includes(d) ? '✔️' : '')).join(' | ')}`).join('\n')}
`

formula	carbon_intensity_2023_in	carbon_intensity_rate_in	energy_intensity_2023_in	energy_intensity_rate_in	per_capita_gdp_2023_in	per_capita_gdp_rate_in	population_2023_in	population_2050_in	population_2100_in	year_in

population							✔️	✔️	✔️	✔️
population_factor							✔️	✔️	✔️	✔️
per_capita_gdp_factor						✔️				✔️
per_capita_gdp					✔️	✔️				✔️
energy_intensity_factor				✔️						✔️
energy_intensity			✔️	✔️						✔️
carbon_intensity_factor		✔️								✔️
carbon_intensity	✔️	✔️								✔️
technology_factor		✔️		✔️						✔️
income_factor						✔️				✔️
carbon_emissions	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️

Code

fixedDot = {
 let start = introspection.dot.split('\n')

  let subgraph = []

  inputs.forEach(input => {
    subgraph.push(start.find(s => s.indexOf(`${input}" [`) != -1))
    subgraph.push(start.find(s => s.indexOf(`${input.slice(0,-3)}" [`) != -1))
  })

  let out = start.filter(d => subgraph.indexOf(d) == -1)

// https://graphviz.org/Gallery/directed/cluster.html
  out[0] = out[0] + `subgraph cluster_0 { style=filled;color=lightgrey; node [style=filled,color=white];label = "inputs";` + subgraph.join(';') + "}";
  
  return out.join('\n')
}


g = dot`${fixedDot}`

// todo svg download button

DOM.download(() => serializeSVG(g), undefined, "Save as SVG")

fixedDot = `digraph {subgraph cluster_0 { style=filled;color=lightgrey; node [style=filled,color=white];label = "inputs"; "0_carbon_intensity_2023_in" [cul_scope_id=0,name=carbon_intensity_2023_in,cul_source_scope_id=0,reason="input definition",inputs=carbon_intensity_2023_in,label="0_carbon_intensity_2023_in inputs: carbon_intensity_2023_in"]; "0_carbon_intensity_2023" [cul_scope_id=0,name=carbon_intensity_2023,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=carbon_intensity_2023_in,label="0_carbon_intensity_2023 inputs: carbon_intensity_2023_in"]; "0_carbon_intensity_rate_in" [cul_scope_id=0,name=carbon_intensity_rate_in,cul_source_scope_id=0,reason="input definition",inputs=carbon_intensity_rate_in,label="0_carbon_intensity_rate_in inputs: carbon_intensity_rate_in"]; "0_carbon_intensity_rate" [cul_scope_id=0,name=carbon_intensity_rate,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=carbon_intensity_rate_in,label="0_carbon_intensity_rate inputs: carbon_intensity_rate_in"]; "0_energy_intensity_2023_in" [cul_scope_id=0,name=energy_intensity_2023_in,cul_source_scope_id=0,reason="input definition",inputs=energy_intensity_2023_in,label="0_energy_intensity_2023_in inputs: energy_intensity_2023_in"]; "0_energy_intensity_2023" [cul_scope_id=0,name=energy_intensity_2023,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=energy_intensity_2023_in,label="0_energy_intensity_2023 inputs: energy_intensity_2023_in"]; "0_energy_intensity_rate_in" [cul_scope_id=0,name=energy_intensity_rate_in,cul_source_scope_id=0,reason="input definition",inputs=energy_intensity_rate_in,label="0_energy_intensity_rate_in inputs: energy_intensity_rate_in"]; "0_energy_intensity_rate" [cul_scope_id=0,name=energy_intensity_rate,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=energy_intensity_rate_in,label="0_energy_intensity_rate inputs: energy_intensity_rate_in"]; "0_per_capita_gdp_2023_in" [cul_scope_id=0,name=per_capita_gdp_2023_in,cul_source_scope_id=0,reason="input definition",inputs=per_capita_gdp_2023_in,label="0_per_capita_gdp_2023_in inputs: per_capita_gdp_2023_in"]; "0_per_capita_gdp_2023" [cul_scope_id=0,name=per_capita_gdp_2023,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=per_capita_gdp_2023_in,label="0_per_capita_gdp_2023 inputs: per_capita_gdp_2023_in"]; "0_per_capita_gdp_rate_in" [cul_scope_id=0,name=per_capita_gdp_rate_in,cul_source_scope_id=0,reason="input definition",inputs=per_capita_gdp_rate_in,label="0_per_capita_gdp_rate_in inputs: per_capita_gdp_rate_in"]; "0_per_capita_gdp_rate" [cul_scope_id=0,name=per_capita_gdp_rate,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=per_capita_gdp_rate_in,label="0_per_capita_gdp_rate inputs: per_capita_gdp_rate_in"]; "0_population_2023_in" [cul_scope_id=0,name=population_2023_in,cul_source_scope_id=0,reason="input definition",inputs=population_2023_in,label="0_population_2023_in inputs: population_2023_in"]; "0_population_2023" [cul_scope_id=0,name=population_2023,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=population_2023_in,label="0_population_2023 inputs: population_2023_in"]; "0_population_2050_in" [cul_scope_id=0,name=population_2050_in,cul_source_scope_id=0,reason="input definition",inputs=population_2050_in,label="0_population_2050_in inputs: population_2050_in"]; "0_population_2050" [cul_scope_id=0,name=population_2050,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=population_2050_in,label="0_population_2050 inputs: population_2050_in"]; "0_population_2100_in" [cul_scope_id=0,name=population_2100_in,cul_source_scope_id=0,reason="input definition",inputs=population_2100_in,label="0_population_2100_in inputs: population_2100_in"]; "0_population_2100" [cul_scope_id=0,name=population_2100,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=population_2100_in,label="0_population_2100 inputs: population_2100_in"]; "0_year_in" [cul_scope_id=0,name=year_in,cul_source_scope_id=0,reason="input definition",inputs=year_in,label="0_year_in inputs: year_in"]; "0_year" [cul_scope_id=0,name=year,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs=year_in,label="0_year inputs: year_in"]} "0_population" [cul_scope_id=0,name=population,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,population_2023_in,population_2050_in,population_2100_in",label="0_population inputs: year_in,population_2023_in,population_2050_in,population_2100_in"] "0_population_factor" [cul_scope_id=0,name=population_factor,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,population_2023_in,population_2050_in,population_2100_in",label="0_population_factor inputs: year_in,population_2023_in,population_2050_in,population_2100_in"] "0_per_capita_gdp_factor" [cul_scope_id=0,name=per_capita_gdp_factor,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,per_capita_gdp_rate_in",label="0_per_capita_gdp_factor inputs: year_in,per_capita_gdp_rate_in"] "0_per_capita_gdp" [cul_scope_id=0,name=per_capita_gdp,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="per_capita_gdp_2023_in,year_in,per_capita_gdp_rate_in",label="0_per_capita_gdp inputs: per_capita_gdp_2023_in,year_in,per_capita_gdp_rate_in"] "0_energy_intensity_factor" [cul_scope_id=0,name=energy_intensity_factor,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,energy_intensity_rate_in",label="0_energy_intensity_factor inputs: year_in,energy_intensity_rate_in"] "0_energy_intensity" [cul_scope_id=0,name=energy_intensity,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="energy_intensity_2023_in,year_in,energy_intensity_rate_in",label="0_energy_intensity inputs: energy_intensity_2023_in,year_in,energy_intensity_rate_in"] "0_carbon_intensity_factor" [cul_scope_id=0,name=carbon_intensity_factor,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,carbon_intensity_rate_in",label="0_carbon_intensity_factor inputs: year_in,carbon_intensity_rate_in"] "0_carbon_intensity" [cul_scope_id=0,name=carbon_intensity,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="carbon_intensity_2023_in,year_in,carbon_intensity_rate_in",label="0_carbon_intensity inputs: carbon_intensity_2023_in,year_in,carbon_intensity_rate_in"] "0_technology_factor" [cul_scope_id=0,name=technology_factor,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,energy_intensity_rate_in,carbon_intensity_rate_in",label="0_technology_factor inputs: year_in,energy_intensity_rate_in,carbon_intensity_rate_in"] "0_income_factor" [cul_scope_id=0,name=income_factor,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,per_capita_gdp_rate_in",label="0_income_factor inputs: year_in,per_capita_gdp_rate_in"] "0_carbon_emissions" [cul_scope_id=0,name=carbon_emissions,cul_source_scope_id=0,reason=definition,loc="[object Object]",inputs="year_in,population_2023_in,population_2050_in,population_2100_in,per_capita_gdp_2023_in,per_capita_gdp_rate_in,energy_intensity_2023_in,energy_intensity_rate_in,carbon_intensity_2023_in,carbon_intensity_rate_in",label="0_carbon_emissions inputs: year_in,population_2023_in,population_2050_in,population_2100_in,per_capita_gdp_2023_in,per_capita_gdp_rate_in,energy_intensity_2023_in,energy_intensity_rate_in,carbon_intensity_2023_in,carbon_intensity_rate_in"] "0_year_in" -> "0_year" [value="[object Object]",label="input +year_in"] "0_population_2023_in" -> "0_population_2023" [value="[object Object]",label="input +population_2023_in"] "0_population_2050_in" -> "0_population_2050" [value="[object Object]",label="input +population_2050_in"] "0_population_2100_in" -> "0_population_2100" [value="[object Object]",label="input +population_2100_in"] "0_year" -> "0_population" [value="[object Object]",label="call -"] "0_population_2023" -> "0_population" [value="[object Object]",label="call -"] "0_population_2050" -> "0_population" [value="[object Object]",label="call -"] "0_population_2100" -> "0_population" [value="[object Object]",label="call -"]Show 32 truncated lines

population = ({ year_in, population_2023_in, population_2050_in, population_2100_in }) => {
  if (year({ year_in }) <= 2023) return population_2023({ population_2023_in });else
  if (year({ year_in }) <= 2050)
  return (
    population_2023({ population_2023_in }) +
    (population_2050({ population_2050_in }) - population_2023({ population_2023_in })) * (year({ year_in }) - 2023) / (
    2050 - 2023));else


  return (
    population_2050({ population_2050_in }) +
    (population_2100({ population_2100_in }) - population_2050({ population_2050_in })) * (year({ year_in }) - 2050) / (
    2100 - 2050));

};

population_factor = ({ year_in, population_2023_in, population_2050_in, population_2100_in }) => population({ year_in, population_2023_in, population_2050_in, population_2100_in }) / population_2023({ population_2023_in });

per_capita_gdp_factor = ({ year_in, per_capita_gdp_rate_in }) => {
  if (year({ year_in }) <= 2023) return 1;else

  return (
    per_capita_gdp_factor({ per_capita_gdp_rate_in, year_in: year({ year_in }) - 1 }) * (
    1 + per_capita_gdp_rate({ per_capita_gdp_rate_in })));

};

per_capita_gdp = ({ per_capita_gdp_2023_in, year_in, per_capita_gdp_rate_in }) =>
per_capita_gdp_2023({ per_capita_gdp_2023_in }) * per_capita_gdp_factor({ year_in, per_capita_gdp_rate_in });

energy_intensity_factor = ({ year_in, energy_intensity_rate_in }) => {
  // energy intensity of economy

  if (year({ year_in }) <= 2023) return 1;else

  return (
    energy_intensity_factor({ energy_intensity_rate_in, year_in: year({ year_in }) - 1 }) * (
    1 + energy_intensity_rate({ energy_intensity_rate_in })));

};

energy_intensity = ({ energy_intensity_2023_in, year_in, energy_intensity_rate_in }) =>
energy_intensity_2023({ energy_intensity_2023_in }) * energy_intensity_factor({ year_in, energy_intensity_rate_in });

carbon_intensity_factor = ({ year_in, carbon_intensity_rate_in }) => {
  // carbon intensity of energy sources

  if (year({ year_in }) <= 2023) return 1;else

  return (
    carbon_intensity_factor({ carbon_intensity_rate_in, year_in: year({ year_in }) - 1 }) * (
    1 + carbon_intensity_rate({ carbon_intensity_rate_in })));

};

carbon_intensity = ({ carbon_intensity_2023_in, year_in, carbon_intensity_rate_in }) =>
carbon_intensity_2023({ carbon_intensity_2023_in }) * carbon_intensity_factor({ year_in, carbon_intensity_rate_in });

technology_factor = ({ year_in, energy_intensity_rate_in, carbon_intensity_rate_in }) =>
energy_intensity_factor({ year_in, energy_intensity_rate_in }) * carbon_intensity_factor({ year_in, carbon_intensity_rate_in });

income_factor = ({ year_in, per_capita_gdp_rate_in }) => per_capita_gdp_factor({ year_in, per_capita_gdp_rate_in });

carbon_emissions = ({ year_in, population_2023_in, population_2050_in, population_2100_in, per_capita_gdp_2023_in, per_capita_gdp_rate_in, energy_intensity_2023_in, energy_intensity_rate_in, carbon_intensity_2023_in, carbon_intensity_rate_in }) =>
population({ year_in, population_2023_in, population_2050_in, population_2100_in }) * per_capita_gdp({ per_capita_gdp_2023_in, year_in, per_capita_gdp_rate_in }) * energy_intensity({ energy_intensity_2023_in, year_in, energy_intensity_rate_in }) * carbon_intensity({ carbon_intensity_2023_in, year_in, carbon_intensity_rate_in });

raw = "nomemo"

population_2023_in	population_2050_in	population_2100_in	per_capita_gdp_2023_in	energy_intensity_2023_in	carbon_intensity_2023_in	per_capita_gdp_rate_in	energy_intensity_rate_in	carbon_intensity_rate_in	year_in	formula	value
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,023	carbon_emissions	38,237,760,000,000
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,023	population_factor	1
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,023	per_capita_gdp_factor	1
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,023	energy_intensity_factor	1
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,023	carbon_intensity_factor	1
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,030	carbon_emissions	37,923,539,881,066.43
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,030	population_factor	1.065
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,030	per_capita_gdp_factor	1.072
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,030	energy_intensity_factor	0.932
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,030	carbon_intensity_factor	0.932
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,050	carbon_emissions	36,339,592,789,115.5
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,050	population_factor	1.25
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,050	per_capita_gdp_factor	1.308
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,050	energy_intensity_factor	0.762
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,050	carbon_intensity_factor	0.762
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,070	carbon_emissions	30,849,537,347,483.902
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,070	population_factor	1.3
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,070	per_capita_gdp_factor	1.596
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,070	energy_intensity_factor	0.624
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,070	carbon_intensity_factor	0.624
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,090	carbon_emissions	26,150,158,830,197.906
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,090	population_factor	1.35
8,000,000,000	10,000,000,000	11,000,000,000	15,300	1.42	0.22	0.01	-0.01	-0.01	2,090	per_capita_gdp_factor	1.948

OJS Runtime Error

spec_post_process is not defined

You may ignore notebook workings below this line

debug

formula must be defined in here:

OJS Error

TypeError: input_combos_projection is not a function

Object {population_2023_in: Array(1), population_2050_in: Array(1), population_2100_in: Array(1), per_capita_gdp_2023_in: Array(1), energy_intensity_2023_in: Array(1), carbon_intensity_2023_in: Array(1), per_capita_gdp_rate_in: Array(1), energy_intensity_rate_in: Array(1), carbon_intensity_rate_in: Array(1), year_in: Array(6), formulae: Array(5)}

Object {cul_functions: Object, cul_links: Array(48), cul_scope_ids_to_resource: Object, import_sources_to_resource: Object, cul_input_map: Object, dot: "digraph {\n \"0_year\" [cul_scope_id=0,name=year,cul…ions\" [value=\"[object Object]\",label=\"call -\"]\n}\n"}

Array(11) ["population", "population_factor", "per_capita_gdp_factor", "per_capita_gdp", "energy_intensity_factor", "energy_intensity", "carbon_intensity_factor", "carbon_intensity", "technology_factor", "income_factor", "carbon_emissions"]

Object {year_in: Array(6), formulae: Array(5)}

Object {population_2023_in: 8000000000, population_2050_in: 10000000000, population_2100_in: 11000000000, per_capita_gdp_2023_in: 15300, energy_intensity_2023_in: 1.42, carbon_intensity_2023_in: 0.22, per_capita_gdp_rate_in: 0.01, energy_intensity_rate_in: -0.01, carbon_intensity_rate_in: -0.01}

here

Object {year_in: Array(6), formulae: Array(5)}

Array(30) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Array(2) ["year_in", "formulae"]

Array(30) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Array(1) [Object]

encodings:

Array(4) [Object, Object, Object, Object]

other

Code

q = new URLSearchParams(location.search)

formulae = Object.values(introspection.cul_functions).filter(d => d.reason == 'definition' && inputs.indexOf(d.name+'_in') == -1).map(d => d.name)

formulae_next = Object.values(introspection_next.cul_functions).filter(d => d.reason == 'definition' && inputs.indexOf(d.name+'_in') == -1).map(d => d.name)

formulae_objs = Object.values(introspection.cul_functions).filter(d => d.reason == 'definition' && inputs.indexOf(d.name+'_in') == -1)

mutable inputs_history = inputs_default

domains

q = URLSearchParams {}

formulae = Array(11) ["population", "population_factor", "per_capita_gdp_factor", "per_capita_gdp", "energy_intensity_factor", "energy_intensity", "carbon_intensity_factor", "carbon_intensity", "technology_factor", "income_factor", "carbon_emissions"]

formulae_next = Array(11) ["population", "population_factor", "per_capita_gdp_factor", "per_capita_gdp", "energy_intensity_factor", "energy_intensity", "carbon_intensity_factor", "carbon_intensity", "technology_factor", "income_factor", "carbon_emissions"]

formulae_objs = Array(11) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object]

mutable inputs_history = Mutable {}

inputs_history = Array(1) [Object]

Object {year_in: Array(6), formulae: Array(5)}

Code

input_domains = {
  // if formula mapped => not something to include
  var o = {}
  mapped.filter(d => !formulae.includes(d)).forEach(i => { // only use mapped
    
    o[i] = domains[i]
  })
  if (mapped.includes("interaction"))
    o.interaction = inputs_history.map((d,i) => i)
  return o
}

input_domains_next = {
  // if formula mapped => not something to include
  var o = {}
  mapped_next.filter(d => !formulae_next.includes(d)).forEach(i => { // only use mapped
    o[i] = domains_next[i]
  })
  if (mapped_next.includes("interaction"))
    o.interaction = inputs_history.map((d,i) => i)
  return o
}


input_combos_projection = cartesianProduct(Object.entries(input_domains_projection).map(([k,v]) => ({[k == 'formulae' ? 'formula' : k]: v})))

input_combos_projection_next = cartesianProduct(Object.entries(input_domains_projection_next).map(([k,v]) => ({[k == 'formulae' ? 'formula' : k]: v})))

input_domains = Object {year_in: Array(6), formulae: Array(5)}

input_domains_next = Object {year_in: Array(6), formulae: Array(5)}

input_combos_projection = Array(30) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

input_combos_projection_next = Array(30) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Code

// cursor shouldn't include invalid inputs

// this is not designed to replace existing code - e.g. formulae needs to change to formula in specs
// this relies on domains.formula having revelent list of model formulae if formula is mapped

function projection_fn ({mapped, domains, cursor}) {
  
  let input_domains_projection = {}
  Object.entries(cursor).forEach(([k,v]) => {
    input_domains_projection[k] = [v] // inputs defined in cursor => a one-entry array
  })
  
  Object.entries(domains).forEach(([k,v]) => {
    if (mapped.includes(k)) input_domains_projection[k] = v // mapped domain => include that domain
  })

  let input_combos_projection = cartesianProduct(Object.entries(input_domains_projection).map(([k,v]) => ({[k]:v})))

  // inputs...|formula|value
  // ^ whenever a set of formulae mapped

  // inputs...|formulae...
  // ^ whenever >1 formula is mapped
  // =1 case also falls here, its ok

  /*let sets = 0; // I could just look for formula in mapped, since
  mapped.forEach(m => {
    if (m.slice(-3) == '_in') return;
    if (m != 'interaction')
      sets++;
    // not even checking domains keys
  })*/

  if (mapped.includes('formula')) {

    //return input_combos_projection.map(combos => ({...combos, value: /*+*/model[combos.formula](combos)}))

    let o = []

    input_combos_projection.forEach(combos => {
      let ans = 'ERROR'; // or NaN for viz purposes?
      try {
        ans = /*+*/model[combos.formula](combos)
      } catch(e) {
        console.log(e)
      }

      o.push({...combos, value:ans})
    })

    return o;

  } else {
    
    let o = [];

    input_combos_projection.forEach(combos => {
      let oo = {...combos};

      mapped.filter(m => m.slice(-3) != '_in') // 'interaction' case todo
        .forEach(f => {
          oo[f] = /*+*/model[f](oo);
        })

      o.push(oo);

    })

    return o;

  }
}

projection_fn = ƒ(…)

Code

projection = {
  if (mapped.includes('formulae'))
    return input_combos_projection.map(combos => {
      if (!mapped.includes('interaction')) return combos
      if (combos.interaction == inputs_history.length-1) return combos
      else
        return ({...combos, ...inputs_history[combos.interaction]/*adding too much here e.g. t_interval, restrict to inputs?*/}) // cant set to input histories here !
    }).map(combos => ({...combos, value: /*+*/model[combos.formula](combos)/*.toFixed(2)*/}))
  else {
    // do all mapped formulae at once
    var o = [];
   input_combos_projection.forEach(combo => {
    var oo = (combos => {
          if (!mapped.includes('interaction')) return combos
      if (combos.interaction == inputs_history.length-1) return combos
else
        return ({...combos, ...inputs_history[combos.interaction]/*adding too much here e.g. t_interval, restrict to inputs?*/}) // cant set to input histories here !
    //mapped.filter(d => formulae.includes(d)).for
    })(combo);
      mapped.filter(d => formulae.includes(d)).forEach(formula => {
        var oooo = {}
        Object.keys(oo).forEach(k => {
          if (k.slice(-3) == '_in') oooo[k] = oo[k]
        })
        oo[formula] = /*+*/model[formula](oooo); // interaction going into call and other things on scatters messes up memo when its needed where randomness is used in model
      })
     o.push(oo);
   })

    return o
     
     
     //combos => ({...combos, [formula]}))
    
    //mapped.filter(formula => formulae.includes(formula)).map(formula => input_combos_projection.map(combos => ({...combos, [formula]}))
    // here look for mapped functions and loop/flatten
    // what will be in c-p for functions? nothing?
  }
}

projection_next = {
  if (mapped_next.includes('formulae'))
    return input_combos_projection_next.map(combos => {
      if (!mapped_next.includes('interaction')) return combos
      if (combos.interaction == inputs_history.length-1) return combos
      else
        return ({...combos, ...inputs_history[combos.interaction]/*adding too much here e.g. t_interval, restrict to inputs?*/}) // cant set to input histories here !
    }).map(combos => ({...combos, value: /*+*/model_next[combos.formula](combos)/*.toFixed(2)*/}))
  else {
    // do all mapped formulae at once
    var o = [];
   input_combos_projection_next.forEach(combo => {
    var oo = (combos => {
          if (!mapped_next.includes('interaction')) return combos
      if (combos.interaction == inputs_history.length-1) return combos
else
        return ({...combos, ...inputs_history[combos.interaction]/*adding too much here e.g. t_interval, restrict to inputs?*/}) // cant set to input histories here !
    //mapped.filter(d => formulae.includes(d)).for
    })(combo);
      mapped_next.filter(d => formulae.includes(d)).forEach(formula => {
        var oooo = {}
        Object.keys(oo).forEach(k => {
          if (k.slice(-3) == '_in') oooo[k] = oo[k]
        })
        oo[formula] = /*+*/model_next[formula](oooo); // interaction going into call and other things on scatters messes up memo when its needed where randomness is used in model
      })
     o.push(oo);
   })

    return o
     
     
     //combos => ({...combos, [formula]}))
    
    //mapped.filter(formula => formulae.includes(formula)).map(formula => input_combos_projection.map(combos => ({...combos, [formula]}))
    // here look for mapped functions and loop/flatten
    // what will be in c-p for functions? nothing?
  }
}

projection = Array(30) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

projection_next = Array(30) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Code

inputs = Object.values(introspection.cul_functions).filter(d => d.reason == 'input definition').map(d => d.name).sort()

inputs_next = Object.values(introspection_next.cul_functions).filter(d => d.reason == 'input definition').map(d => d.name).sort()


input_domains_projection = { // think RE blanket using all here ! filter for inputs? See t_interval
  var o = {}
  Object.entries(/*viz_spec.cursor*/ui).filter(([k,v]) => inputs.includes(k)).forEach(([k,v]) => {
    
    o[k] = [v]
  })
  Object.entries(input_domains).forEach(([k,v]) => {
    o[k] = v
  })
  return o
}

input_domains_projection_next = { // think RE blanket using all here ! filter for inputs? See t_interval
  var o = {}
  Object.entries(/*viz_spec.cursor*/ui).filter(([k,v]) => inputs_next.includes(k)).forEach(([k,v]) => {
    
    o[k] = [v]
  })
  Object.entries(input_domains_next).forEach(([k,v]) => {
    o[k] = v
  })
  return o
}

inputs = Array(10) ["carbon_intensity_2023_in", "carbon_intensity_rate_in", "energy_intensity_2023_in", "energy_intensity_rate_in", "per_capita_gdp_2023_in", "per_capita_gdp_rate_in", "population_2023_in", "population_2050_in", "population_2100_in", "year_in"]

inputs_next = Array(10) ["carbon_intensity_2023_in", "carbon_intensity_rate_in", "energy_intensity_2023_in", "energy_intensity_rate_in", "per_capita_gdp_2023_in", "per_capita_gdp_rate_in", "population_2023_in", "population_2050_in", "population_2100_in", "year_in"]

input_domains_projection = Object {population_2023_in: Array(1), population_2050_in: Array(1), population_2100_in: Array(1), per_capita_gdp_2023_in: Array(1), energy_intensity_2023_in: Array(1), carbon_intensity_2023_in: Array(1), per_capita_gdp_rate_in: Array(1), energy_intensity_rate_in: Array(1), carbon_intensity_rate_in: Array(1), year_in: Array(6), formulae: Array(5)}

input_domains_projection_next = Object {population_2023_in: Array(1), population_2050_in: Array(1), population_2100_in: Array(1), per_capita_gdp_2023_in: Array(1), energy_intensity_2023_in: Array(1), carbon_intensity_2023_in: Array(1), per_capita_gdp_rate_in: Array(1), energy_intensity_rate_in: Array(1), carbon_intensity_rate_in: Array(1), year_in: Array(6), formulae: Array(5)}

Code

function cartesianProduct(input, current) {
    if (!input || !input.length) { return []; }
 
    var head = input[0];
    var tail = input.slice(1);
    var output = [];
 
     for (var key in head) {
       for (var i = 0; i < head[key].length; i++) {
             var newCurrent = copy(current);         
             newCurrent[key] = head[key][i];
             if (tail.length) {
                  var productOfTail = 
                          cartesianProduct(tail, newCurrent);
                  output = output.concat(productOfTail);
             } else output.push(newCurrent);
        }
      }    
     return output;
 }

// https://stackoverflow.com/questions/18957972/cartesian-product-of-objects-in-javascript

function copy(obj) {
   var res = {};
   for (var p in obj) res[p] = obj[p];
   return res;
 }

cartesianProduct = ƒ(input, current)

copy = ƒ(obj)

Code

json2csv = require("json2csv@5.0.7/dist/json2csv.umd.js")

// thx to https://observablehq.com/@palewire/saving-csv
// Ben Welsh and comments from Christophe Yamahata
function serialize (data) {
 let parser = new json2csv.Parser();
 let csv = parser.parse(data);
 return new Blob([csv], {type: "text/csv"}) 
}


function serializeJSON (data) {
 return new Blob([JSON.stringify(data,null,2)], {type: "text/json"}) 
}

json2csv = Object {AsyncParser: ƒ(opts, transformOpts), Parser: ƒ(opts), Transform: ƒ(opts, transformOpts), default: Object, parse: ƒ(data, opts), parseAsync: ƒ(data, opts, transformOpts), transforms: Object}

serialize = ƒ(data)

serializeJSON = ƒ(data)

Code

cql = require("compassql")

schema = cql.schema.build([...projection, ...projection, ...projection, ...projection])

encodings = Object.entries(spec.channels).filter(([k,v]) => k != 'detail_only_proj').map(([k,v]) => ({type:v.type??'nominal', channel:k, field:(v.name??v) == 'formulae' ? 'formula' : (v.name??v)/*nominal, type todo*/}))

output = cql.recommend({
  spec: {data: projection,
    mark: spec.mark == 'bar' ? 'line' : spec.mark,
    encodings
  },
  chooseBy: "effectiveness",
}, schema);

vlTree = cql.result.mapLeaves(output.result, function (item) {
  return item//.toSpec();
});

c_spec = vlTree.items[0].toSpec()

c_spec1 = {
  var s = c_spec;
  s.data = {name: 'projection'};
  s.width = /*viz.width ??*/ 500;
  s.height = spec.height;
  s.datasets = {projection:projection/*.filter(d => d.y>-100)*/}
  var r = {}
  // todo independent scales Object.entries(viz_spec.independent_scales).filter(([k,v]) => v == true).forEach(([k,v]) => { r[k] = 'independent' })
  //s.resolve = {scale: r}
  if (spec.mark == "bar") s.mark = "bar"; //{"type": "bar", "tooltip": true};
  var p = s.mark;
  s.mark = {type: p, tooltip:true}
  if (spec.mark == "line") {s.mark.point = true; s.encoding.order={field:spec.channels.detail_only_proj}/*s.encoding.size = {value:20}*/}
  if (spec.mark == "point") {s.encoding.size = {value:100};
  s.mark.strokeWidth = 5};
  
  //s["config"] ={"legend": {"disable": true}}

  //s['encoding']['x']['axis']['labelAngle'] = 0 //.x.axis.labelAngle=0// = {"orient": "top", "labelAngle":0};

  return s
}

OJS Runtime Error

mapped_0 is not defined

encodings = Array(4) [Object, Object, Object, Object]

OJS Runtime Error

spec_0 is not defined

OJS Runtime Error

spec_0 is not defined

OJS Runtime Error

spec_0 is not defined

OJS Runtime Error

spec_0 is not defined

Code

vega_interactive = { // credit to Mike Bostock (starting point): https://observablehq.com/@mbostock/hello-vega-embed
  const v = window.vega = await require("vega");
  const vl = window.vl = await require("vega-lite");
  const ve = await require("vega-embed");
  async function vega(spec, options) {
    const div = document.createElement("div");
        div.setAttribute('id','chart-out');
    div.value = (await ve(div, spec, options)).view;
    div.value.addEventListener('mousemove', (event, item) => {
      //console.log(item);
      if (item != undefined && item.datum != undefined && item.datum.formula != undefined) {
              /*DN off viewof formula_select.value = item.datum.formula;
      viewof formula_select.dispatchEvent(new CustomEvent("input"))
*/
      }
    })
    div.value.addEventListener('click', (event, item) => {
      console.log(item.datum);
      /*viewof formula_select.value = item.datum.formula;
      viewof formula_select.dispatchEvent(new CustomEvent("input"));
      viewof month_select.value = item.datum.month_in;
      viewof month_select.dispatchEvent(new CustomEvent("input"));*/
      //if (item.datum.age_0_in != undefined) {viewof inputs.value = {...viewof inputs.value, age_0_in:item.datum.age_0_in}; //cursor.month_in.push(item.datum.month_in);
      //viewof inputs.dispatchEvent(new CustomEvent("input"))};

       //     if (item.datum.age_in != undefined) {viewof inputs.value = {...viewof inputs.value, age_in:item.datum.age_in}; //cursor.month_in.push(item.datum.month_in);
      //viewof inputs.dispatchEvent(new CustomEvent("input"))};

      //viewof inputs.value = { ...viewof inputs.value, ...item.datum };
      //viewof inputs.dispatchEvent(new CustomEvent("input"))

      /*viewof formula_select.value = item.datum.formula;
      viewof formula_select.dispatchEvent(new CustomEvent("input"))
*/
//y.value.insert('selection_age_0_in_store', [{unit: "concat_1_layer_0", fields: [{"field":"age_0_in","channel":"x","type":"E"}]    , values:[10]}])
//y.value.insert('selection_dampener_in_store', [{unit: "concat_2_concat_2_layer_0", fields: [{"field":"dampener_in","channel":"x","type":"E"}] , values:[0.95]}])
  //.run()
      
// NOW TODO addSignalListener stuff... is it trigerred for above??
// until there is a selection api...
//https://github.com/vega/vega-lite/issues/2790#issuecomment-976633121
// https://github.com/vega/vega-lite/issues/1830#issuecomment-926138326
      
    }) // DN
    return div;
  }
  vega.changeset = v.changeset;
  return vega;
}

Code

// reference: https://observablehq.com/@mbostock/saving-svg
serializeSVG = {
  const xmlns = "http://www.w3.org/2000/xmlns/";
  const xlinkns = "http://www.w3.org/1999/xlink";
  const svgns = "http://www.w3.org/2000/svg";
  return function serialize(svg) {
    svg = svg.cloneNode(true);
    const fragment = window.location.href + "#";
    const walker = document.createTreeWalker(svg, NodeFilter.SHOW_ELEMENT);
    while (walker.nextNode()) {
      for (const attr of walker.currentNode.attributes) {
        if (attr.value.includes(fragment)) {
          attr.value = attr.value.replace(fragment, "#");
        }
      }
    }
    svg.setAttributeNS(xmlns, "xmlns", svgns);
    svg.setAttributeNS(xmlns, "xmlns:xlink", xlinkns);
    const serializer = new window.XMLSerializer;
    const string = serializer.serializeToString(svg);
    return new Blob([string], {type: "image/svg+xml"});
  };
}

serializeSVG = ƒ(svg)

Code

import {inputs_default} from "./kaya.ojs" // for enable/disable of inputs

import {viewof ui} with {uis, introspection, mutable inputs_history} from "./kaya.ojs" // for enable/disable of inputs

  // AA:
  import {introspection as introspection_0, spec_post_process/*, viewof ui*//*, domains*//*, mutable inputs_history*/, viewof field, uis as uis_0, spec as spec_0, mapped as mapped_0 } from "./kaya.ojs"




  import { domains as domains_0 } with {viewof ui} from "./kaya.ojs"


model_0 = require('../../models/kaya/kaya.js');
//introspection_0 = await FileAttachment('../../models/kaya/kaya-nomemo.introspection.json').json({typed:true});

cul_0_0 = await FileAttachment('../../models/kaya/kaya-nomemo_esm/cul_scope_0.cul.js').text();
esm_0_0 = await FileAttachment('../../models/kaya/kaya-nomemo_esm/cul_scope_0.mjs').text();

  import {inputs_default as inputs_default} from "./kaya.ojs"

  import {viewof ui as viewof ui, ui as ui} with {uis as uis, introspection as introspection, mutable inputs_history as mutable inputs_history, inputs_history as inputs_history} from "./kaya.ojs"

  import {introspection as introspection_0, spec_post_process as spec_post_process, viewof field as viewof field, field as field, uis as uis_0, spec as spec_0, mapped as mapped_0} from "./kaya.ojs"

  import {domains as domains_0} with {viewof ui as viewof ui, ui as ui} from "./kaya.ojs"

model_0 = Module {year$m: ƒ(key), year: ƒ(a), population_2023$m: ƒ(key), population_2023: ƒ(a), population_2050$m: ƒ(key), population_2050: ƒ(a), population_2100$m: ƒ(key), population_2100: ƒ(a), population$m: ƒ(key), population: ƒ(a), population_factor$m: ƒ(key), population_factor: ƒ(a), per_capita_gdp_2023$m: ƒ(key), per_capita_gdp_2023: ƒ(a), per_capita_gdp_rate$m: ƒ(key), per_capita_gdp_rate: ƒ(a), per_capita_gdp_factor$m: ƒ(key), per_capita_gdp_factor: ƒ(a), per_capita_gdp$m: ƒ(key), per_capita_gdp: ƒ(a), …}

cul_0_0 = `export const year = () => year_in; // modelling population linearly between some points: export const population_2023 = () => population_2023_in; export const population_2050 = () => population_2050_in; export const population_2100 = () => population_2100_in; export const population = () => { if (year() <= 2023) return population_2023(); else if (year() <= 2050) return ( population_2023() + ((population_2050() - population_2023()) * (year() - 2023)) / (2050 - 2023) ); else return ( population_2050() + ((population_2100() - population_2050()) * (year() - 2050)) /Show 73 truncated lines

esm_0_0 = `export const year = ({ year_in }) => year_in; // modelling population linearly between some points: export const population_2023 = ({ population_2023_in }) => population_2023_in; export const population_2050 = ({ population_2050_in }) => population_2050_in; export const population_2100 = ({ population_2100_in }) => population_2100_in; export const population = ({ year_in, population_2023_in, population_2050_in, population_2100_in }) => { if (year({ year_in }) <= 2023) return population_2023({ population_2023_in });else if (year({ year_in }) <= 2050) return ( population_2023({ population_2023_in }) + (population_2050({ population_2050_in }) - population_2023({ population_2023_in })) * (year({ year_in }) - 2023) / ( 2050 - 2023));else return ( population_2050({ population_2050_in }) + (population_2100({ population_2100_in }) - population_2050({ population_2050_in })) * (year({ year_in }) - 2050) / (Show 72 truncated lines

Code

models = [model_0,]
uis1 = [uis_0,]
specs = [spec_0,]
cul_0s = [cul_0_0,]
esm_0s = [esm_0_0,]
introspections = [introspection_0,]
domains1 = [domains_0,]
mappeds = [mapped_0,]



viewof step = Inputs.range([0,1], {label:'spec', value:q.get('spec') ?? 0, step:1})

viewof shadow_step = Inputs.range([0,1], {label:'shadow spec', value:q.get('spec') ?? 0, step:0.5}) // hmmmm

viewof step_next = Inputs.range([0,1], {label:'spec next', value: step ==0 ? 0 : step + 1, step:1})


emojis = [/*"🅾️"*/"⏮️","➡️1️⃣","➡️2️⃣","➡️3️⃣","➡️4️⃣","➡️5️⃣","➡️6️⃣"]//➡️


model = models[step]
uis = uis1[step]
spec = specs[step]
cul_0 = cul_0s[step]
esm_0 = esm_0s[step]
introspection = introspections[step]
domains = domains1[step]
mapped = mappeds[step]



domains_next = domains1[step_next]
mapped_next = mappeds[step_next]

model_next = models[step_next]
introspection_next = introspections[step_next]

models = Array(1) [Module]

uis1 = Array(1) [Object]

specs = Array(1) [Object]

cul_0s = Array(1) ["export const year = () => year_in;\n\n// modelling p…gdp() * energy_intensity() * carbon_intensity();\n"]

esm_0s = Array(1) ["export const year = ({ year_in }) => year_in;\n\n// …ty_2023_in, year_in, carbon_intensity_rate_in });"]

introspections = Array(1) [Object]

domains1 = Array(1) [Object]

mappeds = Array(1) [Array(2)]

step = 0

shadow_step = 0

step_next = 0

emojis = Array(7) ["⏮️", "➡️1️⃣", "➡️2️⃣", "➡️3️⃣", "➡️4️⃣", "➡️5️⃣", "➡️6️⃣"]

model = Module {year$m: ƒ(key), year: ƒ(a), population_2023$m: ƒ(key), population_2023: ƒ(a), population_2050$m: ƒ(key), population_2050: ƒ(a), population_2100$m: ƒ(key), population_2100: ƒ(a), population$m: ƒ(key), population: ƒ(a), population_factor$m: ƒ(key), population_factor: ƒ(a), per_capita_gdp_2023$m: ƒ(key), per_capita_gdp_2023: ƒ(a), per_capita_gdp_rate$m: ƒ(key), per_capita_gdp_rate: ƒ(a), per_capita_gdp_factor$m: ƒ(key), per_capita_gdp_factor: ƒ(a), per_capita_gdp$m: ƒ(key), per_capita_gdp: ƒ(a), …}

uis = Object {population_2023_in: HTMLFormElement, population_2050_in: HTMLFormElement, population_2100_in: HTMLFormElement, per_capita_gdp_2023_in: HTMLFormElement, energy_intensity_2023_in: HTMLFormElement, carbon_intensity_2023_in: HTMLFormElement, per_capita_gdp_rate_in: HTMLFormElement, energy_intensity_rate_in: HTMLFormElement, carbon_intensity_rate_in: HTMLFormElement}

spec = Object {mark: "text", height: 40, channels: Object}

cul_0 = `export const year = () => year_in; // modelling population linearly between some points: export const population_2023 = () => population_2023_in; export const population_2050 = () => population_2050_in; export const population_2100 = () => population_2100_in; export const population = () => { if (year() <= 2023) return population_2023(); else if (year() <= 2050) return ( population_2023() + ((population_2050() - population_2023()) * (year() - 2023)) / (2050 - 2023) ); else return ( population_2050() + ((population_2100() - population_2050()) * (year() - 2050)) /Show 73 truncated lines

esm_0 = `export const year = ({ year_in }) => year_in; // modelling population linearly between some points: export const population_2023 = ({ population_2023_in }) => population_2023_in; export const population_2050 = ({ population_2050_in }) => population_2050_in; export const population_2100 = ({ population_2100_in }) => population_2100_in; export const population = ({ year_in, population_2023_in, population_2050_in, population_2100_in }) => { if (year({ year_in }) <= 2023) return population_2023({ population_2023_in });else if (year({ year_in }) <= 2050) return ( population_2023({ population_2023_in }) + (population_2050({ population_2050_in }) - population_2023({ population_2023_in })) * (year({ year_in }) - 2023) / ( 2050 - 2023));else return ( population_2050({ population_2050_in }) + (population_2100({ population_2100_in }) - population_2050({ population_2050_in })) * (year({ year_in }) - 2050) / (Show 72 truncated lines

introspection = Object {cul_functions: Object, cul_links: Array(48), cul_scope_ids_to_resource: Object, import_sources_to_resource: Object, cul_input_map: Object, dot: "digraph {\n \"0_year\" [cul_scope_id=0,name=year,cul…ions\" [value=\"[object Object]\",label=\"call -\"]\n}\n"}

domains = Object {year_in: Array(6), formulae: Array(5)}

mapped = Array(2) ["year_in", "formulae"]

domains_next = Object {year_in: Array(6), formulae: Array(5)}

mapped_next = Array(2) ["year_in", "formulae"]

model_next = Module {year$m: ƒ(key), year: ƒ(a), population_2023$m: ƒ(key), population_2023: ƒ(a), population_2050$m: ƒ(key), population_2050: ƒ(a), population_2100$m: ƒ(key), population_2100: ƒ(a), population$m: ƒ(key), population: ƒ(a), population_factor$m: ƒ(key), population_factor: ƒ(a), per_capita_gdp_2023$m: ƒ(key), per_capita_gdp_2023: ƒ(a), per_capita_gdp_rate$m: ƒ(key), per_capita_gdp_rate: ƒ(a), per_capita_gdp_factor$m: ƒ(key), per_capita_gdp_factor: ƒ(a), per_capita_gdp$m: ƒ(key), per_capita_gdp: ƒ(a), …}

introspection_next = Object {cul_functions: Object, cul_links: Array(48), cul_scope_ids_to_resource: Object, import_sources_to_resource: Object, cul_input_map: Object, dot: "digraph {\n \"0_year\" [cul_scope_id=0,name=year,cul…ions\" [value=\"[object Object]\",label=\"call -\"]\n}\n"}