diff --git a/package-lock.json b/package-lock.json
index dda0cac..1271732 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -9,6 +9,7 @@
       "version": "3.0.0",
       "license": "ISC",
       "dependencies": {
+        "@evoposter/evaluator": "file:src/@evoposter/evaluator",
         "@evoposter/nlp": "file:src/@evoposter/nlp",
         "cors": "^2.8.5",
         "cross-env": "^7.0.3",
@@ -17,7 +18,6 @@
         "pm2": "^5.3.0"
       },
       "devDependencies": {
-        "@evoposter/evaluator": "file:src/@evoposter/evaluator",
         "@rollup/plugin-json": "^6.0.0",
         "@rollup/plugin-node-resolve": "^15.1.0",
         "@rollup/plugin-terser": "^0.4.3",
@@ -10289,7 +10289,6 @@
     },
     "src/@evoposter/evaluator": {
       "version": "0.0.1",
-      "dev": true,
       "license": "ISC",
       "devDependencies": {
         "@rollup/plugin-terser": "^0.4.3",
diff --git a/package.json b/package.json
index 67bcfdd..c98efca 100644
--- a/package.json
+++ b/package.json
@@ -5,6 +5,7 @@
   "main": "src/server.js",
   "type": "module",
   "dependencies": {
+    "@evoposter/evaluator": "file:src/@evoposter/evaluator",
     "@evoposter/nlp": "file:src/@evoposter/nlp",
     "cors": "^2.8.5",
     "cross-env": "^7.0.3",
@@ -33,7 +34,6 @@
   },
   "homepage": "https://github.com/sergiomrebelo/evo-poster#readme",
   "devDependencies": {
-    "@evoposter/evaluator": "file:src/@evoposter/evaluator",
     "@rollup/plugin-json": "^6.0.0",
     "@rollup/plugin-node-resolve": "^15.1.0",
     "@rollup/plugin-terser": "^0.4.3",
diff --git a/src/@evoposter/evaluator/lib/evaluator.min.js b/src/@evoposter/evaluator/lib/evaluator.min.js
index 0bd461e..b85863a 100644
--- a/src/@evoposter/evaluator/lib/evaluator.min.js
+++ b/src/@evoposter/evaluator/lib/evaluator.min.js
@@ -1,2 +1,2 @@
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diff --git a/src/@evoposter/evaluator/lib/evaluator.min.js.map b/src/@evoposter/evaluator/lib/evaluator.min.js.map
index 77124c5..390f657 100644
--- a/src/@evoposter/evaluator/lib/evaluator.min.js.map
+++ b/src/@evoposter/evaluator/lib/evaluator.min.js.map
@@ -1 +1 @@
-{"version":3,"file":"evaluator.min.js","sources":["../src/utils.js","../src/metrics.config.js","../src/metrics/Legibility.mjs","../src/metrics/SemanticsLayout.mjs","../src/metrics/SemanticsEmphasis.mjs","../semantics-visual.config.js","../src/metrics/SemanticVisuals.mjs","../src/metrics/Alignment.mjs","../src/metrics/WhiteSpaceFraction.mjs","../src/metrics/TypefaceParing.mjs","../src/metrics/VisualBalance.mjs","../src/index.mjs","../src/metrics/GridAppropriateSize.mjs","../src/metrics/Regularity.mjs"],"sourcesContent":["export const map = (value, minA, maxA, minB, maxB) => {\n    return minB + (maxB - minB) * ((value - minA) / (maxA - minA));\n}\n\nexport const constraint = (value, min, max) => {\n    return Math.min(max, Math.max(min, value));\n}\n\nexport const arrMean = (arr) => {\n    const sum = arr.reduce((a, b) => a + b, 0);\n    return (sum / arr.length) || 0;\n}\n\nexport const arrSum = (arr) => {\n    return arr.reduce((partialSum, a) => partialSum + a, 0);\n}\n\nexport const arrMax = (arr) => {\n    return Math.max(...arr);\n}\n\nexport const arrMin = (arr) => {\n    return Math.min(...arr);\n}\n\nexport const arrUnique = (arr) => {\n    return arr.filter((value, index, array) => array.indexOf(value) === index);\n}\n\nexport const sumProduct = (arr, weights) => {\n    return arr.reduce((s, v, i) => s + v * weights[i], 0);\n}\n\nexport const hexToRGB = (hex) => {\n    if (hex[\"levels\"]) {\n        return {\n            r: parseInt(hex[\"levels\"][0]),\n            g: parseInt(hex[\"levels\"][1]),\n            b: parseInt(hex[\"levels\"][2])\n        }\n    }\n\n    let result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})$/i.exec(hex);\n    return result ? {\n        r: parseInt(result[1], 16),\n        g: parseInt(result[2], 16),\n        b: parseInt(result[3], 16)\n    } : null;\n}\n\nexport const colorDistance = (a, b) => {\n    return Math.sqrt(Math.pow(a.r - b.r, 2) + Math.pow(a.g - b.g, 2) + Math.pow(a.b - b.b, 2));\n}","export const ALIGNMENT = {\n    \"A\": 10, // limit to the non-linear function\n    \"WEIGHTS\": [.8, .2] // alignment parts weights\n}\n\nexport const LEGIBILITY = {\n    \"MAX_CONSTRAINT\": 1,\n    \"WHITE_SPACE_FACTOR\": 3,\n    \"MODES\": [`OVERSET`, `JUSTIFY`,`ATTEMPT_JUSTIFY`],\n    \"DEFAULT_MAX_LIMIT_SCALE\": 1\n}\n\nexport const REGULARITY = {\n    \"A\": 10, // limit to the non-linear function\n}\n\nexport const SEMANTICS_EMPHASIS = {\n    \"MIN_RANGE\": 50,\n    \"THRESHOLD_VALID\": 0.2,\n    \"MODES\": [`DIF`, `MIN`]\n}\n\nexport const SEMANTICS_VISUALS = {\n    \"MAX_COLOR_DISTANCE\": 441.67\n}\n\nexport const SEMANTICS_LAYOUT = {\n    \"MODES\": [`RELATIVE`, `FIXED`]\n}\n\nexport const TYPEFACE_PARING = {\n    \"MODES\": [`BOTH`, `TYPE_FAMILY`, `CATEGORY`]\n}\n\nexport const VISUAL_BALANCE = {\n    \"VISUAL_CENTER_FT\": 20,\n}\n\nexport const WHITE_SPACE_FRACTION = {\n    \"OPTIMAL\": .5,\n    \"MIN_DISTANCE\": 10\n}\n\n\nexport default {\n    \"DEBUG\": false,\n}","/**\n * Legibility\n *\n * Measure the legibility of the text in the poster\n * it is related to the legibility of the sentence\n * and not the typeface shapes\n *\n * Expected return a value between 0 (good) and (1) bad\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 August 2018 (as part of evoPoster)\n * v2.0.0 November 2020 (as part of evoPoster)\n * v2.5.0 November 2021 (as part of evoPoster)\n * v3.0.0 November 2023\n */\n\nimport {arrMean, map} from \"../utils.js\";\nimport {LEGIBILITY} from \"../metrics.config.js\";\n\nconst MAX_CONSTRAINT = LEGIBILITY[\"MAX_CONSTRAINT\"];\nconst WHITE_SPACE_FACTOR = LEGIBILITY[\"WHITE_SPACE_FACTOR\"];\nconst DEFAULT_MAX_LIMIT_SCALE = LEGIBILITY[\"DEFAULT_MAX_LIMIT_SCALE\"];\nconst AVAILABLE_MODES = LEGIBILITY [\"MODES\"]\n\nexport const compute = (sentencesLength = [], minSize, mode= 'OVERSET', maxLimitScale= DEFAULT_MAX_LIMIT_SCALE) => {\n    if (!AVAILABLE_MODES.includes(mode)) {\n        mode = `OVERSET`;\n    }\n    let results = [];\n    let max = minSize * maxLimitScale;\n    for (let sentence of sentencesLength) {\n        let dif = minSize-sentence;\n        let value = MAX_CONSTRAINT;\n        switch (mode) {\n            case `JUSTIFY`:\n                value = justify(dif, max);\n                break;\n            case `ATTEMPT_JUSTIFY`:\n                value = attemptJustify(dif, max);\n                break;\n            default:\n                value = overset(dif, max);\n                break;\n        }\n        results.push(value);\n    }\n\n    // calculate mean\n    const mean = arrMean([...results]);\n\n    return mean;\n}\n\nconst overset = (value, max) => {\n    // only prejudice when text overfits the poster\n    // if dif bigger than 0\n    value = value >= 0 ? 0 : value;\n    // if dif lower than limit\n    value = value <= -max ? -max : value;\n    // transform in scale of 1 (bad) to 0 (good)\n    return map (value, -max, 0, MAX_CONSTRAINT, 0);\n}\n\nconst justify = (value, max) => {\n    // prejudice both overset and small lettering\n    value = Math.abs(value)\n    // if the dif is bigger than max\n    value = value > max ? max : value;\n    // transform in scale of 1 (bad) to 0 (good)\n    return map (value, max, 0, MAX_CONSTRAINT, 0);\n}\n\n// attempt to justify the text\n// prejudice more when the text overset box than when it is smaller\nconst attemptJustify = (value, max) => {\n    // if dif bigger than 0 (it is not overset), soften the value\n    value = value >= 0 ? value/WHITE_SPACE_FACTOR : value;\n    return justify(value, max);\n}\n\nexport { compute as default };","/**\n * Layout Semantics\n *\n * Measure the appropriate of the layout\n * based on the importance of the content\n * it considers that most important parts of content\n * are those have more emotions related\n *\n * return the mean of difference between all textboxes,\n * a value between 1 (good) and 0 (bad)\n *\n * two modes: RELATIVE and FIXED\n * RELATIVE mode is related to the composition height\n * FIXED height is related to the container's height\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 April 2020\n * v2.0.0 November 2023\n */\n\nimport {arrMean, arrSum} from \"../utils.js\";\nimport {SEMANTICS_LAYOUT} from \"../metrics.config.js\";\n\nconst AVAILABLE_MODES = SEMANTICS_LAYOUT[\"MODES\"];\n\nexport const compute = (textboxesHeights = [], dist = null, mode=`RELATIVE`, size = {height: 100, margin:[0,0,0,0]}) => {\n    // mode validation\n    if (!AVAILABLE_MODES.includes(mode)) mode = `RELATIVE`;\n\n    // define max height\n    let height = 0;\n    if (mode === `RELATIVE`) {\n        // using the textbox height\n        height = arrSum(textboxesHeights);\n    } else if (mode === `FIXED`) {\n        // using container height\n        height = size.height - ((size.height*size.margin[1]) + (size.height*size.margin[3]));\n    }\n    const percents = textboxesHeights.map((p) => p/height);\n\n    // calculate distances\n    let distances = [];\n    for (let i in dist) {\n        const dif = Math.abs(dist[i][3] - percents[i]);\n        distances.push(dif);\n    }\n\n    // return average\n    return (1-arrMean(distances));\n}\n\nexport { compute as default };","/**\n * Semantics Emphasis on Visuals\n *\n * Measure the appropriate of the visual features\n * based on the importance of the content\n * it considers that most important parts of content\n * are those have more emotions related\n *\n * return the mean of difference between all textboxes,\n * a value between 1 (good) and 0 (bad)\n *\n * two modes: MIN and DIF\n * MIN takes into account that the most important parts\n * are typeset in the min value possible in the container\n * DIF: take into account only the difference between the parts\n * selects a style and compare based on the distribution of emotions.\n *\n *\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 November 2023\n */\nimport {arrMax, arrMean, arrMin, arrSum, constraint, map} from \"../utils.js\";\nimport {SEMANTICS_EMPHASIS} from \"../metrics.config.js\";\n\nlet MIN_RANGE = SEMANTICS_EMPHASIS[\"MIN_RANGE\"];\nlet THRESHOLD_VALID = SEMANTICS_EMPHASIS[\"THRESHOLD_VALID\"];\nlet AVAILABLE_MODES = SEMANTICS_EMPHASIS[\"MODES\"];\n\n// by tradition, use more that a method to emphasize\n// the text is considered \"typecrime\".\n// this method enables turn on/off this feature\n// by using the param allowMultiple\nexport const compute = (textboxes, dist, noCurrentTypefaces = 1, allowMultiple = true, weights = [0.4, 0.3, 0.3]) => {\n    // if textboxes size is 1 ---> 1\n    const perDist = dist.map((e) => e[3]);\n\n    const fontWeight = checkDifferenceVariableFeature(textboxes.map((b) => b[\"weight\"]), perDist);\n    const fontStretch = checkDifferenceVariableFeature(textboxes.map((b) => b[\"font-stretch\"]), perDist);\n    let typefaceDesign = noCurrentTypefaces > 1 ? (checkDifferenceUniqueFeatures(textboxes.map((b) => b[\"font-stretch\"]), perDist)) : 0;\n\n    // way of combine and only checks one\n    let res = [fontWeight, fontStretch, typefaceDesign];\n    let weightedRes = res.map((x,i) => x*weights[i]);\n\n    let value;\n\n    if (!allowMultiple) {\n        // if not allowed multiple emphasis\n        // we check what fields are active\n        // and penalty when there is active fields\n        let active = res.map((r) => r > THRESHOLD_VALID);\n        let c = 0;\n        for (let a of active) {\n            if (a) {\n                c++;\n            }\n        }\n        value = arrMax(res)/c;\n    } else {\n        value = arrSum(weightedRes);\n    }\n\n    return value;\n}\n\n// check the levels\nconst checkDifferenceUniqueFeatures = (currentFeatures, dist) => {\n    // available semantic level\n    const uniqueValues = dist.filter((value, index, array) => array.indexOf(value) === index);\n    const target = [];\n\n    // define the target typeface for each semantic level\n    for (let uq of uniqueValues) {\n        for (let i=0; i<dist.length; i++) {\n            if (dist[i] === uq) {\n                target.push(currentFeatures[i]);\n                break;\n            }\n        }\n    }\n\n    // check if the target has duplicates\n    const duplicates = target.filter((item, index) => target.indexOf(item) !== index);\n\n    let value = 1;\n    // if there is duplicate in levels\n    // (if yes, difference is max)\n    if (!duplicates.length >= 1) {\n        // count the amount of tb not in the same typeface\n        let c = 0;\n        // for each typeface\n        for (let i in dist) {\n            let level = dist[i];\n            let currentValue = currentFeatures[i];\n            // get unique index of current semantic level\n            let index = uniqueValues.indexOf(level);\n            // get target value\n            let targetValue = target[index];\n            if (currentValue !== targetValue) {\n                // if not the same as target\n                c++;\n            }\n        }\n        // map value to a value between 0 (no difference) and 1 (max difference)\n        value = map(c, 0, currentFeatures.length, 0, 1);\n    }\n\n    return value;\n}\n\nconst checkDifferenceVariableFeature = (currentFeatures, dist, mode = `DIF`) => {\n    if (!AVAILABLE_MODES.includes(mode)) {\n        mode = `DIF`;\n    }\n    // max feature range\n    const maxFeature = arrMax(currentFeatures);\n    const minFeature = arrMin(currentFeatures);\n    let range = Math.abs(maxFeature - minFeature);\n    if (range < MIN_RANGE) {\n        return 1;\n    }\n\n    // semantic data range\n    const maxSemantic = arrMax(dist);\n    const minSemantic = arrMin(dist);\n\n    // consider the current variable minimum\n    let def = minFeature;\n    if (mode === `DIF`) {\n        // selects a style used in the first min semantic textbox\n        for (let i in dist) {\n            if (dist[i] === minSemantic) {\n                // consider the difference\n                def = currentFeatures[i];\n                break;\n            }\n        }\n    }\n\n    // create target feature values\n    const target = dist.map((e) => {\n        // The most neutral sentence are the most regular\n        let v = map(e, minSemantic, maxSemantic, 0, range);\n        v = constraint(v, 0, range);\n        return v;\n    });\n\n    // distance to target\n    const current = [];\n    for (let i in currentFeatures) {\n        let w = currentFeatures[i];\n        let currentDistance = Math.abs(w - def);\n        let dif = Math.abs(currentDistance - target[i]);\n        current.push(dif);\n    }\n\n    let mean = map(arrMean(current), 0, range, 1, 0);\n    return constraint(mean, 0, 1);\n}\n\n\n\n\nexport { compute as default };","export default {\n    anger : {\n        color: {\n            typography: [`#ff0000`, `#00ff00`],\n            background: [`#ff0000`]\n        },\n        typefaces: [`sans-serif`, `neo-grotesque`]\n    },\n    joy: {\n        color: {\n            typography: [],\n            background: [`#ffff00`, `#00ff00`]\n        },\n        typefaces: [`sans-serif`, `serif`]\n    },\n    trust: {\n        color: {\n            typography: [],\n            background: [`#0000ff`, `#00ff00`]\n        },\n        typefaces: [`neo-grotesque`]\n    },\n    sadness: {\n        color: {\n            typography: [],\n            background: [`#0071b6`]\n        },\n        typefaces: []\n    },\n    disgust: {\n        color: {\n            typography: [`#800080`],\n            background: []\n        },\n        typefaces: []\n    }\n}","/**\n * Semantic Visuals Measure\n *\n * This function assesses the appropriateness of visual features (type design and colour)\n * based on the emotions collected. It takes into account the configuration file \"visual-semantics.config.js\".\n *\n * The function returns the mean difference between all textboxes,\n * yielding a value between 1 (good) and 0 (bad).\n * If there is no information pertaining to a particular emotion, the value defaults to 1.\n *\n * As of now, the function exclusively considers the predominant emotions from ML analysis.\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version: 1.0.0 (November 2023)\n */\n\nimport {SEMANTICS_VISUALS} from \"../metrics.config.js\";\n\nconst MAX_COLOR_DISTANCE = SEMANTICS_VISUALS[\"MAX_COLOR_DISTANCE\"];\n\nimport * as configurationFile from \"../../semantics-visual.config.js\";\nimport {arrMean, colorDistance, hexToRGB, constraint} from \"../utils.js\";\n\n\nexport const compute = async (data, textboxes, background, typefaceData, config = configurationFile) => {\n\n    let emotion = data.predominant.emotion;\n\n    if (config[\"default\"][emotion] === undefined) return 1;\n\n    const targetTypefaceColors = config[\"default\"][emotion][\"color\"][\"typography\"];\n    const targetBackgroundColors = config[\"default\"][emotion][\"color\"][\"background\"];\n    const targetTypographyFeatures = config[\"default\"][emotion][\"typefaces\"];\n\n    // typography colour\n    let meanTypefaceColorDistance = 1;\n    if (targetTypefaceColors !== undefined && targetTypefaceColors.length > 0) {\n        let typefaceColorsDistances = [];\n        for (let t of textboxes) {\n            let c = hexToRGB(t.color);\n            let typefaceColorsDist = Number.MAX_VALUE;\n            for (let targetColor of targetTypefaceColors) {\n                targetColor = hexToRGB(targetColor);\n                let distance = colorDistance(c, targetColor)\n                if (distance < typefaceColorsDist) {\n                    typefaceColorsDist = distance;\n                }\n            }\n            typefaceColorsDistances.push(typefaceColorsDist);\n        }\n\n        meanTypefaceColorDistance = typefaceColorsDistances.length < 1 ? 1 : arrMean(typefaceColorsDistances);\n        meanTypefaceColorDistance /= MAX_COLOR_DISTANCE;\n        meanTypefaceColorDistance = constraint(1-meanTypefaceColorDistance, 0, 1);\n    }\n\n    // background colour\n    let meanTypefaceBackgroundDistance = 1;\n    if (targetBackgroundColors !== undefined && targetBackgroundColors.length !== 0) {\n        let backgroundColorsDistances = [];\n        meanTypefaceBackgroundDistance = 0;\n        for (let c of background) {\n            c = hexToRGB(c);\n            let backgroundColorsDist = Number.MAX_VALUE;\n            for (let targetColor of targetBackgroundColors) {\n                targetColor = hexToRGB(targetColor);\n                let distance = colorDistance(c, targetColor);\n                if (distance < backgroundColorsDist) {\n                    backgroundColorsDist = distance;\n                }\n            }\n            backgroundColorsDistances.push(backgroundColorsDist);\n        }\n\n        meanTypefaceBackgroundDistance = meanTypefaceBackgroundDistance.length < 1 ? 1 : arrMean(backgroundColorsDistances);\n        meanTypefaceBackgroundDistance /= MAX_COLOR_DISTANCE;\n        meanTypefaceBackgroundDistance = constraint(1-meanTypefaceBackgroundDistance, 0, 1);\n    }\n\n    // typeface\n    let meanTypefaceError = 1;\n    if (targetTypographyFeatures !== undefined && targetTypographyFeatures.length > 0) {\n        let fontsTags = [];\n        for (let t of textboxes) {\n            let tbTagsValue = 0;\n            const typefaceIndex = typefaceData.map(t => t.family).indexOf(t[\"typeface\"]);\n            const tags = typefaceData[typefaceIndex][\"tags\"];\n            for (let t of targetTypographyFeatures) {\n                if (tags.includes(t)) {\n                    tbTagsValue += (1/targetTypographyFeatures.length)\n                }\n            }\n            fontsTags.push(tbTagsValue);\n        }\n\n        meanTypefaceError = fontsTags.length < 1 ? 1 : arrMean(fontsTags);\n        meanTypefaceError = constraint(meanTypefaceError, 0, 1);\n    }\n\n    return (meanTypefaceColorDistance + meanTypefaceBackgroundDistance + meanTypefaceError)/3;\n\n\n}\n\n\nexport {compute as default};","/**\n * Alignment metric\n *\n * Estimate if the horizontal alignment of text boxes follows a regular pattern\n * Based on Harrington et al. (2004).\n *\n * Include the check if the text Alignment is the same (part B).\n * User can modify the value of WEIGHTS when necessary.\n *\n * return a value between 1 (good) and 0 (bad)\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nimport {arrMean, arrUnique, sumProduct} from \"../utils.js\";\nimport {ALIGNMENT} from \"../metrics.config.js\";\n\nconst A = ALIGNMENT[\"A\"]\nconst WEIGHTS = ALIGNMENT[\"WEIGHTS\"];\n\nexport const compute = (sentenceWidth, textAlignment, weights = WEIGHTS) => {\n    if (sentenceWidth.length < 2) {\n        return 1;\n    }\n\n    let histogram = sentenceWidth;\n    let results = [];\n\n    for (let i = 0; i<histogram.length-1; i++) {\n        let z = Math.abs(histogram[i] - histogram [i+1]);\n        let v = A / (A + z);\n        results.push(v);\n    }\n\n    let resHistogramDif = arrMean(results);\n    let availableTextAligns = arrUnique(textAlignment).length;\n    let resTextAlign = 1/availableTextAligns;\n\n    let res = sumProduct([resHistogramDif, resTextAlign], weights);\n    return res;\n}\n\nexport {compute as default};","import {colorDistance, hexToRGB} from \"../utils.js\";\nimport {WHITE_SPACE_FRACTION} from \"../metrics.config.js\";\n\n/**\n * White Space Fraction metric\n *\n * Assess the white space factor, i.e. the amount of the result that is background.\n * Based on Harrington et al. (2004).\n *\n * Traditionally, white space (including margins) should total about half of the total page area.\n *\n * We decided made this in other way, using the pixels of image that are equals to background color\n *\n * return a value between 1 (good) and 0 (bad)\n * in first version, we used a no-linear method and onlyy works with solid backgrounds\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nconst OPTIMAL = WHITE_SPACE_FRACTION[\"OPTIMAL\"];\nconst MIN_DISTANCE = WHITE_SPACE_FRACTION[\"MIN_DISTANCE\"];\n\nexport const compute = (img, color, amount = null, optimal = OPTIMAL) => {\n    if (amount === null) {\n        color = hexToRGB(color);\n        amount = percentTypographyColor(img, color, img.pixelDensity());\n    }\n\n    const res = 1-4*Math.pow((amount - optimal), 2);\n\n    return res;\n}\n\nconst percentTypographyColor = (img, c, d=1) => {\n    let amount = 0;\n    let distances = [];\n    let size = img.width * d * img.height * d ;\n    img.loadPixels();\n    for (let i = 0; i < 4 * size; i += 4) {\n        let current = {\n            r: img.pixels[i],\n            g: img.pixels[i+1],\n            b: img.pixels[i+2]\n        }\n        const distance = colorDistance(current, c);\n        distances.push(distance);\n        if (distance < MIN_DISTANCE) {\n            amount++;\n        }\n    }\n    return amount/size;\n}\n\nexport {compute as default};","import {arrUnique} from \"../utils.js\";\nimport {TYPEFACE_PARING} from \"../metrics.config.js\";\n\n/**\n * Typeface Pairing Metric\n *\n * Checks if the employed typefaces pair well together.\n * Since each typeface is related to a line of text,\n * this metric considers the typeface name and the classification.\n * Typefaces should be included in the configuration file with corresponding classification values.\n *\n * It had three modes:\n * TYPEFACE: values the use of the same typeface\n * CATEGORY: values the use of typefaces in the same category\n * BOTH: values both\n *\n *\n * Returns a value between 1 (good) and 0 (bad) to indicate compatibility.\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Updated Version: 1.5.0 (November 2023)\n */\n\nconst AVAILABLE_MODES = TYPEFACE_PARING[\"MODES\"]\n\nexport const compute = (typefaces, availableTypefaces, mode = `BOTH`) => {\n    if (!AVAILABLE_MODES.includes(mode))  {\n        mode = `BOTH`;\n    }\n\n    let weights = [.5, .5]; // heights to BOTH mode\n    if (mode === `TYPE_FAMILY`) {\n        weights = [1, 0];\n    } else if (mode === `CATEGORY`) {\n        weights = [0, 1];\n    }\n\n    let categories = [];\n    let usedTypefaces = arrUnique(typefaces);\n    let categoriesFactor = 0, typefaceFactor = 0;\n\n    if (mode !== `TYPE_FAMILY`) {\n        const typefacesNames = availableTypefaces.map(a => a[\"family\"]);\n        const typefacesClassification = availableTypefaces.map(a => a[\"category\"]);\n\n        for (let typeface of usedTypefaces) {\n            const index = typefacesNames.indexOf(typeface);\n            if (index === -1) {\n                categories.push(`undefined`);\n            } else {\n                categories.push(typefacesClassification[index]);\n            }\n        }\n\n        categories = arrUnique(categories);\n        categoriesFactor = 1/categories.length;\n    }\n\n    if (mode !== `CATEGORY`) {\n        typefaceFactor = 1 / usedTypefaces.length;\n    }\n\n    const res = [typefaceFactor, categoriesFactor].reduce((s, v, i) => s + v * weights[i], 0);\n\n    return res;\n}\n\nexport {compute as default};","/**\n * Visual Balance metric\n *\n * Estimate the visual balance (centred) of the composition.\n * Based on Harrington et al. (2004)\n *\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nimport {arrSum} from \"../utils.js\";\nimport {VISUAL_BALANCE} from \"../metrics.config.js\";\n\n// visual center factor\n// By default, the visual center is taken to be offset a twentieth of the page height towards the top\nconst VISUAL_CENTER_FT = VISUAL_BALANCE[\"VISUAL_CENTER_FT\"];\n\nexport const compute = async (img = null, size, rows, widths, heights, visualWeights = null) => {\n    const dx = size[\"width\"];\n    const dy = size[\"height\"];\n\n    const vw = visualWeights === null ? await visualWeight(img, rows, widths, heights) : visualWeights;\n    const bo = balanceCenter(vw, widths, heights);\n    const vo = visualCenter(dx, dy);\n\n    // calculate central Balance\n    let cb = Math.pow(((bo.x-vo.x)/dx), 2) + Math.pow(((bo.y-vo.y)/dy), 2);\n    cb = 1-Math.pow(Math.abs(cb/2), 1/2);\n\n    return cb;\n}\n\n\nconst balanceCenter = (vws, widths, heights) => {\n    // get text box center\n    const vc = [];\n    for (let i in heights) {\n        vc.push(visualCenter(widths[i], heights[i]));\n    }\n\n    // get page balance center\n    let posX = 0, posY = 0;\n    const m = arrSum(vws);\n\n    for (let i in vws) {\n        posX += vc[i].x*vws[i];\n        posY += vc[i].y*vws[i];\n    }\n\n    posX /= m;\n    posY /= m;\n\n    return {\n        x: posX,\n        y: posY\n    }\n}\n\n\n/**\n* Calculate the visual centre of a element\n* The visual center lies halfway between the left and right edges\n* the visual center is taken to be offset a twentieth of the page height towards the top\n*/\nconst visualCenter = (width, height) => {\n    return {\n        x: width/2,\n        y: height/2 - (height/VISUAL_CENTER_FT)\n    }\n}\n\n/**\n * calculate the visual weight of a object\n * visual weight = area x optical density\n */\nconst visualWeight = async (img, rows, widths, heights) => {\n    let areas = [];\n    let opticalDensity = [];\n\n    for (let i in widths) {\n\n        // compute areas\n        areas.push(widths[i] * heights[i]);\n\n        // compute visual weight\n        const rendering = await img.get(\n            0,\n            img.height/2 + rows[\"center\"][i],\n            img.width,\n            rows[\"l\"][i]\n        );\n\n        await rendering.loadPixels();\n\n        let r = 0, g = 0, b = 0;\n        const realPixelsSize = rendering.pixels.length/4;\n\n        for (let i=0; i < rendering.pixels.length; i+=4) {\n            r += rendering.pixels[i];\n            g += rendering.pixels[i+1];\n            b += rendering.pixels[i+2];\n        }\n\n        r = Math.round(r/realPixelsSize);\n        g = Math.round(g/realPixelsSize);\n        b = Math.round(b/realPixelsSize);\n\n        const avgLuma = (0.2126*r + 0.7152*g + 0.0722*b);\n        const t = avgLuma / 255;\n        opticalDensity.push(-Math.log10(t));\n    }\n\n    return areas.map((a, i) => a * opticalDensity[i]);\n}\n\n\nexport {compute as default};\n","/**\n * Evaluation metrics for evo-poster\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v0.0.1 July 2023\n */\n\nimport * as Legibility from \"./metrics/Legibility.mjs\";\nimport * as GridAppropriateSize from \"./metrics/GridAppropriateSize.mjs\";\nimport * as SemanticsLayout from \"./metrics/SemanticsLayout.mjs\";\nimport * as SemanticsEmphasis from \"./metrics/SemanticsEmphasis.mjs\";\nimport * as SemanticsVisuals from \"./metrics/SemanticVisuals.mjs\";\nimport * as Alignment from \"./metrics/Alignment.mjs\";\nimport * as Regularity from \"./metrics/Regularity.mjs\";\nimport * as WhiteSpaceFraction from \"./metrics/WhiteSpaceFraction.mjs\";\nimport * as TypefaceParing from \"./metrics/TypefaceParing.mjs\";\nimport * as VisualBalance from \"./metrics/VisualBalance.mjs\";\n\n\nexport const legibility = Legibility.compute;\nexport const gridAppropriateSize = GridAppropriateSize.compute;\nexport const semanticsLayout = SemanticsLayout.compute;\nexport const semanticsEmphasis = SemanticsEmphasis.compute;\nexport const semanticsVisuals = SemanticsVisuals.compute;\nexport const alignment = Alignment.compute;\nexport const regularity = Regularity.compute;\nexport const whiteSpaceFraction = WhiteSpaceFraction.compute;\nexport const typefaceParing = TypefaceParing.compute;\nexport const visualBalance = VisualBalance.compute;\n\nexport default () => {\n    console.log (`@evo-poster · evaluator v2.00`)\n}\n","/**\n * Grid Size Appropriateness\n *\n * Measure the appropriate of the used grid to the size of container\n * it is related to if the width and height of the grid is\n * in accordance with poster size\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 November 2023\n */\n\nimport * as CONFIG from \"../metrics.config.js\";\n\nconst DEBUG = CONFIG[\"default\"][\"DEBUG\"];\n\nexport const compute = (containerWidth, containerHeight, rows = [], columns = [], margins = {left:0, top:0, right:0, bottom:0}) => {\n    let invalid = false;\n    // debug\n    let msg = \"\";\n\n    // height calculation\n    let height = Math.abs(margins.top)+Math.abs(margins.bottom);\n    for (let r of rows) {\n        height = height + parseFloat(r);\n    }\n    // width calculation\n    let width = Math.abs(margins.left)+Math.abs(margins.right);\n    for (let r of columns) {\n        width = width + parseFloat(r);\n    }\n\n    width = Math.round(width);\n    height = Math.round(height);\n\n    if (height > containerHeight) {\n        invalid = true;\n        msg += `Grid height is bigger than container (grid:${height}, container:${containerHeight}). `;\n    } else if (height < containerHeight) {\n        msg += `Grid and container height are not the same (grid:${height}, container:${containerHeight}). `;\n    }\n\n    if (width > containerWidth) {\n        invalid = true;\n        msg += `Grid width is bigger than container (grid:${width}, container:${containerWidth}). `;\n    } else if (width < containerWidth) {\n        msg += `Grid and container width are not the same (grid:${width}, container:${containerWidth}). `;\n    }\n\n    if (msg !== \"\" && DEBUG) {\n        console.warn(msg);\n    }\n\n    return invalid ? 1 : 0;\n}\n\nexport { compute as default };","/**\n * Alignment metric\n *\n * Estimate if the text boxes follows a regular vertical pattern\n * Based on Harrington et al. (2004).\n *\n *\n * return a value between 1 (good) and 0 (bad)\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nimport {arrMean} from \"../utils.js\";\nimport {REGULARITY} from \"../metrics.config.js\";\n\nconst A = REGULARITY[\"A\"];\n\nexport const compute = (heights) => {\n    if (heights.length < 2) {\n        return 1;\n    }\n\n    const histogram = heights;\n\n    let results = [];\n\n    for (let i = 0; i<histogram.length-1; i++) {\n        let z = Math.abs(histogram[i] - histogram [i+1]);\n        let v = A / (A + z);\n        results.push(v);\n    }\n\n    const res = arrMean(results);\n\n    return res;\n}\n\nexport {compute as 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\ No newline at end of file
+{"version":3,"file":"evaluator.min.js","sources":["../src/utils.js","../src/metrics.config.js","../src/metrics/Legibility.mjs","../src/metrics/SemanticsLayout.mjs","../src/metrics/SemanticsEmphasis.mjs","../semantics-visual.config.js","../src/metrics/SemanticVisuals.mjs","../src/metrics/Alignment.mjs","../src/metrics/WhiteSpaceFraction.mjs","../src/metrics/TypefaceParing.mjs","../src/metrics/VisualBalance.mjs","../src/index.mjs","../src/metrics/GridAppropriateSize.mjs","../src/metrics/Regularity.mjs"],"sourcesContent":["export const map = (value, minA, maxA, minB, maxB) => {\n    return minB + (maxB - minB) * ((value - minA) / (maxA - minA));\n}\n\nexport const constraint = (value, min, max) => {\n    return Math.min(max, Math.max(min, value));\n}\n\nexport const arrMean = (arr) => {\n    const sum = arr.reduce((a, b) => a + b, 0);\n    return (sum / arr.length) || 0;\n}\n\nexport const arrSum = (arr) => {\n    return arr.reduce((partialSum, a) => partialSum + a, 0);\n}\n\nexport const arrMax = (arr) => {\n    return Math.max(...arr);\n}\n\nexport const arrMin = (arr) => {\n    return Math.min(...arr);\n}\n\nexport const arrUnique = (arr) => {\n    return arr.filter((value, index, array) => array.indexOf(value) === index);\n}\n\nexport const sumProduct = (arr, weights) => {\n    return arr.reduce((s, v, i) => s + v * weights[i], 0);\n}\n\nexport const hexToRGB = (hex) => {\n    if (hex[\"levels\"]) {\n        return {\n            r: parseInt(hex[\"levels\"][0]),\n            g: parseInt(hex[\"levels\"][1]),\n            b: parseInt(hex[\"levels\"][2])\n        }\n    }\n\n    let result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})$/i.exec(hex);\n    return result ? {\n        r: parseInt(result[1], 16),\n        g: parseInt(result[2], 16),\n        b: parseInt(result[3], 16)\n    } : null;\n}\n\nexport const colorDistance = (a, b) => {\n    return Math.sqrt(Math.pow(a.r - b.r, 2) + Math.pow(a.g - b.g, 2) + Math.pow(a.b - b.b, 2));\n}","export const ALIGNMENT = {\n    \"A\": 10, // limit to the non-linear function\n    \"WEIGHTS\": [.8, .2] // alignment parts weights\n}\n\nexport const LEGIBILITY = {\n    \"MAX_CONSTRAINT\": 1,\n    \"WHITE_SPACE_FACTOR\": 3,\n    \"MODES\": [`OVERSET`, `JUSTIFY`,`ATTEMPT_JUSTIFY`],\n    \"DEFAULT_MAX_LIMIT_SCALE\": 1\n}\n\nexport const REGULARITY = {\n    \"A\": 10, // limit to the non-linear function\n}\n\nexport const SEMANTICS_EMPHASIS = {\n    \"MIN_RANGE\": 50,\n    \"THRESHOLD_VALID\": 0.2,\n    \"MODES\": [`DIF`, `MIN`]\n}\n\nexport const SEMANTICS_VISUALS = {\n    \"MAX_COLOR_DISTANCE\": 441.67\n}\n\nexport const SEMANTICS_LAYOUT = {\n    \"MODES\": [`RELATIVE`, `FIXED`]\n}\n\nexport const TYPEFACE_PARING = {\n    \"MODES\": [`BOTH`, `TYPE_FAMILY`, `CATEGORY`]\n}\n\nexport const VISUAL_BALANCE = {\n    \"VISUAL_CENTER_FT\": 20,\n    \"MODES\": [\n        `CENTER`,\n        `LEFT-CENTER`, `RIGHT-CENTER`,\n        `LEFT-TOP`, `RIGHT-TOP`,\n        `LEFT-BOTTOM`, `RIGHT-BOTTOM`]\n}\n\nexport const WHITE_SPACE_FRACTION = {\n    \"OPTIMAL\": .5,\n    \"MIN_DISTANCE\": 10\n}\n\n\nexport default {\n    \"DEBUG\": false,\n}","/**\n * Legibility\n *\n * Measure the legibility of the text in the poster\n * it is related to the legibility of the sentence\n * and not the typeface shapes\n *\n * Expected return a value between 0 (good) and (1) bad\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 August 2018 (as part of evoPoster)\n * v2.0.0 November 2020 (as part of evoPoster)\n * v2.5.0 November 2021 (as part of evoPoster)\n * v3.0.0 November 2023\n */\n\nimport {arrMean, map} from \"../utils.js\";\nimport {LEGIBILITY} from \"../metrics.config.js\";\n\nconst MAX_CONSTRAINT = LEGIBILITY[\"MAX_CONSTRAINT\"];\nconst WHITE_SPACE_FACTOR = LEGIBILITY[\"WHITE_SPACE_FACTOR\"];\nconst DEFAULT_MAX_LIMIT_SCALE = LEGIBILITY[\"DEFAULT_MAX_LIMIT_SCALE\"];\nconst AVAILABLE_MODES = LEGIBILITY [\"MODES\"]\n\nexport const compute = (sentencesLength = [], minSize, mode= 'OVERSET', maxLimitScale= DEFAULT_MAX_LIMIT_SCALE) => {\n    if (!AVAILABLE_MODES.includes(mode)) {\n        mode = `OVERSET`;\n    }\n    let results = [];\n    let max = minSize * maxLimitScale;\n    for (let sentence of sentencesLength) {\n        let dif = minSize-sentence;\n        let value = MAX_CONSTRAINT;\n        switch (mode) {\n            case `JUSTIFY`:\n                value = justify(dif, max);\n                break;\n            case `ATTEMPT_JUSTIFY`:\n                value = attemptJustify(dif, max);\n                break;\n            default:\n                value = overset(dif, max);\n                break;\n        }\n        results.push(value);\n    }\n\n    // calculate mean\n    const mean = arrMean([...results]);\n\n    return mean;\n}\n\nconst overset = (value, max) => {\n    // only prejudice when text overfits the poster\n    // if dif bigger than 0\n    value = value >= 0 ? 0 : value;\n    // if dif lower than limit\n    value = value <= -max ? -max : value;\n    // transform in scale of 1 (bad) to 0 (good)\n    return map (value, -max, 0, MAX_CONSTRAINT, 0);\n}\n\nconst justify = (value, max) => {\n    // prejudice both overset and small lettering\n    value = Math.abs(value)\n    // if the dif is bigger than max\n    value = value > max ? max : value;\n    // transform in scale of 1 (bad) to 0 (good)\n    return map (value, max, 0, MAX_CONSTRAINT, 0);\n}\n\n// attempt to justify the text\n// prejudice more when the text overset box than when it is smaller\nconst attemptJustify = (value, max) => {\n    // if dif bigger than 0 (it is not overset), soften the value\n    value = value >= 0 ? value/WHITE_SPACE_FACTOR : value;\n    return justify(value, max);\n}\n\nexport { compute as default };","/**\n * Layout Semantics\n *\n * Measure the appropriate of the layout\n * based on the importance of the content\n * it considers that most important parts of content\n * are those have more emotions related\n *\n * return the mean of difference between all textboxes,\n * a value between 1 (good) and 0 (bad)\n *\n * two modes: RELATIVE and FIXED\n * RELATIVE mode is related to the composition height\n * FIXED height is related to the container's height\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 April 2020\n * v2.0.0 November 2023\n */\n\nimport {arrMean, arrSum} from \"../utils.js\";\nimport {SEMANTICS_LAYOUT} from \"../metrics.config.js\";\n\nconst AVAILABLE_MODES = SEMANTICS_LAYOUT[\"MODES\"];\n\nexport const compute = (textboxesHeights = [], dist = null, mode=`RELATIVE`, size = {height: 100, margin:[0,0,0,0]}) => {\n    // mode validation\n    if (!AVAILABLE_MODES.includes(mode)) mode = `RELATIVE`;\n\n    // define max height\n    let height = 0;\n    if (mode === `RELATIVE`) {\n        // using the textbox height\n        height = arrSum(textboxesHeights);\n    } else if (mode === `FIXED`) {\n        // using container height\n        height = size.height - ((size.height*size.margin[1]) + (size.height*size.margin[3]));\n    }\n    const percents = textboxesHeights.map((p) => p/height);\n\n    // calculate distances\n    let distances = [];\n    for (let i in dist) {\n        const dif = Math.abs(dist[i][3] - percents[i]);\n        distances.push(dif);\n    }\n\n    // return average\n    return (1-arrMean(distances));\n}\n\nexport { compute as default };","/**\n * Semantics Emphasis on Visuals\n *\n * Measure the appropriate of the visual features\n * based on the importance of the content\n * it considers that most important parts of content\n * are those have more emotions related\n *\n * return the mean of difference between all textboxes,\n * a value between 1 (good) and 0 (bad)\n *\n * two modes: MIN and DIF\n * MIN takes into account that the most important parts\n * are typeset in the min value possible in the container\n * DIF: take into account only the difference between the parts\n * selects a style and compare based on the distribution of emotions.\n *\n *\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 November 2023\n */\nimport {arrMax, arrMean, arrMin, arrSum, constraint, map} from \"../utils.js\";\nimport {SEMANTICS_EMPHASIS} from \"../metrics.config.js\";\n\nlet MIN_RANGE = SEMANTICS_EMPHASIS[\"MIN_RANGE\"];\nlet THRESHOLD_VALID = SEMANTICS_EMPHASIS[\"THRESHOLD_VALID\"];\nlet AVAILABLE_MODES = SEMANTICS_EMPHASIS[\"MODES\"];\n\n// by tradition, use more that a method to emphasize\n// the text is considered \"typecrime\".\n// this method enables turn on/off this feature\n// by using the param allowMultiple\nexport const compute = (textboxes, dist, noCurrentTypefaces = 1, allowMultiple = true, weights = [0.4, 0.3, 0.3]) => {\n    // if textboxes size is 1 ---> 1\n    const perDist = dist.map((e) => e[3]);\n\n    const fontWeight = checkDifferenceVariableFeature(textboxes.map((b) => b[\"weight\"]), perDist);\n    const fontStretch = checkDifferenceVariableFeature(textboxes.map((b) => b[\"font-stretch\"]), perDist);\n    let typefaceDesign = noCurrentTypefaces > 1 ? (checkDifferenceUniqueFeatures(textboxes.map((b) => b[\"font-stretch\"]), perDist)) : 0;\n\n    // way of combine and only checks one\n    let res = [fontWeight, fontStretch, typefaceDesign];\n    let weightedRes = res.map((x,i) => x*weights[i]);\n\n    let value;\n\n    if (!allowMultiple) {\n        // if not allowed multiple emphasis\n        // we check what fields are active\n        // and penalty when there is active fields\n        let active = res.map((r) => r > THRESHOLD_VALID);\n        let c = 0;\n        for (let a of active) {\n            if (a) {\n                c++;\n            }\n        }\n        value = arrMax(res)/c;\n    } else {\n        value = arrSum(weightedRes);\n    }\n\n    return value;\n}\n\n// check the levels\nconst checkDifferenceUniqueFeatures = (currentFeatures, dist) => {\n    // available semantic level\n    const uniqueValues = dist.filter((value, index, array) => array.indexOf(value) === index);\n    const target = [];\n\n    // define the target typeface for each semantic level\n    for (let uq of uniqueValues) {\n        for (let i=0; i<dist.length; i++) {\n            if (dist[i] === uq) {\n                target.push(currentFeatures[i]);\n                break;\n            }\n        }\n    }\n\n    // check if the target has duplicates\n    const duplicates = target.filter((item, index) => target.indexOf(item) !== index);\n\n    let value = 1;\n    // if there is duplicate in levels\n    // (if yes, difference is max)\n    if (!duplicates.length >= 1) {\n        // count the amount of tb not in the same typeface\n        let c = 0;\n        // for each typeface\n        for (let i in dist) {\n            let level = dist[i];\n            let currentValue = currentFeatures[i];\n            // get unique index of current semantic level\n            let index = uniqueValues.indexOf(level);\n            // get target value\n            let targetValue = target[index];\n            if (currentValue !== targetValue) {\n                // if not the same as target\n                c++;\n            }\n        }\n        // map value to a value between 0 (no difference) and 1 (max difference)\n        value = map(c, 0, currentFeatures.length, 0, 1);\n    }\n\n    return value;\n}\n\nconst checkDifferenceVariableFeature = (currentFeatures, dist, mode = `DIF`) => {\n    if (!AVAILABLE_MODES.includes(mode)) {\n        mode = `DIF`;\n    }\n    // max feature range\n    const maxFeature = arrMax(currentFeatures);\n    const minFeature = arrMin(currentFeatures);\n    let range = Math.abs(maxFeature - minFeature);\n    if (range < MIN_RANGE) {\n        return 1;\n    }\n\n    // semantic data range\n    const maxSemantic = arrMax(dist);\n    const minSemantic = arrMin(dist);\n\n    // consider the current variable minimum\n    let def = minFeature;\n    if (mode === `DIF`) {\n        // selects a style used in the first min semantic textbox\n        for (let i in dist) {\n            if (dist[i] === minSemantic) {\n                // consider the difference\n                def = currentFeatures[i];\n                break;\n            }\n        }\n    }\n\n    // create target feature values\n    const target = dist.map((e) => {\n        // The most neutral sentence are the most regular\n        let v = map(e, minSemantic, maxSemantic, 0, range);\n        v = constraint(v, 0, range);\n        return v;\n    });\n\n    // distance to target\n    const current = [];\n    for (let i in currentFeatures) {\n        let w = currentFeatures[i];\n        let currentDistance = Math.abs(w - def);\n        let dif = Math.abs(currentDistance - target[i]);\n        current.push(dif);\n    }\n\n    let mean = map(arrMean(current), 0, range, 1, 0);\n    return constraint(mean, 0, 1);\n}\n\n\n\n\nexport { compute as default };","export default {\n    anger : {\n        color: {\n            typography: [`#ff0000`, `#00ff00`],\n            background: [`#ff0000`]\n        },\n        typefaces: [`sans-serif`, `neo-grotesque`]\n    },\n    joy: {\n        color: {\n            typography: [],\n            background: [`#ffff00`, `#00ff00`]\n        },\n        typefaces: [`sans-serif`, `serif`]\n    },\n    trust: {\n        color: {\n            typography: [],\n            background: [`#0000ff`, `#00ff00`]\n        },\n        typefaces: [`neo-grotesque`]\n    },\n    sadness: {\n        color: {\n            typography: [],\n            background: [`#0071b6`]\n        },\n        typefaces: []\n    },\n    disgust: {\n        color: {\n            typography: [`#800080`],\n            background: []\n        },\n        typefaces: []\n    }\n}","/**\n * Semantic Visuals Measure\n *\n * This function assesses the appropriateness of visual features (type design and colour)\n * based on the emotions collected. It takes into account the configuration file \"visual-semantics.config.js\".\n *\n * The function returns the mean difference between all textboxes,\n * yielding a value between 1 (good) and 0 (bad).\n * If there is no information pertaining to a particular emotion, the value defaults to 1.\n *\n * As of now, the function exclusively considers the predominant emotions from ML analysis.\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version: 1.0.0 (November 2023)\n */\n\nimport {SEMANTICS_VISUALS} from \"../metrics.config.js\";\n\nconst MAX_COLOR_DISTANCE = SEMANTICS_VISUALS[\"MAX_COLOR_DISTANCE\"];\n\nimport * as configurationFile from \"../../semantics-visual.config.js\";\nimport {arrMean, colorDistance, hexToRGB, constraint} from \"../utils.js\";\n\n\nexport const compute = async (data, textboxes, background, typefaceData, config = configurationFile) => {\n\n    let emotion = data.predominant.emotion;\n\n    if (config[\"default\"][emotion] === undefined) return 1;\n\n    const targetTypefaceColors = config[\"default\"][emotion][\"color\"][\"typography\"];\n    const targetBackgroundColors = config[\"default\"][emotion][\"color\"][\"background\"];\n    const targetTypographyFeatures = config[\"default\"][emotion][\"typefaces\"];\n\n    // typography colour\n    let meanTypefaceColorDistance = 1;\n    if (targetTypefaceColors !== undefined && targetTypefaceColors.length > 0) {\n        let typefaceColorsDistances = [];\n        for (let t of textboxes) {\n            let c = hexToRGB(t.color);\n            let typefaceColorsDist = Number.MAX_VALUE;\n            for (let targetColor of targetTypefaceColors) {\n                targetColor = hexToRGB(targetColor);\n                let distance = colorDistance(c, targetColor)\n                if (distance < typefaceColorsDist) {\n                    typefaceColorsDist = distance;\n                }\n            }\n            typefaceColorsDistances.push(typefaceColorsDist);\n        }\n\n        meanTypefaceColorDistance = typefaceColorsDistances.length < 1 ? 1 : arrMean(typefaceColorsDistances);\n        meanTypefaceColorDistance /= MAX_COLOR_DISTANCE;\n        meanTypefaceColorDistance = constraint(1-meanTypefaceColorDistance, 0, 1);\n    }\n\n    // background colour\n    let meanTypefaceBackgroundDistance = 1;\n    if (targetBackgroundColors !== undefined && targetBackgroundColors.length !== 0) {\n        let backgroundColorsDistances = [];\n        meanTypefaceBackgroundDistance = 0;\n        for (let c of background) {\n            c = hexToRGB(c);\n            let backgroundColorsDist = Number.MAX_VALUE;\n            for (let targetColor of targetBackgroundColors) {\n                targetColor = hexToRGB(targetColor);\n                let distance = colorDistance(c, targetColor);\n                if (distance < backgroundColorsDist) {\n                    backgroundColorsDist = distance;\n                }\n            }\n            backgroundColorsDistances.push(backgroundColorsDist);\n        }\n\n        meanTypefaceBackgroundDistance = meanTypefaceBackgroundDistance.length < 1 ? 1 : arrMean(backgroundColorsDistances);\n        meanTypefaceBackgroundDistance /= MAX_COLOR_DISTANCE;\n        meanTypefaceBackgroundDistance = constraint(1-meanTypefaceBackgroundDistance, 0, 1);\n    }\n\n    // typeface\n    let meanTypefaceError = 1;\n    if (targetTypographyFeatures !== undefined && targetTypographyFeatures.length > 0) {\n        let fontsTags = [];\n        for (let t of textboxes) {\n            let tbTagsValue = 0;\n            const typefaceIndex = typefaceData.map(t => t.family).indexOf(t[\"typeface\"]);\n            const tags = typefaceData[typefaceIndex][\"tags\"];\n            for (let t of targetTypographyFeatures) {\n                if (tags.includes(t)) {\n                    tbTagsValue += (1/targetTypographyFeatures.length)\n                }\n            }\n            fontsTags.push(tbTagsValue);\n        }\n\n        meanTypefaceError = fontsTags.length < 1 ? 1 : arrMean(fontsTags);\n        meanTypefaceError = constraint(meanTypefaceError, 0, 1);\n    }\n\n    return (meanTypefaceColorDistance + meanTypefaceBackgroundDistance + meanTypefaceError)/3;\n\n\n}\n\n\nexport {compute as default};","/**\n * Alignment metric\n *\n * Estimate if the horizontal alignment of text boxes follows a regular pattern\n * Based on Harrington et al. (2004).\n *\n * Include the check if the text Alignment is the same (part B).\n * User can modify the value of WEIGHTS when necessary.\n *\n * return a value between 1 (good) and 0 (bad)\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nimport {arrMean, arrUnique, sumProduct} from \"../utils.js\";\nimport {ALIGNMENT} from \"../metrics.config.js\";\n\nconst A = ALIGNMENT[\"A\"]\nconst WEIGHTS = ALIGNMENT[\"WEIGHTS\"];\n\nexport const compute = (sentenceWidth, textAlignment, weights = WEIGHTS) => {\n    if (sentenceWidth.length < 2) {\n        return 1;\n    }\n\n    let histogram = sentenceWidth;\n    let results = [];\n\n    for (let i = 0; i<histogram.length-1; i++) {\n        let z = Math.abs(histogram[i] - histogram [i+1]);\n        let v = A / (A + z);\n        results.push(v);\n    }\n\n    let resHistogramDif = arrMean(results);\n    let availableTextAligns = arrUnique(textAlignment).length;\n    let resTextAlign = 1/availableTextAligns;\n\n    let res = sumProduct([resHistogramDif, resTextAlign], weights);\n    return res;\n}\n\nexport {compute as default};","import {colorDistance, hexToRGB} from \"../utils.js\";\nimport {WHITE_SPACE_FRACTION} from \"../metrics.config.js\";\n\n/**\n * White Space Fraction metric\n *\n * Assess the white space factor, i.e. the amount of the result that is background.\n * Based on Harrington et al. (2004).\n *\n * Traditionally, white space (including margins) should total about half of the total page area.\n *\n * We decided made this in other way, using the pixels of image that are equals to background color\n *\n * return a value between 1 (good) and 0 (bad)\n * in first version, we used a no-linear method and onlyy works with solid backgrounds\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nconst OPTIMAL = WHITE_SPACE_FRACTION[\"OPTIMAL\"];\nconst MIN_DISTANCE = WHITE_SPACE_FRACTION[\"MIN_DISTANCE\"];\n\nexport const compute = (img, color, amount = null, optimal = OPTIMAL) => {\n    if (amount === null) {\n        color = hexToRGB(color);\n        amount = percentTypographyColor(img, color, img.pixelDensity());\n    }\n\n    const res = 1-4*Math.pow((amount - optimal), 2);\n\n    return res;\n}\n\nconst percentTypographyColor = (img, c, d=1) => {\n    let amount = 0;\n    let distances = [];\n    let size = img.width * d * img.height * d ;\n    img.loadPixels();\n    for (let i = 0; i < 4 * size; i += 4) {\n        let current = {\n            r: img.pixels[i],\n            g: img.pixels[i+1],\n            b: img.pixels[i+2]\n        }\n        const distance = colorDistance(current, c);\n        distances.push(distance);\n        if (distance < MIN_DISTANCE) {\n            amount++;\n        }\n    }\n    return amount/size;\n}\n\nexport {compute as default};","import {arrUnique} from \"../utils.js\";\nimport {TYPEFACE_PARING} from \"../metrics.config.js\";\n\n/**\n * Typeface Pairing Metric\n *\n * Checks if the employed typefaces pair well together.\n * Since each typeface is related to a line of text,\n * this metric considers the typeface name and the classification.\n * Typefaces should be included in the configuration file with corresponding classification values.\n *\n * It had three modes:\n * TYPEFACE: values the use of the same typeface\n * CATEGORY: values the use of typefaces in the same category\n * BOTH: values both\n *\n *\n * Returns a value between 1 (good) and 0 (bad) to indicate compatibility.\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Updated Version: 1.5.0 (November 2023)\n */\n\nconst AVAILABLE_MODES = TYPEFACE_PARING[\"MODES\"]\n\nexport const compute = (typefaces, availableTypefaces, mode = `BOTH`) => {\n    if (!AVAILABLE_MODES.includes(mode))  {\n        mode = `BOTH`;\n    }\n\n    let weights = [.5, .5]; // heights to BOTH mode\n    if (mode === `TYPE_FAMILY`) {\n        weights = [1, 0];\n    } else if (mode === `CATEGORY`) {\n        weights = [0, 1];\n    }\n\n    let categories = [];\n    let usedTypefaces = arrUnique(typefaces);\n    let categoriesFactor = 0, typefaceFactor = 0;\n\n    if (mode !== `TYPE_FAMILY`) {\n        const typefacesNames = availableTypefaces.map(a => a[\"family\"]);\n        const typefacesClassification = availableTypefaces.map(a => a[\"category\"]);\n\n        for (let typeface of usedTypefaces) {\n            const index = typefacesNames.indexOf(typeface);\n            if (index === -1) {\n                categories.push(`undefined`);\n            } else {\n                categories.push(typefacesClassification[index]);\n            }\n        }\n\n        categories = arrUnique(categories);\n        categoriesFactor = 1/categories.length;\n    }\n\n    if (mode !== `CATEGORY`) {\n        typefaceFactor = 1 / usedTypefaces.length;\n    }\n\n    const res = [typefaceFactor, categoriesFactor].reduce((s, v, i) => s + v * weights[i], 0);\n\n    return res;\n}\n\nexport {compute as default};","/**\n * Visual Balance metric\n *\n * Estimate the visual balance (centred) of the composition.\n * Based on Harrington et al. (2004)\n *\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nimport {arrSum, constraint} from \"../utils.js\";\nimport {VISUAL_BALANCE} from \"../metrics.config.js\";\n\n// visual center factor\n// By default, the visual center is taken to be offset a twentieth of the page height towards the top\nconst VISUAL_CENTER_FT = VISUAL_BALANCE[\"VISUAL_CENTER_FT\"];\nconst AVAILABLE_MODES = VISUAL_BALANCE[\"MODES\"];\n\nexport const compute = async (img = null, size, rows, widths, heights, mode= `CENTER`, visualWeights = null) => {\n    const dx = size[\"width\"];\n    const dy = size[\"height\"];\n    if (!AVAILABLE_MODES.includes(mode)) mode = `CENTER`;\n\n    const vw = visualWeights === null ? await visualWeight(img, rows, widths, heights) : visualWeights;\n    const bo = balanceCenter(mode, vw, widths, heights);\n    let vo = visualCenter(mode, dx, dy, size[\"margin\"]);\n\n    // calculate central Balance\n    let cb = Math.pow(((bo.x-vo.x)/dx), 2) + Math.pow(((bo.y-vo.y)/dy), 2);\n    cb = 1-Math.pow(Math.abs(cb/2), 1/2);\n\n    return constraint(cb, 0, 1);\n}\n\n\nconst balanceCenter = (mode, vws, widths, heights) => {\n\n    // get text box center\n    const vc = [];\n    for (let i in heights) {\n        vc.push(visualCenter(mode, widths[i], heights[i]));\n    }\n\n    // get page balance center\n    let posX = 0, posY = 0;\n    const m = arrSum(vws);\n\n    for (let i in vws) {\n        posX += vc[i].x*vws[i];\n        posY += vc[i].y*vws[i];\n    }\n\n    posX /= m;\n    posY /= m;\n\n    return {\n        x: posX,\n        y: posY\n    }\n}\n\n\n/**\n* Calculate the visual centre of a element\n* The visual center lies halfway between the left and right edges\n* the visual center is taken to be offset a twentieth of the page height towards the top\n*/\nconst visualCenter = (mode, width, height, margins = [0,0,0,0]) => {\n    mode = String(mode)\n    const posX = mode.includes(`LEFT`) ? 1 : mode.includes(`RIGHT`) ? 2 : 0;\n    const posY = mode.includes(`TOP`) ? 1 : mode.includes(`BOTTOM`) ? 2 : 0;\n\n    let x = posX === 0 ? width/2 :\n        posX === 1 ? (width * margins[0]) : width * (1 - margins[2]) ;\n    let y = posY === 0 ? height/2 - (height/VISUAL_CENTER_FT) :\n        posY === 1 ? (height * margins[1]) : height - (height * margins[3])\n\n\n\n    return {\n        x: x,\n        y: y\n    }\n}\n\n/**\n * calculate the visual weight of a object\n * visual weight = area x optical density\n */\nconst visualWeight = async (img, rows, widths, heights) => {\n    let areas = [];\n    let opticalDensity = [];\n\n    for (let i in widths) {\n\n        // compute areas\n        areas.push(widths[i] * heights[i]);\n\n        // compute visual weight\n        const rendering = await img.get(\n            0,\n            img.height/2 + rows[\"center\"][i],\n            img.width,\n            rows[\"l\"][i]\n        );\n\n        await rendering.loadPixels();\n\n        let r = 0, g = 0, b = 0;\n        const realPixelsSize = rendering.pixels.length/4;\n\n        for (let i=0; i < rendering.pixels.length; i+=4) {\n            r += rendering.pixels[i];\n            g += rendering.pixels[i+1];\n            b += rendering.pixels[i+2];\n        }\n\n        r = Math.round(r/realPixelsSize);\n        g = Math.round(g/realPixelsSize);\n        b = Math.round(b/realPixelsSize);\n\n        const avgLuma = (0.2126*r + 0.7152*g + 0.0722*b);\n        const t = avgLuma / 255;\n        opticalDensity.push(-Math.log10(t));\n    }\n\n    return areas.map((a, i) => a * opticalDensity[i]);\n}\n\n\nexport {compute as default};\n","/**\n * Evaluation metrics for evo-poster\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v0.0.1 July 2023\n */\n\nimport * as Legibility from \"./metrics/Legibility.mjs\";\nimport * as GridAppropriateSize from \"./metrics/GridAppropriateSize.mjs\";\nimport * as SemanticsLayout from \"./metrics/SemanticsLayout.mjs\";\nimport * as SemanticsEmphasis from \"./metrics/SemanticsEmphasis.mjs\";\nimport * as SemanticsVisuals from \"./metrics/SemanticVisuals.mjs\";\nimport * as Alignment from \"./metrics/Alignment.mjs\";\nimport * as Regularity from \"./metrics/Regularity.mjs\";\nimport * as WhiteSpaceFraction from \"./metrics/WhiteSpaceFraction.mjs\";\nimport * as TypefaceParing from \"./metrics/TypefaceParing.mjs\";\nimport * as VisualBalance from \"./metrics/VisualBalance.mjs\";\n\n\nexport const legibility = Legibility.compute;\nexport const gridAppropriateSize = GridAppropriateSize.compute;\nexport const semanticsLayout = SemanticsLayout.compute;\nexport const semanticsEmphasis = SemanticsEmphasis.compute;\nexport const semanticsVisuals = SemanticsVisuals.compute;\nexport const alignment = Alignment.compute;\nexport const regularity = Regularity.compute;\nexport const whiteSpaceFraction = WhiteSpaceFraction.compute;\nexport const typefaceParing = TypefaceParing.compute;\nexport const visualBalance = VisualBalance.compute;\n\nexport default () => {\n    console.log (`@evo-poster · evaluator v2.00`)\n}\n","/**\n * Grid Size Appropriateness\n *\n * Measure the appropriate of the used grid to the size of container\n * it is related to if the width and height of the grid is\n * in accordance with poster size\n *\n * Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * srebelo[at]dei.uc.pt\n *\n * v1.0.0 November 2023\n */\n\nimport * as CONFIG from \"../metrics.config.js\";\n\nconst DEBUG = CONFIG[\"default\"][\"DEBUG\"];\n\nexport const compute = (containerWidth, containerHeight, rows = [], columns = [], margins = {left:0, top:0, right:0, bottom:0}) => {\n    let invalid = false;\n    // debug\n    let msg = \"\";\n\n    // height calculation\n    let height = Math.abs(margins.top)+Math.abs(margins.bottom);\n    for (let r of rows) {\n        height = height + parseFloat(r);\n    }\n    // width calculation\n    let width = Math.abs(margins.left)+Math.abs(margins.right);\n    for (let r of columns) {\n        width = width + parseFloat(r);\n    }\n\n    width = Math.round(width);\n    height = Math.round(height);\n\n    if (height > containerHeight) {\n        invalid = true;\n        msg += `Grid height is bigger than container (grid:${height}, container:${containerHeight}). `;\n    } else if (height < containerHeight) {\n        msg += `Grid and container height are not the same (grid:${height}, container:${containerHeight}). `;\n    }\n\n    if (width > containerWidth) {\n        invalid = true;\n        msg += `Grid width is bigger than container (grid:${width}, container:${containerWidth}). `;\n    } else if (width < containerWidth) {\n        msg += `Grid and container width are not the same (grid:${width}, container:${containerWidth}). `;\n    }\n\n    if (msg !== \"\" && DEBUG) {\n        console.warn(msg);\n    }\n\n    return invalid ? 1 : 0;\n}\n\nexport { compute as default };","/**\n * Alignment metric\n *\n * Estimate if the text boxes follows a regular vertical pattern\n * Based on Harrington et al. (2004).\n *\n *\n * return a value between 1 (good) and 0 (bad)\n *\n * Author: Sérgio M. Rebelo\n * CDV lab. (CMS, CISUC, Portugal)\n * Contact: srebelo[at]dei.uc.pt\n *\n * Version 1.0.0 (March 2020)\n * Version: 1.5.0 (November 2023)\n */\n\nimport {arrMean} from \"../utils.js\";\nimport {REGULARITY} from \"../metrics.config.js\";\n\nconst A = REGULARITY[\"A\"];\n\nexport const compute = (heights) => {\n    if (heights.length < 2) {\n        return 1;\n    }\n\n    const histogram = heights;\n\n    let results = [];\n\n    for (let i = 0; i<histogram.length-1; i++) {\n        let z = Math.abs(histogram[i] - histogram [i+1]);\n        let v = A / (A + z);\n        results.push(v);\n    }\n\n    const res = arrMean(results);\n\n    return res;\n}\n\nexport {compute as 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\ No newline at end of file
diff --git a/src/@evoposter/evaluator/src/metrics.config.js b/src/@evoposter/evaluator/src/metrics.config.js
index 4965820..9a80c2a 100644
--- a/src/@evoposter/evaluator/src/metrics.config.js
+++ b/src/@evoposter/evaluator/src/metrics.config.js
@@ -34,6 +34,11 @@ export const TYPEFACE_PARING = {
 
 export const VISUAL_BALANCE = {
     "VISUAL_CENTER_FT": 20,
+    "MODES": [
+        `CENTER`,
+        `LEFT-CENTER`, `RIGHT-CENTER`,
+        `LEFT-TOP`, `RIGHT-TOP`,
+        `LEFT-BOTTOM`, `RIGHT-BOTTOM`]
 }
 
 export const WHITE_SPACE_FRACTION = {
diff --git a/src/@evoposter/evaluator/src/metrics/VisualBalance.mjs b/src/@evoposter/evaluator/src/metrics/VisualBalance.mjs
index 231910b..cb7e1e3 100644
--- a/src/@evoposter/evaluator/src/metrics/VisualBalance.mjs
+++ b/src/@evoposter/evaluator/src/metrics/VisualBalance.mjs
@@ -13,34 +13,37 @@
  * Version: 1.5.0 (November 2023)
  */
 
-import {arrSum} from "../utils.js";
+import {arrSum, constraint} from "../utils.js";
 import {VISUAL_BALANCE} from "../metrics.config.js";
 
 // visual center factor
 // By default, the visual center is taken to be offset a twentieth of the page height towards the top
 const VISUAL_CENTER_FT = VISUAL_BALANCE["VISUAL_CENTER_FT"];
+const AVAILABLE_MODES = VISUAL_BALANCE["MODES"];
 
-export const compute = async (img = null, size, rows, widths, heights, visualWeights = null) => {
+export const compute = async (img = null, size, rows, widths, heights, mode= `CENTER`, visualWeights = null) => {
     const dx = size["width"];
     const dy = size["height"];
+    if (!AVAILABLE_MODES.includes(mode)) mode = `CENTER`;
 
     const vw = visualWeights === null ? await visualWeight(img, rows, widths, heights) : visualWeights;
-    const bo = balanceCenter(vw, widths, heights);
-    const vo = visualCenter(dx, dy);
+    const bo = balanceCenter(mode, vw, widths, heights);
+    let vo = visualCenter(mode, dx, dy, size["margin"]);
 
     // calculate central Balance
     let cb = Math.pow(((bo.x-vo.x)/dx), 2) + Math.pow(((bo.y-vo.y)/dy), 2);
     cb = 1-Math.pow(Math.abs(cb/2), 1/2);
 
-    return cb;
+    return constraint(cb, 0, 1);
 }
 
 
-const balanceCenter = (vws, widths, heights) => {
+const balanceCenter = (mode, vws, widths, heights) => {
+
     // get text box center
     const vc = [];
     for (let i in heights) {
-        vc.push(visualCenter(widths[i], heights[i]));
+        vc.push(visualCenter(mode, widths[i], heights[i]));
     }
 
     // get page balance center
@@ -67,10 +70,21 @@ const balanceCenter = (vws, widths, heights) => {
 * The visual center lies halfway between the left and right edges
 * the visual center is taken to be offset a twentieth of the page height towards the top
 */
-const visualCenter = (width, height) => {
+const visualCenter = (mode, width, height, margins = [0,0,0,0]) => {
+    mode = String(mode)
+    const posX = mode.includes(`LEFT`) ? 1 : mode.includes(`RIGHT`) ? 2 : 0;
+    const posY = mode.includes(`TOP`) ? 1 : mode.includes(`BOTTOM`) ? 2 : 0;
+
+    let x = posX === 0 ? width/2 :
+        posX === 1 ? (width * margins[0]) : width * (1 - margins[2]) ;
+    let y = posY === 0 ? height/2 - (height/VISUAL_CENTER_FT) :
+        posY === 1 ? (height * margins[1]) : height - (height * margins[3])
+
+
+
     return {
-        x: width/2,
-        y: height/2 - (height/VISUAL_CENTER_FT)
+        x: x,
+        y: y
     }
 }
 
diff --git a/src/@evoposter/evaluator/test/unit/VisualBalance.test.js b/src/@evoposter/evaluator/test/unit/VisualBalance.test.js
index a3c7569..ba60b52 100644
--- a/src/@evoposter/evaluator/test/unit/VisualBalance.test.js
+++ b/src/@evoposter/evaluator/test/unit/VisualBalance.test.js
@@ -88,6 +88,7 @@ const TESTING_PARAMS = [
         heights: [12.38095238095238, 11.38095238095238, 12.38095238095238, 11.38095238095238, 12.38095238095238, 12.38095238095238, 12.38095238095238, 12.38095238095238, 12.38095238095238, 12.38095238095238],
         widths: [264.93060302734375, 103.13275146484375, 98.85130310058594, 91.71133422851562, 80.71405029296875, 14.855758666992188, 73.46426391601562, 70.31559753417969, 109.78497314453125, 109.39274597167969],
         visualWeights: [911.5736477154408, 319.2937844631935, 346.174007090054, 434.42082642696874, 345.92923073281816, 77.71624681230338, 312.8142084364499, 268.67683328540517, 426.86878515497756, 441.40525053069166],
+        mode: `CENTER`,
         res: 0.6874696913950737,
         valid: true
     },
@@ -173,6 +174,7 @@ const TESTING_PARAMS = [
         widths: [264.93060302734375, 103.13275146484375, 98.85130310058594, 91.71133422851562, 80.71405029296875, 14.855758666992188, 73.46426391601562, 70.31559753417969, 109.78497314453125, 109.39274597167969],
         visualWeights: [911.5736477154408, 319.2937844631935, 346.174007090054, 434.42082642696874, 345.92923073281816, 77.71624681230338, 312.8142084364499, 268.67683328540517, 426.86878515497756, 441.40525053069166],
         res: 0.5,
+        mode: `CENTER`,
         valid: false
     },
     {
@@ -290,6 +292,7 @@ const TESTING_PARAMS = [
             291.6050576564197
         ],
         res: 0.6983685363606276,
+        mode: `CENTER`,
         valid: true
     },
     {
@@ -320,6 +323,7 @@ const TESTING_PARAMS = [
         widths: [276.33221435546875],
         visualWeights: [7973.376582108727],
         res: 0.37440557682834674,
+        mode: `CENTER`,
         valid: true,
     },
     {
@@ -350,6 +354,7 @@ const TESTING_PARAMS = [
         widths: [276],
         visualWeights: [7973],
         res: 0,
+        mode: `CENTER`,
         valid: false,
     },
     {
@@ -404,6 +409,7 @@ const TESTING_PARAMS = [
             1108.3613454753233
         ],
         res: 0.7099021460649569,
+        mode: `CENTER`,
         valid: true
     },
     {
@@ -458,7 +464,255 @@ const TESTING_PARAMS = [
             1108.3613454753233
         ],
         res: 1,
+        mode: `CENTER`,
         valid: false
+    },
+    {
+        rows: {
+            "x": {
+                "left": -45,
+                "right": 45
+            },
+            "center": {
+                "0": -60.39999999999999,
+                "1": -30.39999999999999,
+                "2": -1.3999999999999915,
+                "3": 27.60000000000001,
+                "4": 56.60000000000001
+            },
+            "gap": {
+                "0": {
+                    "bottom": -60.39999999999999,
+                    "top": -60.39999999999999
+                },
+                "1": {
+                    "bottom": -28.284999999999993,
+                    "top": -32.51499999999999
+                },
+                "2": {
+                    "bottom": 0.7150000000000083,
+                    "top": -3.5149999999999912
+                },
+                "3": {
+                    "bottom": 29.715000000000007,
+                    "top": 25.48500000000001
+                },
+                "4": {
+                    "bottom": 56.60000000000001,
+                    "top": 56.60000000000001
+                }
+            },
+            "l": [
+                30.95,
+                29.95,
+                29.95,
+                29.95
+            ]
+        },
+        heights: [
+            342.25732421875,
+            287.4759826660156,
+            293.0855712890625,
+            437.04632568359375
+        ],
+        widths: [
+            29.476190476190474,
+            28.523809523809522,
+            28.523809523809522,
+            28.523809523809522
+        ],
+        visualWeights: [
+            3727.5057306443678,
+            3622.4177405315445,
+            3234.7133591917686,
+            4203.703294362204
+        ],
+        res: 0.5303072425405706,
+        mode: `RIGHT-TOP`,
+        valid: true
+    },
+    {
+        rows: {
+            "x": {
+                "left": -45,
+                "right": 45
+            },
+            "center": {
+                "0": -63.45,
+                "1": -31.450000000000003,
+                "2": -1.4500000000000028,
+                "3": 28.549999999999997,
+                "4": 58.55
+            },
+            "gap": {
+                "0": {
+                    "bottom": -63.45,
+                    "top": -63.45
+                },
+                "1": {
+                    "bottom": -29.335000000000004,
+                    "top": -33.565000000000005
+                },
+                "2": {
+                    "bottom": 0.6649999999999969,
+                    "top": -3.5650000000000026
+                },
+                "3": {
+                    "bottom": 30.664999999999996,
+                    "top": 26.435
+                },
+                "4": {
+                    "bottom": 58.55,
+                    "top": 58.55
+                }
+            },
+            "l": [
+                32,
+                30.95,
+                30.95,
+                30.95
+            ]
+        },
+        heights: [
+            170.4186553955078,
+            297.0517272949219,
+            174.01736450195312,
+            230.81512451171875
+        ],
+        widths: [
+            30.476190476190474,
+            29.476190476190474,
+            29.476190476190474,
+            29.476190476190474
+        ],
+        visualWeights: [
+            2144.2423788076026,
+            3100.417377039685,
+            2374.310169650738,
+            2925.722717489419
+        ],
+        res: 0.5367634140653805,
+        mode: `RIGHT-TOP`,
+        valid: true
+    },
+    {
+        rows: {
+            "x": {
+                "left": -45,
+                "right": 45
+            },
+            "center": {
+                "0": -62.425,
+                "1": -30.424999999999997,
+                "2": 1.5750000000000028,
+                "3": 30.575000000000003,
+                "4": 60.575
+            },
+            "gap": {
+                "0": {
+                    "bottom": -62.425,
+                    "top": -62.425
+                },
+                "1": {
+                    "bottom": -28.31,
+                    "top": -32.54
+                },
+                "2": {
+                    "bottom": 3.6900000000000026,
+                    "top": -0.5399999999999969
+                },
+                "3": {
+                    "bottom": 32.690000000000005,
+                    "top": 28.460000000000004
+                },
+                "4": {
+                    "bottom": 60.575,
+                    "top": 60.575
+                }
+            },
+            "l": [
+                32,
+                32,
+                29.9,
+                30.95
+            ]
+        },
+        widths: [
+            30.476190476190474,
+            30.476190476190474,
+            28.476190476190474,
+            29.476190476190474
+        ],
+        heights: [
+            133.9654083251953,
+            382.00225830078125,
+            343.7714538574219,
+            477.23687744140625
+        ],
+        visualWeights: [
+            1381.1239085281425,
+            5107.805296971529,
+            3543.086645087024,
+            4825.177613113833
+        ],
+        mode: `RIGHT-TOP`,
+        res:  0.5367276624494475,
+        valid: true
+    },
+    {
+        rows: {
+            "x": {
+                "left": -45,
+                "right": 45
+            },
+            "center": {
+                "0": -55.39999999999999,
+                "1": -16.39999999999999,
+                "2": 23.60000000000001,
+                "3": 61.60000000000001
+            },
+            "gap": {
+                "0": {
+                    "bottom": -55.39999999999999,
+                    "top": -55.39999999999999
+                },
+                "1": {
+                    "bottom": -14.284999999999991,
+                    "top": -18.51499999999999
+                },
+                "2": {
+                    "bottom": 25.715000000000007,
+                    "top": 21.48500000000001
+                },
+                "3": {
+                    "bottom": 61.60000000000001,
+                    "top": 61.60000000000001
+                }
+            },
+            "l": [
+                39.9,
+                40,
+                38.95
+            ]
+        },
+        widths: [
+            38,
+            38.095238095238095,
+            37.095238095238095
+        ],
+        heights: [
+            594.3903198242188,
+            760.325927734375,
+            456.15655517578125
+        ],
+        mode: `RIGHT-BOTTOM`,
+        visualWeights: [
+            9704.27382043399,
+            15381.23748966179,
+            10104.313922673464
+        ],
+        res: 0,
+        valid: true
     }
 ];
 
@@ -466,7 +720,7 @@ describe(`Testing visual Balance metric`, () => {
     for (let i in TESTING_PARAMS) {
         let params = TESTING_PARAMS[i];
         test(`Test ${i}`, async () => {
-            const res = await visualBalance(null, SIZE, params.rows, params.widths, params.heights, params.visualWeights);
+            const res = await visualBalance(null, SIZE, params.rows, params.widths, params.heights, params.mode,  params.visualWeights);
             if (params.valid) {
                 expect(res).toBe(params.res);
             } else {