This package provides detailed nutritional values for 528 key foods in India, based on direct measurements across six regions. Data was obtained from the book Indian Food Composition Tables 2017, published by the National Institute of Nutrition, Hyderabad.
import * as ifct2017 from "jsr:@nodef/ifct2017";
await ifct2017.loadCompositions();
await ifct2017.loadColumns();
await ifct2017.loadIntakes();
// Load corpus first
ifct2017.compositions('pineapple');
ifct2017.compositions('ananas comosus');
// → [ { code: 'E053',
// → name: 'Pineapple',
// → scie: 'Ananas comosus',
// → lang: 'A. Ahnaros; B. Anarasa; G. Anenas; H. Ananas; Kan. Ananas; Kash. Punchitipul; Kh. Soh trun; Kon. Anas; Mal. Kayirha chakka; M. Kihom Ananas; O. Sapuri; P. Ananas; Tam. Annasi pazham; Tel. Anasa pandu; U. Ananas.',
// → ... } ]
ifct2017.columns('vitamin c');
ifct2017.columns('c-vitamin');
// → [ { code: 'vitc',
// → name: 'Total Ascorbic acid',
// → tags: 'ascorbate water soluble vitamin c vitamin c essential' } ]
ifct2017.pictures.unpkg('A001');
// → https://unpkg.com/@ifct2017/pictures/assets/A001.jpeg
ifct2017.intakes('his');
ifct2017.intakes('Histidine');
// → [ { code: 'his',
// → whorda: -0.01,
// → usear: NaN,
// → usrdam: -0.014,
// → usrdaf: NaN,
// → euprim: NaN,
// → euprif: NaN,
// → ulus: NaN,
// → uleu: NaN,
// → uljapan: NaN } ]
// Negative value indicates amount per kg of body weight.| Method | Action |
|---|---|
| compositions | Detailed nutrient composition of 528 key foods in India. |
| columns | Codes and names of nutrients, and its components. |
| pictures | Single representative photo of each foods (JPEG, 307x173). |
| intakes | Recommended daily intakes of nutrients. |
| hierarchy | Tree-like hierarchy of nutrients, and its components. |
| representations | Representations of columns (as factors and units). |
| codes | Uniquely identifiable codes for each food. |
| groups | Categorization of food by their common names. |
| descriptions | Names of each food in local languages, including scientific name. |
| abbreviations | Full forms of abbreviations used in the original book. |
| languages | Full form of language abbreviations. |
| methods | Analytical methods of nutrient and bioactive components. |
| energies | Metabolizable energy conversion factors. |
| nutrients | Detailed description of various nutrients, and its components. |
| jonesFactors | Jones factors for conversion of nitrogen to protein. |
| carbohydrates | Conversion of carbohydrate weights to monosaccharide equivalents. |
| regions | Categorization of the States/UTs into six different regions. |
| samplingUnits | Number of primary sampling units in each State/UT. |
| compositingCentres | Regional compositing centres and sample size of each region. |
| frequencyDistribution | Frequency distribution of States/UTs for fixing the number of districts to be sampled. |
| about | On the history of malnutrition, current status, and data details. |
| contents | Contents in the original book. |
NOTE:
.pictures(code) -> nullas it is not included locally.
Use.pictures.unpkg(code), or.pictures.jsDelivr(code)instead.
Full forms of abbreviations used in the original book.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadAbbreviations() → corpus
// ifct2017.abbreviationsSql([table], [options]) → SQL statements
// ifct2017.abbreviationsCsv() → Path of CSV file
// ifct2017.abbreviations(query)
// → {abbr, full} if found, null otherwise.
await ifct2017.loadAbbreviations();
// Load corpus first
ifct2017.abbreviations('GLV');
ifct2017.abbreviations('g l v');
// → { abbr: 'GLV', full: 'Green Leafy Vegetables' }
ifct2017.abbreviations('what is D.R.I.');
ifct2017.abbreviations('d. r. i. stands for?');
// → { abbr: 'DRI', full: 'Dietary reference intake' }
// Note:
// Full stops must immediately follow character, if present.
// For single character abbreviations, full stop is mandatory.On the history of malnutrition, current status, and data details.
Supported topics include: 1937, 1951, 1963, 1971, 1989, 2017, challenge, column, credit, data, father, form, funder, group, interest, learn, limitation, publisher, source, supporter, use, user, what, when, why.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadAbout() → corpus
// ifct2017.about(query)
// → text if matched, null otherwise
await ifct2017.loadAbout();
// Load corpus first
ifct2017.about('who is you publisher');
ifct2017.about('which organization issued you');
// → Indian Food Composition Tables 2017 was published by:
// → T. Longvah, R. Ananthan, K. Bhaskarachary and K. Venkaiah
// → National Institute of Nutrition
// → Indian Council of Medical Research
// → Department of Health Research
// → Ministry of Health and Family Welfare, Government of India
// → Jamai Osmania (PO), Hyderabad – 500 007
// → Telangana, India
// → Phone: +91 40 27197334, Fax: +91 40 27000339, Email: [email protected]
ifct2017.about('can i know the food groups');
ifct2017.about('i want to know what types of food are there');
// → There are 20 food groups:
// → - A: Cereals and Millets. 24 foods.
// → - B: Grain Legumes. 25 foods.
// → - C: Green Leafy Vegetables. 34 foods.
// → - D: Other Vegetables. 78 foods.
// → - E: Fruits. 68 foods.
// → - F: Roots and Tubers. 19 foods.
// → - G: Condiments and Spices. 33 foods.
// → - H: Nuts and Oil Seeds. 21 foods.
// → - I: Sugars. 2 foods.
// → - J: Mushrooms. 4 foods.
// → - K: Miscellaneous Foods. 2 foods.
// → - L: Milk and Milk Products. 4 foods.
// → - M: Egg and Egg Products. 15 foods.
// → - N: Poultry. 19 foods.
// → - O: Animal Meat. 63 foods.
// → - P: Marine Fish. 92 foods.
// → - Q; Marine Shellfish. 8 foods.
// → - R: Marine Mollusks. 7 foods.
// → - S: Fresh Water Fish and Shellfish. 10 foods.
// → - T: Edible Oils and Fats. 9 foods.
ifct2017.about('what happened in 1951');
ifct2017.about('what was the situation in nineteen fifty');
// → Between 1938 and 1951, there was a notable transition in the Indian nutrition
// → scenario. Among tropical regions, India contributed substantially in the field
// → of nutrition (Nicholls, 1945). The incidence of pellagra was noticed and the
// → role of niacin in its cure was successfully demonstrated in India (Raman, 1940;
// → Aykroyd & Swaminathan, 1940). The agricultural practices in India also underwent
// → modifications with concomitant increase in the crop yields. However, the basic
// → diet of individuals remained inadequate, devoid of animal fats and proteins,
// → due to poor economic conditions (Day, 1944). The translation of nutrition research
// → into sustained public health was hindered by obstacles of weak economy, ignorance
// → and poverty (Aykroyd, 1941). Other deficiency diseases such as maternal anaemia,
// → infant beriberi and osteomalacia continued to be rampant. Sustained nutritional
// → issues prompted the revision of Indian FCT resulting in the publication of fourth
// → edition of the Health Bulletin No. 23 by Aykroyd, Patwardhan, and Ranganathan (1951).
// Note:
// Can convert textual number to number.
// 1950-1959 is considered for 1951 event.Conversion of carbohydrate weights to monosaccharide equivalents.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadCarbohydrates() → corpus
// ifct2017.carbohydratesSql([table], [options]) → SQL statements
// ifct2017.carbohydratesCsv() → path of CSV file
// ifct2017.carbohydrates(query)
// → matches [{sno, carbohydrate, hydrolysis, monosaccharide}]
await ifct2017.loadCarbohydrates();
// Load corpus first
ifct2017.carbohydrates('monosaccharide');
ifct2017.carbohydrates('Glucose');
// → [ { sno: '1',
// → carbohydrate: 'Monosaccharides e.g. glucose',
// → hydrolysis: 100,
// → monosaccharide: 1 } ]
ifct2017.carbohydrates('what is carbohydrate conversion factor of disaccharides?');
ifct2017.carbohydrates('maltose conversion factor');
// → [ { sno: '2',
// → carbohydrate: 'Disaccharides e.g. sucrose, lactose, maltose',
// → hydrolysis: 105,
// → monosaccharide: 1.05 } ]Uniquely identifiable codes for each food.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadCodes() → corpus
// ifct2017.codesSql([table], [options]) → SQL statements
// ifct2017.codesCsv() → path of CSV file
// ifct2017.codes(query)
// → matches [{name, code}]
await ifct2017.loadCodes();
// Load corpus first
ifct2017.codes('mango green');
ifct2017.codes('Raw mango');
// → [ { name: 'Mango, green, raw (Common)', code: 'D057' } ]
ifct2017.codes('what is food code of atta?');
ifct2017.codes('atta code');
// → [ { name: 'Atta (H., P.)', code: 'A019' },
// → { name: 'Gahama atta (O.)', code: 'A019' },
// → { name: 'Wheat flour, atta (Common)', code: 'A019' } ]Codes and names of nutrients, and its components.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadColumns() → corpus
// ifct2017.columnsSql([table], [options]) → SQL statements
// ifct2017.columnsCsv() → path of CSV file
// ifct2017.columns(query)
// → matches [{code, name, tags}]
ifct2017.columns('vitamin c');
ifct2017.columns('c-vitamin');
// → [ { code: 'vitc',
// → name: 'Ascorbic acids (C)',
// → tags: 'total ascorbate water soluble vitamin c vitamin c essential' } ]
ifct2017.columns('what is butyric acid?');
ifct2017.columns('c4:0 stands for?');
// → [ { code: 'f4d0',
// → name: 'Butyric acid (C4:0)',
// → tags: 'c40 c 40 4 0 bta butanoic propanecarboxylic carboxylic saturated fatty fat triglyceride lipid colorless liquid unpleasant vomit body odor' } ]Regional compositing centres and sample size of each region.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadCompositingCentres() → corpus
// ifct2017.compositingCentresSql([table], [options]) → SQL statements
// ifct2017.compositingCentresCsv() → path of CSV file
// ifct2017.compositingCentres(query)
// → matches [{region, centre, samples}]
await ifct2017.loadCompositingCentres();
// Load corpus first
ifct2017.compositingCentres('west');
ifct2017.compositingCentres('Mumbai');
// → [ { region: 'West', centre: 'Mumbai', samples: 12 } ]
ifct2017.compositingCentres('what is compositing centre of north east?');
ifct2017.compositingCentres('North East compositing centre');
// → [ { region: 'North East', centre: 'Guwahati', samples: 11 } ]Detailed nutrient composition of 528 key foods in India.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadCompositions() → corpus
// ifct2017.compositionsSql([table], [options]) → SQL statements
// ifct2017.compositionsCsv() → path of CSV file
// ifct2017.compositions(query)
// → matches [{code, name, scie, lang, grup, regn, tags, ...}]
await ifct2017.loadCompositions();
// Load corpus first
ifct2017.compositions('pineapple');
ifct2017.compositions('ananas comosus');
// → [ { code: 'E053',
// → name: 'Pineapple',
// → scie: 'Ananas comosus',
// → lang: 'A. Ahnaros; B. Anarasa; G. Anenas; H. Ananas; Kan. Ananas; Kash. Punchitipul; Kh. Soh trun; Kon. Anas; Mal. Kayirha chakka; M. Kihom Ananas; O. Sapuri; P. Ananas; Tam. Annasi pazham; Tel. Anasa pandu; U. Ananas.',
// → ... } ]
ifct2017.compositions('tell me about cow milk.');
ifct2017.compositions('gai ka doodh details.');
// → [ { code: 'L002',
// → name: 'Milk, Cow',
// → scie: '',
// → lang: 'A. Garoor gakhir; B. Doodh (garu); G. Gai nu dhudh; H. Gai ka doodh; Kan. Hasuvina halu; Kash. Doodh; Kh. Dud masi; M. San Sanghom; Mar. Doodh (gay); O. Gai dudha; P. Gaan da doodh; S. Gow kshiram; Tam. Pasumpaal; Tel. Aavu paalu.',
// → ... } ]Contents in the original book.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadContents() → corpus
// ifct2017.contentsSql([table], [options]) → SQL statements
// ifct2017.contentsCsv() → path of CSV file
// ifct2017.contents(query)
// → matches [{sno, title, pagenos}]
await ifct2017.loadContents();
// Load corpus first
ifct2017.contents('table 2');
ifct2017.contents('Water soluble vitamins');
// → [ { sno: '6.2.',
// → title: 'Table 2: Water Soluble Vitamins',
// → pagenos: '31' } ]
ifct2017.contents('what is page number of table 3?');
ifct2017.contents('fat soluble vitamin page number');
// → [ { sno: '6.3.',
// → title: 'Table 3: Fat Soluble Vitamins',
// → pagenos: '61' } ]Names of each food in local languages, including scientific name.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadDescriptions() → corpus
// ifct2017.descriptionsSql([table], [options]) → SQL statements
// ifct2017.descriptionsCsv() → path of CSV file
// ifct2017.descriptions(query)
// → matches [{code, name, scie, desc}]
await ifct2017.loadDescriptions();
// Load corpus first
ifct2017.descriptions('pineapple');
ifct2017.descriptions('ananas comosus');
// → [ { code: 'E053',
// → name: 'Pineapple',
// → scie: 'Ananas comosus',
// → desc: 'A. Ahnaros; B. Anarasa; G. Anenas; H. Ananas; Kan. Ananas; Kash. Punchitipul; Kh. Soh trun; Kon. Anas; Mal. Kayirha chakka; M. Kihom Ananas; O. Sapuri; P. Ananas; Tam. Annasi pazham; Tel. Anasa pandu; U. Ananas.' } ]
ifct2017.descriptions('tell me about cow milk.');
ifct2017.descriptions('gai ka doodh details.');
// → [ { code: 'L002',
// → name: 'Milk, Cow',
// → scie: '',
// → desc: 'A. Garoor gakhir; B. Doodh (garu); G. Gai nu dhudh; H. Gai ka doodh; Kan. Hasuvina halu; Kash. Doodh; Kh. Dud masi; M. San Sanghom; Mar. Doodh (gay); O. Gai dudha; P. Gaan da doodh; S. Gow kshiram; Tam. Pasumpaal; Tel. Aavu paalu.' } ]Metabolizable energy conversion factors.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadEnergies() → corpus
// ifct2017.energiesSql([table], [options]) → SQL statements
// ifct2017.energiesCsv() → path of CSV file
// ifct2017.energies(query)
// → matches [{component, kj, kcal}]
await ifct2017.loadEnergies();
// Load corpus first
ifct2017.energies('dietary fibre');
ifct2017.energies('Soluble fibre');
// → [ { component: 'Fibre', kj: 8, kcal: 2 } ]
ifct2017.energies('what is energy conversion factor of fat?');
ifct2017.energies('conversion factor of fat');
// → [ { component: 'Fat', kj: 37, kcal: 9 } ]Frequency distribution of States/UTs for fixing the number of districts to be sampled.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadFrequencyDistribution() → corpus
// ifct2017.frequencyDistributionSql([table], [options]) → SQL statements
// ifct2017.frequencyDistributionCsv() → path of CSV file
// ifct2017.frequencyDistribution(districts)
// → {districts, states, selected, sampled} if found, null otherwise
await ifct2017.loadFrequencyDistribution();
// Load corpus first
ifct2017.frequencyDistribution(2);
ifct2017.frequencyDistribution(5);
// → { districts: '1-5', states: 9, selected: 1, sampled: 9 }
ifct2017.frequencyDistribution(32);
ifct2017.frequencyDistribution(37);
// → { districts: '31-40', states: 4, selected: 5, sampled: 20 }Categorization of food by their common names.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadGroups() → corpus
// ifct2017.groupsSql([table], [options]) → SQL statements
// ifct2017.groupsCsv() → path of CSV file
// ifct2017.groups(query)
// → matches [{code, group, entries, tags}]
await ifct2017.loadGroups();
// Load corpus first
ifct2017.groups('cereals');
ifct2017.groups('Millet');
// → [ { code: 'A',
// → group: 'Cereals and Millets',
// → entries: 24,
// → tags: 'vegetarian eggetarian fishetarian veg' } ]
ifct2017.groups('what is vegetable?');
ifct2017.groups('vegetable group code?');
// → [ { code: 'D',
// → group: 'Other Vegetables',
// → entries: 78,
// → tags: 'vegetarian eggetarian fishetarian veg' },
// → { code: 'C',
// → group: 'Green Leafy Vegetables',
// → entries: 34,
// → tags: 'vegetarian eggetarian fishetarian veg' } ]Tree-like hierarchy of nutrients, and its components.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadHierarchy() → corpus
// ifct2017.hierarchySql([table], [options]) → SQL statements
// ifct2017.hierarchyCsv() → path of CSV file
// ifct2017.hierarchy(query)
// → {parents, ancestry, children} if found, null otherwise
await ifct2017.loadHierarchy();
// Load corpus first
ifct2017.hierarchy('soluble oxalic acid');
ifct2017.hierarchy('Soluble Oxalic Acid');
// → { parents: 'oxalt', ancestry: 'oxalt orgac', children: '' }
ifct2017.hierarchy('what is ifct2017.hierarchy of total saturated fat?');
ifct2017.hierarchy('who are children of total saturated fat?');
// → { parents: 'fatce',
// → ancestry: 'fatce',
// → children:
// → 'f4d0 f6d0 f8d0 f10d0 f11d0 f12d0 f14d0 f15d0 f16d0 f18d0 f20d0 f22d0 f24d0' }Recommended daily intakes of nutrients.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadIntakes() → corpus
// ifct2017.intakesSql([table], [options]) → SQL statements
// ifct2017.intakesCsv() → path of CSV file
// ifct2017.intakes(query)
// → matches [{code, whorda, usear, usrdam, usrdaf, euprim, euprif, ulus, uleu, uljapan}]
await ifct2017.loadIntakes();
// Load corpus first
ifct2017.intakes('his');
ifct2017.intakes('Histidine');
// → [{ code: 'his',
// → whorda: -0.01,
// → usear: NaN,
// → usrdam: -0.014,
// → usrdaf: NaN,
// → euprim: NaN,
// → euprif: NaN,
// → ulus: NaN,
// → uleu: NaN,
// → uljapan: NaN }]
ifct2017.intakes('intake of total fibre?');
ifct2017.intakes('what is rda of total fiber?');
// → [{ code: 'fibtg',
// → whorda: NaN,
// → usear: NaN,
// → usrdam: 38,
// → usrdaf: 25,
// → euprim: NaN,
// → euprif: NaN,
// → ulus: NaN,
// → uleu: NaN,
// → uljapan: NaN }]
// Note:
// +ve value indicates amount in grams.
// -ve value indicates amount in grams per kg of body weight.
// NaN indicates no recommentation given.
// Note:
// whorda: WHO Recommended Dietary Allowance
// usear: US Estimated Average Requirement
// usrdam: US Recommended Dietary Allowance (Male)
// usrdaf: US Recommended Dietary Allowance (Female)
// euprim: EU Population Reference Intake (Male)
// euprif: EU Population Reference Intake (Female)
// ulus: Tolerable intake Upper Level (US)
// uleu: Tolerable intake Upper Level (EU)
// uljapan: Tolerable intake Upper Level (Japan)Jones factors for conversion of nitrogen to protein.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadJonesFactors() → corpus
// ifct2017.jonesFactorsSql([table], [options]) → SQL statements
// ifct2017.jonesFactorsCsv() → path of CSV file
// ifct2017.jonesFactors(query)
// → matches [{food, factor}]
await ifct2017.loadJonesFactors();
// Load corpus first
ifct2017.jonesFactors('maida');
ifct2017.jonesFactors('Refined wheat');
// → [ { food: 'Refined wheat flour (Maida)', factor: '5.70' } ]
ifct2017.jonesFactors('what is jones factor of barley?');
ifct2017.jonesFactors('jones factor of oats');
// → [ { food: 'Barley and its flour;Rye and its flour;Oats',
// → factor: '5.83' } ]Full form of language abbreviations.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadLanguages() → corpus
// ifct2017.languagesSql([table], [options]) → SQL statements
// ifct2017.languagesCsv() → path of CSV file
// ifct2017.languages(query)
// → {abbr, lang} if found, null otherwise.
await ifct2017.loadLanguages();
// Load corpus first
ifct2017.languages('mal.');
ifct2017.languages('Mal');
// → { abbr: 'Mal.', lang: 'Malayalam' }
ifct2017.languages('what is s.?');
ifct2017.languages('S. stands for?');
// → { abbr: 'S.', lang: 'Sanskrit' }
// Note:
// Full stops must immediately follow character, if present.
// For single character abbreviations, full stop is mandatory.Analytical methods of nutrient and bioactive components.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadMethods() → corpus
// ifct2017.methodsSql([table], [options]) → SQL statements
// ifct2017.methodsCsv() → path of CSV file
// ifct2017.methods(query)
// → {analyte, method, reference} if found, null otherwise
await ifct2017.loadMethods();
// Load corpus first
ifct2017.methods('soluble oxalic acid');
ifct2017.methods('Insoluble Oxalic Acid');
// → { analyte: 'Oxalic acid (Total), Soluble oxalic acid, Insoluble oxalic acid',
// → method: 'Fast- HPLC',
// → reference: 'Moreau & Savage (2009)' }
ifct2017.methods('what is analytical method of saponin?');
ifct2017.methods('how is total saponin measured?');
// → { analyte: 'Total Saponin',
// → method: 'Colorimetry',
// → reference: 'Dini et al. (2009)' }Single representative photo of each foods (JPEG, 307x173).
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.picturesUnpkg(code) → UNPKG URL | null
// ifct2017.picturesJsDelivr(code) → jsDelivr URL | null
// ifct2017.pictures(code)
// → path is present, null otherwise
ifct2017.pictures('A001');
// C:\Documents\pictures\A001.jpeg
ifct2017.picturesUnpkg('A001');
// https://unpkg.com/@ifct2017/pictures/assets/A001.jpeg
ifct2017.picturesJsDelivr('A001');
// https://cdn.jsdelivr.net/npm/@ifct2017/pictures/assets/A001.jpegCategorization of the States/UTs into six different regions.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadRegions() → corpus
// ifct2017.regionsSql([table], [options]) → SQL statements
// ifct2017.regionsCsv() → path of CSV file
// ifct2017.regions(query)
// → matches [{region, states}]
await ifct2017.loadRegions();
// Load corpus first
ifct2017.regions('central');
ifct2017.regions('Uttaranchal');
// → [ { region: 'Central',
// → states: 'Chhattisgarh;Madhya Pradesh;Uttar Pradesh;Uttaranchal' } ]
ifct2017.regions('which region andhra pradesh belongs to?');
ifct2017.regions('details of south region');
// → [ { region: 'South',
// → states: 'Andaman & Nicobar Islands;Andhra Pradesh;Karnataka;Kerala;Lakshadweep;Pondicherry;Telangana;Tamil Nadu' } ]Representations of columns (as factors and units).
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadRepresentations() → corpus
// ifct2017.representationsSql([table], [options]) → sql statements
// ifct2017.representationsCsv() → path of csv file
// ifct2017.representations(query)
// → {type, factor, unit} if found, null otherwise
await ifct2017.loadRepresentations();
// Load corpus first
ifct2017.representations('his');
ifct2017.representations('Histidine');
// → { type: 'mass', factor: 1000, unit: 'mg' }
ifct2017.representations('representation of vitamin d?');
ifct2017.representations('what is unit of ergocalciferol?');
// → { type: 'mass', factor: 1000000000, unit: 'ng' }
// Note:
// type: Type of physical quantity
// factor: Multiplication factor
// unit: Unit symbolNumber of primary sampling units in each State/UT.
import * as ifct2017 from "jsr:@nodef/ifct2017";
// ifct2017.loadSamplingUnits() → corpus
// ifct2017.samplingUnitsSql([table], [options]) → SQL staments
// ifct2017.samplingUnitsCsv() → path of CSV file
// ifct2017.samplingUnits(query)
// → matches [{sno, state, districts, selected}]
await ifct2017.loadSamplingUnits();
// Load corpus first
ifct2017.samplingUnits('andaman');
ifct2017.samplingUnits('Nicobar');
// → [ { sno: 'A',
// → state: 'Andaman & Nicobar',
// → districts: 3,
// → selected: 1 } ]
ifct2017.samplingUnits('sampling units in orissa?');
ifct2017.samplingUnits('orissa\'s sampling units');
// → [ { sno: '20', state: 'Orissa', districts: 30, selected: 4 } ]As of 18 April 2025, this project is licensed under AGPL-3.0. Previous versions remain under MIT.
