/** * Copyright (c) 2017 mol* contributors, licensed under MIT, See LICENSE file for more info. * * @author David Sehnal */ import { Database, Table, Column, ColumnHelpers } from 'mol-data/db' import { Tensor } from 'mol-math/linear-algebra' import * as Data from './data-model' export function toDatabase = Database>(schema: Schema, frame: Data.Frame): Frame { return createDatabase(schema, frame) as Frame; } export function toTable = Table>(schema: Schema, category: Data.Category): R { return new CategoryTable(category, schema, true) as any; } type ColumnCtor = (field: Data.Field, category: Data.Category, key: string) => Column function getColumnCtor(t: Column.Schema): ColumnCtor { switch (t.valueType) { case 'str': return (f, c, k) => createColumn(t, f, f.str, f.toStringArray); case 'int': return (f, c, k) => createColumn(t, f, f.int, f.toIntArray); case 'float': return (f, c, k) => createColumn(t, f, f.float, f.toFloatArray); case 'tensor': throw new Error(`Use createTensorColumn instead.`); } } function createColumn(schema: Column.Schema, field: Data.Field, value: (row: number) => T, toArray: Column['toArray']): Column { return { schema, '@array': field['@array'], isDefined: field.isDefined, rowCount: field.rowCount, value, valueKind: field.valueKind, areValuesEqual: field.areValuesEqual, toArray }; } function createTensorColumn(schema: Column.Schema.Tensor, category: Data.Category, key: string): Column { const space = schema.space; const first = category.getField(`${key}[1]`) || Column.Undefined(category.rowCount, schema); const value = (row: number) => Data.getTensor(category, key, space, row); const toArray: Column['toArray'] = params => ColumnHelpers.createAndFillArray(category.rowCount, value, params) return { schema, '@array': void 0, isDefined: first.isDefined, rowCount: category.rowCount, value, valueKind: first.valueKind, areValuesEqual: (rowA, rowB) => Tensor.areEqualExact(value(rowA), value(rowB)), toArray }; } class CategoryTable implements Table { // tslint:disable-line:class-name _rowCount: number; _columns: ReadonlyArray; _schema: any; [k: string]: any; constructor(category: Data.Category, schema: Table.Schema, public _isDefined: boolean) { const fieldKeys = Object.keys(schema); this._rowCount = category.rowCount; this._columns = fieldKeys; this._schema = schema; const cache = Object.create(null); for (const k of fieldKeys) { Object.defineProperty(this, k, { get: function() { if (cache[k]) return cache[k]; const fType = schema[k]; if (fType.valueType === 'tensor') { cache[k] = createTensorColumn(fType, category, k); } else { const ctor = getColumnCtor(fType); const field = category.getField(k); cache[k] = !!field ? ctor(field, category, k) : Column.Undefined(category.rowCount, fType); } return cache[k]; }, enumerable: true, configurable: false }); } } } function createDatabase(schema: Database.Schema, frame: Data.Frame): Database { const tables = Object.create(null); for (const k of Object.keys(schema)) { tables[k] = createTable(k, (schema as any)[k], frame); } return Database.ofTables(frame.header, schema, tables); } function createTable(key: string, schema: Table.Schema, frame: Data.Frame) { const cat = frame.categories[key]; return new CategoryTable(cat || Data.Category.empty(key), schema, !!cat); }