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Licensed to the Apache Software Foundation (ASF) under one\n// or more contributor license agreements.  See the NOTICE file\n// distributed with this work for additional information\n// regarding copyright ownership.  The ASF licenses this file\n// to you under the Apache License, Version 2.0 (the\n// \"License\"); you may not use this file except in compliance\n// with the License.  You may obtain a copy of the License at\n//\n//   http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing,\n// software distributed under the License is distributed on an\n// \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n// KIND, either express or implied.  See the License for the\n// specific language governing permissions and limitations\n// under the License.\n\nimport { Table } from '../table';\nimport { Vector } from '../vector';\nimport { IntVector } from '../vector/int';\nimport { Field, Schema } from '../schema';\nimport { Predicate, Col } from './predicate';\nimport { RecordBatch } from '../recordbatch';\nimport { VectorType as V } from '../interfaces';\nimport { DataType, Int, Struct, Dictionary } from '../type';\n\n/** @ignore */\nexport type BindFunc = (batch: RecordBatch) => void;\n/** @ignore */\nexport type NextFunc = (idx: number, batch: RecordBatch) => void;\n\nTable.prototype.countBy = function(this: Table, name: Col | string) { return new DataFrame(this.chunks).countBy(name); };\nTable.prototype.scan = function(this: Table, next: NextFunc, bind?: BindFunc) { return new DataFrame(this.chunks).scan(next, bind); };\nTable.prototype.scanReverse = function(this: Table, next: NextFunc, bind?: BindFunc) { return new DataFrame(this.chunks).scanReverse(next, bind); };\nTable.prototype.filter = function(this: Table, predicate: Predicate): FilteredDataFrame { return new DataFrame(this.chunks).filter(predicate); };\n\nexport class DataFrame<T extends { [key: string]: DataType } = any> extends Table<T> {\n    public filter(predicate: Predicate): FilteredDataFrame<T> {\n        return new FilteredDataFrame<T>(this.chunks, predicate);\n    }\n    public scan(next: NextFunc, bind?: BindFunc) {\n        const batches = this.chunks, numBatches = batches.length;\n        for (let batchIndex = -1; ++batchIndex < numBatches;) {\n            // load batches\n            const batch = batches[batchIndex];\n            if (bind) { bind(batch); }\n            // yield all indices\n            for (let index = -1, numRows = batch.length; ++index < numRows;) {\n                next(index, batch);\n            }\n        }\n    }\n    public scanReverse(next: NextFunc, bind?: BindFunc) {\n        const batches = this.chunks, numBatches = batches.length;\n        for (let batchIndex = numBatches; --batchIndex >= 0;) {\n            // load batches\n            const batch = batches[batchIndex];\n            if (bind) { bind(batch); }\n            // yield all indices\n            for (let index = batch.length; --index >= 0;) {\n                next(index, batch);\n            }\n        }\n    }\n    public countBy(name: Col | string) {\n        const batches = this.chunks, numBatches = batches.length;\n        const count_by = typeof name === 'string' ? new Col(name) : name as Col;\n        // Assume that all dictionary batches are deltas, which means that the\n        // last record batch has the most complete dictionary\n        count_by.bind(batches[numBatches - 1]);\n        const vector = count_by.vector as V<Dictionary>;\n        if (!DataType.isDictionary(vector.type)) {\n            throw new Error('countBy currently only supports dictionary-encoded columns');\n        }\n\n        const countByteLength = Math.ceil(Math.log(vector.length) / Math.log(256));\n        const CountsArrayType = countByteLength == 4 ? Uint32Array :\n                                countByteLength >= 2 ? Uint16Array : Uint8Array;\n\n        const counts = new CountsArrayType(vector.dictionary.length);\n        for (let batchIndex = -1; ++batchIndex < numBatches;) {\n            // load batches\n            const batch = batches[batchIndex];\n            // rebind the countBy Col\n            count_by.bind(batch);\n            const keys = (count_by.vector as V<Dictionary>).indices;\n            // yield all indices\n            for (let index = -1, numRows = batch.length; ++index < numRows;) {\n                let key = keys.get(index);\n                if (key !== null) { counts[key]++; }\n            }\n        }\n        return new CountByResult(vector.dictionary, IntVector.from(counts));\n    }\n}\n\n/** @ignore */\nexport class CountByResult<T extends DataType = any, TCount extends Int = Int> extends Table<{ values: T,  counts: TCount }> {\n    constructor(values: Vector<T>, counts: V<TCount>) {\n        type R = { values: T, counts: TCount };\n        const schema = new Schema<R>([\n            new Field('values', values.type),\n            new Field('counts', counts.type)\n        ]);\n        super(new RecordBatch<R>(schema, counts.length, [values, counts]));\n    }\n    public toJSON(): Object {\n        const values = this.getColumnAt(0)!;\n        const counts = this.getColumnAt(1)!;\n        const result = {} as { [k: string]: number | null };\n        for (let i = -1; ++i < this.length;) {\n            result[values.get(i)] = counts.get(i);\n        }\n        return result;\n    }\n}\n\n/** @ignore */\nexport class FilteredDataFrame<T extends { [key: string]: DataType } = any> extends DataFrame<T> {\n    private _predicate: Predicate;\n    constructor (batches: RecordBatch<T>[], predicate: Predicate) {\n        super(batches);\n        this._predicate = predicate;\n    }\n    public scan(next: NextFunc, bind?: BindFunc) {\n        // inlined version of this:\n        // this.parent.scan((idx, columns) => {\n        //     if (this.predicate(idx, columns)) next(idx, columns);\n        // });\n        const batches = this._chunks;\n        const numBatches = batches.length;\n        for (let batchIndex = -1; ++batchIndex < numBatches;) {\n            // load batches\n            const batch = batches[batchIndex];\n            const predicate = this._predicate.bind(batch);\n            let isBound = false;\n            // yield all indices\n            for (let index = -1, numRows = batch.length; ++index < numRows;) {\n                if (predicate(index, batch)) {\n                    // bind batches lazily - if predicate doesn't match anything\n                    // in the batch we don't need to call bind on the batch\n                    if (bind && !isBound) {\n                        bind(batch);\n                        isBound = true;\n                    }\n                    next(index, batch);\n                }\n            }\n        }\n    }\n    public scanReverse(next: NextFunc, bind?: BindFunc) {\n        const batches = this._chunks;\n        const numBatches = batches.length;\n        for (let batchIndex = numBatches; --batchIndex >= 0;) {\n            // load batches\n            const batch = batches[batchIndex];\n            const predicate = this._predicate.bind(batch);\n            let isBound = false;\n            // yield all indices\n            for (let index = batch.length; --index >= 0;) {\n                if (predicate(index, batch)) {\n                    // bind batches lazily - if predicate doesn't match anything\n                    // in the batch we don't need to call bind on the batch\n                    if (bind && !isBound) {\n                        bind(batch);\n                        isBound = true;\n                    }\n                    next(index, batch);\n                }\n            }\n        }\n    }\n    public count(): number {\n        // inlined version of this:\n        // let sum = 0;\n        // this.parent.scan((idx, columns) => {\n        //     if (this.predicate(idx, columns)) ++sum;\n        // });\n        // return sum;\n        let sum = 0;\n        const batches = this._chunks;\n        const numBatches = batches.length;\n        for (let batchIndex = -1; ++batchIndex < numBatches;) {\n            // load batches\n            const batch = batches[batchIndex];\n            const predicate = this._predicate.bind(batch);\n            // yield all indices\n            for (let index = -1, numRows = batch.length; ++index < numRows;) {\n                if (predicate(index, batch)) { ++sum; }\n            }\n        }\n        return sum;\n    }\n    public *[Symbol.iterator](): IterableIterator<Struct<T>['TValue']> {\n        // inlined version of this:\n        // this.parent.scan((idx, columns) => {\n        //     if (this.predicate(idx, columns)) next(idx, columns);\n        // });\n        const batches = this._chunks;\n        const numBatches = batches.length;\n        for (let batchIndex = -1; ++batchIndex < numBatches;) {\n            // load batches\n            const batch = batches[batchIndex];\n            // TODO: bind batches lazily\n            // If predicate doesn't match anything in the batch we don't need\n            // to bind the callback\n            const predicate = this._predicate.bind(batch);\n            // yield all indices\n            for (let index = -1, numRows = batch.length; ++index < numRows;) {\n                if (predicate(index, batch)) { yield batch.get(index) as any; }\n            }\n        }\n    }\n    public filter(predicate: Predicate): FilteredDataFrame<T> {\n        return new FilteredDataFrame<T>(\n            this._chunks,\n            this._predicate.and(predicate)\n        );\n    }\n    public countBy(name: Col | string) {\n        const batches = this._chunks, numBatches = batches.length;\n        const count_by = typeof name === 'string' ? new Col(name) : name as Col;\n        // Assume that all dictionary batches are deltas, which means that the\n        // last record batch has the most complete dictionary\n        count_by.bind(batches[numBatches - 1]);\n        const vector = count_by.vector as V<Dictionary>;\n        if (!DataType.isDictionary(vector.type)) {\n            throw new Error('countBy currently only supports dictionary-encoded columns');\n        }\n\n        const countByteLength = Math.ceil(Math.log(vector.length) / Math.log(256));\n        const CountsArrayType = countByteLength == 4 ? Uint32Array :\n                                countByteLength >= 2 ? Uint16Array : Uint8Array;\n\n        const counts = new CountsArrayType(vector.dictionary.length);\n\n        for (let batchIndex = -1; ++batchIndex < numBatches;) {\n            // load batches\n            const batch = batches[batchIndex];\n            const predicate = this._predicate.bind(batch);\n            // rebind the countBy Col\n            count_by.bind(batch);\n            const keys = (count_by.vector as V<Dictionary>).indices;\n            // yield all indices\n            for (let index = -1, numRows = batch.length; ++index < numRows;) {\n                let key = keys.get(index);\n                if (key !== null && predicate(index, batch)) { counts[key]++; }\n            }\n        }\n        return new CountByResult(vector.dictionary, IntVector.from(counts));\n    }\n}\n"]}