// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
 
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Xunit;
 
namespace Microsoft.Data.Analysis.Tests
{
    public class DataFrameGroupByTests
    {
        [Fact]
        public void TestGroupingWithTKeyTypeofString()
        {
            const int length = 11;
 
            //Create test dataframe (numbers starting from 0 up to length)
            DataFrame df = MakeTestDataFrameWithParityAndTensColumns(length);
 
            var grouping = df.GroupBy<string>("Parity").Groupings;
 
            //Check groups count
            Assert.Equal(2, grouping.Count());
 
            //Check number of elements in each group
            var oddGroup = grouping.Where(gr => gr.Key == "odd").FirstOrDefault();
            Assert.NotNull(oddGroup);
            Assert.Equal(length / 2, oddGroup.Count());
 
            var evenGroup = grouping.Where(gr => gr.Key == "even").FirstOrDefault();
            Assert.NotNull(evenGroup);
            Assert.Equal(length / 2 + length % 2, evenGroup.Count());
 
 
        }
 
        [Fact]
        public void TestGroupingWithTKey_CornerCases()
        {
            //Check corner cases
            var df = MakeTestDataFrameWithParityAndTensColumns(0);
            var grouping = df.GroupBy<string>("Parity").Groupings;
            Assert.Empty(grouping);
 
 
            df = MakeTestDataFrameWithParityAndTensColumns(1);
            grouping = df.GroupBy<string>("Parity").Groupings;
            Assert.Single(grouping);
            Assert.Equal("even", grouping.First().Key);
        }
 
 
        [Fact]
        public void TestGroupingWithTKeyPrimitiveType()
        {
            const int length = 55;
 
            //Create test dataframe (numbers starting from 0 up to length)
            DataFrame df = MakeTestDataFrameWithParityAndTensColumns(length);
 
            //Group elements by int column, that contain the amount of full tens in each int
            var groupings = df.GroupBy<int>("Tens").Groupings.ToDictionary(g => g.Key, g => g.ToList());
 
            //Get the amount of all number based columns
            int numberColumnsCount = df.Columns.Count - 2; //except "Parity" and "Tens" columns
 
            //Check each group
            for (int i = 0; i < length / 10; i++)
            {
                Assert.Equal(10, groupings[i].Count());
 
                var rows = groupings[i];
                for (int colIndex = 0; colIndex < numberColumnsCount; colIndex++)
                {
                    var values = rows.Select(row => Convert.ToInt32(row[colIndex]));
 
                    for (int j = 0; j < 10; j++)
                    {
                        Assert.Contains(i * 10 + j, values);
                    }
                }
            }
 
            //Last group should contain smaller amount of items
            Assert.Equal(length % 10, groupings[length / 10].Count());
        }
 
        [Fact]
        public void TestGroupingWithTKeyOfWrongType()
        {
            var message = string.Empty;
 
            //Create test dataframe (numbers starting from 0 up to length)
            DataFrame df = MakeTestDataFrameWithParityAndTensColumns(1);
 
            //Use wrong type for grouping
            Assert.Throws<InvalidCastException>(() => df.GroupBy<double>("Tens"));
        }
 
 
        private DataFrame MakeTestDataFrameWithParityAndTensColumns(int length)
        {
            DataFrame df = DataFrameTests.MakeDataFrameWithNumericColumns(length, false);
            DataFrameColumn parityColumn = new StringDataFrameColumn("Parity", Enumerable.Range(0, length).Select(x => x % 2 == 0 ? "even" : "odd"));
            DataFrameColumn tensColumn = new Int32DataFrameColumn("Tens", Enumerable.Range(0, length).Select(x => x / 10));
            df.Columns.Insert(df.Columns.Count, parityColumn);
            df.Columns.Insert(df.Columns.Count, tensColumn);
 
            return df;
        }
    }
}