public static void CanAggregateLettersUsingChunkWhile()
{
var nums =
new SeriesBuilder <int, char>
{
{ 0, 'a' }, { 10, 'b' }, { 11, 'c' }
}.Series;
var actual =
nums.Aggregate(Aggregation.ChunkWhile <int>((k1, k2) => k2 - k1 < 10),
segment => segment.Data.Keys.First(),
segment => new string(segment.Data.Values.ToArray()));
var expected =
new SeriesBuilder <int, string> {
{ 0, "a" },
{ 10, "bc" }
}.Series;
Assert.AreEqual(expected, actual);
}
public static void Samples([CallerFilePath] string file = "")
{
var root = Path.GetDirectoryName(file);
// ------------------------------------------------------------
// Creating time series
// ------------------------------------------------------------
// [create-builder]
var numNames = new SeriesBuilder <int, string>()
{
{ 1, "one" }, { 2, "two" }, { 3, "three" }
}.Series;
numNames.Print();
// [/create-builder]
// [create-heterogen]
// Create series builder and use it via 'dynamic'
var nameNumsBuild = new SeriesBuilder <string, int>();
dynamic nameNumsDyn = nameNumsBuild;
nameNumsDyn.One = 1;
nameNumsDyn.Two = 2;
nameNumsDyn.Three = 3;
// Build series and print it
var nameNums = nameNumsBuild.Series;
nameNums.Print();
// [/create-heterogen]
// [create-ordinal]
var rnd = new Random();
var randNums = Enumerable.Range(0, 100)
.Select(_ => rnd.NextDouble()).ToOrdinalSeries();
randNums.Print();
// [/create-ordinal]
// [create-kvp]
var sin = Enumerable.Range(0, 1000)
.Select(x => KeyValue.Create(x, Math.Sin(x / 100.0)))
.ToSeries();
sin.Print();
// [/create-kvp]
// [create-sparse]
var opts = Enumerable.Range(0, 10)
.Select(x => KeyValue.Create(x, OptionalValue.OfNullable <int>(x)))
.ToSparseSeries();
opts.Print();
// [/create-sparse]
// [create-csv]
var frame = Frame.ReadCsv(Path.Combine(root, "../data/stocks/msft.csv"));
var frameDate = frame.IndexRows <DateTime>("Date").SortRowsByKey();
var msftOpen = frameDate.GetColumn <double>("Open");
msftOpen.Print();
// [/create-csv]
// ------------------------------------------------------------
// Lookup and slicing
// ------------------------------------------------------------
// [lookup-key]
// Get value for a specified int and string key
var tenth = randNums[10];
var one = nameNums["One"];
// Get first and last value using index
var fst = nameNums.GetAt(0);
var lst = nameNums.GetAt(nameNums.KeyCount - 1);
// [/lookup-key]
// [lookup-opt]
// Get value as OptionalValue<T> and use it
var opt = opts.TryGet(5);
if (opt.HasValue)
{
Console.Write(opt.Value);
}
// For value types, we can convert to nullable type
int?value1 = opts.TryGet(5).AsNullable();
int?value2 = opts.TryGetAt(0).AsNullable();
// [/lookup-opt]
// [lookup-ord]
// Get value exactly at the specified key
var jan3 = msftOpen
.Get(new DateTime(2012, 1, 3));
// Get value at a key or for the nearest previous date
var beforeJan1 = msftOpen
.Get(new DateTime(2012, 1, 1), Lookup.ExactOrSmaller);
// Get value at a key or for the nearest later date
var afterJan1 = msftOpen
.Get(new DateTime(2012, 1, 1), Lookup.ExactOrGreater);
// [/lookup-ord]
// [lookup-slice]
// Get a series starting/ending at
// the specified date (inclusive)
var msftStartIncl = msftOpen.StartAt(new DateTime(2012, 1, 1));
var msftEndIncl = msftOpen.EndAt(new DateTime(2012, 12, 31));
// Get a series starting/ending after/before
// the specified date (exclusive)
var msftStartExcl = msftOpen.After(new DateTime(2012, 1, 1));
var msftEndExcl = msftOpen.Before(new DateTime(2012, 12, 31));
// Get prices for 2012 (both keys are inclusive)
var msft2012 = msftOpen.Between
(new DateTime(2012, 1, 1), new DateTime(2012, 12, 31));
// [/lookup-slice]
// ------------------------------------------------------------
// Statistics and calculations
// ------------------------------------------------------------
// [calc-stat]
// Calculate median & mean price
var msftMed = msft2012.Median();
var msftAvg = msft2012.Mean();
// Calculate sum of square differences
var msftDiff = msft2012 - msftAvg;
var msftSq = (msftDiff * msftDiff).Sum();
// [/calc-stat]
// [calc-diff]
// Subtract previous day value from current day
var msftChange = msft2012 - msft2012.Shift(1);
// Use built-in Diff method to do the same
var msftChangeAlt = msft2012.Diff(1);
// Get biggest loss and biggest gain
var minMsChange = msftChange.Min();
var maxMsChange = msftChange.Max();
// [/calc-diff]
// [calc-custom]
var wackyStat = msft2012.Observations.Select(kvp =>
kvp.Value / (kvp.Key - msft2012.FirstKey()).TotalDays).Sum();
// [/calc-custom]
// ------------------------------------------------------------
// Missing data
// ------------------------------------------------------------
// [fill-const-drop]
// Fill missing data with constant
var fillConst = opts.FillMissing(-1);
fillConst.Print();
// Drop keys with no value from the series
var drop = opts.DropMissing();
drop.Print();
// [/fill-const-drop]
// [fill-dir]
// Fill with previous available value
var fillFwd = opts.FillMissing(Direction.Forward);
fillFwd.Print();
// Fill with the next available value
var fillBwd = opts.FillMissing(Direction.Backward);
fillBwd.Print();
// [/fill-dir]
// ------------------------------------------------------------
// Windows and chunks, grouping
// ------------------------------------------------------------
// [aggreg-group]
// Group random numbers by the first digit & get distribution
var buckets = randNums
.GroupBy(kvp => (int)(kvp.Value * 10))
.Select(kvp => OptionalValue.Create(kvp.Value.KeyCount));
buckets.Print();
// [/aggreg-group]
// [aggreg-win]
// Average over 25 element floating window
var monthlyWinMean = msft2012.WindowInto(25, win => win.Mean());
// Get floating window over 5 elements as series of series
// and then apply average on each series individually
var weeklyWinMean = msft2012.Window(5).Select(kvp =>
kvp.Value.Mean());
// [/aggreg-win]
// [aggreg-chunk]
// Get chunks of size 25 and mean each (disjoint) chunk
var monthlyChunkMean = msft2012.ChunkInto(25, win => win.Mean());
// Get series containing individual chunks (as series)
var weeklyChunkMean = msft2012.Chunk(5).Select(kvp =>
kvp.Value.Mean());
// [/aggreg-chunk]
// [aggreg-pair]
// For each key, get the previous value and average them
var twoDayAvgs = msft2012.Pairwise().Select(kvp =>
(kvp.Value.Item1 + kvp.Value.Item2) / 2.0);
// [/aggreg-pair]
// [aggreg-any]
msft2012.Aggregate
( // Get chunks while the month & year of the keys are the same
Aggregation.ChunkWhile <DateTime>((k1, k2) =>
k1.Month == k2.Month && k2.Year == k1.Year),
// For each chunk, return the first key as the key and
// either average value or missing value if it was empty
chunk => KeyValue.Create
(chunk.Data.FirstKey(),
chunk.Data.ValueCount > 0 ?
OptionalValue.Create(chunk.Data.Mean()) :
OptionalValue.Empty <double>()));
// [/aggreg-any]
// ------------------------------------------------------------
// Operations (Select, where)
// ------------------------------------------------------------
// [linq-methods]
var overMean = msft2012
.Select(kvp => kvp.Value - msftAvg)
.Where(kvp => kvp.Value > 0.0).KeyCount;
// [/linq-methods]
// [linq-query]
var underMean =
(from kvp in msft2012
where kvp.Value - msftAvg < 0.0
select kvp).KeyCount;
// [/linq-query]
Console.WriteLine(overMean);
Console.WriteLine(underMean);
// ------------------------------------------------------------
// Indexing and sampling & resampling
// ------------------------------------------------------------
// [index-keys]
// Turn DateTime keys into DateTimeOffset keys
var byOffs = msft2012.SelectKeys(kvp =>
new DateTimeOffset(kvp.Key));
// Replace keys with ordinal numbers 0 .. KeyCount-1
var byInt = msft2012.IndexOrdinally();
// [/index-keys]
// [index-with]
// Replace keys with explictly specified new keys
var byDays = numNames.IndexWith(new[] {
DateTime.Today,
DateTime.Today.AddDays(1.0),
DateTime.Today.AddDays(2.0)
});
// [/index-with]
}
