Using a Deedle Data frame - f#

I'm new to F# and would like to convert my code below to output a deedle data frame or even convert the output array into a data frame.
let GetDataBitstamp() =
async {
let! csv = sprintf "http://api.bitcoincharts.com/v1/trades.csv?symbol=bitstampUSD" |> fetch
return
[|
for row in csv.Split([|'\n'|], StringSplitOptions.RemoveEmptyEntries) do
match row.Split([|','|]) with
| [|d; p; v |]-> yield (d,p)
| _ -> yield! [||]
|] |> Map.ofArray
} |> Async.RunSynchronously

You can construct deedle data frame directly from CSV file using
Frame.ReadCsv("csvPath")

I found a solution that works for me see code below:
module may =
let simples() =
let time,price =Map.toArray(Bitcharts.GetDataBitstamp())|> Array.unzip
time, price
The next step is to put the time, price arrays into the data series:
let df1 : Frame<int, string> = frame[]
let d,a = may.simples()
df1.AddSeries("ts",d)
df1.AddSeries("price",a)

Related

Categorical data with Deedle

Suppose I have CSV data with a categorical variable in it, like
Entry Color
0 -> 1 Red
1 -> 2 Blue
I would like to translate the variable into a discriminated union. I have tried row.GetAs<Color>, this results in an InvalidCastException. If I use fromString/toString, I have to keep track of which variable is already cast/read from records and which is not/ read from csv data. Is there a better solution?
#r "nuget: Deedle"
open Deedle
//https://stackoverflow.com/questions/21559497/create-discriminated-union-case-from-string
module Util =
open Microsoft.FSharp.Reflection
let toString (x:'a) =
let (case, _ ) = FSharpValue.GetUnionFields(x, typeof<'a>)
case.Name
let fromString<'a> (s:string) =
match FSharpType.GetUnionCases typeof<'a> |> Array.filter (fun case -> case.Name = s) with
|[|case|] -> (FSharpValue.MakeUnion(case,[||]) :?> 'a)
|_ -> failwith $"Unknown union case {s}"
type Color =
| Red
| Blue
| Green
override this.ToString() = Util.toString this
static member fromString s = Util.fromString<Color> s
let data = "Entry;Color\n1;Red\n2;Blue"
//https://stackoverflow.com/questions/44344061/converting-a-string-to-a-stream/44344794
let bytes = System.Text.Encoding.UTF8.GetBytes data
let stream = new MemoryStream( bytes )
let df:Frame<int,string> = Frame.ReadCsv(
stream = stream,
separators = ";",
hasHeaders = true
)
df.Print()
//let col = df |> Frame.mapRowValues (fun row -> row.GetAs<Color>"Color")
//Invalid cast from 'System.String' to 'FSI_...+Color'.
let col' = df |> Frame.mapRowValues (fun row -> Color.fromString (row.GetAs<string> "Color"))
//works
df.ReplaceColumn("Color", col')
df.SaveCsv(__SOURCE_DIRECTORY__ + "/df.csv",includeRowKeys=false)
let df' = Frame.ReadCsv(__SOURCE_DIRECTORY__ + "/df.csv", schema="int,Color")
df |> Frame.mapRowValues (fun row -> row.GetAs<Color> "Color")
//works
df' |> Frame.mapRowValues (fun row -> row.GetAs<Color> "Color")
//breaks
Unfortunately, there is no way to tell Deedle to convert particular columns to a discriminated union when reading CSV data. (This would not really work with unions that have cases with arguments and Deedle also does not know what types are defined in your F# code.)
The best way is something along the lines of what you are currently doing - that is, read the CSV file with categorical values as string and then parse those manually and replace the column. I would probably do this by getting the specified series and using Series.mapValues to transform the data (as that is a bit more direct than using Frame.mapRowValues):
let df = Frame.ReadCsv(stream = stream, separators = ";", hasHeaders = true)
let newCol = df.Columns.["Color"].As<string>() |> Series.mapValues Color.fromString
df.ReplaceColumn("Color", newCol)

How do I do an efficient temporal join of records in an array?

I would like to join a record to the next record at least X days/minutes/seconds into the future. I need to do this with arrays with a few hundred thousand records. I am open to sequences/lists/arrays but I believe arrays are likely to be fastest.
I can do this quickly in Deedle with Frame.joinAlign JoinKind.Left Lookup.ExactOrGreater, but I have an easier time reasoning about transformations using standard arrays/sequences/lists.
The following example is fine with 1000 records but very slow when 100k. A comment here suggests a binary search but I do not see how to do that here where the search is based on an inequality.
type Test1 = {
Date : DateTime
Value : float
}
type Test2 = {
Date1 :DateTime
Value1 : float
Date2 : DateTime
Value2 : float
}
let rng = System.Random()
let rng2 = System.Random()
let rs =
[| for i = 1 to 1000 do
let baseDay = DateTime(2016,1,1).AddDays(float i)
let actualDay = baseDay.AddDays(float (rng2.Next(7)))
yield {Date = actualDay; Value = rng.NextDouble() }|]
[| for r in rs do
let futureDay = r.Date.AddDays(float 4)
let r2 =
rs
|> Array.filter (fun x -> x.Date > futureDay)
|> Array.tryHead
let nr =
match r2 with
| Some x -> Some {Date1 = r.Date;Value1 = r.Value; Date2=x.Date;Value2 = x.Value}
| None -> None
if nr.IsSome then yield nr.Value |]
The problem is this expression:
let r2 =
rs
|> Array.filter (fun x -> x.Date > futureDay)
|> Array.tryHead
This filters the entire array and creates a new array with all the matching items, when you really just want the first matching item. And this is happening for every r. Try this instead:
let r2 = rs |> Array.tryFind (fun x -> x.Date > futureDay)
N.b. your logic would have been fine if you were dealing with sequences rather than arrays as the filter would have been evaluated lazily, but of course sequences are going to be slower than arrays in general. The thing to keep in mind is that whereas the Seq module is lazy (with some exceptions), when using the Array and List (and Set and Map, etc.) modules, every step in the chain/pipeline will eagerly allocate a new list/array and consequently can be very expensive when working with large collections.
If sorting rs doesn't affect your logic or expected output, a further improvement can be made by using Array.FindIndex to start searching at r's index rather than from the beginning of the array each time:
Array.sortInPlace rs
rs
|> Seq.mapi (fun i r ->
let futureDay = r.Date.AddDays 4.0
let r2Index = Array.FindIndex (rs, i, (fun x -> x.Date > futureDay))
match r2Index with
| -1 -> None
| i' -> let x = rs.[i']
Some { Date1=r.Date; Value1=r.Value; Date2=x.Date; Value2=x.Value })
|> Seq.choose id
|> Array.ofSeq
This should offer a significant improvement over even the Array.tryFind approach as only a handful of array elements will need to be scanned each time.
Here are FSI timings from my ageing tablet with the system under otherwise nil load:
10k elements:
unsorted + Array.filter (original code): 00:00:08.783
unsorted + Array.tryFind: 00:00:03.844
Array.sort + Seq.mapi: 00:00:00.027
100k elements:
unsorted + Array.filter: I didn't bother.
unsorted + Array.tryFind: 00:06:14.288
Array.sort + Seq.mapi: 00:00:00.305

How to cumulate (scan) Deedle data frame values

I'm loading a sequence of records into a deedle data frame (from a database table). Is it possible to accumulate (for example sum cumulatively) the values, and get back a data frame? For example there is Series.scanValues but there is no Frame.scanValues. There is Frame.map, but it didn't do what I expected, it left all values as they were.
#if INTERACTIVE
#r #"Fsharp.Charting"
#load #"..\..\Deedle.fsx"
#endif
open FSharp.Charting
open FSharp.Charting.ChartTypes
open Deedle
type SeriesX = {
DataDate:DateTime
Series1:float
Series2:float
Series3:float
}
let rnd = new System.Random()
rnd.NextDouble() - 0.5
let data =
[for i in [100..-1..1] ->
{SeriesX.DataDate = DateTime.Now.AddDays(float -i)
SeriesX.Series1 = rnd.NextDouble() - 0.5
SeriesX.Series2 = rnd.NextDouble() - 0.5
SeriesX.Series3 = rnd.NextDouble() - 0.5
}
]
# now comes the deedle frame:
let df = data |> Frame.ofRecords
let df = df.IndexRows<DateTime>("DataDate")
df.["Series1"] |> Chart.Line
df.["Series1"].ScanValues((fun acc x -> acc + x),0.0) |> Chart.Line
let df' = df |> Frame.mapValues (Seq.scan (fun acc x -> acc + x) 0.0)
df'.["Series1"] |> Chart.Line
The last two lines just give me back the original values while I would like to have the accumulated values like in df.["Series1"].Scanvalues for Series1, Series2, and Series3.
For filtering and projection, series provides Where and Select methods
and corresponding Series.map and Series.filter functions (there is
also Series.mapValues and Series.mapKeys if you only want to transform
one aspect).
So you just apply your function to each Series:
let allSum =
df.Columns
|> Series.mapValues(Series.scanValues(fun acc v -> acc + (v :?> float)) 0.0)
|> Frame.ofColumns
and use Frame.ofColumns that to convert the result to the Frame.
Edit:
If you need to select only numerics columns, you can use the Frame.getNumericCols:
let allSum =
df
|> Frame.getNumericCols
|> Series.mapValues(Series.scanValues (+) 0.0)
|> Frame.ofColumns
without an explicit type cast code has become more beautiful :)
There is a Series.scanValues function. You can obtain a series from every column in your data frame like this: frame$column, which gets you a Series.
If you need all the columns at once to do the scan, you could first map each row into a single value (a tuple, for example) and the apply the Series.scanValues to that new column.

F#: Flattening a sequence of sequences into a single Seq

I am trying to build a single sequence that contains the contents of multiple files so that it can be sorted and then passed to a graphing component. However I am stuck trying to fold the contents of each file together. The pseudo code below wont compile but hopefully will show the intention of what I am trying to achieve.
Any help, greatly appreciated.
open System.IO
let FileEnumerator filename = seq {
use sr = System.IO.File.OpenText(filename)
while not sr.EndOfStream do
let line = sr.ReadLine()
yield line
}
let files = Directory.EnumerateFiles(#"D:\test_Data\","*.csv",SearchOption.AllDirectories)
let res =
files
|> Seq.fold(fun x item ->
let lines = FileEnumerator(item)
let sq = Seq.concat x ; lines
sq
) seq<string>
printfn "%A" res
You are essentially trying to reimplement Files.Readlines, which returns the file contents as seq<string>. This can then be concatenated with Seq.concat:
let res = Directory.EnumerateFiles(#"D:\test_Data","*.csv",SearchOption.AllDirectories)
|> Seq.map File.ReadLines
|> Seq.concat
To fix the problem in your original approach, you need to use Seq.append instead of Seq.concat. The initial value for fold should be an empty sequence, which can be written as Seq.empty:
let res =
files |> Seq.fold(fun x item ->
let lines = FileEnumerator(item)
let sq = Seq.append x lines
sq ) Seq.empty
If you wanted to use Seq.concat, you'd have to write Seq.concat [x; lines], because concat expects a sequence of sequences to be concatenated. On the other hand, append simply takes two sequences, so it is easier to use here.
Another (simpler) way to concatenate all the lines is to use yield! in sequence expressions:
let res =
seq { for item in files do
yield! FileEnumerator(item) }
This creates a sequence by iterating over all the files and adding all lines from the files (in order) to the resulting sequence. The yield! construct adds all elements of the sequenece to the result.

Handy F# snippets [closed]

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Closed 10 years ago.
There are already two questions about F#/functional snippets.
However what I'm looking for here are useful snippets, little 'helper' functions that are reusable. Or obscure but nifty patterns that you can never quite remember.
Something like:
open System.IO
let rec visitor dir filter=
seq { yield! Directory.GetFiles(dir, filter)
for subdir in Directory.GetDirectories(dir) do
yield! visitor subdir filter}
I'd like to make this a kind of handy reference page. As such there will be no right answer, but hopefully lots of good ones.
EDIT Tomas Petricek has created a site specifically for F# snippets http://fssnip.net/.
Perl style regex matching
let (=~) input pattern =
System.Text.RegularExpressions.Regex.IsMatch(input, pattern)
It lets you match text using let test = "monkey" =~ "monk.+" notation.
Infix Operator
I got this from http://sandersn.com/blog//index.php/2009/10/22/infix-function-trick-for-f go to that page for more details.
If you know Haskell, you might find yourself missing infix sugar in F#:
// standard Haskell call has function first, then args just like F#. So obviously
// here there is a function that takes two strings: string -> string -> string
startsWith "kevin" "k"
//Haskell infix operator via backQuotes. Sometimes makes a function read better.
"kevin" `startsWith` "K"
While F# doesn't have a true 'infix' operator, the same thing can be accomplished almost as elegantly via a pipeline and a 'backpipeline' (who knew of such a thing??)
// F# 'infix' trick via pipelines
"kevin" |> startsWith <| "K"
Multi-Line Strings
This is pretty trivial, but it seems to be a feature of F# strings that is not widely known.
let sql = "select a,b,c \
from table \
where a = 1"
This produces:
val sql : string = "select a,b,c from table where a = 1"
When the F# compiler sees a back-slash followed by a carriage return inside a string literal, it will remove everything from the back-slash to the first non-space character on the next line. This allows you to have multi-line string literals that line up, without using a bunch of string concatenation.
Generic memoization, courtesy of the man himself
let memoize f =
let cache = System.Collections.Generic.Dictionary<_,_>(HashIdentity.Structural)
fun x ->
let ok, res = cache.TryGetValue(x)
if ok then res
else let res = f x
cache.[x] <- res
res
Using this, you could do a cached reader like so:
let cachedReader = memoize reader
Simple read-write to text files
These are trivial, but make file access pipeable:
open System.IO
let fileread f = File.ReadAllText(f)
let filewrite f s = File.WriteAllText(f, s)
let filereadlines f = File.ReadAllLines(f)
let filewritelines f ar = File.WriteAllLines(f, ar)
So
let replace f (r:string) (s:string) = s.Replace(f, r)
"C:\\Test.txt" |>
fileread |>
replace "teh" "the" |>
filewrite "C:\\Test.txt"
And combining that with the visitor quoted in the question:
let filereplace find repl path =
path |> fileread |> replace find repl |> filewrite path
let recurseReplace root filter find repl =
visitor root filter |> Seq.iter (filereplace find repl)
Update Slight improvement if you want to be able to read 'locked' files (e.g. csv files which are already open in Excel...):
let safereadall f =
use fs = new FileStream(f, FileMode.Open, FileAccess.Read, FileShare.ReadWrite)
use sr = new StreamReader(fs, System.Text.Encoding.Default)
sr.ReadToEnd()
let split sep (s:string) = System.Text.RegularExpressions.Regex.Split(s, sep)
let fileread f = safereadall f
let filereadlines f = f |> safereadall |> split System.Environment.NewLine
For performance intensive stuff where you need to check for null
let inline isNull o = System.Object.ReferenceEquals(o, null)
if isNull o then ... else ...
Is about 20x faster then
if o = null then ... else ...
Active Patterns, aka "Banana Splits", are a very handy construct that let one match against multiple regular expression patterns. This is much like AWK, but without the high performance of DFA's because the patterns are matched in sequence until one succeeds.
#light
open System
open System.Text.RegularExpressions
let (|Test|_|) pat s =
if (new Regex(pat)).IsMatch(s)
then Some()
else None
let (|Match|_|) pat s =
let opt = RegexOptions.None
let re = new Regex(pat,opt)
let m = re.Match(s)
if m.Success
then Some(m.Groups)
else None
Some examples of use:
let HasIndefiniteArticle = function
| Test "(?: |^)(a|an)(?: |$)" _ -> true
| _ -> false
type Ast =
| IntVal of string * int
| StringVal of string * string
| LineNo of int
| Goto of int
let Parse = function
| Match "^LET\s+([A-Z])\s*=\s*(\d+)$" g ->
IntVal( g.[1].Value, Int32.Parse(g.[2].Value) )
| Match "^LET\s+([A-Z]\$)\s*=\s*(.*)$" g ->
StringVal( g.[1].Value, g.[2].Value )
| Match "^(\d+)\s*:$" g ->
LineNo( Int32.Parse(g.[1].Value) )
| Match "^GOTO \s*(\d+)$" g ->
Goto( Int32.Parse(g.[1].Value) )
| s -> failwithf "Unexpected statement: %s" s
Maybe monad
type maybeBuilder() =
member this.Bind(v, f) =
match v with
| None -> None
| Some(x) -> f x
member this.Delay(f) = f()
member this.Return(v) = Some v
let maybe = maybeBuilder()
Here's a brief intro to monads for the uninitiated.
Option-coalescing operators
I wanted a version of the defaultArg function that had a syntax closer to the C# null-coalescing operator, ??. This lets me get the value from an Option while providing a default value, using a very concise syntax.
/// Option-coalescing operator - this is like the C# ?? operator, but works with
/// the Option type.
/// Warning: Unlike the C# ?? operator, the second parameter will always be
/// evaluated.
/// Example: let foo = someOption |? default
let inline (|?) value defaultValue =
defaultArg value defaultValue
/// Option-coalescing operator with delayed evaluation. The other version of
/// this operator always evaluates the default value expression. If you only
/// want to create the default value when needed, use this operator and pass
/// in a function that creates the default.
/// Example: let foo = someOption |?! (fun () -> new Default())
let inline (|?!) value f =
match value with Some x -> x | None -> f()
'Unitize' a function which doesn't handle units
Using the FloatWithMeasure function http://msdn.microsoft.com/en-us/library/ee806527(VS.100).aspx.
let unitize (f:float -> float) (v:float<'u>) =
LanguagePrimitives.FloatWithMeasure<'u> (f (float v))
Example:
[<Measure>] type m
[<Measure>] type kg
let unitize (f:float -> float) (v:float<'u>) =
LanguagePrimitives.FloatWithMeasure<'u> (f (float v))
//this function doesn't take units
let badinc a = a + 1.
//this one does!
let goodinc v = unitize badinc v
goodinc 3.<m>
goodinc 3.<kg>
OLD version:
let unitize (f:float -> float) (v:float<'u>) =
let unit = box 1. :?> float<'u>
unit * (f (v/unit))
Kudos to kvb
Scale/Ratio function builder
Again, trivial, but handy.
//returns a function which will convert from a1-a2 range to b1-b2 range
let scale (a1:float<'u>, a2:float<'u>) (b1:float<'v>,b2:float<'v>) =
let m = (b2 - b1)/(a2 - a1) //gradient of line (evaluated once only..)
(fun a -> b1 + m * (a - a1))
Example:
[<Measure>] type m
[<Measure>] type px
let screenSize = (0.<px>, 300.<px>)
let displayRange = (100.<m>, 200.<m>)
let scaleToScreen = scale displayRange screenSize
scaleToScreen 120.<m> //-> 60.<px>
Transposing a list (seen on Jomo Fisher's blog)
///Given list of 'rows', returns list of 'columns'
let rec transpose lst =
match lst with
| (_::_)::_ -> List.map List.head lst :: transpose (List.map List.tail lst)
| _ -> []
transpose [[1;2;3];[4;5;6];[7;8;9]] // returns [[1;4;7];[2;5;8];[3;6;9]]
And here is a tail-recursive version which (from my sketchy profiling) is mildly slower, but has the advantage of not throwing a stack overflow when the inner lists are longer than 10000 elements (on my machine):
let transposeTR lst =
let rec inner acc lst =
match lst with
| (_::_)::_ -> inner (List.map List.head lst :: acc) (List.map List.tail lst)
| _ -> List.rev acc
inner [] lst
If I was clever, I'd try and parallelise it with async...
F# Map <-> C# Dictionary
(I know, I know, System.Collections.Generic.Dictionary isn't really a 'C#' dictionary)
C# to F#
(dic :> seq<_>) //cast to seq of KeyValuePair
|> Seq.map (|KeyValue|) //convert KeyValuePairs to tuples
|> Map.ofSeq //convert to Map
(From Brian, here, with improvement proposed by Mauricio in comment below. (|KeyValue|) is an active pattern for matching KeyValuePair - from FSharp.Core - equivalent to (fun kvp -> kvp.Key, kvp.Value))
Interesting alternative
To get all of the immutable goodness, but with the O(1) lookup speed of Dictionary, you can use the dict operator, which returns an immutable IDictionary (see this question).
I currently can't see a way to directly convert a Dictionary using this method, other than
(dic :> seq<_>) //cast to seq of KeyValuePair
|> (fun kvp -> kvp.Key, kvp.Value) //convert KeyValuePairs to tuples
|> dict //convert to immutable IDictionary
F# to C#
let dic = Dictionary()
map |> Map.iter (fun k t -> dic.Add(k, t))
dic
What is weird here is that FSI will report the type as (for example):
val it : Dictionary<string,int> = dict [("a",1);("b",2)]
but if you feed dict [("a",1);("b",2)] back in, FSI reports
IDictionary<string,int> = seq[[a,1] {Key = "a"; Value = 1; } ...
Tree-sort / Flatten a tree into a list
I have the following binary tree:
___ 77 _
/ \
______ 47 __ 99
/ \
21 _ 54
\ / \
43 53 74
/
39
/
32
Which is represented as follows:
type 'a tree =
| Node of 'a tree * 'a * 'a tree
| Nil
let myTree =
Node
(Node
(Node (Nil,21,Node (Node (Node (Nil,32,Nil),39,Nil),43,Nil)),47,
Node (Node (Nil,53,Nil),54,Node (Nil,74,Nil))),77,Node (Nil,99,Nil))
A straightforward method to flatten the tree is:
let rec flatten = function
| Nil -> []
| Node(l, a, r) -> flatten l # a::flatten r
This isn't tail-recursive, and I believe the # operator causes it to be O(n log n) or O(n^2) with unbalanced binary trees. With a little tweaking, I came up with this tail-recursive O(n) version:
let flatten2 t =
let rec loop acc c = function
| Nil -> c acc
| Node(l, a, r) ->
loop acc (fun acc' -> loop (a::acc') c l) r
loop [] (fun x -> x) t
Here's the output in fsi:
> flatten2 myTree;;
val it : int list = [21; 32; 39; 43; 47; 53; 54; 74; 77; 99]
LINQ-to-XML helpers
namespace System.Xml.Linq
// hide warning about op_Explicit
#nowarn "77"
[<AutoOpen>]
module XmlUtils =
/// Converts a string to an XName.
let xn = XName.op_Implicit
/// Converts a string to an XNamespace.
let xmlns = XNamespace.op_Implicit
/// Gets the string value of any XObject subclass that has a Value property.
let inline xstr (x : ^a when ^a :> XObject) =
(^a : (member get_Value : unit -> string) x)
/// Gets a strongly-typed value from any XObject subclass, provided that
/// an explicit conversion to the output type has been defined.
/// (Many explicit conversions are defined on XElement and XAttribute)
/// Example: let value:int = xval foo
let inline xval (x : ^a when ^a :> XObject) : ^b =
((^a or ^b) : (static member op_Explicit : ^a -> ^b) x)
/// Dynamic lookup operator for getting an attribute value from an XElement.
/// Returns a string option, set to None if the attribute was not present.
/// Example: let value = foo?href
/// Example with default: let value = defaultArg foo?Name "<Unknown>"
let (?) (el:XElement) (name:string) =
match el.Attribute(xn name) with
| null -> None
| att -> Some(att.Value)
/// Dynamic operator for setting an attribute on an XElement.
/// Example: foo?href <- "http://www.foo.com/"
let (?<-) (el:XElement) (name:string) (value:obj) =
el.SetAttributeValue(xn name, value)
OK, this has nothing to do with snippets, but I keep forgetting this:
If you are in the interactive window, you hit F7 to jump back to the code window (without deselecting the code which you just ran...)
Going from code window to F# window (and also to open the F# window) is Ctrl Alt F
(unless CodeRush has stolen your bindings...)
Weighted sum of arrays
Calculating a weighted [n-array] sum of a [k-array of n-arrays] of numbers, based on a [k-array] of weights
(Copied from this question, and kvb's answer)
Given these arrays
let weights = [|0.6;0.3;0.1|]
let arrs = [| [|0.0453;0.065345;0.07566;1.562;356.6|] ;
[|0.0873;0.075565;0.07666;1.562222;3.66|] ;
[|0.06753;0.075675;0.04566;1.452;3.4556|] |]
We want a weighted sum (by column), given that both dimensions of the arrays can be variable.
Array.map2 (fun w -> Array.map ((*) w)) weights arrs
|> Array.reduce (Array.map2 (+))
First line: Partial application of the first Array.map2 function to weights yields a new function (Array.map ((*) weight) which is applied (for each weight) to each array in arr.
Second line: Array.reduce is like fold, except it starts on the second value and uses the first as the initial 'state'. In this case each value is a 'line' of our array of arrays. So applying an Array.map2 (+) on the first two lines means that we sum the first two arrays, which leaves us with a new array, which we then (Array.reduce) sum again onto the next (in this case last) array.
Result:
[|0.060123; 0.069444; 0.07296; 1.5510666; 215.40356|]
Performance testing
(Found here and updated for latest release of F#)
open System
open System.Diagnostics
module PerformanceTesting =
let Time func =
let stopwatch = new Stopwatch()
stopwatch.Start()
func()
stopwatch.Stop()
stopwatch.Elapsed.TotalMilliseconds
let GetAverageTime timesToRun func =
Seq.initInfinite (fun _ -> (Time func))
|> Seq.take timesToRun
|> Seq.average
let TimeOperation timesToRun =
GC.Collect()
GetAverageTime timesToRun
let TimeOperations funcsWithName =
let randomizer = new Random(int DateTime.Now.Ticks)
funcsWithName
|> Seq.sortBy (fun _ -> randomizer.Next())
|> Seq.map (fun (name, func) -> name, (TimeOperation 100000 func))
let TimeOperationsAFewTimes funcsWithName =
Seq.initInfinite (fun _ -> (TimeOperations funcsWithName))
|> Seq.take 50
|> Seq.concat
|> Seq.groupBy fst
|> Seq.map (fun (name, individualResults) -> name, (individualResults |> Seq.map snd |> Seq.average))
DataSetExtensions for F#, DataReaders
System.Data.DataSetExtensions.dll adds the ability to treat a DataTable as an IEnumerable<DataRow> as well as unboxing the values of individual cells in a way that gracefully handles DBNull by supporting System.Nullable. For example, in C# we can get the value of an integer column that contains nulls, and specify that DBNull should default to zero with a very concise syntax:
var total = myDataTable.AsEnumerable()
.Select(row => row.Field<int?>("MyColumn") ?? 0)
.Sum();
There are two areas where DataSetExtensions are lacking, however. First, it doesn't support IDataReader and second, it doesn't support the F# option type. The following code does both - it allows an IDataReader to be treated as a seq<IDataRecord>, and it can unbox values from either a reader or a dataset, with support for F# options or System.Nullable. Combined with the option-coalescing operator in another answer, this allows for code such as the following when working with a DataReader:
let total =
myReader.AsSeq
|> Seq.map (fun row -> row.Field<int option>("MyColumn") |? 0)
|> Seq.sum
Perhaps a more idiomatic F# way of ignoring database nulls would be...
let total =
myReader.AsSeq
|> Seq.choose (fun row -> row.Field<int option>("MyColumn"))
|> Seq.sum
Further, the extension methods defined below are usable from both F# and from C#/VB.
open System
open System.Data
open System.Reflection
open System.Runtime.CompilerServices
open Microsoft.FSharp.Collections
/// Ported from System.Data.DatasetExtensions.dll to add support for the Option type.
[<AbstractClass; Sealed>]
type private UnboxT<'a> private () =
// This class generates a converter function based on the desired output type,
// and then re-uses the converter function forever. Because the class itself is generic,
// different output types get different cached converter functions.
static let referenceField (value:obj) =
if value = null || DBNull.Value.Equals(value) then
Unchecked.defaultof<'a>
else
unbox value
static let valueField (value:obj) =
if value = null || DBNull.Value.Equals(value) then
raise <| InvalidCastException("Null cannot be converted to " + typeof<'a>.Name)
else
unbox value
static let makeConverter (target:Type) methodName =
Delegate.CreateDelegate(typeof<Converter<obj,'a>>,
typeof<UnboxT<'a>>
.GetMethod(methodName, BindingFlags.NonPublic ||| BindingFlags.Static)
.MakeGenericMethod([| target.GetGenericArguments().[0] |]))
|> unbox<Converter<obj,'a>>
|> FSharpFunc.FromConverter
static let unboxFn =
let theType = typeof<'a>
if theType.IsGenericType && not theType.IsGenericTypeDefinition then
let genericType = theType.GetGenericTypeDefinition()
if typedefof<Nullable<_>> = genericType then
makeConverter theType "NullableField"
elif typedefof<option<_>> = genericType then
makeConverter theType "OptionField"
else
invalidOp "The only generic types supported are Option<T> and Nullable<T>."
elif theType.IsValueType then
valueField
else
referenceField
static member private NullableField<'b when 'b : struct and 'b :> ValueType and 'b:(new:unit -> 'b)> (value:obj) =
if value = null || DBNull.Value.Equals(value) then
Nullable<_>()
else
Nullable<_>(unbox<'b> value)
static member private OptionField<'b> (value:obj) =
if value = null || DBNull.Value.Equals(value) then
None
else
Some(unbox<'b> value)
static member inline Unbox =
unboxFn
/// F# data-related extension methods.
[<AutoOpen>]
module FsDataEx =
type System.Data.IDataReader with
/// Exposes a reader's current result set as seq<IDataRecord>.
/// Reader is closed when sequence is fully enumerated.
member this.AsSeq =
seq { use reader = this
while reader.Read() do yield reader :> IDataRecord }
/// Exposes all result sets in a reader as seq<seq<IDataRecord>>.
/// Reader is closed when sequence is fully enumerated.
member this.AsMultiSeq =
let rowSeq (reader:IDataReader) =
seq { while reader.Read() do yield reader :> IDataRecord }
seq {
use reader = this
yield rowSeq reader
while reader.NextResult() do
yield rowSeq reader
}
/// Populates a new DataSet with the contents of the reader. Closes the reader after completion.
member this.ToDataSet () =
use reader = this
let dataSet = new DataSet(RemotingFormat=SerializationFormat.Binary, EnforceConstraints=false)
dataSet.Load(reader, LoadOption.OverwriteChanges, [| "" |])
dataSet
type System.Data.IDataRecord with
/// Gets a value from the record by name.
/// DBNull and null are returned as the default value for the type.
/// Supports both nullable and option types.
member this.Field<'a> (fieldName:string) =
this.[fieldName] |> UnboxT<'a>.Unbox
/// Gets a value from the record by column index.
/// DBNull and null are returned as the default value for the type.
/// Supports both nullable and option types.
member this.Field<'a> (ordinal:int) =
this.GetValue(ordinal) |> UnboxT<'a>.Unbox
type System.Data.DataRow with
/// Identical to the Field method from DatasetExtensions, but supports the F# Option type.
member this.Field2<'a> (columnName:string) =
this.[columnName] |> UnboxT<'a>.Unbox
/// Identical to the Field method from DatasetExtensions, but supports the F# Option type.
member this.Field2<'a> (columnIndex:int) =
this.[columnIndex] |> UnboxT<'a>.Unbox
/// Identical to the Field method from DatasetExtensions, but supports the F# Option type.
member this.Field2<'a> (column:DataColumn) =
this.[column] |> UnboxT<'a>.Unbox
/// Identical to the Field method from DatasetExtensions, but supports the F# Option type.
member this.Field2<'a> (columnName:string, version:DataRowVersion) =
this.[columnName, version] |> UnboxT<'a>.Unbox
/// Identical to the Field method from DatasetExtensions, but supports the F# Option type.
member this.Field2<'a> (columnIndex:int, version:DataRowVersion) =
this.[columnIndex, version] |> UnboxT<'a>.Unbox
/// Identical to the Field method from DatasetExtensions, but supports the F# Option type.
member this.Field2<'a> (column:DataColumn, version:DataRowVersion) =
this.[column, version] |> UnboxT<'a>.Unbox
/// C# data-related extension methods.
[<Extension; AbstractClass; Sealed>]
type CsDataEx private () =
/// Populates a new DataSet with the contents of the reader. Closes the reader after completion.
[<Extension>]
static member ToDataSet(this:IDataReader) =
this.ToDataSet()
/// Exposes a reader's current result set as IEnumerable{IDataRecord}.
/// Reader is closed when sequence is fully enumerated.
[<Extension>]
static member AsEnumerable(this:IDataReader) =
this.AsSeq
/// Exposes all result sets in a reader as IEnumerable{IEnumerable{IDataRecord}}.
/// Reader is closed when sequence is fully enumerated.
[<Extension>]
static member AsMultipleEnumerable(this:IDataReader) =
this.AsMultiSeq
/// Gets a value from the record by name.
/// DBNull and null are returned as the default value for the type.
/// Supports both nullable and option types.
[<Extension>]
static member Field<'T> (this:IDataRecord, fieldName:string) =
this.Field<'T>(fieldName)
/// Gets a value from the record by column index.
/// DBNull and null are returned as the default value for the type.
/// Supports both nullable and option types.
[<Extension>]
static member Field<'T> (this:IDataRecord, ordinal:int) =
this.Field<'T>(ordinal)
Handling arguments in a command line application:
//We assume that the actual meat is already defined in function
// DoStuff (string -> string -> string -> unit)
let defaultOutOption = "N"
let defaultUsageOption = "Y"
let usage =
"Scans a folder for and outputs results.\n" +
"Usage:\n\t MyApplication.exe FolderPath [IncludeSubfolders (Y/N) : default=" +
defaultUsageOption + "] [OutputToFile (Y/N): default=" + defaultOutOption + "]"
let HandlArgs arr =
match arr with
| [|d;u;o|] -> DoStuff d u o
| [|d;u|] -> DoStuff d u defaultOutOption
| [|d|] -> DoStuff d defaultUsageOption defaultOutOption
| _ ->
printf "%s" usage
Console.ReadLine() |> ignore
[<EntryPoint>]
let main (args : string array) =
args |> HandlArgs
0
(I had a vague memory of this technique being inspired by Robert Pickering, but can't find a reference now)
A handy cache function that keeps up to max (key,reader(key)) in a dictionary and use a SortedList to track the MRU keys
let Cache (reader: 'key -> 'value) max =
let cache = new Dictionary<'key,LinkedListNode<'key * 'value>>()
let keys = new LinkedList<'key * 'value>()
fun (key : 'key) -> (
let found, value = cache.TryGetValue key
match found with
|true ->
keys.Remove value
keys.AddFirst value |> ignore
(snd value.Value)
|false ->
let newValue = key,reader key
let node = keys.AddFirst newValue
cache.[key] <- node
if (keys.Count > max) then
let lastNode = keys.Last
cache.Remove (fst lastNode.Value) |> ignore
keys.RemoveLast() |> ignore
(snd newValue))
Creating XElements
Nothing amazing, but I keep getting caught out by the implicit conversion of XNames:
#r "System.Xml.Linq.dll"
open System.Xml.Linq
//No! ("type string not compatible with XName")
//let el = new XElement("MyElement", "text")
//better
let xn s = XName.op_Implicit s
let el = new XElement(xn "MyElement", "text")
//or even
let xEl s o = new XElement(xn s, o)
let el = xEl "MyElement" "text"
Pairwise and pairs
I always expect Seq.pairwise to give me [(1,2);(3;4)] and not [(1,2);(2,3);(3,4)]. Given that neither exist in List, and that I needed both, here's the code for future reference. I think they're tail recursive.
//converts to 'windowed tuples' ([1;2;3;4;5] -> [(1,2);(2,3);(3,4);(4,5)])
let pairwise lst =
let rec loop prev rem acc =
match rem with
| hd::tl -> loop hd tl ((prev,hd)::acc)
| _ -> List.rev acc
loop (List.head lst) (List.tail lst) []
//converts to 'paged tuples' ([1;2;3;4;5;6] -> [(1,2);(3,4);(5,6)])
let pairs lst =
let rec loop rem acc =
match rem with
| l::r::tl -> loop tl ((l,r)::acc)
| l::[] -> failwith "odd-numbered list"
| _ -> List.rev acc
loop lst []
Naive CSV reader (i.e., won't handle anything nasty)
(Using filereadlines and List.transpose from other answers here)
///Given a file path, returns a List of row lists
let ReadCSV =
filereadlines
>> Array.map ( fun line -> line.Split([|',';';'|]) |> List.ofArray )
>> Array.toList
///takes list of col ids and list of rows,
/// returns array of columns (in requested order)
let GetColumns cols rows =
//Create filter
let pick cols (row:list<'a>) = List.map (fun i -> row.[i]) cols
rows
|> transpose //change list of rows to list of columns
|> pick cols //pick out the columns we want
|> Array.ofList //an array output is easier to index for user
Example
"C:\MySampleCSV"
|> ReadCSV
|> List.tail //skip header line
|> GetColumns [0;3;1] //reorder columns as well, if needs be.
Date Range
simple but useful list of dates between fromDate and toDate
let getDateRange fromDate toDate =
let rec dates (fromDate:System.DateTime) (toDate:System.DateTime) =
seq {
if fromDate <= toDate then
yield fromDate
yield! dates (fromDate.AddDays(1.0)) toDate
}
dates fromDate toDate
|> List.ofSeq
toggle code to sql
More trivial than most on this list, but handy nonetheless:
I'm always taking sql in and out of code to move it to a sql environment during development. Example:
let sql = "select a,b,c "
+ "from table "
+ "where a = 1"
needs to be 'stripped' to:
select a,b,c
from table
where a = 1
keeping the formatting. It's a pain to strip out the code symbols for the sql editor, then put them back again by hand when I've got the sql worked out. These two functions toggle the sql back and forth from code to stripped:
// reads the file with the code quoted sql, strips code symbols, dumps to FSI
let stripForSql fileName =
File.ReadAllText(fileName)
|> (fun s -> Regex.Replace(s, "\+(\s*)\"", ""))
|> (fun s -> s.Replace("\"", ""))
|> (fun s -> Regex.Replace(s, ";$", "")) // end of line semicolons
|> (fun s -> Regex.Replace(s, "//.+", "")) // get rid of any comments
|> (fun s -> printfn "%s" s)
then when you are ready to put it back into your code source file:
let prepFromSql fileName =
File.ReadAllText(fileName)
|> (fun s -> Regex.Replace(s, #"\r\n", " \"\r\n+\"")) // matches newline
|> (fun s -> Regex.Replace(s, #"\A", " \""))
|> (fun s -> Regex.Replace(s, #"\z", " \""))
|> (fun s -> printfn "%s" s)
I'd love to get rid of the input file but can't even begin to grok how to make that happen. anyone?
edit:
I figured out how to eliminate the requirement of a file for these functions by adding a windows forms dialog input/output. Too much code to show, but for those who would like to do such a thing, that's how I solved it.
Pascal's Triangle (hey, someone might find it useful)
So we want to create a something like this:
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
Easy enough:
let rec next = function
| [] -> []
| x::y::xs -> (x + y)::next (y::xs)
| x::xs -> x::next xs
let pascal n =
seq { 1 .. n }
|> List.scan (fun acc _ -> next (0::acc) ) [1]
The next function returns a new list where each item[i] = item[i] + item[i + 1].
Here's the output in fsi:
> pascal 10 |> Seq.iter (printfn "%A");;
[1]
[1; 1]
[1; 2; 1]
[1; 3; 3; 1]
[1; 4; 6; 4; 1]
[1; 5; 10; 10; 5; 1]
[1; 6; 15; 20; 15; 6; 1]
[1; 7; 21; 35; 35; 21; 7; 1]
[1; 8; 28; 56; 70; 56; 28; 8; 1]
[1; 9; 36; 84; 126; 126; 84; 36; 9; 1]
[1; 10; 45; 120; 210; 252; 210; 120; 45; 10; 1]
For the adventurous, here's a tail-recursive version:
let rec next2 cont = function
| [] -> cont []
| x::y::xs -> next2 (fun l -> cont <| (x + y)::l ) <| y::xs
| x::xs -> next2 (fun l -> cont <| x::l ) <| xs
let pascal2 n =
set { 1 .. n }
|> Seq.scan (fun acc _ -> next2 id <| 0::acc)) [1]
Flatten a List
if you have something like this:
let listList = [[1;2;3;];[4;5;6]]
and want to 'flatten' it down to a singe list so the result is like this:
[1;2;3;4;5;6]
it can be done thusly:
let flatten (l: 'a list list) =
seq {
yield List.head (List.head l)
for a in l do yield! (Seq.skip 1 a)
}
|> List.ofSeq
List comprehensions for float
This [23.0 .. 1.0 .. 40.0] was marked as deprecated a few versions backed.
But apparently, this works:
let dl = 9.5 / 11.
let min = 21.5 + dl
let max = 40.5 - dl
let a = [ for z in min .. dl .. max -> z ]
let b = a.Length
(BTW, there's a floating point gotcha in there. Discovered at fssnip - the other place for F# snippets)
Parallel map
let pmap f s =
seq { for a in s -> async { return f s } }
|> Async.Parallel
|> Async.Run

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