Category Archives: How-to

How-to entries are descriptions of how to complete a particular action, code snippet, code pattern, architecture, or otherwise.

Linux Containers

Docker Tips n’ Tricks for Devs – #0001 – 3 Second to Postgresql

The easiest implementation of a docker container with Postgresql that I’ve found recently allows the following commands to pull and run a Postgresql server for you.

docker pull postgres:latest
docker run -p 5432:5432 postgres

Then you can just connect to the Postgresql Server by opening up pgadmin with the following connection information:

  • Host: localhost
  • Port: 5432
  • Maintenance DB: postgres
  • Username: postgres
  • Password:

With that information you’ll be able to connect and use this as a development database that only takes about 3 seconds to launch whenever you need it.

______10 |> F# – Moar Thinking Functionally (Notes)

More notes on the “Thinking Functionally” series. Previous notes are @ “_______1 |> F# – Getting Started, Thinking Functionally“.

#6 Partial Application

Breaking down functions into single parameter functions is the mathematically correct way of doing it, but that is not the only reason it is done — it also leads to a very powerful technique called partial function application.

For example:

let add42 = (+) 42 // partial application
add42 1
add42 3

[1;2;3] |> List.map add42

let twoIsLessThan = (<) 2 // partial application twoIsLessThan 1 twoIsLessThan 3 // filter each element with the twoIsLessThan function [1;2;3] |> List.filter twoIsLessThan

let printer = printfn "printing param=%i"

[1;2;3] |> List.iter printer

Each case a partially applied function above it can then be reused in multiple contexts. It can also fix function parameters.

let add1 = (+) 1
let add1ToEach = List.map add1   // fix the "add1" function

add1ToEach [1;2;3;4]

let filterEvens =
   List.filter (fun i -> i%2 = 0) // fix the filter function

filterEvens [1;2;3;4]

Then the following shows plug in behavior that is transparent.

let adderWithPluggableLogger logger x y =
    logger "x" x
    logger "y" y
    let result = x + y
    logger "x+y"  result
    result 

let consoleLogger argName argValue =
    printfn "%s=%A" argName argValue 

let addWithConsoleLogger = adderWithPluggableLogger consoleLogger
addWithConsoleLogger  1 2
addWithConsoleLogger  42 99

let popupLogger argName argValue =
    let message = sprintf "%s=%A" argName argValue
    System.Windows.Forms.MessageBox.Show(
                                 text=message,caption="Logger")
      |> ignore

let addWithPopupLogger  = adderWithPluggableLogger popupLogger
addWithPopupLogger  1 2
addWithPopupLogger  42 99

Designing Functions for Partial Application

Sample calls to the list library:

List.map    (fun i -> i+1) [0;1;2;3]
List.filter (fun i -> i>1) [0;1;2;3]
List.sortBy (fun i -> -i ) [0;1;2;3]

Here are the same examples using partial application:

let eachAdd1 = List.map (fun i -> i+1)
eachAdd1 [0;1;2;3]

let excludeOneOrLess = List.filter (fun i -> i>1)
excludeOneOrLess [0;1;2;3]

let sortDesc = List.sortBy (fun i -> -i)
sortDesc [0;1;2;3]

Commonly accepted guidelines to multi-parameter function design.

  1. Put earlier: parameters ore likely to be static. The parameters that are most likely to be “fixed” with partial application should be first.
  2. Put last: the data structure or collection (or most varying argument). Makes it easier to pipe a structure or collection from function to function. Like:
    let result =
       [1..10]
       |> List.map (fun i -> i+1)
       |> List.filter (fun i -> i>5)
    
  3. For well-known operations such as “subtract”, put in the expected order.

Wrapping BCL Function for Partial Application

Since the data parameter is generally last versus most BCL calls that have the data parameter first, it’s good to wrap the BCL.

let replace oldStr newStr (s:string) =
  s.Replace(oldValue=oldStr, newValue=newStr)

let startsWith lookFor (s:string) =
  s.StartsWith(lookFor)

Then pipes can be used with the BCL call in the expected way.

let result =
     "hello"
     |> replace "h" "j"
     |> startsWith "j"

["the"; "quick"; "brown"; "fox"]
     |> List.filter (startsWith "f")

…or we can use function composition.

let compositeOp = replace "h" "j" >> startsWith "j"
let result = compositeOp "hello"

Understanding the Pipe Function

The pipe function is defined as:

let (|>) x f = f x

It allows us to put the function argument in front of the function instead of after.

let doSomething x y z = x+y+z
doSomething 1 2 3

If the function has multiple parameters, then it appears that the input is the final parameter. Actually what is happening is that the function is partially applied, returning a function that has a single parameter: the input.

let doSomething x y  =
   let intermediateFn z = x+y+z
   intermediateFn        // return intermediateFn

let doSomethingPartial = doSomething 1 2
doSomethingPartial 3
3 |> doSomethingPartial

#7 Function Associativity and Composition

Function Associativity

This…

let F x y z = x y z

…means this…

let F x y z = (x y) z

Also three equivalent forms.

let F x y z = x (y z)
let F x y z = y z |> x
let F x y z = x <| y z

Function Composition

Here’s an example

let f (x:int) = float x * 3.0  // f is int->float
let g (x:float) = x > 4.0      // g is float->bool

We can create a new function h that takes the output of “f” and uses it as the input for “g”.

let h (x:int) =
    let y = f(x)
    g(y)                   // return output of g

A much more compact way is this:

let h (x:int) = g ( f(x) ) // h is int->bool

//test
h 1
h 2

These are notes, to read more check out the Function Composition.

______11 |> F# – Some Hackery – A String Calculator Kata

Now for some F# hacking. The first thing I did was actually go through a Code Kata, which I’ll present here.

The first step I took was to get a project started. For that I used the ProjectScaffold to build a clean project via bash.

First cloned…

git clone git@github.com:fsprojects/ProjectScaffold.git sharpKataStringCalc

…then I navigated into the directory and executed the build.sh script…

cd sharpKataStringCalc/
./build.sh

…then I got prompted for some input.

  #####################################################

# Project Scaffold Init Script
# Please answer a few questions and we will generate
# two files:
#
# build.fsx               This will be your build script
# docs/tools/generate.fsx This script will generate your
#                         documentation
#
# NOTE: Aside from the Project Name, you may leave any
# of these blank, but you will need to change the defaults
# in the generated scripts.
#

  #####################################################

Project Name (used for solution/project files): sharpKataStringCalc
Summary (a short description): A code kata for the string calculator exercise.
Description (longer description used by NuGet): The code kata, kicked off my Roy Osherove, this is my iteration of it (at least my first iteration of it).
Author: Adron Hall
Tags (separated by spaces): fsharp f# code kata stringcalculator
Github User or Organization: adron
Github Project Name (leave blank to use Project Name):

Once I hit enter after entering the information I’ve gotten more than a few of these broken builds.

Time Elapsed 00:00:00.1609190
Running build failed.
Error:
Building /Users/adronhall/Coderz/sharpKataStringCalc/sharpKataStringCalc.sln failed with exitcode 1.

---------------------------------------------------------------------
Build Time Report
---------------------------------------------------------------------
Target         Duration
------         --------
Clean          00:00:00.0019508
AssemblyInfo   00:00:00.0107624
Total:         00:00:00.6460652
Status:        Failure
---------------------------------------------------------------------
  1) Building /Users/adronhall/Coderz/sharpKataStringCalc/sharpKataStringCalc.sln failed with exitcode 1.
  2) : /Users/adronhall/Coderz/sharpKataStringCalc/src/sharpKataStringCalc/sharpKataStringCalc.fsproj(0,0): Target named 'Rebuild' not found in the project.
  3) : /Users/adronhall/Coderz/sharpKataStringCalc/tests/sharpKataStringCalc.Tests/sharpKataStringCalc.Tests.fsproj(0,0): /Users/adronhall/Coderz/sharpKataStringCalc/tests/sharpKataStringCalc.Tests/sharpKataStringCalc.Tests.fsproj: The required attribute "Project" in Import is empty
---------------------------------------------------------------------

This problem I was able to solve once, based on what I did in a previous blog entry “That Non-Windows Scaffolding for OS-X and Linux |> I Broke It! But…“. Which seemed odd that I fixed it previously. To help with the build I actually opened it up in Xamarin Studio. Now, one of the problems with doing this, is that it’s only available on Windows & OS-X. I’m however interested in using this stuff on Linux too, but that’s looking a bit more difficult the more I work with the toolchain unfortunately.

After working through the issue I found that on one OS-X box I’d installed Mono via make and F# via make and that messes things up. Do one or the other and you should be ok. So on my other two OS-X boxes (I’ve a personal retina and a work retina) the build worked flawlessly, and when it works flawlessly it looks like this toward the end of the build execution.

Finished Target: GenerateReferenceDocs
Starting Target: GenerateDocs (==> GenerateReferenceDocs, GenerateReferenceDocs)
Finished Target: GenerateDocs
Starting Target: All (==> GenerateDocs)
Finished Target: All

---------------------------------------------------------------------
Build Time Report
---------------------------------------------------------------------
Target                  Duration
------                  --------
Clean                   00:00:00.0035253
AssemblyInfo            00:00:00.0103142
Build                   00:00:04.9369669
CopyBinaries            00:00:00.0052210
RunTests                00:00:00.6568475
CleanDocs               00:00:00.0025772
GenerateHelp            00:00:08.6989318
GenerateReferenceDocs   00:00:11.7627584
GenerateDocs            00:00:00.0003409
All                     00:00:00.0000324
Total:                  00:00:26.1162623
Status:                 Ok
---------------------------------------------------------------------

I’ve gotten this to work on OS-X and Windows just fine using the straight up ProjectScaffold and the ./build.sh. So all is good, I’m going to move forward with writing the kata based on that and loop back around to straighten out the Linux issues.

To run the tests, execute the following script after creating the project scaffold.

./build.sh RunTests

First off, what are the ideas behind the string calculator kata? Well here’s how Roy Osherove lays it out this particular code kata.

Before you start:

  • Try not to read ahead.
  • Do one task at a time. The trick is to learn to work incrementally.
  • Make sure you only test for correct inputs. there is no need to test for invalid inputs for this kata.

String Calculator

  1. Create a simple String calculator with a method int Add(string numbers)
    1. The method can take 0, 1 or 2 numbers, and will return their sum (for an empty string it will return 0) for example “” or “1” or “1 2”
    2. Start with the simplest test case of an empty string and move to 1 and two numbers
    3. Remember to solve things as simply as possible so that you force yourself to write tests you did not think about
    4. Remember to refactor after each passing test
  2. Allow the Add method to handle an unknown amount of numbers.
  3. Allow the Add method to handle new lines between numbers (instead of an empty space).
    1. the following input is ok: “1\n2 3” (will equal 6)
    2. the following input is NOT ok: “1 \n” (not need to prove it – just clarifying)
  4. Support different delimiters
    1. to change a delimiter, the beginning of the string will contain a separate line that looks like this: “//[delimiter]\n[numbers…]” for example “//;\n1;2” should return three where the default delimiter is ‘;’ .
    2. the first line is optional. all existing scenarios should still be supported
  5. Calling Add with a negative number will throw an exception “negatives not allowed” – and the negative that was passed.if there are multiple negatives, show all of them in the exception message
  6. Numbers bigger than 1000 should be ignored, so adding 2 + 1001 = 2
  7. Delimiters can be of any length with the following format: “//[delimiter]\n” for example: “//[***]\n1***2***3” should return 6
  8. Allow multiple delimiters like this: “//[delim1][delim2]\n” for example “//[*][%]\n1*2%3” should return 6.
  9. Make sure you can also handle multiple delimiters with length longer than one char.

Ok, so now that we’re clear on the string calculator, I’m going to dig into knocking out the first item, “Create a simple string calculator with a method int Add (string numbers)”

But first, in TDD fashion let’s write the test and make it fail first. I changed the code in the Tests.fs file in the tests directory and tests project to read as follows.

module sharpKataStringCalc.Tests

open System
open sharpKataStringCalc
open NUnit.Framework

[<TestFixture>]
type CalculatorTests() =
  [<Test>]
  member x.add_empty_string() =
    let calculator = Calculator()
    let result = calculator.Add ""
    Assert.That(result, Is.EqualTo 0)

That gets us a failing test, since we don’t even have any implementation yet. So now I’ll add the first part of the implementation code. First I created a Calculator.fs file and deleted the other file that ProjectScaffold put in there in the first place.

namespace sharpKataStringCalc

open System

type Calculator() = 
  member x.Add express = 
    0

Ok, that gives me a passing test for the first phase of all this. Now since I’m a total F# newb still I’ve got to kind of dig around and read documentation while I’m working through this. So I’m taking a couple of hours while Roy’s suggestion is to use 30 minutes to do this kata. But I figured it is a good way to force myself to learn the syntax and start getting into an F# refactoring practice.

The first thing I started to do was write a test where I set the Calculator() again that looked something like this. I didn’t like that so I tried to pull it out of the test.

  [<TestCase("1", Result = 1)>]
  member x.Add_single_number_returns_that_number expression =
    let calculator = Calculator()
    calculator.Add expression

I ended up with something like this then.

let calculator = Calculator()

[<TestFixture>]
type CalculatorTests() =
  [<Test>]
  member x.add_empty_string() =
    let result = calculator.Add ""
    Assert.That(result, Is.EqualTo 0)

  [<TestCase("1", Result = 1)>]
  member x.Add_single_number_returns_that_number expression =
    calculator.Add expression

After adding that code with that little refactor I ran it, red light fail, so I then moved on to implementation for this test.

type Calculator() = 
  member x.Add expression = 
    match expression with
    | "" -> 0
    | _ -> 1

Everything passed. So now on to the next scenario other subsequent number strings. I add another test and result condition.

  [<TestCase("1", Result = 1)>]
  [<TestCase("2", Result = 2)>]
  member x.Add_single_number_returns_that_number expression =
    calculator.Add expression

It runs, gets a red light fail, I then implement with this minor addition.

type Calculator() = 
  member x.Add expression = 
    match expression with
    | "" -> 0
    | _ -> Int32.Parse expression

Before moving on, I’m just going to cover some of the syntax I’ve been using. The | delimits individual matches, individual discriminated union cases, and enumeration values. In this particular case I’m just using it to match the empty string or the
wildcard. Which speaking of, the _ is a wildcard match or specifies a generic parameter. To learn more about these in detail check out match expressions or generics. There are lots of good things in there.

The other syntax is somewhat more self-explanatory so I’m going to leave it as is for the moment. It is, in the end, when executing the tests evident what is going on at least. Alright, back to the kata. Let’s actually add two numbers. For the test I’m just going to add another TestCase with two actual numbers.

  [<TestCase("1", Result = 1)>]
  [<TestCase("2", Result = 2)>]
  [<TestCase("1 2", Result = 3)>]
  member x.Add_single_number_returns_that_number expression =
    calculator.Add expression

Fails, so on to implementation. I’m just going to do this the cheap “it works” way and do something dumb.

type Calculator() = 
  member x.Add expression = 
    match expression with
    | "" -> 0
    | _ when expression.Contains " " -> 3
    | _ -> Int32.Parse expression

That’ll give me a passing green light, but I’ll add another bit of attribute to the test and get another failing test.

  [<TestCase("1", Result = 1)>]
  [<TestCase("2", Result = 2)>]
  [<TestCase("1 2", Result = 3)>]
  [<TestCase("2 3", Result = 5)>]
  member x.Add_single_number_returns_that_number expression =
    calculator.Add expression

I’ll add the following code to implement and get a passing test.

type Calculator() = 
  member x.Add expression = 
    match expression with
    | "" -> 0
    | _ when expression.Contains " " -> 
        let numbers = expression.Split [| ' ' |]
        (Int32.Parse numbers.[0]) + (Int32.Parse numbers.[1])
    | _ -> Int32.Parse expression

Ok. So that part of the match looks for an empty space, and then takes the two numbers opposite sides of that empty space (array item 0 and 1) and then parses them and adds them together. Keep in mind that ‘ ‘ signifies a single character, and not a string, even though for the contains method that executes on a string, passing in a string with ” ” is ok and the appropriate actions are taken by the compiler.

For the tests I’m going to do a refactor and break them apart just a bit and rename them using the “ xyz “ technique of methods. After the refactor the code looked like this. I got this idea from the “Use F# to write unit tests with readable names” tip.

[<TestFixture>]
type CalculatorTests() =
  [<Test>]
  member x.``should return zero if no string value is passed in.``() =
    let result = calculator.Add ""
    Assert.That(result, Is.EqualTo 0)

  [<TestCase("1", Result = 1)>]
  [<TestCase("2", Result = 2)>]
  member x.``take one number and return that number`` expression =
    calculator.Add expression

  [<TestCase("1 2", Result = 3)>]
  [<TestCase("2 3", Result = 5)>]
  member x.``add single number to single number and return sum`` expression =
    calculator.Add expression

At this point I’m going to take a break, and wrap this up in a subsequent part of this series. It’s been a fun troubleshooting and getting started string calculator kata. So stay tuned and I’ll be slinging some F# math at ya real soon.

Reference:

_______1 |> F# – Getting Started, Thinking Functionally (Notes)

Recently I took the plunge into writing F# on OS-X and Linux (Ubuntu specifically). This is the first of a new series I’m starting (along with my other ongoing series on JavaScript workflow and practices). In this article particularly I’m just going to provide an overview and notes of key paradigms in functional programming. Most of these notes are derived from a great series called “Thinking Functionally“.

#1 Thinking Functionally: Introduction

This is the article that kicks off the series. The emphasis is focused around realizing that a different way of thinking needs applied when using a functional language. The difference between functional thinking and imperative. The key topics of the series includes:

  • Mathematical Functions
  • Functions and Values
  • Types
  • Functions with Multiple Parameters
  • Defining Functions
  • Function Signatures
  • Organizing Functions

#2 Mathematical Functions

Functional programming, its paradigms and its origins, are all rooted in mathematics. A mathematical function looks something like this:

Add1(x) = x + 1

  • The set of values that can be used as input to the function is called the domain.
  • The set of possible output values from the function is called the range.
  • The function is said to map the domain to the range.

If F# the definition would look like this.

let add1 x = x + 1

The signature of the function would look like this.

val add1 : int -> int

Key Properties of Mathematical Functions

  • A function always gives the same output value for a given input value.
  • A function has no side effects.

Pure functions

  • They are trivially parallelizable.
  • I can use a function lazily.
  • A function only needs to be evaluated once. (i.e. memoization)
  • The function can be evaluated in any order.

Prospectively “Unhelpful” properties of mathematical functions

  • The input and output values are immutable.
  • A function always has exactly one input and one output

#3 Function Values and Simple Values

Looking at this function again:

let add1 x = x + 1

The x means:

  • Accept some value from the input domain.
  • Use the name “x” to represent that value so that we can refer to it later.

It is also referred to as x is bound to the input value. In this binding it will only ever be that input value. This is not an assignment of a variable. Emphasis on the fact that there are no “variables”, only values. This is a critical part of functional thinking.

Function Values: this is a value that provides binding for a function to a name.

Simple Values: This is what one might think of as constants.

let c = 5

Simple Values vs. Function Values

Both are bound to names using the keyword let. The key difference is a function needs to be application to an argument to get a result, and a simple value doesn’t, as it is the value it is.

“Values” vs. “Objects”

In F# most things are referred to as “values”, contrary to “objects” in C# (or other languages like JavaScript for that matter). A value is just a member of a domain, such as a domain of ints, string, or functions that map ints to strings. In theory a value is immutable and has no behavior attached to it. However in F#, even some primitive values have some object-like behavior such as calling a member or property with dot notation syntax like “theValue”.Length or something similar.

Naming Values

Most of the naming is the general ruleset that applies to practically every language. However there is one odd feature that comes in handy in some scenarios. Let’s say you want to have a name such as “this is my really long and flippantly ridiculous name” for a domain. Well, what you can use is the “this is my really long and flippantly ridiculous name“ to have that be the name. It’s a strange feature, but I’ll cover more of it in subsequent articles.

In F# it is also practice to name functions and values with camel case instead of pascal case (camelCase vs. PascalCase).

#4 How Types Work with Functions

Function signatures look like this, with the arrow notation.

val functionName : domain -> range

Example functions:

let intToString x = sprintf "x is %i" x  // format int to string
let stringToInt x = System.Int32.Parse(x)

Example function signatures:

val intToString : int -> string
val stringToInt : string -> int

This means:

  • intToString has a domain of int which it maps onto the range string.
  • stringToInt has a domain of string which it maps onto the range int.

Primitive Types

Standard known types you’d expect: string, int, float, bool, char, byte, etc.

Type Annotations

Sometimes type might not be inferred, if that is the case, the compiler can take annotations to resolve type.

let stringLength (x:string) = x.Length   
let stringLengthAsInt (x:string) :int = x.Length 

Function Types as Parameters

A function that takes other functions as parameters, or returns a function, is called a higher-order function (sometimes abbreviated as HOF).

Example:

let evalWith5ThenAdd2 fn = fn 5 + 2 

The signature looks like:

val evalWith5ThenAdd2 : (int -> int) -> int

Looking at an example executing. The example:

let times3 x = x * 3
evalWith5ThenAdd2 times3

The signature looks like:

val times3 : int -> int
val it : int = 17

Also these are very sensitive to types, so beward.

let times3float x = x * 3.0
evalWith5ThenAdd2 times3float

Gives an error:

error FS0001: Type mismatch. Expecting a int -> int but 
              given a float -> float

Function as Output

A function value can also be the output of a function. For example, the following function will generate an “adder” function that adds using the input value.

let adderGenerator numberToAdd = (+) numberToAdd

Using Type Annotations to Constrain Function Types

This example:

let evalWith5ThenAdd2 fn = fn 5 +2

Becomes this:

let evalWith5AsInt (fn:int->int) = fn 5

…or maybe this:

let evalWith5AsFloat (fn:int->float) = fn 5

The “unit” Type

When a function returns no output, it still requires a range, and since there isn’t a void function in mathematical functions this supplants the void as return output. This special range is called a “unit” and has one specific value only, a “()”. It is similar to a void type or a null in C# but also is not, in that it is the value “()”.

Parameterless Functions

For a WAT moment here, parameterless functions come up. In this example we might expect “unit” and “unit”.

let printHello = printf "hello world" 

But what we actually get is:

hello world
val printHello : unit = ()

Like I said, a very WAT kind of moment. To get what we expect, we need to force the definition to have a “unit” argument, as shown.

let printHelloFn () = printf "hello world"

Then we get what we expect.

val printHelloFn : unit -> unit

So why is this? Well, you’ll have to dive in a little deeper and check out the F# for fun and profitThinking Functional” entry on “How Types Work with Functions” for the full low down on that. Suffice it I’ve documented how you get what you would expect, the writer on the site goes into the nitty gritty of what is actually happening here. Possibly, you might have guessed what is happen already. There is also some strange behavior that takes place with forcing unit types with the ignore function that you may want to read on that article too, but if you’re itching to move on, and get going faster than digging through all of this errata, keep going. I’m going to jump ahead to the last section I want to cover for this blog entry of notes, currying.

#5 Currying

Haskell Curry

Haskell Curry

Breaking multi-parameter functions into smaller one-parameter functions. The way to write a function with more than one parameter is to use a process called currying. For more history on this check out Haskell Curry the mathematician to dive deep into his work (which also covers a LOT of other things beside merely currying, like combinatory logic and Curry’s paradox for instance).

Basic example:

let printTwoParameters x y = 
   printfn "x=%i y=%i" x y

This of course looks neat enough right? Well, if we look at the compiler rewrite, it gets rather interesting.

let printTwoParameters x =
   let subFunction y = 
      printfn "x=%i y=%i" x y
   subFunction

That Non-Windows Scaffolding for OS-X and Linux |> I Broke It! But…

I dove into the ProjectScaffold solution recently to see where I could get with this project. Just like in the instructions I first cloned the project.

git clone

Then executed the shell script to get an appropriate download of Paket and related tools.

$ ./build.sh

On OS-X my first attempt I got scaffolding but a bad build right off. See the video below for that example.

Trying the same thing on Ubuntu gave me this issue. This issue seemed to be different from the OS-X issue so I’m working to resolve it separately.

adron@ubuntu ~/C/ProjectScaffold> ./build.sh
No version specified. Downloading latest stable.
Github download failed. Try downloading Paket directly from 'nuget.org'.
Error getting response stream (Write: The authentication or decryption has failed.): SendFailure

Then trying it on OS-X gave me this issue.  :-/

Grumble grumble, fuss, fuss, alright, going into debugging and troubleshooting mode. I made a video of the exact steps I went through.

So if you have any ideas, let me know, I’m currently looking through the code and trying out some things. Once I get this working I’ll update this blog entry below the video with the updated resolution. Thanks!

UPDATED: Dammit, the dumbest things are what always punch me in the face the hardest. I fixed it, and it didn’t require a pull request after all! When naming a project be sure to use only string characters to be safe. As I wrote in the github issue, the name “sharp-kata-01” breaks the build in a way that gives completely erroneous messages. Once I renamed it things moved forward.

OS-X and F# [Clone It, Build It, Install It, Hack It]

Ok.

Mission: Get F# running on OS-X and executing via repl.

There are a number of steps here, that if you don’t have everything covered things just won’t work. So let’s knock out the prerequisites first. This will include getting autoconf installed. There are some other tools that I’ve added below that are good to have installed too, so get autoconf, automake, and libtool installed.

export build=~/devtools # or wherever you'd like to build
mkdir -p $build
 
cd $build
curl -OL http://ftpmirror.gnu.org/autoconf/autoconf-2.68.tar.gz
tar xzf autoconf-2.68.tar.gz
cd autoconf-2.68
./configure --prefix=/usr/local
make
sudo make install
export PATH=/usr/local/bin
  
cd $build
curl -OL http://ftpmirror.gnu.org/automake/automake-1.11.tar.gz
tar xzf automake-1.11.tar.gz
cd automake-1.11
./configure --prefix=/usr/local
make
sudo make install
 
cd $build
curl -OL http://ftpmirror.gnu.org/libtool/libtool-2.4.tar.gz
tar xzf libtool-2.4.tar.gz
cd libtool-2.4
./configure --prefix=/usr/local
make
sudo make install

Now we’re ready to get some F# (64-bit flavors too) running on OS-X. This is very likely to take more than a few minutes because we’ve got two actual builds to do here.

First step is to get mono built and installed.

git clone https://github.com/mono/mono
cd mono
./autogen.sh --prefix=/mono64 --enable-nls=no
make
sudo make install

Now clone fsharp, build and install it.

git clone https://github.com/fsharp/fsharp
cd fsharp
./autogen.sh --prefix=/mono64
make
sudo make install

Finally get the binary executable paths added to the paths file.

vi /etc/paths

Add the following paths to the paths file.

/mono64/bin/mono
/mono64/bin

Once that’s done, run the tests to confirm everything has built right and is operable.

cd tests/fsharp/core
./run-all.sh

Now… that should all be ok.

repl

What I wanted now is something to sling some code and execute it. The easiest way, is to use the ‘fsharpi’ repl. At the OS-X terminal launch the repl.
Enter this line…

let x = 5;;

Then let y to a value…

let y = 20;;

Then enter…

y + x;;

…and you should see the math calculated. Then you can quit out of the repl with the following command.

#quit;;

The overall repl should come out looking like this.

> let x = 5;;

val x : int = 5

> let y = 20;;

val y : int = 20

> y + x;;
val it : int = 25
> #quit;;

- Exit...

Compilation – A Step Further

Just to show one more step. Let’s do a compile of a file with F# Code in it. Create a file called process.fs and enter the following code.

open System

[<EntryPoint>]
let main (argv :string[]) = 
    printfn "Hello World" 
    Console.ReadLine() |> ignore
    0

Now run the compiler ‘fsharpc’.

fsharpc process.fs

Now before you freak out screaming “OMG WTF am I supposed to do with a process.exe file…!!!” just calm down and execute the following command.

mono process.exe

There you’ll see the execution of “Hello World”. Welcome to F# on OS-X. More to come, Keep thrashing code!

Note: I’ve got a github repo for this and coming F# coding available at sharpgrounds.

Documentation First w/ README.md && Project Tree Build

I’m sitting here trying to get the folder structure for my project into a kind of ASCII Tree or something. I wasn’t going to manually do this, it would be insane. Especially on any decent size Enterprise Project with an endless supply of folders and nested content. I went digging to come up with a better solution. On Linux I immediately found the Tree Utility which was perfect.

Except I was on OS-X.

First option I gave a go to was to build the thing. Because I like to do things the hard way sometimes. First I needed to get the source, which is available here.

curl -O ftp://mama.indstate.edu/linux/tree/tree-1.7.0.tgz

Once downloaded, unzip the source into a directory and find the following section for the particular operating system you want to use the utility on. The section for OS settings looked like this when I finished editing it.

# Uncomment options below for your particular OS:

# Uncomment for OS X:
CC=cc
CFLAGS=-O2 -Wall -fomit-frame-pointer -no-cpp-precomp
LDFLAGS=
MANDIR=/usr/share/man/man1
OBJS+=strverscmp.o

Now get a good build of the command file.

./configure
make

Now let’s get tree into the executable path.

sudo mkdir -p /usr/local/bin
sudo cp tree /usr/local/bin/tree

Make sure your ~/.bash_profile is setup right, include this.

export PATH="/usr/local/bin:$PATH"

Reload the shell and tree should be available as a command.

The other option which is really simple, if you don’t want to compile to code, is to just use brew to install it.

brew install tree

So now you can use tree, and do cool stuff like pipe it out to a file. If you’re running this against a Node.js Project you may want to delete the node_modules directory and then just reinstall it after running the tree command.

tree > prof-tree.md

Then in your README.md file you can include the folder structure in the description of the project. Here’s a sample output!

.
├── README.md
├── client
│   └── README.md
├── package.json
├── proj-tree.md
├── server
│   ├── boot
│   │   ├── authentication.js
│   │   ├── explorer.js
│   │   ├── rest-api.js
│   │   └── root.js
│   ├── config.json
│   ├── datasources.json
│   ├── middleware.json
│   ├── model-config.json
│   └── server.js
└── test
    └── test_exists.js

4 directories, 14 files

That’s a super easy way to offer better documentation that provides some real insight into what various parts of the project structure are actually for.