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a port to Ruby of Perl's Data::RandomPerson
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README.markdown

RandomPerson

A port to Ruby of Perl's Data::RandomPerson.

http://search.cpan.org/~peterhi/Data-RandomPerson-0.4/

Build status

Master branch:
Build Status for master branch

Development branch:
Build Status for development branch

QUICK NOTE!

I'd really, really appreciate it that if you see something is wrong or doesn't work for you or you've an idea for improvement let me know. Wow, Github makes this so easy, just go for it! I'll be happy to help and happy for the help. Bonus points for a topic branch too!

Why did I write this?

Because the Faker library and a few of the other random ones are excellent, but they don't have the precision I needed. I wanted believable male names between a certain age range for a football game I was writing. Producing names like Mrs Albert Wiggins wasn't really going to cut it.

And I'm a masochist.

It also has unicode characters, so it'll give your database a real test. At least, if it's MySQL, all the other databases won't even notice.

Installation:

    gem install randomperson

Super quick start!

For those with a short attention span:

    require 'randomperson'

    r = RandomPerson() # don't forget the brackets!
    r.generate  # => each time this will generate a new person.

The demographics will be randomly selected.

USAGE:

For those with more willpower:

The original API (if you can call it that) was very clunky so I've endeavoured to improve it by adding a nice facade over the top. I'll start off using that but as the examples move along I might do some things "the old fashioned way" by using some of the classes underlying everything directly, just so you know what's going on under there. If you see several ways of doing the same thing don't freak out! You're probably best using the stuff just below here.

So, to generate 1000 random people with Spanish names, between the ages of 16 and 35 with a ratio of 3 males to every 5 females:

    require 'randomperson'

    r = RandomPerson() # don't forget the brackets!

    r.demographic("Spain", gender_ratio: [3,5] , age_lower:16, age_upper:35 ).add_Spanish

    people = [ ]
    1000.times { people << r.generate }

    10.times { |i| puts "#{people[i].first} #{people[i].last} age: #{people[i].age} born: #{people[i].dob.strftime("%d-%b-%Y")}" }

Output:

Bartolomé Andrés de Elixaeberna age: 20 born: 28-Apr-1990
Fabiana Cordero Balmaceda age: 21 born: 14-Jun-1989
Jorge Alas Albarracin age: 29 born: 16-Apr-1981
Eufemia Berlanga de Sergi age: 33 born: 25-Jan-1977
David Puig Villaroel age: 19 born: 17-Apr-1991
Alba Henchoz de Hurtado age: 30 born: 14-Feb-1980
Natalia Jara de Guevara age: 33 born: 28-Jul-1977
Tito Cresaco Campo age: 20 born: 14-Feb-1990
Susana Pitillas de Vaime age: 32 born: 12-Aug-1978
Gustavo Hierro Carbajal y Plazas age: 31 born: 08-Feb-1979

Here's an example using the Thai Romanised data:

    r.demographic("Thai").add_Thai_Romanised  
    people = [ ]
    10.times { people << r.generate( "Thai") }
    10.times { |i| puts "#{people[i].first} #{people[i].last} age: #{people[i].age} born: #{people[i].dob.strftime("%d-%b-%Y")}" }

Output:

Yongchaiyuth Sripituksakul Puntasrima age: 39 born: 29-Mar-1971
Sri-Patana Wattanasin age: 77 born: 04-Feb-1933
Wattana Rojjanasukchai age: 7 born: 27-Jun-2003
Ban Sukbunsung age: 85 born: 07-Sep-1925
Daeng Prasongsanti age: 65 born: 27-Mar-1945
Proi Paowsong Sriwarunyu age: 60 born: 06-May-1950
Ban Yongjaiyut age: 4 born: 06-Nov-2006
Siam Narkhirunkanok age: 15 born: 12-Aug-1995
Tong Punyawong Kadesadayurat age: 64 born: 01-Sep-1946
Sarai Wattanapanit Maneerattana age: 44 born: 21-Dec-1966

DEMOGRAPHICS

The Demographic class sets the parameters that will be used to generate people.

r.demographic("My fancy demo")

is the same as

r.demographic("My fancy demo", gender_ratio: [1,1] , age_lower: 0, age_upper: 115 )

So you'd end up with a population of roughly 50:50 male/female with ages from 0 to 115 years.

If you don't give a demographic name it will be given a number. To see the demographics use:

r.demographics

To see a particular one, use its name (it's a hash):

r.demographics["My fancy demo"]

LAST PERSON

r.demographic("Who was last?").add_English
r.person.lastname # generates a new person if none.

=> "Fletcher"

r.person.lastname # gets the last person if there was one.

=> "Fletcher"

r.generate.lastname # generates a new name every time

=> "Ford"

r.person.lastname

=> "Ford"

r.generate.lastname

=> "Bradley"

r.generate.lastname

=> "King"

r.person.lastname

=> "King"

# an undefined demographic will return nil
r.person("What?").lastname

NoMethodError: undefined method `lastname' for nil:NilClass

r.person("What?") 

=> nil

r.person.lastname # it still remembers the last person.

=> "King"

r.person("What?") 

=> nil

r.person("Who was last?").lastname

=> "King"

r.demographic("Not English").add_Thai
r.person.lastname # not using Thais yet

=> "King"

r.generate.lastname # generate will automatically assume you want to use the last loaded demographic, unless specified otherwise

=> "Ornlamai Sriwarunyu"

r.generate("Who was last?").lastname # specify to the demographic to generate using it.

=> "Bradley-Adams"

r.person.lastname

=> "Bradley-Adams"

r.generate("Not English").lastname

=> "Suntornnitikul Parnthong"

r.person("Who was last?").lastname # this will generate a new name due to the switch between demographics. It's only keeping one lastname, not a lastname for every demographic.

=> "Jones"

WHY NAME THE DEMOGRAPHIC?

Because you can mix and match.

These will produce the same results (Spanish names), but are named differently.

r.demographic("Spain").add_Spanish  
r.demographics.keys

=> ["Spain"]

r.demographic.add_Spanish # the name is handled for you.  
r.demographics.keys

=> ["0"]

But, you can play about a bit with the name files. For example:

r.demographic("Mix n match").add_Spanish  
person = r.generate  
puts "#{person.prefix if person.prefix} #{person.first} #{person.last} #{person.suffix if person.suffix}  age: #{person.age} born: #{person.dob.strftime("%d-%b-%Y")}"  

=> Sra. Enriqueta Sedeno Arbizu age: 53 born: 22-Nov-1958

That is what we expect, but we can change the prefixes to British, the females' first names to be Finnish, and the males' first names to be French, while keeping the Spanish last names, and an American suffix for good measure!

r.demographic("Mix n match").add_BritishPrefix.add_SpanishLast.add_FinnishFemaleFirst.add_FrenchMaleFirst.add_AmericanSuffix  
person = r.generate  
puts "#{person.prefix if person.prefix} #{person.first} #{person.last} #{person.suffix if person.suffix}  age: #{person.age} born: #{person.dob.strftime("%d-%b-%Y")}"  

=> Miss Johanna Sanchez Questi age: 94 born: 20-May-1917

and the last 2 lines again:

=> Mr Rémy Escriba Sanroma age: 103 born: 16-Mar-1908

or:

r.demographic("French Spaniards").add_SpanishLast.add_French_First  
person = r.generate # a couple of times and then puts...  

=> Denise-Juliette Parrado Heras age: 80 born: 06-Apr-1931
=> Gustav Cordona Belsue age: 20 born: 27-Oct-1991

LOADING NAMEFILES

r.demographic.add_Spanish_Female would just load the SpanishFemaleFirst class into r.demographics.

r.demographic.add_Spanish would load:

SpanishFemaleFirst into r.demographic["0"].femalefirst
SpanishMaleFirst into r.demographic["0"].malefirst
SpanishLast into r.demographic["0"].last

etc etc

or you can do things the old fashioned way (but why? anyway...)

    require 'namefiles/spanish-female-first'   
    # obviously you need to use the path from where you are 
    # or where the script will run from

    r.demographic["My fancy demo"].femalefirst = RandomPerson::Names::SpanishFemaleFirst.new

The rule is, put_underscores_between_the_important_words

and

always begin with add_

and

make sure each word is capitalised, add_male will actually pick up Fe_male_ whereas add_Female and add_Male will get what you want.

If you want EnglishLast names loaded:

r.demographic.add_English_Last

All English files:

r.demographic.add_English

English males:

r.demographic.add_English_Male

If you need to check what's loaded, use loaded_classes

    r.demographics["French"].loaded_classes

=> {:femalefirst=>"French_Female_First", :last=>"French_Last", :malefirst=>"French_Male_First"}

Or, see all the demographics' loaded classes:

r.demographics.loaded_classes

And (because I'm really lazy and like convenience), this does the same:

r.loaded_classes

NEGATIONS

Sometimes you'll want to load something but not another, you can do this by prepending not to the things you don't want. For example, to get the Thai names that are in Thai script and not romanised:

    # an already load demographic gets Thai, non romanised
  r.demographic["Thai"].male.add_Thai_notRomanised

or to also get rid of the female names.

    # a new demographic gets non romanised Thai and no female names
  r.demographic("Thai").add_Thai_notRomanised_notFemale

This is an experimental thing. Seems to work, but may change. You cannot do this (at the moment)

  r.demographic.notRomanised_add_Thai

Always begin with add_

Here's an example:

r.demographic("SuperThai").add_Thai_notRomanised  
people = [ ]  
10.times { people << r.generate( "SuperThai") }  
10.times { |i| puts "#{people[i].first} #{people[i].last} age: #{people[i].age} born: #{people[i].dob.strftime("%d-%b-%Y")}" }

Output:

ทักษิณ ชัยภูมิ age: 53 born: 14-May-1958
สุริยา พิษณุโลก age: 47 born: 21-Aug-1964
แก้วเก้า เจริญปุระ age: 61 born: 14-Mar-1950
บัณฑิตา สระบุรี age: 113 born: 06-Nov-1898
วิศิษฏ์ สุโขทัย age: 83 born: 23-Sep-1928
สุประภา โลโซ age: 50 born: 09-Apr-1961
สริตา ตรัง age: 111 born: 27-Jan-1900
ณัฎฐา ลพบุรี age: 86 born: 24-Dec-1925
สุชาย ลพบุรี age: 88 born: 06-Apr-1923
ปราณี สะเมิง age: 85 born: 04-Mar-1926

RATIOS VS ODDS

The default gender ratio is 1:1. The male part is the left side (or Array#first), the female is the right side (or Array#last). All ratios are given as an array i.e. [1,1] or [3,2]

A ratio of 1:1 will not necessarily give you a population with exactly half male, half female. The ratio is the odds of getting that. So a ratio of [1,3] will give a 25% chance of producing a male and a 75% chance of being a female.

If you wish to have an exact ratio within the population then create two choices each with a 0% chance of producing the other. For example:

  r.demographic("Men", gender_ratio:[1,0] )
  r.demographic("Women", gender_ratio:[0,1] )
  #...more code here...

  25.times { people << r.generate("Men") }
  75.times { people << r.generate("Women") }

This would give you a population of 25 males and 75 females. Which sounds great unless you really think about it.

FORMATTING

Each culture has it's own conventions around names. This makes sticking some monolithic algorithm in the centre of the code to sort out how names should be displayed impossible, so each data file describes how it thinks the names should be formatted.

For example, in the EnglishLast.rb file:

    @formats = [
      ->(n)   { n.rand },
        ->(n)   { n.rand + '-' + n.rand },
    ]

This tells us that there are two ways of formatting last names defined in the file. All formats are described as lambda functions in an array.

BIT MORE ON RATIOS

Behind the scenes, ratios like this [1,3] are converted to an array of ranges like this [0..24, 25..99], called ratiod. Just so you know for this next bit.

In the EnglishLast.rb file:

@formats_ratiod = [ 0..96, 97..99 ]

This says that the chances of a name being single barrelled is 97%, and double barrelled with a hypen is 3%. I made up those figures from my own experience, but if you disagree with either the ratios or the formatting then you can change it. Either directly in the file or while running the code. It's your choice. Just make sure the numbers are right, length of arrays should be the same ( e.g. four formatting options should have a ratio with four parts like [a,b,c,d]) or it will break.

PREFIXES AND SUFFIXES

Each generator starts by producing a person with a gender, an age and a date of birth. These are then passed on to the subsequent name portions so that a male always gets a male name and a male prefix (if you've specified a prefix file).

Suffix files will also (I hope) do a bit of checking at what's already been set, so you won't get Dr. Bobby Horliton PhD, as it should be either Dr. or PhD (apparently). Stuff like that should get caught.

ADDING YOUR OWN NAMEFILES

My suggestion is to find the type of name that is closest (if you're looking for Spanishy names then look at the Spanish files as they'll have similar formatting rules there for free...) and copy and paste it in to a new one. It's easier that way.

If you do make your own name file then fork this project and send me a pull request! (See the Github help if you don't know how).

These are current name files in the library:

American_Female_First
American_Last
American_Male_First
American_Prefix
American_Suffix
Ancient_Greek_Female_First
Ancient_Greek_Last
Ancient_Greek_Male_First
Any_Last
Basque_Female_First
Basque_Last
Basque_Male_First
British_Prefix
British_Suffix
English_Female_First
English_Last
English_Male_First
English_Prefix
Finnish_Female_First
Finnish_Last
Finnish_Male_First
Finnish_Prefix
French_Female_First
French_Last
French_Male_First
German_Female_First
German_Last
German_Male_First
Scottish_Female_First
Scottish_Last
Scottish_Male_First
Scottish_Prefix
Spanish_Female_First
Spanish_Last
Spanish_Male_First
Spanish_Prefix
Swedish_Female_First
Swedish_Last
Swedish_Male_First
Thai_Female_First
Thai_First
Thai_Last
Thai_Male_First
Thai_Romanised_Female_First
Thai_Romanised_Last
Thai_Romanised_Male_First
Welsh_Female_First
Welsh_Last
Welsh_Male_First
Welsh_Prefix

ACCURACY OF NAMES AND RATIOS

I've taken bits and pieces from wherever I could get them, so if you see something is wrong then either let me know or produce a patch, just have a look at the help links on Github for how to do it. I'll also add you name to this readme, and worldly fame will be yours.

PASS A BLOCK TO HANDLE ERRORS

If you try calling a demographic that doesn't exist yet, an error will be raised. There is a default handler for this, and it will output a warning, and the result returned will be nil. If you wish, you may supply a block to handle the error.

e.g.

r.person # => this will produce a person, with a randomised demographic
r.demographic("French").add_French 
r.person # => this will produce a person too, with a French demographic
r.person "French" # => French again

# This raises an error, caught by the default block
# which prints a warning to stdout
r.person "British" # => That demographic does not exist!

r.person "British" do |error|
  warn "There are no British people here, this is Scotland!"
end
# => "There are no British people here, this is Scotland!"

The default block can be set via:

RandomPerson::Facade.default_error_block= #... put your block here, e.g
RandomPerson::Facade.default_error_block= ->(error){ warn "-> #{error}" }

Or if you're lazy like me and have an instance floating around:

r.class.default_error_block= ->(error){ warn "-> #{error}" }

TODO:

There's lots to do. Lot of repetition and ugly bits here and there, but it works so I'll get round to it when I can.

THANKS GO TO

Peter Hickman for writing the original library in Perl that inspired this on in Ruby.

My good friends:
Johan Bergsten for helping me out with the Swedish names.
HALLOJULIA (hayl yeah!) for helping me with the German names.
Aino Rissanen for helping me with the Finnish names.
Fabiola Fernández Gutiérrez for help with the Spanish prefixes.
Aled Coe and Annette Smith for help with Welsh surnames.

Special thanks to my agent, all the people at Marvel and DC for such fine comics, my wife and my mom for supporting me when no one else believed in me, but (sniff) most thanks goes to Noel Edmonds and Jeremy Kyle, for making daytime TV so creepy and inane that I rarely waste time watching it.

THE END UNLESS YOU WANT TO READ THE LICENCE

Iain Barnett

LICENCE:

It's an MIT Licence, I didn't take any code from the Perl one just names and a slight idea on how to structure things, so this ain't gonna be under the GPL. MIT is better anyway ;)

Copyright (c) 2012 Iain Barnett

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

In other words, be good.

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