View Source StreamData (StreamData v0.6.0)
Functions to create and combine generators.
A generator is a StreamData
struct. Generators can be created through the
functions exposed in this module, like constant/1
, and by combining other
generators through functions like bind/2
.
Similar to the Stream
module, the functions in this module return a lazy
construct. We can get values out of a generator by enumerating the generator.
Generators always generate an infinite stream of values (which are randomized
most of the time).
For example, to get an infinite stream of integers that starts with small
integers and progressively grows the boundaries, you can use integer/0
:
Enum.take(StreamData.integer(), 10)
#=> [-1, 0, -3, 4, -4, 5, -1, -3, 5, 8]
As you can see above, values emitted by a generator are not unique.
In many applications of generators, the longer the generator runs the larger the generated values will be. For integers, a larger integer means a bigger number. For lists, it may mean a list with more elements. This is controlled by a parameter that we call the generation size (see the "Generation size" section below).
StreamData is often used to generate random values. It is also the foundation
for property-based testing. See ExUnitProperties
for more information.
Enumeration
Generators implement the Enumerable
protocol. The enumeration starts with a
small generation size, which increases when the enumeration continues (up to a
fixed maximum size).
Since generators are proper streams, functions from the Stream
module can be
used to stream values out of them. For example, to build an infinite stream of
positive even integers, you can do:
StreamData.integer()
|> Stream.filter(& &1 > 0)
|> Stream.map(& &1 * 2)
|> Enum.take(10)
#=> [4, 6, 4, 10, 14, 16, 4, 16, 36, 16]
Generators that are manipulated via the Stream
and Enum
modules are no
longer shrinkable (see the section about shrinking below). If you want
generation through the Enumerable
protocol to be reproducible, see seeded/2
.
Generation size
Generators have access to a generation parameter called the generation
size, which is a non-negative integer. This parameter is meant to bind the
data generated by each generator in a way that is completely up to the
generator. For example, a generator that generates integer can use the size
parameter to generate integers inside the -size..size
range. In a similar
way, a generator that generates lists could use this parameter to generate a
list with 0
to size
elements. During composition, it is common for the
"parent generator" to pass the size to the composed generators.
When creating generators, they can access the generation size using the
sized/1
function. Generators can be resized to a fixed generation size using
resize/2
.
Shrinking
StreamData
generators are also shrinkable. The idea behind shrinking is
to find the simplest value that respects a certain condition. For example,
during property-based tests, we use shrinking to find the integer closest to 0
or the smallest list that makes a test fail. By reporting the simplest data
structure that triggers an error, the failure becomes easier to understand
and reproduce.
Each generator has its own logic to shrink values. Those are outlined in each generator documentation.
Note that the generation size is not related in any way to shrinking: while intuitively one may think that shrinking just means decreasing the generation size, in reality the shrinking rule is bound to each generated value. One way to look at it is that shrinking a list is always the same, regardless of its generated length.
Special generators
Some Elixir types are implicitly converted to StreamData
generators when
composed or used in property-based testing. These types are:
atoms - they generate themselves. For example,
:foo
is equivalent toStreamData.constant(:foo)
.tuples of generators - they generate tuples where each value is a value generated by the corresponding generator, exactly like described in
tuple/1
. For example,{StreamData.integer(), StreamData.boolean()}
generates entries like{10, false}
.
Note that these terms must be explicitly converted to StreamData generators.
This means that these terms are not full-fledged generators. For example, atoms
cannot be enumerated directly as they don't implement the Enumerable
protocol.
However, StreamData.constant(:foo)
is enumerable as it has been wrapped in
a StreamData
function.
Summary
Types
An opaque type that represents a StreamData
generator that generates values
of type a
.
Functions
Generates atoms of various kind
s.
Generates binaries.
Binds each element generated by data
to a new generator returned by applying fun
.
Binds each element generated by data
and to a new generator returned by
applying fun
or filters the generated element.
Generates bitstrings.
Generates boolean values.
Generates bytes.
Generates chardata.
Checks the behaviour of a given function on values generated by data
.
Generates an integer corresponding to a valid UTF-8 codepoint of the given kind.
A generator that always generates the given term.
Filters the given generator data
according to the given predicate
function.
Generates a list of fixed length where each element is generated from the
corresponding generator in data
.
Generates maps with fixed keys and generated values.
Generates floats according to the given options
.
Generates values from different generators with specified probability.
Generates integers bound by the generation size.
Generates an integer in the given range
.
Generates iodata.
Generates iolists.
Generates keyword lists where values are generated by value_data
.
Generates lists where each values is generated by the given data
.
Generates lists where each values is generated by the given data
.
Maps the given function fun
over the given generator data
.
Generates maps with keys from key_data
and values from value_data
.
Generates sets where values are generated by data
.
Generates lists of elements out of first
with a chance of them being
improper with the improper ending taken out of improper
.
Generates elements taken randomly out of enum
.
Generates non-negative integers bound by the generation size.
Constrains the given enum_data
to be non-empty.
Generates non-empty improper lists where elements of the list are generated
out of first
and the improper ending out of improper
.
Generates values out of one of the given datas
.
Generates maps with fixed but optional keys and generated values.
Generates positive integers bound by the generation size.
Calls the provided zero argument function to generate values.
Resize the given generated data
to have fixed generation size new_size
.
Scales the generation size of the given generator data
according to
size_changer
.
Makes the given generator data
always use the same given seed
when generating.
Returns the generator returned by calling fun
with the generation size.
Generates a string of the given kind or from the given characters.
Generates any term.
Generates trees of values generated by leaf_data
and subtree_fun
.
Generates tuples where each element is taken out of the corresponding
generator in the tuple_datas
tuple.
Generates a list of elements generated by data
without duplicates (possibly
according to a given uniqueness function).
Makes the values generated by data
not shrink.
Types
@opaque t(a)
An opaque type that represents a StreamData
generator that generates values
of type a
.
Functions
Generates atoms of various kind
s.
kind
can be:
:alphanumeric
- this generates alphanumeric atoms that don't need to be quoted when written as literals. For example, it will generate:foo
but not:"foo bar"
.:alias
- generates Elixir aliases likeFoo
orFoo.Bar.Baz
.
These are some of the most common kinds of atoms usually used in Elixir applications. If you
need completely arbitrary atoms, you can use a combination of map/2
, String.to_atom/1
,
and string-focused generators to transform arbitrary strings into atoms:
printable_atom =
StreamData.map(
StreamData.string(:printable, max_length: 255),
&String.to_atom/1
)
Bear in mind the system limit of 255 characters in an atom when doing so.
Examples
Enum.take(StreamData.atom(:alphanumeric), 3)
#=> [:xF, :y, :B_]
Shrinking
Shrinks towards smaller atoms and towards "simpler" letters (like towards only alphabet letters).
Generates binaries.
The length of the generated binaries is limited by the generation size.
Options
:length
- (non-negative integer) sets the exact length of the generated binaries (same as inlist_of/2
).:min_length
- (non-negative integer) sets the minimum length of the generated binaries (same as inlist_of/2
). Ignored if:length
is present.:max_length
- (non-negative integer) sets the maximum length of the generated binaries (same as inlist_of/2
). Ignored if:length
is present.
Examples
Enum.take(StreamData.binary(), 3)
#=> [<<1>>, "", "@Q"]
Shrinking
Values generated by this generator shrink by becoming smaller binaries and by
having individual bytes that shrink towards 0
.
Binds each element generated by data
to a new generator returned by applying fun
.
This function is the basic mechanism for composing generators. It takes a
generator data
and invokes fun
with each element in data
. fun
must
return a new generator that is effectively used to generate items from
now on.
Examples
Say we wanted to create a generator that returns two-element tuples where the first element is a non-empty list, and the second element is a random element from that list. To do that, we can first generate a list and then bind a function to that list; this function will return the list and a random element from it.
StreamData.bind(StreamData.list_of(StreamData.integer(), min_length: 1), fn list ->
StreamData.bind(StreamData.member_of(list), fn elem ->
StreamData.constant({list, elem})
end)
end)
Shrinking
The generator returned by bind/2
shrinks by first shrinking the value
generated by the inner generator and then by shrinking the outer generator
given as data
. When data
shrinks, fun
is once more applied on the
shrunk value and returns a whole new generator, which will most likely
emit new items.
@spec bind_filter( t(a), (a -> {:cont, t(b)} | :skip) | (a, non_neg_integer() -> {:cont, t(b)} | :skip), non_neg_integer() ) :: t(b) when a: term(), b: term()
Binds each element generated by data
and to a new generator returned by
applying fun
or filters the generated element.
Works similarly to bind/2
but allows to filter out unwanted values. It takes
a generator data
and invokes fun
with each element generated by data
.
fun
must return one of:
{:cont, generator}
-generator
is then used to generate the next element:skip
- the value generated bydata
is filtered out and a new element is generated
Since this function acts as a filter as well, it behaves similarly to
filter/3
: when more than max_consecutive_failures
elements are filtered
out (that is, fun
returns :skip
), a StreamData.FilterTooNarrowError
is
raised. See the documentation for filter/3
for suggestions on how to avoid
such errors.
The function can accept one or two arguments. If a two-argument function is
passed, the second argument will be the number of tries left before raising
StreamData.FilterTooNarrowError
.
Examples
Say we wanted to create a generator that generates two-element tuples where the first element is a list of integers with an even number of members and the second element is a member of that list. We can do that by generating a list and, if it has even length, taking an element out of it, otherwise filtering it out.
require Integer
list_data = StreamData.list_of(StreamData.integer(), min_length: 1)
data =
StreamData.bind_filter(list_data, fn
list when Integer.is_even(length(list)) ->
inner_data = StreamData.bind(StreamData.member_of(list), fn member ->
StreamData.constant({list, member})
end)
{:cont, inner_data}
_odd_list ->
:skip
end)
Enum.at(data, 0)
#=> {[-6, -7, -4, 5, -9, 8, 7, -9], 5}
Shrinking
This generator shrinks like bind/2
but values that are skipped are not used
for shrinking (similarly to how filter/3
works).
Generates bitstrings.
The length of the generated bitstring is limited by the generation size.
Options
:length
- (non-negative integer) sets the exact length of the generated bitstrings (same as inlist_of/2
).:min_length
- (non-negative integer) sets the minimum length of the generated bitstrings (same as inlist_of/2
). Ignored if:length
is present.:max_length
- (non-negative integer) sets the maximum length of the generated bitstrings (same as inlist_of/2
). Ignored if:length
is present.
Examples
Enum.take(StreamData.bitstring(), 3)
#=> [<<0::size(1)>>, <<2::size(2)>>, <<5::size(3)>>]
Shrinking
Values generated by this generator shrink by becoming smaller bitstrings and
by having the individual bits go towards 0
.
Generates boolean values.
Examples
Enum.take(StreamData.boolean(), 3)
#=> [true, true, false]
Shrinking
Shrinks towards false
.
Generates bytes.
A byte is an integer between 0
and 255
.
Examples
Enum.take(StreamData.byte(), 3)
#=> [102, 161, 13]
Shrinking
Values generated by this generator shrink like integers, so towards bytes
closer to 0
.
@spec chardata() :: t(IO.chardata())
Generates chardata.
Chardata are values of the IO.chardata/0
type.
Examples
Enum.take(StreamData.chardata(), 3)
#=> ["", [""], [12174]]
Shrinking
Shrinks towards less nested chardata and ultimately towards smaller binaries.
@spec check_all(t(a), Keyword.t(), (a -> {:ok, term()} | {:error, b})) :: {:ok, map()} | {:error, map()} when a: term(), b: term()
Checks the behaviour of a given function on values generated by data
.
This function takes a generator and a function fun
and verifies that that
function "holds" for all generated data. fun
is called with each generated
value and can return one of:
{:ok, term}
- means that the function "holds" for the given value.term
can be anything and will be used for internal purposes byStreamData
.{:error, term}
- means that the function doesn't hold for the given value.term
is the term that will be shrunk to find the minimal value for whichfun
doesn't hold. See below for more information on shrinking.
When a value is found for which fun
doesn't hold (returns {:error, term}
),
check_all/3
tries to shrink that value in order to find a minimal value that
still doesn't satisfy fun
.
The return value of this function is one of:
{:ok, ok_map}
- if all generated values satisfyfun
.ok_map
is a map of metadata that contains no keys for now.{:error, error_map}
- if a generated value doesn't satisfyfun
.error_map
is a map of metadata that contains the following keys::original_failure
- iffun
returned{:error, term}
for a generated value, this key in the map will beterm
.:shrunk_failure
- the value returned in{:error, term}
byfun
when invoked with the smallest failing value that was generated.:nodes_visited
- the number of nodes (a positive integer) visited in the shrinking tree in order to find the smallest value. See also the:max_shrinking_steps
option.:successful_runs
- the number of successful runs before a failing value was found.
Options
This function takes the following options:
:initial_seed
- three-element tuple with three integers that is used as the initial random seed that drives the random generation. This option is required.:initial_size
- (non-negative integer) the initial generation size used to start generating values. The generation size is then incremented by1
on each iteration. See the "Generation size" section of the module documentation for more information on generation size. Defaults to1
.:max_runs
- (non-negative integer) the total number of elements to generate out ofdata
and check throughfun
. Defaults to100
.:max_run_time
- (non-negative integer) the total number of time (in milliseconds) to run a given check for. This is not used by default, so unless a value is given, then the length of the check will be determined by:max_runs
. If both:max_runs
and:max_run_time
are given, then the check will finish at whichever comes first,:max_runs
or:max_run_time
.:max_shrinking_steps
- (non-negative integer) the maximum numbers of shrinking steps to perform in casecheck_all/3
finds an element that doesn't satisfyfun
. Defaults to100
.
Examples
Let's try out a contrived example: we want to verify that the integer/0
generator generates integers that are not 0
or multiples of 11
. This
verification is broken by design because integer/0
is likely to generate
multiples of 11
at some point, but it will show the capabilities of
check_all/3
. For the sake of the example, let's say we want the values that
fail to be represented as strings instead of the original integers that
failed. We can implement what we described like this:
options = [initial_seed: :os.timestamp()]
{:error, metadata} = StreamData.check_all(StreamData.integer(), options, fn int ->
if int == 0 or rem(int, 11) != 0 do
{:ok, nil}
else
{:error, Integer.to_string(int)}
end
end)
metadata.nodes_visited
#=> 7
metadata.original_failure
#=> 22
metadata.shrunk_failure
#=> 11
As we can see, the function we passed to check_all/3
"failed" for int = 22
, and check_all/3
was able to shrink this value to the smallest failing
value, which in this case is 11
.
Generates an integer corresponding to a valid UTF-8 codepoint of the given kind.
kind
can be:
:ascii
- only ASCII characters are generated. Shrinks towards lower codepoints.:alphanumeric
- only alphanumeric characters (?a..?z
,?A..?Z
,?0..?9
) are generated. Shrinks towards?a
following the order shown previously.:printable
- only printable codepoints (String.printable?(<<codepoint::utf8>>)
returnstrue
) are generated. Shrinks towards lower codepoints.:utf8
- all valid codepoints (<<codepoint::utf8>>)
does not raise) are generated. Shrinks towards lower codepoints.
Defaults to :utf8
.
Examples
Enum.take(StreamData.codepoint(), 3)
#=> [889941, 349615, 1060099]
Enum.take(StreamData.codepoint(:ascii), 3)
#=> ~c"Kk:"
@spec constant(a) :: t(a) when a: var
A generator that always generates the given term.
Examples
iex> Enum.take(StreamData.constant(:some_term), 3)
[:some_term, :some_term, :some_term]
Shrinking
This generator doesn't shrink.
@spec filter(t(a), (a -> as_boolean(term())), non_neg_integer()) :: t(a) when a: term()
Filters the given generator data
according to the given predicate
function.
Only elements generated by data
that pass the filter are kept in the
resulting generator.
If the filter is too strict, it can happen that too few values generated by data
satisfy it.
In case more than max_consecutive_failures
consecutive values don't satisfy the filter, a
StreamData.FilterTooNarrowError
will be raised. There are a few ways you can avoid risking
StreamData.FilterTooNarrowError
errors.
Try to make sure that your filter filters out only a small subset of the elements generated by
data
. For example, having something likeStreamData.filter(StreamData.integer(), &(&1 != 0))
is usually fine because only a very tiny part of the generation space (integers) is being filtered out.Keep an eye on how the generation size affects the generator being filtered. For example, take something like
StreamData.filter(StreamData.positive_integer(), &(&1 not in 1..5)
. While it seems like this filter is not that strict (as we're filtering out only a handful of numbers out of all natural numbers), this filter will fail with small generation sizes. Sincepositive_integer/0
returns an integer between0..size
, ifsize
is small (for example, less than 10) then the probability of generating many consecutive values in1..5
is high.Try to restructure your generator so that instead of generating many values and taking out the ones you don't want, you instead generate values and turn all of them into values that are suitable. A good example is a generator for even integers. You could write it as
def even_integers() do StreamData.filter(StreamData.integer(), &Integer.is_even/1) end
but this would generate many unused values, increasing likeliness of
StreamData.FilterTooNarrowError
errors and performing inefficiently. Instead, you can usemap/2
to turn all integers into even integers:def even_integers() do StreamData.map(StreamData.integer(), &(&1 * 2)) end
Shrinking
All the values that each generated value shrinks to satisfy predicate
as
well.
Generates a list of fixed length where each element is generated from the
corresponding generator in data
.
Examples
data = StreamData.fixed_list([StreamData.integer(), StreamData.binary()])
Enum.take(data, 3)
#=> [[1, <<164>>], [2, ".T"], [1, ""]]
Shrinking
Shrinks by shrinking each element in the generated list according to the corresponding generator. Shrunk lists never lose elements.
Generates maps with fixed keys and generated values.
data_map
is a map or keyword list of fixed_key => data
pairs. Maps generated by this
generator will have the same keys as data_map
and values corresponding to values generated by
the generator under those keys.
See also optional_map/1
.
Examples
data = StreamData.fixed_map(%{
integer: StreamData.integer(),
binary: StreamData.binary(),
})
Enum.take(data, 3)
#=> [%{binary: "", integer: 1}, %{binary: "", integer: -2}, %{binary: "R1^", integer: -3}]
Shrinking
This generator shrinks by shrinking the values of the generated map.
Generates floats according to the given options
.
The complexity of the generated floats grows proportionally to the generation size.
Options
:min
- (float) if present, the generated floats will be greater than or equal to this value.:max
- (float) if present, the generated floats will be less than or equal to this value.
If neither of :min
or :max
is provided, then unbounded floats will be generated.
Shrinking
Values generated by this generator will shrink towards simpler floats. Such values are not
guaranteed to shrink towards smaller or larger values (but they will never violate the :min
or
:max
options).
@spec frequency([{pos_integer(), t(a)}]) :: t(a) when a: term()
Generates values from different generators with specified probability.
frequencies
is a list of {frequency, data}
where frequency
is an integer
and data
is a generator. The resulting generator will generate data from one
of the generators in frequency
, with probability frequency / vsum_of_frequencies
.
Examples
Let's build a generator that returns a binary around 25% of the time and an
integer around 75% of the time. We'll use integer/0
first so that generated values
will shrink towards integers.
ints_and_some_bins = StreamData.frequency([
{3, StreamData.integer()},
{1, StreamData.binary()},
])
Enum.take(ints_and_some_bins, 3)
#=> ["", -2, -1]
Shrinking
Each generated value is shrunk, and then this generator shrinks towards
values generated by generators earlier in the list of frequencies
.
Generates integers bound by the generation size.
Examples
Enum.take(StreamData.integer(), 3)
#=> [1, -1, -3]
Shrinking
Generated values shrink towards 0
.
Generates an integer in the given range
.
The generation size is ignored since the integer always lies inside range
.
Examples
Enum.take(StreamData.integer(4..8), 3)
#=> [6, 7, 7]
Shrinking
Shrinks towards the smallest absolute value that still lie in range
.
Generates iodata.
Iodata are values of the iodata/0
type.
Examples
Enum.take(StreamData.iodata(), 3)
#=> [[""], <<198>>, [115, 172]]
Shrinking
Shrinks towards less nested iodata and ultimately towards smaller binaries.
Generates iolists.
Iolists are values of the iolist/0
type.
Examples
Enum.take(StreamData.iolist(), 3)
#=> [[164 | ""], [225], ["" | ""]]
Shrinking
Shrinks towards smaller and less nested lists and towards bytes instead of binaries.
Generates keyword lists where values are generated by value_data
.
Keys are always atoms.
Examples
Enum.take(StreamData.keyword_of(StreamData.integer()), 3)
#=> [[], [sY: 1], [t: -1]]
Shrinking
This generator shrinks equivalently to a list of key-value tuples generated by
list_of/1
, that is, by shrinking the values in each tuple and also reducing
the size of the generated keyword list.
Generates lists where each values is generated by the given data
.
The same as calling list_of/2
with []
as options.
Generates lists where each values is generated by the given data
.
Each generated list can contain duplicate elements. The length of the
generated list is bound by the generation size. If the generation size is 0
,
the empty list will always be generated. Note that the accepted options
provide finer control over the size of the generated list. See the "Options"
section below.
Options
:length
- (integer or range) if an integer, the exact length the generated lists should be; if a range, the range in which the length of the generated lists should be. If provided,:min_length
and:max_length
are ignored.:min_length
- (integer) the minimum length of the generated lists.:max_length
- (integer) the maximum length of the generated lists.
Examples
Enum.take(StreamData.list_of(StreamData.binary()), 3)
#=> [[""], [], ["", "w"]]
Enum.take(StreamData.list_of(StreamData.integer(), length: 3), 3)
#=> [[0, 0, -1], [2, -1, 1], [0, 3, -3]]
Enum.take(StreamData.list_of(StreamData.integer(), max_length: 1), 3)
#=> [[1], [], []]
Shrinking
This generator shrinks by taking elements out of the generated list and also
by shrinking the elements of the generated list. Shrinking still respects any
possible length-related option: for example, if :min_length
is provided, all
shrunk list will have more than :min_length
elements.
Maps the given function fun
over the given generator data
.
Returns a new generator that returns elements from data
after applying fun
to them.
Examples
iex> data = StreamData.map(StreamData.integer(), &Integer.to_string/1)
iex> Enum.take(data, 3)
["1", "0", "3"]
Shrinking
This generator shrinks exactly like data
, but with fun
mapped over the
shrunk data.
@spec map_of(t(key), t(value), keyword()) :: t(%{optional(key) => value}) when key: term(), value: term()
Generates maps with keys from key_data
and values from value_data
.
Since maps require keys to be unique, this generator behaves similarly to
uniq_list_of/2
: if more than max_tries
duplicate keys are generated
consequently, it raises a StreamData.TooManyDuplicatesError
exception.
Options
:length
- (non-negative integer) same as inlist_of/2
.:min_length
- (non-negative integer) same as inlist_of/2
.:max_length
- (non-negative integer) same as inlist_of/2
.
Examples
Enum.take(StreamData.map_of(StreamData.integer(), StreamData.boolean()), 3)
#=> [%{}, %{1 => false}, %{-2 => true, -1 => false}]
Shrinking
Shrinks towards smallest maps and towards shrinking keys and values according to the respective generators.
Generates sets where values are generated by data
.
Options
:max_tries
- (non-negative integer) the maximum number of times that this generator tries to generate the next element of the set before giving up and raising aStreamData.TooManyDuplicatesError
in case it can't find a unique element to generate.
Examples
Enum.take(StreamData.mapset_of(StreamData.integer()), 3)
#=> [#MapSet<[-1]>, #MapSet<[1, 2]>, #MapSet<[-3, 2, 3]>]
Shrinking
This generator shrinks in the same way as uniq_list_of/2
, by removing
elements and shrinking elements as well.
Generates lists of elements out of first
with a chance of them being
improper with the improper ending taken out of improper
.
Behaves similarly to nonempty_improper_list_of/2
but can generate empty
lists and proper lists as well.
Examples
data = StreamData.maybe_improper_list_of(StreamData.byte(), StreamData.binary())
Enum.take(data, 3)
#=> [[60 | "."], [], [<<212>>]]
Shrinking
Shrinks towards smaller lists and shrunk elements in those lists, and ultimately towards proper lists.
@spec member_of(Enumerable.t()) :: t(term())
Generates elements taken randomly out of enum
.
enum
must be a non-empty and finite enumerable. If given an empty
enumerable, this function raises an error. If given an infinite enumerable,
this function will not terminate.
Examples
Enum.take(StreamData.member_of([:ok, 4, "hello"]), 3)
#=> [4, 4, "hello"]
Shrinking
This generator shrinks towards elements that appear earlier in enum
.
@spec non_negative_integer() :: t(non_neg_integer())
Generates non-negative integers bound by the generation size.
Examples
Enum.take(StreamData.non_negative_integer(), 3) #=> [0, 2, 0]
Shrinking
Generated values shrink towards 0
.
@spec nonempty(t(Enumerable.t())) :: t(Enumerable.t())
Constrains the given enum_data
to be non-empty.
enum_data
must be a generator that emits enumerables, such as lists
and maps. nonempty/1
will filter out enumerables that are empty
(Enum.empty?/1
returns true
).
Examples
Enum.take(StreamData.nonempty(StreamData.list_of(StreamData.integer())), 3)
#=> [[1], [-1, 0], [2, 1, -2]]
@spec nonempty_improper_list_of(t(a), t(b)) :: t(nonempty_improper_list(a, b)) when a: term(), b: term()
Generates non-empty improper lists where elements of the list are generated
out of first
and the improper ending out of improper
.
Examples
data = StreamData.nonempty_improper_list_of(StreamData.byte(), StreamData.binary())
Enum.take(data, 3)
#=> [[42], [56 | <<140, 137>>], [226 | "j"]]
Shrinking
Shrinks towards smaller lists (that are still non-empty, having the improper ending) and towards shrunk elements of the list and a shrunk improper ending.
Generates values out of one of the given datas
.
datas
must be a list of generators. The values generated by this generator
are values generated by generators in datas
, chosen each time at random.
Examples
data = StreamData.one_of([StreamData.integer(), StreamData.binary()])
Enum.take(data, 3)
#=> [-1, <<28>>, ""]
Shrinking
The generated value will be shrunk first according to the generator that
generated it, and then this generator will shrink towards earlier generators
in datas
.
Generates maps with fixed but optional keys and generated values.
data_map
is a map or keyword list of fixed_key => data
pairs. Maps generated by this
generator will have a subset of the keys of data_map
and values corresponding to the values
generated by the generator unders those keys.
By default, all keys are considered optional. A list of exactly which keys are optional can be provided as the second argument, allowing for a map of mixed optional and required keys. The second argument is available since StreamData 0.6.0.
See also fixed_map/1
.
Examples
data = StreamData.optional_map(%{
integer: StreamData.integer(),
binary: StreamData.binary(),
})
Enum.take(data, 3)
#=> [%{binary: "", integer: 1}, %{integer: -2}, %{binary: "R1^"}]
data = StreamData.optional_map(%{
integer: StreamData.integer(),
binary: StreamData.binary(),
}, [:integer])
Enum.take(data, 3)
#=> [%{binary: ""}, %{binary: "R1^", integer: -2}, %{binary: "R2^"}]
Shrinking
This generator shrinks by first shrinking the map by taking out keys until the map is empty, and then by shrinking the generated values.
@spec positive_integer() :: t(pos_integer())
Generates positive integers bound by the generation size.
Examples
Enum.take(StreamData.positive_integer(), 3)
#=> [1, 1, 3]
Shrinking
Generated values shrink towards 1
.
Calls the provided zero argument function to generate values.
Examples
Generating a UUID
uuid = StreamData.repeatedly(&Ecto.UUID.generate/0)
Enum.take(uuid, 3)
#=> ["2712ec5b-bc50-4b4a-8a8a-ca85d37a457b", "2092570d-8fb0-4e67-acbe-92db4c8a2bae", "1bef1fb1-8f86-46ac-a49e-3bffaa51e40b"]
Generating a unique integer
integer = StreamData.repeatedly(&System.unique_integer([:positive, :monotonic]))
Enum.take(integer, 3)
#=> [1, 2, 3]
Shrinking
By nature, this generator is not shrinkable.
Resize the given generated data
to have fixed generation size new_size
.
The new generator will ignore the generation size and always use new_size
.
See the "Generation size" section in the documentation for StreamData
for
more information about the generation size.
Examples
data = StreamData.resize(StreamData.integer(), 10)
Enum.take(data, 3)
#=> [4, -5, -9]
Scales the generation size of the given generator data
according to
size_changer
.
When generating data from data
, the generation size will be the result of
calling size_changer
with the generation size as its argument. This is
useful, for example, when a generator needs to grow faster or slower than
the default.
See the "Generation size" section in the documentation for StreamData
for
more information about the generation size.
Examples
Let's create a generator that generates much smaller integers than integer/0
when size grows. We can do this by scaling the generation size to the
logarithm of the generation size.
data = StreamData.scale(StreamData.integer(), fn size ->
trunc(:math.log(size))
end)
Enum.take(data, 3)
#=> [0, 0, -1]
Another interesting example is creating a generator with a fixed maximum generation size. For example, say we want to generate binaries but we never want them to be larger than 64 bytes:
small_binaries = StreamData.scale(StreamData.binary(), fn size ->
min(size, 64)
end)
Makes the given generator data
always use the same given seed
when generating.
This function is useful when you want a generator to have a predictable generating
behaviour. It's especially useful when using a generator with the Enumerable
protocol
since you can't set the seed specifically in that case (while you can with check_all/3
for example).
seed
must be an integer.
Examples
int = StreamData.seeded(StreamData.integer(), 10)
Enum.take(int, 3)
#=> [-1, -2, 1]
Enum.take(int, 4)
#=> [-1, -2, 1, 2]
Returns the generator returned by calling fun
with the generation size.
fun
takes the generation size and has to return a generator, that can use
that size to its advantage.
See the "Generation size" section in the documentation for StreamData
for
more information about the generation size.
Examples
Let's build a generator that generates integers in double the range integer/0
does:
data = StreamData.sized(fn size ->
StreamData.resize(StreamData.integer(), size * 2)
end)
Enum.take(data, 3)
#=> [0, -1, 5]
Generates a string of the given kind or from the given characters.
kind_or_codepoints
can be:
:ascii
- strings containing only ASCII characters are generated. Such strings shrink towards lower codepoints.:alphanumeric
- strings containing only alphanumeric characters (?a..?z
,?A..?Z
,?0..?9
) are generated. Such strings shrink towards?a
following the order shown previously.:printable
- printable strings (String.printable?/1
returnstrue
) are generated. Such strings shrink towards lower codepoints.:utf8
- valid strings (String.valid?/1
returnstrue
) are generated. Such strings shrink towards lower codepoints. Available since 0.6.0.a range - strings with characters from the range are generated. Such strings shrink towards characters that appear earlier in the range.
a list of ranges or single codepoints - strings with characters from the ranges or codepoints are generated. Such strings shrink towards earlier elements of the given list and towards the beginning of ranges.
Options
See the documentation of list_of/2
for the possible values of options.
Examples
Enum.take(StreamData.string(:ascii), 3)
#=> ["c", "9A", ""]
Enum.take(StreamData.string(Enum.concat([?a..?c, ?l..?o])), 3)
#=> ["c", "oa", "lb"]
Shrinking
Shrinks towards smaller strings and as described in the description of the
possible values of kind_or_codepoints
above.
@spec term() :: t(simple | [simple] | %{optional(simple) => simple} | tuple()) when simple: boolean() | integer() | binary() | float() | atom() | reference()
Generates any term.
The terms that this generator can generate are simple terms or compound terms. The simple terms are:
- integers (through
integer/0
) - binaries (through
binary/1
) - floats (through
float/0
) - booleans (through
boolean/0
) - atoms (through
atom/1
) - references (which are not shrinkable)
Compound terms are terms that contain other terms (which are generated recursively with
term/0
):
Examples
Enum.take(StreamData.term(), 3)
#=> [0.5119003572251588, {{true, ""}}, :WJg]
Shrinking
The terms generated by this generator shrink based on the generator used to create them (see the list of possible generated terms above).
Generates trees of values generated by leaf_data
and subtree_fun
.
leaf_data
generates the leaf nodes. subtree_fun
is a function that is
called by tree
, if an inner node of the tree shall be generated. It takes a
generator child_gen
for child nodes and returns a generator for an inner
node using child_gen
to go "one level deeper" in the tree. The frequency
between leaves and inner nodes is 1:2.
This is best explained with an example. Say that we want to generate binary
trees of integers, and that we represent binary trees as either an integer (a
leaf) or a %Branch{}
struct:
defmodule Branch do
defstruct [:left, :right]
end
Now, we can generate trees by using the integer()
generator to generate
the leaf nodes. Then we can use the subtree_fun
function to generate inner
nodes (that is, %Branch{}
structs or integer()
s).
tree_data =
StreamData.tree(StreamData.integer(), fn child_data ->
StreamData.map({child_data, child_data}, fn {left, right} ->
%Branch{left: left, right: right}
end)
end)
Enum.at(StreamData.resize(tree_data, 10), 0)
#=> %Branch{left: %Branch{left: 4, right: -1}, right: -2}
Examples
A common example is a nested list:
data = StreamData.tree(StreamData.integer(), &StreamData.list_of/1)
Enum.at(StreamData.resize(data, 10), 0)
#=> [[], '\t', '\a', [1, 2], -3, [-7, [10]]]
A more complex example is generating data that could represent the Elixir equivalent of a JSON document. The code below is slightly simplified compared to the JSON spec.
scalar_generator =
StreamData.one_of([
StreamData.integer(),
StreamData.boolean(),
StreamData.string(:ascii),
nil
])
json_generator =
StreamData.tree(scalar_generator, fn nested_generator ->
StreamData.one_of([
StreamData.list_of(nested_generator),
StreamData.map_of(StreamData.string(:ascii, min_length: 1), nested_generator)
])
end)
Enum.at(StreamData.resize(json_generator, 10), 0)
#=> [%{"#" => "5"}, true, %{"4|B" => nil, "7" => true, "yt(3y" => 4}, [[false]]]
Shrinking
Shrinks values and shrinks towards less deep trees.
Generates tuples where each element is taken out of the corresponding
generator in the tuple_datas
tuple.
Examples
data = StreamData.tuple({StreamData.integer(), StreamData.binary()})
Enum.take(data, 3)
#=> [{-1, <<170>>}, {1, "<"}, {1, ""}]
Shrinking
Shrinks by shrinking each element in the generated tuple according to the corresponding generator.
Generates a list of elements generated by data
without duplicates (possibly
according to a given uniqueness function).
This generator will generate lists where each list is unique according to the
value returned by applying the given uniqueness function to each element
(similarly to how Enum.uniq_by/2
works). If more than the value of the
:max_tries
option consecutive elements are generated that are considered
duplicates according to the uniqueness function, a
StreamData.TooManyDuplicatesError
error is raised. For this reason, try to
make sure to not make the uniqueness function return values out of a small
value space. The uniqueness function and the max number of tries can be
customized via options.
Options
:uniq_fun
- (a function of arity one) a function that is called with each generated element and whose return value is used as the value to compare with other values for uniqueness (similarly toEnum.uniq_by/2
).:max_tries
- (non-negative integer) the maximum number of times that this generator tries to generate the next element of the list before giving up and raising aStreamData.TooManyDuplicatesError
in case it can't find a unique element to generate. Note that the generation size often affects this: for example, if you have a generator likeuniq_list_of(integer(), min_length: 4)
and you start generating elements out of it with a generation size of1
, it will fail by definition becauseinteger/0
generates in-size..size
so it would only generate in a set ([-1, 0, 1]
) with three elements. Useresize/2
orscale/2
to manipulate the size (for example by setting a minimum generation size of3
) in such cases.:length
- (non-negative integer) same as inlist_of/2
.:min_length
- (non-negative integer) same as inlist_of/2
.:max_length
- (non-negative integer) same as inlist_of/2
.
Examples
data = StreamData.uniq_list_of(StreamData.integer())
Enum.take(data, 3)
#=> [[1], [], [2, 3, 1]]
Shrinking
This generator shrinks like list_of/1
, but the shrunk values are unique
according to the :uniq_fun
option as well.
Makes the values generated by data
not shrink.
Examples
Let's build a generator of bytes (integers in the 0..255
) range. We can
build this on top of integer/1
, but for our purposes, it doesn't make sense for
a byte to shrink towards 0
:
byte = StreamData.unshrinkable(StreamData.integer(0..255))
Enum.take(byte, 3)
#=> [190, 181, 178]
Shrinking
The generator returned by unshrinkable/1
generates the same values as data
,
but such values will not shrink.