# StreamData v0.4.3 StreamData View Source

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 sized.

## 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 to`StreamData.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.

# Link to this section 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

Checks the behaviour of a given function on values generated by `data`

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 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`

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

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 used 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

# Link to this section Types

# t(a)
View Source
(opaque)
t(a)

t(a)

An opaque type that represents a `StreamData`

generator that generates values
of type `a`

.

# Link to this section Functions

# atom(kind) View Source

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"`

.`:operator`

- this generates Elixir operators (such as`:<>`

or`:+`

). These don't need to be quoted when written as literals.`:alias`

- generates Elixir aliases like`Foo`

or`Foo.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), &String.to_atom/1)`

## Examples

```
Enum.take(StreamData.atom(:alphanumeric), 3)
#=> [:xF, :y, :B_]
```

## Shrinking

Shrinks towards smaller atoms and towards "simpler" letters (like towards only alphabet letters).

# binary(options \\ []) View Source

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 in`list_of/2`

).`:min_length`

- (non-negative integer) sets the minimum length of the generated binaries (same as in`list_of/2`

). Ignored if`:length`

is present.`:max_length`

- (non-negative integer) sets the maximum length of the generated binaries (same as in`list_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`

.

# bind(data, fun) View Source

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 value and returns a whole new generator, which will most likely
emit new items.

# bind_filter(data, fun, max_consecutive_failures \\ 10) View Source

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 by`data`

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.

## 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).

# bitstring(options \\ []) View Source

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 in`list_of/2`

).`:min_length`

- (non-negative integer) sets the minimum length of the generated bitstrings (same as in`list_of/2`

). Ignored if`:length`

is present.`:max_length`

- (non-negative integer) sets the maximum length of the generated bitstrings (same as in`list_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`

.

# boolean() View Source

Generates boolean values.

## Examples

```
Enum.take(StreamData.boolean(), 3)
#=> [true, true, false]
```

## Shrinking

Shrinks towards `false`

.

# byte() View Source

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`

.

# check_all(data, options, fun) View Source

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 by`StreamData`

.`{: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 which`fun`

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 satisfy`fun`

.`ok_map`

is a map of metadata that contains no keys for now.`{:error, error_map}`

- if a generated value doesn't satisfy`fun`

.`error_map`

is a map of metadata that contains the following keys:`:original_failure`

- if`fun`

returned`{:error, term}`

for a generated value, this key in the map will be`term`

.`:shrunk_failure`

- the value returned in`{:error, term}`

by`fun`

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 by`1`

on each iteration. See the "Generation size" section of the module documentation for more information on generation size. Defaults to`1`

.`:max_runs`

- (non-negative integer) the total number of elements to generate out of`data`

and check through`fun`

. Defaults to`100`

.`: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 case`check_all/3`

finds an element that doesn't satisfy`fun`

. Defaults to`100`

.

## 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`

.

# constant(term)
View Source
constant(a) :: t(a) when a: var

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.

# filter(data, predicate, max_consecutive_failures \\ 25)
View Source
filter(t(a), (a -> as_boolean(term())), non_neg_integer()) :: t(a)
when a: term()

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 like`StreamData.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. Since`positive_integer/0`

returns an integer between`0..size`

, if`size`

is small (for example, less than 10) then the probabilitty of generating many consecutive values in`1..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 use`map/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.

# fixed_list(datas) View Source

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.

# fixed_map(data) View Source

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.

## 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.

# float(options \\ []) View Source

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).

# frequency(frequencies)
View Source
frequency([{pos_integer(), t(a)}]) :: t(a) when a: term()

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 times and a
integer around 75% of times. 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`

.

# integer() View Source

Generates integers bound by the generation size.

## Examples

```
Enum.take(StreamData.integer(), 3)
#=> [1, -1, -3]
```

## Shrinking

Generated values shrink towards `0`

.

# integer(range) View Source

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 with the smallest absolute value that still lie in `range`

.

# iodata() View Source

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.

# iolist() View Source

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.

# keyword_of(value_data) View Source

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.

# list_of(data) View Source

Generates lists where each values is generated by the given `data`

.

The same as calling `list_of/2`

with `[]`

as options.

# list_of(data, options) View Source

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
shrinked list will have more than `:min_length`

elements.

# map(data, fun) View Source

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.

# map_of(key_data, value_data, options \\ []) View Source

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 in`list_of/2`

.`:min_length`

- (non-negative integer) same as in`list_of/2`

.`:max_length`

- (non-negative integer) same as in`list_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.

# maybe_improper_list_of(first, improper) View Source

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.

# member_of(enum)
View Source
member_of(Enumerable.t()) :: t(term())

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`

.

# nonempty(enum_data)
View Source
nonempty(t(Enumerable.t())) :: t(Enumerable.t())

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]]
```

# nonempty_improper_list_of(first, improper) View Source

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)
#=> [["\f"], [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.

# one_of(datas) View Source

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`

.

# optional_map(data) View Source

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.

## Examples

```
data = StreamData.optional_map(%{
integer: StreamData.integer(),
binary: StreamData.binary(),
})
Enum.take(data, 3)
#=> [%{binary: "", int: 1}, %{int: -2}, %{binary: "R1^"}]
```

## 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.

# positive_integer()
View Source
positive_integer() :: t(pos_integer())

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`

.

# resize(data, new_size) View Source

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]
```

# scale(data, size_changer) View Source

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)
```

# seeded(data, seed) View Source

Makes the given generator `data`

always used 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]
```

# sized(fun) View Source

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]
```

# string(kind_or_codepoints, options \\ []) View Source

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`

returns`true`

) are generated. Such strings shrink towards lower codepoints.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.

# term() View Source

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).

# tree(leaf_data, subtree_fun) View Source

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]]]
```

## Shrinking

Shrinks values and shrinks towards less deep trees.

# tuple(tuple_datas) View Source

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.

# uniq_list_of(data, options \\ []) View Source

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 to`Enum.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 a`StreamData.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 like`uniq_list_of(integer(), min_length: 4)`

and you start generating elements out of it with a generation size of`1`

, it will fail by definition because`integer/0`

generates in`-size..size`

so it would only generate in a set (`[-1, 0, 1]`

) with three elements. Use`resize/2`

or`scale/2`

to manipulate the size (for example by setting a minimum generation size of`3`

) in such cases.`:length`

- (non-negative integer) same as in`list_of/2`

.`:min_length`

- (non-negative integer) same as in`list_of/2`

.`:max_length`

- (non-negative integer) same as in`list_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.

# unshrinkable(data) View Source

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.