one(x)
one(T::type)

Return a multiplicative identity for x: a value such that one(x)*x == x*one(x) == x. Alternatively one(T) can take a type T, in which case one returns a multiplicative identity for any x of type T.

If possible, one(x) returns a value of the same type as x, and one(T) returns a value of type T. However, this may not be the case for types representing dimensionful quantities (e.g. time in days), since the multiplicative identity must be dimensionless. In that case, one(x) should return an identity value of the same precision (and shape, for matrices) as x.

If you want a quantity that is of the same type as x, or of type T, even if x is dimensionful, use oneunit instead.

See also the identity function, and I in LinearAlgebra for the identity matrix.

Examples

julia> one(3.7)
1.0

julia> one(Int)
1

julia> import Dates; one(Dates.Day(1))
1

all(itr) -> Bool

Test whether all elements of a boolean collection are true, returning false as soon as the first false value in itr is encountered (short-circuiting). To short-circuit on true, use any.

If the input contains missing values, return missing if all non-missing values are true (or equivalently, if the input contains no false value), following three-valued logic.

See also: all!, any, count, &, , &&, allunique.

Examples

julia> a = [true,false,false,true]
4-element Vector{Bool}:
 1
 0
 0
 1

julia> all(a)
false

julia> all((println(i); v) for (i, v) in enumerate(a))
1
2
false

julia> all([missing, false])
false

julia> all([true, missing])
missing

all(p, itr) -> Bool

Determine whether predicate p returns true for all elements of itr, returning false as soon as the first item in itr for which p returns false is encountered (short-circuiting). To short-circuit on true, use any.

If the input contains missing values, return missing if all non-missing values are true (or equivalently, if the input contains no false value), following three-valued logic.

Examples

julia> all(i->(4<=i<=6), [4,5,6])
true

julia> all(i -> (println(i); i < 3), 1:10)
1
2
3
false

julia> all(i -> i > 0, [1, missing])
missing

julia> all(i -> i > 0, [-1, missing])
false

julia> all(i -> i > 0, [1, 2])
true

all(A; dims)

Test whether all values along the given dimensions of an array are true.

Examples

julia> A = [true false; true true]
2×2 Matrix{Bool}:
 1  0
 1  1

julia> all(A, dims=1)
1×2 Matrix{Bool}:
 1  0

julia> all(A, dims=2)
2×1 Matrix{Bool}:
 0
 1

all(p, A; dims)

Determine whether predicate p returns true for all elements along the given dimensions of an array.

Examples

julia> A = [1 -1; 2 2]
2×2 Matrix{Int64}:
 1  -1
 2   2

julia> all(i -> i > 0, A, dims=1)
1×2 Matrix{Bool}:
 1  0

julia> all(i -> i > 0, A, dims=2)
2×1 Matrix{Bool}:
 0
 1

updatewith!(m::T, w::T)::T where {T<:AbstractModel}

Update the model m with the values from w and return the updated model.

Examples

julia> using UserApp.User

julia> SearchLight.updatewith!(user, Dict("name" => "John Doe", "email" => "foo@bar.com"))

julia> save!(user)

updatewith!(m::T, w::T)::T where {T<:AbstractModel}

Update the model m with the values from w and return the updated model.

Examples

julia> using UserApp.User

julia> SearchLight.updatewith!(user, Dict("name" => "John Doe", "email" => "foo@bar.com"))

julia> save!(user)

update_or_create(m::T; ignore = Symbol[], skip_update = false, filters...)::T where {T<:AbstractModel}

Examples

julia>

to_models(m::Type{T}, df::DataFrames.DataFrame)::Vector{T} where {T<:AbstractModel}

Return an array of type Model

Examples

julia> DataFrame(Stat, SQLWhereExpression("date >= ? AND date <= ?", startdate, enddate), order=["stats.date"])
8160×9 DataFrame
  Row │ stats_id  stats_package_uuid                 stats_package_name   stats_status  stats_region  stats_date  stats_request_count  stats_year  stats_month
      │ Int64     String                             String               Int64         String        String      Int64                Int64       String
──────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
    1 │        1  00000000-1111-2222-3333-44444444…  REPLTreeViews                 200  cn-northeast  2021-11-25                    1        2021  2021-11
    2 │       17  00701ae9-d1dc-5365-b64a-a3a3ebf5…  BioAlignments                 200  au            2021-11-25                    1        2021  2021-11
    3 │      217  00701ae9-d1dc-5365-b64a-a3a3ebf5…  BioAlignments                 200  us-west       2021-11-25                    1        2021  2021-11
    4 │      314  009559a3-9522-5dbb-924b-0b6ed2b2…  XGBoost                       200  cn-northeast  2021-11-25                    1        2021  2021-11
    5 │      406  009559a3-9522-5dbb-924b-0b6ed2b2…  XGBoost                       200  eu-central    2021-11-25                    5        2021  2021-11
    6 │      461  009559a3-9522-5dbb-924b-0b6ed2b2…  XGBoost                       200  sa            2021-11-25                    1        2021  2021-11
    ⋮
 8160 │   623498  fff527a3-8410-504e-9ca3-60d5e79b…  SimpleANOVA                   200  eu-central    2021-11-25                    1        2021  2021-11

julia> SearchLight.to_models(Stat, DataFrame(Stat, SQLWhereExpression("date >= ? AND date <= ?", startdate, enddate), order=["stats.date"]))
8160-element Vector{Stat}:
 Stat
| KEY                  | VALUE                                |
|----------------------|--------------------------------------|
| date::Date           | 2021-11-25                           |
| id::DbId             | 1                                    |
| month::String        | 2021-11                              |
| package_name::String | REPLTreeViews                        |
| package_uuid::String | 00000000-1111-2222-3333-444444444444 |
| region::String       | cn-northeast                         |
| request_count::Int64 | 1                                    |
| status::Int64        | 200                                  |
| year::Int64          | 2021                                 |
 ⋮
 Stat
| KEY                  | VALUE                                |
|----------------------|--------------------------------------|
| date::Date           | 2021-11-25                           |
| id::DbId             | 623498                               |
| month::String        | 2021-11                              |
| package_name::String | SimpleANOVA                          |
| package_uuid::String | fff527a3-8410-504e-9ca3-60d5e79bb1e4 |
| region::String       | eu-central                           |
| request_count::Int64 | 1                                    |
| status::Int64        | 200                                  |
| year::Int64          | 2021                                 |

columnsfromjoins(joins::Vector{SQLJoin})::Vector{SQLColumn}

Extracts columns from joins param and adds to be used for the SELECT part

escape_column_name(c::SQLColumn) :: SQLColumn
escape_column_name(s::String)

Sanitizes input to be use as column names in SQL queries.

escape_value(i::SQLInput)

Sanitizes input to be used as values in SQL queries.

add_quotes(str::String) :: String

Adds quotes around str and escapes any previously existing quotes.

strip_quotes(str::String) :: String

Unquotes str.

isquoted(str::String) :: Bool

Checks weather or not str is quoted.

NamedTuple

NamedTuples are, as their name suggests, named Tuples. That is, they're a tuple-like collection of values, where each entry has a unique name, represented as a Symbol. Like Tuples, NamedTuples are immutable; neither the names nor the values can be modified in place after construction.

Accessing the value associated with a name in a named tuple can be done using field access syntax, e.g. x.a, or using getindex, e.g. x[:a] or x[(:a, :b)]. A tuple of the names can be obtained using keys, and a tuple of the values can be obtained using values.

The @NamedTuple macro can be used for conveniently declaring NamedTuple types.

Examples

julia> x = (a=1, b=2)
(a = 1, b = 2)

julia> x.a
1

julia> x[:a]
1

julia> x[(:a,)]
(a = 1,)

julia> keys(x)
(:a, :b)

julia> values(x)
(1, 2)

julia> collect(x)
2-element Vector{Int64}:
 1
 2

julia> collect(pairs(x))
2-element Vector{Pair{Symbol, Int64}}:
 :a => 1
 :b => 2

In a similar fashion as to how one can define keyword arguments programmatically, a named tuple can be created by giving a pair name::Symbol => value or splatting an iterator yielding such pairs after a semicolon inside a tuple literal:

julia> (; :a => 1)
(a = 1,)

julia> keys = (:a, :b, :c); values = (1, 2, 3);

julia> (; zip(keys, values)...)
(a = 1, b = 2, c = 3)

As in keyword arguments, identifiers and dot expressions imply names:

julia> x = 0
0

julia> t = (; x)
(x = 0,)

julia> (; t.x)
(x = 0,)