Flatline user manual

S-expressions vs. JSON

Flatline expressions in this manual use its lisp-like syntax, based on symbolic expressions or sexps. When sending them to BigML via our API, you can also use their JSON representation, which is trivially obtained by using JSON lists for each paranthesised sexp. For instance:

(if (< (f "a") 3) 0 4) => ["if", ["<", ["f", "a"], 3], 0, 4]

Literal values

Constant numbers, symbols, booleans and strings, using Java/Clojure syntax are valid expressions.


"a string"


While running over an input dataset, Flatline keeps track of the (zero-based) number of the input row that’s being used, which can be accessed with the function row-number, which takes no arguments:

(row-number) => current input row (0-based)

A typical use of this function is to generate a unique identifier for each row. The row number will start at 0 unless you skip some rows of the input dataset, and increase by one on each new row (unless you’re specifying a input row step when generating a dataset).

Field accessors

Field values

Input field values are accessed using the field operator:

(field <field-designator> [<shift>] [<default-value>])
<field-designator> := field id | field name | column_number
<shift> := integer expression
<default-value> := output value if the requested row is out-of-range

where <field-designator> can be either the identifier, name or column number of the desired field, and the optional <shift> (an integer, defaulting to 0) denotes the offset with respect to the current input row. The optional <default-value> is the output value if the value of the field for the given row (taking into account the shift, if any) is outside the limits of our dataset. It can be a constant value or an expression. If <default-value> is not set, the accessor will return a missing in those cases.

So, for instance, these sexps denote field values extracted from the current row:

(field 0)
(field 0 0)
(field 0 -1 "default-string")
(field 0 -1 (mean (field 0)))
(field 0 -1 3)
(field "000004")
(field "a field name" 0)


(field "000001" -2)

denotes the value of the cell corresponding to a field with identifier “000001” two rows before the current one. Positive shift values denote rows after the current one.

(field "a field" 3)
(field "another field" 2)

For convenience, and since field is probably going to be your most often user operator, it can be abbreviated to f:

(f "000001" -2)
(f 3 1)
(f 1 -1 3)
(f "a field" 23)

We also provide a predicate, missing?, that will tell you whether the value of the field for the given row (taking into account the shift, if any) is a missing token:

(missing? <field-designator> [<shift>])


(missing? "species")
(missing? "000001" -2)
(missing? 3 1)
(missing? "a field" 23)

will all yield boolean values. For backwards compatibility, missing is an alias for missing?.

Randomized field values

There are two Flatline functions that will let you generate a random value in the domain of a given field, given its designator:

(random-value <field-designator>)
(weighted-random-value <field-designator>)


(random-value "age")
(weighted-random-value "000001")
(weighted-random-value 3)

Both functions generate a value with the constrain that it belongs to the domain of the given field, but while random-value uses a uniform probability of the field’s range of values, weighted-random-value uses de distribution of the field values (as computed in its histogram) as the probability measure for the random generator.

These two functions work for numeric, categorical and text fields, with generated values satisfying:

  • For numeric fields, generated values are in the interval [(minimum <fid>),  (maximum <fid>)]
  • For categorical fields, generated values belong to the set (categories <fid>)
  • For text fields, we generate terms in the field’s tag cloud (generated values correspond to single terms in the cloud).
  • Datetime parent fields are not supported, since they don’t have a defined distribution: you can use any of their numeric children for generating values following their distributions.

A common use of these functions is replacing missing values with random data, which in Flatline you could write as, say:

(if (missing? "00000") (random-value "000000") (f "000000"))

We provide a shortcut for those common operations with the functions ensure-value and ensure-weighted-value:

(ensure-value <fdes>) :=
  (if (missing? <fdes>) (random-value <fdes>) (field <fdes>))

(ensure-weighted-value <fdes>) :=
  (if (missing? <fdes>) (weighted-random-value <fdes>) (field <fdes>))

We them, our example above can be simply written as:

(ensure-value "000000")

or, if you want that the generated random values follow the same distribution as the field “000000”:

(ensure-weighted-value "000000")

Normalized field values

For numeric fields, it’s often useful to normalize their values to a standard interval (usually [0, 1]). To that end, you can use the Flatline primitive normalize, which takes as arguments the designator for the field you want to normalize and, optionally, the two bounds of the resulting interval:

(normalize <id> [<from> <to>])
=> (+ from (* (- to from)
              (/ (- (f id) (minimum id))
                 (- (maximum id) (minimum id)))))

For instance:

(normalize "000001") ;; = (normalize "000001" 0 1)
(normalize "width" -1 1)
(normalize "length" 8 23)

As shown in the formula above, normalize linearly maps the minimum value of the field to from (0 by default) and the maximum value to to (1 by default).

Besides this linear normalization, it’s also common to standardize numeric data values by mapping them to a gaussian, according to the equation:

x[i] -> (x[i] - mean(x)) / standard_deviation(x)

or, in flatline terms:

(/ (- (f <id>) (mean <id>)) (standard-deviation <id>))

This normalization function is called the Z score, and we provide it as the function z-score:

(z-score <field-designator>)


(z-score "000034")
(z-score "a numeric field")
(z-score 23)

As with normalize, the field used must have a numeric optype.

Vectorized categorical or text fields

It may be useful to convert categorical or text fields to numeric values for models which accept only numeric data as input. This can be accomplished with the Flatline primitive vectorize:

(vectorize <field-designator> [<max-fields>])

For categorical fields, the output is a binary indicator vector. In other words, it is a list of numeric fields, one per possible categorical value, and for each instance, the numeric field corresponding to the category of that instance will have a value of 1, whereas the remaining numeric fields will have a value of 0.

For text fields, the output is a list of numeric fields, each corresponding to a term in the field’s tag cloud. The value of each field is the number of times that term appears in that instance.

A numeric expression or literal can be passed as an optional second argument to limit the number of generated fields to the n most frequent categories or text terms.

Field properties

Summary properties

Field descriptors contain lots of properties with metadata about the field, including its summary. These propeties (when they’re atomic) can be accessed via field-prop:

(field-prop <type> <field-descriptor> <property-name> ...)
<type> := string | numeric | boolean

For instance, you can access the name for field “00023” via:

(field-prop string "00023" name)

or the value of the nested property missing_count inside the summary with:

(field-prop numeric "00023" summary missing_count)

We provide several shortcuts for concrete summary properties, to save you typing:

(maximum <field-designator>)
(mean <field-designator>)
(median <field-designator>)
(minimum <field-designator>)
(missing-count <field-designator>)
(population <field-designator>)
(sum <field-designator>)
(sum-squares <field-designator>)
(standard-deviation <field-designator>)
(variance <field-designator>)

(preferred? <field-designator>)

(category-count <field-designator> <category>)
(bin-center <field-designator> <bin-number>)
(bin-count <field-designator> <bin-number>)

As you can see, the category and count accessors take an additional parameter designating either the category (a string or order number) and the bin (a 0-based integer index) you refer to:

(category-count "species" "Iris-versicolor")
(category-count "species" (f "000004"))
(bin-count "age" (f "bin-selector"))
(bin-center "000003" 3)
(bin-center (field "field-selector") 4)

Discretization of numeric fields

A simple way to discretize a numeric field is to assign a label to each of a finite set of segments, defined by a sequence of upper bounds. For instance:

(let (v (f "age"))
  (cond (< v 2) "baby"
        (< v 10) "child"
        (< v 20) "teenager"

Flatline provides a shortcut for the above expression via its segment-label primitive:

(segment-label "000000" "baby" 2 "child" 10 "teenager" 20 "adult")

As you can see, the first argument is the field designator (as usual, a name, column number or identifier), followed by alternating labels and upper bounds. More generally:

(segment-label <fdes> <l1> <b1> ... <ln-1> <bn-1> <ln>)
<l1> ... <ln> strings, <b1> ... <bn-1> numbers
=> (cond (< (f <fdes>) <b1>) <l1>
         (< (f <fdes>) <b2>) <l2>
         (< (f <fdes>) <bn-1>) <ln-1>

The alternating labels and bounds must be constant strings and numbers. If you want to use segments of equal length between the minimum and maximum value of the field, you can omit the upper bounds and give simply the list of labels, e.g.

(segment-label 0 "1st fourth" "2nd fourth" "3rd fourth" "4th fourth")

which would be equivalent to:

(let (max (maximum 0)
      min (minimum 0)
      step (/ (- max min) 4))
  (segment-label 0 "1st fourth" (+ min step)
                   "2nd fourth" (+ min step step)
                   "3rd fourth" (+ min step step step)
                   "4th fourth"))

or, in general:

(segment-label <fdes> <l1> ... <ln>) with  <l1> ... <ln> strings

=> (let (min (minimum <fdes>)
         step (- (maximum <fdes>) min)
         shift (- (f <fdes>) min))
     (cond (< shift step) <l1>
           (< shift (* 2 step)) <l2>
           (< shift (* (- n 1) step)) <ln-1>

Items and itemsets

A common operation on fields of optype items is to check whether they contain a list of items. That can be used, for instance, to filter the rows of a dataset that satisfy a given association rule, but calling contains-items? with the list of items in the antecedent and consequent of the desired rule.

(contains-items? <field-designator> <item_0> ... <item_n>)
;; with <item_i> of type string for i in [0, n]

The contains-items primitive takes as first argument the descriptor of the field we want to check (which must have optype items), followed by the one or more items we want to check, which must all have type string. For instance, the predicate:

(contains-items? "000000" "blue" "green" "darkblue")

will filter the rows whose first column satisfies the association rule blue, green -> darkblue.

It is also possible to check whether an items field contains only the given list of items (in any order), using equal-to-items?, which works exactly as contains-items? except for the fact that it’s exclusive:

(equals-to-items? <field-designator> <item_0> ... <item_n>)
;; with <item_i> of type string for i in [0, n]

Field population, percentiles &co for numeric fields

Besides direct readings from the field summaries, there exist other derived statistical properties available as Flatline functions. In particular, these are the ones related to population percentiles and their distribution for numeric fields:

(percentile <field-designator> <fraction>)    ;; fraction in [0.0, 1.0]
(within-percentiles? <field-designator> <lower> <upper>)
(population-fraction <field-designator> <sexp>)
(percentile-label <field-designator> <label-0> ... <label-n>)

The first one, percentile, gives you the value that a numeric field must have in order to be in the given population fraction. Thus, you could use, for instance, the following predicate in a filter to remove outliers:

(<= (percentile "age" 0.5) (f "age") (percentile "age" 0.95))

We provide syntactic sugar for the above expression via within-percentiles?:

(within-percentiles? "age" 0.5 0.95)

Related to percentile is population-fraction, which, given a field identifier and a value, computes the number of instances of this field whose value is less than the given one. As with the case of percentile, the designated field must be numeric.

Finally, percentile-label computes the percentile the input value belongs to and generates the label you provided. For instance, this generator:

(percentile-label "000023" "1st" "2nd" "3rd" "4th")

will generate the label “1st” if the value of the field 000023 is in the first population “quartile” (since we’re providing 4 labels, we use 4 segments), “2nd” to the second, etc. The sexp above is equivalent to:

(cond (within-percentiles? "000023" 0 0.25) "1st"
      (within-percentiles? "000023" 0.25 0.5) "2nd"
      (within-percentiles? "000023" 0.5 0.75) "3rd"

and, as you see, it easily generalizes to any number of labels: if you had provided 5 labels we’d be computing “quintiles”; had them been 10, the labels would correspond to “deciles,” and so forth. As with all functions in this section, the target field must be numeric.

Note that we’re using scare quotes around quartile, quintiles, etc. above. That’s because percentile-label will assign to each value the label of the lowest percentile it belongs to, and therefore, it won’t really discretize your variable by exact quantiles: if the population is skewed around a value, so it’ll be the resulting labels’ population.

Strings and regular expressions

Coercion and substrings

Any value can be coerced to a string using the str operator, which will also concatenate the corresponding strings when called with more than one argument:

(str <sexp0> ...)

For instance:

(str 1 "hello " (field "a"))   ;; =>  "1hello <value of field a>"
(str "value_" (+ 3 4) "/" (name "000001"))  ;; => "value_7/a"

It is also possible to take a substring of a string value using the subs operator:

(subs <string> <start> [<end>])
<start> in [0 (length <string>))
<end> in (0 (length <string>)]

It returns the substring of <string> beginning at start inclusive, and ending at end (defaults to length of string), exclusive.

String utilities

The number of characters in a string value is given by length:

(length <string>)


(length "abc") => 3
(length "") => 0

Note that the length of a missing value is a missing value, not zero.

The primitive join allows joining a list of string values using a given separator, optionally skipping any missing values in the list:

(join <list of string> [<sep-string>] [<skip-missings?>])

For instance:

(join (list "a" "b" "zz")) => "abzz"
(join (list "a" "b") "|") => "a|b"
(join (list "a" "b" "c") "x") => "axbxc"
(join (list "a" (f 1) "b") ",") => MISSING (if (missing? 1))
(join (list "a" (f 1) "b") "," true) => "a,b" (if (missing? 1))
(join (list "a" (f 1) "b") true) => "ab" (if (missing? 1))

The primitive levenshtein computes, as an integer, the distance between two given string values:

(levenshtein <str-sexp0> <str-sexp1>)

Arbitrary arguments are allowed, provided they’re strings:

(levenshtein (f 0) "a random string")
(if (< (levenshtein (f 0) "bluething") 5) "bluething" (f 0))

You can also compute the number of times a word appears in a given string by means of the occurrences function. It takes an input string and the term to look for as mandatory parameters, and, optionally, a language code, and a boolean controlling case sensitivity:

(occurrences <string> <term> [<case-insensitive?> <lang>])
<case-insensitive?> := true | false (defaults to false)
<lang> := "en" | "es" | "ca" | "nl" | "none" (defaults to "none")

By default, terms matching is case sensitive and exact. The optional third argument is a boolean flag to turn on case insensitivity. Finally, if you provide a fourth constant argument specifying one of the known languages (English, Spanish, Catalan or Dutch), words are compared using their stems (e.g., in English, “day” and “days” will be considered the same term).

For instance:

(occurrences "howdy woman, howdy" "howdy") => 2
(occurrences "howdy woman" "Man" true) => 0
(occurrences "howdy man" "Man" true) => 1
(occurrences "hola, Holas" "hola" true "es") => 2

Hashing functions

There are several hashing functions available: md5, sha1 and sha256. These functions act on the stream of bytes of their input string and return a string representing the bytes that the cryptographic digest they name produces, in their hexadecimal representation:

(md5 <string>) => string of length 32
(sha1 <string>) => string of length 40
(sha256 <string>) => string of length 64


(md5 "a text") => "b229386ec4627869d2c71b7df3c9600a"
(sha1 "a text") => "7081f2babbafff16b4bae16282859c844baa14ef"
(sha256 "") =>

As shown, the returned strings use charaters in [0-9a-f] to represent the values of the output bytes: md5 produces 16 bytes (128 bits), sha-1 produces 20 bytes (160 bits) and sha-256 produces 32 bytes (256 bits).

Regular expression matching

The matches? function takes a regular expression as a string and a form evaluating to a string and returns a boolean telling you if the latter matches the former.

(matches? <string> <regex-string>)  => boolean
<regex-string> := a string form representing a regular expression
<string> := a string expression to be tested against the regexp

Regular expressions follow the Perl and Java syntax and extensions (see for instance this summary), including flags modifiers such as “(?i)” to turn on case-insensitive mode.

For instance, to check if the field “name” contains the word “Hal” anywhere, you could use:

(matches? (field "name") ".*\\sHal\\s.*")
(matches? (field "name") "(?i).*\\shal\\s.*")

where the second form performs case-insensitive pattern matching.

It’s possible to use non-constant string values for the regular expression, but take into account that any special character in the string will be treated as such when it’s converted to a regular expression. If what you want is to match literally the contents of a field, use re-quote:

(re-quote <string>)  => regexp that matches <string> literally

and then you can write things like:

(if (matches? (f "result") (re-quote (f "target"))) "GOOD" "MISS")

and you can use the string concatenation operator str to construct regular expressions strings out of smaller pieces:

(matches? (f "name") (str "^" (re-quote (f "salutation")) "\\s *$"))

Regular expression search and replace

Given a string expression, you can substitute matches of a given regexp by a given replacement string using replace and replace-first:

(replace <string> <regexp> <replacement>)
(replace-first <string> <regexp> <replacement>)


(replace "Target string ss" "\\Ws" "S") => "TargetStringSs"

The replacement is literal, except that “$1”, “$2”, etc. in the replacement string are substituted with the string that matched the corresponding parenthesized group in the pattern. If you want them to appear literally in the replacement string, just use “\$1” and the like.

For example:

(replace "Almost Pig Latin" "\\b(\\w)(\\w+)\\b" "$2$1ay")
 => "lmostAay igPay atinLay"

While replace replaces all occurrences of the regular expression, replace-first stops after the first match:

(replace-first "swap first two words" "(\\w+)(\\s+)(\\w+)" "$3$2$1")
 => "first swap two words"

Text analysis

Flatline provides a primitive function, language, that tries to detect the language of a given string value. It returns the ISO 639 code of the detected language, as a string.

(language <string>) => <ISO 639 string code>

For instance:

(language "this is an English phrase") => "en"

Note that language detectors will do in general a very poor job for short texts, and that we currently limit the set of detected languages to those used in BigML’s text analysis facility (English, Spanish, Catalan or Dutch as of this writing, represented as “en”, “es”, “ca” and “nl”, respectively.)

Relational operators and equality

You can compare numeric and datetime values with any of the relational operators <, <=, >, and >=, which can be applied to two or more arguments and always result in a boolean value. For example:

(< (field 0) (field 1))
(<= (field 0 -1) (field 0) (field 0 1))
(> (field "date") "07-14-1969")
(>= 23 (f "000004" -2))

The equality (=) and inequality (!=) operators can be applied to operators of any kind:

(= "Dante" (field "Author"))
(= 1300 (field "Year"))
(= (field "Year" -2) (field "Year" -1) (field "Year"))
(!= (field "00033" -1) (field "00033" 1))

Comparing numerical values can be tricky, especially when they’re the result of mathematical operations, but Flatline makes an effort to be sensible and considers things like 1 and 1.0 equal (for numeric values, it actually uses Clojure’s == operator); but of course it cannot fix rounding errors or the like for you! For convenience, and to help readability in some contexts, we provide the trivial predicate zero?, which simply expands to a comparison with 0:

(zero? <x>) := (= 0 <x>)

Logical operators

The basic logical connectives and, or and not, acting on boolean values, are available, with their usual meanings.

(and (= 3 (field 1)) (= "meh" (f "a")) (< (f "pregnancies") 5))
(not true)

For additional convenience, and and or can be applied to lists (described below):

(and (list <sexp0> ... <sexpn>)) := (and <sexp0> ... <sexpn>)
(or (list <sexp0> ... <sexpn>)) := (or <sexp0> ... <sexpn>)

Arithmetical operators

The usual arithmetical operators +, -, * and / taking any number of arguments (or zero, for``+and*`) are available. Of course their operands must evaluate to a numeric value; otherwise, the result will be nil, representing a missing value.

When not coerced, the result of the / operator has type double. If needed, you can transform it to an integer via the coercion function integer or use instead the integer division operator div (see below).

Numerical coercions

You can coerce arbitrary values to explicit numeric types. When the input sexp is a string (or a category name), we try to parse it as a number and afterwards perform a pure numerical coercion if needed. Boolean values are mapped to 0 (false) and 1 (true).

(integer <sexp>)
(real <sexp>)

If the input value cannot be coerced to a number the result is a missing value.

Mathematical functions

We provide a host of mathematical functions:

(odd? <x>)
(even? <x>)

(max <x0> ... <xn>)
(min <x0> ... <xn>)

(abs <x>)     ;; Absolute value
(mod <n> <m>) ;; Modulus
(div <n> <m>) ;; Integer division (quotient)
(sqrt <x>)
(pow <x> <n>)
(square <x>)  ;; (* <x> <x>)

(ln <x>)      ;; Natural logarithm
(log <x>)     ;; Base-2 logarithm
(log10 <x>)   ;; Base-10 logarithm
(exp <x>)     ;; Exponential

(ceil <x>)
(floor <x>)
(round <x>)

(cos <x>)     ;; <x> := radians
(sin <x>)     ;; <x> := radians
(tan <x>)     ;; <x> := radians

(to-radians <x>) ;; <x> := degrees
(to-degrees <x>) ;; <x> := radians

(acos <x>)
(asin <x>)
(atan <x>)

(cosh <x>)
(sinh <x>)
(tanh <x>)

As well as two primitives for generating random numbers:

(rand)            ;; a random double in [0, 1)
(rand-int <n>)    ;; a random integer in [0, n) or (n, 0]

Currently there’s no way of specifying the seed used for random number generation, but it’s coming shortly to a selected data generation language very near to you.


It’s also possible to compute the slope, intercept and Pearson coeffient of the linear regression of a set of points given as a list of alternating x and y coordinates:

(linear-regression <x0> <y0> <x1> <y1> ... <xn> <yn>)
   => (<slope> <intercept> <pearson>) ;; 3 double values


(linear-regression 1 1 2 2 3 3 4 4) => (1.0 0 1.0)
(linear-regression 2.0 3.1 2.3 3.3 24.3 45.2) => (1.89 -0.87 0.9999)

Statistical functions

The function chi-square-p-value computes the p-value of a Chi-square distribution with the given number of degrees of freedom and a given cut value:

(chi-square-p-value <d> <x>)
  ;; => <p-value>, with <d> integer <x> a number

Thus, the value x passes the Chi-square test if the value returned by (chi-square-p-value d x) is less than or equal to x. For instance, the expression:

(<= (chi-square-p-value 2 (field "000000")) 0.05)

will compute a boolean that tells you whether the field “000000” passes a Chi-square test for two degrees of freedom with significance level 0.05.

Fuzzy logic

Flatline provides some functions to work with fuzzy logic features, fields or values. Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.

Check this link to know more about fuzy logic: - Wikipedia: Fuzzy logic

Triangular norms (t-norms) and conorms (t-cnorms are operations which generalize the logical conjunction and logical disjunction to fuzzy logic.

You can find more information about t-norms and t-conorms in the following links:

All the norms are computed from two numeric values that can be specified either by referencing a numeric input field (giving its name or id) or by any valid flatline numeric expression. For instance:

(tnorm-min "field21" "field4")
(tnorm-min "000002" "000001")
(tnorm-min 0.70 0.24)
(tnorm-min "000002" 0.1)
(tnorm-min 0.2 (field 1))

Numeric values used by these norms must be between 0 and 1. As they are fuzzy logic values it doesn’t make sense having values outside this range. If you pass a field to the norms with more than 80% of its values outside this range, an exception will be raised. When some sparse out-of-range values are found during calculations, the generated field will contain a missing value for this specific row.

If your input fields are out of range, consider normalizing or truncating your fields, before passing them to the fuzzy logic norms:

(max 0 (min 1 (field "000001"))) ;; Truncating field
(normalize "000001") ;; Normalizing field

You could then write expressions like these:

(tnorm-min (normalize "000001")  (normalize "000002"))
(tnorm-min (max 0 (min 1 (field "000001")))
           (max 0 (min 1 (field "000002"))))

Basic T-norms

As members of the family of fuzzy logics, t-norm fuzzy logics primarily aim at generalizing classical two-valued logic by admitting intermediary truth values between 1 (truth) and 0 (falsity) representing degrees of truth of propositions. In fuzzy logic, continuous t-norms are often found playing the role of conjunctive connectives. We provide the following basic t-norms. All of them need 2 parameters.

(tnorm-min <f1> <f2>) ;; Minimum t-norm. Also called the Gödel t-norm.
(tnorm-product <f1> <f2>) ;; Product t-norm. The ordinary product of real numbers.
(tnorm-lukasiewicz <f1> <f2>) ;; Łukasiewicz t-norm.
(tnorm-drastic <f1> <f2>) ;; Drastic t-norm
(tnorm-nilpotent-min <f1> <f2>) ;; Nilpotent minimum t-norm

Basic T-conorms

T-conorms (also called S-norms) are dual to t-norms under the order-reversing operation which assigns 1 – x to x on [0, 1]. T-conorms are used to represent logical disjunction in fuzzy logic and union in fuzzy set theory. We provide the following basic t-conorms. All of them need 2 parameters.

(tconorm-max <f1> <f2>) ;; Maximum t-norm. Dual to the minimum t-norm, is the smallest t-conorm.
(tconorm-probabilistic <f1> <f2>) ;; Probabilistic t-norm. It's dual to the product t-norm.
(tconorm-bounded <f1> <f2>) ;; Bounded t-norm. It'ss dual to the Łukasiewicz t-norm.
(tconorm-drastic <f1> <f2>) ;; Drastic t-conorm. It's dual to the drastic t-norm.
(tconorm-nilpotent-max <f1> <f2>) ;; Nilpotent maximum t-conorm. It's dual to the nilpotent minumum.
(tconorm-einstein-sum <f1> <f2>) ;; Einstein t-conorm. It's a dual to one of the Hamacher t-norms.

Parametric T-norms

We provide the following parametrized t-norms. All of them need 3 parameters, the two fields were t-norms will be applied, and the parameter p that provides a way to vary the gain on the function so that it can be very restrictive or very permissive. This parameter must be a real number.

(tnorm-schweizer-sklar <f1> <f2>) ;;  Parameter p in the range [-∞, ∞]
(tnorm-hamacher <f1> <f2>) ;; Parameter p in the range [0, ∞]
(tnorm-frank <f1> <f2>) ;; Parameter p in the range [0, ∞]
(tnorm-yager <f1> <f2>) ;; Parameter p in the range [0, ∞]
(tnorm-aczel-alsina <f1> <f2>) ;; Parameter p in the range [0, ∞]
(tnorm-dombi <f1> <f2>) ;; Parameter p in the range [0, ∞]
(tnorm-sugeno-weber <f1> <f2>) ;; Parameter p in the range [-1, ∞]

Dates and times

Epoch fields

A numerical field can be interpreted as an epoch, that is, the number of milliseconds since 1970. Flatline provides the following functions to expand an epoch to its date-time components:

(epoch-year <n>)
(epoch-month <n>)
(epoch-week <n>)
(epoch-day <n>)
(epoch-weekday <n>)
(epoch-hour <n>)
(epoch-minute <n>)
(epoch-second <n>)
(epoch-millisecond <n>)

(epoch-fields <n>)
  => (list (epoch-year <n>) (epoch-month <n>) (epoch-day <n>)
           (epoch-weekday <n>) (epoch-hour <n>) (epoch-minute <n>)
           (epoch-second <n>) (epoch-millisecond <n>))
<n> ::= numerical value

For instance:

(epoch-fields (f "milliseconds"))
(epoch-year (* 1000 (f "seconds")))

The epoch functions also accept negative integers, which represent dates prior to 1970.

The day of the week (given by epoch-weekday) is a number from 1 (Monday) to 7 (Sunday).

The week within the year, given by epoch-week, is a number between 1 and 52. Note that it is not included in the oputput of epoch-fields.

Datetime arithmetic

Since epochs are just integers, date arithmetic can be performed at that level by simply using Flatline’s arithmetic operations.

As a convenience, if a field of type datetime is used in an arithmetic operation, it’s automatically converted to an epoch (i.e., an integer value) for you. For instance, the two following expressions for computing the number of seconds since 1970 are equivalent:

(/ (f "a-datetime-string") 1000)
(/ (epoch (f "a-datetime-string")) 1000)

Datetime parsing

Conversely, string values representing dates can be transformed to a numerical epoch by using the epoch coercion function:

(epoch <str>)
(epoch <str> <format>)

If you don’t specify a datetime format for parsing, we try a long list of available formats in sequence, which is less efficient than if you provide the format explicitly. Datetime format specifiers follow the well known *JodaTime* specification for datetime patterns.

For instance:

(epoch-fields (epoch "1969-14-07T06:00:12")) => [1969 14 07 06 00 12 0]
(epoch-hour (epoch "11~22~30" "hh~mm~ss")) => 11

The datetime formate pattern letters are:

Symbol  Meaning                      Presentation  Examples
------  -------                      ------------  -------
 G       era                          text          AD
 C       century of era (>=0)         number        20
 Y       year of era (>=0)            year          1996

 x       weekyear                     year          1996
 w       week of weekyear             number        27
 e       day of week                  number        2
 E       day of week                  text          Tuesday; Tue

 y       year                         year          1996
 D       day of year                  number        189
 M       month of year                month         July; Jul; 07
 d       day of month                 number        10

 a       halfday of day               text          PM
 K       hour of halfday (0~11)       number        0
 h       clockhour of halfday (1~12)  number        12

 H       hour of day (0~23)           number        0
 k       clockhour of day (1~24)      number        24
 m       minute of hour               number        30
 s       second of minute             number        55
 S       fraction of second           number        978

 z       time zone                    text          Pacific Standard Time; PST
 Z       time zone offset/id          zone          -0800; -08:00; America/Los_Angeles

 '       escape for text              delimiter
 ''      single quote                 literal       '

The count of pattern letters determine the format, according to the following rules:

  • Text: If the number of pattern letters is 4 or more, the full form is used; otherwise a short or abbreviated form is used if available. Thus, “EEEE” might output “Monday” whereas “E” might output “Mon” (the short form of Monday).
  • Number: The minimum number of digits. Shorter numbers are zero-padded to this amount. Thus, “HH” might output “09” whereas “H” might output “9” (for the hour-of-day of 9 in the morning).
  • Year: Numeric presentation for year and weekyear fields are handled specially. For example, if the count of y is 2, the year will be displayed as the zero-based year of the century, which is two digits.
  • Month: 3 or over, use text, otherwise use number. Thus, “MM” might output “03” whereas “MMM” might output “Mar” (the short form of March) and “MMMM” might output “March”.
  • Zone: Z outputs offset without a colon, ZZ outputs the offset with a colon, ZZZ or more outputs the zone id.
  • Zone names: Time zone names (z) cannot be parsed.

Any characters in the pattern that are not in the ranges of ['a'..'z'] and ['A'..'Z'] will be treated as quoted text. For instance, characters like :, ., `,#and?` will appear in the resulting time text even they are not embraced within single quotes.

Local bindings

You can define lexically scoped variables using the let special form:

(let <bindings> <body>)
<bindings> := (<varname0> <val0> ...  <varnamen> <valn>)
<body> := <expression with varname0 ... varnamen>

The binding values are evaluated sequentially and can then be referenced in the body of the let expression by their names:

(let (x (+ (window "a" -10 10))
      a (/ (* x 3) 4.34)
      y (if (< a 10) "Good" "Bad"))
  (list x (str (f 10) "-" y) a y))

As shown in the example above, value expressions can use any identifier previously defined in the same list:

(let (x 43
      x (+ x 1)
      y (+ x 1))
  (list x y))  =>  (44 45)

Finally, let expressions can nested and they can appear wherever a Flatline expression is valid:

(list (let (z (f 0)) (* 2 (* z z) (log z)))
      (let (pi 3.141592653589793 r (f "radius")) (* 4 pi r r)))

Control structures


The if operator can be applied to a boolean conditional to yield one of a couple of values, with the “else” clause being optional:

(if <cond> <then> [<else>])
<cond> := boolean value

You can use arbitrary expressions for <cond>, <then> and <else>, with the only restriction that <cond> must be a boolean value. If not provided, <else> defaults to a “nil” value that denotes a missing token.

(if (< (field "age") 18) "non-adult" "adult")

(if (= "oh" (field "000000")) "OH")

(if (> (field "000001") (mean "000001"))
    "above average"
    (if (< (field "000001") (mean "000001"))
        "below average"

Flatline won’t let you give <then> and <else> different types.

Another caveat is that in Flatline boolean expressions can have 3 values, namely true, false and missing. If the <cond> in an if expression is a missing value, the whole expression will evaluate to a missing value. That means that, for instance:

(if (< (f 0) 3) 0 1)

will evaluate to null (and not to 1) when the field 0 has a missing value. That’s because the <else> branch is not even evaluated. Therefore:

(if (< (f 0) 3) 0 (if (missing? 0) 2 1))

will again evaluate to null when the field 0 is missing: it will not evaluate to 2, because the <else> branch is never reached. If you need to test for a missing value, the test must always come first:

(if (missing? 0) 2 (if (< (f 0) 3) 0 1))

We also provide the cond operator, which allows a more compact representation of a chain of nested if clauses:

(cond <cond0> <then0>
      <cond1> <then1>
      ... ...
      <default>) := (if <cond0> <then0> (if <cond1> <then1> ... <default>))

Conditions are checked in order, and the first one that matches provides the value of the cond expression. If none of the conditions is met, the expression evaluates to <default> or nil (missing token) if it’s not provided.

For instance:

(cond (> (f "000001") (mean "000001")) "above average"
      (= (f "000001") (mean "000001")) "below average"

(cond (or (= "a" (f 0)) (= "a+" (f 0))) 1
      (or (= "b" (f 0)) (= "b+" (f 0))) 0
      (or (= "c" (f 0)) (= "c+" (f 0))) -1)

(cond (< (f "age") 2) "baby"
      (and (<= 2 (f "age") 10) (= "F" (f "sex"))) "girl"
      (and (<= 2 (f "age") 10) (= "M" (f "sex"))) "boy"
      (< 10 (f "age") 20) "teenager"

The same caveat with if regarding missing values applies to cond: if any of the conditions evaluates to a missing value, the whole expression evaluates to a missing value. Therefore, checks using missing? must always come first:

(cond (missing? "age") 0
      (< (f "age") 10) 1

;;; INCORRECT (the missing? test is never reached)
(cond (< (f "age")) 1
      (missing? "age") 0


It’s possible to create a list of values using the list operator:

(list <sexp-0> ...)

with the values any valid Flatline expression, e.g.:

(list (field "age")
      (field "weight" -1)
      (population "age"))

(list 1.23
      (if (< (field "age") 10) "child" "adult")
      (field 3))

and we also provide the classical cons to create a list from its head and tail, which can in turn be accessed via head and tail:

(cons <head> <tail>)
<tail> := list
(head <list>)
(tail <list>)

so that:

(head (cons x lst)) ==> x
(tail (cons x lst)) ==> lst

It is also possible to access the nth element of a list using its 0-based position index:

(nth <list> <pos>)
<pos> := positive integer

When the given position is out of bounds, the expression evaluates to nil (a missing token).

There are operators to take and drop the first n elements of a list:

(drop <list> <n>)
(take <list> <n>)
<n> := positive integer

For example:

(take 3 (list 1 2 "3" 4)) ;; => (1 2 "3")
(drop 3 (list 1 2 "3" 4)) ;; => (4)

Taking more elements than a list contains returns the full list, and droping more elements that the list length evaluates to a missing value.

It is also possible to take a slice in a list in a semi-open range [from, to) with the function slice, that can be defined in terms of take and drop:

(slice <lst> <from> <to>)
:= (take (- <to> <from>) (drop <from> <lst>))

List operators

Given a list value, you can count its elements, obtain their median, mode and, when its values are numeric, compute the maximum minimum and average:

(count <list>)         ;; (count (list (f 1) (f 2))) => 2
(mode <list>)          ;; (mode (list a b b c b a c c c)) => "c"
(max <list>)           ;; (max (list -1 2 -2 0.38)) => 2
(min <list>)           ;; (min (list -1.3 2 1)) => -1.3
(avg <list>)           ;; (avg (list -1 -2 1 2 0.8 -0.8)) => 0
(list-median <list>)   ;; (list-median (list -1 -2 1 2 0.8 -0.8) => 1

And, as we have mentioned, the arithmetic operators +, -, * and / are, like max and min, overloaded to distribute over the elements of a numeric list:

(+ (list x y ...)) := (+ x y ...)
(- (list x y ...)) := (- x y ...)
(* (list x y ...)) := (* x y ...)
(/ (list x y ...)) := (/ x y ...)

One can reverse and sort (in ascending lexicographical order) any list:

(reverse <list>)
(sort <list>)


(reverse (list "a" 0 2 "b")) => ("b" 2 0 "a")
(sort (list 1 3 -1 2))  => (-1 2 1 3)
(sort (list "a" "b" "aa")) => ("a" "aa" "b")

Finally, you can check whether a value appears in a list using the in operator:

(in <x> (<x0> <x1> ... <xn>))

which evaluates to true if any of the <xi> equals <x>, e.g.:

(in 3 (1 2 3 2)) => true
(in "abc" (1 2 3)) => false
(in (f "size") ("X" "XXL"))

Maps and filters

It’s also possible to apply an expression template (a Flatline expression with one free variable, marked as _) to each element of a list, yielding a list of results, using the map primitive:

(map <fn> (list <a0> <a1> ... <an>))
  := (list (call <fn> <a0>) (call <fn> <a1>) ... (call <fn> <an>))
<fn> := expression template

An expression template is any valid Flatline expression that uses _ as a placeholder:

(< _ 3)
(+ (f "000001" _) 3)
(< -18 _ (f 3))

and when you call a template with an argument, a new expression is generated by the simple device of substituting the argument for _ in the template. For instance:

(map (* 2 _) (list (f 0 -1) (f 0) (f 0 1)))

expands to

((* 2 (f 0 1)) (* 2 (f 0)) (* 2 (f 0 1)))

A second common list transformation is filter, which allows you to apply a predicate to each element of a list and retain only those values that satisfy it:

(filter <fn> (list <a0> ... <an>)) := [ai | (call <fn> <ai>) is true]

For instance,

(+ (filter (< _ 3) (fields "a" "b" "c")))

will add those values of the fields with names a, b and c whose values are less than three.

Currently, maps and filter are implemented as macro expansions (for simplicity, and also for performance) and their second argument must therefore be a list, fields or window (see below) form. If needed, future versions of Flatline will provide slow real functions.

Field lists and windows

(Almost) all fields

We provide several primitives for creating lists of field values. The first one is all, which specifies that all input fields should be copied, without any modification. For cases where you want to copy all but a few fields, there’s all-but, which takes as argument designators of those fields not to include in the list:

(all) := (list (f 0) ... (f <field-count>))
(all-but  <fd0> ... <fdn>)
  := (list (f i0) ... (f in)) | i0...in not in fd0...fdn

and, conversely, fields, which lets you select a list of fields from the current input row:

(fields <field-designator> ... <field-designator-n>) :=
   (list (f <field-designator>) .. <field-designator-n>)

In both all-but and fields, fields can be designated, as usual, with either their identifier, name or column_number:

(all-but "id" "000023")
(fields "000003" 3 "a field" "another" "0002a3b-3")

Sometimes one needs to fill-in missing values in one pass: an easy way for that is provided by the function all-with-defaults, that copies all input rows, but replacing missing values with given ones:

(all-with-defaults <field-designator-0> <field-value-0>
                   <field-designator-1> <field-value-1>
                   <field-designator-n> <field-value-n>)

The list of designator/value pairs does not need to be exhaustive or ordered, and again the designator can be a field id, name, or column number:

(all-with-defaults "species" "Iris-versicolor"
                   "petal-width" 2.8
                   "000002" 0)

It is also possible to provide a default for all missing numeric fields in a row at once, using all-with-numeric-default:

(all-with-numeric-default <value>)
<value> := "mean" | "median" | "minimum" | "maximum" | <number>

As shown, we can specify that missing numeric fields be filled with their mean, median, minimum or maximum values (as read from their respective field descriptors) or with any concrete numeric value. For example:

(all-with-numeric-default "median")
(all-with-numeric-default 0)

A word of caution: for the case of concrete values, the given number is cast to the datatype of the target field, i.e., it’ll be mapped to value range of the given field (for instance, if you give a default value of 128 and a field of type int8 is missing, it’ll receive the value -1).


In addition to horizontally selecting different fields in the same row, we can keep the field fixed and select vertical windows of its value, via the window and related operators. They’re just syntactic sugar over the shifted field accessors we’ve already seen:

(window <field-designator> <start> <end> [<padding-value>])

 := (list (f <field-designator> <start> <padding-value>)
          (f <field-designator> <start + 1> <padding-value>)
          (f <field-designator> <end> <padding-value>))

So, for instance, the window:

(window "000001" -1 2)

denotes the list of values:

(list (f "000001" -1) (f "000001" 0) (f "000001" 1) (f "000001" 2))

As shown, both start and end must be integers, and the values corresponding to their shifts are included in the resulting list.

In the same way that the shifted field accessors accept a default-value to handle with the out-of-range rows, you can use the padding-value to indicate the value that will be used in those cases for windows. padding-value can be also an expression:

(window "Temp" -2 0 (+ 273 (* 40 (rand))))

It’s possible to apply arithmetic operators, filter and map to any window. For instance, you could compute the average of the last 3 values of a field as:

(/ (+ (window "Temp" -2 0) 3))

Or convert all the values to Fahrenheit degrees and select those below 99.9 with:

(filter (< _ 99.9) (map (+ 32 (* 1.8 _)) (window "Temp" -2 0)))

In addition to the plain window generator, we provide some other convenience window primitives computing, respectively, the average value, median and of the values in a window, their sum and the sequence of their differences:

(window-median <field-designator> <start> <end> [<padding-value>])
  := (list-median (window <field-designator> <start> <end> <padding-value>))

(window-mean <field-designator> <start> <end> [<padding-value>])
  := (avg (window <field-designator> <start> <end> <padding-value>))

(window-mode <field-designator> <start> <end> [<padding-value>])
  := (mode (window <field-designator> <start> <end> <padding-value>))

(window-sum <field-designator> <start> <end> [<padding-value>])
  := (+ (window <field-designator> <start> <end> <padding-value>))

(diff-window <fdes> <start> <end> [<padding-value>])
  := (list (- (f <fdes> <start> <padding-value>)
              (f <fdes> (- <start> 1) <padding-value>))
           (- (f <fdes> (- <start> 1) <padding-value>)
              (f <fdes> (- <start> 2) <padding-value>))
           (- (f <fdes> (- <end> 1) <padding-value>)
              (f <fdes> <end> <padding-value>)))

These window generator forms can also be combined with filter, map and all the other window operators.

Conditional window limits

There are scenarios in which you might be interested in forming a window whose width depends on some condition. For instance, say you want to compute the average of a temperature for the last four minutes in a dataset with aperiodic entries: cond-window to the rescue:

(let (now (f "epoch"))
  (avg (cond-window "temperature" (< (- (f "epoch") now) 240))))

As you see in this example, cond-window takes a field designator and a predicate; the latter is applied sequentially to the current and future rows (up to a standard maximum value), and a list of the values of the requested fields for the rows satisfying the predicate is returned.

(cond-window <fdes> <sexp>)
  := (list (f <fdesc> 0) ... (f <fdesc> n)) | for [0..n] (<sexp>)

Note that, as mentioned, <sexp> is a Flatline expression computed with the corresponding (future) full row as input.

Acummulating values in cells

It is possible to store and retrieve values global to the full generation using named cells. To retrieve a value previously stored in a cell (most probably, when computing the field values of a previous row), you just call cell, providing the cell name as a string-valued expression (it doesn’t have to be constant) and a default value in case the cell hasn’t been set yet (this also tells flatline what the type of the cell’s values are going to be). To store a value in a cell, you simply call set-cell, with a name and a value. set-cell returns the value just set.

For instance, this flatline expression will generate a new field containing the running sum of values in the input field “price”:

(let (s (cell "sum" 0))
  (set-cell "sum" (+ (f "price") s)))

Of course, set-cell doesn’t need to be the final call in your expression. Here’s an example of a more complicated, if a bit contrieved, usage case for a string cell:

(let (s (cell "ff" "flip")
      ns (set-cell "ff" (if (= s "flip") "flop" "flip")))
  (if (= s "flip")
    (- (f "A") (f "B"))
    (- (f "B") (f "A"))))

As mentioned, cell names can be computed values:

(let (cat (f "cat-field")
      cat-count (cell cat 0)
      new-count (set-cell cat (+ 1 cat-count)))
   (if (> cat-count 1000) (something) (something-else)))