data:image/png;base64,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data:image/png;base64,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 a text string as input, and reports a new text string containing all but the first character of the input.pt:_ sem o primeiro caractere
resulti2Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)pt:_ sem a primeira palavra
0Takes a text string as input, and reports a new text string containing all but the last letter of the input.pt:_ sem o último caractere
resulti1Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)pt:_ sem a última palavra
011111Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing only the first word, with no spaces before or after it.pt:a primeira palavra de _
Takes a text string as input, and reports the last character in the string.pt:o último caractere de _
0Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing only the last word of the input, with no spaces before or after it.pt:a última palavra de _
index011Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.pt:a palavra _ está vazia
0Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.pt:a frase _ está vazia
index10Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs.
Consider using SENTENCE (Lists palette) instead.de:füge Wörter zusammen _
ca:uneix les paraules _
es:unir las palabras _
fr:fusionne les mots _
pt:uma frase com as palavras _
1121Takes a sentence in list form and reports the sentence as a text string.de:Liste $arrowRight Satz _
ca:llista $arrowRight frase _
es:lista $arrowRight frase _
fr:liste $arrowRight phrase _
pt:uma frase com as palavras em _
Takes a sentence in text string form and reports the sentence as a list of its words.de:Satz $arrowRight Liste _
ca:frase $arrowRight llista _
es:frase $arrowRight lista _
fr:phrase $arrowRight liste _
pt:uma lista com as palavras da frase _
report a list in which each item is one letter from the input wordde:Wort $arrowRight Liste _
ca:paraula $arrowRight llista _
es:palabra $arrowRight lista _
fr:mot $arrowRight liste _
pt:uma lista com os caracteres da palavra _
join all the items of the input list
into a single word, and report itde:Liste $arrowRight Wort _
ca:llista $arrowRight paraula _
es:lista $arrowRight palabra _
fr:liste $arrowRight mot _
pt:uma palavra com os caracteres em _
SENTENCE is the main constructor for sentences, represented
as lists of words. It takes zero or more inputs, each of which can
be either a list or a text string. If a list, the input is assumed to be
a list of words. If a text string, it is converted to a list of words using
SENTENCE→LIST. Then all the lists of words are appended to form a new list of words.
If the inputs are lists of lists rather than lists of words, SENTENCE,
like APPEND, does only one level of flattening, reporting a list of
all the items of all the input lists.The first input is a shape list as in
SHAPE OF. The output is an array with those dimensions containing the atomic items of the second input,
repeating values if more are needed.Reports a flat list of the maximum size of the input array along
each dimension: number of rows, number of columns, etc.
"Maximum" because it works even if the array isn't uniformly shaped.Reports the greater of its two inputs. Works on strings too.Reports a flat list of all the atomic elements
of sublists of the input list.Given two arrays A and B, reports an array whose dimensions are
APPEND(SHAPE OF (A), SHAPE OF (B))
in which each atomic item of the result is computed by applying the dyadic function input to an item of A and an item of B.1Reports its input.
This is useful to fit a value into a different-type input slot, e.g., number into list slot.Reports the number of dimensions of the input.111Takes a multidimensional array, and
reports an array whose dimensions
are reversed (as reported by
SHAPE OF). In the case of a
two-dimensional array, does the usual transposition of rows and columns.If there are no atomic data, we don't have any shape information, so we can't really transpose it. But an empty list is 0 rows of some number of columns, so the transpose should be 0 columns in some number of rows. We arbitrarily make it 0 columns of one row. This at least has one dimension of the shape correct.1The transpose of a vector is just the vector itself.1This is the opposite no-atomic-data situation from above. We know that there are (length array) rows of 0 columns, so the transpose is 0 rows, i.e., an empty list. But we lose the number of original rows, transpose columns.atomnodeThis is one row, n columns. So the transpose is n rows, one column.Reverses the order of the (toplevel) items of the input.
If the input is a matrix, this means it reverses the order of the rows, which is a reflection through a horizontal axis, as the ⦵ symbol suggests.1Recursively deep-inserts
the items of the first input
into the items of the second
input. If the first input is not
a list, then it is inserted with
IN FRONT OF in the second
unless the second also isn't
a list, in which case they are
combined with LIST.1Like MAP, but can take any number of lists
as inputs. The lists must all be the same size.
The function input must take a number of inputs
equal to the number of lists. MULTIMAP calls
the function with all the first items, then all the
second items, and so on.1Takes a (possibly deep) list as input,
and reports a human-readable text form
of the list (namely, Lisp notation).()Computes a generalized matrix multiplication.
In normal matrix multiplication, each cell of the result
is computed by multiplying individual numbers within
a row of the left input and a column of the right input,
and then adding those products. In APL terms this is
+.× ("plus dot times")
Any dyadic functions can replace addition and multiplication in this algorithm; a common case is
∨.∧ ("or dot and")111111Reports the smaller of its two inputs.Computes logarithms in any base.
The base is the left input. It's usual in APL that if there's a main data input and some sort of control input, the latter comes on the left. This is because APL syntax, unless you use parentheses, groups computations from right to left.
APL has a monadic version of this function that computes natural logs (log to the base e).Computes the number of combinations of right-input things taken left-input at a time, otherwise known as the elements of Pascal's triangle. This block shares the ! symbol with the monadic factorial function, because the formula for computing this function uses factorials.The factorial of a positive integer n is the product of the integers from 1 to n.
In real APL, the domain of this function is extended beyond integers to compute the gamma function.This function has two names because there are two ways
to understand it.
Lisp way: A matrix is a list of rows. This block combines the numbers in each row, producing one value for the entire row.
APL way: A matrix is made of vectors. This block takes each column as a vector, and does vector arithmetic on the columns, producing one column as the result.pt:lança o erro _
Acts just like the function selected from
the pulldown menu, but hyperized, so
comparing two equal-sized lists reports
a list of the same length as the inputs,
with the results of item-by-item comparisons.﹦﹦
≠
identical to
and
or
is _ a _?Report a list with left-input random integers in the range 1 to right-input.
No number appears more than once
in the result.choicechoice11Reports 1 if the input is positive,
0 if the input is zero,
or -1 if the input is negative.0falsereports 1 divided
by its input.This block reports a random integer between 1 and its input. To roll more than one die, use (for three dice)
roll (reshape as 3 items of 6)
APL: ?3⍴6
Don't use reshape as 3 items of roll 6, because that would roll one die and report 3 copies of the same random roll. Reports the greatest common divisor of its inputs.
If the inputs are values in {0,1} then this is equivalent to the logical OR of the values, with 0=False, 1=True. Hence the APL symbol ∨.
Also accepts Snap! Booleans as inputs.abTakes a dyadic scalar function as input, and
hyperizes it, so that it can take lists as inputs.abReports True if the input is an APL scalar,
i.e., either an atomic (non-list) value, or
an array (list of lists) of any depth with only
one atomic item, e.g., (list (list (list (3)))).1The input must be a value for which SCALAR? reports true, i.e., either an atom or a list of any depth but only one scalar item of item of... etc. This function returns the underlying scalar (number, etc.).1Reports the least common multiple of its inputs.
If the inputs are values in {0,1} then this is equivalent to the logical AND of the values, with 0=False, 1=True. Hence the APL symbol ∧.
Also accepts Snap! Booleans as inputs.abReports the not-and of its inputs, in the form
0 for false, 1 for true.Reports 1 if the input is False or 0;
otherwise reports 0.Reports the not-and of its inputs, in the form
0 for false, 1 for true.Reports True if the left input is
less than or equal to the right input.
Reports a Snap! Boolean, not an integer 0 or 1.Reports True if the left input is
greater than than or equal to
the right input.
Reports a Snap! Boolean, not an integer 0 or 1.Reports 0 if its inputs are equal;
reports 1 if its inputs are not equal.
The inputs can have any non-list values.
(Lists are hyperized.) If the inputs are
Booleans (True/False or 1/0), this is
also the exclusive-or function.reports True iff the input is 0 or False.Reports a Snap! Boolean False if the input
is False or 0; reports True otherwise.Make scalar called with non-singleton input If the input is a positive integer, reports a list of the numbers
from 1 to that input. (If the input is 0, reports an empty list.)
If the input is a list of positive integers, reports an array with
the shape specified by the input (as in ⍴ reshape) in which
each item is a list of the indices of that item in the array
(so technically the shape has one more dimension
than the input, whose size is the size of the input).If the rank of the left input is one more than the rank of the right input,
reports the index of the right input in the left input, or if not found,
reports one more than the length of the left input.
If the rank of the left input is equal to the rank of the right input,
reports a vector of the indices of the items of the right input
in the left input (mapping this function over the right input).
If the rank of the left input is more than that of the right input by 2 or more,
reports a vector, the location of the right input in the left in each dimension.
It is an error if the rank of the left input is less than that of the right input.1resultThis isn't an APL function, although it's related to the outer product.
It takes any number of lists, and reports a list of all possible tuples with one item from each of the lists. The length of the result is the product of the lengths of the inputs.Reports an array of Booleans the same shape
as the left input, indicating which of the atoms
in the left input appear anywhere in the right
input.
(The structure of the right input doesn't matter.)Like append, but:
A scalar input is treated as an array the same shape as the other input except that the last item of the shape is 1.
If the two inputs are of different ranks,
the function is mapped over the larger ranked input.
Catenate adds new columns, by appending to each row.If the input is a nesting of length=1 lists, which
APL treats as a scalar (the innermost item)
for many purposes, report that innermost scalar.
Otherwise, report the input as is.
Exposing this block for users is important because Snap! /does not/ treat such a nesting
as a scalar, so you might need to use this in
translating an APL program to Snap!.
(But the functions in the APL library already use
this block as needed.)Reports a vector of indices of the items of the input, in order of the values of the items, so that
item (grade up (foo)) of (foo)
reports the items in sorted order, smallest to largest. For a matrix, sorts the rows based on their first items, or if those are equal, based on their second items, etc.Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.ca:ordena _ segons criteri _
copy of datasplitmerge11#1#2Compares two vectors for sorting.
Compare first items; if those are equal compare second items; etc.1111Reports a vector of indices of the items of the input, in order of the values of the items, so that
item (grade down (foo)) of (foo)
reports the items in sorted order, largest to smallest. For a matrix, sorts the rows based on their first items, or if those are equal, based on their second items, etc.The left input must be a vector of Booleans
(either Snap! form or APL form); the right input must be an array whose first dimension is equal to the length of the left input. The block reports an array of the same rank as the right input, containing only those items (rows, for a matrix) for which the corresponding Boolean is True (or 1).The left input must be a vector of Booleans
(either Snap! form or APL form); the right input must be an array whose last dimension is equal to the length of the left input. The block reports an array of the same rank as the right input, containing only those items (columns, for a matrix) for which the corresponding Boolean is True (or 1).Turns a row-wise (in Lisp terminology) function
into a column-wise one.Reverses the order of the columns of the input, which is a reflection through a vertical axis, as the ⏀ symbol suggests.This function has two names because there are two ways
to understand it.
Lisp way: A matrix is a list of rows. This block turns it into a list of columns, and combines the numbers in each column, producing one value for the entire column.
APL way: A matrix is made of vectors. This block takes each row as a vector, and does vector arithmetic on the rows, producing one row as the result.Like append, but:
A scalar input is treated as a vector
of length 1.
If the two inputs are of different ranks,
the function is mapped over the larger ranked input.
Catenate vertically adds new rows, by appending to each column.A hyperblock version of JOIN. The regular JOIN isn't hyperized because it can accept a list as input, representing it as text.A positive left input selects the first n items of the right input.
A negative left input selects the last abs(n) items
of the right input.
If the right input is a matrix, a numeric left input selects rows;
the left input may also be a two-item vector, in which case
the first number is applied to the rows
and the second number is applied to the columns.
Similarly for higher-dimension arrays. 10valueindexA positive left input selects all but the first n items of the right input.
A negative left input selects all but the last abs(n) items
of the right input.
If the right input is a matrix, a numeric left input selects rows;
the left input may also be a two-item vector, in which case
the first number is applied to the rows
and the second number is applied to the columns.
Similarly for higher-dimension arrays. 0valueindex0partialpartialApplies the function to each "atom" of the data. What's an atom?
Something for which the predicate input reports true.11acres,adult,advice,arrangement,attempt,August,Autumn,border,breeze,brick,calm,canal,Casey,cast,chose,claws,coach,constantly,contrast,cookies,customs,damage,Danny,deeply,depth,discussion,doll,donkey,Egypt,Ellen,essential,exchange,exist,explanation,facing,film,finest,fireplace,floating,folks,fort,garage,grabbed,grandmother,habit,happily,Harry,heading,hunter,Illinois,image,independent,instant,January,kids,label,Lee,lungs,manufacturing,Martin,mathematics,melted,memory,mill,mission,monkey,Mount,mysterious,neighborhood,Norway,nuts,occasionally,official,ourselves,palace,Pennsylvania,Philadelphia,plates,poetry,policeman,positive,possibly,practical,pride,promised,recall,relationship,remarkable,require,rhyme,rocky,rubbed,rush,sale,satellites,satisfied,scared,selection,shake,shaking,shallow,shout,silly,simplest,slight,slip,slope,soap,solar,species,spin,stiff,swung,tales,thumb,tobacco,toy,trap,treated,tune,University,vapor,vessels,wealth,wolf,zoocookiesy6c,o,o,k,i,e,s_,_,_,_,_,_,_