WARNING do not use this project as a reliable source this is only for fun and learning. please use trusted websites and apps for weather infomation. thank you. Also music is annoying and loud. Instuctions: For this project to work you need to click edit, then go to settings, then click "JavaScript exstentions", and run the project from there. This project is a weather tracker. This is probably my favorite project to work on. It is live weather data that looks like its taken from multiple defferant places around the world. MORE INSTUCTIONS: arrow keys to move around the map. longitude/latitude is based on where your mouse pointer is on the map. press space bar to open editor select a color, press - to clear, and press C to go out of editing mode click on the lists and expand them to see the weather statements and other cool stuff! scroll mouse wheel up and the map will zoom in, and scroll down to zoom out. press S to scan for weather a black dot will apear and start to out line where the weather is (It takes about one minute and thirty seconds) pressing S also allows you to see what kind of weather it is. and finnally MUSIC(if you can't tell I don't like the music I added, It sounds... weird) press M to play and press C to stop. BUGS FIXED: Latitude no longer says NaN. BUGS: screen animation is kinda crazy. Music is loud and really weird (I'll make it where you can choose defferant music). More updates to come: better music, better animation, and more! KI5IZO- yo yo old 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WARNING do not use this project as a reliable source this is only for fun and learning. please use trusted websites and apps for weather infomation. thank you. Also music is annoying and loud. Instuctions: For this project to work you need to click edit, then go to settings, then click "JavaScript exstentions", and run the project from there. This project is a weather tracker. This is probably my favorite project to work on. It is live weather data that looks like its taken from multiple defferant places around the world. MORE INSTUCTIONS: arrow keys to move around the map. longitude/latitude is based on where your mouse pointer is on the map. press space bar to open editor select a color, press - to clear, and press C to go out of editing mode click on the lists and expand them to see the weather statements and other cool stuff! scroll mouse wheel up and the map will zoom in, and scroll down to zoom out. press S to scan for weather a black dot will apear and start to out line where the weather is (It takes about one minute and thirty seconds) pressing S also allows you to see what kind of weather it is. and finnally MUSIC(if you can't tell I don't like the music I added, It sounds... weird) press M to play and press C to stop. BUGS FIXED: Latitude no longer says NaN. BUGS: screen animation is kinda crazy. Music is loud and really weird (I'll make it where you can choose defferant music). More updates to come: better music, better animation, and more! KI5IZO- yo yo old joo/jo

pt:os dados JSON _ em formato de lista

pt:o valor com chave _ em _

pt:a resposta a _ de _ enviando _ e cabeçalhos _ GETGET POST PUT DELETEhttps://snap.berkeley.edu

pt:um par (chave: _ , valor: _ ) Reports a three-item list containing the latitude and longitude of the user, and the precision of the measurements. Works only if the user allows snap.berkeley.edu access to location data. Some browsers also require an HTTPS connection to Snap!.

pt:a localização actual do dispositivo 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.

Reports the number of dimensions of the input.

11list1Takes 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.

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.

Like 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.

colscolumns

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")

listlast111last111Reports the smaller of its two inputs.

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.

choicesindexchoicesReports 1 if the input is positive, 0 if the input is zero, or -1 if the input is negative.

0falseabsreports 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 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 not-and of its inputs, in the form 0 for false, 1 for true.

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 False if its inputs are equal; reports True 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.

Boolean0Turns list of list of ... a single scalar (e.g., ((((x)))) ) into just the scalar. Error if called with anything else.

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 input is a list that includes 0, the result is an array whose shape is the part of the input list before the 0, in which every element is empty. If you'd like some other value in every element, MD-MAP a constant function over the result. For list inputs, the size of the result grows very quickly, more or less the factorial of the size of the input. Snap! will not attempt to compute a result bigger than a few million atomic items. ⍳(⍳ 9) will work (≈ 3 million atoms) but ⍳(⍳ 10) will give an error.

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. The result gets very big very quickly. Snap! will refuse to do this computation if the result would be more than a few million atomic items. (crossproduct (⍳(⍳9))) makes about 3 million atomic items; (crossproduct (⍳(⍳10))) gives an error message.

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.

listIf 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.

Compares 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.

listA 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.

1length0absvalueindexA 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.

0valueindex

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#2

1The identity function reports its input.

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

Takes a dyadic scalar function as input, and hyperizes it, so that it can take lists as inputs. Don't use on slow functions (this has compiled map calls). Meant for use on primitives.

abComputes 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.

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 _?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.

Reports 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.

Reports 1 if the input is False or 0; otherwise reports 0.

Reports its input. This is useful to fit a value into a different-type input slot, e.g., number into list slot.

Report a list with left-input random integers in the range 1 to right-input. No number appears more than once in the result. The left input must be less than or equal to the right input.

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.

11

de:$globe zeige aktuellen Standort pt:mostra a localização corrente do dispositivo no $globe ca:$globe centra a la meva ubicació

de:$globe Zoom pt:o zoom do $globe ca:$globe zoom

de:setze $globe Zoom auf _ pt:altera o zoom do $globe para _ ca:$globe fixa el zoom a _ 10

de:$globe Längengrad von x _ pt:a longitude no $globe da coordenada x _ ca:$globe longitud per a x _ 0

de:$globe Breitengrad von y _ pt:a latitude no $globe da coordenada y _ ca:$globe latitud per a y _ 0

de:setze $globe auf Länge: _ Breite: _ pt:altera a posição no $globe para (longitude: _ , latitude: _ ) ca:$globe centra a longitud: _ latitud: _ -122.25785237.872099

de:y von $globe Breitengrad _ pt:a coordenada y da latitude _ no $globe ca:$globe y per a latitud _

de:x von $globe Längengrad _ pt:a coordenada x da longidude _ no $globe ca:$globe x per a longitud _

de:ändere $globe um x: _ y: _ Pixel pt:desloca a posição do mapa de (x: _ , y: _ ) ca:$globe canvia la ubicació en x: _ y: _ píxels 1010

de:$globe Entfernung in km zu _ pt:a tua distância em km a _ no $globo ca:$globe distància en km fins a _ §_objectsMenumap_dist(lat1, lon1, lat2, lon2)y positionx position

de:$globe aktuelle Position pt:a localização corrente do dispositivo no $globe ca:$globe ubicació actual

de:$globe aktueller Breitengrad pt:a latitude corrente do dispositivo no $globe ca:$globe latitud actual

de:$globe aktueller Längengrad pt:a longitude corrente do dispositivo no $globe ca:$globe longitud actual

de:aktualisiere $globe pt:actualiza o $globe ca:$globe força refresc

de:$globe als Kostüm pt:a parte visível do $globe na forma de traje ca:$globe vestit del mapa actual

de:setze $globe Stil auf _ pt:altera o estilo do $globe para _ ca:$globe fixa l'estil de mapa a _ OpenStreetMapOpenStreetMap Wikimedia Watercolor Toner Terrain Topographic Satellite Streets Shading Mapbox (experimental)

ca:desa clau: _ amb valor: _ al navegador

ca:dades desades al navegador

ca:esborra clau: _ del navegador

ca:esborra dades del navegador Reports the value previously stored under the input key in the browser's local storage. Reports False if the key is not found.

ca:obté valor de clau: _ al navegador

de:sprich _ mit _ Aussprache _ und Höhe _ Geschwindigkeit _ pt:fala _ na língua _ _ com tom _ e velocidade _ ca:digues _ amb accent _ _ to _ i velocitat _ Hello, World!en-USالعربية=ar Български=bg বাংলা=bn Català=ca Català - Valencià=ca-VA Česky=cs Deutsch=de Dansk=dk Ελληνικά=el English-American=en-US English-British=en-GB Esperanto=eo Español=es Eesti=et Euskara=eu suomi=fi Français=fr Galego=gl Hrvatski=hr Magyar=hu Interlingua=ia Bahasa Indonesia=id Italiano=it 日本語=ja にほんご=ja-HIRA ಕನ್ನಡ=kn 한국어=ko Malayalam=ml Nederlands=nl Norsk=no Polski=pl Português=pt Português do Brasil=pt-BR Român=ro Русский=ru Slovenščina=si svenska=sv Tamil=ta Telagu=te Türkçe=tr Українська=ua 简体中文=zh-CN 繁體中文=zh-TW11

de:sprich _ mit _ Aussprache _ und Höhe _ Geschwindigkeit _ und warte pt:fala _ na língua _ _ com tom _ e velocidade _ , e espera ca:digues _ amb accent _ _ to _ velocitat _ i espera Hello, World!en-USالعربية=ar Български=bg বাংলা=bn Català=ca Català - Valencià=ca-VA Česky=cs Deutsch=de Dansk=dk Ελληνικά=el English-American=en-US English-British=en-GB Esperanto=eo Español=es Eesti=et Euskara=eu suomi=fi Français=fr Galego=gl Hrvatski=hr Magyar=hu Interlingua=ia Bahasa Indonesia=id Italiano=it 日本語=ja にほんご=ja-HIRA ಕನ್ನಡ=kn 한국어=ko Malayalam=ml Nederlands=nl Norsk=no Polski=pl Português=pt Português do Brasil=pt-BR Român=ro Русский=ru Slovenščina=si svenska=sv Tamil=ta Telagu=te Türkçe=tr Українська=ua 简体中文=zh-CN 繁體中文=zh-TW11Takes 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 ca:_ sense la primera lletra All but first of empty word.letterTakes 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 ca:_ sense la primera paraula All but first of empty sentence.Takes 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 ca:_ sense la darrera lletra reversereverseAll but first of empty word.letterTakes 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 ca:_ sense la darrera paraula reversereverseAll but last of empty sentence.Takes 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 _ ca:primera paraula de _ Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra 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 _ ca:darrera paraula de _ lastLast of empty sentence.Takes 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 ca:paraula buida? _ 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 ca:frase buida? _ Like 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 _ firstbffirst1list11bf Takes 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 word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _ letterjoin all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _ listlistSENTENCE 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.

ca:frase _ Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ ( )Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _ Catch errors. Runs the first script. If it succeeds, nothing else happens. But if it has an error (something that would otherwise result in a red halo around the block), then the second script is run, with the text of the error message that would have been shown in the variable ERROR.

pt:tenta executar _ e, em caso de erro _ , executa _ ca:prova de forma segura _ i si _ _ Throw an error. Makes a red halo appear around the script that runs it, with the input text shown in a speech balloon next to the script, just like any Snap! error. This is useful to put in the second script of SAFELY TRY after some other instructions to undo the partial work of the first script.

pt:lança o erro _ Catch errors in a reporter. Evaluates its first input. If that expression successfully reports a value, this block reports that value. If the expression causes a Snap! error, then the final input slot is evaluated with the text of what would have been the error message in variable ERROR. SAFELY TRY then reports the value of that final expression. Sometimes you'll want to throw an error in the final expression. You can put an ERROR block inside a CALL block to do that.

ca:prova de forma segura reportant _ i si _ reportant _ 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AgACBQECAA3STBAgQIEBAgN0AAQIECBAIBAQ4QDdJgAABAgQE2A0QIECAAIFAQIADdJMECBAgQECA3QABAgQIEAgEBDhAN0mAAAECBATYDRAgQIAAgUBAgAN0kwQIECBAQIDdAAECBAgQCAQEOEA3SYAAAQIEBNgNECBAgACBQECAA3STBAgQIEBAgN0AAQIECBAIBAQ4QDdJgAABAgQE2A0QIECAAIFAQIADdJMECBAgQECA3QABAgQIEAgEBDhAN0mAAAECBATYDRAgQIAAgUBAgAN0kwQIECBAQIDdAAECBAgQCAQEOEA3SYAAAQIEBNgNECBAgACBQECAA3STBAgQIEBAgN0AAQIECBAIBAQ4QDdJgAABAgQE2A0QIECAAIFA4A/5KH1pF6pmtQAAAABJRU5ErkJggg==redgreenyellowpurple-1924123https://api.weather.gov/alerts/activeSpecial Weather StatementerrorNOAA Connections are unstable (Try again in about a minute, if it still dosn't work restart the project)1924123https://api.weather.gov/alerts/activeSpecial Merine WarningerrorNOAA Connections are unstable (Try again in about a minute, if it still dosn't work restart the project)lon.cordinates1112223123https://api.weather.gov/alerts/activeerrorlat.cordinates2112223123https://api.weather.gov/alerts/activeerrorWeather Type:1924223https://api.weather.gov/alerts/activeSpecial Weather StatementerrorNOAA Connections are unstable (Try again in about a minute, if it still dosn't work restart the project)1924223https://api.weather.gov/alerts/activeHeat AdvisoryerrorNOAA Connections are unstable (Try again in about a minute, if it still dosn't work restart the project)1924223https://api.weather.gov/alerts/activeSpecial Merine WarningerrorNOAA Connections are unstable (Try again in about a minute, if it still dosn't work restart the project)LON. cordinates1112223223https://api.weather.gov/alerts/activeerrorLAT. cordinates2112223223https://api.weather.gov/alerts/activeerrorWeather type:-1466.620000000000174.0165641759018818.104888888888908-17.848003990122976event,Special Weather Statement000