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 reportPipe receiveUserEdit getPosition reportMousePosition doDefineBlock doSetBlockAttribute doDeleteBlock reportBlockAttribute reportThisContext reportJSFunction
de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ contThis is a breakpoint block, to be used in debugging. The Boolean input specifies the condition under which the program should pause. (Use True for an unconditional breakpoint.) The intent is that the script should have SHOW VARIABLE blocks for the process's local variables. After the pause, this block automatically hides all temporary variables.
de:falls _ dann _ und pause $pause-1-255-220-0 ca:si _ fes _ i atura-ho tot $pause-1-255-220-0 es:si _ haz _ y páralo todo $pause-1-255-220-0 fr:si _ faire _ et mettre tout en pause $pause-1-255-220-0 pt:se _ , então _ e faz pausa em tudo $pause-1-255-220-0 Call f(f(f(...(f(x))))) n times where the three input slots are n, f, and x from left to right. The # variable can be used inside f to represent how many times f has been called.
pt:o resultado de _ invocações em cascata de _ com argumento inicial _ _ ca:en cascada _ vegades _ _ _ Call f(f(f(...(f(x))))) until condition is true, where the three input slots are condition, f, and x from left to right. The # variable can be used inside f or condition to indicate how many times f has been called.
pt:o resultado da invocação em cascata até que _ de _ com argumento inicial _ _ ca:en cascada fins _ _ _ _ Returns the function x↦f(g(x)) where f and g are the two inputs.
pt:a composição de _ com _ ca:composa _ _ Like the built-in REPEAT UNTIL block, except that the ending condition is not tested until the script has been run the first time. So the script is run at least once.
pt:repete _ até que _ $loop-0.7 ca:repeteix _ fins _ Run the script repeatedly, as long as the given condition is true. Runs the script at least once before testing the condition.
pt:repete _ enquanto _ $loop-0.7 ca:repeteix _ mentre _ Runs the script repeatedly, as long as the condition is true. Tests the condition before the first time the script is run. Like the built in REPEAT UNTIL except that in this block the condition must be true, not false.
pt:enquanto _ , repete _ $loop-0.7 Runs the script the specified number of times, like the built-in REPEAT block, but this one provides the # variable that can be used inside the script. Try REPEAT (200) MOVE (#) STEPS RIGHT 92 with the pen down.
pt:repete _ vezes _ _ $loop-0.7 ca:repeteix _ _ _ The primitive FOR block uses an implicit step of ±1 depending on which of the starting and ending values is larger. This version allows you to provide an explicit step value. If the sign of the step input is incompatible with the ordering of the starting and ending values, the script will not be run at all.
pt:para _ de _ com passo _ a _ _ $loop-0.7 ca:per _ = _ incrementant _ fins _ _ 1110Provides LOOP as a function of one input that runs the body of the LET with A set to the function input, so the body can run itself recursively. See COPY block in Variables for an example of use.
pt:tendo _ o valor inicial _ , executa _ definido como _ ca:fes que _ sigui _ al _ _ new valueReports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.
ca:elimina els duplicats de _ The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.
ca:associació _ _ This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.
ca:_ per cada _ de _ _ ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.
This block allows you to set the pen's color, transparency (on a scale from 0=opaque to 100=invisible), or size (in pixels). The color can be set in many ways, including RGB values, HSV values, HSL values, fair HSV or HSL (based on a modified spectrum that gives each spectral color equal space and promotes brown to a color), color number, crayon, or X11/W3C color name. See Appendix A in the Snap! Reference Manual for details.
ca:$brush fixa el _ del llapis a _ _ color color number crayon fair hue ~1 size transparency X11/W3C name ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB(A) vector RGB(A) hex } }§_ext_clr_numbersNote to myself: When the user sets a fair dimension, say fair HSL, variable HSL contains the corresponding "unfair" values, and "raw HSL" contains the numbers the user actually gave. It has to be this way because in the case of HSV, there is no "HSV" variable; rather, we use the pen's actual remembered-by-Javascript color's HSV coordinates. So our HSL variable has to be analogous to that. In both cases "raw" means the numbers the user gave us.
1
HSV✐ raw HSL100HSV100
This block reports the pen size, color, or transparency, in all the same ways that the SET PEN block allows you to set those pen properties. (It will also report the current crayon number, if the pen color was most recently set with SET PEN TO CRAYON.) See Appendix A of the Snap! Reference Manual for details.
ca:$brush _ del llapis color color number crayon fair hue ~1 size transparency X11/W3C name ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB vector RGBA vector RGB(A) hex } }huesaturation (HSV)value (brightness)transparencyRGB vectorRGBA vectorredgreenblueRGB(A) hex1616HSV vectorHSL vectorsaturation (HSL)lightnesscolor numbercolorfair huenot setfair saturation (HSL)not setfair lightnessnot setfair HSL vectornot setfair saturation (HSV)2not setfair value (brightness)3not setfair HSV vectornot setcrayonX11/W3C nameChanges any pen property by the specified amount. Some color scales can't be adjusted this way unless the color was first SET using that scale. Vectors (e.g., RGB color as a list of three numbers) can be incremented by a vector as the amount. X11/W3C names can't be incremented.
ca:$brush augmenta _ del llapis en _ color number crayon fair hue ~1 size transparency ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB vector RGBA vector } }crayonRGB hexI don't know how to handle
de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _ pt:ignora _
combines any number of colors, reporting the result of mixing them, either additively (like colored light beams) or subtractively (like paints). If the result is too bright (additive) or too dark (subtractive), try the "averaged" versions, which will make the brightness more like the brightnesses of the input colors.
ca:$brush barreja colors _ amb criteri _ additive (light) additive (averaged) subtractive (ideal) subtractive (averaged) simulated paint paint (sRGB corrected)waveformssum of weightscombined waveformweighted geometric mean of waveformssum of weightscombined waveformTback to RGBT5.47813E-05 0.000184722 0.000935514 0.003096265 0.009507714 0.017351596 0.022073595 0.016353161 0.002002407 -0.016177731 -0.033929391 -0.046158952 -0.06381706 -0.083911194 -0.091832385 -0.08258148 -0.052950086 -0.012727224 0.037413037 0.091701812 0.147964686 0.181542886 0.210684154 0.210058081 0.181312094 0.132064724 0.093723787 0.057159281 0.033469657 0.018235464 0.009298756 0.004023687 0.002068643 0.00109484 0.000454231 0.000255925-4.65552E-05 -0.000157894 -0.000806935 -0.002707449 -0.008477628 -0.016058258 -0.02200529 -0.020027434 -0.011137726 0.003784809 0.022138944 0.038965605 0.063361718 0.095981626 0.126280277 0.148575844 0.149044804 0.14239936 0.122084916 0.09544734 0.067421931 0.035691251 0.01313278 -0.002384996 -0.009409573 -0.009888983 -0.008379513 -0.005606153 -0.003444663 -0.001921041 -0.000995333 -0.000435322 -0.000224537 -0.000118838 -4.93038E-05 -2.77789E-050.00032594 0.001107914 0.005677477 0.01918448 0.060978641 0.121348231 0.184875618 0.208804428 0.197318551 0.147233899 0.091819086 0.046485543 0.022982618 0.00665036 -0.005816014 -0.012450334 -0.015524259 -0.016712927 -0.01570093 -0.013647887 -0.011317812 -0.008077223 -0.005863171 -0.003943485 -0.002490472 -0.001440876 -0.000852895 -0.000458929 -0.000248389 -0.000129773 -6.41985E-05 -2.71982E-05 -1.38913E-05 -7.35203E-06 -3.05024E-06 -1.71858E-06newsRGBRGB vector255addmax255RGB vectorThis block reports a color. The color can be set in many ways, including RGB values, HSV values, HSL values, fair HSV or HSL (based on a modified spectrum that gives each spectral color equal space and promotes brown to a color), color number, crayon, or X11/W3C color name. See Appendix A in the Snap! Reference Manual for details.
ca:$brush color amb _ _ _ color color number crayon fair hue X11/W3C name ~ fair HSL=fair HSL vector fair HSV=fair HSV vector HSL=HSL vector HSV=HSV vector RGB (0-255)=RGB vector RGB hex§_ext_clr_numberscolor numbercolor300index15scale1515255fromto122crayon132X11/W3C nameRGB hexcolorRGB vectorHSV vectorHSL vectorfair HSV vectorindex-1scale1index1color100fair HSL vectorindex-1scale1index1color110050
ca:$brush _ del color _ nearest color number crayon number fair hue ~1 transparency X11/W3C name ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB vector RGBA vector RGB hex } }X11/W3C namenearest color numberrgbhslhsvrgbtransparencytransparencyRGB vectorRGBA vectorRGB hexredgreenbluehsvfalseHSV vectorhuesaturation (HSV)value (brightness)hsltrueHSL vectorsaturation (HSL)lightnessHSLlightnessfair lightness310030.9833.333333330.986.9444444516.944444451.96100fair lightnessfair saturation (HSL)210075.9493610075.949366.9444444516.944444451.96100fair saturation (HSL)fair HSL vectorfair lightness310030.9833.333333330.986.9444444516.944444458.823529100fair lightnessfair saturation (HSL)210075.9493610075.949366.9444444516.944444458.823529100fair saturation (HSL)fair HSL vectorfair hue11111111fair huefair saturation (HSL)fair lightnessfair HSL vectorbrownfair value (brightness)310054.5166.66666666754.516.9444444516.944444451.96100fair value (brightness)fair saturation (HSV)210086.3310086.336.9444444516.944444451.96100fair saturation (HSV)fair HSV vectorfair value (brightness)310054.5166.66666666754.516.9444444516.944444451.96100fair value (brightness)fair saturation (HSV)210086.3310086.336.9444444516.944444451.96100fair saturation (HSV)fair HSV vectorfair hue11111111fair huefair saturation (HSV)fair value (brightness)fair HSV vectorbrownfair somethingCan't get here -- please post project and screenshot to forum.This block reports the pen color (as a color, not as a list of numbers).
ca:✐ color de llapis actual
de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont
de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ pt:lança _ _ catchtagSets the pen color to one of 100 preselected colors, like a box of 100 crayons. The colors have names that are meant to be evocative. They are organized in families, more or less corresponding to spectral (rainbow) colors; the input slot has a two-level menu in which you can hover over a family name to see the colors of that family, each including a crayon number and an RGB value. See Appendix A of the Snap! Reference Manual for more details.
ca:✐ fixa el llapis de color _ _ grays={ 0 black #000000=0 1 gray7 #121212=1 2 gray14 #242424=2 3 gray21 #363636=3 4 gray28 #484848=4 5 gray36 #5c5c5c=5 6 gray43 #6d6d6d=6 7 gray50 #7f7f7f=7 8 gray57 #919191=8 9 gray64 #a3a3a3=9 10 gray71 #b5b5b5=10 11 gray78 #c8c8c8=11 12 gray85 #dadada=12 13 gray92 #ececec=13 14 white #ffffff=14 } pinks={ 15 deep pink #ff1493=15 16 hot pink #ff69b4=16 17 bright pink #ff007f=17 18 raspberry #e30b5d=18 19 amaranth #e52b50=19 } reds={ 20 red #ff0000=20 21 burgundy #900020=21 22 cherry #990000=22 23 dark candy apple red #a40000=23 24 sanguine #c00000=24 25 maroon #800000=25 26 crimson #c90016=26 27 Lists #d94d11=27 28 candy apple red #ff0800=28 29 coquelicot #ff3800=29 } browns={ 30 saddle brown #8b4513=30 31 chocolate #7b3f00=31 32 kobicha #6b4423=32 33 sepia #704214=33 34 chestnut #954535=34 35 dark brown #654321=35 36 brown #964b00=36 37 golden brown #996515=37 38 cinnamon #b87333=38 39 copper #d2691e=39 } oranges={ 40 orange #ff7f00=40 41 Pantone orange #ff5800=41 42 pumpkin #ff7518=42 43 Variables #f3761d=43 44 Spanish orange #e86100=44 45 burnt orange #cc5500=45 46 sinopia #cb410b=46 47 ochre #cc7722=47 48 carrot #ed9121=48 49 tangerine #f28500=49 } yellows={ 50 yellow #ffff00=50 51 Control #e6a822=51 52 dark goldenrod #b8860b=52 53 goldenrod #daa520=53 54 saffron #f4c430=54 55 sandstorm #ecd540=55 56 mustard #ffdb58=56 57 gold #ffd700=57 58 egg yolk #fee33e=58 59 rubber duck #fbe108=59 } greens={ 60 lime #00ff00=60 61 apple green #8db600=61 62 Operators #62c213=62 63 forest green #228b22=63 64 green #008000=64 65 dark green #006400=65 66 dark pastel green #03c03c=66 67 emerald #50c878=67 68 mint #3eb489=68 69 Pen #00a178=69 } cyans={ 70 aqua (cyan) #00ffff=70 71 dark cyan #008b8b=71 72 cerulean #007ba7=72 73 iceberg #71a6d2=73 74 Sensing #0494dc=74 75 teal #008080=75 76 light sky blue #87cefa=76 77 deep sky blue #00bfff=77 78 dodger blue #1e90ff=78 79 azure #007fff=79 } blues={ 80 blue #0000ff=80 81 midnight blue #191970=81 82 dark powder blue #003399=82 83 cobalt #0047ab=83 84 denim #1560bd=84 85 navy blue #000080=85 86 steel blue #4682b4=86 87 Motion #4a6cd4=87 88 cornflower #6495ed=88 89 slate blue #6a5acd=89 } purples={ 90 violet #8000ff=90 91 Looks #8f56e3=91 92 grape #6f2da8=92 93 indigo #4b0082=93 94 x11 purple #a020f0=94 95 magenta (fuchia) #ff00ff=95 96 dark orchid #9932cc=96 97 Sound #cf4ad9=97 98 purple #7f007f=98 99 dark magenta #8b008b=99 }✐ last set ascrayon✐ fair?false1231Takes three inputs for hue, saturation, and value ("brightness") values, each between 0 and 1. 0,0,0 is black; 0,0,1 is white. 0.15,1,1 is yellow, and so on. The SET PEN block in this library lets you set individual hue, saturation, or value without changing the others, lets you provide a list of three HSV color components, and lets you use the very much superior HSL (hue, saturation, lightness) color space. See Appendix A of the Snap! Reference Manual for details.
pt:altera a cor da caneta para matiz _ , saturação _ e brilho _ (0 a 1) ca:fixa el llapis al color h: _ s: _ v: _ 0.30.70.6Reports the current pen color as a list of three RGB values in the range 0-255. The PEN block in this library provides a large set of ways to examine the color. See Appendix A in the Snap! Reference Manual for details.
pt:a cor da caneta em RGB (vermelho, verde e azul; 0 a 255) Reports the current pen color as a list of three HSV values in the range 0-1. See Appendix A in the Snap! Reference Manual for details.
pt:a cor da caneta em HSV (matiz, saturação e brilho, 0 a 1) Takes a value from 0 to 15 and reports the corresponding hexadecimal digit. For internal use of the color library.
Don't ask. Only for internal use by the SET PEN block.
truetrue
not a crayon
not an X11 color
11000hues1fair hues00hue101100021000✐ fair hue table10
11
1
smallest componentrgb255
smallest component2rgb255
2Red family8.7Brown/orange family17yellow501427.7522.334green52cyan67blue83.33333333purple9550505left of black
falsehue603.61001003.6100100
hue mod 100, other components cut off at [0, 100]
value100color3.6index1index-1scale1255fromto1newHSVHSL✐ fair?true
color300index15scale15grayscale15255fromto121✐ last set ascolor number✐ fair?true✐ raw HSL10050✐ raw HSV100100
015866161216341656✐ last set asRGB✐ fair?false1
44partial matchestrue4matchhead matchestrueSUBSET throws here if it handles a unique match1
44partial matchesfalse4matchhead matchesfalseSUBSET throws here if it handles a unique match2
015866161216341656
fair hue transparency ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) } HSL={ hue saturation (HSL) lightness } HSV={ hue saturation (HSV) value (brightness) } RGB (0-255)={ red green blue } }255100We have to do this last test to rule out the vector options, which aren't numbers; their code makes three recursive calls and we catch range issues then.huesaturation (HSV)value (brightness)saturation (HSL)lightnesstransparencyredgreenbluefair huefair saturation (HSV)fair value (brightness)fair saturation (HSL)fair lightness
✐ last set asHSL33✐ fair?false1
✐ last set asRGB✐ fair?false
✐ last set asRGB✐ fair?false
✐ last set asRGB✐ fair?false
✐ last set asRGB✐ fair?false
0
4value0255131✐ last set asRGB✐ fair?false
✐ last set asHSL22✐ fair?false1
✐ last set asHSL✐ HSL11000100✐ raw HSL✐ fair?false1
✐ last set asHSV✐ fair?false✐ raw HSV1310031
100if USED TO BE fairHSL✐ fair?false1
✐ last set asHSL2222✐ fair?true1
✐ last set asHSL3333✐ fair?true1
2313
22✐ last set asHSV✐ fair?true1
33✐ last set asHSV✐ fair?true1
23131Sets the pen color to one of 100 preselected colors, like a box of 100 crayons. The colors have names that are meant to be evocative. They are organized in families, more or less corresponding to spectral (rainbow) colors; the input slot has a two-level menu in which you can hover over a family name to see the colors of that family, each including a crayon number and an RGB value. See Appendix A of the Snap! Reference Manual for more details.
ca:fixa el llapis de color _ grays={ 0 black #000000=0 1 gray7 #121212=1 2 gray14 #242424=2 3 gray21 #363636=3 4 gray28 #484848=4 5 gray36 #5c5c5c=5 6 gray43 #6d6d6d=6 7 gray50 #7f7f7f=7 8 gray57 #919191=8 9 gray64 #a3a3a3=9 10 gray71 #b5b5b5=10 11 gray78 #c8c8c8=11 12 gray85 #dadada=12 13 gray92 #ececec=13 14 white #ffffff=14 } pinks={ 15 deep pink #ff1493=15 16 hot pink #ff69b4=16 17 bright pink #ff007f=17 18 raspberry #e30b5d=18 19 amaranth #e52b50=19 } reds={ 20 red #ff0000=20 21 burgundy #900020=21 22 cherry #990000=22 23 dark candy apple red #a40000=23 24 sanguine #c00000=24 25 maroon #800000=25 26 crimson #c90016=26 27 Lists #d94d11=27 28 candy apple red #ff0800=28 29 coquelicot #ff3800=29 } browns={ 30 saddle brown #8b4513=30 31 chocolate #7b3f00=31 32 kobicha #6b4423=32 33 sepia #704214=33 34 chestnut #954535=34 35 dark brown #654321=35 36 brown #964b00=36 37 golden brown #996515=37 38 cinnamon #b87333=38 39 copper #d2691e=39 } oranges={ 40 orange #ff7f00=40 41 Pantone orange #ff5800=41 42 pumpkin #ff7518=42 43 Variables #f3761d=43 44 Spanish orange #e86100=44 45 burnt orange #cc5500=45 46 sinopia #cb410b=46 47 ochre #cc7722=47 48 carrot #ed9121=48 49 tangerine #f28500=49 } yellows={ 50 yellow #ffff00=50 51 Control #e6a822=51 52 dark goldenrod #b8860b=52 53 goldenrod #daa520=53 54 saffron #f4c430=54 55 sandstorm #ecd540=55 56 mustard #ffdb58=56 57 gold #ffd700=57 58 egg yolk #fee33e=58 59 rubber duck #fbe108=59 } greens={ 60 lime #00ff00=60 61 apple green #8db600=61 62 Operators #62c213=62 63 forest green #228b22=63 64 green #008000=64 65 dark green #006400=65 66 dark pastel green #03c03c=66 67 emerald #50c878=67 68 mint #3eb489=68 69 Pen #00a178=69 } cyans={ 70 aqua (cyan) #00ffff=70 71 dark cyan #008b8b=71 72 cerulean #007ba7=72 73 iceberg #71a6d2=73 74 Sensing #0494dc=74 75 teal #008080=75 76 light sky blue #87cefa=76 77 deep sky blue #00bfff=77 78 dodger blue #1e90ff=78 79 azure #007fff=79 } blues={ 80 blue #0000ff=80 81 midnight blue #191970=81 82 dark powder blue #003399=82 83 cobalt #0047ab=83 84 denim #1560bd=84 85 navy blue #000080=85 86 steel blue #4682b4=86 87 Motion #4a6cd4=87 88 cornflower #6495ed=88 89 slate blue #6a5acd=89 } purples={ 90 violet #8000ff=90 91 Looks #8f56e3=91 92 grape #6f2da8=92 93 indigo #4b0082=93 94 x11 purple #a020f0=94 95 magenta (fuchia) #ff00ff=95 96 dark orchid #9932cc=96 97 Sound #cf4ad9=97 98 purple #7f007f=98 99 dark magenta #8b008b=99 }123Takes three inputs for red, green, and blue values, each between 0 and 255. 0,0,0 is black; 255,255,255 is white. 255,255,0 is yellow, and so on. The SET PEN block in this library lets you set individual red, green, or blue without changing the others, lets you provide a list of three RGB color components, and lets you provide a six-digit hexadecimal number, the form in which RGB color values are usually found online. See Appendix A of the Snap! Reference Manual for details.
pt:altera a cor da tua caneta para vermelho _ , verde _ e azul _ (0 a 255) 25500Creates a bunch of global variables. For internal use by Color library.
global✐ last crayon✐ crayon colors
ca:canvia de llapis de color saltant _
ca:llapis de color call with True to turn on the entire Scheme numeric tower, including infinite-precision integers, exact rationals, and complex numbers; call with False to restore native JavaScript arithmetic.
pt:altera utilização de aritmética do Scheme para _ ca:sistema numèric d'Scheme _ Provides Scheme arithmetic functions not in JavaScript
pt:_ de _ ca:Scheme _ de _ number? complex? real? rational? integer? exact? inexact? exact inexact finite? infinite? nan? numerator denominator real-part imag-part magnitude angleMulti-branched conditional. If the first (Boolean) input is True, then the script in the second (C-slot) input is run, and this block stops. Additional tests can be added by putting one or more ELSE IF blocks in the third (variadic) Boolean slot. Use the arrowheads to get the number of Boolean slots you need. Each ELSE IF block provides a Boolean slot for a condition to test and a script slot for what to do if the condition is True. You can put an ELSE block in the last Boolean input to CASES for a script to run if all the tests turn out False. As soon as a condition is True, no further tests are done and no further scripts are run.
pt:se _ , então _ _ ca:casos: si _ llavors _ _
pt:captura _ _ ca:agafa _ _ cont
pt:lança _ ca:llança _ catchtagFor use with the CASES block. See its help message.
pt:senão, se _ , então _ ca:altrament si _ llavors _ For use with the CASES block. See its help message.
pt:senão, _ ca:si no _ 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 _ _ 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 _ errAccepts any number of scripts as inputs. Launches a separate thread for each of them, so they are done in parallel, then waits for them all to complete. This doesn't change how the Snap! scheduler works; the threads are not truly asynchronous. And there is no increase in speed. Rather, the point of this block is to allow starting synchronized but independent scripts.
pt:executa _ em paralelo e espera ca:executa en paral·lel _ i espera each scripttestAccepts any number of scripts as inputs. Launches a separate thread for each of them, so they are done in parallel, then continues with the current script while they all run. This doesn't change how the Snap! scheduler works; the threads are not truly asynchronous. And there is no increase in speed. Rather, the point of this block is to allow starting synchronized but independent scripts.
pt:executa _ em paralelo ca:executa en paral·lel _ Allows reading global settings programmatically. Eisenberg's Law: Anything you can do from the user interface you should be able to do in your program, and vice versa. This library is just a beginning; there are many UI controls outside of the Settings menu.
pt:o valor da configuração _ ca:paràmetre _ Project notes Project name User Presentation mode Language Zoom blocks Stage size Stage scale Retina display support Long form input dialog Plain prototype labels Input sliders Execute on slider change Clicking sound Turbo mode Flat design Keyboard editing Visible stepping Thread safe scripts Prefer smooth animations Flat line ends Codification support Inheritance support Hyper blocks support Visible paletteAllows changing global settings programmatically. This block is for Boolean (checkbox) settings; use SET VALUE for numeric or text values. Eisenberg's Law: Anything you can do from the user interface you should be able to do in your program, and vice versa. This library is just a beginning; there are many UI controls outside of the Settings menu.
pt:altera o valor da configuração _ para _ ca:fixa el paràmetre _ a _ Presentation mode Retina display support Long form input dialog Plain prototype labels Input sliders Execute on slider change Clicking sound Turbo mode Flat design Keyboard editing Visible stepping Thread safe scripts Prefer smooth animations Flat line ends Codification support Inheritance support Hyper blocks supportAllows changing global settings programmatically. This block is for numeric or text settings; use SET FLAG for Boolean (checkbox) values. Eisenberg's Law: Anything you can do from the user interface you should be able to do in your program, and vice versa. This library is just a beginning; there are many UI controls outside of the Settings menu.
pt:altera o valor da configuração _ para _ ca:fixa el valor de _ a _ Project notes Project name Language Zoom blocks Stage size Stage scale Visible palette
00
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)
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)
what's your name?
Alert!
choose!
title
§_objectsMenuoption
leftleft righthey!open my definition to look at examples how to use the menu blocks
de:Kostüm aus Text _ Größe _ pt:um traje com o texto _ de tamanho _ ca:vestit amb el text _ i mida _ A72
§_costumesMenu2002Use this block to make "clickable" buttons:
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-TW11
de:animiere Setter _ Getter _ Delta _ über _ Sek. beschleunigt _ ca:modifica setter _ i getter _ amb delta _ en _ segons de forma _ pt:anima com modificador _ e inspector _ de _ em _ s de forma _ 501
de:Fehler _ pt:assinala o erro _
de:gleite _ in _ Sek. beschleunigt: _ ca:llisca _ en _ segons de forma _ fr:glisser _ en _ sec. accélérer _ pt:desliza _ em _ s de forma _ 501linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elasticend time1000deltaXdeltaY
de:animiere _ um _ in _ Sek. beschleunigt: _ ca:modifica _ amb _ en _ segons de forma _ pt:anima _ de _ em _ s de forma _ x positionx position y position direction size ghost effect color effect saturation effect brightness effect fisheye effect whirl effect pixelate effect mosaic effect negative effect tempo volume balance501linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elasticy positiondirectionsizetempovolumebalance1 12
de:Beschleunigung _ ca:de forma _ pt:a forma _ linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elasticquadratic-inquadratic-outquadratic-in-outcubic-incubic-outcubic-in-outquart-inquart-outquart-in-outsine-insine-outsine-in-outelasticunknown easing function ""
de:für _ = _ bis _ _ in _ Sek. beschleunigt: _ _ ca:per _ = _ fins _ _ en _ segons de forma _ _ pt:para _ de _ a _ _ em _ s de forma _ _ 01001linearlinear quadratic={ quadratic-in quadratic-out quadratic-in-out } cubic={ cubic-in cubic-out cubic-in-out } quart={ quart-in quart-out quart-in-out } sinusoidal={ sine-in sine-out sine-in-out } elastic
pt:executa no modo turbo _ generate a list of samples representing a sine wave at the given frequency, duration and sample rate
pt:as amostras de som com _ Hz de _ s amostrado a _ Hz ca:_ Hz durant _ segons amb mostreig de _ Hz 440a 55=55 a 110=110 a 220=220 a 440=440 a 880=880 a 1760=1760 a 3520=352014410022.05 kHz=22050 44.1 kHz=44100 88.2 kHz=88200 96 kHz=96000
pt:π
pt:_ radianos em graus ca:graus de _ radiants 0.5
pt:o som _ ca:so de nom _ §_soundsMenuquickly plot the samples of a sound, a list of samples (single channel), or a list of lists (multiple channels) to the stage at a lower resolution.
pt:desenha o gráfico do som _ ca:dibuixa el so _ §_soundsMenu1offset-2records an audio snippet and reports it as a new sound, or zero if the user cancels
pt:$circleSolid-1-255-0-0 uma nova gravação ca:$circleSolid-1-255-0-0 enregistra
pt:a nota com _ Hz ca:nota de _ Hz 440
pt:a frequência da nota _ ca:freqüència (hz) de la nota _ 69
pt:o nome da nota _ ca:nom de la nota _ 69returns a frequency analysis of the argument list, represented as a two-column table, where the first column contains the unique values and the second column their occurrences
pt:$flash as frequências dos itens de _ ca:$flash analitza _
pt:o agrupamento dos itens de _ de acordo com _ ca:$flash agrupa _ per _
pt:uma ordenação de _ com critério _ ca:$flash ordena _ per _
pt:o histograma de _ entre _ e _ com intervalo _ ca:histograma de _ des de: _ fins: _ interval: _ 1
pt:o histograma de _ agrupado de acordo com _ entre _ e _ com passo _ ca:histograma de _ agrupat per: _ des de: _ fins: _ interval: _ add missing entries to a sorted list. Used for histograms
pt:os itens de _ com a chave _ entre _ e _ com passo _ completados por _ ca:omple _ clau: _ des de: _ fins: _ interval: _ amplada: _ 11
pt:_ em minúsculas ca:_ $arrowRight minúscules
pt:desenha gráfico de barras de _ em (x: _ , y: _ ) com largura _ e altura _ ca:dibuixa _ _ a x: _ y: _ i ample: _ alçada: _ barsbars lines pie chart-200-10040020022100startsettingratio1step2lines
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 Takes 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.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 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 All but first of empty word.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 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 All 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 _ Last 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 _ firstbffirst111bf 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 _ join 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 _ 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.
ca:frase _ 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 _
pt:o texto multilinha _ ca:multilínia _ Reports the part of the first string up to the first instance of the second string inside it. If the second string isn't found, reports the entire first string.
pt:o texto de _ antes de _ ca:part del text _ anterior a _ Reports the portion of the first input string starting after the first occurrence of the second string. If the second string isn't found in the first string, reports an empty string.
pt:o texto de _ depois de _ ca:part del text _ posterior a _ Reports the character position (starting from 1) of the beginning of the first input string inside the second input string. If it's not found, reports 0.
pt:a posição de _ em _ ca:posició de _ en _ If input is TRUE, comparisons made by functions in the string library will be case-independent (so "FOO" = "foo"). This is the default. If input is FALSE, comparisons will be exact.
pt:altera comparações ignorando distinção minúsculas/maiúsculas para _ ca:ignora la diferenciació de majúscules _ Reports the portion of the first input (string) starting at the position given by the second input (counting from 1, like LETTER n OF) and ending at the position given by the third input (also counting from 1). If the third input is empty, reports the portion from the first position to the end of the string. If a position number is negative, counts from the end backward, so -1 is the last character, -2 is the next to last, etc.
pt:o texto de _ entre as posições _ e _ , inclusive ca:part del text _ des de la posició _ fins _ Reports True if the first input string contains the second input string, otherwise false. Comparison is case-independent by default; use USE CASE-INDEPENDENT COMPARISONS to change that.
pt:o texto de _ antes de _ ca:text _ conté _ ? Reports the input text with lower case letters instead of capital letters in the input. Uses the user's locale's rules for case conversion.
pt:em minúsculas _ ca:_ a minúscules
ca:comparacions ignorant majúscules? err_resetThe 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.
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.
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.
cols
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.
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.
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 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.
0Turns 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.
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.
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).
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.
0valueindexThrow 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 _
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
1Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.
( )The 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.)
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.
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.
11The stream version of IN FRONT OF. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is here in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:a prefixação de _ ao canal _ The stream version of ITEM 1 OF. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:o primeiro item do canal _ The stream version of ALL BUT FIRST OF. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:um canal com todos os itens de _ menos o primeiro 423true4The stream version of variadic MAP (any number of input lists, like MULTIMAP in the list utilities library). Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:a aplicação de _ aos itens dos canais _ The stream version of KEEP. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:os itens tais que _ do canal _ The inputs are a stream and a positive integer. SHOW STREAM reports a regular finite list of length less than (for short finite streams) or equal to the second input. It's useful because an infinite stream can't be displayed in a finite amount of time.
pt:uma lista com os itens do canal _ até ao _ º 1001Make a stream from a finite collection of items, like the LIST primitive. Since this block is typically used for small amounts of data, it does not delay computation of its inputs.
pt:um canal com _ 1Call this block with STREAM WITH NUMBERS FROM 2 as its input to get the infinite stream of all the prime numbers. It's called SIEVE because the algorithm it uses is the Sieve of Eratosthenes (clickable link): https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes Look inside; it's a beautifully elegant algorithm. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:a crivagem do canal _ The stream version of NUMBERS FROM 1 TO, with no ending number because it reports the infinite stream of all the integers greater than or equal to its input. Read the code! It's deceptively simple. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!
pt:um canal com os números a partir de _ 1Read the code while running it,
Takes a table (typically from a CSV data set) as input and reports a summary of the table grouped by the field in the specified column number. The remaining three inputs are used only if the field values are numbers, in which case they can be grouped into buckets (e.g., decades, centuries, etc.). Those three inputs specify the smallest and largest values of interest and, most importantly, the width of a bucket (10 for decades, 100 for centuries). If the field isn't numeric, leave these three inputs empty or set them to zero. In that case, each string value of the field is its own bucket, and they appear sorted alphabetically. The block reports a new table with three columns. The first column contains the bucket name or smallest number. The second column contains a nonnegative integer that says how many records in the input table fall into this bucket. The third column is a subtable containing the actual records from the original table that fall into the bucket. If your buckets aren't of constant width, or you want to group by some function of more than one field, load the "Frequency Distribution Analysis" library instead.
ca:gràfica de la taula _ agrupada pel camp: _ des de: _ fins: _ interval: _ 1110add missing entries to a sorted list. Used for histograms
pt:os itens de _ com a chave _ entre _ e _ com passo _ completados por _ ca:gràfica omplint _ amb clau: _ des de: _ fins: _ interval: _ amb _ 11
pt:desenha gráfico de barras de _ em (x: _ , y: _ ) com largura _ e altura _ ca:dibuixa gràfic _ a x: _ y: _ amplada: _ alçada: _ -200-100400200If smallest value < 0, the x axis isn't at the bottom. Note that sort is by > not by <. Not clear this can happen in a histogram!max y1ratio1label delta y5010step2190draw x axis at y=00draw y axis902012i15Ready to draw bars.2data90
ca:ordena taula _ pel camp _ en ordre _ ascending descending
ca:agrupa taula _ pel camp _ per intervals de _
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 This block creates new variables on the selected scope: global (for all sprites), sprite (for this sprite only) or script (only for that blocks stack) with the names given (in 'names' list). If there is already a variable with that name in that scope, it does nothing: no errors and no overwrites.
pt:cria as variáveis _ _ ca:crea les _ variables _ es:crear las _ variables _ de:erstellen _ var _ globalglobal sprite scriptThis block deletes all the variables with the names given (inside input list). Each name will make only one variable deletion, and this will be the variable found following the scope order: 'script' -> 'sprite' -> 'global'. If we have a "testing" sprite variable and also a "testing" global one, deleting "testing" will delete only the sprite one (Yes! we can also do "delete var (testing, testing)" to delete both. If one variable does not exists (in any scope) an error happens, stopping block action in that point. You can check it before with the "does var (name) exists?"block.
pt:remove as variáveis _ ca:esborra les variables _ es:borrar variables _ de:löschen var _ This block sets the given value (last input) to the variable named with the name givent (var input). It looks for that variable following the scope order 'script' -> 'sprite' -> 'global' (the first match it finds). If that variable does not exist (in any scope) an error happens, stopping their script. You can check it before using the "does var (name) exists?" block.
ca:assigna a _ el valor _ es:asignar a _ el valor _ pt:altera _ para _ de:setze var _ auf _ §_getVarNamesDictThis block reports the value of the variable with the name given. It looks for that variable following the scope order 'script' -> 'sprite' -> 'global' (the first match it finds). If that variable does not exist (in any scope) an error happens, stopping their script. You can check it before using the "does var (name) exists?" block.
pt:o valor de _ §_getVarNamesDictThis block reports "true" if there is a variable with this given name (input slot) in that context. It can be a global, sprite or script variable. Otherwise it reports "false".
pt:a variável _ existe ca:existeix la variable _ ? es:existe la variable _ ? de:existiert var _ ? err_resetThis block turns on (show) the watcher view on stage (if it was not already activated) of the variable with the given name (slot input). It can only access to the closest variable scope (if there different variables with the same name in different scopes) following the order 'script' -> 'sprite' -> 'global'. No errors if that variable does not exist.
ca:mostra la variable _ es:mostrar variable _ pt:mostra a variável _ de:zeige var _ §_getVarNamesDictThis block turns off (hide) the watcher view on stage (if it was not already activated) of the variable with the given name (slot input). It can only access to the closest variable scope (if there different variables with the same name in different scopes) following the order 'script' -> 'sprite' -> 'global'. No errors if that variable does not exist.
ca:amaga la variable _ es:esconder variable _ pt:esconde a variável _ de:verstecke var _ §_getVarNamesDict
Initialization of SciSnap!
Creates global variables "SciSnap!Properties", "SciSnap!Data", and "SciSnap!Messages". Sets the default values of global SciSnap!-properties for items typeOfData width height minValue maxValue columns rows maxSetValue
Returns the value of an existing global SciSnap!-property or "not found". Items: typeOfData width height minValue maxValue columns rows maxSetValue
minValuetypeOfData width height minValue maxValue columns rows maxSetValueSets the value of an existing global SciSnap!-property or inserts it anew. Items: typeOfData width height minValue maxValue columns rows maxSetValue
minValuetypeOfData width height minValue maxValue columns rows maxSetValueShows the message with headline in the middle of the window.
headlinetheMessageAdds a new entry to SciSnap!Messages and shows the error message.
something wrong!Reports a copy of a costume.
Reports the costume of a sprite.
Returns a random number between 0 and 1.
Reports Pi.
Reports Euler's number.
Rounds value x to n digits.
1.23572Reports n faculty.
50Reports a binomial coefficient.
531result1n1i1Test on vector, matrix, table, predicate, interval, list of intervals, set, or complex-number.
vectorvector transposed-vector matrix table complex-number complex-number-Cartesian-style complex-number-polar-style set comparisonOperator interval listOfIntervals numericalListcomplex-numbercomplex-number-Cartesian-stylecomplex-number-polar-styletransposed-vectorvectormatrixwidth11table1width1SciS_isTable(data)intervalfalselistOfIntervalsset2344listOfIntervals55numericalListfalseReports a substring of a string.
thisString14Reports a string without first/all instances of substring.
allall firstthisthisStringReports a string in upper case.
thisStringReports a string in lower case.
ThisStringStores a string in a file.
this textthis fileReports the index of first appearance of substring in string.
ringthisStringReports a string with replacement instead of substring.
allall firstthisthatthisStringReports date and time in standard notation.
day10hour10minute10second10--T::
seconds todayJulian Date decimal years days this year hours this year minutes this year seconds this year hours today minutes today seconds todayYYYY17MM54DD21h12m45s7daysi11402Julian Date2JD365.2530.6001172099531121531101215821260360024Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE only if all of the inputs are TRUE, otherwise FALSE. Like AND but for multiple inputs.
pt:todas as afirmações _ são verdadeiras 1Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE if at least one input is TRUE, otherwise FALSE.
pt:alguma das afirmações _ é verdadeira 1Returns an empty costume of the given width and height with background color r/g/b.
400300245245245Tests the value of a property.
falsetypeOfConfigurationMathPadReports the row or column name of the row or column with the number entered, or reports an error message.
columncolumn row1SciSnap!Datatable0columnrowTurns a row-wise (in Lisp terminology) function into a column-wise one.
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.
ab
pt:a aplicação a _ de _ 1Evaluates the predicate with a given parameter.
ringified predicateEvaluates the intervals with a given parameter.
ringified predicateitem3<trueReports the intersection of two sets.
set1set2are these two sets?setresultset2234element34element344Reports the union of two sets.
set1set2are these two sets?setresultset223344Reports the difference between two sets.
set1set2are these two sets?setresultset2234element32set1set24433m14n1m1compare the mth interval with all intervals of set144Reports the Cartesian product between two sets.
set1set2set4040resultset22item_133implication
10equivalence
10Converts numbers to booleans
1Converts booleans to numbers
trueIntervals should be of structure [leftLimit,operator1,operator2,rightLimit]
2resultn1
1111111111212121212121212Intervals should be of structure [leftLimit,operator1,operator2,rightLimit]
11define left edge and operator44define right edge and operator
11resultn1m1n1
11resultn1
0listOfIntervalsitemfalseReturns all elements of a list not covered by the predicate.
Returns all elements of a list not covered by the intervals.
result3lst_sort(list, fn)34Test wether the predicate is translatable to a list of intervals.
ringified predicate1comparisonOperator112233falseIntervals should be of structure [leftLimit,operator1,operator2,rightLimit]
interval1interval2intersection0result1144341444341444
1141413<2≤3≤2<3<2<4414142<3≤2≤3<2<3<trueCreates a new empty costume of the specified size and color and sets the local properties
thisSpritethisSprite theStage anotherSprite400300245245245costumeWidthcostumeHeightscaleProperties2212126-10-102211600falsefalsefalsefalsetruetruefalseStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage 8: front color red 9: front color green 10: front color blue line properties: 1: line width 2: line style 3: line color red 4: line color green 5: lineColor blue marker properties: 1: marker style 2: marker width 3: marker connected 4: marker color red 5: marker color green 6: marker color blue data properties: 1: minValue 2: maxValue 3: columns 4: rows scale offsets: 1:left offset 2: right offset 3: upper offset 4: lower offset labels: 1: title 2: x label 3: y label 4: title height 5: x label height 6: y label height 7: x unit 8: y unit ranges: 1: x left 2: x right 3: y lower 4: y upper scale properties: 1: scales x precision 2: scales y precision 3: scales x textheight 4: scales y textheight 5: scales x intervals 6: scales y intervals 7: scales x start 8: scales y start 9: scales x step 10: scales y step 11: scales x scaling 12: scales y scaling 13: scales ticlength 14: scales x minitics 15: scales y minitics 16: scales x grid 17: scales y grid 18: scales x centered 19: scales y centered 20: scales show x scale 21: scales show y scale 22: plot border
thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.
costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties markerProperties dataProperties scaleOffsets labels ranges scalePropertiesthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.
costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties markerProperties dataProperties scaleOffsets labels ranges scalePropertiesthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage 8: front color red 9: front color green 10: front color blue
40030024524524518018018000thisSpritethisSprite theStage anotherSpriteStagea simple way to set some net attributes: line properties: 1: line style 2: lineWidth 3: line color red 4: line color green 5: lineColor blue
continuouscontinuous dashed dash-dot dot-dot rainbow inverse-rainbow1000thisSpritethisSprite theStage anotherSpritelinePropertiesa simple way to set some net attributes: marker properties: 1: marker style 2: marker width 3: marker connected 4: marker color red 5: marker color green 6: marker color blue
squarenone o_circle ._point +_plus x_ex square triangle5000falsethisSpritethisSprite theStage anotherSpritemarkerPropertiesSets some properties for scales.
2212121010thisSpritethisSprite theStage anotherSpritescalePropertiesscaleProperties123456scalePropertiesSets the labels and textheights of the diagrams of a PlotPad.
thisSpritethisSprite theStage anotherSpriteDiagram Title18x-label16y-label16labelsSets the distances of the diagram axes to the edges of the Sketchpad.
thisSpritethisSprite theStage anotherSpriteupperOffset13scalePropertiesUPPER OFFSET1labels013scaleProperties0lowerOffset13scalePropertiesLOWER OFFSET19scalePropertiesrightOffset1.54scalePropertiesRIGHT OFFSET.13scaleProperties0leftOffset14scalePropertiesLEFT OFFSET18scaleProperties13scaleProperties0scaleOffsetsSets the ranges for the axes of the diagrams of a Sketchpad.
-1010-1010false0.1truethisSpritethisSprite theStage anotherSpriterangesrangesDraws the graph of a function given as rigified Snap!-function or coefficient-list of a polynomial.
ringified operator or polynomialthisSpritethisSprite theStage anotherSpritevectorSciS_addGraphToPlotpad(costume,ranges,offsets,lineattributes,aFunction,proc)rangesscaleOffsetslinePropertiesDraws the data points of a two-dimensional table with numerical values. With a lot of additions from Rick Hessman. Thanks!
myDatathisSpritethisSprite theStage anotherSpritemyDatamatrix12SciS_addNumericDataplotToPlotpad(costume,ranges,offsets,labels,lineattributes,dataattributes,data)rangesscaleOffsetslabelslinePropertiesmarkerPropertiesDraws the data points of a two-dimensional table with numerical values in column 2. Text values should be used to set the x-label.
myDatatruetruethisSpritethisSprite theStage anotherSpritemyDatatableSciS_addMixedDataplotToPlotpad(costume,ranges,offsets,labels,lineattributes,dataattributes,data)rangesscaleOffsetslabelslinePropertiesmarkerPropertiesscalePropertiesscalePropertieswasXscaled?20wasYscaled?212021scalePropertiesDraws a histogram of histogram data.With a lot of additions by Rick Hessman. Thanks!
myData10truethisSpritethisSprite theStage anotherSpritemyDatavector0transposed-vectorminmaxdeltah2resulti1221i1source0.1SciS_addHistogramToPlotpad(costume,ranges,offsets,lineattributes,data,datapointattributes)rangesscaleOffsetslinePropertiesmarkerPropertiesDraws and labels the axes of a PlotPad. With lot of additions of Rick Hessman. Thanks!
thisSpritethisSprite theStage anotherSpriteSciS_addAxesAndScalesToPlotpad(costume,scaleattributes,labels,offsets,ranges,plotcolors)scalePropertieslabelsscaleOffsetsrangescostumeProperties
thisSpriteSciS_newcostume(w,h,r,g,b)3costumeProperties4costumeProperties5costumePropertiesSets the ranges to "pretty" values. Many thanks to Rick Hessman!
thisSpritethisSprite theStage anotherSpritescalePropertiesscalePropertiesrangesrangespretty125get dictionary of pretty values.72192311245631261427pretty346get dictionary of pretty values.821102312246842261527rangesscaleProperties"Pretty" scaling from Rick Hessman. Many thanks!
-1010620002000Numbers are too big for pretty printing.nintpretty0.0010.0020.0050.010.020.050.10.20.512510205010020050010002000between91341341341341341341places33322211100000000000sign2index1i21dticnintresultdeltanstartstart*scaling is the actual starting value (start is the value displayed next to the tic)stop1stepstep*scaling is the actual stepping value (step is the difference between the tic labels).scalingintervalsprecisionminitics
thisSpritethisSprite theStage anotherSpritemyData0.1myData2dy43ranges1234scalePropertiesscaleProperties7192151111418310436121151scalePropertiesCalculates the ranges of two columns of a table.
Conversion of coordinates.
100xpxp yp x ythisSpritethisSprite theStage anotherSpriteoffsetsscaleOffsetsrangesrangesdiagramWidth1diagramHeight34x0112y0443coordinate1 xpypxyObtaining coordinates of a PlotPad by mouse.
costume-coordinatescostume-coordinates graph-coordinatesthisSpritethisSprite theStage anotherSpriteselection1 targetCostumetargetCostumeWidthtargetCostumeHeighttargetXPositiontargetYPositiontargetSizecostume-coordinatescostume coordinates.graph-coordinatescostume coordinates.
400300xycontinuouso_circle000
400300xycontinuous000
00600400continuousnone continuous dashed dash-dot dot-dot rainbow inverse rainbowsquarenone o_circle ._point +_plus x_ex square triangle000Changes the Snap!-Logo. ;-)
Creates SQL variables and sets some properties.
Establishes connection and sets the corresponding properties. Change parameters if necessary.
connectionhttps://snapextensions.uni-goettingen.de/mysqlquery.php?server=db1&user=snapexuser&password=snap!userconnection&type=connect12ok0115ERRORImports the results of an SQL-query to SQLData and sets the corresponding properties.
typeOfDatatable0minValuenotSetmaxValuenotSetReports a list of databases on the server and sets the corresponding properties.
dbsconnection&type=getDBs000115ERRORSelects one of the databases and sets the corresponding properties.
2databasescurrentDatabasedatabasesresultconnection&command=USE currentDatabasetablestablesconnection&command=SHOW TABLES FROM currentDatabase&type=getTables000115ERRORReports a list of tables of the chosen database and sets the corresponding properties.
tblsconnection&command=SHOW TABLES FROM currentDatabase&type=getTables000115ERRORReads the attributes of the specified table and sets the corresponding properties.
1databases0115ERRORSelects one of the tables and sets the corresponding properties.
1tables0115ERRORGenerates a simplified SQL query.
* DISTINCTi11result FROM 0result2Generates an almost complete SQL query.
* DISTINCTASCASC DESC10i11result FROM 0result20result20resultASCExecutes an SQL query.
1:ERRORReturns an SQL predicate.
Returns an SQL predicate.
Returns an SQL predicate.
Returns an SQL predicate.
Returns an SQL predicate.
Returns an SQL predicate.
Returns an SQL predicate.
Returns an SQL predicate.
Returns an SQL value.
Returns an SQL value.
Returns an SQL value.
Returns an SQL value.
Returns an SQL value.
Creates a new empty costume of the specified size and color and sets local properties. If sprite is the stage, the old costume# of stage is stored. So you can draw graphs on maps, for example.
thisSpritethisSprite theStage anotherSprite400300245245245targettheStageStagethisSpriteStagetheStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue vertex properties: 1: ranges for random coordinates (xLeft,xRight, yUpper,yLower) 2: minimal radius of a vertex, should be changed is vertices are not growing with number of edges 3: boolean: vertices grow with number of connected edges 4: boolean: vertex content is shown. If there is no content, the vertex number is shown edge properties: 1: line width 2: line color red 3: line color green 4: line color blue 5: boolean: edge is directed 6: boolean: edge has a weight 7: boolean: edge shows weight
thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.
costumePropertiestypeOfConfiguration typeOfData costumeProperties vertexProperties edgePropertiesthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.
costumePropertiestypeOfConfiguration typeOfData costumeProperties vertexProperties edgePropertiesthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes. 1: width 2: height 3: back color red 4: back color green 5: back color blue
400300245245245thisSpritethisSprite theStage anotherSpritecostumePropertiesa simple way to set some vertex attributes: 1: ranges for random coordinates (xLeft,xRight, yUpper,yLower) 2: minimal radius of a vertex, should be changed is vertices are not growing with number of edges 3: boolean: vertices grow with number of connected edges 4: boolean: vertex content is shown. If there is no content, the vertex number is shown
3truefalsethisSpritethisSprite theStage anotherSpritevertexProperties1vertexPropertiesThe ranges are maintained. a simple way to set some edge attributes: 1: line width 2: line color red 3: line color green 4: line color blue 5: boolean: edge is directed 6: boolean: edge has a weight 7: boolean: edge shows weight
1000falsefalsefalsethisSpritethisSprite theStage anotherSpriteedgeProperties
100100thisSpritethisSprite theStage anotherSprite1vertexListvertexListadds n vertices to the vertexList of a GraphPad: Vertex attributes have the following order: 1: x 2: y 3: size 4: content 5: isMarked 6: colorNr 7: numberOfLinks
1thisSpritethisSprite theStage anotherSpritevertexListSciS_addVerticesToVertexlist(n,vlist,vAttributes)vertexListvertexPropertiesadjacencyMatrixSciS_addVerticesToAdjacencymatrix(n,amatrix)adjacencyMatrixMoves a vertex to another position measured in Snap! sprite-coordinates
1thisSpritethisSprite theStage anotherSprite100100vertexListCreates n new edges randomly.
1thisSpritethisSprite theStage anotherSpriteresultSciS_addRandomEdgesToGraph(amatrix,n,lAttributes,vlist)adjacencyMatrixedgePropertiesvertexListadjacencyMatrix1vertexList2Creates a new edge between two vertices.
12thisSpritethisSprite theStage anotherSprite0vertexList0vertexListadjacencyMatrixXadjacencyMatrixX5edgePropertiesdraws the graph on a GraphPad.
thisSpritethisSprite theStage anotherSpriteStageoldCostumeStageSciS_drawGraph(amatrix,vlist,cAttributes,vAttributes,lAttributes,oldCostume)adjacencyMatrixvertexListcostumePropertiesvertexPropertiesedgePropertiesDeletes a vertex.
1thisSpritethisSprite theStage anotherSprite0vertexListvertexListadjacencyMatrixDeletes an edge.
12thisSpritethisSprite theStage anotherSprite0vertexList0vertexListadjacencyMatrix05edgeProperties
12thisSpritethisSprite theStage anotherSprite0vertexList0vertexListSets the weight of an edge.
121thisSpritethisSprite theStage anotherSprite0vertexList0vertexList
thisSpritethisSprite theStage anotherSpritefrom vertex nrInput for start vertex.
thisSpritethisSprite theStage anotherSpritenew start vertexwidth (1....20)
1thisSpritethisSprite theStage anotherSprite0vertexListSets the content of a vertex.
1thisSpritethisSprite theStage anotherSprite0vertexListInput for a new vertex content.
thisSpritethisSprite theStage anotherSpritevertex numberTells a vertex to be marked.
1thisSpritethisSprite theStage anotherSprite0vertexListDeletes a marker.
1thisSpritethisSprite theStage anotherSprite0vertexListDeletes all markers.
thisSpritethisSprite theStage anotherSpriteitemvertexListDepth first search in a graph.
1thisSpritethisSprite theStage anotherSprite0vertexListBreadth first search in a graph.
1thisSpritethisSprite theStage anotherSprite0vertexListReports the distance of two vertices.
thisSpritethisSprite theStage anotherSprite120vertexList0vertexListShortes path between two vertices, using Dijkstra-method.
12thisSpritethisSprite theStage anotherSprite0vertexList0vertexListShortes paths between startVertex and all connected vertices, using Dijkstra-method.
1thisSpritethisSprite theStage anotherSprite0vertexListReturns the vertexnumber of a vertex near (x|y), if there is one.
10050thisSpritethisSprite theStage anotherSpriteSciS_vertexnumberAtGraph(vlist,cAttributes,vAttributes,x,y)vertexListcostumePropertiesvertexProperties
00thisSpritethisSprite theStage anotherSpritetargetSize10011001Reports the vertexnumber of the vertex with the specified content. If not found then reports 0.
PeterthisSpritethisSprite theStage anotherSpritenr0itemvertexListFamilyTree
numberXdiagramSpriteDiagramSpriteDiagramSprite400300245245245DiagramSpriteEdges per Node18Number of Edges16Number of Vertices1610trueDiagramSpriteDiagramSprite
spriteName
spriteNameImports a stored sprite from a file.
new nameImports stored blocks to another palette. All blocks should be from the same category!
Lookslibrarybegincategory=10end"50oldcat2libraryall""""SciS_importLibrary2(src)Creates a new empty costume of the specified size and color ans sets local properties for a NeuralNet.
thisSpritethisSprite theStage anotherSprite400300245245245costumeWidthcostumeHeightStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for NN position on stage 7: upper offset for NN position on stage neural net properties: 1: number of layers 2: layer width 3: width of NN image 4: height of NN image
thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.
netPropertiestypeOfConfiguration typeOfData costumeProperties netPropertiesthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.
netPropertiestypeOfConfiguration typeOfData costumeProperties netPropertiesthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for NN position on stage 7: upper offset for NN position on stage
40030024524524500thisSpritethisSprite theStage anotherSpritecostumePropertiesa simple way to set some net attributes: 1: number of layers 2: layer width 3: width of NN image 4: height of NN image
23400300thisSpritethisSprite theStage anotherSpritenetPropertiesReports the output of the nth layer of a neural network with given input.
last1 lastthisSpritethisSprite theStage anotherSpritevector2netPropertiesCreates a fully connected neural network of perceptrons. Inputs should be connected to layer 1, outputs to last layer.
23thisSpritethisSprite theStage anotherSpritemyDataone matrix for each layer1myDatapropertiesnetPropertiesnetProperties34typeOfDataweightsDraws the nodes and current status of connections of an NN as new costume.
thisSpritethisSprite theStage anotherSpritenetWidth2netPropertiesSciS_NNshowStatus(cAttributes,nAttributes,weights,outputs,costume,sprite)costumePropertiesnetPropertiesmyDataA block for teaching a neural net.
0.1thisSpritethisSprite theStage anotherSpritewidth2netPropertiesmyDataSciS_NNteach(weights,width,depth,input,output,eta)myData1netProperties
thisSpritethisSprite theStage anotherSpritecreates a temporary or static Sprite.
truenewSprite
Removes the calling sprite.
Reports the row or column number of the row or column with the name entered, or reports an error message. If the the name is a number You can mark this with a # (e.g. #123).
columncolumn rownameSciSnap!Datatable010columnfirstlast1#n0i10i100ERROR: name not found!rowfirstlast1#n0i10i10ERROR: name not found!ERROR: something wrong!Input dialog for a list of items. Click item by mouse and then ok. Returns the selected item.
thisSpritethisSprite theStage anotherSpritetitlewidth9costume400245245245continuous1000245245245 (click on it)102018truei12530505281218true70301005ok751218trueokfalse70100305Returns an empty table.
Reports a new table initialized with value.
23resultReports a table with labeled columns.
20i1rowERROR: labels required!Reports the copy of a list or an empty list.
Imports table-CSV-data, costume-data, SQL-data or FITS-data to SciSnap!Data.
costume-(RGB)-datatable-(CSV)-data costume-(RGB)-data SQL-(query)-data FITS-datacurrent-costumecurrent-costume filepicker other-sourceFITS-datatable-(CSV)-datacolumns10rows1000hmaxcolumnSciSnap!DatatrueminValuemaxValuecostume-(RGB)-datahminValuemaxValueSQL-(query)-datatypeOfDatatable0columns10rows1000hmaxcolumntrueminValuemaxValueReads a file using the filepicker.
Stores a list in a CSV file. JS-code copied from Snap!-code.
SciSnap!DatafilenameReports randomly distributed points in the form of a rectangle, circle, or ring.
100-100100-100100squaresquare circle ringcircleringCreates new random data in the specified ranges swaying by a straight.
10-55102Generates a list of points "near" a given graph.
20-552Creates the transpose of a table or a list.
01valuevaluetableERROR: data are not transposable!Adds a row, a column, or column headers to a table.
rowrow column column-headersSciSnap!Datachoice1 tablerowi1column0i1column-headersi11Reports the row or column of a table identified by number or name, or reports an error message. If the the name is a number You can mark this with a # (e.g. #123).
rowrow columnnumberOrNamefirst last numberOrNameSciSnap!Datatruechoice1 tablerowlast1columnlast11ERROR: something wrong!Deletes a row or column of a table. identified by number or name, or reports an error message. If the the name is a number You can mark this with a # (e.g. #123).
rowrow columnnumberOrNamefirst last numberOrNameSciSnap!Datatablerowlastcolumnlast11itemReports the element a position x|y of a table, identified by numbers or names.
numberOrNamenumberOrNameSciSnap!Datatablexcolumnyrow001Replaces the value at position x|y of a table.
numberOrNamenumberOrNameSciSnap!Datatablexcolumnyrow001Reports the columns from row startnr to row endnr.
SciSnap!DatanumberOrNamelastlast numberOrNametablelaststartnrrowendnrrowcolNumbersitemSciS_columncopy(data,cols,start,stop)Reports a subsection of an image or table.
RGB-datatable-data matrix-data list-data RGB-data FITS-dataSciSnap!DatanumberOrNamenumberOrNamenumberOrNamenumberOrNamelist-dataresultdummyvalueindextable-datamatrix-datab1columnb2rowe1columne2rowbeginendSciS_subsection(data,begin,end)RGB-dataFITS-dataRGB-datawidthwidthheightheightFITS-datawidthwidthheightheightbeginendSciS_subsectionImage(data,begin,end,width,height)Determines the rows of a table with certain properties.
SciSnap!DatanumberOrNameless-thanless-than greater-than equal-to different-fromtablencolumn11op1less-thangreater-thanequal-todifferent-fromcounts the appearances of the values of a list.
sortedDatacountedValuesvalue1n0itemreturns the entropy of a list of data
Copy of corresponding Snap!-library-block: Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.
SciSnap!DatatableNormalizes a table by dividing it by the specified value.
SciSnap!Datameanmean max number sum median softmaxchoice1 vector0Reports a compressed version of a vector or matrix.
SciSnap!Data2isMatrixmatrixvector11resulti11iresultcolumnNr11Pooling operation with matrices, images and vectors.
maxmax meanSciSnap!Data21typeOfPooling1 matrixvectorFITSRGBmatrixFITSRGBSciS_pooling(data,width,height,typeOfPooling,stride,typeOfData)Reports a sorted list.
Sorts a list or table.
SciSnap!DatanumberOrNametruetrueisTabletableresult1Determines a group of properties of a table.
meanmin max number sum meannumberOrNameSciSnap!DatanumberOrNametruetablegroupcolumncolumn11valuecolumncolumn11choice1 copytruecolumn0SciS_groupeddata(aTable,compaircolumn,operation,groupcolumn)Calculates the ranges, the covariance or the correlation between two columns of a table.
rangesranges covariance correlationnumberOrNamenumberOrNameSciSnap!Datatruetablencolumn11mcolumn11copychoice1 1SciS_propertiesoftable(selection,table,x,y)1 Calculates the parameters of a regression line.
SciSnap!DatamatrixsumX0sumY0numerator0denominator0dummymeanXmeanYdummymbDetermines the next k neighbours of a point in data. The class of the point has to be in column 3.
5SciSnap!Datatablepoint2neighbors3truetruetable-data114Applies a convolution to a table with FITS, RGB, or table values.
tableimage tableSciSnap!Data100100matrixSciS_convolution(kernel,data,width,height,typeOfData,mIndex,kWidth)Custering of n-dimensional data with k-means-method. Cluster numbers are attached to the data.
3SciSnap!DatamatrixSciS_k-means-clustering(k,data)Custering of n-dimensional data with k-means-method. A metric has to be delivered as a ringified term. Cluster numbers are attached to the data.
3SciSnap!Dataringified operatormatrixresultadd cluster number 0 to datadimension1minmaxi1calculate min and max for all columnscentersi1choose k random centerscalculate min and max for all columnsanyChangestruen0anyChangesfalsedummycenter1pointbuild clustersadjust centersMetric for Strtings.
SaturdaySundaylabels of data: -1 not visited, 0 visited, -2 noise, >0 clusternr
SciSnap!Data505matrix2SciS_DBSCAN(data,r,minMembers)-1reports a ID3 decision tree constructed on the base of labeled data
0empty datasetcountedValuescolumnlasttrue1only one result --> ready11only one attribut left --> readyinformation gainsi111calculate information gains for all attributesinformation gains2falsetruecompareColumn11take attribute with max information gain as compare criterioncountedValuescolumntruelinksitemrecursive construction of ID3 trees for all other attributesnode11
thisSpritethisSprite theStage anotherSpriteReturns the vertexnumber of a vertex near mouse position, if there is one.
thisSpritethisSprite theStage anotherSpriteSciS_vertexnumberAtGraph(vlist,cAttributes,vAttributes,x,y)vertexListcostumePropertiesvertexPropertiesreturns the class of a dataset using an ID3-decision-tree
1leaf (unambiguous)1nodei1ERROR: no sensefull data to compare found!Reports a vector, built by the items.
Returns a new vector of dimension n with random elements.
3110Returns a matrix formed from the input vectors.
length1resultitemReturns a new nxm-matrix with random elements.
32110i1Creates the transpose of a vector or a matrix.
valuevalueDetermines a property of a vector.
minmin max minpos maxpos number sum mean median variance standard-deviation softmaxdatatransposed-vector1choice1 softmaxmedianminmaxnumbersummeanminposmaxposvariancestandard-deviationvariancestandard-deviationERROR: unknown option!Linear operations with scalars, vectors and matrices.
*+ - * X-*ERROR: illegal operation between numbers!vectormatrixERROR: illegal operation with numbers!vectorvector+-*Xmatrixvector matrix operationERROR: illegal operation with vectors!matrixvectormatrix vector operationmatrixmatrix matrix operation-*ERROR: wrong operation!ERROR: illegal operationApplies a mapping matrix to an object that is given as a matrix of points.
Soves a linear system of max. 50 equations.
some tests for correct data01transposed-vectormatrixcreates matrix with attached column bsolvedtruen50i1dTry to solve matrixr1rowswap rowshelpnormalize row1k11k1k1diagonalizer1Reports a list of matrix A in triangle form, the rank of A, column change and column positions.
0Aba copy of A is used to let A unchangedrankrow1columchangefalsecolumn positions11i1search downwardsi1search to the rightc0row1row1row-1transform to diagonal form ECalculates the coefficients of the interpolation polynomial for a 2xn-matrix of points.
212matrixcreate matrix of powers of x and y-valuesolvedtruen50i1j-12dTry to solve matrixr1rowswap rowshelpnormalize row1k11k1k1diagonaizer1Calculates the value of a polynomial for input x.
polynomial as list of coefficients11result12i3Applies an affine transformation to a list of points.
SciSnap!DataoktrueP11P22P33Q11Q22Q33S1221221220ERROR: denominator is zeroReports a complex number in Cartesian style.
23Reports a complex number in polar style.
230Reports a complex number in polar style.
Reports a complex number in Cartesian style.
Reports properties of complex numbers.
real-partabsolute-value real-part imaginary-part phase conjugatereal-partimaginary-partphaseconjugateERROR: unknown operator!Reports arithmetic results of two complex numbers.
++ - * /complex-numbercomplex-number-polar-stylecomplex-number-polar-style+-*/complexNumberCartesianStyle22333223Creates a new empty costume of the specified size and color and sets local properties
thisSpritethisSprite theStage anotherSprite400300245245245theStagecostumeWidthcostumeHeighttheStagecostume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for MathPad position on stage 7: upper offset for MathPad position on stage other attributes: 1: line width 2: draw only ponts, not arrows 3: dimension of coordinate system 4: max. value on scales 5: start point of arrows
thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.
costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dimension maxValue startPointthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.
costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dimension maxValue startPointthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for MathPad position on stage 7: upper offset for MathPad position on stage
40030024524524500thisSpritethisSprite theStage anotherSpritecostumePropertiesa simple way to set some MathPad attributes: 1: line width 2: draw only ponts, not arrows 3: dimension of coordinate system 4: max. value on scales 5: start point of arrows
1false310000thisSpritethisSprite theStage anotherSpriteoldDimensiondimensionoldMaxValuemaxValuelinePropertiesdimensionmaxValuestartPointDraws axes on a MathPad in 2 or 3 dimensions.
thisSpritethisSprite theStage anotherSpriteSciS_mathpadaddaxes(cAttributes,type,maxValue,dimension,costume)costumePropertiestypeOfDatamaxValuedimensionDraws a vector, complex number or object of points from the start point stored in the properties.
vectorvector complex-number line-to object-of25500thisSpritethisSprite theStage anotherSpritefalsedataobject-ofdimension1SciS_mathpadplot(costume,data,r,g,b,maxValue,linewidth,dimension,onlypoints,cAttributes,startpoint,choice)maxValue1linePropertiesdimension2linePropertiescostumePropertiesstartPointcomplex-numberdata23dimensionSciS_mathpadplot(costume,data,r,g,b,maxValue,linewidth,dimension,onlypoints,cAttributes,startpoint,choice)maxValue1linePropertiesdimension2linePropertiescostumePropertiesstartPoint
Returns a root of an equation calculated with Newton's method.
ringified term1epsilon0.00001fi110001000Reports an element of a sequence.
ringified term1Returns a list of the n first elements of a sequence.
1ringified termresulti1Returns a list of secant slopes calculated with the given sequence.
ringified term21/nresulti1Reports the secant slope "nearby" the given point.
ringified term1epsilon0.00001Returns a finite sum.
10ringified term10result0iReturns the integral of a term calculated with n trapezoids.
2ringified term11000result0dxx1Reports either - data Fourier transformed to frequency-space - "raw" FFT (Fast Forier Transformation)data formatted as SciSnap! complex numbers - or iFFT real data
frequency_spectrumfrequency_spectrum complex_FFTdata iFFT_of_FFTdata100iFFT_of_FFTdataSciS_FFTops(data,freq,choice)Reports coefficient of the binomial distribution b(N,p,k)=(N choose k)*p^k(1-p)^(N-k)
100.12ERROR: wrong parameter!Reports binomial distribution B(N,p,k).
1100.1ERROR: wrong parameter!Reports coefficient of the hypergeometric distribution.
10352ERROR: wrong parameter!Reports hypergeometric distribution.
11035ERROR: wrong parameter!Reports coefficient of the Poisson distribution.
0.052ERROR: wrong parameter!Reports Poisson distribution.
20.05ERROR: wrong parameter!Reports coefficient of the Pareto distribution.
132Reports coefficient of the normal distribution.
101The empty set.
The real (computer-) numbers.
Returns a set by listing the elements, predicates, and defined intervals. The sequence is: set isNumerical list of elements list of intervals predicate.
Creation of a set by a predicate. Intervals and elements should be defined by another block. If possible the predicate is translated to a list of intervals. The sequence is: set isNumerical list of elements list of intervals predicate.
ringified predicatepredicateTreeERROR: pure predicate sets are not supported yet.Returns a set with one numerical interval. The sequence is: set isNumerical list of elements list of intervals predicate.
-Infinity-Infinity≤≤ <≤≤ <InfinityInfinitysettrueReports whether "element" is element of "set".
elementset43Reports the intersection, union, difference, or cross product of two sets.
set1∩∩ ∪ \ Xset2are these two sets?set∩∪\set1set24433m14n1m1compare the mth interval with all intervals of set144Xresultset22item_133Is set1 subset of set2?
set1set2are these two sets?setinterval4element3Compare elementstrueAre theses two sets equal?
set1set2setReports up to n elements of a set. Numers are natural numbers smaller than maxSetValue in SciSnap!-properties.
10setresult340i1maxSetValue should be close to infinity ;-)resultvalueindexlistclean up1Reports simple types, sets and lists as string.
i1maxSetValue should be close to infinity ;-)elementsvalueindexlist20item18is first elementtrueitem18ERROR: no simple type!Converts a string to a list of elements.
1,2,[3,4],{6,7,8..12}]10]0]0}10}0}0find listsCreates a new empty costume of the specified size and color and sets local properties
thisSpritethisSprite theStage anotherSprite400300245245245costumeWidthcostumeHeightStagecostume properties: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage line properties: 1: lineWidth 2: line color red 3: line color green 4: lineColor blue data properties: 1: minValue 2: maxValue 3: columns 4: rows image properties: 1: type (RGB,FITS) 2: imageWidth 3: imageHeight grid colors: list of usable colors: 1:black 2:gray 3:white 4: red 5:yellow 6:magenta 7:green 8:cyan 9:blue
thisSpritethisSprite theStage anotherSpriteReads the value of an existing property.
costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dataProperties imageProperties gridProperties gridColorsthisSpritethisSprite theStage anotherSpriteresultnot foundname1 hmyPropertiesSets the value of an existing property or inserts it anew.
costumePropertiestypeOfConfiguration typeOfData costumeProperties lineProperties dataProperties imageProperties gridProperties gridColorsthisSpritethisSprite theStage anotherSpritename1 donefalsehmyPropertiesmyPropertiesa simple way to set some costume attributes: 1: width 2: height 3: back color red 4: back color green 5: back color blue 6: left offset for Image position on stage 7: upper offset for Image position on stage
40030024524524500thisSpritethisSprite theStage anotherSpriteStagea simple way to set some net attributes: line properties: 1: line style 2: lineWidth 3: line color red 4: line color green 5: lineColor blue 6: fill color red 6: fill color green 8: fill color blue
continuouscontinuous dashed dash-dot dot-dot1000180180180thisSpritethisSprite theStage anotherSpritelinePropertiesa simple way to set some grid attributes: 1: number of horizontal cells 2: number of vertical cells 3: cell width 4: cell height grid data are stored in myData
thisSpritethisSprite theStage anotherSprite400400wh1costumePropertiesws2costumeProperties11gridPropertiesmyData2Imports costume-data or FITS-data to a data list.
costume(RGB)datacostume(RGB)data FITSDatacurrentCostumecurrentCostume filepicker list with dimensions at topthisSpritethisSprite theStage anotherSpritechoice1 filepickerFITSData6typeOfConfigurationImagePadmyPropertiestypeOfDataFITSmyPropertieslineProperties1000myPropertiesdataProperties16myPropertiesimagePropertiesFITSmyPropertiesgridProperties40404040myPropertiesgridColors000black150150150gray255255255white25500red2552550yellow2550255magenta02550green0255255cyan00255bluemyPropertiesheader5011Stagecostume(RGB)dataw1h211costumeDatacostumePropertiesminmin123maxmax123typeOfConfigurationImagePadmyPropertiestypeOfDataRGBmyPropertieslineProperties1000myPropertiesdataProperties1myPropertiesimagePropertiesRGBmyPropertiesgridProperties40404040myPropertiesgridColors000black150150150gray255255255white25500red2552550yellow2550255magenta02550green0255255cyan00255bluemyPropertiesStageGenerates an image from the FITS or RGB data of the DataSprite in gray or false colors
graygray false-color RGBmyData0255falsethisSpritethisSprite theStage anotherSpritecolor1 sourcemyDatamyDatatypeOfDataFITS1costumePropertiestheStage2costumePropertiestheStage1costumeProperties2costumeProperties1costumeProperties2costumeProperties1costumePropertiestheStage2costumePropertiestheStage1costumeProperties2costumePropertiesDraws a grid on an ImagePad.
myDatathisSpritethisSprite theStage anotherSpritetruemyDatamatrix1gridProperties12gridPropertiesDraws a line on present costume using line properties. Attention: JS coordinates are used.
1010100100thisSpritethisSprite theStage anotherSpriteDraws a rectangle on present costume using line properties. Attention: JS coordinates are used.
1010100100thisSpritethisSprite theStage anotherSpriteSciS_drawRectangleOnImagepad(costume,x1,y1,width,height,lineAttributes)linePropertiesFills a rectangle on present costume using surface properties. Attention: JS coordinates are used.
1010100100thisSpritethisSprite theStage anotherSpriteSciS_fillRectangleOnImagepad(costume,x1,y1,width,height,lineAttributes)linePropertiesDraws a circle on present costume using line properties. Attention: JS coordinates are used.
10010020thisSpritethisSprite theStage anotherSpriteSciS_drawCircleOnImagepad(costume,x,y,radius,lineAttributes)linePropertiesFills a circle on present costume using surface properties. Attention: JS coordinates are used.
10010020thisSpritethisSprite theStage anotherSpriteSciS_fillCircleOnImagepad(costume,x,y,radius,lineAttributes)linePropertiesDraws a text on present costume using line properties. Attention: JS coordinates are used.
my text1005012truethisSpritethisSprite theStage anotherSpriteSciS_drawTextOnImagepad(costume,x,y,text,height,horizontal,lineAttributes)linePropertiesDraws a list of "point" as "balls". Attention: JS-coordinates are used!
myDatacirclescircles squares5thisSpritethisSprite theStage anotherSpritesourcemyDatamatrix012SciS_drawListOfPoints(costume,data,shape,size,lineAttributes)121lastlinePropertiesSets the RGB value at (x|y) on costume. Attention: JS-coordinates are used!
11thisSpritethisSprite theStage anotherSprite255100301costumeProperties2costumeProperties11Reports the RGB value at the position (x|y) of the costume. JS-code-snippets copied from Snap!-code. Attention: JS-coordinates are used!
11thisSpritethisSprite theStage anotherSprite1costumeProperties2costumeProperties11Sets the value at (x|y) in myData. Attention: JS-coordinates are used!
11thisSpritethisSprite theStage anotherSpritetypeOfDataRGBtypeOfDataFITSReads the data value at the position (x/y) of an image stored in myData. Attention: JS-coordinates are used!
11thisSpritethisSprite theStage anotherSpritetypeOfDataRGBtypeOfDataFITSObtaining data with the mouse from an image or grid stored in myData.
image-valueimage-value costume-coordinates slice-data line-data circle-data brightness cell-valuethisSpritethisSprite theStage anotherSpriteselection1 targetCostumetargetCostumeWidthtargetCostumeHeightStagecostume-coordinatescostume coordinates.line-dataokfalseokfalsexold1yold21212Stageline dataimage-value11image valueslice-dataslice datacircle-datacircle dataokfalseokfalsecontinuous32552000180180180112112continuous1255200018018018012Stagebrightnessbrightnesscell-valuevalue of a grid cellApplies an affine transformation to a costume.
currentCostumeoktrueP11P22P33Q11Q22Q33S1221221220ERROR: denominator is zeroReports the brightness of a FITS or RGB image around x|y in a circle with radius r. Attention: JS-coordinates are used!
10010010thisSpritethisSprite theStage anotherSpritedatamyDatatypeOfDataFITStypeOfDataRGBFills a grid or a part of a grid with one of the specified values.
thisSpritethisSprite theStage anotherSprite1xMax1yMaxgridPropsgridPropertiesxMaxyMaxmyDataSciS_fillOnImagePadGridRandomlyOnImagePad(xMin,xMax,yMin,yMax,numbers,data)myDatasets the value of a grid cell
thisSpritethisSprite theStage anotherSprite1targetCostumetargetCostumeWidthtargetCostumeHeightStage11myDatatrueSets the value of a grid cell an shows the result.
thisSpritethisSprite theStage anotherSprite112truegridPropsgridProperties1112Reports the Moore- or v.-Neumann-neighborhood of a cell. Order: north,east,south,west
MooreMoore vonNeumannfalse11thisSpritethisSprite theStage anotherSpriteSciS_neighborhoodInGridOnImagePad(data,gridProperties,x,y,isTorus,typeOfNeighborhood)myDatagridPropertiesSwaps the cell values of a grid randomly.
thisSpritethisSprite theStage anotherSpritetrue111xMax1yMaxgridPropsgridPropertiesxMaxyMaxSciS_swapCellsOfGridOnImagePad(data,gridProperties,n,isTorus,range,xMin,xMax,yMin,yMax)myDatagridPropertiesCounts the surrounding values and changes it if necessary.
thisSpritethisSprite theStage anotherSpritetrueany or numberany22greater-thangreater-than equal-to smaller-than different-from41false51xMax1yMaxgridPropsgridPropertiesxMaxyMaxSciS_changeSurroundingValuesOfGridOnImagePag(data,gridProperties,ifValue,elseValue,surrValue,op,n,isTorus,withNoise,noise,xMin,xMax,yMin,yMax,oldValue)myDatagridProperties1 1 Replaces the cell values of a grid depending on the features of the neighbors.
thisSpritethisSprite theStage anotherSpritetruesumsum min max mean1xMax1yMax1gridPropsgridPropertiesxMaxyMaxSciS_replaceValuesOfGridOnImagePad(data,gridProperties,operation,isTorus,xMin,xMax,yMin,yMax,range)myDatagridProperties1 Combines two grids.
1orand or xor not-and not-or not-xor minus212xMax1111yMaxSciS_combineGridsOnImagePad(grid1,grid2,value1,operator,value2,ifValue,elseValue,xMax,yMax)Applies a Wolfram Cellular Automaton to a grid.
3031SciS_applyWolframAutomatonToAgridOnImagePad(no,grid,color0,color1)
Sets the global instrument used for playing notes.
PianoPiano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Drums={ Cabasa Snare Drum Bass Drum Closed Hi-Hat Open Hi-Hat Mid Tom High Tom Crash Cymbal } Other={ Accordion Marimba Organ Vibraphone }Sets the global volume to the selected percentage.
50Sets the default volume of the selected instrument to the specified percentage.
PianoPiano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Drums={ Cabasa Snare Drum Bass Drum Closed Hi-Hat Open Hi-Hat Mid Tom High Tom Crash Cymbal } Other={ Accordion Marimba Organ Vibraphone }50Plays the selected note for the specified musical duration. This block waits for the selected note to finish playing. A note name and duration (whole, half, quarter, etc.) can be selected from the drop-down menu.
C3 D3 E3 F3 G3 A3 B3 C4 D4 E4 F4 G4 A4 B4 Sharps={ C#3 D#3 E#3 F#3 G#3 A#3 B#3 C#4 D#4 E#4 F#4 G#4 A#4 B#4 } Flats={ Cb3 Db3 Eb3 Fb3 Gb3 Ab3 Bb3 Cb4 Db4 Eb4 Fb4 Gb4 Ab4 Bb4 }Whole Half Quarter Eighth Sixteenth Thirtysecond Dotted Notes={ Dotted Half Dotted Quarter Dotted Eighth Dotted Sixteenth } Triplet Notes={ Half Triplet Quarter Triplet Eighth Triplet Sixteenth Triplet }Plays the selected note for the specified musical duration. This block does note wait for the selected note to finish playing. A note name and duration (whole, half, quarter, etc.) can be selected from the drop-down menu.
C3 D3 E3 F3 G3 A3 B3 C4 D4 E4 F4 G4 A4 B4 Sharps={ C#3 D#3 E#3 F#3 G#3 A#3 B#3 C#4 D#4 E#4 F#4 G#4 A#4 B#4 } Flats={ Cb3 Db3 Eb3 Fb3 Gb3 Ab3 Bb3 Cb4 Db4 Eb4 Fb4 Gb4 Ab4 Bb4 }Whole Half Quarter Eighth Sixteenth Thirtysecond Dotted Notes={ Dotted Half Dotted Quarter Dotted Eighth Dotted Sixteenth } Triplet Notes={ Half Triplet Quarter Triplet Eighth Triplet Sixteenth Triplet }Waits for the selected musical duration.
Whole Half Quarter Eighth Sixteenth Thirtysecond Dotted Notes={ Dotted Half Dotted Quarter Dotted Eighth Dotted Sixteenth } Triplet Notes={ Half Triplet Quarter Triplet Eighth Triplet Sixteenth Triplet }Each note duration is assigned a numerical value, with a whole note equallying four beats. Dotted Notes are equal to the named note plus half of the named note's duration. Triplet notes are equal in duration to two of the named notes divided by three. Since there are 60 seconds in a minute, dividing 60 by the tempo and then multiplying it by a note's duration alters the duration of the notes to make them match the global tempo.
Plays a list of notes simultaneously for the specified musical duration.
Whole Half Quarter Eighth Sixteenth Thirtysecond Dotted Notes={ Dotted Half Dotted Quarter Dotted Eighth Dotted Sixteenth } Triplet Notes={ Half Triplet Quarter Triplet Eighth Triplet Sixteenth Triplet }Reports the note in the specified position within the specified major scale.
11 2 3 4 5 6 7C40 1 2 3 4 5 6 7
Creates a list consisting of the specified scale, starting in the first octave and ending in the seventh octave.Finds the position of Notes 1 and 2 in the extended scale.Checks to see if the specified notes are in the selected major scale.Reports the numerical interval between the specified notes in the selected major scale.Reports the note in the specified position within the specified minor scale.
11 2 3 4 5 6 7C40 1 2 3 4 5 6 7Extracts and reports the name of a note from its scientific pitch notation. (e.g., "C#4" reports as "C#".)
Creates a list consisting of the specified scale, starting in the first octave and ending in the seventh octave.Finds the position of Notes 1 and 2 in the extended scale.Checks to see if the specified notes are in the selected minor scale.Reports the numerical interval between the specified notes in the selected minor scale.Reports all notes in a specified scale using the provided note and octave number as the starting note of the scale.
ChromaticChromatic Major Minor0 1 2 3 4 5 6 7Reports the note in a specific position within a major or minor musical scale. (e.g., The third note in a C Major scale starting in the fourth octave is E4.)
11 2 3 4 5 6 7MajorMajor Minor0 1 2 3 4 5 6 7Reports the distance in steps between two notes in the selected scale. (e.g., In the C Major scale, there are two intervals between C4 and E4.)
MajorMajor MinorMinorThis block must be called. It executes a specific commands when corresponding conditions are met.
This block reports a list that consists of a specified note name and duration. These values can be selected using the drop-down menu or entered directly.
C3 D3 E3 F3 G3 A3 B3 C4 D4 E4 F4 G4 A4 B4 Sharps={ C#3 D#3 E#3 F#3 G#3 A#3 B#3 C#4 D#4 E#4 F#4 G#4 A#4 B#4 } Flats={ Cb3 Db3 Eb3 Fb3 Gb3 Ab3 Bb3 Cb4 Db4 Eb4 Fb4 Gb4 Ab4 Bb4 }Whole Half Quarter Eighth Sixteenth Thirtysecond Dotted Notes={ Dotted Half Dotted Quarter Dotted Eighth Dotted Sixteenth } Triplet Notes={ Half Triplet Quarter Triplet Eighth Triplet Sixteenth Triplet }Reports all notes in the western chromatic scale. A drop down menu determines whether the scale will be reported using sharps or flats.
Reports all notes in a given major scale in order, beginning with the input note and octave.
CC D E F G A B Sharps={ C# D# E# F# G# A# B# } Flats={ Cb Db Eb Fb Gb Ab Bb }40 1 2 3 4 5 6 7Reports all notes in a given minor scale in order, beginning with the input note and octave.
CC D E F G A B Sharps={ C# D# E# F# G# A# B# } Flats={ Cb Db Eb Fb Gb Ab Bb }40 1 2 3 4 5 6 7Reports the notes of a major chord, given the starting note and octave of the chord.
CC D E F G A B Sharps={ C# D# E# F# G# A# B# } Flats={ Cb Db Eb Fb Gb Ab Bb }40 1 2 3 4 5 6 7
This block reports the chord positions in a major scale that use major chords.
This block reports the chord positions in a major scale that use minor chords.
This block reports the chord positions in a minor scale that use major chords.
This block reports the chord positions in a major scale that use major chords.
Selects the notes of the Diminished scale from the Chromatic scale.Reports all notes in a given diminished scale in order, beginning with the input note. Does not include octave.Reports all notes in a given diminished scale in order, beginning with the input note and octave.
Converts roman numerals to arabic numerals using an association table.
Reports the notes of a diminished chord, given the starting note and octave of the chord.
Reports the notes of a minor chord, given the starting note and octave of the chord.
CC D E F G A B Sharps={ C# D# E# F# G# A# B# } Flats={ Cb Db Eb Fb Gb Ab Bb }40 1 2 3 4 5 6 7
CC D E F G A B Sharps={ C# D# E# F# G# A# B# } Flats={ Cb Db Eb Fb Gb Ab Bb }40 1 2 3 4 5 6 7II ii iii IV V vi vii°Outputs the major chords in a scale.Outputs the minor chords in a scale.7Outputs the diminished chord in a scale.
CC D E F G A B Sharps={ C# D# E# F# G# A# B# } Flats={ Cb Db Eb Fb Gb Ab Bb }40 1 2 3 4 5 6 7ii ii° III iv v VI VIIOutputs the major chords in a scale.Outputs the minor chords in a scale.2Outputs the diminished chord in a scale.Each major scale is listed, along with the notes it contains and the number of notes in the scale before an octave change.
Each minor scale is listed, along with the notes it contains and the number of notes in the scale before an octave change.
Reports the number of notes in the specified scale before an octave change.
Reports the number of notes in the selected scale before an octave change.
Reports the notes in the specified scale.
Reports the notes in the specified minor scale.
Reports the notes comprising a major or minor chord, using the specified note and octave as the root note of the chord.
MajorMajor MinorThis block finds the notes in a specified chord along the selected scale. Roman numerals differentiate chord positions from octave numbers (e.g., the Roman numeral “I” reports the notes of the first chord in the sequence). Upper case Roman numerals represent major chords within the scale. Lower case Roman numerals represent minor chords within the scale.
MajorMajor Minor0 1 2 3 4 5 6 7Major Positions={ I ii iii IV V vi vii° } Minor Positions={ i ii° III iv v VI VII }This block reports a chord and appends an additional note.
May be paired with a musical duration to insert a rest into a sequence of chords.
This block is used to group sequences of notes and chords into measures. Each note or chord in the measure is paired with a note duration. The pairs are entered into the block using a list.
This code block groups measures together. This can be helpful for separating groups of measures that repeat in various places throughout a song, like verses and choruses. This block works with both notes and chords. To add measures to a section, put the measures into a list.
This block determines if the combined durations of notes or chords within a measure are correct. The combined duration of notes within a measure must equal the number of beats specified by the time signature. For example, in 4/4 time, there can be four quarter notes within a measure. The Beats in Measure reports the discrepancy between the number of actual beats in a measure versus the required number of beats.
4/44/4 3/4 2/4 6/8 5/4 7/4 9/8 12/8ofbeatsEach note duration is assigned a numerical value, with a whole note equallying four beats. Dotted Notes are equal to the named note plus half of the named note's duration. Triplet notes are equal in duration to two of the named notes divided by three. Unlike the "Adjusted Note Duration" block, the values reported here are not linked to the global temp reporter.
Piano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Other={ Accordion Marimba Organ Vibraphone }Melody
Piano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Other={ Accordion Marimba Organ Vibraphone }Loop-Melody
Piano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Other={ Accordion Marimba Organ Vibraphone }
Piano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Other={ Accordion Marimba Organ Vibraphone }This block reports a series of beats that tell the drum when to play. Beats are represented as an “X,” which can be typed into the block. Beats can be added or subtracted using the arrows on the right of the block.
This block reports a drum pattern, assigns a drum to it, and sets the duration value of each item in the pattern. Different drums can be selected from the drop-down menu. Beat durations can be selected from the drop-down menu or entered directly.
Snare DrumBass Drum Snare Drum Crash Cymbal Cabasa Toms={ High Tom Mid Tom } Hi-Hats={ Closed Hi-hat Open Hi-hat }EighthQuarter Eighth SixteenthThis code block specifies the type of track to be played and assigns it an instrument. Both the track type and instrument are set using drop-down menus. This block accepts notes or chords paired with corresponding durations. This block also allows for the creation of note or chord loops that will repeat for the whole duration of the song. Both types of loops may be selected from the track drop-down menu. For a loop to play, it must have an accompanying track that is not a loop.
MelodyMelody Chords Loops={ Loop-Melody Loop-Chords }PianoPiano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Guitar, Overdrive Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Drums={ Cabasa Snare Drum Bass Drum Closed Hi-Hat Open Hi-Hat Mid Tom High Tom Crash Cymbal } Other={ Accordion Marimba Music Box Organ Vibraphone }This block is used to play multiple music tracks. In most cases, several tracks with musical instruments are played in parallel.
4/44/4 3/4 5/4 7/4 6/8 9/8 12/8Reports the corresponding note (in scientific pitch notation) for the specified MIDI number.
Reports the midi number of the specified note. The note should be entered using scientific pitch notation (e.g., "C4").
Extracts the note name from the provided scientific pitch notation. (e.g., An input of "C4" produces an output of "C".)
12Association list for note names and their corresponding MIDI numbers in the first octave. Used to convert note names to MIDI.
The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.
11Extracts the octave number from the provided scientific pitch notation. (e.g., An input of "C4" produces an output of "4".)
3
Passes through the signal from a MIDI controller, allowing the user to play the controller using the assigned instrument. The name of the MIDI controller must be input exactly as it appears on the user's computer.
MPK mini 3MPK mini 3 MPK mini play MPK Mini Mk II M-audio Keystation 49esPianoPiano Brass={ French Horn Trumpet Tuba } Strings={ Banjo Bass, Acoustic Bass, Electric (Finger) Cello Guitar, Acoustic Guitar, Electric Guitar, Overdrive Harp Koto Sitar Violin } Woodwinds={ Bassoon Clarinet Flute Oboe Saxophone Shakuhachi } Drums={ Cabasa Snare Drum Bass Drum Closed Hi-Hat Open Hi-Hat Mid Tom High Tom Crash Cymbal } Other={ Accordion Marimba Music Box Organ Vibraphone }This block assigns a number, frequency, and amplitude to a tone.
This block turns the specified tone on or off. This block will play a sine wave.
This block turns off all the tones currently playing.
1152009600 19200 38400 57600 115200Close port.
port
portWrite to the port a list containing numbers
portConnect to a broker The options are not required but can be filled if needed 1. username (some brokers need this) 2. password (some brokers need this) 3. keepalive (default time is 60 seconds but some brokers need a shorter time) 4. connection callback (ringed set of scripts to be run when connection is made or remade) Note: The connection callback doesn''t have to be the 4th option - it just has to be the last (or only) option. ADVANCED: The brokers offered in the menu are accessed using default ports and basepaths (if needed). If accessing other brokers, the complete urls should be formed like this: siteURL:port/basepath e.g mqtt.eclipseprojects.io:443/mqtt
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netconnection statuscallbackconnection statusconnectingcallbackoptionsusername1password2keepalive3mqt_connect(broker,callback,options)connectingconnectedPublish a message to an MQTT broker The options are not required but can be filled if needed 1. qos (quality of service 0,1 or 2) 2. retain flag (boolean)
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netnamehellooptionsqos1retain2mqt_pub(broker,topic,payload,options)Subscribe to a topic on a broker Make sure you have already run an MQTT connect block before using this one You should add a script to be run inside he grey ring. It will be run each time a message is received If you click on the arrow on the grey ring twice, you can use #1 and #2 inside your script. #1 will contain the received payload #2 will contain the received topic name
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netname
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netname
allall broker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.net
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netnameerr_resetcallbackTopic@received payloadreceived topic
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netnamename2helloreceived payloadreceived topic/@
hellonameThe response topic should be a unique value for each call e.g. use timer There is only 1 option at present 1. Timeout value - default is 3 seconds
broker.emqx.iobroker.emqx.io mqtt.eclipseprojects.io test.mosquitto.org broker.xmqtt.netnamename2helloreceived payloadreceived topic/@timer011000
ca:🤖 connecta't a _ 192.168.4.1
ca:🤖 desconnecta't
ca:🤖 connectat
ca:toca la nota _ a l'octava _ durant _ ms CC C# D D# E F F# G G# A A# B11 2 3 4 5100
ca:botó _ 🆗 ❌ ➡️ ⬆️ ⬇️ ⬅️
ca:dibuixa a la pantalla _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ falsefalsefalsefalsefalsefalsetruefalsetruefalsefalsefalsefalsefalsefalsetruefalsefalsefalsetruefalsetruetruetruefalse
ca:mou motor _ _ passos _ 1128clockwiseclockwise counter-clockwise
ca:mou motor 1 en _ motor 2 en _ _ passos clockwiseclockwise counter-clockwisecounter-clockwiseclockwise counter-clockwise64
ca:mou motor _ angle _ ° 1-90
ca:mou motor _ _ voltes completes 13
ca:escriu _ a x: _ y: _ color _ escala _ Hello, MicroBlocks!1030🔵🔵 🟢 ​🔴 ​🟣 ​🟡 ​⚪️1
ca:neteja pantalla
ca:posa el LED x: _ y: _ a _ 33true
ca:mostra caràcter _ A
ca:posa el pin digital _ a _ 1true
ca:posa el pin analògic _ a _ 1512
ca:llegeix el pin digital _
ca:llegeix el pin analògic _
ca:inclinació _ xx y zmemoized response,calling
ca:acceleració
ca:nivell de llum
ca:temperatura °C takes a snapshot with the webcam and reports it as a new costume, or zero if the user cancels
pt:$camera a imagem actual do vídeo
§_costumesMenu
§_costumesMenuthing
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.11433