data:image/png;base64,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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

The identity function reports its input.

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) hexfloor1616HSV 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 hexnumberlistI don't know how to handle

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 RGBTword5.47813E-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 numbercolor300indexfloor15scale1515255fromto122crayon132X11/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 _ _ contSets 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?false1231numberTakes 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

1last1000huescolumns1lengthcolumnsfair hues00hue10110002last1000✐ fair hue table10

11

1number

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?truetransparency

color300indexfloor15scale15grayscale15255fromto121number✐ last set ascolor number✐ fair?true✐ raw HSLhue10050✐ raw HSVhue100100

015numberlength8length66length161216341656✐ last set asRGB✐ fair?false1number

44partial matchestrue4matchhead matchestrueSUBSET throws here if it handles a unique match1number

44partial matchesfalse4matchhead matchesfalseSUBSET throws here if it handles a unique match2

015numberlength8length66length161216341656

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?false1number

✐ last set asRGB✐ fair?false

✐ last set asRGB✐ fair?false

✐ last set asRGB✐ fair?false

✐ last set asRGB✐ fair?false

0

4numbervalue0255131number✐ last set asRGB✐ fair?false

✐ last set asHSL22✐ fair?false1number

✐ last set asHSL✐ HSL11000100✐ raw HSL✐ fair?false1number

✐ last set asHSV✐ fair?false✐ raw HSV13100length31number

hue100if USED TO BE fairHSL✐ fair?false1number

✐ last set asHSL2222✐ fair?true1number

✐ last set asHSL3333✐ fair?true1number

231length3

saturationsaturation22✐ last set asHSV✐ fair?true1number

brightnessbrightness33✐ last set asHSV✐ fair?true1number

231length31numberReports 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 _ 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 _ 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 _ 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 _ 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 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 _ 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 _

err_resetSets 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 colorslist

ca:canvia de llapis de color saltant _

pt:lança o erro _ 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 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_reset

pt:o texto multilinha _

ca:llapis de color

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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:Grad _ ca:graus de _ radians pt:_ radianos em graus 0.5

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 } elastictime in milliseconds

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 reporter12

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 } elasticreporter

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont

de:wirf _ ca:llança _ es:lanzar _ fr:lance _ pt:lança _ catchtag

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont

de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ pt:lança _ _ catchtagThis 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 This block does nothing. Use it when you are calling a reporter for its side effect and don't care about its return value.

de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _ pt:ignora _ 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 valueThe inputs are a starting value and one or more monadic functions. The first function is called with the starting value as its input. The second function is called with the value reported by the first function as its input, and so on. PIPE reports the value reported by the last function. PIPE provides an alternative notation for function composition instead of the usual nesting. nested acronym: report (combine (keep items (capital-letter? ( )) from (map (letter (1) of ( )) over (sentence→list (phrase)))) using (join ( ) ( ))) piped acronym: report (pipe (phrase) → (sentence→list ( )) (map (letter (1) of ( )) over ( )) (keep items (capital-letter? ( )) from ( )) (combine ( ) using (join ( ) ( ))) Some people find PIPE easier to read because the functions are called in the same order in which you read them, whereas in nested composition the innermost function is called first.

pt:a aplicação a _ de _ ca:canalitza _ $arrowRight _ 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