data:image/png;base64,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

Sprite

Check if player 1 wins horizontallycounter0i1lengthCheck if player 1 wins vertically 411321231141511421331241421331241151611521431341521431341251431341251161621531441351531441351261441351261171631541451361541451361271641551461371641531421311651541431321541431321211661551441331551441331221441331221111671561451341561451341231451341231121571461351241461351241131471361251141falseCheck if player 1 wins diagonally

Checks if player 2 wins horizontallycounter0i1lengthChecks if player 2 wins vertically412322232142512422332242422332242152612522432342522432342252432342252162622532442352532442352262442352262172632542452362542452362272642552462372642532422312652542432322542432322212662552442332552442332222442332222112672562452342562452342232452342232122572462352242462352242132472362252142falseChecks if player 2 wins diagonally

Each time you press space and the last tile of the column is empty, this algorithm will check if player 1 (red chip) has win.Each time you press space and the last tile of the column is empty, this algorithm will check if player 1 (black chip) has win.Each time you press space and the last tile of the column is empty, this algorithm will check if player 2 (black chip) has win.anythingtruei1move madetrue42Each time you press space and the last tile of the column is empty, this algorithm will check if player 2 (black chip) has win.anythingtrueSad TromboneLooks like a tie2not valid if there are no more empty tiles to put a chipStart GameThis would change from player 1 to player 2 (changes the costumes, red = player 1; black = player 2)1804.75145.251

#inoutLABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.

de:drucke _ in Größe _ ca:etiqueta _ de mida _ es:etiqueta _ de tamaño _ fr:étiquette _ d'une taille de _ Hello!12

de:behalte Elemente, die _ aus _ ca:manté els elements on _ de _ es:mantener los elementos donde _ de _ fr:garder les items tels que _ de _ 1

de:kombiniere mit _ die Elemente von _ ca:combina amb _ els elements de _ es:combinar con _ los elementos de _ fr:combine avec _ les items de _ 1

de:für _ = _ bis _ _ ca:per _ = _ fins _ _ es:para _ = _ hasta _ _ fr:pour _ allant de _ à _ _ 110i

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ 121

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ cont3

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

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

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

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

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _

de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _

de:Zahlen von _ bis _ ca:nombres des de _ a _ es:números de _ a _ fr:nombres de _ à _ 1101

de:wende _ an auf _ ca:mapeja _ sobre _ es:mapear _ sobre _ fr:appliquer _ à _ 1Take any number of input lists, and create a new list containing the items of the input lists. So APPEND [A B] [C D] where the [,,,] are lists reports the list [A B C D] not [[A B] [C D]].

length11111Reports a new list containing the items of the input list, but in the opposite order.

1inputresultReports 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.

1Reports 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.

1even itemsmerge11#1#2The 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.

11

#11datacountmapmany##1#1data listscount

#1datacount1

durationname duration length number of channels sample rate samplessamplessoundvar base64, binaryString, len, bytes, i, arrayBuffer, audioCtx; if (sound.audioBuffer) { return; } base64 = sound.audio.src.split(',')[1]; binaryString = window.atob(base64); len = binaryString.length; bytes = new Uint8Array(len); for (i = 0; i < len; i += 1) { bytes[i] = binaryString.charCodeAt(i); } arrayBuffer = bytes.buffer; Note.prototype.setupContext(); audioCtx = Note.prototype.audioContext; audioCtx.decodeAudioData(arrayBuffer, function(buffer) { sound.audioBuffer = buffer; });soundreturn !isNil(sound.audioBuffer);soundchoiceswitch (choice) { case 'sample rate': return sound.audioBuffer.sampleRate; case 'duration': return sound.audioBuffer.duration; case 'length': return sound.audioBuffer.length; case 'number of channels': return sound.audioBuffer.numberOfChannels; default: return sound.audioBuffer; }

play back a sound, a list of samples (single channel), or a list of lists (multiple channels) at the given sample rate. for demo purposes this block also fast-plots the samples on the stage

4410022.05 kHz=22050 44.1 kHz=44100 88.2 kHz=88200 96 kHz=96000samplesratevar audioCtx, channels, frameCount, arrayBuffer, i, source; Note.prototype.setupContext(); audioCtx = Note.prototype.audioContext; channels = (samples.at(1) instanceof List) ? samples.length() : 1; frameCount = (channels === 1) ? samples.length() : samples.at(1).length(); arrayBuffer = audioCtx.createBuffer(channels, frameCount, rate); if (!arrayBuffer.copyToChannel) { arrayBuffer.copyToChannel = function (src, channel) { var buffer = this.getChannelData(channel); for (i = 0; i < src.length; i += 1) { buffer[i] = src[i]; } } } if (channels === 1) { arrayBuffer.copyToChannel(Float32Array.from(samples.asArray()), 0, 0); } else { for (i = 0; i < channels; i += 1) { arrayBuffer.copyToChannel(Float32Array.from(samples.at(i + 1).asArray()), i, 0); } } source = audioCtx.createBufferSource(); source.buffer = arrayBuffer; source.connect(audioCtx.destination); source.start(); source.pause = source.stop; this.parentThatIsA(StageMorph).activeSounds.push(source);generate a list of samples representing a sine wave at the given frequency, duration and sample rate

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

quickly 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.

1listoffset-2

records an audio snippet and reports it as a new sound, or zero if the user cancels

ca:volum del so es:volumen del sonido return typeof meter != 'undefined'return Math.floor(meter.volume * 100);reports a list of all pixels in the given costume. Each pixel is represented by a 4-item sublist of RGBA values

pixelsname pixels width heightwidthheightcostume var i, pixels = [], src = costume.contents.getContext('2d').getImageData( 0, 0, costume.width(), costume.height() ); for (i = 0; i < src.data.length; i += 4) { pixels.push(new List([ src.data[i], src.data[i + 1], src.data[i + 2], src.data[i + 3] ])); } return new List(pixels);overwrite the pixels in the given costume with another list of pixels. Caution! This operation directly changes the costume. Make sure to use a copy in case you would like to revert changes later.

reports the sprite's actual current costume, which may or may not be part of its wardrobe e.g. if it is a copy

reports a copy of the given costume. This is especially useful when modifying a costume, so you can keep the original around

display the given bitmap in a copy of the current costume (so you can switch back to the original costume again)

takes a snapshot with the webcam and reports it as a new costume, or zero if the user cancels

501

0.5

1

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

x positionx position y position direction size501linearlinear 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 } elasticgettergettergettergetterreporter

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 ""

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 } elasticreporterdata:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAAQAElEQVR4AezdaYxkV3nG8bequ6c9q2fMjIMXwJjVYHZsCBgbEjAJxCERSxIpQoqJFIUoICxHgkiJLCS+RIkURSQRUaLIX7LwIUpIsBRjG5vFEIclZokxi03ALF7H28z09FZ5np654/JML7Xc9Zz/uM7cqlu37jnnd07fp+6t6nE/+IMAAggggAACtQsQwLWTUyECCCCAAAIReQcwMwABBBBAAIGGBAjghuCpFgEEEEAgbwECON/xp+cIIIAAAg0KEMAN4lM1AggggEC+AgRwvmOfd8/pPQIIINCwAAHc8ABQPQIIIIBAngIEcJ7jTq/zFqD3CCDQAgECuAWDQBMQQAABBPITIIDzG3N6jEDeAvQegZYIEMAtGQiagQACCCCQlwABnNd401sEEMhbgN63SIAAbtFg0BQEEEAAgXwECOB8xpqeIoAAAnkLtKz3BHDLBoTmIIAAAgjkIUAA5zHO9BIBBBBAoGUCNQdwy3pPcxBAAAEEEGhIgABuCJ5qEUAAAQTyFiCAaxx/qkIAAQQQQKAQIIALCZYIIIAAAgjUKEAA14idd1X0HgEEEEBgWIAAHtbgPgIIIIAAAjUJEMA1QVNN3gL0HgEEEDhZgAA+WYTHCCCAAAII1CBAANeATBUI5C1A7xFAYD0BAng9FdYhgAACCCBQsQABXDEwu0cAgbwF6D0CGwkQwBvJsB4BBBBAAIEKBQjgCnHZNQIIIJC3AL3fTIAA3kyH5xBAAAEEEKhIgACuCJbdIoAAAgjkLbBV7wngrYR4HgEEEEAAgQoECOAKUNklAggggAACWwmkHcBb9Z7nEUAAAQQQaEiAAG4InmoRQAABBPIWIIDTHX96hgACCCDQYgECuMWDQ9MQQAABBNIVIIDTHdu8e0bvEUAAgZYLEMAtHyCahwACCCCQpgABnOa40qu8Beg9Agh0QIAA7sAg0UQEEEAAgfQECOD0xpQeIZC3AL1HoCMCBHBHBopmIoAAAgikJUAApzWe9AYBBPIWoPcdEiCAOzRYNBUBBBBAIB0BAjidsaQnCCCAQN4CHes9AdyxAaO5CCCAAAJpCBDAaYwjvUAAAQQQ6JhAyQHcsd7TXAQQQAABBBoSIIAbgqdaBBBAAIG8BQjgEsefXSGAAAIIIDCqAAE8qhTbIYAAAgggUKIAAVwiZt67ovcIIIAAAuMIEMDjaLEtAggggAACJQkQwCVBspu8Beg9AgggMK4AATyuGNsjgAACCCBQggABXAIiu0AgbwF6jwACkwgQwJOo8RoEEEAAAQSmFCCApwTk5QggkLcAvUdgUgECeFI5XocAAggggMAUAgTwFHi8FAEEEMhbgN5PI0AAT6PHaxFAAAEEEJhQgACeEI6XIYAAAgjkLTBt7wngaQV5PQIIIIAAAhMIEMAToPESBBBAAAEEphXodgBP23tejwACCCCAQEMCBHBD8FSLAAIIIJC3AAHc3fGn5QgggAACHRYggDs8eDQdAQQQQKC7AgRwd8cu75bTewQQQKDjAgRwxweQ5iOAAAIIdFOAAO7muNHqvAXoPQIIJCBAACcwiHQBAQQQQKB7AgRw98aMFiOQtwC9RyARAQI4kYGkGwgggAAC3RIggLs1XrQWAQTyFqD3CQkQwAkNJl1BAAEEEOiOAAHcnbGipQgggEDeAon1ngBObEDpDgIIIIBANwQI4G6ME61EAAEEEEhMYMwATqz3dAcBBBBAAIGGBAjghuCpFgEEEEAgbwECeIzxZ1MEEEAAAQTKEiCAy5JkPwgggAACCIwhQACPgZX3pvQeAQQQQKBMAQK4TE32hQACCCCAwIgCBPCIUGyWtwC9RwABBMoWIIDLFmV/CCCAAAIIjCBAAI+AxCYI5C1A7xFAoAoBArgKVfaJAAIIIIDAFgIE8BZAPI0AAnkL0HsEqhIggKuSZb8IIIAAAghsIkAAb4LDUwgggEDeAvS+SgECuEpd9o0AAggggMAGAgTwBjCsRgABBBDIW6Dq3hPAVQuzfwQQQAABBNYRIIDXQWEVAggggAACVQu0O4Cr7j37RwABBBBAoCEBArgheKpFAAEEEMhbgABu7/jTMgQQQACBhAUI4IQHl64hgAACCLRXgABu79jk3TJ6jwACCCQuQAAnPsB0DwEEEECgnQIEcDvHhVblLUDvEUAgAwECOINBposIIIAAAu0TIIDbNya0CIG8Beg9ApkIEMCZDDTdRAABBBBolwAB3K7xoDUIIJC3AL3PSIAAzmiw6SoCCCCAQHsECOD2jAUtQQABBPIWyKz3BHBmA053EUAAAQTaIUAAt2McaAUCCCCAQGYCJwVwZr2nuwgggAACCDQkQAA3BE+1CCCAAAJ5CxDAQ+PPXQQQQAABBOoSIIDrkqYeBBBAAAEEhgQI4CGMvO/SewQQQACBOgUI4Dq1qQsBBBBAAIHjAgTwcQgWeQvQewQQQKBuAQK4bnHqQwABBBBAQAIEsBC4IZC3AL1HAIEmBAjgJtSpEwEEEEAgewECOPspAAACeQvQewSaEiCAm5KnXgQQQACBrAUI4KyHn84jgEDeAvS+SQECuEl96kYAAQQQyFaAAM526Ok4AgggkLdA070ngJseAepHAAEEEMhSgADOctjpNAIIIIBA0wLNBnDTvad+BBBAAAEEGhIggBuCp1oEEEAAgbwFCODmxp+aEUAAAQQyFiCAMx58uo4AAggg0JwAAdycfd4103sEEEAgcwECOPMJQPcRQAABBJoRIICbcafWvAXoPQIIIBAEMJMAAQQQQACBBgQI4AbQqRKBrAXoPAIIrAkQwGsM/IUAAggggEC9AgRwvd7UhgACeQvQewROCBDAJyi4gwACCCCAQH0CBHB91tSEAAII5C1A758kQAA/iYMHCCCAAAII1CNAANfjTC0IIIAAAnkLnNJ7AvgUElYggAACCCBQvQABXL0xNSCAAAIIIHCKQFYBfErvWYEAAggggEBDAgRwQ/BUiwACCCCQtwABnM3401EEEEAAgTYJEMBtGg3aggACCCCQjQABnM1Q591Reo8AAgi0TYAAbtuI0B4EEEAAgSwECOAshplO5i1A7xFAoI0CBHAbR4U2IYAAAggkL0AAJz/EdBCBvAXoPQJtFSCA2zoytAsBBBBAIGkBAjjp4aVzCCCQtwC9b7MAAdzm0aFtCCCAAALJChDAyQ4tHUMAAQTyFmh77wngto8Q7UMAAQQQSFKAAE5yWOkUAggggEDbBaoN4Lb3nvYhgAACCCDQkAAB3BA81SKAAAII5C1AAFc3/uwZAQQQQACBDQUI4A1peAIBBBBAAIHqBAjg6mzz3jO9RwABBBDYVIAA3pSHJxFAAAEEEKhGgACuxpW95i1A7xFAAIEtBQjgLYnYAAEEEEAAgfIFCODyTdkjAnkL0HsEEBhJgAAeiYmNEEAAAQQQKFeAAC7Xk70hgEDeAvQegZEFCOCRqdgQAQQQQACB8gQI4PIs2RMCCCCQtwC9H0uAAB6Li40RQAABBBAoR4AALseRvSCAAAII5C0wdu8J4LHJeAECCCCAAALTCxDA0xuyBwQQQAABBMYWSCqAx+49L0AAAQQQQKAhAQK4IXiqRQABBBDIW4AATmb86QgCCCCAQJcECOAujRZtRQABBBBIRoAATmYo8+4IvUcAAQS6JkAAd23EaC8CCCCAQBICBHASw0gn8hag9wgg0EUBAriLo0abEUAAAQQ6L0AAd34I6QACeQvQewS6KkAAd3XkaDcCCCCAQKcFCOBODx+NRwCBvAXofZcFCOAujx5tRwABBBDorAAB3Nmho+EIIIBA3gJd7z0B3PURpP0IIIAAAp0UIIA7OWw0GgEEEECg6wLTBXDXe0/7EUAAAQQQaEiAAG4InmoRQAABBPIWIIAnH39eiQACCCCAwMQCBPDEdLwQAQQQQACByQUI4Mnt8n4lvUcAAQQQmEqAAJ6KjxcjgAACCCAwmQABPJkbr8pbgN4jgAACUwsQwFMTsgMEEEAAAQTGFyCAxzfjFQjkLUDvEUCgFAECuBRGdoIAAggggMB4AgTweF5sjQACeQvQewRKEyCAS6NkRwgggAACCIwuQACPbsWWCCCAQN4C9L5UAQK4VE52hgACCCCAwGgCBPBoTmyFAAIIIJC3QOm9J4BLJ2WHCCCAAAIIbC1AAG9txBYIIIAAAgiULtCpAC699+wQAQQQQACBhgQI4IbgqRYBBBBAIG8BArgz409DEUAAAQRSEiCAUxpN+oIAAggg0BkBArgzQ5V3Q+k9AgggkJoAAZzaiNIfBBBAAIFOCBDAnRgmGpm3AL1HAIEUBQjgFEeVPiGAAAIItF6AAG79ENFABPIWoPcIpCpAAKc6svQLAQQQQKDVAgRwq4eHxiGAQN4C9D5lAQI45dGlbwgggAACrRUggFs7NDQMAQQQyFsg9d4TwKmPMP1DAAEEEGilAAHcymGhUQgggAACqQtsHsCp957+IYAAAggg0JAAAdwQPNUigAACCOQtQABvPP48gwACCCCAQGUCBHBltOwYAQQQQACBjQUI4I1t8n6G3iOAAAIIVCpAAFfKy84RQAABBBBYX4AAXt+FtXkL0HsEEECgcgECuHJiKkAAAQQQQOBUAQL4VBPWIJC3AL1HAIFaBAjgWpipBAEEEEAAgScLEMBP9uARAgjkLUDvEahNgACujZqKEEAAAQQQeEKAAH7CgnsIIIBA3gL0vlYBArhWbipDAAEEEEDgmAABfMyBvxFAAAEE8haovfcEcO3kVIgAAggggEAEAcwsQAABBBBAoAGBVgVwA/2nSgQQQAABBBoRIIAbYadSBBBAAIHcBQjg1swAGoIAAgggkJMAAZzTaNNXBBBAAIHWCBDArRmKvBtC7xFAAIHcBAjg3Eac/iKAAAIItEKAAG7FMNCIvAXoPQII5ChAAOc46vQZAQQQQKBxAQK48SGgAQjkLUDvEchVgADOdeTpNwIIIIBAowIEcKP8VI4AAnkL0PucBQjgnEefviOAAAIINCZAADdGT8UIIIBA3gK5954Azn0G0H8EEEAAgUYECOBG2KkUAQQQQCBvgQgCOPcZQP8RQAABBBoRIIAbYadSBBBAAIHcBXIO4NzHnv4jgAACCDQoQAA3iE/VCCCAAAL5ChDAuY49/UYAAQQQaFSAAG6Un8oRQAABBHIVIIBzHfm8+03vEUAAgcYFCODGh4AGIIAAAgjkKEAA5zjq9DlvAXqPAAKtECCAWzEMNAIBBBBAIDcBAji3Eae/COQtQO8RaI0AAdyaoaAhCCCAAAI5CRDAOY02fUUAgbwF6H2rBAjgVg0HjUEAAQQQyEWAAM5lpOknAgggkLdA63pPALduSGgQAggggEAOAgRwDqNMHxFAAAEEWidQawC3rvc0CAEEEEAAgYYECOCG4KkWAQQQQCBvAQK4tvGnIgQQQAABBJ4QIICfsOAeAggggAACtQkQwLVR510RvUcAAQQQeLIAAfxkdx8LnAAAEABJREFUDx4hgAACCCBQiwABXAszleQtQO8RQACBUwUI4FNNWIMAAggggEDlAgRw5cRUgEDeAvQeAQTWFyCA13dhLQIIIIAAApUKEMCV8rJzBBDIW4DeI7CxAAG8sQ3PIIAAAgggUJkAAVwZLTtGAAEE8hag95sLEMCb+/AsAggggAAClQgQwJWwslMEEEAAgbwFtu49Aby1EVsggAACCCBQugABXDopO0QAAQQQQGBrgZQDeOveswUCCCCAAAINCRDADcFTLQIIIIBA3gIEcKrjT78QQAABBFotQAC3enhoHAIIIIBAqgIEcKojm3e/6D0CCCDQegECuPVDRAMRQAABBFIUIIBTHFX6NCzQ0wMXz/UZ3V+v+Dlvo6cTuNGFNgt4nrl4zq03F73Oz3mbNveDtpUg4IEuYTfsAoHWCfgANqtWzavsVNmtsud4Of340o+9fpce71Dxtj4A+rV6yA2B0gQ8p5iPpXGmsSMCeIxxHET0VGZU5lTmVbYPzo4dayViux7Pq2xTmVXBNhr5Y/dtqtmhu0/Ls1WeqfI8lQtUXnBSeb4eP0flGSpPVdmr4jCe09L70qKdN82xZOfjFH1r22B5DmUxH9sG34X2eHJ0oZ2NtfHEgeDs2KH/zlrcFy9cPiNev3QgfmPxQHxgYSX+cHElPrR8Zly9tD/efeTM+KUj++JVcVo8fbA/dn8pwmHtd7+N9SGTim3s0HTw7lefn6HisH2Flq9VuUzl51R+/nh54/HlG7S8VOXVKi9VcVCfq+UZKt7XrJbetxbN31Kej4n1zXMm+fnY/E9Et1vQ73bzq229Dgj92BP7FKivXlqKK5d2xgdjLj64si2uGvTjvTET7+n14rcGvbhypR+/rXW/2+/H+2a2xdVH98SHFmfjvRcciDce2R7nal9z1bY26737svEOCRxQeZbKy1QuUXHYvllLl8u1dOi+6fjS91382MXbeOmQfo22ebHKeSoO4nktfUDVormb5lA/1fmYWN+am4/NTU9qnkCAAF4HzQeDwemxb2F/XLq4Pd6rQP0DhevvKGjfHv14s677+QB9oV76NB2Vf2atDOLs6MUFeu4Vevx63f8VPX/lXD+u6u+ODxw5EL+6cHqcr337jEpPcStJYE778WVjh+XLdf8yFYfpL2jpAPZYOZBfpMc+I/YlZ1+KLpYv1PqXqLxSxWfCDmW/1oHtxw7ic/ScPyf2gVV3671pzvRTnY8J9i35+Vjv7E+7NgL4pPHVAWF7HIiXLM/Hlf3ZuFpPv1vlMgXqc1R+Rvf3arlLZV73HaZ9Lft6PKOyTWWHgnqPzoyfovUOhVf1+vGOmX68v789fm/hKXHpI3viDNWjnNYW3KYR2KYXH1BxoF6ipUPT5VLdd+g+V0tfTvY2e3W/+NKVly7+AtbpWu/Pij22T9d97+siLX1p2kHus+KL9fh8Fb+mr2VtN82T7anOxwT7lvx8rG3iT1ZR515V68GkzTo6GMwcfkqco891r1jsxft1xvsetfd1KucpVPeobNPZ74yW/aHS0/3hMvyct51TGO/UPs7Sdi/T/V/rz8VV20+LXz96IJ7tz4f1HLfxBXp6ybyKQ9Nnth4nB+bPap3PaJ+upUN3l5bezmclM7q/UfEbKR88fRnbgezXnqftve9LtPSZ9Gu19OfDDuu+7ld6S3k+Jti35OdjpZM9451XfiDpgq0OCLOxN56ms9ZfVPC+W0H5Zq17lkJzt0J3Tks7+YcsxvzTW3ttL2a1PE2vPasXcYn2+Zuq553P2xfP+2aED/x6itsYAg5Vh68vHfts1yHp+w5NX3lwkM5pf7Mq/eNF9JKPtaJVGpFj94vni+WMnvT+d2npL3P5zNeXpy/T49epPF/FIeztdLf8m+bebOxNcz4m2jfPl2TnY/CnMgEfdErbeRd39OlQOJ4W5yzNxVt1qfid/V5crCDer8PznPrTUynr5n31Fe57tMMLdfR+27bZuOLcPfGMtYOSVnIbScCh6pD1pWKflfqs1/f9K0S7tAcfDMW7FrR9PXbpDS19/+TibVy83ksX78P78hmxL2M74B30/kz5Wdqfx9Hb6W55t5TnY6J9S3o+ljez2dN6AqUfQNarpK3rFHwzF+2M/Yu74jUKxl/WIfvlWneGwtc/VL2K2t1TXbtULlDgXzE/H285ejohHKP98Zjs06YOXAfhq3Tfn/P6krHPej1untNF8fZF0aYa2diwhP4U2xav99JvxPxZ8Vl63l+8K+o9X4/98YIW5dw095Kdj4n2zfMl2flYzqxmL5sJ+ACz2fPJPqcDQi92x765HXGJLge/Q2H4Mv007dPh2Wc+E/R7rJf0VM9OhfCF/Zl4e5wWVyzsDf+qUm+sveS3sUPWwefg9Relni2CfSq+vO9x83x2KRy9HC7adMPbydv5sffl4n1v1yv3q/gStOv3N659ZlzKRwgpz8eE+5bsfNQ851aDgA8uNVTTyirmF7bFixWEb9MlZ/8jDPt03wfauhrbV327dHBaa0PMxWVxIEo9o6qrIzXV47HxZWYHnz+TfY7q3afiAOxr6eLQ1F3JxlqJCf94P8PF+3b9Dvoztc8XqFys4jPiM7T081pMdUt5PqbYN8+HlOfjVJOZF48mUMaBY7SaWrSVQq+3cHqc1e/Hm3SY9meIB3Q26suXdbeyp/p39yJepLa8NVbjAret7kaUUV/F+xBR+DNXX3q+SHX528g+G53Xfc9hP6+70oy1EiX+Gd73jPbrs56ztfS3rf2vbD1T972u2E4Px7t5zFOdj4n2zWOd7Hwcb/ay9TQCPnhN8/quvnZ2sC0u1me+b9Dh+mwtmwjfws5jsFftuGhhJt5ycN9a0BTPsTwm4KB16Pns18F3plb7bNR2PhjqoQT9dzVluA7PFX/Zy5ef/WtKPgv2Z9BeP2ntKc/HFPuW+nycdB7zujEFfAAb8yUJbL4zzpjtx+W69OzPEP3DVBxgm+lcL3xm9dR+Ly7X6bAvbwZ/Tgj0dc+B528e28ZfhvLnsTYrxq1YatNKb67H7fFlb58BPV21XRARDuPJz4J3Jjwf0+tbX2Oe9nxUB7nVI+DJVE9NLarl6M54qS45+1dK9ui8qR0GvZjXG4Lnrc7G5XdH+OyuRWKNNsVh58vN/vLTeWqJD36zWvZVHIha1HIbrst1+42bP//1G4NnqgV7VdwuLca7pTwfE+xb8vNxvNnL1tMI+EAyzes799q130UMffYbcY5CeKIDZkWd9q8n7VYIX3rWnrUzquCP3h7F2hfTniaL56r4Uq+Dz/O2CMRiqadruRX1uQ0+6/Wl8fNVs/8hBr9xKp7Xqq1vKczHjXqZYN88tv6iZLLzcaOxZH01Aj6IVLPnFu7VXwi5cEccUMj5XzQa+2AZVf8ZxIzaeH5vW1ykpX/Yq66x7fv3GyT/Qxj+Xwu67FGDhy8962Gtt2JMvPTPjs+GfBbstjmIfXD2+pEa5TFOdT4m2rek5+NIk5aNShUY+WBRaq3N7Wxm9454uQLYlwzb2He3ae+gH7487oN7c1LN1+yQs4F/1ciftfqLVyef/TbZSrfPbwYcuv51FAew3yD4ID1qu1Kej6n1zeO9znwM/8z6uVHHvKrt3IZp52NVbWO/Gwh48mzwVHqr74mY00XNN+rS824tPWHb18mePv/txQtjexzwWUT7Glhbizw+vkrhy87nqFafCc9p6fVaaAT9d7Olr+r9puApWjqE/TnwcBu1euNbyvMxwb553iU9HzeeqTxTlYAPIFXtu3X73XFGzOns8jUxiHHOUurrR28tVGZ6EftjPvzlnqzG5yRo932H1u1XOVPF971OPHp07DZ8/9iaev92/Q5cn/n6jYLP1n2Q9votW5LyfEywb557noPJzsctJ+w6G7BqOgFPqun20JFX+2xSH9bt0ZHRlzPb3G//Dxt2LsyFv9jT5nZWPfLuu3/dyGe+Lr78p+Grutot9+82FMUbu50OXbfRQewzYj/v5zYsKc/HRPvmcU52Pm44UXmiUgFPqkoraNHOZ2I19vd7urgba2ea0cY/PnKrbNPA+JKmFm1sZS1tct8dZv61Ix/4/LiWiseoREMVbtecXuPPgn2G5DcKXqdVm95Sno8p9s1jmvJ83HSy8uR6AtOv86Safi/d2ENfZ5W71dRW99lnDyqzq/1w8PgAryZnd3O/Z9Rrh9lwqHm9Vrfu5jnlg7PfKMyqdaO0M+X5mFrfPJ6pz0dNW251C/jAUXedjdU3O4i+ws0/TI21YZSKNSg6UY/t90R7z9Sj+j9iCJ9ZuvjgV4xbsay+BaPV4Pa4rW6jw9dLP97y1SnPxwT75jH1XHTxGHvcPcbF0vfbUNwet9VtHGs+tqHxubXBA9XVPqfcbv8Q+Ycn5T5u1jf333NzuHidi19XLH2/6eK2DLfT95tuU9n1u4+pzsdR+uZtPK7DxetcbF0sfb/p4rYMt9P3m24T9W8gkN3gaHbqJHgDjfasXo1BLJwb+rs9baqzJR6jVVXoZVH08ISH1/lxG4rb4lK0Zfh+sW7DZcrzMaG+eUyzmI8bTlSeqEQgpwAeLK3EshT9w6RFe2/6SV8dDOLo9zdrYtrPeYxcltTNRZUVFT/W4kQI+34bitulIVubW55fvu+yVdtSno+p9c1j7JLyfNxqvvJ8BQI5BfDqzFIcjEGstvyd+UDtW+oN4uB5EaMcyCuYFq3Ypfvu8F1Qa7z0Yx8E9bA1N7fHxW8Q3Ea31Qdpr9uqkSnPxxT75vlXjLGXfjzKOG81D8p83u1xmWQ+ltkO9jWiQDYBrFBbWRrEg3J5RDNUN91r522g8F2Y7cWPvhx6u9DONtbRKh9EHGiPqbIjKn5cjJseNnY7uQ1+7NA9pBY9rlIcnHV341vK8zHRvnn+JTsfN56pPFOlQDYBbMSdh+KQwu0O3fe7Vy1adhusBe6qjuiPry7Gd++KrM+AfcBz8D6sUXpExcEmGt174nby4yeeqfae63VxLZ5LPjC7jY9qxVGV4jnd3fiW8nxMsG/Jz8eNZyrPVCWQVQALcUnpdouWi4q6kQ6S2rbu25KuQf947pG4+10R/qGvu/621FcE20Nq0AMqDjmvK8atWOqpxm5ug8fIZ75uo98sjBzAavVk81EvrPE26XxMrW+ee56DKc/HGqcVVVkgtwBeXlmJm3XUPKjOa6G/W3YbDOJIbyW+omb5jEqLbG/DB7yfSMGXov0lJ4+bi1Y1enMb3EafmTt471VrvBwngFOej6n1zWNdBHCq81FTmFudAlkFsD+b2j6IO3QZ+n+F7B8oLVp1W9WAPNBfic+6ra1qWTONcZj5jOMeVX+/isOuyXFz6KoZazffd1sO69F9Kj9V8SVov0nQ3a1vHuNU52OFfdsadrQtJvlZS3o+jsbGVmUK6Hhf5u46sK+D4TOpW3SAOKTL0D6AtqfRvVjSUf3O2YX4ensa1WhLfHnXVwJ+oFY4hP1FJ4+ZmCavcvoAAAmOSURBVLTm2G34/rE11f7t+oriL1/5rNdtcwC7fW7z6C1IeT6m1zePbdrzcfSZy5YlCGQXwAre1ZXF8OfA35PfkkLYB1Pdbfg2CP/u70H9hN8ch8Nnew03qBXViyMcag6476pF/ha7Q68I4abGzvW6Df6SmIP3brXNl6B9iVJ3R7+lPB8T7Fvz83H9qVXafFx/96ytSiC7ADbk0sraZehPKXz9xRkfSL26uTJQSyL8De0v6c3BDTpwjXwZs7lG11azQ80h52+v+98m8RUMHwh90CkaMXy/WFf2crgOzxlfDvcbAr8xcPFc8puDsetNeT4m2Lfk5+PYE5gXTCyQZQDveTQeWV6OT0ntdkXf4yo+oOphQzddelYbvt9biX/fdTB8MG+oIa2s1m9G/KW5b6l1vjTvL8D4zNNj5lB00VMS9N/VlOE6XK/b5M97fWn8G6qyeGPg5/RwvFvK8zHBvnnsk56P483eWrdOrrIsA1hnmCunLcQ3tLy+1wtfij6qw3dxkK13kAexPBjEvar8ltXFuFlt8jvs4M+TBPzllx9qzf+o3KniM0+vKwJPfFpbza3Yt5curtNfvPIbAX+Z75uq1l/CmujsV68NjXmy8zHRvnnuJTsfPScp9QhkGcBrtIfioTgatwxW40aF7/9pXf2fBw/Cl1Lv7w3i1pml+I/5R8M/1GoKt5MEHHw+4/Rl6C/rOb9p8lmIQ8+BqFVrN29XlLUVU/7lfXkXxdJ1+ezbges3Av51sbu0gX8PuNhGDye4pTwf0+ubxzrt+TjBFOYl4wuMFcDj7769r9A786W5h+O7q4P4hALwuuMhXN8/0DEI/5vU/repPxfL8fEjS+Fg8Tvr9qI12zJf+vuxmmAnFwefv5FahLAPikXRZhrROFH8eNRS7MNLv8ZLF4ev6/LZty+H/7ee9CVxf/brN1J6OPkt5fmYaN+Sno+Tz2ReOY5AtgF8HOnQkeX4+srqWgj7TNjftq3+TFjhq2h4VOH/NYXvJ7YtxK27H4uHdKDygf5401isI+BvRH9b629VuU3F3z72l7IcjA7Iws/LomgzaT8Rxl7vdUXx4+Hi9cOPfd/79psj/06y6/+CNvovFV858XrdLeWW8nxMsW+pz8dSJjU72Vgg6wB24P3FwXjsgcX4usLw33SUvVlUG4SwninjpsvOqvdBffD3hViJfz68Ep+NQ/GA1qn6MipIeh8+03QI+otPn1dPv6TiL0D5TNhBaEMXh6aLnl43fP1cUTbbxvtynd636y3C1wHsL8s5/L0f72PqojkwSHU+Jto3zw3PiyTn49QTmh1sKZB1AFvnmojVcx+Nh+ePxm0ry/FPuhz9SR1RfXmz3C9mDRQECl/t+97BIG5aWYlr5w/FdXsfjh/p4OTLWW4OZWsBn+36d279hSy/YXIYfkcve1DFX2BzaIpZj0Lmx8ragxH/8mu9DxcfYP2Zr38NygdZ1+fiS9D+Bzi8zYi7HW2zayLd+Zho35Kej6PNWraaVCD7ADacAnA1FMKH+/HFxcX4Wx22P6Yg/pye82e0y3q8qjI4UfTElrcntvb/XtAH8kN6/TcUvtf2V+OvlhfihjgSP1HdnQjfLftb3wYOSP8OrkP4q6r2RhX/wypf09JXL4qzYbs6IL19sfT94eL1LsU633fxax3mDlmfYfvzXv/a2qdVh7/57C+AeUz1sPyb5kSy8zHBvnnuJD0fy5/h7LEQIICPS/jAcOb98fjOg3HH8iD+sbcSH9Fl6Y/p6a8oOP2NRx9w/cOmVSPfHNxHBqvxvViNf1ldiT+Jo/E3s/fHbcc/8/U+R94ZG54Q8Dj4oOcz0yKEHZB+0+QvRv1QW/rS4GEtfYZiZwfrZsWh6336G80+m/bnu7fr9T7j/U8tHfL+FrZD2fvRqupuKc/HBPuW/HysbqbnvWcC+KTx18Fhaed9cd/sA/FFh+VgOT6s8PxLnbneouXdWt6rQH5I5ZAeL2npkF3VfZ8pH9bjR3T2fJ+2+6EC/Dbd/3s99+GlpfizheW47rSH4weqY0HFP7Qn1c7DMQTs589m/c92Ohgdvjfo9TepfFHFv5/7fS19puzQdLA6kP3FGV9W9n0XP/YbLAe2f7fXv+Lk4PX+btTrb4wIf+HLH0v47NpBrdX13DRPkp2PifUti/lYz6zPpxYCeJ2x1oFhoLLgsHy0F7cc6cffzazGNSp/HCvxp73VuFZntdf3Im5X0H5b5U4Fry8v36TQ/XhvOT6qy8zXxHL80WAlPrptKa771sG4Q5/3PqLXrAZ/yhLwQc9nrb4k7G9Ef0U7/oyKg9jh6fsOT1+e9u/t+rNiB6y/QFUUf7HKl5X9mbKD22e8n9I+/PrPaukzal/a9heuag1f1b1205xJdj4m1rcs5uPapOSvUgQI4E0YdXBYPXB/HLr+3viBLxvPrMQnVw7HP6wuxF+vrMZHer24Wi//fZX3rfbjqpXl+PDyUvz5Yytx7dxi/OtpD8bntz0Y34lH4uFXRixpf/4B1ebcShSwqS8z+yzWZ7AOWn872peMb1A9DlMvb9T9m1Q+fVLxOj/n7Vx839+w9qVtB7bPoH2mvKLXNXrT/GlkPtbR6YT6ls18rGNepF4HAbzFCOvAMHhXxIqWi3EwHt1xOO6dfzTu2vFA3D7707h1/t74jMtdP43P73gwvrrrobhz38G4x6Gr1/hSs1/rH8otauLpKQTs64B0UPps2J8B+8zWIeowdeg6XK9XHf48d7h4nQPaZ8s+A/bZsoPX/+iHL137y1jet17a/E1zKtn5mFDfspmPzf9EdLsFBPAY4+cDhIr/BSuH6rLu+6x2UcvFF0Z46XV+ztv4h3CMvbNpCQI299mwP+N1ePqfjPT/MMGB6s+EfTnZAevPeL108a8X+TNkb+Nt/Zmyz6YdvL7k7H2W0LTyd6F550vTnmuec557yczH9vRtqnHz3MlmPk4llemLCeBMBz7xbvvA57NWfz7ss2J/fuszY3/Ryt9wLoofe72/XOUvaTl0fcD05/TeR+JMdK8mAc8l5mNN2F2qhgDu0mjR1nEFfOBz8cHPxWe06xU/R+iOq8v24wp4Lrp4vrmsNxe9zs+1Yj6O20G2H0+AAB7Pi60RQAABBBAoRYAALoWRnSCAAAIIpCVQfW8I4OqNqQEBBBBAAIFTBAjgU0hYgQACCCCAQPUCbQ7g6ntPDQgggAACCDQkQAA3BE+1CCCAAAJ5CxDAbR1/2oUAAgggkLQAAZz08NI5BBBAAIG2Cvw/AAAA///umLzMAAAABklEQVQDABUxz4UlvP9KAAAAAElFTkSuQmCC-1590,0,0,0,0,0,00,0,0,0,0,0,00,0,0,0,0,0,00,0,0,0,0,0,00,0,0,0,0,0,01,0,2,1,1,2,02-1440,0,0,0,0,11,0,2,1,1,2,05