<snapdata remixID="14999018"><project name="Binary and Linear Search Practice" app="Snap! 11.0.8, https://snap.berkeley.edu" version="2"><notes></notes><thumbnail>data:image/png;base64,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</thumbnail><scenes select="1"><scene name="Binary and Linear Search Practice"><notes></notes><hidden></hidden><headers></headers><code></code><blocks><block-definition s="task 1 - linear search - goal: %&apos;target&apos; list: %&apos;list&apos;" type="command" category="other"><header></header><code></code><translations></translations><inputs><input type="%s" initial="1"></input><input type="%s" initial="1"></input></inputs><script><block s="doForEach"><l>item</l><block var="list"/><script><block s="doIf"><block s="reportVariadicEquals"><list><block var="item"/><block var="target"/></list></block><script><block s="bubble"><l>true</l></block><block s="doReport"><block s="reportBoolean"><l><bool>true</bool></l></block></block></script><list></list></block></script></block><block s="bubble"><l>false</l></block><block s="doReport"><block s="reportBoolean"><l><bool>false</bool></l></block></block></script></block-definition><block-definition s="task 2 - binary search - goal: %&apos;target&apos; list: %&apos;list&apos;" type="command" category="other"><header></header><code></code><translations></translations><inputs><input type="%s" initial="1"></input><input type="%s" initial="1"></input></inputs><script><block s="doDeclareVariables"><list><l>low</l><l>high</l><l>index</l><l>item</l></list></block><block s="doSetVar"><l>low</l><l>1</l></block><block s="doSetVar"><l>high</l><block s="reportListAttribute"><l><option>length</option></l><block var="list"/></block></block><block s="doUntil"><block s="reportVariadicGreaterThan"><list><block var="low"/><block var="high"/></list></block><script><block s="doSetVar"><l>index</l><block s="reportMonadic"><l><option>floor</option></l><block s="reportQuotient"><block s="reportVariadicSum"><list><block var="low"/><block var="high"/></list></block><l>2</l></block></block></block><block s="doSetVar"><l>item</l><block s="reportListItem"><block var="index"/><block var="list"/></block></block><block s="doIfElse"><block s="reportVariadicEquals"><list><block var="item"/><block var="target"/></list></block><script><block s="bubble"><l>true</l></block><block s="doReport"><block s="reportBoolean"><l><bool>true</bool></l></block></block></script><script><block s="doIfElse"><block s="reportVariadicGreaterThan"><list><block var="item"/><block var="target"/></list></block><script><block s="doSetVar"><l>high</l><block s="reportDifference"><block var="index"/><l>1</l></block></block></script><script><block s="doSetVar"><l>low</l><block s="reportVariadicSum"><list><block var="index"/><l>1</l></list></block></block></script></block></script></block></script></block><block s="bubble"><l>false</l></block><block s="doReport"><block s="reportBoolean"><l><bool>false</bool></l></block></block></script></block-definition></blocks><primitives></primitives><stage name="Stage" width="480" height="360" costume="0" color="255,255,255,1" tempo="60" threadsafe="false" penlog="false" volume="100" pan="0" lines="round" ternary="false" hyperops="true" codify="false" inheritance="true" sublistIDs="false" id="131"><pentrails>data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAAQAElEQVR4AezVC27kNhAEUCP3P3SC/NZeez6SSDWb9YJg1zsjkV2vDdQfH/4jQIAAAQIEbhdQwLeTu5AAAQIECHx8ZBew3wACBAgQIFAkoICL4F1LgAABAtkCCjh3/5ITIECAQKGAAi7EdzUBAgQI5Aoo4NzdZyeXngABAsUCCrh4Aa4nQIAAgUwBBZy5d6mzBaQnQGABAQW8wBKMQIAAAQJ5Ago4b+cSE8gWkJ7AIgIKeJFFGIMAAQIEsgQUcNa+pSVAIFtA+oUEFPBCyzAKAQIECOQIKOCcXUtKgACBbIHF0ivgxRZiHAIECBDIEFDAGXuWkgABAgQWE7i5gBdLbxwCBAgQIFAkoICL4F1LgAABAtkCCvjG/buKAAECBAj8J6CA/5PwNwECBAgQuFFAAd+InX2V9AQIECDwWUABf9bwMwECBAgQuElAAd8E7ZpsAekJECDwu4AC/l3EvwkQIECAwA0CCvgGZFcQyBaQngCBnwQU8E8qPiNAgAABApMFFPBkYMcTIJAtID2BRwIK+JGMzwkQIECAwEQBBTwR19EECBDIFpD+mYACfqbjOwIECBAgMElAAU+CdSwBAgQIZAu8Sq+AXwn5ngABAgQITBBQwBNQHUmAAAECBF4J7F3Ar9L7ngABAgQIFAko4CJ41xIgQIBAtoAC3nf/khEgQIDAwgIKeOHlGI0AAQIE9hVQwPvuNjuZ9AQIEFhcQAEvviDjESBAgMCeAgp4z71KlS0gPQECDQQUcIMlGZEAAQIE9hNQwPvtVCIC2QLSE2gioICbLMqYBAgQILCXgALea5/SECCQLSB9IwEF3GhZRiVAgACBfQQU8D67lIQAAQLZAs3SK+BmCzMuAQIECOwhoID32KMUBAgQINBMYHABN0tvXAIECBAgUCSggIvgXUuAAAEC2QIKeOD+HUWAAAECBN4VUMDvSnmOAAECBAgMFFDAAzGzj5KeAAECBI4IKOAjWp4lQIAAAQKDBBTwIEjHZAtIT4AAgaMCCviomOcJECBAgMAAAQU8ANERBLIFpCdA4IyAAj6j5h0CBAgQIHBRQAFfBPQ6AQLZAtITOCuggM/KeY8AAQIECFwQUMAX8LxKgACBbAHprwgo4Ct63iVAgAABAicFFPBJOK8RIECAQLbA1fQK+Kqg9wkQIECAwAkBBXwCzSsECBAgQOCqQO8Cvpre+wQIECBAoEhAARfBu5YAAQIEsgUUcN/9m5wAAQIEGgso4MbLMzoBAgQI9BVQwH13lz259AQIEGguoICbL9D4BAgQINBTQAH33JupswWkJ0BgAwEFvMESRSBAgACBfgIKuN/OTEwgW0B6ApsIKOBNFikGAQIECPQSUMC99mVaAgSyBaTfSEABb7RMUQgQIECgj4AC7rMrkxIgQCBbYLP0CnizhYpDgAABAj0EFHCPPZmSAAECBDYTOFjAm6UXhwABAgQIFAko4CJ41xIgQIBAtoACPrB/jxIgQIAAgVECCniUpHMIECBAgMABAQV8ACv7UekJECBAYKSAAh6p6SwCBAgQIPCmgAJ+E8pj2QLSEyBAYLSAAh4t6jwCBAgQIPCGgAJ+A8kjBLIFpCdAYIaAAp6h6kwCBAgQIPBCQAG/API1AQLZAtITmCWggGfJOpcAAQIECDwRUMBPcHxFgACBbAHpZwoo4Jm6ziZAgAABAg8EFPADGB8TIECAQLbA7PQKeLaw8wkQIECAwA8CCvgHFB8RIECAAIHZAmsX8Oz0zidAgAABAkUCCrgI3rUECBAgkC2ggNfdv8kIECBAYGMBBbzxckUjQIAAgXUFFPC6u8meTHoCBAhsLqCAN1+weAQIECCwpoACXnMvpsoWkJ4AgQABBRywZBEJECBAYD0BBbzeTkxEIFtAegIhAgo4ZNFiEiBAgMBaAgp4rX2YhgCBbAHpgwQUcNCyRSVAgACBdQQU8Dq7MAkBAgSyBcLSK+CwhYtLgAABAmsIKOA19mAKAgQIEAgT+K2Aw9KLS4AAAQIEigQUcBG8awkQIEAgW0ABf9q/HwkQIECAwF0CCvguafcQIECAAIFPAgr4E0b2j9ITIECAwJ0CCvhObXcRIECAAIF/BRTwvxD+yhaQngABAncLKOC7xd1HgAABAgT+ElDAfyH4n0C2gPQECFQIKOAKdXcSIECAQLyAAo7/FQBAIFtAegJVAgq4St69BAgQIBAtoICj1y88AQLZAtJXCijgSn13EyBAgECsgAKOXb3gBAgQyBaoTq+AqzfgfgIECBCIFFDAkWsXmgABAgSqBWoLuDq9+wkQIECAQJGAAi6Cdy0BAgQIZAso4Lr9u5kAAQIEggUUcPDyRSdAgACBOgEFXGeffbP0BAgQCBdQwOG/AOITIECAQI2AAq5xd2u2gPQECBD4UMB+CQgQIECAQIGAAi5AdyWBaAHhCRD4W0AB/83gDwIECBAgcK+AAr7X220ECGQLSE/gfwEF/D+FHwgQIECAwH0CCvg+azcRIEAgW0D6LwIK+AuHfxAgQIAAgXsEFPA9zm4hQIAAgWyBb+kV8DcSHxAgQIAAgfkCCni+sRsIECBAgMA3gagC/pbeBwQIECBAoEhAARfBu5YAAQIEsgUUcMz+BSVAgACBlQQU8ErbMAsBAgQIxAgo4JhVZweVngABAqsJKODVNmIeAgQIEIgQUMARaxYyW0B6AgRWFFDAK27FTAQIECCwvYAC3n7FAhLIFpCewKoCCnjVzZiLAAECBLYWUMBbr1c4AgSyBaRfWUABr7wdsxEgQIDAtgIKeNvVCkaAAIFsgdXTK+DVN2Q+AgQIENhSQAFvuVahCBAgQGB1gbkFvHp68xEgQIAAgSIBBVwE71oCBAgQyBZQwPP272QCBAgQIPBQQAE/pPEFAQIECBCYJ6CA59lmnyw9AQIECDwVUMBPeXxJgAABAgTmCCjgOa5OzRaQngABAi8FFPBLIg8QIECAAIHxAgp4vKkTCWQLSE+AwFsCCvgtJg8RIECAAIGxAgp4rKfTCBDIFpCewNsCCvhtKg8SIECAAIFxAgp4nKWTCBAgkC0g/SEBBXyIy8MECBAgQGCMgAIe4+gUAgQIEMgWOJxeAR8m8wIBAgQIELguoICvGzqBAAECBAgcFtiqgA+n9wIBAgQIECgSUMBF8K4lQIAAgWwBBbzN/gUhQIAAgU4CCrjTtsxKgAABAtsIKOBtVpkdRHoCBAh0E1DA3TZmXgIECBDYQkABb7FGIbIFpCdAoKOAAu64NTMTIECAQHsBBdx+hQIQyBaQnkBXAQXcdXPmJkCAAIHWAgq49foMT4BAtoD0nQUUcOftmZ0AAQIE2goo4LarMzgBAgSyBbqnV8DdN2h+AgQIEGgpoIBbrs3QBAgQINBd4FoBd09vfgIECBAgUCSggIvgXUuAAAEC2QIK+Pz+vUmAAAECBE4LKODTdF4kQIAAAQLnBRTwebvsN6UnQIAAgUsCCvgSn5cJECBAgMA5AQV8zs1b2QLSEyBA4LKAAr5M6AACBAgQIHBcQAEfN/MGgWwB6QkQGCKggIcwOoQAAQIECBwTUMDHvDxNgEC2gPQEhgko4GGUDiJAgAABAu8LKOD3rTxJgACBbAHphwoo4KGcDiNAgAABAu8JKOD3nDxFgAABAtkCw9Mr4OGkDiRAgAABAq8FFPBrI08QIECAAIHhAq0KeHh6BxIgQIAAgSIBBVwE71oCBAgQyBZQwG32b1ACBAgQ2ElAAe+0TVkIECBAoI2AAm6zquxBpSdAgMBuAgp4t43KQ4AAAQItBBRwizUZMltAegIEdhRQwDtuVSYCBAgQWF5AAS+/IgMSyBaQnsCuAgp4183KRYAAAQJLCyjgpddjOAIEsgWk31lAAe+8XdkIECBAYFkBBbzsagxGgACBbIHd0yvg3TcsHwECBAgsKaCAl1yLoQgQIEBgd4HnBbx7evkIECBAgECRgAIugnctAQIECGQLKODH+/cNAQIECBCYJqCAp9E6mAABAgQIPBZQwI9tsr+RngABAgSmCijgqbwOJ0CAAAECPwso4J9dfJotID0BAgSmCyjg6cQuIECAAAEC3wUU8HcTnxDIFpCeAIFbBBTwLcwuIUCAAAECXwUU8FcP/yJAIFtAegK3CSjg26hdRIAAAQIEfgko4F8WfiJAgEC2gPS3CijgW7ldRoAAAQIE/hFQwP84+JMAAQIEsgVuT6+Abyd3IQECBAgQ+PhQwH4LCBAgQIBAgcBSBVyQ35UECBAgQKBEQAGXsLuUAAECBNIFFPAyvwEGIUCAAIEkAQWctG1ZCRAgQGAZAQW8zCqyB5GeAAECaQIKOG3j8hIgQIDAEgIKeIk1GCJbQHoCBBIFFHDi1mUmQIAAgXIBBVy+AgMQyBaQnkCqgAJO3bzcBAgQIFAqoIBL+V1OgEC2gPTJAgo4efuyEyBAgECZgAIuo3cxAQIEsgXS0yvg9N8A+QkQIECgREABl7C7lAABAgSyBT4+FHD6b4D8BAgQIFAioIBL2F1KgAABAukCyQWcvnv5CRAgQKBQQAEX4ruaAAECBHIFFHDq7uUmQIAAgVIBBVzK73ICBAgQSBVQwKmbz84tPQECBMoFFHD5CgxAgAABAokCCjhx6zJnC0hPgMASAgp4iTUYggABAgTSBBRw2sblJZAtID2BZQQU8DKrMAgBAgQIJAko4KRty0qAQLaA9EsJKOCl1mEYAgQIEEgRUMApm5aTAAEC2QLLpVfAy63EQAQIECCQIKCAE7YsIwECBAgsJ3BrAS+X3kAECBAgQKBIQAEXwbuWAAECBLIFFPBt+3cRAQIECBD4JaCAf1n4iQABAgQI3CaggG+jzr5IegIECBD4KqCAv3r4FwECBAgQuEVAAd/C7JJsAekJECDwXUABfzfxCQECBAgQmC6ggKcTu4BAtoD0BAj8LKCAf3bxKQECBAgQmCqggKfyOpwAgWwB6Qk8FlDAj218Q4AAAQIEpgko4Gm0DiZAgEC2gPTPBRTwcx/fEiBAgACBKQIKeAqrQwkQIEAgW+B1egX82sgTBAgQIEBguIACHk7qQAIECBAg8Fpg5wJ+nd4TBAgQIECgSEABF8G7lgABAgSyBRTwrvuXiwABAgSWFlDAS6/HcAQIECCwq4AC3nWz2bmkJ0CAwPICCnj5FRmQAAECBHYUUMA7blWmbAHpCRBoIaCAW6zJkAQIECCwm4AC3m2j8hDIFpCeQBsBBdxmVQYlQIAAgZ0EFPBO25SFAIFsAelbCSjgVusyLAECBAjsIqCAd9mkHAQIEMgWaJdeAbdbmYEJECBAYAcBBbzDFmUgQIAAgXYCQwu4XXoDEyBAgACBIgEFXATvWgIECBDIFlDAw/bvIAIECBAg8L6AAn7fypMECBAgQGCYgAIeRpl9kPQECBAgcExAAR/z8jQBAgQIEBgioICHMDokW0B6AgQIHBdQwMfNvEGAAAECBC4LKODLhA4gkC0gPQECv+uE6gAAAkNJREFU5wQU8Dk3bxEgQIAAgUsCCvgSn5cJEMgWkJ7AeQEFfN7OmwQIECBA4LSAAj5N50UCBAhkC0h/TUABX/PzNgECBAgQOCWggE+xeYkAAQIEsgWup1fA1w2dQIAAAQIEDgso4MNkXiBAgAABAtcFOhfw9fROIECAAAECRQIKuAjetQQIECCQLaCAu+7f3AQIECDQWkABt16f4QkQIECgq4AC7rq57LmlJ0CAQHsBBdx+hQIQIECAQEcBBdxxa2bOFpCeAIEtBBTwFmsUggABAgS6CSjgbhszL4FsAekJbCOggLdZpSAECBAg0ElAAXfallkJEMgWkH4rAQW81TqFIUCAAIEuAgq4y6bMSYAAgWyB7dIr4O1WKhABAgQIdBBQwB22ZEYCBAgQ2E7gUAFvl14gAgQIECBQJKCAi+BdS4AAAQLZAgr47f17kAABAgQIjBNQwOMsnUSAAAECBN4WUMBvU2U/KD0BAgQIjBVQwGM9nUaAAAECBN4SUMBvMXkoW0B6AgQIjBdQwONNnUiAAAECBF4KKOCXRB4gkC0gPQECcwQU8BxXpxIgQIAAgacCCvgpjy8JEMgWkJ7APAEFPM/WyQQIECBA4KGAAn5I4wsCBAhkC0g/V0ABz/V1OgECBAgQ+FFAAf/I4kMCBAgQyBaYn14Bzzd2AwECBAgQ+CaggL+R+IAAAQIECMwXWLmA56d3AwECBAgQKBJQwEXwriVAgACBbAEFvOr+zUWAAAECWwso4K3XKxwBAgQIrCrwJwAAAP//lVwoVAAAAAZJREFUAwDU5ALRcl29HAAAAABJRU5ErkJggg==</pentrails><costumes><list struct="atomic" id="132"></list></costumes><sounds><list struct="atomic" id="133"></list></sounds><variables></variables><blocks></blocks><scripts></scripts><sprites select="1"><sprite name="Sprite" idx="1" x="0" y="0" heading="90" scale="1" volume="100" pan="0" rotation="1" draggable="true" costume="0" color="80,80,80,1" pen="tip" id="138"><costumes><list struct="atomic" id="139"></list></costumes><sounds><list struct="atomic" id="140"></list></sounds><blocks></blocks><variables></variables><scripts><script x="182" y="275"><custom-block s="task 1 - linear search - goal: %s list: %s"><l>30</l><block var="numbers - 1"/></custom-block></script><script x="181" y="323"><custom-block s="task 2 - binary search - goal: %s list: %s"><l>2</l><block var="numbers - 2"/></custom-block></script><script x="152" y="380.33333333333337"><block s="receiveGo"></block><block s="doSetVar"><l>timer</l><l>0</l></block><custom-block s="task 1 - linear search - goal: %s list: %s"><l>99</l><block var="numbers - 3"/></custom-block><block s="bubble"><block var="timer"/></block></script><script x="500" y="384.33333333333337"><block s="receiveGo"></block><block s="doForever"><script><block s="doChangeVar"><l>timer</l><l>1</l></block></script></block></script><script x="174" y="35.83333333333334"><block s="receiveGo"></block></script><script x="345" y="63.83333333333334"><block s="doSetVar"><l>numbers - 1</l><block s="reportNewList"><list><l>10</l><l>20</l><l>30</l><l>40</l><l>50</l></list></block></block><block s="doSetVar"><l>numbers - 2</l><block s="reportNewList"><list><l>1</l><l>2</l><l>3</l><l>4</l><l>5</l><l>6</l></list></block></block><block s="doSetVar"><l>numbers - 3</l><block s="reportNewList"><list></list></block></block><block s="doFor"><l>i</l><l>1</l><l>100</l><script><block s="doAddToList"><block var="i"/><block var="numbers - 3"/></block></script></block></script><script x="372" y="549"><custom-block s="task 2 - binary search - goal: %s list: %s"><l>99</l><block var="numbers - 3"/></custom-block></script></scripts></sprite><watcher var="numbers - 1" style="normal" x="10" y="10" color="243,118,29" extX="80" extY="70"/><watcher var="numbers - 2" style="normal" x="10" y="103.000002" color="243,118,29" extX="80" extY="70"/><watcher var="numbers - 3" style="normal" x="10" y="196.000004" color="243,118,29" extX="80" extY="70"/><watcher scope="Stage" s="getTimer" style="normal" x="10" y="289.000006" color="4,148,220" hidden="true"/><watcher var="timer" style="normal" x="10" y="310.000008" color="243,118,29"/></sprites></stage><variables><variable name="numbers - 1"><list struct="atomic" id="214">10,20,30,40,50</list></variable><variable name="numbers - 2"><list struct="atomic" id="215">1,2,3,4,5,6</list></variable><variable name="numbers - 3"><list struct="atomic" id="216">1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100</list></variable><variable name="timer"><l>1085899</l></variable></variables></scene></scenes></project><media name="Binary and Linear Search Practice" app="Snap! 11.0.8, https://snap.berkeley.edu" version="2"></media></snapdata>