<snapdata remixID="14691463"><project name="AbstractDataType Lecture Template" app="Snap! 11.0.4, https://snap.berkeley.edu" version="2"><notes></notes><thumbnail>data:image/png;base64,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</thumbnail><scenes select="1"><scene name="AbstractDataType Lecture Template"><notes></notes><hidden></hidden><headers></headers><code></code><blocks><block-definition s="Coordinate Points, X Point: %&apos;X&apos; Y Point %&apos;Y&apos;" type="reporter" category="other"><header></header><code></code><translations></translations><inputs><input type="%n" initial="1"></input><input type="%n" initial="1"></input></inputs><script><block s="doReport"><block s="reportNewList"><list><block var="X"/><block var="Y"/></list></block></block></script></block-definition><block-definition s="Draw Shape: Shape Dataset: %&apos;Dataset (table)&apos;" type="command" category="other"><header></header><code></code><translations></translations><inputs><input type="%l" initial="1"></input></inputs><script><block s="doForEach"><l>item</l><block var="Dataset (table)"/><script><block s="gotoXY"><custom-block s="Select X coordinate from Data Record: %l"><block var="item"/></custom-block><custom-block s="Select Y coordinate from Data Record: %l"><block var="item"/></custom-block></block><block s="down"></block></script></block></script></block-definition><block-definition s="Select X coordinate from Data Record: %&apos;SingleRecord&apos;" type="reporter" category="other"><header></header><code></code><translations></translations><inputs><input type="%l" initial="1"></input></inputs><script><block s="doReport"><block s="reportListItem"><l>1</l><block var="SingleRecord"/></block></block></script></block-definition><block-definition s="Select Y coordinate from Data Record: %&apos;SingleData&apos;" type="reporter" category="other"><header></header><code></code><translations></translations><inputs><input type="%l" initial="1"></input></inputs><script><block s="doReport"><block s="reportListItem"><l>2</l><block var="SingleData"/></block></block></script></block-definition></blocks><primitives></primitives><stage name="Stage" width="480" height="360" costume="1" 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="44"><pentrails>data:image/png;base64,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</pentrails><costumes><list id="45"><item><ref mediaID="AbstractDataType Lecture Template_Stage_cst_XY Grid"></ref></item></list></costumes><sounds><list struct="atomic" id="46"></list></sounds><variables></variables><blocks></blocks><scripts><script x="306.47783251231533" y="57.01847290640404"><block s="doForEach"><l>item</l><l/><script></script></block></script></scripts><sprites select="1"><sprite name="Sprite" idx="1" x="-240" y="175" heading="90" scale="1" volume="100" pan="0" rotation="1" draggable="true" costume="0" color="145,26,68,1" pen="tip" id="55"><costumes><list struct="atomic" id="56"></list></costumes><sounds><list struct="atomic" id="57"></list></sounds><blocks></blocks><variables></variables><scripts><script x="463.2758620689656" y="124.14532019704433"><block s="reportNewList"><list><l>240</l><l>0</l><l>0</l><l>100</l><l>-240</l><l>0</l><l>240</l><l></l></list><comment w="165.3694581280788" collapsed="false">odd index numbers are x points&#xD;even index numbers are y points&#xD;draws a Triangle</comment></block></script><comment x="521.8226600985222" y="193.74384236453204" w="307.2413793103449" collapsed="false">The list of numbers is confusing because it is not obvious which numbers are x and which are y coordinates. It will also be difficult to maintain (add or delete coordinates to the lists).&#xD;It would be better to create a matrix (a list of list). Each item in the list will be a list of x and y coordinates.</comment><script x="368.69458128078827" y="388.13546798029563"><block s="doSayFor"><l>Hello!</l><l>2</l></block></script><script x="445.967660687827" y="54.60385878489319"><block s="reportNewList"><list><l>240</l><l>0</l><l>0</l><l>100</l><l>-240</l><l>0</l><l>0</l><l>-100</l><l>240</l><l>0</l></list><comment w="165.3694581280788" collapsed="false">odd index numbers are x points&#xD;even index numbers are y points&#xD;draws a diamond</comment></block></script><script x="15.492610837438406" y="162.39367816091948"><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l></l><l></l></custom-block></script><script x="380.51724137931046" y="321.5903119868641"><block s="doForEach"><l>item</l><l/><script></script></block></script><comment x="15.492610837438463" y="14.21510673234802" w="414.1379310344828" collapsed="false">Abstract Data Types (ADT) is a custom data type created by the programmer to provide meaning to the data in your program. It is not built into the programming language. ADT are created with custom constructor and selector functions (blocks).&#xD;ADT are a form of abstraction because they make are program easier to understand, read, and debug.</comment><comment x="505.56650246305423" y="10" w="290.9852216748768" collapsed="false">Lists and Numbers are examples of Primitive Data Types</comment><comment x="15.911330049261096" y="106.05911330049264" w="413.1527093596059" collapsed="false">Step 1: Create A Reporter block (the constructor) that will take two numbers as the domain (x and y coordinates) and output a list as the range.</comment><comment x="10.492610837438434" y="196.20689655172413" w="421.5270935960591" collapsed="false">Step 2: Create two report blocks (the selectors) that will take the list created by the contructor block as the domain and output a single number as the range (one the x coordinate the second block the y coordinate).</comment><comment x="10" y="319.3596059113301" w="343.69458128078816" collapsed="false">Step 3: Create a list that stores the necessary Coordinte Points to draw your specified shape </comment><comment x="523.7931034482759" y="304.58128078817737" w="304.2857142857142" collapsed="false">Step 4: Create a Draw Shape Command Block whose domain will be the list of Coordinate Points create in step 3. The command block will draw the shape on the screen by using a the for each item block and the selector blocks created in Step 2.</comment><script x="37.8768472906404" y="376.83333333333337"><block s="receiveGo"></block><block s="clear"></block><block s="up"></block><block s="doSetVar"><l>Draw Square</l><block s="reportNewList"><list><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>240</l><l>175</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>240</l><l>-175</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>-240</l><l>-175</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>-240</l><l>175</l></custom-block></list></block></block><block s="doSetVar"><l>Diamond Shape</l><block s="reportNewList"><list><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>240</l><l>0</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>0</l><l>100</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>-240</l><l>0</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>0</l><l>-100</l></custom-block><custom-block s="Coordinate Points, X Point: %n Y Point %n"><l>240</l><l>0</l></custom-block></list></block></block><custom-block s="Draw Shape: Shape Dataset: %l"><block var="Diamond Shape"/></custom-block><block s="up"></block><block s="setColor"><color>145,26,68,1</color></block><block s="setSize"><l>10</l></block><custom-block s="Draw Shape: Shape Dataset: %l"><block var="Draw Square"/></custom-block></script></scripts></sprite><watcher var="Diamond Shape" style="normal" x="10" y="10" color="243,118,29" extX="128.5615234375" extY="103"/><watcher var="Draw Square" style="normal" x="10" y="136.000002" color="243,118,29" extX="128.5615234375" extY="86"/></sprites></stage><variables><variable name="Diamond Shape"><list id="158"><item><list struct="atomic" id="159">240,0</list></item><item><list struct="atomic" id="160">0,100</list></item><item><list struct="atomic" id="161">-240,0</list></item><item><list struct="atomic" id="162">0,-100</list></item><item><list struct="atomic" id="163">240,0</list></item></list></variable><variable name="Draw Square"><list id="164"><item><list struct="atomic" id="165">240,175</list></item><item><list struct="atomic" id="166">240,-175</list></item><item><list struct="atomic" id="167">-240,-175</list></item><item><list struct="atomic" id="168">-240,175</list></item></list></variable></variables></scene></scenes></project><media name="AbstractDataType Lecture Template" app="Snap! 11.0.4, https://snap.berkeley.edu" version="2"><costume name="XY Grid" center-x="240" center-y="180" image="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAAQAElEQVR4Aezdz5EcyXUH4MZGyI2VDzoqZIDOtIBkkOJZHqwHOjOooGgBzzJAoaN84LrBA9S/Bt5sTk33TE93/cmq/BB4qKysqsx8X3bhobG75A9fO//xt7/9rfMVjrU8+7H9fv/md//29VZsv7pxV+Dd6G/ve9+TH05+ECCwK4E//+mPV9d7q//qzToJENhcYOwCvDm/BRB4TGBabKfnj43qKQIE1hRQgNfUNheBGQVSdP/rP399ynHGYQ1FgMBKAgrwStAdTmNJBAgQILChgAK8Ib6pCSwt8Nvf/+GUWHqeXsdP7ole12ddYwsowGPv/7jZD5B5FZ5Xf0X905fTKXH6/iPtxPfTq4dcb+PqTd86v5yHT3w7O53STpw++JF7EtduS38b03vaa2m31yv3smivaRPYWkAB3noHzE+gZ4EU3qzvp6/nwn2OS/tcZXOcKaZFsx22rn09T53Itepr27mWaPvSFgR6FlCAe94dazu8QIpJ4laiuZa4df1Wf33jq2+AL/elkJ5O52J6LqJtca0b0peo808e2yJY666+DJW+RNoV7fXqm/NYBmUy59jGIvCMgAL8jJ5nCTwpUMVnWpQybPXVPembJaoIZ7C2nfPOonKPRSLLq760BYE9CyjAe949aycwt0AKcuKJcdsC2bYzZM4Tad8TbdGt56rvnuev3qOTQCcCCnAnG2EZ4wpcKyxVZOrarDrtXzG37VknMRgBAh8JKMAfCblOYAWBKrQpvIlMWX1pzxZVcPMtN5GBq6/a7Xn63on8c9XEO7e8upTcEq86nawpYK6OBBTgjjbDUsYWaAtu235EZbZ/8agt0lWYq++RhV15pi3IbTu3lkP6E21f2861RNuXdqL+gFAm6RMEehBQgHvYBWsgsJZAimei5ks7ces8/bneRvo+iBTOxPS29CXa/py30V5Lu72WdvraSF8b7TVtAq8EOjtRgDvbEMshMJdAfeOrb4BzjTsdp+aZ9vdwXrn3vMYenKxhGwEFeBt3sxJYRSCFJ7HUZEuOPceas77EHGMZg8DcAisX4LmXbzwCBAgQILBPAQV4n/tm1QcVqH+WedD0pEWAQCOgADcYSzeNT4AAAQIESkABLglHAgQIECCwooACvCL22FPJngABAgRaAQW41dAmQIAAAQIrCSjAK0GbZmwB2RMgQGAqoABPRZwTIECAAIEVBBTgFZBNQWBsAdkTIHBNQAG+pqKPAAECBAgsLKAALwxseAIExhaQPYFbAgrwLRn9BAgQIEBgQQEFeEFcQxMgQGBsAdm/J6AAv6fjGgECBAgQWEhAAV4I1rAECBAgMLbAR9krwB8JuU6AAAECBBYQUIAXQDUkAQIECBD4SODYBfij7F0nQIAAAQIbCSjAG8GblgABAgTGFlCAj7v/MiNAgACBjgUU4I43x9IIECBA4LgCCvBx93bszGRPgACBzgUU4M43yPIIECBA4JgCCvAx91VWYwvIngCBHQgowDvYJEskQIAAgeMJKMDH21MZERhbQPYEdiKgAO9koyyTAAECBI4loAAfaz9lQ4DA2AKy35GAAryjzbJUAgQIEDiOgAJ8nL2UCQECBMYW2Fn2CvDONsxyCRAgQOAYAgrwMfZRFgQIECCwM4GZC/DOsrdcAgQIECCwkYACvBG8aQkQIEBgbAEFeMb9NxQBAgQIELhXQAG+V8p9BAgQIEBgRgEFeEbMsYeSPQECBAh8RkAB/oyWewkQIECAwEwCCvBMkIYZW0D2BAgQ+KyAAvxZMfcTIECAAIEZBBTgGRANQWBsAdkTIPCIgAL8iJpnCBAgQIDAkwIK8JOAHidAYGwB2RN4VEABflTOcwQIECBA4AkBBfgJPI8SIEBgbAHZPyOgAD+j51kCBAgQIPCggAL8IJzHCBAgQGBsgWezV4CfFfQ8AQIECBB4QEABfgDNIwQIECBA4FmBfRfgZ7P3PAECBAgQ2EhAAd4I3rQECBAgMLaAArzf/bdyAgQIENixgAK8482zdAIECBDYr4ACvN+9G3vlsidAgMDOBRTgnW+g5RMgQIDAPgUU4H3um1WPLSB7AgQOIKAAH2ATpUCAAAEC+xNQgPe3Z1ZMYGwB2RM4iIACfJCNlAYBAgQI7EtAAd7XflktAQJjC8j+QAIK8IE2UyoECBAgsB8BBXg/e2WlBAgQGFvgYNn/8PPPP596jnj3vL7R1mY/+npffvWrv3f9/o70fng3+no38tnrfU9++PHHH089RwB7Xt9oa7Mffb0vf/3rP3T9/o70fng3+no38tnrfU8++VfQSUcQIECAAAECzwoowM8Kep7AwgL/+z8LT2B4AgQ2EVCAP8HuVgJrCqTwJjJnjom0BQECxxBQgI+xj7I4mEAV23/+l9MpUelVf507EiCwXwEFeL97t/LKTbelQFuEt1yHuQkQmE9AAZ7P0kgEVhHwLXgVZpMQWFxAAV6c2ARHEFgjh7aw1jfea31rrMUcBAgsL6AAL29sBgLvCqTIJnJTjom0K6bn1e9IgMC+BRTgfe+f1e9coIprvvEmKp30T8/Tl+ttf86XDzMQILCEgAK8hKoxCTwoMC2uOU9kuBwTaQsCBPYvoADvfw9lcFCB+sab9BTeKGwTZiWwlIACvJSscQk0Aimmiabr0qzC2l6rvssNfiFA4LACCvBht1ZiPQq0hXa6vveuTe91TmAdAbMsKaAAL6lrbAJngWlhnZ6333hzLXF+7NX/AlbOBQECxxJQgI+1n7LpVCBFNnFrebmWyPUcE2kLAgS2E1h6ZgV4aWHjDy/QFtNq17fcOhZSXa9zRwIEjiugAB93b2XWuUAV3zp2vlzLI0BgZoG+C/DMyRqOQG8C+cab6G1d1kOAwPICCvDyxmYg8CJQ33ZTdBMvFzQIEBhOQAHud8ut7IACKbqJA6YmJQIEPimgAH8SzO0ECBAgQGAOAQV4DkVjzC9gRAIECBxcQAE++AZLjwABAgT6FFCA+9wXqxpbQPYECAwgoAAPsMlSJECAAIH+BBTg/vbEigiMLSB7AoMIKMCDbLQ0CRAgQKAvAQW4r/2wGgIExhaQ/UACCvBAmy1VAgQIEOhHQAHuZy+shAABAmMLDJa9AjzYhkuXAAECBPoQUID72AerIECAAIHBBCYFeLDspUuAAAECBDYSUIA3gjctAQIECIwtoAA3+69JgAABAgTWElCA15I2DwECBAgQaAQU4AZj7KbsCRAgQGBNAQV4TW1zESBAgACB7wIK8HcIh7EFZE+AAIG1BRTgtcXNR4AAAQIEzgIK8BnBTwJjC8ieAIEtBBTgLdTNSYAAAQLDCyjAw38EABAYW0D2BLYSUIC3kjcvAQIECAwtoAAPvf2SJ0BgbAHZbymgAG+pb24CBAgQGFZAAR526yVOgACBsQW2zl4B3noHzE+AAAECQwoowENuu6QJECBAYGuBbQvw1tmbnwABAgQIbCSgAG8Eb1oCBAgQGFtAAd5u/81MgAABAgMLKMADb77UCRAgQGA7AQV4O/uxZ5Y9AQIEBhdQgAf/AEifAAECBLYRUIC3cTfr2AKyJ0CAwEkB9iEgQIAAAQIbCCjAG6CbksDQApInQOAioABfGPxCgAABAgTWFVCA1/U2GwECYwvInsCLgAL8QqFBgAABAgTWE1CA17M2EwECBMYWkP0rAQX4FYcTAgQIECCwjoACvI6zWQgQIEBgbIE32SvAb0h0ECBAgACB5QUU4OWNzUCAAAECBN4IDFWA32SvgwABAgQIbCSgAG8Eb1oCBAgQGFtAAR5m/yVKgAABAj0JKMA97Ya1ECBAgMAwAgrwMFs9dqKyJ0CAQG8CCnBvO2I9BAgQIDCEgAI8xDZLcmwB2RMg0KOAAtzjrlgTAQIECBxeQAE+/BZLkMDYArIn0KuAAtzrzlgXAQIECBxaQAE+9PZKjgCBsQVk37OAAtzz7lgbAQIECBxWQAE+7NZKjAABAmML9J69Atz7DlkfAQIECBxSQAE+5LZKigABAgR6F1i2APeevfURIECAAIGNBG4W4N/+/g+nxEbr6nbamCS6XaCFETgL5DOaODd3+TNrT+xy8YMvOvuWGJUhuSfuyf9qAa6H//ynP/4yxk9fTqfE6fuPtBPfTz91yHNtvPPwj3/+x7dX22fTfnvHS8+X87IT1ZF2os6vHXO9jfaeMimj9tqk7ZTAJgL12azP6mUReU8Sl5PzL2knzs2bP3O9jZs3nk71vpy+/5ief+9+dah76therLVXLu017X4Far9q/y4rrc/Q5eT8y/T83PXmZ91Txzc3NB25pzm9NNN3jkv9OB8vfTd+mX7+pufXHqt76tjeU7mXRXtt2r5agKc3zXpeGD99PRf0c2Tw6ku7jWv91XfP8+1Yd7YDmlu/npeWSLv60hYEhhDwng2xzV0m+ZnPXt3bJlJ95xrx82//9u1K9X07e+rXqgepD4kMVn1pfybeFOCq2lXFXwY7J3NpJ5FETqrv0j5/1az+nN+KPJO4db367xmr7n3n2AIVUvXlsfQl0r43yqas7n1uqPsku4lAfSbrM/qyiHrn8l4lcqH6Lu0739/ceyXqncq7lMgt1Zd2+hJp3xuVQ+V073Pu20ag9qn27WUV9TnL5y6RC9V3aU8+e7mWyLX3osZ67547rtXnNJ/PRB6pvrTTl0j73iiDMrn13JsCfOvGS3+L0rYvFx/4pQCnY6V/2vfA8B4hQKARaN+ptt3coklgEYH289a2P5ostSD3TJ9J/7Qv9+0sPleA30suGIm65wL0/U82aVd/HauvfSbX0j/tS/8T0f5ppm1nyJwn0hYEZhLY3zB55xJPrLx9j9p2hsx5Im1B4JVAPneJV53nk9SC8+E0vZb+aV/ueyLaz2bbzpA5T6Q9d3yuACfxWkHbrr72GKA2Xl07F+ac53qO08jYiepv29XnSIDA5wTa96htf24UdxP4vED7eWvbt0aqew5eI+4vwC1IoVRfENNOpP1e1D01xvTe9FfUtZxX+4Nj/s498cFtL5fzd/uJlw4NAkcUaN+7ep+qL/mmnUj7jsg7lrh665XOvGOJK5d0HV2gPlf53CWSb/VVe3p+6f+aX99Gxqioqzmv9gfHfG4TH9z2cjmf28RLx4yNNwX43n94/PQaAt7GvQMWdD2b56ov7Sej/qoh4IkMV31pJ2rzyip9gkAPAvWZrM/ow2uqd8p79jDhaA/O9tkruPrs1bH6Pzo2n93Lf4aU+6sv7Sej6kHqQyLDVV/aiXr/yiR91+JNAb5206UvCSQuJ+df0k6cm5efaScuJ+/8knuuxbVHzve9/Gvk7fVz/+WfC9SxvXajHaDE9HL6Em1/zttor2kT2KXA9F356DxJ1j11TN8HUe/N9LZr/dVXx+kzOz237KnA9PNz73ndV8fpuDm/de17/6V+pJ17P4hbn8Nr/dVXxw+Gvnn5agGuql1V/ObTnV6o9S+xvDJZco4l1m3McQTqs1mf1aUyP1xYSAAAEABJREFUr3mWGL/WvuQcS6x79DFrv2r/evWodS6xvsr9njmuFuAsKg8n0t5TLL3mjJ/Yk4m1jieQz2hiqcyXHDtrzviJtMWOBM5Lzb4lzs0ufy69toyfuCf5mwX4nofdQ4AAAQIECDwm8EN9XX7scU8RILCVQN7d3/zuL/5PU7baAPMSeC3wqbO8v5dvwGl86kk3EyCwqcD0nZ2eb7o4kxMg8K5Ava+XApw7qyNtQYBAvwK33tVb/f1mYmUExhNo39OXAnxhqP/eqqfjf//76XTnetz3ZXkr+7G88Uef99M7Pz561vXl9s+7sZzto5/bHvekeX0nBfjrGbCz+Nf/6G9N+e/KRg37sf3nsXmB3zRH/Vz2kLd3Y/t3Y/o56HFPmpf2pQDf+69NN89qdiVgMaMI3HpXb/WP4iJPAnsQaN/TSwFuO/aQgDUSGF1g+s5Oz0f3WSz///vLYkMb+EGBHe5Jva8/VOPB1Od5bIeA8yTe8Sg725OOJRdbWt7d//rPX59yXGwSA38TyPuQyFmOibTFdgLZg0RWkGMi7Z1E3tvLN+DN1huwRBaQYyJtsZ1A9iCRFeSYSFsQGFWg3oF/+vXplCiH6q9zx/UEyj77kaiZq7/OOz9uV4ALKniJgqr+OndcT6Dssx+Jmrn669yxMwHLWVWgfTdWndhkNwV2uifbFeCp5E4Bp2kc6tyeHGo7JbOAgD+cLoD65JA72pN+CnBrviPAdtmHbtuTQ2/vnpNbZO3t573+IHqtb5HJDXpV4Jr/tb6rDy/cWf+d8rVp3rm2XgFuobJIH+oobBv2ZFt/s/cnkHcikZXlmEi7Ynpe/Y7LCcQ8kRlyTKRdMT2v/jWP+e+PM1+KbY4VdV7Xq//7cfkCHJxEJswxkXYb1/ra69rzCsQ7kVFzTKTdxrW+9ro2gaMJ1Gc+Xw4SlV/6p+fpy/W2P+erx8EnbJ1b6/RPz9MXjrY/5x3HsgW4BWlR2v7CSV8i5+29ORfzCbTGrXPbX7OlL5Hz9t6cCwJHF5h+5nOeSN45JtIW6wlMzXOeyApyTKS9RdS33JdvvV++raL6v529+nXZAvxqqvPJNZz0Jc6XL/+Kf7VzLpYXuOadvkRmzzGRtiAwqkD9QTT5ex+isEy0zh/N8H9/Ob3c0sueVLF9KcJfX5Z4rTF/Ab4XcHpfL4DXlEbpsyej7PTYeeZznpgq1O9B7bXqm97rfF6BmCcyah3TLv9rfbm+85ivAAcoEZA6HhwvqXYd2YdEu0h70mpojywwfTdai/eutfdpPy9Q1vm9KZERqy/timt9da2X4/Sbb53fWN9zBbgGLZjgJdJffWknpufpE+sI3LK/1b/OqsxCYH2B6Wd+el6/f2VluZZIu+3PuZhX4D3f9lr2I5HZ2/6cbx1VbF/+Gvr7Xz9X/5X1zVOAM/AtjLY/cIn37s818bxAOddI7bk9KRXHEQXy+U/cyj3XErmeYyJtsZ1A9iCRFeSYSHvnMV8Bfg8iWInck2Mi7X1H/6uPc+LaStOfyLUcE2kLAkcWaD/n1a4/nNax8q/rde64jMB7ztmTRM383r11zxbH+pZb335rDXVe16v/+3GeAvweSvASmfC9+3JdzCfQWle79qGOma2upS0IjCpQ70QdR3XoKe/aiz38HpVCm7jml/7ElWvzFOArA1+69gR4WfAAv9Se1PHRlD1H4GgC+Y0+cbS89pTP9Pelg+/H/AV4MMA9fbZf1poPdeKlQ4PAgAL1e1XehcSABN2mnP1IdLvAeRb2XAHOBzhxbS3BS1y7pm85gexHop2hzrMfifaa9iMCnjmCQN6FxBFyOUoO2Y/EUfL5II/HC3D9pj6dIHiJab/z5QXsyfLGZiBAgMBMAo8V4PqNPoU2MdNiDPOEgD15As+jnxJwMwECswg8VoBTdBOzLMEgswhkPxKzDGYQAgQIEFha4LECvPSqjE+AAIE+BayKwGwCCvBslAYiQIAAAQL3CyjA91u5kwABAmMLyH5WAQV4Vk6DESBAgACB+wQU4Puc3EWAAAECYwvMnr0CPDupAQkQIECAwMcCCvDHRu4gQIAAAQKzC+yqAM+evQEJECBAgMBGAgrwRvCmJUCAAIGxBRTg3ey/hRIgQIDAkQQU4CPtplwIECBAYDcCCvButmrshcqeAAECRxNQgI+2o/IhQIAAgV0IKMC72CaLHFtA9gQIHFFAAT7irsqJAAECBLoXUIC73yILJDC2gOwJHFVAAT7qzsqLAAECBLoWUIC73h6LI0BgbAHZH1lAAT7y7sqNAAECBLoVUIC73RoLI0CAwNgCR89eAT76DsuPAAECBLoUUIC73BaLIkCAAIGjC7xfgI+evfwIECBAgMBGAgrwRvCmJUCAAIGxBRTg2/vvCgECBAgQWExAAV6M1sAECBAgQOC2gAJ822bsK7InQIAAgUUFFOBFeQ1OgAABAgSuCyjA1130ji0gewIECCwuoAAvTmwCAgQIECDwVkABfmuih8DYArInQGAVAQV4FWaTECBAgACB1wIK8GsPZwQIjC0gewKrCSjAq1GbiAABAgQI/CKgAP9ioUWAAIGxBWS/qoACvCq3yQgQIECAwDcBBfibg18JECBAYGyB1bNXgFcnNyEBAgQIEDidFGCfAgIECBAgsIFAVwV4g/xNSYAAAQIENhFQgDdhNykBAgQIjC6gAHfzCbAQAgQIEBhJQAEeabflSoAAAQLdCCjA3WzF2AuRPQECBEYTUIBH23H5EiBAgEAXAgpwF9tgEWMLyJ4AgREFFOARd13OBAgQILC5gAK8+RZYAIGxBWRPYFQBBXjUnZc3AQIECGwqoABvym9yAgTGFpD9yAIK8Mi7L3cCBAgQ2ExAAd6M3sQECBAYW2D07BXg0T8B8idAgACBTQQU4E3YTUqAAAECYwucTgrw6J8A+RMgQIDAJgIK8CbsJiVAgACB0QVGLsCj7738CRAgQGBDAQV4Q3xTEyBAgMC4AgrwqHsvbwIECBDYVEAB3pTf5AQIECAwqoACPOrOj5237AkQILC5gAK8+RZYAAECBAiMKKAAj7jrch5bQPYECHQhoAB3sQ0WQYAAAQKjCSjAo+24fAmMLSB7At0IKMDdbIWFECBAgMBIAgrwSLstVwIExhaQfVcCCnBX22ExBAgQIDCKgAI8yk7LkwABAmMLdJe9AtzdllgQAQIECIwgoACPsMtyJECAAIHuBFYtwN1lb0EECBAgQGAjAQV4I3jTEiBAgMDYAgrwavtvIgIECBAg8IuAAvyLhRYBAgQIEFhN4Ieff/751HNEouf1jba2R/djNKe18v3Vr/7e9fu7lkMP83g3+qslve/JDz/++OOp5whgz+sbbW32o6/35a9//Yeu39+R3g/vRl/vRj57ve+Jv4LODgkCiwoYnAABAm8FFOC3JnoIdCXwv//T1XIshgCBmQQU4JkgDUNgboEU3kTGzTGR9t7CegkQuC6gAF930UtgU4Eqtv/8L6dTohZT/XXuSIDAfgUU4P3unZUPJNAW4YHSPkCqUiBwW0ABvm3jCoEuBXwL7nJbLIrApwUU4E+TeYDA/ALTolrfeNv+6pt/diMSWEbAqO8LKMDv+7hKYFGBFNhEJskxkXYb1/ra69oECOxTQAHe575Z9QEEqrDmm22iUmr7275r/XXdkQCB3gQ+Xo8C/LGROwisItAW4ZowfYmc55hIWxAgsH8BBXj/eyiDAwrUt91KTeEtCUcCxxE4cgE+zi7JZPcCKaiJNpEqqm1/9bX3aRMgcEwBBfiY+yqrTgXaYtsu8VZ/e482AQLHElCAj7Wfv2Sj1Y3AtLi25+033vQnsvC2P+eCAIHjCSjAx9tTGXUokIKauLa09CdyLcdE2oIAgWMLKMDH3t9Rs+sq77agVru+6dYxC65raQsCBI4voAAff49l2KlAFd86drpMyyJAYCEBBXghWMMSuEcg33oT99x79z1uJEBgFwIK8C62ySL3KJBvtol27XWeoptor2kTIDCWgAI81n7LdiWBKrTT6VJ0E9N+57MJGIjAbgQU4N1slYXuRaCKbwptYi/rtk4CBNYVUIDX9TbbAAIpuokBUpVibwLWsysBBXhX22WxBAgQIHAUAQX4KDspDwIECIwtsLvsFeDdbZkFEyBAgMARBBTgI+yiHAgQIEBgdwKzFuDdZW/BBAgQIEBgIwEFeCN40xIgQIDA2AIK8Gz7byACBAgQIHC/gAJ8v5U7CRAgQIDAbAIK8GyUYw8kewIECBD4nIAC/DkvdxMgQIAAgVkEFOBZGA0ytoDsCRAg8HkBBfjzZp4gQIAAAQJPCyjATxMagMDYArInQOAxAQX4MTdPESBAgACBpwQU4Kf4PEyAwNgCsifwuIAC/LidJwkQIECAwMMCCvDDdB4kQIDA2AKyf05AAX7Oz9MECBAgQOAhAQX4ITYPESBAgMDYAs9nrwA/b2gEAgQIECDwaQEF+NNkHiBAgAABAs8L7LkAP5+9EQgQIECAwEYCCvBG8KYlQIAAgbEFFOC97r91EyBAgMCuBRTgXW+fxRMgQIDAXgUU4L3u3Njrlj0BAgR2L6AA734LJUCAAAECexRQgPe4a9Y8toDsCRA4hIACfIhtlAQBAgQI7E1AAd7bjlkvgbEFZE/gMAIK8GG2UiIECBAgsCcBBXhPu2WtBAiMLSD7QwkowIfaTskQIECAwF4EFOC97JR1EiBAYGyBw2WvAB9uSyVEgAABAnsQUID3sEvWSIAAAQKHE/hUAT5c9hIiQIAAAQIbCSjAG8GblgABAgTGFlCA795/NxIgQIAAgfkEFOD5LI1EgAABAgTuFlCA76Ya+0bZEyBAgMC8AgrwvJ5GI0CAAAECdwkowHcxuWlsgf1m/9vf/+GU2G8Gy6w8JollRjcqgfsEFOD7nNxFYHcCVWD+/Kc//rL2n76cTonT9x9pJ76fXj3kehtXb/rW+eU8fOLb2emUduL04I88m7j2ePrbmN7TXku7vV4mZdRe0yawloACvJa0eQjsUeCnL6fLsn/6ei7c58hJinGOC8e0aLbT1bWv5yUlcq362nauJdq+tAWBHgQU4B52wRoIzCxQ3+zqm97L8CmkOUkRTVza5yqWYyJ9ibQfiLbYVUGsvgyXvkTaH0X73Ef3PnK9bMrqkTE8Q+AZAQX4GT3PEtijQBXhrL1t53wnUcU5xTyRZVdf2vOFkQgsJ6AAL2drZAL7E0hBTjyx8rYQtu0MmfNE2okUzzbSd0/kmdyXsRJpV1/agsAeBBTgPeySNRKYU6D9K+a2Peccd46V4tnGnY+5bSUB0ywroAAv62t0An0JVMHNt9xEVld91W7P0/dO5J+fJt655dWlfEtNvOp0QmBQAQV40I2X9rEFZvsXjNoiXYW5+hYmbAt12860+dacY/oTaVdf2861RNuXdqL+4FBW6RMEfhFYvqUAL29sBgL9CKR4JmpFaSdunac/19tI3weRYpiY3txVvDAAAACmSURBVJa+xLT/2nnua2N6T3st7c9en97vnMDaAgrw2uLmI7CSQH2zq296S01b8yw1/hLjlske176EhzG3Eei5AG8jYlYCBxJIgUksldKSYy+15oybdSfSFgS2ElCAt5I3L4EnBfIt7je/+4v/recnHT1OYCsBBXgr+Y/mdZ3AOwIpvu3l6Xl7TZsAgT4FFOA+98WqCNwUuFVsb/XfHMgFAgQ2Ffh/AAAA///yZpTFAAAABklEQVQDACNsa8BYRSkmAAAAAElFTkSuQmCC" mediaID="AbstractDataType Lecture Template_Stage_cst_XY Grid"/></media></snapdata>