--- layout: exercise permalink: "/Modules/Pyodide/PlotTenHeads" title: "CS 477: Module 1: Numpy Exercise" excerpt: "CS 477: Module 1: Numpy Exercise" language: "pyodide" canvasasmtid: "183698" canvaspoints: "1.5" canvashalftries: 5 info: comments: "true" points: 1.5 instructions: "

The code below does one trial (experiment) of the 10 heads in a row game, but that doesn't give us a very good idea of how long it takes in general. We should do the experiment many times to get a better sense. Your task in this exercise is to put the starter code inside of a for loop that loops through num_trials times and stores the number of flips it took for each trial as an element in the trials numpy array. All you should have to do is add a for loop, put all of the code inside, and assign num_flips to an index in trials. Then, we can use the power of matplotlib to plot a histogram showing us a distribution of the number of heads it took over all experiments.

" packages: "numpy,matplotlib" goals: - To apply array indexing in numpy - To use for loops in numpy processor: correctfeedback: "Correct!!" incorrectfeedback: "Try again" submitformlink: false feedbackprocess: | let imageRef = 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woKCrK5GgAAbhxut1tRUVGe36XlAQGrhC5cFgwKCiJgAQBwFcrTLTbl40IoAABAGSJgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxfiyZ5tFD/3E7hJK5MCYrnaXAADADYMzWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAW84mANWXKFDVv3lxBQUEKCgpSXFycPv30U09/SkqKHA6H15aYmGhjxQAAwJf5212AFWrVqqUxY8aoQYMGMsZoxowZ6tGjhzZv3qymTZtKkhITEzVt2jTPa5xOp13lAgAAH+cTAat79+5e+6+++qqmTJmiNWvWeAKW0+lUeHi4HeUBAIByxicuEV6soKBAs2fP1qlTpxQXF+dpX7FihUJDQxUbG6t+/frp2LFjNlYJAAB8mU+cwZKk7du3Ky4uTmfPnlVgYKAWLFigJk2aSPrv5cEHHnhAMTEx2rdvn5577jklJSVp9erV8vPzK3a+/Px85efne/bdbneZHAcAALjx+UzAio2N1ZYtW5Sbm6u5c+cqOTlZK1euVJMmTdS7d2/PuGbNmql58+aqV6+eVqxYoU6dOhU7X3p6ukaOHFlW5QMAAB/iM5cIK1WqpPr166t169ZKT09XixYt9NZbbxU7tm7dugoJCdHevXsvOd+wYcOUm5vr2Q4dOlRapQMAAB/jM2ewfqmwsNDrEt/FDh8+rGPHjikiIuKSr3c6nXzSEAAAXBWfCFjDhg1TUlKSateurZMnT2rWrFlasWKFli5dqry8PI0cOVK9evVSeHi49u3bpyFDhqh+/fpKSEiwu3QAAOCDfCJgZWdn65FHHtGRI0fkcrnUvHlzLV26VPfdd5/OnDmjbdu2acaMGcrJyVFkZKQ6d+6sl19+mTNUAACgVPhEwPrnP/95yb4qVapo6dKlZVgNAAAo73zmJncAAIDrBQELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALOYTAWvKlClq3ry5goKCFBQUpLi4OH366aeefmOMXnzxRUVERKhKlSqKj4/Xnj17bKwYAAD4Mp8IWLVq1dKYMWO0ceNGbdiwQffee6969Oihr7/+WpI0btw4TZw4UVOnTtXatWsVEBCghIQEnT171ubKAQCAL/KJgNW9e3d16dJFDRo0UMOGDfXqq68qMDBQa9askTFGEyZM0AsvvKAePXqoefPmmjlzpn788UctXLjQ7tIBAIAP8omAdbGCggLNnj1bp06dUlxcnPbv36/MzEzFx8d7xrhcLrVr106rV6++5Dz5+flyu91eGwAAQEn4TMDavn27AgMD5XQ69dRTT2nBggVq0qSJMjMzJUlhYWFe48PCwjx9xUlPT5fL5fJsUVFRpVo/AADwHT4TsGJjY7VlyxatXbtW/fr1U3Jysr755purnm/YsGHKzc31bIcOHbKwWgAA4Mv87S7AKpUqVVL9+vUlSa1bt9b69ev11ltv6dlnn5UkZWVlKSIiwjM+KytLLVu2vOR8TqdTTqezVGsGAAC+yWfOYP1SYWGh8vPzFRMTo/DwcGVkZHj63G631q5dq7i4OBsrBAAAvsonzmANGzZMSUlJql27tk6ePKlZs2ZpxYoVWrp0qRwOh9LS0vTKK6+oQYMGiomJ0fDhwxUZGamePXvaXToAAPBBPhGwsrOz9cgjj+jIkSNyuVxq3ry5li5dqvvuu0+SNGTIEJ06dUpPPvmkcnJy1KFDBy1ZskSVK1e2uXIAAOCLHMYYY3cRNwK32y2Xy6Xc3FwFBQVZNm/00E8sm6s0HRjT1e4SAAA3qNL6HXo989l7sAAAAOxCwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACL+UTASk9PV9u2bVWtWjWFhoaqZ8+e2rVrl9eYlJQUORwOry0xMdGmigEAgC/ziYC1cuVKpaamas2aNfrss8/0888/q3Pnzjp16pTXuMTERB05csSzffDBBzZVDAAAfJm/3QVYYcmSJV7706dPV2hoqDZu3Ki77rrL0+50OhUeHl7W5QEAgHLGJ85g/VJubq4kqXr16l7tK1asUGhoqGJjY9WvXz8dO3bsknPk5+fL7XZ7bQAAACXhcwGrsLBQaWlpat++vW655RZPe2JiombOnKmMjAyNHTtWK1euVFJSkgoKCoqdJz09XS6Xy7NFRUWV1SEAAIAbnMMYY+wuwkr9+vXTp59+qlWrVqlWrVqXHPfdd9+pXr16WrZsmTp16lSkPz8/X/n5+Z59t9utqKgo5ebmKigoyLJ6o4d+YtlcpenAmK52lwAAuEG53W65XC7Lf4dez3zqDFb//v318ccfa/ny5ZcNV5JUt25dhYSEaO/evcX2O51OBQUFeW0AAAAl4RM3uRtjNGDAAC1YsEArVqxQTEzMr77m8OHDOnbsmCIiIsqgQgAAUJ74xBms1NRUvffee5o1a5aqVaumzMxMZWZm6syZM5KkvLw8DR48WGvWrNGBAweUkZGhHj16qH79+kpISLC5egAA4Gt8ImBNmTJFubm56tixoyIiIjzbhx9+KEny8/PTtm3b9Jvf/EYNGzbU448/rtatW+vLL7+U0+m0uXoAAOBrfOYS4eVUqVJFS5cuLaNqAABAeecTZ7AAAACuJwQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACL2RqwNm3apO3bt3v2//Wvf6lnz5567rnndO7cORsrAwAAuHq2Bqw//elP2r17tyTpu+++U+/evVW1alXNmTNHQ4YMsbM0AACAq2ZrwNq9e7datmwpSZozZ47uuusuzZo1S9OnT9e8efPsLA0AAOCq2RqwjDEqLCyUJC1btkxdunSRJEVFReno0aN2lgYAAHDVbA1Ybdq00SuvvKL//d//1cqVK9W1a1dJ0v79+xUWFmZnaQAAAFfN1oD15ptvatOmTerfv7+ef/551a9fX5I0d+5c3XHHHXaWBgAAcNX87XzzFi1aeH2K8ILXXntN/v62lgYAAHDVbD2DVbduXR07dqxI+9mzZ9WwYUMbKgIAALh2tgasAwcOqKCgoEh7fn6+Dh8+bENFAAAA186W63CLFi3y/Lx06VK5XC7PfkFBgTIyMhQTE2NHaQAAANfMloDVs2dPSZLD4VBycrJXX8WKFRUdHa033njDhsoAAACunS0B68Kzr2JiYrR+/XqFhITYUQYAAECpsPWjevv377fz7QEAAEqF7c9CyMjIUEZGhrKzsz1nti549913baoKAADg6tkasEaOHKlRo0apTZs2ioiIkMPhsLMcAAAAS9gasKZOnarp06fr4YcftrMMAAAAS9n6HKxz587xlTgAAMDn2Bqw/vjHP2rWrFnXPE96erratm2ratWqKTQ0VD179tSuXbu8xhhj9OKLLyoiIkJVqlRRfHy89uzZc83vDQAA8Eu2XiI8e/as3n77bS1btkzNmzdXxYoVvfrHjx9fonlWrlyp1NRUtW3bVufPn9dzzz2nzp0765tvvlFAQIAkady4cZo4caJmzJihmJgYDR8+XAkJCfrmm29UuXJly48NAACUX7YGrG3btqlly5aSpB07dnj1XckN70uWLPHanz59ukJDQ7Vx40bdddddMsZowoQJeuGFF9SjRw9J0syZMxUWFqaFCxeqd+/e13YgAAAAF7E1YC1fvrxU5s3NzZUkVa9eXdJ/n7eVmZmp+Ph4zxiXy6V27dpp9erVBCwAAGAp25+DZbXCwkKlpaWpffv2uuWWWyRJmZmZkqSwsDCvsWFhYZ6+X8rPz1d+fr5n3+12l1LFAADA19gasO65557LXgr8/PPPr3jO1NRU7dixQ6tWrbqW0pSenq6RI0de0xy+JHroJ3aXUGIHxnS1uwQAQDln66cIW7ZsqRYtWni2Jk2a6Ny5c9q0aZOaNWt2xfP1799fH3/8sZYvX65atWp52sPDwyVJWVlZXuOzsrI8fb80bNgw5ebmerZDhw5dcT0AAKB8svUM1ptvvlls+0svvaS8vLwSz2OM0YABA7RgwQKtWLFCMTExXv0xMTEKDw9XRkaG56Z6t9uttWvXql+/fsXO6XQ65XQ6S1wDAADABbaewbqUP/zhD1f0PYSpqal67733NGvWLFWrVk2ZmZnKzMzUmTNnJP33E4lpaWl65ZVXtGjRIm3fvl2PPPKIIiMj1bNnz1I6CgAAUF5dlze5r169+oqeTTVlyhRJUseOHb3ap02bppSUFEnSkCFDdOrUKT355JPKyclRhw4dtGTJEp6BBQAALGdrwHrggQe89o0xOnLkiDZs2KDhw4eXeB5jzK+OcTgcGjVqlEaNGnXFdQIAAFwJWwOWy+Xy2q9QoYJiY2M1atQode7c2aaqAAAAro2tAWvatGl2vj0AAECpuC7uwdq4caO+/fZbSVLTpk3VqlUrmysCAAC4erYGrOzsbPXu3VsrVqxQcHCwJCknJ0f33HOPZs+erZo1a9pZHgAAwFWx9TENAwYM0MmTJ/X111/r+PHjOn78uHbs2CG3262nn37aztIAAACumq1nsJYsWaJly5apcePGnrYmTZpo8uTJ3OQOAABuWLaewSosLFTFihWLtFesWFGFhYU2VAQAAHDtbA1Y9957r5555hn9+OOPnrYffvhBf/nLX9SpUycbKwMAALh6tgasv/3tb3K73YqOjla9evVUr149xcTEyO12a9KkSXaWBgAAcNVsvQcrKipKmzZt0rJly7Rz505JUuPGjRUfH29nWQAAANfEljNYn3/+uZo0aSK32y2Hw6H77rtPAwYM0IABA9S2bVs1bdpUX375pR2lAQAAXDNbAtaECRP0xBNPKCgoqEify+XSn/70J40fP96GygAAAK6dLQFr69atSkxMvGR/586dtXHjxjKsCAAAwDq2BKysrKxiH89wgb+/v3766acyrAgAAMA6tgSsm2++WTt27Lhk/7Zt2xQREVGGFQEAAFjHloDVpUsXDR8+XGfPni3Sd+bMGY0YMULdunWzoTIAAIBrZ8tjGl544QXNnz9fDRs2VP/+/RUbGytJ2rlzpyZPnqyCggI9//zzdpQGAABwzWwJWGFhYfrPf/6jfv36adiwYTLGSJIcDocSEhI0efJkhYWF2VEaAADANbPtQaN16tTR4sWLdeLECe3du1fGGDVo0EA33XSTXSUBAABYwtYnuUvSTTfdpLZt29pdBgAAgGVs/S5CAAAAX0TAAgAAsBgBCwAAwGIELAAAAIvZfpM7YLXooZ/YXUKJHBjT1e4SAAClhDNYAAAAFiNgAQAAWIyABQAAYDGfCFhffPGFunfvrsjISDkcDi1cuNCrPyUlRQ6Hw2tLTEy0p1gAAODzfCJgnTp1Si1atNDkyZMvOSYxMVFHjhzxbB988EEZVggAAMoTn/gUYVJSkpKSki47xul0Kjw8vIwqAgAA5ZlPnMEqiRUrVig0NFSxsbHq16+fjh07dtnx+fn5crvdXhsAAEBJlIuAlZiYqJkzZyojI0Njx47VypUrlZSUpIKCgku+Jj09XS6Xy7NFRUWVYcUAAOBG5hOXCH9N7969PT83a9ZMzZs3V7169bRixQp16tSp2NcMGzZMAwcO9Oy73W5CFgAAKJFycQbrl+rWrauQkBDt3bv3kmOcTqeCgoK8NgAAgJIolwHr8OHDOnbsmCIiIuwuBQAA+CCfuESYl5fndTZq//792rJli6pXr67q1atr5MiR6tWrl8LDw7Vv3z4NGTJE9evXV0JCgo1VAwAAX+UTAWvDhg265557PPsX7p1KTk7WlClTtG3bNs2YMUM5OTmKjIxU586d9fLLL8vpdNpVMgAA8GE+EbA6duwoY8wl+5cuXVqG1QAAgPKuXN6DBQAAUJoIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxf7sLAMqr6KGf2F1CiRwY09XuEgDghsMZLAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALOYTAeuLL75Q9+7dFRkZKYfDoYULF3r1G2P04osvKiIiQlWqVFF8fLz27NljT7EAAMDn+UTAOnXqlFq0aKHJkycX2z9u3DhNnDhRU6dO1dq1axUQEKCEhASdPXu2jCsFAADlgU982XNSUpKSkpKK7TPGaMKECXrhhRfUo0cPSdLMmTMVFhamhQsXqnfv3mVZKgAAKAd84gzW5ezfv1+ZmZmKj4/3tLlcLrVr106rV6+2sTIAAOCrfOIM1uVkZmZKksLCwrzaw8LCPH3Fyc/PV35+vmff7XaXToEAAMDn+PwZrKuVnp4ul8vl2aKiouwuCQAA3CB8PmCFh4dLkrKysrzas7KyPH3FGTZsmHJzcz3boUOHSrVOAADgO3w+YMXExCg8PFwZGRmeNrfbrbVr1youLu6Sr3M6nQoKCvLaAAAASsIn7sHKy8vT3r17Pfv79+/Xli1bVL16ddWuXVtpaWl65ZVX1KBBA8XExGj48OGKjIxUz5497SsaAAD4LJ8IWBs2bNA999zj2R84cKAkKTk5WdOnT9eQIUN06tQpPfnkk8rJyVGHDh20ZMkSVa5c2a6SAQCAD3MYY4zdRdwI3G63XC6XcnNzLb1cGD30E8vmAkrDgTFd7S4BwA2utH6HXs98/h4sAACAskbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACzmb3cBAK5v0UM/sbuEEjswpqvdJQCAJM5gAQAAWI6ABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgsXIRsF566SU5HA6vrVGjRnaXBQAAfFS5+S7Cpk2batmyZZ59f/9yc+gAAKCMlZuU4e/vr/DwcLvLAAAA5UC5uEQoSXv27FFkZKTq1q2rvn376uDBg5cdn5+fL7fb7bUBAACURLkIWO3atdP06dO1ZMkSTZkyRfv379edd96pkydPXvI16enpcrlcni0qKqoMKwYAADcyhzHG2F1EWcvJyVGdOnU0fvx4Pf7448WOyc/PV35+vmff7XYrKipKubm5CgoKsqyW6KGfWDYXUN4dGNPV7hIAFMPtdsvlcln+O/R6Vm7uwbpYcHCwGjZsqL17915yjNPplNPpLMOqAACArygXlwh/KS8vT/v27VNERITdpQAAAB9ULgLWX//6V61cuVIHDhzQf/7zH91///3y8/NTnz597C4NAAD4oHJxifDw4cPq06ePjh07ppo1a6pDhw5as2aNatasaXdpAADAB5WLgDV79my7SwAAAOVIubhECAAAUJYIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDF/O0uAADKm+ihn9hdQokcGNPV7hJK5EZZT+nGWVNcO85gAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxhzHG2F3EjcDtdsvlcik3N1dBQUGWzXsjfbwYAFA+WP04idL6HXo94wwWAACAxQhYAAAAFiNgAQAAWKxcBazJkycrOjpalStXVrt27bRu3Tq7SwIAAD6o3ASsDz/8UAMHDtSIESO0adMmtWjRQgkJCcrOzra7NAAA4GPKTcAaP368nnjiCT366KNq0qSJpk6dqqpVq+rdd9+1uzQAAOBj/O0uoCycO3dOGzdu1LBhwzxtFSpUUHx8vFavXl3sa/Lz85Wfn+/Zz83NlfTfj5paqTD/tKXzAQBwraz+XXdhvvL0ZKhyEbCOHj2qgoIChYWFebWHhYVp586dxb4mPT1dI0eOLNIeFRVVKjUCAHC9cE0onXlPnjwpl8tVOpNfZ8pFwLoaw4YN08CBAz37hYWFOn78uGrUqCGHw3HN87vdbkVFRenQoUPl5qFrZYW1LR2sa+lhbUsH61p6rnRtjTE6efKkIiMjy6C660O5CFghISHy8/NTVlaWV3tWVpbCw8OLfY3T6ZTT6fRqCw4Otry2oKAg/sMvJaxt6WBdSw9rWzpY19JzJWtbXs5cXVAubnKvVKmSWrdurYyMDE9bYWGhMjIyFBcXZ2NlAADAF5WLM1iSNHDgQCUnJ6tNmza67bbbNGHCBJ06dUqPPvqo3aUBAAAfU24C1u9+9zv99NNPevHFF5WZmamWLVtqyZIlRW58LytOp1MjRowochkS1461LR2sa+lhbUsH61p6WNtf5zDl6TOTAAAAZaBc3IMFAABQlghYAAAAFiNgAQAAWIyABQAAYDEClk0mT56s6OhoVa5cWe3atdO6devsLum6kZ6errZt26patWoKDQ1Vz549tWvXLq8xxhi9+OKLioiIUJUqVRQfH689e/Z4jTl79qxSU1NVo0YNBQYGqlevXkUeNnv8+HH17dtXQUFBCg4O1uOPP668vLxSP8brwZgxY+RwOJSWluZpY12v3g8//KA//OEPqlGjhqpUqaJmzZppw4YNnn7W9soVFBRo+PDhiomJUZUqVVSvXj29/PLLXt9nx7qWzBdffKHu3bsrMjJSDodDCxcu9Oovy3U8ePCgunbtqqpVqyo0NFSDBw/W+fPnS+W4bWVQ5mbPnm0qVapk3n33XfP111+bJ554wgQHB5usrCy7S7suJCQkmGnTppkdO3aYLVu2mC5dupjatWubvLw8z5gxY8YYl8tlFi5caLZu3Wp+85vfmJiYGHPmzBnPmKeeespERUWZjIwMs2HDBnP77bebO+64w+u9EhMTTYsWLcyaNWvMl19+aerXr2/69OlTZsdql3Xr1pno6GjTvHlz88wzz3jaWderc/z4cVOnTh2TkpJi1q5da7777juzdOlSs3fvXs8Y1vbKvfrqq6ZGjRrm448/Nvv37zdz5swxgYGB5q233vKMYV1LZvHixeb555838+fPN5LMggULvPrLah3Pnz9vbrnlFhMfH282b95sFi9ebEJCQsywYcNK9fjtQMCywW233WZSU1M9+wUFBSYyMtKkp6fbWNX1Kzs720gyK1euNMYYU1hYaMLDw81rr73mGZOTk2OcTqf54IMPPPsVK1Y0c+bM8Yz59ttvjSSzevVqY4wx33zzjZFk1q9f7xnz6aefGofDYX744YeyODRbnDx50jRo0MB89tln5u677/YELNb16j377LOmQ4cOl+xnba9O165dzWOPPebV9sADD5i+ffsaY1jXq/XLgFWW67h48WJToUIFk5mZ6RkzZcoUExQUZPLz80vleO3CJcIydu7cOW3cuFHx8fGetgoVKig+Pl6rV6+2sbLrV25uriSpevXqkqT9+/crMzPTaw1dLpfatWvnWcONGzfq559/9hrTqFEj1a5d2zNm9erVCg4OVps2bTxj4uPjVaFCBa1du7bUj8suqamp6tq1q9faSKzrtVi0aJHatGmjBx98UKGhoWrVqpXeeecdTz9re3XuuOMOZWRkaPfu3ZKkrVu3atWqVUpKSpLEulqlLNdx9erVatasmddDvhMSEuR2u/X111+X6nGWtXLzJPfrxdGjR1VQUFDkCfJhYWHauXOnTVVdvwoLC5WWlqb27dvrlltukSRlZmZKUrFreKEvMzNTlSpVKvIF3b8cExoa6tXv7++v6tWre8b4mtmzZ2vTpk1av359kT7W9ep99913mjJligYOHKjnnntO69ev19NPP61KlSopOTmZtb1KQ4cOldvtVqNGjeTn56eCggK9+uqr6tu3ryT+zFqlLNcxMzOz2Pe5uA5fQcDCdS01NVU7duzQqlWr7C7lhnfo0CE988wz+uyzz1S5cmW7y/EphYWFatOmjUaPHi1JatWqlXbs2KGpU6cqOTnZ5upuXB999JHef/99zZo1S02bNtWWLVuUlpamyMhI1hXXPS4RlrGQkBD5+fkV+eRFVlaWwsPDbarq+tS/f399/PHHWr58uWrVquVpv7BOl1vD8PBwnTt3Tjk5OZcdk52d7dV//vx5HT9+3Cf/XWzcuFHZ2dm69dZb5e/vL39/f61cuVITJ06Uv7+/52+RrOuVi4iIUJMmTbzaGjdurIMHD0riz+zVGjx4sIYOHarevXurWbNmevjhh/WXv/xF6enpklhXq5TlOoaHhxf7PhfX4SsIWGWsUqVKat26tTIyMjxthYWFysjIUFxcnI2VXT+MMerfv78WLFigzz//XDExMV79MTExCg8P91pDt9uttWvXetawdevWqlixoteYXbt26eDBg54xcXFxysnJ0caNGz1jPv/8cxUWFqpdu3aleYi26NSpk7Zv364tW7Z4tjZt2qhv377asmWL6taty7pepfbt2xd5lMju3btVp04dSfyZvVqnT59WhQrev6b8/PxUWFgoiXW1SlmuY1xcnLZv3+4VxD777DMFBQUV+UvKDc/uu+zLo9mzZxun02mmT59uvvnmG/Pkk0+a4OBgr09VlGf9+vUzLpfLrFixwhw5csSznT592jNmzJgxJjg42PzrX/8y27ZtMz169Cj2I8W1a9c2n3/+udmwYYOJi4szcXFxXu+VmJhoWrVqZdauXWtWrVplGjRo4FMfzf41F3+K0BjW9WqtW7fO+Pv7m1dffdXs2bPHvP/++6Zq1armvffe84xhba9ccnKyufnmmz2PaZg/f74JCQkxQ4YM8YxhXUvm5MmTZvPmzWbz5s1Gkhk/frzZvHmz+f77740xZbeOFx7T0LlzZ7NlyxazZMkSU7NmTR7TAOtMmjTJ1K5d21SqVMncdtttZs2aNXaXdN2QVOw2bdo0z5jCwkIzfPhwExYWZpxOp+nUqZPZtWuX1zxnzpwxf/7zn81NN91kqlatau6//35z5MgRrzHHjh0zffr0MYGBgSYoKMg8+uij5uTJk2VxmNeFXwYs1vXq/fvf/za33HKLcTqdplGjRubtt9/26mdtr5zb7TbPPPOMqV27tqlcubKpW7euef75570+zs+6lszy5cuL/f9qcnKyMaZs1/HAgQMmKSnJVKlSxYSEhJhBgwaZn3/+uVSP3w4OYy56JC4AAACuGfdgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAytyBAwfkcDi0ZcsWu0vx2Llzp26//XZVrlxZLVu2vKo5OnbsqLS0NM9+dHS0JkyYYEl9AG4sBCygHEpJSZHD4dCYMWO82hcuXCiHw2FTVfYaMWKEAgICtGvXLq/vW7vYhXX75bZ3795ix69fv15PPvlkaZYN4DpFwALKqcqVK2vs2LE6ceKE3aVY5ty5c1f92n379qlDhw6qU6eOatSocclxiYmJOnLkiNf2yy8kv6BmzZqqWrXqVdcE4MZFwALKqfj4eIWHhys9Pf2SY1566aUil8smTJig6Ohoz35KSop69uyp0aNHKywsTMHBwRo1apTOnz+vwYMHq3r16qpVq5amTZtWZP6dO3fqjjvuUOXKlXXLLbdo5cqVXv07duxQUlKSAgMDFRYWpocfflhHjx719Hfs2FH9+/dXWlqaQkJClJCQUOxxFBYWatSoUapVq5acTqdatmypJUuWePodDoc2btyoUaNGyeFw6KWXXrrkmjidToWHh3ttfn5+xY795SVCh8OhKVOmKCkpSVWqVFHdunU1d+5cT/+5c+fUv39/RUREqHLlyqpTp85l//0AuH4RsIByys/PT6NHj9akSZN0+PDha5rr888/148//qgvvvhC48eP14gRI9StWzfddNNNWrt2rZ566in96U9/KvI+gwcP1qBBg7R582bFxcWpe/fuOnbsmCQpJydH9957r1q1aqUNGzZoyZIlysrK0kMPPeQ1x4wZM1SpUiV99dVXmjp1arH1vfXWW3rjjTf0+uuva9u2bUpISNBvfvMb7dmzR5J05MgRNW3aVIMGDdKRI0f017/+9ZrW43KGDx+uXr16aevWrerbt6969+6tb7/9VpI0ceJELVq0SB999JF27dql999/3yvMArhxELCAcuz+++9Xy5YtNWLEiGuap3r16po4caJiY2P12GOPKTY2VqdPn9Zzzz2nBg0aaNiwYapUqZJWrVrl9br+/furV69eaty4saZMmSKXy6V//vOfkqS//e1vatWqlUaPHq1GjRqpVatWevfdd7V8+XLt3r3bM0eDBg00btw4xcbGKjY2ttj6Xn/9dT377LPq3bu3YmNjNXbsWLVs2dJzdik8PFz+/v4KDAxUeHi4AgMDL3msH3/8sQIDAz3bgw8+eEVr9eCDD+qPf/yjGjZsqJdffllt2rTRpEmTJEkHDx5UgwYNPJcqO3TooD59+lzR/ACuD/52FwDAXmPHjtW99957TWdtmjZtqgoV/u/va2FhYbrllls8+35+fqpRo4ays7O9XhcXF+f52d/fX23atPGczdm6dauWL19ebNjZt2+fGjZsKElq3br1ZWtzu9368ccf1b59e6/29u3ba+vWrSU8wv9zzz33aMqUKZ79gICAK3r9xcd8Yf/CpylTUlJ03333KTY2VomJierWrZs6d+58xTUCsB8BCyjn7rrrLiUkJGjYsGFKSUnx6qtQoYKMMV5tP//8c5E5Klas6LXvcDiKbSssLCxxXXl5eerevbvGjh1bpC8iIsLz85UGnGsVEBCg+vXrl8rct956q/bv369PP/1Uy5Yt00MPPaT4+Hiv+7QA3Bi4RAhAY8aM0b///W+tXr3aq71mzZrKzMz0CllWPrtqzZo1np/Pnz+vjRs3qnHjxpL+Gza+/vprRUdHq379+l7blYSqoKAgRUZG6quvvvJq/+qrr9SkSRNrDuQKXHzMF/YvHLP033p/97vf6Z133tGHH36oefPm6fjx42VdJoBrxBksAGrWrJn69u2riRMnerV37NhRP/30k8aNG6ff/va3WrJkiT799FMFBQVZ8r6TJ09WgwYN1LhxY7355ps6ceKEHnvsMUlSamqq3nnnHfXp00dDhgxR9erVtXfvXs2ePVv/+Mc/LvnJveIMHjxYI0aMUL169dSyZUtNmzZNW7Zs0fvvv2/JcVyJOXPmqE2bNurQoYPef/99rVu3znPf2fjx4xUREaFWrVqpQoUKmjNnjsLDwxUcHFzmdQK4NpzBAiBJGjVqVJFLeI0bN9bf//53TZ48WS1atNC6dess/YTdmDFjNGbMGLVo0UKrVq3SokWLFBISIkmes04FBQXq3LmzmjVrprS0NAUHB3vd71USTz/9tAYOHKhBgwapWbNmWrJkiRYtWqQGDRpYdiwlNXLkSM2ePVvNmzfXzJkz9cEHH3jOpFWrVk3jxo1TmzZt1LZtWx04cECLFy++4uMFYD+H+eUNFgCAUuFwOLRgwQL17NnT7lIAlDL+WgQAAGAxAhYAAIDFuMkdAMoId2QA5cf/B464Zjfej0dDAAAAAElFTkSuQmCC";; let zeroRef = 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"; let resetRef = 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"; correctcheck: | pyodide.globals.get("img_str") == imageRef incorrectchecks: - incorrectcheck: | pyodide.globals.get("img_str") == zeroRef feedback: "Try again. It looks like all of the elements of trials are still zero. Make sure each element is the number of flips in a particular trial" - incorrectcheck: | pyodide.globals.get("img_str") == resetRef feedback: "Try again. It looks like you forgot to reset num_flips and heads_in_row at the beginning of each trial" files: - filename: "student.py" name: driver ismain: false isreadonly: false isvisible: true code: | import matplotlib.pyplot as plt import numpy as np # Make the results repeatable (aka "pseudorandom") np.random.seed(0) num_trials = 100 # Make a numpy array to hold the trials trials = np.zeros(num_trials) num_flips = 0 heads_in_row = 0 while heads_in_row < 10: if np.random.randint(2) == 1: heads_in_row += 1 else: heads_in_row = 0 num_flips += 1 - filename: "main.py" ismain: true name: main isreadonly: true isvisible: true code: | plt.figure(figsize=(6, 4), dpi=100) plt.hist(trials) plt.xlabel("Number of Flips") plt.ylabel("Counts") plt.title("10 Heads Histogram") save_figure_js() openFilesOnLoad: ["main.py", "student.py"] ---