fix cthulhutech.ipynb

notebooks/cthulhutech.ipynb

@@ -28,7 +28,7 @@
     },
     {
       "cell_type": "code",
-      "source": "%pip install icepool\n\nimport icepool\n\nclass CthulhuTechEval(icepool.MultisetEvaluator):\n    def next_state(self, state, outcome, count):\n        score, run = state or (0, 0)\n        if count > 0:\n            set_score = outcome * count\n            run_score = 0\n            run += 1\n            if run >= 3:\n                # This could be the triangular formula, but it's clearer this way.\n                for i in range(run): run_score += (outcome - i)\n            score = max(set_score, run_score, score)\n        else:\n            # No dice rolled this number, so the score remains the same.\n            run = 0\n        return score, run\n\n    def final_outcome(self, final_state):\n        # Return just the score.\n        return final_state[0]\n    \n    # Outcomes should be seen in consecutive order.\n    extra_outcomes = icepool.MultisetEvaluator.consecutive\n\nimport matplotlib.pyplot as plt\n\ndefault_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\nevaluator = CthulhuTechEval()\nfigsize = (16, 9)\nfig, ax = plt.subplots(figsize=figsize)\n\nfor num_dice in range(1, 11):\n    pool = icepool.d10.pool(num_dice)\n    result = evaluator.evaluate(pool)\n    ax.plot([x for x in range(101)], [result.probability('>=', percent=True) for x in range(101)])\n    marker_size = 64 if num_dice < 10 else 128\n    ax.scatter(result.median(), 50.0,\n               marker=('$%d$' % num_dice),\n               facecolor=default_colors[num_dice-1],\n               s=marker_size)\n\nax.set_xticks(range(0, 61, 5))\nax.set_yticks(range(0, 101, 10))\nax.set_xlim(0, 60)\nax.set_ylim(0, 100)\nax.set_xlabel('Result')\nax.set_ylabel('Chance of getting at least (%)')\nax.grid()\nplt.show()",
+      "source": "%pip install icepool\n\nimport icepool\n\nclass CthulhuTechEval(icepool.MultisetEvaluator):\n    def next_state(self, state, outcome, count):\n        score, run = state or (0, 0)\n        if count > 0:\n            set_score = outcome * count\n            run_score = 0\n            run += 1\n            if run >= 3:\n                # This could be the triangular formula, but it's clearer this way.\n                for i in range(run): run_score += (outcome - i)\n            score = max(set_score, run_score, score)\n        else:\n            # No dice rolled this number, so the score remains the same.\n            run = 0\n        return score, run\n\n    def final_outcome(self, final_state):\n        # Return just the score.\n        return final_state[0]\n    \n    # Outcomes should be seen in consecutive order.\n    extra_outcomes = icepool.MultisetEvaluator.consecutive\n\nimport matplotlib.pyplot as plt\n\ndefault_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\nevaluator = CthulhuTechEval()\nfigsize = (16, 9)\nfig, ax = plt.subplots(figsize=figsize)\n\nfor num_dice in range(1, 11):\n    pool = icepool.d10.pool(num_dice)\n    result = evaluator.evaluate(pool)\n    ax.plot([x for x in range(101)], [result.probability('>=', x, percent=True) for x in range(101)])\n    marker_size = 64 if num_dice < 10 else 128\n    ax.scatter(result.median(), 50.0,\n               marker=('$%d$' % num_dice),\n               facecolor=default_colors[num_dice-1],\n               s=marker_size)\n\nax.set_xticks(range(0, 61, 5))\nax.set_yticks(range(0, 101, 10))\nax.set_xlim(0, 60)\nax.set_ylim(0, 100)\nax.set_xlabel('Result')\nax.set_ylabel('Chance of getting at least (%)')\nax.grid()\nplt.show()",
       "metadata": {
         "trusted": true
       },