258 lines
118 KiB
Plaintext
258 lines
118 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
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"plt.rcParams[\"font.size\"] = 16\n",
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"\n",
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"g_label_fontsize = 16\n",
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"\n",
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"colors = [\n",
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" \"#5D7599\",\n",
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" \"#233142\",\n",
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" \"#F95959\",\n",
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"]\n",
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"\n",
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"edgecolors = [\n",
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" \"#FFFFFF\",\n",
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" \"#FFFFFF\",\n",
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" \"#FFFFFF\",\n",
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"]\n",
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"\n",
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"hatches = [\"\\\\\\\\\", \"\", \"\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'plt' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39msubplots(\n\u001b[1;32m 2\u001b[0m figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m14\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m5\u001b[39m), ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, nrows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, constrained_layout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m\n\u001b[1;32m 3\u001b[0m )\n",
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"\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
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]
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}
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],
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"source": [
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"fig, ax = plt.subplots(\n",
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" figsize=(7, 14 / 5), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"labels_name = [\n",
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" \"ModelA\\n\" + r\"2DP$\\times $4TP\",\n",
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" \"ModelA\\n\" + r\"2DP$\\times $8TP\",\n",
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" \"ModelB\\n\" + r\"2DP$\\times $4TP\",\n",
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" \"ModelB\\n\" + r\"2DP$\\times $8TP\",\n",
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" \"ModelC\\n\" + r\"2DP$\\times $4TP\",\n",
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" \"ModelC\\n\" + r\"2DP$\\times $8TP\",\n",
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" \"ModelD\\n\" + r\"2DP$\\times $4TP\",\n",
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" \"ModelD\\n\" + r\"2DP$\\times $8TP\",\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# memory cost\n",
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"data_a = {\n",
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" labels_name[0]: [0, 53, 1200], # 22.64\n",
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" labels_name[1]: [0, 53, 1200], # 22.64\n",
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" labels_name[2]: [0, 35, 1200], # 37\n",
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" labels_name[3]: [0, 35, 1200], # 37\n",
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" labels_name[4]: [0, 53, 1200], # 22.64\n",
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" labels_name[5]: [0, 53, 1200], # 22.64\n",
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" labels_name[6]: [0, 53, 1200], # 22.64\n",
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" labels_name[7]: [0, 53, 1200], # 22.64\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"legend_labels = [\"Megatron-LM\", \"XLA\", \"DLRover-Lynx\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bar_width = 0.2\n",
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"group_spaing = 0.15\n",
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"\n",
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"group_positions = {}\n",
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"current_pos = 0\n",
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"\n",
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"for x_label, y_data in data_a.items():\n",
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" group_positions[x_label] = []\n",
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" for i in range(len(y_data)):\n",
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" group_positions[x_label].append(current_pos)\n",
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" current_pos += bar_width\n",
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" current_pos += group_spaing\n",
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"\n",
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"group_centers = {}\n",
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"for x_label, positions in group_positions.items():\n",
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" group_centers[x_label] = sum(positions) / len(positions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, '(b)')"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"label_set = set()\n",
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"\n",
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"for x_label, y_data in data_a.items():\n",
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" positions = group_positions[x_label]\n",
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" for i, (pos, value, color, edgecolor, label, hatch) in enumerate(\n",
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" zip(\n",
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" positions,\n",
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" y_data,\n",
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" colors,\n",
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" edgecolors,\n",
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" legend_labels,\n",
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" hatches,\n",
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" )\n",
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" ):\n",
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" if label in label_set:\n",
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" local_label = None\n",
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" else:\n",
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" local_label = label\n",
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" label_set.add(local_label)\n",
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"\n",
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" ax.bar(\n",
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" pos,\n",
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" value,\n",
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" width=bar_width,\n",
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" color=color,\n",
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" edgecolor=edgecolor,\n",
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" hatch=hatch,\n",
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" label=local_label,\n",
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" )\n",
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"\n",
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"\n",
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"ax.set_xticks(list(group_centers.values()))\n",
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"ax.set_xticklabels(list(data_a.keys()))\n",
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"\n",
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"ax.set_ylim(0, 1500)\n",
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"ax.set_yticks([0, 500, 1000, 1500])\n",
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"ax.set_yticklabels([\"0\", \"500\", \"1000\", \"1500\"], rotation=90, ha=\"center\", va=\"center\")\n",
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"\n",
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"ax.tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
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"ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
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"\n",
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"ax.set_ylabel(\"Time (s)\", fontsize=g_label_fontsize)\n",
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"ax.set_title(\"(b)\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x7f63985869c0>"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ax.legend(\n",
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" ncol=3,\n",
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" loc=\"upper center\",\n",
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" frameon=False,\n",
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" shadow=False,\n",
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" fontsize=g_label_fontsize,\n",
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" bbox_to_anchor=(0.5, 1)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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PnpQk5c+fX7Vr11bz5s2VLl06q8dHlPgCAEh4OnfurNy5c1utu3//frjV/GCdl5eXzbqQkJA4jAQAkFiRWAEg1iWWm/lcB2wtQ3/nzp0oLYdsj3Xr1pn3fyxVqpSqVKni0PYRd86ePavq1avr4cOHzg4FiBFeAwEg6Zk0aZIyZ85stW79+vWaNWuWzXP79Omj3bt3S5JKlCgRayscJRav3jDr1q2bTCaTkiVLpg4dOlg9fsGCBeZtQwAACZ+Hh4e6d+9us37s2LF68eJFHEaUsISGhurevXs26zNlyhSH0QAAEisSKwDEicRyM5/rSNrefPNN1axZ02rd6NGjFRoa6rC+Ro8ebf63rZuZseHevXuaPHmymjRposKFCytVqlRyd3dX5syZ9eabb6p9+/ZasmSJ/P39Hdrv3r17NWjQIFWsWFF58uRRypQp5enpqcyZM6tUqVLq1KmTFi9erICAAIf2K73cZ3Pw4MGqVauW8ubNq3Tp0snNzU3e3t7Knj27KlWqpF69emnTpk1R/n+8adMmVahQweZS2VevXtWePXusPiJbpvLAgQPq1q2b0qdPL5PJZFHv5+enESNGqHjx4kqWLJkyZMigZs2a6dy5c1G6Bkm6cOGChg8frnr16il37tzy9vaWu7u70qVLp5IlS6pdu3aaN2+enj59GuW2rQkKCtL8+fNVsWJFmUwmDRkyxOpxz58/15w5c1SnTh1lzpxZ7u7uypgxo6pXr645c+YwI8XBksJrIAAgvAwZMmjq1Kk263v16qXr169blM+fP9+8H3yaNGm0bNkyJUuWLNbiTOiuXbum1atXS5K8vb3Vvn17c12XLl2sftbz9/fX7Nmz4ypEAEAcaNu2rdUtGKWX4zXbtm2L44gSjgsXLkQ4BvDmm44f7zx+/LgGDRqk6tWry8fHR15eXvL09FTGjBlVtmxZffrpp1q+fLkCAwOj1O6FCxd05syZKD8iWyX1/v37drUT1TG/gwcP6ptvvlGVKlXk4+Oj5MmTK3ny5MqVK5cqVKigb7/9Vn///XeU2nzd5cuX9f3336tAgQIymUzasWNHuHrDMLRgwQJVqlRJKVOmlLe3typXrqzNmzfHqN+EJnfu3DKZTFF+ROby5ct2tdOuXbsI29mxY4eaN28uT09PVa5c2eZxmzdvVuvWrZU7d255enoqderUKlu2rIYNG6bHjx9H7YcCOJibswMAkHRs3HVc0sub968Kex5WH99xHUlbv379tHHjRovy8+fPa/ny5WrcuHGM+zh06JB27twp6eUHYltLMDvSrVu3NHjwYM2ePdvqF7G7d+/q7t27OnHihGbPnq0sWbJo8ODB6tSpk1xdXaPd74YNGzR48GD9888/5jJvb29lzJhR9+/fN/d7+PBhzZgxQylTplTPnj3Vt2/fCPfOtMfu3bvVu3dvHTx40Gq9n5+f/Pz8dPPmTe3atUu//PKL8ubNqx9//FHNmzePsO0rV65o6NChmj17tgzDsHncrFmzbM70nDVrlsUXkuvXr+uPP/7QnDlzdObMGZvtHjlyRI0bN9aFCxfMZYGBgVq8eLE2bdqkPXv2qFixYhFegyTt27dPgwcPtvlF9NGjR3r06JH+/fdfzZkzR97e3urUqZO+/fZbpU2bNtL2X3fhwgVNnTpVs2bN0v379yM8dtmyZerdu7euXr0arvz+/fvavHmzNm/erPHjx2vVqlXy8fGJciywLrG+BgIAbGvQoIHatGmjuXPnWtQ9efJEnTp10oYNG8xlx48fV6dOnSRJJpNJv//+u/LmzRtn8SZEkydPNt8M+vjjj5U6dWpzXd68eVWjRg2r779TpkzRF198EWdxAgBiV4YMGVShQgWLm8ZhFi9erOrVq8dtUAnEypUrbdZlzJhRJUuWdFhfGzZs0JAhQ3TgwAGr9ffv39f9+/d14MABTZs2TenTp1fPnj3Vp08fJU+ePNL233//fV25ciXKcV26dMnmdjKS9Ouvv2ro0KGRtrN9+/YIb3qH2bJli7766qtwY3qvunr1qq5evaq//vpLP/zwg6pUqaKRI0eqdOnSkbYtSU+fPtXSpUs1Z84c7dq1y+b42sOHD9W8eXOLsaudO3dq165dmj17tj7++GO7+kzodu3apWXLlmnMmDE2J3lJkqurq4YNG6ZWrVopW7ZskbabO3duBQQE6MqVK/r666+1bNmycPXvvfeehg4dqrJly1qc++jRI82ZM0dTp06NcCxTko4ePaquXbvqr7/+ClceFBSkAwcO6MCBA/rll1+0ZMkSVapUKdKYHfF3NGTIELv+btq2bUvScxLBihUA4lRiWSmB60i6qlatavPL2KhRoxzSx6vt9OrVK0aJC/ZYvHix3njjDU2fPl2GYahJkyb67bfftHnzZq1Zs0bffvutcuTIEe6c27dvq0uXLqpXr160VikIDg7W559/rtq1a5u/gJUpU0bbt2/XkydPdPHiRT158kR79uwJ90H56dOn+v7775U6dWqb2dFubm6qXbt2hP1/8803qly5sjmpwsXFRc2bN9e8efO0bds2rVixQgMHDrRY+vrixYtq0aKFvvnmmwjbHzx4sGbNmhVhUoW9/P399ccff+iDDz5Qrly5NHDgwAi/iOzfv1+VKlUKl1TxqidPnmjgwIGR9tmlSxe9++675i+mBQoU0NChQ7Vu3Tpt3LhR06ZN00cffRRuNs2zZ880duxYFSpUSOvXr7fr+l68eKEVK1aoRo0aKlCggEaNGhVhUkVQUJA6duyoxo0bWyRVvO7w4cOqXr26w1bSQOJ8DQQARG78+PHKnj271bqNGzdq5syZkqTHjx/rww8/NM90/PLLL1WvXr04izMhCgwM1PTp083PrS0D361bN6vnnjlzRlu3bo212AAAcc/WKoGSwiUy4v8EBQWFey99XdeuXe2alR+ZBw8eqFGjRqpdu7Y5qaJEiRIaOXKkNm7cqHXr1mnixImqXr26XFxcwp337bffqlixYnat3HD48GGtW7dOderUifTY/v3768aNGzIMI8KkCunlDeLAwED9999/ql+/fri6DBkyaNasWbpx40akN6yfP3+ujh076oMPPtA///yjbNmyqW/fvlq6dKm2bdumP/74Q61bt5aHh0e487Zv367y5ctrypQpNtsODQ3V5s2b1aZNG2XJkkUdOnTQzp07bY6v3b9/X++9957NCUGGYahnz56RruaRWOTMmVO9evXSkSNHVKhQIZvHde7cWf3791f27Nnt/ttIliyZChUqpOHDh4crf++997RlyxZVqVJFXl5e5vK///5b7du3V/bs2dWrV69IkyomTpyoMmXKWCRVvO7evXuqXbu2jh49GuFxJ06c0LFjxzR06FClT58+wmNdXFw0bdo0PXz40OLv6Ntvv9XZs2dVo0YNi/NSpUqlwYMH68yZM5o2bVqEfSDxILECQJxLLDfzk8J1VC1fxAkRxX/9+vWzWn7gwAGbswrsdfnyZS1dulSSlDZtWnXs2DFG7UVm2LBhatasmR4/fqwyZcro5MmTWrx4sdq3b69q1aqpbt26+u6773Tu3Dn17NnT4vwNGzbovffe07Nnz+zu88WLF6pfv364faQ//PBD7dmzR5UrVzZ/+TSZTKpQoYK2b98ebjnkyISEhNjMlpde7vf9448/mrct8PDw0KpVq7RgwQK1bNlSVapUUcOGDTVs2DCdO3fO6gfnH3/8UWvWrLHZR9hKFa8+cuXKZXHc4MGDLY4Le7Rr104nTpxQ5syZ1aZNG23ZsiXSrRYuXryoevXqydfXN8LjLl26ZLPuwYMHeu+998xfdD08PDRu3DidPn1agwYNUu3atVWjRg116tRJy5Yt0+nTpy0y0u/du6e6devq119/jTCO8ePHK1euXProo4/0559/RpqI8uTJE1WrVk2//fZbhMe9KixuOE5ieg0EANgnTZo0mjFjhs363r1768qVK2rdurU5ubNq1ar6/vvv4yrEBGvhwoXmpNJq1aqpSBHL72C1a9e2ebNk8uTJsRkeACCORTSb/+bNm7p3714cRpMwjBgxwua2p5kyZbI6nhVVly5dUunSpbV8+XJJL2+ozp8/X//++6/69eunGjVqqHbt2uratas2bdqkf/75x+LG9sWLF1WpUiVzG7akS5dOtWvX1tq1azV27Fibx5UqVUojRoywa8WBMB4eHipQoIDGjRtnLnNzc9OaNWvUrl07ZcuWLcIb7Y8fP1alSpX022+/yWQy6csvv9T58+c1atQoNWrUSFWqVFGrVq00d+5cnTx50mILlhcvXqhLly5WPyOGhoaqQIECql69uv74449ItyQJCgpSvXr1dOrUqQiPe/TokZ48eRLhMYlNhgwZtHTpUnl6elqtf+ONN6Ld9quTl9zc3DRnzpxwSTRbtmzR22+/rXLlymn27NmRbusclvzSvXt3BQUF2RWDv7+/OnbsGOE4qbe3t958800NGjRIx44dU9GiRW0e+9Zbb6lTp05WV991dXVVwYIF9cMPP4Qrz5o1q/766y8NGTJEhQoVskgkQuJFYgUAp0gKSQmJ4ToaVX/HCdHEf02aNLF6k1ySRo4cGaO2x44da14GuEuXLkqRIkWM2ovIr7/+qq+//lqSVKNGDe3atUsFCxa0eqynp6fGjh1r9fqOHj2qDh062N3vp59+Gm4pYx8fH82ZM8fmPqImk0lTp061uXWFp6dnuIeXl5fKly9v9diVK1fq559/Dlc2YMAA1a1b1+rxKVOm1OLFi5UlSxaLuq+++srqOY5UsGBBLVq0SPfu3dOzZ8/CJaO8Ljg4WM2aNVNAQIAWL16sR48eadiwYVY/2L/33ntW23j69KmqVq2qw4cPS3r5s1+wYIE+//xzm6sG5MuXT7t27VLVqlXDlRuGoR49ekQ4a8RkMql58+aaPXu2hg8fHuH2Lk+fPlXNmjW1e/dupU2bVv3799e+ffvk6+srf39/HTp0SK1bt7Z67uTJkxl8cqDE8hoIAIiamjVrmrf4eJ2vr6/eeecdrVu3TpKUPXt2LVy4kFWH7DBx4kTzv3v06GH1GBcXF3322WdW61atWqWbN2/GSmwAgLhXokSJCOuPHDkSJ3EkFAsXLtSQIUOs1rm4uOiPP/5QmjRpYtTHjRs3VLFiRV2+fFnSy3GojRs3qkWLFjbPKVmypPbv328xlhUYGKhmzZrZvfpIz5491apVK6t1Mdlq7fbt2+Z/d+vWTeXKlYv0nODgYNWqVcu8Wsdvv/2m4cOH29zeJH/+/Nq7d6/VtgcPHmwxYcnFxUVTp07V1atXFRAQoK1bt0a4zWy/fv20f/9+ffvtt3r8+LGWLFmirFmzWhxXuHBhZciQIdLrS2yKFStm828jsmSUiLy6pXKDBg2UJ0+ecPWenp4qXry4pk2bpqlTp0a4cob0crW2cePGyc3NTa1bt9aGDRt07949BQYG6vz58xo6dKjVsc1Dhw5p7dq1dsWcLVs2bdy40ervh/Ry25rAwMAI23h165/UqVPrzz//jDBZA4lXokusuHTpklauXKkxY8aoS5cuqlOnjipWrKiSJUuqUKFCKly4sEqUKKH//e9/atSokbp3765ffvlFf/75Z7g3EwCxL7EnJSSW64AlV1dX9erVy2rdhg0bdOLEiWi1++jRI/NMfE9PT5sDq47w77//qk+fPpJe7jm3ePFim1nMr+rbt6+qVKliUb5kyRItXLgw0vPXr19vsdpA165dlTJlygjPc3d31/jx4y3KTSaTNm7cqOfPn5sffn5+Wr16tdV2+vfvb1HWuHHjCPtOlSqVOnfubFF+4sQJm9ttOIqHh4dq166tDBkyKEWKFOrevbuqVatm9djhw4fr33//1cqVK9WkSROlSZNGAwcO1P79+1WqVCnzcZUrV7ZYti9Mp06ddOzYMfPzTz75RB999JFdca5YscLiy5T08ub4/v37rZ7Xo0cPjRkzRm3bttWXX36pH3/80epxwcHB+uijj7R//3598sknunjxokaMGKFy5copZcqUSp48uUqWLKm5c+eqa9euFucHBgbq999/j/Q6YJ/E8BoIAIieMWPG2EyuC0tidHd315IlS5QxY8a4DC1B+vvvv80rreXJk8dmsq8kdezY0ern9RcvXrD0MAAkIunTp48wwTyy5fSTitDQUP3yyy9q06aN1Vnrbm5umjlzpj744IMY9fPixQs1a9ZM169fN5cNGjTI5oSeV6VJk0YbNmxQ6tSpLdps2rRphKuJvmrYsGFyc3OzKL948aJd51sTtn2ru7u7BgwYYNc5X3/9tXl8p3fv3mrXrl2k56RMmVKzZ89WsmTJwpUbhqH27dtbrIBbrVo15ciRQ8mSJVPVqlXNY5ev27lzpyZMmKBBgwbpu+++U+rUqdW4cWMdPXpUTZs2Na+6kTt3bi1YsMCu60uM+vTpo/z581uUr1q1KtJVcW0JW2VUktXx0vfee09z5sxRp06d1Llz53DHv27IkCGaNGmSKlSooBMnTmju3LmqWbOmMmTIIA8PD+XLl0+DBg3SH3/8YfX8iCZzvc7Hx0eLFi2ymvj94MGDcKu4vO7Zs2fmsVQXFxctWLDA5gRAJH4JPrHi3r17mjZtmlq0aCEfHx/lz59fjRo1Uv/+/TVt2jRt3LhRe/fu1ZEjR3Tu3Dn9999/OnbsmPbt26eVK1dq8uTJ6tOnj2rVqqXs2bMrX758atu2rRYvXsx+3EAcSOxJCYnlOmCpY8eONrOmoztje8qUKeYvFGF7CcaG58+fq0WLFubl1X788ccIVwp4lclk0uDBg63W/fjjjxFu5WAYhtUvRBENIL+qSpUqFlnOYasi2OPw4cNWl4a059pr165ttTwmGd7RVaZMGavlw4YN06BBgywSL95++20dPHhQp0+f1rlz57R9+3arszUWLlyoRYsWmZ97eHjYzGy3JlWqVBargUgvt2bp0KFDpJnfkmzuwf7rr79q586d+v333zV9+vQIZ5t8//33VmdLbNmyJdL+Yb+E/BoIAIi+lClTmpd+tmXw4MF23WyAwq1G1q1bt3D7sb8uQ4YMatq0qdW66dOn68WLFw6PDwDgHNmzZ7dZ9+jRoziMJP4JDQ3Vpk2bVLFiRfXq1cvq+1+2bNnMW1vE1OjRo7V3717z84wZM9qcaGCNj4+P1bGVZ8+e6ZNPPol0S1RJypkzp5o0aWJRfvjw4XAJH1ERNjmqUaNGNmfxv2rbtm0aPXq0pJfJP7bGBq0pVKiQmjdvblH+4MGDSLc0szUGNnz4cFWtWtXiZ5sxY0YtWrRI169f17Fjx/Tff/9FugpMYubu7q5vv/3WovzmzZtatWpVlNu7fPmyeXwtd+7cdiUuFStWzOqWdocPH9bQoUPVvXt37dixI8KVLZo0aWKxDbEkbd++PUoJIu+9957NFYiHDBlicwLdN998Y/5bGzJkiGrVqmV3n0h8EmRixfPnz83ZhtmzZ1eXLl20ePFi3bx50+Y+5dLLm0FhX/5tHXfp0iX98ccfatGihTJmzKhGjRpp7dq10c7eAhC5xJ6UkFiuA+F5e3urS5cuVusWLlyoa9euRam9oKAg88CqyWSymZHtCPPmzdPZs2clSWnTprX65SwiZcqUsTqYfuLECe3cudPmeQcPHrSYWeHu7m51H2lbrA0mnzhxItyXXFtsfTiO6MZAGFsxOmO1q3Tp0lktz58/vwYOHGjzvMKFC1vNUpekgIAAixkKdevWjdI+nZLUsGFDVa9e3aL89OnTGjZsWKTn58iRw+oNhYCAAC1fvlxt2rSJtI106dKpcuXKFuXRXUUB1iXk10AAQMxUrVpV3bp1s1m/efNmu24SJHV3797VkiVLJEleXl7q2LFjpOdYW5lLiv7gOAAgfopoVc8nT57EYSTOExoaqidPnujatWs6dOiQZs6cqa5duypPnjyqWbOm1XGglClTqk+fPjp9+rRq1qwZ4xhu375tMZbRpk0bm1tf2NK9e3erWwZs27ZNs2fPtqsNa5+9DMOI1uqc27dvN088srXV2OsGDx5s/nzXvHlzuydohbF2U1wKvyWaNbbGwEwmk6ZNm2ZzTC9btmx68803bW47nJS0aNFCOXLksCgfNWpUlNuaOXOm+ffgk08+sWtMVZLVFe+ePn2qgQMHasKECVZXZHmdtRV1/fz87F75JcygQYPCrewbJiAgQJ9++qlF+c6dO82rKFetWtW8rTaSrgSVWHH79m1988038vHxUefOnbVt2za9ePHCnBQRljhh6xEmsuPC2gsKCtLKlSvVoEED5c+fX5MmTdLz58+d+BMAEq/EnpSQWK4D4fXo0cPqcrzBwcEaO3ZslNqaN2+ebt26JenlDe3ChQs7JEZrXl3a7IMPPojyl4zkyZPbXDEgolUBrO17lzp1ars+PIcpXbq01fLt27dHeq6tL332LFOdKlUqeXt7W5Q7Y0DD1hf40aNHR/sL48yZM3X16tVwZQ0aNIhWW9a2W5Fezsj08/OL8FwXFxerS55+8cUXdq9sIln/wn7r1i0SZR0sob4GAgBibsSIETYTNnfu3BnpIDmkadOmmVeQa9OmjV37v5crV05vv/221bpJkyY5MjwAgBNFdOPe19c3DiOJe3PmzJHJZJKrq6vSpEmjnDlzqnTp0vrkk080efJki7ELDw8PVa5cWb/++qtu3Lih0aNHR/mmvy2jR4+2WNU8OmMlbm5uNle5GDlypF0JqRUqVNBbb71lUT5t2rQoj3VMnTpV0stJRJUqVYr0+MOHD2vPnj3m53Xq1IlSf5JsTty5cuWKzp8/b/M8W38LX3zxhfLmzRvlOJIid3d39e7d26J83759EU6Qe11ISIh5+1Y3Nzd16NDB7nOt/U2WLl3arklYYWwl50R11RY3NzfNnj1bHh4eFnVbt24Nt+2Ir6+v2rZtK8MwlCFDBv3xxx8RrjCHpCFB/AY8ffpUAwYMUJ48eTR8+HA9fPjQajKFo1hLsrh8+bJ69OihfPnyacaMGQzMA7EgsSclJMTrWPbnP84OI17LkiWLzRn006dP1+PHj+1ua8yYMeZ/9+vXL6ah2bRnzx4dP37c/Dy6+8HZ2u9z165dNs+xtvejtZuyEbEV78mTJyM9t1y5chZbF5QrV05eXl529W3tmsMGw+OSreSJ6M7GMAwj3DLUYcqVKxet9qpUqWJ12dJHjx5pzpw5kZ5v7UtzRPvLWmNticGQkJAo/U0icgnxNRAA4BheXl7q3r27zfovv/wyyrPHkpIXL15oypQp5ucR/SxfZ2u1kG3btplXpQMAJGwR3XuIyuSUxK5IkSJ68OCBtm/frm7dukW40kdU+fn5aebMmeHKXF1dbU74iUzjxo2VLFkyi/IzZ85ow4YNdrVhbdXIK1euWJ3IZMu9e/e0YsUKSbI6O9+a15M3ozOWGNG4TkRjiY4eA0uqOnXqZHX1j+HDh9vdxrp163Tz5k1JLyfE2LOFTJjYGuuTXm4pE1XFihWzuf1x3759zRPpevTooStXrkiSZs2aFaVrRuIV7xMrZsyYoQIFCmj06NEKDAyMNJnC1hYf9j5e93qSxa1bt/Tpp5+qZMmSOnDgQFz8CIAkJTElJSSG69i277SzQ4j3+vTpY/X96NmzZ3bPGtuwYYM5MaBs2bJ67733HBrjq7Zu3Rru+aBBgyJdycnaw1Y28I0bN2z2fffuXYuy1zP/I2NrJp89e4ymTp1aCxYsMH8ILlGihDnT2h7WvgAHBwfbfb6juLq6OrS9PXv26L///gtX5ubmZnMWamRcXFzUqlUrq3WLFi2K9HxHLNOYPn16q+WsPOZ4Ce01EADgGBcuXLA5GCm9vBnRsWNHtgSxYcWKFebPzVWqVInSDYoWLVrY/EzMqhUAkDgEBATYrLN3ckhClTFjRu3Zs0e7d+82P5o1a2b12NOnT0drKwx7LF++3GKyQM6cOaP980+dOrXq169vtc6esRJJat26tdWZ/5MnT7Y7jtmzZysoKEjJkydX27Zt7Trn9bHEnDlzRnkcsVq1ajbbj2gs0dFjYElVihQprCbybtq0SUeOHLGrjenTp5v/3blz5yj1Hx/H+vr376933nnHovzOnTv66quvtHTpUvPrS48ePaK0ki4St3ib3nj37l21a9dOmzZtMn8Rf3XQ1tqX83Tp0snHx0c+Pj7KnDmzUqdOrdSpUytVqlTy8PCQu7u73N3dFRISoqCgIAUFBenp06d68OCBHjx4oNu3b+vSpUu6du2axY2SVxM5DMPQsWPHVKFCBX3xxRf66aefyBQFHGjjrpez6VvWLR+uPOx5WH18l1iuAxErXLiw6tWrp9WrV1vUjR8/Xr1797Z6Q/5Vo0ePNv+7b9++Do/xVX/99Vestn///n2bddaWWHv69KlevHhh9/uoreUc7d3fskaNGrp586b8/Pzszox+9uyZli5dqocPH1rUOeNmgSNX6ZJkdT/urFmzxmhpu8qVK2vkyJEW5X///beeP38e4d+EI7402/p9cEYiTGKX0F4DAQAx5+/vr48++sh8syFZsmRWBzS3b9+uyZMnq2vXrnEcYfz366+/mv/9+eefR+lcLy8vtW/f3uq2W7///ruGDx+e6G+6AUBiF9Hqf5kzZ467QJzAw8NDFSpUCFdWvHhxHThwwOpqWP369dP777+vQoUKOTQOa2MlPj4+MWqzcuXKWrx4sUW5vdsxpEiRQm3btrVYdXTTpk26ePFipFtjGIZhvjnerFkzu7Yhu3Xrli5fvmxXfNEV0Viio8fAkrIePXpo9OjR8vf3D1c+fPjwSJN7rl+/bl5ZJVeuXKpRo0aU+o6PY32urq6aM2eO3n77bQUGBoarmzJliubNmyfp5evPqFGjotUHEqd4mQ2wadMmffzxx7p//755hQrp/25epEiRQqVKlVLp0qVVvHhxFS5cWEWKFHHYUk9hW38cO3ZMR48e1aFDh7R7927zB5qweEJCQjR27Fjt2rVLixcvtrkUDYCoSyxJCYnlOhCxfv36Wb2peOfOHc2ZMyfCpfUOHz6sbdu2SZLy5cunjz76KNbiNAxD+/fvD1fWr18/mxnz0RHRF548efJYjenkyZNW94m0xtasDWtbT0TEnqSK/fv3a/r06Vq8eLGePXsWpfZjk6O/VIb9/r0qpp+pbO39HRgYqH/++SfWVySw9YWNWbOxI6G8BgIAHKNz5846duyYJKlly5Zq1KiRGjVqZPXYAQMGqHbt2oyXvOL48ePmJa9z5cqlevXqRbmNLl266JdffrH4bPP48WPNnz9fn3zyiUNiBQDEPcMwIpzBnytXrjiMJn5IlSqV5s2bp4oVK+rFixfh6vz9/dW6dWvt27fPoZNf43Ks5MqVK7p27Zpy5MgRaRtdu3a1SKwwDEOTJ0+O9Obv9u3bde7cOUnWtxWxZt8+yxWZt2/f7tCfdZYsWWzWkVjhOBkyZFDHjh0tfn+WLl2q8+fPR7hy7W+//aaQkBBJUseOHWM0GSu6YmOsr0iRIvruu+80YMCAcOWhoaF68uSJkidPrgULFkR5K2skbvEusWLKlCn6/PPPzX+k0ss/jBIlSujDDz9U9erVVbp06VhdAshkMilPnjzKkyePGjRoYI7hyJEj2rBhgxYtWhRuf/qDBw+qTJkyWrVqlcqXL2+rWQBRlFiSEhLLdcC2//3vfypXrpxF0oIkjRkzRp06dbL5gfPVmdq9e/eO1Q+mvr6+8vX1DVeWKVMm/e9//4u1Pl9VoUIFiw/vknTgwAG7EytsZbFXrFgxRrGF8fPz0x9//KFJkyaZbxi4ubmpUaNG2rZtm11bjiQkwcHB5i0YXhXTLwxZsmRRlixZdPv2bYs6W9vIhHHWl+YdO3aoSpUqMWqjbdu2mj17tmMCSkASymsgACDmJkyYYJ69VaxYMU2fPl1eXl5q2rSp1RmYz5490yeffKLNmzczMP7/vfp5uGvXrtEa3ypQoIA++OAD/fnnnxZ1kydPJrECABKwa9euKSgoyGZ94cKF4zCa+KN8+fIaNGiQBg0aZFF38OBBDRkyRD/88IND+rpy5YrV8Z+YjpUUL15cLi4uCg0Ntai7fv26XYkVhQsXVpUqVbR9+/Zw5bNmzdL3338f4YqR06ZNkySVLFlSZcqUsSvma9euWZQVK1ZMGTJksOv8+CR37ty6cuVKjNpI6BN2+vTpo8mTJ4dLUAoNDdXIkSPNvx+vCw0NNW+l7Orqqo4dO0a53/j8PaBPnz5avny5/v77b4u6pk2b6o033nBCVIjP4tXI5cCBA9WtWzeFhITIMAy5urrqk08+0dGjR3X48GF9++23Klu2rFP2VTKZTHr77bf11Vdf6ejRozp16pQ+++wz8/Iz9+/f1/vvv69169bFeWxAYrZx13HNX2uZGduybnnVrPimEyKKnsRyHbCtX79+VsvPnTunFStWWK27evWqlixZIull1nD79u1jLT5JVr8UPnnyJFb7fFXdunWtZvcvWLDA7jbCkh1e5e3trTp16sQotrt37+qrr75Sjhw59Nlnn+nYsWPKnj27vv/+e127dk1Lly61uQ1JQnblyhWrAzYR7edqL1vLcEa0xCMSroTwGggAiJm9e/eqT58+kl7OHF22bJl5y4lff/1VGTNmtHre1q1bNXXq1DiLMz57/PixOTFFermiR1T3KA97WEuqkF6uBmVtYBgAkDAcPnzYZp2Xl5eKFSsWh9HEL1999ZXNyUE//fSTw7a/DVvV4XUxHSvx8vKymTwRlbESa9usPXjwIMLtHO7du2f+bh7RqpKvc/ZYIhwrV65catasmUX5nDlzdPPmTavnbNq0yZyQUqdOHWXLli1WY4xrYVuCWEtKmj9/vo4ePeqEqBCfxZvEigEDBmjkyJEyDEOGYahZs2Y6deqUpk2bpjffjH83HQsXLqxJkybp6tWr6tmzp9zd3fX8+XM1atSI5ArAwRJLUkJiuQ5Y17BhQ5tLptlaiu+XX34xZwh37drV5l5xjmLty9C9e/ditc9XpUiRQr1797Yo37FjR7iVoCJibY/Lr7/+2q6tPax58OCB+vTpo9y5c2v48OF69OiR3nrrLc2bN0+XL1/WN998E+GShAmdrX1bHbH1SerUqa2Wv75qChKHhPAaCACIvtu3b6tJkybmPYx/++03FSxY0FyfMWNG/frrrzbP79evX4xnCCYGv/32m8W+1rFh0qRJsd4HACB2WFsJMEyFChWcMuk0vnB1ddW8efOUJk0ai7qQkBC1adNGT58+jXE/8X2spGHDhlZvbkf0/j979mwFBQUpZcqUatmypd19OXssEY73+rYXkhQUFKSff/7Z6vHTp083/7tz586xFpczFSpUSN99951FeXBwsNq2bWv+DgRI8SSxYtSoURo1apQMw1CWLFm0fv16LViwIMI9feKLdOnS6eeff9bRo0dVtmxZBQUFqWnTpvrnn3+cHRqQqCSWpITEch2w5OLiYp7B97q///5bO3fuDFf25MkTzZgxQ5KULFkyde/ePdZjtDaI+++//8Z6v6/68ssvLRImDcNQp06drC6F+KoLFy5o4cKF4creeecdq8ka9pg5c6YKFCign3/+WQEBAcqcObPmzp2rf//9Vy1btnTofpHxla3lRR2R/GBrhY+Y7kmK+CkhvAYCAKInODhYTZo00a1btyRJffv2VaNGjSyOa9q0qdVy6eWNiOgsG5yYhIaGmm94mEwmrVy5Urt3747Ro1u3blb7Wrx4sR48eBCXlwcAcJCIJm2GbVuelOXMmVNTpkyxWnfx4kV98cUXMe4jvo+VuLm5Wb3BfeDAAasrnhiGYd7moXXr1vL29ra7r/gwlgjHevPNN1W7dm2L8qlTp1ok0ty+fVtr1qyRJOXIkUO1atWKkxidwdakv6NHjzpsmyEkDk5PrNiyZYsGDhwoSapXr56OHz+umjVrOjmqqCtcuLB2796tnj17KiAgQA0aNLC5dA6A6EksSQmJ5TpgqW3btjaXQB45cmS451OnTjVn0bdr187meY5k7cvb0aNHI9y709GSJUumNWvWKHfu3OHK//77b3Xo0CHcHn+vunfvnho3bhxu2cWCBQtq/fr18vDwiFIMjx8/Vt26dfXJJ5+YvzDUrFlTJ06cUOvWreP1vn+OljZtWqvlvr6+evjwYYzatjULI3369DFqF/FXfH8NBABET9++fbVnzx5JUsWKFTV8+HCbx06aNMnme31S3xJkw4YNunDhgiSpRo0aatCggf73v//F6DFw4ECrM5efP39u3gsbAJBwnDlzRidOnLBa5+7ubjOBMalp1qyZ2rZta7Vu1qxZNrejtJetsZLLly/HqF3JcWMlnTt3tjohaOLEiRZl27Zt0/nz5yVJn332WZT6sTaWeODAgSi1gfjH2qoVz549s1iBbvbs2eax2o4dO8rFxem3lGPF3LlzNXfuXEmy+tl62LBhJBTBzKl/Bbdu3VLLli0VGhqqPn36aOXKlQl6sN3NzU0///yzxo4dqzt37ujjjz92dkhAopNYkhISy3UgvOTJk9ucdb1hwwbzl+Pg4GCNHz9e0stZ3tFdcSGq0qVLZ1EWGBgY53sw58qVS/v27VPVqlXDlc+ZM0fvvPOOFi1apLt37yooKEiXL1/WhAkTVLx4cR05csR8bP369bVv3z5lyJAhSn3fvn1b5cuXDzcDpFGjRlqzZk2U20oMIvrcderUqRi1bWt50jfeeCNG7caWypUrm7eki+5j9uzZzr4Mp4rvr4EAgKibP3+++TU7a9asWrRoUYSremXKlEkTJkywWd+vXz9dvXrV4XEmBK8OVDtqpabs2bOrfv36VuumTp0qwzAc0g8AIG5ElIDYqFGjRL1VaVT9+uuvypcvn9W6zp07m1faig5bYyW+vr66fv16tNuVHDdWkjVrVjVs2NCifOHChRZbmYStVlG+fHkVL148Sv1YG0vcvXt3lNqILy5fvhzjcZ/EomLFiipXrpxF+fjx482rlBiGYV5p1NXVNdGuPnfu3Dl17dpV0ssV+F6fGCRJL168UNu2beN0ciLiL6cmVnTt2lX379/XwIEDNWrUqEQzQ/SLL77QuHHjtG3bNv3yyy/ODgdIdBJLUkJiuQ6E161bN3l5eVmUG4ahUaNGSZIWLFigGzduSHq5jGOBAgXiJDZbXwxnzpzpkPYDAgJ06dIlu47NkiWLtm7darF/3ZEjR9S8eXNlzpxZnp6eypMnjz7//HPdvn1bklSqVCktXLhQq1atsvrlLrL4atSooTNnzpjLcufOrd9//z1JbPthTcaMGW3OxDh58mSM2vbz87Mo8/T0VJEiRWLULmJm6NChWrVqVay1H59fAwEAUXP8+HF16tRJ0suJJIsWLbLrhk6LFi1sLlX+9OlTffLJJw6N01m6deumffssv89Zc+7cOW3atEmSlC9fPocuoxw2EPy6CxcumPsEAMR/9+7dM9/EfJ2Li4u+/PLLOI4oeqLy/hgT3t7emj9/vtXxnPv376tDhw7RbrtAgQI271XFxlhJrly5bI7NRMTalmD+/v7hJn3cu3dPK1eulCR16dIlyn1Ym4R04cIF7dixI8ptWXPu3Dmbq9ci6vbt22dzq7jXWVu14v79+5o+fbqklyudhK22VqtWLfn4+Dgu0HgiKChIzZs317Nnz5QzZ05NnTpVPXv2VIUKFSyOPX78uIYOHeqEKBHfOC2xYtmyZVq1apU6duyoH3/80VlhxJru3bura9euGjx4cIyyIwFYl1iSEhLLdeD/pE+fXu3bt7dat2DBAl27dk2jR482l/Xr1y+uQlOyZMlUuHBhi/LFixdb7KEXHQsWLFClSpXsPn779u0aO3assmbNqt27d2v+/Pn67LPPVKNGDZUsWVIlSpRQ1apV1aJFC/3yyy86cuSIDh48qGbNmkUrvu+++07Hjh0LV/bll19avQmcVJhMJqtfFqSXX6BiwtpgQaVKlZQsWbIYtYvou3fvnoYPHx6rq7PE59dAAID9Hj9+rA8//NA8Y23EiBF677337D5/ypQpNm8QbN682Txgm1AdP35ckydPVo4cOew6fuLEieZZjl26dHHoMsrvv/++zSTFSZMmOawfAEDsGjp0qJ49e2a1rlWrVnrrrbfiOKKoi+r7Y0yVKVPGYsJOmI0bN1rdFsMeadOmVdGiRa3WxcZYSY0aNaLVVuXKla2udDFlyhTz545Zs2YpKChI6dOnV5MmTaLcR4kSJayWO2p7t44dO9r8f4ioGz16tN2rKjRo0MDqOPGYMWMUHBwc7vN6586dHRZjfPLll1/q8OHDcnV11bx585QmTRq5uLho1qxZSp48ucXxI0eO1MGDB50QKeITpyRWBAcHq1+/fnrvvfc0ZcoUZ4QQJ8aNG6cCBQro22+/dXYoQKKUWJISEst14P/07t3b6tJ+wcHBatKkiY4fPy5JqlChgsqXLx+nsVkbEA8ICIjxe5VhGJowYYKKFStm1/ErVqxQzZo1FRgYqM2bN+t///ufWrRoocmTJ2vjxo06dOiQ/v33X23dulXz58/XF198EaNBBH9/f6ufOWrXrh3tNhOLypUrWy3/888/YzRrIGyVkVfZWqoacWP8+PFKnz693n333VjtJz6/BgIAImcYhtq0aWOeoda4ceMob9uUJUsWjRs3zmZ93759E/SWICNHjlTZsmXtmrnn5+dnnjnq5eUVoxm01phMJpv7pa9bty5B/5wBIKnYs2ePJk+ebLUuQ4YM4ZLT47OovD86yoABA2xO8unXr1+4VUujwtZYyatby0aHo8dKrK1CcfbsWW3dulWGYZhvjrdt2zZaE11KlixpdULSkiVLYry18JEjR7R79267xxIRsf/++0+rVq1S48aN7TreZDKpf//+FuXXrl3TL7/8ohUrVkiSfHx8EuX46fr16807Dnz99df63//+Z64rUKCAhg0bZnHOixcv1K5dOwUGBsZVmIiHnJJYMXHiRPn6+mr+/Pk295RKDFxdXTV9+nTNnz/f7qXRAURNYklKSCzXgZfy5s2rjz76yGrdq186+vbtG1chmVWsWNFq+aRJk7Rz585ot7to0SIdOXJE77//fqTHrlmzRk2bNlVQUJAGDx5scxaAI+3YscNij0np5Z6UMRHT/RVDQkJidL4jtGrVSu7u7hbljx8/1vbt26Pd7uuDF8mTJ1fLli2j3R5i5sGDBxo3bpw+/PDDWN9+Lz6/BgIAIvf9999r7dq1kqRChQrpt99+i1Y7bdq0Ud26da3W+fr6qkOHDglyr+rTp09r/vz5atSokV3H//7773ry5IkkqWXLltFa6jsy7dq1s3qzJDQ01GEzSgEAseP27dtq2bKlQkNDrdZPnTpVmTJliuOooi6q74+O4uLioj/++MPq+2tAQIBatWpl9wz+V7Vr185q+cmTJ3Xu3LkotxcWz+sJj9mzZ1fNmjWj1Z4kffzxx/L29rYonzRpkrZt26bz58/LZDLp008/jVb77u7uVidEhISEqEOHDjG6wfzll1/K1dU1SqvfwrYhQ4YoVapUqlq1qt3ntGrVStmzZ7co//LLL81/Nx06dEh093Fv3bqldu3ayTAMvfvuuxo0aJDFMV988YXVCYonT57U4MGD4yJMxFNOSazo3Lmzrl27ZvUPNrF5++23dffu3US5/xAQXySWpITEch14KbLl7QsWLOiU2fsNGzZUmjRpLMoNw1DLli11/vz5KLd5//599e3bV+7u7mrTpk2Ex545c0bNmzc3r4TQqlWrKPcXHZcvX7Zabi3ZIiqikhhh7UvI06dPY9T/q2wNwkQmS5YsNm+CRzTTNCIPHz60mIXRvHnzWLmRkBA54ybSwIED9fTpU7tnLsRUfH0NBABEbOXKlRoyZIgkKUWKFFq+fLlSpkwZ7famTp1q9bOnJG3dutU8Syy6nPGe2rNnT4WGhtp148gwjHDXaO+e11GVLl06m5/npk2bpufPn8dKvwCQmEX3O3ZU+Pr6qn79+rp27ZrV+q+//trm63tE4vv7o2Q7xujE7uPjY3ObscOHD2vAgAFRbrNUqVIqW7as1brojpWcPn3a4veqc+fOMbppnSpVKrVu3dqifPXq1eYtNqpUqaKCBQtGuw9b232eOnVK7dq1i9akoQULFmjTpk2qW7euMmfOHOXz4+LvMybi+m9w7969WrhwoerXr2918pQtHh4e6tWrl0V52M/XxcVFn3zyicPijA9CQ0PVunVr3bt3T6lTp7a5AIDJZNKsWbOsrtgyevToGK/YgoTLKYkVXl5eVvenSay8vb2j9GIGxBdVyxdxdgh2SyxJCYnlOhKDV7PZo5PZ/s4770SYcd2nT59o7a1sKxZ7Y/T29raZpX7z5k1VrlxZ//33n93x+Pv7q2nTprpx44aaNWsW6SyK/v37m/fqlmRz8MDRwmYJvm7//v0xajcqvxvWPvvY+lnfvn1bnTp1srpEpK0vrLb2YrVH2CyB161fv16nT5+OcnuvL43p4eFh93YzCXHmalRFNKMjODjY4f1t2bJFM2bMUMaMGa1m21uTWF8DAQC2/fPPP2rdurX5vXjKlClW9+2OimzZsmnMmDE26wcOHKgjR45Eu/24fk/97bff9Oeff6pkyZLKkydPpMevXr3a/HmvZMmSNvcpdwRbCc7379/X3LlzY61fAEisYnup9/v376tatWr6559/rNZ36tRJ33//fbTaju/vj5Ljv9s1atRIHTt2tFr36rYGUfHNN99YLZ81a5YePnwY5fZeHytJnz69evbsGeV2Xte1a1eLspCQEO3atUuSbG4ZZq+mTZvanDi8cOFCffzxx1H6vTpw4IB5bLJ79+4RHhsbY2BxwdbfYGz8/fn5+alTp04yDCNaK8Z8+umnNidC1axZUzly5IhpiPFqrG/48OHatm2bpJffd3LlymXz2Hz58mn48OEW5SEhIWrXrh3Jy0mUUxIrACQMjaq/k6Bu5ieWpITEch0J3as3/1/9d1TYmrGdKVMmffzxxzGOy55yaz7//HOlSpXKat2NGzdUsmRJ/frrr5F+6D137pyqVq2q7du3y8vLSz/++GOEx9+4cUNr1qwJV9a4cWNNnDhRp06d0uPHjxUUFBQrWefp0qWzWj5+/PhIzw0MDFS/fv2sJoH4+flZlNkaFMmSJYtF2c6dO3X//v1wZaGhofr888+1b98+q+fY+nJma1UOe5QoUUKff/65RblhGPr666+j3N7ixYvDPe/evbvdgysBAQEWZWErnMRUfLn5HtFKJTFdReV1Z8+eVcuWLWUYhho2bGj3TJjE/BoIALB08uRJ1alTx/zZpnnz5lZnP0ZHhw4dwu1Z/KrAwEA1bdpUvr6+0Wo7Lt9T9+7dax78t3e1irDVPyTF+qpR77//vtWlwCXpp59+ipWBfABIzB49ehRrbR88eFClS5e2OX7Qs2dPTZ06NdrbOMbn98cw1sZTIiq3x7hx42yuzNCmTRsdPXo0Su3VrVtXH374oUW5v7+/fvjhhyi1ZRiGlixZEq5s8ODBNsfnouLNN9+0+VkrS5YsatiwYYzad3d315dffmmzfv78+XrnnXf077//RtrWvHnzVK1aNT19+lR169ZVtWrVIjw+NsbA4oKtv0FH//2FhITo448/1unTp5UyZUpVr149ym14e3tbTc6RXq6o4gjxZaxv586d5m08WrVqpebNm0d6Tvfu3a1uh3PmzJkI/y6QeJFY8f8ZhqH79+9H+8s8kFgltJv5iSUpIbFcR0J269Ytq/+Oitq1a1ud5de9e3ereyBHNa5X3bx50+42smXLFuFey35+furRo4cKFiyoH374QQcPHtTdu3cVHBys27dva926dWrfvr3efPNN87JngwcPVs6cOSPs19oelDdu3FD37t1VtGhRpU2bVp6ennJ1dZXJZJLJZJKLi4vc3Nzk6ekpb29vpUuXTj4+PipSpIgqVaqkrl27auHChVY/oL+qePHiVss3b96skSNH2jxvw4YNevvttzV69GirCR9Xrlwx/9swDHX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",
|
|
"text/plain": [
|
|
"<Figure size 2100x840 with 1 Axes>"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig.savefig(\"compile_time_hybrid.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
|
|
"fig"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|