Repository: Breakthrough-Energy/PostREISE (language: python)

Issue:
Generation stacked bar chart
Integrate generation stacked bar charts for comparison between any number of scenarios side by side. This plot is adopted in the macro grid report as following:

![image.png](https://images.zenhubusercontent.com/5d2f805ed7f15a57af533d83/50d4a821-ab6b-4833-9dfb-3285613a4e84)


Discussion / hints:

Fix patch:
diff --git a/postreise/plot/demo/plot_bar_generation_stack_demo.ipynb b/postreise/plot/demo/plot_bar_generation_stack_demo.ipynb
new file mode 100644
index 00000000..dfac0a0d
--- /dev/null
+++ b/postreise/plot/demo/plot_bar_generation_stack_demo.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from powersimdata.utility.helpers import PrintManager\n",
+    "\n",
+    "from postreise.plot.plot_bar_generation_stack import plot_bar_generation_stack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stacked Bar Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scenario_ids = ['824', '1149', '1502', '1452', '1441', '1270']\n",
+    "scenario_names = [\n",
+    "    '2020',\n",
+    "    'Current\\n2030 Goals',\n",
+    "    'Ambitious\\n2030 Goals\\nDesign 1',\n",
+    "    'Ambitious\\n2030 Goals\\nDesign 2a',\n",
+    "    'Ambitious\\n2030 Goals\\nDesign 2b',\n",
+    "    'Ambitious\\n2030 Goals\\nDesign 3'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resources = [\n",
+    "    'curtailment', \n",
+    "    'wind_curtailment', \n",
+    "    'solar_curtailment', \n",
+    "    'wind', \n",
+    "    'solar', \n",
+    "    'ng', \n",
+    "    'coal', \n",
+    "    'dfo', \n",
+    "    'other', \n",
+    "    'geothermal', \n",
+    "    'hydro', \n",
+    "    'nuclear'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t2c = {\n",
+    "    'nuclear': '#173FA5',\n",
+    "    'hydro': '#0090FF',\n",
+    "    'geothermal': '#CC67F3',\n",
+    "    'other': '#8B36FF',\n",
+    "    'dfo': '#31E8CB',\n",
+    "    'coal': '#37404C',\n",
+    "    'ng': '#72818F',\n",
+    "    'solar': '#FFBB45',\n",
+    "    'wind': '#78D911',\n",
+    "    'curtailment': 'xkcd:red',\n",
+    "    'solar_curtailment': '#FFBB45',\n",
+    "    'wind_curtailment': '#78D911',\n",
+    "}\n",
+    "\n",
+    "t2hc = {\n",
+    "    \"solar_curtailment\": \"#996100\",\n",
+    "    \"wind_curtailment\": \"#4e8e0b\",\n",
+    "}\n",
+    "\n",
+    "t2l = {\n",
+    "    \"nuclear\": \"Nuclear\",\n",
+    "    \"hydro\": \"Hydro\",\n",
+    "    \"geothermal\": \"Geothermal\",\n",
+    "    \"other\": \"Other\",\n",
+    "    \"dfo\": \"Distillate Fuel Oil\",\n",
+    "    \"coal\": \"Coal\",\n",
+    "    \"ng\": \"Natural Gas\",\n",
+    "    \"solar\": \"Solar\",\n",
+    "    \"wind\": \"Wind\",\n",
+    "    \"wind_offshore\": \"Wind Offshore\",\n",
+    "    \"biomass\": \"Biomass\",\n",
+    "    \"storage\": \"Storage\",\n",
+    "    \"wind_curtailment\": \"Wind Curtailment\",\n",
+    "    \"solar_curtailment\": \"Solar Curtailment\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAI4CAYAAAAF0yrwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABzcklEQVR4nO3deXgURf7H8U9lMslkQuQMgciteAArohzegrBeuIuIIIqgqKvi4oEuIoi6HuCJqKCr+PNYb1BUFEVdBNZrdxVEPEEREBAJN4RMjkmmfn/MJIZcnJmukPfreeZhpru6uqa+zKS/U9XdxlorAAAAAED8JXjdAAAAAACorUjIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDgBrOGGONMQeXWfZ3Y8wLpV6PMcYsN8ZsN8asNsZMraCei2N1nRePdgMAABIyANjvGWMukjRYUi9rbR1JnSV9WEHRiyRtkjQkjs0DAKBWIyEDgP1fF0nvW2t/liRr7Vpr7ZTSBYwxLSWdLOlySacZY5rEv5kAANQ+JGQAsP/7r6QhxpiRxpjOxhhfBWWGSJpvrZ0u6QdJg+LaQgAAaikSMgDYz1lrX5B0taTTJP1b0jpjzKgyxYZIein2/CUxbREAgLggIQOAmq9Ikr/MMr+kcPELa+2L1tpekupJulLSncaY0yTJGHO8pNaSXokVf0nSH4wxR1ZvswEAAAkZANR8KyW1KrOstaRfyha01oatta9K+lpSh9jiiyQZSV8ZY9ZK+l+p5QAAoBqRkAFAzTdV0lhjTDNjTIIxppekP0l6TSq5nH1vY0xabP0ZktpL+p8xJiBpgKIX8ziy1ONqSRcYYxLj/m4AAKhFSMgAoOa7Q9Jnkj6RtFnSfZIGWWu/ja3fJmmMoiNpW2Lrh1lrP5F0tqRcSc/Frr641lq7VtLTkhIlnR7H9wEAQK1jrLVetwEAAAAAaiVGyAAAAADAIyRkAAAAAOAREjIAAAAA8AgJGQAAAAB4ZL+/nHGjRo1sq1atvG4GAAAAHLNgwYIN1tp0r9uB2m2/T8hatWql+fPne90MAAAAOMYY84vXbQCYsggAAAAAHiEhAwAAAACPkJABAAAAgEdIyAAAAADAI/v9RT0AAACA3bFgwYLUxMTERZJaet0W1GzGmNxwOHzP0UcfPb6yMiRkAAAAQCkJCQnPpaWltWzZsmWBz+fzujmooYqKipSbm5u0YsWKvy9YsECVJWVMWQQAAABK8fl8Z2RmZhaSjGFv+Hw+1alTp6hVq1YRv99/U2XlSMgAAACAUqy1/qSkpIjX7cD+ISUlpcham1LZehIyAAAAoIyEBA6TsW/sbKSV/2kAAAAA4BESMgAAAGA/NGvWrMRWrVoF93R7Y0zw22+/dTZfGDVqVPKAAQNSJGnx4sUJxphgQUGB183abVxlEQAAAKjC6B8blCQ1ORsjWv1loZodlajUhjvPVXal/N2HbArtSjtuuumm5M8++yzho48+yi1e1rJly2DLli1t2WU333xz+PLLLw+vWLGicFfq3hOvvfaa/5577vH/8MMPSkpK0sEHH6xrr702fOGFF4b3pL6uXbsGBw4cWHj99dfvUlZ177335u/JfvY1Y0zwm2++yevQocMenXfobMYLAAAAuKQ6krHd0aNHj6KFCxeacDia76xYscKEw2F9//33OyxbuXKl/vjHPxbt9Q6r8PTTT/svvvhi/wUXXFD4yy+/5K5fvz7097//veDtt9/e7QGfoqIiFRZWW97oPBIyAAAAYCe8TsYk6aSTTioMh8P6z3/+45Ok2bNnJx533HG2devWOyxr3ry5WrduHZk5c6Y/IyOjZHQvMzMzeMsttyQfcsghwbS0tOCZZ56ZkpOTU1L/2LFjk9PT04Pp6enBiRMnJlXWjqKiIt18883+6667ruj6668vaNSokU1MTFTv3r0Lp06dmitJI0aMCPTp06fkyoJlpxR27do1OHz48ECnTp2CqampwXPOOSdlwYIFGj16dGIwGAwOGTIkIElDhw4NNGnSJFinTp1g+/btg7NmzSpJ+Mruo7SuXbsGr7766kCnTp2CwWAw2KNHj5TffvvN/PnPf06pU6dOsEOHDsHFixeXBGbhwoW+4447LqVu3brBVq1aBf/v//7PX7yuX79+KYMHDw6cfPLJKampqcEjjjgi+N133yVIUufOnYOS1KVLl0AwGAw++eST/vKtqRoJGQAAAFAFF5IxSUpJSVHHjh01Z86cREn66KOPfMcff3xRt27dIqWXde3a1VZWx5tvvul7//3385YuXZq7ePFiM3ny5CRJevXVV/3/+Mc/fO+8807+0qVLQ3PmzKn00oBff/21b+3atRo0aNAeTU0sNn369IQpU6bkb9u2LTRt2rTco48+WnfffXdhKBQKPffcc3mS1KVLl8jChQtzN23aFOrXr1/R4MGDk0onkVWZMWNGwgsvvJC3cuXK3JUrV5pjjjkmZejQoYWbNm0KtW3b1o4dOzZZkrZu3aozzjgjecCAAUXr168PvfDCC/kjR470L1iwoKQP3n777YS///3vBZs3bw61atXK3njjjcmSNH/+/JAkffHFF3mhUCj0l7/8Zbf7hIQMAAAAqEJ1JmM5G3fvtKPjjjsu8umnnyZI0v/+97+EXr16FZ588slFpZedfPLJlU5XHDZsWGHr1q0jGRkZ9tRTT40sWrQoQZKmTp2aOGDAgEjXrl2L6tatq3HjxlV6fta6deuMJLVo0WKv7tU2YMCASJcuXYqSkpIUCAQqLHPVVVcVNG3a1CYlJemOO+7ILygo0Ndff71Ld+weOHBgUfv27SONGjWyPXr0iLRs2dL27ds3nJSUpHPPPbfw22+/NZI0bdo0/4EHHqjrrruuICkpSSeccELR6aefHnnxxRdLRuNOPfVU26NHj6KkpCQNGjSo8Pvvvzd7895LIyEDAAAAqlCdydjqL3fv3KkePXoUzZ8/32RlZZlNmzbpD3/4Q+SUU04Jf/nllyYrK8ssXbpUvXr1qrTSpk2bloyepaSkKCcnx0hSVlaWmjdvXpJgHXzwwZUmW40bN7aStHLlyr3KJXYlobvllluS27RpE0xLSwumpaUFt2/frqysrF3ab5MmTXZ4r+np6SXrgsGgQqHotVRWrFiR8PXXX6t4H2lpacEZM2YkZGVllSRdGRkZJXWlpqaWbLsvkJABAAAAVajOZKzZUbt3DYwePXqEt2/frocffjjp6KOPtpLUsGFDNW7cWA8//HBSenq6Dj/88N0eucrIyNCqVatKGv7zzz9X+iaOOOKIoiZNmuill16q9Hyp1NRUm5tbcuFH/frrr+VGlIwxVb6eNWtW4qOPPup7+eWX8zdv3hzKzs4O1alTR9ZWOiNzj7Ro0cJ26dLFZmdnh4ofoVAo9OKLL+bt0x1VgoQMAAAA2AvxPMcsLS1NHTp00BNPPOE7/vjjSxKvrl27Rp544okqzx+ryoABAwpfffXVhAULFvi2bdum4vOrKuLz+TRu3LjwQw895HvooYeSNm7cqMLCQr333nuJAwcOTJGko446KvLFF1+YH3/8MWHDhg1m/PjxlV4kpFijRo1s6URw27ZtxufzqUmTJpFwOKwbbrgheVfPH9sdAwYMKFixYoV59NFHk/Ly8pSXl6d58+b5vvzyy12aGtmwYUMtWbJkl8pWhPuQAQAAAFXYpfuEHbuble5u+dKbHnts5Kuvvkro3r17ydTEE088sei5555LOOGEE/bocvcDBgwIL1y4MOG0005LNsZozJgxhW+99ValucIll1wSTktL0z333OMfM2ZMYnJystq2bavrr78+LEnnnHNO+I033vAdffTRgfr16+vaa68tLL7wSGVGjBgRvvTSS5Oef/754DnnnBN58skn81599dXE9u3bp6SkpOjyyy8vysjI2JO3V6X69evr3Xffzbv22muTx44dG7TW6rDDDtOECRN26T5nI0eOLLzyyiv9F198sX/ixInhyy67bLcu7GH29ZCfazp37mznz5/vdTMAAADgGGPMAmtt57LLFy1aFO7YseMu3ZwY2BWLFi1K6tixY4VTPJmyCAAAAAAeYcoiAOeM/rFBhcure46+6+XvPmTTTreJh2v+U8/J/vG6vCvxKf78uNY/Xpd3JT5Sxd9xXveP1+Vdig8Qb4yQAagRXD+Y8Lp8PLnwfl0s7xIX+8el8q5xrX9cKw/s7/gUAHCeawcHrpV3jWv9E6/yrnC1f1wp7xrX+se18kBtwCcBgNNcOzhwrbxrXOufeJZ3hav940J517jWP66VB2oLPg0AnOXawYFr5V3jWv+4Vj5eXHm/rpV3jWv941p5oDbhEwHASa4dHLhW3jWu9Y9r5ePJhffrYnmXuNg/LpUHapu4fSqMMSuMMd8YY74yxsyPLWtgjPmXMean2L/1Y8uNMeYRY8xSY8zXxpijStVzUaz8T8aYi+LVfgDx49rBgWvlXeNa/7hW3jWu9U9tO8dPYlopgB3F+5PRw1p7ZKkb8N0k6UNrbVtJH8ZeS9IZktrGHpdL+ocUTeAk3Sapm6Sukm4rTuIA7D9cOjhwrbxrXOsf18q7xrX+qY3n+ElMK4U3Fi9enGCMCRYU7D/3u/7xxx8TgsFgMBwOS5K6du0afPDBB5M8btZu8/obqo+k7rHn/5Q0T9Ko2PLnrLVW0n+NMfWMMU1jZf9lrd0kScaYf0k6XdLL8W02gOrkysGBa+Vd41r/uFbeNa71j2vl48mF9+taeefNPjVY1eo1WZs0+5Ov1euEI5SZ0WCn1ZUr3+uD0K425b333kscPXp00tKlS5WQkKA2bdpo4sSJ+d27dy/a1Tr2lRUrVpiRI0cG5s2bZ0KhkBo3bqw+ffpEbr/99ry6devudn2PPPJI0rPPPpv45Zdf7lJ/HHLIIZFQKLTLfVdd+vXrl5KZmWknTZqUtyfbx/MTYiV9YIxZYIy5PLYsw1r7W+z5WkkZsecHSlpVatvVsWWVLQewH3Hh4MDF8i5xsX9cKu8a1/rHtfKuca1/atv32+7a62RsN2zcuFH9+/dPuvLKKws3bdoUWr16dWjs2LHhQCCwp83fJRWNqmVlZZnjjjsuJS8vTx9//HFeTk5O6IMPPsjbunWrfvjhB9++2EdtEc8RshOstb8aYxpL+pcxZnHpldZaa4yx+2JHsYTvcknKyMjQvHnz9kW1AOIls+rVLh5MxKP8vPx5Oy0bDznJbvaP1+Wd+VtT5vPjSv94Xd6Z+EhVfsfVlP7c1+Vd+X7bXfFMxiTpu+++80nSFVdcUSBJfr9f/fr1CxevLyws1OjRo5NffPFFX15enk4++WT75JNP5jVq1KjcMfZDDz2U9MgjjyRmZWWpfv36uuaaawpvvPHGAkmaOXOm/9JLL/VfeumlRVOmTPGdcMIJ9s0338wtvf1dd92VnJqaqjfeeCM3MTGaUrRt2zbyzDPP5EnRKZKHH354ID8/P5SUFJ1F2LVr1+DAgQMLr7/++oJHHnkk6Zlnnkk86qijItOnT0/o1q2bnTdvniksLFQwGAz6fD5lZ2eHXnnlFf/tt9/uX7VqlerUqaPzzz8/MnHixEr3Uax0/dOmTUuoW7eunn766YLFixcnjB8/PrGgoEC333574dVXX10gSaFQSNddd13g7bffTigoKNBpp50WeeKJJ/LS0tJK+uOKK64omjx5ss/n8+nmm28uvO666wruv//+pBkzZhhjjHnqqaeC3bp1s3Pnzt2hr3YmbgmZtfbX2L/rjDFvKHoOWJYxpqm19rfYlMR1seK/SmpeavNmsWW/6vcpjsXL51WwrymSpkhS586dbffu3csWAeCw93+sfJ2rBxPxKN/92O47LR8PjzzqZv94Xd6VvzWlPz8u9Y/X5V2Jj1T5d1xN6s99Xd6V77fdEe9kTJLat29f5PP51Ldv35Tzzz+/sHv37oWNGzcuSbYmTZqUNHXqVN/s2bPzMjMzIwMGDEi57LLLAmWTKUlq0qSJffvtt/MOO+ywyHvvvZfYr1+/pGOPPbboxBNPLJKio3GbNm0yK1euDBUVlZ8N+dFHHyX07t07UpyM7YlvvvlG/fr1i6xbty4vPz9fzzzzTLkpi3Xq1NGzzz6bf/TRRxfNnz/f17t37+ROnTr5hwwZEq6q7uL6L7roosjjjz+ed/311wcuuuiipF69ekWWLVsWmjVrln/IkCH+wYMHF9SrV09XX311YPny5earr77KTUpKsuecc07KqFGjAo899lhecX9s3brVrFmzJjRjxgz/RRdd5L/gggvCI0eOLPjvf//rc37KojEm1RiTVvxc0qmSvpX0lqTiKyVeJGlG7PlbkobErrZ4jKStsamN70s61RhTP3Yxj1NjywDUAi4fTLhQPl5ceb+ulXeNa/3jWnnXuNY/rpV3jRfJmCQ1bNhQc+bMyU9ISNDVV1/tz8zMTDnllFNSVq1aZSRp2rRpicOGDSts165dpF69errvvvvyZ86caSqaDjhw4MBw+/btIz6fT7179y484YQT7Ny5c0uyK2OMJkyYkBcMBpWWllZu+y1btqhp06Z7Nd80PT1dY8eOLUhKSqpwH5J01llnhbt161aUmJioY445pqhPnz6Rf//737s0JfLAAw/UddddV+D3+3XhhReG165dq/Hjx+cHg0H169cv7Pf79d133/mKior00ksvJUyaNCk/IyPD1q9fXzfffHN4xowZJf85ExMTdf/99+cFAgGdd9554WAwqG+++Waf/OeN1whZhqQ3jDHF+3zJWvueMeYLSdOMMZdK+kXSgFj5dyWdKWmppJCkoZJkrd1kjLlT0hexcncUX+ADwP7NtYMD18rHkwvv18XyLnGxf1wq7xrX+se18q7xKhkrdtRRRxVNnz49V5IWLlzou/DCC5OHDRsWmDlzZm5WVpZat25dMmLWtm3bSFFRkVavXl2uo6dNm+YfN26cf8WKFYpEIsrLy1O7du1M8fr69esrNTW10nbUq1dPv/32214FsEmTJjstM2fOHN/o0aOTf/zxRxUWFqqgoEBnnHHGLp3m1LBhw5LnqampVpKaN29esm1ycrKys7PNb7/9ZvLy8nTMMcfscDJe6ZHBunXrqvS0yEAgoG3btiVI2uuLqcTlU2CtXWat7Rh7tLfWjost32it7WmtbWut7VWcXNmov1prD7LW/sFaO79UXU9baw+OPZ6JR/sBeMu1gwPXyrvGtf6JV3lXuNo/rpR3jWv941p513idjJXVqVOnogsuuKBwyZIlRopeO2H58uUlSdXPP/+c4PP51KxZsx1+NQqFQhoyZIj/2muvDWdlZYWys7NDJ598cuwC51GxgZRKnXTSSZF33303obCw4u+/tLQ0K0nZ2dkly9avX79DmbL7qGifF110UfIZZ5xRtHr16lB2dnZo0KBBkdLt3BeaNGlik5OTtWjRorzs7OxQ8WNXr+C4s77amZr3SQBQq7h2cOBaede41j/xLO8KV/vHhfKuca1/XCvvGheSsYULF/puvfXW5KVLlyZI0k8//ZTw2muvJR511FFWkvr371/4+OOPJ/7www8JW7Zs0Y033pjcu3dvW/aCF/n5+QqHw8rIyLB+v1/Tpk3zf/LJJ7uVVYwdOzZ/+/btOvfcc1OWLFmSIEnLli1LuOyyywL//e9/fQceeKBt3LixpkyZkhQOhzVx4sSkVatWVVlnkyZN7Nq1a5Wb+/spbzk5OWrYsKFNTU3V3LlzfW+++eY+/8+TmJio888/PzJ8+PDk1atXm+L38tprr/l3Zfv09HS7YsWKPc7K3PkLAgBluHZw4Fp517jWP66VjxdX3q9r5V3jWv+4Vt45vT4IZUoaMmjXN9nd8ruibt26dv78+QmPP/54IDs7W2lpaerZs2dk8uTJeZJ0zTXXFPz666+mR48egfz8fJ100kn2ySefLHehifr16+vOO+8sHDJkSFJBQUFSz549bc+ePXdr2CkjI8N++umneSNHjkw+/vjjA7m5uWrcuLH69u1b1K5duyJJmjRpUnjEiBH+8ePHJ5533nmRTp06VVnnWWedFZ48eXJikyZNggkJCdq8eXPogQceCI8dO9Z/0003JXbt2tWeddZZka1bt+7dkFQFJk+enHfjjTcGjjnmmJQtW7aocePGGjp06C5NR7zqqqsK+vfvH0hLSwt269bNzp49e7eusmj29ZCfazp37mznz5+/84IAnDH6xwbOHRy4UP7uQ9w4ZXb0j+V/6XWhf7wu73J8yqoJ/bmvyz9y7JadlouXa/5Tz7n+8bq8V58fY8wCa23nsssXLVoU7tixY+29MRb2uUWLFiV17NixwhE3RsgAOMeFgwOXy3vt7pU7HrsUT8u56YQjlJnTQMqpevv9tvwhVW/nCtf+P8ftHL9jd1o0blzsH1fKA7URCRkA53R+LuT+wbcX5R084K/ucypqennXuHbwXRvP8ZOkyfUO8/77xLXyDn6/AfHi1jcUAEjOHEy7Vt41rvWPa+W9UnYEs5hTB99elHeIC/8/XSsP1GaMHQNwjgsHBy6Wd4mL/eNSede41j+ulXeNa/1T277fgHgjIQNQ47h4MBGP8q5wtX9cKe8a1/rHtfKuca1/atv3G+AFEjIANYqrBxPxKO8KV/vHhfKuca1/XCvvGtf6pzZ+vwFeICEDUGO4fDDhQvl4ceX9ulbeNa71j2vlXeNa/7hWHtifkZABqBFcOzhwrXw8ufB+XSzvEhf7x6XyrnGtf1wrD+zvSMgAOM+1gwPXyrvGtf6pbefAuNo/rpR3jWv941p57LlZs2YltmrVKrin2xtjgt9++y25Qhxw2XsATnPt4MC18q5xrX9q4zkwrvaPC+Vd41r/uFbeJaN/bLDHic2uuPuQTaFdKXfTTTclf/bZZwkfffRRbvGyli1bBlu2bGnLLrv55pvDK1as2KV64S2yXgDOcu3gwLXyrnGtf1wrHy+uvF/XyrvGtf5xrTwq1qNHj6KFCxeacDgsSVqxYoUJh8P6/vvvd1i2cuVK/fGPfyzysq3YdSRkAJzk2sGBa+Vd41r/uFY+nlx4vy6Wd4mL/eNSeVTupJNOKgyHw/rPf/7jk6TZs2cnHnfccbZ169Y7LGvevLm+++47X0ZGRsnIXmZmZvCWW25JPuSQQ4JpaWnBM888MyUn5/c7rI8dOzY5PT09mJ6eHpw4cWJS3N9cLUZCBsA5rh0cuFbeNa71j2vlXeNa/9S2c/wkppViz6WkpKhjx46aM2dOoiR99NFHvuOPP76oW7dukdLLunbtaiva/s033/S9//77eUuXLs1dvHixmTx5cpIkvfrqq/5//OMfvnfeeSd/6dKloTlz5vji965AQgbAOS4dHLhW3jWu9Y9r5V3jWv/UxnP8JKaVYu8cd9xxkU8//TRBkv73v/8l9OrVq/Dkk08uKr3s5JNPrnC64rBhwwpbt24dycjIsKeeempk0aJFCZI0derUxAEDBkS6du1aVLduXY0bNy4/fu8IJGQAnOPKwYFr5V3jWv+4Vt41rvWPa+XjyYX361p57LoePXoUzZ8/32RlZZlNmzbpD3/4Q+SUU04Jf/nllyYrK8ssXbpUvXr1Kqxo26ZNm5aMnKWkpCgnJ8dIUlZWlpo3bx4pXnfwwQdHKtoe1YOEDIBzXDg4cLG8S1zsH5fKu8a1/nGtvGtc65/a9v3muh49eoS3b9+uhx9+OOnoo4+2ktSwYUM1btxYDz/8cFJ6eroOP/zw3UqoMjIytGrVqpK84OeffyZHiCM6G0CN4+LBRG06B8bV/nGlvGtc6x/XyrvGtf6pbd9vNUFaWpo6dOigJ554wnf88ceXJF5du3aNPPHEE5WeP1aVAQMGFL766qsJCxYs8G3btk1jx45N3retRlW4DxmAGsXVg4nadA6Mq/3jQnnXuNY/rpV3jWv9Uxu/3yqzq/cJi5djjz028tVXXyV07969ZGriiSeeWPTcc88lnHDCCbt9ufsBAwaEFy5cmHDaaaclG2M0ZsyYwrfeeos8IU6MtbudRNconTt3tvPnz/e6GQB2x+xTK1zs8sFEXMr3+mCn28TDmhc7u9k/Hpd3JT7Fnx/X+sfz8q7ER6rwO87z/vG6vEfxMcYssNZ2Lrt80aJF4Y4dOxZ40SbsnxYtWpTUsWNHf0XrmLIIoEZw/mDC4/Lx5ML7dbG8S1zsH5fKu8a1/nGtPLC/IyED4DzXDg5cK+8a1/qntp0D42r/uFLeNa71j2vlgdqAhAyA01w7OHCtvGtc65/aeA6Mq/3jQnnXuNY/rpUHagsSMgDOcu3gwLXyrnGtf1wrHy+uvF/XyrvGtf5xrTxQm5CQAXCSawcHrpV3jWv941r5eHLh/bpY3iUu9o9L5YHahoQMgHNcOzhwrbxrXOsf18q7xrX+qW3n+ElMKwWwIxIyAM5x6eDAtfKuca1/XCvvGtf6pzae4ycxrRTAjkjIADjHlYMD18q7xrX+ca28a1zrH9fKx5ML79e18kBtRkIGwDkuHBy4WN4lLvaPS+Vd41r/uFbeNa71T237fttfLV68OMEYEywo4H7Xrkn0ugGAF0b/uOMfiJyNEa3+slDNjkpUasOd/06xP5a/+5Ca8wfRxYOJeJQfMminRePC1f5xpbxrXOsf18q7xrX+qW3fb5WafWqwWuvv9UFod4q/9957iaNHj05aunSpEhIS1KZNG02cODG/e/fuRdXVRFQfEjLUSnev7FzyvPiPwU0nHKHMnAZSTtXb7rflD6l6O1e4ejBRm86BcbV/XCjvGtf6x7XyrnGtf2rj91tNsHHjRvXv3z/pgQceKLzkkksK8vLy9MEHH/gDgUC17bOgoEBJSUnVVn9tx5RF1Gou/3FyobxrXOsf18rHiyvv17XyrnGtf1wr7xrX+se18vjdd99955OkK664osDv9ystLU39+vULH3PMMUWFhYUaOXJkcmZmZrBBgwbBvn37pmzYsMFUVM9DDz2U1KZNm2BqamqwWbNmwfvuu68k45o5c6Y/IyMjOGbMmORGjRoFBwwYkBKv91cbkZCh1nLtj41r5V3jWv+4Vj6eXHi/LpZ3iYv941J517jWP66Vx47at29f5PP51Ldv35Rp06b5161bV5JwTZo0KWnq1Km+2bNn5y1btiyUk5Ojyy67rMKhsyZNmti33347b9u2baEnnnii4NZbb038+OOPfcXrN27cqE2bNpmVK1eGnn/++dx4vLfaioQMtZJrf2xcK+8a1/rHtfKuca1/4lXeFa72jyvlXeNa/7hWHuU1bNhQc+bMyU9ISNDVV1/tz8zMTDnllFNSVq1aZaZNm5Y4bNiwwnbt2kXq1aun++67L3/mzJmmogt5DBw4MNy+ffuIz+dT7969C0844QQ7d+7cktOZjDGaMGFCXjAYVFpaWlzfY23DOWSolXqO/FL166bq6c+WSVomSRpwcoau+nNzhfKKdObNC0vKFhSEtXlrji7/UwtlZjTQhq0FOveO8gdfw/7UTOd1b6Ivvl+rIfd8W67+G85tqT8dm64lq3J0xUM/lKv/tosOVmZGA321NFvX/WNJufrHX3KwjmtfTzM+XqmbnvyxXP0PDTtURx6cptlfbtRdLy4vV/8TI9opM6OB3v7Pek147Zdy9c9btCc9WT263zC/5Hlx+wef2kyZGQ3KxafYxadm6uLTMvXt0nXqf8fX5fqnOD6r1uVp8L3flqt/1PltlJnRoFx8io0d1Fq9jmqoDz5frWsnLy5Xf3F8Pvtui8Y8vbRc/Q8PP0yZGQ3KxafYE9cdrkObp+rZWct078vLytX//KgOar4nnVkNSsenWPHn5+eVG3TW2K/Ktb84PmU/P8X9c02/VsrMaFAuPsWKPz8fLVyjKyZ+X67+4viU/fwU13/PXw5RZkaDcvEpVvz5eeXDFbr9n0vL1V8cn7Kfn+L6n7upw550ZbXg+63895tLnx/p989Qcfvr103Vdn+Orvpz1d9xpx4Z0Bv/WqQX/xfYoX+kir/jStdfP7NImRmq8juu3YFGT725SLO+LV9/Rd9xpes/oqNfmRmq8jsuLSlf97/wlf63IqVc/S79DaoJjjrqqKLp06fnStLChQt9F154YfKwYcMCWVlZat26tS0u17Zt20hRUZFWr15dbhBm2rRp/nHjxvlXrFihSCSivLw8tWvXrmS0rX79+kpNTY3PG6rlSMhQK+U09ylHeZLySpbNqJ+nVS2yFM61WhbY/nvhgKQDfJrder3Wtdim0KaIlgXKXznj5UbL9FWL1drmj1RY/z/Tf9JnLX7RxsIiLQuEytU/49DftKjFBmXlFGpZoPzMgH9kLNbbLRK1+vDCCut/pOl3ymiRqBWrw1oWyCtX/wt/WKVZLdbop6UFWhbI3+0+i6dlgezfX8Ta/++WG5TdYn75+MS82nC5lrRYo1Cdivu/dHx2iF+s/plt12pJi03l4xPzVOMf9WELv7JyKu7/kvhsKBO/WP2vtF+tuS3Wlo9PzISm36phC59+altQYf33HPi1Ht1518XFDvGJKfn8pNsK218Sn7Kfn1j/vH/QOq1ssaV8fGJKf34qqr90fCrq/9cOX6P/tlhXPj4xJZ+fQ8MV1l8Sn7Kfn1j9z3ZcqS4777q44Put/PebS58fqdRnKNb+HOVpRv1fKo5RzKsNl2tJl2SFDkrV6h9yVLp/pEq+40rVX2mMYp5q/KNadfErq06KVo/PLVd/hd9xpeqvNEYxE5p+q4ZtfFqVHdDqZ/LK1Y8916lTp6ILLrig8Nlnn03MyMjQ8uXLS5Kqn3/+OcHn86lZs2aRZcuWlSRloVBIQ4YM8T/22GPhCy64IBwIBNSrV68Ua0tyORlT4alnqAamdMfvjzp37mznzy//ay5qt7KXvYdbl70nPhVzJUbEp2LEx22uxEciRhXxKj7GmAXW2s5lly9atCjcsWPH3+f5OXTZ+4ULF/reeOONxCFDhoQPPvjgyE8//ZQwYMCAwCGHHGK7detW9PDDDye+9957eU2bNo0MHDgwJTk5WTNmzMhdvHhxwuGHHx7Iz88P5eTkqFGjRsG33nqr4PTTTy+cPn26f8iQIf6//OUvkUmTJuXNnDnTf+mll/qzsrJ263L8qNyiRYuSOnbs6K9oHSNkAAAAQFV28z5h1alu3bp2/vz5CY8//nggOztbaWlp6tmzZ2Ty5Ml5devW1a+//mp69OgRyM/P10knnWSffPLJcsOR9evX15133lk4ZMiQpIKCgqSePXvanj177t+jNA4jIQMAAABqiDZt2kTefffdSq96OGHChPwJEyaUW37YYYdFrLUlieWYMWMKxowZU/5qH5LOOuuscFZWVnifNBg7xVUWAQAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAOwz/fr1S7n66qsDXrejpkj0ugEAAACAy26446FgddY/4dbrQrtaNjMzM5iXl6fly5eH6tatK0l68MEHk1555ZXEzz//fKf1jBgxIrBs2TIzY8aM3L1o8l5ZsWKFGTVqVGDOnDkmJydHDRo0ULdu3ezYsWMLOnXqVORVu7zCCBkAAABQg0QiEY0fPz7Zi30XFBTs1fa//fabOf7441Nyc3M1b968vOzs7NDChQtzTzzxxKJ33nnHt4+aWaOQkAEAAAA1yLBhw4oef/xx3/r1601F64cOHRpo0qRJsE6dOsH27dsHZ82alShJr776qn/y5MkJ77zzjgkGg8FDDz00KEVH3d544w1/8fYjRowI9OnTJ0WSFi9enGCMCT744INJTZs2DR533HFBSTr99NNTGjVqFExLSwt27tw5+MUXX+xSMjV+/PjkOnXq6PXXX89t3759xOfzKT093V533XUFY8eOLcn2qqp/6tSp/oMOOiiYmpoabNy4cfCWW27xJDndV0jIAAAAgBqka9eukW7dutk77rijwkSkS5cukYULF+Zu2rQp1K9fv6LBgwcn5eTkqH///uHhw4dHevfubUOhUGjJkiW7PFXy448/9i1evDg0d+7ckCSddtppRT/99FMoKysrdMQRR0QGDx68S0nRRx99lHDmmWdGEhOrPnOqqvqHDx/unzx5ckFOTk7o22+/zT311FMLd/V9uIiEDAAAAKhh7rrrroJnn3024ddffy03SnbVVVcVNG3a1CYlJemOO+7ILygo0Ndff71X0wHHjRuXX7duXaWlpUmSRowYUVC/fn0Fg0Hde++9+UuWLNGGDRsqHLErbfPmzWrSpEmk+PULL7zgT0tLC6ampgaPP/74lOLlVdWfmJiob775JmHjxo1q3LixPfHEE2v0eWckZAAAAEAN07Vr16IePXrY2267rdzI1C233JLcpk2bYFpaWjAtLS24fft2ZWVl7dVxf5s2bUqSqHA4rGHDhgWaN28erFOnTrBNmzYpkpSVlbXThKxevXr67bffStpy4YUXhrOzs0N33nlnYTgcNrtS/8svv5z//vvv+1q3bh3s0qVLcPbs2TX6QoUkZAAAAEANNH78+PxXXnklYfXq1SWJ0KxZsxIfffRR38svv5y/efPmUHZ2dqhOnTqy1kqSjDG2bD3BYFA5OTklr7OyssrtKyHh97RhypQpSe+//37CBx98kLd169bQsmXLciWV7KMqJ554YmTWrFkJhYWVzzLcWf3du3cv+vDDD3PXrVsX6t27d9GFF16YtNMdO4yEDAAAAKiBOnToEOndu7d9+umnS6Yjbtu2zfh8PjVp0iQSDod1ww03JJdOtjIyMuyqVatM6YSoXbt2durUqYl5eXn66KOPfLNmzaoyR9i2bZuSkpLUuHHjyPbt2zVixIhdvqjGzTffnL9t2zadc845Kd99911CUVGRNm/erK+++qpkn1XVn5ubq8ceeyxpw4YNJhAI6IADDrClk8WaqEYP7wEAAADVbXfuExZvd911V/6bb75ZchPmc845J/zqq68mtm/fPiUlJUWXX355UUZGRkn5IUOGhKdOnZrYoEGDYLNmzfT999+H7rnnnvyBAwcGGjRoEOzSpYvt06dPZPPmzZVOPxw2bFjB7NmzfS1atAjWrVtXY8aMKXzxxRd3KSvKzMy0n332Wd6NN96Y3L1790BOTo4aNmyozp0728cffzx/V+p/+eWXfTfddFNiJBJRq1at9PTTT+/dtfg9ZnZlaLEm69y5s50/f77XzYBjRv/YwOsmOOfuQzZ53YQSxKdirsSI+FSM+LjNlfhIxKgiXsXHGLPAWtu57PJFixaFO3bsWKMP8uGWRYsWJXXs2NFf0bqaPb4HAAAAADUYCRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAOratWvwwQcfTPK6HbVNotcNAAAAAFx2TI8/Bauz/v/OfTu0u9s8/vjjSZMmTUpcvny5UlNTdfjhh9sxY8aETz/99MLqaCOqDyNkAAAAQA1y2223JY8ZMyZx5MiR4d9++y20atWq0BVXXFH4xhtvMNhSA5GQAQAAADXEhg0bzP333++7//77wxdffHG4bt26CgQCGjRoUPiJJ57IC4VCuuSSSwLp6enB9PT04CWXXBIIhaIDcOvWrTM9evRIqV+/fvCAAw4I9ujRI2Xp0qXkAx4ji64m3Ts2KLdswMkZuurPzfXzyg06a+xXql83VUlJ/pL1F5+aqYtPy9SGrQU6946vS5YXFIS1eWuOrunXSsP6HKxV6/I0+N5vy9V/w7kt9adj0/XRwjW6YuL35eofO6i1eh3VUF8tzdZ1/1hSrv57/nKI+pzYQp99t0Vjnl5arv6Hhh2qIw9O0ysfrtDt/1xarv4nrjtchzZP1dv/Wa8Jr/1Srv4f1kR2owcBAABQ1ty5cxMLCgo0ePDgcEXrR48eHViwYEHCwoULc40xOuuss1JuuummwCOPPJJXVFSkIUOGFM6YMSNcVFSk888/P+XKK69Mnj17dm683wd+R0JWTZYFssstm1E/T6taZCmcbpXT3Kcc5UnKK1n/asPlWtJijUKbIloWyPl9w4CkA3x6/6B1Wtlii7b5y6yP+Wf6T/qsxS/aWFhUYf1PNf5RH7bwKyunUMsCueXqf+3wNfpvi3VavaHM+phHmn6njBaJWnFouML6JzT9Vg1b+PTT0gItC+SXqx8AAAB7Z/369aZevXpKSqr42huvv/56wgMPPBBu1qyZlaSbb745fM011/gfeeQRNW3a1A4dOrQkkbvlllsKzjjjjOQ4NR2VICGrJoOeT6t0nT/FVLk+2CChyvUHNK16fcM2virXZxyeWOX6ZkdVvb7VcX61Os5f6fq2pySp7SlcoAcAAGBfS09Pt1u2bFFBQUGFSdn69evVpk2bkmlJBx10UGT9+vWSpG3btumKK64I/Pvf/07Ytm2bJCknJ0fhcFh+f+XHdqhezBkFAAAAaogePXoU+v1+vfDCCxVmUOnp6Vq2bFnJMf6yZcsS0tPTJUl33HFH8s8//5zwn//8J3f79u2h999/P1+SrLVxaTsqxggZAAAAUEM0atTIjhw5smjkyJH+xMREnX322eGkpCS99dZb/jlz5vjOPvvsyD333OM/4YQTCo0xGjdunL9fv34RSdq+fbsJBAK2UaNGNisry/z9739nSpMDSMgAAACAKuzJfcKq0x133JHfpEmTpHvvvdd/xRVX+FNTU9W+fXt78803h48//vjCv/71r4EjjzwyRZJ69+4dueeee/IkadSoUQXnnXde8RUY9de//rVw9uzZ5AMeIwAAAABADXPVVVcVXHXVVQUVrXv22WfzKlreunXryOeff75DcnnjjTeW1FF2HeKDc8gAAAAAwCMkZAAAAADgERIyAAAAAPBIXBMyY4zPGLPQGDMz9rq1MeZ/xpilxpipxpik2PLk2OulsfWtStUxOrZ8iTHmtHi2HwAAAAD2pXiPkF0r6YdSr++VNNFae7CkzZIujS2/VNLm2PKJsXIyxrSTNFBSe0mnS3rMGOOLU9sBAAAAYJ+KW0JmjGkmqbek/4u9NpJOkfRarMg/JZ0de94n9lqx9T1j5ftIesVam2+tXS5pqaSucXkDAAAAALCPxXOE7CFJN0qKxF43lLTFWlsYe71a0oGx5wdKWiVJsfVbY+VLllewDQAAAADUKHG5D5kx5ixJ66y1C4wx3eOwv8slXS5JGRkZmjdvXnXvsrzM+O/SdZ7EoTLEpxzi4z5nYkR8KkR83OZMfCRiVAGn4gPEWbxuDH28pD8bY86UFJB0gKSHJdUzxiTGRsGaSfo1Vv5XSc0lrTbGJEqqK2ljqeXFSm9Twlo7RdIUSercubPt3r17dbynKr3/Y9x36Twv4lAZ4lMe8XGfKzEiPhUjPm5zJT4SMaqIS/HZH1xwwQWBzMxM+8ADD+TvznY//vhjwpFHHhnYunVryO/3q2vXrsGBAwcWXn/99QWPPPJI0rPPPpv45Zdf1sibR++L9p900kkp/fv3L7r66qv3aX/EJSGz1o6WNFqSYiNkf7PWDjLGvCrpXEmvSLpI0ozYJm/FXv8ntn6OtdYaY96S9JIx5kFFf19qK+nzeLwHAAAA1E5HZc0PVmf9X2Z03uWD+szMzOCmTZvk8/mUkJCggw8+WAMHDiy84YYbChITo4f2L730Ut6u1vXoo4+G+/btG5akQw45JBIKhfY6wRgxYkRg2bJlZsaMGbl7uv2kSZMSkpKSSi8rGjdu3G4lmLsjFArpb3/7W+CNN95I2Lx5szIyMjR48OCi22+/Pd/ni15D8KOPPtqj97Mz8Rohq8woSa8YY+6StFDSU7HlT0l63hizVNImRa+sKGvtd8aYaZK+l1Qo6a/W2qL4NxsAAADwxssvvxzu27dveMOGDea9995LHDVqlP+LL77wvfbaa9WSMHihd+/edk8Tuj1x1llnpaxfv97MmDEj/8gjjyz6+OOPfZdcckny6tWrA88+++wuJbh7Ku4JmbV2nqR5sefLVMFVEq21eZL6V7L9OEnjqq+FqA3uXtnZ6ya45xCvGwAAAHZHo0aN7IUXXhg+8MADIz179kz+/PPPfV27di3q169fSmZmpp00aVLemjVrzKBBgwLz5883xSNq//vf/0Lnnntuytq1a3X++ef7ExIS/CNGjCgaPHhw+PDDDw/k5+eHSo9OVWTo0KGBWbNmJWzfvl0tW7bUAw88UHDGGWcUvvrqq/7JkycnWGsVDAaDzZs315IlS0IbNmwww4YNC8ybN88YY3TeeedFHnzwwTy/37/L77fsyNvixYsTSrd3T/fx5ptv+j/99FPz7bff5rVt2zYiST179ix65pln8v/4xz8m/+1vf0vo0KFDpPQUzl1u9C6I933IAAAAAOxDPXr0KMrIyNCcOXPK3Z/3rrvuSm7atKldv359KCsrK3TXXXcVGGP05ptv5jZp0kQvv/xyOBQKhXZ3OmCXLl0iCxcuzN20aVOoX79+RYMHD07KyclR//79w8OHD4/07t3bhkKh0JIlS0KSdP755wcSExPt0qVLQ19++WXuvHnzEiZOnFh11reb9nQf77//vu+II45QcTJW7JRTTinKyMjQu+++W62DWCRkAAAAQA3XuHFjbdq0yZRd7vf7lZWVZZYuXZoQCAR0xhlnFBafE7U3rrrqqoKmTZvapKQk3XHHHfkFBQX6+uuvK6x41apV5t///reZMmVKXt26ddWsWTM7fPjwwunTp1ea6Lz77rsmLS0tWPxYvnx5lXnLnuyj2MaNG03jxo1tRevS09O1YcOGcv26L3l9DhkAAACAvZSVlaUGDRqUSyr+/ve/540cOTJw+umnByRp8ODBRXffffdeXxzjlltuSX7xxRd969evlyTl5OQoKysrQVK56zssXbo0obCwUJmZmSUXR7HWqkmTJpXWf+aZZ+7WOWR7so9iDRs2tJUlfOvXr1ejRo0qTNb2FRIyAAAAoAabN2+eb926derRo0e5ZKh+/fr6v//7vzxJ+vzzz32nn356crdu3fxnn3122Jg9G/iZNWtW4qOPPuqbNWtW/tFHH12UmJioAw44IGhtNG8xxlhJJZW3bt06kpSUpI0bN+703LSqpKam2tzc3JJ6f/31132yj1NPPbXo6aefTvjpp58SSk9bnDNnjm/t2rU644wzCve40buAKYsAAABADbRx40a9/PLL/sGDByf36dPHduvWrVxC9vLLL/u//fbbhKKiItWvX9/6fD4VT1ls1KiRli5duttZ2bZt24zP51OTJk0i4XBYN9xwQ3JOTk7J+oyMDLtq1SpTWBjNY1q1amWPP/54e8UVVwQ2btyowsJCfffddwnvvPPObg0OHXXUUZEvvvjC/PjjjwkbNmww48ePL8m89mYfffv2DR9zzDH2nHPOCXzxxRe+cDisDz/80Dd06NDkQYMGRf7whz9EdlbH3mCEDAAAAKjC7twnLB7OP/98v8/n8yckJOiggw7SsGHDCkeOHFnhlf+WLFmScMMNN/g3b96sAw44QBdddFHRn/70p7Ak/e1vfwvfeOON/jvuuCPxuuuuKxo0aFB4V/Z/zjnnhF999dXE9u3bp6SkpOjyyy8vysjIKFk/ZMiQ8NSpUxMbNGgQbNasmb7//vvQ1KlT86699trkdu3aBUOhkJo1a6YRI0bs0v5K7/eNN97wHX300YH69evr2muvLZwzZ05JPrM3+3j33Xdzb7jhhsCf/vSn5C1btqhx48YaNGhQ0Z133llt9z4rZoqHFvdXnTt3tvPnz4/7fkf/2CDu+3Td3Yds8roJv5t9qtctcE+vD7xuQQk+PxVz5TNEfCpGfNzmSnwkYlQRr+JjjFlgrS13L5xFixaFO3bsuE8vbY7abdGiRUkdO3as8Pr7TFkEAAAAAI8wZbGacOPhCnDjYQAAAGAHjJABAAAAgEdIyAAAAADAIyRkAAAAAOAREjIAAAAA8AgJGQAAAAB4hIQMAAAAADzCZe+ryQ2fnel1E5wzoZfXLQAAAKidunbtGhw4cGDh9ddfzw2vHUNCBgAAAFThgW4KVmf9f/ufQru7zSOPPJI0adKkxFWrVqlOnTo67bTTIg899FB+enq6HTFiRGDZsmVmxowZudXRXuxbTFkEAAAAapBbb701+fbbb08cP358eMuWLaFPPvkkb/Xq1aZHjx4pubnVm4MVFRWpsLCwWvdR25CQAQAAADXExo0b9cADD/juvffecP/+/cOBQECHHXZYZMaMGbm//vqrHnrooaTJkycnvPPOOyYYDAYPPfTQktG9FStWJHTq1CmYmpoaPP7441PWrFljitd9+OGHvk6dOgXT0tKChx56aHDmzJn+4nVdu3YNDh8+PFC87eLFi8kh9iE6EwAAAKgh5syZ4y8oKNCQIUPCpZfXq1dP3bt3t59++qlv+PDhkd69e9tQKBRasmRJyXTIN954I+Gf//xn3tq1a0PhcNjceeedyZK0bNmyhH79+iXfdNNN4c2bN4fuvvvu8IUXXuj/9ddfSxK26dOnJ0yZMiV/27ZtoYMPPjgSv3e8/yMhAwAAAGqI9evXm3r16ikpKancuiZNmthNmzZVuu3AgQOLjjjiiEhaWpr69u1b+O233xpJ+r//+z9/9+7d7XnnnRdOTEzUOeecE+7QoYN9/fXXS0bJBgwYEOnSpUtRUlKSAoFAdby1WouLegAAAAA1RHp6ut2yZYsKCgrKJWVr1641DRo0qHTbpk2b2uLnqampysnJMZK0cuVKM2vWLJOWllYyvbGwsFAnnnhiyQhZixYtGBWrJoyQAQAAADVEjx49Cv1+v5577jl/6eVbtmzRv//9b3PKKacUGWNsZdtXpFmzZvbss8+22dnZoeJHbm5u6O67784vLmOMqaoK7AUSMgAAAKCGaNSokb3++uuLRo0a5X/11Vf9eXl5Wrx4cUKfPn1SmjRpoiuvvLIgIyPDrlq1yuzq1RAvvfTS8Icffmhee+01fzgcVk5OjmbOnOlfunQpuUIcMGURAAAAqMKe3CesOo0bNy6/YcOGSWPGjPEPHjzYn5qaqlNPPTXy6quv5gWDQQ0ZMiQ8derUxAYNGgSbNWum77//vsr2t23bNjJt2rT8UaNGJV966aX+hIQEHXHEEfbJJ58sitd7qs1IyAAAAIAa5vrrry+4/vrrCypa17RpU/vll1/ukIR9/vnnO7y+5pprCq655pqS7U855ZSiL774osLErey22LcYhgQAAAAAj5CQAQAAAIBHmLKIWumGz870ugnOmdDL6xYAAADUPoyQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAECFZs6c6c/IyAh63Y6KuNy23cFl7wEAAIAqLLhye7Ue9B/9eJ3Q7m4zZcoU/+TJk/0//fSTUlJS1KxZM51//vmFN954Y4HP59vjthhjgt98801ehw4dIntcCXYLI2QAAABADXLrrbcmjx492n/dddeF16xZE1q/fn3osccey//Pf/7jy8/P97p5lSooKPC6CU4iIQMAAABqiA0bNpj777/f98ADD4QvueSScP369eXz+XTCCScUvfXWW7nBYFChUEiXX355oGnTpsGGDRsGL7jggkB2dnZJHRMmTEhq0aJFsG7dusFTTjklZfny5QmS1Llz56AkdenSJRAMBoNPPvmkv3ibW2+9NblBgwbB9PT04EMPPZRUvLyqfRVPKRwzZkxyo0aNggMGDEgZMWJE4PTTT0/p06dPSmpqarBt27bBr7/+OuGmm25KbtCgQbBJkybB6dOnl+z3oYceSmrTpk0wNTU12KxZs+B9991Xsu/9BQkZAAAAUEPMnTs3MRwOa9CgQeHKylx99dWBpUuXmq+++ip36dKlod9++82MGjUqIElvvfWW/6677kp88cUX83/77bdQ8+bNbf/+/QOSNH/+/JAkffHFF3mhUCj0l7/8JSxJGzdu1NatW82aNWtCkydPDt90002J69atMzvbV/G2mzZtMitXrgw9//zzubH3YAYPHly4efPmUIcOHewZZ5wRiEQiWrNmTWjkyJGF11xzTUlC1qRJE/v222/nbdu2LfTEE08U3HrrrYkff/zxns/JdBAJGQAAAFBDrF+/3tSrV09JSb8PFHXq1CmYlpYWDAQCwXfeeSfxpZdeSpg0aVJ+RkaGrV+/vm6++ebwjBkzEiTp+eefTzzvvPMiJ554YlEwGNTDDz+c99VXX2nx4sWV5gWJiYm6//778wKBgM4777xwMBjUN998k1BUVKSq9iVJxhhNmDAhLxgMKi0tTZJ09NFH23PPPTeclJSkc889t3Dz5s0aN25cfiAQ0CWXXFKwZs0arV+/3kjSwIEDw+3bt4/4fD717t278IQTTrBz587dr66DsV+9GQAAAGB/1qhRI7tlyxYVFBSUJGULFy4MSVJGRkbwt99+M3l5eTrmmGMCpbcrKiqSJGVlZalTp04lF+yoV6+e6tWrp19++SXhsMMOq/BCHnXr1t0hAQwEAtq2bVvCb7/9FqlqX5JUv359paam7lBfenp6yfNgMKh69erJ748OihWX3bp1q0lPT7fTpk3zjxs3zr9ixQpFIhHl5eWpXbt2Zlf7qyZghAwAAACoIXr06FHo9/v14osv+ita37hxYyUnJ2vRokV52dnZoeJHKBQqTtq0cuXKkhxg69at2rJli1q2bLnbV1Vs0qSJrWpfUnSEbE+FQiENGTLEf+2114azsrJC2dnZoZNPPtlaa/e4TheRkAEAAAA1RHp6ur3++uuL/va3v/mffvpp/+bNm1VYWKhPP/3Ul5ubq4SEBJ1//vmR4cOHJ69evdpI0rJlyxJee+01vyRdcMEFhVOnTk349NNPfaFQSNddd13giCOOUPHoWMOGDbVkyZJdOkcrMTGxyn3trfz8fIXDYWVkZFi/369p06b5P/nkk/1qdExiyiIAAABQpT25T1h1GjduXP6BBx6YNHHiRP9VV13lT0lJUfPmzXXLLbcU9uzZM9yjR4/wjTfeGDjmmGNStmzZosaNG2vo0KFFktS3b9/wjz/+aAYOHJi8bds2HXXUUXbatGl5xXWPHDmy8Morr/RffPHF/okTJ4abNGlSZVsmT56cV9m+9lb9+vV15513Fg4ZMiSpoKAgqWfPnrZnz5771/CYJLO/DfmV1blzZzt//vy47/eGOx6K+z5dN+HW67xuQgniU55L8Rn9YwOvm+Ckuw/Z5HUTJBGfyhAft7kSH4kYVcSr+BhjFlhrO5ddvmjRonDHjh25aRb2mUWLFiV17NixwpFDpiwCAAAAgEdIyAAAAADAIyRkAAAAAOAREjIAAAAA8AgJGQAAAAB4hIQMAAAAADxCQgYAAAAAHiEhAwAAAACPkJABAAAAtdCIESMCffr0SfG6HbVdotcNAAAAAFxmHlawOuu31yq0q2UzMzODjz76aLhv377h4mWPPPJI0rPPPpv45Zdf7nI9cAcjZAAAAAB2UFBQ4HUTag0SMgAAAGA/MHbs2OTTTjtthymIF198cWDo0KEBSfrhhx8SunTpEkxNTQ0ed9xxKRs3biwpt3jx4gRjTPDBBx9Matq0afC4444LFhYWauTIkcmZmZnBBg0aBPv27ZuyYcMGE+e3td8jIQMAAAD2A5dddlnBRx99ZNavX2+k6CjXW2+9lTB06NCwJJ1//vmBjh07RtavXx8aO3ZsePr06eVygY8//ti3ePHi0Ny5c0OTJk1Kmjp1qm/27Nl5y5YtC+Xk5Oiyyy4LxPt97e84hwwAAACoQQYNGuT3+Xz+4tfhcFjt2rVTq1atbJcuXexzzz3nv+GGGwqmT5/ur1+/vk466aSiH3/8MeHbb7/Vv//977xgMKgzzzyz8JRTTvGXrXvcuHH5devWlSRNmzYtcdiwYYXt2rWLSNJ9992X37lz50BBQYGSkpLi9n73d4yQAQAAADXIiy++GM7Ozg4VP+67777C4nUXXnhh4SuvvJIoSS+88EJi//79iyRp1apVJi0tTcXJliQ1b97clq27TZs2keLnWVlZat26dUmZtm3bRoqKirR69WpyiH2IzgQAAAD2E4MGDQovWbJEn3/+uW/u3LnmkksuCUtSs2bNbHZ2trZu3VpSdvXq1eXOB0tI+D09yMjI0PLly0vK/Pzzzwk+n0/NmjWLlN0Oe46EDAAAANhPpKam6owzzrAXXnhh8hFHHKFDDjkkIkmHHnpopH379ho5cmQgNzdX7733XuKcOXOqvEBH//79Cx9//PHEH374IWHLli268cYbk3v37m2ZrrhvcQ4ZAAAAUIXduU+YC4YOHRqeNm1a0sSJEwtLL3/55ZfzBg8eHGjYsGHwyCOPtOecc05k69atlSZl11xzTcGvv/5qevToEcjPz9dJJ51kn3zyybzqfwe1CwkZAAAAUEOsWbOmXHJ4zTXXFFxzzTUlNw5r06ZNJDk5WYMHD97hZmLt2rWLLFiwoMLk8rDDDotYa3dYl5iYqAkTJuRPmDBhXzUfFWDKIgAAALCfKCws1D333JN01lln2YYNG3rdHOwCRsgAAACA/cDWrVvVtGnTYGZmpmbNmsXUwhqChAwAAADYD9StW1ehUKhGne8GpiwCAAAAgGdIyAAAAADAIyRkAAAAAOAREjIAAAAA8AgX9QDgnLtXdva6CW46xOsGoCbg81MJhz4/xKgCDsUHiDcSMgDAbuFgshIcUAKowTIzM4OPPvpouG/fvmGv21LbkJABAAAAVWjS/aVgdda/dt4Fu3yp+szMzGBeXp6WL18eqlu3riTpwQcfTHrllVcSP//8cy55XwNxDhkAAABQg0QiEY0fPz7Z63bsinCYAbedISEDAAAAapBhw4YVPf74477169eb0ssXL16cYIwJFhQUlCzr2rVr8MEHH0wqfj1hwoSkNm3aBFNTU4MHHXRQ8OOPP/aVrb+wsFCjR49Obt68ebBevXrBM888MyUrK6tkX6effnpKo0aNgmlpacHOnTsHv/jii5I6+vXrlzJo0KDAySefnJKSkhKcOXOmf593wH6GhAwAAACoQbp27Rrp1q2bveOOO3ZrlOypp57y33vvvYlPP/10/rZt20IzZszIa9y4sS1bbvz48cnvvvuub+7cuXlr1qwJ1a1b11566aWB4vWnnXZa0U8//RTKysoKHXHEEZHBgwfv0I633norYcyYMeHs7OzQqaeeyhDZTnAOGQAAAFDD3HXXXQU9e/ZMvummm8zOS0c988wzicOHDy/q3r17kSR16NAhUlG5Z5991vfggw+GDz744Igk3X333fkHH3xwSkFBgZKSkjRixIiSIbh77703v3HjxikbNmwwjRo1spLUq1cve9pppxVKUmIi6cbOMEIGAAAA1DBdu3Yt6tGjh73tttt2eZTst99+M23btq0wCSttzZo1uvDCC/1paWnBtLS04B/+8IcUn8+n1atXJ4TDYQ0bNizQvHnzYJ06dYJt2rRJkaTSUxoPPPDAcqNuqBwJGQAAAFADjR8/Pv+VV15JWL16tZGktLQ0K0nZ2dklZdavX1/yvGnTpvann37a6fF/kyZNNH369ILs7OxQ8SM/Pz/Upk2byJQpU5Lef//9hA8++CBv69atoWXLluVKkrW/52DG7PKgHURCBgAAANRIHTp0iPTu3ds+/fTTPik6MtW4cWNNmTIlKRwOa+LEiUmrVq0qKT906NDCxx57zPfRRx/5ioqK9O233yYsWbKkXD5w8cUXF91yyy1Jxet+/fVX8/zzz/sladu2bUpKSlLjxo0j27dv14gRI2rE1R5dxqROAAAAoAq7c5+weLvrrrvy33zzzZILbkyaNCk8YsQI//jx4xPPO++8SKdOnUrKXnrppeENGzaYIUOGJK9bt04HHnignnnmmfxDDz10hzrHjh2bb61NPu200wLr169XgwYN9Oc//zkyePDg8LBhwwpmz57ta9GiRbBu3boaM2ZM4Ysvvsggz16IS0JmjAlI+khScmyfr1lrbzPGtJb0iqSGkhZIGmytLTDGJEt6TtLRkjZKOs9auyJW12hJl0oqknSNtfb9eLwHAAAAwGtr1qzZITls27ZtJD8/v2TZgAEDwgMGDKj0yoajRo0qGDVqVEHZ5aXrTUxM1O23355/++23l9u+Xr16+vDDD3NLLxs+fHhJfdOnT88ttxGqFK9sNl/SKdbajpKOlHS6MeYYSfdKmmitPVjSZkUTLcX+3RxbPjFWTsaYdpIGSmov6XRJjxljyt07AQAAAABqgrgkZDZqe+ylP/awkk6R9Fps+T8lnR173if2WrH1PU307MA+kl6x1uZba5dLWiqpa/W/AwAAAADY9+I239MY4zPGfCVpnaR/SfpZ0hZrbWGsyGpJB8aeHyhplSTF1m9VdFpjyfIKtgEAAACAGiVuF/Ww1hZJOtIYU0/SG5IOq659GWMul3S5JGVkZGjevHnVtSvsBuLgNpfi093rBjjKlRh197oBjiI+bnMlPhIxqohL8QHiLe5XWbTWbjHGzJV0rKR6xpjE2ChYM0m/xor9Kqm5pNXGmERJdRW9uEfx8mKltym9jymSpkhS586dbffu3avp3VTu7Y++ivs+XedFHCpDfMpzKT6aPd7rFjjJmRgRnwoRH7c5Ex+JGFXAqfgAcRaXKYvGmPTYyJiMMSmS/ijpB0lzJZ0bK3aRpBmx52/FXiu2fo6N3m3uLUkDjTHJsSs0tpX0eTzeAwAAAADsa/EaIWsq6Z+xKyImSJpmrZ1pjPle0ivGmLskLZT0VKz8U5KeN8YslbRJ0Ssrylr7nTFmmqTvJRVK+mtsKiQAAAAA1DhxScistV9L6lTB8mWq4CqJ1to8Sf0rqWucpHH7uo0AAAAAEG/cVRsAAABAtVq8eHGCMSZYUFDuntS1HgkZAAAAUENkZmYGA4FAMDU1NZiWlhbs1KlT8N57700qLCzc+cZwEgkZAAAAUIO8/PLL4ZycnNDy5ctzb7jhhvAjjzySOHDgwBSv24U9Q0IGAAAA1ECNGjWyF154YfiFF17If/31183nn3/uC4VCuvzyywNNmzYNNmzYMHjBBRcEsrOzJUkzZ870Z2RkBG+++ebkBg0aBNPT04PPPfecf+rUqf6WLVsG69atGxw1alRycf1z5871HXnkkcG0tLRgenp6cMiQIYHc3NyS/Rtjgvfee29Sy5Ytg2lpacHBgwcHioqi19sLh8O6/PLLA/Xr1w82a9Ys+Prrr8f9dls1BR0DAAAAVKFr167Bssv69u1bOHr06IJt27apV69e5dZfeOGFhddcc03BmjVrzNlnn11u9Oovf/lL+C9/+Uv4p59+Smjbtm1kb9rXo0ePooyMDM2ZM8f3xBNP+JcvX26++uqr3KSkJHvOOeekjBo1KvDYY4/lSdLGjRuVl5enNWvWhB599NGka6+91n/CCSfYr776KrR06VLfiSeemHzxxRcnHH744RGfz6cJEybkn3jiiUXLli1LOPPMMwP33Xdf8m233ZZfvO/33nvPN3/+/NzNmzebbt26BV5//XV///79ww8++GDSv/71r4TPP/8874ADDrB//vOfGcGrBCNkAAAAQA3XuHFjbdq0ybz00ksJkyZNys/IyLD169fXzTffHJ4xY0bJMX9iYqLuueee/EAgoEsuuaRgy5YtGjFiREH9+vXVpUuXooMOOkhffPGFT5JOOumkop49exYlJSXpsMMOi1x00UVFn3zyyQ75w6hRo8Lp6en2kEMOiRx77LH2yy+/TJCkN954I/HKK68sbNu2bSQjI8PeeOON4fj2SM3BCBkAAABQhc8//zxU2boDDjigyvWZmZm2qvV7OzpWLCsrS4WFhcrLy9MxxxwTKL2ueBqhJNWtW1d+v1+SlJqaWtLG4vXJycnavn27kaSvv/464dprr03+5ptvTF5engoLC9W+ffsd9tusWbOS9qekpJRsm5WVpZYtW5bUe9BBBxVJ8u+L97q/YYQMAAAAqMHmzZvnW7dunfr371+YnJysRYsW5WVnZ4eKH6FQqNKEsCpXXHFFoG3btvann34Kbd++PTR69Ogia+3ON1R0xO6XX34xxa+XLVvm25M21AYkZAAAAEANtHHjRr388sv+wYMHJ/fp08cee+yxReeff35k+PDhyatXrzaStGzZsoTXXnttj0amtm/frrS0NHvAAQdo4cKFvmeeeWaXk6q+ffsWTpkyJXHp0qUJ69atM/fffz+jY5UgIQMAAABqkPPPP9+fmpoabNWqVfDee+/1Dxs2rHDatGm5kjR58uS81q1b22OOOSalTp06wV69egV++OGHPTrmv/feewtef/11X1paWvCyyy5L7tOnzy5Pr7z++usLunfvHuncuXOgU6dOKX/605+4UVolOIcMAAAAqCHWrFlT5fTD1NRUPfroo3mPPvpouXVnnXVWOCsrq+TiGklJSbLW7lDfl19+WfL6zDPPLFy+fHmliVTZbadPn15yTfykpCQ99dRTeU899VTJ+jFjxhRU1fbaihEyAAAAAPAICRkAAAAAeGSXpiwaY1pLGifpSEl1Sq+z1rbY980CAAAAgP3frp5D9pKknyXdIGmPLpsJAAAAANjRriZk7SUdb63dJzeuAwAAAFxWVFQkn49bZ2Hvlb4xd0V29RyyjyR12uvWAAAAAO7buGHDBv/ODqSBqkQiEeXl5SX88ssvSZJ+qaxcpSNkxpg7Sr1cIek9Y8wbktaWLmetvXUv2woAAAA4o7Cw8JS1a9fOWbt2bUOv24KazRgTLioqeisSiQyprExVUxabl3k9U5K/zHK7F+0DAAAAnHP00Ud/L6mJ1+1A7VBpQmatHRrPhgAAAABAbVPlRT2MMe9K+rei55B9Ya2t9E7dAAAAAIDds7OLenwiqYek9yRtNcZ8aIy5zRjTwxgTqP7mAQAAAMD+q8qEzFo73lp7uqT6kk6W9LakjpJekbTFGPNJ9TcRAAAAAPZPu3Qfstj9x+YbY36UtDT2GCKpQzW2DQAAAAD2azs7hyxd0kmKjo6dJKmRpM8Uncp4pqSvqrl9AAAAALDf2tkIWZakHyQ9LOlha+3P1d8kAAAAAKgddnZRj1skrZJ0l6SnjDF3GmNONcbUqf6mAQAAAMD+rcoRMmvtOEkyxiRI6iTpRElXSnrWGLNG0sfW2hHV3koAAAAA2A/tbIRMUvSiHtbaBZKejj1ekNRM0jXV2DYAAAAA2K/t6kU9ih8dJP2q6I2ib1H0ptEAAAAAgD2wKxf1+EnSx5ImSvq3tfaXam8VAAAAANQCO0vILrTWvhSXlgAAAABALbOzc8gej0srAAAAAKAW2llCZuLSCgAAAACohXY2ZdFnjOmhKhIza+2cfdskAAAAAKgddpaQJUt6SpUnZFZSm33aIgAAAACoJXaWkOVYa0m4AAAAAKAa7NKNoQEAAAAA+x4X9QAAAAAAj1SZkFlr0+LVEAAAAACobZiyCAAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHgk0esGAEBZN3x2ptdNcNKEXl63IIr4VIz4uM2V+EjEqCIuxQeIN0bIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR7gxdDX5dO6HXjfBPbde53ULAAAAAKcwQgYAAAAAHiEhAwAAAACPkJABAAAAgEdIyAAAAADAIyRkAAAAAOAREjIAAAAA8AgJGQAAAAB4hIQMAAAAADxCQgYAAAAAHiEhAwAAAACPkJABAAAAgEdIyAAAAADAIyRkAAAAAOAREjIAAAAA8Eii1w0AvPDp3A+9boJ7br3O6xYAAADUOoyQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgkbgkZMaY5saYucaY740x3xljro0tb2CM+Zcx5qfYv/Vjy40x5hFjzFJjzNfGmKNK1XVRrPxPxpiL4tF+AAAAAKgO8RohK5R0g7W2naRjJP3VGNNO0k2SPrTWtpX0Yey1JJ0hqW3scbmkf0jRBE7SbZK6Seoq6bbiJA4AAAAAapq4JGTW2t+stV/GnmdL+kHSgZL6SPpnrNg/JZ0de95H0nM26r+S6hljmko6TdK/rLWbrLWbJf1L0unxeA8AAAAAsK8lxnuHxphWkjpJ+p+kDGvtb7FVayVlxJ4fKGlVqc1Wx5ZVtrzsPi5XdGRNGRkZmjdv3r57A9hjxMFtxMd9xMhtxMdtxMdtxAe1WVwTMmNMHUnTJV1nrd1mjClZZ621xhi7L/ZjrZ0iaYokde7c2Xbv3n1fVLt7bp8Q/306zpM4VIb4lONSfN7+6Cuvm+AkV2JEfCpGfNzmSnwkYlQRl+IDxFvcrrJojPErmoy9aK19PbY4KzYVUbF/18WW/yqpeanNm8WWVbYcAAAAAGqceF1l0Uh6StIP1toHS616S1LxlRIvkjSj1PIhsastHiNpa2xq4/uSTjXG1I9dzOPU2DIAAAAAqHHiNWXxeEmDJX1jjPkqtmyMpHskTTPGXCrpF0kDYuvelXSmpKWSQpKGSpK1dpMx5k5JX8TK3WGt3RSXdwAAAAAA+1hcEjJr7SeSTCWre1ZQ3kr6ayV1PS3p6X3XOgAAAADwRtzOIQMAAAAA7IiEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPBIXG4MDQDYf3w690Ovm+CmW6/zugWSiE+lHImPRIwq5FB8gHhjhAwAAAAAPMIIGQDn8OtxJfgFGQCA/Q4jZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBHSMgAAAAAwCMkZAAAAADgERIyAAAAAPAICRkAAAAAeCTR6wbsr84Nve11EwAAAAA4jhEyAAAAAPAICRkAAAAAeISEDAAAAAA8wjlkqJU4xw8AAAAuICED4BwSZrcRH7cRH/cRIwClkZBVkx6dtnvdBAfV8boBAAAAgFNIyKpJ58NJPsqyXjcAAAAAcAwX9QAAAAAAj5CQAQAAAIBHmLKIWolz/CrCNFsAAIB4IyFDrcQ5fuW5dI4fCXNl3Ph/S3wqQ3zc5kZ8JGJUMXfiA8RbXBIyY8zTks6StM5a2yG2rIGkqZJaSVohaYC1drMxxkh6WNKZkkKSLrbWfhnb5iJJY2PV3mWt/Wc82g8gvkiYK+ZK0kx8KkZ83OZKfCRiVBGX4gPEW7zOIXtW0ulllt0k6UNrbVtJH8ZeS9IZktrGHpdL+odUksDdJqmbpK6SbjPG1K/2lgMAAABANYlLQmat/UjSpjKL+0gqHuH6p6SzSy1/zkb9V1I9Y0xTSadJ+pe1dpO1drOkf6l8kgcAAAAANYaX55BlWGt/iz1fKykj9vxASatKlVsdW1bZ8nKMMZcrOrqmjIwMzZs3b9+1epd192CfbvMmDpXp7nUDnEN83OdOjLp73QAnER+3uRMfiRiV51Z8gPhy4qIe1lprjNln04ettVMkTZGkzp072+7du++rqnfdovjv0nWexKEyxKcc4uM+Z2JEfCpEfNzmTHwkYlQBp+IDxJmX9yHLik1FVOzfdbHlv0pqXqpcs9iyypYDAAAAQI3kZUL2lqSLYs8vkjSj1PIhJuoYSVtjUxvfl3SqMaZ+7GIep8aWAQAAAECNFK/L3r+s6ITpRsaY1YpeLfEeSdOMMZdK+kXSgFjxdxW95P1SRS97P1SSrLWbjDF3SvoiVu4Oa23ZC4UAAAAAQI0Rl4TMWnt+Jat6VlDWSvprJfU8Lenpfdg0AAAAAPCMl1MWAQAAAKBWIyEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4JFErxuwv8p44yWvm+Ceay/wugUAAACAUxghAwAAAACPkJABAAAAgEdIyAAAAADAI5xDhlqJc/wqwDl+AAAAcUdCBsA5JMyVcCRpJj6VID5ucyQ+EjGqkEPxAeKNKYsAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAAAAAMAjJGQAAAAA4BESMgAAAADwCAkZAAAAAHiEhAwAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR2pkQmaMOd0Ys8QYs9QYc5PX7QEAAACAPVHjEjJjjE/So5LOkNRO0vnGmHbetgoAAAAAdl+NS8gkdZW01Fq7zFpbIOkVSX08bhMAAAAA7DZjrfW6DbvFGHOupNOttZfFXg+W1M1aO7xUmcslXR57eaikJXFvqFsaSdrgdSNQKeLjNuLjNuLjNuLjNuIjtbTWpnvdCNRuiV43oDpYa6dImuJ1O1xhjJlvre3sdTtQMeLjNuLjNuLjNuLjNuIDuKEmTln8VVLzUq+bxZYBAAAAQI1SExOyLyS1Nca0NsYkSRoo6S2P2wQAAAAAu63GTVm01hYaY4ZLel+ST9LT1trvPG6W65i+6Tbi4zbi4zbi4zbi4zbiAzigxl3UAwAAAAD2FzVxyiIAAAAA7BdIyAAAAADAIyRkAGolY8wKY0yvStadaIyp9P6FxpgWxpjtxhhf9bWwdiM+biM+biM+QM1CQlZDGGOSjTFPGWN+McZkG2O+MsacUWp9T2PMYmNMyBgz1xjTstS6B4wxP8W2W2yMGVKm7iONMQti2y4wxhwZx7dW4xhjLjDGzI/9wfrNGDPLGHOCA+262BjzidftiAdjzDxjzGZjTHJ11G+t/dhae2ip/e1wcGOtXWmtrWOtLaqO/dd0xMdtxMdtxAeofUjIao5ESasknSyprqSxkqYZY1oZYxpJel3SLZIaSJovaWqpbXMk/Sm23UWSHjbGHCdJsVsHzJD0gqT6kv4paUZsOcowxlwv6SFJ4yVlSGoh6TFJfXaznnJXOK1oGcozxrSSdKIkK+nP3rYGZREftxEftxEfoHYiIashrLU51tq/W2tXWGsj1tqZkpZLOlrSOZK+s9a+aq3Nk/R3SR2NMYfFtr3NWrs4tt3/JH0s6dhY1d0VTfYestbmW2sfkWQknRLXN1gDGGPqSrpD0l+tta/HYhK21r5trR1pjHnWGHNXqfLdjTGrS71eYYwZZYz5WlKOMeZgY4w1xlxqjFkpaU6s3CXGmB9iv5C+X2a00xpjroyNeG4xxjxqog6X9LikY2Mjd1vi1C1eGCLpv5KeVfQHBklSrP8fi41YbjfGfGqMaWKMeSjWl4uNMZ3K1NXFGPN9bP0zxphArK6S2Bljnlc08X47Vu+NsR9CbHESbYzJNMa8ZYzZZIxZaoz5S5l2VfX/YpQx5tfYCPYSY0zPfd5j8UV83EZ83EZ8gFqIhKyGMsZkSDpE0neS2ktaVLzOWpsj6efY8rLbpUjqEttOsTJf2x3vf/B1RdtCx0oKSHpjL+o4X1JvSfUkFcaWnSzpcEmnGWP6SBqjaJKdrmjy/HKZOs5SNIZHSBog6TRr7Q+SrpT0n9hUk3p70UbXDZH0YuxxWuyzUGyAoqPHjSTlS/qPpC9jr1+T9GCZugZJOk3SQYp+nsaW3Zm1drCklZL+FOvb+ypo0yuSVkvKlHSupPHGmJ3+qGGMOVTScEldrLVpsbas2Nl2jiM+biM+biM+QC1EQlYDGWP8in5Z/9Nau1hSHUlbyxTbKimtgs0fVzR5ez/2ene2re0aStpgrS3cacnKPWKtXWWtzS217O+x0bZcRZOqu621P8T2M17SkabUKJmke6y1W6y1KyXNlXTkXrSnRjHRc/VaSppmrV2g6A8PF5Qq8oa1dkFspPgNSXnW2udi50JMlVT2F+TJsXhskjRO0YR5d9vUXNLxkkZZa/OstV9J+j9FD6x2pkhSsqR2xhh/bAT8591tgyuIj9uIj9uID1B7kZDVMMaYBEnPSypQ9JcnSdou6YAyRQ+QlF1m2/sldZA0oNSI2C5tC0nSRkmNzN6d67VqJ8taKnqO3xYTnXa4SdEppAeWKrO21POQokl1bXGRpA+stRtir19SqWk9krJKPc+t4HXZvird978o+gvw7sqUtMlaW/oz84t2jFmFrLVLJV2n6DTjdcaYV4wxe9IGVxAftxEftxEfoJYiIatBjDFG0lOKXkyin7U2HFv1naSOpcqlKjpF4btSy26XdIakU62120pV+52kI2J1Fzui9LYo8R9Fp4mcXcn6HEnBUq+bVFDG7mTZKklXWGvrlXqkWGs/24X2VVT3fiM23XaApJONMWuNMWsljVD0fMmOVW9dqealnreQtKaSclX17RpJDYwxpUeVW0j6Nfa8yv8X1tqXrLXFv4xbSffuQrudQ3zcRnzcRnyA2o2ErGb5h6LnGv2pzJS3NyR1MMb0i520e6ui54UtliRjzGhFpz30stZuLFPnPEWnFVxjopfWLx51m1ON76NGstZuVbRvHzXGnG2MCRpj/MaYM4wx90n6StKZxpgGxpgmiv4yuLselzTaGNNeil5IxBjTfxe3zZLUzOy/V8g8W9H/q+0UnaZ5pKKfh4+1a9NnKvJXY0wzY0wDSTdrx6uTlpYlqU1FK6y1qyR9JuluY0zAGHOEpEsVvXKpVMX/C2PMocaYU0z08tZ5iv7KHdnD9+K1s0V8XHa2iI/LzhbxAWotErIaInYO0RWKfkmvNdGrIW03xgyy1q6X1E/ROeKbJXWTNLDU5uMV/UVraantxkiStbZA0T8EQyRtkXSJpLNjy1GGtXaCpOsVPTl6vaIjWsMlvanoVNJFip60/IEq/+NXVf1vKPoL4ivGmG2SvlV0ZHNXzFF0ZHOtMWbDzgrXQBdJesZG75GztvghabKiJ6/vyVTSlxSN1TJFz9e4q5Jyd0saG5tK+rcK1p8vqZWivya/Iek2a+3s2Lqq/l8kS7pH0gZFp6I2ljR6D96HC4iP24iP24gPUIsZa/frWU4AAAAA4CxGyAAAAADAIyRkAAAAAOAREjIAAAAA8AgJGQAAAAB4hIQMqAWMMfOMMZd53Q5UjPi4jfi4jfi4jfgAO0dChlordt+1p4wxvxhjso0xXxljzihTpqcxZrExJmSMmRu7/UDxuvuMMauMMdtidYwps+2RxpgFsW0XGGOO3El7/hjbR7YxZmOsPaNi95ardYiP24iP24iP24gPgNJIyFCbJSp6H7GTJdVV9N5i04wxrSTJGNNI0uuSbpHUQNJ87XiPlackHWatPUDScZIGGWPOiW2bJGmGojfPrC/pn5JmmEpu2myiN39+TdH7xrS01jaUdJ6kZpKa77u3XKMQH7cRH7cRH7cRHwC/s9by4MEj9pD0taR+seeXS/qs1LpUSbmK/hEsu92Bkr6RdGPs9amSflXsXn+xZSslnV7BtkbRP8w37KRtyZIeUvTmnGtiz5Nj6+pLmqnozao3x543K7XtPEmXxZ4fLOnfkrYqesPOqV73O/EhPsSH+BAf4sODR219MEIGxBhjMiQdIum72KL2khYVr7fW5kj6Oba8eJubjDHbJa1W9A/mS6W2/dpaW/rO61+X3raUQxX9JXL6Tpp4s6RjJB0pqaOkror+qipFR7ufkdRSUgtF/3BPrqSeOyV9oOgf0WaSJu1kv04gPm4jPm4jPm4jPkDtRkIGSDLG+CW9KOmf1trFscV1FP0Vr7StktKKX1hr74m9PkrS86XK73TbUhrF/l1bqj2vGGO2xOb/D44tHiTpDmvtOmvtekm3Sxoca8dGa+10a23IWpstaZyiU2EqElb0D2emtTbPWvtJJeWcQXzcRnzcRnzcRnwAkJCh1jPGJCj6x6xA0vBSq7ZLOqBM8QMkZZdeYKMWKvqr4O27s23Mxti/TUvVOdBaW0/Sl5J8scWZkn4ptd0vsWUyxgSNMU/ETu7eJukjSfWMMT6Vd6Oi01Q+N8Z8Z4y5pIIyziA+xKeybWOITxWID/GpbNsY4gM4gIQMtZoxxih6cnSGonP3w6VWf6fo1IzisqmSDtLvU0rKSoytL972iFj9xY6oZNslis73P2cnzV2j6C+LxVrElknSDYpOPelmoyd5n1Tc7LKVWGvXWmv/Yq3NlHSFpMeMMQfvZN+eID7Ep9R64rObiA/xKbWe+AAOIyFDbfcPSYdL+pO1NrfMujckdTDG9DPRS//equi8/MXGmARjzBXGmPomqqukv0r6MLbtPElFkq4x0csbF//yOadsA6y1EUX/oN1mjPlLqTrbKvqHutjLksYaY9JN9Apctyp6FS0pOhUlV9IWY0wDSbdV9oaNMf2NMc1iLzdLspIiO+knrxAf4kN89hzxIT7EB6gJrANXFuHBw4uHor/2WUl5ik7xKH4MKlWml6TFiv6xmSepVWx5gqT3JG2KbfOjpDHa8apWnSQtiG37paROO2nP6YpefWq7otNIFkoaKSk1tj4g6RFJv8Uej0gKxNZlxtpX3JYrYu8tMbZ+nn6/ytV9iv4iul3Rk8Qv9zoWxIf4EB/iQ3yIDw8etfVhrC19ER4AAAAAQLwwZREAAAAAPEJCBgAAAAAeISEDAAAAAI+QkAEAAACAR0jIAKAME71haXev24HfERO3eRUfY8w8Y8xl8d5vTcfnCXALCRmAGskYs8IYk2uMyTbGbDHGfGaMudIYs9ffa9ba9tbaefugmTswxjQ1xrxljFljjLHGmFb7eh9eqqEx6W2M+STW3rXGmP8zxqTt6/24gPjULDU0Xj2MMd/E2rvRGPOGMebAfb0fYH9DQgagJvuTtTZN0Xv63CNplKSnvG1SlSKK3j+on9cNqUY1LSZ1Jd2l6L2UDpd0oKT7PW1R9SI+NUtNi9f3kk6z1tZTNGY/KXoDbABVICEDUONZa7daa9+SdJ6ki4wxHSTJGJNsjHnAGLPSGJNljHncGJMSW9fIGDMz9kvuJmPMx8W/PMd+me4Ve55ijPmnMWazMeYHY8yNxpjVxfuOlf2bMeZrY8xWY8xUY0ygknZmWWsfk/RFNXeJ52pQTF6y1r5nrQ1ZazdLelLS8aXqGhrbR7YxZpkx5orq6rN42l/iE3OQMeZzY8w2Y8wMY0yDfd5hHqtB8cqy1q4ptahI0sHV0yvA/oOEDMB+w1r7uaTVkk6MLbpH0iGSjlT0oOBASbfG1t0QK5suKUPSGEm2gmpvk9RKUhtJf5R0YQVlBkg6XVJrSUdIungv38p+owbG5CRJ35V6vU7SWZIOkDRU0kRjzFG7WJfz9oP4SNIQSZdIaiqpUNIju1hXjVMT4mWMaWGM2SIpV9LfJN23S28OqMVIyADsb9ZIamCMMZIulzTCWrvJWpstabykgbFyYUUP4Fpaa8PW2o+ttRUdrAyQNN5au9lau1oVH+w9Yq1dY63dJOltRQ+O8LsaERNjzB8lXaTfD2hlrX3HWvuzjfq3pA/0+8Hw/qLGxifmeWvtt9baHEm3SBpgjPHtrL4azOl4WWtXxqYsNpI0VtLiPXqXQC1CQgZgf3OgpE2K/ioclLQgNmVni6Lnb6XHyt0vaamkD2JT0W6qpL5MSatKvV5VQZm1pZ6HJNXZ8+bvl5yPiTHmGEkvSTrXWvtjqeVnGGP+G5vytUXSmYoeaO5Pamx8Kqj/F0l+7X8xKs35eElSLHn7p6QZxpjEnZUHajMSMgD7DWNMF0UPVj6RtEHRKTPtrbX1Yo+61to6kmStzbbW3mCtbSPpz5KuN8b0rKDa3yQ1K/W6efW+i/1LTYiJMaaTpLckXWKt/bDU8mRJ0yU9ICkj9qv/u5LM3uzPJTU5PpXU30LRkaENe7NPV9WEeJWRKKmxolN+AVSChAxAjWeMOcAYc5akVyS9YK39xlobUfQCABONMY1j5Q40xpwWe36WMebg2LSfrYqefB6poPppkkYbY+qb6OWbh+9lWwOSkmMvkys7Ob6mqykxiV0c4T1JV1tr3y6zOknRWK2XVGiMOUPSqXu6L5fsJ/EpdqExpp0xJijpDkmvWWuL9nSfLqpB8TrHGHOoMSbBGJMu6UFJC2OjZQAqQUIGoCZ72xiTregUm5sV/eM/tNT6UYpO2fmvMWabpNmSDo2taxt7vV3SfyQ9Zq2dW8E+7lD0xPjlsfKvScrfizbnxvYpRc+tyN2LulxU02Jyg6JTvJ4yxmyPPb6ToiMMkq5R9IB1s6QLFB2pqcn2m/iU8rykZxWdVhdQNGb7i5oWrwMVTaCzJX2jaALYdw/rAmoNU/H5nQCAihhjhkkaaK092eu2IIqYuI341CzEC4g/RsgAoArGmKbGmONjU3AOVfQX+ze8bldtRkzcRnxqFuIFeI+r3gBA1ZIkPaHo/Xe2KHoOx2NeNgjExHHEp2YhXoDHmLIIAAAAAB5hyiIAAAAAeISEDAAAAAA8QkIGAAAAAB4hIQMAAAAAj5CQAQAAAIBH/h81cS/TDBY26AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with PrintManager():\n",
+    "    ax_list = plot_bar_generation_stack(\n",
+    "        [\"all\", \"Texas\"], \n",
+    "        scenario_ids, \n",
+    "        resources,\n",
+    "        area_types=[None, \"interconnect\"],\n",
+    "        scenario_names=scenario_names, \n",
+    "        t2c=t2c,\n",
+    "        t2l=t2l,\n",
+    "        t2hc=t2hc,\n",
+    "        titles={\"all\": \"USA\", \"Texas\": \"ERCOT\"},\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<AxesSubplot:title={'center':'USA'}, ylabel='TWh'>,\n",
+       " <AxesSubplot:title={'center':'ERCOT'}, ylabel='TWh'>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with PrintManager():\n",
+    "    plot_bar_generation_stack(\n",
+    "        [\"all\", \"Texas\"], \n",
+    "        scenario_ids, \n",
+    "        resources,\n",
+    "        area_types=[None, \"interconnect\"],\n",
+    "        scenario_names=scenario_names,\n",
+    "        curtailment_split=False,\n",
+    "        t2c=t2c,\n",
+    "        t2l=t2l,\n",
+    "        t2hc=t2hc,\n",
+    "        titles={\"all\": \"USA\", \"Texas\": \"ERCOT\"},\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/postreise/plot/plot_bar_generation_stack.py b/postreise/plot/plot_bar_generation_stack.py
new file mode 100644
index 00000000..ddb6435e
--- /dev/null
+++ b/postreise/plot/plot_bar_generation_stack.py
@@ -0,0 +1,234 @@
+import os
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from powersimdata.network.model import ModelImmutables, area_to_loadzone
+from powersimdata.scenario.scenario import Scenario
+
+from postreise.analyze.generation.curtailment import (
+    calculate_curtailment_time_series_by_areas_and_resources,
+)
+from postreise.analyze.generation.summarize import sum_generation_by_type_zone
+
+
+def plot_bar_generation_stack(
+    areas,
+    scenario_ids,
+    resources,
+    area_types=None,
+    scenario_names=None,
+    curtailment_split=True,
+    t2c=None,
+    t2l=None,
+    t2hc=None,
+    titles=None,
+    plot_show=True,
+    save=False,
+    filenames=None,
+    filepath=None,
+):
+    """Plot any number of scenarios as generation stack bar for selected resources in
+    each specified areas.
+
+    :param list/str areas: list of area(s), each area is one of *loadzone*, *state*,
+        *state abbreviation*, *interconnect*, *'all'*.
+    :param int/list/str scenario_ids: list of scenario id(s), defaults to None.
+    :param str/list resources: one or a list of resources. *'curtailment'*,
+        *'solar_curtailment'*, *'wind_curtailment'*, *'wind_offshore_curtailment'*
+        are valid entries together with all available generator types in the area(s).
+        The order of the resources determines the stack order in the figure.
+    :param list/str area_types: list of area_type(s), each area_type is one of
+        *'loadzone'*, *'state'*, *'state_abbr'*, *'interconnect'*, defaults to None.
+    :param list/str scenario_names: list of scenario name(s) of same len as scenario
+        ids, defaults to None.
+    :param bool curtailment_split: if curtailments are split into different
+        categories, defaults to True.
+    :param dict t2c: user specified color of resource type to overwrite pre-defined ones
+        key: resource type, value: color code.
+    :param dict t2l: user specified label of resource type to overwrite pre-defined ones
+        key: resource type, value: label.
+    :param dict t2hc: user specified color of curtailable resource hatches to overwrite
+        pre-defined ones. key: resource type, valid keys are *'curtailment'*,
+        *'solar_curtailment'*, *'wind_curtailment'*, *'wind_offshore_curtailment'*,
+        value: color code.
+    :param dict titles: user specified figure titles, key: area, value: new figure
+        title in string, use area as title if None.
+    :param bool plot_show: display the generated figure or not, defaults to True.
+    :param bool save: save the generated figure or not, defaults to False.
+    :param dict filenames: user specified filenames, key: area, value: new filename
+        in string, use area as filename if None.
+    :param str filepath: if save is True, user specified filepath, use current
+        directory if None.
+    :return: (*list*) -- matplotlib.axes.Axes object of each plot in a list.
+    :raises TypeError:
+        if resources is not a list/str and/or
+        if titles is provided but not in a dictionary format and/or
+        if filenames is provided but not in a dictionary format.
+    :raises ValueError:
+        if length of area_types and areas is different and/or
+        if length of scenario_names and scenario_ids is different.
+    """
+    if isinstance(areas, str):
+        areas = [areas]
+    if isinstance(scenario_ids, (int, str)):
+        scenario_ids = [scenario_ids]
+    if not isinstance(scenario_ids, list):
+        raise TypeError("ERROR: scenario_ids should be a int/str/list")
+    if isinstance(resources, str):
+        resources = [resources]
+    if not isinstance(resources, list):
+        raise TypeError("ERROR: resources should be a list/str")
+    if isinstance(area_types, str):
+        area_types = [area_types]
+    if not area_types:
+        area_types = [None] * len(areas)
+    if len(areas) != len(area_types):
+        raise ValueError(
+            "ERROR: if area_types are provided, number of area_types must match number of areas"
+        )
+    if isinstance(scenario_names, str):
+        scenario_names = [scenario_names]
+    if scenario_names and len(scenario_names) != len(scenario_ids):
+        raise ValueError(
+            "ERROR: if scenario names are provided, number of scenario names must match number of scenario ids"
+        )
+    if titles is not None and not isinstance(titles, dict):
+        raise TypeError("ERROR: titles should be a dictionary if provided")
+    if filenames is not None and not isinstance(filenames, dict):
+        raise TypeError("ERROR: filenames should be a dictionary if provided")
+    s_list = []
+    for sid in scenario_ids:
+        s_list.append(Scenario(sid))
+    mi = ModelImmutables(s_list[0].info["grid_model"])
+    type2color = mi.plants["type2color"]
+    type2label = mi.plants["type2label"]
+    type2hatchcolor = mi.plants["type2hatchcolor"]
+    if t2c:
+        type2color.update(t2c)
+    if t2l:
+        type2label.update(t2l)
+    if t2hc:
+        type2hatchcolor.update(t2hc)
+    all_loadzone_data = dict()
+    for sid, scenario in zip(scenario_ids, s_list):
+        curtailment = calculate_curtailment_time_series_by_areas_and_resources(
+            scenario, areas={"loadzone": mi.zones["loadzone"]}
+        )
+        for area in curtailment:
+            for r in curtailment[area]:
+                curtailment[area][r] = curtailment[area][r].sum().sum()
+        curtailment = (
+            pd.DataFrame(curtailment).rename(columns=mi.zones["loadzone2id"]).T
+        )
+        curtailment.rename(
+            columns={c: c + "_curtailment" for c in curtailment.columns}, inplace=True
+        )
+        curtailment["curtailment"] = curtailment.sum(axis=1)
+        all_loadzone_data[sid] = pd.concat(
+            [
+                sum_generation_by_type_zone(scenario).T,
+                scenario.state.get_demand().sum().T.rename("load"),
+                curtailment,
+            ],
+            axis=1,
+        ).rename(index=mi.zones["id2loadzone"])
+
+    width = 0.4
+    x_scale = 0.6
+    ax_list = []
+    for area, area_type in zip(areas, area_types):
+        fig, ax = plt.subplots(figsize=(10, 8))
+        for ind, s in enumerate(s_list):
+            patches = []
+            fuels = []
+            bottom = 0
+            loadzone_set = area_to_loadzone(s.info["grid_model"], area, area_type)
+            data = (
+                all_loadzone_data[scenario_ids[ind]]
+                .loc[loadzone_set]
+                .sum()
+                .divide(1e6)
+                .astype("float")
+                .round(2)
+            )
+            for i, f in enumerate(resources[::-1]):
+                if f == "load":
+                    continue
+                if curtailment_split and f == "curtailment":
+                    continue
+                if not curtailment_split and f in {
+                    "wind_curtailment",
+                    "solar_curtailment",
+                    "wind_offshore_curtailment",
+                }:
+                    continue
+                fuels.append(f)
+                if "curtailment" in f:
+                    patches.append(
+                        ax.bar(
+                            ind * x_scale,
+                            data[f],
+                            width,
+                            bottom=bottom,
+                            color=type2color.get(f, "red"),
+                            hatch="//",
+                            edgecolor=type2hatchcolor.get(f, "black"),
+                            lw=0,
+                        )
+                    )
+                else:
+                    patches.append(
+                        ax.bar(
+                            ind * x_scale,
+                            data[f],
+                            width,
+                            bottom=bottom,
+                            color=type2color[f],
+                        )
+                    )
+                bottom += data[f]
+
+            # plot load line
+            xs = [ind * x_scale - 0.5 * width, ind * x_scale + 0.5 * width]
+            ys = [data["load"]] * 2
+            line_patch = ax.plot(xs, ys, "--", color="black")
+
+        if scenario_names:
+            labels = scenario_names
+        else:
+            labels = [s.info["name'"] for s in s_list]
+        ax.set_xticks([i * x_scale for i in range(len(s_list))])
+        ax.set_xticklabels(labels, fontsize=12)
+        ax.set_ylabel("TWh", fontsize=12)
+        bar_legend = ax.legend(
+            handles=patches[::-1] + line_patch,
+            labels=[type2label.get(f, f.capitalize()) for f in fuels[::-1]]
+            + ["Demand"],
+            fontsize=12,
+            bbox_to_anchor=(1, 1),
+            loc="upper left",
+        )
+        ax.add_artist(bar_legend)
+        ax.set_axisbelow(True)
+        ax.grid(axis="y")
+        if titles is not None and area in titles:
+            ax.set_title(titles[area])
+        else:
+            ax.set_title(area)
+        fig.tight_layout()
+        ax_list.append(ax)
+        if plot_show:
+            plt.show()
+        if save:
+            if filenames is not None and area in filenames:
+                filename = filenames[area]
+            else:
+                filename = area
+            if not filepath:
+                filepath = os.getcwd()
+            fig.savefig(
+                f"{os.path.join(filepath, filename)}.pdf",
+                bbox_inches="tight",
+                pad_inches=0,
+            )
+    return ax_list


Test patch:
diff --git a/postreise/plot/tests/test_plot_bar_generation_stack.py b/postreise/plot/tests/test_plot_bar_generation_stack.py
new file mode 100644
index 00000000..78e8690a
--- /dev/null
+++ b/postreise/plot/tests/test_plot_bar_generation_stack.py
@@ -0,0 +1,62 @@
+import pytest
+
+from postreise.plot.plot_bar_generation_stack import plot_bar_generation_stack
+
+
+def test_plot_bar_generation_stack_throws_error_for_scenario_ids_not_a_list():
+    with pytest.raises(TypeError):
+        plot_bar_generation_stack(
+            "Western",
+            {823, 824},
+            ["wind", "solar", "coal"],
+        )
+
+
+def test_plot_bar_generation_stack_throws_error_for_resources_not_a_list():
+    with pytest.raises(TypeError):
+        plot_bar_generation_stack(
+            "Western",
+            [823, 824],
+            {"wind", "solar", "coal"},
+        )
+
+
+def test_plot_bar_generation_stack_throws_error_for_different_length_of_areas_and_area_types():
+    with pytest.raises(ValueError):
+        plot_bar_generation_stack(
+            ["Western", "Eastern"],
+            [823, 824],
+            ["wind", "solar", "coal"],
+            area_types="interconnect",
+        )
+
+
+def test_plot_bar_generation_stack_throws_error_for_different_length_of_scenario_ids_and_scenario_names():
+    with pytest.raises(ValueError):
+        plot_bar_generation_stack(
+            ["Western", "Eastern"],
+            [823, 824],
+            ["wind", "solar", "coal"],
+            scenario_names="USA Basecase",
+        )
+
+
+def test_plot_bar_generation_stack_throws_error_for_titles_not_a_dict():
+    with pytest.raises(TypeError):
+        plot_bar_generation_stack(
+            ["Western", "Eastern"],
+            [823, 824],
+            ["wind", "solar", "coal"],
+            titles=["WECC", "EI"],
+        )
+
+
+def test_plot_bar_generation_stack_throws_error_for_filenames_not_a_dict():
+    with pytest.raises(TypeError):
+        plot_bar_generation_stack(
+            ["Western", "Eastern"],
+            [823, 824],
+            ["wind", "solar", "coal"],
+            save=True,
+            filenames=["WECC", "EI"],
+        )