From 2b5551cce063b20ab2fa8ae1e990b9d6f602c5c3 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Mon, 10 Sep 2018 17:49:26 -0500
Subject: [PATCH 01/35] master us

---
 input/master_us/depinst.xml                  |  411 +++
 input/master_us/get_deployment.ipynb         |  671 +++++
 input/master_us/images/legacy_power.png      |  Bin 0 -> 11451 bytes
 input/master_us/input.xml                    |  512 ++++
 input/master_us/reactor_with_coordinates.xml | 2672 ++++++++++++++++++
 input/master_us/recipes.xml                  | 1533 ++++++++++
 6 files changed, 5799 insertions(+)
 create mode 100644 input/master_us/depinst.xml
 create mode 100644 input/master_us/get_deployment.ipynb
 create mode 100644 input/master_us/images/legacy_power.png
 create mode 100644 input/master_us/input.xml
 create mode 100644 input/master_us/reactor_with_coordinates.xml
 create mode 100644 input/master_us/recipes.xml

diff --git a/input/master_us/depinst.xml b/input/master_us/depinst.xml
new file mode 100644
index 00000000..30226b02
--- /dev/null
+++ b/input/master_us/depinst.xml
@@ -0,0 +1,411 @@
+<root>
+          <prototypes>
+	<val>OYSTER CREEK</val>
+<val>NINE MILE POINT-1</val>
+<val>DRESDEN-2</val>
+<val>GINNA</val>
+<val>DRESDEN-3</val>
+<val>INDIAN POINT-2</val>
+<val>QUAD CITIES-1</val>
+<val>QUAD CITIES-2</val>
+<val>PALISADES</val>
+<val>ROBINSON-2</val>
+<val>TURKEY POINT-3</val>
+<val>TURKEY POINT-4</val>
+<val>BROWNS FERRY-1</val>
+<val>BROWNS FERRY-2</val>
+<val>MONTICELLO</val>
+<val>POINT BEACH-1</val>
+<val>OCONEE-1</val>
+<val>OCONEE-2</val>
+<val>OCONEE-3</val>
+<val>VERMONT YANKEE</val>
+<val>PEACH BOTTOM-2</val>
+<val>PEACH BOTTOM-3</val>
+<val>DIABLO CANYON-1</val>
+<val>THREE MILE ISLAND-1</val>
+<val>CALVERT CLIFFS-1</val>
+<val>CALVERT CLIFFS-2</val>
+<val>COOPER</val>
+<val>FORT CALHOUN-1</val>
+<val>PRAIRIE ISLAND-1</val>
+<val>SURRY-1</val>
+<val>SURRY-2</val>
+<val>BROWNS FERRY-3</val>
+<val>POINT BEACH-2</val>
+<val>PILGRIM-1</val>
+<val>FITZPATRICK</val>
+<val>SALEM-1</val>
+<val>SALEM-2</val>
+<val>HATCH-1</val>
+<val>ANO-1</val>
+<val>INDIAN POINT-3</val>
+<val>ANO-2</val>
+<val>COOK-1</val>
+<val>COOK-2</val>
+<val>PRAIRIE ISLAND-2</val>
+<val>MILLSTONE-2</val>
+<val>BRUNSWICK-1</val>
+<val>BRUNSWICK-2</val>
+<val>SEQUOYAH-1</val>
+<val>SEQUOYAH-2</val>
+<val>DUANE ARNOLD-1</val>
+<val>BEAVER VALLEY-1</val>
+<val>ST. LUCIE-1</val>
+<val>DAVIS BESSE-1</val>
+<val>FARLEY-1</val>
+<val>FARLEY-2</val>
+<val>DIABLO CANYON-2</val>
+<val>NORTH ANNA-1</val>
+<val>NORTH ANNA-2</val>
+<val>MCGUIRE-1</val>
+<val>MCGUIRE-2</val>
+<val>HATCH-2</val>
+<val>COLUMBIA</val>
+<val>FERMI-2</val>
+<val>WATTS BAR-1</val>
+<val>SUMMER-1</val>
+<val>LASALLE-1</val>
+<val>LASALLE-2</val>
+<val>SUSQUEHANNA-1</val>
+<val>SUSQUEHANNA-2</val>
+<val>CATAWBA-1</val>
+<val>CATAWBA-2</val>
+<val>BEAVER VALLEY-2</val>
+<val>GRAND GULF-1</val>
+<val>LIMERICK-1</val>
+<val>LIMERICK-2</val>
+<val>MILLSTONE-3</val>
+<val>PERRY-1</val>
+<val>WATERFORD-3</val>
+<val>COMANCHE PEAK-1</val>
+<val>COMANCHE PEAK-2</val>
+<val>BYRON-1</val>
+<val>BYRON-2</val>
+<val>BRAIDWOOD-1</val>
+<val>BRAIDWOOD-2</val>
+<val>NINE MILE POINT-2</val>
+<val>CALLAWAY-1</val>
+<val>CLINTON-1</val>
+<val>SOUTH TEXAS-1</val>
+<val>SOUTH TEXAS-2</val>
+<val>HOPE CREEK-1</val>
+<val>PALO VERDE-1</val>
+<val>PALO VERDE-2</val>
+<val>PALO VERDE-3</val>
+<val>VOGTLE-1</val>
+<val>VOGTLE-2</val>
+<val>RIVER BEND-1</val>
+<val>WOLF CREEK</val>
+<val>ST. LUCIE-2</val>
+<val>HARRIS-1</val>
+
+</prototypes>
+<build_times>
+	<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+
+</build_times>
+<n_build>
+	<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
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+<val>1</val>
+<val>1</val>
+<val>1</val>
+<val>1</val>
+
+</n_build>
+<lifetimes>
+	<val>192</val>
+<val>196</val>
+<val>200</val>
+<val>198</val>
+<val>212</val>
+<val>241</val>
+<val>222</val>
+<val>228</val>
+<val>217</val>
+<val>209</val>
+<val>234</val>
+<val>241</val>
+<val>244</val>
+<val>255</val>
+<val>211</val>
+<val>210</val>
+<val>240</val>
+<val>246</val>
+<val>256</val>
+<val>227</val>
+<val>244</val>
+<val>255</val>
+<val>372</val>
+<val>253</val>
+<val>257</val>
+<val>283</val>
+<val>250</val>
+<val>243</val>
+<val>247</val>
+<val>230</val>
+<val>238</val>
+<val>279</val>
+<val>229</val>
+<val>229</val>
+<val>259</val>
+<val>283</val>
+<val>327</val>
+<val>256</val>
+<val>255</val>
+<val>275</val>
+<val>307</val>
+<val>261</val>
+<val>298</val>
+<val>260</val>
+<val>270</val>
+<val>281</val>
+<val>263</val>
+<val>326</val>
+<val>342</val>
+<val>251</val>
+<val>276</val>
+<val>276</val>
+<val>291</val>
+<val>291</val>
+<val>336</val>
+<val>388</val>
+<val>299</val>
+<val>325</val>
+<val>339</val>
+<val>360</val>
+<val>302</val>
+<val>369</val>
+<val>386</val>
+<val>512</val>
+<val>354</val>
+<val>350</val>
+<val>370</val>
+<val>352</val>
+<val>372</val>
+<val>380</val>
+<val>396</val>
+<val>411</val>
+<val>352</val>
+<val>380</val>
+<val>435</val>
+<val>393</val>
+<val>397</val>
+<val>382</val>
+<val>443</val>
+<val>479</val>
+<val>381</val>
+<val>404</val>
+<val>409</val>
+<val>418</val>
+<val>409</val>
+<val>377</val>
+<val>406</val>
+<val>418</val>
+<val>430</val>
+<val>398</val>
+<val>385</val>
+<val>396</val>
+<val>414</val>
+<val>406</val>
+<val>431</val>
+<val>390</val>
+<val>385</val>
+<val>361</val>
+<val>404</val>
+
+</lifetimes>
+
+</root>
\ No newline at end of file
diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
new file mode 100644
index 00000000..23b0aa07
--- /dev/null
+++ b/input/master_us/get_deployment.ipynb
@@ -0,0 +1,671 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3 as lite\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import collections\n",
+    "sys.path.insert(0, '../../scripts/')\n",
+    "jupyter_directory = os.getcwd()\n",
+    "from IPython.display import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "from shutil import copyfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import analysis as an"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get sqlite cursor and simulation time data\n",
+    "cur = an.get_cursor('./cyclus.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sink_Source_Inst has no values\n",
+      "Sink_Source_Inst has no values\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get number of reactors and capacity timeseries\n",
+    "an.plot_power(cur)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('power_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_dep_array_once(sfr_avail, growth_rate, old_cap):\n",
+    "    # automatically calculated values\n",
+    "    scenario_name = '%i_%s_growth' %(sfr_avail, str(growth_rate).replace('.','_'))\n",
+    "    demand_eq = '110*(%s)**(t/12)' %str(1+growth_rate)\n",
+    "    avail_timestep = (sfr_avail* 12) - (2013*12 + 5)\n",
+    "\n",
+    "    # get lwr_only, find deployment scheme for lwr and sfr\n",
+    "    cur = an.get_cursor('./output/2020_lwr.sqlite')\n",
+    "    init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
+    "    power_dict = an.get_power_dict(cur)\n",
+    "    dep_array, dep_power = an.get_new_deployment(power_dict, ['us_government'],\n",
+    "                                                 demand_eq, old_cap, 240)\n",
+    "    return dep_array\n",
+    "\n",
+    "\n",
+    "dep_dict = {}\n",
+    "new_avail = 2050\n",
+    "growth_rate = 0.015\n",
+    "scenario_name = 'smr_msr'\n",
+    "smr_dep=get_dep_array_once(new_avail, growth_rate, 0.72)\n",
+    "dep_dict[scenario_name] = {'smr': smr_dep}\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121757860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121928320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121757710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112593e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x123650f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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xzVrt9vam7hjF2lp2x1yHY9u3ADBs8nQzR9KcTPSS1EMY6uqMib2yEkNNjWm7YmuL2tXV1N8uE/uN0Wm1nPplP4FRIwifOMXc4TTT4oQpRVFWKYpSqCjK0Uu2vaooyllFUQ42/jf9kn0vKIqSrShKpqIoXeu7lSxKYmJis4lMN2rcuHFcafju6tWrefLJJ9t8/a5ICIHQ6dCVlNBw+jR16enUZ2eju3ABQ20tamdnrH18sA0ORhMUhE2/fsbuGZnkb0jescN8/Mg86qoq6TMg2NzhXKY1LfrVwIfAp7/Z/q4QYumlGxRFGYJx0fBQoC+wWVGUYCGEvh1ilSTpGoROh6Gmxthqr6m5rDtGbW+PytkZlUaDopKT4tvTllXLaaitYcyd9xI57VZzh3OZFn/aQohk4GIrrzcT+FwIUS+EOAVkAzFtiE/qRt555x3CwsIICwvjr3/9K/BrOeFHHnmE0NBQJk+eTO0lY7MBtmzZwqxZs0xfb9q0idmzZ192/S1bthAZGcnQoUN58MEHmxVHa5KUlERwcDAJCQns3Lmznb/DziWEQF9VjbaoiPpTp6jLyKDhzBn0paVgMKB2c8PGzw9NaCi2gYFY9+mD2t5eJvl2VllSTEn+GfzChjHy9rvNsiZsS9rSR/+koij3AfuB/yeEKAV8gN2XHJPfuE3qQv6y9y9kXMxo12sOch/EophFV92flpZGUlISe/bsQQhBbGwsCQkJuLm5kZWVxdq1a1m5ciV33nknX3/9Nffee6/p3AkTJvDEE09QVFSEl5cXSUlJl9WgqaurIzExkS1bthAcHMx9993Hxx9/zDPPPGM6pqCggFdeeYW0tDRcXFwYP348kZGR7fo6dDRhMKAvKzPORq2uNo2OQVFQOzmhavpPjo7pNOdOGP+Wxtw138yRXN2N/tP+MTAAiAAKgGWN26/UwXfFGguKoixQFGW/oij7ixoX8pW6rx07djBr1iwcHBxwdHRk9uzZpKSkABAQEGAqPRwdHU1ubm6zcxVFYf78+Xz22WeUlZWRmprKtGnTmh2TmZlJQEAAwcHG/tH777+f5OTkZsfs2bOHcePG4eXlhY2NDXfddVcHfbftx6DVoq+spOHsWerS06k7fhztuXMYqqpQ2dlh5eWF7YABaAYPxqZ/f6zc3WWS72QF2Zmora3pHdi1hlRe6oZa9EKIC02fK4qyEmha+icf8L3k0H7AuatcYwWwAoy1bm4kDunGXKvl3VGuVVPJ1tbW9Llarb6s6wbggQceYMaMGWg0GubMmYOVVfNf3dbWbOrqDxuFXo+httY4C7WmGkNjlU0AxdoaK1dX4/BHR0cUuRSi2ekaGkj7YT3eQSGorbruP7A31KJXFMX7ki9nAU0jcr4D7lYUxVZRlAAgCJCrNkjEx8ezYcMGampqqK6uZv369c0qWLakb9++9O3blzfeeOOKKz4NGjSI3NxcsrOzAfjnP/9JQkLzErGxsbFs27aNkpIStFqtqTSyuelratAVFVN/8iR16ek05Oaiu1iC0OpQu7hg7eODZtAgNCEhWHt7G8e3yyTfJRSdPgVA//Cu3QXYYoteUZS1wDjAU1GUfOAVYJyiKBEYu2VygUcBhBDHFEX5AjgO6IAn5IgbCYxlihMTE4mJMT6bf/jhh4mMjLysm+Za5s2bR1FREUOGDLlsn0ajISkpiTlz5qDT6RgxYgQLFy5sdoy3tzevvvoqo0aNwtvbm6ioKPT6zv31FAaDcXRMebmx5V5VhTAYTPvVTs6onBxR2dmhsjPfYtJS65QWnAVg8NiuUY74amSZ4h7CEssU/9aTTz5JZGQkDz30kLlDuS6G+npjUbCqKvQVlSB+TewqOztUjo6/zkLtZi317vB7dy37vv+G5M9W8WTSF2YZbSPLFEvdSnR0NA4ODixbtqzlg82sWXmB6mqEVmvap9JojGPZHRxkUbBuoKa8DCtrG2y6+Lsvmegli5CWlmbuEK5ICAFCoL+0K6ahwbRfsbFB7eaO2skRlYNDt2ux93Q1ZaXYu7p2+Yf8MtFL0nUSWq2xO6ayEkNVFYZLJmYp1tbGao+OjsZqj5eMKJK6n+ryMuxdXM0dRotkopekFgiDwdgVU16OqKtrPuTRygq1iysqJ0fUTk6gUnX51p3UPhrqajl9+BcCo7v+5H+Z6CXpN4QQxtZ6Y1fMpcvlKTY2piqPKkdHOTmpB8vemwpAwLBoM0fSMpnopR7PNEmpuhp9eQWi4ZKumKbl8hwcjIlddsVIjTJTU3Dp1ZvwSVPNHUqLZHUjqVOUlZXx0UcftXhcbm4u//rXv1p1XFhY2A3FIvR6Y1mBc+eoz875dZJSURHotMZJSr37YBscjO2gQdj4+WHl4SGTvGTSUFfLyQP78AsbhkrV9R+wy0QvdYr2TvTXQwiBvrIS7YULv84+PX0a/cWLCL0OtbML1t7extmnQ4Zg4+uLlZcnKhubVld61Ol07Rqz1LUdT94KgO+QoWaOpHVkopc6xeLFi8nJySEiIoLnn38eIQTPP/88YWFhDB06lHXr1pmOS0lJISIignfffZfc3Fzi4uKIiooiKiqKXbt2XfM+27ZtIz4+nttmzGBwcDCPzJ1LzZEjNJw+zb/+8Q+ip05l+O238/KKFdgGBfHdoUMs/uu7WHl48P7//R+BgYEA5OTkMHbsWMA4tDMhIYHo6GimTJlCQUEBYFzQ5MUXXyQhIYH33nuvA189qaspzM3B2lbDoLHjzB1Kq8g++h7o/J/+RH16+5Ypth08iD4vvnjV/W+99RZHjx7l4MGDAHz99dccPHiQQ4cOUVxczIgRI4iPj+ett95i6dKl/PCDsU5eTU0NmzZtQqPRkJWVxdy5c5utFiWEQGi1iIYGDI1VHvfu2cOBDRvw69uXmY89xve7djF6zFj+8MEH7E9Lw93dncmTJ/Pdjz8SHx/PkiVLAEhJScHDw4OzZ8+yY8cO4uLi0Gq1PPXUU3z77bd4eXmxbt06XnrpJVatWgUY36ls3769XV9Lqeu7cDIb76AQixlhJRO9ZBY7duxg7ty5qNVqevfuTUJCAvv27cPZ2bnZcVqtlieffJKDBw+iVqs5ceIEhvp6dBUVCK2W+qysZhOU0OsZERFB8IgRqF1cuOfBB9l99Ch23t6MGz+eXr16Aca6OcnJydx2221UVVVRWVlJXl4e99xzD8nJyaSkpDB79mwyMzM5evQokyZNary8Hm/vX2v6WUKpY6l9nc04TuGpHEbOtpyfvUz0PdC1Wt6dpbU1lt599116eXmRtnUruooKnIODqc/KQldQAAYDikqFys3NOCrG3h6b4mJUGg1Wnp6AcdSMoijXvN+oUaNISkoiJCSEuLg4Vq1aRWpqKsuWLePMmTOEhoaSmpp6xXMdZBmDHqcgOxOAQWPGmTeQ6yD76KVO4eTkRGVlpenr+Ph41q1bh16vp6ioiOTkZGJiYnC0t6eyogJdcTENeXmUnDqFl1qN/vx5/rlmDXq9HrWbG9Y+Pig2NtgOHIiNj4+xTruNDQB79+7l1KlTGAwG1q1bx9ixY4mNjWX79u0UFxej1+tZu3atqYxxfHw8S5cuJT4+nsjISLZu3YqtrS0uLi6EhIRQVFRkSvRarZZjx451/gsodRnVZaVYWdvg7tPP3KG0mmzRS53Cw8ODMWPGEBYWxrRp03j77bdJTU1l2LBhKELwpxdfxK2uDnsbG1QNDUSNGcO9M2fy6L33Mvd3v2P9tm2MnzABBwcHbHx8UGu1cJX+0VGjRrF48WKOHDlCfHw8s2bNQqVS8ec//5nx48cjhGD69OnMnDkTgLi4OPLy8oiPj0etVuPr68ugQYMAsLGx4auvvuLpp5+mvLwcnU7HM888Q2hoaKe9dlLXUlpwDkcPD4vpnwdZprjH6CrlYoVOZ5ycVFuHoaoSQ00tTatNKio1isYWtbMziq2tsaTAddq2bVuzh7mSeXWV37v29NHD9xAYHcPUx55p+eAOJssUS12C0OmM5XorK40jYy6tE6NSGWvEODig2NujsrOzqFaS1PPUVVVRW1mBRz8/c4dyXWSil9qNMBgQWq2x+FdDg7Fk7yUTiVQaDWoXF2OdGI0G5TomJLXWuHHjGDduXLteU5KalJ43rijl1qevmSO5PjLRSzdM6HQY6uoxVJQbE3xVFVzSFajY2GLl4WKsEaPRoMgCYJKFKys4B4Cbt0z0UjdlaGgwLolXUYHQ6jDU1pj2KdbWxha7s7MxsXfxFXck6Ub8svHfqNRWuPbxbvngLqQ1i4OvAm4BCoUQYY3blgAzgAYgB3hACFGmKIo/kA5kNp6+Wwix8LKLSl2eEAJRW2t8cFpTi6hvXoddpdGgdnIyLotnZ4dKozFjtJLU8QpzT1JwIgP3vv1QW1nWu9PWtOhXAx8Cn16ybRPwghBCpyjKX4AXgEWN+3KEEBHtGqXU4YRWi766GlFXh66kpFkXDIDKzt64cpKzs3HlJNkNI/UgQgjWvWpMcWPn3mfmaK5fi0/ChBDJwMXfbNsohGh6yrYbsJyZA5Kx/npdHbriEhry86nLyqIuMxNtfj664mJQFNROTlj16o1tUBCawYOxHRBoHL/u5NSjkvy2bdtaLKR2vcaNG8eNDid+9dVXWbp0abvG01arV6/m3Llz5g6jQ2Xs3E5DbS3RN89k4IhR5g7nurVHH/2DwLpLvg5QFOUXoAL4XyFEypVOUhRlAbAAwM/PsoYqWRp9VRWGmhq0Fy40lubVN9uv2Ngal8NzsEfl6IhibW3Rwxx1Oh1WVu3z+Gnbtm04OjoyevRos9zfEqxevZqwsDD69rWsB5StpW2o578f/xWAUXfcY5F/G20a26YoykuADljTuKkA8BNCRALPAv9SFMX5SucKIVYIIYYLIYZ7eXm1JQzpN/RlZVz89J9cWLKE0/fO58TwEejLytAVFRmTvKJg7e2NbeAANKGhaIKDsPHth5W7u7EGewf8Iufm5jJ48GAeeeQRQkNDmTx5MrWNS/Tl5OQwdepUoqOjiYuLIyMjA71eT2BgIEIIysrKUKlUJCcnA8aZrNnZ2c2uv3r1aubMmcOMGTOYPHkyAEuWLGHEiBGEh4fzyiuvmI697bbbiI6OJjQ0lBUrVpi2//TTT0RFRTFs2DAmTpxIbm4uy5cv59133yUiIoKUlBROnz7NxIkTCQ8PZ+LEiZw5cwaAxMREnn32WcaPH8+iRYuaxVZbW8vdd99NeHg4d911l+n7Bti4cSOjRo0iKiqKOXPmUFVVBYC/vz+LFi0iJiaGmJiYy75fgIMHDzJy5EjCw8OZNWsWpaWl5OTkEBUVZTomKyuL6Oho0zVffPFFRo0axfDhwzlw4ABTpkxhwIABLF++3HTOlV63q/38vvrqK/bv38+8efOIiIho9r11FxdystDrdARGjcDW3jJrG91ws0NRlPsxPqSdKBqn1woh6oH6xs/TFEXJAYIBOe21nemKiih45VXUTo4YGhqoT89AX10FBoG+vBwax69b9fXGIS6Oco0GzaBBoFaz48ssin863a7xePo6Endn8DWPycrKYu3ataxcuZI777yTr7/+mnvvvZcFCxawfPlygoKC2LNnD48//jg///wzwcHBHD9+nFOnThEdHU1KSgqxsbHk5+czcODAy66fmprK4cOHcXd3Z+PGjWRlZbF3716EENx6660kJycTHx/PqlWrcHd3p7a2lhEjRnD77bdjMBh45JFHSE5OJiAggIsXL+Lu7s7ChQtxdHTkueeeA2DGjBncd9993H///axatYqnn36aDRs2AHDixAk2b96MWt18xaGPP/4Ye3t7Dh8+zOHDh02JuLi4mDfeeIPNmzfj4ODAX/7yF9555x1efvllAJydndm7dy+ffvopzzzzzGWzfe+77z4++OADEhISePnll3nttdf461//iouLCwcPHiQiIoKkpCQSExNN5/j6+pKamsrvf/97EhMT2blzJ3V1dYSGhrJw4cKrvm5+fn5X/fl9+OGHLF26lOHDW5ygaZEqi4sASJj/kJkjuXE3lOgVRZmK8eFrghCi5pLtXsBFIYReUZRAIAg42S6R9mCioYG8p55Cd64AYTCAToe2sgJxsZQGGxU1Dla4ljaAlwdDe23GAAAgAElEQVSuN92EytEBp5tuwi4iwtQ6T09PRzFzd0JAQAAREcbn9NHR0eTm5lJVVcWuXbuYM2eO6bj6euOarXFxcSQnJ3Pq1CleeOEFVq5cSUJCAiNGjLji9SdNmoS7uztgbClv3LiRyMhIAKqqqsjKyiI+Pp7333+f9evXA5CXl0dWVhZFRUXEx8cTEBAAYLrOb6WmpvLNN98AMH/+fP7nf/7HtG/OnDmXJXmA5ORknn76aQDCw8MJDw8HYPfu3Rw/fpwxY8YA0NDQwKhRv/b/zp071/Tx97//fbNrlpeXU1ZWZirMdv/995tew4cffpikpCTeeecd1q1bx969e03n3XrrrQAMHTqUqqoqnJyccHJyQqPRUFZWdtXXzc/P74o/v57gzLHDoCg4eVpuz0NrhleuBcYBnoqi5AOvYBxlYwtsakwkTcMo44HXFUXRAXpgoRDi4hUvLDVjqKkx9qVXV6MvLaXu2HF0F0sQNTXUZGZQt3svmX5qyhwU9LYGdI6Cc2EqDk0PQlEU8gqzGOjZm7W3vdJi10tLLe+OYnvJmqtqtZra2loMBgOurq6mBUkuFRcXx/Llyzl37hyvv/46S5YsMa0gdSWXlgwWQvDCCy/w6KOPNjtm27ZtbN68mdTUVOzt7Rk3bhx1dXUIIW6oy+rSc65VsvhK1xZCMGnSJNauXdviOdcT2+23385rr73GhAkTiI6OxsPDw7Sv6WegUqma/TxUKhU6ne6qr1tubu4Vf37d3enDBzm6dROefv5Y21jumsGtGXUzVwjhLYSwFkL0E0L8XQgxUAjhK4SIaPxvYeOxXwshQoUQw4QQUUKI7zv+W7B89Tk5ZI4aTXZ8AienTef0PfO48OablHy8nPOfryE/6xcO+SvsffYmLr70AJUvPULD/z7G8EVvseG2DayfuR43N2+OVZzg0+OfkpJ/xeffXZKzszMBAQF8+eWXgDH5HTp0CIDY2Fh27dqFSqVCo9EQERHBJ598QlxcXIvXnTJlCqtWrTL1eZ89e5bCwkLKy8txc3PD3t6ejIwMdu/eDRgrXm7fvp1Tp04BcPGisX3y2/LKo0eP5vPPPwdgzZo1puUGryU+Pp41a4yPsY4ePcrhw4cBGDlyJDt37jT1v9fU1HDixAnTeU3LK65bt65ZSx/AxcUFNzc3UlKMP+t//vOfpta9RqNhypQpPPbYYzzwwAMtxnepq71u1/Lb16g7+fcHS1Cp1cz4/QvmDqVNes7QADPTV1RQ8re/U3voEGpnJxry89EVFWOorkY0toy+j1G44KpQ5ALlDgp1A/pib+OAp50nPo4+vByzGI3VlScmrZy0kpnfzmTpfuPQu59u/wkfR59O+/7aYs2aNTz22GO88cYbaLVa7r77boYNG4atrS2+vr6MHDkSMLbw165dy9ChLS/IPHnyZNLT000J0tHRkc8++4ypU6eyfPlywsPDCQkJMV3by8uLFStWMHv2bAwGA7169WLTpk3MmDGDO+64g2+//ZYPPviA999/nwcffJAlS5bg5eVFUlJSi7E0Jdzw8HAiIiKIiYkx3XP16tXMnTvX1F31xhtvEBxsfMdVX19PbGwsBoPhiq3+f/zjHyxcuJCamhoCAwObxTJv3jy++eYb04Pp1rra63alLqkmiYmJLFy4EDs7O1JTU7HrJrOij/y8kdqKckJGxeHe1zL+lq5GlinuANV79lKzZw8VP/2ExyOPUJ+RzsV/fHrZcYf9FfI8QWcFhS4KIx9/hT4Ofejr0Bc3jRsedh5XuPrVldWVcbTkKI9tfowl8UuYGjDVtK87lovtzvz9/dm/fz+ejStlXa+lS5dSXl7OH//4x3aO7PpY6u/d+Zws1rxofC6SuOxjPPr5mjmiK5NlijtJQ34+lf/9Lw5jx2Lt04+affvIf+wx0/6CF359y7c2XsXhAAXPCkGFvcKQiXcwwHUA1iprxjj6ENev5S6Ja3HVuBLrHYut2pajxUebJXqp55g1axY5OTn8/PPP5g7FIuUeTGPD0jcAWPDRapw8buwf265EJvrrJAwGyr/7jop//we7yAiKftiAcjIPliw1rnj0m3dI345UyOinkDZQ4b0J7/O4xxCKaopwsXXBz7n9J4pZq6zxdvDmQs2Fdr+21HnaMqKlaUSRdGO2fvo39Fot0596rlskeZCJ/qqEEJSv34Dd0DCq9++n8J13QAhEVbXpmOqUFC4dC/GfaMjqq+Kch8KpPgoBLgHcP+R+ZnmGEeIeYjquj0OfDo3dXeNOYc21H6BJknS5hrpaLp7No3dgEIPHjjN3OO1GJvpL6CsryXv4EfTl5ehKSzGUlwOg01hTZqNlzyAFUChyVkj3U/Bo7II51Qd83AOYEzyHiV7h9HPqh6ed+VoCHnYebDq9ibK6Mlw1rmaLQ5IsTek548Ii0bfcZuZI2lePTfQ1Bw5Q9N77cEndl4aKMnQnsvllgIqavgJ/NeR7KWjVOrJH9CX8tgcBGO0SwCL3QRTVFOGuccfTzrNL1b8Y7zueTac3Eb8unicinuDRYY+2fJIk9XB6nY4v//gSAB4+XfPh643qEYm+9IsvqE5Nbb7tyC8YLhRyup+NaZtBGCgaonD+93fgZO/GlpoL9LLvhUpRca/vBIZ6NR/W56658uxJc7s58GbK68tZl7mOLWe2yEQvSS0oOn2KlH+tpr6mmuhbZtHLP9DcIbWrbpnoC/7wMjVN076trGjIyaHGXk2146/frs6gY+9oW6rm39zsXC97L16KfAqV0r5rmXYmlaLi3iH3crHuIiuPrCQ5PxkvLG/6dmJiIrfccgt33HGHuUORujEhBBs/eZ8Lp3LwGxrB2Lvmmzukdmfxib6hvobqihJEWTmqo1lUbtpMxdafOd1b4ZyHgk8JuNnDF3d6UTt8ULNzJ/hNYE7wnKtc2fLd1P8mVh5ZydL9S3ltwGvmDkeSuqRDm37kfE4WA4aP5Lbn/9fc4XQIi0/0uyaNondhQ7NtGb6w7/5ovIcMZ2N5Luerz7MoZhERvXrWwldDPIbw+LDH+ejQR5TWl95wPZf28s4777Bq1SrAWHjrmWeeITc3l2nTpjF27Fh27dqFj48P3377bbPZlVu2bOHDDz80DRvctGkTH3/8sam4WBN/f3/uv/9+vv/+e7RaLV9++SWDBg2iurqap556iiNHjqDT6Xj11VeZOXMm06dP56233iI8PJzIyEhmzZrFyy+/zB/+8Af69+/Pww8/3HkvjmQWF05ms+XvH+Ho4cm0J37f8gkWyuITvWtpA2cGOtMwNpJcL0GFtzN6Nyd+H/4ovR16mzs8s3ss4jH6OvZFlArq9fVorDRsXb2CwtPtW1S0V/9AxicuuOr+tLQ0kpKS2LNnD0IIYmNjSUhIwM3N7arlb5tMmDCBJ554gqKiIlPZgavVcPH09OTAgQN89NFHLF26lL/97W+8+eabTJgwgVWrVlFWVkZMTAw33XQT8fHxpKSk4O/vj5WVFTt37gRgx44dze4vdU8VRYVsWvkhAJMfedJia823hsUnegTogvszY/Hylo/toYZ5DSOnNIei2iJ8ncwzmmDHjh3MmjXLVOFx9uzZpKSkcOutt7ZY/lZRFObPn89nn33GAw88QGpqKp9+enlJiabrNl2nqcW/ceNGvvvuO9MSfHV1dZw5c4a4uDjef/99AgICuPnmm9m0aRM1NTXk5uYSEhJyxetLli9j53b2fvsVRaeNBexiZt5BQGT3rKXfxPITvdQifxd/TnISncG4GMm1Wt4d5Vo1lVpT/vaBBx5gxowZaDQa5syZc9Wl+pqupVar0TUuviKE4Ouvv74seTc0NLB//34CAwOZNGkSxcXFrFy50rQik9T9VJVe5N/vL8FaY8egMQkMGpNAYDdP8tDGpQS7AgWMpQeka9JYaWjQN1DVUGWW+8fHx7NhwwZqamqorq5m/fr1rSo33KRv37707duXN954o9mKSa0xZcoUPvjgA9M/Nr/88gsANjY2+Pr68sUXXzBy5Eji4uJYunTpdcUlWYaKokLWvPQsnyy8D4AZzyzi5qefZ0B0DIrK4tNgi7r/dygBYKWyQmfQcbriNAZh6PT7R0VFkZiYSExMDLGxsTz88MOmVYxaa968efj6+jJkyJDrOu8Pf/gDWq2W8PBwwsLC+MMf/mDaFxcXR+/evbG3tycuLo78/HyZ6LuZ8sLz7PryX5zPPkHwqDjufOXP3b6r5rcsvkzxodDB5E8L5+al69o5qu4lPT2dPgF9OFd1DmdbZ7P11bfFk08+SWRkJA89ZLlrd/Y05ixTfPrIQbasWk7puXwAvPwDue8v75sllo7Ss8oUy66bVnGxdaGguoAGfUPLB3cx0dHRODg4sGzZMnOHIlmAmvIyfk76hMqSIoZOmMyA4SPxDuq5D9hblegVRVkF3AIUCiHCGre5A+sAfyAXuFMIUaoYB2q/B0wHaoBEIcSB9g+9MbaOunA3pFJUuNq6Utlgecu+paWlmTsEqQszGPQUZJ0gY+d28tOPUnwmF4ABw2OZ/OjT5g2uC2hti3418CFw6Zi2xcAWIcRbiqIsbvx6ETANCGr8Lxb4uPFjh1AEyHTfOkIIU1+9QRgsusyD1PV1ZrfwphUfcnTrJgDsXVwZNCaBgSNGEhQzutNi6MpaleiFEMmKovj/ZvNMYFzj5/8AtmFM9DOBT4Xxp7xbURRXRVG8hRAF7RGwdGM0Gg0lJSVYORh/5DqDDhu1TQtnSdKNEUJQUlKCRnPlNY7bg16no+x8AZmpKRzdugnf0HAS7n2Q3oEDO+yelqotffS9m5K3EKJAUZRejdt9gLxLjstv3NZxiV720beoX79+5OfnU36+nJLaEuoL6rGz6h6LOEtdk0ajoV+/fu1+3dxDBzie/DPZ+3ajra8z3svRiXH3Pdztqk62l454GHulrHvZezhFURYACwD8/Np/ST2pOWtrawICAiirK2P+OmN1vsP3He5SdfQlqSXlhRf4+k8vA+Dp25+BMaMYNDoBt759UanUZo6u62pLor/Q1CWjKIo30LR2XT5w6di9fsC5354shFgBrADj8MobDUIRyBb9dXDVuDKp/yQ2nd5ElbYKJxsnc4ckSdckDAay9u4iJ20vGTuTAbj3z3+VXTTXoS2J/jvgfuCtxo/fXrL9SUVRPsf4ELZc9s93LU0rUBXXFstEL3VplReL+duTD2PQG8tZePTzI2r6rTLJX6fWDq9ci/HBq6eiKPnAKxgT/BeKojwEnAGaCrv/B+PQymyMwyuvXGawnSim/0mt1bQ4+cHCgwS4BJg5Gkm6XOXFYs4cOcSx7Vsw6HUMm3wzY+6ch52Ts7lDs0itHXUz9yq7Jl7hWAE80ZagpI4V6hEKwNqMtcwKmmXmaCTJ6NyJDE4e2EvuoQNcOJlt2u4fEc34+x9BfZVCdlLLLP+Vk330183e2p7J/Sez8fRGMi9mEuLec2cMSuajbajnbMZxsvemkr1/N9WlFwHjCJpBYxIYEB1D/2FR2DnK7sW2svxEL92QBeEL2Hh6I3d8fwcH5x9ELUcsSJ3g4rmz5KTt4fThX8g7dhiDXg+Ag5s7sbPuJGR0PB4+vqjU8vexPVl8oleBHCJ4A0LcQ0yjb/525G88OuxRc4ckdUMGvZ7TRw6SuSuF/PQjlBdeMO3zGRRKyKixDB47HlsHB/l33IEsPtEDsuvmBr0Q8wKbTm9i85nNMtFL7aauqor0HVvJTE3hbGY6NJZCcOndh7DxkwkeOYb+4RFy3Hsn6h6JXrohXvZezA6aTXJ+srlDkSxYQ20NF05mk71vN5mpKVSXlQKgUqvpPzQC/2FRBEbF4N7Xx8yR9lzdJNHLFv2N8rTz5GLdRbQGLdYqa3OHI1mI0vPnyD10gNyDaZz6JQ3RuJiNo4cnwyZNZ2DMKHyCB2PdgbVupNaz6ERvMHT+SkndTYBLAAZh4FT5KYLdgs0djtRFCSE4feRg4/DHX0yLeaAo9BsUyoDhsQSPHIOzZ69rX0gyC4tO9E2tCNlHf+MGuxtX//km6xvuDrkbfxd/8wYkdQl6nY7ywguc2L2DzNQUSvLOmP7enDy8CE24ieCRY/ANHYq1rWy1d3UWneiltvN39sfN1o016Ws4VHiItbesNXdIkpnUVJSTn36Ukwf2kbVnFw21NQBY2doSFDuaXv6BhIyOx6VXbzlCxsJYdKIXTV038nfuhqlVar6f9T1/2vMntuZtNXc4UicSQnDmyCEKsjPJPXSAsxnHTPs8ffsTGB1DyKg4Wfq3G7DoRC+1DxdbF/yd/anV1XKw8CARvSLMHZLUAYQQaOvrSE/ZRtbeXeQdO2IqFmZtq2HA8FgGRMcSGDUCB1c3M0crtSeLTvSyj779TPCbwEeHPiK7LFsm+m6ktrKCsxnHOXP0EDlpe6koMk5YUhQVPoOH4Bc2jIHDR+Lu0w+1lRx11V1ZdKKX2k+gi/Ht+fsH3uf2oNtlH6yFMhj0nMtIJz/j2GXdMY5u7oSNn8yA4bH0Dxsmhz72IBad6Jta9IrspG8za7U13g7eFFQXUFhTSG+H3uYOSWql+ppqTuzeaZyJmnEcXUM9YHyI2j88kuCRYwmOHYPG0dHMkUrmYtGJXmpfi2MW87utv6O4rlgm+i6svqaG/PQjZO/bw6lf9plmoqIo+IQMwT88Et+wYXgPDJbFwSTA0hN903wplWzRt4de9sbJLl9mfkno6FAzRyM10et0nM/JInNXMkVnTnE2/fivY9o9vYiaPhPfIUPxGRwqS/pKV2TZid5EJvr2MNh9MHZWdnyd9TUzB84kslekuUPqsUry8xrL+R6gIDsLbV0tAPYurviGDiVkdByDRsdjY2dv5kglS2DRiV4gSyC0J7VKzSeTPuG+H+/jdz//jvUz1+Nh52HusLo9YTCg0zaQvmM7Jw/s5XxOlmkRDrWVFT6Dw/AdMpSAyOH0Dhhg5mglS3TDiV5RlBBg3SWbAoGXAVfgEaCocfuLQoj/3HCErSG7btpNZK9IonpFcaDwAMn5yXKpwQ5SdbGEojO55OzfTebundRVVpj29Q0eTMjIsQyMGUWfAUGyxIDUZjec6IUQmUAEgKIoauAssB7jYuDvCiGWtkuE12AQskXfEVZMXsHwz4ZTVFvU8sFSq2gb6jlz5BAnUlMoyjtNUe5J0z6X3n0IjR9Pn4EhDBgei7WNrRkjlbqj9uq6mQjkCCFOd+b4ayGrV3YIW7UtzjbOLD+0nNVHV2OlsmLZuGWM6DPC3KFZlPPZJ8jal0ruwQMU5uaYtrv17UdQzGgGxoyi/9AIOQtV6nDtlejvBi6thvWkoij3AfuB/yeEKG2n+0id5MXYFzlafBSAdZnrSMlPkYn+Ghpqayi7cJ6svamcTT9KQVYmOm0DALYODgSPisN3cBj9h0Xi1qevmaOVepo2J3pFUWyAW4EXGjd9DPwREI0flwEPXuG8BcACAD8/vxu6t2nClJzF2e5uDryZmwNvBiDtQhobsjfwQNgDuGlk6xOMM1ALT+aQl36U3EMHyDt22PQO08raBt+wcPoMCCZk1Fhc+/RFbWXR4x4kC9cev33TgANCiAsATR8BFEVZCfxwpZOEECuAFQDDhw8X7RCH1EGGeQ0j/WI6r6e+zptj38TOyq5H/uN67kQ6BVmZnDywjwsns6mvqQZAUanoGzy4cS3USDx8fM0cqSQ11x6Jfi6XdNsoiuIthCho/HIWcLQd7nFFwiCabtpRt5CAZ4c/y6GiQ2w+s5nN/9rMXSF38b8j/9fcYXUog15PXVUlGTu3czYzndOHfzEldhSF/kMj8A4aRL9BofiGDpUzUKUurU2JXlEUe2AS8Oglm99WFCUCY9dN7m/2SRbIzsqOZeOWseX0Fn7M/ZEfTv5AXmXeNc8Z5T2KxLDEzgmwHeh1WsounCd7326Kck9y8sA+tPV1pv19BgThFzaMkNHxuPbuIycqSRalTYleCFEDePxm2/w2RXR9ERg/yBZ9h/N18iUxLJFA10BWHF5BlbbqqseerzrP4aLDaKyuPv7bwdqBmwNvRqWoOiLcFgkhKMjK4Hz2CU4fOUh++jHTikooCh4+vgSPHEO/wUPxDg6RQx4li9ZNnhD17ERfXa/jVHF1p9zLXRnG4mH/d81jUi9s4f0jr/DmnjeveVxuSQX+Tp23ILmuqoqqjAxqsnOoOXUKQ72xyiMqFRofH9wDR2DXrx/2AwagqBRKgBLqOZRzuNNilC7n6+pFhLe/ucOwaBad6EUPnzBV26Cnsk7Loq8PszWzK01usgXVyyjKVX4+qjocBrzDivS3OiwClR40DWoCChzwKLehX5EdNrpf3z0Uu9RT4FPHmd41XHTWolefAnZAKcZBwVKXIQxWfDTuM7wcXABwt3eit6OLmaOyLBad6JsoPbAEQoPOwNi//ExJtXGs9qQhvZkT3c/MUbVeXrU/5Q0l7XY9IQQUlkDBBeN/p/NBq/v1AHdXCPADPx/wcMPLToMXEN5uEUgdIbv0NCkXV/FE8t2mbcJgxbrp3zHYy6fZsSqVeboBLYFFJ/qePDP2eEEFJdUNJI72J6SPEzcN7o2XkyX1I/dp09nCYODUoTQKsjI5c+QQF8/mUVdtfG6gKCp6BQQSEDmCvkEh+EdE98jhoN2BwWDgz8m9Ka0z1gIqqinhQOXn3P3T9GbHCaEw138xL427xxxhdnkWneh/1fP+iE+XGPvk58X6EdS7+9cg1zbUU3Aig7zjR8nas5OS/DOmfbb2DngHD6JX/wD8wiLoNyRUrn/aTahUqmbJ22Aw8Nx/XSmubT7Z/peyH1h7aimfn/yg2fYAu1F8N/edTom1K7PoRC9Ez51nda7MOPTP29XOzJF0jOqyUorzTpOzfw/nTqRz4WS2aZ+1xo6Q0fH08g9k4IhRuPTqLWee9hAqlYp3pj1x2fYlKSEk5+9qtu1cbSYn638m8u9TAAhyGs4Xd157gEB3ZdF/HaaHsd38bbneIFj09WHOl/86rjunqAofVzscbS36R2hSWnDWVASssriI8zlZpn2Obu4Ej4rDJ2Qwg8YkoHF0RKWSE5SkXz0fN4fnmdNs208nDvCX3R9gUAyU685xvObfjErKQq1Y8eeE/yUuYIiZou183SNLdHP5pTV8lZZPoKcDbg42APR1tWNGuLeZI7txtZUVnNi9gzNHD3PhZBblhabKGfQODCIwOobg2DH4DArFtXfb+vOlnmlqcBRTg5MA+D59L2/s/gs60UClksEzW/8H9x3GwQuzgm/h8dhbzBlqh7PoRN8TWvSl1Q18+LOx2+KN28IYPdDTzBG1nV6n5W9PPURDrXF5vN6BA+kfHknQiFF4+vnj6C5XtZLa14zBMcwY/DUA09Y8ybm6oxQ2ZKBXVfJxxk7cNE7MHZZg5ig7jkUn+p7g6c9/ISWrmD7OGkL6WO5D19qqSkryz3DmyCEKsjNpqK0l+ubbiJ19l1zQWupUP8770PT5Mz/+H1sKl/Ong0/iZvcPpgZHmTGyjmPZib7xYWx3HTqXd7GGlKxi7G3U7H5xornDuS56ndb0IDU//TgXTv7a525la4t38CBibpsjk7xkVn+d9gQLvqshtfRTXkh5gWOFC/j96Fndbky+ZSf6bi6v1Fh7ZcX84WaO5PqcOXqIL//4kulrRzd3gmJHExAxHDfvvviEDEHpZn9IkuVacevz3PlFGem137E651WOFZ/gb7cu6lbJ3qITfXcuU1xQXsv+XONYYT93y6iUWFlSTPGZXL5b9icAJj70OP7hkbj07tNt33VJ3cO/bn+d5Ny7+N2Oeewr/xffHIvnjqFjzB1Wu7HoRG/SzZLIhYo6xrz1MwYBno42+Lp3zbHyJfl5FGRnkrN/N+VFhc0WvA6/aSoRk6df42xJ6jqs1GomDAhnYdGfWJ75Ir+cz5SJvqvorkXNcoqqMAh4dlIwU8O6Vmu4vPA8//5gKZUlxVSVFJu2eweFEBQzmuCRY+gbMgRnTy8zRilJN2besAksz4T/nFnLm5evgGqxLDrRm3ShRNhWsz/ayYEzZQDcOqwv/p4OZo7oV3qdll1frKHgRAaBUSMYODyWoJjRePr2x97F1dzhSVKbudo5YG8IpsbqBMP+PoHU+37E3tqSakhdmUUn+u7Woj9fXseBM2XcNLg3CcGe9PfoOn3zZ44e5rt33qS+uhrf0HBmLXrF3CFJUof4aPLbvPDzEgqsdjLy08m8P/7/GBcYZu6w2qRbPFbuSl0bbXGy2Fh98cEx/swf5d9lvq+q0ot8t8yY5GNn3cXkBU+ZOyRJ6jDRPgNYd/tSBtpOQ1hd5MXtfzR3SG1m0Ym+uxU1++7gOaDrFSrbsfZT6muqGTphMmPvno9rH8stvSBJreFm78j6u99GretFpeo4T/zwV3OH1CZtTvSKouQqinJEUZSDiqLsb9zmrijKJkVRsho/urU91Mv92nXTNVq+bVGn1fP5vjxUCvi6da1EX5yXi6dvf256+PKqgZLUnb0w4lUAkgvXoNPrzRtMG7RXi368ECJCCNE0s2cxsEUIEQRsafxauobXvj8OGEfaWKm7xhut8sLzZO1LpeRsHn5DI1CpZcVIqWe5KzyOKb2fAHUdRwvzzB3ODeuoh7EzgXGNn/8D2AYsave7iO4xYUpvEHy+z7iQxkNjA80cjdGPHy7jeMpW09d9AgeaMRpJMp9At35wAT7Z/w0fz3jW3OHckPZI9ALYqCiKAD4RQqwAegshCgCEEAWKovT67UmKoiwAFgD4+fm1QxiWq7iqHiHgj7eFYWdj/lazEILM1BQAZr/wGs6eXrj3tZz1aCWpPY3tH8bHGbDjYhLV9U/gYGt5wy3bo49gjBAiCpgGPKEoSnxrThJCrBBCDBdCDPfyurHJNU1rxnaV0Sk36myZsVyvj6vGzJEYVZeVotfpSJj/EAER0Xj085O1aaQeK7yPP7UXOesAABuQSURBVGPcEwG4+5vFNOh01z6hC2rzX68Q4lzjx0JgPRADXFAUxRug8WNhW+/Tne3PvQiAt0vXeAi74W3jcDIXueCHJAHw4fRnEEIht2EzL2xeae5wrlubEr2iKA6Kojg1fQ5MBo4C3wH3Nx52P/BtW+5zNYKmPvqOuHrnOHaunD/9JwMAny4w2ibv+BEunMxi0JgEBkbHmjscSeoSrNRqkm76BoCN5z41czTXr60t+t7ADkVRDgF7gX8LIX4C3gImKYqSBUxq/LrjKJbbrbAruwSA5fdG4ayxNnM08HPSJwBETv3/7Z15eBzF3ec/NYek0T2SdduyLN82h40NNjaYK5wvR8iy+5CwvJxxNgT2DcnmDSS770uSzbvkIpCQAAlH3iW8gJNwxSHh9AZsg7GML3zKxrJuaXSM5tSctX90ayTZsi3Jkrt7qM/z6FF3dfX09zfV+qq6qrrqatVco1AM4eyps5juvATsAT7t6TjxCSbipDpjpZSfAmeOkN4NTPpKGQNt9FbkoCfA53+1gUAkTnVRNlecZvxLSL3trXQ1NjBv5QVUzplvtByFwnR8ruZCnqp/h61tB6ktKjNazqix9Fw3A1ixM/YHa3fj74+zelUtF8wxx0yP3vY2ABacf5HBShQKczKneBrUw/4ua42pt/SzuVWnQJBSsr3Jy4wpOXznqvmsNMmC31v+8goARVVqKKVCMRIrquchpZ1tnTuNljImLG30KSxWoz/oCdIbivHVC2YaLSWFTCY5vGMrALlFxQarUSjMSaErh6xkNYf8nxgtZUxY2+gtWqN/av0hAJbUTMoUQOMiHPADcNGtq7E7jO8UVijMSoVrJmFajZYxJixt9KlJzSxWo39vv4dMh41aEy0qUr9pIwA5hUUGK1EozE15diXYw2xpOWi0lFFjaaO3IoFInBZvmK9/bo6pOpG9HVpHbM2ZZxmsRKEwN8urzgDgsbo/Gqxk9Fja6Ac6Y81kmCfC448AUF5gnvky2g/s52DdJtwVlWRmm2dVK4XCjNyx9HLs8VL2ea3TTm9po7ciA0Y/JdccRh+PRnnxgfvobWuh5swlRstRKCxBeeY8vGwjFIsYLWVUWNrordhGP2D0JXnmMPqupsPEY1Euuf2rXHTraqPlKBSWYEmZVin6n28/bbCS0WFpo09hIaPv8PUD5qnRdzZoHUo1i5ZYqglMoTCSf73oHwHY6tlssJLRYWmjt+IUCANDK93ZGQYr0ejraMdmt1NQctSSAQqF4hhkOBwUs5QuuZkd7Q1Gyzkhljb6AaxSE93d6qPFG+b82VOw28yhOeTz4covUBOYKRRj5AuzPw/At97+scFKToyl/7qtNgXCu3u1Ge++c5U5JgyLhIJ8su5NsvPyjZaiUFiOu5dfA4Av1mWwkhNjaaNPYYEafSIpeeSdemaX5jK/whzG+sGfXgCgYs48g5UoFNbDZrNRxFn0J/uMlnJCrG30FqrR72nzEUtI00xgFg2H2LL2ZQAuvu0rBqtRKKxJntNNXCijPyVYoY1+s75c4OpVtQYr0djyF23RryvuulfNbaNQjJOirClgD5p+PL2ljT41jt4CPLOhgapCF5WFxi8XKKWkbq22LNq8lRcYrEahsC6l2doT+qfd5l5xytJGP4A0+aKxHb5+GntC1JaYYxIzn6eDaDjM3BWrsDvSYu0ZhcIQat3a2g2/3/GGwUqOz7iNXggxTQixTgixRwixSwjxT3r6A0KIFiHENv3nqomTOxyrjLpp7g0DcPvKGQYr0ehqagTgrCuvMViJQmFtrp23AoB1LX8zWMnxOZnqXBz4ppTyYyFEHrBFCPGWfuznUsqfnry84zPQdCNMMib9WDR0BQGY6ja+2QYg2Kv1F6gFRhSKk2NqQRHVzos4HNlAKBYh22mON96PZNw1eillm5TyY33bD+wBqiZKWDqxpbGXvEwHtSW5RktBJpO8+7snAMgpNM/CJwqFVVlRuRxhi/LnPR8ZLeWYTEgbvRCiBlgMbNKT7hZC7BBCPC2EmDQ3GWy6MXeNflujl0XVhaZ4G9bf000iFqOkukaNtlEoJoDr5p0HwENbHsYbDhqsZmRO2uiFELnAn4CvSyl9wGPATGAR0Ab87BjnrRZC1Akh6jwez8lqOKnzJ5umnhAzTVCbB60jFmDVf73dYCUKRXpwWnk1WYmZhGz7OX/Ncr7z1lNGSzqKkzJ6IYQTzeSfk1K+BCCl7JBSJqTWgP5b4JyRzpVS/kZKuVRKubSkpGRc17fC8Mq+cAx/JE5FQZbRUgDweToByC8pM1iJQpE+PH75z1hZdCsk8tnY+r7Rco7iZEbdCOApYI+U8qEh6RVDsl0PTPoyLEKYd5To0/pslWaZ9iBl9FPG989VoVAczZKqmTx+zTepcJ5ON1u47eV/M1rSME7GIVcCNwMXHzGU8sdCiJ1CiB3ARcC9EyF0RJLmHl75151tPPJOPQDLas2x6Hafp5OcQjeODHNMk6xQpBP3LNWaRLf1vGewkuGMe3illHI9I/eCvj5+OePEhG30sUSSe57fCsCjX1pMpsNusCJtxE1b/V7y1dzzCsWkcM38c3hy22V8mjBX84152zxGhXlr9D95Yx/xpGT1qlquPqPSaDkAfLq1ju7mRspqZxstRaFIW8pyysEepiNgnsnOLG70OiaM4sXNTQB87aJZBivR2Pnum6x95EcArLrpVmPFKBRpzNQ8rZvyoQ1rDFYyiAktcvSYdSnBUDROXzjGty6fS4HLHGPVGz/ZjsPh5LKv/HecmeYYAaRQpCMX1iwG4PX2X9AfixqsRsPSRj+IucJYo9fmpxVlG6xkkKC3l6Kp1Zx+8WVGS1Eo0ppVMxayJP9GAL791m8MVqNhLoccI2ac1Gxbk5cH/rwbgKXTzTHFQCwaobPhoFoAXKE4Rfzyqm8AsL79rwYr0bC40euTmplk1E0wEucnb+wF4MXVy00x9zyAp+EQkWCQ2ctWGC1FofhMkJfpopBFROk1WgpgcaM3E7FEkiseeY8NB7o5p6aIZbXmmBkyGg6x7c2/AFAxa67BahSKzw61efPA7qfJ203S4P5ESxv9QNON0TX6pp4QZzzwJk09YW5aVs2jX1psqJ6h/P3Zp9nz/jqmLThdTUusUJxCzq48A4CrXr2Qi581dm6p9FheyGCjf/BvewnHEnzj0jncef4MsjPM8bW27N3Njnf+Rm7xFK6//wGj5SgUnynuXHI5B3obWd/2Bt3UG6rF0jV6TNIZ29wTItNh456LZ5nG5AEObasD4B/u/h84M8y5IIJCka5kOTN4+MqvsaDwHLD7WPTUJXx/3e8N0WJto9cxclKz5z9qZHtzH59fVGV4E9JQmnbv5PDObZRU1zB1wWlGy1EoPrPcufh6ym0rSYgwrze8aogGSxu9kcMrff0xvvWH7fzob9oom2sXmWOaA4Du5ibWfO9+2g/sp3LufKPlKBSfaVbNWMhbNz9OVcZiQrLNEA2WNvoUp7giHYrGuem3m/jDlmaKcjJ44uYlrJw15dSKOA4fvvQCAJ//53/hwltWG6xGoUhfulsDHNjSOaq8pdkVSHsfa3aun2RVR2Ntozdg4ZH+WIIv/986drb0Ma3IxVv3XsDlC8tPuY7j4e/2AlB71tk4nMefgiHki9Kws+tUyFIo0o4Xvv8Rb/x2dEtuXDR9GQAPbv4hv9l8al+ksrbRDzDJbeOd/n7WbG7ixc2NPPjXvWw40E2By8m6b15oinVgj6S1vg2bc8awPoOwP0pXsx+ZlDTu6qZpTw9SStY+up2//GoH8WgCT6OfA1s6SSYl3S0BQj5zzNOhUJiJRDxJy/7hL0LFo4kTnnfbkkuZ77qWmL2ZR3f+cLLkjYh5hoiMAynlpLbabGvy0hOM8NyHjbyzd/DxrCgngw/vvwSH3Zz/J2UyiM0+fAWpF//3RwT7oiy7dgabXtNWvbrszoX0dYYACAdirPm3zQAsu66WTa9+Sma2gzsfWnVqxSsUBtPnCZFTkIkjY3ANie6WAEWVOQgh+PMvt9Gyz8uN/zK4SmrIHyW/WHsTPhZJ4GnyU1qdR6A3Ql5RFv6efvKmZPHEqu9w93onhz0beHjjK9iFoKawgmvmj7ji6oRhaaMfYCJH3TR0BfH1x+gORrntmc2p9MsWlPHAtQsBKHA5yXCY0+T7gwGQQYR9+Dw7wT6tdt68d7Am4u0IYXPYgMSwx09vu2b+kVBc+x2OY7eLYTe+QpGOJJOS3/+vD6k5Ywr/cJf2wlNrvZeXf/YxF3xxDpWz3bTs05pGo6E4NpsgmZSE/bGU0X/46kF2vNtMXnEW/u5+Zp9dRv3mDuacU8b+jzpYMmMpKw6t4j/ED/C5uigRy5TRHw8pk+Ou0Usp8fgjJIaM3Gn19nPD4xuHDc9/4uYllOdnMas0l5zMCfq6EjGwT+z0xVJKGrZ/TOehgwAIeykyKYlGEjgzBw06OWT5xUgwnmr16jjkG5SXGN738eS975Ff4uKm7y1HCO31BdtxmqyklMikxHacJ55kIomwCVMNSVWYk2Qiedx76VgMvQ9PdE9KKZES4hGtCaZhh9ZvlUxKupr9AHQe9pM3ZXD+qmgkgTPLTiQUx9sRoqQ6Dykl7Qe1BUf83f0A1G/uGPY7vyOfCHHumfUDMirsVOZN/jKjk2b0QogrgEcAO/CklPLBCb/IgCOfwCyklMSPWF/22Q8O8/21u0fM/9P/fCbubCeF2RksmegZKA9/AM9cAbe/CdXLBtOTSbCN/ylh/4c7WPvwv+p7dmz2Mp785vtEw/Fh+doODK56s/3dphE/60Dd0aMIfJ4wj921jvkrKtizsY2vPX7xMbXseLeZ9X+o546fnU9WztH/0OKxBE/c83fOvnoG51w9YxTRKT6r9LQFef57m7jyv51O7aKxLWhf93oDH/35EF/5xQXsWt/K+jX13PHT88nKPfqe/H/P7WP3+lZufXBlKk0mJY/dtS61v2djG3s2Dg6PXPvL7antt5/ZzdvP7KZqrpvOw/4R9QzY1cCT8oW1p1NeWzCmmMbLpBi9EMIO/Aq4FGgGNgshXpNSjuysJ3+94x6/89/rhrWxDzDV7eLuI1aAKsnL5JL5ZWMTsOYW8LXAnW8Ppj1yJsy7Gi7/ITy2Ejo+gRueBl+rdnzbc4NG722CR5fCOV+Gjb+E65+Al78C1/0aXr0L/tNT8Kc74Au/hTP+i3ZO+054/Dz46kYoW8jrv34WgCk1N+PvdSFsrqNMfiIYuNETiST2Y9SOdr3fAkDQGxnR6PsDMQB2vNukjF5xFFJKnrjn7+S6M+nzhAE4+HEntYtKCPZF+N23N3D13Wcy/bTBuZte+8U2XLlOLr19YSpt+ztaRSYciLHrPe2eDHj7eeXnWymfWcCFX5rLxpcOsPXNxtQ5v7tvQ2r710NMfrS07Bv9bJXxaGLcTytjRUzGS0dCiHOBB6SUl+v79wNIKf/PSPmXLl0q6+rqxnydT95/BfuX78f7g7uwZV1Gw7at9DRvx5HYT5+004I2//rM5GFsGVlE8mtS57qiPRRFW3FUnzP4RJCIQXMdlC2EjGxtu2QuREMQ7ITKxdp24weQTIDNDrml4NP/y2dkQyIOiTGMVimZC559o8ubkQ0152nbnn3QexiKaojGoKXdh7BXUFJ7G76u/tFf/wSs+MIsNr504Kj0C2+ay75N7cxcXMruDa1c8MW5vPfCPhacV8X7L+5P5Vt2XS09LQHsThvu8hx8XWH6gzEOfuwBYNHnpiGEwNPkZ/ppxez9oI1VN87hvRfqWXh+JXs2tjHzrBK6mgM4M+zkFmXRuKubOeeUs3t9C/POraBhZxdnXDSNbW83UjXHTX8wRtAboby2gPq6DhasrGT3hlZmLy2jo8GHK9dJLJKguyWgn99N6fQ8kklJT2uQafOL2PdhOwvPr2TvB23MOLOEjgYfQW+E+Ssq2PV+K3OXl9O8t5fCMhdhf4zulgALVlZycGvnMA0ZLgc9rUFqF5fQcchHqC/CvHMHP6NxVzeJeDL1PRZX5eLKddLnCZOV66S3PcSMM4rpPOwn0Ktff30rBSUuHE7bkBi6kEnIK8rC1x2mao6b3vYg3o4wxVNzsAlBIiGx2QTdrQHyp7hwZNjobh78DmRSklecha8rjN1hI6cwE29HiMxsJ1VzCqmv66C4MpdIKEbIF8VdkUPYFyXan6Cg1EWgt1/TMMJnZOU4ycp10tMaJNedSUaWg66WAIUlLuxOG55GP4l4kopZhXQ1+eluCR51z81dVs6+j9pTS0WXTs/DXZFDyBelaXcPAJWzC1Nt4631Wlt6rjuTQG/kqM8rnZ53zNr3qcJdnk35zAIuvnl8LzYKIbZIKZeeMN8kGf0NwBVSyjv1/ZuBZVLKu0fKP16jf+XnD9KwaQdSQDI52MYsbMU4bEkGxuSI1CLiQ2v+I6UdmX7kdzNS2qlmQO+gDokggQtn9hU47LkkUZ2mCoVVWDLnEMu/cce4zh2t0U9WG/1IbSnDHFIIsRpYDVBdXT2ui+QUTcEmMsFpw4Ebm3MaSdtcpuccwJGZhS0jR8sY7tU6PzNyB09OxiDYBfpCvil8rZBfoYXga4W8cq2GHvFBTqkWhm/Ia8yZuRAJaNt2B9gcEOuHjByIBgfzFM+CoAf6WsBVoGnpa4H8Suj3atcbyJ9foV0jvwL6+7SnCNC0DB1hJJPafsRHS7AWYbdTWaA9rjb2VDHV3UpLbwV2e4LltVto9ZZzoLOGxdW7qO+YQV84H4CZJQ0kpeBQ13QtDFscKW0kpY3SfA9OWxx/JAdfOJ/czACBSC7Ti5to6qmiyt1GU08VM0saOOipYXpxE4e7p6UkluZ58PfnIoQkOyNMfyyTTEeU7mARNpEgL0v77oKRHMryPbR4K1KfVVPcREP3NErzPfjCedhFAldGP12B4tR1prpbafWWMdXdTlNvBe7sPqJxJ7GEE3eOl/a+stTnlOd30hsqwGmPkeGI0RN0U+Vupb2vjLysAEkpCEVdlOZ10+otT12jNK8Lf38O4Zgr9VmVhe14/MW4nGHstiS9oUKqi1qO0lDg8tPpn5L6HsIxV+pzKwvb6fQXk0jamap/j+5sL4mknXAsE3d2H53+EkryughGsglFs1PXz80MkumI0B0s0mMoRUpBSV4Pnf7ilIZAJJfi3B7C0SykFORkhugKFJOTGSTLGaE7UERVYRsdvhKS+vkefzE2W4KiHC+dvhIyHRHc2X20+0opyevC359LfyyL0jwP3lAB0YSTsvwuugLuYRrsR3xGvsuPxz8FV0aYnAxNR25mIFUWQiQpzevG4y/CbktSnNNLh38KUmr3/JTcbkJRF7GEA6c9TiiandJQ6W7HJpI0dFVTkteNx18MSJLSjtMepSinjw5fCVnOfvpjWVQVttHiraCysJ1WbzkZ9igIyLDHCERysNsSJJI2QJCdEcJhj+ML52O3xVkyfQfxpINwNIt8l5++cD790Sy9XAJkZ/STnRmiJ+Bmac12Pu2qpidQiK8/H4c9RqY9it2ewBfOZ3bZp8xbOPkvflq66UahUCg+y4y2Rj9ZvQCbgdlCiBlCiAzgRuC1SbqWQqFQKI7DpDTdSCnjQoi7gTfQhlc+LaXcNRnXUigUCsXxmbRx9FLK14HXJ+vzFQqFQjE6zPkev0KhUCgmDGX0CoVCkeYoo1coFIo0Rxm9QqFQpDnK6BUKhSLNmZQXpsYsQggPcPgkPmIKkA7r4aVLHKBiMSvpEku6xAEnF8t0KeUJp/U0hdGfLEKIutG8HWZ20iUOULGYlXSJJV3igFMTi2q6USgUijRHGb1CoVCkOeli9L8xWsAEkS5xgIrFrKRLLOkSB5yCWNKijV6hUCgUxyZdavQKhUKhOAaWNnohxBVCiH1CiANCiPuM1nMihBDThBDrhBB7hBC7hBD/pKcXCSHeEkLU67/deroQQvxCj2+HEOIsYyMYjhDCLoTYKoRYq+/PEEJs0uN4UZ+iGiFEpr5/QD9eY6TuIxFCFAoh/iiE2KuXzbkWLpN79XvrEyHE80KILKuUixDiaSFEpxDikyFpYy4HIcQtev56IcQtJorlJ/o9tkMI8bIQonDIsfv1WPYJIS4fkj4xHieltOQP2vTHB4FaIAPYDiwwWtcJNFcAZ+nbecB+YAHwY+A+Pf0+4Ef69lXAX9FW7FoObDI6hiPi+QbwH8BafX8NcKO+/TjwVX37LuBxfftG4EWjtR8Rx78Dd+rbGUChFcsEqAIOAa4h5XGrVcoFWAWcBXwyJG1M5QAUAZ/qv936ttsksVwGOPTtHw2JZYHuX5nADN3X7BPpcYbfnCfxRZ4LvDFk/37gfqN1jTGGV4FLgX1AhZ5WAezTt58Avjgkfyqf0T/AVOAd4GJgrf4H1zXkRk6VD9q6BOfq2w49nzA6Bl1Pvm6O4oh0K5ZJFdCkm5xDL5fLrVQuQM0R5jimcgC+CDwxJH1YPiNjOeLY9cBz+vYw7xool4n0OCs33Qzc1AM062mWQH9MXgxsAsqklG0A+u9SPZuZY3wY+GdgYMHLYsArpYzr+0O1puLQj/fp+c1ALeABntGboZ4UQuRgwTKRUrYAPwUagTa073kL1iyXAcZaDqYtnyO4He2JBE5BLFY2+hMuQG5WhBC5wJ+Ar0spfcfLOkKa4TEKIa4GOqWUW4Ymj5BVjuKY0TjQHrEfk1IuBoJoTQTHwrSx6O3X16E9/lcCOcCVI2S1QrmciGNpN31MQojvAnHguYGkEbJNaCxWNvpmYNqQ/alAq0FaRo0Qwolm8s9JKV/SkzuEEBX68QqgU083a4wrgWuFEA3AC2jNNw8DhUKIgVXLhmpNxaEfLwB6TqXg49AMNEspN+n7f0QzfquVCcDngENSSo+UMga8BKzAmuUywFjLwczlg945fDVwk9TbYzgFsVjZ6C23ALkQQgBPAXuklA8NOfQaMDA64Ba0tvuB9H/URxgsB/oGHmONREp5v5RyqpSyBu17f1dKeROwDrhBz3ZkHAPx3aDnN0UtS0rZDjQJIebqSZcAu7FYmeg0AsuFENn6vTYQi+XKZQhjLYc3gMuEEG79CecyPc1whBBXAN8GrpVShoYceg24UR8FNQOYDXzERHqckR0vE9DZcRXayJWDwHeN1jMKveehPXrtALbpP1ehtYu+A9Trv4v0/AL4lR7fTmCp0TGMENOFDI66qdVv0APAH4BMPT1L3z+gH681WvcRMSwC6vRyeQVttIYlywT4HrAX+AR4Fm0khyXKBXgerW8hhlabvWM85YDW/n1A/7nNRLEcQGtzH/jbf3xI/u/qsewDrhySPiEep96MVSgUijTHyk03CoVCoRgFyugVCoUizVFGr1AoFGmOMnqFQqFIc5TRKxQKRZqjjF6hUCjSHGX0CoVCkeYoo1coFIo05/8DdMEmBdU2xC4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121901128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110460278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1217ad780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BwcHExMQghOBkYRkWq4PBPYI9HZripaSUFBYWkpubS79+2rzpP6X/MN4P/B83fXYneY5sT4fTKi3OkFLKU8BSIBtngi8G0oEiKaWtqlku0Kuu/YUQdwkhdgshdufn57c0DKUJLBYLERERrhnEDgmq10ZpS0IIIiIiXJ8itWpo92i6GKIpc5zzdCit0uI/dyFEOHAt0A/oCQQCV9XRtM7+FSnl61LKkVLKkZGRkS0LQnXdNFl1kq+0OSi1WNGpshFKG/OW0iRRAb2x689TVlHh6VBarDXXdVcCJ6SU+VJKK/ARMB4IE0JUdwn1Bk63MsbGecfvU7swVTg/bAV5sAb9k08+ydKlSz12fkVpjn6hfRFCkn76mKdDabHWJPpsYKwQIkA437qnAgeBTcDPqtrcAnzSuhAVd6q0ORBAV1WxssVsNlvjjRSvEddtAAD3bv25ZodatqaPfiewBvgO+KHqWK8DS4CHhBDHgAjgLTfEWV8MbXVor2VzONDrdO3edfPss88yePBgrrzySo4cOeLanpmZycyZM0lKSiI5OZnDhw8DsGjRIhYvXkxycjKDBg3is88+q3XMzZs3k5KSwuzZs4mNjWXx4sU4HM5CdytXrmTYsGHExcWxZMkSAFavXs1DDz0EwCuvvEL//v1dMUycOBGA9PR0Jk2aRFJSEjNmzODMmTMATJ48mUceeYRJkybxyiuvtNGrpHREs2PH4WePAeBwfm7DjTuoVn1+l1I+ATxxyebjwOjWHLe5hE7dWWyql786ytGzJvzduKpUbM8Qnpg1tN7n09PTef/99/n++++x2WwkJiaSlJQEwF133cWyZcsYOHAgO3fu5J577mHjxo0AZGVlsWXLFjIzM5kyZQrHjh3DaKy5vu2uXbs4ePAgffv2ZebMmXz00UeMHz+eJUuWkJ6eTnh4ONOnT+fjjz8mJSWF5593DpHbtm0bERERnDp1iu3bt5OcnIzVauWXv/wln3zyCZGRkaxatYpHH32U5cuXA1BUVMSWLVvc9rop2mDw8eHmIXfwRsb/cbpUmzNl1cIjnYxDQnvfI9u2bRuzZ88mICAAgGuuca7oYzKZ2LFjB/PmzXO1rbjohtf111+PTqdj4MCB9O/fn8OHD5OQULPa5ujRo11X5jfeeCPbt2/H19eXyZMnU32Tf8GCBWzdupXrrrsOk8lEaWkpOTk5zJ8/n61bt7Jt2zbmzJnDkSNH2L9/P9OmTQPAbrcTFRXlOtcNN9zQBq+OogXdgpxVLd/Y82/mDB3n4WiaT9OJXqp69M22eNIADHodMV0D2/W8dY3AcDgchIWFsWfPnibtU9cx6mrTUJfeuHHjSE1NZfDgwSQnJ7N8+XLS0tJ44YUXyM7OZujQoaSlpdW5b2Bg+75mSseRENUfvodc22bST2WS1GuAp0NqFq/o8xBq2E2T2R0SfTvXBkpJSWHt2rWUl5dTWlrKf/7zHwBCQkLo168fH3zwAeC857J3717Xfh988AEOh4PMzEyOHz/O4MGDax17165dnDhxAofDwapVq5g4cSJjxoxhy5YtFBQUYLfbWblyJZMmTXLFsnTpUlJSUhgxYgSbNm3Cz8+P0NBQBg8eTH5+vivRW61WDhzwvvVDleYbEtmbxYP/CMA/0z/ycDTN5xWJXmk6TyT6xMREbrjhBhISEpg7dy7Jycmu51asWMFbb73F8OHDGTp0KJ988uMgrcGDBzNp0iSuuuoqli1bVqt/HpxX6A8//DBxcXH069eP2bNnExUVxZ/+9CemTJnC8OHDSUxM5NprrwUgOTmZnJwcUlJS0Ov1REdHu27EGgwG1qxZw5IlSxg+fDgJCQns2LGjjV8dRSvmDnX+nmQUHfRwJM2n6a4bNWGqeYrMlTikxMcD1T4fffRRHn300Vrb+/XrxxdffFHnPhMmTOCll15q8LgBAQGsWrWq1vb58+czf/78WtsHDBhQo2tn3bp1NZ5PSEhg69attfbbvHlzg3Eo3q9HcDihchjFtrOeDqXZvOOKXq0w1SgpJcXlVgBC/bVXZEpROoJIY28qdXmuYbxaoe0reqXJsgrNlFqsBBt98fN139DKtvL222832mby5MlMnjy5zWNRlGp9QvpwrKKSo4WnGRLZu0XHOPbtN+xd/z/X4xEzZ9E/cZS7QqyTti+FVc9Nk1VY7QBEhdbu51YUpWkGR8QAsPtURov2v3DmFDvXruL00cNUlJVRUVaG3Wp1Y4R184oreqFWmGqUQ0JEoAGjBq7mFaWjSowaCIfhYP5xYEqz91/9h0cxFRYwfNrVXHnHPe4PsB5ekeiVxjmkRKfeEBWlVRKi+iGljhNFJ5u9b4XZjKmwgIQZPyHlptvaILr6abrrRk2YahopqxK9l5SNVRRPMfoa0NvDKbA0vz79V2/+HYBeg2PxNbRvUUFNJ/pq3lL3uq3YqkYIdJREr8oUK1rmI4Iw20ubvZ+5pBiAgWPGuzukRnlFolcaVmFzJnp/38733y2lrDUUzm63eygaxRv4iSAqHM1P9BZTKf0TR6H3af/hzZr+y1dlipvG7nB22wR6cLGRtihTbDKZmDp1KomJiQwbNsw1qzYrK4vLL7+ce+65h8TERHJycggKCuLxxx9nzJgxpKWl8fTTTzNq1Cji4uK46667kFKSmZlJYmKi6/gZGRmuKpuKUs3fJ5gKfRanS5pXydJiMmEMDGqjqBrmHTdjO0iXREdkqrBhtTkw+OicXVyfPwxnf3DvSXoMg6ueq/fptipTbDQaWbt2LSEhIRQUFDB27FhXZcwjR46QmprKP/7xDwDKysqIi4vj6aefBiA2NpbHH38cgIULF/LZZ58xa9YsQkND2bNnDwkJCaSmprJo0SL3vlaK5g0MG0Le+W/YdGIfC4ZPbrS93Wal6OxZyi4UEhjepe0DrIOmr+iVxv389TQsNgd+Pp77r764THFISEidZYoTEhL4xS9+4VroA+ouU3wxKSWPPPII8fHxXHnllZw6dYpz55w3yfr27cvYsWNdbfV6PXPnznU93rRpE2PGjGHYsGFs3LjRVbzsjjvuIDU1FbvdzqpVq+oso6B0bjfH/wSAg3nHm9R+3T//ytu/vhu7zUa3mP5tGVq9tH1Fr7puGnSqqJz9p0rwHxtCz1B/58YGrrzbUluUKV6xYgX5+fmkp6fj6+tLTEwMFosFqF1S2Gg0otc75xBYLBbuuecedu/eTXR0NE8++aRrv7lz5/LUU09xxRVXkJSURERERMt+YMVrJfSsGmJZnN2k9sV5Z+ka3Zfx1y9o8xmw9fGKK3qhat3U6c+fO6+AAwx6fD14Rd9WZYqLi4vp1q0bvr6+bNq0iZMnmza2uTqpd+3aFZPJxJo1a1zPGY1GZsyYwd13382tt97aqp9b8U4Bvn7o7RGcNec0qb3FZCK8Zy8Gjh7vkRux4CWJXqnbycIyYqNC3LpsYEu0VZniBQsWsHv3bkaOHMmKFSsYMmRIk+IJCwvjzjvvZNiwYVx33XWMGlXzKmvBggUIIZg+fXorfmrFmwXpu5Mvd1HZwELxRWfPsGH5Mkry8zAGBbdjdLVpuutGTZhqWFahmVnDoxpv2A7aokxx165d610Nav/+/TUem0ymGo+feeYZnnnmmTr33b59O7fddpurq0dRLtXVrycllfv5/Gg618aOqbPNga0b2fPlZwSEhhF9eVw7R1iTphN9NTVhqqYDp4t5dO1+isutxEQEAhWN7qM4zZ49m8zMTNfIH0Wpyz0jb+I3O9axPy+z3kRvLrqAf0god7/+XjtHV5tXJHqlpg2H8tiTU8TMoT2YFtsdc17Tbhp1JE0pU9wW1q5d65HzKtoyutdAAI6cr3/kTVlxEYGhYe0VUoO03UevRt3U6bXNmXQJNLBsYRJ9I9SC1oribuEBQWAP5vvSVfz9m//U2cZcUkSASvRupEbd1CAE9OuqEryitKWbBz4IwP78umvTm4tVolfaiJQSi9XOuP5q/LeitKVfjZsNQJnVXOu50vMFFJ87qxK9W6iem1oqbA4cEo/WtVGUzsDg44N0+FJuq53o9677HIDeQ4a2d1h1alWiF0KECSHWCCEOCyEOCSHGCSG6CCHWCyEyqr6GuyvYBuJo61NoRlmFc1xvoJ+2hwYuWrSoxkSmlpo8eTK7d++utf3tt9/mvvvua/Xxlc5NSL86E332fuds7/5Jo9s7pDq19or+FeALKeUQYDhwCHgY2CClHAhsqHqstBNzpbMEb4BBXdErSlvTST8q7OU1tlVayjmTcYTLk6eg9+kYf4ctTvRCiBAgBXgLQEpZKaUsAq4F3qlq9g5wXWuDrI+aMFVbXqlzen+XQM9Mta7Piy++SFxcHHFxcbz88svAj+WE77zzToYOHcr06dMpL6/5R7NhwwZmz57terx+/XrmzJlT6/gbNmxgxIgRDBs2jNtuu42KitpzB1JTUxk0aBCTJk3i66+/dvNPqHRGOoxUOGr+zn73v08B6Brd1xMh1ak1bzf9gXwgVQgxHEgHfgV0l1KeAZBSnhFCdGt9mI1QXTcuWQXOj5Ex9Qyr/POuP3P4/OE6n2upIV2GsGT0knqfT09PJzU1lZ07dyKlZMyYMUyaNInw8HAyMjJYuXIlb7zxBtdffz0ffvghN910k2vfK664gnvvvZf8/HwiIyNJTU2tVYPGYrGwaNEiNmzYwKBBg7j55pt57bXXeOCBB1xtzpw5wxNPPEF6ejqhoaFMmTKFESNGuPV1UDofH2HE6rDU2Ga64KxTP/Kns+vaxSNa03XjAyQCr0kpRwBlNKObRghxlxBitxBid35+fivCUKoVmio4dKYEnYDe4QGeDsdl+/btzJ49m8DAQIKCgpgzZw7btm0DnCUQEhISAEhKSiIrK6vGvkIIFi5cyHvvvUdRURFpaWlcddVVNdocOXKEfv36MWjQIABuueUWtm7dWqPNzp07mTx5MpGRkRgMBm644YY2+mmVzsRXGLHKmoneYiolPKonug5UQqM1V/S5QK6UcmfV4zU4E/05IURU1dV8FJBX185SyteB1wFGjhzZsvEzasKUy0ff5fLQamflx5iIAAz1VKts6Mq7rTS0Epif34+LJOv1+lpdNwC33nors2bNwmg0Mm/ePHwu6fds6kpj6qa94m4GnZFyW82VpiymUoyBni1idqkWX9FLKc8COUKI6tqxU4GDwKfALVXbbgE+qWN3t+rsf8BSSt5JO0mAQc+zs+N49caO1SWRkpLCxx9/jNlspqysjLVr19aoYNmYnj170rNnT5555pk6V3waMmQIWVlZHDt2DIB3332XSZMm1WgzZswYNm/eTGFhIVar1VUaWVFaw08fgP2iWlIl+Xmc3Pc9QR1sHYPW3hL+JbBCCGEAjgO34nzzWC2EuB3IBua18hxKI/afKmFvThH9IwNZMKbj3ACqlpiYyKJFixg92jnU7I477mDEiBG1umkasmDBAvLz84mNja31nNFoJDU1lXnz5mGz2Rg1ahSLFy+u0SYqKoonn3yScePGERUVRWJiolokXGk1f59AHNYyHA4HOp2O//1tKQBRlw1uZM/21apEL6XcA4ys46mprTluM87fHqfp8P62yTkF+8XrEzwcSf0eeughHnrooRrbYmJiapQT/s1vfuP6/tKiZtu3b+fOO++s9/hTp07l+++/r7V98+bNru9vvfVWtZiI4la9gnqRWVHB+sy9zBg4ggtnTtNzcCxJP22zwYYtou2ZsVU68wpTFTY7Xx5wrpMaGxXi4WjaRlJSEvv27asxGkdROoIpfZ2L1qw68AXWCgvm4iJiho9Ap+s4N2JBlSnWrEJTBb9bs4+icisAL90wvN4bsFqXnp7u6RAUpU4/GzaBp74NxvzdV3ywuwCAiN59PBxVbZrODJ256+ab4+fZcDgPq91ByqBIxg/o6umQFKVTMtKNIdk+FOWdpd+IkUQPjfd0SLV4xRV9Zxx1c/J8GQD/vnMsQaqAmaJ4zE+7X01Q+aecH9Sdex5+0tPh1EnTV/SdWUFpJUF+PirJK4qHJVuCADjqU8ju3GMejqZuGk/0nbfWTVF5JaH+HauejaJ0Rsc3bwDg+27fceuG2Xxx9DsPR1SbxhO9k9B5xY9RrwqbnbXf5/L5u+BdAAAgAElEQVT+rmzXv8NnSgkL0EaiLyoq4h//+Eej7bKysvj3v//dpHZxcXHuCE1RWs1cUkLYoEFM6+6cu/FN7v5G9mh/3p0hvcSXB87x4Kq9PPzRD65/B8+UaGa5QHcn+o7GZrN5OgTFQ3IO7MNqKScuaRxPXXEbUgoyL2R5OqxatJ3oO0HPzffZF3hr+wmEgG2/m0La769w/Xv5ho47QepiDz/8MJmZmSQkJPDb3/4WKSW//e1viYuLY9iwYaxatcrVbtu2bSQkJPDSSy+RlZVFcnIyiYmJJCYmsmPHjgbPs3nzZlJSUpg9ezaxsbEsXrwYh8P5S7Jy5UqGDRtGXFwcS5Y46/2sXr3aNYnrlVdeoX///gBkZmYyceJEwDm0c9KkSSQlJTFjxgzOnDkDOBc0eeSRR5g0aRKvvPKK+180RRNOHTkEwJDxyQT7+aOzh7PH9AGbjv/g4chq8pI7ed476uat7Sc4cKqYmUN7EN2l9RUpz/7xj1Qccm+ZYr/Lh9DjkUfqff65555j//797NnjXHXnww8/ZM+ePezdu5eCggJGjRpFSkoKzz33HEuXLuWzzz4DwGw2s379eoxGIxkZGdx44411rhZ1sV27dnHw4EH69u3LzJkz+eijjxg/fjxLliwhPT2d8PBwpk+fzscff0xKSgrPP/88ANu2bSMiIoJTp06xfft2kpOTsVqt/PKXv+STTz4hMjKSVatW8eijj7J8+XLA+Ully5Yt7ngJFY0qLcgnIDSM0G49ABgSMpFD5k/527crmNL/OQ9H9yMvSfTe6ei5Uj7bd4bkgV157aYkT4fjNtu3b+fGG29Er9fTvXt3Jk2axLfffktISM2ZvVarlfvuu489e/ag1+s5evRoo8cePXq068r8xhtvZPv27fj6+rpKFIOzbs7WrVu57rrrMJlMlJaWkpOTw/z589m6dSvbtm1jzpw5HDlyhP379zNt2jQA7HY7UVFRrnOpUsdKpaUcg7+/6/Hqec8y4q3d5FlOeTCq2rSd6L18wtS3Wc7ypz+Nj2qkZdM1dOXdXpo60e2ll16ie/fu7N27F4fDgdFobHSfS+dUCCEaPN+4ceNITU1l8ODBJCcns3z5ctLS0njhhRfIzs5m6NChpKWl1blvYKA27pEobafSUo6v0b/GNn99F8rtRR6KqG7a7qP3ckVmZ3mDaxN6eTiS1gkODqa0tNT1OCUlhVWrVmG328nPz2fr1q2MHj26Vrvi4mKioqLQ6XS8++67Tao2uWvXLk6cOIHD4WDVqlVMnDiRMWPGsGXLFgoKCrDb7axcudJVxjglJYWlS5eSkpLCiBEj2LRpE35+foSGhjJ48GDy8/Ndid5qtXLgwAE3vzqKllktFgyXXID464OwyjIPRVQ3bV/RV9N5Zx99cbkVo68Oo2/HKpDUXBEREUyYMIG4uDiuuuoq/vKXv5CWlsbw4cMRQvCXv/yFHj16EBERgY+PD8OHD2fRokXcc889zJ07lw8++IApU6Y06Qp63LhxPPzww/zwww+uG7M6nY4//elPTJkyBSklV199Nddeey0AycnJ5OTkkJKSgl6vJzo6miFDhgBgMBhYs2YN999/P8XFxdhsNh544AGGDh3apq+Xoh1WSznG4JpdjkE+IeQ58im2mAk1doyV3jSd6L19cfBCUyXhAQZPh+EWlw6bfP755103Qqv5+vqyYcOGGtv27dvn+v5Pf/oTULu88cUCAgJco3guNn/+fObPn19r+4ABA2p07axbt67G8wkJCbWWJYSa5Y+VzqvSYiGka81lsbsF9OB4JTzw+cukzvZ8VymorpsOqdhs5Y2tx9l98rxbRtooitI2rBZLrT76F2feB8B359d7IqQ6eUWi97Z69P/94QzP/u8QJwvNjOnXxdPhaMbkyZNdQzMVpT1YLeX4XtJHH+znj5+9Hw6fAk6XnK9nz/al6QzprWWKz5c516D84cnp/Hp6x1qSTFGUH1VaLDWGV1a7/jLnIjmv7fq0vUOqk6YTvbcqMlvx99UTbNRGLRtF6YxsVisOuw2DsXainx2bDMDBQvdOTmwpTd+MreYt9eiLzJXMW5ZGzgUzXbzkJqyieKvP/+pcCPzSrhuAgV2jELZwTNaS9g6rThpP9N7VdbP/VAkZeSamx3bn6mHumySlKIr7Ze1zLkZ/2ahxdT7vQyBme2mdz7U31XXTgVSvGvXkNUO5boS2J0l5g82bNzdaSK25Jk+e3Gi9nvo8+eSTLF261K3xtNbbb7/N6dOnPR1Gu6swm6ksN5M8fxEhXSPrbOOnC6bcXtzOkdXNKxK9t4y6OW+qBKBrkJ+HI9Eud5YMbkmi72wliztroj912DlDOrxHz3rbhBm6U8E5/rRlFTlFhe0VWp00nSGlw7u6borKrQQa9Bh8NP3fUktWVhaXX345d955J0OHDmX69OmUl5cDzpLAM2fOJCkpieTkZA4fPozdbqd///5IKSkqKkKn07kmLSUnJ3PsWM3l2t5++23mzZvHrFmzmD59OuCckDVq1Cji4+N54oknXG2vu+46kpKSGDp0KK+//rpr+xdffEFiYiLDhw9n6tSpZGVlsWzZMl566SUSEhLYtm0bJ0+eZOrUqcTHxzN16lSys7MBWLRoEQ899BBTpkxxlUCuVl5ezs9//nPi4+O54YYbXD83OCdnjRs3jsTERObNm4fJZAKcE8KWLFnC6NGjGT16dK2fF2DPnj2MHTuW+Ph4Zs+ezYULF8jMzCQxMdHVJiMjg6SkJNcxH3nkEcaNG8fIkSP57rvvmDFjBgMGDGDZsmWufep63er7/1uzZg27d+9mwYIFJCQk1PjZvN2h7ZsB6D7gsnrbDO4yBPRm/p31DL9e/2I7RVY3jffRe5f1B8+1+fKA21YfpSDH5NZjdo0OIvn6QQ22ycjIYOXKlbzxxhtcf/31fPjhh9x0003cddddLFu2jIEDB7Jz507uueceNm7cyKBBgzh48CAnTpwgKSmJbdu2MWbMGHJzc7nsstp/XGlpaezbt48uXbqwbt06MjIy2LVrF1JKrrnmGrZu3UpKSgrLly+nS5culJeXM2rUKObOnYvD4eDOO+9k69at9OvXj/Pnz9OlSxcWL15MUFAQv/nNbwCYNWsWN998M7fccgvLly/n/vvv5+OPPwbg6NGjfPXVV+j1NctVvPbaawQEBLBv3z727dvnSsQFBQU888wzfPXVVwQGBvLnP/+ZF198kccffxyAkJAQdu3axb/+9S8eeOCBWvMDbr75Zv76178yadIkHn/8cZ566ilefvllQkND2bNnDwkJCaSmprJo0SLXPtHR0aSlpfHggw+yaNEivv76aywWC0OHDmXx4sX1vm59+vSp9//vb3/7G0uXLmXkyJHN+6XRuNLCfCL7xNSaFXuxpdMXsyN7Mvdt+gVFFZ4dT+8Vid4bRt04HJKcC2ZiIryzImK/fv1ISHAulJKUlERWVhYmk4kdO3Ywb948V7uKCuccguTkZLZu3cqJEyf4/e9/zxtvvMGkSZMYNWpUncefNm0aXbo4J5etW7eOdevWMWLECABMJhMZGRmkpKTw6quvsnbtWgBycnLIyMggPz+flJQU+vXrB+A6zqXS0tL46KOPAFi4cCG/+93vXM/NmzevVpIH2Lp1K/fffz8A8fHxxMfHA/DNN99w8OBBJkyYAEBlZSXjxv14U+/GG290fX3wwQdrHLO4uJiioiJXYbZbbrnF9RrecccdpKam8uKLL7Jq1Sp27drl2u+aa64BYNiwYZhMJoKDgwkODsZoNFJUVFTv69anT586//86s+JzZ+k7PLHBNj56PSn9huK7KYRym2dvymo60QsvGnWzNSMfKeGulP5tep7Grrzbip/fj/cd9Ho95eXlOBwOwsLCXAuSXCw5OZlly5Zx+vRpnn76aZ5//nnXClJ1ubjgmZSS3//+9/ziF7+o0Wbz5s189dVXpKWlERAQwOTJk7FYLEgpW3SxcPE+DRVcq+vYUkqmTZvGypUrG92nObHNnTuXp556iiuuuIKkpCQiIiJcz1X/H+h0uhr/HzqdDpvNVu/rlpWVVef/X2dlrazAdOE8YVWLjTTGIIIokkcYl3o9L099ljF9BrZxhLW1ujNYCKEXQnwvhPis6nE/IcROIUSGEGKVEEINCG+C3VkXAJh4WVcPR9J+QkJC6NevHx988AHgTH579+4FYMyYMezYsQOdTofRaCQhIYF//vOfJCcnN3rcGTNmsHz5clef96lTp8jLy6O4uJjw8HACAgI4fPgw33zzDeCseLllyxZOnDgBwPnzzo/Zl5ZNHj9+PO+//z4AK1ascC032JCUlBRWrFgBwP79+11F2saOHcvXX3/t6n83m801FlapLsy2atWqGlf6AKGhoYSHh7Nt2zYA3n33XdfVvdFoZMaMGdx9993ceuutjcZ3sfpet4Zc+hp1BiV55wAI7d60RH9VzDUE0AeT7hCrDnzVlqHVyx13/X4FHLro8Z+Bl6SUA4ELwO1uOEeDtD7qJq/Ewt82HaN3uH+nK2K2YsUK3nrrLYYPH87QoUP55JNPAOfVZ3R0NGPHjgWcV/ilpaUMGzas0WNOnz6d+fPnM27cOIYNG8bPfvYzSktLmTlzJjabjfj4eB577DHXsSMjI3n99deZM2cOw4cPd60cNWvWLNauXeu6Gfvqq6+SmppKfHw87777bpPWir377rsxmUzEx8fzl7/8hdGjR7vO+fbbb3PjjTcSHx/P2LFjOXz4x1mUFRUVjBkzhldeeYWXXnqp1nHfeecdfvvb3xIfH8+ePXtcffvgXEFLCOG6Md1U9b1uDVm0aBGLFy/uVDdjM3Y6R2KFNvGK/vEpN/H1zauRDl9Olpxsy9DqJ6Vs8T+gN7ABuAL4DOfirQWAT9Xz44AvGztOUlKSbImN/3xcHhw8RGYd+KZF+3cUO44VyL5LPpN//vxQmxz/4MGDbXJcpW307dtX5ufnt3j/559/Xv7f//2fGyNqOW/83fvPS8/Jpdf/RFaUm5u137A3U+SV//qFW2MBdssm5OrW9tG/DPwOCK56HAEUSSmrBxPnAmrmTyOKy53j538aX/+YXEVpitmzZ5OZmcnGjRs9HYrXspSZiBo4uM4aNw3xFUGU2z1TEqHFiV4I8VMgT0qZLoSYXL25jqZ13jEVQtwF3AXQp0+floZRfbDW7e9hF6qWDAwPVEXMFFo1oqV6RJHSdiymUgJCw5q9n58Iolj8wAWzifCAoDaIrH6t6dyeAFwjhMgC3sfZffMyECaEqH4D6Q3UOW1OSvm6lHKklHJkZGTdU4gb5SVligtKnUMKvWU1KUXxZuaSYoxBwY03vESIr3OgxYs71rg7pEa1ONFLKX8vpewtpYwBfg5slFIuADYBP6tqdgvwSauj9GLH8ky8sP4o3UP8NL82rKJ4O6vFQmlhAeFRze9m/XCec3bsf7NX896eTe4OrUFtMVxlCfCQEOIYzj77t9rgHDVoecLUh9/lAnDNcNU/rygd3fkzp0BKIno3v7s50M+PHroJVIrTvLn3nTaIrn5uSfRSys1Syp9WfX9cSjlaSnmZlHKelLLCHeeo57xtdeh2c7KwjP6RgTz6k1hPh6IoSiOK884CENa9ZWXE1y9cRriIo8Cxl4S3ppLw1lSe3vSeO0Osk7YHoGtcqcXK8fwyry170BqLFi1izZr278tUlIYUn3Mm+tBu3Vt8jEVxC+nuM5IIQ38iDP2JCmrhPcpm0HQJhGpCp733q3/vzOaRtT8AMKETzYZVFC0rzjuLMTgEv4CWX5zdPnIGt4+c4caoGqe9DHkxDXfd/GfvacICfHliVmyb17fpCF588UXi4uKIi4vj5ZdfBhouX1xtw4YNzJ492/V4/fr1zJkzp9bxY2JieOKJJ0hMTGTYsGGuWaZlZWXcdtttjBo1ihEjRrhm3l599dWucgQjRozg6aefBuCxxx7jzTffdP8LoHiFonNnCWvF1byneMUVvdZYrHbSjhcSGxXCrRP6teu5N739Onknj7v1mN369mfKorvqfT49PZ3U1FR27tyJlJIxY8YwadIkwsPD6y1/W+2KK67g3nvvJT8/n8jISFJTU+ut4dK1a1e+++47/vGPf7B06VLefPNNnn32Wa644gqWL19OUVERo0eP5sorryQlJYVt27YRExODj48PX3/9NQDbt2+vcX5FqWYxmTi573sGjWu83lJHo+0r+ipaq3Wz64SzaNYNo6I9HEn72L59O7NnzyYwMJCgoCDmzJnjKsjVWPlbIQQLFy7kvffeo6ioiLS0NK666qo6z1N9pX/xcdatW8dzzz1HQkKCq1pldna2qwzy9u3b+clPfoLJZMJsNpOVlcXgwYPb5oVQNEs6HHy5zFnbKDq28XpLHY22r+g12nWz6YizImDywPbvm2/oyrutNDQ6qinlb2+99VZmzZqF0Whk3rx5+PjU/WtbfSy9Xu9a0k9KyYcfflgreVdWVrJ792769+/PtGnTKCgo4I033nCtyKQoFyvIzebYt2kAXD5xsmeDaQFtXQp7ASklK3ZmM6RHMP0j23catKekpKTw8ccfYzabKSsrY+3atU0qN1ytZ8+e9OzZk2eeeabGiklNMWPGDP7617+63my+//57AAwGA9HR0axevZqxY8eSnJzM0qVLmxWX0nmUlzhr1Mx77I/4BWivwqxXJHotTZjKN1VQaXPQpxOVI05MTGTRokWMHj2aMWPGcMcdd7hWMWqqBQsWEB0dTWxs8+YbPPbYY1itVuLj44mLi+Oxxx5zPZecnEz37t0JCAggOTmZ3NxcleiVOlnKnOWa/YObX/qgIxAdYdLRyJEj5e7du5u938Z/PErUqx8R8tlKel2W0AaRud+3WeeZtyyN1FtHMWVw/etNutOhQ4e4/PLL2+VcbeW+++5jxIgR3H57my9voLiRN/zumUuKee3OBQDc9Y+3CY7oOMOhhRDpUspGF+zVdB99R3iTaq6iqkqVkUF+jbRUqiUlJREYGMgLL7zg6VCUTijvuHMVsMtGjSWoS0QjrTsmTSf6aloadWOudN4kDDCoAmZNlZ6e7ukQlE6sqGrpwCtuW6ypbuKLaSdDeomyCjsAgX5e8R6rKF6vOO8sel9fgsK6eDqUFtN2otdg101ZhWeu6LXYzaVom7f8zhWdPUNotx6aLLVSTbuRX0xDH6fKXF037XdFbzQaKSws9Jo/PKXjk1JSWFiI0Wj0dCitUpibTeGpHMK6N20h8I5K9R+0M3OlHX9fPXpd+7059e7dm9zcXPLz89vtnIpiNBrp3bu3p8Nosbys47y75H4A+ieO8nA0raPtRK/BK9SyChuBfu3bbePr60u/fu1bU0dRtCz30H7Wvf43AK6+79cMGDnGwxG1jrYTfRUt3Qk3V9rbtdtGUZTmO/btNxSfO8PwaVczZOJkTeWYuqiM087KKmxqaKWidGBH0raRsSuN0O5RXHnHPZ4Oxy00fTNWazcXK2x20k9eUEMrFaWDklLy1VuvYTGVMGS895TD0HSir6aVYU9/23iMwrJKIgINng5FUZQ6nM08iqW0hLjJ0xg/b4Gnw3EbbWRIL3HojLMw0h/naK+etaJ0BqUFzpFpQyZM8nAk7qXtRC8dno6gWU4WljEttjtdVZ0bRemQLCYTAEER2qxpUx+v6CwWdPw74vmlFWTkmZgypH0qViqK0nQHtmzgxJ50zp/OBcAYpM1yxPXxikSvBVuOOj8SDusV6uFIFEW51M61qygrKiIwLJzLRo3F1+Bdn7q1neg1NOgmq6AMvU4wY6i2p1IrirepLDdz4cxpRs6aw6SbbvN0OG1C2330VbQw6iarsIxeYf4YfDp+rIrSmRz95msAuvTSbrmGxqis006yCsvoG9F5lg9UFK3YuXY1AENTpno4krbT4kQvhIgWQmwSQhwSQhwQQvyqansXIcR6IURG1ddw94Vbk1YmTNnsDvafKqFf10BPh6IoyiUqzGVEXTYYnd57Z6y3po/eBvxaSvmdECIYSBdCrAcWARuklM8JIR4GHgaWtD7UBnTwOhSbjjhvxEaF+ns4EkXpnMwlxax45CEsptJaz1WWm0m8+loPRNV+WpzopZRngDNV35cKIQ4BvYBrgclVzd4BNtPWib6DKzBVAHBNQk8PR9IxWC0Wvvv8U6wVFZ4ORekkSgryKMk/x+UTJ+MfUnPkm06vZ+gk7+22ATeNuhFCxAAjgJ1A96o3AaSUZ4QQdQ4cF0LcBdwF0KdPn5adWCNdNxfMlQCq9EGVE3vT2f7+v5xr/XbsD2OKFwmK6MqVd96Lwdj5Plm3OtELIYKAD4EHpJQlTS3nKaV8HXgdYOTIka3K2B19cfBisxU/Hx1GX+/tA7xYSX4e2Qf21ft89g97ALjnzX9jDApqr7AUpdNqVaIXQvjiTPIrpJQfVW0+J4SIqrqajwLyWhuk1p0pthAZ7F0TMBqy+d03ydi5o8E2QV0i8AtUN6cVpT20ONEL56X7W8AhKeWLFz31KXAL8FzV109aFWGDtNF1k1VY5jUjbswlxa5p4vUpzMkmemg8Mxb/qt42/iEhml/MQVG0ojVX9BOAhcAPQog9VdsewZngVwshbgeygXmtC7FxHTlhSCk5UVDGdQm9PB2KW3z8/B84c/Rwo+1iEpII7da9HSJSFKUxrRl1s536b6V59y3sZigyWym12DQ/Wcpus2ExlXLhVC6XjRpLwvSf1t9YQNRlg9ovOEVRGqTpWjdamDB1orAMQPNdNx8++xg5B38AoOfgWPrGJ3g4IkVRmkrTib5aRx51c7Iq0feN0HaiP3cikz7DEhg8LplBYyd4OhxFUZrBKxJ9R/Zh+ikAortod+zuJ0ufobLcTL/hicRPneHpcBRFaaaOeyncFBroujlyrpRgow9+PtodQ5+9fy86vZ4hEyd7OhRFUVpA24m+SkftunnqPwfIL61g8aQBng6lxSrMZVSWlzPxxlsICu/i6XAURWmBjpkhvcR/9p4G4Ko47S42UpLvnO8W0lUtgagoWqXtPvoO3HUjpaSk3MbiSQPoH6mtaf52m42NqcsoLy3BXFwMQEjXSA9HpShKS2k70VcRuo43YepEQRmVdgdhAb6eDqXZzp/KYd9XXxAcEYnB35/esXF0je7r6bAURWkhr0j0HdFNb+4EICrU6OFImq/C7BwSOn3x/cTEj/BwNIqitJa2++g7aNeNxWrndLGFyYMj+cmwKE+H02wVZjMAfgHans2rKIqTthN9tQ5W6ybnvDNRzh7RCx+9tl5iKSUHtnwFgF+Atid5KYripK0spBEnCrQ7G/b8qRxXieHAMDWcUlG8gaYTfUetdZNVXd9Gg4m+9HwhALOXPKG6bhTFS3jFzdiONGHqvn9/x6bDeYQF+BKqwRE3+9Z/DkBYD7W+raJ4C69I9B3FhbJKPt9/ltioEBaO095wRLvNRsYuZ7dNcNeuHo5GUTo+6XD2KnTEId4X6ziXwi3RgbpuThWVM+rZr7A7JLdNjOH6kdGeDqnZDmx23oS96t6H8DV0nqUPFaWlvnxzP/+4Z5Onw2iUthN9FU933ZRX2ln9bQ42h+S3MwZzVZz2hlQClBVdAOCy0eM8HImiaEPmd/meDqFJvCLRe9qKnSd5ZUMGBh8dN43ti9FXm5UqK8xl+Pj5YTBqt6Syoii1aTvRd5Cumw925xIe4Mu2300h1F97N2AB8k6WkJ9diN7H6BxLv+0UlRabp8NSFMUNtJ3oq3hycXApJUfzSgn086F7iPbKHVRb/ex2sn/YRmW5jqM7z7J5xRG2r87wdFiKogkOR8e46KyPGnXTSuVWO1LCTWO1N8rmYtJeAIDOpycWs/NKvry00pMhKUq7qzBb2bY6g+QbBuHn/2N6tFsdbF55hDGz+lOQW8r502Ukzvjxb95hc6AzOLtsz50oIfO7PCJ6B2GrtGPw96GizEpAiB+l5y0Mn9r+AzU0nug9/y56wWwFIEyjXTbVpLQAoPdL6Agvq6J4xJ6vcjjyzVlCI/0Z9ZN+ru0n9hVweMcZrBab6wbsxYnebpeuZLrmz7sbPIdK9C0kdO3fA/Wfvaf53Zp92BwOAMICDO0egztJR5HzG2Fk+weqy0ZRmsNhc3g6hAZ5RaL3hP/uO4NeJ7hlfH/8ffUkD2ziBCO7DUpOQXgbdvVYSsDHCD4XvflUloHQg68R7FawmsEYCtZykBK7NRMAoatd9sB0wYKPQY+PQYd0gNCBTwMji2xWO9IBvn71t7GYrBiDtP0pSPEO1go7dpvD+ftdNcDD4ZBUmK34Bfhis9opKSwHwG778eNuWVGF63tzSSW+Rj2yCfnebnOg92nfi1NNJ3rpoRsgNruDLw44Z8A+fNWQ5u288Q/w9ctwdxp0j22bAJ+LhphkWPTZj9v+2BPC+8Gv9sDHd8MPH8ATRfDCYOcbg7weoQtHiJrJ11Jm5Z3fO2fLhnbzpzivnMAwPxY9N6He06965luKzpm5d9kVdT6f+X0eX/xzP3N/l0SP/qGt/3kVpRVe/9UWAPxDDMROcM6B2f3fLHb/N4vFf5/MP3+5xdU2a1+B6/u3H/7a9f37f9hFYKiBsuLG72t9+soeZv860V3hN4mXjLpp3x9j3ynn8nozq9eCPfUdlBXUbnjuABSfcl7FH3POOiVru/Nrcc6P7aSEjK+cCffENrBa4MRWsFVC5sa6gzCfh5xdtbdXDznN2lb7uQsn4OwPziQPzqt5SzEgQVYg9BE12wuBxWR1PSzOc17VXHwlU5eic+YGnz912DkxK+9kaYPtFMWdcg6dx26v/5K7vKSy1gi+4983fUJUU5I8wOmMIgpyTU0+rju0SYYUQswUQhwRQhwTQjzcFufwpP/tOwPAtNjuzg1vTIE3r6zd8LXx8FIsbHkO3pvrTPL6qu6U8gs/tjv8X1gx13kl/s5P4d/XwzuznBi3FgAAAAkuSURBVF/fnQ1n9tU+9qqF8NY0Z7K+WGVZw8GnXv3j9xfFIGUFQtQue2CtsDd8PEXRgDOZxXz6yh52fXqiwXaXVsRd9+aBNoln1TN1XKS1Ibd33Qgh9MDfgWlALvCtEOJTKeVBd5+rLYeHlJsq+eKfn3K6SzlZQfGu7b7FZjjwHRt9nkf/35mcTLoNysKg7AK89zAYAiEwEk5/79wO8PkbQBj8fUHVUcLgvSWweyv0TITNfwJH2I8n37/X2eaH751f1/wRwmNg2DzQVfV7Z5wAaxh8sx7O7oPMTdArCaKG/3jej/7i/ITQa+SP28qqzg/wzHgKK3qzJa8/SBPUmejVpClF+zLT8wA4f6bmhdCxqu3eri366EcDx6SUxwGEEO8D1wJtkOiruX/C1Ob3vufkvoMM8NvGPsN41/ZBhUfBUcpa4iA7F9Y/DQxzPpm9/5KjDGv4JNnZQDZweSPtTMB++M/Fx+/v/PK316se+8P3B3G+zNXxbK167ttGYpHo/Yaj9xte65lKS8uv6D1x00lR6rJ3o7Or9NK5lV++cenfbPtpz78P4e7FO4QQPwNmSinvqHq8EBgjpbyvvn1Gjhwpd+9ueOxpXZbdfieWsrI26YCy20uqvtPX6LeTUqDz6UVoYH902EHnA452uurV6Z0jZwDsVf2BQg/y4mSsA5o+1KvcEYpNRKDTh9f5fJBvESZrWK3t4X7n6j3mhQpnl1aYXx6ijk9dxZUROKQP/j6lGPUN9+crijtU/05Czd/di7cD+OnNVNjbZ8Gd6r+PUZMDGTj7mhYdQwiRLqUc2Vi7triir+vyutZfuxDiLuAugD59+rToRIaQACrNNnS+ereXQfAlEBsDGdjNiM4vyLXdYjNwrjiSnr7fOjeERIAp78dkr/cFvyDQ+4GlyHljNWKA8w2h/AI4rM5++tJzzsuLoG7Ofna71fnV1wj+4VByBkKiwJTvbGe3Qsgli4FYip1DJC8+f0hPQDqP7x/mvGnrHwrlxWDwB7sdArqA6VzVjds8MisuIzK4gPzSmkNE+0dmIQBfs6TSZiDAUE6F1YCvj40w/0vuDVzEx1JAeaWRiJC6k3gXcsnMj6FneGEz/kcUpeV8K/LJK4lkQGRWje3WEhOmikBAEBlcQIjRRGZ+DCHGUsoq/bE7fBjQ7QRS6jie3xe9zsaVsdvIKexFYVkYIUYTBaYuXDCHERlcgEPqMPpUkFfaFavdl27B+dilHqvNhwC/cvJKIgn4//bOLcSqMorjvz9OM5ZZznRjSskRJPAppQenIqKLpogR+DAiZLeXIujyUA6+1FNYERJEGmVEmFlmJWJImM9TSmVTOjlm5ZSlERlUDwqrh732uD3N7Yxn3JfWDzbn2+tbZ+b7n7XPYu+1v32+5n8wY/D70TL18gnXPxFn9J3AU2a20Pe7AczsmeHeM94z+iAIgv8zYz2jn4gC0WfAbEkdkpqBLmDbBPyfIAiCYAw0vHRjZqckPQzsBCYBG8xsYuYoBUEQBKMyIU/GmtkOYMdE/O0gCIKgPmLuWxAEQcWJRB8EQVBxItEHQRBUnEj0QRAEFScSfRAEQcVp+ANT4xqEdBz4YZxvvxQY4jeCS0loKSZV0VIVHRBaUq42s8tGcypEoj8bJO0Zy5NhZSC0FJOqaKmKDggt9RKlmyAIgooTiT4IgqDiVCHRvzK6S2kILcWkKlqqogNCS12UvkYfBEEQjEwVzuiDIAiCESh1oi/TIuSSZkjaLWm/pK8lPeL2NkkfSzror61ul6QXXds+SfPyVfBfJE2S9Lmk7b7fIanHtWz2n6lGUovv93v/zDzHXYukaZK2SDrg8eksa1wkPebHV6+kTZImlyUukjZIOiapN2OrOw6SVrr/QUkrC6LjOT++9kl6X9K0TF+36+iTtDBjb1x+M7NSbiQ/gXyIZPHUZuBLYE7e4xphvO3APG9PBb4F5gDPAqvcvgpY4+3FwEckK3bNB3ry1jCEpseBt4Dtvv8O0OXtdcCD3n4IWOftLmBz3mOv0fEG8IC3m0lWTy9dXICrgMPA+Zl43FOWuAA3AfOA3oytrjgAbcB3/trq7dYC6FgANHl7TUbHHM9dLUCH57RJjc5vuR+cZ/FhdgI7M/vdQHfe46pj/B8CtwN9QLvb2oE+b68Hlmf8B/2KsAHTgV3ALcB2/8L9ljmYB+NDsjZBp7eb3E95a/DxXOTJUTX20sXFE/0RT3JNHpeFZYoLMLMmQdYVB2A5sD5jP8MvLx01fXcBG719Rt5KY9Lo/Fbm0k16UKcMuK3w+CXyXKAHuMLMjgL4a7qAZNH1rQWe4PRK5JcAf5hZulJ6dryDWrz/hPsXgVnAceB1L0O9KmkKJYyLmf0EPA/8CBwl+Zz3Us64pNQbh8LGJ8N9JFcjcI50lDnRj2kR8qIh6ULgPeBRM/tzJNchbIXQJ2kJcMzM9mbNQ7jaGPryponkMvtlM5sL/EVSIhiOwmrx+vWdJCWAK4EpwKIhXMsQl9EYbuyF1iRpNXAK2JiahnBruI4yJ/oBYEZmfzrwc05jGROSziNJ8hvNbKubf5XU7v3twDG3F1nfDcBSSd8Db5OUb9YC0ySlq5ZlxzuoxfsvBn4/lwMegQFgwMx6fH8LSeIvY1xuAw6b2XEzOwlsBa6nnHFJqTcOhY2P3xheAqwwr8dwjnSUOdGXahFySQJeA/ab2QuZrm1AOjNgJUntPrXf7bML5gMn0kvYvDGzbjObbmYzST73T8xsBbAbWOZutVpSjcvcvxBnWWb2C3BE0jVuuhX4hhLGhaRkM1/SBX68pVpKF5cM9cZhJ7BAUqtf4SxwW65IugN4ElhqZn9nurYBXT4DqgOYDXxKo/NbnjdeGnDDYzHJ7JVDwOq8xzPKWG8kufTaB3zh22KSmugu4KC/trm/gJdc21fAdXlrGEbXzZyedTPLD9J+4F2gxe2Tfb/f+2flPe4aDdcCezw2H5DM1ihlXICngQNAL/AmyWyOUsQF2ERyb+EkyRnt/eOJA0kNvN+3ewuio5+k5p5+99dl/Fe7jj5gUcbesPwWT8YGQRBUnDKXboIgCIIxEIk+CIKg4kSiD4IgqDiR6IMgCCpOJPogCIKKE4k+CIKg4kSiD4IgqDiR6IMgCCrOv+2JSQj2Uzl7AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11119f550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121901e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121957400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1227bfc88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1228f94a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x122801e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10dbb38d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1228f9cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x123468748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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q2F1m707UaX2ICYpor1A7lOZMDr4IuA44LaWMa1j2KjAdqAeKgTullJVCiGhgD7C3YfcNUsr5bRC30kbqS0o48oc/UF+4F9/MTKpz1qKx2L82F/aEdUM09CqTmDwEPvfdTUJoPwBMFivfbTGydG0gB0rdqC8fx58mD+S34/o58+0oLsBiseDm1rI2Z4R/CBDSaFmlUc/J2tNIJDZpwWitpLCsDoGgp18PfD27Tl2j5nxv/hCYct6yn4A4KWU8sA947Jx1xVLKhIb/VJLvoKTNhqWsjMPz5rFn0GCKp0ylKD2D4ilTqd9TCFJyesNaqj1tvHyThpdv0rD+gbFc/8i7+D3+Xxy56T6OH7+GjTti2Lgjhh82RLJmeyAHy2rxcNPym4wYleTb0AsvvMDAgQO55ppr2Lt3r2N5cXExU6ZMITk5mbS0NAoLCwHIyspi/vz5pKWlMWDAAL755psLjrlmzRrS09OZMWMGQ4YMYf78+dga+saXLFnC0KFDiYuL45FHHgHg008/5eGHHwbgrbfeIiYmxhHD2LFjASgoKCAjI4Pk5GQmT57MiRP27r7MzEwef/xxMjIyeOutt9rkHAV6+TIoNIbBoX0J9+mJwB0bZizUcrjmKEXlhykqP0xZbXWbvH5HctmPUSlldkNL/dxlP57zdANwU+uGpbQ2/bp1YJNULl+GIW8D1jONRzKUnyqhyhu8/GBVvGDDIA2+g4bgrnHn8RGP46ZxI8o/ipOVVtYf7cZ/1hXjpzuJp9sv49qHRQby+fxRuGtds9/9ajz79S52H2/dRDEkwp+np8dedH1BQQH/+te/+Pnnn7FYLCQlJZGcnAzAvHnzeO+99+jfvz8bN27k/vvvZ9WqVQCUlJSwdu1aiouLGTduHPv370d3XrXO/Px8du/eTVRUFFOmTOHLL79k9OjRPPLIIxQUFBAUFMSkSZNYvnw56enpvPrqqwDk5OQQEhLCsWPHyM3NJS0tDbPZzO9+9zu++uorwsLCWLp0KU888QSLFi0CoLKykrVr17bqubuYYG9fgr37A7C3rAQLRuqlGZCcMtag1UQR5OXbLrE4Q2v00d8FLD3neR8hxM9ANfD/pJQ5Te0khJgHzAPo3bt3K4ShnK/6u+8489HHaPx8qc1u/Gswa2HVMEG1N+h1gkPjBhASEE6tuZa5g+cyLyyBHj6/9IWerDLxRcEpvtp6nPyDZ/Dx0PLN78aq/nYnyMnJYcaMGXg31PG5/vrrAdDr9axfv57Zs2c7tq2rq3M8vvnmm9FoNPTv35+YmBgKCwtJSEhodOzhw4c7WuZz584lNzcXd3d3MjMzCQuzXxS99dZbyc7O5sYbb0Sv11NTU8ORI0f41a9+RXZ2Njk5OcycOZO9e/eyc+dOJk6cCIDVaiU8/JcRVrfccksbnJ3LGxga7Xh8qPIkeks5x2sP4aaJwc+zc84/0KJEL4R4ArAAnzQsOgH0llKWCyGSgeVCiFgp5QVNHinl+8D7ACkpKfL89cqVMfz8M6Vvv43Gx4f6AwcRWg2Go4fRGus5Hu5BBLCzt+B4CHybqsE9OoqbBsymn3co3m7eZPTKQKvRXvT4b60sYkn+YQBmJvXk9ZsTLrptV3KplndbamoUhs1mIzAwkK3n1AO61D5NHaOpbc4fSnuuUaNGsXjxYgYOHEhaWhqLFi0iLy+P1157jcOHDxMbG0teXl6T+/r4OL+REBXYg4MVEoP1DEdqjhJm6Uaot3+nG1V11d+xhRB3YL9Ie6ts+EuQUtZJKcsbHhdgv1A7oDUCVRqrP3SI4mnXUnLLHA7MmMGhub/CkLeB/TtzqTh+gOLKA+RHW3h+joZ/P5nJ75/vxYEFd+D7+H/x8fxVfDPzP2TFZXFdzHWM7z3+kkneWG9lVeEpYiP8yX9iAq/eNKwd36lyvvT0dJYtW4bRaKSmpoavv/4aAH9/f/r06cNnn30G2EdPbdu2zbHfZ599hs1mo7i4mAMHDjBw4MALjp2fn8/Bgwex2WwsXbqUsWPHMmLECNauXUtZWRlWq5UlS5aQkZHhiGXhwoWkp6eTmJjI6tWr8fT0JCAggIEDB1JaWupI9GazmV27Ol7ZiujAHngIPyT1nDYe5Uj11Y9X76iuqkUvhJgCPAJkSCkN5ywPA85IKa1CiBigP3CgVSLtwmwGA8UzZmA5cRKsVvt/DY5011LtIzD3EezuLai8OY2TtSfo5t2NvkH9eDJ6KoNDBrfo9V/4djenqusYFRNCN7+uM1Kho0pKSuKWW24hISGBqKgo0tLSHOs++eQT7rvvPhYsWIDZbGbOnDkMG2b/YB44cCAZGRmcOnWK995774L+ebC30B999FF27NjhuDCr0Wh48cUXGTduHFJKpk2bxg032O9dTktL48iRI6Snp6PVaomMjGTQoEEAeHh48Pnnn/Pggw9SVVWFxWLhoYceIjbWOd+CLkYIQUxQL6rrDByvPUSNuZQKo3en6rMXl/paBiCEWAJkAqHAKeBp7KNsPIHyhs02SCnnCyFmAc9h786xAk9LKb++XBApKSly8+bNV/seOoW6Awewlpdj1euxVlRi2rULy5lybAYDpgMHsB45SnasoCwAbMJ+l+mJYPCZOhl/D380QsOwsGHc0K91iwfYbJLMhWuwWG3858E0gny6xm3nl7Jnzx4GD27Zh2d7y8rK4rrrruOmmy4+bmLNmjUsXLiwyRE5XcWJmjOcqTtBkEd3IvxDnR1OI0393QkhCqSUKZfbtzmjbuY2sfj/LrLtF8AXlzumYh/eKI1GrLW1GPLzOf7HP12wTXmoJyYPKPUycypJIH53JwND+qAVWrQaLZF+kSR2S2zTOG96bz2Hzxh4dOogleSVTi/MO4AzdSeorDtDBB0r0beEujO2nRh37aL0zbeozc1FGxKCtaKiURcMwLvTNJT5Q5m/wOrvTUzvYfi4+xDqFUpP357cPuT2S/alt6Z7/r6JjQfPUGOy0L+bL3NT1cgoV/bhhx9edpvMzEwyMzPbPJaOzE2rReCBFPXsLitiUHBfNBrXHy6sEn0rk1JS+fnnVH/1bwybNxM45xbMJ05Se854YWtZGQA/JAqOhgksWjgdAH+872NCvUKJ8I1AIzRohHP+wKw2yZq9pcT1DGBEn2DuSYshwPvqyqMqiqvp7deLYzUnsWCgsHw/vf17u/xdtCrRt5Bx2zbKPviAgOuuw7NfP2rWraP0xZcc6yv/9cstBi/O1nCwhyCoBvRecNuE/yIpsC9uGjd6ePcgJjDGGW/hAh/llWCxSWYl9+K2kVHODkdR2pWvpxcxbpEcrDyOmRqO1hxnkGfH+Ld5tVSibwYpJdbKSpASaTRSsfRTqr//Ht2gQVT+vAltaQX6FSsv2K+kG/wnVUNJhBvW6AjeGv8WUf5RVNVV4evui7d7x5y4+ostRwFI7995+igV5Uq4a90YENKbXaWFWIWRo1Wl9AoIu/yOHZRK9BchLRZK//xnfEaOombTRir+990LtjEfPszZHvNDYbAyQUNRhOB0IMREJTAgaAB3951OTEAMAZ4Bjv26eXdrp3dx5dbuK2XnsWp+N15VmVSU7t4RnDIeocpcRk8Z6rI3UqlEfw5LWRnF06/HVl3lmECj/L2/AnAkFH5M0jgeH+omCNJDnZ8nRl93xkdN4NqoicSHxRPoGei0/vWWsNkk9/2jAICJQ7o7ORqlubpSmeL2Furjj9ESRrW5FJOlHi93T2eHdFW6bKKvWbWak88/D+dMYGCtq0NWVvFDkkCvEwTrocIXJFA5fADXX2ev1BflH0VP357UW+s7bPfL1dh9ohpDvZU5qZHE9wp0djhKBySlRErZaCSK1WpFq22f0WDO4Kn1ADOUGaqIDGi9b+NSSqxmM0Ij0Lq17WCHLpHoS99+B/15VfL0R0sw1dWycWDjr2KVQwThv3+YSK8gTFYTPdzsRY5Se6QS6RfZaFs3Tec6fWV6ewGs2SmuPyNPZ/fCCy/w0UcfERkZSVhYmKN6ZXFxMb/97W8pLS3F29ubDz74gEGDBpGVlYVOp2PXrl2cOnWK119/neuuazxXr16v54YbbqCiogKz2cyCBQu44YYbKCkpYerUqYwbN468vDyWL19ObGwsDz/8MD/88AOvvfYaq1at4uuvv8ZoNDJ69Gj++te/cuDAAWbPns2WLfYJ4YuKipgzZw4FBQXtfr5awsdDR6kJqs2l2GRYiycml1KiLy/DqK/BZrWi8/UlsHvbTqfZuTJVg5I7szDmb7I/0bpBfT2ngjWcCvml1WENs7I7NoDIX93ZaN8h3mHM6DfDZfviWqLolB6AIG91Y1SzffconNzRusfsMRSmvnTR1W1Vplin07Fs2TL8/f0pKytj5MiRjsqYe/fuZfHixfzlL38BoLa2lri4OJ577jkAhgwZwlNPPQXAbbfdxjfffMP06dMJCAhg69atJCQksHjxYrKyslr3XLUDHw8dXtogjNYKSipP0CcwvEX5waSvobaqEo1Wi09gEJ7tUNzN5RP96aP7KD1UiDhTRWBxKcaVq6kv2s/WPoLicIg4YyakGnJv6kdg0vBG+07qlUZ6r3QnRd6xSCl54ds9AIT6uWY/ZFfRVmWKpZQ8/vjjZGdno9FoOHbsGKdOnQIgKiqKkSNHOrbVarXMmvXLfMCrV6/mlVdewWAwcObMGWJjY5k+fTr33HMPixcv5vXXX2fp0qXk5+e3zUlpY1EBPSg8U4HRWsGJGs+GGa2ujtlkAiAsqk+7NShdPtEXz5hBYI39wmkVYHKHvKGC8tsmM2TQWEqqS9hTe5L7ht3XYcapd0TVJgsA1w+LwF+nbo5qtku0vNtSW5Qp/uSTTygtLaWgoAB3d3eio6MxNSSl80sK63Q6R7+8yWTi/vvvZ/PmzURGRvLMM8849ps1axbPPvss48ePJzk5mZCQq0+QzqTVaOjl24ej+oNU1J8mgqt7H4bqKgzVVWjd3du118DlE72XycbBYd3Q3Xgth3poMft7ofPU8fsBN+Hv4e/s8FzGv7ceA2BSrBpt09Glp6eTlZXFo48+isVi4euvv+Y3v/lNozLFs2fPRkrJ9u3bHdUrP/vsM+644w4OHjzYZJniqqoqunXrhru7O6tXr+bQoUPNiudsUg8NDUWv1/P55587iqfpdDomT57Mfffdx//9X5MlslxGgM6bU7W+mKWeOosZzyu8gGqz2aitOIPGTUtAtwsnOG9LLp/okSB7dmf83P92diQu7VC5vdr0+EEdd4y/YtdWZYpvvfVWpk+fTkpKCgkJCY5yw5cTGBjIvffey9ChQ4mOjiY1NfWC43755ZdMmjSphe/c+fw9/Cmv02Mw111xojfV1GC1WPAP64ZHEyWi25LrJ3qlxX7/r59ZXXiamFAfvD3Un4QreOKJJ3jiiScuWN6nTx++//77JvcZM2YMb7zxxkWPGRoaetHZoHbu3NnouV6vb/R8wYIFLFiwoMl9c3NzueuuuzrFEEydmwfUQZ2l/or3tTbs4+XX/j0NLv+vWgB0wREyrUVfZ+GrrccZ1MOPO8dEOzscpZOZMWMGxcXFjpE/rs7XQwe1YDCbrnhfq9mCm4eHU0b0uXyiV1rmRKURgPsy+3JDQk8nR6O0leaUKW4Ly5Ytc8rrtpWzZYzrbIbLb9zAZrVSdfok9UYjnt7OKSvievfpn0/S0KxXrkbhyRoAYkI7z7RpitKW3IUnNszN3t5YU02dwYC7Tod3gHPuOHf9RA+q66YF9pyoxk0jGBTu5+xQFMUluGvcARtG8+X76aWUmGr1CI2G4IheeHh5tX2ATWhWohdCLBJCnBZC7DxnWbAQ4ichRFHDz6CG5UII8bYQYr8QYrsQIqmtggfVmG8Jm03y1dbjdPfX4a7tHJ/5itLWvNztyfp07ZlLbmezWik7cgizyYSbh3PvNm/uv+4PgSnnLXsUWCml7A+sbHgOMBXo3/DfPODC+r6tSEhQ6f7qbCo5w7FKI6G+quSBojRXd99AkFpMFuMlt6s3GbGazXj6+BDYvX3HzZ+vWYleSpkNnP/xdQPw94bHfwduPGf5R9JuAxAohGjbij3KVfn5SCUAr908zMmRKG0tKyuLzz//vMXHyczMZPPmzRcs//DDD3nKvV6AAAAgAElEQVTggQdafHxX4a7xwoIRKeVFt7Ga7f34AWHd27w65eW05Pt6dynlCYCGn2fvtOkJHDlnu6MNy9qO6qO/YiazlZe+KwSgd7CaYERRroSXmxcgOVZddtFt6owGNFotmg5w/0BbdMw2lXUv+NgTQswTQmwWQmwuLS1tgzCUS/nLmmIAHrqmPx5uqn/e1bz++uvExcURFxfHm2++CdirUw4ePJh7772X2NhYJk2ahNHYuHth5cqVzJgxw/H8p59+YubMmRccf+XKlSQmJjJ06FDuuuuuRsXRzlq8eDEDBgwgIyODdevWtfI77Ngi/OzTbBosTQ+zlDYb9QYDGm3HGMHekihOCSHCpZQnGrpmTjcsPwqcW7i9F3D8/J2llO8D7wOkpKRc/PvPZQiJatFfoXqLjbdXFuGmETwwrp+zw3FpL+e/TOGZwlY95qDgQTwy/JGLri8oKGDx4sVs3LgRKSUjRowgIyODoKAgioqKWLJkCR988AE333wzX3zxBb/+9a8d+44fP95Rrz4sLIzFixdz552NS3WbTCaysrJYuXIlAwYM4Pbbb+fdd9/loYcecmxz4sQJnn76aQoKCggICGDcuHEkJia26nnoyLQaDVq8MUs9JnM9OvfG17kMNdUAeAcENLV7u2tJU+7fwB0Nj+8Avjpn+e0No29GAlVnu3iUjmH3Cfsf4f2ZfXFTo21cTm5uLjNmzMDHxwdfX19mzpxJTk4OYC+BcLb0cHJyMiUlJY32FUJw22238Y9//IPKykry8vKYOnVqo2327t1Lnz59GDBgAAB33HEH2dnZjbbZuHEjmZmZhIWF4eHhwS233NJG77bj8ve0lzI4Un2y0XKb1UpNmb2XQufbMYYtN6tFL4RYAmQCoUKIo8DTwEvAp0KIu4HDwNki2N8C04D9gAG484IDtiLh+J/SXMcb7oadFq+ukbfUpVrebeVSFwA9PX+ZS0Cr1V7QdQNw5513Mn36dHQ6HbNnz8bNrXEauNTxz9UVJ+c5V7hvMBV1J7HIxjdP1Zvs59zLz7/RlIvO1NxRN3OllOFSSncpZS8p5f9JKcullBOklP0bfp5p2FZKKX8rpewrpRwqpbzwEr3iVGdq7Td6BKuZpFxSeno6y5cvx2AwUFtby7JlyxpVsLyciIgIIiIiWLBgQZMzPg0aNIiSkhL2798PwMcff0xGRkajbUaMGMGaNWsoLy/HbDbz2Wefteg9uSIhBFq8kdLaaLnNYp/bwTe449Te7xhXClpC9dFfESkl7zZciA1Uid4lJSUlkZWVxfDh9hnT7rnnHhITEy/oprmUW2+9ldLSUoYMGXLBOp1Ox+LFi5k9ezYWi4XU1FTmz5/faJvw8HCeeeYZRo0aRXh4OElJSVit1guO1dlphRYrjQucWS0WhBAdYrTNWaK5X9PaUkpKimxqbG5z7Bo0mEMzU5j2Px+3clSd08GyWsYtXENEgI71j01wdjguac+ePQwePNjZYbTIAw88QGJiInfffbezQ3FpByqOY7RWMDh4sKObpvLUCcx1dYT1jm7V12rq704IUSClTLncvh2jA6kFXP4NtLM9DRdi37/9sn8bSieVnJzM9u3bG43GUa6Ou8beKVJntXfXmGr1mPR63Nw71nScrt91A6rr5gqcvRAbGeTt5EgUZykoKHB2CJ2Gh9YDzFBp0uPlHoxJb68G6xsc6uTIGlMN4i7mWKURHw8t/l6d4zNeUZwpUGe/q7zSVOFozet8fXE/Z/RTR+DS/9ptNlvDI9Wib64TlSYiAr26/NA4RWkNnm7uuOGDR10dldX224WcMVXg5bh0oleu3MGyWiICnVMTW1E6Iz8PXzRVZjTu7oSE90TbwfrnwcW7bqRsaNGr1mmzFBw6w95TNcRGdLwWh6K4Kl+NfZiyQWPpkEkeXDzRK833ycZDzP1gIwBzh/d2cjRKS1RWVvKXv/zlstuVlJTwz3/+s1nbxcXFtUZoXZKbzT5E3ehRz66y3ZysufSEJM7g0olenu2jVw36y1q/vxxfTzdemz2MyGA14saVtXai72gsDXeWugqzyYTQaPBw8wcEVXXVzg7pAi6d6JXmO1ZpZEi4P7OSezk7FKWFHn30UYqLi0lISOBPf/oTUkr+9Kc/ERcXx9ChQ1m6dKlju5ycHBISEnjjjTcoKSkhLS2NpKQkkpKSWL9+/SVfZ82aNaSnpzNjxgyGDBnC/PnzHQMglixZwtChQ4mLi+ORR+z1fj799FMefvhhAN566y1iYmIAKC4uZuzYsYB9aGdGRgbJyclMnjyZEyfsFzAzMzN5/PHHycjI4K233mr9k9ZGbFYrplo9Om8f+gb3wg0vLNRypOr05XduRy59MVb10TeP2Wpj78kabkmNvPzGyhU5+T//Q92e1i1T7Dl4ED0ef/yi61966SV27tzJ1q1bAfjiiy/YunUr27Zto6ysjNTUVNLT03nppZdYuHAh33zzDQAGg4GffvoJnU5HUVERc+fObXK2qHPl5+eze/duoqKimDJlCl9++SWjR4/mkUceoaCggKCgICZNmsTy5ctJT0/n1VdfBSAnJ4eQkBCOHTtGbm4uaWlpmM1mfve73/HVV18RFhbG0qVLeeKJJ1i0aBFg/6aydu3a1jiF7cZsMiFtNrz87de9QryCOWWspcZcxS9zMTmfSyd6pXn2nKjGaLaSHBXk7FCUNpCbm8vcuXPRarV0796djIwMNm3ahL9/44vuZrOZBx54gK1bt6LVatm3b99ljz18+HBHy3zu3Lnk5ubi7u7uKFEM9ro52dnZ3Hjjjej1empqajhy5Ai/+tWvyM7OJicnh5kzZ7J371527tzJxIkTAbBarYSH/1JB1RVLHVsbupm0DfXoQ338qTD5US9rnRnWBVw60Z9t0QvVSX9R+0/X8L+r7VUIU6JVom9tl2p5t5fm1qt644036N69O9u2bcNms6HT6S67z/n3WwghLvl6o0aNYvHixQwcOJC0tDQWLVpEXl4er732GocPHyY2Npa8vLwm9/Xxcb0pLW0NpQ/OLWDmrnGn3mrDYrXi1kEKm3WOPnqNSvRNMdZbmfP+Bn7YdYphvQIID1Dj5zsDPz8/ampqHM/T09NZunQpVquV0tJSsrOzGT58+AXbVVVVER4ejkaj4eOPP25Wtcn8/HwOHjyIzWZj6dKljB07lhEjRrB27VrKysqwWq0sWbLEUcY4PT2dhQsXkp6eTmJiIqtXr8bT05OAgAAGDhxIaWmpI9GbzWZ27drVymenfdmsVjRabaMPRA+tfYhlmaHKWWFdwKVb9Nguv0lX9lnBEcr09cxJjeTFmUOdHY7SSkJCQhgzZgxxcXFMnTqVV155hby8PIYNG4YQgldeeYUePXoQEhKCm5sbw4YNIysri/vvv59Zs2bx2WefMW7cuGa1oEeNGsWjjz7Kjh07HBdmNRoNL774IuPGjUNKybRp07jhhhsASEtL48iRI6Snp6PVaomMjGTQoEEAeHh48Pnnn/Pggw9SVVWFxWLhoYceIjY2tk3PV1s6m+jP5ePhRUU9lNedpLtvUIe4C92lyxTXGw0UJyZTMmcMU5/5WxtE5toe/nQr3+88yc5nJqNR33paTWcoU9wca9asaXQxV7nQqQP7cdfpCI5oPJptf/kR6mQ1wZ49CPdrnQlIumyZYqma9BdVZTBTUlZLbIS/SvKK0gYs5nqklGg0F/bDRwXaLzJX1HWMm6euuutGCDEQWHrOohjgKSAQuBcobVj+uJTy26uO8BJsZ4dXqkTWyPFKI+mvrMZik8xM7OnscBQXlZmZSWZmprPD6LBqK+xJ3CfowkEO7lo3NHhio2Pc/HXViV5KuRdIABBCaIFjwDLsk4G/IaVc2CoRKlds4Q97sdgkj04dxPXDIpwdjqJ0ShazGY1Wi5tH0yWJPbXeGK0VWG02tE6eJLy1LsZOAIqllIfa88KDowSC0kh5w+Tfv0mP6RAXghSls7FaLJjrTPgEXPxiq7ebDqMVCs8U4iF86R/ivBpTrfUxMwdYcs7zB4QQ24UQi4QQavB2O6sw1JM5MEwleUVpI6ZaPchL154P9QnASxOIkG7US0M7RnehFid6IYQHcD3wWcOid4G+2Lt1TgCvXWS/eUKIzUKIzaWlpU1tclmOG6ZUQmvkTG09wd4ezg5DUTots8mE1s0NN4+L/ztz02iJCe6Jzs0XsLKnrJjT+sr2C/IcrdGinwpskVKeApBSnpJSWqU9C38ADG9qJynl+1LKFCllytlbqZWWqzaZOV5pVBUqlQ5nzZo1ly2kdqUyMzMvW6/nYp555hkWLry6S4nmOhPunpe/sxggWOffcGG2jjOmS4/C+fDDDzl+/PhVxXQprZHo53JOt40QIvycdTOAna3wGk2SDXWgVVEze+GyN1fs44llO7FJSI0OdnZISifQmiWDrybRd8SSxXUGA1azGXdd8+aFDfTyZXBoP9yFD1bqL7lth0z0QghvYCLw5TmLXxFC7BBCbAfGAX9oyWsozZO7v4w3VxSxYvcpYkJ9SIoKdHZIShspKSlh8ODB3HvvvcTGxjJp0iSMRiNgLwk8ZcoUkpOTSUtLo7CwEKvVSkxMDFJKKisr0Wg0ZGdnA/Y7Wffv39/o+B9++CGzZ89m+vTpTJo0CYBXX32V1NRU4uPjefrppx3b3njjjSQnJxMbG8v777/vWP7999+TlJTEsGHDmDBhAiUlJbz33nu88cYbJCQkkJOTw6FDh5gwYQLx8fFMmDCBw4cPA5CVlcXDDz/MuHHjHCWQzzIajcyZM4f4+HhuueUWx/sG+PHHHxk1ahRJSUnMnj0bvV4PQHR0NI888gjDhw9n+PDhF7xfgK1btzJy5Eji4+OZMWMGFRUVFBcXk5SU5NimqKiI5ORkLPUmUjPG8fyLLzNq1ChSUlLYsmULkydPpm/fvrz33nuOfc49b39++S3Ays69hU3+/j7//HM2b97MrbfeSkJCQqP31lItGnUjpTQAIectu61FEV1ZBPYfqkXPp5uOoNUICp68Bm8P165s4UpyPt1H2RF9qx4zNNKXtJsHXHKboqIilixZwgcffMDNN9/MF198wa9//WvmzZvHe++9R//+/dm4cSP3338/q1atYsCAAezevZuDBw+SnJxMTk4OI0aM4OjRo/Tr1++C4+fl5bF9+3aCg4P58ccfKSoqIj8/Hykl119/PdnZ2aSnp7No0SKCg4MxGo2kpqYya9YsbDYb9957L9nZ2fTp04czZ84QHBzM/Pnz8fX15Y9//CMA06dP5/bbb+eOO+5g0aJFPPjggyxfvhyAffv2sWLFCrTnlRd499138fb2Zvv27Wzfvt2RiMvKyliwYAErVqzAx8eHl19+mddff52nnnoKAH9/f/Lz8/noo4946KGHLrjb9/bbb+edd94hIyODp556imeffZY333yTgIAAtm7dSkJCAosXLyYrKwur2YIAoqKjyXv5Zf7whz+QlZXFunXrMJlMxMbGMn/+/AvO2+RpU9m8fjMRvaIu+vv785//zMKFC0lJuezNrlekk2SErp3oj1ca+W7nSaJCvFWS7yL69OlDQkICAMnJyZSUlKDX61m/fj2zZ892bFdXVwfYW+7Z2dkcPHiQxx57jA8++ICMjAxSU1ObPP7EiRMJDrZ3//3444/8+OOPJCYmAqDX6ykqKiI9PZ23336bZcuWAXDkyBGKioooLS0lPT2dPn36ADiOc768vDy+/NLeGXDbbbfx3//93451s2fPviDJA2RnZ/Pggw8CEB8fT3x8PAAbNmxg9+7djBkzBoD6+npGjRrl2G/u3LmOn3/4Q+NOhqqqKiorKx2F2e644w7HObznnntYvHgxr7/+OkuXLiU/Px9LnRGE4Prrrwdg6NCh6PV6/Pz88PPzQ6fTUVlZ2eR5O3rwFOG9wunZu9cFv7+25NJZwTHxSBd1usZEub6eR7/cAcBjUzt//ZWO5nIt77bi6flL/7BWq8VoNGKz2QgMDHRMSHKutLQ03nvvPY4fP85zzz3Hq6++6phBqinnFjyTUvLYY4/xm9/8ptE2a9asYcWKFeTl5eHt7U1mZiYmkwkp5VWNhDt3n0sVXGvq2FJKJk6cyJIlS5rYo/E+VxLbrFmzePbZZxk/fjzJyckEBwVx+lAFQgjH70Cj0TT6fWg0GiwWS5PnzWazkb19Ax6e7lSaahGAVdra/FqES9e6OUt0wRIIJrOVCQvXMvWtHLYdqWTcwDAmx3Z3dliKE/n7+9OnTx8++8w+0llKybZt2wAYMWIE69evR6PRoNPpSEhI4K9//StpaWmXPe7kyZNZtGiRo8/72LFjnD59mqqqKoKCgvD29qawsJANGzYA9oqXa9eu5eDBgwCcOWMfaXJ+2eTRo0fzr3/9C4BPPvnEMd3gpaSnp/PJJ58AsHPnTrZv3w7AyJEjWbdunaP/3WAwNJpY5ez0ikuXLm3U0gcICAggKCiInJwcAD7++GNH616n0zF58mTuu+8+7rzzTixms30QSDM+LJo6b2VlZei09tE6x/QlHNWXUFn3yzk5/xy1Ftdu0XfhO2N3Ha+mps7Cg+P7EdszgFF9Q9T9BAqffPIJ9913HwsWLMBsNjNnzhyGDRuGp6cnkZGRjBw5ErC38M/O+3o5kyZNYs+ePY4E6evryz/+8Q+mTJnCe++9R3x8PAMHDnQcOywsjPfff5+ZM2dis9no1q0bP/30E9OnT+emm27iq6++4p133uHtt9/mrrvu4tVXXyUsLIzFixdfNpazCTc+Pp6EhASGDx/ueM0PP/yQuXPnOrqrFixYwIAB9m9cdXV1jBgxApvN1mSr/+9//zvz58/HYDAQExPTKJZbb72VL7/8kkmTJmGpMwHN6yy+2HkL8wlEK9wI8bQPUPT18MFWZwbsF6Lnz5+Pl5cXeXl5eHm1zhwSLl2muKr8BMfHjOfwHeOZ/Nj/tkFkHdfyn4/x0NKtrPyvDPqG+To7nC6lq5Qp7iyio6PZvHkzoaGhV7X/woULqaqq4vnnn8dQVUl1WSmhvaNwc2/fmxJbUqbYpVv0XdmxSvvQqwg1a5SitJkZM2ZQXFzMqlWrANBXlCM0GrRu7k6O7Mq4dKJ3XIzt5F0WZquN+R8XcLLa5Fh2vNJI72BvvDw6xpyUitJRtWREy9kRRQDG6mpsVhte/v4u103q0om+qzhyxsDKwtMM7RlAd3/71f3wAB1T48Ivs6eiKK1B2mxUl9trcvkEut5d5y6d6LtCi/50tYmXvy8E4IlrBzMypnWmJVMUpfkMNdVIm42gHhG4ubtWtw24eKLv7KSUzPu4gK1HKhkc7s/gHhcviaooStuwWa3oy8vQurvj4e2axQJdO9E3jBhytf6y5jpUbmDrkUpCfDz47veXH++sKErrs9TXIaXEJ/Dik4x0dJ3ihqnO6uzImv+9NekyWyrKpWVlZfH55587OwyXZG24a9VD57oj3Fw60XfmMsV7T9aQXWS/+NMryHX/wBTFlUlpw1SrRwiB1s11O0BcOtE7dLJEf6zSyOQ3s/nr2gOEB+joGagSvdLY66+/TlxcHHFxcbz55pvApcsXn7Vy5UpmzJjheP7TTz8xc+bMC44fHR3N008/TVJSEkOHDqWw0D4goLa2lrvuuovU1FQSExP56quvAJg2bZqjHEFiYiLPPfccAE8++SR/+9vfWv8EtBNDVRV1tbV4+vginDzBd0u47kcUnbOomcVqY8/xagCeum4Ik+N6uGy/YFew+sP3OX3oQKses1tUDOOy5l10fUFBAYsXL2bjxo1IKRkxYgQZGRkEBQVdtPztWePHj+e3v/0tpaWljrIDd955Z5OvExoaypYtW/jLX/7CwoUL+dvf/sYLL7zA+PHjWbRoEZWVlQwfPpxrrrmG9PR0cnJyiI6Oxs3NjXXr1gGQm5vb6PVdiZQS/ZlyAAK6uXYdKdf9iDpXJ0mEUkqmvZ3DPR/Zy0FMHNJdteaVC+Tm5jJjxgx8fHzw9fVl5syZjoJcTZUvPpcQgttuu41//OMfVFZWkpeXx9SpU5t8nbMt/XOP8+OPP/LSSy+RkJDgqFZ5+PBhRxnk3Nxcrr32WvR6PQaDgZKSEgYOHNg2J6IN2Ww2aspKkVLi5efn8o0t1aLvQI5Xmdh3Ss/1wyKYMLibmvfVBVyq5d1WLlWfqqnyxee78847mT59OjqdjtmzZ+N2kb7ns8fSarWOMrpSSr744osLknd9fT2bN28mJiaGiRMnUlZWxgcffEBycvIVv7+OwFBViaG6Cq27u0veIHW+TtGid/VP27NKymoBmDM8khsSejo5GqWjSk9PZ/ny5RgMBmpra1m2bFmzyg2fFRERQUREBAsWLCArK+uKXnvy5Mm88847jg+bn3/+GQAPDw8iIyP59NNPGTlyJGlpaSxcuPCK4uooTHo9+jPlCCEI6dUbN4/2LV7WFlw60XeEyput6YstRwFUd41ySUlJSWRlZTF8+HBGjBjBPffc45jFqLluvfVWIiMjGTJkyBXt9+STT2I2m4mPjycuLo4nn3zSsS4tLY3u3bvj7e1NWloaR48edclEb6iuBCC4VyQaF74Ae64WlykWQpQANYAVsEgpU4QQwcBSIBooAW6WUlZc7BhXW6b49NF9lF9zA0fnTWPiw69dRfQdh6HewpCnfsDTTcPu56ag7YKTqbiKzlCm+IEHHiAxMZG7777b2aF0KDabldMHD+AbFIxvcMcqN9KSMsWt9XE1TkqZcM4LPgqslFL2B1Y2PFcu4fGG6QD/OGmgSvJKm0pOTmb79u0uOxqmLZkaZnfSdoLumnO11cXYG4DMhsd/B9YAj7T6q8jOccOU1SZZvvU4ALeNinJyNEpnV1BQ4OwQOiSrxUJ1mf0mxfaeVKSttUaLXgI/CiEKhBBnhyB0l1KeAGj42e38nYQQ84QQm4UQm0tLS1shDNd1usZeZ/6FGXHo3FV9eUVpb/UmI6WH7HPcBnTrgfs5o5c6g9Zo0Y+RUh4XQnQDfhJCFDZnJynl+8D7YO+jv5oXPjtnrKuPujmuZotyOVJKl/+7U+xsVitVp08B4BcSis63403N2dJrqS1u0Uspjzf8PA0sA4YDp4QQ4QANP0+39HUuycX/va3Za/9GE6FG27gEnU5HeXl5pxv11VXpK8qxms3ofHw7ZIVKKSXl5eXodLqrPkaLWvRCCB9AI6WsaXg8CXgO+DdwB/BSw8+vWvI6FyNx/X9o245U8s6q/QD0VMXLXEKvXr04evQoXb3LsTOoNxow6fUIrQbfoBBOVFQ6O6Qm6XQ6evXqddX7t7TrpjuwrOET0A34p5TyeyHEJuBTIcTdwGFgdgtf59KEa451NVtt5O4vA+CTe0bg6+nSNyp3Ge7u7vTp08fZYSgtYKiuonBdNqs//CsAv37xTbrH9HNyVG2nRZlFSnkAGNbE8nJgQkuO3azXt7luCYR9p2q47u1c6q02YsJ8GNMv1NkhKUqXsOrDv/Lzd18DIDQarrn7/k6d5MHFa92c1dH61JrjmX/vot5q4w/XDGBsf5XkFaWtmWr17MlZzdYf/oNGq2XyfQ/Rf8Ro3D061wibprh0onfVi2E2m2TnsSoGdvfj99f0d3Y4itLp7duQy9dvvOR4ftOTL9A77oLOiE7LpRO9g4u16PeX6qk2WXhqeoyzQ1GUTs1YU01xQT4/vPsm7jovpv72D/RLHeWSvQAt4dqJ3kVb9O9n2yeqSIkKcnIkitJ5lWzbwhf/85TjefK1N9J/+GgnRuQ8Lp3oHfXoXejTWUpJblEZvp5uRIWoevOK0toM1VUc2bWD/OWfAXDdQ4/Qa3AcPoFdt2Hl0oneFdXUWThZbeKxqYO63NdHRWkrtZUVbP3xWw7t+JkT+365OT/+mikMHOV6pZJbm0sn+rMXY10pYZbW1AHQ3f/q73JTFKWx5a8+z8n9+3DXeRGdkMyg0en0Hz4KDy/1rRlcPNG7orKGRB/q2/mHdClKW7HZrOjPlFO4Lpv9m/I4uX8fUfGJzHrsWUQnmSykNbl0onfFPvpSvT3Rh/mpRK8oV8pmtZL9zw/Zk7MaQ5W9XIHWzY1+qaMYd8e9KslfhEsnegcXSvQnKu0liVWiV5Qrt+2nbyn4Zhn+Yd1JmnYD/UeMptegWGeH1eG5dKJ3xRIIi9YdRCMg0Mvd2aEoiks4sms7+zaup2jjOmor7TOSznn2ZfxC1B3lzeXSif4sV7kYu+NoFSeqTIwbGIZGTReoKJdVcfI4nz73OAC+QcGkTJ/JsInTVJK/Qi6d6F2tBMKqQntZ/ieuHeLkSBSl46ozGDi8YytFm/IozF0LwG0vv01Y72jVB3+VXDrRO7hAi95stfHmyn0M6uFHv24dbwYbRXGmOkMt21f+wP78PI7v2+NYHtY7mtjMiXSLVuVCWsK1E70Lteh3Ha9GShiryhErCpb6esqPHmbfxnXsy8ul8tQJADRaLf2HjyY6IZno+ET8wy6Yblq5Cq6d6Bu4Qh/95pIzANyTplomStdUXVbK4Z3bOPjzZg4U5GMx1wOg8/ElNuMa+qaOIHpoIu4tmDJPaZpLJ3rHOHoXsHhdCb2CvOgRoP6Ila5BSsmxvbs5sGUTJdu2UFpywLGue0w/+qWOYvDYDAK69XBilF2Diyd6e9eN7OCzg5+sMnGs0kjGgDBnh6IobUZKidVioXDdWoo3b+TQjq2YTUYAvPwDGDBiDANGjaVPYgoeOjU/cnu66kQvhIgEPgJ6ADbgfSnlW0KIZ4B7gbMzJz8upfy2pYG6smOVBgCyxkQ7NxBFaWVWi5myI4fZtyGXA1s2UXa4xLEuvN9AesUOZfCYDEIjo9SIGSdqSYveAvyXlHKLEMIPKBBC/NSw7g0p5cKWh3dpZ7tuRAcfk36wzJ7oI4NUK0ZxfYaqSkq2/8yBgnwObt1MvbGh1e7nz6AxGcQkptA3dQU6JTIAABZTSURBVKRqtXcgV53opZQngBMNj2uEEHuAnq0VWGdScOgMfjo3YkLVsErFNZ0uOUBRfh4lWzdzsrjIsTy8/0Ci4pPolzKi00+w7cpapY9eCBENJAIbgTHAA0KI24HN2Fv9Fa3xOuf75Yapjt2i//lwJUm9g9TdsIpLkFJSW1lB4bq1lGzbwvG9ezDX2Ws0ueu87K32pFR6xw3r0pN5uJIWJ3ohhC/wBfCQlLJaCPEu8DwgG36+BtzVxH7zgHkAvXv3bmkMLdq/rR2tMDIyJsTZYSjK/2/vzIPjKs4E/usZjY6RNNLolizJOm0jGxvbso2MIUAAmxyw7JICig3ZJOQiyYbdDVkcKqnNplIku2zCJksCBHKWc4dwLVknBSSxwZd825Jlj3VrdEsz0mhGc/b+8Z5GI0W+Jc1B/6pU897X/eZ93+s3n/r4uvuc+L2T2E+fCtfanQP94bSlq9dSXLOM2k3XkbOkjCSTWqcp3rgiRy+EMKE5+R1SyhcApJT9EenfB16d61op5bPAswD19fWXNfMpHsIrnW4/Lm+AkmwVVqmILfpbbfSeaeHsof10Hj9KKBgAIKuwiDW33k71+k1Urq2PspaK+eBKom4E8DzQLKX8ZoS8WO+/B7gLOHFlKl6MLrE7mv/cbi12uK44K8qaKN7phIJBTu97i7ZDB2g/dji8njtotfaKNeuoXFtP7pKyKGqpWAiupEZ/HfBB4LgQ4ogu+yJwnxDiGrSum3bgE1ek4fkIxfYSCK8ctfOdN2wIARsrc6KtjuIdhn9ykv72s9gO7MV+unnGXqp55RVcteVGljdcj7V4CakZKlAgkbmSqJvdzD0Kuvgx8zHYR+8LhPjcLw4D8L3715OcFLutDkXiMNDeSp/tNG1HGmk9dIBQMAhAaqaFmg0NVK5dz/KG60kxp0dZU8ViEtczY7VGQ2zy+O+bCUn45Luq2bZKTfFWLAyhYBDbgT3YGvfRefxIeGMOgCUrVlK1bgO1GxuwFqvI53cyce7odWKwsvzbg90APHRTdZQ1USQSPo+bEXuPvurjrhnRMbml5dRs3Mzyhi0UVFSpWrsiTFw7+ljdStDtCzA2GeCRrcuxpKpQNMXlEwz4Gexop+1wI50njtJzqikcbZaakcmqm26lsLKGmo0NZFjVOJBibuLa0U8TW1X6HXs7ASjPMUdZE0U8MtzdSU9LM2cb99J18nh4spLRZKJs1Wqq12+ipn6TWqtdcdHEtaOPxa0ED3aM8LXXtB1y6ivUrEHF+ZFSEvB5ad79Z/rOnqb96CHGhwbD6aVXraJizTqKa1dQVrdKLQymuCzi3NHri5rFSNTN+KSfJ3aeBuC3n9pMcZZa1Enx10y6XPTaWmg73EjH8SOM9HSF07ILi1l3+x3UbtxMTmkZZouaf6G4cuLa0ccS3kCQbU/uosfhoaEql/VLVW1eoeH3TmJvOUXbkUYGO1rpPHEsnJaebaXuhpupWreByrX1mFJSY6biokgc4trRT3XdRPuH0TXi5pZv/hlvIMQDDUv57M21UdVHEX36bKfpPnUS24E99LQ0h/c3Tsu0UH71NdRuaKB202a1KJhiUYhrRx8myo7+8d834w2EeGTrcj5yXSVpycao6qNYXAJ+P26ng5a3/0Jfq42Oo4fwuicAEAYDVWvrqbymnuLa5RRUVke9YqJ45xHfjj5GBmN7Rj2kmYw8dKP6Eb9TGLF303pwP31nz3D24H4CPm84Lb+iiur1m1jesAVLXj7JaSr6ShFd4tvR60RzUbMd+zo42u3knvoy5eQTGEd/H/aWJk7ve4uhzvbwRCUhDOQvraS6fiNLlq9k6eproqypQvHXxLWjj2Z4pdPj5yuvnOT15gEA7lxbEjVdFPOLlJJJ1zjNu/+MvaWJnpYmXCPD4fSi6lpK665mxeYbKF+1BoNRddUpYpu4dvRhFrkiPeENcN+ze2nqHaOmIIMnPrCGzdV5i6uEYl5xDvRhP9NCT/MJbAf2zlgzprCqhpoNDdTUX0tuWbmagaoIM9zjYqR3gtr6wmircl7i29FHYeORSX+Qj/74AE29Y1Tkmtn58A0Y1RaBccdob4/Wv964j77WMzj7+8JplvwC1tz2Xmo3NlBSuwJTqto0RjE3v/jqfgDl6BeFBe4bHxib5I1TA0jgVO8Ye1tHsJpNvP4vNyonHye4nQ5sjftoP3KQwc42HH294bSCimqufvdWyleupmr9RpJT1UQ3xbnpbhmluDoLY8TS48FAaMZ5rBHXjl5KuaC9Noc6Rxlx+dixr4M3W6anpedlJLNn+7uVk49RggE/nrExWvbsor/VRtvRQ0yOj4XTCyqrWXXTbSzbtJn8pZVk5Kj9fBXTOAbcpGenYIoIkx7ucZFTks5Ql4uXvnWYsroc7vjH6YH3yQk/6VkpAPgmAwx1jZO/1MLEqJfMnFTGhj1Y8tNwDnjILkhjtN9NbsnibfYS145+ivmMumkbmmDM42dkwseHf3QgLN+6spCv3LEKAEtaEiZj7P73fqchpWSgvVUfOG2m7fABfB5POD2npJRVN95Cxep15FdUqmUFFOckFJLs+PJeKq7O5b2fXgOA3ebgd08c4vp7lpFdoLX2uppGcAy4w9dNuqYd/d6XWjn+ZjeW/DTGBj3UrC/AdnCA2g2FnDnQz4rNxZx6u5f7//1asgsWJ/Q2rh29lKHLrtFLKRkY9xKM2I6w1+nh7qf3zAjP//4D9RRZUqkuSMecPE+PK+CDpOT5+a65CIUACYbzRIME/WCcYwnlc8ljjP5WG/Yzp2g9uJ/+VhueiBp7/tJKqtZtpGTZCirX1quw1wQjGAxhnFXRCoW01r2IaGVLKZEhicFoQEpJKCQxGg0z8sqQJDQ1wx7wTWo7crUfHyYY1PzLSI8LgMGucSx50+M17jGfdpHUWgHWIjNCCPpbnQCMDWqVDdtBLTLvTKMWkttxQovg8oz54t/RCyG2Af8NGIHnpJRfn/ebTHnkC/yQpZQEZu0v+9M9Hfz7q01z5v+vD6zBmm4i25zMuvJ5nqLesQd+uA0+shPKr52Wh0IwXysT/uQO6NwDXx6eO731T/CTO+HBN6B0/bR8sAWe2gj37ICr3jc/uswDwUCA8eEhzux/W+tj72ib4djLVq6mqLqWspWrKV+1GmNS7P+jUlweEw4vP3r0LW76+xXUbZkOaf7BI7vIyE7h3i9tCssaX2tn/yttfOLb7+LkLju7f32Gjz5xPc9/fhflK3N5/2fX8McfNnHmQP9ct+LpT/9pxrnBKPjfp6bXKfrdE4fCx//3zAkAVt6whIGO8bmV111QwBeMPF0UFsTRCyGMwFPArUA3cEAI8bKUcm7PeuX3O2/6gz9u5PVTA38lL7Wm8dmba2bI8jNTuHnFJY6g/+oBcPbAx16flj25Gq56P2z9Gny3AQaa4O+eh3F9EPDIz6YdvaML/qceNn4M3v4O/M3T8OIn4c7vwksPwd8+By88CHc9C2vu0a7pPQbPXA+ffAuKVs3Up32X9rnzMWh+BR4+Bn/4EjS9CJs+BTu3a+lvf1uTAeRUwUirdtz0Erz2eVh5F2x7XJP94Utw9OfwiO3Sns1l4B5zMtLdha1xL/aWZnptLeE0YTBQVreK/IpqljdswVqkNrZOZMZHJvnJF9/mjoevoWxFDo5+rbvk1J7eGY7eOxHAOxGYce3R17VVQT0uPyd39QDgcmgzmDtPapWgczn5uWjaZb9gnpN/6blgHr/eaggFFi9qUCzEpCMhRAPwb1LKrfr5dgAp5eNz5a+vr5eNjY2XfJ8Tu17E+LHtOL76EIbU22g/cpiR7qMkBU/jlEZ60DZmqA51YEhOxWupCF+b5hshx2cnqXzjdIsg6IfuRihcCclm7Th/OfjcMDEAJWu14849EApqXSPp+TCuh+aZ0iAU0L7nYslfrtWkL4ZkM1Rs0Y4HW2C0A3IqQBhgWHfSpeuh++DM6wxGTd/LwVIMY9MRKhTWafbKEFgrISNfkwcmoecQFK+BgFd7dsnpUL4JDHp9ov+k9oysFVp60AcZhZA3vQicZ2ycvrPTz8OUmkGapYKCiiqkrCAzv5iMnHS6m0epqS+gabeduutKaD0ySHF1Fj5vkLEhDyW12Zze38/KLSU0v91L9bp8BjtdJKcZychOoat5hNoNhZzcZeeqzcW0HR2iqCqLgC+IY8BDWoaJ0X43FVfn4hzwIKXEWmSm7egQy68tpml3D9mFZrILzPSedVJ1TT5Nb9mp21KCrXEAU6qR0hVWTu/vJ3fJ9JZ+1kLtO6xF6UxO+LHkpzHp8jPc46LuuhLOHh7AmGSgfGUuLXv7yC1Nx2A0EPAFMRoNDNtd1NYX0nN6FK87wLKNmg05Jdo9RuwT1F1Xgu2gpoM5M5nRvgkq1+TR3z7OhMNLTnE6Hpcfr9vPso2FNO22Yy1KRwgYtk+QW5JOKCRx9LvJXZJBksmA1xMgyWRk2O4iMycVU6qR4e4JsgvSMCQZwoOVgrm/I+ALMTbsIbckA6/bj3vMh7UoHc+4D99kgKwCM702B0nJRnJK0rE1zqqYCbDkpYW7QwCWbSoM63D2sBYsUbuhkOEeF7lLMsJOfKqvfDbZhebwP45oUb2ugG0fX3XhjOdACHFQSll/wXwL5OjvBrZJKR/Uzz8IbJJSfmau/Jfr6F/81tdp33cMKSAUmm7KC0MuSYYQUzE5ItxIiqz5zyWbLZ/9bOaSLTZT+kZbjykupM+5nrmcM09QGkGkY0hehiFpCQajWt1RkdhcXdXFDV/40GVde7GOfqH66OfqS5nhCYQQHwc+DlBeXn5ZN0nPycMgUsBkIAkrBlMZIcNylqbbSEpJxZCs16Q8o9oAY3JEEz/kh4khyCye+aVjdq0Wi9COM4u0mqd3DNILNDMia7gpGeDVBmswJmm1V/+kVpv1TUznya2BiUGtiyctS9PF2QOWEph0aPebyj9Vi7YUw6RTa0WApktkhJEMaefeMU0Ho0lrYSBhvB8yCrR7CiNkl0HQC85uyK0FZ5em59T9ZEi7RiscbfzDbNXuLQxaiQaDkJoJHifIgPY8DBGv0NSzi/wuS8TSEJ4RMCZrto/bwZii6ZximVEEZwcrWJrbRcewlYLMIcYn07GkjeNwWzAZg6SYvAy7rJTn9NA5UkqZtYceRxFZaeMEQkYm/SnkZYzS6yykIreL9uEyCi2DOD2ZGA1BUk1ehl05lOd20zlcSqnVjt1RSFbaOMGQEY8/BavZycB4PvmZQ0x4zUgpyEydYGA8T9etjMzUcZKMAZzuLJZk99I1uoTynG46R5ZgMvrDOuRlDDPh0wbdMlImGBzPoyBzkFF3FmkmL0ZDkFF3NmU5PfSMFmMwBCnIHMbuKCI3Y4RJXwpBaSQz1cXgeB5FWf2MTmTjDaSEdclKG0MIicOdNUOHSDtc3nQ8vjQKModwejJnXG9JG8OgX281OwiGjIxNZpKbPoI3kEIgNH1/c7Ibc7KHIVcuGSkukpP8jExYZ+hgNTsIhIyMT2ZqNvhTmPCma3pMpuPxp1GQOYjDk4UvkEyBZZARl5VAKIlssxOHe2Zk1FQ5TpFm8mBO8TDsyiEjxYXLq/22rWYHo+5srGYH3kAybp95RvrUtR5/GuZkN269XAQhpL4l6dLcLuyOQvzBZExGP/6gifVLj+L0WLANVLKp6iCBYBI1he28euQWbr7qLRxuC43ta/D40yiyDJBkDNA9WjLjHiXZffiDSaSnuGkfKicjxUUglIQlZwEDM3TiuutGoVAo3slcbI1+oYLBDwC1QohKIUQycC/w8gLdS6FQKBTnYUG6bqSUASHEZ4CdaOGVP5BSnlyIeykUCoXi/CxYHL2U8jXgtYX6foVCoVBcHGoev0KhUCQ4ytErFApFgqMcvUKhUCQ4ytErFApFgqMcvUKhUCQ4CzJh6pKVEGIQ6LiCr8gDhuZJnWiSKHaAsiVWSRRbEsUOuDJblkop8y+UKSYc/ZUihGi8mNlhsU6i2AHKllglUWxJFDtgcWxRXTcKhUKR4ChHr1AoFAlOojj6Z6OtwDyRKHaAsiVWSRRbEsUOWARbEqKPXqFQKBTnJlFq9AqFQqE4B3Ht6IUQ24QQLUIImxDi0WjrcyGEEGVCiDeFEM1CiJNCiM/p8hwhxB+FEGf0T6suF0KIb+v2HRNCrIuuBTMRQhiFEIeFEK/q55VCiH26Hb/Ul6hGCJGin9v09Ipo6j0bIUS2EOI3QohTetk0xHGZ/JP+bp0QQvxcCJEaL+UihPiBEGJACHEiQnbJ5SCE+JCe/4wQ4vK2bloYW/5Tf8eOCSF+J4TIjkjbrtvSIoTYGiGfHx8npYzLP7Tlj88CVUAycBSoi7ZeF9C5GFinH2cCp4E64D+AR3X5o8A39OP3AL9H29/pWmBftG2YZc8/Az8DXtXPfwXcqx8/DXxKP34IeFo/vhf4ZbR1n2XHj4EH9eNkIDseywRYArQBaRHl8Q/xUi7ADcA64ESE7JLKAcgBWvVPq35sjRFbbgOS9ONvRNhSp/uvFKBS92vG+fRxUX85r+BBNgA7I863A9ujrdcl2vAScCvQAhTrsmKgRT9+BrgvIn84X7T/gFLgdeBm4FX9BzcU8SKHywdtX4IG/ThJzyeibYOuj0V3jmKWPB7LZAnQpTu5JL1ctsZTuQAVs5zjJZUDcB/wTIR8Rr5o2jIr7S5gh348w3dNlct8+rh47rqZeqmn6NZlcYHeTF4L7AMKpZS9APpngZ4tlm18EvgCENLPcwGHlDKgn0fqGrZDT3fq+WOBKmAQ+KHeDfWcECKdOCwTKWUP8ATQCfSiPeeDxGe5THGp5RCz5TOLj6C1SGARbIlnR3/BDchjFSFEBvBb4GEp5dj5ss4hi7qNQoj3AQNSyoOR4jmyyotIizZJaE3s70kp1wITaF0E5yJmbdH7r+9Ea/6XAOnA7XNkjYdyuRDn0j3mbRJCPAYEgB1Tojmyzast8ezou4GyiPNSwB4lXS4aIYQJzcnvkFK+oIv7hRDFenoxMKDLY9XG64A7hBDtwC/Qum+eBLKFEFO7lkXqGrZDT88CRhZT4fPQDXRLKffp579Bc/zxViYAtwBtUspBKaUfeAHYTHyWyxSXWg6xXD7og8PvA+6Xen8Mi2BLPDv6uNuAXAghgOeBZinlNyOSXgamogM+hNZ3PyV/QI8wuBZwTjVjo4mUcruUslRKWYH23N+QUt4PvAncrWebbceUfXfr+WOiliWl7AO6hBDLddG7gSbirEx0OoFrhRBm/V2bsiXuyiWCSy2HncBtQgir3sK5TZdFHSHENuBfgTuklO6IpJeBe/UoqEqgFtjPfPq4aA68zMNgx3vQIlfOAo9FW5+L0HcLWtPrGHBE/3sPWr/o68AZ/TNHzy+Ap3T7jgP10bZhDptuZDrqpkp/QW3Ar4EUXZ6qn9v09Kpo6z3LhmuARr1cXkSL1ojLMgG+ApwCTgA/RYvkiItyAX6ONrbgR6vNfvRyygGt/9um/304hmyxofW5T/32n47I/5huSwtwe4R8XnycmhmrUCgUCU48d90oFAqF4iJQjl6hUCgSHOXoFQqFIsFRjl6hUCgSHOXoFQqFIsFRjl6hUCgSHOXoFQqFIsFRjl6hUCgSnP8HltrSPrzVr+8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x123443550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1228f97b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n",
+      "timestep 0 is not done correctly\n",
+      "It has a powercap of 0.000000 when it should have 110.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x121cbbf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "def get_dep_arrays(sfr_avail, growth_rate, old_cap, new_cap, old_life, new_life):\n",
+    "    # automatically calculated values\n",
+    "    scenario_name = '%i_%s_growth' %(sfr_avail, str(growth_rate).replace('.','_'))\n",
+    "    demand_eq = '110*(%s)**(t/12)' %str(1+growth_rate)\n",
+    "    avail_timestep = (sfr_avail* 12) - (2013*12 + 5)\n",
+    "\n",
+    "    # get lwr_only, find deployment scheme for lwr and sfr\n",
+    "    cur = an.get_cursor('./output/2020_lwr.sqlite')\n",
+    "    init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
+    "    power_dict = an.get_power_dict(cur)\n",
+    "    dep_array, dep_power = an.get_new_deployment(power_dict, ['us_government'],\n",
+    "                                                 demand_eq, old_cap, old_life)\n",
+    "    for indx in range(len(dep_array)):\n",
+    "        if indx > avail_timestep:\n",
+    "            dep_array[indx] = 0\n",
+    "    # recalculate power\n",
+    "    dep_power = np.zeros(len(dep_array))\n",
+    "    for indx, val in enumerate(dep_array):\n",
+    "        if val != 0:\n",
+    "            top = min(indx+960, duration)\n",
+    "            for i in range(indx, top):\n",
+    "                dep_power[i] += val * old_cap\n",
+    "    plt.plot(dep_power, label='dep power')\n",
+    "    plt.plot(dep_array, label='dep array')\n",
+    "\n",
+    "    new_power = {'power': power_dict['us_government'] + dep_power}\n",
+    "    plt.plot(new_power['power'], label='only old')\n",
+    "\n",
+    "    sfr_dep, sfr_power = an.get_new_deployment(new_power, ['power'], demand_eq,\n",
+    "                                               new_cap, new_life)\n",
+    "    plt.plot(new_power['power'] + sfr_power, label='total power')\n",
+    "    plt.plot(sfr_dep, label='new reactor deployment')\n",
+    "    plt.plot(sfr_power, label='only new')\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "    plt.close()\n",
+    "    return dep_array, sfr_dep\n",
+    "\n",
+    "dep_dict = {}\n",
+    "for new_avail in [2030, 2035, 2040, 2045, 2050]:\n",
+    "    for growth_rate in [0.0, 0.005, 0.01, 0.015]:\n",
+    "        scenario_name = '%i_%s_growth' %(new_avail, str(growth_rate).replace('0.', ''))\n",
+    "        dep1, dep2 = get_dep_arrays(new_avail, growth_rate, 1, 0.4, 960, 960)\n",
+    "        dep_dict[scenario_name] = {'lwr': dep1, 'sfr': dep2}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./2030_0_growth/input.xml DOES NOT EXIST\n",
+      "./2030_005_growth/input.xml DOES NOT EXIST\n",
+      "./2030_01_growth/input.xml DOES NOT EXIST\n",
+      "./2030_015_growth/input.xml DOES NOT EXIST\n",
+      "./2035_0_growth/input.xml DOES NOT EXIST\n",
+      "./2035_005_growth/input.xml DOES NOT EXIST\n",
+      "./2035_01_growth/input.xml DOES NOT EXIST\n",
+      "./2035_015_growth/input.xml DOES NOT EXIST\n",
+      "./2040_0_growth/input.xml DOES NOT EXIST\n",
+      "./2040_005_growth/input.xml DOES NOT EXIST\n",
+      "./2040_01_growth/input.xml DOES NOT EXIST\n",
+      "./2040_015_growth/input.xml DOES NOT EXIST\n",
+      "./2045_0_growth/input.xml DOES NOT EXIST\n",
+      "./2045_005_growth/input.xml DOES NOT EXIST\n",
+      "./2045_01_growth/input.xml DOES NOT EXIST\n",
+      "./2045_015_growth/input.xml DOES NOT EXIST\n",
+      "./2050_0_growth/input.xml DOES NOT EXIST\n",
+      "./2050_005_growth/input.xml DOES NOT EXIST\n",
+      "./2050_01_growth/input.xml DOES NOT EXIST\n",
+      "./2050_015_growth/input.xml DOES NOT EXIST\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write these deploy schemes to file\n",
+    "for key in dep_dict.keys():\n",
+    "    if not os.path.isdir(\"./%s\" %key):\n",
+    "        print('FOLDER %s NOT THERE,' %key)\n",
+    "        os.makedirs(key)    \n",
+    "        print('%s FOLDER MADE!' %key)\n",
+    "    try:\n",
+    "        os.remove('./%s/input.xml' %key)\n",
+    "    except:\n",
+    "        print('./%s/input.xml DOES NOT EXIST'%key)\n",
+    "    copyfile('./udb_recipe.xml', './%s/udb_recipe.xml' %key)\n",
+    "    copyfile('./udb_precise.xml', './%s/udb_precise.xml' %key)\n",
+    "    copyfile('./udb_no.xml', './%s/udb_no.xml' %key)\n",
+    "    for key2 in dep_dict[key]:\n",
+    "        an.write_deployinst(dep_dict[key][key2], key2, './%s/%s_dep.xml' %(key, key2),\n",
+    "                            960)\n",
+    "        if key2 == 'sfr':\n",
+    "            an.write_deployinst(dep_dict[key][key2], 'blanket', './%s/blanket_dep.xml' %(key), 960)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete sqlite files\n",
+    "for key in dep_dict.keys():\n",
+    "    if os.path.isfile('./%s/output.sqlite' %key):\n",
+    "        os.remove('./%s/output.sqlite' %key)\n",
+    "        print('REMOVED %s' %key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## "
+   ]
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/input/master_us/images/legacy_power.png b/input/master_us/images/legacy_power.png
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diff --git a/input/master_us/input.xml b/input/master_us/input.xml
new file mode 100644
index 00000000..596c38a2
--- /dev/null
+++ b/input/master_us/input.xml
@@ -0,0 +1,512 @@
+
+<simulation xmlns:xi="http://www.w3.org/2001/XInclude">
+  <control>
+    <duration>1764</duration>
+    <startmonth>5</startmonth>
+    <startyear>2013</startyear>
+    <decay>lazy</decay>
+  </control>
+
+  <archetypes>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Source</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Sink</name>
+    </spec>
+    <spec>
+        <lib>cycamore</lib>
+        <name>Reactor</name>
+    </spec>
+    <spec>
+      <lib>agents</lib>
+      <name>NullRegion</name>
+    </spec>
+    <spec>
+      <lib>agents</lib>
+      <name>NullInst</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Separations</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>DeployInst</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Separations</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Enrichment</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Mixer</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Storage</name>
+    </spec>
+  </archetypes>
+
+
+
+
+<!-- La Hague Model from Schneider & Marignac -->
+  <facility>
+    <name>reprocessing</name>
+    <config>
+      <Separations>
+         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> </feed_commods>
+         <feed_commod_prefs> <val>1.0</val> <val>1.0</val> </feed_commod_prefs>
+         <feed_recipe>uox_used_fuel_recipe</feed_recipe>
+         <feedbuf_size>1e100</feedbuf_size>
+         <!-- 1100 tons/year 91.6 tons/timestep -->
+         <throughput>1e100</throughput>
+         <leftover_commod>reprocess_waste</leftover_commod>
+         <leftoverbuf_size>1e100</leftoverbuf_size>
+         <streams>
+          <item>
+            <commod>lwr_pu</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Pu</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_u</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>U</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+        </streams>
+      </Separations>
+    </config>
+  </facility>
+
+  <facility>
+    <name>sfr_reprocessing</name>
+    <config>
+      <Separations>
+         <feed_commods>   <val>cooled_sfr</val> <val>cooled_blanket</val> </feed_commods>
+         <feed_commod_prefs> <val>1.0</val> <val>1.0</val>  </feed_commod_prefs>
+         <feed_recipe>driver_used</feed_recipe>
+         <feedbuf_size>1e100</feedbuf_size>
+         <!-- 1100 tons/year 91.6 tons/timestep -->
+         <throughput>1e100</throughput>
+         <leftover_commod>reprocess_waste</leftover_commod>
+         <leftoverbuf_size>1e100</leftoverbuf_size>
+         <streams>
+          <item>
+            <commod>sfr_pu</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Pu</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>sfr_u</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>U</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+        </streams>
+      </Separations>
+    </config>
+  </facility>
+
+
+<!-- Infinite UOX_Pu to MOX mixer -->
+<facility>
+    <name>sfr_mixer_lwr</name>
+    <config>
+      <Mixer>
+        <in_streams>
+          <stream>
+            <info>
+              <mixing_ratio>0.15</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_pu</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.85</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_u</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+        </in_streams>
+        <out_commod>sfr_fuel_legacy</out_commod>
+        <out_buf_size>1e100</out_buf_size>
+        <throughput>1e100</throughput>
+      </Mixer>
+    </config>
+</facility>
+
+
+<!-- Infinite UOX_Pu to MOX mixer -->
+<facility>
+    <name>sfr_mixer_sfr</name>
+    <config>
+      <Mixer>
+        <in_streams>
+          <stream>
+            <info>
+              <mixing_ratio>0.15</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>sfr_pu</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.85</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>sfr_u</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+        </in_streams>
+        <out_commod>sfr_fuel_new</out_commod>
+        <out_buf_size>1e100</out_buf_size>
+        <throughput>1e100</throughput>
+      </Mixer>
+    </config>
+</facility>
+
+
+<facility>
+    <name>sfr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>driver_fresh</val>      <val>driver_fresh</val>  </fuel_inrecipes>
+        <fuel_outrecipes> <val>driver_used</val> <val>driver_used</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>sfr_fuel_legacy</val> <val>sfr_fuel_new</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>sfr_used</val> <val>sfr_used</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val> <val>3.0</val>                   </fuel_prefs>
+        <cycle_time>9</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>3076</assem_size>
+        <n_assem_core>5</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>400</power_cap>
+      </Reactor>
+    </config>
+</facility>
+
+<facility>
+    <name>blanket</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>natl_u_recipe</val>        </fuel_inrecipes>
+        <fuel_outrecipes> <val>blanket_used</val>   </fuel_outrecipes>
+        <fuel_incommods>  <val>blanket</val>                    </fuel_incommods>
+        <fuel_outcommods> <val>blanket_used</val>              </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                    </fuel_prefs>
+        <cycle_time>14</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>768</assem_size>
+        <n_assem_core>5</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>0</power_cap>
+      </Reactor>
+    </config>
+</facility>
+
+
+<facility>
+    <name>smr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>smr_recipe</val>        </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>   </fuel_outrecipes>
+        <fuel_incommods>  <val>smr_fuel</val>                    </fuel_incommods>
+        <fuel_outcommods> <val>smr_used</val>              </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                    </fuel_prefs>
+        <cycle_time>24</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>447</assem_size>
+        <n_assem_core>37</n_assem_core>
+        <n_assem_batch>12</n_assem_batch>
+        <power_cap>50</power_cap>
+      </Reactor>
+    </config>
+</facility>
+
+
+<facility>
+    <name>fhr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>fhr_recipe</val>        </fuel_inrecipes>
+        <fuel_outrecipes> <val>used_fhr_recipe</val>   </fuel_outrecipes>
+        <fuel_incommods>  <val>fhr_fuel</val>                    </fuel_incommods>
+        <fuel_outcommods> <val>fhr_used</val>              </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                    </fuel_prefs>
+        <cycle_time>1</cycle_time>
+        <refuel_time>0</refuel_time>
+        <!-- 470,000 Pebbles in core, with lifetime of 1.4 years -->
+        <!-- 1.5g of LEU in each pebble -->
+        <assem_size>41.47</assem_size>
+        <n_assem_core>17</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>100</power_cap>
+      </Reactor>
+    </config>
+</facility>
+
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>lwr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1000</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+<facility>
+        <!-- Stores uox unf for 48 months (4years) -->
+    <name>sfr_storage</name>
+    <config>
+        <Storage>
+            <in_commods>
+                <val>sfr_used</val>
+            </in_commods>
+            <residence_time>36</residence_time>
+            <out_commods>
+                <val>cooled_sfr</val>
+            </out_commods>
+        </Storage>
+    </config>
+</facility>
+
+<facility>
+        <!-- Stores uox unf for 48 months (4years) -->
+    <name>blanket_storage</name>
+    <config>
+        <Storage>
+            <in_commods>
+                <val>blanket_used</val>
+            </in_commods>
+            <residence_time>12</residence_time>
+            <out_commods>
+                <val>cooled_blanket</val>
+            </out_commods>
+        </Storage>
+    </config>
+</facility>
+
+<facility>
+        <!-- Stores uox unf for 48 months (4years) -->
+    <name>uox_storage</name>
+    <config>
+        <Storage>
+            <in_commods>
+                <val>uox_waste</val>
+            </in_commods>
+            <residence_time>36</residence_time>
+            <out_commods>
+                <val>cooled_uox</val>
+            </out_commods>
+        </Storage>
+    </config>
+</facility>
+
+
+
+  <facility>
+    <name>enrichment</name>
+    <config>
+      <Enrichment>
+        <feed_commod>natl_u</feed_commod>
+        <feed_recipe>natl_u_recipe</feed_recipe>
+        <product_commod>uox</product_commod>
+        <tails_assay>0.003</tails_assay>
+        <tails_commod>tailings</tails_commod>
+        <swu_capacity>1e100</swu_capacity>
+        <initial_feed>1e100</initial_feed>
+      </Enrichment>
+    </config>
+  </facility>
+
+  <facility>
+    <name>nat_u_source</name>
+    <config>
+      <Source>
+        <outcommod>natl_u</outcommod>
+        <outrecipe>natl_u_recipe</outrecipe>
+        <throughput>1e100</throughput>
+      </Source>
+    </config>
+  </facility>
+
+  <facility>
+    <name>blanket_source</name>
+    <config>
+      <Source>
+        <outcommod>blanket</outcommod>
+        <outrecipe>natl_u_recipe</outrecipe>
+        <throughput>1e100</throughput>
+      </Source>
+    </config>
+  </facility>
+
+  <facility>
+    <name>unf_source</name>
+    <config>
+      <Source>
+        <outcommod>pre_2013_unf</outcommod>
+        <outrecipe>udb_precise</outrecipe>
+        <throughput>1e100</throughput>
+        <inventory_size>68072030</inventory_size>
+      </Source>
+    </config>
+  </facility>
+
+  <facility>
+    <name>SomeSink</name>
+    <config>
+      <Sink>
+        <in_commods>
+          <val>tailings</val>
+          <val>reprocess_waste</val>
+        </in_commods>
+        <capacity>1e100</capacity>
+      </Sink>
+    </config>
+  </facility>
+
+  <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
+
+  <region>
+    <name>us</name>
+    <config><NullRegion/></config>
+    <institution>
+      <name>us_government</name>
+      <config>
+        <DeployInst>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/prototypes)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/build_times)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/n_build)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/lifetimes)'/>
+        </DeployInst>
+      </config>
+    </institution>
+  </region>
+
+<region>
+    <name>Sink_Source</name>
+    <config><NullRegion/></config>
+        <institution>
+         <name>Sink_Source_Inst</name>
+         <config><NullInst/></config>
+        <initialfacilitylist>
+          <entry>
+            <number>1</number>
+            <prototype>nat_u_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>blanket_storage</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>enrichment</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>SomeSink</prototype>
+          </entry>
+
+          <entry>
+            <number>1</number>
+            <prototype>blanket_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>reprocessing</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>sfr_reprocessing</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>sfr_mixer_sfr</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>sfr_mixer_lwr</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>sfr_storage</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>uox_storage</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>unf_source</prototype>
+          </entry>
+        </initialfacilitylist>
+        </institution>
+</region>
+<xi:include href='recipes.xml' xpointer='xpointer(/root/recipe)'/>
+</simulation>
\ No newline at end of file
diff --git a/input/master_us/reactor_with_coordinates.xml b/input/master_us/reactor_with_coordinates.xml
new file mode 100644
index 00000000..344a5dc5
--- /dev/null
+++ b/input/master_us/reactor_with_coordinates.xml
@@ -0,0 +1,2672 @@
+<root>
+
+<!-- Deleted Fort St. Vraine Becuase it is a natural gas now -->
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>DRESDEN-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>150</n_assem_core>
+        <n_assem_batch>50</n_assem_batch>
+        <power_cap>197</power_cap>
+	<longitude>-88.268100</longitude>
+	<latitude>41.389700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>INDIAN POINT-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>49</n_assem_core>
+        <n_assem_batch>16</n_assem_batch>
+        <power_cap>257</power_cap>
+	<longitude>-73.952222</longitude>
+	<latitude>41.269722</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>YANKEE NPS</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>32</n_assem_core>
+        <n_assem_batch>10</n_assem_batch>
+        <power_cap>167</power_cap>
+	<longitude>-73.952222</longitude>
+	<latitude>41.269722</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>HADDAM NECK</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>108</n_assem_core>
+        <n_assem_batch>36</n_assem_batch>
+        <power_cap>560</power_cap>
+	<longitude>-73.952222</longitude>
+	<latitude>41.269722</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SAN ONOFRE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>84</n_assem_core>
+        <n_assem_batch>28</n_assem_batch>
+        <power_cap>436</power_cap>
+	<longitude>-117.555000</longitude>
+	<latitude>33.368900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>OYSTER CREEK</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>472</n_assem_core>
+        <n_assem_batch>157</n_assem_batch>
+        <power_cap>619</power_cap>
+	<longitude>-74.205000</longitude>
+	<latitude>39.814700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>NINE MILE POINT-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>474</n_assem_core>
+        <n_assem_batch>158</n_assem_batch>
+        <power_cap>621</power_cap>
+	<longitude>-76.406900</longitude>
+	<latitude>43.520800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>DRESDEN-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>674</n_assem_core>
+        <n_assem_batch>224</n_assem_batch>
+        <power_cap>883</power_cap>
+	<longitude>-88.268100</longitude>
+	<latitude>41.389700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>GINNA</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>111</n_assem_core>
+        <n_assem_batch>37</n_assem_batch>
+        <power_cap>580</power_cap>
+	<longitude>-77.310000</longitude>
+	<latitude>43.277800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>MILLSTONE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>489</n_assem_core>
+        <n_assem_batch>163</n_assem_batch>
+        <power_cap>641</power_cap>
+	<longitude>-72.168611</longitude>
+	<latitude>41.311944</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>DRESDEN-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>662</n_assem_core>
+        <n_assem_batch>220</n_assem_batch>
+        <power_cap>867</power_cap>
+	<longitude>-88.268100</longitude>
+	<latitude>41.389700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>INDIAN POINT-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>196</n_assem_core>
+        <n_assem_batch>65</n_assem_batch>
+        <power_cap>1020</power_cap>
+	<longitude>-73.952222</longitude>
+	<latitude>41.269722</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>QUAD CITIES-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>693</n_assem_core>
+        <n_assem_batch>231</n_assem_batch>
+        <power_cap>908</power_cap>
+	<longitude>-90.310000</longitude>
+	<latitude>41.726400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>QUAD CITIES-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>696</n_assem_core>
+        <n_assem_batch>232</n_assem_batch>
+        <power_cap>911</power_cap>
+	<longitude>-90.310000</longitude>
+	<latitude>41.726400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>PALISADES</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>153</n_assem_core>
+        <n_assem_batch>51</n_assem_batch>
+        <power_cap>793</power_cap>
+	<longitude>-86.314400</longitude>
+	<latitude>42.322800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ROBINSON-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>143</n_assem_core>
+        <n_assem_batch>47</n_assem_batch>
+        <power_cap>741</power_cap>
+	<longitude>-80.158300</longitude>
+	<latitude>34.402800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>TURKEY POINT-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>154</n_assem_core>
+        <n_assem_batch>51</n_assem_batch>
+        <power_cap>802</power_cap>
+	<longitude>-80.330600</longitude>
+	<latitude>25.434200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>TURKEY POINT-4</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>154</n_assem_core>
+        <n_assem_batch>51</n_assem_batch>
+        <power_cap>802</power_cap>
+	<longitude>-80.330600</longitude>
+	<latitude>25.434200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>BROWNS FERRY-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>841</n_assem_core>
+        <n_assem_batch>280</n_assem_batch>
+        <power_cap>1101</power_cap>
+	<longitude>-87.118600</longitude>
+	<latitude>34.703900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>BROWNS FERRY-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>843</n_assem_core>
+        <n_assem_batch>281</n_assem_batch>
+        <power_cap>1104</power_cap>
+	<longitude>-87.118600</longitude>
+	<latitude>34.703900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>MONTICELLO</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>441</n_assem_core>
+        <n_assem_batch>147</n_assem_batch>
+        <power_cap>578</power_cap>
+	<longitude>-93.849200</longitude>
+	<latitude>45.333600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>POINT BEACH-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>114</n_assem_core>
+        <n_assem_batch>38</n_assem_batch>
+        <power_cap>591</power_cap>
+	<longitude>-87.536700</longitude>
+	<latitude>44.281100</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>OCONEE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>163</n_assem_core>
+        <n_assem_batch>54</n_assem_batch>
+        <power_cap>846</power_cap>
+	<longitude>-82.898100</longitude>
+	<latitude>34.793900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>OCONEE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>163</n_assem_core>
+        <n_assem_batch>54</n_assem_batch>
+        <power_cap>846</power_cap>
+	<longitude>-82.898100</longitude>
+	<latitude>34.793900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>OCONEE-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>163</n_assem_core>
+        <n_assem_batch>54</n_assem_batch>
+        <power_cap>846</power_cap>
+	<longitude>-82.898100</longitude>
+	<latitude>34.793900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>VERMONT YANKEE</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>462</n_assem_core>
+        <n_assem_batch>154</n_assem_batch>
+        <power_cap>605</power_cap>
+	<longitude>-72.513100</longitude>
+	<latitude>42.778900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>PEACH BOTTOM-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>859</n_assem_core>
+        <n_assem_batch>286</n_assem_batch>
+        <power_cap>1125</power_cap>
+	<longitude>-76.268100</longitude>
+	<latitude>39.758300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>PEACH BOTTOM-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>869</n_assem_core>
+        <n_assem_batch>289</n_assem_batch>
+        <power_cap>1138</power_cap>
+	<longitude>-76.268100</longitude>
+	<latitude>39.758300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>DIABLO CANYON-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>216</n_assem_core>
+        <n_assem_batch>72</n_assem_batch>
+        <power_cap>1122</power_cap>
+	<longitude>-120.856000</longitude>
+	<latitude>35.210800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>THREE MILE ISLAND-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>158</n_assem_core>
+        <n_assem_batch>52</n_assem_batch>
+        <power_cap>819</power_cap>
+	<longitude>-76.724911</longitude>
+	<latitude>40.152047</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>CALVERT CLIFFS-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>167</n_assem_core>
+        <n_assem_batch>55</n_assem_batch>
+        <power_cap>866</power_cap>
+	<longitude>-76.442200</longitude>
+	<latitude>38.431900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>CALVERT CLIFFS-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>164</n_assem_core>
+        <n_assem_batch>54</n_assem_batch>
+        <power_cap>850</power_cap>
+	<longitude>-76.442200</longitude>
+	<latitude>38.431900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>COOPER</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>586</n_assem_core>
+        <n_assem_batch>195</n_assem_batch>
+        <power_cap>768</power_cap>
+	<longitude>-95.641400</longitude>
+	<latitude>40.361900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>FORT CALHOUN-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>93</n_assem_core>
+        <n_assem_batch>31</n_assem_batch>
+        <power_cap>482</power_cap>
+	<longitude>-96.077200</longitude>
+	<latitude>41.520300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>PRAIRIE ISLAND-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>100</n_assem_core>
+        <n_assem_batch>33</n_assem_batch>
+        <power_cap>522</power_cap>
+	<longitude>-92.633100</longitude>
+	<latitude>44.621700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SURRY-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>161</n_assem_core>
+        <n_assem_batch>53</n_assem_batch>
+        <power_cap>838</power_cap>
+	<longitude>-76.697800</longitude>
+	<latitude>37.165600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SURRY-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>161</n_assem_core>
+        <n_assem_batch>53</n_assem_batch>
+        <power_cap>838</power_cap>
+	<longitude>-76.697800</longitude>
+	<latitude>37.165600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>BROWNS FERRY-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>844</n_assem_core>
+        <n_assem_batch>281</n_assem_batch>
+        <power_cap>1105</power_cap>
+	<longitude>-87.118600</longitude>
+	<latitude>34.703900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>POINT BEACH-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>114</n_assem_core>
+        <n_assem_batch>38</n_assem_batch>
+        <power_cap>591</power_cap>
+	<longitude>-87.536700</longitude>
+	<latitude>44.281100</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>KEWAUNEE</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>109</n_assem_core>
+        <n_assem_batch>36</n_assem_batch>
+        <power_cap>566</power_cap>
+	<longitude>-87.536100</longitude>
+	<latitude>44.342200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>PILGRIM-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>517</n_assem_core>
+        <n_assem_batch>172</n_assem_batch>
+        <power_cap>677</power_cap>
+	<longitude>-70.578300</longitude>
+	<latitude>41.945000</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>FITZPATRICK</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>621</n_assem_core>
+        <n_assem_batch>207</n_assem_batch>
+        <power_cap>813</power_cap>
+	<longitude>-76.398300</longitude>
+	<latitude>43.523300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SALEM-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>225</n_assem_core>
+        <n_assem_batch>75</n_assem_batch>
+        <power_cap>1168</power_cap>
+	<longitude>-75.534225</longitude>
+	<latitude>39.462977</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SALEM-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>223</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1158</power_cap>
+	<longitude>-75.534225</longitude>
+	<latitude>39.462977</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>CRYSTAL RIVER-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>165</n_assem_core>
+        <n_assem_batch>55</n_assem_batch>
+        <power_cap>860</power_cap>
+	<longitude>-82.698300</longitude>
+	<latitude>28.957500</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>HATCH-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>669</n_assem_core>
+        <n_assem_batch>223</n_assem_batch>
+        <power_cap>876</power_cap>
+	<longitude>-82.343900</longitude>
+	<latitude>31.934200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ANO-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>161</n_assem_core>
+        <n_assem_batch>53</n_assem_batch>
+        <power_cap>836</power_cap>
+	<longitude>-82.343900</longitude>
+	<latitude>31.934200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>MAINE YANKEE</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>165</n_assem_core>
+        <n_assem_batch>55</n_assem_batch>
+        <power_cap>860</power_cap>
+	<longitude>-69.696111</longitude>
+	<latitude>43.950556</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>INDIAN POINT-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>200</n_assem_core>
+        <n_assem_batch>66</n_assem_batch>
+        <power_cap>1040</power_cap>
+	<longitude>-73.952222</longitude>
+	<latitude>41.269722</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ZION-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>200</n_assem_core>
+        <n_assem_batch>66</n_assem_batch>
+        <power_cap>1040</power_cap>
+	<longitude>-87.803000</longitude>
+	<latitude>42.446400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ZION-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>200</n_assem_core>
+        <n_assem_batch>66</n_assem_batch>
+        <power_cap>1040</power_cap>
+	<longitude>-87.803000</longitude>
+	<latitude>42.446400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ANO-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>191</n_assem_core>
+        <n_assem_batch>63</n_assem_batch>
+        <power_cap>993</power_cap>
+	<longitude>-87.803000</longitude>
+	<latitude>42.446400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>COOK-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>198</n_assem_core>
+        <n_assem_batch>66</n_assem_batch>
+        <power_cap>1030</power_cap>
+	<longitude>-86.565900</longitude>
+	<latitude>41.975400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>COOK-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>207</n_assem_core>
+        <n_assem_batch>69</n_assem_batch>
+        <power_cap>1077</power_cap>
+	<longitude>-86.565900</longitude>
+	<latitude>41.975400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>RANCHO SECO-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>168</n_assem_core>
+        <n_assem_batch>56</n_assem_batch>
+        <power_cap>873</power_cap>
+	<longitude>-121.122000</longitude>
+	<latitude>38.345300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>PRAIRIE ISLAND-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>99</n_assem_core>
+        <n_assem_batch>33</n_assem_batch>
+        <power_cap>518</power_cap>
+	<longitude>-92.633100</longitude>
+	<latitude>44.621700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>MILLSTONE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>167</n_assem_core>
+        <n_assem_batch>55</n_assem_batch>
+        <power_cap>869</power_cap>
+	<longitude>-72.168611</longitude>
+	<latitude>41.311944</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>THREE MILE ISLAND-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>169</n_assem_core>
+        <n_assem_batch>56</n_assem_batch>
+        <power_cap>880</power_cap>
+	<longitude>-76.724911</longitude>
+	<latitude>40.152047</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>TROJAN</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>211</n_assem_core>
+        <n_assem_batch>70</n_assem_batch>
+        <power_cap>1095</power_cap>
+	<longitude>-122.885000</longitude>
+	<latitude>46.038300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>BRUNSWICK-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>716</n_assem_core>
+        <n_assem_batch>238</n_assem_batch>
+        <power_cap>938</power_cap>
+	<longitude>-78.010300</longitude>
+	<latitude>33.958300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>BRUNSWICK-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>702</n_assem_core>
+        <n_assem_batch>234</n_assem_batch>
+        <power_cap>920</power_cap>
+	<longitude>-78.010300</longitude>
+	<latitude>33.958300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SEQUOYAH-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>222</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1152</power_cap>
+	<longitude>-85.091700</longitude>
+	<latitude>35.226400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SEQUOYAH-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>217</n_assem_core>
+        <n_assem_batch>72</n_assem_batch>
+        <power_cap>1125</power_cap>
+	<longitude>-85.091700</longitude>
+	<latitude>35.226400</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>DUANE ARNOLD-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>459</n_assem_core>
+        <n_assem_batch>153</n_assem_batch>
+        <power_cap>601</power_cap>
+	<longitude>-91.777200</longitude>
+	<latitude>42.100600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>BEAVER VALLEY-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>177</n_assem_core>
+        <n_assem_batch>59</n_assem_batch>
+        <power_cap>921</power_cap>
+	<longitude>-80.430600</longitude>
+	<latitude>40.623300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ST. LUCIE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>189</n_assem_core>
+        <n_assem_batch>63</n_assem_batch>
+        <power_cap>982</power_cap>
+	<longitude>-80.246400</longitude>
+	<latitude>27.348600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>DAVIS BESSE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>172</n_assem_core>
+        <n_assem_batch>57</n_assem_batch>
+        <power_cap>894</power_cap>
+	<longitude>-80.246400</longitude>
+	<latitude>27.348600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>FARLEY-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>168</n_assem_core>
+        <n_assem_batch>56</n_assem_batch>
+        <power_cap>874</power_cap>
+	<longitude>-85.111700</longitude>
+	<latitude>31.223100</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>FARLEY-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>170</n_assem_core>
+        <n_assem_batch>56</n_assem_batch>
+        <power_cap>883</power_cap>
+	<longitude>-85.111700</longitude>
+	<latitude>31.223100</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>DIABLO CANYON-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>215</n_assem_core>
+        <n_assem_batch>71</n_assem_batch>
+        <power_cap>1118</power_cap>
+	<longitude>-120.856000</longitude>
+	<latitude>35.210800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>NORTH ANNA-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>181</n_assem_core>
+        <n_assem_batch>60</n_assem_batch>
+        <power_cap>943</power_cap>
+	<longitude>-77.789400</longitude>
+	<latitude>38.060600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>NORTH ANNA-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>181</n_assem_core>
+        <n_assem_batch>60</n_assem_batch>
+        <power_cap>943</power_cap>
+	<longitude>-77.789400</longitude>
+	<latitude>38.060600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>MCGUIRE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>223</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1158</power_cap>
+	<longitude>-80.948300</longitude>
+	<latitude>35.432500</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>MCGUIRE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>223</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1158</power_cap>
+	<longitude>-80.948300</longitude>
+	<latitude>35.432500</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>HATCH-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>674</n_assem_core>
+        <n_assem_batch>224</n_assem_batch>
+        <power_cap>883</power_cap>
+	<longitude>-82.343900</longitude>
+	<latitude>31.934200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>COLUMBIA</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>845</n_assem_core>
+        <n_assem_batch>281</n_assem_batch>
+        <power_cap>1107</power_cap>
+	<longitude>-119.334000</longitude>
+	<latitude>46.471100</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>FERMI-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>792</n_assem_core>
+        <n_assem_batch>264</n_assem_batch>
+        <power_cap>1037</power_cap>
+	<longitude>-83.257500</longitude>
+	<latitude>41.962800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>SHOREHAM</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>626</n_assem_core>
+        <n_assem_batch>208</n_assem_batch>
+        <power_cap>820</power_cap>
+	<longitude>-72.865000</longitude>
+	<latitude>40.961100</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>WATTS BAR-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>216</n_assem_core>
+        <n_assem_batch>72</n_assem_batch>
+        <power_cap>1123</power_cap>
+	<longitude>-84.789400</longitude>
+	<latitude>35.602800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SUMMER-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>187</n_assem_core>
+        <n_assem_batch>62</n_assem_batch>
+        <power_cap>971</power_cap>
+	<longitude>-81.314700</longitude>
+	<latitude>34.298600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>LASALLE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>868</n_assem_core>
+        <n_assem_batch>289</n_assem_batch>
+        <power_cap>1137</power_cap>
+	<longitude>-88.669200</longitude>
+	<latitude>41.245600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>LASALLE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>870</n_assem_core>
+        <n_assem_batch>290</n_assem_batch>
+        <power_cap>1140</power_cap>
+	<longitude>-88.669200</longitude>
+	<latitude>41.245600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>SUSQUEHANNA-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>960</n_assem_core>
+        <n_assem_batch>320</n_assem_batch>
+        <power_cap>1257</power_cap>
+	<longitude>-76.148900</longitude>
+	<latitude>41.088900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>SUSQUEHANNA-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>960</n_assem_core>
+        <n_assem_batch>320</n_assem_batch>
+        <power_cap>1257</power_cap>
+	<longitude>-76.148900</longitude>
+	<latitude>41.088900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SAN ONOFRE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>206</n_assem_core>
+        <n_assem_batch>68</n_assem_batch>
+        <power_cap>1070</power_cap>
+	<longitude>-117.555000</longitude>
+	<latitude>33.368900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SAN ONOFRE-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>208</n_assem_core>
+        <n_assem_batch>69</n_assem_batch>
+        <power_cap>1080</power_cap>
+	<longitude>-117.555000</longitude>
+	<latitude>33.368900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>CATAWBA-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>221</n_assem_core>
+        <n_assem_batch>73</n_assem_batch>
+        <power_cap>1146</power_cap>
+	<longitude>-81.070000</longitude>
+	<latitude>35.051700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>CATAWBA-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>221</n_assem_core>
+        <n_assem_batch>73</n_assem_batch>
+        <power_cap>1146</power_cap>
+	<longitude>-81.070000</longitude>
+	<latitude>35.051700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>BEAVER VALLEY-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>170</n_assem_core>
+        <n_assem_batch>56</n_assem_batch>
+        <power_cap>885</power_cap>
+	<longitude>-80.430600</longitude>
+	<latitude>40.623300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>GRAND GULF-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>1084</n_assem_core>
+        <n_assem_batch>361</n_assem_batch>
+        <power_cap>1419</power_cap>
+	<longitude>-91.048300</longitude>
+	<latitude>32.006700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>LIMERICK-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>863</n_assem_core>
+        <n_assem_batch>287</n_assem_batch>
+        <power_cap>1130</power_cap>
+	<longitude>-75.587200</longitude>
+	<latitude>40.226700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>LIMERICK-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>866</n_assem_core>
+        <n_assem_batch>288</n_assem_batch>
+        <power_cap>1134</power_cap>
+	<longitude>-75.587200</longitude>
+	<latitude>40.226700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>MILLSTONE-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>235</n_assem_core>
+        <n_assem_batch>78</n_assem_batch>
+        <power_cap>1218</power_cap>
+	<longitude>-72.168611</longitude>
+	<latitude>41.311944</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>PERRY-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>959</n_assem_core>
+        <n_assem_batch>319</n_assem_batch>
+        <power_cap>1256</power_cap>
+	<longitude>-81.143300</longitude>
+	<latitude>41.800800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>WATERFORD-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>225</n_assem_core>
+        <n_assem_batch>75</n_assem_batch>
+        <power_cap>1168</power_cap>
+	<longitude>-90.471100</longitude>
+	<latitude>29.995300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>COMANCHE PEAK-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>233</n_assem_core>
+        <n_assem_batch>77</n_assem_batch>
+        <power_cap>1209</power_cap>
+	<longitude>-97.785000</longitude>
+	<latitude>32.298300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>COMANCHE PEAK-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>231</n_assem_core>
+        <n_assem_batch>77</n_assem_batch>
+        <power_cap>1197</power_cap>
+	<longitude>-97.785000</longitude>
+	<latitude>32.298300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>BYRON-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>224</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1164</power_cap>
+	<longitude>-89.281900</longitude>
+	<latitude>42.074200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>BYRON-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>219</n_assem_core>
+        <n_assem_batch>73</n_assem_batch>
+        <power_cap>1136</power_cap>
+	<longitude>-89.281900</longitude>
+	<latitude>42.074200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>BRAIDWOOD-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>227</n_assem_core>
+        <n_assem_batch>75</n_assem_batch>
+        <power_cap>1178</power_cap>
+	<longitude>-88.229200</longitude>
+	<latitude>41.243600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>BRAIDWOOD-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>222</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1152</power_cap>
+	<longitude>-88.229200</longitude>
+	<latitude>41.243600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>NINE MILE POINT-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>974</n_assem_core>
+        <n_assem_batch>324</n_assem_batch>
+        <power_cap>1276</power_cap>
+	<longitude>-76.406900</longitude>
+	<latitude>43.520800</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>CALLAWAY-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>234</n_assem_core>
+        <n_assem_batch>78</n_assem_batch>
+        <power_cap>1215</power_cap>
+	<longitude>-91.780000</longitude>
+	<latitude>38.761700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>CLINTON-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>813</n_assem_core>
+        <n_assem_batch>271</n_assem_batch>
+        <power_cap>1065</power_cap>
+	<longitude>-88.835000</longitude>
+	<latitude>40.172200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SOUTH TEXAS-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>247</n_assem_core>
+        <n_assem_batch>82</n_assem_batch>
+        <power_cap>1280</power_cap>
+	<longitude>-96.048900</longitude>
+	<latitude>28.795600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>SOUTH TEXAS-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>247</n_assem_core>
+        <n_assem_batch>82</n_assem_batch>
+        <power_cap>1280</power_cap>
+	<longitude>-96.048900</longitude>
+	<latitude>28.795600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>HOPE CREEK-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>895</n_assem_core>
+        <n_assem_batch>298</n_assem_batch>
+        <power_cap>1172</power_cap>
+	<longitude>-75.538056</longitude>
+	<latitude>39.467778</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>PALO VERDE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>253</n_assem_core>
+        <n_assem_batch>84</n_assem_batch>
+        <power_cap>1311</power_cap>
+	<longitude>-112.865000</longitude>
+	<latitude>33.389200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>PALO VERDE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>253</n_assem_core>
+        <n_assem_batch>84</n_assem_batch>
+        <power_cap>1314</power_cap>
+	<longitude>-112.865000</longitude>
+	<latitude>33.389200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>PALO VERDE-3</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>253</n_assem_core>
+        <n_assem_batch>84</n_assem_batch>
+        <power_cap>1312</power_cap>
+	<longitude>-112.865000</longitude>
+	<latitude>33.389200</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>VOGTLE-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>221</n_assem_core>
+        <n_assem_batch>73</n_assem_batch>
+        <power_cap>1150</power_cap>
+	<longitude>-81.760600</longitude>
+	<latitude>33.143300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>VOGTLE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>222</n_assem_core>
+        <n_assem_batch>74</n_assem_batch>
+        <power_cap>1152</power_cap>
+	<longitude>-81.760600</longitude>
+	<latitude>33.143300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- BWR -->
+    <name>RIVER BEND-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>180</assem_size>
+        <n_assem_core>738</n_assem_core>
+        <n_assem_batch>246</n_assem_batch>
+        <power_cap>967</power_cap>
+	<longitude>-91.333300</longitude>
+	<latitude>30.756700</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>WOLF CREEK</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>230</n_assem_core>
+        <n_assem_batch>76</n_assem_batch>
+        <power_cap>1195</power_cap>
+	<longitude>-95.688900</longitude>
+	<latitude>38.238900</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>ST. LUCIE-2</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>190</n_assem_core>
+        <n_assem_batch>63</n_assem_batch>
+        <power_cap>987</power_cap>
+	<longitude>-80.246400</longitude>
+	<latitude>27.348600</latitude>
+      </Reactor>
+    </config>
+  </facility>
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>HARRIS-1</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>446.0</assem_size>
+        <n_assem_core>179</n_assem_core>
+        <n_assem_batch>59</n_assem_batch>
+        <power_cap>928</power_cap>
+	<longitude>-78.955000</longitude>
+	<latitude>35.633300</latitude>
+      </Reactor>
+    </config>
+  </facility>
+</root>
\ No newline at end of file
diff --git a/input/master_us/recipes.xml b/input/master_us/recipes.xml
new file mode 100644
index 00000000..6fb7f5c2
--- /dev/null
+++ b/input/master_us/recipes.xml
@@ -0,0 +1,1533 @@
+<root>
+  <recipe>
+    <name>natl_u_recipe</name>
+    <basis>mass</basis>
+    <nuclide> <id>U235</id> <comp>0.711</comp> </nuclide>
+    <nuclide> <id>U238</id> <comp>99.289</comp> </nuclide>
+  </recipe>
+
+<recipe>
+  <name>msr_input_recipe</name>
+  <basis>mass</basis>
+<nuclide> <id>Cl37</id>  <comp>0.26</comp>  </nuclide>
+<nuclide> <id>Na23</id> <comp>0.13</comp>  </nuclide>
+<nuclide> <id>U238</id> <comp>0.55</comp>  </nuclide>
+<nuclide> <id>Pu239</id>  <comp>0.06</comp>  </nuclide>
+</recipe>
+
+<recipe>
+  <name>na</name>
+  <basis>mass</basis>
+<nuclide> <id>Na23</id> <comp>1</comp>  </nuclide>
+</recipe>
+
+
+<recipe>
+  <name>cl</name>
+  <basis>mass</basis>
+<nuclide> <id>Cl37</id> <comp>1</comp>  </nuclide>
+</recipe>
+
+
+<recipe>
+  <name>pu</name>
+  <basis>mass</basis>
+<nuclide> <id>Pu239</id> <comp>0.84</comp>  </nuclide>
+<nuclide> <id>Pu238</id> <comp>0.16</comp>  </nuclide>
+
+</recipe>
+
+
+<recipe>
+    <name>msr_output_recipe</name>
+    <basis>atom</basis>
+<nuclide> <id>Hg198</id>  <comp>1.08996e-20</comp>  </nuclide>
+<nuclide> <id>Si28</id> <comp>5.73733e-14</comp>  </nuclide>
+<nuclide> <id>Si29</id> <comp>1.15683e-14</comp>  </nuclide>
+<nuclide> <id>Ac225</id>  <comp>2.05857e-18</comp>  </nuclide>
+<nuclide> <id>Ac227</id>  <comp>4.62223e-17</comp>  </nuclide>
+<nuclide> <id>S33</id>  <comp>6.3413e-10</comp> </nuclide>
+<nuclide> <id>S32</id>  <comp>1.82036e-13</comp>  </nuclide>
+<nuclide> <id>S34</id>  <comp>1.22575e-06</comp>  </nuclide>
+<nuclide> <id>S36</id>  <comp>5.95223e-07</comp>  </nuclide>
+<nuclide> <id>B11</id>  <comp>5.98529e-20</comp>  </nuclide>
+<nuclide> <id>He3</id>  <comp>3.99343e-08</comp>  </nuclide>
+<nuclide> <id>He4</id>  <comp>2.17138e-05</comp>  </nuclide>
+<nuclide> <id>Ga71</id> <comp>2.03701e-09</comp>  </nuclide>
+<nuclide> <id>Cm243</id>  <comp>5.34165e-08</comp>  </nuclide>
+<nuclide> <id>Cm242</id>  <comp>2.60007e-07</comp>  </nuclide>
+<nuclide> <id>Cm241</id>  <comp>3.20746e-13</comp>  </nuclide>
+<nuclide> <id>Cm247</id>  <comp>5.97719e-09</comp>  </nuclide>
+<nuclide> <id>Cm246</id>  <comp>6.06841e-08</comp>  </nuclide>
+<nuclide> <id>Cm245</id>  <comp>1.94544e-07</comp>  </nuclide>
+<nuclide> <id>Cm244</id>  <comp>1.11646e-06</comp>  </nuclide>
+<nuclide> <id>Cm249</id>  <comp>7.44061e-15</comp>  </nuclide>
+<nuclide> <id>Cm248</id>  <comp>1.17723e-09</comp>  </nuclide>
+<nuclide> <id>Ca44</id> <comp>1.19464e-20</comp>  </nuclide>
+<nuclide> <id>Np235</id>  <comp>4.11626e-14</comp>  </nuclide>
+<nuclide> <id>Ca42</id> <comp>1.09159e-17</comp>  </nuclide>
+<nuclide> <id>Ca43</id> <comp>5.67445e-20</comp>  </nuclide>
+<nuclide> <id>Np238</id>  <comp>6.4896e-09</comp> </nuclide>
+<nuclide> <id>Np239</id>  <comp>1.89852e-06</comp>  </nuclide>
+<nuclide> <id>Cl37</id> <comp>0.0147552</comp>  </nuclide>
+<nuclide> <id>Cl35</id> <comp>3.10159e-10</comp>  </nuclide>
+<nuclide> <id>N14</id>  <comp>1.08883e-18</comp>  </nuclide>
+<nuclide> <id>Li7</id>  <comp>1.47675e-18</comp>  </nuclide>
+<nuclide> <id>Am244M</id> <comp>1.83048e-11</comp>  </nuclide>
+<nuclide> <id>Bk249</id>  <comp>3.46793e-11</comp>  </nuclide>
+<nuclide> <id>Am244</id>  <comp>3.0031e-11</comp> </nuclide>
+<nuclide> <id>Am241</id>  <comp>2.25977e-06</comp>  </nuclide>
+<nuclide> <id>Am243</id>  <comp>1.01406e-06</comp>  </nuclide>
+<nuclide> <id>Am242</id>  <comp>1.47774e-09</comp>  </nuclide>
+<nuclide> <id>Np236</id>  <comp>3.36297e-11</comp>  </nuclide>
+<nuclide> <id>Np237</id>  <comp>3.04446e-06</comp>  </nuclide>
+<nuclide> <id>Na23</id> <comp>0.0121816</comp>  </nuclide>
+<nuclide> <id>N15</id>  <comp>8.53675e-13</comp>  </nuclide>
+<nuclide> <id>Ar38</id> <comp>3.97154e-05</comp>  </nuclide>
+<nuclide> <id>Ar36</id> <comp>4.77175e-15</comp>  </nuclide>
+<nuclide> <id>Es255</id>  <comp>4.72515e-20</comp>  </nuclide>
+<nuclide> <id>Es253</id>  <comp>2.52381e-16</comp>  </nuclide>
+<nuclide> <id>Bi209</id>  <comp>1.16567e-15</comp>  </nuclide>
+<nuclide> <id>Mg26</id> <comp>2.15601e-10</comp>  </nuclide>
+<nuclide> <id>Mg24</id> <comp>3.00634e-05</comp>  </nuclide>
+<nuclide> <id>Mg25</id> <comp>8.09669e-08</comp>  </nuclide>
+<nuclide> <id>Ga69</id> <comp>4.81988e-10</comp>  </nuclide>
+<nuclide> <id>F19</id>  <comp>1.95659e-09</comp>  </nuclide>
+<nuclide> <id>Al27</id> <comp>4.90307e-14</comp>  </nuclide>
+<nuclide> <id>Cf249</id>  <comp>3.7773e-11</comp> </nuclide>
+<nuclide> <id>Cu65</id> <comp>1.59347e-13</comp>  </nuclide>
+<nuclide> <id>U237</id> <comp>2.41529e-08</comp>  </nuclide>
+<nuclide> <id>H1</id> <comp>5.44207e-06</comp>  </nuclide>
+<nuclide> <id>H2</id> <comp>6.88042e-08</comp>  </nuclide>
+<nuclide> <id>H3</id> <comp>3.96039e-07</comp>  </nuclide>
+<nuclide> <id>Pb208</id>  <comp>9.8009e-13</comp> </nuclide>
+<nuclide> <id>Pb207</id>  <comp>6.09026e-16</comp>  </nuclide>
+<nuclide> <id>Bk250</id>  <comp>3.49268e-15</comp>  </nuclide>
+<nuclide> <id>Am242M</id> <comp>1.74259e-07</comp>  </nuclide>
+<nuclide> <id>Th234</id>  <comp>7.17987e-14</comp>  </nuclide>
+<nuclide> <id>Th232</id>  <comp>8.05624e-13</comp>  </nuclide>
+<nuclide> <id>Th233</id>  <comp>3.19318e-18</comp>  </nuclide>
+<nuclide> <id>Th230</id>  <comp>2.68464e-12</comp>  </nuclide>
+<nuclide> <id>Pb206</id>  <comp>2.9541e-19</comp> </nuclide>
+<nuclide> <id>Cr54</id> <comp>1.07305e-20</comp>  </nuclide>
+<nuclide> <id>Co59</id> <comp>3.16673e-19</comp>  </nuclide>
+<nuclide> <id>Co58</id> <comp>3.29408e-19</comp>  </nuclide>
+<nuclide> <id>Ti49</id> <comp>1.02435e-20</comp>  </nuclide>
+<nuclide> <id>Ti48</id> <comp>1.0487e-20</comp> </nuclide>
+<nuclide> <id>P31</id>  <comp>3.97879e-10</comp>  </nuclide>
+<nuclide> <id>Hg200</id>  <comp>1.66079e-20</comp>  </nuclide>
+<nuclide> <id>Si30</id> <comp>1.99852e-10</comp>  </nuclide>
+<nuclide> <id>Ar40</id> <comp>6.16725e-13</comp>  </nuclide>
+<nuclide> <id>K39</id>  <comp>3.58614e-13</comp>  </nuclide>
+<nuclide> <id>Cf254</id>  <comp>5.4359e-18</comp> </nuclide>
+<nuclide> <id>Cf253</id>  <comp>2.30788e-16</comp>  </nuclide>
+<nuclide> <id>Cf252</id>  <comp>1.69805e-13</comp>  </nuclide>
+<nuclide> <id>Cf251</id>  <comp>1.98895e-12</comp>  </nuclide>
+<nuclide> <id>Cf250</id>  <comp>2.38926e-11</comp>  </nuclide>
+<nuclide> <id>Hf176</id>  <comp>1.6724e-20</comp> </nuclide>
+<nuclide> <id>Ra226</id>  <comp>1.32022e-16</comp>  </nuclide>
+<nuclide> <id>Ra224</id>  <comp>1.62035e-15</comp>  </nuclide>
+<nuclide> <id>Ra225</id>  <comp>3.09608e-18</comp>  </nuclide>
+<nuclide> <id>Hf178</id>  <comp>3.26107e-20</comp>  </nuclide>
+<nuclide> <id>K40</id>  <comp>2.39579e-15</comp>  </nuclide>
+<nuclide> <id>K41</id>  <comp>1.11877e-15</comp>  </nuclide>
+<nuclide> <id>Pa233</id>  <comp>1.0747e-13</comp> </nuclide>
+<nuclide> <id>Pa232</id>  <comp>4.45048e-16</comp>  </nuclide>
+<nuclide> <id>Pa231</id>  <comp>3.6507e-13</comp> </nuclide>
+<nuclide> <id>Pu246</id>  <comp>9.98956521939e-21</comp>  </nuclide>
+<nuclide> <id>Pu244</id>  <comp>3.699136e-10</comp> </nuclide>
+<nuclide> <id>Pu242</id>  <comp>4.66966221913e-06</comp>  </nuclide>
+<nuclide> <id>Pu243</id>  <comp>2.54914724174e-10</comp>  </nuclide>
+<nuclide> <id>Pu240</id>  <comp>0.000125687710609</comp>  </nuclide>
+<nuclide> <id>Pu241</id>  <comp>1.56203834435e-05</comp>  </nuclide>
+<nuclide> <id>Cm250</id>  <comp>5.5527e-16</comp> </nuclide>
+<nuclide> <id>Pu239</id>  <comp>0.000429894945391</comp>  </nuclide>
+<nuclide> <id>Pu238</id>  <comp>3.00084541217e-06</comp>  </nuclide>
+<nuclide> <id>Hf180</id>  <comp>2.8226e-20</comp> </nuclide>
+<nuclide> <id>Pu237</id>  <comp>2.57584934957e-12</comp>  </nuclide>
+<nuclide> <id>Pu236</id>  <comp>2.03001950609e-11</comp>  </nuclide>
+<nuclide> <id>Th227</id>  <comp>1.95575e-18</comp>  </nuclide>
+<nuclide> <id>Th229</id>  <comp>8.34683e-14</comp>  </nuclide>
+<nuclide> <id>Th228</id>  <comp>3.09366e-13</comp>  </nuclide>
+<nuclide> <id>Es254</id>  <comp>1.21416e-18</comp>  </nuclide>
+<nuclide> <id>Fe56</id> <comp>1.17836e-20</comp>  </nuclide>
+<nuclide> <id>Fe58</id> <comp>2.3068e-17</comp> </nuclide>
+<nuclide> <id>Ra223</id>  <comp>1.21068e-18</comp>  </nuclide>
+<nuclide> <id>Ni62</id> <comp>1.16164e-20</comp>  </nuclide>
+<nuclide> <id>Ni64</id> <comp>1.0487e-20</comp> </nuclide>
+<nuclide> <id>Ti50</id> <comp>1.02435e-20</comp>  </nuclide>
+<nuclide> <id>U238</id> <comp>0.00487348109679</comp> </nuclide>
+<nuclide> <id>U239</id> <comp>1.31832e-08</comp>  </nuclide>
+<nuclide> <id>U232</id> <comp>1.4603e-11</comp> </nuclide>
+<nuclide> <id>U233</id> <comp>1.1898e-11</comp> </nuclide>
+<nuclide> <id>U234</id> <comp>1.58283926139e-07</comp>  </nuclide>
+<nuclide> <id>U235</id> <comp>8.14637561925e-07</comp>  </nuclide>
+<nuclide> <id>U236</id> <comp>1.69692331594e-06</comp>  </nuclide>
+<nuclide> <id>W184</id> <comp>6.102e-20</comp>  </nuclide>
+<nuclide> <id>W182</id> <comp>2.96916e-19</comp>  </nuclide>
+</recipe>
+
+  <recipe>
+    <name>msr_pu_recipe</name>
+    <basis>mass</basis>
+     <nuclide> <id>Pu239</id>  <comp>0.84</comp> </nuclide> 
+     <nuclide> <id>Pu238</id>  <comp>0.16</comp> </nuclide> 
+  </recipe> 
+
+   <recipe>
+    <name>waste_recipe</name>
+    <basis>atom</basis>
+     <nuclide> <id>Hg198</id> <comp>1.08996e-20</comp> </nuclide>
+<nuclide> <id>Si28</id> <comp>5.73733e-14</comp> </nuclide>
+<nuclide> <id>Si29</id> <comp>1.15683e-14</comp> </nuclide>
+<nuclide> <id>Ac225</id> <comp>2.05857e-18</comp> </nuclide>
+<nuclide> <id>Ac227</id> <comp>4.62223e-17</comp> </nuclide>
+<nuclide> <id>S33</id> <comp>6.3413e-10</comp> </nuclide>
+<nuclide> <id>S32</id> <comp>1.82036e-13</comp> </nuclide>
+<nuclide> <id>S34</id> <comp>1.22575e-06</comp> </nuclide>
+<nuclide> <id>S36</id> <comp>5.95223e-07</comp> </nuclide>
+<nuclide> <id>B11</id> <comp>5.98529e-20</comp> </nuclide>
+<nuclide> <id>He3</id> <comp>3.99343e-08</comp> </nuclide>
+<nuclide> <id>He4</id> <comp>2.17138e-05</comp> </nuclide>
+<nuclide> <id>Ga71</id> <comp>2.03701e-09</comp> </nuclide>
+<nuclide> <id>Ca44</id> <comp>1.19464e-20</comp> </nuclide>
+<nuclide> <id>Ca42</id> <comp>1.09159e-17</comp> </nuclide>
+<nuclide> <id>Ca43</id> <comp>5.67445e-20</comp> </nuclide>
+<nuclide> <id>N14</id> <comp>1.08883e-18</comp> </nuclide>
+<nuclide> <id>Li7</id> <comp>1.47675e-18</comp> </nuclide>
+<nuclide> <id>Bk249</id> <comp>3.46793e-11</comp> </nuclide>
+<nuclide> <id>N15</id> <comp>8.53675e-13</comp> </nuclide>
+<nuclide> <id>Ar38</id> <comp>3.97154e-05</comp> </nuclide>
+<nuclide> <id>Ar36</id> <comp>4.77175e-15</comp> </nuclide>
+<nuclide> <id>Es255</id> <comp>4.72515e-20</comp> </nuclide>
+<nuclide> <id>Es253</id> <comp>2.52381e-16</comp> </nuclide>
+<nuclide> <id>Bi209</id> <comp>1.16567e-15</comp> </nuclide>
+<nuclide> <id>Mg26</id> <comp>2.15601e-10</comp> </nuclide>
+<nuclide> <id>Mg24</id> <comp>3.00634e-05</comp> </nuclide>
+<nuclide> <id>Mg25</id> <comp>8.09669e-08</comp> </nuclide>
+<nuclide> <id>Ga69</id> <comp>4.81988e-10</comp> </nuclide>
+<nuclide> <id>F19</id> <comp>1.95659e-09</comp> </nuclide>
+<nuclide> <id>Al27</id> <comp>4.90307e-14</comp> </nuclide>
+<nuclide> <id>Cu65</id> <comp>1.59347e-13</comp> </nuclide>
+<nuclide> <id>H1</id> <comp>5.44207e-06</comp> </nuclide>
+<nuclide> <id>H2</id> <comp>6.88042e-08</comp> </nuclide>
+<nuclide> <id>H3</id> <comp>3.96039e-07</comp> </nuclide>
+<nuclide> <id>Pb208</id> <comp>9.8009e-13</comp> </nuclide>
+<nuclide> <id>Pb207</id> <comp>6.09026e-16</comp> </nuclide>
+<nuclide> <id>Bk250</id> <comp>3.49268e-15</comp> </nuclide>
+<nuclide> <id>Pb206</id> <comp>2.9541e-19</comp> </nuclide>
+<nuclide> <id>Cr54</id> <comp>1.07305e-20</comp> </nuclide>
+<nuclide> <id>Co59</id> <comp>3.16673e-19</comp> </nuclide>
+<nuclide> <id>Co58</id> <comp>3.29408e-19</comp> </nuclide>
+<nuclide> <id>Ti49</id> <comp>1.02435e-20</comp> </nuclide>
+<nuclide> <id>Ti48</id> <comp>1.0487e-20</comp> </nuclide>
+<nuclide> <id>P31</id> <comp>3.97879e-10</comp> </nuclide>
+<nuclide> <id>Hg200</id> <comp>1.66079e-20</comp> </nuclide>
+<nuclide> <id>Si30</id> <comp>1.99852e-10</comp> </nuclide>
+<nuclide> <id>Ar40</id> <comp>6.16725e-13</comp> </nuclide>
+<nuclide> <id>K39</id> <comp>3.58614e-13</comp> </nuclide>
+<nuclide> <id>Hf176</id> <comp>1.6724e-20</comp> </nuclide>
+<nuclide> <id>Ra226</id> <comp>1.32022e-16</comp> </nuclide>
+<nuclide> <id>Ra224</id> <comp>1.62035e-15</comp> </nuclide>
+<nuclide> <id>Ra225</id> <comp>3.09608e-18</comp> </nuclide>
+<nuclide> <id>Hf178</id> <comp>3.26107e-20</comp> </nuclide>
+<nuclide> <id>K40</id> <comp>2.39579e-15</comp> </nuclide>
+<nuclide> <id>K41</id> <comp>1.11877e-15</comp> </nuclide>
+<nuclide> <id>Hf180</id> <comp>2.8226e-20</comp> </nuclide>
+<nuclide> <id>Es254</id> <comp>1.21416e-18</comp> </nuclide>
+<nuclide> <id>Fe56</id> <comp>1.17836e-20</comp> </nuclide>
+<nuclide> <id>Fe58</id> <comp>2.3068e-17</comp> </nuclide>
+<nuclide> <id>Ra223</id> <comp>1.21068e-18</comp> </nuclide>
+<nuclide> <id>Ni62</id> <comp>1.16164e-20</comp> </nuclide>
+<nuclide> <id>Ni64</id> <comp>1.0487e-20</comp> </nuclide>
+<nuclide> <id>Ti50</id> <comp>1.02435e-20</comp> </nuclide>
+<nuclide> <id>W184</id> <comp>6.102e-20</comp> </nuclide>
+<nuclide> <id>W182</id> <comp>2.96916e-19</comp> </nuclide>
+
+  </recipe> 
+
+
+  <recipe>
+    <name>uox_fuel_recipe</name>
+    <basis>mass</basis>
+     <nuclide> <id>U234</id>  <comp>0.0002558883</comp> </nuclide> 
+     <nuclide> <id>U235</id>  <comp>0.0319885317</comp> </nuclide> 
+     <nuclide> <id>U238</id>  <comp>0.96775558</comp> </nuclide> 
+  </recipe> 
+
+  <recipe>
+    <name>smr_recipe</name>
+    <basis>mass</basis>
+     <nuclide> <id>U235</id>  <comp>0.0495</comp> </nuclide> 
+     <nuclide> <id>U238</id>  <comp>0.9505</comp> </nuclide> 
+  </recipe> 
+
+  <recipe>
+    <name>fhr_recipe</name>
+    <basis>mass</basis>
+     <nuclide> <id>U235</id>  <comp>0.199</comp> </nuclide> 
+     <nuclide> <id>U238</id>  <comp>0.801</comp> </nuclide> 
+  </recipe> 
+
+  <recipe>
+    <name>depleted_u</name>
+    <basis>mass</basis>
+    <nuclide> <id>U235</id><comp>0.003</comp></nuclide>
+    <nuclide><id>U238</id><comp>0.997</comp></nuclide>
+  </recipe>
+
+
+<recipe>
+  <name>recipe</name>
+  <basis>mass</basis>
+  <nuclide> <id>Se79</id> <comp>0.000005</comp> </nuclide>
+  <nuclide> <id>Kr85</id> <comp>0.000008</comp> </nuclide>
+  <nuclide> <id>Rb85</id> <comp>0.000021</comp> </nuclide>
+  <nuclide> <id>Sr90</id> <comp>0.000329</comp> </nuclide>
+  <nuclide> <id>Zr90</id> <comp>0.000223</comp> </nuclide>
+  <nuclide> <id>Zr93</id> <comp>0.000869</comp> </nuclide>
+  <nuclide> <id>Mo95</id> <comp>0.000002</comp> </nuclide>
+  <nuclide> <id>Tc99</id> <comp>0.000870</comp> </nuclide>
+  <nuclide> <id>Pd106</id> <comp>0.000083</comp> </nuclide>
+  <nuclide> <id>Pd107</id> <comp>0.000269</comp> </nuclide>
+  <nuclide> <id>Sn126</id> <comp>0.000024</comp> </nuclide>
+  <nuclide> <id>Te125</id> <comp>0.000010</comp> </nuclide>
+  <nuclide> <id>I129</id> <comp>0.000182</comp> </nuclide>
+  <nuclide> <id>Cs134</id> <comp>0.000001</comp> </nuclide>
+  <nuclide> <id>Cs135</id> <comp>0.000570</comp> </nuclide>
+  <nuclide> <id>Cs137</id> <comp>0.000805</comp> </nuclide>
+  <nuclide> <id>Ba134</id> <comp>0.000103</comp> </nuclide>
+  <nuclide> <id>Ba137</id> <comp>0.000517</comp> </nuclide>
+  <nuclide> <id>Nd144</id> <comp>0.000120</comp> </nuclide>
+  <nuclide> <id>Pm147</id> <comp>0.000004</comp> </nuclide>
+  <nuclide> <id>Sm147</id> <comp>0.000139</comp> </nuclide>
+  <nuclide> <id>Sm151</id> <comp>0.000014</comp> </nuclide>
+  <nuclide> <id>Eu151</id> <comp>0.000003</comp> </nuclide>
+  <nuclide> <id>Eu154</id> <comp>0.000007</comp> </nuclide>
+  <nuclide> <id>Gd154</id> <comp>0.000024</comp> </nuclide>
+  <nuclide> <id>Gd155</id> <comp>0.000008</comp> </nuclide>
+  <nuclide> <id>U234</id> <comp>0.000215</comp> </nuclide>
+  <nuclide> <id>U235</id> <comp>0.010475</comp> </nuclide>
+  <nuclide> <id>U236</id> <comp>0.004469</comp> </nuclide>
+  <nuclide> <id>U238</id> <comp>0.965956</comp> </nuclide>
+  <nuclide> <id>Np237</id> <comp>0.000591</comp> </nuclide>
+  <nuclide> <id>Pu238</id> <comp>0.000206</comp> </nuclide>
+  <nuclide> <id>Pu239</id> <comp>0.007554</comp> </nuclide>
+  <nuclide> <id>Pu240</id> <comp>0.002669</comp> </nuclide>
+  <nuclide> <id>Pu241</id> <comp>0.000657</comp> </nuclide>
+  <nuclide> <id>Pu242</id> <comp>0.000589</comp> </nuclide>
+  <nuclide> <id>Am241</id> <comp>0.001226</comp> </nuclide>
+  <nuclide> <id>Am242M</id> <comp>0.000001</comp> </nuclide>
+  <nuclide> <id>Am243</id> <comp>0.000151</comp> </nuclide>
+  <nuclide> <id>Cm244</id> <comp>0.000025</comp> </nuclide>
+  <nuclide> <id>Cm245</id> <comp>0.000004</comp> </nuclide>
+</recipe>
+
+<recipe>
+    <name>udb_recipe</name>
+    <basis>mass</basis>
+    <nuclide> <id>10030000</id> <comp>1.9184808098049048e-08</comp> </nuclide>
+<nuclide> <id>20030000</id> <comp>3.956345371613027e-08</comp> </nuclide>
+<nuclide> <id>60140000</id> <comp>1.0360464010398925e-07</comp> </nuclide>
+<nuclide> <id>70140000</id> <comp>2.8712213685339276e-10</comp> </nuclide>
+<nuclide> <id>340790000</id> <comp>5.187052988793343e-06</comp> </nuclide>
+<nuclide> <id>350790000</id> <comp>2.50308298451531e-10</comp> </nuclide>
+<nuclide> <id>360830000</id> <comp>2.319063855993136e-14</comp> </nuclide>
+<nuclide> <id>360850000</id> <comp>8.424756008698851e-06</comp> </nuclide>
+<nuclide> <id>370850000</id> <comp>2.127031116369025e-05</comp> </nuclide>
+<nuclide> <id>380900000</id> <comp>0.0003289876659941197</comp> </nuclide>
+<nuclide> <id>390900000</id> <comp>8.34495070922332e-08</comp> </nuclide>
+<nuclide> <id>400900000</id> <comp>0.000222719370202569</comp> </nuclide>
+<nuclide> <id>400930000</id> <comp>0.0008688223421184692</comp> </nuclide>
+<nuclide> <id>400950000</id> <comp>3.945879275981622e-19</comp> </nuclide>
+<nuclide> <id>410930000</id> <comp>8.51532625763373e-09</comp> </nuclide>
+<nuclide> <id>410940000</id> <comp>1.5983982416214802e-09</comp> </nuclide>
+<nuclide> <id>410950000</id> <comp>4.75426146122346e-19</comp> </nuclide>
+<nuclide> <id>420940000</id> <comp>1.2428149729729447e-12</comp> </nuclide>
+<nuclide> <id>420950000</id> <comp>2.404925446423266e-06</comp> </nuclide>
+<nuclide> <id>430990000</id> <comp>0.000870144497402629</comp> </nuclide>
+<nuclide> <id>440990000</id> <comp>6.50443357215483e-08</comp> </nuclide>
+<nuclide> <id>441010000</id> <comp>1.0849852499220995e-12</comp> </nuclide>
+<nuclide> <id>441030000</id> <comp>5.870829352094033e-15</comp> </nuclide>
+<nuclide> <id>441060000</id> <comp>6.715421015138686e-08</comp> </nuclide>
+<nuclide> <id>451030000</id> <comp>6.284930623680953e-08</comp> </nuclide>
+<nuclide> <id>451050000</id> <comp>2.1348336834111952e-16</comp> </nuclide>
+<nuclide> <id>451060000</id> <comp>6.286136086281296e-14</comp> </nuclide>
+<nuclide> <id>461050000</id> <comp>1.1635691814396126e-12</comp> </nuclide>
+<nuclide> <id>461060000</id> <comp>8.313562949985759e-05</comp> </nuclide>
+<nuclide> <id>461070000</id> <comp>0.00026931631219451527</comp> </nuclide>
+<nuclide> <id>461080000</id> <comp>8.021515802937837e-13</comp> </nuclide>
+<nuclide> <id>471070000</id> <comp>6.537994266813962e-10</comp> </nuclide>
+<nuclide> <id>471080001</id> <comp>2.227825969748037e-10</comp> </nuclide>
+<nuclide> <id>471090000</id> <comp>6.389919922425404e-13</comp> </nuclide>
+<nuclide> <id>471100001</id> <comp>2.5363295423474083e-11</comp> </nuclide>
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+<nuclide> <id>541360000</id> <comp>1.98576307819239e-12</comp> </nuclide>
+<nuclide> <id>551330000</id> <comp>1.2474172753844895e-12</comp> </nuclide>
+<nuclide> <id>551340000</id> <comp>1.9951198205648614e-06</comp> </nuclide>
+<nuclide> <id>551350000</id> <comp>0.0005826264089377535</comp> </nuclide>
+<nuclide> <id>551370000</id> <comp>0.0008701278548625033</comp> </nuclide>
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+<nuclide> <id>561370000</id> <comp>0.000491339242010577</comp> </nuclide>
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+<nuclide> <id>581440000</id> <comp>2.7087594006671897e-08</comp> </nuclide>
+<nuclide> <id>591440000</id> <comp>1.1409358978994215e-12</comp> </nuclide>
+<nuclide> <id>601430000</id> <comp>1.6121917393952859e-12</comp> </nuclide>
+<nuclide> <id>601440000</id> <comp>0.00012465345675659713</comp> </nuclide>
+<nuclide> <id>601450000</id> <comp>1.2788212091920111e-12</comp> </nuclide>
+<nuclide> <id>611470000</id> <comp>3.987741588377756e-06</comp> </nuclide>
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+<nuclide> <id>611490000</id> <comp>2.591658144687706e-16</comp> </nuclide>
+<nuclide> <id>621470000</id> <comp>0.00013383241155837808</comp> </nuclide>
+<nuclide> <id>621480000</id> <comp>2.28445756837165e-17</comp> </nuclide>
+<nuclide> <id>621490000</id> <comp>7.241081321911669e-13</comp> </nuclide>
+<nuclide> <id>621500000</id> <comp>5.412063624261078e-15</comp> </nuclide>
+<nuclide> <id>621510000</id> <comp>1.4265289466899098e-05</comp> </nuclide>
+<nuclide> <id>621520000</id> <comp>1.2646658280874399e-08</comp> </nuclide>
+<nuclide> <id>621540000</id> <comp>4.432561191599591e-09</comp> </nuclide>
+<nuclide> <id>631510000</id> <comp>2.4902354297125444e-06</comp> </nuclide>
+<nuclide> <id>631520000</id> <comp>9.922673575528767e-09</comp> </nuclide>
+<nuclide> <id>631530000</id> <comp>3.2110654295826987e-13</comp> </nuclide>
+<nuclide> <id>631540000</id> <comp>8.368426867779915e-06</comp> </nuclide>
+<nuclide> <id>631550000</id> <comp>9.654306211902801e-07</comp> </nuclide>
+<nuclide> <id>641520000</id> <comp>4.898489066040203e-09</comp> </nuclide>
+<nuclide> <id>641540000</id> <comp>2.4620692601970257e-05</comp> </nuclide>
+<nuclide> <id>641550000</id> <comp>8.046404267557515e-06</comp> </nuclide>
+<nuclide> <id>641560000</id> <comp>1.3262129816880616e-13</comp> </nuclide>
+<nuclide> <id>641570000</id> <comp>9.378181401623675e-14</comp> </nuclide>
+<nuclide> <id>641580000</id> <comp>6.531373985974515e-14</comp> </nuclide>
+<nuclide> <id>661600000</id> <comp>2.0528511243073202e-16</comp> </nuclide>
+<nuclide> <id>661610000</id> <comp>1.733917220538732e-14</comp> </nuclide>
+<nuclide> <id>661620000</id> <comp>1.0125964768236053e-14</comp> </nuclide>
+<nuclide> <id>661630000</id> <comp>6.331605506671839e-15</comp> </nuclide>
+<nuclide> <id>661640000</id> <comp>3.743929766179579e-15</comp> </nuclide>
+<nuclide> <id>671650000</id> <comp>2.1531766789343777e-15</comp> </nuclide>
+<nuclide> <id>681660000</id> <comp>5.012220241967963e-16</comp> </nuclide>
+<nuclide> <id>681670000</id> <comp>9.616446809552053e-19</comp> </nuclide>
+<nuclide> <id>802060000</id> <comp>9.067089720628601e-29</comp> </nuclide>
+<nuclide> <id>812050000</id> <comp>1.729087406023592e-19</comp> </nuclide>
+<nuclide> <id>812060000</id> <comp>3.2218900827450714e-27</comp> </nuclide>
+<nuclide> <id>812070000</id> <comp>9.646532883521067e-20</comp> </nuclide>
+<nuclide> <id>812080000</id> <comp>3.8666890495950874e-17</comp> </nuclide>
+<nuclide> <id>812090000</id> <comp>3.2369852908960147e-23</comp> </nuclide>
+<nuclide> <id>812100000</id> <comp>5.907056385636831e-25</comp> </nuclide>
+<nuclide> <id>822060000</id> <comp>1.840360352522321e-15</comp> </nuclide>
+<nuclide> <id>822070000</id> <comp>1.0291960604917079e-13</comp> </nuclide>
+<nuclide> <id>822080000</id> <comp>2.2794742367576077e-10</comp> </nuclide>
+<nuclide> <id>822090000</id> <comp>1.3206039394649454e-19</comp> </nuclide>
+<nuclide> <id>822100000</id> <comp>6.82754064356723e-15</comp> </nuclide>
+<nuclide> <id>822110000</id> <comp>7.462700505070432e-19</comp> </nuclide>
+<nuclide> <id>822120000</id> <comp>2.293086357200626e-14</comp> </nuclide>
+<nuclide> <id>822140000</id> <comp>5.96576010571667e-20</comp> </nuclide>
+<nuclide> <id>832090000</id> <comp>4.3700224750466354e-15</comp> </nuclide>
+<nuclide> <id>832100000</id> <comp>4.21315458332643e-18</comp> </nuclide>
+<nuclide> <id>832110000</id> <comp>4.423850702276451e-20</comp> </nuclide>
+<nuclide> <id>832120000</id> <comp>2.1749071627631957e-15</comp> </nuclide>
+<nuclide> <id>832130000</id> <comp>3.162163120836608e-20</comp> </nuclide>
+<nuclide> <id>832140000</id> <comp>4.388075005328531e-20</comp> </nuclide>
+<nuclide> <id>832150000</id> <comp>1.2465620245837413e-25</comp> </nuclide>
+<nuclide> <id>842100000</id> <comp>1.1113171091930022e-16</comp> </nuclide>
+<nuclide> <id>842110000</id> <comp>4.906738949069394e-25</comp> </nuclide>
+<nuclide> <id>842120000</id> <comp>1.1466437162926667e-25</comp> </nuclide>
+<nuclide> <id>842130000</id> <comp>4.206038004191382e-29</comp> </nuclide>
+<nuclide> <id>842140000</id> <comp>6.036831178218889e-27</comp> </nuclide>
+<nuclide> <id>842150000</id> <comp>6.252910256629493e-25</comp> </nuclide>
+<nuclide> <id>842160000</id> <comp>8.844626364305672e-20</comp> </nuclide>
+<nuclide> <id>842180000</id> <comp>6.95936195844197e-21</comp> </nuclide>
+<nuclide> <id>852150000</id> <comp>8.075037767769477e-32</comp> </nuclide>
+<nuclide> <id>852170000</id> <comp>3.80450352895755e-25</comp> </nuclide>
+<nuclide> <id>852180000</id> <comp>1.1232009398912311e-26</comp> </nuclide>
+<nuclide> <id>852190000</id> <comp>1.666041434796187e-26</comp> </nuclide>
+<nuclide> <id>862170000</id> <comp>4.452312970927491e-31</comp> </nuclide>
+<nuclide> <id>862180000</id> <comp>2.620765007621978e-31</comp> </nuclide>
+<nuclide> <id>862190000</id> <comp>1.416246175407419e-21</comp> </nuclide>
+<nuclide> <id>862200000</id> <comp>3.4544097567104784e-17</comp> </nuclide>
+<nuclide> <id>862220000</id> <comp>1.2595773317654631e-17</comp> </nuclide>
+<nuclide> <id>872210000</id> <comp>3.526906178801007e-21</comp> </nuclide>
+<nuclide> <id>872230000</id> <comp>6.664959949509621e-21</comp> </nuclide>
+<nuclide> <id>882230000</id> <comp>3.596511867006751e-16</comp> </nuclide>
+<nuclide> <id>882240000</id> <comp>1.98512889575316e-13</comp> </nuclide>
+<nuclide> <id>882250000</id> <comp>1.5730683028707203e-17</comp> </nuclide>
+<nuclide> <id>882260000</id> <comp>1.9619280847714446e-12</comp> </nuclide>
+<nuclide> <id>882270000</id> <comp>4.990078149761968e-28</comp> </nuclide>
+<nuclide> <id>882280000</id> <comp>2.5453742900264505e-18</comp> </nuclide>
+<nuclide> <id>892250000</id> <comp>1.0468688062158938e-17</comp> </nuclide>
+<nuclide> <id>892270000</id> <comp>2.559077873059754e-13</comp> </nuclide>
+<nuclide> <id>892280000</id> <comp>3.1055811713894496e-22</comp> </nuclide>
+<nuclide> <id>902270000</id> <comp>5.916540557775588e-16</comp> </nuclide>
+<nuclide> <id>902280000</id> <comp>3.8843537837290955e-11</comp> </nuclide>
+<nuclide> <id>902290000</id> <comp>3.0948210985299835e-12</comp> </nuclide>
+<nuclide> <id>902300000</id> <comp>1.4641209497904833e-08</comp> </nuclide>
+<nuclide> <id>902310000</id> <comp>4.606439572148806e-14</comp> </nuclide>
+<nuclide> <id>902320000</id> <comp>8.029437100992847e-09</comp> </nuclide>
+<nuclide> <id>902340000</id> <comp>1.4007404453426799e-11</comp> </nuclide>
+<nuclide> <id>912310000</id> <comp>8.758753980317061e-10</comp> </nuclide>
+<nuclide> <id>912330000</id> <comp>2.083978564248122e-11</comp> </nuclide>
+<nuclide> <id>912340000</id> <comp>1.6225707442525229e-13</comp> </nuclide>
+<nuclide> <id>922320000</id> <comp>1.4083361993177135e-09</comp> </nuclide>
+<nuclide> <id>922330000</id> <comp>8.283157946474767e-09</comp> </nuclide>
+<nuclide> <id>922340000</id> <comp>0.00021983372059405972</comp> </nuclide>
+<nuclide> <id>922350000</id> <comp>0.011332550927198985</comp> </nuclide>
+<nuclide> <id>922360000</id> <comp>0.0045365441420002735</comp> </nuclide>
+<nuclide> <id>922370000</id> <comp>2.0689289173450176e-11</comp> </nuclide>
+<nuclide> <id>922380000</id> <comp>0.9647551461773404</comp> </nuclide>
+<nuclide> <id>922400000</id> <comp>1.2951503022003924e-18</comp> </nuclide>
+<nuclide> <id>932370000</id> <comp>0.000615580339743037</comp> </nuclide>
+<nuclide> <id>932390000</id> <comp>1.5568830759986837e-10</comp> </nuclide>
+<nuclide> <id>932400000</id> <comp>9.476319242680662e-20</comp> </nuclide>
+<nuclide> <id>942380000</id> <comp>0.0002479747002327807</comp> </nuclide>
+<nuclide> <id>942390000</id> <comp>0.0076466897137470015</comp> </nuclide>
+<nuclide> <id>942400000</id> <comp>0.002648183687988528</comp> </nuclide>
+<nuclide> <id>942410000</id> <comp>0.0006593917342549193</comp> </nuclide>
+<nuclide> <id>942420000</id> <comp>0.0006170230325763445</comp> </nuclide>
+<nuclide> <id>942430000</id> <comp>8.787989788730932e-19</comp> </nuclide>
+<nuclide> <id>942440000</id> <comp>6.557054262812302e-08</comp> </nuclide>
+<nuclide> <id>952410000</id> <comp>0.0011193620127406107</comp> </nuclide>
+<nuclide> <id>952420001</id> <comp>1.1553967875959674e-06</comp> </nuclide>
+<nuclide> <id>952430000</id> <comp>0.00018071999673381287</comp> </nuclide>
+<nuclide> <id>962420000</id> <comp>1.0025569880643576e-11</comp> </nuclide>
+<nuclide> <id>962430000</id> <comp>4.331751409418092e-07</comp> </nuclide>
+<nuclide> <id>962440000</id> <comp>4.395754254755778e-05</comp> </nuclide>
+<nuclide> <id>962450000</id> <comp>7.681625644028616e-06</comp> </nuclide>
+<nuclide> <id>962460000</id> <comp>1.089711565510341e-06</comp> </nuclide>
+<nuclide> <id>962470000</id> <comp>2.4648351286400143e-08</comp> </nuclide>
+<nuclide> <id>962480000</id> <comp>3.1579582405138782e-09</comp> </nuclide>
+<nuclide> <id>982490000</id> <comp>3.1585309196431825e-11</comp> </nuclide>
+<nuclide> <id>982510000</id> <comp>1.231561926303681e-11</comp> </nuclide>
+</recipe>
+
+  <recipe>
+    <name>mox_fuel_recipe</name>
+    <basis>mass</basis>
+     <nuclide> <id>U232</id>  <comp>1.2872E-10</comp> </nuclide> 
+     <nuclide> <id>U233</id>  <comp>1.38E-09</comp> </nuclide> 
+     <nuclide> <id>U234</id>  <comp>.000143528</comp> </nuclide> 
+     <nuclide> <id>U235</id>  <comp>.007420728</comp> </nuclide> 
+     <nuclide> <id>U236</id>  <comp>.003556698</comp> </nuclide>
+     <nuclide> <id>U237</id>  <comp>3.34366E-11</comp> </nuclide>  
+     <nuclide> <id>U238</id>  <comp>0.868879045</comp> </nuclide>
+     <nuclide> <id>U239</id>  <comp>2.26477E-19</comp> </nuclide> 
+     <nuclide> <id>Pu238</id>  <comp>0.001440424</comp> </nuclide> 
+     <nuclide> <id>Pu239</id>  <comp>0.066449749</comp> </nuclide> 
+     <nuclide> <id>Pu240</id>  <comp>0.029770312</comp> </nuclide> 
+     <nuclide> <id>Pu241</id>  <comp>0.016094726</comp> </nuclide> 
+     <nuclide> <id>Pu242</id>  <comp>0.006244625</comp> </nuclide>
+     <nuclide> <id>Pu243</id>  <comp>3.95537E-19</comp> </nuclide> 
+     <nuclide> <id>Pu244</id>  <comp>1.64638E-07</comp> </nuclide> 
+     <nuclide> <id>Pu245</id>  <comp>1.43248E-24</comp> </nuclide> 
+  </recipe> 
+
+   <recipe>
+    <name>mox_used_fuel_recipe</name>
+    <basis>mass</basis>
+     <nuclide> <id>He4</id>  <comp>2.51087058608741E-05</comp> </nuclide> 
+     <nuclide> <id>Ra226</id>  <comp>6.85864649540962E-14</comp> </nuclide> 
+     <nuclide> <id>Ra228</id>  <comp>1.0769444927185E-19</comp> </nuclide> 
+     <nuclide> <id>Pb206</id>  <comp>3.63781250186619E-18</comp> </nuclide> 
+     <nuclide> <id>Pb207</id>  <comp>1.05894542041679E-15</comp> </nuclide> 
+     <nuclide> <id>Pb208</id>  <comp>2.00189681933373E-12</comp> </nuclide> 
+     <nuclide> <id>Pb210</id>  <comp>1.1829390296063E-19</comp> </nuclide> 
+     <nuclide> <id>Th228</id>  <comp>4.90174735683015E-12</comp> </nuclide> 
+     <nuclide> <id>Th229</id>  <comp>1.43792588721374E-12</comp> </nuclide> 
+     <nuclide> <id>Th230</id>  <comp>2.39987630689358E-09</comp> </nuclide> 
+     <nuclide> <id>Th232</id>  <comp>8.76554821092882E-10</comp> </nuclide> 
+     <nuclide> <id>Bi209</id>  <comp>2.68786146372081E-16</comp> </nuclide> 
+     <nuclide> <id>Ac227</id>  <comp>2.46087316302713E-14</comp> </nuclide> 
+     <nuclide> <id>Pa231</id>  <comp>7.06963562072402E-10</comp> </nuclide> 
+     <nuclide> <id>U232</id>  <comp>5.93369416879439E-10</comp> </nuclide> 
+     <nuclide> <id>U233</id>  <comp>1.03594660580906E-08</comp> </nuclide> 
+     <nuclide> <id>U234</id>  <comp>0.0002656863</comp> </nuclide> 
+     <nuclide> <id>U235</id>  <comp>0.0043397763</comp> </nuclide> 
+     <nuclide> <id>U236</id>  <comp>0.0051097366</comp> </nuclide> 
+     <nuclide> <id>U238</id>  <comp>0.8283573053</comp> </nuclide> 
+     <nuclide> <id>Np237</id>  <comp>0.0043297768</comp> </nuclide> 
+     <nuclide> <id>Pu238</id>  <comp>0.0060396887</comp> </nuclide> 
+     <nuclide> <id>Pu239</id>  <comp>0.0410078864</comp> </nuclide> 
+     <nuclide> <id>Pu240</id>  <comp>0.0283985363</comp> </nuclide> 
+     <nuclide> <id>Pu241</id>  <comp>0.0146892429</comp> </nuclide> 
+     <nuclide> <id>Pu242</id>  <comp>0.0098784908</comp> </nuclide> 
+     <nuclide> <id>Pu244</id>  <comp>2.18888718157919E-07</comp> </nuclide> 
+     <nuclide> <id>Am241</id>  <comp>0.0021278903</comp> </nuclide> 
+     <nuclide> <id>Am242m</id>  <comp>5.0357404506317E-05</comp> </nuclide> 
+     <nuclide> <id>Am243</id>  <comp>0.0020828926</comp> </nuclide> 
+     <nuclide> <id>Cm242</id>  <comp>0.0002752858</comp> </nuclide> 
+     <nuclide> <id>Cm243</id>  <comp>1.26393485496395E-05</comp> </nuclide> 
+     <nuclide> <id>Cm244</id>  <comp>0.0010179475</comp> </nuclide> 
+     <nuclide> <id>Cm245</id>  <comp>0.0001275934</comp> </nuclide> 
+     <nuclide> <id>Cm246</id>  <comp>6.14068350026396E-06</comp> </nuclide> 
+     <nuclide> <id>Cm247</id>  <comp>1.20593784421403E-07</comp> </nuclide> 
+     <nuclide> <id>Cm248</id>  <comp>9.15852795618264E-09</comp> </nuclide> 
+     <nuclide> <id>Cm250</id>  <comp>3.73380755414193E-17</comp> </nuclide> 
+     <nuclide> <id>Cf249</id>  <comp>4.05679090711136E-11</comp> </nuclide> 
+     <nuclide> <id>Cf250</id>  <comp>2.9328488367162E-11</comp> </nuclide> 
+     <nuclide> <id>Cf251</id>  <comp>1.4479253718258E-11</comp> </nuclide> 
+     <nuclide> <id>Cf252</id>  <comp>7.53461165518465E-12</comp> </nuclide> 
+     <nuclide> <id>H3</id>  <comp>1.02694706965821E-07</comp> </nuclide> 
+     <nuclide> <id>C14</id>  <comp>3.95879595791321E-11</comp> </nuclide> 
+     <nuclide> <id>Kr81</id>  <comp>7.34462144755557E-11</comp> </nuclide> 
+     <nuclide> <id>Kr85</id>  <comp>2.05489408777763E-05</comp> </nuclide> 
+     <nuclide> <id>Sr90</id>  <comp>0.000408279</comp> </nuclide> 
+     <nuclide> <id>Tc99</id>  <comp>0.0011189423</comp> </nuclide> 
+     <nuclide> <id>I129</id>  <comp>0.0003505819</comp> </nuclide> 
+     <nuclide> <id>Cs134</id>  <comp>0.0002101892</comp> </nuclide> 
+     <nuclide> <id>Cs135</id>  <comp>0.0009355518</comp> </nuclide> 
+     <nuclide> <id>Cs137</id>  <comp>0.0018309056</comp> </nuclide> 
+  </recipe> 
+
+   <recipe>
+    <name>uox_used_fuel_recipe</name>
+    <basis>atom</basis>
+    <nuclide> <id>He4</id>  <comp>9.47457840128509E-07</comp> </nuclide> 
+   <nuclide> <id>Ra226</id>  <comp>9.78856442957042E-14</comp> </nuclide> 
+   <nuclide> <id>Ra228</id>  <comp>2.75087759176098E-20</comp> </nuclide> 
+   <nuclide> <id>Pb206</id>  <comp>5.57475193532078E-18</comp> </nuclide> 
+   <nuclide> <id>Pb207</id>  <comp>1.68592497990149E-15</comp> </nuclide> 
+   <nuclide> <id>Pb208</id>  <comp>3.6888358546006E-12</comp> </nuclide> 
+   <nuclide> <id>Pb210</id>  <comp>3.02386544437848E-19</comp> </nuclide> 
+   <nuclide> <id>Th228</id>  <comp>8.47562285269577E-12</comp> </nuclide> 
+   <nuclide> <id>Th229</id>  <comp>2.72787861516683E-12</comp> </nuclide> 
+   <nuclide> <id>Th230</id>  <comp>2.6258831537493E-09</comp> </nuclide> 
+   <nuclide> <id>Th232</id>  <comp>4.17481422959E-10</comp> </nuclide> 
+   <nuclide> <id>Bi209</id>  <comp>6.60770597104927E-16</comp> </nuclide> 
+   <nuclide> <id>Ac227</id>  <comp>3.0968621961773E-14</comp> </nuclide> 
+   <nuclide> <id>Pa231</id>  <comp>9.24658854635179E-10</comp> </nuclide> 
+   <nuclide> <id>U232</id>  <comp>0.000000001</comp> </nuclide> 
+   <nuclide> <id>U233</id>  <comp>2.21390148606282E-09</comp> </nuclide> 
+   <nuclide> <id>U234</id>  <comp>0.0001718924</comp> </nuclide> 
+   <nuclide> <id>U235</id>  <comp>0.0076486597</comp> </nuclide> 
+   <nuclide> <id>U236</id>  <comp>0.0057057461</comp> </nuclide> 
+   <nuclide> <id>U238</id>  <comp>0.9208590237</comp> </nuclide> 
+   <nuclide> <id>Np237</id>  <comp>0.0006091729</comp> </nuclide> 
+   <nuclide> <id>Pu238</id>  <comp>0.000291487</comp> </nuclide> 
+   <nuclide> <id>Pu239</id>  <comp>0.0060657301</comp> </nuclide> 
+   <nuclide> <id>Pu240</id>  <comp>0.0029058707</comp> </nuclide> 
+   <nuclide> <id>Pu241</id>  <comp>0.0017579218</comp> </nuclide> 
+   <nuclide> <id>Pu242</id>  <comp>0.0008638616</comp> </nuclide> 
+   <nuclide> <id>Pu244</id>  <comp>2.86487251922763E-08</comp> </nuclide> 
+   <nuclide> <id>Am241</id>  <comp>6.44271331287386E-05</comp> </nuclide> 
+   <nuclide> <id>Am242m</id>  <comp>8.53362027193319E-07</comp> </nuclide> 
+   <nuclide> <id>Am243</id>  <comp>0.0001983912</comp> </nuclide> 
+   <nuclide> <id>Cm242</id>  <comp>2.58988475560194E-05</comp> </nuclide> 
+   <nuclide> <id>Cm243</id>  <comp>0.000000771</comp> </nuclide> 
+   <nuclide> <id>Cm244</id>  <comp>8.5616190260478E-05</comp> </nuclide> 
+   <nuclide> <id>Cm245</id>  <comp>5.72174539442251E-06</comp> </nuclide> 
+   <nuclide> <id>Cm246</id>  <comp>7.29567535786554E-07</comp> </nuclide> 
+   <nuclide> <id>Cm247</id>  <comp>0.00000001</comp> </nuclide> 
+   <nuclide> <id>Cm248</id>  <comp>7.69165773748653E-10</comp> </nuclide> 
+   <nuclide> <id>Cm250</id>  <comp>4.2808095130239E-18</comp> </nuclide> 
+   <nuclide> <id>Cf249</id>  <comp>1.64992658175413E-12</comp> </nuclide> 
+   <nuclide> <id>Cf250</id>  <comp>2.04190913935875E-12</comp> </nuclide> 
+   <nuclide> <id>Cf251</id>  <comp>9.86556100338561E-13</comp> </nuclide> 
+   <nuclide> <id>Cf252</id>  <comp>6.57970721693466E-13</comp> </nuclide> 
+   <nuclide> <id>H3</id>  <comp>8.58461800264195E-08</comp> </nuclide> 
+   <nuclide> <id>C14</id>  <comp>4.05781943561107E-11</comp> </nuclide> 
+   <nuclide> <id>Kr81</id>  <comp>4.21681236076192E-11</comp> </nuclide> 
+   <nuclide> <id>Kr85</id>  <comp>3.44484671160181E-05</comp> </nuclide> 
+   <nuclide> <id>Sr90</id>  <comp>0.0007880649</comp> </nuclide> 
+   <nuclide> <id>Tc99</id>  <comp>0.0011409492</comp> </nuclide> 
+   <nuclide> <id>I129</id>  <comp>0.0002731878</comp> </nuclide> 
+   <nuclide> <id>Cs134</id>  <comp>0.0002300898</comp> </nuclide> 
+   <nuclide> <id>Cs135</id>  <comp>0.0006596706</comp> </nuclide> 
+   <nuclide> <id>Cs137</id>  <comp>0.0018169192</comp> </nuclide> 
+   <nuclide> <id>H1</id>  <comp>0.0477938151</comp> </nuclide> 
+
+  </recipe> 
+
+<!-- BR 1.089 
+   <recipe>
+    <name>sfr_spent_mox_recipe</name>
+    <basis>mass</basis>
+   <nuclide> <id>He4</id>  <comp>8.2631E-05</comp> </nuclide> 
+   <nuclide> <id>Ra226</id>  <comp>2.306E-13</comp> </nuclide> 
+   <nuclide> <id>Ra228</id>  <comp>6.029E-21</comp> </nuclide> 
+   <nuclide> <id>Pb206</id>  <comp>5.2269E-18</comp> </nuclide> 
+   <nuclide> <id>Pb207</id>  <comp>1.0722E-15</comp> </nuclide> 
+   <nuclide> <id>Pb208</id>  <comp>4.4347E-10</comp> </nuclide> 
+   <nuclide> <id>Pb210</id>  <comp>1.3841E-16</comp> </nuclide> 
+   <nuclide> <id>Th228</id>  <comp>7.7910E-10</comp> </nuclide> 
+   <nuclide> <id>Th229</id>  <comp>3.5259E-11</comp> </nuclide> 
+   <nuclide> <id>Th230</id>  <comp>1.1419E-08</comp> </nuclide> 
+   <nuclide> <id>Th232</id>  <comp>6.3415E-11</comp> </nuclide> 
+   <nuclide> <id>Bi209</id>  <comp>2.5042E-13</comp> </nuclide> 
+   <nuclide> <id>Ac227</id>  <comp>2.8317E-14</comp> </nuclide> 
+   <nuclide> <id>Pa231</id>  <comp>8.8076E-10</comp> </nuclide> 
+   <nuclide> <id>U232</id>  <comp>1.4693E-07</comp> </nuclide> 
+   <nuclide> <id>U233</id>  <comp>4.0461E-08</comp> </nuclide> 
+   <nuclide> <id>U234</id>  <comp>0.0010</comp> </nuclide> 
+   <nuclide> <id>U235</id>  <comp>0.0003</comp> </nuclide> 
+   <nuclide> <id>U236</id>  <comp>0.0005</comp> </nuclide> 
+   <nuclide> <id>U238</id>  <comp>0.5864</comp> </nuclide> 
+   <nuclide> <id>Np237</id>  <comp>0.0038</comp> </nuclide> 
+   <nuclide> <id>Pu238</id>  <comp>0.0096</comp> </nuclide> 
+   <nuclide> <id>Pu239</id>  <comp>0.0981</comp> </nuclide> 
+   <nuclide> <id>Pu240</id>  <comp>0.0890</comp> </nuclide> 
+   <nuclide> <id>Pu241</id>  <comp>0.0155</comp> </nuclide> 
+   <nuclide> <id>Pu242</id>  <comp>0.0273</comp> </nuclide> 
+   <nuclide> <id>Pu244</id>  <comp>1.779E-07</comp> </nuclide> 
+   <nuclide> <id>Am241</id>  <comp>0.0077</comp> </nuclide> 
+   <nuclide> <id>Am242m</id>  <comp>0.0005</comp> </nuclide> 
+   <nuclide> <id>Am243</id>  <comp>0.0091</comp> </nuclide> 
+   <nuclide> <id>Cm242</id>  <comp>0.0004</comp> </nuclide> 
+   <nuclide> <id>Cm243</id>  <comp>0.0000</comp> </nuclide> 
+   <nuclide> <id>Cm244</id>  <comp>0.0067</comp> </nuclide> 
+   <nuclide> <id>Cm245</id>  <comp>0.0017</comp> </nuclide> 
+   <nuclide> <id>Cm246</id>  <comp>0.0009</comp> </nuclide> 
+   <nuclide> <id>Cm247</id>  <comp>0.0000</comp> </nuclide> 
+   <nuclide> <id>Cm248</id>  <comp>4.0265E-06</comp> </nuclide> 
+   <nuclide> <id>Cm250</id>  <comp>1.076E-12</comp> </nuclide> 
+   <nuclide> <id>Cf249</id>  <comp>1.6590E-07</comp> </nuclide> 
+   <nuclide> <id>Cf250</id>  <comp>9.5219E-09</comp> </nuclide> 
+   <nuclide> <id>Cf251</id>  <comp>3.2032E-10</comp> </nuclide> 
+   <nuclide> <id>Cf252</id>  <comp>8.3754E-12</comp> </nuclide> 
+   <nuclide> <id>H3</id>  <comp>3.1829E-07</comp> </nuclide> 
+   <nuclide> <id>Kr81</id>  <comp>1.5156E-11</comp> </nuclide> 
+   <nuclide> <id>Kr85</id>  <comp>0.0000</comp> </nuclide> 
+   <nuclide> <id>Sr90</id>  <comp>0.0009</comp> </nuclide> 
+   <nuclide> <id>Tc99</id>  <comp>0.0029</comp> </nuclide> 
+   <nuclide> <id>I129</id>  <comp>0.0009</comp> </nuclide> 
+   <nuclide> <id>Cs134</id>  <comp>0.0001</comp> </nuclide> 
+   <nuclide> <id>Cs135</id>  <comp>0.0051</comp> </nuclide> 
+   <nuclide> <id>Cs137</id>  <comp>0.0046</comp> </nuclide>
+
+  </recipe> 
+-->
+
+<recipe> 
+  <name>driver_fresh</name>
+  <basis>mass</basis>
+  <nuclide>
+    <id>U236</id>
+    <comp>0.000860</comp>
+  </nuclide>
+  <nuclide>
+    <id>U238</id>
+    <comp>0.846452</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU238</id>
+    <comp>0.000404</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU239</id>
+    <comp>0.103806</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU240</id>
+    <comp>0.040913</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU241</id>
+    <comp>0.004767</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU242</id>
+    <comp>0.002736</comp>
+  </nuclide>
+</recipe>
+
+<recipe> 
+  <name>driver_used</name>
+  <basis>mass</basis>
+  <nuclide>
+    <id>KR83</id>
+    <comp>0.000100</comp>
+  </nuclide>
+  <nuclide>
+    <id>KR84</id>
+    <comp>0.000178</comp>
+  </nuclide>
+  <nuclide>
+    <id>KR86</id>
+    <comp>0.000275</comp>
+  </nuclide>
+  <nuclide>
+    <id>RB85</id>
+    <comp>0.000150</comp>
+  </nuclide>
+  <nuclide>
+    <id>RB87</id>
+    <comp>0.000354</comp>
+  </nuclide>
+  <nuclide>
+    <id>SR88</id>
+    <comp>0.000462</comp>
+  </nuclide>
+  <nuclide>
+    <id>SR90</id>
+    <comp>0.000682</comp>
+  </nuclide>
+  <nuclide>
+    <id>Y89</id>
+    <comp>0.000566</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR91</id>
+    <comp>0.000775</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR92</id>
+    <comp>0.001017</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR93</id>
+    <comp>0.001272</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR94</id>
+    <comp>0.001385</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR95</id>
+    <comp>0.000122</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR96</id>
+    <comp>0.001671</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO95</id>
+    <comp>0.001351</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO97</id>
+    <comp>0.001714</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO98</id>
+    <comp>0.001962</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO100</id>
+    <comp>0.002227</comp>
+  </nuclide>
+  <nuclide>
+    <id>TC99</id>
+    <comp>0.001925</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU100</id>
+    <comp>0.000137</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU101</id>
+    <comp>0.002134</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU102</id>
+    <comp>0.002441</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU103</id>
+    <comp>0.000116</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU104</id>
+    <comp>0.002259</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU106</id>
+    <comp>0.000626</comp>
+  </nuclide>
+  <nuclide>
+    <id>RH103</id>
+    <comp>0.002054</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD104</id>
+    <comp>0.000245</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD105</id>
+    <comp>0.001644</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD106</id>
+    <comp>0.001164</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD107</id>
+    <comp>0.001048</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD108</id>
+    <comp>0.000873</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD110</id>
+    <comp>0.000259</comp>
+  </nuclide>
+  <nuclide>
+    <id>AG109</id>
+    <comp>0.000566</comp>
+  </nuclide>
+  <nuclide>
+    <id>CD111</id>
+    <comp>0.000154</comp>
+  </nuclide>
+  <nuclide>
+    <id>CD112</id>
+    <comp>0.000104</comp>
+  </nuclide>
+  <nuclide>
+    <id>SN126</id>
+    <comp>0.000136</comp>
+  </nuclide>
+  <nuclide>
+    <id>TE128</id>
+    <comp>0.000386</comp>
+  </nuclide>
+  <nuclide>
+    <id>TE130</id>
+    <comp>0.001073</comp>
+  </nuclide>
+  <nuclide>
+    <id>I127</id>
+    <comp>0.000235</comp>
+  </nuclide>
+  <nuclide>
+    <id>I129</id>
+    <comp>0.000621</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE131</id>
+    <comp>0.001718</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE132</id>
+    <comp>0.002457</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE134</id>
+    <comp>0.003372</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE136</id>
+    <comp>0.003233</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS133</id>
+    <comp>0.002893</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS134</id>
+    <comp>0.000163</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS135</id>
+    <comp>0.003383</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS137</id>
+    <comp>0.002870</comp>
+  </nuclide>
+  <nuclide>
+    <id>BA137</id>
+    <comp>0.000112</comp>
+  </nuclide>
+  <nuclide>
+    <id>BA138</id>
+    <comp>0.002996</comp>
+  </nuclide>
+  <nuclide>
+    <id>LA139</id>
+    <comp>0.002790</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE140</id>
+    <comp>0.002582</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE141</id>
+    <comp>0.000104</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE142</id>
+    <comp>0.002390</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE144</id>
+    <comp>0.000603</comp>
+  </nuclide>
+  <nuclide>
+    <id>PR141</id>
+    <comp>0.002542</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND143</id>
+    <comp>0.002084</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND144</id>
+    <comp>0.001358</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND145</id>
+    <comp>0.001532</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND146</id>
+    <comp>0.001389</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND148</id>
+    <comp>0.000893</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND150</id>
+    <comp>0.000539</comp>
+  </nuclide>
+  <nuclide>
+    <id>PM147</id>
+    <comp>0.000527</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM147</id>
+    <comp>0.000269</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM148</id>
+    <comp>0.000286</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM149</id>
+    <comp>0.000624</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM150</id>
+    <comp>0.000132</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM151</id>
+    <comp>0.000341</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM152</id>
+    <comp>0.000434</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM154</id>
+    <comp>0.000159</comp>
+  </nuclide>
+  <nuclide>
+    <id>EU153</id>
+    <comp>0.000186</comp>
+  </nuclide>
+  <nuclide>
+    <id>GD156</id>
+    <comp>0.000103</comp>
+  </nuclide>
+  <nuclide>
+    <id>U236</id>
+    <comp>0.000720</comp>
+  </nuclide>
+  <nuclide>
+    <id>U238</id>
+    <comp>0.768194</comp>
+  </nuclide>
+  <nuclide>
+    <id>NP237</id>
+    <comp>0.000417</comp>
+  </nuclide>
+  <nuclide>
+    <id>NP239</id>
+    <comp>0.000182</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU238</id>
+    <comp>0.000368</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU239</id>
+    <comp>0.096609</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU240</id>
+    <comp>0.040990</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU241</id>
+    <comp>0.005191</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU242</id>
+    <comp>0.002772</comp>
+  </nuclide>
+  <nuclide>
+    <id>AM241</id>
+    <comp>0.000624</comp>
+  </nuclide>
+  <nuclide>
+    <id>AM243</id>
+    <comp>0.000256</comp>
+  </nuclide>
+</recipe>
+
+<recipe> 
+  <name>blanket_fresh</name>
+  <basis>mass</basis>
+  <nuclide>
+    <id>U235</id>
+    <comp>0.007109</comp>
+  </nuclide>
+  <nuclide>
+    <id>U238</id>
+    <comp>0.992891</comp>
+  </nuclide>
+</recipe>
+
+<recipe> 
+  <name>blanket_used</name>
+  <basis>mass</basis>
+  <nuclide>
+    <id>KR86</id>
+    <comp>0.000102</comp>
+  </nuclide>
+  <nuclide>
+    <id>RB87</id>
+    <comp>0.000130</comp>
+  </nuclide>
+  <nuclide>
+    <id>SR88</id>
+    <comp>0.000178</comp>
+  </nuclide>
+  <nuclide>
+    <id>SR90</id>
+    <comp>0.000250</comp>
+  </nuclide>
+  <nuclide>
+    <id>Y89</id>
+    <comp>0.000222</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR91</id>
+    <comp>0.000297</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR92</id>
+    <comp>0.000360</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR93</id>
+    <comp>0.000434</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR94</id>
+    <comp>0.000445</comp>
+  </nuclide>
+  <nuclide>
+    <id>ZR96</id>
+    <comp>0.000530</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO95</id>
+    <comp>0.000469</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO97</id>
+    <comp>0.000542</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO98</id>
+    <comp>0.000600</comp>
+  </nuclide>
+  <nuclide>
+    <id>MO100</id>
+    <comp>0.000671</comp>
+  </nuclide>
+  <nuclide>
+    <id>TC99</id>
+    <comp>0.000598</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU101</id>
+    <comp>0.000651</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU102</id>
+    <comp>0.000707</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU104</id>
+    <comp>0.000617</comp>
+  </nuclide>
+  <nuclide>
+    <id>RU106</id>
+    <comp>0.000108</comp>
+  </nuclide>
+  <nuclide>
+    <id>RH103</id>
+    <comp>0.000608</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD105</id>
+    <comp>0.000456</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD106</id>
+    <comp>0.000326</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD107</id>
+    <comp>0.000259</comp>
+  </nuclide>
+  <nuclide>
+    <id>PD108</id>
+    <comp>0.000201</comp>
+  </nuclide>
+  <nuclide>
+    <id>AG109</id>
+    <comp>0.000138</comp>
+  </nuclide>
+  <nuclide>
+    <id>TE128</id>
+    <comp>0.000127</comp>
+  </nuclide>
+  <nuclide>
+    <id>TE130</id>
+    <comp>0.000327</comp>
+  </nuclide>
+  <nuclide>
+    <id>I129</id>
+    <comp>0.000194</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE131</id>
+    <comp>0.000520</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE132</id>
+    <comp>0.000734</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE134</id>
+    <comp>0.001028</comp>
+  </nuclide>
+  <nuclide>
+    <id>XE136</id>
+    <comp>0.000959</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS133</id>
+    <comp>0.000899</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS135</id>
+    <comp>0.000994</comp>
+  </nuclide>
+  <nuclide>
+    <id>CS137</id>
+    <comp>0.000833</comp>
+  </nuclide>
+  <nuclide>
+    <id>BA138</id>
+    <comp>0.000903</comp>
+  </nuclide>
+  <nuclide>
+    <id>LA139</id>
+    <comp>0.000857</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE140</id>
+    <comp>0.000800</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE142</id>
+    <comp>0.000741</comp>
+  </nuclide>
+  <nuclide>
+    <id>CE144</id>
+    <comp>0.000116</comp>
+  </nuclide>
+  <nuclide>
+    <id>PR141</id>
+    <comp>0.000799</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND143</id>
+    <comp>0.000673</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND144</id>
+    <comp>0.000510</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND145</id>
+    <comp>0.000497</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND146</id>
+    <comp>0.000441</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND148</id>
+    <comp>0.000281</comp>
+  </nuclide>
+  <nuclide>
+    <id>ND150</id>
+    <comp>0.000165</comp>
+  </nuclide>
+  <nuclide>
+    <id>PM147</id>
+    <comp>0.000148</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM147</id>
+    <comp>0.000137</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM149</id>
+    <comp>0.000199</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM151</id>
+    <comp>0.000107</comp>
+  </nuclide>
+  <nuclide>
+    <id>SM152</id>
+    <comp>0.000114</comp>
+  </nuclide>
+  <nuclide>
+    <id>U235</id>
+    <comp>0.003575</comp>
+  </nuclide>
+  <nuclide>
+    <id>U236</id>
+    <comp>0.000736</comp>
+  </nuclide>
+  <nuclide>
+    <id>U238</id>
+    <comp>0.916420</comp>
+  </nuclide>
+  <nuclide>
+    <id>NP237</id>
+    <comp>0.000256</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU239</id>
+    <comp>0.049541</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU240</id>
+    <comp>0.003661</comp>
+  </nuclide>
+  <nuclide>
+    <id>PU241</id>
+    <comp>0.000197</comp>
+  </nuclide>
+</recipe>
+
+
+
+
+</root>
\ No newline at end of file

From 9c5de56d4357128482d8b1093109b6af0d8a1fd3 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 11 Sep 2018 08:48:36 -0500
Subject: [PATCH 02/35] fix vermont yankee and fort calhourn update decom time

---
 input/master_us/depinst.xml | 808 ++++++++++++++++++------------------
 1 file changed, 404 insertions(+), 404 deletions(-)

diff --git a/input/master_us/depinst.xml b/input/master_us/depinst.xml
index 30226b02..7f2ca5d1 100644
--- a/input/master_us/depinst.xml
+++ b/input/master_us/depinst.xml
@@ -1,411 +1,411 @@
 <root>
-          <prototypes>
-	<val>OYSTER CREEK</val>
-<val>NINE MILE POINT-1</val>
-<val>DRESDEN-2</val>
-<val>GINNA</val>
-<val>DRESDEN-3</val>
-<val>INDIAN POINT-2</val>
-<val>QUAD CITIES-1</val>
-<val>QUAD CITIES-2</val>
-<val>PALISADES</val>
-<val>ROBINSON-2</val>
-<val>TURKEY POINT-3</val>
-<val>TURKEY POINT-4</val>
-<val>BROWNS FERRY-1</val>
-<val>BROWNS FERRY-2</val>
-<val>MONTICELLO</val>
-<val>POINT BEACH-1</val>
-<val>OCONEE-1</val>
-<val>OCONEE-2</val>
-<val>OCONEE-3</val>
-<val>VERMONT YANKEE</val>
-<val>PEACH BOTTOM-2</val>
-<val>PEACH BOTTOM-3</val>
-<val>DIABLO CANYON-1</val>
-<val>THREE MILE ISLAND-1</val>
-<val>CALVERT CLIFFS-1</val>
-<val>CALVERT CLIFFS-2</val>
-<val>COOPER</val>
-<val>FORT CALHOUN-1</val>
-<val>PRAIRIE ISLAND-1</val>
-<val>SURRY-1</val>
-<val>SURRY-2</val>
-<val>BROWNS FERRY-3</val>
-<val>POINT BEACH-2</val>
-<val>PILGRIM-1</val>
-<val>FITZPATRICK</val>
-<val>SALEM-1</val>
-<val>SALEM-2</val>
-<val>HATCH-1</val>
-<val>ANO-1</val>
-<val>INDIAN POINT-3</val>
-<val>ANO-2</val>
-<val>COOK-1</val>
-<val>COOK-2</val>
-<val>PRAIRIE ISLAND-2</val>
-<val>MILLSTONE-2</val>
-<val>BRUNSWICK-1</val>
-<val>BRUNSWICK-2</val>
-<val>SEQUOYAH-1</val>
-<val>SEQUOYAH-2</val>
-<val>DUANE ARNOLD-1</val>
-<val>BEAVER VALLEY-1</val>
-<val>ST. LUCIE-1</val>
-<val>DAVIS BESSE-1</val>
-<val>FARLEY-1</val>
-<val>FARLEY-2</val>
-<val>DIABLO CANYON-2</val>
-<val>NORTH ANNA-1</val>
-<val>NORTH ANNA-2</val>
-<val>MCGUIRE-1</val>
-<val>MCGUIRE-2</val>
-<val>HATCH-2</val>
-<val>COLUMBIA</val>
-<val>FERMI-2</val>
-<val>WATTS BAR-1</val>
-<val>SUMMER-1</val>
-<val>LASALLE-1</val>
-<val>LASALLE-2</val>
-<val>SUSQUEHANNA-1</val>
-<val>SUSQUEHANNA-2</val>
-<val>CATAWBA-1</val>
-<val>CATAWBA-2</val>
-<val>BEAVER VALLEY-2</val>
-<val>GRAND GULF-1</val>
-<val>LIMERICK-1</val>
-<val>LIMERICK-2</val>
-<val>MILLSTONE-3</val>
-<val>PERRY-1</val>
-<val>WATERFORD-3</val>
-<val>COMANCHE PEAK-1</val>
-<val>COMANCHE PEAK-2</val>
-<val>BYRON-1</val>
-<val>BYRON-2</val>
-<val>BRAIDWOOD-1</val>
-<val>BRAIDWOOD-2</val>
-<val>NINE MILE POINT-2</val>
-<val>CALLAWAY-1</val>
-<val>CLINTON-1</val>
-<val>SOUTH TEXAS-1</val>
-<val>SOUTH TEXAS-2</val>
-<val>HOPE CREEK-1</val>
-<val>PALO VERDE-1</val>
-<val>PALO VERDE-2</val>
-<val>PALO VERDE-3</val>
-<val>VOGTLE-1</val>
-<val>VOGTLE-2</val>
-<val>RIVER BEND-1</val>
-<val>WOLF CREEK</val>
-<val>ST. LUCIE-2</val>
-<val>HARRIS-1</val>
+	<prototypes>
+		<val>OYSTER CREEK</val>
+		<val>NINE MILE POINT-1</val>
+		<val>DRESDEN-2</val>
+		<val>GINNA</val>
+		<val>DRESDEN-3</val>
+		<val>INDIAN POINT-2</val>
+		<val>QUAD CITIES-1</val>
+		<val>QUAD CITIES-2</val>
+		<val>PALISADES</val>
+		<val>ROBINSON-2</val>
+		<val>TURKEY POINT-3</val>
+		<val>TURKEY POINT-4</val>
+		<val>BROWNS FERRY-1</val>
+		<val>BROWNS FERRY-2</val>
+		<val>MONTICELLO</val>
+		<val>POINT BEACH-1</val>
+		<val>OCONEE-1</val>
+		<val>OCONEE-2</val>
+		<val>OCONEE-3</val>
+		<val>VERMONT YANKEE</val>
+		<val>PEACH BOTTOM-2</val>
+		<val>PEACH BOTTOM-3</val>
+		<val>DIABLO CANYON-1</val>
+		<val>THREE MILE ISLAND-1</val>
+		<val>CALVERT CLIFFS-1</val>
+		<val>CALVERT CLIFFS-2</val>
+		<val>COOPER</val>
+		<val>FORT CALHOUN-1</val>
+		<val>PRAIRIE ISLAND-1</val>
+		<val>SURRY-1</val>
+		<val>SURRY-2</val>
+		<val>BROWNS FERRY-3</val>
+		<val>POINT BEACH-2</val>
+		<val>PILGRIM-1</val>
+		<val>FITZPATRICK</val>
+		<val>SALEM-1</val>
+		<val>SALEM-2</val>
+		<val>HATCH-1</val>
+		<val>ANO-1</val>
+		<val>INDIAN POINT-3</val>
+		<val>ANO-2</val>
+		<val>COOK-1</val>
+		<val>COOK-2</val>
+		<val>PRAIRIE ISLAND-2</val>
+		<val>MILLSTONE-2</val>
+		<val>BRUNSWICK-1</val>
+		<val>BRUNSWICK-2</val>
+		<val>SEQUOYAH-1</val>
+		<val>SEQUOYAH-2</val>
+		<val>DUANE ARNOLD-1</val>
+		<val>BEAVER VALLEY-1</val>
+		<val>ST. LUCIE-1</val>
+		<val>DAVIS BESSE-1</val>
+		<val>FARLEY-1</val>
+		<val>FARLEY-2</val>
+		<val>DIABLO CANYON-2</val>
+		<val>NORTH ANNA-1</val>
+		<val>NORTH ANNA-2</val>
+		<val>MCGUIRE-1</val>
+		<val>MCGUIRE-2</val>
+		<val>HATCH-2</val>
+		<val>COLUMBIA</val>
+		<val>FERMI-2</val>
+		<val>WATTS BAR-1</val>
+		<val>SUMMER-1</val>
+		<val>LASALLE-1</val>
+		<val>LASALLE-2</val>
+		<val>SUSQUEHANNA-1</val>
+		<val>SUSQUEHANNA-2</val>
+		<val>CATAWBA-1</val>
+		<val>CATAWBA-2</val>
+		<val>BEAVER VALLEY-2</val>
+		<val>GRAND GULF-1</val>
+		<val>LIMERICK-1</val>
+		<val>LIMERICK-2</val>
+		<val>MILLSTONE-3</val>
+		<val>PERRY-1</val>
+		<val>WATERFORD-3</val>
+		<val>COMANCHE PEAK-1</val>
+		<val>COMANCHE PEAK-2</val>
+		<val>BYRON-1</val>
+		<val>BYRON-2</val>
+		<val>BRAIDWOOD-1</val>
+		<val>BRAIDWOOD-2</val>
+		<val>NINE MILE POINT-2</val>
+		<val>CALLAWAY-1</val>
+		<val>CLINTON-1</val>
+		<val>SOUTH TEXAS-1</val>
+		<val>SOUTH TEXAS-2</val>
+		<val>HOPE CREEK-1</val>
+		<val>PALO VERDE-1</val>
+		<val>PALO VERDE-2</val>
+		<val>PALO VERDE-3</val>
+		<val>VOGTLE-1</val>
+		<val>VOGTLE-2</val>
+		<val>RIVER BEND-1</val>
+		<val>WOLF CREEK</val>
+		<val>ST. LUCIE-2</val>
+		<val>HARRIS-1</val>
 
-</prototypes>
-<build_times>
-	<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
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-<val>1</val>
-<val>1</val>
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-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
+	</prototypes>
+	<build_times>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 
-</build_times>
-<n_build>
-	<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
-<val>1</val>
+	</build_times>
+	<n_build>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 
-</n_build>
-<lifetimes>
-	<val>192</val>
-<val>196</val>
-<val>200</val>
-<val>198</val>
-<val>212</val>
-<val>241</val>
-<val>222</val>
-<val>228</val>
-<val>217</val>
-<val>209</val>
-<val>234</val>
-<val>241</val>
-<val>244</val>
-<val>255</val>
-<val>211</val>
-<val>210</val>
-<val>240</val>
-<val>246</val>
-<val>256</val>
-<val>227</val>
-<val>244</val>
-<val>255</val>
-<val>372</val>
-<val>253</val>
-<val>257</val>
-<val>283</val>
-<val>250</val>
-<val>243</val>
-<val>247</val>
-<val>230</val>
-<val>238</val>
-<val>279</val>
-<val>229</val>
-<val>229</val>
-<val>259</val>
-<val>283</val>
-<val>327</val>
-<val>256</val>
-<val>255</val>
-<val>275</val>
-<val>307</val>
-<val>261</val>
-<val>298</val>
-<val>260</val>
-<val>270</val>
-<val>281</val>
-<val>263</val>
-<val>326</val>
-<val>342</val>
-<val>251</val>
-<val>276</val>
-<val>276</val>
-<val>291</val>
-<val>291</val>
-<val>336</val>
-<val>388</val>
-<val>299</val>
-<val>325</val>
-<val>339</val>
-<val>360</val>
-<val>302</val>
-<val>369</val>
-<val>386</val>
-<val>512</val>
-<val>354</val>
-<val>350</val>
-<val>370</val>
-<val>352</val>
-<val>372</val>
-<val>380</val>
-<val>396</val>
-<val>411</val>
-<val>352</val>
-<val>380</val>
-<val>435</val>
-<val>393</val>
-<val>397</val>
-<val>382</val>
-<val>443</val>
-<val>479</val>
-<val>381</val>
-<val>404</val>
-<val>409</val>
-<val>418</val>
-<val>409</val>
-<val>377</val>
-<val>406</val>
-<val>418</val>
-<val>430</val>
-<val>398</val>
-<val>385</val>
-<val>396</val>
-<val>414</val>
-<val>406</val>
-<val>431</val>
-<val>390</val>
-<val>385</val>
-<val>361</val>
-<val>404</val>
+	</n_build>
+	<lifetimes>
+		<val>192</val>
+		<val>196</val>
+		<val>200</val>
+		<val>198</val>
+		<val>212</val>
+		<val>241</val>
+		<val>222</val>
+		<val>228</val>
+		<val>217</val>
+		<val>209</val>
+		<val>234</val>
+		<val>241</val>
+		<val>244</val>
+		<val>255</val>
+		<val>211</val>
+		<val>210</val>
+		<val>240</val>
+		<val>246</val>
+		<val>256</val>
+		<val>19</val>
+		<val>244</val>
+		<val>255</val>
+		<val>372</val>
+		<val>253</val>
+		<val>257</val>
+		<val>283</val>
+		<val>250</val>
+		<val>41</val>
+		<val>247</val>
+		<val>230</val>
+		<val>238</val>
+		<val>279</val>
+		<val>229</val>
+		<val>229</val>
+		<val>259</val>
+		<val>283</val>
+		<val>327</val>
+		<val>256</val>
+		<val>255</val>
+		<val>275</val>
+		<val>307</val>
+		<val>261</val>
+		<val>298</val>
+		<val>260</val>
+		<val>270</val>
+		<val>281</val>
+		<val>263</val>
+		<val>326</val>
+		<val>342</val>
+		<val>251</val>
+		<val>276</val>
+		<val>276</val>
+		<val>291</val>
+		<val>291</val>
+		<val>336</val>
+		<val>388</val>
+		<val>299</val>
+		<val>325</val>
+		<val>339</val>
+		<val>360</val>
+		<val>302</val>
+		<val>369</val>
+		<val>386</val>
+		<val>512</val>
+		<val>354</val>
+		<val>350</val>
+		<val>370</val>
+		<val>352</val>
+		<val>372</val>
+		<val>380</val>
+		<val>396</val>
+		<val>411</val>
+		<val>352</val>
+		<val>380</val>
+		<val>435</val>
+		<val>393</val>
+		<val>397</val>
+		<val>382</val>
+		<val>443</val>
+		<val>479</val>
+		<val>381</val>
+		<val>404</val>
+		<val>409</val>
+		<val>418</val>
+		<val>409</val>
+		<val>377</val>
+		<val>406</val>
+		<val>418</val>
+		<val>430</val>
+		<val>398</val>
+		<val>385</val>
+		<val>396</val>
+		<val>414</val>
+		<val>406</val>
+		<val>431</val>
+		<val>390</val>
+		<val>385</val>
+		<val>361</val>
+		<val>404</val>
 
-</lifetimes>
+	</lifetimes>
 
 </root>
\ No newline at end of file

From 1f01664cace710b5b1d67685364b27c64c8c5af7 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 11 Sep 2018 08:54:41 -0500
Subject: [PATCH 03/35] added watts bar and seabrook

---
 input/master_us/depinst.xml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/input/master_us/depinst.xml b/input/master_us/depinst.xml
index 7f2ca5d1..ad0da8ea 100644
--- a/input/master_us/depinst.xml
+++ b/input/master_us/depinst.xml
@@ -99,6 +99,8 @@
 		<val>WOLF CREEK</val>
 		<val>ST. LUCIE-2</val>
 		<val>HARRIS-1</val>
+		<val>SEABROOK</val>
+		<val>WATTS BAR-2</val>
 
 	</prototypes>
 	<build_times>
@@ -201,6 +203,8 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>1</val>
+		<val>37</val>
 
 	</build_times>
 	<n_build>
@@ -303,6 +307,8 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 
 	</n_build>
 	<lifetimes>
@@ -405,6 +411,8 @@
 		<val>385</val>
 		<val>361</val>
 		<val>404</val>
+		<val>442</val>
+		<val>720</val>
 
 	</lifetimes>
 

From 22181ce230a5eba0f9e76b64ae9a91624408948b Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 11 Sep 2018 08:58:09 -0500
Subject: [PATCH 04/35] Added watts bar 2 and seabrook

---
 input/master_us/reactor_with_coordinates.xml | 66 ++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/input/master_us/reactor_with_coordinates.xml b/input/master_us/reactor_with_coordinates.xml
index 344a5dc5..14a83272 100644
--- a/input/master_us/reactor_with_coordinates.xml
+++ b/input/master_us/reactor_with_coordinates.xml
@@ -1819,6 +1819,72 @@
     </config>
   </facility>
 
+  <facility>
+  <!-- us -->
+  <!-- PWR -->
+  <name>WATTS BAR-2</name>
+  <config>
+    <Reactor>
+      <fuel_inrecipes>
+        <val>uox_fuel_recipe</val>
+      </fuel_inrecipes>
+      <fuel_outrecipes>
+        <val>uox_used_fuel_recipe</val>
+      </fuel_outrecipes>
+      <fuel_incommods>
+        <val>uox</val>
+      </fuel_incommods>
+      <fuel_outcommods>
+        <val>uox_waste</val>
+      </fuel_outcommods>
+      <fuel_prefs>
+        <val>1.0</val>
+      </fuel_prefs>
+      <cycle_time>18</cycle_time>
+      <refuel_time>2</refuel_time>
+      <assem_size>446.0</assem_size>
+      <n_assem_core>224</n_assem_core>
+      <n_assem_batch>74</n_assem_batch>
+      <power_cap>1165</power_cap>
+      <longitude>-84.789400</longitude>
+      <latitude>35.602800</latitude>
+    </Reactor>
+  </config>
+</facility>
+
+<facility>
+  <!-- us -->
+  <!-- PWR -->
+  <name>SEABROOK</name>
+  <config>
+    <Reactor>
+      <fuel_inrecipes>
+        <val>uox_fuel_recipe</val>
+      </fuel_inrecipes>
+      <fuel_outrecipes>
+        <val>uox_used_fuel_recipe</val>
+      </fuel_outrecipes>
+      <fuel_incommods>
+        <val>uox</val>
+      </fuel_incommods>
+      <fuel_outcommods>
+        <val>uox_waste</val>
+      </fuel_outcommods>
+      <fuel_prefs>
+        <val>1.0</val>
+      </fuel_prefs>
+      <cycle_time>18</cycle_time>
+      <refuel_time>2</refuel_time>
+      <assem_size>446.0</assem_size>
+      <n_assem_core>240</n_assem_core>
+      <n_assem_batch>80</n_assem_batch>
+      <power_cap>1246</power_cap>
+      <longitude>-70.850833</longitude>
+      <latitude>42.898889</latitude>
+    </Reactor>
+  </config>
+</facility>
+
  <facility>
     <!-- us -->
     <!-- PWR -->

From 15509691692ba8102bf8369ad1df29b29e77436b Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 11 Sep 2018 17:24:18 -0500
Subject: [PATCH 05/35] lwr_dep

---
 input/master_us/get_deployment.ipynb |  506 +------
 input/master_us/input.xml            |   83 +-
 input/master_us/lwr_dep.xml          | 2086 ++++++++++++++++++++++++++
 3 files changed, 2201 insertions(+), 474 deletions(-)
 create mode 100644 input/master_us/lwr_dep.xml

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 23b0aa07..d09fa7f2 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,18 +30,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "# get sqlite cursor and simulation time data\n",
-    "cur = an.get_cursor('./cyclus.sqlite')\n",
+    "cur = an.get_cursor('./2020.sqlite')\n",
     "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -60,17 +60,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,443 +82,32 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_dep_array_once(sfr_avail, growth_rate, old_cap):\n",
-    "    # automatically calculated values\n",
-    "    scenario_name = '%i_%s_growth' %(sfr_avail, str(growth_rate).replace('.','_'))\n",
-    "    demand_eq = '110*(%s)**(t/12)' %str(1+growth_rate)\n",
-    "    avail_timestep = (sfr_avail* 12) - (2013*12 + 5)\n",
-    "\n",
-    "    # get lwr_only, find deployment scheme for lwr and sfr\n",
-    "    cur = an.get_cursor('./output/2020_lwr.sqlite')\n",
-    "    init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
-    "    power_dict = an.get_power_dict(cur)\n",
-    "    dep_array, dep_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                 demand_eq, old_cap, 240)\n",
-    "    return dep_array\n",
-    "\n",
-    "\n",
-    "dep_dict = {}\n",
-    "new_avail = 2050\n",
-    "growth_rate = 0.015\n",
-    "scenario_name = 'smr_msr'\n",
-    "smr_dep=get_dep_array_once(new_avail, growth_rate, 0.72)\n",
-    "dep_dict[scenario_name] = {'smr': smr_dep}\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x121757860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x121928320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x121757710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x112593e80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x123650f60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x121901128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110460278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1217ad780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11119f550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
+      "[0. 0. 1. ... 0. 1. 0.]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x121901e10>"
+       "[<matplotlib.lines.Line2D at 0x7f2ceec12be0>]"
       ]
      },
+     "execution_count": 29,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x121957400>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1227bfc88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1228f94a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x122801e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10dbb38d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1228f9cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x123468748>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x123443550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1228f97b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n",
-      "timestep 0 is not done correctly\n",
-      "It has a powercap of 0.000000 when it should have 110.000000\n"
-     ]
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x121cbbf28>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -526,51 +115,22 @@
     }
    ],
    "source": [
-    "\n",
-    "def get_dep_arrays(sfr_avail, growth_rate, old_cap, new_cap, old_life, new_life):\n",
-    "    # automatically calculated values\n",
-    "    scenario_name = '%i_%s_growth' %(sfr_avail, str(growth_rate).replace('.','_'))\n",
-    "    demand_eq = '110*(%s)**(t/12)' %str(1+growth_rate)\n",
-    "    avail_timestep = (sfr_avail* 12) - (2013*12 + 5)\n",
-    "\n",
-    "    # get lwr_only, find deployment scheme for lwr and sfr\n",
-    "    cur = an.get_cursor('./output/2020_lwr.sqlite')\n",
-    "    init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
-    "    power_dict = an.get_power_dict(cur)\n",
-    "    dep_array, dep_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                 demand_eq, old_cap, old_life)\n",
-    "    for indx in range(len(dep_array)):\n",
-    "        if indx > avail_timestep:\n",
-    "            dep_array[indx] = 0\n",
-    "    # recalculate power\n",
-    "    dep_power = np.zeros(len(dep_array))\n",
-    "    for indx, val in enumerate(dep_array):\n",
-    "        if val != 0:\n",
-    "            top = min(indx+960, duration)\n",
-    "            for i in range(indx, top):\n",
-    "                dep_power[i] += val * old_cap\n",
-    "    plt.plot(dep_power, label='dep power')\n",
-    "    plt.plot(dep_array, label='dep array')\n",
-    "\n",
-    "    new_power = {'power': power_dict['us_government'] + dep_power}\n",
-    "    plt.plot(new_power['power'], label='only old')\n",
-    "\n",
-    "    sfr_dep, sfr_power = an.get_new_deployment(new_power, ['power'], demand_eq,\n",
-    "                                               new_cap, new_life)\n",
-    "    plt.plot(new_power['power'] + sfr_power, label='total power')\n",
-    "    plt.plot(sfr_dep, label='new reactor deployment')\n",
-    "    plt.plot(sfr_power, label='only new')\n",
-    "    plt.legend()\n",
-    "    plt.show()\n",
-    "    plt.close()\n",
-    "    return dep_array, sfr_dep\n",
-    "\n",
-    "dep_dict = {}\n",
-    "for new_avail in [2030, 2035, 2040, 2045, 2050]:\n",
-    "    for growth_rate in [0.0, 0.005, 0.01, 0.015]:\n",
-    "        scenario_name = '%i_%s_growth' %(new_avail, str(growth_rate).replace('0.', ''))\n",
-    "        dep1, dep2 = get_dep_arrays(new_avail, growth_rate, 1, 0.4, 960, 960)\n",
-    "        dep_dict[scenario_name] = {'lwr': dep1, 'sfr': dep2}\n"
+    "cur = an.get_cursor('./2020.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
+    "power_dict = an.get_power_dict(cur)\n",
+    "deploy_array, deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
+    "                                                     '100*(1.01)**(t/12)', 1, 960, 0, True)\n",
+    "print(deploy_array)\n",
+    "plt.plot(deploy_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an.write_deployinst(deploy_array, 'lwr', 'lwr_dep.xml', 960)"
    ]
   },
   {
diff --git a/input/master_us/input.xml b/input/master_us/input.xml
index 596c38a2..04760adf 100644
--- a/input/master_us/input.xml
+++ b/input/master_us/input.xml
@@ -319,6 +319,7 @@
     </config>
   </facility>
 
+
 <facility>
         <!-- Stores uox unf for 48 months (4years) -->
     <name>sfr_storage</name>
@@ -385,8 +386,74 @@
   </facility>
 
   <facility>
-    <name>nat_u_source</name>
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>lwr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1000</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+
+    <name>na
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>lwr</name>
     <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1000</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+
+    <config>
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>lwr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1000</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+
       <Source>
         <outcommod>natl_u</outcommod>
         <outrecipe>natl_u_recipe</outrecipe>
@@ -447,8 +514,22 @@
         </DeployInst>
       </config>
     </institution>
+
+  <institution>
+  <name>lwr</name>
+  <config>
+    <DeployInst>
+      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
+      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
+      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+    </DeployInst>
+    </config>
+  </institution>
+
   </region>
 
+
 <region>
     <name>Sink_Source</name>
     <config><NullRegion/></config>
diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
new file mode 100644
index 00000000..ea4f7c5c
--- /dev/null
+++ b/input/master_us/lwr_dep.xml
@@ -0,0 +1,2086 @@
+<root>
+<prototypes>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
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+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+</lifetimes>
+</root>

From 14439fc5798622131e8f176851005f5e7e6cb230 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 11 Sep 2018 17:28:57 -0500
Subject: [PATCH 06/35] lwr dep

---
 input/master_us/lwr_dep.xml | 1068 +++++++++++++++++------------------
 1 file changed, 521 insertions(+), 547 deletions(-)

diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
index ea4f7c5c..2f57475c 100644
--- a/input/master_us/lwr_dep.xml
+++ b/input/master_us/lwr_dep.xml
@@ -1,5 +1,5 @@
 <root>
-<prototypes>
+	<prototypes>
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
@@ -513,542 +513,523 @@
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-</prototypes>
-<build_times>
-		<val>2</val>
-		<val>14</val>
-		<val>20</val>
-		<val>30</val>
-		<val>50</val>
-		<val>61</val>
-		<val>73</val>
-		<val>84</val>
-		<val>95</val>
-		<val>106</val>
-		<val>117</val>
-		<val>128</val>
-		<val>139</val>
-		<val>150</val>
-		<val>160</val>
-		<val>171</val>
-		<val>181</val>
-		<val>192</val>
-		<val>196</val>
-		<val>199</val>
-		<val>201</val>
-		<val>205</val>
-		<val>210</val>
-		<val>212</val>
-		<val>213</val>
-		<val>217</val>
-		<val>219</val>
-		<val>223</val>
-		<val>229</val>
-		<val>230</val>
-		<val>231</val>
-		<val>235</val>
-		<val>239</val>
-		<val>241</val>
-		<val>242</val>
-		<val>245</val>
-		<val>247</val>
-		<val>251</val>
-		<val>252</val>
-		<val>254</val>
-		<val>256</val>
-		<val>257</val>
-		<val>258</val>
-		<val>260</val>
-		<val>261</val>
-		<val>262</val>
-		<val>264</val>
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-		<val>284</val>
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-		<val>292</val>
-		<val>299</val>
-		<val>300</val>
-		<val>301</val>
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-		<val>328</val>
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-		<val>361</val>
-		<val>362</val>
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-		<val>371</val>
-		<val>373</val>
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-		<val>378</val>
-		<val>381</val>
-		<val>382</val>
-		<val>383</val>
-		<val>385</val>
-		<val>386</val>
-		<val>387</val>
-		<val>389</val>
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-		<val>397</val>
-		<val>398</val>
-		<val>399</val>
-		<val>405</val>
-		<val>407</val>
-		<val>410</val>
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-		<val>415</val>
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-		<val>443</val>
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-		<val>576</val>
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-		<val>591</val>
-		<val>598</val>
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-		<val>951</val>
-		<val>957</val>
-		<val>962</val>
-		<val>968</val>
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-		<val>974</val>
-		<val>978</val>
-		<val>980</val>
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-</build_times>
-<n_build>
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-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
+	</prototypes>
+	<build_times>
+		<val>   4       </val>
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+		<val>   26      </val>
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+		<val>   48      </val>
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+		<val>   80      </val>
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+		<val>   167     </val>
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+		<val>   1109    </val>
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+		<val>   1119    </val>
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+		<val>   1123    </val>
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+		<val>   1134    </val>
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+		<val>   1138    </val>
+		<val>   1140    </val>
+		<val>   1141    </val>
+		<val>   1142    </val>
+		<val>   1144    </val>
+		<val>   1145    </val>
+		<val>   1149    </val>
+		<val>   1151    </val>
+		<val>   1154    </val>
+		<val>   1156    </val>
+		<val>   1157    </val>
+		<val>   1158    </val>
+		<val>   1160    </val>
+		<val>   1162    </val>
+		<val>   1164    </val>
+		<val>   1167    </val>
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+		<val>   1171    </val>
+		<val>   1172    </val>
+		<val>   1175    </val>
+		<val>   1179    </val>
+		<val>   1180    </val>
+		<val>   1181    </val>
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+		<val>   1221    </val>
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+		<val>   1237    </val>
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+		<val>   1243    </val>
+		<val>   1245    </val>
+		<val>   1249    </val>
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+		<val>   1253    </val>
+		<val>   1255    </val>
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+		<val>   1258    </val>
+		<val>   1261    </val>
+		<val>   1262    </val>
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+		<val>   1273    </val>
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+		<val>   1279    </val>
+		<val>   1281    </val>
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+		<val>   1299    </val>
+		<val>   1300    </val>
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+		<val>   1388    </val>
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+		<val>   1400    </val>
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+		<val>   1421    </val>
+		<val>   1424    </val>
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+		<val>   1428    </val>
+		<val>   1431    </val>
+		<val>   1433    </val>
+		<val>   1435    </val>
+		<val>   1438    </val>
+		<val>   1441    </val>
+		<val>   1442    </val>
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+		<val>   1448    </val>
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+		<val>   1452    </val>
+		<val>   1455    </val>
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+		<val>   1459    </val>
+		<val>   1462    </val>
+		<val>   1464    </val>
+		<val>   1465    </val>
+		<val>   1469    </val>
+		<val>   1471    </val>
+		<val>   1472    </val>
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+		<val>   1478    </val>
+		<val>   1479    </val>
+		<val>   1482    </val>
+		<val>   1485    </val>
+		<val>   1486    </val>
+		<val>   1489    </val>
+		<val>   1492    </val>
+		<val>   1493    </val>
+		<val>   1495    </val>
+		<val>   1498    </val>
+		<val>   1500    </val>
+		<val>   1502    </val>
+		<val>   1505    </val>
+		<val>   1507    </val>
+		<val>   1508    </val>
+		<val>   1511    </val>
+		<val>   1514    </val>
+		<val>   1515    </val>
+		<val>   1518    </val>
+		<val>   1521    </val>
+		<val>   1522    </val>
+		<val>   1524    </val>
+		<val>   1527    </val>
+		<val>   1529    </val>
+		<val>   1530    </val>
+		<val>   1534    </val>
+		<val>   1536    </val>
+		<val>   1537    </val>
+		<val>   1540    </val>
+		<val>   1543    </val>
+		<val>   1546    </val>
+		<val>   1549    </val>
+		<val>   1550    </val>
+		<val>   1553    </val>
+		<val>   1556    </val>
+		<val>   1557    </val>
+		<val>   1559    </val>
+		<val>   1562    </val>
+		<val>   1563    </val>
+		<val>   1565    </val>
+		<val>   1568    </val>
+		<val>   1570    </val>
+		<val>   1571    </val>
+		<val>   1574    </val>
+		<val>   1577    </val>
+		<val>   1580    </val>
+		<val>   1583    </val>
+		<val>   1584    </val>
+		<val>   1586    </val>
+		<val>   1589    </val>
+		<val>   1590    </val>
+		<val>   1592    </val>
+		<val>   1595    </val>
+		<val>   1597    </val>
+		<val>   1598    </val>
+		<val>   1601    </val>
+		<val>   1604    </val>
+		<val>   1607    </val>
+		<val>   1610    </val>
+		<val>   1613    </val>
+		<val>   1616    </val>
+		<val>   1617    </val>
+		<val>   1619    </val>
+		<val>   1622    </val>
+		<val>   1623    </val>
+		<val>   1625    </val>
+		<val>   1628    </val>
+		<val>   1630    </val>
+		<val>   1631    </val>
+		<val>   1634    </val>
+		<val>   1636    </val>
+		<val>   1637    </val>
+		<val>   1640    </val>
+		<val>   1642    </val>
+		<val>   1643    </val>
+		<val>   1645    </val>
+		<val>   1648    </val>
+		<val>   1651    </val>
+		<val>   1654    </val>
+		<val>   1655    </val>
+		<val>   1657    </val>
+		<val>   1660    </val>
+		<val>   1661    </val>
+		<val>   1663    </val>
+		<val>   1665    </val>
+		<val>   1667    </val>
+		<val>   1668    </val>
+		<val>   1671    </val>
+		<val>   1673    </val>
+		<val>   1674    </val>
+		<val>   1677    </val>
+		<val>   1680    </val>
+		<val>   1682    </val>
+
+	</build_times>
+	<n_build>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -1561,15 +1542,8 @@
 		<val>1</val>
 		<val>2</val>
 		<val>1</val>
-</n_build>
-<lifetimes>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
+	</n_build>
+	<lifetimes>
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
@@ -2082,5 +2056,5 @@
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
-</lifetimes>
+	</lifetimes>
 </root>

From bf307af0dc8933470573b45a8b312b946525d221 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 11 Sep 2018 17:33:48 -0500
Subject: [PATCH 07/35] error fix

---
 input/master_us/input.xml   | 75 +++----------------------------------
 input/master_us/lwr_dep.xml |  1 -
 2 files changed, 5 insertions(+), 71 deletions(-)

diff --git a/input/master_us/input.xml b/input/master_us/input.xml
index 04760adf..c6d45a03 100644
--- a/input/master_us/input.xml
+++ b/input/master_us/input.xml
@@ -385,76 +385,11 @@
     </config>
   </facility>
 
-  <facility>
- <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>lwr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1000</power_cap>
-      </Reactor>
-    </config>
-  </facility>
 
-
-    <name>na
- <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>lwr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1000</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
-
-    <config>
- <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>lwr</name>
+  <facility>
+    <name>nat_u_source</name>
     <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1000</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
-
-      <Source>
+        <Source>
         <outcommod>natl_u</outcommod>
         <outrecipe>natl_u_recipe</outrecipe>
         <throughput>1e100</throughput>
@@ -516,7 +451,7 @@
     </institution>
 
   <institution>
-  <name>lwr</name>
+  <name>lwr_inst</name>
   <config>
     <DeployInst>
       <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
@@ -524,7 +459,7 @@
       <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
       <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
     </DeployInst>
-    </config>
+  </config>
   </institution>
 
   </region>
diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
index 2f57475c..0aaa450d 100644
--- a/input/master_us/lwr_dep.xml
+++ b/input/master_us/lwr_dep.xml
@@ -512,7 +512,6 @@
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
-		<val>lwr</val>
 	</prototypes>
 	<build_times>
 		<val>   4       </val>

From 947b19be7cc06537de275e53c450d2ca9431537a Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Wed, 12 Sep 2018 10:25:47 -0500
Subject: [PATCH 08/35] fix reverse error

---
 input/master_us/lwr_dep.xml | 1068 ++++++++++++++++++-----------------
 1 file changed, 548 insertions(+), 520 deletions(-)

diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
index 0aaa450d..df11e871 100644
--- a/input/master_us/lwr_dep.xml
+++ b/input/master_us/lwr_dep.xml
@@ -1,5 +1,5 @@
 <root>
-	<prototypes>
+<prototypes>
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
@@ -512,523 +512,544 @@
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
-	</prototypes>
-	<build_times>
-		<val>   4       </val>
-		<val>   15      </val>
-		<val>   26      </val>
-		<val>   37      </val>
-		<val>   48      </val>
-		<val>   59      </val>
-		<val>   70      </val>
-		<val>   80      </val>
-		<val>   91      </val>
-		<val>   101     </val>
-		<val>   112     </val>
-		<val>   116     </val>
-		<val>   119     </val>
-		<val>   121     </val>
-		<val>   125     </val>
-		<val>   130     </val>
-		<val>   132     </val>
-		<val>   133     </val>
-		<val>   137     </val>
-		<val>   139     </val>
-		<val>   143     </val>
-		<val>   149     </val>
-		<val>   150     </val>
-		<val>   151     </val>
-		<val>   155     </val>
-		<val>   159     </val>
-		<val>   161     </val>
-		<val>   162     </val>
-		<val>   165     </val>
-		<val>   167     </val>
-		<val>   171     </val>
-		<val>   172     </val>
-		<val>   174     </val>
-		<val>   176     </val>
-		<val>   177     </val>
-		<val>   178     </val>
-		<val>   180     </val>
-		<val>   181     </val>
-		<val>   182     </val>
-		<val>   184     </val>
-		<val>   189     </val>
-		<val>   191     </val>
-		<val>   196     </val>
-		<val>   197     </val>
-		<val>   200     </val>
-		<val>   202     </val>
-		<val>   204     </val>
-		<val>   209     </val>
-		<val>   212     </val>
-		<val>   219     </val>
-		<val>   220     </val>
-		<val>   221     </val>
-		<val>   223     </val>
-		<val>   228     </val>
-		<val>   231     </val>
-		<val>   241     </val>
-		<val>   246     </val>
-		<val>   247     </val>
-		<val>   248     </val>
-		<val>   257     </val>
-		<val>   258     </val>
-		<val>   260     </val>
-		<val>   263     </val>
-		<val>   264     </val>
-		<val>   271     </val>
-		<val>   272     </val>
-		<val>   273     </val>
-		<val>   275     </val>
-		<val>   281     </val>
-		<val>   282     </val>
-		<val>   283     </val>
-		<val>   290     </val>
-		<val>   291     </val>
-		<val>   293     </val>
-		<val>   295     </val>
-		<val>   298     </val>
-		<val>   301     </val>
-		<val>   302     </val>
-		<val>   303     </val>
-		<val>   305     </val>
-		<val>   306     </val>
-		<val>   307     </val>
-		<val>   309     </val>
-		<val>   311     </val>
-		<val>   314     </val>
-		<val>   316     </val>
-		<val>   317     </val>
-		<val>   318     </val>
-		<val>   319     </val>
-		<val>   325     </val>
-		<val>   327     </val>
-		<val>   330     </val>
-		<val>   332     </val>
-		<val>   335     </val>
-		<val>   339     </val>
-		<val>   348     </val>
-		<val>   351     </val>
-		<val>   352     </val>
-		<val>   356     </val>
-		<val>   360     </val>
-		<val>   363     </val>
-		<val>   364     </val>
-		<val>   373     </val>
-		<val>   381     </val>
-		<val>   389     </val>
-		<val>   397     </val>
-		<val>   400     </val>
-		<val>   404     </val>
-		<val>   412     </val>
-		<val>   420     </val>
-		<val>   428     </val>
-		<val>   433     </val>
-		<val>   435     </val>
-		<val>   443     </val>
-		<val>   450     </val>
-		<val>   458     </val>
-		<val>   466     </val>
-		<val>   473     </val>
-		<val>   481     </val>
-		<val>   489     </val>
-		<val>   496     </val>
-		<val>   504     </val>
-		<val>   511     </val>
-		<val>   518     </val>
-		<val>   526     </val>
-		<val>   533     </val>
-		<val>   540     </val>
-		<val>   547     </val>
-		<val>   554     </val>
-		<val>   562     </val>
-		<val>   569     </val>
-		<val>   576     </val>
-		<val>   583     </val>
-		<val>   590     </val>
-		<val>   597     </val>
-		<val>   603     </val>
-		<val>   610     </val>
-		<val>   617     </val>
-		<val>   624     </val>
-		<val>   630     </val>
-		<val>   637     </val>
-		<val>   644     </val>
-		<val>   650     </val>
-		<val>   657     </val>
-		<val>   663     </val>
-		<val>   670     </val>
-		<val>   676     </val>
-		<val>   677     </val>
-		<val>   682     </val>
-		<val>   688     </val>
-		<val>   695     </val>
-		<val>   701     </val>
-		<val>   707     </val>
-		<val>   713     </val>
-		<val>   720     </val>
-		<val>   726     </val>
-		<val>   732     </val>
-		<val>   738     </val>
-		<val>   744     </val>
-		<val>   750     </val>
-		<val>   756     </val>
-		<val>   762     </val>
-		<val>   768     </val>
-		<val>   774     </val>
-		<val>   780     </val>
-		<val>   786     </val>
-		<val>   792     </val>
-		<val>   798     </val>
-		<val>   804     </val>
-		<val>   810     </val>
-		<val>   815     </val>
-		<val>   821     </val>
-		<val>   827     </val>
-		<val>   832     </val>
-		<val>   838     </val>
-		<val>   844     </val>
-		<val>   849     </val>
-		<val>   855     </val>
-		<val>   860     </val>
-		<val>   866     </val>
-		<val>   871     </val>
-		<val>   877     </val>
-		<val>   882     </val>
-		<val>   888     </val>
-		<val>   893     </val>
-		<val>   894     </val>
-		<val>   898     </val>
-		<val>   900     </val>
-		<val>   904     </val>
-		<val>   909     </val>
-		<val>   910     </val>
-		<val>   914     </val>
-		<val>   920     </val>
-		<val>   925     </val>
-		<val>   930     </val>
-		<val>   935     </val>
-		<val>   941     </val>
-		<val>   946     </val>
-		<val>   951     </val>
-		<val>   953     </val>
-		<val>   956     </val>
-		<val>   961     </val>
-		<val>   964     </val>
-		<val>   966     </val>
-		<val>   971     </val>
-		<val>   975     </val>
-		<val>   976     </val>
-		<val>   981     </val>
-		<val>   986     </val>
-		<val>   991     </val>
-		<val>   996     </val>
-		<val>   997     </val>
-		<val>   1001    </val>
-		<val>   1006    </val>
-		<val>   1008    </val>
-		<val>   1011    </val>
-		<val>   1016    </val>
-		<val>   1019    </val>
-		<val>   1021    </val>
-		<val>   1026    </val>
-		<val>   1030    </val>
-		<val>   1035    </val>
-		<val>   1040    </val>
-		<val>   1045    </val>
-		<val>   1049    </val>
-		<val>   1051    </val>
-		<val>   1054    </val>
-		<val>   1059    </val>
-		<val>   1061    </val>
-		<val>   1064    </val>
-		<val>   1068    </val>
-		<val>   1072    </val>
-		<val>   1073    </val>
-		<val>   1076    </val>
-		<val>   1077    </val>
-		<val>   1079    </val>
-		<val>   1081    </val>
-		<val>   1082    </val>
-		<val>   1085    </val>
-		<val>   1087    </val>
-		<val>   1090    </val>
-		<val>   1091    </val>
-		<val>   1092    </val>
-		<val>   1093    </val>
-		<val>   1096    </val>
-		<val>   1097    </val>
-		<val>   1099    </val>
-		<val>   1100    </val>
-		<val>   1103    </val>
-		<val>   1105    </val>
-		<val>   1109    </val>
-		<val>   1110    </val>
-		<val>   1111    </val>
-		<val>   1114    </val>
-		<val>   1115    </val>
-		<val>   1118    </val>
-		<val>   1119    </val>
-		<val>   1121    </val>
-		<val>   1122    </val>
-		<val>   1123    </val>
-		<val>   1125    </val>
-		<val>   1127    </val>
-		<val>   1131    </val>
-		<val>   1132    </val>
-		<val>   1134    </val>
-		<val>   1136    </val>
-		<val>   1137    </val>
-		<val>   1138    </val>
-		<val>   1140    </val>
-		<val>   1141    </val>
-		<val>   1142    </val>
-		<val>   1144    </val>
-		<val>   1145    </val>
-		<val>   1149    </val>
-		<val>   1151    </val>
-		<val>   1154    </val>
-		<val>   1156    </val>
-		<val>   1157    </val>
-		<val>   1158    </val>
-		<val>   1160    </val>
-		<val>   1162    </val>
-		<val>   1164    </val>
-		<val>   1167    </val>
-		<val>   1169    </val>
-		<val>   1171    </val>
-		<val>   1172    </val>
-		<val>   1175    </val>
-		<val>   1179    </val>
-		<val>   1180    </val>
-		<val>   1181    </val>
-		<val>   1183    </val>
-		<val>   1184    </val>
-		<val>   1188    </val>
-		<val>   1191    </val>
-		<val>   1192    </val>
-		<val>   1196    </val>
-		<val>   1200    </val>
-		<val>   1201    </val>
-		<val>   1205    </val>
-		<val>   1206    </val>
-		<val>   1207    </val>
-		<val>   1208    </val>
-		<val>   1209    </val>
-		<val>   1213    </val>
-		<val>   1217    </val>
-		<val>   1218    </val>
-		<val>   1220    </val>
-		<val>   1221    </val>
-		<val>   1223    </val>
-		<val>   1224    </val>
-		<val>   1225    </val>
-		<val>   1229    </val>
-		<val>   1231    </val>
-		<val>   1232    </val>
-		<val>   1233    </val>
-		<val>   1235    </val>
-		<val>   1237    </val>
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-		<val>   1242    </val>
-		<val>   1243    </val>
-		<val>   1245    </val>
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-		<val>   1250    </val>
-		<val>   1251    </val>
-		<val>   1253    </val>
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-		<val>   1257    </val>
-		<val>   1258    </val>
-		<val>   1261    </val>
-		<val>   1262    </val>
-		<val>   1263    </val>
-		<val>   1265    </val>
-		<val>   1266    </val>
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-		<val>   1269    </val>
-		<val>   1271    </val>
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-		<val>   1274    </val>
-		<val>   1276    </val>
-		<val>   1277    </val>
-		<val>   1278    </val>
-		<val>   1279    </val>
-		<val>   1281    </val>
-		<val>   1285    </val>
-		<val>   1287    </val>
-		<val>   1289    </val>
-		<val>   1290    </val>
-		<val>   1292    </val>
-		<val>   1293    </val>
-		<val>   1295    </val>
-		<val>   1297    </val>
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-		<val>   1300    </val>
-		<val>   1304    </val>
-		<val>   1308    </val>
-		<val>   1311    </val>
-		<val>   1312    </val>
-		<val>   1316    </val>
-		<val>   1319    </val>
-		<val>   1320    </val>
-		<val>   1323    </val>
-		<val>   1324    </val>
-		<val>   1327    </val>
-		<val>   1331    </val>
-		<val>   1333    </val>
-		<val>   1335    </val>
-		<val>   1338    </val>
-		<val>   1341    </val>
-		<val>   1342    </val>
-		<val>   1346    </val>
-		<val>   1349    </val>
-		<val>   1353    </val>
-		<val>   1357    </val>
-		<val>   1360    </val>
-		<val>   1364    </val>
-		<val>   1368    </val>
-		<val>   1371    </val>
-		<val>   1372    </val>
-		<val>   1375    </val>
-		<val>   1378    </val>
-		<val>   1380    </val>
-		<val>   1382    </val>
-		<val>   1386    </val>
-		<val>   1388    </val>
-		<val>   1389    </val>
-		<val>   1393    </val>
-		<val>   1395    </val>
-		<val>   1396    </val>
-		<val>   1400    </val>
-		<val>   1403    </val>
-		<val>   1407    </val>
-		<val>   1410    </val>
-		<val>   1414    </val>
-		<val>   1417    </val>
-		<val>   1418    </val>
-		<val>   1421    </val>
-		<val>   1424    </val>
-		<val>   1426    </val>
-		<val>   1428    </val>
-		<val>   1431    </val>
-		<val>   1433    </val>
-		<val>   1435    </val>
-		<val>   1438    </val>
-		<val>   1441    </val>
-		<val>   1442    </val>
-		<val>   1445    </val>
-		<val>   1448    </val>
-		<val>   1449    </val>
-		<val>   1452    </val>
-		<val>   1455    </val>
-		<val>   1456    </val>
-		<val>   1459    </val>
-		<val>   1462    </val>
-		<val>   1464    </val>
-		<val>   1465    </val>
-		<val>   1469    </val>
-		<val>   1471    </val>
-		<val>   1472    </val>
-		<val>   1475    </val>
-		<val>   1478    </val>
-		<val>   1479    </val>
-		<val>   1482    </val>
-		<val>   1485    </val>
-		<val>   1486    </val>
-		<val>   1489    </val>
-		<val>   1492    </val>
-		<val>   1493    </val>
-		<val>   1495    </val>
-		<val>   1498    </val>
-		<val>   1500    </val>
-		<val>   1502    </val>
-		<val>   1505    </val>
-		<val>   1507    </val>
-		<val>   1508    </val>
-		<val>   1511    </val>
-		<val>   1514    </val>
-		<val>   1515    </val>
-		<val>   1518    </val>
-		<val>   1521    </val>
-		<val>   1522    </val>
-		<val>   1524    </val>
-		<val>   1527    </val>
-		<val>   1529    </val>
-		<val>   1530    </val>
-		<val>   1534    </val>
-		<val>   1536    </val>
-		<val>   1537    </val>
-		<val>   1540    </val>
-		<val>   1543    </val>
-		<val>   1546    </val>
-		<val>   1549    </val>
-		<val>   1550    </val>
-		<val>   1553    </val>
-		<val>   1556    </val>
-		<val>   1557    </val>
-		<val>   1559    </val>
-		<val>   1562    </val>
-		<val>   1563    </val>
-		<val>   1565    </val>
-		<val>   1568    </val>
-		<val>   1570    </val>
-		<val>   1571    </val>
-		<val>   1574    </val>
-		<val>   1577    </val>
-		<val>   1580    </val>
-		<val>   1583    </val>
-		<val>   1584    </val>
-		<val>   1586    </val>
-		<val>   1589    </val>
-		<val>   1590    </val>
-		<val>   1592    </val>
-		<val>   1595    </val>
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+		<val>	1821	</val>
+		<val>	1823	</val>
+		<val>	1825	</val>
+		<val>	1827	</val>
+		<val>	1828	</val>
+		<val>	1831	</val>
+		<val>	1833	</val>
+		<val>	1834	</val>
+		<val>	1837	</val>
+		<val>	1840	</val>
+		<val>	1842	</val>
 
-	</build_times>
-	<n_build>
+</build_times>
+<n_build>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -1541,8 +1562,15 @@
 		<val>1</val>
 		<val>2</val>
 		<val>1</val>
-	</n_build>
-	<lifetimes>
+</n_build>
+<lifetimes>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
@@ -2055,5 +2083,5 @@
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
-	</lifetimes>
+</lifetimes>
 </root>

From eb6865a38d05d33aaa0c4a25e0580fceab0f8506 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Sun, 16 Sep 2018 17:50:08 -0500
Subject: [PATCH 09/35] organize get deployment

---
 input/master_us/get_deployment.ipynb | 217 +++++++++++++++++----------
 1 file changed, 139 insertions(+), 78 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index d09fa7f2..3b75148e 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,18 +30,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sqlite file with only the legacy lwrs from 2013/05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "# get sqlite cursor and simulation time data\n",
-    "cur = an.get_cursor('./2020.sqlite')\n",
+    "cur = an.get_cursor('./legacy_only.sqlite')\n",
     "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -60,17 +69,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,31 +90,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cur = an.get_cursor('./legacy_only.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
+    "power_dict = an.get_power_dict(cur)\n",
+    "for key, val in power_dict.items():\n",
+    "    # start from 2020 -> 6 years and 7 months (80 months later)\n",
+    "    power_dict[key] = val[80:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0. 0. 1. ... 0. 1. 0.]\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f2ceec12be0>]"
+       "[<matplotlib.lines.Line2D at 0x7f9bfce0f1d0>]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFbBJREFUeJzt3X+wXPVd//Hnu7khYJPyK5eWhoRAi1rq2EKvlFrLMIItYAV/VKU6Fms7Uaed0lFHqXVQ6x/K9zviDNIppsIUOrWg/aGxBiuF2h8qsUkafoRAudAiKWkSQptAgWLK2z/23JvNZu+9Zzd7755z9vmYWe7Zs5+7+94zy2s/ed/zIzITSdJoeMGwC5AkLRxDX5JGiKEvSSPE0JekEWLoS9IIMfQlaYQY+pI0Qgx9SRohhr4kjZCxYb3w8uXLc/Xq1cN6eUmqpU2bNj2emeP9/v7QQn/16tVs3LhxWC8vSbUUEY8czu/b3pGkEWLoS9IIMfQlaYQY+pI0Qgx9SRohc4Z+RBwZEf8dEXdFxNaI+NMuY5ZExC0RMRkRGyJi9XwUK0k6PGVm+t8DfjIzXwW8GrggIs7uGPMO4NuZ+XLgr4CrBlumJGkQ5gz9bHmquLu4uHVeY/ES4MZi+RPAeRERA6uyAnbte5bb7tvJzn3P8rn7dg67HEnqS6mDsyJiEbAJeDnwwczc0DFkBfAoQGbuj4i9wPHA4x3PswZYA7Bq1arDq3yB/fLaO/n649/lpUcfyWN7n+Ubf/HTwy5JknpW6g+5mfn9zHw1cBJwVkT8SMeQbrP6Q664nplrM3MiMyfGx/s+ingoHtnzXQAe2/vskCuRpP71tPdOZn4H+Hfggo6HtgMrASJiDDgaeGIA9UmSBqjM3jvjEXFMsXwUcD5wf8ewdcBlxfJbgDsy85CZfpM0/O1JaqgyPf0TgRuLvv4LgL/PzM9ExAeAjZm5Drge+GhETNKa4V86bxVXRCY060/VkkbBnKGfmXcDZ3RZf2Xb8rPALw62tGpzni+pjjwiV5JGiKHfJ3v6kurI0O+TkS+pjgx9SRohhn6f7O5IqiNDv09pg0dSDRn6kjRCDP0+2d6RVEeGviSNEEO/T870JdWRoS9JI8TQ75N770iqI0O/T7Z3JNWRoS9JI8TQ75MTfUl1ZOj3ybNsSqojQ79PRr6kOjL0JWmEGPp9srsjqY4M/R5NXwzd0JdUQ4a+JI0QQ79HU20dj8iVVEeGfo+m2jv29CXVkaHfo5h7iCRV1pyhHxErI+LzEbEtIrZGxOVdxpwbEXsjYktxu3J+yh2+7PgpSXUyVmLMfuB3M3NzRCwDNkXEbZl5X8e4L2XmmwdfYjV5RK6kOppzpp+ZOzJzc7H8JLANWDHfhVWVe2xKqrOeevoRsRo4A9jQ5eHXRcRdEXFrRLxyht9fExEbI2Lj7t27ey62CiLs6kuqr9KhHxFLgU8C783MfR0PbwZOzsxXAX8N/GO358jMtZk5kZkT4+Pj/dY8VFNtHbs7kuqoVOhHxGJagf+xzPxU5+OZuS8znyqW1wOLI2L5QCutGPfTl1RHZfbeCeB6YFtmXj3DmJcU44iIs4rn3TPIQqvC9o6kOiuz987rgV8D7omILcW6PwRWAWTmdcBbgN+OiP3AM8Cl2fTdW5r97iQ11Jyhn5lfZo5jkjLzWuDaQRVVB2a+pDryiFxJGiGGfp8a3ryS1FCGfo8OHJxl6kuqH0O/R55lU1KdGfqSNEIM/R4duIiKJNWPod+jA+0dY19S/Rj6PQovoyKpxgz9Hk3tteNEX1IdGfqSNEIM/R7Z3pFUZ4Z+n2zvSKojQ79PHpErqY4M/T4505dUR4Z+r2zpS6oxQ79PTvQl1ZGh36Pps2za35FUQ4a+JI0QQ79H2fFTkurE0O/RgfbOUMuQpL4Y+j2K6b13TH1J9WPoS9IIMfR7NH0RFSf6kmpoztCPiJUR8fmI2BYRWyPi8i5jIiKuiYjJiLg7Is6cn3KHb/oiKsMtQ5L6MlZizH7gdzNzc0QsAzZFxG2ZeV/bmAuB04rba4EPFT8bx7NsSqqzOWf6mbkjMzcXy08C24AVHcMuAW7KljuBYyLixIFXWwFeREVSnfXU04+I1cAZwIaOh1YAj7bd386hXwy19nxHyE+F/39OPs7r/+IOrrn9wSFUJUm9KR36EbEU+CTw3szc1/lwl185ZC4cEWsiYmNEbNy9e3dvlQ7R3qf/d3q5s73zK3+7gW9+5xmuvu1rPPadZxa6NEnqSanQj4jFtAL/Y5n5qS5DtgMr2+6fBDzWOSgz12bmRGZOjI+P91PvUHy/Sy+nW3vneXs+kiquzN47AVwPbMvMq2cYtg54W7EXz9nA3szcMcA6h6rbydXMd0l1VGbvndcDvwbcExFbinV/CKwCyMzrgPXARcAk8DTw9sGXWi1eOUtSHc0Z+pn5Zea4dEi2psLvGlRRVRbusSmpxjwit0+2dyTVkaHfIyf6kurM0C/BSb2kpjD0ezR9ERW/CSTVkKHfo+mLqDj/l1RDhn4J7bP6cPcdSTVm6Pdo6kAt2zuS6sjQ75OZL6mODP0S2vv3U+2dbqdmkKSqM/R7ZEdfUp0Z+mXkoYvO8yXVkaHfJ7s7kurI0O+R7R1JdWbol9B9Uu9UX1L9GPq9Kqb6tnck1ZGh3yczX1IdGfolOKuX1BSGfp/8IpBUR4Z+j6bPsmnqS6ohQ78ET6MsqSkM/R55RK6kOjP0e3SgvTPUMiSpL4Z+CV5ERVJTGPoltE/qpy+iYoNHUg3NGfoRcUNE7IqIe2d4/NyI2BsRW4rblYMvs4LMfEk1NFZizEeAa4GbZhnzpcx880AqqqD23TOnL6IyrGIk6TDMOdPPzC8CTyxALZV1cE9/eHVI0uEaVE//dRFxV0TcGhGvHNBzVpp770iqozLtnblsBk7OzKci4iLgH4HTug2MiDXAGoBVq1YN4KWHxz/kSqqjw57pZ+a+zHyqWF4PLI6I5TOMXZuZE5k5MT4+frgvvWCc1UtqisMO/Yh4SRR/3YyIs4rn3HO4z1sl3Wb1fhFIqqM52zsR8XHgXGB5RGwH/hhYDJCZ1wFvAX47IvYDzwCXZoPPRjZ9RO5Qq5Ck/swZ+pn51jkev5bWLp2N1dyvMEmjxiNyS8guyw3+x4ykBjP0e2R7R1KdGfoldDsi19SXVEeGfgnmu6SmMPR75Fk2JdWZoV9Ct/Pp+3dcSXVk6JfS1tMfYhWSdLgM/RLaZ/XZZV23cZJURYZ+n8x3SXVk6JfQHvC2dyTVmaFfQvdWjnN9SfVj6PfIY7Mk1ZmhX4KnVpbUFIZ+CWUD3i8CSVVn6PfNhJdUP4Z+CQfP4D0iV1J9GfollD3PjufjkVR1hn4JB8/qs+2/klQvhn7PbO9Iqi9Dv0cH9tN3N05J9WPol2CYS2oKQ79HU18AfhFIqiNDv4T2Vs5sp2Hwe0BS1Rn6JRx05azhlSFJh23O0I+IGyJiV0TcO8PjERHXRMRkRNwdEWcOvszq8SybkuqozEz/I8AFszx+IXBacVsDfOjwy6qWsvHuF4Gkqpsz9DPzi8ATswy5BLgpW+4EjomIEwdVYBUY5pKaYmwAz7ECeLTt/vZi3Y4BPHdX/7DxUZYuGWPZkYt5cNeTvGx8KQ/vfooff/lyPv3Vb/Kb55zKVf/6AJefdxrX3PEgf/TTr+CqW+/nnW84lQ9/6WHWnHMqf/OFh/mtc1/Gh/59kiPHFvG3X/46Rx+1mPecdxp/9pn7WHHMUSxftoRFAZv/5zvTr73rye8BcPnNW7j85i0H1fWem7/Ki5cdye337+LEo4/krFOO4zfPeRmnv/RF87UpJKknUWYWGxGrgc9k5o90eexfgD/PzC8X928Hfj8zN3UZu4ZWC4hVq1a95pFHHumr6NVX/EvX9ce/8Aj2fPc5Ln7VS1l312PT6zvvL6SjFi9i25/N1h2TpPIiYlNmTvT7+4PYe2c7sLLt/klA14TNzLWZOZGZE+Pj4wN46YN9v/gCe77ji6zz/kIa5mtLUqdBhP464G3FXjxnA3szc95aO5Kk/s3Z04+IjwPnAssjYjvwx8BigMy8DlgPXARMAk8Db5+vYiVJh2fO0M/Mt87xeALvGlhFkqR54xG5kjRCDH1JGiGGviSNEENfkkZII0O/c8/4Ye4p7176kqqkkaEvSequkaHfec77YZ4D3/PvS6qSRoa+JKk7Q1+SRkgjQ98/nkpSd40MfVNfkrprZuhLkrpqZOinU31J6qqZoW/mS1JXIxH6HpErSS3NDH2jVpK6amTod/KIXElqaWTo29OXpO4aFfrOqiVpdo0K/ez4KUk6WKNCf4rtHUnqrlGhf6C9c3Dqu8umJLU0KvSn2zsmrSR11ajQn4m7bEpSS6nQj4gLIuKBiJiMiCu6PP7rEbE7IrYUt3cOvtTyvEauJHU3NteAiFgEfBD4KWA78JWIWJeZ93UMvSUz3z0PNZY2NatO+zuS1FWZmf5ZwGRmPpyZzwE3A5fMb1n9mSnqbe9IUkuZ0F8BPNp2f3uxrtMvRMTdEfGJiFg5kOr65DxfkrorE/rdJqudufrPwOrM/FHgc8CNXZ8oYk1EbIyIjbt37+6t0hKcVUvS7MqE/nagfeZ+EvBY+4DM3JOZ3yvufhh4Tbcnysy1mTmRmRPj4+P91Dsrd9mUpNmVCf2vAKdFxCkRcQRwKbCufUBEnNh292Jg2+BK7J2ZL0ndzbn3Tmbuj4h3A58FFgE3ZObWiPgAsDEz1wHviYiLgf3AE8Cvz2PNM5pp7x132ZSkljlDHyAz1wPrO9Zd2bb8PuB9gy1NkjRoHpHb4NeWpE6NDH2vkStJ3TUz9I1aSeqqkaHfyfaOJLU0MvTdT1+SuhuJ0LenL0ktzQx9o1aSumpk6Heypy9JLY0Mfds7ktRdM0N/2AVIUkU1KvSrGPa2dyRVSbNCf/rcyp0PLHQllXhpSTpEo0J/SufeO+7NI0ktjQz9KrG9I6lKGhX6U+fRP2TvHds7kgQ0LvRbP5/vSPnO+5I0qhoV+pKk2Rn688yevqQqaVToT7VxDtlj056+JAENC/0pVToNgyRVSSNDv0ps70iqkkaF/owH5NrekSSgYaE/3dPvSPnO+5I0qhoV+pKk2ZUK/Yi4ICIeiIjJiLiiy+NLIuKW4vENEbF60IVKkg7fnKEfEYuADwIXAqcDb42I0zuGvQP4dma+HPgr4KpBF1rG8x6RK0mzKjPTPwuYzMyHM/M54Gbgko4xlwA3FsufAM6LCHdckaSKGSsxZgXwaNv97cBrZxqTmfsjYi9wPPD4IIps94Wv7Z7xsef2Pw/Avd/cd9D6/3hoz6DLKO25/c/zU1d/YWivL6l6fvnHVvLON5w6lNcuE/rdZuyd/ZIyY4iINcAagFWrVpV46UMtXTLG8qVLOOqIF/DCI8b4nyeeZuWxP8D2bz/Nq1Yew38+tIcLXvkS/nXrt3jTK1/MZ7fu5PxXnMD6e77FeT98Arffv4vzX3ECn9u2i/Nf8WI+t20nYy8I9he9oZ/84RO44/5d06/32lOOY8PXnziohmVHjvHks/sPqe3U5S9k2ZFj3LV970G/f/zSI/p6r5KaafnSJUN77TKhvx1Y2Xb/JOCxGcZsj4gx4GjgiY4xZOZaYC3AxMREX43215x8LBv/6Px+flWSRl6Znv5XgNMi4pSIOAK4FFjXMWYdcFmx/BbgjnTneEmqnDln+kWP/t3AZ4FFwA2ZuTUiPgBszMx1wPXARyNiktYM/9L5LFqS1J8y7R0ycz2wvmPdlW3LzwK/ONjSJEmD5hG5kjRCDH1JGiGGviSNEENfkkaIoS9JIySGtTt9ROwGHunz15czD6d4WAB1rNuaF4Y1L5w61t1e88mZOd7vEw0t9A9HRGzMzIlh19GrOtZtzQvDmhdOHeseZM22dyRphBj6kjRC6hr6a4ddQJ/qWLc1LwxrXjh1rHtgNdeypy9J6k9dZ/qSpD7ULvTnukj7sETEyoj4fERsi4itEXF5sf5PIuKbEbGluF3U9jvvK97HAxHxpiHV/Y2IuKeobWOx7riIuC0iHix+Hlusj4i4pqj57og4cwj1/lDbttwSEfsi4r1V3M4RcUNE7IqIe9vW9bxtI+KyYvyDEXFZt9ea55r/f0TcX9T16Yg4pli/OiKeadvm17X9zmuKz9Vk8b7m7fKpM9Tc8+dhIbNlhppvaav3GxGxpVg/2O2cmbW50Tq180PAqcARwF3A6cOuq6jtRODMYnkZ8DVaF5L/E+D3uow/vah/CXBK8b4WDaHubwDLO9b9P+CKYvkK4Kpi+SLgVlpXSjsb2FCBz8O3gJOruJ2Bc4AzgXv73bbAccDDxc9ji+VjF7jmNwJjxfJVbTWvbh/X8Tz/DbyueD+3AhcucM09fR4WOlu61dzx+F8CV87Hdq7bTL/MRdqHIjN3ZObmYvlJYButawfP5BLg5sz8XmZ+HZik9f6qoP1C9zcCP9u2/qZsuRM4JiJOHEaBhfOAhzJztoP8hradM/OLHHoFuV637ZuA2zLzicz8NnAbcMFC1pyZ/5aZU9cHvZPW1fNmVNT9osz8r2wl000ceJ8DN8N2nslMn4cFzZbZai5m678EfHy25+h3O9ct9LtdpH22YB2KiFgNnAFsKFa9u/in8Q1T/5ynOu8lgX+LiE3RuoYxwIszcwe0vsyAE4r1Val5yqUc/D9GlbfzlF63bdXq/w1aM8opp0TEVyPiCxHxhmLdClp1ThlWzb18Hqq0nd8A7MzMB9vWDWw71y30S12AfZgiYinwSeC9mbkP+BDwMuDVwA5a/2yD6ryX12fmmcCFwLsi4pxZxlalZqJ16c6LgX8oVlV9O89lpjorU39EvB/YD3ysWLUDWJWZZwC/A/xdRLyIatTc6+ehCjVPeSsHT2YGup3rFvplLtI+NBGxmFbgfywzPwWQmTsz8/uZ+TzwYQ60FirxXjLzseLnLuDTtOrbOdW2KX7uKoZXoubChcDmzNwJ1d/ObXrdtpWov/gD8puBXy1aCRQtkj3F8iZaPfEfpFVzewtowWvu4/NQle08Bvw8cMvUukFv57qFfpmLtA9F0Ye7HtiWmVe3rW/vef8cMPXX+nXApRGxJCJOAU6j9UeZBRMRL4yIZVPLtP5gdy8HX+j+MuCf2mp+W7GnydnA3qlWxRAcNBuq8nbu0Ou2/Szwxog4tmhRvLFYt2Ai4gLgD4CLM/PptvXjEbGoWD6V1rZ9uKj7yYg4u/j/4m0ceJ8LVXOvn4eqZMv5wP2ZOd22Gfh2nq+/Ts/XjdZeDl+j9W33/mHX01bXT9D6p9XdwJbidhHwUeCeYv064MS233l/8T4eYB73bpil5lNp7aVwF7B1ansCxwO3Aw8WP48r1gfwwaLme4CJIW3rHwD2AEe3ravcdqb1pbQD+F9as7J39LNtafXRJ4vb24dQ8yStfvfU5/q6YuwvFJ+bu4DNwM+0Pc8EraB9CLiW4kDQBay558/DQmZLt5qL9R8Bfqtj7EC3s0fkStIIqVt7R5J0GAx9SRohhr4kjRBDX5JGiKEvSSPE0JekEWLoS9IIMfQlaYT8H4lCsWro1Kg/AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -115,96 +131,141 @@
     }
    ],
    "source": [
-    "cur = an.get_cursor('./2020.sqlite')\n",
-    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
-    "power_dict = an.get_power_dict(cur)\n",
-    "deploy_array, deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                     '100*(1.01)**(t/12)', 1, 960, 0, True)\n",
-    "print(deploy_array)\n",
-    "plt.plot(deploy_array)"
+    "lwr_deploy_array, lwr_deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
+    "                                                     '100*(1.01)**(t/12)', 1, 960, 240, False)\n",
+    "power_dict['lwr'] = lwr_deployed_power\n",
+    "plt.plot(lwr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "an.write_deployinst(deploy_array, 'lwr', 'lwr_dep.xml', 960)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With LWRs deployed, the power looks like:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "./2030_0_growth/input.xml DOES NOT EXIST\n",
-      "./2030_005_growth/input.xml DOES NOT EXIST\n",
-      "./2030_01_growth/input.xml DOES NOT EXIST\n",
-      "./2030_015_growth/input.xml DOES NOT EXIST\n",
-      "./2035_0_growth/input.xml DOES NOT EXIST\n",
-      "./2035_005_growth/input.xml DOES NOT EXIST\n",
-      "./2035_01_growth/input.xml DOES NOT EXIST\n",
-      "./2035_015_growth/input.xml DOES NOT EXIST\n",
-      "./2040_0_growth/input.xml DOES NOT EXIST\n",
-      "./2040_005_growth/input.xml DOES NOT EXIST\n",
-      "./2040_01_growth/input.xml DOES NOT EXIST\n",
-      "./2040_015_growth/input.xml DOES NOT EXIST\n",
-      "./2045_0_growth/input.xml DOES NOT EXIST\n",
-      "./2045_005_growth/input.xml DOES NOT EXIST\n",
-      "./2045_01_growth/input.xml DOES NOT EXIST\n",
-      "./2045_015_growth/input.xml DOES NOT EXIST\n",
-      "./2050_0_growth/input.xml DOES NOT EXIST\n",
-      "./2050_005_growth/input.xml DOES NOT EXIST\n",
-      "./2050_01_growth/input.xml DOES NOT EXIST\n",
-      "./2050_015_growth/input.xml DOES NOT EXIST\n"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# write these deploy schemes to file\n",
-    "for key in dep_dict.keys():\n",
-    "    if not os.path.isdir(\"./%s\" %key):\n",
-    "        print('FOLDER %s NOT THERE,' %key)\n",
-    "        os.makedirs(key)    \n",
-    "        print('%s FOLDER MADE!' %key)\n",
-    "    try:\n",
-    "        os.remove('./%s/input.xml' %key)\n",
-    "    except:\n",
-    "        print('./%s/input.xml DOES NOT EXIST'%key)\n",
-    "    copyfile('./udb_recipe.xml', './%s/udb_recipe.xml' %key)\n",
-    "    copyfile('./udb_precise.xml', './%s/udb_precise.xml' %key)\n",
-    "    copyfile('./udb_no.xml', './%s/udb_no.xml' %key)\n",
-    "    for key2 in dep_dict[key]:\n",
-    "        an.write_deployinst(dep_dict[key][key2], key2, './%s/%s_dep.xml' %(key, key2),\n",
-    "                            960)\n",
-    "        if key2 == 'sfr':\n",
-    "            an.write_deployinst(dep_dict[key][key2], 'blanket', './%s/blanket_dep.xml' %(key), 960)"
+    "x = np.arange(0, 1684)\n",
+    "years = 2020 + x/12\n",
+    "y = 100*(1.01)**(x/12)\n",
+    "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
+    "plt.plot(years, y, label='power demand')\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Power Capacity [MWe]')\n",
+    "plt.legend()\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## To fill in the lack from 2020, deploy MSRs:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9bfcbd9eb8>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# delete sqlite files\n",
-    "for key in dep_dict.keys():\n",
-    "    if os.path.isfile('./%s/output.sqlite' %key):\n",
-    "        os.remove('./%s/output.sqlite' %key)\n",
-    "        print('REMOVED %s' %key)"
+    "# rebus\n",
+    "msr_power = 3.7*0.44\n",
+    "lifetime = 60*12\n",
+    "msr_deploy_array, msr_deployed_power = an.get_new_deployment(power_dict, ['us_government', 'lwr'],\n",
+    "                                                     '100*(1.01)**(t/12)', msr_power, lifetime, 240, True)\n",
+    "power_dict['msr'] = msr_deployed_power\n",
+    "plt.plot(msr_deploy_array)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## "
+    "## Now the total deployed power is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0, 1684)\n",
+    "years = 2020 + x/12\n",
+    "y = 100*(1.01)**(x/12)\n",
+    "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
+    "plt.plot(years, power_dict['us_government'] + power_dict['lwr'] + power_dict['msr'], label='legacy + lwr + msr')\n",
+    "plt.plot(years, y, label='power demand')\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Power Capacity [MWe]')\n",
+    "plt.legend()\n",
+    "plt.grid()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 49ceb2c0abf3ec159236eff0793ce258d2ddbefa Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 18 Sep 2018 10:09:57 -0500
Subject: [PATCH 10/35] 1% growth -> 0.4% growth

---
 input/master_us/get_deployment.ipynb | 36 ++++++++++++++--------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 3b75148e..d08b71ea 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -112,16 +112,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9bfce0f1d0>]"
+       "[<matplotlib.lines.Line2D at 0x7fc8f058cb38>]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -132,18 +132,18 @@
    ],
    "source": [
     "lwr_deploy_array, lwr_deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                     '100*(1.01)**(t/12)', 1, 960, 240, False)\n",
+    "                                                     '100*(1.004)**(t/12)', 1, 960, 240, False)\n",
     "power_dict['lwr'] = lwr_deployed_power\n",
     "plt.plot(lwr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(deploy_array, 'lwr', 'lwr_dep.xml', 960)"
+    "an.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep.xml', 960)"
    ]
   },
   {
@@ -155,12 +155,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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W1X+Knff4YdVPr3WG6f7yFmfYbj8rD8N9pwPnqmqKiIQBP4nIid7JP6vql6e1HwK0cR89gTfdr8YYE5j2rHHubzi83bn5rceYgBimu7T4rUC4VSrFfRnmPvLvqYVhwIfuevNFpJaINFTVXf7K6A/Nmzdn1apVXscwxvjbqq9g4t3OOEojvodmvQtfJ8j4tQ9CREJFZBmwF/hRVRe4bz0rIitE5GUROXHpS2NgZ67Vk9xlRaYFXDFkApP9m5mgkZ0FUx+DL0fBGZ3httnlsjgASFn8xxSRWsA3wD3AAWA3UBl4G9isqk+JyA/AP1X1J3edGcBfVDXxtG2NAcYAREVFxYwfP/6Uz4qIiCAqKorIyMgCL6/Mzs4u9Tmc/SmY8hY1q6py5MgR9uzZc/Ku7rKUkpJCREREmX9ucVhW//Elb1jGYTqsfoFaR1aR1PhiNre6GQ0JK6OEvynpvo2Li0tU1djC2pVJgQAQkSeAY6r6Yq5lA4EHVXWoiPwHSFDVz9z31gMDCzrFFBsbq6cPQJeZmUlSUtLJa/Pzk5aWRnh4eLG/n7IWTHmLkzU8PJwmTZoQFlb2/9kSEhIYOHBgmX9ucVhW/yk0785FMOEmOH4QLnkVoq8ts2ynK+m+FRGfCoTf+iBEpD6QqaqHRaQqcB4w9kS/gjh/3l8GnDhh/y1wt4iMx+mcPlKc/oewsDBatGhRaLuEhAS6du1a1M17JpjyBlNWYwqlCgv/C1MfhZoNYfQ0aBjtdaoy4c+rmBoCH4hIKE5fxwRV/V5EZrrFQ3Auc73dbT8JuAjnMtdU4GY/ZjPGmMKlHYFv74U1/4M2F8Dl/4FqdbxOVWb8eRXTCuB3f0aq6rn5tFfgLn/lMcaYItm1Ar4YAYe2w3lPQp/7IKRi3VtcvsdiMsaYolKFxPed0aCr1YGR30OzPl6n8oQVCGOMOSE9Bb6/H1Z+4QzRfcV/oXo9r1N5xgqEMcYA1VO2wdsPwsHNEPc49PtThTuldDorEMYYs/Rjui35M1SrDTdNhBb9vU4UEKxAGGMqrrSj8MOfYOUEjtbqRO3RX0GNKK9TBQwrEMaYiilpMXw1Gg7vgIGPsFy7M9CKwykq9gk2Y0zFk5MDc16C9wZDTjaMnAQDHwYJjqFsypIdQRhjKo6ju+Cb22DrLGh/GVzyClSt7XWqgGUFwhhTMayfAhPvhIxUuOQ16HZTuZq7wR+sQBhjyrfMNPjxb7DwPxDVCa56F+qf5XWqoGAFwhhTfu1dB1/dAntWQs/b4by/Q1hwjIocCKxAGGPKn5wcWPAWTH8SqkTAdZ/DWRd6nSroWIEwxpQvR5Lgf3fA1tnQ9kK49HWIaOB1qqCUb4EQkRU+rL9PVQeVYh5jjCkeVVj5pXPjW06WdUSXgoKOIEJx5mfIj+BM8mOMMd5KPegUhtVfQ5MecMV/oE5Lr1MFvYIKxG2qur2glUXkzgLeCwdmA1Xcz/lSVZ8QkRbAeKAOsAT4g6pmiEgV4EMgBmfe6mtUdVtRvhljTAW0aQZMvAuO7YNz/wp974dQO3teGvK9k1pVfzrxXESqisjvrgvL3SYP6cC5qhoNdAEuFJFewFjgZVVtAxwCRrvtRwOHVLU18LLbzhhj8paRCpP+DB9fAVVqwi0zoP+DVhxKUaFDbYjIpThTg05xX3cRkUJPLakjxX0Z5j4UOBf40l3+Ac681ADD3Ne47w9y5602xphT7ZgPb50DC9+GnnfAbbOgURevU5U7vozF9ATQAzgMoKrLgOa+bFxEQkVkGbAX+BHYDBxW1Sy3SRLQ2H3eGNjpfkYWcASo69N3YYypGDJSYcqj8N6FkJ3pDM095DkIq+p1snJJnKmgC2ggskBVe4rIUlXt6i5boaqdff4QkVrAN8DfgPfd00iISFNgkqp2EpHVwGBVTXLf2wz0UNUDp21rDDAGICoqKmb8+PG+xjhFSkoKERERxVrXC8GUN5iyQnDlrchZIw+v4az1r1Pt+K/80mgIW1reRHalaqW2/Yq0b+Pi4hJVNbbQhqpa4AN4F7geWAG0AV4H3ipsvTy28wTwZ2A/UMld1huY6j6fCvR2n1dy20lB24yJidHiio+PL/a6XgimvMGUVTW48lbIrOnHVCc/rPpEpOrLHVU3J5TOdk9TkfYtsFh9+L3tyymme4AOOJ3On+Kc+rm/sJVEpL575ICIVAXOA9YC8cBVbrMRwET3+bfua9z3Z7rfiDGmoto+D97qC/P/Dd1Hwx3zoOUAr1NVGAXdKNcFWK6qqcBj7qMoGgIfiEgoTl/HBFX9XkTWAONF5BlgKc4RCu7Xj0RkE3AQuLaIn2eMKS8yUmHm0zD/TajVFEZ8Z9OAeqCg68HeAVqIyBJgLvAzMF9Vj/qyYVVdAXTNY/kWnE7v05enAcN92bYxphzbMgu+uw8ObYXutzgD7FUJjr6B8ibfAqGqsSJSDeeXeR/gXpy/8HcDc1U135vkjDGmyFIPwrS/wrKPoXYLO2oIAAXeUeKeXkoQkUXAAqAvcBNgwyIaY0qHKqz6CqY87BSJvvc7U4DapaueK6gP4nqcI4cuOB3UJ4rEOaq6u2ziGWPKtcM7nTGUNk6FRl3hxq+hoc9X0Bs/K+gI4m1gHfAWMFtVN5RNJGNMuZeTDQv/63REaw4M/oczoU9IqNfJTC4FFYhIIBrnKOJJdyymXcA8YJ6qziyDfMaY8mbPavj2XvhlMbQ+Dy5+CWo38zqVyUNBndTZOKOtLgH+JSJROPcn/BF4Cmc4cGOM8U1GKsx+AX5+DcIj4Yp3oNNVNl9DACuoD6IzztHDiUdlnKOH13EuezXGGN+smwSTH4IjOyD6erjgGahuQ60FuoJOMY3DufdhMvBXLWRuCGOM+Z1D253CsGEy1D8bRk6C5n29TmV8VNAppm5lGcQYU45kZXDm9i/gp69AQuD8p6HXHRAa5nUyUwTFmZNacKZ7sGvRjDG/t2UWTHqQlvs3wNmXwIXPQWQTr1OZYijoFFMOzgQ/nwLfAcfLJJExJjgl74Zpj8PKL6B2c1Z0+iudr3zQ61SmBAo6xdRFRNoB1+EUiTXu12n624Q/xpiKLivDmdlt1ljISoMBD8E5f+Tg3AVeJzMlVOBw36q6TlWfcPsjvgM+xLnM1RhjYON0eLMPTHsMmvZwhuOOe9SGySgnChyLSUQa4wy7fTlwCKc4fFMGuYwxgezAZpj6mHN1Up2WcN3n0Haw3dNQzhTUST0LqAFMAEbizNEAUFlE6qjqwfzWNcaUU+kpMOdFmPcGhFZ2huLudQdUquJ1MuMHBR1BNMPppL4Ndw5ol7jLW/oxlzEmkKjCigkw/QlI3gXR18GgJ6BmQ6+TGT8qqJO6eUk2LCJNcfoszsC5IuptVX1VRJ4EbgX2uU0fVdVJ7jqPAKOBbOBeVZ1akgzGmFLw61LnZredC5wRV6/+CJp29zqVKQMFnWI6o7BhvQtpkwX8SVWXiEgNIFFEfnTfe1lVXzxtW+1x+js6AI2A6SLS1h0TyhhT1o4kwYynYMXnUL0+DHvDGSYjxJep7E15UNAppklAYXdT59tGVXfhjP6KqiaLyFqgcQHbGgaMV9V0YKs7N3UPnPGfjDFlJT0ZfnrZ6WdQdSbw6feAM8CeqVAKKhDRIlLQ/NMC+DQ/tYg0x5mf+sSsdHeLyE3AYpyjjEM4xWN+rtWSKLigGGNKU3YWLP0Q4v8Bx/ZBp+Ew6G9Q60yvkxmPiKr69wNEIoBZwLOq+rU7bPh+nI7up4GGqjpKRN7AmWfiY3e9d4FJqvrVadsbg9tpHhUVFTN+/Phi5UpJSSEiIngmQg+mvMGUFYIrr1+yqlLn4BJabX6f6qk7ORzZns2tbia5ZtsSbTaY9isEV96SZo2Li0tU1dhCG6qq3x5AGDAVeCCf95sDq9znjwCP5HpvKtC7oO3HxMRoccXHxxd7XS8EU95gyqoaXHlLPeuuFaofXKr6RE3VV7uorvlWNSenVDYdTPtVNbjyljQrsFh9+B1e4I1yJSEiArwLrFXVl3Itb6hO/wQ4N+Ctcp9/C3wqIi/hdFK3ARb6K58xFdqRJIj/Jyz7BKrWcgbUix0NlSp7ncwEEL8VCJy+hj8AK0VkmbvsUeA6EemCc4ppG859FqjqahGZgDPmUxZwl9oVTMaUrtSDMOf/nPmgUeh9F/R/EKrW9jqZCUCFFggReRF4X1VXF2XDqvoTTkf26SYVsM6zwLNF+RxjjA/SU2D+m850nxkpzuWqAx+GWk29TmYCmC9HEOuAt0WkEvA+8JmqHvFvLGNMqcjKgCUfwKzn4dheaDcUzn0cGpztdTITBAotEKr6DvCOiJwF3AysEJG5wH9VNd7fAY0xxZCTA6u+hJnPwOHt0KwvXPuJM+KqMT7yqQ9CREKBdu5jP7AceEBEblPVa/2YzxhTFKqw8UeY8XfYswqiOsENX0HrQTbSqikyX/ogXgIuAWYC/1DVE1cWjRWR9f4MZ4zxkSpsiXducktaBLWbw5XvQocrbGgMU2y+HEGsAh5X1dQ83rPjVWO8tnW2Uxh2zIOaTWDoK9DlBrtk1ZSYLwXiBlV9L/cCEZmhqoOss9oYD23/2SkM2+ZAjYZw0YvQ7Sabm8GUmoJGcw0HqgH1RKQ2v12yWhPnRjZjjBd2LnQKw5Z4qN4ALhwLMSMhLNzrZKacKegI4jbgfpxisCTX8qPAG/4MZYzJwy+JdFrxFCQkQrV6cMGzEDsKKlfzOpkppwqaMOhV4FURuUdVXy/DTMaY3HYsgNkvwKYfqVmpBpz3JPQYA5Wre53MlHMFnWI6V1VnAr+IyBWnv6+qX/s1mTEVmarT+Tz7BaePoVpdOPevzM9oT79zLvI6nakgCjrFNADn0tZL8nhPASsQxpQ2Vdg03SkMOxdAxBkw+B9OH0Pl6mQnJHid0FQgBZ1iesL9enPZxTGmgsrJgfU/OIVh13KIbAoX/x90udE6n41nfLlR7h/A86p62H1dG2cWuMf9Hc6Yci8nG1Z/A7NfhH1roU5LuPRf0Pkau4/BeM6X+yCGqOqjJ16o6iERuQiwAmFMcWUeh+Wfwc+vw8EtUL8dXPEOdLgcQv05Cr8xvvPlJzFURKqoajqAiFQF7E4cY4oj9SAsfhcW/MeZ97lRN7j6Q2h3iQ2JYQKOLwXiY2CGiLyP0zk9CvigsJVEpCnwIXAGkAO8raqvikgd4HOc6Ua3AVe7RyUCvApcBKQCI1V1SV7bNiboHN4J8/8NiR9A5jFocwH0vc8ZZdUG0TMBypfhvp8XkZXAIJy7qZ9W1ak+bDsLp69iiYjUABJF5EdgJDBDVZ8TkYeBh4GHgCE404y2AXoCb7pfjQleu1c5k/Ss/NIpBJ2GQ597IKqD18mMKZRPJztVdTIwuSgbdued3uU+TxaRtUBjYBgw0G32AZCAUyCGAR+6E2rPF5Fap81fbUxwUHXuXZj7qnPJalh16Hk79LrDZnAzQcWXq5h6Aa8DZwOVgVDgmKrW9PVDRKQ50BVYAESd+KWvqrtEpIHbrDGwM9dqSe4yKxAmOGRlOFckzf837FoG1evDuX+F7qNtzmcTlMT5g72ABiKLgWuBL4BY4Cagtao+5tMHiEQAs4BnVfVrETmsqrVyvX9IVWuLyA/AP925rBGRGcBfVDXxtO2NAcYAREVFxYwfP97Hb/VUKSkpREREFGtdLwRT3mDKCiXPG5ZxlEa/TqHRr5OoknGIY9WakNTkEvZEnUtOaOleqhpM+zaYskJw5S1p1ri4uERVjS20oaoW+AAWu19X5Fr2c2Hrue3CgKnAA7mWrQcaus8bAuvd5/8BrsurXX6PmJgYLa74+Phir+uFYMobTFlVS5B392rViXerPt1A9Ymaqh9errrxR9Xs7FLNl1sw7dtgyqoaXHlLmvXE7/XCHr70QaSKSGVgmYg8j3PKp9BRwtyrkt4F1qrqS7ne+hYYATznfp2Ya/ndIjIep3P6iFr/gwk0OTlOv8L8fzvDbVeqCtHXOX0MDdp5nc6YUuVLgfgDEALcDfwRaApc6cN6fd11V4rIMnfZoziFYYKIjAZ2AMPd9ybhXOK6CecyVxviwwSOjGPOjW3z34IDG50Jegb9DWJuhmp1vE5njF/4cpnrdvcIojlo0RNKAAAda0lEQVTOAH3rVTXDh/V+4rdJhk43KI/2CtxV2HaNKVP7N8Gid2DZp5B+BBp1de94vgxCw7xOZ4xf+XIV08XAW8BmnF/4LUTkNnUufTWm/MnOgg1TnMKwJR5CwqD9MOhxKzTtaTe2mQrDl1NM/wfEqeomABFpBfxAEe+LMCbgpeyDJR/A4vfhaBLUbAxxj0PMCIhoUPj6xpQzvhSIvSeKg2sLsNdPeYwpW6rUPLIWvvoYVv8PcjKhxQAY8hy0HWID55kKzZef/tUiMgmYgDMW03Bg0YlZ5tRmljPBKD0ZVn0Fi96h2+6VUKWmM79z91ugfluv0xkTEHwpEOHAHpwZ5gD2AXVwZpqzmeVM8FCFX5c4A+at+goyUqBBeza0uZ22V/0VqgTHTVLGlBVfrmKyy01NcEs7AismOP0Lu1c69y50vMKZxrNJd36dNYu2VhyM+R1frmIKB0YDHXCOJgBQ1VF+zGVMyajCzoVOUVj1NWQdhzM6OdN4dhoO4ZFeJzQm4PlyiukjYB0wGHgKuAFY689QxhRb6kFY8blzGmnfWqgcAdHXQLcRzj0MdomqMT7zpUC0VtXhIjJMVT8QkU9xxlcyJjDkZDv3Kyz7FNZ+D9np0DgGLnkNOl5pfQvGFJMvBSLT/XpYRDoCu3HuqjbGW/s2wPJPYfl4SN7lDKkdMwK63eScTjLGlIgvBeJtEakNPI4zoF4E8De/pjImP8cPw+qvnaOFpEUgodDmfBgyFtpeCJVsunRjSosvVzG94z6dDbT0bxxj8pCTDVsSnKKw7nvISoP6Z8MFz0Cnq6FGlNcJjSmX8i0QIvIAzpDb7562/B4gVFVf8Xc4U8HtXet0OC//HJJ/hfBa0PUP0OV663A2pgwUdAQxCuiWx/K3gUWAFQhT+o78Aqu+hBVfwJ6VICHQ+ny48J9w1hA7hWRMGSqoQGhew3qraro7GZAxpeP4YVgzEVZ+Adt+AtS5CunCsc4NbTZQnjGeKLAPQkSiVHXP6cv8G8lUCJlpsHGqc4fzxmmQnQF1WsHAh50b2eq28jqhMRVeQQXiBeAHEfkTsMRdFgM8D7xY2IZF5D1gKM5osB3dZU8Ct+KM5wTwqKpOct97BOeO7WzgXlW1ey3Km5xs5whh5QRY850zAU/1BhA7GjoPh0bdrF/BmACSb4FQ1Q9FZB/O3dMdcQbmWw084eNkQeOAfwEfnrb8ZVU9pcCISHvgWpzhPBoB00Wkrapm+/qNmACVkw075sPqb5zTSMf2QuUacPYl0OkqZ2htG1LbmIBU4P9MtxAUa2IgVZ0tIs19bD4MGK+q6cBWEdkE9ADmFeezjcc0B7bP+60opOx2BshrewF0uNy5XyGsqtcpjTGFEGcqaD9t3CkQ3592imkkcBRYDPxJVQ+JyL+A+ar6sdvuXWCyqn6ZxzbHAGMAoqKiYsaPH1+sbCkpKUREBM8QDAGfV3OoeXQ9DfbOpd7enwjPPER2SGUO1olhb4O+HKwTS3alwCwKAb9vc7Gs/hNMeUuaNS4uLlFVYwtrV9bH9m8CT+OcrnoaZzrTUThzXZ8uz8qlqm/jXGpLbGysDhw4sFhBEhISKO66XgjIvKqQtNg9UvgfHP0FQquwr3YXwgeMIbTtYOpXqUF9r3MWIiD3bT4sq/8EU96yylrYVUwhwFWqOqE0Piz3FVEi8l/ge/dlEtA0V9MmwK+l8ZmmlGVnwfa5zh3N635wi0JlaH0enPcktL2Q1fOXMLDTQI+DGmNKqrA+iBwRuRtnutESE5GGqrrLfXk5sMp9/i3wqYi8hNNJ3QZYWBqfaUpB5nHYHA9rv4MNk+H4IagU7hSFc/8K7S6y+RWMKYd8OcX0o4g8CHwOHDuxUFUPFrSSiHwGDATqiUgS8AQwUES64Jw+2gbc5m5rtYhMANYAWcBddgWTx44fdu5PWPsdbJoBmcecItD2Qmg3FFoPgsrVvU5pjPEjXwrEiZnj7sq1TClk4D5VvS6Pxe/msexE+2eBZ33IY/wleQ+s/8GZU2HrbMjJhIgoZ8Kdsy+B5v0gNMzrlMaYMuLLaK4tyiKI8YAq7FkF66fAhinwSyKgUKcl9LrDKQqNYyEkxOukxhgP+DIndTXgAeBMVR0jIm2As1T1+0JWNYEoMw22zYH1k2HDVDia5CxvHANxj0K7i6FBe7uj2Rjj0ymm94FEoI/7Ogn4gt+uQDKBLnm3Uww2THWm5sxMhbBq0OpcZ+yjNhfYnArGmN/xpUC0UtVrROQ6AFU9bqO5BjhV2L3CPXU0GX5d6iyv2cSZS6HtEGh+DoSFe5vTGBPQfCkQGSJSFffGNRFpBaT7NZUputSDsHmmc8XR5hmQsgcQaBIL5z7uFIWoDnbqyBjjM18KxJPAFKCpiHwC9MUZLsN4KScbflkCm6Y7jxMdzFVrO6eOWg1y5mq2uRSMMcXky1VM00QkEeiFMyTGfaq63+/JzO8l73aOEjb+6PQlHD8EiNPBPOAh58a1xt0gJNTrpMaYcsCXq5g+AmYDc1R1nf8jmZOy0tmxPB42z6TDlmmQsNVZXr2Bc8qo9SDnaKFaHW9zGmPKJV+vYjoHeF1EWgLLgNmq+qpfk1VEOTkkrVtA5sZ40tbPoEXqSs4knUwNZYm2YUWT2xl0yQ0Q1dHuTTDG+J0vp5hmisgsoDsQB9yOM7GPFYhSkLx7I6lrp7Nn2VRaJCfSJOcoABtyGvNlyLnsr9+Ldr2G8N/ZW1mzEwZMz+Syrnu5sOMZHic3xpR3vpximgFUx5m8Zw7QXVX3+jtYeZVxZC+Zm+I5sno6lXfOoV7mLmoAOVqHmdKV+IwOnNX7Yvp06cg1jWoSFuocKRzc9QtZ2yozZfVupqzezXNXdGJIp4ZEVrWhL4wx/uHLKaYVOHNRdwSOAIdFZJ6qHvdrsnIi+9hBdq+YSVjSz2RvmU3D4xupDGRrNebltGdllaE0ix1C41adGdamPsPy2U6jiBAm3n0OP2/az/XvLODhr1fy8NcrmffIuTSMDMyJeIwxwc2XU0x/BBCRCOBmnD6JM4Aq/o0WpFIPcmRdAvNnTqRz1iqi0jbTGCVNw1iS04aPcq7m55yO3HTFMIZEN2Vw5aJdcdSndT2mP9Cfd3/axmcLdzBhURL3ndfGT9+MMaYi8+UU091AP5yjiO3AezinmgzAsf1sXjyVrC1zqPrrfM7M3Eok0F8rk5jThknh17Krdnd697uAto3r8ufaVSnpjeitG9Tg2cs68s3SJF6evoEcVf54ftvS+X6MMcblyymmqsBLQKKqZvk5T+BL3sPGxdMI2f4TlXbOo1n2dloBqVqFRG3Lsvqj+LVWDAciO/HQxZ04J9Q/VxuFhAjPXtaJR75ZyRvxm1iy4xAPXdiOjo1t4h5jTOnw5RTTCyISDdzu/uU7R1WXF7aeiLwHDAX2qmpHd1kdnImHmuNMGHS1qh5yx3Z6FbgISAVGquqSYn1HpSknh+x969mw6Eeq7lpE5V2LaJSzizbAMa3C4pyzmFVzBEk1Yxh83mB6NW9wslO5LFwZ04RuzWrz+P9WsmjbQR7+egXf39OvzD7fGFO++XKK6V5gDPC1u+hjEXlbVV8vZNVxwL+AD3MtexiYoarPicjD7uuHgCE404y2AXoCb7pfy1ZmGqnbF7Nt6Qyq7lpE3UPLqKnJnA0c0BoszjmLHyMvYk+trsTFXUDzWhEMqOvtrGot6lXnk1t6MeK9hczasI/JK3cxpFNDTzMZY8oHX04x3QL0VNVjACIyFueS1wILhKrOFpHmpy0ehjMNKcAHQAJOgRgGfKiqCswXkVqnzV/tH8cOcHD9HPatTqDSLwtpmraeamTRHtic05DJOTGkNuzO/lpduOTcfnQID2Nw7Wp+jVRcdw5sxawN+7jjkyV8dmsvereq63UkY0yQ86VACJB7fuhsd1lxRJ34pa+qu0TkxEhyjYGdudolucv8UiD2LvmelrP+DAm/UgeI0FBWaksSZAjapCdZjXtwQfcOnF+tMnWqV/ZHhFLXs2Vdpt7fn8GvzGbKql1WIIwxJebrUBsLROQb9/VlFDC3dDHlVXA0z4YiY3BOeREVFUVCQkKRP2zH1iQaZUUxMbQ/h2uejdRtzVn1q9EsXKgUIsBxdqxezI4ib9l/UlJSfPpe29UJ4YN52+kWvo/IKt4M7e1r1kARTHktq/8EU94yy6qqhT6AbsC9wH1AV1/WcddrDqzK9Xo90NB93hBY7z7/D3BdXu0KesTExGhxpKZn6deTZ2hOTk6x1vdCfHy8T+0+XbBdmz30vX7w81a/5imIr1kDRTDltaz+E0x5S5oVWKw+/A7P95IbEQkXkftF5F844zD9W1VfVdWlJahH3wIj3OcjgIm5lt8kjl7AEfVj/0PVyqHUDg8p8f0Igeja7k2pF1GFJ75dfaLYGmNMsRR0TeYHQCywEucqoxeLsmER+QynM/ssEUkSkdHAc8D5IrIRON99DTAJ2AJsAv4L3FmUzzK/ERH6t6mHKlz11jxycqxIGGOKp6A+iPaq2glARN4FFhZlw6p6XT5vDcqjrQJ3FWX7Jn9jr+rMzPV7Sdx+iB/X7mFwBxv51RhTdAUdQWSeeKJ2B3VQCQsNYfZf4gBYsuOQx2mMMcGqoCOIaBE56j4XoKr7WnD+6K/p93Sm2GqGhxHdtBb/mbWFUBH+cmE7ryMZY4JMvkcQqhqqqjXdRw1VrZTruRWHIPDQ4LMA+HfCZo6l20GgMaZobN7KcqxP63r8bWh7AN6I3+RxGmNMsLECUc5d3b0p4BxFZGXneJzGGBNMrECUcxFVKjGqbwsAzntpFqkZdqrJGOMbKxAVwKMXtaNxrapsO5DKazM28eOaPVYojDGFsgJRAVQKDWHGnwZQrXIob83azK0fLubZH9Z6HcsYE+CsQFQQ4WGhJDw4kEn39qPbmbX4ZMEOLntjLonb7T4JY0zefBnN1ZQTDWqG06BmOK9c05Vr3p7H6l+P8Ng3K7m1X8vfta1cKYTBHc6gciX7G8KYisoKRAV0Zt1qzHtkEG/Eb+KFqev50xd5zyAbHhZCuzNOveVFBEb0bk6T2lXz3f7GQ9lEbDtYqpmLKzRE6NQ4kkplOBWsMeWFFYgK7I4Brbg0uhGnD/qqKK9M38iBYxmnLE/LzGbh1oMs3bGs8I0vmFeKSUumQY0qdG9RJ9/39+1N44tfvZ8C3RcH96VRt/URIsIr0bxutXI5IrEJHFYgKrCQEKFpnbynUH35mi55Ll+/O5m9yWkFbnf58hVER3cucb7S8PrMTRxISWfdrqP5tklNzeFAdv7vB4oDxzI4nJrNJf/6CYA2DSJoVT+Ca7o35YzIcNo0iLAjJVOqrECYIjnrjBqcdUaNAttk/xJKvzb1yyhRwXzJkZCQwMCBA/0fpoRUlTe+mkmzNmfz3zlbSEnLYsrq3UxZvRuA2tXC6HpmbWKa1aZv63q0jYqgWmX7L26Kz356jAkSIkLHeqEMjG7EJdGNAFiZdIRdR47z+aKd7E1OZ+a6vcxct5cXpq4nNETo06ouzetW59IujWhRrzr1Iqp4/F2YYGIFwpgg1qlJJJ2aRHKBO+fH/pR0ViYdYdqaPazbfZSfNu1nzsb9fDR/OwC9W9aleb3qXBLdkNYNImhQI9zL+CbAeVIgRGQbkAxkA1mqGisidYDPceax3gZcrap2kb4xRVAvogpx7RoQ164BAMczslm64xALtx3k580HWLjtIPO2HOCzhTsA6NOqLv93dTQNI/O/Ks1UXF4eQcSp6v5crx8GZqjqcyLysPv6IW+iGVM+VK0cSp/W9ejTuh73n+cUjBVJh1m49SCfL97Jz5sP0PufM0l8/Dzq2uknc5pAuuRhGM482LhfL/MwizHlUtXKofRsWZd7BrVhzl/iuHNgKwBinpnOH95dYGN0mVOInn4RfFl8qMhW4BCgwH9U9W0ROayqtXK1OaSqtfNYdwwwBiAqKipm/PjxxcqQkpJCREREsdb1QjDlDaasEFx5/ZH1q40ZbDmczeoDOVzVJoyhrSqXynaDab9CcOUtada4uLhEVY0ttKGqlvkDaOR+bQAsB/oDh09rc6iw7cTExGhxxcfHF3tdLwRT3mDKqhpcef2VNScnR7s/86M2e+h7ffir5Xr0eEaJtxlM+1U1uPKWNCuwWH34Xe3JKSZV/dX9uhf4BugB7BGRhgDu171eZDOmIhIRXr+uK2GhwmcLd9LpyWks23nY61jGY2VeIESkuojUOPEcuABYBXwLjHCbjQAmlnU2Yyqyni3rsvLJwdw3qA0A4+Zu9TiR8ZoXVzFFAd+4Y8hUAj5V1SkisgiYICKjgR3AcA+yGVOhhYeF8sfz27I/JZ1PFuwgOS2L16/vandkV1Bl/q+uqluA6DyWHwAGlXUeY8zv3T6gFVNX72HGur088Ply3vpDjNeRjAcC6TJXY0yAaFqnGgsfHcTZDWsyZfVuDqSkex3JeMAKhDEmTyEhwt1xrQH4fPFOj9MYL1iBMMbk6+LODakRXonnp6znyPFMr+OYMmYFwhhToCu7NQHg4tfmnLhHyVQQViCMMQV68tIO9GlVl6RDx5m47Fev45gyZAXCGFOo90Z2B2DVL0c8TmLKkhUIY0yhwsNCaXdGDdbvSfY6iilDViCMMT5p36gmczbuZ9v+Y15HMWXECoQxxie39msJwINfLCctM9vjNKYsWIEwxvjk7IY16dWyDou3HyL679P49fBxryMZP7MCYYzx2X9viuX89lGkZ+XQ57mZdod1OWcFwhjjsxrhYbz9hxgeOL8tAC9OW+9xIuNPViCMMUUiItw2wOmP+GzhTpLT7A7r8soKhDGmyKpUCmXslZ0AeOb7tXaqqZyyAmGMKZZrup9J7WphfL54J6PGLfI6jvGDgCsQInKhiKwXkU0i8rDXeYwx+Zt8X39u69+S5UlHuOyNuRw6luF1JFOKAqpAiEgo8AYwBGgPXCci7b1NZYzJzxmR4dwZ15o+reqybOdhRn1gRxLlSUAVCKAHsElVt6hqBjAeGOZxJmNMASKrhvHprb3o16YeS3cc5so3f2btrqNexzKlINAmmm0M5J6ZJAno6VEWY0wRvHFDN4a+9hOJ2w8x5NU5NKouVF8yy+tYPjuWmho0eY+lpjIqdAu3uHe3+4sE0vjuIjIcGKyqt7iv/wD0UNV7crUZA4wBiIqKihk/fnyxPislJYWIiIiShy4jwZQ3mLJCcOUN9KzZOcqyfdks2JVFZmYWoZUC7W/Q/GVnBU/e7KwsujcOp0+j4uWNi4tLVNXYQhuqasA8gN7A1FyvHwEeya99TEyMFld8fHyx1/VCMOUNpqyqwZXXsvpPMOUtaVZgsfrwOznQ+iAWAW1EpIWIVAauBb71OJMxxlRIAXU8papZInI3MBUIBd5T1dUexzLGmAopoAoEgKpOAiZ5ncMYYyq6QDvFZIwxJkBYgTDGGJMnKxDGGGPyZAXCGGNMnqxAGGOMyVNA3UldVCKyD9hezNXrAftLMY6/BVPeYMoKwZXXsvpPMOUtadZmqlq/sEZBXSBKQkQWqy+3mgeIYMobTFkhuPJaVv8JprxlldVOMRljjMmTFQhjjDF5qsgF4m2vAxRRMOUNpqwQXHktq/8EU94yyVph+yCMMcYUrCIfQRhjjClAuSoQItJUROJFZK2IrBaR+9zldUTkRxHZ6H6t7S6/QURWuI+fRSQ617YuFJH1IrJJRB72Omuu9bqLSLaIXJVr2Qi3/UYRGREIWUVkoIgsc9vPyrXcr/u1OHlFJFJEvhOR5W77m3Nty6t9O9x9nSMisaet84i7/9aLyOBcy736mc0zq4icLyKJIrLS/Xpurvdi3OWbROQ1ERGv8+Za70wRSRGRB3MtC6h9677XWUTmue+vFJFwd3np7VtfJo0IlgfQEOjmPq8BbADaA88DD7vLHwbGus/7ALXd50OABe7zUGAz0BKoDCwH2nuZNVeumTij3V7lLqsDbHG/1naf1/Z4v9YC1gBnuq8blNV+LWbeR3M9rw8cdPN5uW/PBs4CEoDYXO3bu/utCtDC3Z+hHv/M5pe1K9DIfd4R+CXXewtxJggTYDIwpAx/DvLMm2u9r4AvgAfL6ue2GPu2ErACiHZf1wVCS3vflqsjCFXdpapL3OfJwFqcea6HAR+4zT4ALnPb/Kyqh9zl84Em7vMewCZV3aKqGcB4dxueZXXdg/PDuzfXssHAj6p60P1efgQu9Djr9cDXqrrDXedEXr/v12LmVaCG+5dWBE6ByMLDfauqa1V1fR6rDAPGq2q6qm4FNuHsV89+ZvPLqqpLVfVX9+VqIFxEqohIQ6Cmqs5T5zfah5z6c+5JXgARuQznD4Hc89AE3L4FLgBWqOpyd50Dqppd2vu2XBWI3ESkOc5fMAuAKFXdBc4/BNAgj1VG41RbcH6Z7Mz1XpK7zLOsItIYuBx467TVAy4r0BaoLSIJ7qmFm7zIWoS8/8L5S+1XYCVwn6rmlHXe07LmJ79MgZg1tyuBpaqajpMrKdd7Zf1zkF+b6sBDwN9PeysQ921bQEVkqogsEZG/uMtLdd8G3IRBpUFEInD+0r5fVY8WdgpOROJwCsQ5Jxbl0cwvl3sVIesrwEPuXwmnbCKPtl5nrQTEAIOAqsA8EZlfllmhSHkHA8uAc4FWwI8iMgcP921BTfPJlNcfe15nPdG+AzAW569e8PjnoICmfwdeVtWUQPk/VkDTSji/r7oDqcAMEUkE8lqn2FnLXYEQkTCcHfyJqn7tLt4jIg1VdZd7CLY3V/vOwDs45+kOuIuTgKa5NtsE569LL7PGAuPdH9x6wEUikuVmHXha1gSPsyYB+1X1GHBMRGYD0ZTRfi1G3puB59xD8k0ishVoh7f7Nj8F7UOvfmYLat8E+Aa4SVU3u4uT+O10rt+yup9flLw9gatE5HmcfrQcEUkDEgm8fZsEzFLV/e66k4BuwMeU5r4tbudFID5wKv2HwCunLX+BUzsnn3efn4lzDrfPae0r4ZyHbMFvnVIdvMx6WptxnNpJvRWnE7W2+7yOx/v1bGCGux+rAatwOin9vl+LmfdN4En3eRTwC04R9mzf5no/gVM7Jztwaif1FpxOVM9+ZgvIWsvNcWUebRcBvfitI/Wisvo5yC/vae89yW+d1IG4b2sDS9z/X5WA6cDFpb1vS/UfxOsHziGX4vTuL3MfF+H08M8ANrpf67jt3wEO5Wq7ONe2LsK5kmAz8JjXWU9bdxxugXBfj8IpdJuAmwMhK/BnnCuZVuEcLpfJfi3mz0EjYBpO/8Mq4MYA2LeX4/yVmA7sAabmWucxd/+tJ9cVKh7+zOaZFXgcOJar7TJ+u6It1t3Xm3H6gMTrvKet+yRugQjEfeuucyNOZ/oqcv0hWZr71u6kNsYYk6dyexWTMcaYkrECYYwxJk9WIIwxxuTJCoQxxpg8WYEwxhiTJysQxhRCHD+JyJBcy64WkSle5jLG3+wyV2N8ICIdcUb47IpzY9oy4EL97e7g4myzkqpmlVJEY0qdFQhjfOQOwXAMqA4kq+rT4swRcRfOHbY/A3erao6IvI0z9EFV4HNVfcrdRhLwH5xRYV/BGQrhViATWKmqN5bxt2VMvsrdWEzG+NHfcYY3yABi3aOKy3GGaslyi8K1wKc4Q3ocFJFKQLyIfKmqa9ztHFPVvgAisgtopqoZIlKrzL8jYwpgBcIYH6nqMRH5HEhR1XQROQ9nNM3F7iCKVfltWOjrRGQ0zv+xRjiTv5woEJ/n2uxq4GMRmQj8rwy+DWN8ZgXCmKLJcR/gDIb2nqr+NXcDEWkD3Af0UNXDIvIxEJ6rybFczwcDA3AmoHlcRDqqarbf0htTBHYVkzHFNx24WkTqAYhIXRE5E6gJJANH3WHFB+e1soiEAk1UdSbO4Ib1cUbnNCYg2BGEMcWkqitF5O/AdBEJwelovh1YzG8j2W4B5uaziUrApyJSA+ePtbHqTDdpTECwq5iMMcbkyU4xGWOMyZMVCGOMMXmyAmGMMSZPViCMMcbkyQqEMcaYPFmBMMYYkycrEMYYY/JkBcIYY0ye/h9dT0Ssbia6AwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -172,7 +172,7 @@
    "source": [
     "x = np.arange(0, 1684)\n",
     "years = 2020 + x/12\n",
-    "y = 100*(1.01)**(x/12)\n",
+    "y = 100*(1.004)**(x/12)\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
     "plt.plot(years, y, label='power demand')\n",
     "plt.xlabel('Years')\n",
@@ -190,22 +190,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9bfcbd9eb8>]"
+       "[<matplotlib.lines.Line2D at 0x7fc8bf2ba9b0>]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -219,7 +219,7 @@
     "msr_power = 3.7*0.44\n",
     "lifetime = 60*12\n",
     "msr_deploy_array, msr_deployed_power = an.get_new_deployment(power_dict, ['us_government', 'lwr'],\n",
-    "                                                     '100*(1.01)**(t/12)', msr_power, lifetime, 240, True)\n",
+    "                                                     '100*(1.004)**(t/12)', msr_power, lifetime, 240, True)\n",
     "power_dict['msr'] = msr_deployed_power\n",
     "plt.plot(msr_deploy_array)"
    ]
@@ -233,12 +233,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -250,7 +250,7 @@
    "source": [
     "x = np.arange(0, 1684)\n",
     "years = 2020 + x/12\n",
-    "y = 100*(1.01)**(x/12)\n",
+    "y = 100*(1.004)**(x/12)\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'] + power_dict['msr'], label='legacy + lwr + msr')\n",
     "plt.plot(years, y, label='power demand')\n",

From c05638d30fe9bc41df0458ab6e1e84298a61856a Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 18 Sep 2018 12:43:34 -0500
Subject: [PATCH 11/35] get deployment auto fix

---
 input/master_us/get_deployment.ipynb | 61 ++++++++++++++++++++--------
 1 file changed, 45 insertions(+), 16 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index d08b71ea..789c8357 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
    "outputs": [],
    "source": [
     "# get sqlite cursor and simulation time data\n",
-    "cur = an.get_cursor('./legacy_only.sqlite')\n",
+    "cur = an.get_cursor('./with_lwr.sqlite')\n",
     "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
    ]
   },
@@ -74,7 +74,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "metadata": {
     "scrolled": true
    },
@@ -112,10 +112,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc8f058cb38>]"
+       "[<matplotlib.lines.Line2D at 0x7fb5afc0add8>]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -139,11 +139,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep.xml', 960)"
+    "# push back deploy times by five\n",
+    "lwr_deploy_array = np.append(np.array([0]*80), lwr_deploy_array[:-80])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep_auto.xml', 960)"
    ]
   },
   {
@@ -155,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -190,16 +200,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc8bf2ba9b0>]"
+       "[<matplotlib.lines.Line2D at 0x7fb5af6054e0>]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -224,6 +234,25 @@
     "plt.plot(msr_deploy_array)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# push back deploy times by five\n",
+    "msr_deploy_array = np.append(np.array([0]*80), msr_deploy_array[:-80])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep_auto.xml', 720)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -233,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {

From 462dc576e988b984f79c67d76ddde6d9522d7de0 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 18 Sep 2018 13:50:20 -0500
Subject: [PATCH 12/35] 0.9% growth

---
 input/master_us/get_deployment.ipynb |   52 +-
 input/master_us/input.xml            |   48 +-
 input/master_us/lwr_dep.xml          | 2089 ++------------------------
 input/master_us/rebus_dep.xml        | 1050 +++++++++++++
 4 files changed, 1231 insertions(+), 2008 deletions(-)
 create mode 100644 input/master_us/rebus_dep.xml

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 789c8357..933b5c95 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -112,16 +112,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb5afc0add8>]"
+       "[<matplotlib.lines.Line2D at 0x7f5d217f7978>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -132,14 +132,14 @@
    ],
    "source": [
     "lwr_deploy_array, lwr_deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                     '100*(1.004)**(t/12)', 1, 960, 240, False)\n",
+    "                                                     '100*(1.009)**(t/12)', 1, 960, 240, False)\n",
     "power_dict['lwr'] = lwr_deployed_power\n",
     "plt.plot(lwr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,11 +149,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep_auto.xml', 960)"
+    "an.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep.xml', 960)"
    ]
   },
   {
@@ -165,12 +165,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -182,7 +182,7 @@
    "source": [
     "x = np.arange(0, 1684)\n",
     "years = 2020 + x/12\n",
-    "y = 100*(1.004)**(x/12)\n",
+    "y = 100*(1.009)**(x/12)\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
     "plt.plot(years, y, label='power demand')\n",
     "plt.xlabel('Years')\n",
@@ -200,22 +200,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb5af6054e0>]"
+       "[<matplotlib.lines.Line2D at 0x7f5d16656710>]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -229,14 +229,14 @@
     "msr_power = 3.7*0.44\n",
     "lifetime = 60*12\n",
     "msr_deploy_array, msr_deployed_power = an.get_new_deployment(power_dict, ['us_government', 'lwr'],\n",
-    "                                                     '100*(1.004)**(t/12)', msr_power, lifetime, 240, True)\n",
+    "                                                     '100*(1.009)**(t/12)', msr_power, lifetime, 240, True)\n",
     "power_dict['msr'] = msr_deployed_power\n",
     "plt.plot(msr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,11 +246,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep_auto.xml', 720)"
+    "an.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep.xml', 720)"
    ]
   },
   {
@@ -262,12 +262,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -279,7 +279,7 @@
    "source": [
     "x = np.arange(0, 1684)\n",
     "years = 2020 + x/12\n",
-    "y = 100*(1.004)**(x/12)\n",
+    "y = 100*(1.009)**(x/12)\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'] + power_dict['msr'], label='legacy + lwr + msr')\n",
     "plt.plot(years, y, label='power demand')\n",
diff --git a/input/master_us/input.xml b/input/master_us/input.xml
index c6d45a03..d10ec993 100644
--- a/input/master_us/input.xml
+++ b/input/master_us/input.xml
@@ -319,6 +319,27 @@
     </config>
   </facility>
 
+   <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>rebus</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1628</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
 
 <facility>
         <!-- Stores uox unf for 48 months (4years) -->
@@ -450,18 +471,31 @@
       </config>
     </institution>
 
+    <institution>
+    <name>lwr_inst</name>
+    <config>
+      <DeployInst>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+      </DeployInst>
+    </config>
+  </institution>
+
   <institution>
-  <name>lwr_inst</name>
-  <config>
+  <name>rebus_inst</name>
+    <config>
     <DeployInst>
-      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
-      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
-      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
-      <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/build_times)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/n_build)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
     </DeployInst>
-  </config>
+    </config>
   </institution>
 
+
   </region>
 
 
diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
index df11e871..aebf22ab 100644
--- a/input/master_us/lwr_dep.xml
+++ b/input/master_us/lwr_dep.xml
@@ -54,1981 +54,120 @@
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
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+</prototypes>
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+</build_times>
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+</n_build>
+<lifetimes>
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
diff --git a/input/master_us/rebus_dep.xml b/input/master_us/rebus_dep.xml
new file mode 100644
index 00000000..0e7b898d
--- /dev/null
+++ b/input/master_us/rebus_dep.xml
@@ -0,0 +1,1050 @@
+<root>
+<prototypes>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+</prototypes>
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+		<val>1613</val>
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+		<val>1621</val>
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+		<val>1655</val>
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+		<val>1662</val>
+		<val>1668</val>
+		<val>1671</val>
+		<val>1675</val>
+		<val>1682</val>
+		<val>1683</val>
+</build_times>
+<n_build>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
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+		<val>2</val>
+		<val>1</val>
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+		<val>1</val>
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+		<val>1</val>
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+		<val>1</val>
+		<val>1</val>
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+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+</n_build>
+<lifetimes>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
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+		<val>720</val>
+		<val>720</val>
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+		<val>720</val>
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+		<val>720</val>
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+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
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+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
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+		<val>720</val>
+		<val>720</val>
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+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+</lifetimes>
+</root>

From 2eaf5fb7baa5ce7f829cb7a097a678e91768ea10 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Wed, 19 Sep 2018 15:05:58 -0500
Subject: [PATCH 13/35] fix growth rate

---
 input/master_us/get_deployment.ipynb |  47 +-
 input/master_us/lwr_dep_auto.xml     | 186 +++++++
 input/master_us/rebus_dep.xml        | 783 ++++++++++++++++++---------
 3 files changed, 733 insertions(+), 283 deletions(-)
 create mode 100644 input/master_us/lwr_dep_auto.xml

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 933b5c95..50c513c0 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,18 +39,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
     "# get sqlite cursor and simulation time data\n",
-    "cur = an.get_cursor('./with_lwr.sqlite')\n",
+    "cur = an.get_cursor('./cyclus.sqlite')\n",
     "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -69,17 +69,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -112,10 +112,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5d217f7978>]"
+       "[<matplotlib.lines.Line2D at 0x7fd488891f98>]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -200,16 +200,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5d16656710>]"
+       "[<matplotlib.lines.Line2D at 0x7fd485f6e358>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -236,17 +236,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "# push back deploy times by five\n",
-    "msr_deploy_array = np.append(np.array([0]*80), msr_deploy_array[:-80])"
+    "tail = msr_deploy_array[:-80]\n",
+    "msr_deploy_array = np.append(np.array([0]*80), msr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
diff --git a/input/master_us/lwr_dep_auto.xml b/input/master_us/lwr_dep_auto.xml
new file mode 100644
index 00000000..75560a5d
--- /dev/null
+++ b/input/master_us/lwr_dep_auto.xml
@@ -0,0 +1,186 @@
+<root>
+<prototypes>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+</prototypes>
+<build_times>
+		<val>86</val>
+		<val>115</val>
+		<val>145</val>
+		<val>174</val>
+		<val>193</val>
+		<val>197</val>
+		<val>201</val>
+		<val>210</val>
+		<val>211</val>
+		<val>213</val>
+		<val>218</val>
+		<val>223</val>
+		<val>227</val>
+		<val>229</val>
+		<val>230</val>
+		<val>231</val>
+		<val>235</val>
+		<val>239</val>
+		<val>241</val>
+		<val>242</val>
+		<val>245</val>
+		<val>247</val>
+		<val>251</val>
+		<val>252</val>
+		<val>254</val>
+		<val>256</val>
+		<val>257</val>
+		<val>258</val>
+		<val>260</val>
+		<val>261</val>
+		<val>262</val>
+		<val>264</val>
+		<val>271</val>
+		<val>276</val>
+		<val>277</val>
+		<val>280</val>
+		<val>282</val>
+		<val>284</val>
+		<val>286</val>
+		<val>292</val>
+		<val>299</val>
+		<val>300</val>
+		<val>303</val>
+		<val>308</val>
+</build_times>
+<n_build>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+</n_build>
+<lifetimes>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+</lifetimes>
+</root>
diff --git a/input/master_us/rebus_dep.xml b/input/master_us/rebus_dep.xml
index 0e7b898d..1fce1013 100644
--- a/input/master_us/rebus_dep.xml
+++ b/input/master_us/rebus_dep.xml
@@ -260,268 +260,399 @@
 		<val>rebus</val>
 		<val>rebus</val>
 		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+
+
 </prototypes>
 <build_times>
-		<val>326</val>
-		<val>328</val>
-		<val>336</val>
-		<val>340</val>
-		<val>343</val>
-		<val>351</val>
-		<val>353</val>
-		<val>356</val>
-		<val>361</val>
-		<val>368</val>
-		<val>371</val>
-		<val>373</val>
-		<val>378</val>
-		<val>381</val>
-		<val>382</val>
-		<val>383</val>
-		<val>386</val>
-		<val>389</val>
-		<val>391</val>
-		<val>394</val>
-		<val>397</val>
-		<val>398</val>
-		<val>399</val>
-		<val>405</val>
-		<val>407</val>
-		<val>410</val>
-		<val>415</val>
-		<val>418</val>
-		<val>419</val>
-		<val>427</val>
-		<val>431</val>
-		<val>435</val>
-		<val>440</val>
-		<val>444</val>
-		<val>448</val>
-		<val>465</val>
-		<val>480</val>
-		<val>485</val>
-		<val>501</val>
-		<val>513</val>
-		<val>522</val>
-		<val>538</val>
-		<val>553</val>
-		<val>568</val>
-		<val>583</val>
-		<val>598</val>
-		<val>613</val>
-		<val>628</val>
-		<val>642</val>
-		<val>656</val>
-		<val>670</val>
-		<val>684</val>
-		<val>698</val>
-		<val>712</val>
-		<val>725</val>
-		<val>739</val>
-		<val>752</val>
-		<val>757</val>
-		<val>769</val>
-		<val>782</val>
-		<val>795</val>
-		<val>807</val>
-		<val>820</val>
-		<val>833</val>
-		<val>845</val>
-		<val>857</val>
-		<val>869</val>
-		<val>881</val>
-		<val>893</val>
-		<val>905</val>
-		<val>917</val>
-		<val>929</val>
-		<val>940</val>
-		<val>951</val>
-		<val>963</val>
-		<val>974</val>
-		<val>985</val>
-		<val>996</val>
-		<val>1007</val>
-		<val>1018</val>
-		<val>1029</val>
-		<val>1040</val>
-		<val>1044</val>
-		<val>1046</val>
-		<val>1048</val>
-		<val>1054</val>
-		<val>1056</val>
-		<val>1058</val>
-		<val>1060</val>
-		<val>1063</val>
-		<val>1069</val>
-		<val>1071</val>
-		<val>1073</val>
-		<val>1076</val>
-		<val>1081</val>
-		<val>1082</val>
-		<val>1087</val>
-		<val>1088</val>
-		<val>1091</val>
-		<val>1093</val>
-		<val>1095</val>
-		<val>1098</val>
-		<val>1101</val>
-		<val>1102</val>
-		<val>1103</val>
-		<val>1106</val>
-		<val>1108</val>
-		<val>1109</val>
-		<val>1111</val>
-		<val>1114</val>
-		<val>1115</val>
-		<val>1117</val>
-		<val>1118</val>
-		<val>1119</val>
-		<val>1121</val>
-		<val>1125</val>
-		<val>1127</val>
-		<val>1129</val>
-		<val>1130</val>
-		<val>1133</val>
-		<val>1135</val>
-		<val>1138</val>
-		<val>1139</val>
-		<val>1143</val>
-		<val>1147</val>
-		<val>1151</val>
-		<val>1153</val>
-		<val>1155</val>
-		<val>1157</val>
-		<val>1160</val>
-		<val>1162</val>
-		<val>1164</val>
-		<val>1168</val>
-		<val>1170</val>
-		<val>1171</val>
-		<val>1173</val>
-		<val>1177</val>
-		<val>1180</val>
-		<val>1184</val>
-		<val>1185</val>
-		<val>1189</val>
-		<val>1190</val>
-		<val>1192</val>
-		<val>1195</val>
-		<val>1199</val>
-		<val>1200</val>
-		<val>1201</val>
-		<val>1202</val>
-		<val>1205</val>
-		<val>1208</val>
-		<val>1211</val>
-		<val>1212</val>
-		<val>1215</val>
-		<val>1216</val>
-		<val>1217</val>
-		<val>1219</val>
-		<val>1221</val>
-		<val>1224</val>
-		<val>1227</val>
-		<val>1231</val>
-		<val>1233</val>
-		<val>1235</val>
-		<val>1237</val>
-		<val>1240</val>
-		<val>1242</val>
-		<val>1244</val>
-		<val>1245</val>
-		<val>1251</val>
-		<val>1252</val>
-		<val>1255</val>
-		<val>1258</val>
-		<val>1259</val>
-		<val>1262</val>
-		<val>1266</val>
-		<val>1268</val>
-		<val>1273</val>
-		<val>1278</val>
-		<val>1285</val>
-		<val>1288</val>
-		<val>1294</val>
-		<val>1303</val>
-		<val>1312</val>
-		<val>1318</val>
-		<val>1320</val>
-		<val>1329</val>
-		<val>1333</val>
-		<val>1337</val>
-		<val>1346</val>
-		<val>1348</val>
-		<val>1354</val>
-		<val>1362</val>
-		<val>1363</val>
-		<val>1371</val>
-		<val>1376</val>
-		<val>1379</val>
-		<val>1388</val>
-		<val>1390</val>
-		<val>1396</val>
-		<val>1404</val>
-		<val>1412</val>
-		<val>1418</val>
-		<val>1420</val>
-		<val>1428</val>
-		<val>1432</val>
-		<val>1436</val>
-		<val>1444</val>
-		<val>1445</val>
-		<val>1452</val>
-		<val>1459</val>
-		<val>1460</val>
-		<val>1467</val>
-		<val>1472</val>
-		<val>1475</val>
-		<val>1477</val>
-		<val>1483</val>
-		<val>1489</val>
-		<val>1490</val>
-		<val>1498</val>
-		<val>1502</val>
-		<val>1505</val>
-		<val>1513</val>
-		<val>1515</val>
-		<val>1520</val>
-		<val>1527</val>
-		<val>1528</val>
-		<val>1535</val>
-		<val>1540</val>
-		<val>1543</val>
-		<val>1550</val>
-		<val>1553</val>
-		<val>1557</val>
-		<val>1564</val>
-		<val>1565</val>
-		<val>1572</val>
-		<val>1577</val>
-		<val>1579</val>
-		<val>1586</val>
-		<val>1589</val>
-		<val>1593</val>
-		<val>1600</val>
-		<val>1601</val>
-		<val>1607</val>
-		<val>1613</val>
-		<val>1614</val>
-		<val>1621</val>
-		<val>1625</val>
-		<val>1628</val>
-		<val>1635</val>
-		<val>1637</val>
-		<val>1641</val>
-		<val>1648</val>
-		<val>1649</val>
-		<val>1655</val>
-		<val>1660</val>
-		<val>1662</val>
-		<val>1668</val>
-		<val>1671</val>
-		<val>1675</val>
-		<val>1682</val>
-		<val>1683</val>
+		<val>	326	</val>
+<val>	327	</val>
+<val>	330	</val>
+<val>	338	</val>
+<val>	342	</val>
+<val>	347	</val>
+<val>	352	</val>
+<val>	353	</val>
+<val>	355	</val>
+<val>	361	</val>
+<val>	362	</val>
+<val>	370	</val>
+<val>	371	</val>
+<val>	373	</val>
+<val>	377	</val>
+<val>	381	</val>
+<val>	382	</val>
+<val>	383	</val>
+<val>	386	</val>
+<val>	387	</val>
+<val>	389	</val>
+<val>	391	</val>
+<val>	395	</val>
+<val>	397	</val>
+<val>	398	</val>
+<val>	399	</val>
+<val>	405	</val>
+<val>	406	</val>
+<val>	407	</val>
+<val>	410	</val>
+<val>	415	</val>
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+<val>	425	</val>
+<val>	431	</val>
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+<val>	437	</val>
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+<val>	445	</val>
+<val>	459	</val>
+<val>	473	</val>
+<val>	480	</val>
+<val>	491	</val>
+<val>	505	</val>
+<val>	513	</val>
+<val>	523	</val>
+<val>	536	</val>
+<val>	550	</val>
+<val>	563	</val>
+<val>	576	</val>
+<val>	589	</val>
+<val>	602	</val>
+<val>	615	</val>
+<val>	627	</val>
+<val>	639	</val>
+<val>	652	</val>
+<val>	664	</val>
+<val>	676	</val>
+<val>	688	</val>
+<val>	700	</val>
+<val>	711	</val>
+<val>	723	</val>
+<val>	734	</val>
+<val>	746	</val>
+<val>	757	</val>
+<val>	760	</val>
+<val>	771	</val>
+<val>	782	</val>
+<val>	793	</val>
+<val>	804	</val>
+<val>	815	</val>
+<val>	825	</val>
+<val>	836	</val>
+<val>	846	</val>
+<val>	857	</val>
+<val>	867	</val>
+<val>	877	</val>
+<val>	887	</val>
+<val>	897	</val>
+<val>	907	</val>
+<val>	917	</val>
+<val>	927	</val>
+<val>	937	</val>
+<val>	946	</val>
+<val>	956	</val>
+<val>	965	</val>
+<val>	975	</val>
+<val>	984	</val>
+<val>	993	</val>
+<val>	1002	</val>
+<val>	1011	</val>
+<val>	1021	</val>
+<val>	1030	</val>
+<val>	1038	</val>
+<val>	1043	</val>
+<val>	1046	</val>
+<val>	1047	</val>
+<val>	1050	</val>
+<val>	1051	</val>
+<val>	1055	</val>
+<val>	1058	</val>
+<val>	1062	</val>
+<val>	1063	</val>
+<val>	1066	</val>
+<val>	1067	</val>
+<val>	1072	</val>
+<val>	1073	</val>
+<val>	1075	</val>
+<val>	1078	</val>
+<val>	1081	</val>
+<val>	1082	</val>
+<val>	1087	</val>
+<val>	1090	</val>
+<val>	1091	</val>
+<val>	1093	</val>
+<val>	1097	</val>
+<val>	1098	</val>
+<val>	1101	</val>
+<val>	1102	</val>
+<val>	1103	</val>
+<val>	1106	</val>
+<val>	1107	</val>
+<val>	1109	</val>
+<val>	1110	</val>
+<val>	1111	</val>
+<val>	1113	</val>
+<val>	1115	</val>
+<val>	1117	</val>
+<val>	1118	</val>
+<val>	1119	</val>
+<val>	1122	</val>
+<val>	1125	</val>
+<val>	1126	</val>
+<val>	1127	</val>
+<val>	1130	</val>
+<val>	1133	</val>
+<val>	1135	</val>
+<val>	1136	</val>
+<val>	1138	</val>
+<val>	1139	</val>
+<val>	1144	</val>
+<val>	1145	</val>
+<val>	1147	</val>
+<val>	1151	</val>
+<val>	1153	</val>
+<val>	1157	</val>
+<val>	1159	</val>
+<val>	1161	</val>
+<val>	1163	</val>
+<val>	1165	</val>
+<val>	1168	</val>
+<val>	1171	</val>
+<val>	1172	</val>
+<val>	1174	</val>
+<val>	1178	</val>
+<val>	1179	</val>
+<val>	1183	</val>
+<val>	1186	</val>
+<val>	1189	</val>
+<val>	1190	</val>
+<val>	1191	</val>
+<val>	1193	</val>
+<val>	1195	</val>
+<val>	1199	</val>
+<val>	1200	</val>
+<val>	1201	</val>
+<val>	1202	</val>
+<val>	1205	</val>
+<val>	1207	</val>
+<val>	1210	</val>
+<val>	1211	</val>
+<val>	1212	</val>
+<val>	1214	</val>
+<val>	1216	</val>
+<val>	1217	</val>
+<val>	1218	</val>
+<val>	1220	</val>
+<val>	1221	</val>
+<val>	1222	</val>
+<val>	1225	</val>
+<val>	1229	</val>
+<val>	1231	</val>
+<val>	1233	</val>
+<val>	1236	</val>
+<val>	1237	</val>
+<val>	1240	</val>
+<val>	1242	</val>
+<val>	1243	</val>
+<val>	1244	</val>
+<val>	1247	</val>
+<val>	1251	</val>
+<val>	1252	</val>
+<val>	1255	</val>
+<val>	1256	</val>
+<val>	1259	</val>
+<val>	1261	</val>
+<val>	1264	</val>
+<val>	1267	</val>
+<val>	1269	</val>
+<val>	1270	</val>
+<val>	1274	</val>
+<val>	1279	</val>
+<val>	1283	</val>
+<val>	1287	</val>
+<val>	1294	</val>
+<val>	1296	</val>
+<val>	1301	</val>
+<val>	1308	</val>
+<val>	1309	</val>
+<val>	1315	</val>
+<val>	1322	</val>
+<val>	1329	</val>
+<val>	1335	</val>
+<val>	1336	</val>
+<val>	1343	</val>
+<val>	1347	</val>
+<val>	1350	</val>
+<val>	1357	</val>
+<val>	1359	</val>
+<val>	1364	</val>
+<val>	1370	</val>
+<val>	1372	</val>
+<val>	1377	</val>
+<val>	1384	</val>
+<val>	1390	</val>
+<val>	1396	</val>
+<val>	1397	</val>
+<val>	1404	</val>
+<val>	1408	</val>
+<val>	1410	</val>
+<val>	1417	</val>
+<val>	1420	</val>
+<val>	1423	</val>
+<val>	1430	</val>
+<val>	1431	</val>
+<val>	1436	</val>
+<val>	1442	</val>
+<val>	1443	</val>
+<val>	1449	</val>
+<val>	1454	</val>
+<val>	1455	</val>
+<val>	1461	</val>
+<val>	1466	</val>
+<val>	1467	</val>
+<val>	1474	</val>
+<val>	1477	</val>
+<val>	1480	</val>
+<val>	1486	</val>
+<val>	1491	</val>
+<val>	1492	</val>
+<val>	1498	</val>
+<val>	1502	</val>
+<val>	1504	</val>
+<val>	1510	</val>
+<val>	1513	</val>
+<val>	1516	</val>
+<val>	1522	</val>
+<val>	1524	</val>
+<val>	1528	</val>
+<val>	1534	</val>
+<val>	1535	</val>
+<val>	1540	</val>
+<val>	1545	</val>
+<val>	1546	</val>
+<val>	1551	</val>
+<val>	1556	</val>
+<val>	1557	</val>
+<val>	1563	</val>
+<val>	1566	</val>
+<val>	1569	</val>
+<val>	1574	</val>
+<val>	1577	</val>
+<val>	1580	</val>
+<val>	1586	</val>
+<val>	1587	</val>
+<val>	1591	</val>
+<val>	1597	</val>
+<val>	1603	</val>
+<val>	1607	</val>
+<val>	1608	</val>
+<val>	1614	</val>
+<val>	1617	</val>
+<val>	1619	</val>
+<val>	1625	</val>
+<val>	1627	</val>
+<val>	1630	</val>
+<val>	1635	</val>
+<val>	1637	</val>
+<val>	1641	</val>
+<val>	1646	</val>
+<val>	1647	</val>
+<val>	1652	</val>
+<val>	1657	</val>
+<val>	1662	</val>
+<val>	1666	</val>
+<val>	1667	</val>
+<val>	1673	</val>
+<val>	1676	</val>
+<val>	1678	</val>
+<val>	1683	</val>
+<val>	1685	</val>
+<val>	1688	</val>
+<val>	1694	</val>
+<val>	1695	</val>
+<val>	1699	</val>
+<val>	1704	</val>
+<val>	1709	</val>
+<val>	1713	</val>
+<val>	1714	</val>
+<val>	1719	</val>
+<val>	1722	</val>
+<val>	1724	</val>
+<val>	1729	</val>
+<val>	1731	</val>
+<val>	1734	</val>
+<val>	1739	</val>
+<val>	1741	</val>
+<val>	1744	</val>
+<val>	1749	</val>
+<val>	1750	</val>
+<val>	1754	</val>
+<val>	1758	</val>
+<val>	1759	</val>
+<val>	1763	</val>
+
 </build_times>
 <n_build>
 		<val>1</val>
@@ -784,6 +915,72 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+
+
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>2</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>2</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>2</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
+        <val>1</val>
 </n_build>
 <lifetimes>
 		<val>720</val>
@@ -1046,5 +1243,71 @@
 		<val>720</val>
 		<val>720</val>
 		<val>720</val>
+
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+		<val>720</val>
+
 </lifetimes>
 </root>

From f941949db8c44d1298a230b1383b90aa666524a3 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Wed, 19 Sep 2018 15:06:16 -0500
Subject: [PATCH 14/35] added file by mistake

---
 input/master_us/lwr_dep_auto.xml | 186 -------------------------------
 1 file changed, 186 deletions(-)
 delete mode 100644 input/master_us/lwr_dep_auto.xml

diff --git a/input/master_us/lwr_dep_auto.xml b/input/master_us/lwr_dep_auto.xml
deleted file mode 100644
index 75560a5d..00000000
--- a/input/master_us/lwr_dep_auto.xml
+++ /dev/null
@@ -1,186 +0,0 @@
-<root>
-<prototypes>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-</prototypes>
-<build_times>
-		<val>86</val>
-		<val>115</val>
-		<val>145</val>
-		<val>174</val>
-		<val>193</val>
-		<val>197</val>
-		<val>201</val>
-		<val>210</val>
-		<val>211</val>
-		<val>213</val>
-		<val>218</val>
-		<val>223</val>
-		<val>227</val>
-		<val>229</val>
-		<val>230</val>
-		<val>231</val>
-		<val>235</val>
-		<val>239</val>
-		<val>241</val>
-		<val>242</val>
-		<val>245</val>
-		<val>247</val>
-		<val>251</val>
-		<val>252</val>
-		<val>254</val>
-		<val>256</val>
-		<val>257</val>
-		<val>258</val>
-		<val>260</val>
-		<val>261</val>
-		<val>262</val>
-		<val>264</val>
-		<val>271</val>
-		<val>276</val>
-		<val>277</val>
-		<val>280</val>
-		<val>282</val>
-		<val>284</val>
-		<val>286</val>
-		<val>292</val>
-		<val>299</val>
-		<val>300</val>
-		<val>303</val>
-		<val>308</val>
-</build_times>
-<n_build>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>3</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>3</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-</n_build>
-<lifetimes>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-</lifetimes>
-</root>

From 357df35183d0de3d13232c886dddb1b0e762ca46 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 20 Sep 2018 13:43:32 -0500
Subject: [PATCH 15/35] ap 1000 power cap change

---
 scripts/write_input.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/scripts/write_input.py b/scripts/write_input.py
index 4c5ff532..1eabd9ea 100644
--- a/scripts/write_input.py
+++ b/scripts/write_input.py
@@ -254,8 +254,8 @@ def reactor_render(reactor_data, output_file, is_cyborg=False):
 
     ap1000_spec = {'template': pwr_template,
                    'kg_per_assembly': 446.0,
-                   'assemblies_per_core': 157,
-                   'assemblies_per_batch': 52}
+                   'assemblies_per_core': 157 / 1110.0,
+                   'assemblies_per_batch': 157 / 3330.0}
     bwr_spec = {'template': pwr_template,
                 'kg_per_assembly': 180,
                 'assemblies_per_core': 764 / 1000.0,
@@ -411,7 +411,8 @@ def region_render(reactor_data, output_file):
         # file with its `region block`
         for data in reactor_data:
             if data['country'].decode('utf-8') == country:
-
+                if data['lifetime'] == 0:
+                    continue
                 prototype += (valhead
                               + refine_name(data['reactor_name'])
                               + valtail + '\n')
@@ -485,10 +486,13 @@ def main(csv_file, init_date, duration, output_file, reprocessing=True):
     region_output_filename = 'region_output.xml.in'
     # read csv and templates
     csv_database = read_csv(csv_file)
-    for data in csv_database:
-        print(type(int(data['first_crit'])))
+    kill_indx = []
+    
+    for indx, data in enumerate(csv_database):
         entry_time = get_entrytime(init_date, int(data['first_crit']))
         lifetime = get_lifetime(int(data['first_crit']), int(data['shutdown_date']))
+        if int(data['shutdown_date']) < init_date and data['shutdown_date'] != -1:
+            kill_indx.append(indx)
         if entry_time <= 0:
             lifetime = lifetime + entry_time
             if lifetime < 0:

From f6f06004b92272dd20e49f772ce08e9a7c7b7d97 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 20 Sep 2018 17:39:46 -0500
Subject: [PATCH 16/35] new lwr

---
 input/master_us/get_deployment.ipynb |  52 +-
 input/master_us/input.xml            |   2 +-
 input/master_us/lwr_dep.xml          |  58 +-
 input/master_us/rebus_dep.xml        | 807 +++++++++++----------------
 4 files changed, 362 insertions(+), 557 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 50c513c0..582f6d79 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -69,17 +69,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -112,16 +112,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fd488891f98>]"
+       "[<matplotlib.lines.Line2D at 0x7f512c594f28>]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -132,14 +132,14 @@
    ],
    "source": [
     "lwr_deploy_array, lwr_deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                     '100*(1.009)**(t/12)', 1, 960, 240, False)\n",
+    "                                                     '100*(1.009)**(t/12)', 1.11, 960, 240, False)\n",
     "power_dict['lwr'] = lwr_deployed_power\n",
     "plt.plot(lwr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,12 +165,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -200,22 +200,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fd485f6e358>]"
+       "[<matplotlib.lines.Line2D at 0x7f512c4abf28>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,12 +263,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/input/master_us/input.xml b/input/master_us/input.xml
index d10ec993..22d0a723 100644
--- a/input/master_us/input.xml
+++ b/input/master_us/input.xml
@@ -314,7 +314,7 @@
         <assem_size>29565</assem_size>
         <n_assem_core>3</n_assem_core>
         <n_assem_batch>1</n_assem_batch>
-        <power_cap>1000</power_cap>
+        <power_cap>1110</power_cap>
       </Reactor>
     </config>
   </facility>
diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
index aebf22ab..d36e0f93 100644
--- a/input/master_us/lwr_dep.xml
+++ b/input/master_us/lwr_dep.xml
@@ -48,36 +48,28 @@
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
-		<val>lwr</val>
 </prototypes>
 <build_times>
-		<val>83</val>
-		<val>96</val>
-		<val>109</val>
-		<val>122</val>
-		<val>135</val>
-		<val>148</val>
-		<val>161</val>
-		<val>173</val>
-		<val>186</val>
+		<val>84</val>
+		<val>99</val>
+		<val>114</val>
+		<val>128</val>
+		<val>142</val>
+		<val>156</val>
+		<val>170</val>
+		<val>184</val>
 		<val>193</val>
 		<val>197</val>
-		<val>200</val>
-		<val>202</val>
+		<val>201</val>
+		<val>206</val>
 		<val>210</val>
-		<val>211</val>
+		<val>212</val>
 		<val>213</val>
-		<val>217</val>
-		<val>219</val>
+		<val>218</val>
 		<val>223</val>
 		<val>229</val>
 		<val>230</val>
-		<val>232</val>
+		<val>231</val>
 		<val>235</val>
 		<val>239</val>
 		<val>241</val>
@@ -86,30 +78,26 @@
 		<val>247</val>
 		<val>248</val>
 		<val>251</val>
-		<val>252</val>
-		<val>255</val>
+		<val>254</val>
 		<val>256</val>
 		<val>257</val>
 		<val>259</val>
 		<val>261</val>
 		<val>262</val>
 		<val>264</val>
-		<val>267</val>
 		<val>271</val>
 		<val>276</val>
 		<val>277</val>
 		<val>280</val>
 		<val>282</val>
 		<val>284</val>
-		<val>291</val>
+		<val>285</val>
 		<val>292</val>
-		<val>294</val>
 		<val>299</val>
 		<val>300</val>
 		<val>303</val>
 		<val>307</val>
-		<val>308</val>
-		<val>318</val>
+		<val>309</val>
 </build_times>
 <n_build>
 		<val>1</val>
@@ -132,8 +120,6 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
-		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -143,7 +129,6 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>1</val>
 		<val>3</val>
 		<val>2</val>
 		<val>1</val>
@@ -151,16 +136,13 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
 		<val>2</val>
 		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -216,11 +198,5 @@
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
 </lifetimes>
 </root>
diff --git a/input/master_us/rebus_dep.xml b/input/master_us/rebus_dep.xml
index 1fce1013..a09cf5e6 100644
--- a/input/master_us/rebus_dep.xml
+++ b/input/master_us/rebus_dep.xml
@@ -283,376 +283,291 @@
 		<val>rebus</val>
 		<val>rebus</val>
 		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
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 </prototypes>
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-
+		<val>326</val>
+		<val>327</val>
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 </build_times>
 <n_build>
 		<val>1</val>
@@ -661,25 +576,29 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
 		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
@@ -736,6 +655,7 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -747,14 +667,12 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>3</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
@@ -766,25 +684,26 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
+		<val>3</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -803,7 +722,6 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>3</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -811,13 +729,14 @@
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>2</val>
 		<val>1</val>
@@ -830,12 +749,24 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -886,6 +817,10 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -900,6 +835,9 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -915,72 +853,6 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-
-
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>2</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>2</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>2</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
-        <val>1</val>
 </n_build>
 <lifetimes>
 		<val>720</val>
@@ -1202,48 +1074,6 @@
 		<val>720</val>
 		<val>720</val>
 		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-
 		<val>720</val>
 		<val>720</val>
 		<val>720</val>
@@ -1308,6 +1138,5 @@
 		<val>720</val>
 		<val>720</val>
 		<val>720</val>
-
 </lifetimes>
 </root>

From 4080356ec04dd11b776584271086eb829e0c6b9f Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Mon, 24 Sep 2018 15:20:53 -0500
Subject: [PATCH 17/35] init mass comp csv

---
 input/master_us/rebus_init_mass_comp.csv | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 input/master_us/rebus_init_mass_comp.csv

diff --git a/input/master_us/rebus_init_mass_comp.csv b/input/master_us/rebus_init_mass_comp.csv
new file mode 100644
index 00000000..7b1bea59
--- /dev/null
+++ b/input/master_us/rebus_init_mass_comp.csv
@@ -0,0 +1,23 @@
+Na23,0.0675240828
+Cl35,0.2675274456
+Cl37,0.0922682188
+U235,0.0034347457
+U238,0.4736160786
+Np237,0.004590661
+Pu238,0.0020371114
+Pu239,0.046231887
+Pu240,0.0212031681
+Pu241,0.0086553226
+Pu242,0.0061017702
+Am241,0.0049445494
+Am243,0.0014154566
+Cm244,0.0004112467
+Cm245,3.82555080549664E-05
+,
+Na_total,0.0675240828
+Cl_total,0.3597956644
+U_total,0.4770508243
+Np_total,0.004590661
+Pu_total,0.0842292593
+Am_total,0.006360006
+Cm_total,0.0004495022

From 8d40528b8b862c03d8943153c92c28dd2b6644b7 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Mon, 24 Sep 2018 16:09:57 -0500
Subject: [PATCH 18/35] new lifetime 60 -> 30 years

---
 input/master_us/get_deployment.ipynb |   48 +-
 input/master_us/rebus_dep.xml        | 1256 ++++++++++++++++++++------
 2 files changed, 996 insertions(+), 308 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 582f6d79..53013ecc 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -69,17 +69,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -112,10 +112,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f512c594f28>]"
+       "[<matplotlib.lines.Line2D at 0x7f7685cb34e0>]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -200,22 +200,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f512c4abf28>]"
+       "[<matplotlib.lines.Line2D at 0x7f768569f5c0>]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -227,7 +227,7 @@
    "source": [
     "# rebus\n",
     "msr_power = 3.7*0.44\n",
-    "lifetime = 60*12\n",
+    "lifetime = 30*12\n",
     "msr_deploy_array, msr_deployed_power = an.get_new_deployment(power_dict, ['us_government', 'lwr'],\n",
     "                                                     '100*(1.009)**(t/12)', msr_power, lifetime, 240, True)\n",
     "power_dict['msr'] = msr_deployed_power\n",
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,11 +247,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep.xml', 720)"
+    "an.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep.xml', 360)"
    ]
   },
   {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
diff --git a/input/master_us/rebus_dep.xml b/input/master_us/rebus_dep.xml
index a09cf5e6..5b281ae5 100644
--- a/input/master_us/rebus_dep.xml
+++ b/input/master_us/rebus_dep.xml
@@ -283,6 +283,178 @@
 		<val>rebus</val>
 		<val>rebus</val>
 		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
 </prototypes>
 <build_times>
 		<val>326</val>
@@ -337,62 +509,116 @@
 		<val>649</val>
 		<val>663</val>
 		<val>677</val>
+		<val>686</val>
+		<val>687</val>
+		<val>688</val>
 		<val>691</val>
+		<val>697</val>
+		<val>700</val>
 		<val>705</val>
+		<val>706</val>
+		<val>711</val>
+		<val>713</val>
+		<val>715</val>
 		<val>718</val>
+		<val>721</val>
+		<val>722</val>
+		<val>730</val>
+		<val>731</val>
 		<val>732</val>
+		<val>733</val>
+		<val>738</val>
+		<val>741</val>
+		<val>742</val>
 		<val>745</val>
+		<val>746</val>
+		<val>747</val>
+		<val>749</val>
+		<val>754</val>
 		<val>757</val>
+		<val>758</val>
+		<val>760</val>
 		<val>762</val>
+		<val>765</val>
+		<val>767</val>
+		<val>770</val>
+		<val>772</val>
 		<val>775</val>
+		<val>779</val>
 		<val>788</val>
+		<val>791</val>
+		<val>792</val>
+		<val>796</val>
 		<val>801</val>
+		<val>803</val>
+		<val>804</val>
 		<val>814</val>
+		<val>816</val>
 		<val>826</val>
+		<val>833</val>
 		<val>839</val>
+		<val>840</val>
 		<val>851</val>
+		<val>853</val>
 		<val>863</val>
+		<val>869</val>
+		<val>874</val>
 		<val>875</val>
 		<val>887</val>
+		<val>890</val>
 		<val>899</val>
+		<val>905</val>
 		<val>911</val>
+		<val>920</val>
 		<val>923</val>
 		<val>934</val>
+		<val>936</val>
 		<val>946</val>
+		<val>951</val>
 		<val>957</val>
+		<val>965</val>
 		<val>968</val>
 		<val>979</val>
+		<val>980</val>
 		<val>991</val>
+		<val>995</val>
 		<val>1002</val>
+		<val>1009</val>
 		<val>1012</val>
 		<val>1023</val>
 		<val>1034</val>
+		<val>1037</val>
 		<val>1044</val>
 		<val>1046</val>
 		<val>1047</val>
 		<val>1048</val>
+		<val>1051</val>
 		<val>1057</val>
 		<val>1059</val>
 		<val>1060</val>
 		<val>1062</val>
+		<val>1065</val>
 		<val>1066</val>
 		<val>1071</val>
 		<val>1072</val>
 		<val>1073</val>
 		<val>1075</val>
 		<val>1076</val>
+		<val>1078</val>
 		<val>1081</val>
 		<val>1082</val>
 		<val>1086</val>
 		<val>1089</val>
 		<val>1090</val>
 		<val>1091</val>
+		<val>1092</val>
 		<val>1093</val>
 		<val>1098</val>
 		<val>1100</val>
 		<val>1101</val>
 		<val>1102</val>
 		<val>1103</val>
+		<val>1105</val>
 		<val>1106</val>
 		<val>1107</val>
 		<val>1109</val>
@@ -402,6 +628,7 @@
 		<val>1117</val>
 		<val>1118</val>
 		<val>1120</val>
+		<val>1122</val>
 		<val>1125</val>
 		<val>1126</val>
 		<val>1127</val>
@@ -410,11 +637,13 @@
 		<val>1135</val>
 		<val>1139</val>
 		<val>1144</val>
+		<val>1148</val>
 		<val>1151</val>
 		<val>1152</val>
 		<val>1155</val>
 		<val>1156</val>
 		<val>1158</val>
+		<val>1161</val>
 		<val>1162</val>
 		<val>1163</val>
 		<val>1164</val>
@@ -425,6 +654,7 @@
 		<val>1176</val>
 		<val>1178</val>
 		<val>1183</val>
+		<val>1186</val>
 		<val>1189</val>
 		<val>1190</val>
 		<val>1191</val>
@@ -446,11 +676,13 @@
 		<val>1218</val>
 		<val>1221</val>
 		<val>1222</val>
+		<val>1223</val>
 		<val>1224</val>
 		<val>1229</val>
 		<val>1230</val>
 		<val>1233</val>
 		<val>1234</val>
+		<val>1235</val>
 		<val>1236</val>
 		<val>1237</val>
 		<val>1240</val>
@@ -466,107 +698,219 @@
 		<val>1265</val>
 		<val>1267</val>
 		<val>1269</val>
+		<val>1271</val>
 		<val>1276</val>
 		<val>1280</val>
+		<val>1283</val>
 		<val>1285</val>
 		<val>1294</val>
 		<val>1296</val>
 		<val>1303</val>
+		<val>1306</val>
 		<val>1311</val>
 		<val>1312</val>
+		<val>1317</val>
 		<val>1320</val>
 		<val>1325</val>
+		<val>1328</val>
 		<val>1329</val>
 		<val>1337</val>
+		<val>1339</val>
 		<val>1340</val>
 		<val>1346</val>
+		<val>1351</val>
 		<val>1354</val>
 		<val>1355</val>
+		<val>1362</val>
 		<val>1363</val>
 		<val>1369</val>
 		<val>1371</val>
+		<val>1372</val>
 		<val>1379</val>
 		<val>1383</val>
 		<val>1388</val>
+		<val>1394</val>
 		<val>1396</val>
 		<val>1397</val>
 		<val>1404</val>
+		<val>1406</val>
+		<val>1407</val>
+		<val>1408</val>
 		<val>1411</val>
 		<val>1412</val>
+		<val>1417</val>
+		<val>1419</val>
 		<val>1420</val>
+		<val>1422</val>
 		<val>1425</val>
+		<val>1426</val>
 		<val>1428</val>
+		<val>1431</val>
+		<val>1432</val>
+		<val>1433</val>
+		<val>1435</val>
 		<val>1436</val>
 		<val>1438</val>
+		<val>1441</val>
+		<val>1442</val>
 		<val>1444</val>
+		<val>1446</val>
+		<val>1449</val>
+		<val>1450</val>
+		<val>1451</val>
 		<val>1452</val>
+		<val>1453</val>
+		<val>1458</val>
 		<val>1460</val>
+		<val>1461</val>
+		<val>1462</val>
+		<val>1463</val>
 		<val>1465</val>
+		<val>1466</val>
 		<val>1467</val>
+		<val>1469</val>
+		<val>1473</val>
+		<val>1474</val>
 		<val>1475</val>
+		<val>1476</val>
 		<val>1477</val>
+		<val>1478</val>
+		<val>1480</val>
 		<val>1482</val>
 		<val>1483</val>
+		<val>1485</val>
+		<val>1486</val>
+		<val>1487</val>
 		<val>1490</val>
+		<val>1492</val>
 		<val>1495</val>
 		<val>1498</val>
+		<val>1499</val>
+		<val>1504</val>
 		<val>1505</val>
 		<val>1508</val>
+		<val>1511</val>
+		<val>1512</val>
 		<val>1513</val>
+		<val>1515</val>
+		<val>1516</val>
+		<val>1518</val>
 		<val>1520</val>
 		<val>1521</val>
+		<val>1522</val>
+		<val>1523</val>
+		<val>1524</val>
+		<val>1526</val>
 		<val>1528</val>
+		<val>1530</val>
+		<val>1532</val>
 		<val>1534</val>
 		<val>1535</val>
+		<val>1536</val>
+		<val>1538</val>
 		<val>1543</val>
 		<val>1546</val>
+		<val>1549</val>
 		<val>1550</val>
+		<val>1551</val>
+		<val>1553</val>
+		<val>1555</val>
 		<val>1557</val>
 		<val>1559</val>
+		<val>1560</val>
+		<val>1561</val>
+		<val>1562</val>
 		<val>1564</val>
+		<val>1565</val>
+		<val>1567</val>
+		<val>1568</val>
 		<val>1571</val>
 		<val>1572</val>
+		<val>1573</val>
+		<val>1574</val>
+		<val>1576</val>
+		<val>1577</val>
+		<val>1578</val>
 		<val>1579</val>
+		<val>1581</val>
+		<val>1582</val>
 		<val>1583</val>
+		<val>1584</val>
 		<val>1586</val>
+		<val>1589</val>
+		<val>1590</val>
 		<val>1593</val>
+		<val>1594</val>
 		<val>1595</val>
+		<val>1596</val>
+		<val>1597</val>
 		<val>1600</val>
+		<val>1602</val>
+		<val>1604</val>
 		<val>1607</val>
+		<val>1610</val>
+		<val>1612</val>
+		<val>1613</val>
 		<val>1614</val>
 		<val>1619</val>
+		<val>1620</val>
 		<val>1621</val>
+		<val>1623</val>
+		<val>1625</val>
+		<val>1627</val>
 		<val>1628</val>
+		<val>1629</val>
 		<val>1631</val>
 		<val>1635</val>
+		<val>1636</val>
+		<val>1640</val>
 		<val>1641</val>
 		<val>1643</val>
+		<val>1645</val>
 		<val>1648</val>
 		<val>1654</val>
 		<val>1655</val>
+		<val>1656</val>
 		<val>1662</val>
+		<val>1663</val>
 		<val>1666</val>
 		<val>1668</val>
+		<val>1671</val>
+		<val>1672</val>
 		<val>1675</val>
 		<val>1677</val>
+		<val>1680</val>
 		<val>1682</val>
+		<val>1685</val>
 		<val>1688</val>
+		<val>1689</val>
 		<val>1695</val>
+		<val>1697</val>
 		<val>1699</val>
+		<val>1700</val>
 		<val>1701</val>
+		<val>1706</val>
 		<val>1708</val>
 		<val>1711</val>
 		<val>1714</val>
+		<val>1715</val>
 		<val>1721</val>
 		<val>1722</val>
+		<val>1723</val>
 		<val>1727</val>
+		<val>1729</val>
+		<val>1731</val>
 		<val>1732</val>
 		<val>1733</val>
+		<val>1739</val>
 		<val>1740</val>
 		<val>1743</val>
 		<val>1746</val>
+		<val>1748</val>
 		<val>1752</val>
 		<val>1754</val>
+		<val>1756</val>
+		<val>1757</val>
 		<val>1758</val>
 </build_times>
 <n_build>
@@ -641,6 +985,54 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -650,6 +1042,7 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -675,25 +1068,53 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
 		<val>2</val>
 		<val>1</val>
+		<val>2</val>
+		<val>3</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
-		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
@@ -704,6 +1125,12 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -715,11 +1142,64 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -735,27 +1215,39 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>3</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>3</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
+		<val>3</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -769,18 +1261,23 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -788,6 +1285,7 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -798,10 +1296,20 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -831,6 +1339,12 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -847,6 +1361,8 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -855,288 +1371,460 @@
 		<val>1</val>
 </n_build>
 <lifetimes>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
-		<val>720</val>
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+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
+		<val>360</val>
 </lifetimes>
 </root>

From 03a44035b0ecc6fd4e7e4b342973b408c3b42295 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 2 Oct 2018 19:39:20 -0500
Subject: [PATCH 19/35] fourty lifetime

---
 input/master_us/input_fourty.xml     | 706 +++++++++++++++++++++++++++
 input/master_us/rebus_dep_fourty.xml | 366 ++++++++++++++
 2 files changed, 1072 insertions(+)
 create mode 100644 input/master_us/input_fourty.xml
 create mode 100644 input/master_us/rebus_dep_fourty.xml

diff --git a/input/master_us/input_fourty.xml b/input/master_us/input_fourty.xml
new file mode 100644
index 00000000..3bc53f0c
--- /dev/null
+++ b/input/master_us/input_fourty.xml
@@ -0,0 +1,706 @@
+
+<simulation xmlns:xi="http://www.w3.org/2001/XInclude">
+  <control>
+    <duration>1764</duration>
+    <startmonth>5</startmonth>
+    <startyear>2013</startyear>
+    <decay>lazy</decay>
+  </control>
+
+  <archetypes>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Source</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Sink</name>
+    </spec>
+    <spec>
+        <lib>cycamore</lib>
+        <name>Reactor</name>
+    </spec>
+    <spec>
+      <lib>agents</lib>
+      <name>NullRegion</name>
+    </spec>
+    <spec>
+      <lib>agents</lib>
+      <name>NullInst</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Separations</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>DeployInst</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Separations</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Enrichment</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Mixer</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Storage</name>
+    </spec>
+    <spec>
+      <lib>saltproc_reactor.saltproc_reactor</lib>
+      <name>saltproc_reactor</name>
+    </spec>
+  </archetypes>
+
+
+
+
+<!-- La Hague Model from Schneider & Marignac -->
+  <facility>
+    <name>reprocessing</name>
+    <config>
+      <Separations>
+         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> </feed_commods>
+         <feed_recipe>uox_used_fuel_recipe</feed_recipe>
+         <feedbuf_size>1e100</feedbuf_size>
+         <!-- 1100 tons/year 91.6 tons/timestep -->
+         <throughput>1e100</throughput>
+         <leftover_commod>reprocess_waste</leftover_commod>
+         <leftoverbuf_size>1e100</leftoverbuf_size>
+         <streams>
+          <item>
+            <commod>lwr_pu</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Pu</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_u</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>U</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_np</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Np</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_am</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Am</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_cm</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Cm</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+        </streams>
+      </Separations>
+    </config>
+  </facility>
+
+
+
+<!-- La Hague Model from Schneider & Marignac -->
+<facility>
+  <name>salt_reprocessing</name>
+  <config>
+    <Separations>
+      <feed_commods>
+        <val>fuel_out</val>
+      </feed_commods>
+      <feed_recipe>init_salt_recipe</feed_recipe>
+      <feedbuf_size>1e100</feedbuf_size>
+      <!-- 1100 tons/year 91.6 tons/timestep -->
+      <throughput>1e100</throughput>
+      <leftover_commod>salt_reprocess_waste</leftover_commod>
+      <leftoverbuf_size>1e100</leftoverbuf_size>
+      <streams>
+        <item>
+          <commod>salt_pu</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Pu</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_u</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>U</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_np</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Np</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_am</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Am</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_cm</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Cm</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_na</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Na</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_cl</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Cl</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+      </streams>
+    </Separations>
+  </config>
+</facility>
+
+
+
+<!-- Infinite UOX_Pu to MOX mixer -->
+<facility>
+    <name>rebus_fuel_fab_lwr</name>
+    <config>
+      <Mixer>
+        <in_streams>
+          <stream>
+            <info>
+              <mixing_ratio>0.067524083</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>na</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_na</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.359795664</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>cl</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_cl</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.477050824</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>dep_u</commodity>
+                <pref>1.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.00452453985</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_np</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_np</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.00195838076</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_am</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_am</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.00088198336</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_cm</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_cm</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.08827330133</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_pu</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_pu</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+        </in_streams>
+        <out_commod>init_fuel</out_commod>
+        <out_buf_size>1e100</out_buf_size>
+        <throughput>1e100</throughput>
+      </Mixer>
+    </config>
+</facility>
+
+
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>lwr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1110</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+   <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>fake_rebus</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1628</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+
+<facility>
+  <name>rebus</name>
+  <config>
+    <saltproc_reactor>
+      <init_fuel_commod>init_fuel</init_fuel_commod>
+      <final_fuel_commod>fuel_out</final_fuel_commod>
+      <fill_commod>dep_u</fill_commod>
+      <fissile_out_commod>fissile_out</fissile_out_commod>
+      <waste_commod>waste</waste_commod>
+      <db_path>./30_rebus_tru.hdf5</db_path>
+      <power_cap>1628</power_cap>
+    </saltproc_reactor>
+  </config>
+</facility>
+
+<facility>
+        <!-- Stores uox unf for 48 months (4years) -->
+    <name>uox_storage</name>
+    <config>
+        <Storage>
+            <in_commods>
+                <val>uox_waste</val>
+            </in_commods>
+            <residence_time>72</residence_time>
+            <out_commods>
+                <val>cooled_uox</val>
+            </out_commods>
+        </Storage>
+    </config>
+</facility>
+
+
+  <facility>
+    <name>enrichment</name>
+    <config>
+      <Enrichment>
+        <feed_commod>natl_u</feed_commod>
+        <feed_recipe>natl_u_recipe</feed_recipe>
+        <product_commod>uox</product_commod>
+        <tails_assay>0.003</tails_assay>
+        <tails_commod>tailings</tails_commod>
+        <swu_capacity>1e100</swu_capacity>
+        <initial_feed>1e100</initial_feed>
+      </Enrichment>
+    </config>
+  </facility>
+
+
+  <facility>
+    <name>nat_u_source</name>
+    <config>
+        <Source>
+        <outcommod>natl_u</outcommod>
+        <outrecipe>natl_u_recipe</outrecipe>
+        <throughput>1e100</throughput>
+      </Source>
+    </config>
+  </facility>
+
+<facility>
+  <name>tails_storage</name>
+  <config>
+    <Storage>
+      <in_commods>
+        <val>tailings</val>
+      </in_commods>
+      <out_commods>
+        <val>dep_u</val>
+      </out_commods>
+    </Storage>
+  </config>
+</facility>
+
+
+  <facility>
+    <name>unf_source</name>
+    <config>
+      <Source>
+        <outcommod>pre_2013_unf</outcommod>
+        <outrecipe>udb_precise</outrecipe>
+        <throughput>1e100</throughput>
+        <inventory_size>68072030</inventory_size>
+      </Source>
+    </config>
+  </facility>
+
+  <facility>
+  <name>fill_commod_source</name>
+  <config>
+    <Source>
+      <outcommod>dep_u</outcommod>
+      <outrecipe>depleted_u</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+  <facility>
+    <name>SomeSink</name>
+    <config>
+      <Sink>
+        <in_commods>
+          <val>waste</val>
+          <val>fissile_out</val>
+          <val>reprocess_waste</val>
+          <val>salt_reprocess_waste</val>
+          <val>lwr_u</val>
+          <val>salt_u</val>
+        </in_commods>
+        <capacity>1e100</capacity>
+      </Sink>
+    </config>
+  </facility>
+
+  <facility>
+    <name>na_source</name>
+    <config>
+      <Source>
+        <outcommod>na</outcommod>
+        <outrecipe>na_recipe</outrecipe>
+        <throughput>1e100</throughput>
+      </Source>
+    </config>
+  </facility>
+
+<facility>
+  <name>cl_source</name>
+  <config>
+    <Source>
+      <outcommod>cl</outcommod>
+      <outrecipe>cl_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>na_source2</name>
+  <config>
+    <Source>
+      <outcommod>na2</outcommod>
+      <outrecipe>na_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>cl_source2</name>
+  <config>
+    <Source>
+      <outcommod>cl2</outcommod>
+      <outrecipe>cl_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>init_fuel_source</name>
+  <config>
+    <Source>
+      <outcommod>init_fuel</outcommod>
+      <outrecipe>init_salt_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+  <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
+
+  <region>
+    <name>us</name>
+    <config><NullRegion/></config>
+    <institution>
+      <name>us_government</name>
+      <config>
+        <DeployInst>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/prototypes)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/build_times)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/n_build)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/lifetimes)'/>
+        </DeployInst>
+      </config>
+    </institution>
+
+    <institution>
+    <name>lwr_inst</name>
+    <config>
+      <DeployInst>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+      </DeployInst>
+    </config>
+  </institution>
+
+  <institution>
+  <name>rebus_inst</name>
+    <config>
+    <DeployInst>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/prototypes)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/build_times)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/n_build)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/lifetimes)'/>
+    </DeployInst>
+    </config>
+  </institution>
+
+ </region>
+
+
+<region>
+    <name>Sink_Source</name>
+    <config><NullRegion/></config>
+        <institution>
+         <name>Sink_Source_Inst</name>
+         <config><NullInst/></config>
+        <initialfacilitylist>
+          <entry>
+            <number>1</number>
+            <prototype>nat_u_source</prototype>
+          </entry>
+          
+          <entry>
+            <number>1</number>
+            <prototype>enrichment</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>SomeSink</prototype>
+          </entry>
+
+
+          <entry>
+            <number>1</number>
+            <prototype>salt_reprocessing</prototype>
+          </entry>
+
+          
+          <entry>
+            <number>1</number>
+            <prototype>reprocessing</prototype>
+          </entry>
+
+          <entry>
+            <number>1</number>
+            <prototype>rebus_fuel_fab_lwr</prototype>
+          </entry>
+
+          <entry>
+            <number>1</number>
+            <prototype>uox_storage</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>unf_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>tails_storage</prototype>
+          </entry>
+
+<!--
+          <entry>
+            <number>1</number>
+            <prototype>init_fuel_source</prototype>
+          </entry>
+
+          <entry>
+            <number>1</number>
+            <prototype>fill_commod_source</prototype>
+          </entry>
+-->
+          <entry>
+            <number>1</number>
+            <prototype>cl_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>na_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>cl_source2</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>na_source2</prototype>
+          </entry>
+
+        </initialfacilitylist>
+        </institution>
+</region>
+<xi:include href='recipes.xml' xpointer='xpointer(/root/recipe)'/>
+</simulation>
\ No newline at end of file
diff --git a/input/master_us/rebus_dep_fourty.xml b/input/master_us/rebus_dep_fourty.xml
new file mode 100644
index 00000000..95069f3f
--- /dev/null
+++ b/input/master_us/rebus_dep_fourty.xml
@@ -0,0 +1,366 @@
+<root>
+<prototypes>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+		<val>rebus</val>
+</prototypes>
+<build_times>
+		<val>443</val>
+		<val>444</val>
+		<val>513</val>
+		<val>757</val>
+		<val>923</val>
+		<val>924</val>
+		<val>993</val>
+		<val>1159</val>
+		<val>1161</val>
+		<val>1173</val>
+		<val>1178</val>
+		<val>1189</val>
+		<val>1190</val>
+		<val>1199</val>
+		<val>1201</val>
+		<val>1202</val>
+		<val>1205</val>
+		<val>1207</val>
+		<val>1212</val>
+		<val>1214</val>
+		<val>1216</val>
+		<val>1217</val>
+		<val>1218</val>
+		<val>1221</val>
+		<val>1224</val>
+		<val>1236</val>
+		<val>1237</val>
+		<val>1240</val>
+		<val>1244</val>
+		<val>1252</val>
+		<val>1260</val>
+		<val>1263</val>
+		<val>1286</val>
+		<val>1287</val>
+		<val>1297</val>
+		<val>1300</val>
+		<val>1311</val>
+		<val>1313</val>
+		<val>1315</val>
+		<val>1321</val>
+		<val>1330</val>
+		<val>1331</val>
+		<val>1333</val>
+		<val>1338</val>
+		<val>1341</val>
+		<val>1342</val>
+		<val>1343</val>
+		<val>1346</val>
+		<val>1347</val>
+		<val>1351</val>
+		<val>1354</val>
+		<val>1357</val>
+		<val>1359</val>
+		<val>1365</val>
+		<val>1367</val>
+		<val>1370</val>
+		<val>1372</val>
+		<val>1375</val>
+		<val>1379</val>
+		<val>1391</val>
+		<val>1396</val>
+		<val>1403</val>
+		<val>1404</val>
+		<val>1473</val>
+		<val>1639</val>
+		<val>1641</val>
+		<val>1653</val>
+		<val>1658</val>
+		<val>1669</val>
+		<val>1670</val>
+		<val>1679</val>
+		<val>1681</val>
+		<val>1682</val>
+		<val>1685</val>
+		<val>1687</val>
+		<val>1692</val>
+		<val>1694</val>
+		<val>1696</val>
+		<val>1697</val>
+		<val>1698</val>
+		<val>1701</val>
+		<val>1704</val>
+		<val>1716</val>
+		<val>1717</val>
+		<val>1720</val>
+		<val>1724</val>
+		<val>1732</val>
+		<val>1740</val>
+		<val>1743</val>
+</build_times>
+<n_build>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+</n_build>
+<lifetimes>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+</lifetimes>
+</root>

From b95c8837f9647a4f26b5c162ef6b5767dd287b2c Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 2 Oct 2018 19:40:45 -0500
Subject: [PATCH 20/35] Analysis ipynb file

---
 input/master_us/analysis_msr.ipynb | 1146 ++++++++++++++++++++++++++++
 1 file changed, 1146 insertions(+)
 create mode 100644 input/master_us/analysis_msr.ipynb

diff --git a/input/master_us/analysis_msr.ipynb b/input/master_us/analysis_msr.ipynb
new file mode 100644
index 00000000..37e58ce6
--- /dev/null
+++ b/input/master_us/analysis_msr.ipynb
@@ -0,0 +1,1146 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import Necessary Module and Set Path "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/teddy/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.data is not yet QA compliant.\n",
+      "  return f(*args, **kwds)\n",
+      "/home/teddy/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.material is not yet QA compliant.\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sqlite3 as lite\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "from IPython.display import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import collections\n",
+    "sys.path.insert(0, '../../scripts/')\n",
+    "jupyter_directory = os.getcwd()\n",
+    "from decimal import Decimal\n",
+    "from pyne.material import Material"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import analysis as an"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get sqlite cursor and simulation time data\n",
+    "cur = an.get_cursor('one_fuelfab.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "power_dict = an.get_power_dict(cur)\n",
+    "pow = {'legacy lwrs': power_dict['us_government'], 'lwr': power_dict['lwr_inst'], 'msr': power_dict['rebus_inst']}\n",
+    "an.stacked_bar_chart(pow, timestep, 'Year', 'Net Capacity [GWe]', 'Installed nuclear capacity in the U.S.',\n",
+    "                     'power_plot.png', init_year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('power_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total deployed:\n",
+      "lwr: 84\n",
+      "rebus: 95\n"
+     ]
+    }
+   ],
+   "source": [
+    "# total lwrs and msrs deployed\n",
+    "\n",
+    "lwrs = cur.execute('SELECT entertime, lifetime/lifetime from agententry where prototype = \"lwr\"').fetchall()\n",
+    "rebus = cur.execute('SELECT entertime, lifetime/lifetime from agententry where prototype = \"rebus\"').fetchall()\n",
+    "lwrs = an.get_timeseries(lwrs, duration, False)\n",
+    "rebus = an.get_timeseries(rebus, duration, False)\n",
+    "years = 2020 + np.arange(0, duration)/12\n",
+    "plt.plot(years, lwrs, label='LWR deployment')\n",
+    "plt.plot(years, rebus, label='REBUS deployment')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Num. reactors built')\n",
+    "plt.legend()\n",
+    "plt.title('Reactor constuction over time')\n",
+    "plt.savefig('us_dep.png', format='png')\n",
+    "plt.show()\n",
+    "\n",
+    "print('Total deployed:')\n",
+    "print('lwr:', sum(lwrs))\n",
+    "print('rebus:', sum(rebus))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyne import nucname\n",
+    "def get_tru_massfrac(commodity):\n",
+    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
+    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
+    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
+    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
+    "    comp_dict = {}\n",
+    "    for row in massfracs:\n",
+    "        if int(row['nucid']) < 930000000 or int(row['nucid']) > 970000000:\n",
+    "            continue\n",
+    "        key = nucname.name(row['nucid'])\n",
+    "        comp_dict[key] = row['massfrac']\n",
+    "    return comp_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_massfrac(commodity):\n",
+    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
+    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
+    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
+    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
+    "    comp_dict = {}\n",
+    "    for row in massfracs:\n",
+    "        key = nucname.name(row['nucid'])\n",
+    "        comp_dict[key] = row['massfrac']\n",
+    "    return comp_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def nansum_dict(dictionary):\n",
+    "    tot = 0\n",
+    "    for key, val in dictionary.items():\n",
+    "        if math.isnan(val):\n",
+    "            continue\n",
+    "        tot += val\n",
+    "    return tot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tru_massfrac_to_vector(comp_dict):\n",
+    "    tot = sum(comp_dict.values())\n",
+    "    for key, val in comp_dict.items():\n",
+    "        comp_dict[key] = val / tot\n",
+    "    return comp_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRU composition in Spent and initial Fuels:\n",
+    "lwr_unf_tru_comp = get_tru_massfrac('uox_waste')\n",
+    "init_salt_tru_comp = get_tru_massfrac('init_fuel')\n",
+    "eol_salt_tru_comp = get_tru_massfrac('fuel_out')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isotope & Beginning of Life & End of Life \\\\ \n",
+      "Np237 & 4.55E-03 & 5.93E-04\\\\ \n",
+      "Pu238 & 1.74E-03 & 1.90E-03\\\\ \n",
+      "Pu239 & 5.71E-02 & 5.21E-02\\\\ \n",
+      "Pu240 & 1.98E-02 & 2.82E-02\\\\ \n",
+      "Pu241 & 3.37E-03 & 3.29E-03\\\\ \n",
+      "Pu242 & 4.61E-03 & 4.00E-03\\\\ \n",
+      "Am241 & 3.19E-03 & 1.63E-03\\\\ \n",
+      "Am243 & 2.71E-04 & 1.23E-03\\\\ \n",
+      "Cm244 & 7.95E-04 & 1.05E-03\\\\ \n",
+      "Cm245 & 6.75E-05 & 2.79E-04\\\\ \n",
+      "Cm246 & 8.60E-06 & 1.32E-04\\\\ \n",
+      "Total & 0.09550070073265847 & 0.09454567570593689 \\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TRU Composition\n",
+    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
+    "for key, val in eol_salt_tru_comp.items():\n",
+    "    if val < 1e-04:\n",
+    "        continue\n",
+    "    string += key + ' & '\n",
+    "    try:\n",
+    "        bol_comp = init_salt_tru_comp[key]\n",
+    "    except:\n",
+    "        bol_comp = 0\n",
+    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
+    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
+    "\n",
+    "string += 'Total & ' + str(sum(init_salt_tru_comp.values())) + ' & ' + str(sum(eol_salt_tru_comp.values())) + ' \\\\\\ \\n'\n",
+    "print(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Np237': 0.04730891560586033, 'Pu238': 0.022637142727796017, 'Pu239': 0.47107005808831404, 'Pu240': 0.2256725335415319, 'Pu241': 0.13652178893365427, 'Pu242': 0.06708826924103727, 'Pu244': 2.2248857792867743e-06, 'Am241': 0.005003469136455384, 'Am242M': 6.627286296835367e-05, 'Am243': 0.01540723912331845, 'Cm242': 0.002011327807452964, 'Cm243': 5.98765538193152e-05, 'Cm244': 0.00664903038124026, 'Cm245': 0.000444355896302929, 'Cm246': 5.6658871363617076e-05, 'Cm247': 7.766089989535045e-07, 'Cm248': 5.973410615802391e-08, 'Cm250': 3.3245151906201296e-16}\n",
+      "{'Np237': 0.047694674074704346, 'Np238': 3.1547272998811375e-12, 'Np239': 2.4474266707031783e-09, 'Np240': 7.412727783286107e-18, 'Pu238': 0.018233523192184523, 'Pu239': 0.5980201067700567, 'Pu240': 0.20701633286070506, 'Pu241': 0.035311239406065065, 'Pu242': 0.04826525540574554, 'Pu243': 4.406018586348782e-17, 'Pu244': 5.129170626197411e-06, 'Pu246': 2.8704079479690376e-22, 'Am241': 0.03338640393104139, 'Am242': 2.2566782426777774e-10, 'Am242M': 1.7489637802281043e-05, 'Am243': 0.0028409259133844954, 'Am246': 7.1686016799512355e-25, 'Cm242': 4.7261617371794586e-07, 'Cm243': 8.210008652383034e-05, 'Cm244': 0.008328213213882446, 'Cm245': 0.0007067238269110971, 'Cm246': 9.007631433317926e-05, 'Cm247': 1.2357899413626694e-06, 'Cm248': 9.511366897445526e-08, 'Cm250': 5.2874198396126595e-16}\n",
+      "{'Np233': 6.157196175208336e-24, 'Np234': 4.583617746948512e-16, 'Np235': 2.6659256497810293e-10, 'Np236': 4.985598768746053e-07, 'Np236M': 2.2312706910603108e-14, 'Np237': 0.0062718087534495825, 'Np238': 3.7789505796830005e-06, 'Np239': 0.0005460648617620473, 'Np240': 3.0887195506134726e-09, 'Np240M': 6.312646974496896e-10, 'Pu234': 1.0852076936223005e-21, 'Pu235': 8.423767299125435e-19, 'Pu236': 3.624380833961301e-09, 'Pu237': 5.361519500289506e-08, 'Pu237M': 1.80203669526797e-19, 'Pu238': 0.020085946790079637, 'Pu239': 0.5511388110838523, 'Pu240': 0.29807470071188963, 'Pu241': 0.034771857415978324, 'Pu242': 0.042313479869671, 'Pu243': 7.695283430543524e-07, 'Pu244': 1.2328788133185257e-05, 'Pu245': 2.0509433422871233e-10, 'Pu246': 1.0421567617761837e-17, 'Am238': 3.6174765435627224e-27, 'Am239': 6.080836990675323e-14, 'Am240': 8.041794251405208e-10, 'Am241': 0.0172221632501385, 'Am242': 3.5380029924373973e-06, 'Am242M': 9.721435432111467e-06, 'Am243': 0.013062032531247138, 'Am244': 1.0220812397620013e-07, 'Am244M': 6.568549557997798e-08, 'Am245': 4.1433833281083774e-11, 'Cm238': 5.905965211134074e-27, 'Cm239': 4.994177728304086e-22, 'Cm240': 1.3773681136219832e-12, 'Cm241': 4.0484558663567137e-10, 'Cm242': 0.0006956222561349118, 'Cm243': 7.994022143478682e-05, 'Cm244': 0.011102484640905701, 'Cm244M': 9.557346675082354e-14, 'Cm245': 0.00294625448239031, 'Cm246': 0.001391267931319118, 'Cm247': 0.00020582415047767862, 'Cm248': 6.087506234049294e-05, 'Cm249': 1.3510786814377174e-10, 'Cm250': 9.58143806848894e-12, 'Cm251': 2.808716671115108e-18}\n"
+     ]
+    }
+   ],
+   "source": [
+    "lwr_unf_tru_vec = tru_massfrac_to_vector(lwr_unf_tru_comp)\n",
+    "init_salt_tru_vec = tru_massfrac_to_vector(init_salt_tru_comp)\n",
+    "eol_salt_tru_vec = tru_massfrac_to_vector(eol_salt_tru_comp)\n",
+    "\n",
+    "print(lwr_unf_tru_vec)\n",
+    "print(init_salt_tru_vec)\n",
+    "print(eol_salt_tru_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare decay heat / activity of init and final fuel\n",
+    "init_salt_mat = Material(init_salt_tru_vec) * 132659000 * 0.095598353\n",
+    "eol_salt_mat = Material(eol_salt_tru_vec) * 132659000 * 0.09552035\n",
+    "years = np.arange(0, 3000, 100)\n",
+    "init_decay_heat = np.zeros(len(years))\n",
+    "eol_decay_heat = np.zeros(len(years))\n",
+    "for indx, year in enumerate(years):\n",
+    "    sec = year * 365 * 24 * 3600\n",
+    "    decayed_init_mat = init_salt_mat.decay(sec)\n",
+    "    decayed_eol_mat = eol_salt_mat.decay(sec)\n",
+    "    init_decay_heat[indx] = nansum_dict(decayed_init_mat.decay_heat()) * 1e-3\n",
+    "    eol_decay_heat[indx] = nansum_dict(decayed_eol_mat.decay_heat()) * 1e-3\n",
+    "\n",
+    "plt.semilogy(years, init_decay_heat, label='init fuel TRU decay heat')\n",
+    "plt.semilogy(years, eol_decay_heat, label='eol fuel TRU decay heat')\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Decay Heat [kW] normalized by mass')\n",
+    "plt.title('Decay heat of initial and eol fuel salt over time')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isotope & Beginning of Life & End of Life \\\\ \n",
+      "Np237 & 4.77E-02 & 6.27E-03\\\\ \n",
+      "Np239 & 2.45E-09 & 5.46E-04\\\\ \n",
+      "Pu238 & 1.82E-02 & 2.01E-02\\\\ \n",
+      "Pu239 & 5.98E-01 & 5.51E-01\\\\ \n",
+      "Pu240 & 2.07E-01 & 2.98E-01\\\\ \n",
+      "Pu241 & 3.53E-02 & 3.48E-02\\\\ \n",
+      "Pu242 & 4.83E-02 & 4.23E-02\\\\ \n",
+      "Am241 & 3.34E-02 & 1.72E-02\\\\ \n",
+      "Am243 & 2.84E-03 & 1.31E-02\\\\ \n",
+      "Cm242 & 4.73E-07 & 6.96E-04\\\\ \n",
+      "Cm244 & 8.33E-03 & 1.11E-02\\\\ \n",
+      "Cm245 & 7.07E-04 & 2.95E-03\\\\ \n",
+      "Cm246 & 9.01E-05 & 1.39E-03\\\\ \n",
+      "Cm247 & 1.24E-06 & 2.06E-04\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TRU vector\n",
+    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
+    "for key, val in eol_salt_tru_vec.items():\n",
+    "    if val < 1e-04:\n",
+    "        continue\n",
+    "    string += key + ' & '\n",
+    "    try:\n",
+    "        bol_comp = init_salt_tru_vec[key]\n",
+    "    except:\n",
+    "        bol_comp = 0\n",
+    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
+    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
+    "\n",
+    "print(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lwr_unf_tru_comp = get_tru_massfrac('uox_waste')\n",
+    "init_salt_tru_comp = get_tru_massfrac('init_fuel')\n",
+    "eol_salt_tru_comp = get_tru_massfrac('fuel_out')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US UNF UOX generated in 2013-2050: 78281.510000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# French UOX UNF total, in sink, and reprocessed\n",
+    "agent_ids = an.get_agent_ids(cur, 'Reactor')\n",
+    "uox_waste = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'], True, True)\n",
+    "print('US UNF UOX generated in 2013-2050: %f' %uox_waste['uox_waste'][439])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US legacy UOX UNF in 2013: 68072.030000\n"
+     ]
+    }
+   ],
+   "source": [
+    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
+    "legacy_waste = an.facility_commodity_flux(cur, agent_ids, ['pre_2013_unf'], True, True)\n",
+    "print('US legacy UOX UNF in 2013: %f' %legacy_waste['pre_2013_unf'][1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US UNF reprocesed up until 2050: 128974.696000\n"
+     ]
+    }
+   ],
+   "source": [
+    "agent_ids = an.get_prototype_id(cur, 'reprocessing')\n",
+    "us_rep = an.facility_commodity_flux(cur, agent_ids, ['cooled_uox', 'pre_2013_unf'], False, True)\n",
+    "print('US UNF reprocesed up until 2050: %f' %(us_rep['cooled_uox'][439] + us_rep['cooled_uox'][439]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TOTAL UOX UNF inventory in US in 2050: 146353.540000\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_unf = uox_waste['uox_waste'][439] + legacy_waste['pre_2013_unf'][1]\n",
+    "print('TOTAL UOX UNF inventory in US in 2050: %f' %total_unf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total init salt mass sent to MSRs: 0.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f4dd7c24358>]"
+      ]
+     },
+     "execution_count": 263,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Y76KaT7PBbz5PvYHX04OFa6sPAye651f/8xsAXx6p+0sWbkDOAX8x5jgbKRhWvWYW/iIr4EfAU93jI9Ne02us9c+An7DwzZXbu7G7gA902+ex8I2UOeD7wDtGXnt797rjnKPfvJrkmoG/Af575HN9Ctg+7fWs5Wc8cowNHwz+8lmS1PBbSZKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWr8L4G+I6VKUcyzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Salt created\n",
+    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
+    "init_salt = an.facility_commodity_flux(cur, agent_ids, ['init_fuel'], True, False)\n",
+    "print('Total init salt mass sent to MSRs: %f' %sum(init_salt['init_fuel']))\n",
+    "plt.plot(init_salt['init_fuel'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total TRU extracted: 0 MTHM of TRU\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Y76KaT7PBbz5PvYHX04OFa6sPAye651f/8xsAXx6p+0sWbkDOAX8x5jgbKRhWvWYW/iIr4EfAU93jI9Ne02us9c+An7DwzZXbu7G7gA902+ex8I2UOeD7wDtGXnt797rjnKPfvJrkmoG/Af575HN9Ctg+7fWs5Wc8cowNHwz+8lmS1PBbSZKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWr8L4G+I6VKUcyzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# reprocessed TRU -> LWR UNF\n",
+    "init_salt_tru = np.array(init_salt['init_fuel']) * (sum(init_salt_tru_comp.values()))\n",
+    "plt.plot(init_salt_tru)\n",
+    "print('Total TRU extracted:', sum(init_salt_tru), 'MTHM of TRU')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total LWR UNF reprocessed: 0 MTHM\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lwr_unf_reprocessed = init_salt_tru / sum(lwr_unf_tru_comp.values())\n",
+    "plt.plot(lwr_unf_reprocessed)\n",
+    "print('Total LWR UNF reprocessed:', sum(lwr_unf_reprocessed), 'MTHM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average monthly reprocessing of LWR UNF: 0.0 MTHM/month from 2040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# average reprocessing\n",
+    "avg_monthly_rep = sum(lwr_unf_reprocessed) / 1433\n",
+    "print('Average monthly reprocessing of LWR UNF:', avg_monthly_rep, 'MTHM/month from 2040')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146353.53999999998 MTHM of LWR UNF\n"
+     ]
+    }
+   ],
+   "source": [
+    "# LWR UNF leftover after reprocessing:\n",
+    "leftover = total_unf - sum(lwr_unf_reprocessed)\n",
+    "print(leftover, 'MTHM of LWR UNF')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average monthly fabrication of fuel salt: 0.0 MTHM/month from 2040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# average salt fab\n",
+    "avg_monthly_fab = sum(init_salt['init_fuel']) / 1433\n",
+    "print('Average monthly fabrication of fuel salt:', avg_monthly_fab, 'MTHM/month from 2040')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'numpy.float64' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-269-ed56a367b659>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mrepu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0man\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfacility_commodity_flux\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'lwr_u'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'salt_u'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtotal_repu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lwr_u'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'salt_u'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Total reprocessed uranium stockpile: %f'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_repu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'numpy.float64' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "# Total reprocessed U stockpile\n",
+    "agent_ids = an.get_agent_ids(cur, 'Sink')\n",
+    "repu = an.facility_commodity_flux(cur, agent_ids, ['lwr_u', 'salt_u'], False, True)\n",
+    "total_repu = max(repu['lwr_u']) + max(repu['salt_u'])\n",
+    "print('Total reprocessed uranium stockpile: %f' %max(total_repu))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Total Raffinate\n",
+    "raff = an.facility_commodity_flux(cur, agent_ids, ['reprocess_waste', 'salt_reprocess_waste'], False, True)\n",
+    "total_raff = max(raff['reprocess_waste']) + max(raff['salt_reprocess_waste'])\n",
+    "print('Total raffinate stockpile: %f' %max(total_raff))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Waste from MSR\n",
+    "waste = an.facility_commodity_flux(cur, agent_ids, ['waste'], False, False)\n",
+    "plt.plot(waste['waste'])\n",
+    "tot_waste = sum(waste['waste'])\n",
+    "print('Total waste from MSRs: %f' %tot_waste + ' MTHM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tails accumulation\n",
+    "agent_ids = an.get_agent_ids(cur, 'Enrichment')\n",
+    "tails = an.facility_commodity_flux(cur, agent_ids, ['tails'], True, False)\n",
+    "tails_accum = sum(tails['tails'])\n",
+    "print('Total tails: %f' %tails_accum)\n",
+    "\n",
+    "# Tails usage\n",
+    "for_init_salt = sum(init_salt['init_fuel']) * 0.477050824\n",
+    "\n",
+    "# or storage\n",
+    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
+    "fill = an.facility_commodity_flux(cur, agent_ids, ['dep_u'], True, False)\n",
+    "for_fill_commod = sum(fill['dep_u'])\n",
+    "tot = for_init_salt + for_fill_commod\n",
+    "print('Total tails used = (for init salt) + (for fill commod)')\n",
+    "print('Total tails used = %f + %f = %f' %(for_init_salt, for_fill_commod, tot))\n",
+    "\n",
+    "# total outstanding tails\n",
+    "print('Total outstanding tails inventory = %f' %(tails_accum - tot))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Natural U used until 2050\n",
+    "source = an.nat_u_timeseries(cur)\n",
+    "print('Total natural U: %f' %max(source))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ===================================="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get total fuel mass vs time plot up until 2050\n",
+    "fuel_dict = an.fuel_usage_timeseries(cur, ['uox'], True)\n",
+    "fuel_dict['uox'] = fuel_dict['uox']\n",
+    "an.stacked_bar_chart(fuel_dict, timestep,\n",
+    "                  'Years', 'Mass[MTHM]',\n",
+    "                  'Total LWR Fuel Mass vs Time',\n",
+    "                  'total_fuel',\n",
+    "                  init_year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get astrid fuel discharge vs time plot\n",
+    "storageids = an.get_agent_ids(cur, 'storage')\n",
+    "astrid_discharge = an.facility_commodity_flux(cur, storageids, ['astrid_unf'], False, False)\n",
+    "an.stacked_bar_chart(astrid_discharge, timestep,\n",
+    "                  'Years', 'Mass[MTHM]',\n",
+    "                  'ASTRID UNF discharge vs Time',\n",
+    "                  'astrid_unf',\n",
+    "                  init_year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total ASTRID fuel from spent UOX:\n",
+      "17795.32799999986\n",
+      "Total ASTRID fuel from spent ASTRID fuel:\n",
+      "57790.27199999988\n"
+     ]
+    }
+   ],
+   "source": [
+    "# where does the astrid fuel come from?\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
+    "\n",
+    "agent_ids = an.get_agent_ids(cur, 'Reactor')\n",
+    "astrid_loading = an.facility_commodity_flux(cur, agent_ids, ['astrid_fuel_legacy', 'astrid_fuel_new'], False, False)\n",
+    "\n",
+    "print('Total ASTRID fuel from spent UOX:')\n",
+    "print(sum(astrid_loading['astrid_fuel_legacy']))\n",
+    "print('Total ASTRID fuel from spent ASTRID fuel:')\n",
+    "print(sum(astrid_loading['astrid_fuel_new']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "new_dict = collections.OrderedDict()\n",
+    "new_dict = {'fuel from used uox': astrid_loading['astrid_fuel_legacy'],\n",
+    "            'fuel from used mox': astrid_loading['astrid_fuel_new']}\n",
+    "an.stacked_bar_chart(new_dict, timestep,\n",
+    "                  'Years', 'Mass[MTHM]',\n",
+    "                  'ASTRID Fuel Loading vs Time',\n",
+    "                  'where_fuel',\n",
+    "                  init_year)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Pu from Spent UOX:\n",
+      "2669.9276880810953\n",
+      "Total Pu from Spent ASTRID Fuel:\n",
+      "19883.9008139658\n",
+      "Total Pu Reprocessed:\n",
+      "22553.828502046894\n"
+     ]
+    }
+   ],
+   "source": [
+    "# cum plutonium flux\n",
+    "agent_ids = an.get_agent_ids(cur, 'separations')\n",
+    "pu = an.facility_commodity_flux(cur, agent_ids, ['lwr_pu', 'sfr_pu'], True, False)\n",
+    "pu['pu from legacy'] = pu.pop('lwr_pu')\n",
+    "pu['pu from spent sfr fuel'] = pu.pop('sfr_pu')\n",
+    "an.stacked_bar_chart(pu, timestep, 'Years', 'Mass [MTHM]', 'Total Pu Mass vs Time', 'pu', init_year)\n",
+    "\n",
+    "print('Total Pu from Spent UOX:')\n",
+    "print(sum(pu['pu from legacy']))\n",
+    "print('Total Pu from Spent ASTRID Fuel:')\n",
+    "print(sum(pu['pu from spent sfr fuel']))\n",
+    "print('Total Pu Reprocessed:')\n",
+    "print(sum(pu['pu from spent sfr fuel']) + sum(pu['pu from legacy']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Reprocessed U Inventory:\n",
+      "159383.763159\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Total Reprocessed Uranium Stockpile\n",
+    "agent_ids = an.get_agent_ids(cur, 'sink')\n",
+    "repu_inv = an.facility_commodity_flux(cur, agent_ids, ['uox_U', 'astrid_U'], False, True)\n",
+    "an.stacked_bar_chart(repu_inv, timestep, 'Years', 'Mass[MTHM]', 'Total Rep U Mass vs Time', 'repu', init_year)\n",
+    "\n",
+    "print('Total Reprocessed U Inventory:')\n",
+    "print(max([x+y for x, y in zip(repu_inv['uox_U'], repu_inv['astrid_U'])]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Raffinate:\n",
+      "24789.4271143\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Raffinate\n",
+    "agent_ids = an.get_agent_ids(cur, 'separations')\n",
+    "reprocess_waste = an.facility_commodity_flux(cur, agent_ids, ['lahague_raffinate', 'uox_raffinate', 'astrid_raffinate'], True, False)\n",
+    "an.stacked_bar_chart(reprocess_waste, timestep, 'Years', 'Mass [MTHM]',\n",
+    "                     'Raffinate Inventory vs Time', 'raffinate', init_year)\n",
+    "print('Total Raffinate:')\n",
+    "print(sum([x+y+z for x, y, z in zip(reprocess_waste['lahague_raffinate'], reprocess_waste['uox_raffinate'], reprocess_waste['astrid_raffinate']\n",
+    ")]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total UOX Waste in MTHM:\n",
+      "176640.413\n",
+      "Total UOX Waste Stored:\n",
+      "110045.923\n",
+      "Total MOX Waste in MTHM:\n",
+      "6945.558\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get spent fuel no_cum and cumulative plot\n",
+    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
+    "snf_dict_no_cum = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'],\n",
+    "                                        False, False)\n",
+    "snf_dict = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'],\n",
+    "                                        False, True) \n",
+    "dictionary = {}\n",
+    "dictionary['monthly_discharge'] = snf_dict_no_cum['uox_waste'][:960]\n",
+    "dict2 = {}\n",
+    "dict2['cum_mass'] = snf_dict['uox_waste'][:960]\n",
+    "\n",
+    "an.double_axis_bar_line_plot(dictionary,dict2, timestep[:960], 'Years',  'Monthly Discharge Mass [MTHM]','Cumulative Mass [MTHM]',\n",
+    "                    'Spent Fuel Discharge vs Time', 'snf_discharge', init_year)\n",
+    "\n",
+    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
+    "snf_dict = an.facility_commodity_flux(cur, agent_ids, ['uox_waste', 'french_uox_waste'],\n",
+    "                                        False)\n",
+    "print('Total UOX Waste in MTHM:')\n",
+    "print(max([x + y for x, y in zip(snf_dict['uox_waste'], snf_dict['french_uox_waste'])]))\n",
+    "print('Total UOX Waste Stored:')\n",
+    "print(max(snf_dict['uox_waste']))\n",
+    "agent_ids = an.get_agent_ids(cur, 'Sink')\n",
+    "snf_dict = an.facility_commodity_flux(cur, agent_ids, ['mox_waste'],\n",
+    "                                        False)\n",
+    "print('Total MOX Waste in MTHM:')\n",
+    "print(max(snf_dict['mox_waste']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Natural U in MTHM:\n",
+      "1246226.87843\n",
+      "Total UOX usage in MTHM:\n",
+      "176640.413\n",
+      "Total MOX usage in MTHM:\n",
+      "6945.558\n",
+      "Total Tailings in MTHM:\n",
+      "1069586.46543\n",
+      "Finished!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display important simulation results\n",
+    "\n",
+    "# natural uranium demand\n",
+    "dictionary = collections.OrderedDict()\n",
+    "dictionary['natural_uranium'] = an.nat_u_timeseries(cur)\n",
+    "print('Total Natural U in MTHM:')\n",
+    "print(max(dictionary['natural_uranium']))\n",
+    "\n",
+    "# get spent fuel timeseries separated by region\n",
+    "\n",
+    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
+    "comm_dict = an.commodity_flux_region(cur, agent_ids, ['uox_waste', 'french_uox_waste'], False)\n",
+    "\n",
+    "# get fuel usage timeseries in stacked bar chart\n",
+    "fuel_dict = an.fuel_usage_timeseries(cur, ['uox', 'mox'])\n",
+    "\n",
+    "print('Total UOX usage in MTHM:')\n",
+    "print(max(fuel_dict['uox']))\n",
+    "print('Total MOX usage in MTHM:')\n",
+    "print(max(fuel_dict['mox']))\n",
+    "\n",
+    "# get tailings timeseries\n",
+    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
+    "tails_dict = an.facility_commodity_flux(cur, agent_ids, ['tails'], False)\n",
+    "print('Total Tailings in MTHM:')\n",
+    "print(max(tails_dict['tails']))\n",
+    "\n",
+    "print('Finished!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cvHgRlUqFtbU1JiYmSsu7sjz33HO8+eabXLp0CcgbBLhz585KjUFUX++++y7Ozs64ubnh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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display Net Capacity vs Time\n",
+    "from IPython.display import Image\n",
+    "Image(filename='power_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ld33dunW1XrNs2TL69+/P5cuXOXDggNZ7VjeS3A1AV7+3rr7yvPdKozjXGTKp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\nTgghhBiZ/wdObBQ8ztF8zwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display Number of Reactors vs Time\n",
+    "from IPython.display import Image\n",
+    "Image(filename='num_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AqEuXgMaNyZCkeDgxjLGYNQv45x9ymunZk8agdbE+aJ+YF6AAbomJQGBgAUQ1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\nCAAw2he3G2o+0RQFngAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display natural U demand  vs Time\n",
+    "from IPython.display import Image\n",
+    "Image(filename='cum_mass_snf_discharge.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JeRv2e6pXr8p86JAhMHCgNTnCjRuiUPv2hS5dMh4vXVoUKsiY0o4dsh0SYu21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\niEzyD+dE5p/A+1CGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display spent fuel vs Time\n",
+    "from IPython.display import Image\n",
+    "Image(filename='tails_tailings.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qc3Jy1O1yc3NxdXWt0re2bTp37kxJSQllZWXV+v6xT1lZGRcvXqw2DS2EuDu5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\ngQSqEEIIYQQSqEIIIYQR/H9i7ey4/wBKhwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display total fuel usage  vs Time\n",
+    "from IPython.display import Image\n",
+    "Image(filename='total_fuel.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From f59c581216986c3ad9b2f82ee73bf62830103e66 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 9 Oct 2018 14:09:27 -0500
Subject: [PATCH 21/35] jupyter notebook to check output validity

---
 input/master_us/check_output.ipynb | 205 +++++++++++++++++++++++++++++
 1 file changed, 205 insertions(+)
 create mode 100644 input/master_us/check_output.ipynb

diff --git a/input/master_us/check_output.ipynb b/input/master_us/check_output.ipynb
new file mode 100644
index 00000000..ab2b3c0d
--- /dev/null
+++ b/input/master_us/check_output.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3 as lite\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import collections\n",
+    "sys.path.insert(0, '../../scripts/')\n",
+    "jupyter_directory = os.getcwd()\n",
+    "from IPython.display import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "from shutil import copyfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import analysis as an"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cur = an.get_cursor('./output/fill_source.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "95\n"
+     ]
+    }
+   ],
+   "source": [
+    "rebus_ids = an.get_prototype_id(cur, 'rebus')\n",
+    "num = cur.execute('select count() from agententry where prototype = \"rebus\"').fetchone()[0]\n",
+    "print(num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# did it get init fuel?\n",
+    "z = 0 \n",
+    "for id in rebus_ids:\n",
+    "    count = cur.execute('select count() from transactions where commodity = \"init_fuel\" and receiverid = \"' + id + '\"').fetchone()[0]\n",
+    "    if count == 0:\n",
+    "        z += 1\n",
+    "        print(id, count)\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "z=0\n",
+    "for id in rebus_ids:\n",
+    "    count = cur.execute('select count() from transactions where commodity = \"dep_u\" and receiverid = \"' + id + '\"').fetchone()[0]\n",
+    "    entertime = cur.execute('select entertime from agententry where agentid = \"%s\"' %id).fetchone()[0]\n",
+    "    lifetime = min(duration-entertime-1, 478)\n",
+    "    if count != lifetime:\n",
+    "        z += 1\n",
+    "        print(id, count)\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# one reactor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/teddy/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.09034363991813091"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cur = an.get_cursor('./output/one_reactor.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
+    "rebus_id = an.get_prototype_id(cur, 'rebus_3700')\n",
+    "waste_out = an.facility_commodity_flux(cur, rebus_id, ['waste'], True, False)\n",
+    "plt.plot(timestep/12, waste_out['waste'])\n",
+    "plt.title('Waste discharge from a single REBUS 3700')\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Mass [MTHM]')\n",
+    "plt.savefig('rebus_waste.png', format='png')\n",
+    "out = waste_out['waste']\n",
+    "out[out!=0].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 462eb64f1ce8b7a40ab3165c74854b6d85acb290 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Tue, 9 Oct 2018 14:11:40 -0500
Subject: [PATCH 22/35] check output

---
 input/master_us/check_output.ipynb | 195 ++++++++++++++++++-----------
 1 file changed, 120 insertions(+), 75 deletions(-)

diff --git a/input/master_us/check_output.ipynb b/input/master_us/check_output.ipynb
index ab2b3c0d..03b17cf7 100644
--- a/input/master_us/check_output.ipynb
+++ b/input/master_us/check_output.ipynb
@@ -19,6 +19,16 @@
     "from shutil import copyfile"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# check_output\n",
+    "This jupyter notebook is to check whether the simulation is a success, by testing the following:\n",
+    "* Did all the REBUS reactors get their initial fuel?\n",
+    "* Did all the REBUS reactors received their depleted uranium?"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -34,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cur = an.get_cursor('./output/fill_source.sqlite')\n",
+    "cur = an.get_cursor('./output/done.sqlite')\n",
     "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
    ]
   },
@@ -59,14 +69,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\n"
+      "711 0\n",
+      "713 0\n",
+      "715 0\n",
+      "717 0\n",
+      "719 0\n",
+      "721 0\n",
+      "723 0\n",
+      "725 0\n",
+      "727 0\n",
+      "729 0\n",
+      "731 0\n",
+      "733 0\n",
+      "735 0\n",
+      "737 0\n",
+      "739 0\n",
+      "741 0\n",
+      "743 0\n",
+      "745 0\n",
+      "747 0\n",
+      "749 0\n",
+      "751 0\n",
+      "753 0\n",
+      "755 0\n",
+      "757 0\n",
+      "759 0\n",
+      "761 0\n",
+      "763 0\n",
+      "765 0\n",
+      "767 0\n",
+      "769 0\n",
+      "771 0\n",
+      "773 0\n",
+      "775 0\n",
+      "777 0\n",
+      "779 0\n",
+      "781 0\n",
+      "783 0\n",
+      "785 0\n",
+      "787 0\n",
+      "789 0\n",
+      "791 0\n",
+      "793 0\n",
+      "795 0\n",
+      "797 0\n",
+      "799 0\n",
+      "801 0\n",
+      "803 0\n",
+      "805 0\n",
+      "807 0\n",
+      "809 0\n",
+      "811 0\n",
+      "813 0\n",
+      "815 0\n",
+      "817 0\n",
+      "819 0\n",
+      "821 0\n",
+      "823 0\n",
+      "825 0\n",
+      "827 0\n",
+      "829 0\n",
+      "831 0\n",
+      "833 0\n",
+      "835 0\n",
+      "837 0\n",
+      "839 0\n",
+      "841 0\n",
+      "843 0\n",
+      "845 0\n",
+      "847 0\n",
+      "849 0\n",
+      "851 0\n",
+      "853 0\n",
+      "855 0\n",
+      "857 0\n",
+      "859 0\n",
+      "861 0\n",
+      "863 0\n",
+      "865 0\n",
+      "867 0\n",
+      "869 0\n",
+      "871 0\n",
+      "873 0\n",
+      "875 0\n",
+      "877 0\n",
+      "879 0\n",
+      "881 0\n",
+      "883 0\n",
+      "885 0\n",
+      "887 0\n",
+      "889 0\n",
+      "891 0\n",
+      "893 0\n",
+      "895 0\n",
+      "897 0\n",
+      "899 0\n"
      ]
     }
    ],
@@ -78,21 +182,26 @@
     "    if count == 0:\n",
     "        z += 1\n",
     "        print(id, count)\n",
-    "print(z)"
+    "if z ==0:\n",
+    "    print('Success - all REBUS reactors received initial fuel')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 44,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n"
+     "ename": "TypeError",
+     "evalue": "'NoneType' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-44-ba4bf40ad2ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrebus_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'select count() from transactions where commodity = \"dep_u\" and receiverid = \"'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mid\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mentertime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'select entertime from agententry where agentid = \"%s\"'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mlifetime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mentertime\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m478\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlifetime\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
      ]
     }
    ],
@@ -105,72 +214,8 @@
     "    if count != lifetime:\n",
     "        z += 1\n",
     "        print(id, count)\n",
-    "print(z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# one reactor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/teddy/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
-   "source": [
-    "import h5py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.09034363991813091"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cur = an.get_cursor('./output/one_reactor.sqlite')\n",
-    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
-    "rebus_id = an.get_prototype_id(cur, 'rebus_3700')\n",
-    "waste_out = an.facility_commodity_flux(cur, rebus_id, ['waste'], True, False)\n",
-    "plt.plot(timestep/12, waste_out['waste'])\n",
-    "plt.title('Waste discharge from a single REBUS 3700')\n",
-    "plt.xlabel('Years')\n",
-    "plt.ylabel('Mass [MTHM]')\n",
-    "plt.savefig('rebus_waste.png', format='png')\n",
-    "out = waste_out['waste']\n",
-    "out[out!=0].mean()"
+    "if z==0:\n",
+    "    print('Success - all REBUS reactors received their fill material')"
    ]
   },
   {

From 3b06032772e064655b7bb97141f909b57e62f9b4 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 11 Oct 2018 10:22:18 -0500
Subject: [PATCH 23/35] input update

---
 input/master_us/input.xml | 535 ++++++++++++++++++++++++--------------
 1 file changed, 345 insertions(+), 190 deletions(-)

diff --git a/input/master_us/input.xml b/input/master_us/input.xml
index 22d0a723..56ab0af2 100644
--- a/input/master_us/input.xml
+++ b/input/master_us/input.xml
@@ -52,6 +52,10 @@
       <lib>cycamore</lib>
       <name>Storage</name>
     </spec>
+    <spec>
+      <lib>saltproc_reactor.saltproc_reactor</lib>
+      <name>saltproc_reactor</name>
+    </spec>
   </archetypes>
 
 
@@ -62,10 +66,10 @@
     <name>reprocessing</name>
     <config>
       <Separations>
-         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> </feed_commods>
-         <feed_commod_prefs> <val>1.0</val> <val>1.0</val> </feed_commod_prefs>
+         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> <val>ext_uox_waste</val> </feed_commods>
+         <feed_commod_prefs> <val>2</val> <val>2</val> <val>1</val> </feed_commod_prefs>
          <feed_recipe>uox_used_fuel_recipe</feed_recipe>
-         <feedbuf_size>1e100</feedbuf_size>
+         <feedbuf_size>1e300</feedbuf_size>
          <!-- 1100 tons/year 91.6 tons/timestep -->
          <throughput>1e100</throughput>
          <leftover_commod>reprocess_waste</leftover_commod>
@@ -93,42 +97,35 @@
                 </efficiencies>
             </info>
           </item>
-        </streams>
-      </Separations>
-    </config>
-  </facility>
-
-  <facility>
-    <name>sfr_reprocessing</name>
-    <config>
-      <Separations>
-         <feed_commods>   <val>cooled_sfr</val> <val>cooled_blanket</val> </feed_commods>
-         <feed_commod_prefs> <val>1.0</val> <val>1.0</val>  </feed_commod_prefs>
-         <feed_recipe>driver_used</feed_recipe>
-         <feedbuf_size>1e100</feedbuf_size>
-         <!-- 1100 tons/year 91.6 tons/timestep -->
-         <throughput>1e100</throughput>
-         <leftover_commod>reprocess_waste</leftover_commod>
-         <leftoverbuf_size>1e100</leftoverbuf_size>
-         <streams>
           <item>
-            <commod>sfr_pu</commod>
+            <commod>lwr_np</commod>
             <info>
               <buf_size>1e100</buf_size>
               <efficiencies>
                 <item>
-                  <comp>Pu</comp> <eff>.99</eff>
+                  <comp>Np</comp> <eff>.99</eff>
                 </item>
                 </efficiencies>
             </info>
           </item>
           <item>
-            <commod>sfr_u</commod>
+            <commod>lwr_am</commod>
             <info>
               <buf_size>1e100</buf_size>
               <efficiencies>
                 <item>
-                  <comp>U</comp> <eff>.99</eff>
+                  <comp>Am</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_cm</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Cm</comp> <eff>.99</eff>
                 </item>
                 </efficiencies>
             </info>
@@ -139,77 +136,227 @@
   </facility>
 
 
-<!-- Infinite UOX_Pu to MOX mixer -->
+
+<!--
 <facility>
-    <name>sfr_mixer_lwr</name>
+  <name>salt_reprocessing</name>
+  <config>
+    <Separations>
+      <feed_commods>
+        <val>fuel_out</val>
+      </feed_commods>
+      <feed_recipe>init_salt_recipe</feed_recipe>
+      <feedbuf_size>1e100</feedbuf_size>
+      <throughput>1e100</throughput>
+      <leftover_commod>salt_reprocess_waste</leftover_commod>
+      <leftoverbuf_size>1e100</leftoverbuf_size>
+      <streams>
+        <item>
+          <commod>salt_pu</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Pu</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_u</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>U</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_np</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Np</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_am</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Am</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_cm</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Cm</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_na</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Na</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+        <item>
+          <commod>salt_cl</commod>
+          <info>
+            <buf_size>1e100</buf_size>
+            <efficiencies>
+              <item>
+                <comp>Cl</comp>
+                <eff>.99</eff>
+              </item>
+            </efficiencies>
+          </info>
+        </item>
+      </streams>
+    </Separations>
+  </config>
+</facility>
+-->
+
+
+<facility>
+    <name>rebus_fuel_fab_lwr</name>
     <config>
       <Mixer>
         <in_streams>
           <stream>
             <info>
-              <mixing_ratio>0.15</mixing_ratio>
+              <mixing_ratio>0.0675235974</mixing_ratio>
               <buf_size>1e100</buf_size>
             </info>
             <commodities>
               <item>
-                <commodity>lwr_pu</commodity>
+                <commodity>na</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_na</commodity>
                 <pref>2.0</pref>
               </item>
             </commodities>
           </stream>
           <stream>
             <info>
-              <mixing_ratio>0.85</mixing_ratio>
+              <mixing_ratio>0.3597925396</mixing_ratio>
               <buf_size>1e100</buf_size>
             </info>
             <commodities>
               <item>
-                <commodity>lwr_u</commodity>
+                <commodity>cl</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_cl</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.473610929</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>natl_u2</commodity>
+                <pref>1.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.0045906338</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_np</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_np</commodity>
                 <pref>2.0</pref>
               </item>
             </commodities>
           </stream>
-        </in_streams>
-        <out_commod>sfr_fuel_legacy</out_commod>
-        <out_buf_size>1e100</out_buf_size>
-        <throughput>1e100</throughput>
-      </Mixer>
-    </config>
-</facility>
-
-
-<!-- Infinite UOX_Pu to MOX mixer -->
-<facility>
-    <name>sfr_mixer_sfr</name>
-    <config>
-      <Mixer>
-        <in_streams>
           <stream>
             <info>
-              <mixing_ratio>0.15</mixing_ratio>
+              <mixing_ratio>0.0063599406</mixing_ratio>
               <buf_size>1e100</buf_size>
             </info>
             <commodities>
               <item>
-                <commodity>sfr_pu</commodity>
+                <commodity>lwr_am</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_am</commodity>
                 <pref>2.0</pref>
               </item>
             </commodities>
           </stream>
           <stream>
             <info>
-              <mixing_ratio>0.85</mixing_ratio>
+              <mixing_ratio>0.0004494996</mixing_ratio>
               <buf_size>1e100</buf_size>
             </info>
             <commodities>
               <item>
-                <commodity>sfr_u</commodity>
+                <commodity>lwr_cm</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_cm</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.0842285674</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_pu</commodity>
+                <pref>1.0</pref>
+              </item>
+              <item>
+                <commodity>salt_pu</commodity>
                 <pref>2.0</pref>
               </item>
             </commodities>
           </stream>
         </in_streams>
-        <out_commod>sfr_fuel_new</out_commod>
+        <out_commod>init_fuel</out_commod>
         <out_buf_size>1e100</out_buf_size>
         <throughput>1e100</throughput>
       </Mixer>
@@ -217,86 +364,6 @@
 </facility>
 
 
-<facility>
-    <name>sfr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>driver_fresh</val>      <val>driver_fresh</val>  </fuel_inrecipes>
-        <fuel_outrecipes> <val>driver_used</val> <val>driver_used</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>sfr_fuel_legacy</val> <val>sfr_fuel_new</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>sfr_used</val> <val>sfr_used</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val> <val>3.0</val>                   </fuel_prefs>
-        <cycle_time>9</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>3076</assem_size>
-        <n_assem_core>5</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>400</power_cap>
-      </Reactor>
-    </config>
-</facility>
-
-<facility>
-    <name>blanket</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>natl_u_recipe</val>        </fuel_inrecipes>
-        <fuel_outrecipes> <val>blanket_used</val>   </fuel_outrecipes>
-        <fuel_incommods>  <val>blanket</val>                    </fuel_incommods>
-        <fuel_outcommods> <val>blanket_used</val>              </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                    </fuel_prefs>
-        <cycle_time>14</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>768</assem_size>
-        <n_assem_core>5</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>0</power_cap>
-      </Reactor>
-    </config>
-</facility>
-
-
-<facility>
-    <name>smr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>smr_recipe</val>        </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>   </fuel_outrecipes>
-        <fuel_incommods>  <val>smr_fuel</val>                    </fuel_incommods>
-        <fuel_outcommods> <val>smr_used</val>              </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                    </fuel_prefs>
-        <cycle_time>24</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>447</assem_size>
-        <n_assem_core>37</n_assem_core>
-        <n_assem_batch>12</n_assem_batch>
-        <power_cap>50</power_cap>
-      </Reactor>
-    </config>
-</facility>
-
-
-<facility>
-    <name>fhr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>fhr_recipe</val>        </fuel_inrecipes>
-        <fuel_outrecipes> <val>used_fhr_recipe</val>   </fuel_outrecipes>
-        <fuel_incommods>  <val>fhr_fuel</val>                    </fuel_incommods>
-        <fuel_outcommods> <val>fhr_used</val>              </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                    </fuel_prefs>
-        <cycle_time>1</cycle_time>
-        <refuel_time>0</refuel_time>
-        <!-- 470,000 Pebbles in core, with lifetime of 1.4 years -->
-        <!-- 1.5g of LEU in each pebble -->
-        <assem_size>41.47</assem_size>
-        <n_assem_core>17</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>100</power_cap>
-      </Reactor>
-    </config>
-</facility>
-
 
  <facility>
     <!-- us -->
@@ -308,7 +375,6 @@
         <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
         <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
         <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
         <cycle_time>18</cycle_time>
         <refuel_time>1</refuel_time>
         <assem_size>29565</assem_size>
@@ -322,14 +388,13 @@
    <facility>
     <!-- us -->
     <!-- PWR -->
-    <name>rebus</name>
+    <name>fake_rebus</name>
     <config>
       <Reactor>
         <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
         <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
         <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
         <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
         <cycle_time>18</cycle_time>
         <refuel_time>1</refuel_time>
         <assem_size>29565</assem_size>
@@ -342,35 +407,18 @@
 
 
 <facility>
-        <!-- Stores uox unf for 48 months (4years) -->
-    <name>sfr_storage</name>
-    <config>
-        <Storage>
-            <in_commods>
-                <val>sfr_used</val>
-            </in_commods>
-            <residence_time>36</residence_time>
-            <out_commods>
-                <val>cooled_sfr</val>
-            </out_commods>
-        </Storage>
-    </config>
-</facility>
-
-<facility>
-        <!-- Stores uox unf for 48 months (4years) -->
-    <name>blanket_storage</name>
-    <config>
-        <Storage>
-            <in_commods>
-                <val>blanket_used</val>
-            </in_commods>
-            <residence_time>12</residence_time>
-            <out_commods>
-                <val>cooled_blanket</val>
-            </out_commods>
-        </Storage>
-    </config>
+  <name>rebus</name>
+  <config>
+    <saltproc_reactor>
+      <init_fuel_commod>init_fuel</init_fuel_commod>
+      <final_fuel_commod>fuel_out</final_fuel_commod>
+      <fill_commod>dep_u</fill_commod>
+      <fissile_out_commod>fissile_out</fissile_out_commod>
+      <waste_commod>waste</waste_commod>
+      <db_path>./30_rebus_seriously.hdf5</db_path>
+      <power_cap>1628</power_cap>
+    </saltproc_reactor>
+  </config>
 </facility>
 
 <facility>
@@ -381,7 +429,7 @@
             <in_commods>
                 <val>uox_waste</val>
             </in_commods>
-            <residence_time>36</residence_time>
+            <residence_time>102</residence_time>
             <out_commods>
                 <val>cooled_uox</val>
             </out_commods>
@@ -390,7 +438,6 @@
 </facility>
 
 
-
   <facility>
     <name>enrichment</name>
     <config>
@@ -419,15 +466,30 @@
   </facility>
 
   <facility>
-    <name>blanket_source</name>
-    <config>
-      <Source>
-        <outcommod>blanket</outcommod>
-        <outrecipe>natl_u_recipe</outrecipe>
-        <throughput>1e100</throughput>
-      </Source>
-    </config>
-  </facility>
+  <name>nat_u_source2</name>
+  <config>
+    <Source>
+      <outcommod>natl_u2</outcommod>
+      <outrecipe>natl_u_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>tails_storage</name>
+  <config>
+    <Storage>
+      <in_commods>
+        <val>tailings</val>
+      </in_commods>
+      <out_commods>
+        <val>dep_u</val>
+      </out_commods>
+    </Storage>
+  </config>
+</facility>
+
 
   <facility>
     <name>unf_source</name>
@@ -441,19 +503,91 @@
     </config>
   </facility>
 
+
+
+  <facility>
+  <name>fill_commod_source</name>
+  <config>
+    <Source>
+      <outcommod>dep_u</outcommod>
+      <outrecipe>depleted_u</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
   <facility>
     <name>SomeSink</name>
     <config>
       <Sink>
         <in_commods>
-          <val>tailings</val>
+          <val>waste</val>
+          <val>fissile_out</val>
           <val>reprocess_waste</val>
+          <val>fuel_out</val>
+          <val>lwr_u</val>
+          <val>salt_u</val>
         </in_commods>
         <capacity>1e100</capacity>
       </Sink>
     </config>
   </facility>
 
+  <facility>
+    <name>na_source</name>
+    <config>
+      <Source>
+        <outcommod>na</outcommod>
+        <outrecipe>na_recipe</outrecipe>
+        <throughput>1e100</throughput>
+      </Source>
+    </config>
+  </facility>
+
+<facility>
+  <name>cl_source</name>
+  <config>
+    <Source>
+      <outcommod>cl</outcommod>
+      <outrecipe>cl_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>na_source2</name>
+  <config>
+    <Source>
+      <outcommod>na2</outcommod>
+      <outrecipe>na_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>cl_source2</name>
+  <config>
+    <Source>
+      <outcommod>cl2</outcommod>
+      <outrecipe>cl_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
+<facility>
+  <name>init_fuel_source</name>
+  <config>
+    <Source>
+      <outcommod>init_fuel</outcommod>
+      <outrecipe>init_salt_recipe</outrecipe>
+      <throughput>1e100</throughput>
+    </Source>
+  </config>
+</facility>
+
   <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
 
   <region>
@@ -487,16 +621,15 @@
   <name>rebus_inst</name>
     <config>
     <DeployInst>
-      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/prototypes)'/>
-      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/build_times)'/>
-      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/n_build)'/>
-      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/prototypes)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/build_times)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/n_build)'/>
+      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/lifetimes)'/>
     </DeployInst>
     </config>
   </institution>
 
-
-  </region>
+ </region>
 
 
 <region>
@@ -512,8 +645,9 @@
           </entry>
           <entry>
             <number>1</number>
-            <prototype>blanket_storage</prototype>
+            <prototype>nat_u_source2</prototype>
           </entry>
+          
           <entry>
             <number>1</number>
             <prototype>enrichment</prototype>
@@ -523,38 +657,59 @@
             <prototype>SomeSink</prototype>
           </entry>
 
+          
           <entry>
             <number>1</number>
-            <prototype>blanket_source</prototype>
+            <prototype>reprocessing</prototype>
           </entry>
+
           <entry>
             <number>1</number>
-            <prototype>reprocessing</prototype>
+            <prototype>rebus_fuel_fab_lwr</prototype>
           </entry>
+
           <entry>
             <number>1</number>
-            <prototype>sfr_reprocessing</prototype>
+            <prototype>uox_storage</prototype>
           </entry>
           <entry>
             <number>1</number>
-            <prototype>sfr_mixer_sfr</prototype>
+            <prototype>unf_source</prototype>
           </entry>
+
           <entry>
             <number>1</number>
-            <prototype>sfr_mixer_lwr</prototype>
+            <prototype>tails_storage</prototype>
           </entry>
+          
+<!--
           <entry>
+            <prototype>init_fuel_source</prototype>
             <number>1</number>
-            <prototype>sfr_storage</prototype>
           </entry>
+
           <entry>
             <number>1</number>
-            <prototype>uox_storage</prototype>
+            <prototype>fill_commod_source</prototype>
           </entry>
+-->
           <entry>
             <number>1</number>
-            <prototype>unf_source</prototype>
+            <prototype>cl_source</prototype>
           </entry>
+          <entry>
+            <number>1</number>
+            <prototype>na_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>cl_source2</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>na_source2</prototype>
+          </entry>
+
         </initialfacilitylist>
         </institution>
 </region>

From e5f9a3f714bb618be545d884321189822f62cef7 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 11 Oct 2018 12:26:26 -0500
Subject: [PATCH 24/35] clean up input file xml

---
 input/master_us/input.xml     |  239 +---
 input/master_us/lwr_dep.xml   |  126 +-
 input/master_us/rebus_dep.xml | 1828 +++------------------------
 input/master_us/recipes.xml   |  287 ++++-
 input/master_us/sfr_dep.xml   | 2170 +++++++++++++++++++++++++++++++++
 input/master_us/sfr_input.xml |  482 ++++++++
 6 files changed, 3252 insertions(+), 1880 deletions(-)
 create mode 100644 input/master_us/sfr_dep.xml
 create mode 100644 input/master_us/sfr_input.xml

diff --git a/input/master_us/input.xml b/input/master_us/input.xml
index 56ab0af2..8ad08f7a 100644
--- a/input/master_us/input.xml
+++ b/input/master_us/input.xml
@@ -61,13 +61,11 @@
 
 
 
-<!-- La Hague Model from Schneider & Marignac -->
   <facility>
     <name>reprocessing</name>
     <config>
       <Separations>
-         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> <val>ext_uox_waste</val> </feed_commods>
-         <feed_commod_prefs> <val>2</val> <val>2</val> <val>1</val> </feed_commod_prefs>
+         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val>  </feed_commods>
          <feed_recipe>uox_used_fuel_recipe</feed_recipe>
          <feedbuf_size>1e300</feedbuf_size>
          <!-- 1100 tons/year 91.6 tons/timestep -->
@@ -136,112 +134,6 @@
   </facility>
 
 
-
-<!--
-<facility>
-  <name>salt_reprocessing</name>
-  <config>
-    <Separations>
-      <feed_commods>
-        <val>fuel_out</val>
-      </feed_commods>
-      <feed_recipe>init_salt_recipe</feed_recipe>
-      <feedbuf_size>1e100</feedbuf_size>
-      <throughput>1e100</throughput>
-      <leftover_commod>salt_reprocess_waste</leftover_commod>
-      <leftoverbuf_size>1e100</leftoverbuf_size>
-      <streams>
-        <item>
-          <commod>salt_pu</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Pu</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_u</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>U</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_np</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Np</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_am</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Am</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_cm</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Cm</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_na</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Na</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_cl</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Cl</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-      </streams>
-    </Separations>
-  </config>
-</facility>
--->
-
-
 <facility>
     <name>rebus_fuel_fab_lwr</name>
     <config>
@@ -385,26 +277,6 @@
     </config>
   </facility>
 
-   <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>fake_rebus</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1628</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
 
 <facility>
   <name>rebus</name>
@@ -504,18 +376,6 @@
   </facility>
 
 
-
-  <facility>
-  <name>fill_commod_source</name>
-  <config>
-    <Source>
-      <outcommod>dep_u</outcommod>
-      <outrecipe>depleted_u</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
   <facility>
     <name>SomeSink</name>
     <config>
@@ -555,38 +415,6 @@
   </config>
 </facility>
 
-<facility>
-  <name>na_source2</name>
-  <config>
-    <Source>
-      <outcommod>na2</outcommod>
-      <outrecipe>na_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-<facility>
-  <name>cl_source2</name>
-  <config>
-    <Source>
-      <outcommod>cl2</outcommod>
-      <outrecipe>cl_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-<facility>
-  <name>init_fuel_source</name>
-  <config>
-    <Source>
-      <outcommod>init_fuel</outcommod>
-      <outrecipe>init_salt_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
 
   <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
 
@@ -621,10 +449,10 @@
   <name>rebus_inst</name>
     <config>
     <DeployInst>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/prototypes)'/>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/build_times)'/>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/n_build)'/>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/lifetimes)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/build_times)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/n_build)'/>
+      <xi:include href='./rebus_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
     </DeployInst>
     </config>
   </institution>
@@ -635,6 +463,33 @@
 <region>
     <name>Sink_Source</name>
     <config><NullRegion/></config>
+
+
+    <institution>
+    <name>us_fuel_cycle</name>
+    <config>
+      <DeployInst>
+        <prototypes>
+          <val>reprocessing</val>
+          <val>rebus_fuel_fab_lwr</val>
+        </prototypes>
+        <build_times>
+          <val>200</val>
+          <val>200</val>
+        </build_times>
+        <n_build>
+          <val>1</val>
+          <val>1</val>
+        </n_build>
+        <lifetimes>
+          <val>9999</val>
+          <val>9999</val>
+        </lifetimes>
+      </DeployInst>
+    </config>
+    </institution>
+    
+    
         <institution>
          <name>Sink_Source_Inst</name>
          <config><NullInst/></config>
@@ -657,17 +512,6 @@
             <prototype>SomeSink</prototype>
           </entry>
 
-          
-          <entry>
-            <number>1</number>
-            <prototype>reprocessing</prototype>
-          </entry>
-
-          <entry>
-            <number>1</number>
-            <prototype>rebus_fuel_fab_lwr</prototype>
-          </entry>
-
           <entry>
             <number>1</number>
             <prototype>uox_storage</prototype>
@@ -681,18 +525,7 @@
             <number>1</number>
             <prototype>tails_storage</prototype>
           </entry>
-          
-<!--
-          <entry>
-            <prototype>init_fuel_source</prototype>
-            <number>1</number>
-          </entry>
 
-          <entry>
-            <number>1</number>
-            <prototype>fill_commod_source</prototype>
-          </entry>
--->
           <entry>
             <number>1</number>
             <prototype>cl_source</prototype>
@@ -701,14 +534,6 @@
             <number>1</number>
             <prototype>na_source</prototype>
           </entry>
-          <entry>
-            <number>1</number>
-            <prototype>cl_source2</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>na_source2</prototype>
-          </entry>
 
         </initialfacilitylist>
         </institution>
diff --git a/input/master_us/lwr_dep.xml b/input/master_us/lwr_dep.xml
index d36e0f93..b95d2b2b 100644
--- a/input/master_us/lwr_dep.xml
+++ b/input/master_us/lwr_dep.xml
@@ -48,28 +48,38 @@
 		<val>lwr</val>
 		<val>lwr</val>
 		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
+		<val>lwr</val>
 </prototypes>
 <build_times>
-		<val>84</val>
-		<val>99</val>
-		<val>114</val>
-		<val>128</val>
-		<val>142</val>
-		<val>156</val>
-		<val>170</val>
-		<val>184</val>
 		<val>193</val>
-		<val>197</val>
+		<val>199</val>
 		<val>201</val>
-		<val>206</val>
-		<val>210</val>
-		<val>212</val>
+		<val>211</val>
 		<val>213</val>
 		<val>218</val>
 		<val>223</val>
 		<val>229</val>
 		<val>230</val>
-		<val>231</val>
 		<val>235</val>
 		<val>239</val>
 		<val>241</val>
@@ -77,27 +87,59 @@
 		<val>245</val>
 		<val>247</val>
 		<val>248</val>
-		<val>251</val>
+		<val>252</val>
 		<val>254</val>
 		<val>256</val>
 		<val>257</val>
-		<val>259</val>
+		<val>258</val>
 		<val>261</val>
 		<val>262</val>
 		<val>264</val>
-		<val>271</val>
 		<val>276</val>
 		<val>277</val>
 		<val>280</val>
 		<val>282</val>
 		<val>284</val>
-		<val>285</val>
 		<val>292</val>
 		<val>299</val>
 		<val>300</val>
 		<val>303</val>
-		<val>307</val>
-		<val>309</val>
+		<val>308</val>
+		<val>326</val>
+		<val>327</val>
+		<val>328</val>
+		<val>337</val>
+		<val>340</val>
+		<val>343</val>
+		<val>351</val>
+		<val>353</val>
+		<val>355</val>
+		<val>361</val>
+		<val>362</val>
+		<val>370</val>
+		<val>371</val>
+		<val>373</val>
+		<val>378</val>
+		<val>381</val>
+		<val>382</val>
+		<val>383</val>
+		<val>386</val>
+		<val>387</val>
+		<val>389</val>
+		<val>391</val>
+		<val>394</val>
+		<val>397</val>
+		<val>398</val>
+		<val>399</val>
+		<val>405</val>
+		<val>407</val>
+		<val>410</val>
+		<val>412</val>
+		<val>415</val>
+		<val>419</val>
+		<val>431</val>
+		<val>432</val>
+		<val>436</val>
 </build_times>
 <n_build>
 		<val>1</val>
@@ -113,23 +155,34 @@
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>1</val>
+		<val>2</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>2</val>
+		<val>1</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
-		<val>3</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
@@ -141,10 +194,20 @@
 		<val>2</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>1</val>
+		<val>3</val>
+		<val>1</val>
 		<val>1</val>
 		<val>2</val>
+		<val>2</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
+		<val>2</val>
 		<val>1</val>
 		<val>1</val>
 		<val>1</val>
@@ -198,5 +261,26 @@
 		<val>960</val>
 		<val>960</val>
 		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
 </lifetimes>
 </root>
diff --git a/input/master_us/rebus_dep.xml b/input/master_us/rebus_dep.xml
index 5b281ae5..95069f3f 100644
--- a/input/master_us/rebus_dep.xml
+++ b/input/master_us/rebus_dep.xml
@@ -89,1165 +89,109 @@
 		<val>rebus</val>
 		<val>rebus</val>
 		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
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 </n_build>
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-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
-		<val>360</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
+		<val>480</val>
 </lifetimes>
 </root>
diff --git a/input/master_us/recipes.xml b/input/master_us/recipes.xml
index 6fb7f5c2..9204b350 100644
--- a/input/master_us/recipes.xml
+++ b/input/master_us/recipes.xml
@@ -16,14 +16,14 @@
 </recipe>
 
 <recipe>
-  <name>na</name>
+  <name>na_recipe</name>
   <basis>mass</basis>
 <nuclide> <id>Na23</id> <comp>1</comp>  </nuclide>
 </recipe>
 
 
 <recipe>
-  <name>cl</name>
+  <name>cl_recipe</name>
   <basis>mass</basis>
 <nuclide> <id>Cl37</id> <comp>1</comp>  </nuclide>
 </recipe>
@@ -38,6 +38,219 @@
 </recipe>
 
 
+<recipe>
+  <name>sfr_spent_mox_recipe</name>
+  <!--BR 1.089 -->
+  <basis>mass</basis>
+  <nuclide>
+    <id>He4</id>
+    <comp>8.2631E-05</comp>
+  </nuclide>
+  <nuclide>
+    <id>Ra226</id>
+    <comp>2.306E-13</comp>
+  </nuclide>
+  <nuclide>
+    <id>Ra228</id>
+    <comp>6.029E-21</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pb206</id>
+    <comp>5.2269E-18</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pb207</id>
+    <comp>1.0722E-15</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pb208</id>
+    <comp>4.4347E-10</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pb210</id>
+    <comp>1.3841E-16</comp>
+  </nuclide>
+  <nuclide>
+    <id>Th228</id>
+    <comp>7.7910E-10</comp>
+  </nuclide>
+  <nuclide>
+    <id>Th229</id>
+    <comp>3.5259E-11</comp>
+  </nuclide>
+  <nuclide>
+    <id>Th230</id>
+    <comp>1.1419E-08</comp>
+  </nuclide>
+  <nuclide>
+    <id>Th232</id>
+    <comp>6.3415E-11</comp>
+  </nuclide>
+  <nuclide>
+    <id>Bi209</id>
+    <comp>2.5042E-13</comp>
+  </nuclide>
+  <nuclide>
+    <id>Ac227</id>
+    <comp>2.8317E-14</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pa231</id>
+    <comp>8.8076E-10</comp>
+  </nuclide>
+  <nuclide>
+    <id>U232</id>
+    <comp>1.4693E-07</comp>
+  </nuclide>
+  <nuclide>
+    <id>U233</id>
+    <comp>4.0461E-08</comp>
+  </nuclide>
+  <nuclide>
+    <id>U234</id>
+    <comp>0.0010</comp>
+  </nuclide>
+  <nuclide>
+    <id>U235</id>
+    <comp>0.0003</comp>
+  </nuclide>
+  <nuclide>
+    <id>U236</id>
+    <comp>0.0005</comp>
+  </nuclide>
+  <nuclide>
+    <id>U238</id>
+    <comp>0.5864</comp>
+  </nuclide>
+  <nuclide>
+    <id>Np237</id>
+    <comp>0.0038</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pu238</id>
+    <comp>0.0096</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pu239</id>
+    <comp>0.0981</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pu240</id>
+    <comp>0.0890</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pu241</id>
+    <comp>0.0155</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pu242</id>
+    <comp>0.0273</comp>
+  </nuclide>
+  <nuclide>
+    <id>Pu244</id>
+    <comp>1.779E-07</comp>
+  </nuclide>
+  <nuclide>
+    <id>Am241</id>
+    <comp>0.0077</comp>
+  </nuclide>
+  <nuclide>
+    <id>Am242m</id>
+    <comp>0.0005</comp>
+  </nuclide>
+  <nuclide>
+    <id>Am243</id>
+    <comp>0.0091</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm242</id>
+    <comp>0.0004</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm243</id>
+    <comp>0.0000</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm244</id>
+    <comp>0.0067</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm245</id>
+    <comp>0.0017</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm246</id>
+    <comp>0.0009</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm247</id>
+    <comp>0.0000</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm248</id>
+    <comp>4.0265E-06</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cm250</id>
+    <comp>1.076E-12</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cf249</id>
+    <comp>1.6590E-07</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cf250</id>
+    <comp>9.5219E-09</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cf251</id>
+    <comp>3.2032E-10</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cf252</id>
+    <comp>8.3754E-12</comp>
+  </nuclide>
+  <nuclide>
+    <id>H3</id>
+    <comp>3.1829E-07</comp>
+  </nuclide>
+  <nuclide>
+    <id>Kr81</id>
+    <comp>1.5156E-11</comp>
+  </nuclide>
+  <nuclide>
+    <id>Kr85</id>
+    <comp>0.0000</comp>
+  </nuclide>
+  <nuclide>
+    <id>Sr90</id>
+    <comp>0.0009</comp>
+  </nuclide>
+  <nuclide>
+    <id>Tc99</id>
+    <comp>0.0029</comp>
+  </nuclide>
+  <nuclide>
+    <id>I129</id>
+    <comp>0.0009</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cs134</id>
+    <comp>0.0001</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cs135</id>
+    <comp>0.0051</comp>
+  </nuclide>
+  <nuclide>
+    <id>Cs137</id>
+    <comp>0.0046</comp>
+  </nuclide>
+
+</recipe> 
+
+
+
 <recipe>
     <name>msr_output_recipe</name>
     <basis>atom</basis>
@@ -247,6 +460,70 @@
   </recipe> 
 
 
+  <recipe>
+    <name>init_salt_recipe</name>
+    <basis>mass</basis>
+      <nuclide> <id>Na23</id> <comp>0.0675240828</comp> </nuclide>
+      <nuclide>
+  <id>Cl35</id>
+  <comp>0.267527</comp>
+</nuclide>
+      <nuclide>
+  <id>Cl37</id>
+  <comp>0.092268</comp>
+</nuclide>
+      <nuclide>
+  <id>U235</id>
+  <comp>0.0034347457</comp>
+</nuclide>
+      <nuclide>
+  <id>U238</id>
+  <comp>0.4736160786</comp>
+</nuclide>
+      <nuclide>
+  <id>Np237</id>
+  <comp>0.004590661</comp>
+</nuclide>
+      <nuclide>
+  <id>Pu238</id>
+  <comp>0.0020371114</comp>
+</nuclide>
+      <nuclide>
+  <id>Pu239</id>
+  <comp>0.046231887</comp>
+</nuclide>
+      <nuclide>
+  <id>Pu240</id>
+  <comp>0.0212031681</comp>
+</nuclide>
+      <nuclide>
+  <id>Pu241</id>
+  <comp>0.0086553226</comp>
+</nuclide>
+      <nuclide>
+  <id>Pu242</id>
+  <comp>0.0061017702</comp>
+</nuclide>
+<nuclide>
+  <id>Am241</id>
+  <comp>0.0049445494</comp>
+</nuclide>
+      <nuclide>
+  <id>Am243</id>
+  <comp>0.0014154566</comp>
+</nuclide>
+      <nuclide>
+  <id>Cm244</id>
+  <comp>0.0004112467</comp>
+</nuclide>
+      <nuclide>
+  <id>Cm245</id>
+  <comp>0.38255e-05</comp>
+</nuclide>
+</recipe>
+      
+      
+
   <recipe>
     <name>uox_fuel_recipe</name>
     <basis>mass</basis>
@@ -787,7 +1064,7 @@
 
    <recipe>
     <name>uox_used_fuel_recipe</name>
-    <basis>atom</basis>
+    <basis>mass</basis>
     <nuclide> <id>He4</id>  <comp>9.47457840128509E-07</comp> </nuclide> 
    <nuclide> <id>Ra226</id>  <comp>9.78856442957042E-14</comp> </nuclide> 
    <nuclide> <id>Ra228</id>  <comp>2.75087759176098E-20</comp> </nuclide> 
@@ -839,9 +1116,7 @@
    <nuclide> <id>I129</id>  <comp>0.0002731878</comp> </nuclide> 
    <nuclide> <id>Cs134</id>  <comp>0.0002300898</comp> </nuclide> 
    <nuclide> <id>Cs135</id>  <comp>0.0006596706</comp> </nuclide> 
-   <nuclide> <id>Cs137</id>  <comp>0.0018169192</comp> </nuclide> 
-   <nuclide> <id>H1</id>  <comp>0.0477938151</comp> </nuclide> 
-
+   <nuclide> <id>Cs137</id>  <comp>0.0018169192</comp> </nuclide>
   </recipe> 
 
 <!-- BR 1.089 
diff --git a/input/master_us/sfr_dep.xml b/input/master_us/sfr_dep.xml
new file mode 100644
index 00000000..c8dca346
--- /dev/null
+++ b/input/master_us/sfr_dep.xml
@@ -0,0 +1,2170 @@
+<root>
+<prototypes>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
+		<val>astrid</val>
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+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+		<val>960</val>
+</lifetimes>
+</root>
diff --git a/input/master_us/sfr_input.xml b/input/master_us/sfr_input.xml
new file mode 100644
index 00000000..b0a61820
--- /dev/null
+++ b/input/master_us/sfr_input.xml
@@ -0,0 +1,482 @@
+
+<simulation xmlns:xi="http://www.w3.org/2001/XInclude">
+  <control>
+    <duration>1764</duration>
+    <startmonth>5</startmonth>
+    <startyear>2013</startyear>
+    <decay>lazy</decay>
+  </control>
+
+  <archetypes>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Source</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Sink</name>
+    </spec>
+    <spec>
+        <lib>cycamore</lib>
+        <name>Reactor</name>
+    </spec>
+    <spec>
+      <lib>agents</lib>
+      <name>NullRegion</name>
+    </spec>
+    <spec>
+      <lib>agents</lib>
+      <name>NullInst</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Separations</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>DeployInst</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Separations</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Enrichment</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Mixer</name>
+    </spec>
+    <spec>
+      <lib>cycamore</lib>
+      <name>Storage</name>
+    </spec>
+  </archetypes>
+
+
+
+
+<!-- La Hague Model from Schneider & Marignac -->
+  <facility>
+    <name>reprocessing</name>
+    <config>
+      <Separations>
+         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> </feed_commods>
+         <feed_commod_prefs> <val>1.0</val> <val>1.0</val> </feed_commod_prefs>
+         <feed_recipe>uox_used_fuel_recipe</feed_recipe>
+         <feedbuf_size>1e100</feedbuf_size>
+         <!-- 1100 tons/year 91.6 tons/timestep -->
+         <throughput>1e100</throughput>
+         <leftover_commod>lwr_reprocess_waste</leftover_commod>
+         <leftoverbuf_size>1e100</leftoverbuf_size>
+         <streams>
+          <item>
+            <commod>lwr_pu</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Pu</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>lwr_u</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>U</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+        </streams>
+      </Separations>
+    </config>
+  </facility>
+
+  <facility>
+    <name>sfr_reprocessing</name>
+    <config>
+      <Separations>
+         <feed_commods>   <val>cooled_sfr</val> </feed_commods>
+         <feed_commod_prefs> <val>1.0</val>  </feed_commod_prefs>
+         <feed_recipe>sfr_spent_mox_recipe</feed_recipe>
+         <feedbuf_size>1e100</feedbuf_size>
+         <!-- 1100 tons/year 91.6 tons/timestep -->
+         <throughput>1e100</throughput>
+         <leftover_commod>sfr_reprocess_waste</leftover_commod>
+         <leftoverbuf_size>1e100</leftoverbuf_size>
+         <streams>
+          <item>
+            <commod>sfr_pu</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>Pu</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+          <item>
+            <commod>sfr_u</commod>
+            <info>
+              <buf_size>1e100</buf_size>
+              <efficiencies>
+                <item>
+                  <comp>U</comp> <eff>.99</eff>
+                </item>
+                </efficiencies>
+            </info>
+          </item>
+        </streams>
+      </Separations>
+    </config>
+  </facility>
+
+
+<!-- Infinite UOX_Pu to MOX mixer -->
+<facility>
+    <name>sfr_mixer_lwr</name>
+    <config>
+      <Mixer>
+        <in_streams>
+          <stream>
+            <info>
+              <mixing_ratio>0.15</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_pu</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.85</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>lwr_u</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+        </in_streams>
+        <out_commod>astrid_fuel_legacy</out_commod>
+        <out_buf_size>1e100</out_buf_size>
+        <throughput>1e100</throughput>
+      </Mixer>
+    </config>
+</facility>
+
+
+<!-- Infinite UOX_Pu to MOX mixer -->
+<facility>
+    <name>sfr_mixer_sfr</name>
+    <config>
+      <Mixer>
+        <in_streams>
+          <stream>
+            <info>
+              <mixing_ratio>0.15</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>sfr_pu</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+          <stream>
+            <info>
+              <mixing_ratio>0.85</mixing_ratio>
+              <buf_size>1e100</buf_size>
+            </info>
+            <commodities>
+              <item>
+                <commodity>sfr_u</commodity>
+                <pref>2.0</pref>
+              </item>
+            </commodities>
+          </stream>
+        </in_streams>
+        <out_commod>astrid_fuel_new</out_commod>
+        <out_buf_size>1e100</out_buf_size>
+        <throughput>1e100</throughput>
+      </Mixer>
+    </config>
+</facility>
+
+
+
+<facility>
+    <!-- astrid cfv specification from  varaine_pre-conceptual-->
+    <name>astrid</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>mox_fuel_recipe</val>      <val>mox_fuel_recipe</val>  </fuel_inrecipes>
+        <fuel_outrecipes> <val>sfr_spent_mox_recipe</val> <val>sfr_spent_mox_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>astrid_fuel_legacy</val> <val>astrid_fuel_new</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>astrid_unf</val> <val>astrid_unf</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val> <val>3.0</val>                   </fuel_prefs>
+        <cycle_time>12</cycle_time>
+        <refuel_time>2</refuel_time>
+        <assem_size>5568</assem_size>
+        <n_assem_core>4</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>600</power_cap>
+      </Reactor>
+    </config>
+</facility>
+
+
+ <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>lwr</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1110</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+   <facility>
+    <!-- us -->
+    <!-- PWR -->
+    <name>rebus</name>
+    <config>
+      <Reactor>
+        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
+        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
+        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
+        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
+        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
+        <cycle_time>18</cycle_time>
+        <refuel_time>1</refuel_time>
+        <assem_size>29565</assem_size>
+        <n_assem_core>3</n_assem_core>
+        <n_assem_batch>1</n_assem_batch>
+        <power_cap>1628</power_cap>
+      </Reactor>
+    </config>
+  </facility>
+
+
+<facility>
+        <!-- Stores uox unf for 48 months (4years) -->
+    <name>sfr_storage</name>
+    <config>
+        <Storage>
+            <in_commods>
+                <val>astrid_unf</val>
+            </in_commods>
+            <residence_time>36</residence_time>
+            <out_commods>
+                <val>cooled_sfr</val>
+            </out_commods>
+        </Storage>
+    </config>
+</facility>
+
+
+<facility>
+        <!-- Stores uox unf for 48 months (4years) -->
+    <name>uox_storage</name>
+    <config>
+        <Storage>
+            <in_commods>
+                <val>uox_waste</val>
+            </in_commods>
+            <residence_time>36</residence_time>
+            <out_commods>
+                <val>cooled_uox</val>
+            </out_commods>
+        </Storage>
+    </config>
+</facility>
+
+
+
+  <facility>
+    <name>enrichment</name>
+    <config>
+      <Enrichment>
+        <feed_commod>natl_u</feed_commod>
+        <feed_recipe>natl_u_recipe</feed_recipe>
+        <product_commod>uox</product_commod>
+        <tails_assay>0.003</tails_assay>
+        <tails_commod>tailings</tails_commod>
+        <swu_capacity>1e100</swu_capacity>
+        <initial_feed>1e100</initial_feed>
+      </Enrichment>
+    </config>
+  </facility>
+
+
+  <facility>
+    <name>nat_u_source</name>
+    <config>
+        <Source>
+        <outcommod>natl_u</outcommod>
+        <outrecipe>natl_u_recipe</outrecipe>
+        <throughput>1e100</throughput>
+      </Source>
+    </config>
+  </facility>
+
+
+  <facility>
+    <name>unf_source</name>
+    <config>
+      <Source>
+        <outcommod>pre_2013_unf</outcommod>
+        <outrecipe>udb_precise</outrecipe>
+        <throughput>1e100</throughput>
+        <inventory_size>68072030</inventory_size>
+      </Source>
+    </config>
+  </facility>
+
+  <facility>
+    <name>SomeSink</name>
+    <config>
+      <Sink>
+        <in_commods>
+          <val>tailings</val>
+          <val>lwr_reprocess_waste</val>
+          <val>sfr_reprocess_waste</val>
+        </in_commods>
+        <capacity>1e100</capacity>
+      </Sink>
+    </config>
+  </facility>
+
+  <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
+
+  <region>
+    <name>us</name>
+    <config><NullRegion/></config>
+    <institution>
+      <name>us_government</name>
+      <config>
+        <DeployInst>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/prototypes)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/build_times)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/n_build)'/>
+            <xi:include href='./depinst.xml' xpointer='xpointer(/root/lifetimes)'/>
+        </DeployInst>
+      </config>
+    </institution>
+
+    <institution>
+    <name>lwr_inst</name>
+    <config>
+      <DeployInst>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
+        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+      </DeployInst>
+    </config>
+  </institution>
+
+  <institution>
+  <name>sfr_inst</name>
+    <config>
+    <DeployInst>
+      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
+      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/build_times)'/>
+      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/n_build)'/>
+      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
+    </DeployInst>
+    </config>
+  </institution>
+
+
+  </region>
+
+
+<region>
+    <name>Sink_Source</name>
+    <config><NullRegion/></config>
+        <institution>
+         <name>Sink_Source_Inst</name>
+         <config>
+         <DeployInst>
+            <prototypes>
+              <val>reprocessing</val>
+              <val>sfr_reprocessing</val>
+              <val>sfr_mixer_lwr</val>
+              <val>sfr_mixer_sfr</val>
+            </prototypes>
+            <build_times>
+              <val>79</val>
+              <val>79</val>
+              <val>79</val>
+              <val>79</val>
+            </build_times>
+            <n_build>
+              <val>1</val>
+              <val>1</val>
+              <val>1</val>
+              <val>1</val>
+            </n_build>
+            <lifetimes>
+              <val>99999</val>
+              <val>99999</val>
+              <val>99999</val>
+              <val>99999</val>
+            </lifetimes>
+          </DeployInst>
+        </config>
+        <initialfacilitylist>
+          <entry>
+            <number>1</number>
+            <prototype>nat_u_source</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>enrichment</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>SomeSink</prototype>
+          </entry>
+
+          <entry>
+            <number>1</number>
+            <prototype>sfr_storage</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>uox_storage</prototype>
+          </entry>
+          <entry>
+            <number>1</number>
+            <prototype>unf_source</prototype>
+          </entry>
+        </initialfacilitylist>
+        </institution>
+</region>
+<xi:include href='recipes.xml' xpointer='xpointer(/root/recipe)'/>
+</simulation>
\ No newline at end of file

From 8f32c903e01555100c746d5bbd87155a895daf73 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 11 Oct 2018 12:27:02 -0500
Subject: [PATCH 25/35] delete sfr input file

---
 input/master_us/sfr_dep.xml   | 2170 ---------------------------------
 input/master_us/sfr_input.xml |  482 --------
 2 files changed, 2652 deletions(-)
 delete mode 100644 input/master_us/sfr_dep.xml
 delete mode 100644 input/master_us/sfr_input.xml

diff --git a/input/master_us/sfr_dep.xml b/input/master_us/sfr_dep.xml
deleted file mode 100644
index c8dca346..00000000
--- a/input/master_us/sfr_dep.xml
+++ /dev/null
@@ -1,2170 +0,0 @@
-<root>
-<prototypes>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
-		<val>astrid</val>
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-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-		<val>960</val>
-</lifetimes>
-</root>
diff --git a/input/master_us/sfr_input.xml b/input/master_us/sfr_input.xml
deleted file mode 100644
index b0a61820..00000000
--- a/input/master_us/sfr_input.xml
+++ /dev/null
@@ -1,482 +0,0 @@
-
-<simulation xmlns:xi="http://www.w3.org/2001/XInclude">
-  <control>
-    <duration>1764</duration>
-    <startmonth>5</startmonth>
-    <startyear>2013</startyear>
-    <decay>lazy</decay>
-  </control>
-
-  <archetypes>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Source</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Sink</name>
-    </spec>
-    <spec>
-        <lib>cycamore</lib>
-        <name>Reactor</name>
-    </spec>
-    <spec>
-      <lib>agents</lib>
-      <name>NullRegion</name>
-    </spec>
-    <spec>
-      <lib>agents</lib>
-      <name>NullInst</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Separations</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>DeployInst</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Separations</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Enrichment</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Mixer</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Storage</name>
-    </spec>
-  </archetypes>
-
-
-
-
-<!-- La Hague Model from Schneider & Marignac -->
-  <facility>
-    <name>reprocessing</name>
-    <config>
-      <Separations>
-         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> </feed_commods>
-         <feed_commod_prefs> <val>1.0</val> <val>1.0</val> </feed_commod_prefs>
-         <feed_recipe>uox_used_fuel_recipe</feed_recipe>
-         <feedbuf_size>1e100</feedbuf_size>
-         <!-- 1100 tons/year 91.6 tons/timestep -->
-         <throughput>1e100</throughput>
-         <leftover_commod>lwr_reprocess_waste</leftover_commod>
-         <leftoverbuf_size>1e100</leftoverbuf_size>
-         <streams>
-          <item>
-            <commod>lwr_pu</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>Pu</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-          <item>
-            <commod>lwr_u</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>U</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-        </streams>
-      </Separations>
-    </config>
-  </facility>
-
-  <facility>
-    <name>sfr_reprocessing</name>
-    <config>
-      <Separations>
-         <feed_commods>   <val>cooled_sfr</val> </feed_commods>
-         <feed_commod_prefs> <val>1.0</val>  </feed_commod_prefs>
-         <feed_recipe>sfr_spent_mox_recipe</feed_recipe>
-         <feedbuf_size>1e100</feedbuf_size>
-         <!-- 1100 tons/year 91.6 tons/timestep -->
-         <throughput>1e100</throughput>
-         <leftover_commod>sfr_reprocess_waste</leftover_commod>
-         <leftoverbuf_size>1e100</leftoverbuf_size>
-         <streams>
-          <item>
-            <commod>sfr_pu</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>Pu</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-          <item>
-            <commod>sfr_u</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>U</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-        </streams>
-      </Separations>
-    </config>
-  </facility>
-
-
-<!-- Infinite UOX_Pu to MOX mixer -->
-<facility>
-    <name>sfr_mixer_lwr</name>
-    <config>
-      <Mixer>
-        <in_streams>
-          <stream>
-            <info>
-              <mixing_ratio>0.15</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>lwr_pu</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.85</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>lwr_u</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-        </in_streams>
-        <out_commod>astrid_fuel_legacy</out_commod>
-        <out_buf_size>1e100</out_buf_size>
-        <throughput>1e100</throughput>
-      </Mixer>
-    </config>
-</facility>
-
-
-<!-- Infinite UOX_Pu to MOX mixer -->
-<facility>
-    <name>sfr_mixer_sfr</name>
-    <config>
-      <Mixer>
-        <in_streams>
-          <stream>
-            <info>
-              <mixing_ratio>0.15</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>sfr_pu</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.85</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>sfr_u</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-        </in_streams>
-        <out_commod>astrid_fuel_new</out_commod>
-        <out_buf_size>1e100</out_buf_size>
-        <throughput>1e100</throughput>
-      </Mixer>
-    </config>
-</facility>
-
-
-
-<facility>
-    <!-- astrid cfv specification from  varaine_pre-conceptual-->
-    <name>astrid</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>mox_fuel_recipe</val>      <val>mox_fuel_recipe</val>  </fuel_inrecipes>
-        <fuel_outrecipes> <val>sfr_spent_mox_recipe</val> <val>sfr_spent_mox_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>astrid_fuel_legacy</val> <val>astrid_fuel_new</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>astrid_unf</val> <val>astrid_unf</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val> <val>3.0</val>                   </fuel_prefs>
-        <cycle_time>12</cycle_time>
-        <refuel_time>2</refuel_time>
-        <assem_size>5568</assem_size>
-        <n_assem_core>4</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>600</power_cap>
-      </Reactor>
-    </config>
-</facility>
-
-
- <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>lwr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1110</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
-   <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>rebus</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <fuel_prefs>      <val>1.0</val>                   </fuel_prefs>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1628</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
-
-<facility>
-        <!-- Stores uox unf for 48 months (4years) -->
-    <name>sfr_storage</name>
-    <config>
-        <Storage>
-            <in_commods>
-                <val>astrid_unf</val>
-            </in_commods>
-            <residence_time>36</residence_time>
-            <out_commods>
-                <val>cooled_sfr</val>
-            </out_commods>
-        </Storage>
-    </config>
-</facility>
-
-
-<facility>
-        <!-- Stores uox unf for 48 months (4years) -->
-    <name>uox_storage</name>
-    <config>
-        <Storage>
-            <in_commods>
-                <val>uox_waste</val>
-            </in_commods>
-            <residence_time>36</residence_time>
-            <out_commods>
-                <val>cooled_uox</val>
-            </out_commods>
-        </Storage>
-    </config>
-</facility>
-
-
-
-  <facility>
-    <name>enrichment</name>
-    <config>
-      <Enrichment>
-        <feed_commod>natl_u</feed_commod>
-        <feed_recipe>natl_u_recipe</feed_recipe>
-        <product_commod>uox</product_commod>
-        <tails_assay>0.003</tails_assay>
-        <tails_commod>tailings</tails_commod>
-        <swu_capacity>1e100</swu_capacity>
-        <initial_feed>1e100</initial_feed>
-      </Enrichment>
-    </config>
-  </facility>
-
-
-  <facility>
-    <name>nat_u_source</name>
-    <config>
-        <Source>
-        <outcommod>natl_u</outcommod>
-        <outrecipe>natl_u_recipe</outrecipe>
-        <throughput>1e100</throughput>
-      </Source>
-    </config>
-  </facility>
-
-
-  <facility>
-    <name>unf_source</name>
-    <config>
-      <Source>
-        <outcommod>pre_2013_unf</outcommod>
-        <outrecipe>udb_precise</outrecipe>
-        <throughput>1e100</throughput>
-        <inventory_size>68072030</inventory_size>
-      </Source>
-    </config>
-  </facility>
-
-  <facility>
-    <name>SomeSink</name>
-    <config>
-      <Sink>
-        <in_commods>
-          <val>tailings</val>
-          <val>lwr_reprocess_waste</val>
-          <val>sfr_reprocess_waste</val>
-        </in_commods>
-        <capacity>1e100</capacity>
-      </Sink>
-    </config>
-  </facility>
-
-  <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
-
-  <region>
-    <name>us</name>
-    <config><NullRegion/></config>
-    <institution>
-      <name>us_government</name>
-      <config>
-        <DeployInst>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/prototypes)'/>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/build_times)'/>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/n_build)'/>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/lifetimes)'/>
-        </DeployInst>
-      </config>
-    </institution>
-
-    <institution>
-    <name>lwr_inst</name>
-    <config>
-      <DeployInst>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
-      </DeployInst>
-    </config>
-  </institution>
-
-  <institution>
-  <name>sfr_inst</name>
-    <config>
-    <DeployInst>
-      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
-      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/build_times)'/>
-      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/n_build)'/>
-      <xi:include href='./sfr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
-    </DeployInst>
-    </config>
-  </institution>
-
-
-  </region>
-
-
-<region>
-    <name>Sink_Source</name>
-    <config><NullRegion/></config>
-        <institution>
-         <name>Sink_Source_Inst</name>
-         <config>
-         <DeployInst>
-            <prototypes>
-              <val>reprocessing</val>
-              <val>sfr_reprocessing</val>
-              <val>sfr_mixer_lwr</val>
-              <val>sfr_mixer_sfr</val>
-            </prototypes>
-            <build_times>
-              <val>79</val>
-              <val>79</val>
-              <val>79</val>
-              <val>79</val>
-            </build_times>
-            <n_build>
-              <val>1</val>
-              <val>1</val>
-              <val>1</val>
-              <val>1</val>
-            </n_build>
-            <lifetimes>
-              <val>99999</val>
-              <val>99999</val>
-              <val>99999</val>
-              <val>99999</val>
-            </lifetimes>
-          </DeployInst>
-        </config>
-        <initialfacilitylist>
-          <entry>
-            <number>1</number>
-            <prototype>nat_u_source</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>enrichment</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>SomeSink</prototype>
-          </entry>
-
-          <entry>
-            <number>1</number>
-            <prototype>sfr_storage</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>uox_storage</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>unf_source</prototype>
-          </entry>
-        </initialfacilitylist>
-        </institution>
-</region>
-<xi:include href='recipes.xml' xpointer='xpointer(/root/recipe)'/>
-</simulation>
\ No newline at end of file

From fb8307cefba553435dedb207a759889567ab598e Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 11:02:58 -0500
Subject: [PATCH 26/35] delete images

---
 input/master_us/images/legacy_power.png | Bin 11451 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 input/master_us/images/legacy_power.png

diff --git a/input/master_us/images/legacy_power.png b/input/master_us/images/legacy_power.png
deleted file mode 100644
index 8f08f1d802159effdec13c35275b6a3d22e4aa92..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From e10d3b2f8bbd97272f69134126588ebc44f70178 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 11:07:26 -0500
Subject: [PATCH 27/35] delete write input

---
 scripts/write_input.py | 511 -----------------------------------------
 1 file changed, 511 deletions(-)
 delete mode 100644 scripts/write_input.py

diff --git a/scripts/write_input.py b/scripts/write_input.py
deleted file mode 100644
index 1eabd9ea..00000000
--- a/scripts/write_input.py
+++ /dev/null
@@ -1,511 +0,0 @@
-import sys
-import jinja2
-import numpy as np
-import os
-
-# tells command format if input is invalid
-
-if len(sys.argv) < 4:
-    print('Usage: python write_input.py [csv]' +
-          '[init_date] [duration] [output_file_name]')
-
-
-def delete_file(file):
-    """Deletes a file if it exists.
-
-    Parameters
-    ----------
-    file: str
-        filename to delete, if it exists
-
-    Returns
-    -------
-    null
-
-    """
-    if os.path.exists('./' + file) is True:
-        os.system('rm ' + file)
-
-
-def read_csv(csv_file):
-    """This function reads the csv file and returns the list.
-
-    Parameters
-    ---------
-    csv_file: str
-        csv file that lists country, reactor name, net_elec_capacity etc.
-
-    Returns
-    -------
-    reactor_array:  list
-        array with the data from csv file
-    """
-    reactor_array = np.genfromtxt(csv_file,
-                                  skip_header=1,
-                                  delimiter=',',
-                                  dtype=('S128', 'S128',
-                                         'S128', 'int',
-                                         'S128', 'S128', 'int',
-                                         'int', 'S128',
-                                         'int', 'int',
-                                         'int', 'int',
-                                         'int', 'float'),
-                                  names=('country', 'reactor_name',
-                                         'type', 'net_elec_capacity',
-                                         'status', 'operator', 'const_date',
-                                         'cons_year', 'first_crit',
-                                         'entry_time', 'lifetime',
-                                         'first_grid', 'commercial',
-                                         'shutdown_date', 'ucf'))
-    return filter_test_reactors(reactor_array)
-
-
-def filter_test_reactors(reactor_array):
-    """This function filters experimental reactors that have a
-       net electricity capacity less than 100 MWe
-
-    Parameters
-    ---------
-    reactor_array: list
-        array with reactor data.
-
-    Returns
-    -------
-    array
-        array with the filtered data
-    """
-    hitlist = []
-    count = 0
-    for data in reactor_array:
-        if data['net_elec_capacity'] < 100:
-            hitlist.append(count)
-        count += 1
-    return np.delete(reactor_array, hitlist)
-
-
-def get_ymd(yyyymmdd):
-    """This function extracts year and month value from yyyymmdd format
-
-        The month value is rounded up if the day is above 16
-
-    Parameters
-    ---------
-    yyyymmdd: int
-        date in yyyymmdd format
-
-    Returns
-    -------
-    year: int
-        year
-    month: int
-        month
-    """
-
-    year = yyyymmdd // 10000
-    month = (yyyymmdd // 100) % 100
-    day = yyyymmdd % 100
-    if day > 16:
-        month += 1
-    return (year, month)
-
-
-def get_lifetime(start_date, end_date):
-    """This function gets the lifetime for a prototype given the
-       start and end date.
-
-    Parameters
-    ---------
-    start_date: int
-        start date of reactor - first criticality.
-    end_date: int
-        end date of reactor - null if not listed or unknown
-
-    Returns
-    -------
-    lifetime: int
-        lifetime of the prototype in months
-
-    """
-
-    if end_date != -1:
-        end_year, end_month = get_ymd(end_date)
-        start_year, start_month = get_ymd(start_date)
-        dyear = end_year - start_year
-        dmonth = end_month - start_month
-        if dmonth < 0:
-            dyear -= 1
-            start_month += 12
-        dmonth = end_month - start_month
-
-        return (12 * dyear + dmonth)
-    else:
-        return 720
-
-
-def get_entrytime(init_date, start_date):
-    """This function converts the date format and saves it in variables.
-
-        All dates are in format - yyyymmdd
-
-    Parameters
-    ---------
-    init_date: int
-        start date of simulation
-    start_date: int
-        start date of reactor - first criticality.
-
-    Returns
-    -------
-    entry_time: int
-        timestep of the prototype to enter
-
-    """
-
-    init_year, init_month = get_ymd(init_date)
-    start_year, start_month = get_ymd(start_date)
-
-    dyear = start_year - init_year
-    dmonth = start_month - init_month
-    if dmonth < 0:
-        dyear -= 1
-        start_month += 12
-    dmonth = start_month - init_month
-
-    entry_time = 12 * dyear + dmonth
-
-    return entry_time
-
-
-def read_template(template):
-    """ Returns a jinja template
-
-    Parameters
-    ---------
-    template: str
-        template file that is to be stored as variable.
-
-    Returns
-    -------
-    output_template: jinja template object
-        output template that can be 'jinja.render' -ed.
-
-    """
-
-    # takes second argument file as reactor template
-    with open(template, 'r') as fp:
-        input_template = fp.read()
-        output_template = jinja2.Template(input_template)
-
-    return output_template
-
-
-def refine_name(name_data):
-    """ Takes the name data and decodes and refines it.
-
-    Parameters
-    ----------
-    name_data: str
-        reactor name data from csv file
-
-    Returns
-    -------
-    name: str
-        refined and decoded name of reactor
-    """
-    name = name_data.decode('utf-8')
-    start = name.find('(')
-    end = name.find(')')
-    if start != -1 and end != -1:
-        name = name[:start]
-    return name
-
-
-def reactor_render(reactor_data, output_file, is_cyborg=False):
-    """Takes the list and template and writes a reactor file
-
-    Parameters
-    ----------
-    reactor_data: list
-        list of data on reactors
-    template: jinja.template
-        jinja template for reactor file
-    mox_template: jinja.template
-        jinja template for mox reactor file
-    output_file: str
-        name of output file
-    is_cyborg: bool
-        if True, uses Cyborg templates
-
-    Returns
-    -------
-    The reactor section of cyclus input file
-
-    """
-
-    template_path = '../templates/[reactor]_template_cyborg.xml.in'
-
-    if not is_cyborg:
-        template_path = template_path.replace('_cyborg', '')
-
-    pwr_template = read_template(template_path.replace('[reactor]', 'pwr'))
-    mox_reactor_template = read_template(
-        template_path.replace('[reactor]', 'mox'))
-    candu_template = read_template(template_path.replace('[reactor]', 'candu'))
-
-    ap1000_spec = {'template': pwr_template,
-                   'kg_per_assembly': 446.0,
-                   'assemblies_per_core': 157 / 1110.0,
-                   'assemblies_per_batch': 157 / 3330.0}
-    bwr_spec = {'template': pwr_template,
-                'kg_per_assembly': 180,
-                'assemblies_per_core': 764 / 1000.0,
-                'assemblies_per_batch': 764 / 3000.0}
-    phwr_spec = {'template': candu_template,
-                 'kg_per_assembly': 8000 / 473,
-                 'assemblies_per_core': 473 / 500.0,
-                 'assemblies_per_batch': 60}
-    candu_spec = {'template': candu_template,
-                  'kg_per_assembly': 8000 / 473,
-                  'assemblies_per_core': 473 / 500.0,
-                  'assemblies_per_batch': 60}
-    pwr_spec = {'template': pwr_template,
-                'kg_per_assembly': 446.0,
-                'assemblies_per_core': 193 / 1000.0,
-                'assemblies_per_batch': 193 / 3000.0}
-    epr_spec = {'template': pwr_template,
-                'kg_per_assembly': 467.0,
-                'assemblies_per_core': 216,
-                'assemblies_per_batch': 72}
-
-    reactor_specs = {'AP1000': ap1000_spec,
-                     'PHWR': phwr_spec,
-                     'BWR': bwr_spec,
-                     'CANDU': candu_spec,
-                     'PWR': pwr_spec,
-                     'EPR': epr_spec}
-
-    for data in reactor_data:
-        # refine name string
-        name = refine_name(data['reactor_name'])
-        reactor_type = data['type'].decode('utf-8')
-        if reactor_type in reactor_specs.keys():
-            # if the reactor type matches with the pre-defined dictionary,
-            # use the specifications in the dictionary.
-            spec_dict = reactor_specs[reactor_type]
-            reactor_body = spec_dict['template'].render(
-                country=data['country'].decode('utf-8'),
-                type=reactor_type,
-                reactor_name=name,
-                assem_size=round(spec_dict['kg_per_assembly'], 3),
-                n_assem_core=int((spec_dict['assemblies_per_core']
-                                       * data['net_elec_capacity'])),
-                n_assem_batch=int((spec_dict['assemblies_per_batch']
-                                        * data['net_elec_capacity'])),
-                capacity=data['net_elec_capacity'])
-        else:
-            # assume 1000MWe pwr linear core size model if no match
-            reactor_body = pwr_template.render(
-                country=data['country'].decode('utf-8'),
-                reactor_name=name,
-                type=reactor_type,
-                assem_size=523.4,
-                n_assem_core=int(
-                    round(data['net_elec_capacity'] / 1000 * 193)),
-                n_assem_batch=int(
-                    round(data['net_elec_capacity'] / 3000 * 193)),
-                capacity=data['net_elec_capacity'])
-
-        with open(output_file, 'a') as output:
-            output.write(reactor_body)
-
-
-def input_render(init_date, duration, reactor_file,
-                 region_file, output_file, reprocessing):
-    """Creates total input file from region and reactor file
-
-    Parameters
-    ---------
-    init_date: int
-        date of desired start of simulation (format yyyymmdd)
-    reactor_file: str
-        jinja rendered reactor section of cyclus input file
-    region_file: str
-        jinja rendered region section of cylcus input file
-    output_file: str
-        name of output file
-    reprocessing: bool
-        True if reprocessing is done, false if ignored
-
-    Returns
-    -------
-    A complete cylus input file.
-
-    """
-    template = read_template('../templates/input_template.xml.in')
-    with open(reactor_file, 'r') as fp:
-        reactor = fp.read()
-    with open(region_file, 'r') as bae:
-        region = bae.read()
-
-    startyear, startmonth = get_ymd(init_date)
-
-    # has reprocessing chunk if reprocessing boolean is true.
-    if reprocessing is True:
-        reprocessing_chunk = ('<entry>\n'
-                              + '  <number>1</number>\n'
-                              + '  <prototype>reprocessing</prototype>\n'
-                              + '</entry>')
-    else:
-        reprocessing_chunk = ''
-    # renders template
-    rendered_template = template.render(duration=duration,
-                                        startmonth=startmonth,
-                                        startyear=startyear,
-                                        reprocessing=reprocessing_chunk,
-                                        reactor_input=reactor,
-                                        region_input=region)
-
-    with open(output_file, 'w') as output:
-        output.write(rendered_template)
-
-    os.system('rm reactor_output.xml.in region_output.xml.in')
-
-
-def region_render(reactor_data, output_file):
-    """Takes the list and template and writes a region file
-
-    Parameters
-    ---------
-    reactor_data: list
-        list of data on reactors
-    output_file: str
-        name of output file
-
-    Returns
-    -------
-    The region section of cyclus input file
-
-    """
-    # template only has prototype, buildtime, n_build and lifetime
-    template = read_template('../templates/deployinst_template.xml.in')
-    # full template is the bigger template for the `region block'.
-    full_template = read_template('../templates/region_output_template.xml.in')
-    country_list = []
-    empty_country = []
-
-    valhead = '<val>'
-    valtail = '</val>'
-
-    # creates list of countries and turns it into a set
-    for data in reactor_data:
-        country_list.append(data['country'].decode('utf-8'))
-    country_set = set(country_list)
-
-    for country in country_set:
-        prototype = ''
-        entry_time = ''
-        n_build = ''
-        lifetime = ''
-
-        # for every reactor data corresponding to a country, create a
-        # file with its `region block`
-        for data in reactor_data:
-            if data['country'].decode('utf-8') == country:
-                if data['lifetime'] == 0:
-                    continue
-                prototype += (valhead
-                              + refine_name(data['reactor_name'])
-                              + valtail + '\n')
-                entry_time += (valhead
-                               + str(data['entry_time']) + valtail + '\n')
-                n_build += valhead + '1' + valtail + '\n'
-                lifetime += valhead + str(data['lifetime']) + valtail + '\n'
-
-        render_temp = template.render(prototype=prototype,
-                                      start_time=entry_time,
-                                      number=n_build,
-                                      lifetime=lifetime)
-        # if nothing is rendered the length will be less than 100:
-        if len(render_temp) > 100:
-            with open(country, 'a') as output:
-                output.write(render_temp)
-        else:
-            empty_country.append(country)
-
-    # remove the countries with no values
-    for country in empty_country:
-        country_set.remove(country)
-
-    for country in country_set:
-        # jinja render region template for different countries
-        with open(country, 'r') as ab:
-            country_input = ab.read()
-            country_body = full_template.render(
-                country=country,
-                country_gov=(country
-                             + '_government'),
-                deployinst=country_input)
-
-        # write rendered template as 'country'_region
-        with open(country + '_region', 'a') as output:
-            output.write(country_body)
-
-        # concatenate the made file to the final output file and remove temp
-        os.system('cat ' + country + '_region >> ' + output_file)
-        os.system('rm ' + country)
-        os.system('rm ' + country + '_region')
-
-
-def main(csv_file, init_date, duration, output_file, reprocessing=True):
-    """ Generates cyclus input file from csv files and jinja templates.
-
-    Parameters
-    ---------
-    csv_file : str
-        csv file containing reactor data (country, name, net_elec_capacity)
-    init_date: int
-        yyyymmdd format of initial date of simulation
-    input_template: str
-        template file for entire complete cyclus input file
-    output_file: str
-        directory and name of complete cyclus input file
-    reprocessing: bool
-        True if reprocessing is done, False if not
-
-    Returns
-    -------
-    File with reactor section of cyclus input file
-    File with region section of cyclus input file
-    File with complete cyclus input file
-
-    """
-
-    # deletes previously existing files
-    delete_file(output_file)
-    reactor_output_filename = 'reactor_output.xml.in'
-    region_output_filename = 'region_output.xml.in'
-    # read csv and templates
-    csv_database = read_csv(csv_file)
-    kill_indx = []
-    
-    for indx, data in enumerate(csv_database):
-        entry_time = get_entrytime(init_date, int(data['first_crit']))
-        lifetime = get_lifetime(int(data['first_crit']), int(data['shutdown_date']))
-        if int(data['shutdown_date']) < init_date and data['shutdown_date'] != -1:
-            kill_indx.append(indx)
-        if entry_time <= 0:
-            lifetime = lifetime + entry_time
-            if lifetime < 0:
-                lifetime = 0
-            entry_time = 1
-        data['entry_time'] = entry_time
-        data['lifetime'] = lifetime
-    # renders reactor / region / input file.
-    reactor_render(csv_database, reactor_output_filename)
-    region_render(csv_database, region_output_filename)
-    input_render(init_date, duration, reactor_output_filename,
-                 region_output_filename, output_file, reprocessing)
-
-
-if __name__ == "__main__":
-    main(sys.argv[1], int(sys.argv[2]), int(sys.argv[3]), sys.argv[4])

From 551a9341d562ce0cbebecd53a0180d8f58e909cf Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 12:12:42 -0500
Subject: [PATCH 28/35] change write input

---
 input/master_us/get_deployment.ipynb | 158 +++++++++++++--------------
 1 file changed, 76 insertions(+), 82 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 53013ecc..dcef79b5 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,80 +20,46 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import analysis as an"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# sqlite file with only the legacy lwrs from 2013/05"
+    "# get_deployment\n",
+    "This jupyter notebook uses a Cyclus simulation output with only the legacy U.S. fleet and calculates the deployment scheme for a given reactor design and power demand. This process takes three steps:\n",
+    "1. Get current power capacity vs time\n",
+    "2. Deploy lwrs until new reactor designs become available\n",
+    "3. Deploy advanced reactors to meet power demand"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get sqlite cursor and simulation time data\n",
-    "cur = an.get_cursor('./cyclus.sqlite')\n",
-    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
+    "import write_input.demand_deploy as dd\n",
+    "import analysis as an"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sink_Source_Inst has no values\n",
-      "Sink_Source_Inst has no values\n"
+     "ename": "OperationalError",
+     "evalue": "no such table: info",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-c4c26a8d494e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# sqlite file with only the legacy lwrs from 2013/05\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcur\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0man\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./legacy_only.sqlite'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0minit_year\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_month\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimestep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0man\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_timesteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mpower_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0man\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_power_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpower_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/github/master/scripts/analysis.py\u001b[0m in \u001b[0;36mget_timesteps\u001b[0;34m(cur)\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0mlinspace\u001b[0m \u001b[0mup\u001b[0m \u001b[0mto\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[0;32m--> 177\u001b[0;31m     info = cur.execute('SELECT initialyear, initialmonth, '\n\u001b[0m\u001b[1;32m    178\u001b[0m                        'duration FROM info').fetchone()\n\u001b[1;32m    179\u001b[0m     \u001b[0minit_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'initialyear'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOperationalError\u001b[0m: no such table: info"
      ]
     }
    ],
    "source": [
-    "# get number of reactors and capacity timeseries\n",
-    "an.plot_power(cur)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image('power_plot.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "# sqlite file with only the legacy lwrs from 2013/05\n",
     "cur = an.get_cursor('./legacy_only.sqlite')\n",
     "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
     "power_dict = an.get_power_dict(cur)\n",
@@ -104,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 92,
    "metadata": {
     "scrolled": true
    },
@@ -112,16 +78,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f7685cb34e0>]"
+       "[<matplotlib.lines.Line2D at 0x7fea0745d208>]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -131,15 +97,15 @@
     }
    ],
    "source": [
-    "lwr_deploy_array, lwr_deployed_power = an.get_new_deployment(power_dict, ['us_government'],\n",
-    "                                                     '100*(1.009)**(t/12)', 1.11, 960, 240, False)\n",
+    "lwr_deploy_array, lwr_deployed_power = dd.get_new_deployment(power_dict, ['us_government'],\n",
+    "                                                     '100', 1.11, 960, 360, False)\n",
     "power_dict['lwr'] = lwr_deployed_power\n",
     "plt.plot(lwr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,11 +115,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep.xml', 960)"
+    "dd.write_deployinst(lwr_deploy_array, 'lwr', 'lwr_dep.xml', 960)"
    ]
   },
   {
@@ -165,12 +131,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -182,7 +148,7 @@
    "source": [
     "x = np.arange(0, 1684)\n",
     "years = 2020 + x/12\n",
-    "y = 100*(1.009)**(x/12)\n",
+    "y = 100*(1.000)**(x/12)\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
     "plt.plot(years, y, label='power demand')\n",
     "plt.xlabel('Years')\n",
@@ -200,22 +166,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f768569f5c0>]"
+       "95.0"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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t0BeplwYPhzjKM7OqQzNpoRs3hX4CaKPuX2r5dWNFX+A2yPaVtKNzk1ZvlkJfJMEK5XWD5HhBUX752r3LZlVtVHFwVqOv7DwK/QRI2DYlBdTl71jRF7nxbWFRXCM37r1n4t57qFxFQ9/M7jCzLWb2hwKPm5ndaGadZvaSmc2OvkyR/qMWowIJHWnoodqXUO91kNRVXkpP/yfA+X08fgEwM/iZD/y4+rJEpBTZXm5+Z7eiE6/V89w7BZ6qmiNyq1HVlbOS1dEvHvru/jSwvY9ZLgbu8oxFwFgzmxRVgRL/x1eJRj3+jhU9Q4ybl+VdI7eyN6vo6qlEqU/fKP/HVkohZtYG/Mbd3xvy2G+AG9z998H9x4Cvu3tHX222t7d7R0efs/Qbtz29lo079jF+xBDuem4dp00ZwxOrujhu1BA+eNIEhg4ayH0vbCjazojBA/nyeTO57ZnXuPj0E3CHK889iX957FWuv+gUDhw+wv9c+Aojhwzk4tMns3XPATZs38flZ7Xx62VvcO/i17nyQyfx4fccx81PdDJ3+gTeM3EU33noZa698GTWdO3l6Ve7+PJ5M3nq1S7Wbd3LFR9s49an1/C+KWOZPG4Y//bMa/zDRbN4/JUtbN1zgMvmtHLT46s5e0YzY4YN4qfPv861F57M7b9/jVknjObE5hH8+Mk1nHLCaL5+/3I+evLx/G7lZt7fNo4X1u0AYPLYYVw2ZypPruqiY/2Oouth0EBj8MAB7D14hOktI7j2gpP54l0dLLjqbB5Y+gYHjxxly679/Ne50/jFko28ve8gl81p5dGXN7Nu2z7OmdHMn79/Kh/5pyf5P39+Bk+9uoWDh4/iwEWnncCV9yzh3z//fn65ZCObd+3n8rlt3PnsOiaNHcrs1nH870dW8Y15J2MYG3bs4+wZzXztly9y0WmZv8mdz67jhk+fylfuWwbAP1w0ix890cm2vQd7vZbBAwdw8MjR7vtnTZ/Ac2u3lbZhVWDmcSNZvWVPn/O0jh/O69v30TxyMFv3ZGo+sXkEBw8fZfSwQazctAuAC0+dyNquvex85xDTJgxn0drtXD53Gpt37eeRlzfzqdmTeWDpGwDMaRvP8+v66js2vuNGDWHL7gMlzZtdh9NbRrC2a2+Px+74XDsfec/xFddhZkvcvb3i5SMI/YeA7+aF/t+7+5KQeeeTGQKitbX1zPXr11dad6K0Xf1QJO2MGDyQvQeP9Jg277RJPPTSJm7+i9m88tYufvh4Z6/l1t0wr0cNufe/9ol38/2HV3HVh2dw0xOdvR7Pvd0+bRwd63dw/5Uf5NM/frbX4yc2j+C1rXt56mvn8qHvPwnAOTObeWb11khef7lGD21i1/7DoY/Nbh3L0tffrnNFIpn/kyf+7tyKl6829KPYe2cjMDXn/hTgzbAZ3f1Wd2939/aWlpYInlpy37SPVjGOW8oeCEeKtH/kaO/Hw6bVS6HABzjSGJ+0JYXeyeu41VsUob8A+GywF89cYKe7b4qgXSlBgwwTikhCNBWbwcx+CpwLNJvZRuCbwCAAd78FWAhcCHQC+4DP16pYERGpTtHQd/fLijzuwJciq0jKop6+iJRDR+QmXNKOBhSReCn0RURSRKGfcBreEZFyKPQTTpkvIuVQ6Cdcbk+/okvleenLFpsn7PsFfRIRaSwK/QQJPxmVUlVESqfQT5Bip8yo5NSy2WVyly30PMXat5CTzfaHU/6K9CcK/YSrxfCJhmRE+i+FvohIiij0E64WnfJy2myUc4SLSGkU+glXi9Atp01lvkiyKPRFRFJEoZ9wsQ/v1OD5RaR2FPoJV9kBWX0vVE6bGtMXSRaFfsL1vKh0icuEHMXbY1qB/ntY+z2eX0fkijQ8hX7CqactIuVQ6PcjpR79mvs2EX5Ebunt586rI3JFGp9CP4Wi/HSgi7iIJItCv59Qj1pESqHQT5Cws2yWc2rk7mWKPV7W3julz9tQElu4SHUU+gkSNixTyfBKJefFj7L9RpCAEkVqQqGfcNmANStjl82cyAvdZbNAO6G7bIbs6lnJbqT11qh1idSaQj9BohreEX0BLeml0E+QqC6i0mM3y7BdNstoPzc8s7ts5tbZqF8w601S0qqk0Dez881slZl1mtnVIY9/zsy6zGxZ8PPF6EuVMGFDNWUtHzq84wVuF14+t5YkDO+IpFVTsRnMbCBwM/AxYCPwgpktcPeX82b9mbtfVYMapQ+5Y/oiIsWU0tOfA3S6+1p3PwjcB1xc27KkXO61Gd4Jm7dHWzm3jw3v9L1MI9AnEEmrUkJ/MrAh5/7GYFq+T5vZS2b2SzObGkl1UlQl2VXsS8yeQzZF2gpNz8ZP1MavUKQ2Sgn9sL5a/v/M/wXa3P004HfAnaENmc03sw4z6+jq6iqvUulTo/aoRaSxlBL6G4HcnvsU4M3cGdx9m7sfCO7eBpwZ1pC73+ru7e7e3tLSUkm9kq+SI3KLdt9z5y3yqaCS9huAzk4qaVVK6L8AzDSzE81sMHApsCB3BjOblHP3k8DK6EqUvlR0RG4ZbVZyygbFqUjjKrr3jrsfNrOrgIeBgcAd7r7CzL4NdLj7AuDLZvZJ4DCwHfhcDWuWHBUdkRuyG2ahI3KLHqkbtstmCUf3xq1R65L+L+4DA4uGPoC7LwQW5k27Puf2NcA10ZYmpWjE7Ip7oxZpZHF3OHREboKEnYah5+OltVP0Iio95u376NrwI3LLr6ne9MYkaaXQT5DQs2wWOWI2vJ3etwsfkRu+XOjjQZAeraCmemvUuqT/i3vTU+gnXNwbUBgFqkhhcf9/KPQTJKrhHUKGX8o74Vr43j1Jukau3pckrRT6CRI+vFNBO+UckVtsSCdkhqNxd2VKoP30JT7xbnsK/YSr5IyWRcf0e/TkQ3bvLPT83WP65ddUbw1alqRA3P8TCv0ECR3eiXsLCqFetEhhcf93KPQTJLKLqIQs02PZAj317DyFhn+6d9msoKa6i/s/TyQmCv2Eq2x4p8gRuUXa77F8yFBQJbuR1luDliUpEPcnYYV+gvR1jdxG0og1iTSKuP89FPoJUq/hnUJ753QP7xRoLEnDO3H3tkTiotBPuEqukVve3jvF5u293NGjGt4RKSTu/wmFfsLVegOq7DgAESkk7k+ZCv1+pPThnd5DNoWGd8KO3i306SJJJ1wTiUvcnSKFfsJVdO76osM7x4SePK3IgVyJ2HunQesSqTWFfoKE5VQppzzI/zhZbInc+cPaL7ab6NEEBGoSThUh/ZTG9KVUceRUJU+pc9WLFBb3f4dCvx8pNH6e/2YRNuZeeJfN3vMWGgpK0pi+OvqSVgr9Govym/pyzo7Zc7nC7YSN6fdsM+yEax7+eKIuotKghUm/F/e2p9BPkGKnOS68XOUbWWXDO40vCTVK/xT3tqfQr7F6vqmXvMtmlMM7Ibtshu3m2WjU0Ze4xL3tKfRrLMq/b1hbpRyR23t4J+d2kSNyw4ZqCr2mRA3vxN7fEomHQr/GIh2/62P3yL6epvcXuaV/NxC6y2aRUE/GLptxVyBpFXeHQ6EvfaroNAyN2r3PkYASpZ+Ke9srKfTN7HwzW2VmnWZ2dcjjQ8zsZ8Hji82sLepCkyra4Z3CrZXzWLGNrujBW0WeNxl5mowqRaJWNPTNbCBwM3ABMAu4zMxm5c32BWCHu88A/hn4XtSFJlWkozuhQyle8LFKayh2RG6hK2uFLd+oNLwjcYl70yulpz8H6HT3te5+ELgPuDhvnouBO4PbvwTOs9ALukrSVDa8E30dUUvCG5P0UzFvelb8whx2CXC+u38xuH858AF3vypnnj8E82wM7q8J5tlaqN329nbv6Ogou+CnXu3iO795uezl4nLUnTVde2v+PBNHD6VrzwGOhHRhT2oZ0aOGtgnDWbdtHwBDmgZw4PBRBtix3u+0CcNZHzyeezvrhDFDeXPn/oKPTxw9lLd27Y/stYn0N9+YdzJfPGd6Rcua2RJ3b6/0uUvp6Yf12POTpZR5MLP5ZtZhZh1dXV2l1NfLyCFNzDx+ZGJ+3j1xFGfPmMCpk8cwfPDAXq9nSNMApjePKOm1z50+npnHjQRgyrhhHD96CB+bdTwAs6eN5ePBbYD3t41j5nEjGTWkiXdPHMWkMUMBmHHcSGadMBozOH3qWD7ynuMA+MQpEzlz2jgATjlhNO86fiQjhzRxygmjGTxwAKdOHsOH390CwOmtY5nePIKxwwdxygmjATijdSznzGzurmVI0wBmTRrNeUH7Hz35uB6vJf9z4LzTJnU/f1+fEbPPl+v0qWMBup8fYMTggT3uTx47rPv2lHHDul/LjGB9Zr0vaOs9E0f1mD+rZdQQIPPG9r6pY2kdP5z3Ts7UNKRpAAMHZIqfOn4YzSMH0zp+OONHDAagaUDvF5b7PAB/dsbkHq+pGqOHNjFqSFOP13bW9AmMHNLUY90ADG4Kj4JRQ5tCpwOMGTao17RTJ49h8MBMW2Hbddgy+T49ewoAc9rGA3DluSf1mmfkkJ51TZswvGB72b/9x2Ydz/mnTOyePu/UST3mO6M1fJ1PHD2UE4v8j+bW867jRzIh+Ju/6/iRvea94L0TaR45pM/2aqmUnv5ZwLfc/RPB/WsA3P27OfM8HMzznJk1AW8BLd5H45X29EVE0qwePf0XgJlmdqKZDQYuBRbkzbMAuCK4fQnweF+BLyIi8Sj82S3g7ofN7CrgYWAgcIe7rzCzbwMd7r4AuB2428w6ge1k3hhERKTBFA19AHdfCCzMm3Z9zu39wGeiLU1ERKKmI3JFRFJEoS8ikiIKfRGRFFHoi4ikiEJfRCRFih6cVbMnNusC1le4eDNQ8BQPDSyJdavm+lDN9ZPEunNrnubuLZU2FFvoV8PMOqo5Ii0uSaxbNdeHaq6fJNYdZc0a3hERSRGFvohIiiQ19G+Nu4AKJbFu1Vwfqrl+klh3ZDUnckxfREQqk9SevoiIVCBxoV/sIu1xMbOpZvaEma00sxVm9pVg+rfM7A0zWxb8XJizzDXB61hlZp+Iqe51ZrY8qK0jmDbezB41s9XB73HBdDOzG4OaXzKz2THU++6cdbnMzHaZ2VcbcT2b2R1mtiW4slx2Wtnr1syuCOZfbWZXhD1XjWv+vpm9EtT1oJmNDaa3mdk7Oev8lpxlzgy2q87gddXs8qkFai57e6hnthSo+Wc59a4zs2XB9GjXs7sn5ofMqZ3XANOBwcCLwKy46wpqmwTMDm6PAl4lcyH5bwF/FzL/rKD+IcCJwesaGEPd64DmvGn/C7g6uH018L3g9oXAb8lcKW0usLgBtoe3gGmNuJ6BPwZmA3+odN0C44G1we9xwe1xda7540BTcPt7OTW35c6X187zwFnB6/ktcEGday5re6h3toTVnPf4PwHX12I9J62nX8pF2mPh7pvcfWlwezewEpjcxyIXA/e5+wF3fw3oJPP6GkHuhe7vBP40Z/pdnrEIGGtmk8IaqJPzgDXu3tdBfrGtZ3d/msz1JfLrKWfdfgJ41N23u/sO4FHg/HrW7O6PuPvh4O4iYEpfbQR1j3b35zyTTHdx7HVGrsB6LqTQ9lDXbOmr5qC3/l+An/bVRqXrOWmhPxnYkHN/I30HayzMrA04A1gcTLoq+Gh8R/bjPI3zWhx4xMyWmNn8YNrx7r4JMm9mQPYCt41Sc9al9PzHaOT1nFXuum20+v+KTI8y60Qz+08ze8rMzgmmTSZTZ1ZcNZezPTTSej4H2Ozuq3OmRbaekxb6JV2APU5mNhK4H/iqu+8CfgycBJwObCLzsQ0a57Wc7e6zgQuAL5nZH/cxb6PUjGUu3flJ4BfBpEZfz8UUqrNh6jez64DDwL3BpE1Aq7ufAfx34D/MbDSNUXO520Mj1Jx1GT07M5Gu56SF/kZgas79KcCbMdXSi5kNIhP497r7AwDuvtndj7j7UeA2jg0tNMRrcfc3g99bgAfJ1Lc5O2wT/N4SzN4QNQcuAJa6+2Zo/PWco9x12xD1B18gXwT8ZTCUQDBEsi24vYTMmPi7yNScOwRU95or2B4aZT03AZ8CfpadFvV6Tlrol3KR9lgE43C3Ayvd/Qc503PHvP8MyH5bvwC41MyGmNmJwEwyX8rUjZmNMLNR2dtkvrBEDdSSAAABSElEQVT7Az0vdH8F8Oucmj8b7GkyF9iZHaqIQY/eUCOv5zzlrtuHgY+b2bhgiOLjwbS6MbPzga8Dn3T3fTnTW8xsYHB7Opl1uzaoe7eZzQ3+Lz7LsddZr5rL3R4aJVs+Crzi7t3DNpGv51p9O12rHzJ7ObxK5t3uurjryanrj8h8tHoJWBb8XAjcDSwPpi8AJuUsc13wOlZRw70b+qh5Opm9FF4EVmTXJzABeAxYHfweH0w34Oag5uVAe0zrejiwDRiTM63h1jOZN6VNwCEyvbIvVLJuyYyjdwY/n4+h5k4y493Z7fqWYN5PB9vNi8BS4E9y2mknE7RrgJsIDgStY81lbw/1zJawmoPpPwH+Jm/eSNezjsgVEUmRpA3viIhIFRT6IiIpotAXEUkRhb6ISIoo9EVEUkShLyKSIgp9EZEUUeiLiKTI/wfGbp73FkhHsQAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -227,16 +193,17 @@
    "source": [
     "# rebus\n",
     "msr_power = 3.7*0.44\n",
-    "lifetime = 30*12\n",
-    "msr_deploy_array, msr_deployed_power = an.get_new_deployment(power_dict, ['us_government', 'lwr'],\n",
-    "                                                     '100*(1.009)**(t/12)', msr_power, lifetime, 240, True)\n",
+    "lifetime = 40*12\n",
+    "msr_deploy_array, msr_deployed_power = dd.get_new_deployment(power_dict, ['us_government', 'lwr'],\n",
+    "                                                     '100*(1.000)**(t/12)', msr_power, lifetime, 360, True)\n",
     "power_dict['msr'] = msr_deployed_power\n",
-    "plt.plot(msr_deploy_array)"
+    "plt.plot(msr_deploy_array)\n",
+    "sum(msr_deploy_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,11 +214,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(years, lwr_deploy_array, label='LWR construction')\n",
+    "plt.plot(years, msr_deploy_array[:-80], label='MSR construction')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Num. reactors built')\n",
+    "plt.legend()\n",
+    "plt.title('Reactor constuction over time')\n",
+    "plt.savefig('us_dep.png', format='png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
-    "an.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep.xml', 360)"
+    "dd.write_deployinst(msr_deploy_array, 'rebus', 'rebus_dep_fourty.xml', 480)"
    ]
   },
   {
@@ -263,12 +257,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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qlgOFIvIxsBg4HWf8hnMikM0YEyUyUpyvijIrEHElUGd93wG+BozAaaCuKxLjVdVGMTcmjmSkOH0ylVVar67xJNAexBPAeuBxYIGqboxMJGNMtElL8pEgViDiTaACkQ3k4exF3Of2xbQT+BD4UFXfjUA+Y0wUEBEykhMpq7RDTPGk0QKhqjU4o74tB/4kIl1xhgT9CfArnO7AjTFxIj3FZ3sQcSZQG8RwnL2Hulsyzt7DH4FFEUlnjIkaGSmJlNrIcnEl0CGmWThdarwB/KKpsSGMMW1bZkoi5bYHEVcCHWIaFckgxpjolp7sszaIONOSMakFUFUdHp5IxpholJmSyNx1u6mpVXwJ4nUcEwGBDjHV4gw0+Dzwb+BwRBIZY6JSerLzdbGyqJhRJ3XwOI2JhEBdbYwArgQycYrEb3F6dd1h7RHGxJ8bJ54MwN4SG340XgTs7ltV16vqDLc94t/AszinuRpj4kxHt8O+fTb8aNwI2BeTiPQErgAuAQ7gFIeXI5DLGBNl6grEHtuDiBuBxoN4D2evIQmYDkwDXgOSRaRjRNIZY6JGapKPJJ/w75Vfeh3FREigPYg+OI3U3wdubDBd3OknhzGXMSYK9cnJsDOY4kig6yByI5jDGBMDRud24MWPv2D0b+eSf1IHHr863+tIJowCXQfRraluvYOZxxjTdnx3bB9EhFVFxcxbvwtVRcT2KNqqQGcxvR7E8sHMY4xpI4b2zOZ3lwzjoryeHKlRDlVY1xttWaA2iDwRORTgeQECPW+MaaOy05IAKKk4Un/ftD2B2iDC1p23iLQH/goMxWnwvg7YALwI5AJbgW+p6oFwZTDGtFxqsvP1UHHE+mZqywJeKBdGjwBvqupAnEGJ1gE/B+ap6gBgnvvYGBOF0pOcAlFuY1S3aREvECLSDpgIPAmgqlWqWgxcBDzjzvYMcHGksxljgpPm7kEUHbAu2toyUdXIvqDICJzxrtfi7D0sA27F6eOpfYP5DqjqCT2CiciNuNdldO3aNX/OnDktylFaWkpmZmaLlvVCLOWNpawQW3mjJeuXpbXc9f5hRnT2cVt+qt95oiVrsGIpb2uzTpkyZZmqFjQ5o6oGvAEPAkOami/YG1AAVANj3cePAL8Gio+b70BT68rPz9eWmj9/fouX9UIs5Y2lrKqxlTeask6Y+a5e+pdFjT4fTVmDEUt5W5sVWKpBfF8Hc4hpPfCEiCwWkZtEJLsFBauhIqBIVRe7j/8OjAJ2iUh3APfv7la+jjEmjHI7ZbBs2wFqaiN7FMJETpMFQlX/qqqnA9fgnGG0SkSeF5EpLXlBdS6s+0JETnUnnYFzuOlfOP094f59pSXrN8ZExqBuWQAUl1vvrm1VUI3UIuIDBrq3vcBK4HYRaVkDANwCzHZHrRsB/A74PXCmiGwCznQfG2Oi1KDu7QBYus3ORm+rAnb3DSAiDwEXAO8Cv1PVJe5TM0VkQ0teVFU/wWmLON4ZLVmfMSby6grEfhsfos1qskAAa4B7VLXcz3NjQpzHGBMjurVzzl46bNdCtFnBHGL67vHFQUTmAajqwbCkMsZEvdRk5+vjsF1N3WYFGjAo1R0YqJOIdBCRju4tF+gRqYDGmOiU7EvAlyDMXbeL+evtpMO2KNAexPdxLmIbCCx37y/DObvoz+GPZoyJZiJCfp8OrCo6yG9fX+d1HBMGjRYIVX1EVfsC/6mqfRvc8lT1TxHMaIyJUv/7/XFcOaY3uw5W8NGWfV7HMSEW6BDT1927O0Tk0uNvEcpnjIlyuTkZlFRWc+X/fGRnNLUxgc5imoRzausFfp5T4B9hSWSMiSnXnd6X5MQE7n3lU/aUVNIxI9nrSCZEAo0HMcP9e23k4hhjYk1CgtC/i9Nx3L6ySiDL20AmZJo8zVVEfucO8FP3uIOI/Ca8sYwxsaRTZgpgF821NcFcB3GuOuM1AKDOKG/nhS+SMSbW5LiHlf44b7PHSUwoBVMgfCKSUvdARNKAlADzG2PiTE5mCu1SE+2iuTYmmALxHDBPRK4XkeuAdzg68psxxgBwWX4vtu8v58WPt3sdxYRIMN193w/8FhgEDAF+7U4zxph63xzWHYB/LN/hcRITKsF01oeqvgG8EeYsxpgYVpDbkXOGdGPJ1v3c969PKSqqZGf6dq4cc5LX0UwLBXMW02ki8rGIlIpIlYjUiMihSIQzxsSWr/XPoaZWeXnFDt4rquaef66h1kaci1nBtEH8CbgS2ASkAd8D/hjOUMaY2HTNuFxWzjiLlTPOYuopydTUKgcPH/E6lmmhoEaUU9XNgE9Va1T1aaBFw40aY+JHVrIAsM+ujYhZwbRBlItIMvCJiNwP7AQywhvLGBPr6gqEXTwXu4LZg7jane9moAzoDVwWzlDGmNiX5XbJtL+s0tsgpsWa3INQ1W3uHkQuTgd9G1TVfhIYYwJqZ4eYYl4wZzF9E/gMeBSnwXqziJwb7mDGmNiWlSyIwN0vr+GiP73vdRzTAsG0QfwXMMVtqEZE+gGvYddFGGMCSEwQ/mtqHv+3tIgPt+zjSE0tSb6gzosxUSKYf63ddcXBtQWwAWiNMU26dFQvzhvuXGE9d+0uj9OY5gqmQHwqIq+LyHQRmQb8G/jYRpYzxgRj8imdAVjxRXETc5poE0yBSAV24YwwNxnYA3TEGWnu/LAlM8a0Cb07ptOtXSovLLFO/GJNMGcx2YhyxphWaZeWyFeHKigur6J9ug1JGiuCOYspVUR+JCJ/EZGn6m6RCGeMaRt+MLkfALMXb+eAnfYaM4I5xPQ3oBtwNvAe0AsoCWcoY0zb0q9zJiLwwFsbeO6jbV7HMUEKpkD0V9VfAGWq+gzwTWBYeGMZY9qS4b3a88m9Z9EuNZFNu0u9jmOCFEyBqOuKsVhEhgLZOFdVG2NM0LLTkkhJ8vHBZ/u8jmKCFEyBeEJEOgD3AP8C1gKtHlFORHwiskJEXnUf9xWRxSKySURedLv3MMa0IQO7ZZGYIF7HMEEKZsjRv6rqAVVdoKonq2oXVX08BK99K7CuweOZwB9UdQBwALg+BK9hjIkifTtlUFFd43UME6RGC4SI3C4iJ3xJi8gtInJba15URHrhtGX81X0swNeBv7uzPANc3JrXMMZEn7RkH+VVViBiRaA9iOtwzmA63hPuc63xMPAzoNZ9nAMUq2q1+7gI6NnK1zDGRJm0JB9V1bXsK7UuwGNBoAvl1F+33qpa6f7ibxEROR+nf6dlIjK5brK/129k+RuBGwG6du1KYWFhi3KUlpa2eFkvxFLeWMoKsZU31rMe3Omc8/K/b73PoByfB6kaF+vbNixU1e8NWA109TO9K7C6seWaugH/D2cPYSvwFVAOzAb2AonuPOOAt5paV35+vrbU/PnzW7ysF2IpbyxlVY2tvLGedcNXh7TPHa/quN/N1QNllZEPFUCsb9vmAJZqEN/XgQ4xPQC8JiKTRCTLvU3G6azvwVYUpDtVtZeq5gJXAO+q6neB+cDl7mzTgFda+hrGmOh0Usd0Omel8OXBCj60012jXqMFQlWfBX4B/Arn1/7nwC+BGepcMBdqdwC3i8hmnDaJJ8PwGsYYD6Um+Xj1lvGAjTQXCwJ21qeqbxDGgYFUtRAodO9vAcaE67WMMdGhg9tZ334rEFHPhncyxkRUcmICWamJrNt5iB3Fh72OYwKwAmGMibie7dN4Y81XTH3sA6+jmAACFggRSRCRb0UqjDEmPjx97WguG9WLnYcqqKn1e0a7iQIBC4Sq1gI3RyiLMSZOdM9OY3ivbFThQLm1RUSrYA4xvSMi/ykivUWkY90t7MmMMW1axwxrrI52TQ45ytFuNX7UYJoCJ4c+jjEmXuS4BeKjLfs4pWuWx2mMP8GMSd03EkGMMfFlaK9sAA6WH2liTuOVYMakTheRe0TkCffxALc/JWOMabF2qUkk+YTyI9a7a7QKpg3iaaAK+Jr7uAj4TdgSGWPiRlqSj8PW/XfUCqZA9FPV+3GHHlXVw/jvfdUYY5olNcnH7MXbKKmww0zRKJgCUSUiabjdb4tIP8A6czfGtFr39mkcqVHeWPOV11GMH8EUiPuAN4HeIjIbmIcz2I8xxrTK898bC8CK7cUeJzH+BDMm9dvApcB04AWgwO1kzxhjWiUjxTmR8uUVRR4nMf4EcxbT33AKxGeq+qqq7g1/LGNMvLggr4d1txGlgj2LqTvwRxH5TEReEpFbw5zLGBMnTumSyZEapaq6tumZTUQFc6HcuyLyHjAamALcBAwBHglzNmNMHMhMdb6GyiqrSU5M9jiNaajJAiEi84AM4ENgITBaVXeHO5gxJj5kpSYBUFpZTYcMKxDRJJhDTKtwLpQbCgwHhrqnvRpjTKtlug3VJRXVHicxxwvmENNPAEQkE7gWp02iG5AS3mjGmHiQ5R5iKq20AhFtgjnEdDMwAcgHtgFP4RxqMsaYVmvnHmIqtnEhok4w3X2nAQ8By1TVSrwxJqQ6ZjrtDu+s3cVZQ7p5nMY0FMwhpgdEJA+4SUQAFqrqyrAnM8bEhc6ZztHq5dsPeJzEHC+YC+V+DMwGuri350TklnAHM8bEh+TEBC4d1dN6dY1CwRxi+h4wVlXLAERkJs4pr38MZzBjTPxol5pkjdRRKJjTXAVoWNprsO6+jTEhlJWaSGllNarW5UY0CWYP4mlgsYi87D6+GHgyfJGMMfEmMyWRWoXDR2pITw7ma8lEQjCN1A+JSCEwHmfP4VpVXRHuYMaY+FHX3UZpRbUViCjS6L+EiKTi9LvUH1gN/MVOczXGhEPd1dSrig7yjcGpHqcxdQK1QTwDFOAUh3OBByOSyBgTdwZ3bwfAzkMVHicxDQUqEINV9SpV/W/gcmBihDIZY+JMbqcMAN5dt8vjJKahQAWifhRxO7RkjAmnJF8CIlBWaddCRJNArUF5InLIvS9AmvtYAFXVdi15QRHpDTyL0+FfLfCEqj4iIh2BF4FcYCvwLVVt9qWVR44coaioiIqKwLuq2dnZrFu3rrmr90ws5Y2WrKmpqfTq1YukpCSvo5ggnDu0G6+v/oqKIzWkJvm8jmMIUCBUNVz/QtXAf6jqchHJApaJyDs4Y17PU9Xfi8jPgZ8DdzR35UVFRWRlZZGbm4vbNYhfJSUlZGVltewdeCCW8kZDVlVl3759FBUV0bdvX0+zmOB0bec0Tn+0ZR+TT+3icRoDwV0oF1KqulNVl7v3S4B1QE/gIpyGcdy/F7dk/RUVFeTk5AQsDqbtExFycnKa3JM00WPauFwAlm8v9jaIqSdeXrkoIrnAApzBiLaravsGzx1Q1Q5+lrkRuBGga9eu+XPmzDnm+ezsbPr379/ka9fU1ODzxc5ubCzljaasmzdv5uDBgwHnKS0tJTMzM0KJWqctZ62sVr4/t5weGcLvJqSHMZl/bXnbHm/KlCnLVLWgyRlV1ZMbkAksAy51Hxcf9/yBptaRn5+vx1u7du0J0/w5dOhQUPO1REZGRsjXGc68Dc2YMUMfeOCBVq0jUlmDEcznYf78+eEPEiJtPevUxz/Qb/xXYejDBKGtb9uGgKUaxPd0xA8xAYhIEvASMFtV/+FO3iUi3d3nuwM27nWIFRYWMn369LCsu7raTnQzrdc9O5WqmlqvYxhXxAuEOI0DTwLrVPWhBk/9C5jm3p8GvBLpbOHwwAMPMHr0aIYPH86MGTPqp//6179m4MCBnHnmmVx55ZU8+KBzHeL//M//MHr0aPLy8rjssssoLy8HYPfu3VxyySXk5eWRl5fHBx98wC9+8QseeeSR+nXefffdPProoyHJvXv3bvLz8wFYuXIlIsL27dsB6NevH+Xl5UyfPp3bb7+dKVOmcMcdzT6fwJgTJPsSqKq2AhEtvOj05HTgamC1iHziTrsL+D3wvyJyPbAdmNraF/rlvz9l7ZeH/D7X0uPkg3u0Y8YFQ4Ka9+2332bTpk0sWbIEVeXCCy9kwYIFpKen89JLL7FixQqqq6sZNWpU/ZfxpZdeyg033ADAPffcw5NPPsktt9zCz372MyZNmsTLL79MTU0NpaWl9OjRg0svvZRbb72V2tpa5syZw5IlS5r9nvzp0qULFRUVHDp0iIULF1JQUMDChQsZP348Xbp0IT3dOUa8ceN6XogEAAAUNElEQVRG5s6dGzVtDia2JSdagYgmES8Qqvo+jXcXfkYks4Tb22+/zdtvv83IkSMBp2Fp06ZNlJSUcNFFF5GWlgbABRdcUL/MmjVruOeeeyguLqa0tJSzzz4bgPfee4/nn38eAJ/PR3Z2NtnZ2eTk5LBixQp27drFyJEjycnJOSHH2LFjqayspLS0lP379zNixAgAZs6cWb9+f772ta+xaNEiFixYwF133cWbb76JqjJhwoT6eaZOnWrFwYSMFYjo0qa7TQz0Sz8S5+qrKnfeeSff//73j5n+hz/8odFlpk+fzj//+U/y8vKYNWsWhYWFAV/je9/7HrNmzeKrr77iuuuu8zvP4sWLAacNYtasWcyaNSuo/BMmTGDhwoVs27aNiy66iJkzZyIinH/++fXzZGRkBLUuY4KRnJhApRWIqOFJI3W8OPvss3nqqacoLS0FYMeOHezevZvx48fz73//m4qKCkpLS3nttdfqlykpKaF79+4cOXKE2bNn10+fNGkSjz32GOAcHjt0yDl0dskll/Dmm2/y8ccfB9wbaImJEyfy3HPPMWDAABISEujYsSOvv/46p59+ekhfx5g6WSmJVNXUUlltXW5Egza9B+G1s846i3Xr1jFu3DgAMjMzee655xg9ejQXXngheXl59OnTh4KCArKzswGn8Xrs2LH06dOHYcOGUVJSAsD999/P7bffzpNPPonP5+Oxxx5j3LhxJCcnM2XKFNq3bx/yQz25ubmAUygAxo8fT1FRER06nHB5ijEh0TEjBYD9ZVV0z07zOI3x7DqIUNyi9TqIYJSUlKiqallZmebn5+uyZcsCzt9Y3pqaGs3Ly9ONGzeGPGNLeb1tG7LrILzTkqxvrN6pfe54VVcXFYc+UBPa+rZtiGi+DsLAjTfeyIgRIxg1ahSXXXYZo0aNavY61q5dS//+/TnjjDMYMGBAGFIaE1k5mckAzPl4OzW1Nj611+wQk0fqzkhqjcGDB7Nly5YQpDEmOvTu4Jw+/dxH27l4RE8Kcjt6nCi+2R6EMSZqdMtO5anpThdBBw8faWJuE25WIIwxUaWXuxdx+IidyeQ1KxDGmKiS5g4WVF5lBcJrViCMMVElI8VpGn1v4x6PkxgrEGEQK33K+3PffffVdxxojBc6ZjhnMiUl2KBfXrMCEUe86u77vvvuC7p7j9aqqbHDEm3BwG5ZdogpCliBCDPr7js4ubm53HXXXYwbN46CggKWL1/O2WefTb9+/Xj88ccB2LlzJxMnTmTEiBEMHTqUhQsXAs4e27333svYsWP58MMPW5XDRIfUJJ81UkeBtn0dxBs/h69W+30qraYafC14+92Gwbm/D2pW6+67eXr37s2HH37IT37yE6ZPn86iRYuoqKhgyJAh3HTTTTz//POcffbZ3H333dTU1NQXz7KyMoYOHcqvfvWrVmcw0SE92cfCTXupqVV8dqjJM227QHgsnrv7Xr16NVdffTUAX331FcnJyTz88MMAzJs3z2/OCy+8EIBhw4ZRWlpKVlYWWVlZpKamUlxczOjRo7nuuus4cuQIF198cf378Pl8XHbZZY2+DxN76s5k2rKnlAFdw9vrsmlc2y4QAX7pH7buvpvUmu6+hw0bxiefOONB3XfffeTm5jbZ/pGS4nTUlpCQUH+/7nF1dTUTJ05kwYIFvPbaa1x99dX89Kc/5ZprriE1NdXGpGhjvjP2JOat323tEB6zNogwsu6+Q2vbtm106dKFG264geuvv57ly5d7ksOEX1qyU/CtHcJbbXsPwmPW3XdoFRYW8sADD5CUlERmZibPPvusJzlM+NUdYiqtaPzsOBMBwXT5Gq036+7buvtuinX37Z3WZN26t1T73PGq/uKfq0MXqAnxsm1VrbvvqGfdfRvTuD45TttWtXX57Sk7xOQR6+7bmMD6dc7g+cXbmXHBYFIS7SQEL9gehDEmKo06yWnr2lNS6XGS+GUFwhgTlc4a0g2AR+Zu8jhJ/LICYYyJSqNznT2ItTsPeZwkflmBMMZEpfbpyXxzeHcO28VynrECEScmT57M0qVLvY7B1q1bGTp0qNcxTIxIt077PGUFoo0I1N22MbEqLdlHcfkRnv1wK4s27/U6TtyxAhFiW7duZeDAgUybNo3hw4dz+eWX1/c6Om/ePEaOHMmwYcO47rrrqKysZMmSJVx66aUAvPLKK6SlpVFVVUVFRQUnn3wyAJ999hmXXHIJ+fn5TJgwgfXr1wME7G778OHDXHHFFQwfPpxvf/vbHD58uP65t99+m3HjxjFq1CimTp1a3xVIMF1ul5aWcsYZZzBq1CiGDRvGK6+8Uv++Bw0axA033MCYMWM466yz6l9z2bJl5OXlMW7cOP785z+Ha9ObNqhPTgaHj9Rw7yufcsOzS3Gu8TKR0qavg5i5ZCbr96/3+1xNTU2LuqYY2HEgd4wJPPbBhg0bePLJJzn99NO57rrr+Mtf/sLNN9/M9OnTmTdvHqeccgrXXHMNjz32GDfffDMrVqwAYOHChQwdOpSPP/6Y6upqxo4dCzgX1T344IOMHDmSxYsX88Mf/pB3330XaLy77ccee4z09HRWrVrFqlWr6i/E27t3L7/5zW+YO3cuGRkZzJw5k4ceeoh7770XaLrL7dTUVF5++WXatWvH3r17Oe200+p7Yd20aRMvvPACDz30ENdffz0vvfQSV111Fddeey1//OMfmTRpEj/96U+bvc1N/Lp+fF8uHdmTWR9s5ZF5myirqiEzpU1/bUUV29Jh0Lt37/oO7a666ioeffRRzjzzTPr27cspp5wCwLRp0/jzn//MbbfdRv/+/Vm3bh1Llizh9ttvZ8GCBdTU1DBhwgRKS0v54IMPmDZtGgkJzg5fZeXR88Ib6257wYIF/PjHPwZg+PDhDB8+HICPPvqItWvX1uerqqqq7ysKmu5yOyMjg7vuuosFCxaQkJDAjh072LVrFwB9+/ZlxIgRlJSUkJ+fz9atWzl48CDFxcVMmjQJgKuvvpo33ngjdBvbtHkdMpLp1cHpGn9/aZUViAiKqi0tIucAjwA+4K+qGtzIPI0I9Eu/JIzdfYvICY8D7RpPmDCBN954g6SkJL7xjW8wffp0ampqePDBB6mtraV9+/YsWrTIb97Gutv2lwOcvrfOPPNMXnjhBb/LNNXl9uzZs9mzZw/Lli0jKSmJ3NxcKioqjlkWnDEaDh8+jKr6zWFMc+RkOuNU7y2r5KScdI/TxI+oaYMQER/wZ+BcYDBwpYgM9jZVy2zfvr1+6MsXXniB8ePHM3DgQLZu3crmzZsB+Nvf/lb/q3rixIk8/PDDjBs3js6dO7Nv3z7Wr1/PkCFDaNeuHX379uXll18GnC/4lStXNplh4sSJ9d2Fr1mzhlWrVgFw2mmnsWjRovoc5eXlbNy4Mej3dvDgQbp06UJSUhLz589n27ZtAedv37492dnZvP/++wDHdGFuTLA6Z6YCcNucTzxOEl+ipkAAY4DNqrpFVauAOcBFHmdqkUGDBvHMM88wfPhw9u/fzw9+8ANSU1N5+umnmTp1KsOGDSMhIYGbbroJcEZ827VrV3232nWHhOp+ec+ePZtnn32WvLw8hgwZUt8wHMgPfvADSktLGT58OPfffz9jxowBoHPnzsyaNYsrr7yS4cOHc9ppp9U3egfju9/9LkuXLqWgoIDZs2czcODAJpd5+umn+dGPfsS4cePqR9EzpjmG9GhHx4xkDlUc8TpKXJFoOStARC4HzlHV77mPrwbGqurNjS1TUFCgx5/bv27dOgYNGtTk64XrENPWrVs5//zzWbNmTUjXG85DYqEWTVmD+TwUFhYyefLkyARqpXjO+tA7G3l03iYGdMkM2TobKisvIyO98UO20aSsvIw7LxjBBXk9WrS8iCxT1YKm5oumNgh/B6pPqF4iciNwI0DXrl1PGJIzOzu7fpCdQGpqaoKar7lKS0upra0N+brDlTccoilrRUVFk8O2lpaWNjlPtIjnrF0rahnbzUeNHm565hbITKnFJ+FZd6hlptSybdNaCg8Ef3i4RYIZNCISN2Ac8FaDx3cCdwZaJpYHDGquWMobTVltwCDvxFJW1djKG48DBn0MDBCRviKSDFwB/MvjTMYYE7ei5hCTqlaLyM3AWzinuT6lqp+2cF12aqWxq26NaaWoKRAAqvo68Hpr1pGamsq+ffvIycmxIhHHVJV9+/aRmprqdRRjYlZUFYhQ6NWrF0VFRezZsyfgfBUVFTH15RFLeaMla2pqKr169fI6hjExq80ViKSkJPr27dvkfIWFhYwcOTICiUIjlvLGUlZjTOOiqZHaGGNMFLECYYwxxi8rEMYYY/yKmq42WkJE9gCBe4trXCcgloaoiqW8sZQVYiuvZQ2fWMrb2qx9VLVzUzPFdIFoDRFZqkH0RRItYilvLGWF2MprWcMnlvJGKqsdYjLGGOOXFQhjjDF+xXOBeMLrAM0US3ljKSvEVl7LGj6xlDciWeO2DcIYY0xg8bwHYYwxJoA2UyBEpLeIzBeRdSLyqYjc6k7vKCLviMgm928Hd/p3RWSVe/tARPIarOscEdkgIptF5OfRkLfBcqNFpMYdga9u2jR3/k0iMi0asorIZBH5xJ3/vQbTo27biki2iPxbRFa681/bYF1ebdup7uNaESk4bpk73e23QUTObjA9rNu2uVlF5EwRWSYiq92/X2/wXL47fbOIPCph6FmzJdvWff4kESkVkf9sMC2qtq373HAR+dB9frWIpLrTQ7dtgxk0IhZuQHdglHs/C9gIDAbuB37uTv85MNO9/zWgg3v/XGCxe98HfAacDCQDK4HBXudtkO1dnB5vL3endQS2uH87uPc7eLxt2wNrgZPcx12iedsCdzW43xnY7+bzctsOAk4FCoGCBvMPdrdbCtDX3Z6+SGzbFmQdCfRw7w8FdjR4bgnOIGECvAGcG8HPgd+8DZZ7Cfg/4D8j9bltwbZNBFYBee7jHMAX6m3bZvYgVHWnqi5375cA64CewEXAM+5szwAXu/N8oKoH3OkfAXXdfo4BNqvqFlWtAua46/A0r+sWnA/v7gbTzgbeUdX97vt5BzjH46zfAf6hqtvdZeryRuu2VSDL/aWViVMgqvFw26rqOlXd4GeRi4A5qlqpqp8Dm3G2a9i3bXOzquoKVf3SffgpkCoiKSLSHWinqh+q8432LMd+zj3JCyAiF+P8EGg4Fk3UbVvgLGCVqq50l9mnqjWh3rZtpkA0JCK5OL9eFgNdVXUnOP8IQBc/i1yPU2nB+SL5osFzRe60sAkmr4j0BC4BHj9u8YjmDXLbngJ0EJFC99DCNV5kbUbeP+H8UvsSWA3cqqq1kc57XNbGNJYpGrM2dBmwQlUrcXIVNXgu0p+DxubJAO4AfnncU9G4bU8BVETeEpHlIvIzd3pIt22b6+5bRDJxfmXfpqqHmjr8JiJTcArE+LpJfmYL26lezcj7MHCH+yvhmFX4mTcseZuRNRHIB84A0oAPReSjSGaFZuU9G/gE+DrQD3hHRBbi4bYNNGsjmfz92PM6a938Q4CZOL96wePPQYBZfwn8QVVLo+X/WIBZE3G+s0YD5cA8EVkG+FumxVnbVIEQkSScjTtbVf/hTt4lIt1Vdae7+7W7wfzDgb/iHKPb504uAno3WG0vnF+WXuctAOa4H9xOwHkiUu3mnXxc3kKPsxYBe1W1DCgTkQVAHtG7ba8Ffu/ukm8Wkc+BgXi7bRsTaBuGfds2Mysi0gt4GbhGVT9zJxdx9JBu2LK6r9+cvGOBy0Xkfpx2tFoRqQCWEX3btgh4T1X3usu+DowCniOU27aljRfRdsOp8s8CDx83/QGObZi8371/Es7x268dN38izjHIvhxtkBridd7j5pnFsY3Un+M0onZw73f0eNsOAua52zIdWIPTSBmV2xZ4DLjPvd8V2IFThD3btg2eL+TYxskhHNtIvQWnETXs27YFWdu7OS7zM+/HwGkcbUg9L1Kfg8byHvfcfRxtpI7GbdsBWO7+/0oE5gLfDPW2Dek/iJc3nN0txWnZ/8S9nYfTuj8P2OT+7ejO/1fgQIN5lzZY13k4ZxF8BtwdDXmPW3YWboFwH1+HU+w2A9dGQ1bgpzhnMq3B2V2O2m0L9ADexml/WANcFQXb9hKcX4mVwC7grQbL3O1uvw00OEMl3Nu2uVmBe4CyBvN+wtEz2grcbf0ZThuQeJ33uGXvwy0Q0bht3WWuwmlMX0ODH5Kh3LZ2JbUxxhi/2uRZTMYYY1rPCoQxxhi/rEAYY4zxywqEMcYYv6xAGGOM8csKhDFNEMf7InJug2nfEpE3vcxlTLjZaa7GBEFEhuL08DkS58K0T4Bz9OjVwS1ZZ6KqVocoojEhZwXCmCC5XTCUARlAiar+WpwxIn6Ec4XtB8DNqlorIk/gdH2QBryoqr9y11EE/DdOr7AP43SFcANwBFitqldF+G0Z06g21ReTMWH2S5zuDaqAAnev4hKc7lqq3aJwBfA8Tpce+0UkEZgvIn9X1bXuespU9XQAEdkJ9FHVKhFpH/F3ZEwAViCMCZKqlonIi0CpqlaKyDdwetNc6naimMbRbqGvFJHrcf6P9cAZ/KWuQLzYYLWfAs+JyCvAPyPwNowJmhUIY5qn1r2B0xnaU6r6i4YziMgA4FZgjKoWi8hzQGqDWcoa3D8bmIQzAM09IjJUVWvClt6YZrCzmIxpubnAt0SkE4CI5IjISUA7oAQ45HYrfra/hUXEB/RS1XdxOjfsjNM7pzFRwfYgjGkhVV0tIr8E5opIAk5D803AUo72ZLsFWNTIKhKB50UkC+fH2kx1hps0JirYWUzGGGP8skNMxhhj/LICYYwxxi8rEMYYY/yyAmGMMcYvKxDGGGP8sgJhjDHGLysQxhhj/LICYYwxxq//DwLsw08XTjaAAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -280,7 +274,7 @@
    "source": [
     "x = np.arange(0, 1684)\n",
     "years = 2020 + x/12\n",
-    "y = 100*(1.009)**(x/12)\n",
+    "y = 100*(1.000)**(x/12)\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'], label='legacy + lwr')\n",
     "plt.plot(years, power_dict['us_government'] + power_dict['lwr'] + power_dict['msr'], label='legacy + lwr + msr')\n",
     "plt.plot(years, y, label='power demand')\n",
@@ -314,7 +308,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

From 061b017a8b7d379a8d575f6b9cfe7a87343af3a8 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 12:16:53 -0500
Subject: [PATCH 29/35] get deployment ipynb

---
 input/master_us/get_deployment.ipynb | 33 ++++++++++++++++++++++++++--
 1 file changed, 31 insertions(+), 2 deletions(-)

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index dcef79b5..4eacbd1b 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -32,14 +32,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Usage: write_input.main([csv],[init_date], [duration], [country_list], [output_file_name])\n"
+     ]
+    }
+   ],
    "source": [
     "import write_input.demand_deploy as dd\n",
+    "import write_input.from_pris as fp\n",
     "import analysis as an"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db_path = '~/github/pris-input-gen/database/reactors_pris_2016.csv'\n",
+    "fp.main(db_path, 20130501, 1764, ['United States'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then you run it to obtain the power capacity\n",
+    "! cyclus complete_input.xml -o legacy_only.sqlite"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,

From f59c4ad0e8b17bee8bb49a0ef140e3b70aed2ef4 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 12:18:07 -0500
Subject: [PATCH 30/35] Analysis

---
 input/master_us/analysis.ipynb     | 1034 ++++++++++++++++++++++++++++
 input/master_us/check_output.ipynb |  250 -------
 2 files changed, 1034 insertions(+), 250 deletions(-)
 create mode 100644 input/master_us/analysis.ipynb
 delete mode 100644 input/master_us/check_output.ipynb

diff --git a/input/master_us/analysis.ipynb b/input/master_us/analysis.ipynb
new file mode 100644
index 00000000..06934488
--- /dev/null
+++ b/input/master_us/analysis.ipynb
@@ -0,0 +1,1034 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import Necessary Module and Set Path "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/teddy/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.data is not yet QA compliant.\n",
+      "  return f(*args, **kwds)\n",
+      "/home/teddy/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.material is not yet QA compliant.\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sqlite3 as lite\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "from IPython.display import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import collections\n",
+    "sys.path.insert(0, '../../scripts/')\n",
+    "jupyter_directory = os.getcwd()\n",
+    "from decimal import Decimal\n",
+    "from pyne.material import Material"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# analysis\n",
+    "This jupyter notebook obtains important metrics and figures from the U.S. transition from LWR fleet to MSR fleet. This notebook calculates the following metrics:\n",
+    "* power capacity installed\n",
+    "* LWR UNF generated\n",
+    "* LWR UNF inventory\n",
+    "* Fuel salt mass vs time\n",
+    "* Total TRU extracted from LWR UNF\n",
+    "* Total LWR UNF reprocessed\n",
+    "* Average monthly reprocessing / fabrication demand\n",
+    "* Total raffinate stockpile\n",
+    "* Total waste inventory\n",
+    "* Total natural uranium usage\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import analysis as an\n",
+    "! rm power_plot.png"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get sqlite cursor and simulation time data\n",
+    "cur = an.get_cursor('cyclus.sqlite')\n",
+    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "power_dict = an.get_power_dict(cur)\n",
+    "pow = {'legacy lwrs': power_dict['us_government'], 'lwr': power_dict['lwr_inst'], 'msr': power_dict['rebus_inst']}\n",
+    "legacy_only = {'legacy lwrs': power_dict['us_government'][:810]}\n",
+    "an.stacked_bar_chart(pow, timestep, 'Year', 'Net Capacity [GWe]', 'Installed nuclear capacity in the U.S.',\n",
+    "                     'power_plot', init_year)\n",
+    "an.stacked_bar_chart(legacy_only, timestep[:810], 'Year', 'Net Capacity [GWe]', 'Installed nuclear capacity in the U.S.',\n",
+    "                     'legacy_only', init_year)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('power_plot.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('legacy_only.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total deployed:\n",
+      "lwr: 84 ( 92400 )\n",
+      "rebus: 95 ( 154660.0 )\n"
+     ]
+    }
+   ],
+   "source": [
+    "# total lwrs and msrs deployed\n",
+    "\n",
+    "lwrs = cur.execute('SELECT entertime, lifetime/lifetime from agententry where prototype = \"lwr\"').fetchall()\n",
+    "rebus = cur.execute('SELECT entertime, lifetime/lifetime from agententry where prototype = \"rebus\"').fetchall()\n",
+    "lwrs = an.get_timeseries(lwrs, duration, False)\n",
+    "rebus = an.get_timeseries(rebus, duration, False)\n",
+    "years = 2020 + np.arange(0, duration)/12\n",
+    "plt.plot(years, lwrs, label='LWR deployment')\n",
+    "plt.plot(years, rebus, label='REBUS deployment')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Num. reactors built')\n",
+    "plt.legend()\n",
+    "plt.title('Reactor constuction over time')\n",
+    "plt.savefig('us_dep.png', format='png')\n",
+    "plt.show()\n",
+    "\n",
+    "print('Total deployed:')\n",
+    "print('lwr:', sum(lwrs), '(', sum(lwrs) * 1100, ')')\n",
+    "print('rebus:', sum(rebus), '(' , sum(rebus) * 3700 * 0.44, ')')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyne import nucname\n",
+    "def get_tru_massfrac(commodity):\n",
+    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
+    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
+    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
+    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
+    "    comp_dict = {}\n",
+    "    for row in massfracs:\n",
+    "        if int(row['nucid']) < 930000000 or int(row['nucid']) > 970000000:\n",
+    "            continue\n",
+    "        key = nucname.name(row['nucid'])\n",
+    "        comp_dict[key] = row['massfrac']\n",
+    "    return comp_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_u_massfrac(commodity):\n",
+    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
+    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
+    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
+    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
+    "    ufrac = 0\n",
+    "    for row in massfracs:\n",
+    "        if int(row['nucid']) < 920000000 or int(row['nucid']) > 930000000:\n",
+    "            continue\n",
+    "        ufrac += row['massfrac']\n",
+    "    return ufrac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_massfrac(commodity):\n",
+    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
+    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
+    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
+    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
+    "    comp_dict = {}\n",
+    "    for row in massfracs:\n",
+    "        key = nucname.name(row['nucid'])\n",
+    "        comp_dict[key] = row['massfrac']\n",
+    "    return comp_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def nansum_dict(dictionary):\n",
+    "    tot = 0\n",
+    "    for key, val in dictionary.items():\n",
+    "        if math.isnan(val):\n",
+    "            continue\n",
+    "        tot += val\n",
+    "    return tot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tru_massfrac_to_vector(comp_dict):\n",
+    "    tot = sum(comp_dict.values())\n",
+    "    for key, val in comp_dict.items():\n",
+    "        comp_dict[key] = val / tot\n",
+    "    return comp_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TRU composition in Spent and initial Fuels:\n",
+    "lwr_unf_tru_comp = get_tru_massfrac('uox_waste')\n",
+    "init_salt_tru_comp = get_tru_massfrac('init_fuel')\n",
+    "eol_salt_tru_comp = get_tru_massfrac('fuel_out')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isotope & Beginning of Life & End of Life \\\\ \n",
+      "Np237 & 4.61E-03 & 6.00E-04\\\\ \n",
+      "Pu238 & 1.84E-03 & 2.06E-03\\\\ \n",
+      "Pu239 & 5.33E-02 & 5.22E-02\\\\ \n",
+      "Pu240 & 1.95E-02 & 2.81E-02\\\\ \n",
+      "Pu241 & 5.07E-03 & 3.28E-03\\\\ \n",
+      "Pu242 & 4.77E-03 & 3.90E-03\\\\ \n",
+      "Am241 & 5.51E-03 & 1.67E-03\\\\ \n",
+      "Am243 & 9.31E-04 & 1.19E-03\\\\ \n",
+      "Cm244 & 4.03E-04 & 1.00E-03\\\\ \n",
+      "Cm245 & 3.76E-05 & 2.64E-04\\\\ \n",
+      "Cm246 & 4.80E-06 & 1.19E-04\\\\ \n",
+      "Total & 0.0959546936471764 & 0.09453205358076015 \\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TRU Composition\n",
+    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
+    "for key, val in eol_salt_tru_comp.items():\n",
+    "    if val < 1e-04:\n",
+    "        continue\n",
+    "    string += key + ' & '\n",
+    "    try:\n",
+    "        bol_comp = init_salt_tru_comp[key]\n",
+    "    except:\n",
+    "        bol_comp = 0\n",
+    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
+    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
+    "\n",
+    "string += 'Total & ' + str(sum(init_salt_tru_comp.values())) + ' & ' + str(sum(eol_salt_tru_comp.values())) + ' \\\\\\ \\n'\n",
+    "print(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isotope & Beginning of Life & End of Life \\\\ \n",
+      "Np237 & 4.80E-02 & 6.34E-03\\\\ \n",
+      "Pu238 & 1.91E-02 & 2.18E-02\\\\ \n",
+      "Pu239 & 5.55E-01 & 5.52E-01\\\\ \n",
+      "Pu240 & 2.03E-01 & 2.98E-01\\\\ \n",
+      "Pu241 & 5.28E-02 & 3.47E-02\\\\ \n",
+      "Pu242 & 4.97E-02 & 4.13E-02\\\\ \n",
+      "Am241 & 5.74E-02 & 1.77E-02\\\\ \n",
+      "Am243 & 9.70E-03 & 1.26E-02\\\\ \n",
+      "Cm244 & 4.20E-03 & 1.06E-02\\\\ \n",
+      "Cm245 & 3.92E-04 & 2.79E-03\\\\ \n",
+      "Cm246 & 5.00E-05 & 1.26E-03\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TRU Composition\n",
+    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
+    "sum1 = 0\n",
+    "sum2 = 0\n",
+    "for key, val in eol_salt_tru_comp.items():\n",
+    "    if val < 1e-04:\n",
+    "        continue\n",
+    "    val = val / sum(eol_salt_tru_comp.values())\n",
+    "    string += key + ' & '\n",
+    "    try:\n",
+    "        bol_comp = init_salt_tru_comp[key] / sum(init_salt_tru_comp.values())\n",
+    "    except:\n",
+    "        bol_comp = 0\n",
+    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
+    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
+    "\n",
+    "print(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Np237': 0.047308915605860334, 'Pu238': 0.022637142727796017, 'Pu239': 0.47107005808831415, 'Pu240': 0.22567253354153197, 'Pu241': 0.1365217889336543, 'Pu242': 0.0670882692410373, 'Pu244': 2.2248857792867747e-06, 'Am241': 0.005003469136455384, 'Am242M': 6.627286296835368e-05, 'Am243': 0.015407239123318452, 'Cm242': 0.0020113278074529646, 'Cm243': 5.9876553819315204e-05, 'Cm244': 0.006649030381240261, 'Cm245': 0.0004443558963029291, 'Cm246': 5.665887136361708e-05, 'Cm247': 7.766089989535046e-07, 'Cm248': 5.973410615802392e-08, 'Cm250': 3.3245151906201306e-16}\n",
+      "{'Np237': 0.04802950142018718, 'Np238': 1.039337901575956e-11, 'Np239': 8.356128648187144e-09, 'Np240': 6.4344620179821036e-18, 'Pu238': 0.01914812530313312, 'Pu239': 0.5551709091095284, 'Pu240': 0.20329858554911281, 'Pu241': 0.052787326302600454, 'Pu242': 0.04974445133955743, 'Pu243': 2.4460157358746052e-17, 'Pu244': 4.45226838781886e-06, 'Pu246': 1.5942251519941559e-22, 'Am241': 0.05737196207829727, 'Am242': 7.434719410978571e-10, 'Am242M': 5.7620332044017275e-05, 'Am243': 0.009699633628639882, 'Am246': 3.9814504192135768e-25, 'Cm242': 1.7918971176560626e-07, 'Cm243': 4.294319648672978e-05, 'Cm244': 0.004201315229384967, 'Cm245': 0.0003922593227631961, 'Cm246': 4.998776080224266e-05, 'Cm247': 6.860528578281789e-07, 'Cm248': 5.2806510693242906e-08, 'Cm250': 2.9347155426547976e-16}\n",
+      "{'Np233': 6.590807529215453e-24, 'Np234': 4.810661916714748e-16, 'Np235': 2.762276831482792e-10, 'Np236': 5.071449786632528e-07, 'Np236M': 2.3095139242042022e-14, 'Np237': 0.0063430368518328775, 'Np238': 3.817699467880339e-06, 'Np239': 0.0005449893696436123, 'Np240': 3.078736664066157e-09, 'Np240M': 6.292260268363348e-10, 'Pu234': 1.2535419064225444e-21, 'Pu235': 9.586033145399227e-19, 'Pu236': 4.051159679476181e-09, 'Pu237': 5.8294833202379084e-08, 'Pu237M': 2.009832546515702e-19, 'Pu238': 0.02180600925553302, 'Pu239': 0.551809497148822, 'Pu240': 0.29757145587339945, 'Pu241': 0.03466318792176547, 'Pu242': 0.041268248979754764, 'Pu243': 7.51174855408347e-07, 'Pu244': 1.08116919807927e-05, 'Pu245': 1.7926190218492769e-10, 'Pu246': 8.163615464099115e-18, 'Am238': 4.020276240121079e-27, 'Am239': 6.428134571662405e-14, 'Am240': 8.289762804145239e-10, 'Am241': 0.017654707728027366, 'Am242': 3.623232378000574e-06, 'Am242M': 9.956601283235253e-06, 'Am243': 0.012619047640691302, 'Am244': 9.87015222857233e-08, 'Am244M': 6.343184026078238e-08, 'Am245': 3.6340163822269555e-11, 'Cm238': 6.563332955325561e-27, 'Cm239': 5.456005361880801e-22, 'Cm240': 1.478715656337944e-12, 'Cm241': 4.192528490560603e-10, 'Cm242': 0.000713618454441931, 'Cm243': 8.419672236031198e-05, 'Cm244': 0.010605206979192015, 'Cm244M': 9.229451567809788e-14, 'Cm245': 0.0027914115381781606, 'Cm246': 0.001261666747556023, 'Cm247': 0.00018301784680471681, 'Cm248': 5.1003347266915154e-05, 'Cm249': 1.132427554667172e-10, 'Cm250': 7.507634953775834e-12, 'Cm251': 2.192454625456172e-18}\n"
+     ]
+    }
+   ],
+   "source": [
+    "lwr_unf_tru_vec = tru_massfrac_to_vector(lwr_unf_tru_comp)\n",
+    "init_salt_tru_vec = tru_massfrac_to_vector(init_salt_tru_comp)\n",
+    "eol_salt_tru_vec = tru_massfrac_to_vector(eol_salt_tru_comp)\n",
+    "\n",
+    "print(lwr_unf_tru_vec)\n",
+    "print(init_salt_tru_vec)\n",
+    "print(eol_salt_tru_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare decay heat / activity of init and final fuel\n",
+    "init_salt_mat = Material(init_salt_tru_vec) * 132659000 * 0.095598353\n",
+    "eol_salt_mat = Material(eol_salt_tru_vec) * 132659000 * 0.09552035\n",
+    "years = np.arange(0, 3000, 100)\n",
+    "init_decay_heat = np.zeros(len(years))\n",
+    "eol_decay_heat = np.zeros(len(years))\n",
+    "for indx, year in enumerate(years):\n",
+    "    sec = year * 365 * 24 * 3600\n",
+    "    decayed_init_mat = init_salt_mat.decay(sec)\n",
+    "    decayed_eol_mat = eol_salt_mat.decay(sec)\n",
+    "    init_decay_heat[indx] = nansum_dict(decayed_init_mat.decay_heat()) * 1e-3\n",
+    "    eol_decay_heat[indx] = nansum_dict(decayed_eol_mat.decay_heat()) * 1e-3\n",
+    "\n",
+    "plt.semilogy(years, init_decay_heat, label='init fuel TRU decay heat')\n",
+    "plt.semilogy(years, eol_decay_heat, label='eol fuel TRU decay heat')\n",
+    "plt.xlabel('Years')\n",
+    "plt.ylabel('Decay Heat [kW] normalized by mass')\n",
+    "plt.title('Decay heat of initial and eol fuel salt over time')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isotope & Beginning of Life & End of Life \\\\ \n",
+      "Np237 & 4.80E-02 & 6.34E-03\\\\ \n",
+      "Np239 & 8.36E-09 & 5.45E-04\\\\ \n",
+      "Pu238 & 1.91E-02 & 2.18E-02\\\\ \n",
+      "Pu239 & 5.55E-01 & 5.52E-01\\\\ \n",
+      "Pu240 & 2.03E-01 & 2.98E-01\\\\ \n",
+      "Pu241 & 5.28E-02 & 3.47E-02\\\\ \n",
+      "Pu242 & 4.97E-02 & 4.13E-02\\\\ \n",
+      "Am241 & 5.74E-02 & 1.77E-02\\\\ \n",
+      "Am243 & 9.70E-03 & 1.26E-02\\\\ \n",
+      "Cm242 & 1.79E-07 & 7.14E-04\\\\ \n",
+      "Cm244 & 4.20E-03 & 1.06E-02\\\\ \n",
+      "Cm245 & 3.92E-04 & 2.79E-03\\\\ \n",
+      "Cm246 & 5.00E-05 & 1.26E-03\\\\ \n",
+      "Cm247 & 6.86E-07 & 1.83E-04\\\\ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TRU vector\n",
+    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
+    "for key, val in eol_salt_tru_vec.items():\n",
+    "    if val < 1e-04:\n",
+    "        continue\n",
+    "    string += key + ' & '\n",
+    "    try:\n",
+    "        bol_comp = init_salt_tru_vec[key]\n",
+    "    except:\n",
+    "        bol_comp = 0\n",
+    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
+    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
+    "\n",
+    "\n",
+    "print(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lwr_unf_tru_comp = get_tru_massfrac('uox_waste')\n",
+    "init_salt_tru_comp = get_tru_massfrac('init_fuel')\n",
+    "eol_salt_tru_comp = get_tru_massfrac('fuel_out')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US UNF UOX generated in 2013-2050: 78281.510000\n"
+     ]
+    }
+   ],
+   "source": [
+    "metric_dict = {}\n",
+    "agent_ids = an.get_agent_ids(cur, 'Reactor')\n",
+    "uox_waste = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'], True, True)\n",
+    "metric_dict['US UNF UOX generated in 2013-2050'] = uox_waste['uox_waste'][439]\n",
+    "print('US UNF UOX generated in 2013-2050: %f' %uox_waste['uox_waste'][439])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US legacy UOX UNF in 2013: 68072.030000\n"
+     ]
+    }
+   ],
+   "source": [
+    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
+    "legacy_waste = an.facility_commodity_flux(cur, agent_ids, ['pre_2013_unf'], True, True)\n",
+    "metric_dict['US legacy LWR UNF in 2013'] = legacy_waste['pre_2013_unf'][-1]\n",
+    "print('US legacy UOX UNF in 2013: %f' %legacy_waste['pre_2013_unf'][-1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US UNF reprocesed up until 2050: 116457.180000\n"
+     ]
+    }
+   ],
+   "source": [
+    "agent_ids = an.get_prototype_id(cur, 'reprocessing')\n",
+    "us_rep = an.facility_commodity_flux(cur, agent_ids, ['cooled_uox', 'pre_2013_unf'], False, True)\n",
+    "print('US UNF reprocesed up until 2050: %f' %(us_rep['cooled_uox'][439] + us_rep['cooled_uox'][439]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TOTAL UOX UNF inventory in US in 2050: 146353.540000\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_unf = uox_waste['uox_waste'][439] + metric_dict['US legacy LWR UNF in 2013']\n",
+    "metric_dict['Total US LWR UNF inventory in 2050'] =  total_unf\n",
+    "print('TOTAL UOX UNF inventory in US in 2050: %f' %total_unf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TOTAL UOX UNF inventory in US: 265048.158000\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_unf = uox_waste['uox_waste'][-1] + metric_dict['US legacy LWR UNF in 2013']\n",
+    "metric_dict['Total US LWR UNF inventory generated'] =  total_unf\n",
+    "print('TOTAL UOX UNF inventory in US: %f' %total_unf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TOTAL UNF created from 2013: 196976.128000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('TOTAL UNF created from 2013: %f' %(total_unf - metric_dict['US legacy LWR UNF in 2013']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total init salt mass sent to MSRs: 19015.162585\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Salt created\n",
+    "agent_ids = an.get_agent_ids(cur, 'Mixer')\n",
+    "init_salt = an.facility_commodity_flux(cur, agent_ids, ['init_fuel'], True, False)\n",
+    "metric_dict['Total fuel salt mass sent to MSRs'] = sum(init_salt['init_fuel'])\n",
+    "print('Total init salt mass sent to MSRs: %f' %sum(init_salt['init_fuel']))\n",
+    "years = 2013 + np.arange(0, duration)/12\n",
+    "plt.plot(years, init_salt['init_fuel'])\n",
+    "plt.title('MSR fuel loading vs time')\n",
+    "plt.xlabel('years')\n",
+    "plt.ylabel('Fuel Salt [MTHM]')\n",
+    "plt.savefig('msr_fuel', format='png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lwr reprocessed and tru separated\n",
+    "init_salt = an.facility_commodity_flux(cur, agent_ids, ['init_fuel'], True, True)\n",
+    "init_salt_tru = np.array(init_salt['init_fuel']) * sum(init_salt_tru_comp.values())\n",
+    "lwr_unf_reproc = init_salt_tru / sum(lwr_unf_tru_comp.values())\n",
+    "\n",
+    "plt.plot(years, lwr_unf_reproc)\n",
+    "plt.title('LWR UNF reprocessing vs time')\n",
+    "plt.xlabel('years')\n",
+    "plt.ylabel('Mass [MTHM]')\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('lwr_unf_reproc', format='png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total TRU extracted: 1824.5941005008417 MTHM of TRU\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# reprocessed TRU -> LWR UNF\n",
+    "init_salt_tru = np.array(init_salt['init_fuel']) * (sum(init_salt_tru_comp.values()))\n",
+    "metric_dict['TOTAL TRU extracted from LWR UNF'] = init_salt_tru[-1]\n",
+    "plt.plot(init_salt_tru)\n",
+    "print('Total TRU extracted:', init_salt_tru[-1], 'MTHM of TRU')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total LWR UNF reprocessed: 134927.2543777226 MTHM\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lwr_unf_reprocessed = init_salt_tru / sum(lwr_unf_tru_comp.values())\n",
+    "metric_dict['Total LWR UNF reprocessed'] = lwr_unf_reprocessed[-1]\n",
+    "plt.plot(lwr_unf_reprocessed)\n",
+    "print('Total LWR UNF reprocessed:', lwr_unf_reprocessed[-1], 'MTHM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average monthly reprocessing of LWR UNF: 94.15719077300949 MTHM/month from 2040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# average reprocessing\n",
+    "avg_monthly_rep = lwr_unf_reprocessed[-1] / 1433\n",
+    "metric_dict['Average monthly reprocessing demand of LWR UNF'] = avg_monthly_rep\n",
+    "print('Average monthly reprocessing of LWR UNF:', avg_monthly_rep, 'MTHM/month from 2040')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "130120.9036222774 MTHM of LWR UNF\n"
+     ]
+    }
+   ],
+   "source": [
+    "# LWR UNF leftover after reprocessing:\n",
+    "leftover = total_unf - lwr_unf_reprocessed[-1]\n",
+    "metric_dict['LWR UNF leftover inventory'] = leftover\n",
+    "print(leftover, 'MTHM of LWR UNF')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average monthly fabrication of fuel salt: 13.26947842642125 MTHM/month from 2040\n"
+     ]
+    }
+   ],
+   "source": [
+    "# average salt fab\n",
+    "avg_monthly_fab = init_salt['init_fuel'][-1] / 1433\n",
+    "metric_dict['Average monthly fabrication of fuel salt'] = avg_monthly_fab\n",
+    "print('Average monthly fabrication of fuel salt:', avg_monthly_fab, 'MTHM/month from 2040')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.981284662727027\n",
+      "132402.04530472727\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufrac = get_u_massfrac('uox_waste')\n",
+    "print(ufrac)\n",
+    "rep_u = lwr_unf_reprocessed * ufrac\n",
+    "metric_dict['Total reprocessed uranium stockpile'] = rep_u[-1]\n",
+    "print(rep_u[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total raffinate stockpile: 4024.720749\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Total Raffinate\n",
+    "agent_ids = an.get_agent_ids(cur, 'Separations')\n",
+    "raff = an.facility_commodity_flux(cur, agent_ids, ['reprocess_waste'], True, True)\n",
+    "total_raff = max(raff['reprocess_waste']) \n",
+    "metric_dict['Total raffinate stockpile'] = total_raff\n",
+    "print('Total raffinate stockpile: %f' %total_raff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total waste from MSRs: 2972.488523 MTHM\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Waste from MSR\n",
+    "agent_ids = an.get_prototype_id(cur, 'rebus')\n",
+    "waste = an.facility_commodity_flux(cur, agent_ids, ['waste'], True, False)\n",
+    "waste_cum = an.facility_commodity_flux(cur, agent_ids, ['waste'], True, True)\n",
+    "an.double_axis_bar_line_plot(waste, waste_cum, timestep, 'Years', 'Discharge mass [MTHM]',\n",
+    "                             'Cumulative mass [MTHM]', 'Waste from MSRs', 'msr_waste', init_year)\n",
+    "tot_waste = sum(waste['waste'])\n",
+    "metric_dict['Total waste from MSRs'] = tot_waste\n",
+    "print('Total waste from MSRs: %f' %tot_waste + ' MTHM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total tails: 1192722.531289\n",
+      "Total tails used = 2969.479669\n",
+      "Total outstanding tails inventory = 1189753.051620\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Tails accumulation\n",
+    "agent_ids = an.get_agent_ids(cur, 'Enrichment')\n",
+    "tails = an.facility_commodity_flux(cur, agent_ids, ['tailings'], True, False)\n",
+    "tails_accum = sum(tails['tailings'])\n",
+    "metric_dict['Total tails created'] = tails_accum\n",
+    "print('Total tails: %f' %tails_accum)\n",
+    "\n",
+    "# or storage\n",
+    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
+    "fill = an.facility_commodity_flux(cur, agent_ids, ['dep_u'], True, False)\n",
+    "for_fill_commod = sum(fill['dep_u'])\n",
+    "metric_dict['Total tails used'] = for_fill_commod\n",
+    "print('Total tails used = %f' %for_fill_commod)\n",
+    "\n",
+    "metric_dict['Total remaining tails inventory'] = tails_accum - for_fill_commod\n",
+    "# total outstanding tails\n",
+    "print('Total outstanding tails inventory = %f' %(tails_accum - for_fill_commod))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total natural U: 1389698.659289\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Natural U used until 2050\n",
+    "source = an.nat_u_timeseries(cur)\n",
+    "metric_dict['Total natural U used'] = max(source) \n",
+    "print('Total natural U: %f' %max(source))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "US \\gls{UNF} UOX generated in 2013-2050 & 78,281 \\\\\n",
+      "US legacy \\gls{LWR} \\gls{UNF} in 2013 & 68,072 \\\\\n",
+      "Total US \\gls{LWR} \\gls{UNF} inventory in 2050 & 146,353 \\\\\n",
+      "Total US \\gls{LWR} \\gls{UNF} inventory generated & 265,048 \\\\\n",
+      "Total fuel salt mass sent to \\gls{MSR}s & 19,015 \\\\\n",
+      "TOTAL TRU extracted from \\gls{LWR} \\gls{UNF} & 1,824 \\\\\n",
+      "Total \\gls{LWR} \\gls{UNF} reprocessed & 134,927 \\\\\n",
+      "Average monthly reprocessing demand of \\gls{LWR} \\gls{UNF} & 94.16 \\\\\n",
+      "\\gls{LWR} \\gls{UNF} leftover inventory & 130,120 \\\\\n",
+      "Average monthly fabrication of fuel salt & 13.27 \\\\\n",
+      "Total reprocessed uranium stockpile & 132,402 \\\\\n",
+      "Total raffinate stockpile & 4,024 \\\\\n",
+      "Total waste from \\gls{MSR}s & 2,972 \\\\\n",
+      "Total tails created & 1,192,722 \\\\\n",
+      "Total tails used & 2,969 \\\\\n",
+      "Total remaining tails inventory & 1,189,753 \\\\\n",
+      "Total natural U used & 1,389,698 \\\\\n"
+     ]
+    }
+   ],
+   "source": [
+    "# display all the metrics in LATEX table format:\n",
+    "for key, val in metric_dict.items():\n",
+    "    if val > 1000:\n",
+    "        valstr = '{0:,}'.format(int(val))\n",
+    "    else:\n",
+    "        valstr = '{0:,.2f}'.format(val)\n",
+    "    string = key + ' & ' + valstr + ' \\\\\\\\'\n",
+    "    string = string.replace('LWR', '\\gls{LWR}')\n",
+    "    string = string.replace('MSR', '\\gls{MSR}')\n",
+    "    string = string.replace('UNF', '\\gls{UNF}')\n",
+    "    print(string)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get uox fuel discharge vs time plot\n",
+    "storageids = an.get_agent_ids(cur, 'storage')\n",
+    "fuel_discharge = an.facility_commodity_flux(cur, storageids, ['uox_waste'], False, True)\n",
+    "fuel_discharge_nocum = an.facility_commodity_flux(cur, storageids, ['uox_waste'], False, False)\n",
+    "an.double_axis_bar_line_plot(fuel_discharge_nocum, fuel_discharge, timestep, 'Years', 'Mass [MTHM]',\n",
+    "                             'Mass [MTHM]', 'Discharge LWR UNF accumulation vs time', 'us_lwr_unf', init_year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/input/master_us/check_output.ipynb b/input/master_us/check_output.ipynb
deleted file mode 100644
index 03b17cf7..00000000
--- a/input/master_us/check_output.ipynb
+++ /dev/null
@@ -1,250 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sqlite3 as lite\n",
-    "import os\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import collections\n",
-    "sys.path.insert(0, '../../scripts/')\n",
-    "jupyter_directory = os.getcwd()\n",
-    "from IPython.display import Image\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os\n",
-    "from shutil import copyfile"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# check_output\n",
-    "This jupyter notebook is to check whether the simulation is a success, by testing the following:\n",
-    "* Did all the REBUS reactors get their initial fuel?\n",
-    "* Did all the REBUS reactors received their depleted uranium?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import analysis as an"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cur = an.get_cursor('./output/done.sqlite')\n",
-    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "95\n"
-     ]
-    }
-   ],
-   "source": [
-    "rebus_ids = an.get_prototype_id(cur, 'rebus')\n",
-    "num = cur.execute('select count() from agententry where prototype = \"rebus\"').fetchone()[0]\n",
-    "print(num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "711 0\n",
-      "713 0\n",
-      "715 0\n",
-      "717 0\n",
-      "719 0\n",
-      "721 0\n",
-      "723 0\n",
-      "725 0\n",
-      "727 0\n",
-      "729 0\n",
-      "731 0\n",
-      "733 0\n",
-      "735 0\n",
-      "737 0\n",
-      "739 0\n",
-      "741 0\n",
-      "743 0\n",
-      "745 0\n",
-      "747 0\n",
-      "749 0\n",
-      "751 0\n",
-      "753 0\n",
-      "755 0\n",
-      "757 0\n",
-      "759 0\n",
-      "761 0\n",
-      "763 0\n",
-      "765 0\n",
-      "767 0\n",
-      "769 0\n",
-      "771 0\n",
-      "773 0\n",
-      "775 0\n",
-      "777 0\n",
-      "779 0\n",
-      "781 0\n",
-      "783 0\n",
-      "785 0\n",
-      "787 0\n",
-      "789 0\n",
-      "791 0\n",
-      "793 0\n",
-      "795 0\n",
-      "797 0\n",
-      "799 0\n",
-      "801 0\n",
-      "803 0\n",
-      "805 0\n",
-      "807 0\n",
-      "809 0\n",
-      "811 0\n",
-      "813 0\n",
-      "815 0\n",
-      "817 0\n",
-      "819 0\n",
-      "821 0\n",
-      "823 0\n",
-      "825 0\n",
-      "827 0\n",
-      "829 0\n",
-      "831 0\n",
-      "833 0\n",
-      "835 0\n",
-      "837 0\n",
-      "839 0\n",
-      "841 0\n",
-      "843 0\n",
-      "845 0\n",
-      "847 0\n",
-      "849 0\n",
-      "851 0\n",
-      "853 0\n",
-      "855 0\n",
-      "857 0\n",
-      "859 0\n",
-      "861 0\n",
-      "863 0\n",
-      "865 0\n",
-      "867 0\n",
-      "869 0\n",
-      "871 0\n",
-      "873 0\n",
-      "875 0\n",
-      "877 0\n",
-      "879 0\n",
-      "881 0\n",
-      "883 0\n",
-      "885 0\n",
-      "887 0\n",
-      "889 0\n",
-      "891 0\n",
-      "893 0\n",
-      "895 0\n",
-      "897 0\n",
-      "899 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# did it get init fuel?\n",
-    "z = 0 \n",
-    "for id in rebus_ids:\n",
-    "    count = cur.execute('select count() from transactions where commodity = \"init_fuel\" and receiverid = \"' + id + '\"').fetchone()[0]\n",
-    "    if count == 0:\n",
-    "        z += 1\n",
-    "        print(id, count)\n",
-    "if z ==0:\n",
-    "    print('Success - all REBUS reactors received initial fuel')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'NoneType' object is not subscriptable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-44-ba4bf40ad2ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrebus_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'select count() from transactions where commodity = \"dep_u\" and receiverid = \"'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mid\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mentertime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'select entertime from agententry where agentid = \"%s\"'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mlifetime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mentertime\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m478\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlifetime\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
-     ]
-    }
-   ],
-   "source": [
-    "z=0\n",
-    "for id in rebus_ids:\n",
-    "    count = cur.execute('select count() from transactions where commodity = \"dep_u\" and receiverid = \"' + id + '\"').fetchone()[0]\n",
-    "    entertime = cur.execute('select entertime from agententry where agentid = \"%s\"' %id).fetchone()[0]\n",
-    "    lifetime = min(duration-entertime-1, 478)\n",
-    "    if count != lifetime:\n",
-    "        z += 1\n",
-    "        print(id, count)\n",
-    "if z==0:\n",
-    "    print('Success - all REBUS reactors received their fill material')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From ca2dd7e2609b5604ebf3546e961b74bcb1bb9cce Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 12:18:49 -0500
Subject: [PATCH 31/35] rebus dep

---
 input/master_us/analysis_msr.ipynb | 1146 ----------------------------
 1 file changed, 1146 deletions(-)
 delete mode 100644 input/master_us/analysis_msr.ipynb

diff --git a/input/master_us/analysis_msr.ipynb b/input/master_us/analysis_msr.ipynb
deleted file mode 100644
index 37e58ce6..00000000
--- a/input/master_us/analysis_msr.ipynb
+++ /dev/null
@@ -1,1146 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Import Necessary Module and Set Path "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/teddy/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.data is not yet QA compliant.\n",
-      "  return f(*args, **kwds)\n",
-      "/home/teddy/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.material is not yet QA compliant.\n",
-      "  return f(*args, **kwds)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sqlite3 as lite\n",
-    "import os\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "from IPython.display import Image\n",
-    "import matplotlib.pyplot as plt\n",
-    "import collections\n",
-    "sys.path.insert(0, '../../scripts/')\n",
-    "jupyter_directory = os.getcwd()\n",
-    "from decimal import Decimal\n",
-    "from pyne.material import Material"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import analysis as an"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get sqlite cursor and simulation time data\n",
-    "cur = an.get_cursor('one_fuelfab.sqlite')\n",
-    "init_year, init_month, duration, timestep = an.get_timesteps(cur)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "power_dict = an.get_power_dict(cur)\n",
-    "pow = {'legacy lwrs': power_dict['us_government'], 'lwr': power_dict['lwr_inst'], 'msr': power_dict['rebus_inst']}\n",
-    "an.stacked_bar_chart(pow, timestep, 'Year', 'Net Capacity [GWe]', 'Installed nuclear capacity in the U.S.',\n",
-    "                     'power_plot.png', init_year)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image('power_plot.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total deployed:\n",
-      "lwr: 84\n",
-      "rebus: 95\n"
-     ]
-    }
-   ],
-   "source": [
-    "# total lwrs and msrs deployed\n",
-    "\n",
-    "lwrs = cur.execute('SELECT entertime, lifetime/lifetime from agententry where prototype = \"lwr\"').fetchall()\n",
-    "rebus = cur.execute('SELECT entertime, lifetime/lifetime from agententry where prototype = \"rebus\"').fetchall()\n",
-    "lwrs = an.get_timeseries(lwrs, duration, False)\n",
-    "rebus = an.get_timeseries(rebus, duration, False)\n",
-    "years = 2020 + np.arange(0, duration)/12\n",
-    "plt.plot(years, lwrs, label='LWR deployment')\n",
-    "plt.plot(years, rebus, label='REBUS deployment')\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('Num. reactors built')\n",
-    "plt.legend()\n",
-    "plt.title('Reactor constuction over time')\n",
-    "plt.savefig('us_dep.png', format='png')\n",
-    "plt.show()\n",
-    "\n",
-    "print('Total deployed:')\n",
-    "print('lwr:', sum(lwrs))\n",
-    "print('rebus:', sum(rebus))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pyne import nucname\n",
-    "def get_tru_massfrac(commodity):\n",
-    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
-    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
-    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
-    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
-    "    comp_dict = {}\n",
-    "    for row in massfracs:\n",
-    "        if int(row['nucid']) < 930000000 or int(row['nucid']) > 970000000:\n",
-    "            continue\n",
-    "        key = nucname.name(row['nucid'])\n",
-    "        comp_dict[key] = row['massfrac']\n",
-    "    return comp_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_massfrac(commodity):\n",
-    "    commodity_qualid = cur.execute('SELECT qualid FROM transactions INNER JOIN resources ON '\n",
-    "                                   'resources.resourceid = transactions.resourceid WHERE '\n",
-    "                                   'commodity = \"%s\"' %commodity).fetchone()[0]\n",
-    "    massfracs = cur.execute('SELECT nucid, massfrac FROM compositions where qualid = %s' %str(commodity_qualid)).fetchall()\n",
-    "    comp_dict = {}\n",
-    "    for row in massfracs:\n",
-    "        key = nucname.name(row['nucid'])\n",
-    "        comp_dict[key] = row['massfrac']\n",
-    "    return comp_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "def nansum_dict(dictionary):\n",
-    "    tot = 0\n",
-    "    for key, val in dictionary.items():\n",
-    "        if math.isnan(val):\n",
-    "            continue\n",
-    "        tot += val\n",
-    "    return tot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tru_massfrac_to_vector(comp_dict):\n",
-    "    tot = sum(comp_dict.values())\n",
-    "    for key, val in comp_dict.items():\n",
-    "        comp_dict[key] = val / tot\n",
-    "    return comp_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TRU composition in Spent and initial Fuels:\n",
-    "lwr_unf_tru_comp = get_tru_massfrac('uox_waste')\n",
-    "init_salt_tru_comp = get_tru_massfrac('init_fuel')\n",
-    "eol_salt_tru_comp = get_tru_massfrac('fuel_out')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Isotope & Beginning of Life & End of Life \\\\ \n",
-      "Np237 & 4.55E-03 & 5.93E-04\\\\ \n",
-      "Pu238 & 1.74E-03 & 1.90E-03\\\\ \n",
-      "Pu239 & 5.71E-02 & 5.21E-02\\\\ \n",
-      "Pu240 & 1.98E-02 & 2.82E-02\\\\ \n",
-      "Pu241 & 3.37E-03 & 3.29E-03\\\\ \n",
-      "Pu242 & 4.61E-03 & 4.00E-03\\\\ \n",
-      "Am241 & 3.19E-03 & 1.63E-03\\\\ \n",
-      "Am243 & 2.71E-04 & 1.23E-03\\\\ \n",
-      "Cm244 & 7.95E-04 & 1.05E-03\\\\ \n",
-      "Cm245 & 6.75E-05 & 2.79E-04\\\\ \n",
-      "Cm246 & 8.60E-06 & 1.32E-04\\\\ \n",
-      "Total & 0.09550070073265847 & 0.09454567570593689 \\\\ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# TRU Composition\n",
-    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
-    "for key, val in eol_salt_tru_comp.items():\n",
-    "    if val < 1e-04:\n",
-    "        continue\n",
-    "    string += key + ' & '\n",
-    "    try:\n",
-    "        bol_comp = init_salt_tru_comp[key]\n",
-    "    except:\n",
-    "        bol_comp = 0\n",
-    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
-    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
-    "\n",
-    "string += 'Total & ' + str(sum(init_salt_tru_comp.values())) + ' & ' + str(sum(eol_salt_tru_comp.values())) + ' \\\\\\ \\n'\n",
-    "print(string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Np237': 0.04730891560586033, 'Pu238': 0.022637142727796017, 'Pu239': 0.47107005808831404, 'Pu240': 0.2256725335415319, 'Pu241': 0.13652178893365427, 'Pu242': 0.06708826924103727, 'Pu244': 2.2248857792867743e-06, 'Am241': 0.005003469136455384, 'Am242M': 6.627286296835367e-05, 'Am243': 0.01540723912331845, 'Cm242': 0.002011327807452964, 'Cm243': 5.98765538193152e-05, 'Cm244': 0.00664903038124026, 'Cm245': 0.000444355896302929, 'Cm246': 5.6658871363617076e-05, 'Cm247': 7.766089989535045e-07, 'Cm248': 5.973410615802391e-08, 'Cm250': 3.3245151906201296e-16}\n",
-      "{'Np237': 0.047694674074704346, 'Np238': 3.1547272998811375e-12, 'Np239': 2.4474266707031783e-09, 'Np240': 7.412727783286107e-18, 'Pu238': 0.018233523192184523, 'Pu239': 0.5980201067700567, 'Pu240': 0.20701633286070506, 'Pu241': 0.035311239406065065, 'Pu242': 0.04826525540574554, 'Pu243': 4.406018586348782e-17, 'Pu244': 5.129170626197411e-06, 'Pu246': 2.8704079479690376e-22, 'Am241': 0.03338640393104139, 'Am242': 2.2566782426777774e-10, 'Am242M': 1.7489637802281043e-05, 'Am243': 0.0028409259133844954, 'Am246': 7.1686016799512355e-25, 'Cm242': 4.7261617371794586e-07, 'Cm243': 8.210008652383034e-05, 'Cm244': 0.008328213213882446, 'Cm245': 0.0007067238269110971, 'Cm246': 9.007631433317926e-05, 'Cm247': 1.2357899413626694e-06, 'Cm248': 9.511366897445526e-08, 'Cm250': 5.2874198396126595e-16}\n",
-      "{'Np233': 6.157196175208336e-24, 'Np234': 4.583617746948512e-16, 'Np235': 2.6659256497810293e-10, 'Np236': 4.985598768746053e-07, 'Np236M': 2.2312706910603108e-14, 'Np237': 0.0062718087534495825, 'Np238': 3.7789505796830005e-06, 'Np239': 0.0005460648617620473, 'Np240': 3.0887195506134726e-09, 'Np240M': 6.312646974496896e-10, 'Pu234': 1.0852076936223005e-21, 'Pu235': 8.423767299125435e-19, 'Pu236': 3.624380833961301e-09, 'Pu237': 5.361519500289506e-08, 'Pu237M': 1.80203669526797e-19, 'Pu238': 0.020085946790079637, 'Pu239': 0.5511388110838523, 'Pu240': 0.29807470071188963, 'Pu241': 0.034771857415978324, 'Pu242': 0.042313479869671, 'Pu243': 7.695283430543524e-07, 'Pu244': 1.2328788133185257e-05, 'Pu245': 2.0509433422871233e-10, 'Pu246': 1.0421567617761837e-17, 'Am238': 3.6174765435627224e-27, 'Am239': 6.080836990675323e-14, 'Am240': 8.041794251405208e-10, 'Am241': 0.0172221632501385, 'Am242': 3.5380029924373973e-06, 'Am242M': 9.721435432111467e-06, 'Am243': 0.013062032531247138, 'Am244': 1.0220812397620013e-07, 'Am244M': 6.568549557997798e-08, 'Am245': 4.1433833281083774e-11, 'Cm238': 5.905965211134074e-27, 'Cm239': 4.994177728304086e-22, 'Cm240': 1.3773681136219832e-12, 'Cm241': 4.0484558663567137e-10, 'Cm242': 0.0006956222561349118, 'Cm243': 7.994022143478682e-05, 'Cm244': 0.011102484640905701, 'Cm244M': 9.557346675082354e-14, 'Cm245': 0.00294625448239031, 'Cm246': 0.001391267931319118, 'Cm247': 0.00020582415047767862, 'Cm248': 6.087506234049294e-05, 'Cm249': 1.3510786814377174e-10, 'Cm250': 9.58143806848894e-12, 'Cm251': 2.808716671115108e-18}\n"
-     ]
-    }
-   ],
-   "source": [
-    "lwr_unf_tru_vec = tru_massfrac_to_vector(lwr_unf_tru_comp)\n",
-    "init_salt_tru_vec = tru_massfrac_to_vector(init_salt_tru_comp)\n",
-    "eol_salt_tru_vec = tru_massfrac_to_vector(eol_salt_tru_comp)\n",
-    "\n",
-    "print(lwr_unf_tru_vec)\n",
-    "print(init_salt_tru_vec)\n",
-    "print(eol_salt_tru_vec)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# compare decay heat / activity of init and final fuel\n",
-    "init_salt_mat = Material(init_salt_tru_vec) * 132659000 * 0.095598353\n",
-    "eol_salt_mat = Material(eol_salt_tru_vec) * 132659000 * 0.09552035\n",
-    "years = np.arange(0, 3000, 100)\n",
-    "init_decay_heat = np.zeros(len(years))\n",
-    "eol_decay_heat = np.zeros(len(years))\n",
-    "for indx, year in enumerate(years):\n",
-    "    sec = year * 365 * 24 * 3600\n",
-    "    decayed_init_mat = init_salt_mat.decay(sec)\n",
-    "    decayed_eol_mat = eol_salt_mat.decay(sec)\n",
-    "    init_decay_heat[indx] = nansum_dict(decayed_init_mat.decay_heat()) * 1e-3\n",
-    "    eol_decay_heat[indx] = nansum_dict(decayed_eol_mat.decay_heat()) * 1e-3\n",
-    "\n",
-    "plt.semilogy(years, init_decay_heat, label='init fuel TRU decay heat')\n",
-    "plt.semilogy(years, eol_decay_heat, label='eol fuel TRU decay heat')\n",
-    "plt.xlabel('Years')\n",
-    "plt.ylabel('Decay Heat [kW] normalized by mass')\n",
-    "plt.title('Decay heat of initial and eol fuel salt over time')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Isotope & Beginning of Life & End of Life \\\\ \n",
-      "Np237 & 4.77E-02 & 6.27E-03\\\\ \n",
-      "Np239 & 2.45E-09 & 5.46E-04\\\\ \n",
-      "Pu238 & 1.82E-02 & 2.01E-02\\\\ \n",
-      "Pu239 & 5.98E-01 & 5.51E-01\\\\ \n",
-      "Pu240 & 2.07E-01 & 2.98E-01\\\\ \n",
-      "Pu241 & 3.53E-02 & 3.48E-02\\\\ \n",
-      "Pu242 & 4.83E-02 & 4.23E-02\\\\ \n",
-      "Am241 & 3.34E-02 & 1.72E-02\\\\ \n",
-      "Am243 & 2.84E-03 & 1.31E-02\\\\ \n",
-      "Cm242 & 4.73E-07 & 6.96E-04\\\\ \n",
-      "Cm244 & 8.33E-03 & 1.11E-02\\\\ \n",
-      "Cm245 & 7.07E-04 & 2.95E-03\\\\ \n",
-      "Cm246 & 9.01E-05 & 1.39E-03\\\\ \n",
-      "Cm247 & 1.24E-06 & 2.06E-04\\\\ \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# TRU vector\n",
-    "string = 'Isotope & Beginning of Life & End of Life \\\\\\ \\n'\n",
-    "for key, val in eol_salt_tru_vec.items():\n",
-    "    if val < 1e-04:\n",
-    "        continue\n",
-    "    string += key + ' & '\n",
-    "    try:\n",
-    "        bol_comp = init_salt_tru_vec[key]\n",
-    "    except:\n",
-    "        bol_comp = 0\n",
-    "    string += '%.2E' % Decimal(str(bol_comp)) + ' & '\n",
-    "    string += '%.2E' % Decimal(str(val)) + '\\\\\\ \\n'\n",
-    "\n",
-    "print(string)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lwr_unf_tru_comp = get_tru_massfrac('uox_waste')\n",
-    "init_salt_tru_comp = get_tru_massfrac('init_fuel')\n",
-    "eol_salt_tru_comp = get_tru_massfrac('fuel_out')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "US UNF UOX generated in 2013-2050: 78281.510000\n"
-     ]
-    }
-   ],
-   "source": [
-    "# French UOX UNF total, in sink, and reprocessed\n",
-    "agent_ids = an.get_agent_ids(cur, 'Reactor')\n",
-    "uox_waste = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'], True, True)\n",
-    "print('US UNF UOX generated in 2013-2050: %f' %uox_waste['uox_waste'][439])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "US legacy UOX UNF in 2013: 68072.030000\n"
-     ]
-    }
-   ],
-   "source": [
-    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
-    "legacy_waste = an.facility_commodity_flux(cur, agent_ids, ['pre_2013_unf'], True, True)\n",
-    "print('US legacy UOX UNF in 2013: %f' %legacy_waste['pre_2013_unf'][1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "US UNF reprocesed up until 2050: 128974.696000\n"
-     ]
-    }
-   ],
-   "source": [
-    "agent_ids = an.get_prototype_id(cur, 'reprocessing')\n",
-    "us_rep = an.facility_commodity_flux(cur, agent_ids, ['cooled_uox', 'pre_2013_unf'], False, True)\n",
-    "print('US UNF reprocesed up until 2050: %f' %(us_rep['cooled_uox'][439] + us_rep['cooled_uox'][439]))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TOTAL UOX UNF inventory in US in 2050: 146353.540000\n"
-     ]
-    }
-   ],
-   "source": [
-    "total_unf = uox_waste['uox_waste'][439] + legacy_waste['pre_2013_unf'][1]\n",
-    "print('TOTAL UOX UNF inventory in US in 2050: %f' %total_unf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total init salt mass sent to MSRs: 0.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f4dd7c24358>]"
-      ]
-     },
-     "execution_count": 263,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Y76KaT7PBbz5PvYHX04OFa6sPAye651f/8xsAXx6p+0sWbkDOAX8x5jgbKRhWvWYW/iIr4EfAU93jI9Ne02us9c+An7DwzZXbu7G7gA902+ex8I2UOeD7wDtGXnt797rjnKPfvJrkmoG/Af575HN9Ctg+7fWs5Wc8cowNHwz+8lmS1PBbSZKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWr8L4G+I6VKUcyzAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Salt created\n",
-    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
-    "init_salt = an.facility_commodity_flux(cur, agent_ids, ['init_fuel'], True, False)\n",
-    "print('Total init salt mass sent to MSRs: %f' %sum(init_salt['init_fuel']))\n",
-    "plt.plot(init_salt['init_fuel'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total TRU extracted: 0 MTHM of TRU\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Y76KaT7PBbz5PvYHX04OFa6sPAye651f/8xsAXx6p+0sWbkDOAX8x5jgbKRhWvWYW/iIr4EfAU93jI9Ne02us9c+An7DwzZXbu7G7gA902+ex8I2UOeD7wDtGXnt797rjnKPfvJrkmoG/Af575HN9Ctg+7fWs5Wc8cowNHwz+8lmS1PBbSZKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWr8L4G+I6VKUcyzAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# reprocessed TRU -> LWR UNF\n",
-    "init_salt_tru = np.array(init_salt['init_fuel']) * (sum(init_salt_tru_comp.values()))\n",
-    "plt.plot(init_salt_tru)\n",
-    "print('Total TRU extracted:', sum(init_salt_tru), 'MTHM of TRU')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total LWR UNF reprocessed: 0 MTHM\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lwr_unf_reprocessed = init_salt_tru / sum(lwr_unf_tru_comp.values())\n",
-    "plt.plot(lwr_unf_reprocessed)\n",
-    "print('Total LWR UNF reprocessed:', sum(lwr_unf_reprocessed), 'MTHM')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average monthly reprocessing of LWR UNF: 0.0 MTHM/month from 2040\n"
-     ]
-    }
-   ],
-   "source": [
-    "# average reprocessing\n",
-    "avg_monthly_rep = sum(lwr_unf_reprocessed) / 1433\n",
-    "print('Average monthly reprocessing of LWR UNF:', avg_monthly_rep, 'MTHM/month from 2040')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "146353.53999999998 MTHM of LWR UNF\n"
-     ]
-    }
-   ],
-   "source": [
-    "# LWR UNF leftover after reprocessing:\n",
-    "leftover = total_unf - sum(lwr_unf_reprocessed)\n",
-    "print(leftover, 'MTHM of LWR UNF')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average monthly fabrication of fuel salt: 0.0 MTHM/month from 2040\n"
-     ]
-    }
-   ],
-   "source": [
-    "# average salt fab\n",
-    "avg_monthly_fab = sum(init_salt['init_fuel']) / 1433\n",
-    "print('Average monthly fabrication of fuel salt:', avg_monthly_fab, 'MTHM/month from 2040')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'numpy.float64' object is not iterable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-269-ed56a367b659>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mrepu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0man\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfacility_commodity_flux\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'lwr_u'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'salt_u'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtotal_repu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lwr_u'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'salt_u'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Total reprocessed uranium stockpile: %f'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_repu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: 'numpy.float64' object is not iterable"
-     ]
-    }
-   ],
-   "source": [
-    "# Total reprocessed U stockpile\n",
-    "agent_ids = an.get_agent_ids(cur, 'Sink')\n",
-    "repu = an.facility_commodity_flux(cur, agent_ids, ['lwr_u', 'salt_u'], False, True)\n",
-    "total_repu = max(repu['lwr_u']) + max(repu['salt_u'])\n",
-    "print('Total reprocessed uranium stockpile: %f' %max(total_repu))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Total Raffinate\n",
-    "raff = an.facility_commodity_flux(cur, agent_ids, ['reprocess_waste', 'salt_reprocess_waste'], False, True)\n",
-    "total_raff = max(raff['reprocess_waste']) + max(raff['salt_reprocess_waste'])\n",
-    "print('Total raffinate stockpile: %f' %max(total_raff))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Waste from MSR\n",
-    "waste = an.facility_commodity_flux(cur, agent_ids, ['waste'], False, False)\n",
-    "plt.plot(waste['waste'])\n",
-    "tot_waste = sum(waste['waste'])\n",
-    "print('Total waste from MSRs: %f' %tot_waste + ' MTHM')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tails accumulation\n",
-    "agent_ids = an.get_agent_ids(cur, 'Enrichment')\n",
-    "tails = an.facility_commodity_flux(cur, agent_ids, ['tails'], True, False)\n",
-    "tails_accum = sum(tails['tails'])\n",
-    "print('Total tails: %f' %tails_accum)\n",
-    "\n",
-    "# Tails usage\n",
-    "for_init_salt = sum(init_salt['init_fuel']) * 0.477050824\n",
-    "\n",
-    "# or storage\n",
-    "agent_ids = an.get_agent_ids(cur, 'Source')\n",
-    "fill = an.facility_commodity_flux(cur, agent_ids, ['dep_u'], True, False)\n",
-    "for_fill_commod = sum(fill['dep_u'])\n",
-    "tot = for_init_salt + for_fill_commod\n",
-    "print('Total tails used = (for init salt) + (for fill commod)')\n",
-    "print('Total tails used = %f + %f = %f' %(for_init_salt, for_fill_commod, tot))\n",
-    "\n",
-    "# total outstanding tails\n",
-    "print('Total outstanding tails inventory = %f' %(tails_accum - tot))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Natural U used until 2050\n",
-    "source = an.nat_u_timeseries(cur)\n",
-    "print('Total natural U: %f' %max(source))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ===================================="
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get total fuel mass vs time plot up until 2050\n",
-    "fuel_dict = an.fuel_usage_timeseries(cur, ['uox'], True)\n",
-    "fuel_dict['uox'] = fuel_dict['uox']\n",
-    "an.stacked_bar_chart(fuel_dict, timestep,\n",
-    "                  'Years', 'Mass[MTHM]',\n",
-    "                  'Total LWR Fuel Mass vs Time',\n",
-    "                  'total_fuel',\n",
-    "                  init_year)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get astrid fuel discharge vs time plot\n",
-    "storageids = an.get_agent_ids(cur, 'storage')\n",
-    "astrid_discharge = an.facility_commodity_flux(cur, storageids, ['astrid_unf'], False, False)\n",
-    "an.stacked_bar_chart(astrid_discharge, timestep,\n",
-    "                  'Years', 'Mass[MTHM]',\n",
-    "                  'ASTRID UNF discharge vs Time',\n",
-    "                  'astrid_unf',\n",
-    "                  init_year)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total ASTRID fuel from spent UOX:\n",
-      "17795.32799999986\n",
-      "Total ASTRID fuel from spent ASTRID fuel:\n",
-      "57790.27199999988\n"
-     ]
-    }
-   ],
-   "source": [
-    "# where does the astrid fuel come from?\n",
-    "init_year, init_month, duration, timestep = an.get_timesteps(cur)\n",
-    "\n",
-    "agent_ids = an.get_agent_ids(cur, 'Reactor')\n",
-    "astrid_loading = an.facility_commodity_flux(cur, agent_ids, ['astrid_fuel_legacy', 'astrid_fuel_new'], False, False)\n",
-    "\n",
-    "print('Total ASTRID fuel from spent UOX:')\n",
-    "print(sum(astrid_loading['astrid_fuel_legacy']))\n",
-    "print('Total ASTRID fuel from spent ASTRID fuel:')\n",
-    "print(sum(astrid_loading['astrid_fuel_new']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "new_dict = collections.OrderedDict()\n",
-    "new_dict = {'fuel from used uox': astrid_loading['astrid_fuel_legacy'],\n",
-    "            'fuel from used mox': astrid_loading['astrid_fuel_new']}\n",
-    "an.stacked_bar_chart(new_dict, timestep,\n",
-    "                  'Years', 'Mass[MTHM]',\n",
-    "                  'ASTRID Fuel Loading vs Time',\n",
-    "                  'where_fuel',\n",
-    "                  init_year)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Pu from Spent UOX:\n",
-      "2669.9276880810953\n",
-      "Total Pu from Spent ASTRID Fuel:\n",
-      "19883.9008139658\n",
-      "Total Pu Reprocessed:\n",
-      "22553.828502046894\n"
-     ]
-    }
-   ],
-   "source": [
-    "# cum plutonium flux\n",
-    "agent_ids = an.get_agent_ids(cur, 'separations')\n",
-    "pu = an.facility_commodity_flux(cur, agent_ids, ['lwr_pu', 'sfr_pu'], True, False)\n",
-    "pu['pu from legacy'] = pu.pop('lwr_pu')\n",
-    "pu['pu from spent sfr fuel'] = pu.pop('sfr_pu')\n",
-    "an.stacked_bar_chart(pu, timestep, 'Years', 'Mass [MTHM]', 'Total Pu Mass vs Time', 'pu', init_year)\n",
-    "\n",
-    "print('Total Pu from Spent UOX:')\n",
-    "print(sum(pu['pu from legacy']))\n",
-    "print('Total Pu from Spent ASTRID Fuel:')\n",
-    "print(sum(pu['pu from spent sfr fuel']))\n",
-    "print('Total Pu Reprocessed:')\n",
-    "print(sum(pu['pu from spent sfr fuel']) + sum(pu['pu from legacy']))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Reprocessed U Inventory:\n",
-      "159383.763159\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Total Reprocessed Uranium Stockpile\n",
-    "agent_ids = an.get_agent_ids(cur, 'sink')\n",
-    "repu_inv = an.facility_commodity_flux(cur, agent_ids, ['uox_U', 'astrid_U'], False, True)\n",
-    "an.stacked_bar_chart(repu_inv, timestep, 'Years', 'Mass[MTHM]', 'Total Rep U Mass vs Time', 'repu', init_year)\n",
-    "\n",
-    "print('Total Reprocessed U Inventory:')\n",
-    "print(max([x+y for x, y in zip(repu_inv['uox_U'], repu_inv['astrid_U'])]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Raffinate:\n",
-      "24789.4271143\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Raffinate\n",
-    "agent_ids = an.get_agent_ids(cur, 'separations')\n",
-    "reprocess_waste = an.facility_commodity_flux(cur, agent_ids, ['lahague_raffinate', 'uox_raffinate', 'astrid_raffinate'], True, False)\n",
-    "an.stacked_bar_chart(reprocess_waste, timestep, 'Years', 'Mass [MTHM]',\n",
-    "                     'Raffinate Inventory vs Time', 'raffinate', init_year)\n",
-    "print('Total Raffinate:')\n",
-    "print(sum([x+y+z for x, y, z in zip(reprocess_waste['lahague_raffinate'], reprocess_waste['uox_raffinate'], reprocess_waste['astrid_raffinate']\n",
-    ")]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total UOX Waste in MTHM:\n",
-      "176640.413\n",
-      "Total UOX Waste Stored:\n",
-      "110045.923\n",
-      "Total MOX Waste in MTHM:\n",
-      "6945.558\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get spent fuel no_cum and cumulative plot\n",
-    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
-    "snf_dict_no_cum = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'],\n",
-    "                                        False, False)\n",
-    "snf_dict = an.facility_commodity_flux(cur, agent_ids, ['uox_waste'],\n",
-    "                                        False, True) \n",
-    "dictionary = {}\n",
-    "dictionary['monthly_discharge'] = snf_dict_no_cum['uox_waste'][:960]\n",
-    "dict2 = {}\n",
-    "dict2['cum_mass'] = snf_dict['uox_waste'][:960]\n",
-    "\n",
-    "an.double_axis_bar_line_plot(dictionary,dict2, timestep[:960], 'Years',  'Monthly Discharge Mass [MTHM]','Cumulative Mass [MTHM]',\n",
-    "                    'Spent Fuel Discharge vs Time', 'snf_discharge', init_year)\n",
-    "\n",
-    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
-    "snf_dict = an.facility_commodity_flux(cur, agent_ids, ['uox_waste', 'french_uox_waste'],\n",
-    "                                        False)\n",
-    "print('Total UOX Waste in MTHM:')\n",
-    "print(max([x + y for x, y in zip(snf_dict['uox_waste'], snf_dict['french_uox_waste'])]))\n",
-    "print('Total UOX Waste Stored:')\n",
-    "print(max(snf_dict['uox_waste']))\n",
-    "agent_ids = an.get_agent_ids(cur, 'Sink')\n",
-    "snf_dict = an.facility_commodity_flux(cur, agent_ids, ['mox_waste'],\n",
-    "                                        False)\n",
-    "print('Total MOX Waste in MTHM:')\n",
-    "print(max(snf_dict['mox_waste']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Natural U in MTHM:\n",
-      "1246226.87843\n",
-      "Total UOX usage in MTHM:\n",
-      "176640.413\n",
-      "Total MOX usage in MTHM:\n",
-      "6945.558\n",
-      "Total Tailings in MTHM:\n",
-      "1069586.46543\n",
-      "Finished!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Display important simulation results\n",
-    "\n",
-    "# natural uranium demand\n",
-    "dictionary = collections.OrderedDict()\n",
-    "dictionary['natural_uranium'] = an.nat_u_timeseries(cur)\n",
-    "print('Total Natural U in MTHM:')\n",
-    "print(max(dictionary['natural_uranium']))\n",
-    "\n",
-    "# get spent fuel timeseries separated by region\n",
-    "\n",
-    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
-    "comm_dict = an.commodity_flux_region(cur, agent_ids, ['uox_waste', 'french_uox_waste'], False)\n",
-    "\n",
-    "# get fuel usage timeseries in stacked bar chart\n",
-    "fuel_dict = an.fuel_usage_timeseries(cur, ['uox', 'mox'])\n",
-    "\n",
-    "print('Total UOX usage in MTHM:')\n",
-    "print(max(fuel_dict['uox']))\n",
-    "print('Total MOX usage in MTHM:')\n",
-    "print(max(fuel_dict['mox']))\n",
-    "\n",
-    "# get tailings timeseries\n",
-    "agent_ids = an.get_agent_ids(cur, 'Storage')\n",
-    "tails_dict = an.facility_commodity_flux(cur, agent_ids, ['tails'], False)\n",
-    "print('Total Tailings in MTHM:')\n",
-    "print(max(tails_dict['tails']))\n",
-    "\n",
-    "print('Finished!')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cvHgRlUqFtbU1JiYmSsu7sjz33HO8+eabXLp0CcgbBLhz585KjUFUX++++y7Ozs64ubnh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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 233,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display Net Capacity vs Time\n",
-    "from IPython.display import Image\n",
-    "Image(filename='power_plot.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ld33dunW1XrNs2TL69+/P5cuXOXDggNZ7VjeS3A1AV7+3rr7yvPdKozjXGTKp\nShIXQuhz/fp1srOzadKkCW3btuXq1as8evSI5ORkDh06VOj8Hj16cPToUR48eEBWVla+JvTk5GRa\ntWoFwNatW4t1f81rtPXvV0fSLF8DFey3rywPM65QR885BRO/9MUL8XjI63OH3Jr41q1bMTExoXXr\n1owdOxZHR0c6dOiAi4tLoWtbtWrFa6+9hoeHBy1btqRr1640aNAAyB04N2bMGFq1aoWnpydRUVF6\nY/nXv/6Fn58fH3zwAU8++WT5PmglkeQuhBCPueJMXStv2dnZOt9btWoVq1atKnRccxDcxIkTCQgI\nICsri1GjRuHj4wPAiBEjGDFiRKFrly9fnu+1t7c33t7eQG63QF6fPcCbb75ZgiepmiqsWX7atGlY\nW1tjb29f6L3Vq1djZGREQkJu7VJRFF566SXat2+Po6Mj586dq6iwHhulbdYXQojqYPny5Tg7O2Nv\nb4+dnR0jR46s7JCqlAqrufv7+zN37lymTJmS73hMTAwhISG0adNGPfb9998TGRlJZGQkP//8M7Nn\nz+bnn3+uqNAMKrfZuXLmmetK8BW9uM0/Te1zS3WtNM0LIfRZvXp1ZYdQpVVYzb1fv37qNAVNCxYs\nYNWqVRgZGanHgoKCmDJlCkZGRnh6epKUlER8fHxFhWZwRQ2cqwyRGc0rdZCbvkV1ZACeEEKUjUH7\n3Pfv30+rVq3UxQbyxMXF0bp1a/W1jY0NcXFxtGjRolAZgYGBBAYGArnzGTX7YMpbSkpKmctPz5iL\nU3bheZXlwTyzAU53hhTr3NrGWflepxtlEno7tEz3f5hxRcc7uTX2nHRr0iNzf9fce74pkPT/f69j\npH0+aejtUB5mXKFe7W7FikHfedqUx59vRZL4ykbiE48zgyX31NRU3n77bQ4ePFjoPW1zFjVr9poC\nAgIICAgAwM3NTR0QURFCQ0PLXH5I1GyO/1V43EF5cLozhIstvy/WuQVHz3eo/QfeZWz+DomaXeT7\n6ZFzqdPhYwBidNTWdXUReNtdIyRqtt4Y82IozbOUx59vRZL4ykbi060kq8GJ6slgyf3WrVtERUWp\ntfbY2FhcXV05ffo0NjY2xMTEqOfGxsbSsmVLQ4X22Mpr/i5pH3dJms1Lu659ce4hzfdCCKGdwRax\ncXBw4M8//yQ6Opro6GhsbGw4d+4czZs3Z/jw4Wzbtg1FUQgPD6dBgwZam+Sro5q0aUtJF6TRbIrX\nRXNzG22flb77ycY4QlRPlpaWhY6tX7+ebdu2AbmLydy5c0d9z9bWVp1hVVE071/dVVjNfcKECYSG\nhpKQkICNjQ0rVqxg+vTpWs+dNuuFAAAgAElEQVQdOnQo3333He3bt8fCwoLNmzdXVFgGVZkj5QvS\nNqCvumwJW5wR9DLKXojS6zX2/8q1vFO7Xy7VdbNmzVJ/37JlC/b29gZtxdW8f3VXYcl9586dRb4f\nHR2t/m5kZMS6desqKhQhhBDVwPLly7G0tMTW1pazZ88yadIkzM3NCQsLA2Dt2rUcOHCAzMxMvv76\nazp37qxes2jRIgDs7e0JDg7G1taWkSNHEhMTQ3p6OvPmzVPHa1laWjJv3jyCg4MxNzcnKCiIZs2a\n5Strw4YNBAYGkpGRQfv27dm+fTsWFhaV9tmUlKwtL4qlpP3spW0qL8l10hwvRM3k6+uLm5sbO3bs\n4MKFC+pOblZWVpw7d47Zs2cXa577pk2biIiI4OzZs6xZs4b79+8D8Pfff+Pp6cnFixfp168fGzZs\nKHTt6NGjOXPmDBcvXqRLly5s3LixfB+ygklyf4wVNwnrSuwF+8uLW56+Of+lSdqVPXdfCFHxRo8e\nDUD37t3ztf7qsmbNGpycnPD09CQmJobIyEgAateuzbBhw4os6/Lly/Tt2xcHBwd27NjBlSu6pv5W\nTbK2fAWrSovX6FLSpFia5Fvwc8h7XdQGN5EZzdVxAcXtU5e+dyFqLjMzMwBMTEzIyspdu0PXFrGh\noaH89NNPhIWFYWFhgbe3t/qeqampOt1asyxN/v7+7Nu3DycnJ7Zs2VLt1iSQmrsQQogqp169ejx8\n+FDveba2tup+JOfOnVN3gUtOTqZRo0ZYWFhw/fp1wsPDS3T/hw8f0qJFCzIzM9mxY0fJH6CSSXKv\nQFW5T7g0LQql6Usv7dK7pZ0WV5zzhBCVLzU1FRsbG/Xngw8+yPe+v78/s2bNwtnZmbS0NJ3lPPvs\nsyQmJuLs7Mynn35Kx44dAXjqqafIysrC0dGRZcuW4enpWaL43nzzTTw8PBg0aBCdO3cu+QNWMmmW\nF8VSmi8qGTn6/3oVTPwFm+k1m+aLuj7vOn3nCyEKK+3UtbLQbErX5tlnn+XZZ59VX2v2i7u5uanN\n5Obm5lpXPoXcTcm0SUlJUX/39fXF19cXyL8t7OzZs5k9u+hVOKsyqblXI7dTm5RrecWtVVd2C0Rl\n318IIaobqblXkPJewCYvsWsmeCddJ5dQeSfPvC8MpYkvOt2qyEF2mgPmdH0xicxozqBS3FsIIWoK\nqbkLIYQQNYwk9wqkrWZZmqb1oq65ndpE/TG0vGb9gj/lUW5Bmq0L2gbMVYcph0IIYSiS3CtBUcm4\n4PGikvajYgxYqwjllcSLcx9Nld33LqPwhRDVhfS5VwHaEnpbi/uFjsen1qOFhe55n5rnt7W4X64x\nGrpmrO9+uYnWvtA1ef31FbWYTcEELwvmCCGqIqm5CyGEMDgTExOcnZ2xt7dnzJgxpKamFnm+ti1i\nSyM6Ohp7e3v9J1ZzUnOvINqakEvSL66t1q753+JcX9zau6Fr5SWNTXP0fHHmvRccbV8etfiQqC5a\n751Xk5cavKjO3GZ8oP+kEjj7+UK955ibm3PhwgUAJk2axPr161m4UP91ongkuRuYZtN6cZN9UQld\n872CTfYV2UxfUrrGEhQnrpIm+LxzNKfDlWRt+jx552seK/ilTXPt+6KUx5cL+QIhaqq+ffty6dIl\nAD744AM2bdoEwIwZM5g/f36+c1NSUhgxYgQPHjwgMzOTt956ixEjRhAdHc2QIUPo06cPp06dolWr\nVgQFBWFubk5ERATTpk3DwsKCPn36GPz5KoMk90qQl5C19Z8X7FcvKrFn5lT9XpWKGsVf1OC6vC8D\nBRNiUQlSW3IuKqkXPK7vy0ZpBuOlZ8wlJGq2JHVRo2VlZfH999/z1FNPERERwebNm/n5559RFAUP\nDw+8vLxwcXFRz69Tpw579+6lfv36JCQk4OnpyfDhwwGIjIxk586dbNiwgbFjx7Jnzx6ee+45pk6d\nytq1a/Hy8uKVV16prEc1qKqfHYQQQtQ4aWlpODs74+bmRps2bZg+fTonTpxg1KhR1K1bF0tLS0aP\nHs3x48fzXacoCq+99hqOjo4MHDiQuLg47t69C4CdnR3Ozs7AP1u5Jicnk5SUhJeXFwCTJ0827INW\nkgqruU+bNo3g4GCsra25fPkyAK+88goHDhygdu3atGvXjs2bN9OwYUMA3n33XTZu3IiJiQlr1qxh\n8ODBFRWaQZSkH7tg7Tyv9l7c/nVt5RQ1qt5QijNVr2DNXlczva6V6/Q17+f1lUPh2vXDjCuEROVf\nO1qzhp53vrZa+9HETgB4Nb6h8xxNZVnvXn0G6d8XNYhmn3seRVH0Xrdjxw7u3btHREQEpqam2Nra\nqlu55m0JC7kD9tLS0lAURd3e9XFSYTV3f39/fvjhh3zHBg0axOXLl7l06RIdO3bk3XffBeDq1avs\n2rWLK1eu8MMPPzBnzhyys7MrKrRKU5JkXdLEXt7XV0VlHfhXVALWtuNdwWNHEzupPwWPFefeJf1J\nV0zV+x9N7MTnf3jx+R9ehER1kTn3okbq168f+/btIzU1lb///pu9e/fSt2/ffOckJydjbW2Nqakp\nR44c4fbt20WW2bBhQxo0aMCJEycAquX2raVRopr7gwcPiImJwdHRUe+5/fr1y7eLD4CPj4/6u6en\nJ9988w0AQUFBjB8/HjMzM+zs7Gjfvj2nT5+mZ8+eJQmv2qnoBKxZviEH1OXVpkuzSWJJRtJr1vq1\nXadr29i8mm+6YkpMKRfG+eVu7nUOzXJr5PoSfF4NvzQKPsfnf3gxo/lRnQleavaiunJ1dcXf358e\nPXoAuQPqNPvbIXdk/TPPPIObmxvOzs7F2o518+bN6oC66t4qXFx6k7u3tzf79+8nKysLZ2dnmjZt\nipeXV6G9d0tq06ZNjBs3DoC4uLh8e+3a2NgQFxdXpvKFEEIUT3GmrpU3zW1XNS1cuFDrlLi8862s\nrAgLC9N6bV4XMMCiRYvU37t3787FixfV15pbu9ZUepN7cnIy9evX5/PPP2fq1KmsWLGiWDX3orz9\n9tvUqlWLSZMmAdr7WXT1kQQGBhIYGAhAbGysuqdvRUhJSSl1+U5pQwoda5dVu0zxZGWbUMvkn+6K\nJjkWTEot3t5rprEO+V6bGWeVKRbQ3aee9z26QVZdBif2KvR+Zo4xpsZF7OWcWHR8tf//e4Oz6uQ7\nbpaUpfU8TelGmYTeDgWg1qPGNI2eoDsODQ+z6tAv01R93S/vl7u5NXZz08yiC9DyOehjnFWXOr9O\nzb2fxr3NTTOJKaK8vOeraGX5/8MQJD7xONOb3LOysoiPj2f37t28/fbbZb7h1q1bCQ4O5tChQ2oC\nt7GxISYmRj0nNjaWli1bar0+ICCAgIAAANzc3PD29i5zTLqEhoaWuvx/XVxX6Fh4gm2pY0l4WDf3\nF42hCPNM2rPD4qL2CwrQNsCutE31xZ3eNjixFz82PgXo7oLQNfAvb/ldXTHa1knQ2hSueX7eALy8\nvvq81x1qfwxA1s0F3LPdme96zTLzmtLzjuU1xQOkpeQO3DG3fJR7QE9uz2u+LwnNz0/z3mTqKy/3\nmq96ri/xPUuiLP9/GILEp9vhw4cr5b7CcPQm99dff53BgwfTp08f3N3d+e233+jQoUOpbvbDDz/w\n3nvvcfToUSwsLNTjw4cPZ+LEiSxcuJA7d+4QGRmp9rnUBJrJMOFhXazq/V2i69XErue9kpZbEqWd\nr17acQX67qdrcF1RXwiK2ite2xcFzWO/3G2uJnRNhZK8DvmSczH1w1S9TvPe5paPSlWeEOLxUWRy\nz87OJiYmRl05COCJJ55gz549egueMGECoaGhJCQkYGNjw4oVK3j33Xd59OgRgwblrh3m6enJ+vXr\n6datG2PHjqVr167UqlWLdevWYWJiUsZHq7ryErK2ZFww+ReV2LOyy/YZVfQ2scVZZKdg8te2yl5R\nLQzaFgTSvKao7WPrZNUplNQ1k2Ze7bg4iVRb4tekL/mXRFqKWbHK67pvOVdHLi+3+wohqo8ik7uJ\niQn79+9nwYIFJS54586dhY5Nnz5d5/lLly5l6dKlJb6PEEIIIfLT2yzfq1cv5s6dy7hx46hb959a\npKura4UGVp1p2460KAVr53m196Jq7frKKdgqoK+GXBqGmktfkulxmtdo+11Xs7y22rm2PvbSKs31\nORZGpKVqv6445ZlbPqLrvuUAUoMX4jGjN7mfOpU7OOc///mPeszIyEgGZJRAXhIsSbIuaWLXdn1R\nffD69oYvSkUm9ZLEpRlHca/T7HdPyzQtlyb3qkozbrsv3lV/j3puSWWEI0Q+lpaWpKSkEB0dzalT\np5g4cWKR50dHRzNs2LB8092EbnqT+5EjRwwRx2OprAm8JOWXdbCdIVe805aoi1N715fgf7nbHJrp\nrr1D9U3kJaGZ6IsiXwIeH44LPyzX8i59UPyu3OjoaL788ku9yV2UjN4RT8nJySxcuBA3Nzfc3Nx4\n+eWXSU5ONkRsQggharjFixdz/PhxnJ2d+fDDD4mOjqZv3764urri6uqqth5r6tu3b7516Xv37p1v\n4LcoRs192rRp2Nvbs3v3bgC2b9/O1KlT+fbbbys8OFGxKmv9+eJOByxYE9dWey8480DbNQWb3vNG\nyPfTOPY41NiFqIpWrlzJ6tWrCQ4OBiA1NZWQkBDq1KlDZGQkEyZM4OzZs/mumTFjBlu2bOG///0v\nN2/e5NGjR2VeXK2m0Zvcb926lW/q2+uvv65uqScK09yFDCp+ull1oW3QIOjuLijpFw/N8oq69pe7\nzdUpbjk5ugesCSEqR2ZmJnPnzuXChQuYmJhw8+bNQueMGTOGN998k/fff59Nmzbh7+9v+ECrOL3J\n3dzcnBMnTtCnTx8ATp48ibm5eYUHJmqGso4rKKr2rq3s4rQK5NXk+5QpsppPX9/8ZpuavbGTqBwf\nfvghzZo14+LFi+Tk5FCnTp1C51hYWDBo0CCCgoLYvXt3oZq9KEZyX79+PVOmTFH72Rs1asTWrVsr\nPDAhhBA1X7169Xj48J8v8MnJydjY2GBsbMzWrVt1bv89Y8YMnnnmGfr27Uvjxo0NFW61oTe5169f\nn4sXL/LXX3+pr6Oioio8sOpMc1W0mrivenEVZwW9grVvbbXu4vS9ayuzPObyC91+SfyDqcUceV8Z\nFlrY1ej4aspsBkdHR2rVqoWTkxP+/v7MmTOHZ599lq+//pr+/fvnW19FU/fu3alfvz5Tp041cMTV\ng97k/uyzz3Lu3Dnq16+vHvP19SUiIqJCAxPlqzhJtKxllreimtiLO5ZB1mAXQr+STF0rL3lbuJqa\nmnLo0KH88WiMfH/33dwvQLa2tvnmuN+5c4ecnBx8fHwMEG31ozO5X79+nStXrpCcnJxvZPxff/1F\nenq6QYKrjrQNpqvoJFgapdnARvPaiqIrrtIsuqMrsasj4y20vi2EqOK2bdvG0qVL+eCDDzA21r+H\nxeNIZ3K/ceMGwcHBJCUlceDAAfV4vXr12LBhg0GCE1WHIb+gaCZ4zd/LsqoeyHQ3IWqKKVOmMGXK\nlMoOo0rTmdxHjBjBiBEjCAsLo2dPGRUrhBBCVBd62zPWr19PUlKS+vrBgwdMmzatQoOqzqrTYLqE\nh3WL/VMZsWn7vTTSUsyk1i6EeKzoTe6XLl2iYcOG6utGjRpx/vz5Cg1KCF2q+hcmIYSoCvQm95yc\nHB48eKC+TkxMJCsrq0KDEqIgzdq7JHghhCia3qlwL7/8Mr169cLX1xeAr7/+mqVLl1Z4YEIIIWou\nExMTHBwcyMrKws7Oju3bt+drJTakXr16ad2gpjrTm9ynTJlC9+7dOXLkCIqi8O2339K1a1e9BU+b\nNo3g4GCsra3VuYmJiYmMGzeO6OhobG1t2b17N40aNUJRFObNm8d3332HhYUFW7ZswdXVtexPZ2Ah\nUV0A+3zHquI0uOqq4Mh5IUT56PKf8t3y9dob+ufNm5ubqzu7+fn5sW7dukqrONa0xA7FaJYH6Nat\nG2PHjmXEiBFYWlry+++/673G39+fH374Id+xlStXMmDAACIjIxkwYAArV64E4PvvvycyMpLIyEgC\nAwOZPXt2KR6lapENYypX3gA6GUgnRNXXs2dP4uLiAFAUhVdeeQV7e3scHBz46quvAAgNDcXLy4ux\nY8fSsWNHFi9ezI4dO+jRowcODg7cunULgAMHDuDh4YGLiwsDBw7k7t27ACxfvpxp06bh7e3NE088\nwZo1a9T7W1paArkL6wwYMABXV1ccHBwICgoy5MdQrvQm9/3799OhQwfs7Ozw8vLC1taWIUOG6C24\nX79+hdb7DQoKws/PD8j9prZv3z71+JQpUzAyMsLT05OkpCTi4+NL8zyihtPXEiLJXIjqJTs7m0OH\nDjF8+HAAvv32Wy5cuMDFixf56aefeOWVV9R8cPHiRT766CN++eUXtm/fzs2bNzl9+jQzZsxg7dq1\nAPTp04fw8HDOnz/P+PHjWbVqlXqv69ev8+OPP3L69GlWrFhBZmZmvljq1KnD3r17OXfuHEeOHOHl\nl19GURQDfRLlS29yX7ZsGeHh4XTs2JGoqCgOHTpE7969S3Wzu3fv0qJFCwBatGjBn3/+CUBcXByt\nW7dWz7OxsVG/xQkhhKh50tLScHZ2pkmTJiQmJjJo0CAATpw4wYQJEzAxMaFZs2Z4eXlx5swZANzd\n3WnRogVmZma0a9dOXXrWwcGB6OhoAGJjYxk8eDAODg68//77XLlyRb3n008/jZmZGVZWVlhbW6u1\n+jyKovDaa6/h6OjIwIEDiYuLK3ROdaG3z93U1JQmTZqQk5NDTk4O/fv359VXXy3XILR9MzIyMtJ6\nbmBgIIGBgUDuH2JoaGi5xqIpJSWlROWnZ8zFKTt3O9x2WbUByEL/5imlZY0ZL9CpwsovqwqL7yHU\nMskmCxNycnL/nhgbK7m/WwA55P+vDs2MzVhoYVf+8ZUTia9sanp8FflvnyHk9bknJyczbNgw1q1b\nx0svvVRkTdnM7J+WOWNjY/W1sbGxOovrxRdfZOHChQwfPpzQ0FCWL1+u9XoTE5NCM7927NjBvXv3\niIiIwNTUFFtb22q73Lre5N6wYUNSUlLo27cvkyZNwtramlq19F6mVbNmzYiPj6dFixbEx8djbW0N\n5NbUY2Ji1PNiY2Np2bKl1jICAgIICAgAwM3NDW9v71LFUhyhoaHFLj8kqgsxGc3VRWzCE2wrfDDd\nC3RiHTcq9B5lUaHx/f9dINNSS98Mv9DCjg9Sq+4OhxJf2dT0+KJGjy/1tYcPHy71teWtQYMGrFmz\nhhEjRjB79mz69evHZ599hp+fH4mJiRw7doz333+f69evF6u85ORkWrVqBVDi7cmTk5OxtrbG1NSU\nI0eOcPv27RI/T1Wht1k+KCgICwsL/vvf//LUU0/Rrl27fGvNl8Tw4cPVD3vr1q2MGDFCPb5t2zYU\nRSE8PJwGDRqozfdC6CL960LUDC4uLjg5ObFr1y5GjRqFo6MjTk5OPPnkk6xatYrmzYu/u+Py5csZ\nM2YMffv2xcrKSv8FGiZNmsTZs2dxc3Njx44ddO7cuaSPUmXorYLXrVuX27dvExkZiZ+fH6mpqWRn\nZ+steMKECYSGhpKQkICNjQ0rVqxg8eLFjB07lo0bN9KmTRu+/vprAIYOHcp3331H+/btsbCwYPPm\nzWV/skqgufSsEEJUF8WZulbe8rZ8zaNZaXz//fd5//33873v7e2dryVVs1tC8728fVEK0myeB/Jt\nH5sXi5WVFWFhYSV5jCpLb3LfsGEDgYGBJCYmcuvWLeLi4pg1a1ah/XcL2rlzp9bj2q4zMjJi3bp1\nxQxZCCGEEEXR2yy/bt06Tp48Sf369QHo0KGDOspdCCGEEFWP3uRuZmZG7dq11ddZWVk6R7I/ziIz\n8vcJGXpluurU/5y3S1t1ilkIIaoTvc3yXl5evPPOO6SlpRESEsInn3zCM888Y4jYqo3cZWeLP+Cj\nPFXlBJmTY1Sm0exCCCFKR2/NfeXKlTRt2hQHBwc+++wzhg4dyltvvWWI2IQQQghRCnpr7sbGxsyc\nOZOZM2eqx06ePFnqVepqKkOMlC9YS8+xMNL5vrnlI4PHU0gRC8joKsMQcQshRE2nM7lnZ2eze/du\n4uLieOqpp7C3tyc4OFhtoj9//rwh46xWKmLTmJI2v6elmFVYojRUV4Chv6wIIURNoTO5T58+nZiY\nGHr06MFLL71E27ZtCQsLY+XKlYwcOdKQMVY75b0daWmTaXkmR0MmdG2xVuSXFSEed+3f/6Bcy/v1\nlYV6z/njjz+YP38+Z86cwczMDFtbW/773//SsWPHMt17+fLlWFpasmjRomKdb4h95XXFFB0dzbBh\nw7h8+TJnz55l27Zt+XarKwudyf3s2bNcunQJY2Nj0tPTsbKy4tdffy3RSkFCCCFEQYqiMGrUKPz8\n/Ni1axcAFy5c4O7du2VO7iVVVfaVd3Nzw83NrdzK0zmgrnbt2hgb575dp04dOnbsKIldh4LT4Mqi\n4BSx8qoxa04/K82PIem6X1WeGSCEKL4jR45gamrKrFmz1GPOzs6EhITg7OyMs7MzrVq1YurUqQB8\n8cUX9OjRA2dnZ55//nl1ldQffvgBV1dXnJycGDBggFrW1atXte7bro/mvvKQu1Keu7s7jo6OvP76\n60Bubbtz5874+fnh6OiIr68vqampANja2pKQkADkVpA1V9S7ePEiTz75JB06dGDDhg2F7h0aGsqw\nYcOA3BXzpk6dioODA46OjuzZs6fYz5BHZ839+vXrODo6Arnfsm7duoWjoyOKomBkZMSlS5dKfLPH\ngb7+dm1N5dqS1uOeyB735xeiJrt8+TLdu3cvdPyNN97gjTfeIDk5mb59+zJ37lyuXbvGV199xcmT\nJzE1NWXOnDns2LGDIUOGMHPmTI4dO4adnR2JiYlqOdevX+fIkSM8fPiQTp06MXv2bExNTYuMKW9f\n+enTpwNw8OBBIiMjOX36NIqiMHz4cI4dO0abNm24ceMGGzdupHfv3kybNo1PPvlEbzfApUuXCA8P\n5++//8bFxYWnn35a57lvvvkmDRo04JdffgHgwYMHRZatjc7kfu3atRIX9jgKiepCdLq9+rq8+9tF\nftL3LkTNpigKkyZNYsGCBXTv3p2PP/6YiIgI3N3dgdx94K2trQkPD6dfv37Y2eVum9u4cWO1jLx9\n283MzNR9221sbLTeL29f+ejoaLp3767uK3/w4EEOHjyIi4sLkFubjoyMpE2bNrRu3VqdMfbcc8+x\nZs0avcl9xIgRmJubY25uTv/+/Tl9+jTOzs5az/3pp5/U7gqARo0aFeejy0dncm/btm2JCxNFK1gb\nldpp6UiCF6J669atG998843W95YvX46NjY3aJK8oCn5+frz77rv5ztu/f7/O1VL17duuqah95Zcs\nWcLzzz+f7/zo6OhC9817XatWLXJycgAK7QOv6xpt8lrIy0LvIjZCCCFEeXryySd59OhRvr7nM2fO\nsGLFCkJCQvL1kw8YMIBvvvlG3dMkMTGR27dv07NnT44ePUpUVJR6vCzy9pVfvXo1mZmZDB48mE2b\nNqk7xsXFxakx/P777+rucTt37qRPnz5Abp97REQEQKF+8qCgINLT07l//z6hoaFqS4Q2Pj4+fPzx\nx+rrcm2WF6Vn6HXlH0fS6iFE+SnO1LXyZGRkxN69e5k/fz4rV66kTp062Nrakpqayp07d+jRowcA\nw4cP54033uCtt97Cx8eHnJwcTE1NWbduHZ6engQGBjJ69GhycnKwtrYmJCSkTHFp7is/efJkrl27\nRs+ePQGwtLTkiy++wMTEhC5durB161aef/55OnTowOzZswF4/fXXmT59Ou+88w4eHh75yu7RowdP\nP/00v//+O8uWLaNly5ZER0drjePf//43L7zwAvb29piYmPD6668zevToEj2LkaIoirY3BgwYwKFD\nh3j11Vd57733SlSoobi5uXH27NkKKz80NDTfaEdtQqK6EPLXP33u4Qm2OpN7eSekhRZ2fJAaVa5l\nlieJr2wkvrKp6fFFPbek1NcePnyYJ598stTXP84056ZXtmvXrtGlSxet7+msucfHx3P06FH279/P\n+PHjKfgdwNXVtXyjrKbKcxqcEEIIUR50Jvc33niDlStXEhsby8KF+ZtsjIyMOHz4cIUHJ4QQQpTV\n/fv3882Dz3Po0CGaNCnZcuG2trZVotauj87k7uvri6+vL2+++SbLli0r15t++OGHfP755xgZGeHg\n4MDmzZuJj49n/PjxJCYm4urqyvbt2/PtI19dSH+7EEJULU2aNFFXoXtc6B0tv2zZMvbv38+iRYtY\ntGgRwcHBZbphXFwca9as4ezZs1y+fJns7Gx27drFq6++yoIFC4iMjKRRo0Zs3LixTPcxFM3d4Cpi\nwxghhBCipPQm9yVLlvDRRx/RtWtXunbtykcffcSSJaUfyAGQlZVFWloaWVlZpKam0qJFCw4fPoyv\nry+Qu77vvn37ynSPyiAL2AghhKgK9E6F+9///seFCxfUdeb9/PxwcXEptKBAcbVq1YpFixbRpk0b\nzM3N8fHxoXv37jRs2JBatXLDsbGxybe+rxBCCCGKr1jz3JOSktSl/ZKTk8t0wwcPHhAUFERUVBQN\nGzZkzJgxfP/994XO07U6T2BgIIGBgQDExsYSGhpapniKkpKSord8p7Qh6u/tsmqThYnW83JyjMCi\nPKODZsZmLLSwK99Cy5HEVzYSX9nU9Pgq8t8+Q8jbajXPvn37sLW1rbyAahi9yX3JkiW4uLjQv39/\nFEXh2LFjpa61Q+6auXZ2djRt2hSA0aNHc+rUKZKSksjKyqJWrVrExsbSsmVLrdcHBAQQEBAA5M5z\n1zcPvSz0zXMPierCxWLMca+oBVdq+jzeiibxlY3EVzZlnuc+enypry0428nui9L/m65Ncebga261\nqk1ePhClo7fPfcKECWUA7g8AACAASURBVISHhzN69GhGjx5NWFgY48eX/i9VmzZtCA8PJzU1FUVR\nOHToEF27dqV///7qWsNbt25lxIgRpb5HZQhPsK3sEIQQolrbsmULY8aM4ZlnnsHHx4eUlBQGDBiA\nq6srDg4OBAUFAbkLyXTp0oWZM2fSrVs3fHx8SEtLA+DXX39l4MCBODk54erqyq1btwDt27fWZMX6\nWtSiRQuGDx9eLjf08PDA19cXV1dXatWqhYuLCwEBATz99NOMHz+ef//737i4uKjb7gkhhKh58nZj\nA7Czs2Pv3r0AhIWFcenSJRo3bkxWVhZ79+6lfv36JCQk4OnpqeaiyMhIdu7cyYYNGxg7dix79uzh\nueeeY9KkSSxevJhRo0aRnp5OTk6Ozu1b+/XrV2nPX9Eqpc1jxYoVrFixIt+xJ554gtOnT1dGOEII\nIQxMV7P8oEGD1DFeiqLw2muvcezYMYyNjYmLi+Pu3btA7heCvC8H3bt3Jzo6mocPHxIXF8eoUaMA\nqFOnDqB7+1ZJ7kKrgkvPygI2QghRNnXr/vPv6I4dO7h37x4RERGYmppia2urbqVacFvXtLS0Qsuk\n59G1fWtNVmSfe05ODvb29kWdIoQQQlSI5ORkrK2tMTU15ciRI9y+fbvI8+vXr4+NjY26TsqjR49I\nTU0tcvvWmqrI5G5sbIyTkxO///67oeKpVvJWpzPkTnBCCPG4mDRpEmfPnsXNzY0dO3bQuXNnvdds\n376dNWvW4OjoSK9evfjjjz/w8fFh4sSJ9OzZEwcHB3x9fXn48KEBnqDy6G2Wj4+Pp1u3bvTo0SNf\nc8n+/fsrNDAhhBCGUZbtY0srrxatyd/fH39/f/W1lZUVYWFhWq/X3Lxl0aJF6u8dOnTQurHZvHnz\nmDdvXhkirl70JvfHYcqAEEIIUZPoTe5eXl7cvn2byMhIBg4cSGpqKtnZ2YaIrUoLieoCyHgEIYQQ\nVY/eRWw2bNiAr6+vOsowLi6OkSNHVnhg1Yn0twshhKhK9Cb3devWcfLkSerXrw/k9mfU9FGGQggh\nRHWmN7mbmZlRu3Zt9XVWVpbOTV2EEEIIUfn0JncvLy/eeecd0tLSCAkJUdf9fZyFRHUptICNEEII\nUVXoTe4rV66kadOmODg48NlnnzF06FDeeustQ8RWpWnOcRdCCFEy0dHRhRZJW758OatXr8bf31/d\nSCwxMREXFxc2b95cGWFWW3pHyxsbG+Pn54eHhwdGRkZ06tRJmuWFEKIG6bpvebmWd3Vk+ZSXnJzM\n4MGDCQgIYOrUqeVS5uNCb3L/3//+x6xZs2jXrh2KohAVFcVnn33GkCFDDBFflVScJnkZKS+EEKWX\nkpLCkCFDmDhxIrNnz67scKodvcn95Zdf5siRI7Rv3x6AW7du8fTTTz+2yT0kqgvR6TK/XQghKtLC\nhQuZMWMGCxYsqOxQqiW9fe7W1tZqYofcrVmtra0rNKjqRHaCE0KIktPVvZt3/MknnyQoKEimXpeS\nzpr7t99+C0C3bt0YOnQoY8eOxcjIiK+//hp3d3eDBVjVhPwltXYhhCirJk2a8ODBg3zHEhMTsbOz\nA2D8+PH06dOHoUOHcuTIEerVq1cZYVZbOmvuBw4c4MCBA6Snp9OsWTOOHj1KaGgoTZs2LfQHIoQQ\nQpSEpaUlLVq04NChQ0BuYv/hhx/o06ePes78+fMZMGAAo0aNIiMjo7JCrZZ01twrctpBUlISM2bM\n4PLlyxgZGbFp0yY6derEuHHjiI6OxtbWlt27d9OoUaMKi0EIIUTl2rZtGy+88AIvv/wykLtRWbt2\n7fKd89577zF16lQmT57Mzp07MTbW25ssKMaAuqioKNauXUt0dDRZWVnq8bJs+Tpv3jyeeuopvvnm\nGzIyMkhNTeWdd95hwIABLF68mJUrV7Jy5Uree++9Ut+jIshmMUKImqi8pq6VVNeuXTly5Eih41u2\nbMn3Wua4l5ze5D5y5EimT5/OM888Uy7fmP766y+OHTum/uHVrl2b2rVrExQURGhoKAB+fn54e3tX\nveQu/e1CCCGqAb3JvU6dOrz00kvldsPffvuNpk2bMnXqVC5evEj37t356KOPuHv3Li1atACgRYsW\nMkJSCCGEKCUjRVGUok748ssviYyMxMfHBzOzfxZmcXV1LdUNz549i6enJydPnsTDw4N58+ZRv359\n1q5dS1JSknpeo0aNtA7cCwwMJDAwEIDY2Fh27dpVqjiKIyUlBUtLS/V1XNpv+d/Pqk1Wtkmh63Jy\nDLOCXzNjM+7mPDLIvUpD4isbia9sanp8Do1Lv79FTk4OTz75ZKmvF1XDtWvX6NKli9b39Nbcf/nl\nF7Zv387hw4fVZnkjIyMOHz5cqmBsbGywsbHBw8MDAF9fX1auXEmzZs2Ij4+nRYsWxMfH65xLHxAQ\nQEBAAABubm54e3uXKo7iCA0NVcsPierCxQLN8uEJtlrnuaelGmZ1uoUWdnyQGmWQe5WGxFc2El/Z\n1PT4okaPL/W1pf33W1QfepP73r17+e233/Jt+1oWzZs3p3Xr1ty4cYNOnTpx6NAhunbtSteuXdm6\ndSuLFy9m69atjBgxolzuV16kv10IIUR1oTe5Ozk5kZSUVK6r0q1du5ZJkyaR8f/au/Owpq70D+Df\nJCwqi+KCBWkFBFmDyOrCFFBxaRVQtKC2atXBTq1anXHp4q+d6ri0dnHUaQdFS63VumPRCloVq2JR\nlCoollajghRlFxUhyfn9weSaQEJYshHez/Pkechd31wC7z3nnqW2Fs7Ozti2bRukUileeeUVJCYm\n4oUXXsCePXs0dj5CCCGkI1Gb3IuLi+Hu7o7AwECFZ+5t6Qrn6+uLixcvNlouG8ygvVBaJU8TxhBC\niFoCgQBCoRCMMQgEAmzcuBFDhgxpch9LS0tUV1c3uc3s2bOxaNEieHp6ajLcdkdtcv/nP/+pizgI\nIYToyYs/Ldbo8U4P/0TtNp07d0Z2djYAIDU1Fe+88w7S09PbfO4tW7a0+RjGQG1yDw0N1UUchBBC\nOqiqqiqFEUk/+eQT7N69G0+fPsX48eMbFTKlUineeustpKenw8nJCVKpFDNnzsTEiRMRFhaGdevW\nISAgQKGkv3fvXqSkpODrr7/GjBkz0LlzZ+Tl5eH27dvYtm0bkpKSkJGRgeDg4EaD6LRHapO7lZUV\nN0tPbW0t6urqYGFhgaqqKq0HZyiW/Dqp0bLzJY66D4QQQozEkydP4Ovri5qaGhQVFXEt+NPS0pCf\nn4/MzEwwxhAZGYnTp0/jxRdf5Pbdv38/RCIRrl69ivv378PDwwMzZ85s0fnLy8tx4sQJHDp0COPG\njcPZs2exZcsWBAYGIjs7G76+vhr9vLqmNrk/fPhQ4f3BgweRmZmptYAIIYQYP/lq+YyMDEybNg05\nOTlIS0tDWloaBg4cCKB+vJH8/HyF5H7mzBlMmjQJfD4fzz33HMLDw1t8/nHjxoHH40EoFKJ3794Q\nCoUA6mdCFYlExp/cG4qOjsaaNWu0EUu7QaV2QgjRnMGDB6OkpAQPHjwAYwzvvPMO5syZo3J7NWOv\nceTnjK+pqVFYJ2sgzufzFRqL8/l8hXlU2iu1g8Xv37+fe+3duxfLli1TuGAdTVOJnVrKE0JIy+Xl\n5UEikaBHjx4YNWoUtm7dyj0rLywsbDQceUhICPbt2wepVIri4mJuXpKGevfujevXr0MqleLAgQPa\n/hgGRW3J/Ycffni2sYkJHB0dkZycrNWgDImy5+1A425wlNgJIaT5ZM/cgfqSeFJSEgQCAUaOHInr\n169j8ODBAOq7v3377bcKY63ExMTgp59+gre3N/r374/g4GB07dq10TnWrFmDsWPH4vnnn4e3t7fa\nbnTGRG1yp6n2CCHEuDWn65qmSSQSlesWLFiABQsWNFouS858Ph/r1q2DpaUlSktLERQUxD0zly/F\nT5w4ERMnTmx0HPnW8I6OjsjJyVG6rj1Tmdw/+ugjlTvxeDwsX75cKwEZMlmVvLLBawghhOjO2LFj\nUVFRgdraWixfvhzPPdf6iXSMkcrkbmHROIE9evQIiYmJKC0t7XDJnRI7IYQYDlXP2Uk9lcn973//\nO/fzw4cPsX79emzbtg1xcXEK64xZ4ZObgI367eh5OyGEEEPSZGv5srIyvP/++/Dx8YFYLMalS5ew\ndu1ajU4iQwghhBDNUllyX7x4Mfbv34/4+HhcvXoVlpaWuozLIFGVPCGEkPZAZcn9008/xb1797By\n5UrY29vD2toa1tbWsLKygrW1tS5j1IuGXeAosRNCCGkvVJbcpVKpLuMwaDQiHSGEaFbD6Vu//vpr\nXLx4ERs3btRjVMajxcPPdjSU2Akhxi424w2NHu/7wV9p9HjaJhaLYWJiXOlQ7fCzhBBCiC7NmDED\ne/fu5d7L2nydOnUKYWFhmDhxItzd3TF16lRunPkjR47A3d0dISEhmD9/PsaOHQsAyMzMxJAhQzBw\n4EAMGTIEN27cAFBfUzBp0iSMGzcOI0eOxGuvvaYw+urUqVNx6NAhXX1kjdPbrYpEIkFAQAD69OmD\nlJQU3Lp1C3FxcSgrK4Ofnx+2b98OMzMzfYVHCCFEi+SHnwXqe2dFRkaq3e/y5cvIzc2Fvb09hg4d\nirNnzyIgIABz5szB6dOn4eTkhMmTJ3Pbu7u74/Tp0zAxMcHx48fx7rvvYt++fQDqZ6O7cuUKunfv\njvT0dHz++eeIiopCZWUlzp07h6SkJM1/cB3RW8l9/fr18PDw4N4vXboUCxcuRH5+PmxsbJCYmKiv\n0BppqjEd9XEnhJCWk035Kns1NSqqvKCgIDg4OIDP58PX1xcikQh5eXlwdnaGk5MTACgk98rKSkya\nNAne3t5YuHAhcnNzuXURERHo3r07ACA0NBS///477t+/j507dyImJqZdV9XrJbkXFBTg8OHDmD17\nNoD6SQNOnDjBjQE8ffp0HDx4UB+hAXjWUr5abEat5AkhRMdMTEy4Rt2MMdTW1nLr5KdnFQgEEIvF\nTU4Bu3z5coSHhyMnJwc//PCDwtSvDUdife2117Bjxw5s27YNr7/+uqY+jl7oJbm//fbb+Pjjj8Hn\n15++tLQU3bp14+6SHBwcUFhYqI/QCCGE6JmjoyOysrIAAMnJyairq2tye3d3d9y8eRMikQgA8P33\n33PrKisr0adPHwDqJ4WZMWMGvvjiCwCAl5dXK6M3DDqvc0hJSYGtrS38/f25sYGV3XWpmjM+ISEB\nCQkJAOprALQxvvCAJ2MAAObSLpgLtya3lXbR39z2vfnmWNTFSW/nV4fiaxuKr22MPT5jHlv9r3/9\nK6KiohAUFIThw4crnetEXufOnfGf//wHo0ePRs+ePREUFMStW7JkCaZPn47PPvsMw4YNa/I4vXv3\nhoeHB6KjozXyOfSJx5qqz9CCd955B9u3b4eJiQlqampQVVWF8ePHIzU1FX/++SdMTEyQkZGBDz/8\nEKmpqU0eKyAgABcvXtR4jLJq+X53orBe8nuT2+rzmfuiLk747PEtvZ1fHYqvbSi+tjH2+G69+k6r\n9z1x4oTaRNfeVFdXw9LSEowxzJ07F66urli4cGGLjvH48WMIhUJcunRJ6fzwhub69esKbdfk6bxa\nfvXq1SgoKIBIJMKuXbswbNgw7NixA+Hh4VzXh6SkJERFRek6NADPEvv5EkeIJQK9xEAIIaRlNm/e\nDF9fX3h5eaGyshJz5sxp0f7Hjx+Hu7s75s2b1y4SuzoG0xRw7dq1iIuLw/vvv4+BAwdi1qxZ+g6J\nEEJIO7Fw4cIWl9TljRgxAnfu3NFgRPql1+QeFhaGsLAwAICzszMyMzP1GY6C9tZKXlBkDondU32H\nQQghxAAYTMld3xpOFKMNgiLF5/PNScYN92m4r6r1hkw+ZrohIYQQzaPkrkTKLd12gWhNghYUmYP3\nAq/RMhlDSJq8Ol67vPkghJD2jsaWJ4QQQowMldzboK3d4LRZqtXFM3i18b/Q8mMYQo0DIUT7iouL\nsXDhQpw/fx42NjYwMzPDkiVLMH78eH2HZhQouUPxebu2quT1UT2trWp6XX0WdeehGwFCNEPTbY4+\nHrCnyfWMMURHR2P69On47rvvAAC3b99u9ixsEokEAgF1VW4KVcvrgCxJdSniKbyaw/QhT+mrNTFo\n6qVNLTk+Pc8npH06ceIEzMzM8MYbz+aR79u3L+bNmweJRILFixcjMDAQPj4++O9//wugfkS+8PBw\nTJkyBUKhECKRCO7u7pg9eza8vb0xdepUHD9+HEOHDoWrqyvX+6qpKV8nTJiA0aNHw9XVFUuWLAEA\nJCYmKnSp27x5MxYtWqSrS6MxHb7kLrtj1VWJ3bJAyv382K4+SdteaGqQQOXryt354EkUE32dlU4H\nG9SaljxSoC6AhLQ/ubm58PPzU7ouMTERXbt2xYULF/D06VMMHToUI0eOBFCfqHNycuDk5ASRSITf\nf/8de/bsQUJCAgIDA/Hdd9/hzJkzOHToEFatWoWDBw82OeVrdnY2Ll++DHNzc7i5uWHevHmIi4uD\nj48PPv74Y5iammLbtm3cDUZ70uGTOyGEEP2aO3cuzpw5AzMzM/Tt2xdXrlzhRiytrKxEfn4+zMzM\nEBQUxE3rCgBOTk4QCoUA6id6GT58OHg8Hleyl+0/ffp05Ofng8fjKUxCM3z4cG40Ok9PT9y+fRvP\nP/88hg0bhpSUFHh4eKCuro47R3tCyV2Lmiq1A+pK7E2zyZNC4Mhgk/fsmPcD9TeJTUs0p7aBqtwJ\nMV5eXl5c6RkANm3ahJKSEgQEBOCFF17Ahg0bMGrUKIV9Tp061WgCGfnpX/l8Pveez+dDLBYDeDbl\n64EDByASibiB0xruL5s+FgBmz56NVatWwd3dvd1O/dqhn7lrs0re6lInpc/YzSvF3EtGfllzX8q0\n5Zm8NsgeG7S1vYA6dCNASPsybNgw1NTU4Msvv+SWPX78GAAwatQofPnll1wJ+7fffsOjR49afa6W\nTPkqExwcjLt37+K7777D5MmTW31uferwJffWJvbWdINrmJRVJenmHosvYQrHsMl79ussd+dr/Rm8\nJhJ1w2O0NmZZgqfn74QYPh6Ph4MHD2LhwoX4+OOP0atXL1hYWGDt2rWYNGkSRCIR/Pz8wBhDr169\ncPDgwVafqyVTvsp75ZVXkJ2dDRsbm1afW586fHInhJCOTl3XNW2ws7PDrl27lK5btWoVVq1apbBM\nfi4SAHB0dEROTg73Xr5ULr9u8ODB+O2337h1K1asAADMmDEDM2bM4JanpKQonO/MmTNtmohG3zp0\ntXxrtaTUblkg5V66ZJMn1VpVuCFV/TdEVfSEkLaoqKhA//790blzZwwfPlzf4bQaldxbqDmJ3epS\nJy6Zt6XqvaXkz/W0q4lCY7tyd8O5j7O496zq/ZG94k2C6UOe0XTpI4S0P926dVMo6bdXHTq5a6sh\nHaDbpK6MeaUYT7vq99crqFFsza9Mw+QOPEvw6moHmmppT8/eCSEdmeEU5wghhBCiER265N5SbZ0o\nBgBMK5+VKOu6avf5sKqW9DrjqH4T+Sp64FlJ3lCf6RNCSHug8//4d+/exbRp0/Dnn3+Cz+cjPj4e\nCxYsQFlZGWJjYyESieDo6Ijdu3cbVBcEdYldVh0PNB6sRkY+sSt73xLavjHQhIZd9ZRT/Aoqq6ZX\npann81Q1TwjpyHReLW9iYoJPP/0U169fx/nz57Fp0yZcu3YNa9aswfDhw5Gfn4/hw4djzZo1ug5N\nI3rk1iokNNPKp9xLk0wrn4InYQrHb+pcrRkop62v1mhYkld7HaiET0i79a9//QteXl7w8fGBr68v\nfvnlF4SFheHixYsaPY+lpWWjZffu3cPEiRM1eh5DovOSu52dHezs7AAAVlZW8PDwQGFhIZKTk3Hq\n1CkAwPTp0xEWFoa1a9fqOrxWkS+1y2g6mbeUaeXTdlG6b3wTUP+VfGTPU5vo1ZXyqfROSPP850a4\nRo/3pttJtdtkZGQgJSUFly5dgrm5OUpKSlBbW6vROJpib2/PjV9vjPTaoE4kEuHy5csIDg5GcXEx\nl/Tt7Oxw//59fYZGCCFEi4qKitCzZ09ufPeePXvC3t5eYZudO3dCKBTC29sbS5cuBQB8+eWX3PSs\nQP3gNfPmzQMAREdHw9/fH15eXkhISGh0zpKSEgwePBiHDx+GSCSCt7c3gPpc9Je//AV+fn7w8/PD\nuXPntPKZdYnHGNNLp+Lq6mqEhobivffew4QJE9CtWzdUVFRw621sbFBeXt5ov4SEBO6XVlBQoHKE\no+bIrShqcr0tzPGntOk7ScHj+vsjkyeKz9l5Eu1fVpse5igvNdySaXPiYwLF0rf0f++ZQP3xpabP\n9mUC5debmar+PfTmm6NYarjXj+JrG2OPT9j9uVbvK5VKFYZi1UfJvbq6GiEhIXj8+DFGjBiB2NhY\nhIaGIiwsDOvWrYO9vT0GDRqErKws2NjYYOTIkZg/fz6GDh2KwYMH4/fffwcAjBkzBu+99x5CQkJQ\nVlaG7t2748mTJwgMDER6ejp69OgBS0tL/PHHH4iMjMTKlSsREREBkUiEsWPHIicnB48fPwafz0en\nTp2Qn5+PyZMna/zRgDZcv34dHh4eStfppbV8XV0dYmJiMHXqVEyYMAEA0Lt3bxQVFcHOzg5FRUWw\ntbVVum98fDzi4+MBAAEBAQrDEbbUmwc/bHL936Tu+OzxrSa3kVXJ98hVvAnQRbX8pBmu2PN1vtbP\n01rNiU/Zo4Pm9s+vteY1qppX1sBOVdX8oi5Oan+/+kTxtY2xx3drQlyr9z1x4kSr99UUS0tLZGVl\n4eeff8bJkycRGxur0NbqwoULCAsLQ69evQAAU6dOxenTpxEdHQ1nZ2ecP38erq6uuHHjBoYOHQoA\n+Pe//40DBw4AqG+8nZ+fjx49eqCurg7Dhw/Hpk2bEBoa2iiWuro6vPXWW8jOzoZAIKBBbFqDMYZZ\ns2bBw8MDixYt4pZHRkYiKSkJy5YtQ1JSEqKionQdmoIn1eZAl+ZtK5/Y9f2svb1peL3qupq3aAAe\ni3tMIcEra0FPz94JMUwCgYAbM14oFCIpKYlb11SlcmxsLHbv3g13d3eMHz8ePB4Pp06dwvHjx5GR\nkYEuXbogLCwMNTU1AOobcvv7+yM1NVVpcv/888/Ru3dv/Prrr5BKpejUqXE7qvZG58/cz549i+3b\nt+PEiRPw9fWFr68vjhw5gmXLluHYsWNwdXXFsWPHsGzZMl2HRgghREdu3LiB/PxnNXvZ2dno27cv\n9z44OBjp6ekoKSmBRCLBzp07ucQ8YcIEHDx4EDt37kRsbCyA+qldbWxs0KVLF+Tl5eH8+fPcsXg8\nHrZu3Yq8vDylPbEqKythZ2cHPp+P7du3QyKRaOtj64zOS+4hISEq78h++uknHUfTNlaXOjWqjiea\n0byudCaotW5eVzgqvRNiWKqrqzFv3jxUVFTAxMQELi4uSEhI4Lqn2dnZYfXq1QgPDwdjDC+99BJX\no2tjYwNPT09cu3YNQUFBAIDRo0fjq6++go+PD9zc3DBo0CCF8wkEAuzatQvjxo2DtbU1XnrpJW7d\nm2++iZiYGOzZswfh4eGwsLDQ0VXQHhqhTglVA9Yo6/ImQ9XxmtHSLnxmVfU3iuqq5gkhqjWnAZym\n+fv7K22VLusSDQBTpkzBlClTlO7fcIpWc3Nz/Pjjj0q3ra6uBgCYmZkhNTWVWy6bFtbV1RVXrlzh\nlq9evbp5H8KA0djyDVBi17/mXk/50r3FPdbiAXAIIcRYUXInhBBCjAxVy6vRVIm9R24tldq1pDnX\nVdayXv7Zu6z1PLWaJ4R0ZJTc/0dZdbxsgBpi+GTP3uUb2KlK8DK8F3iU8AkhRomSuwpWlzoBfs/e\nK2sV35pSu6DsEfezpHv7b5GpT7LGd/L94hs2sJOfWKapGeSaQsmfENLeUHJH09O5arKrm6DsEVBe\n+ex9Ww8olircLMjrKDcO8gkeaP7odi3RVPKnxE8IMURU70wIIUTnBAIBN5CZr68vRCIRLl68iPnz\n56vdV9kUrq0hP3mMsenwJXdlpXZZIzr5yWBa23BOoWRdXglxaSn3ts0XXyJRqAlQOG9bj60JTdQs\nyGi6hkHWwA5QPyWsJshK9VSCJ+3ZsVvKJx9prQin62q36dy5M7KzsxWWOTo6IiAgQKOxdFQdPrk3\nJD8RDK8/a3tSl0u+8old2fsWE4tVHsMgfrFN3HzINLwJaU2ybzjwjXmlGLXWps0ad15T6Lk9IW13\n6tQprFu3DikpKfjwww9x584d3Lx5E3fu3MHbb7/dqFRfXV2NqKgolJeXo66uDitXrkRUVBREIhHG\njBmDkJAQnDt3Dn369EFycjI6d+6MrKwszJw5E126dEFISIiePqn2GUQO0JemnrVrKqm3OYG3kr7O\nqxiE6psPmYZfwNbUOEi6W3C/L1mSN6tiqLXmKQxsI+siJ8OTKO8ypw3qkr8ystb8DdGNAjEGT548\nga+vLwDAycmJm81NXl5eHk6ePImHDx/Czc0Nf/vb32Bqasqt79SpEw4cOABra2uUlJRg0KBBiIyM\nBADk5+dj586d2Lx5M1555RXs27cPr776Kl5//XVs2LABoaGhWLx4sW4+rB506OROCCFEP5RVyzf0\n8ssvw9zcHObm5rC1tUVxcTEcHBy49YwxvPvuuzh9+jT4fD4KCwtRXFwMoP6GQXbz4O/vD5FIhMrK\nSlRUVHAT0Lz22msqh6xt7yi5/4/8YDXPnXvY+gPpucTe3jS6Tq24brwgIfezYuv55k0sI1+aV8bQ\nxqmnvvmkozA3f1ZzJRAIIBYrTii1Y8cOPHjwAFlZWTA1NYWjoyM3zWvDfZ88eQLGGHg87bfFMQQd\nPrk3HIGuNYldvtEYJXXdY5lXuZ95QcJGCV6mtQ3s1CX/5tD0DUJzqvnpBoAYu8rKStja2sLU1BQn\nT57E7du3m9y+KO6MEgAADcVJREFUW7du6Nq1K86cOYOQkBDs2LFDR5HqXodP7m0l+OOevkMgcljm\nVYWSvOLkMqY6aUGvTGtuEGRtAhpq7o2CqhsASvrEWEydOhXjxo1DQEAAfH194e7urnafbdu2cQ3q\nRo0apYMo9YOSOyGEdHDN6bqmabJpWOWFhYUhLCwMAPDhhx8qrJNNzyq/b8+ePZGRkaH0+PLb/+Mf\n/+B+9vf3x6+//sq9b3geY9Ghk7uy5+wKJfGnjmpL5lQNb3hY5lWYBgmVzgsv33qe34816i7XHrS1\nhX9zW+5TCZ+Q9svgkvvRo0exYMECSCQSzJ49G8uWLdPq+WRJXfbcVqG5RjO6chHDxDKvwgSAOOLZ\ngBhWd+vw8HnTRtsa6jzwspuP1qpw02AwhJB2xaCSu0Qiwdy5c3Hs2DE4ODggMDAQkZGR8PT01No5\n5RtjEeNjcuwi97M4IgBWd+u494JapjLhG4NuN5Qvp6RPiPEzqOSemZkJFxcXODs7AwDi4uKQnJys\nteTe+4tzWjkuMUwmxy4qlORl5BO+IZHdfLSWqpsWVUm/oQqoqb7v18KAiMFgjHWobmHGiLGma/UM\nauKYwsJCPP/889x7BwcHFBYW6jEiQggxPgUFBSgtLVWbIIhhYoyhtLQUnTp1UrkNjxnQb3fPnj1I\nTU3Fli1bAADbt29HZmYmNmzYwG2TkJCAhIQEAPVDEzan60NrPXjwAL169dLa8duK4msbiq9tKL62\n0Wd85eXlOH78ODfgC2l/OnXqBAcHB4XheOUZVLW8g4MD7t69y70vKCiAvb29wjbx8fGIj4/XSTwB\nAQG4ePGi+g31hOJrG4qvbSi+tjH0+Ej7ZlDV8oGBgcjPz8etW7dQW1uLXbt2cZMAEEIIIaR5DKrk\nbmJigo0bN2LUqFGQSCSYOXMmvLy89B0WIYQQ0q4IPjSw4XlcXV0xb948LFiwAC+++KK+w4G/v7++\nQ2gSxdc2FF/bUHxtY+jxkfbLoBrUEUIIIaTtDOqZOyGEEELarkMl95kzZ8LW1hbe3t7csl9//RWD\nBw+GUCjEuHHjUFVVBaB+nmBfX1/uxefzkZ2dDaB+cgM3Nzdu3f379zUS3927dxEeHg4PDw94eXlh\n/fr1AICysjJERETA1dUVERERKC8vB1Df13H+/PlwcXGBj48PLl26xB0rKSkJrq6ucHV1RVJSkl7i\n27FjB3x8fODj44MhQ4YoTNbg6OgIoVAIX19fBAQ0HlhGF/GdOnUKXbt25X6PH330EXeso0ePws3N\nDS4uLlizZo1e4vvkk0+42Ly9vSEQCFBWVgZAt9dvz5498PLyAp/Pb9S6e/Xq1XBxcYGbmxtSU1O5\n5bq8fqriO3bsGPz9/SEUCuHv748TJ05w67TxN9zS+EQiETp37szF8MYbb3DrsrKyIBQK4eLigvnz\n51N/dNJyrANJT09nWVlZzMvLi1sWEBDATp06xRhjLDExkb3//vuN9rty5QpzcnLi3oeGhrILFy5o\nPL579+6xrKwsxhhjVVVVzNXVleXm5rLFixez1atXM8YYW716NVuyZAljjLHDhw+z0aNHM6lUyjIy\nMlhQUBBjjLHS0lLm5OTESktLWVlZGXNycmJlZWU6j+/s2bPceY8cOcLFxxhjffv2ZQ8ePGhzTG2J\n7+TJk+zll19udByxWMycnZ3ZH3/8wZ4+fcp8fHxYbm6uzuOTd+jQIRYeHs691+X1u3btGsvLy2v0\nvc/NzWU+Pj6spqaG3bx5kzk7OzOxWKzz66cqvkuXLrHCwkLGGGNXr15l9vb23Dpt/A23NL5bt24p\n/C+SFxgYyM6dO8ekUikbPXo0O3LkiEZjJcavQ5XcX3zxRXTv3l1h2Y0bN7iGexEREdi3b1+j/Xbu\n3InJkydrPT47Ozv4+fkBAKysrODh4YHCwkIkJydj+vTpAIDp06fj4MGDAIDk5GRMmzYNPB4PgwYN\nQkVFBYqKipCamoqIiAh0794dNjY2iIiIwNGjR3Ue35AhQ2BjYwMAGDRoEAoKCtocgybjU0V+GGQz\nMzNuGGR9xqeL76Cq+Dw8PODm1nhA+uTkZMTFxcHc3BxOTk5wcXFBZmamzq+fqvgGDhzIjZPh5eWF\nmpoaPH2qvZnuWhqfKkVFRaiqqsLgwYPB4/Ewbdo0td9ZQhrqUMldGW9vbxw6dAhAffWZ/CA6Mt9/\n/32jf6yvv/46fH19sWLFCq1UmYlEIly+fBnBwcEoLi6GnZ0dgPp/ILIqRFXD9epiGN/mxCcvMTER\nY8aM4d7zeDyMHDkS/v7+3IiD+ogvIyMDAwYMwJgxY5CbmwtAN8Mgt+T6PX78GEePHkVMTAy3TJfX\nTxVD+f41x759+zBw4ECYmz8bL1+bf8PNje/WrVsYOHAgQkND8fPPPwOov64ODg7cNjQMN2kNg+rn\nrg9bt27F/Pnz8dFHHyEyMhJmZmYK63/55Rd06dJF4Tn9jh070KdPHzx8+BAxMTHYvn07pk2bprGY\nqqurERMTgy+++ALW1tYqt1P2D4nH46lcruv4ZE6ePInExEScOXOGW3b27FnY29vj/v37iIiIgLu7\nu8a6PjY3Pj8/P9y+fRuWlpY4cuQIoqOjkZ+fb3DX74cffsDQoUMVap0M4fqpuk5SqVTpck1p6fXL\nzc3F0qVLkZaWxi3T5t9wc+Ozs7PDnTt30KNHD2RlZSE6Ohq5ubla//6RjqHDl9zd3d2RlpaGrKws\nTJ48Gf36KU51tWvXrkal9j59+gCor3qbMmUKMjMzNRZPXV0dYmJiMHXqVEyYMAEA0Lt3bxQVFQGo\nr7KztbUFoHq43uYM46uL+ADgypUrmD17NpKTk9GjRw9uuSweW1tbjB8/XmPXsCXxWVtbw9LSEgDw\n0ksvoa6uDiUlJQZ1/QDl30FdXj9VDOX715SCggKMHz8e33zzjcLftrb+hlsSn7m5Ofc34e/vj379\n+uG3336Dg4ODwiMsTV4/0nF0+OQuqwKVSqVYuXKlQotVqVSKPXv2IC4ujlsmFotRUlICoP4POSUl\nRaFU3xaMMcyaNQseHh5YtGgRtzwyMpJr8Z6UlISoqChu+TfffAPGGM6fP4+uXbvCzs4Oo0aNQlpa\nGsrLy1FeXo60tDSMGjVK5/HduXMHEyZMwPbt29G/f39u+0ePHuHhw4fcz2lpaRq5hi2N788//+RK\nSZmZmZBKpejRo4fWhkFuaXwAUFlZifT0dIVlur5+qkRGRmLXrl14+vQpbt26hfz8fAQFBen8+qlS\nUVGBl19+GatXr8bQoUO55dr6G25pfA8ePIBEIgEA3Lx5E/n5+XB2doadnR2srKxw/vx5MMbwzTff\nKPz+CWkWnTfh06O4uDj23HPPMRMTE9anTx+2ZcsW9sUXXzBXV1fm6urKli5dyqRSKbf9yZMnWXBw\nsMIxqqurmZ+fHxMKhczT05PNnz+ficVijcT3888/MwBMKBSyAQMGsAEDBrDDhw+zkpISNmzYMObi\n4sKGDRvGSktLGWOMSaVS9uabbzJnZ2fm7e2t0BI3MTGR9evXj/Xr149t3bpVL/HNmjWLdevWjdvW\n39+fMcbYH3/8wXx8fJiPjw/z9PRkK1eu1Et8GzZsYJ6enszHx4cFBwezs2fPcsc6fPgwc3V1Zc7O\nznqLjzHGtm3bxmJjYxWOo+vrt3//ftanTx9mZmbGbG1t2ciRI7l9Vq5cyZydnVn//v0VWnTr8vqp\nim/FihWsS5cu3LYDBgxgxcXFWvsbbml8e/fu5b5/AwcOZIcOHeKOdeHCBebl5cWcnZ3Z3LlzFf4v\nEdIcNEIdIYQQYmQ6fLU8IYQQYmwouRNCCCFGhpI7IYQQYmQouRNCCCFGhpI7IYQQYmQouROiBmMM\nISEh+PHHH7llu3fvxujRo/UYFSGEqEZd4QhphpycHEyaNAmXL1+GRCKBr68vjh492mhEw5YQi8Uw\nMenwI0ATQrSAkjshzbRkyRJYWFjg0aNHsLKywvLly5GUlIRNmzahtrYWQ4YMwcaNG8Hn8xEfH49L\nly7hyZMniI2Nxf/93/8BqB+ydc6cOTh69CjefvttFBQUYPPmzTA1NYVQKMS3336r509JCDEGVGwg\npJk++OAD+Pn5wczMDBcvXkROTg4OHDiAc+fOwcTEBPHx8di1axemTJmCNWvWoHv37hCLxQgPD8fE\niRPh6ekJALCwsMDZs2cB1E8ecvv2bZiZmaGiokKfH48QYkQouRPSTBYWFoiNjYWlpSXMzc1x/Phx\nXLhwAQEBAQCAJ0+ecFOd7ty5E4mJiRCLxbh37x6uXbvGJffY2FjumF5eXnj11VcRFRWF6Oho3X8o\nQohRouROSAvw+Xzw+fXtUBljmDlzJlasWKGwTX5+PtavX4/MzEx069YNr776Kmpqarj1FhYW3M+p\nqalIT09HcnIyVq5ciZycHAgEAt18GEKI0aLW8oS00ogRI7B7925uhrHS0lLcuXMHVVVVsLKygrW1\nNYqKipCamqp0f4lEgoKCAgwbNgyffPIJHjx4gMePH+vyIxBCjBSV3AlpJaFQiA8++AAjRoyAVCqF\nqakpvvrqKwQEBMDT0xPe3t5wdnZWmG5UnlgsxpQpU/Dw4UNIpVIsXboUVlZWOv4UhBBjRK3lCSGE\nECND1fKEEEKIkaHkTgghhBgZSu6EEEKIkaHkTgghhBgZSu6EEEKIkaHkTgghhBgZSu6EEEKIkaHk\nTgghhBiZ/wdObBQ8ztF8zwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display Number of Reactors vs Time\n",
-    "from IPython.display import Image\n",
-    "Image(filename='num_plot.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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AqEuXgMaNyZCkeDgxjLGYNQv45x9ymunZk8agdbE+aJ+YF6AAbomJQGBgAUQ1\nPLY24MvYAHbsLHPkCPDRR/Qw+vlnci8oVMjcUpkZC73ntvrsjI8nA2Z0NEWaGTcOCA6WbiftLBMa\nCmRkUOTSoCBg4EDgyy/1l5hhGJvg2TPyBH3/fZo2vH8/vZXbvRJkTI6PD01Nb9WKxqivXdOunbRp\n9MkTiim6eDEpwSlTLKZHyDCMeTl9msxQiYlAkybA1q3SIYgZxtDcugWsXUs9QW9vehEbPx546y3t\n2ksrQpmMQqGtX0/hIBiGsXtevKCs4AsXUgCobduAtm15WgRjHvz8qH/WqRO9iN25Q56kCqSMmNKm\n0YkTgdat6Uz16pHqtdp00QzD6MuNG0CjRsCCBcCwYcD//keBoVgJ2hbp6UBYGOVKqFGD4sOmplL+\nBH9/+kxLy7/ttGmkMqpVo4wiCnbupDI/P1UqWoDG9ho0oOP26EHpuHRh4kSyTDg6kqn+6dPcixS6\nOctYGLY64MtYMTbuLLNhA3mFFi0KrFxJvUBGAxZ6z7V5doaHk7n7449JMb14AUydCri7U6jo6dNJ\nEc6Ykbvd5ctAr140RnfvHvDBB6qxuqpVKd+klxf1q9asAQICKOpmly5k0vz0U3JHMWVuduke4atX\nNBFo6FBg0CDVwjCM3XDxoipMcLVqFC+UlaDtkpEBHDpEwcQAitzi5kZjcOHhVBYeDmze/Gbb6GhS\naEWKkPOKnx8pxbg4Wvf1peP17El1haB5fmFhmo+rCUVmvoLWkR4j7NeP+sa7dlH/c9Uq6idbAO7u\n7jigyHbPMJbAzJmq9by/TU37LJQXL5yweLEP/v23AlxdZRgyJBGdOt3DzZvZuHnT3NJZARZ6z2Uy\nN4SEqLYjImhRcOsWzf8cOJBytNetS0HS1eVVz0lSEtCwoWpbkWMdeDP3emws8PgxKVlFVr2c9bVl\n+nQK3qAOIUj+nN8xJ9KK8MYNsocoXgV696YxQwsgNTUVoaGh5haDYVQ0a6Zaz2sK07TPArl0ieYC\nXr9OWeQnTiyMChWqAKhibtGsBwu9587O6Th1Sv1+mYxCls2dS2N3kZG5x/Q0oS7Hulyef7m2Odk1\n0bSpKvCZOlq2VL9PuxBrAKnsixeB8uWBhATtJWQYxupYvJhCpJUoAcTE0HRixn7w8qKlQQPaDgsj\nRajIq+7hkTuvet626nKs51depgw55shk1CtUl5NdE/pm7ZMeI4yIoBHR77+n5LkBAcCYMfqdlWEY\niyQujiYjf/IJmbfOnmUlaI+UL09mzKtXaTsmhh79irzqQO686nFxlGEEoDpr1wKvX5M36PXrQP36\nNMZ8/TqVZWZSnY4dqffXrBkTft33AAAgAElEQVSwceObxzUV7DXKMIbESr1GFe+68+eT8WfoUGD0\naMqRzeiBhd5zbZ6d586pPEZ9fanXJZeTw9SdO6q86u7upMT27KF5pQBNOV+6lHp4v/6qcqzavp2S\nGGVnk8/l+PFUfusWOc+kplJyo5UrydnGVKhXhL/8ormlBYRZY0XIWBxWqAhPn6axwMREYMAAeoiV\nL29uqWwEC73nhn52jh5NfpXWGnRMvWl01ChSy48fF2yGIsMwFs8vv1CWCLkcOHYMWLKElSCjOz//\nbBlKcM4cmvohBE39qFMH2L1bup16Z5kzZ8iIu20b+c726gW0aMHhIxjGBjhyhJRgVBTw4Ydk9tLk\nfs4w1sDSpeThumsX8OgR/a4HDqRxb02o7xEGB5Ob0LlzpFqjo2m0dMsWA4vOMIypSEujqCDNmtGY\nzrhxpAxZCTK2gML6vH07KcCgIO0s0tLTJx49Itex//2P/GLz85dlGMbiOXqUHB2Sk4E+fWiOmJub\nuaViGMNRty71/uLjKd7p06cUf1QK9c4yy5YB69ZRiLWwMPoHWZgSZGcZxuKwQGeZ7GyKBzlpEiUr\nXbeOHhiMCbATZxlLQS4nI6avL73kpabSvESp8Uv1itDREahdm3xkgTfHBrU1kWZnAyEhgKcnJSuL\nj1f5ydapA/z9NwWee/2aJqKcPg2ULk3/1sqVNR7aVm8mY8VYmCKMi6MRjqgoep/980/uBZoUVoQm\n5ehRGtVzdSVfzzNnaMywUiXN7dSbRvfvN4xkc+ZQbNKMDNoeOxb44gtVmPElSyjM+JIlQKlSFNJt\n7Vqqt26dYWRgGDsjI4NMQ9On03ysyZOpR8gwtsxnn1Fs1PPngZ9+IveW/v2BgwclGgpjkpgoRPPm\nQsTECNG+vRByuRClSwuRlUX7jx0TolUrWm/ViraFoP2lS1N9Dbi4uBhReIYpAPTeT4su+wzI7t1C\nlC1Lp+nbV4iUFKOejtGEie65rtjqs/Odd+hzyhQhFi/OXaYJLYYR9WDkSFLLitFKTWHGk5JUocmd\nnYGSJal+XhYtIlNrSAgFp2MYBgBFAJk8mWLiv/02cPIkjTyULm1uyRjGNBQvTpaQlSuB9u1pZC4r\nS7qd8RTh1q3kXJNzVF5TmHFtQ5BHRACnTtHiLO30yjC2jlxOUyGaNwemTCGP0NhY5EqzwzD2wLp1\nNBSgCAyRlERRb6TQXpM8f04jkNpy9Cg51GzfTp6nGRnUQ1QXZlwRstzLi/Y/eUJB7BiGUUtiIv2t\nNm0iY8vy5arEqQxjb5Qvnzv6Z8WKqmDgmpDuER47RhPpFcl4z5+niLxSTJtGii4hgZxfmjenpL7q\nwoznDGu+cSPV5yg2DJMvQlAwY39/Mr5MmgTcvs1KkLFvTpygLBfFitFkBCcnGmWTQloRfvEFxatR\nDDQEBQGHDhVc0hkzKLaTnx+NAQ4eTOWDB9O2nx/t1zYLJMPYGQ8f0lSIL76gycPXrtHYYIkS5paM\nYczLsGHAmjX0gvjyJeXV/Pxz6XbamUYVTiwKnJx0ky40VJXUzNeXJjfl5a23KKcHwzD58vw5sHkz\npQN9+BCYNYuUIRtOGEaFnx85yTg5UZi1Ro2k20grQm9vMo86OJBb2m+/qcykDMOYhNhYMppcukR/\n9NhYikfBMIwKFxdSU8HB9MLo4UEvkFJIm0b/+IOydSYlkSPLuXO0zTCM0Xn+nN5qGzakXuDq1cB/\n/7ESZJj8+Ptv6g3Om0e+nYmJwD//SLfjDPUMY0gMGGLt/Hmgd29SfF9/DXzzDc2TYqwIDrFmFUib\nRkeMeLOsZEmapKTw+GQYxmDcvw/89RcwfjzNINqzh1KBMgyTP1JBtS9c0LxfWhG+egVcuQJ060bb\n//wD1KxJMxb37ycfboZh9EYup5lGn35K6WNatSJTKEeGsSAstIdn7zg60q3p3Rvo0AEoWlS39tKm\n0ebNKde9IoqLTEb/0D17KDvF5csFFF1/uHvPWBwFNI3eugX06kUO1UFBwMKFQIMGRpSTKRi6KkIL\nVZy2+Oy8coWmTvz7L019792bVJU2AciknWWSknK73Tx/Dty7R76pRYroITbDMEIA69eT88u1axRT\n4swZVoIMoyvVq1OIwTNnqFfYvz8we7Z2baV15Zgx5IsaGkr/2kOHgHHjSCF+8IF+kjOMHfPffxST\nfvlyioaxfr1kCk6GYdSQlERDC1FRlNFv9mygc2ft2mo2jQpBYdKcnclmIwRQv74qPqiZscXuPWPl\naGEalcEJP0+VYdw4Mqx8+SUwdSrHkLcK2DRqkTRtSuPq3btT1KW8YaqlwlZLjxHWrUtZ4y0QW7uZ\njA0goQg3oiu+wGzchTe6dKHoMNwLtCJYEVoklSurLnXeS+7gQGPwmpB+B23YkBKb1atXcCkZhsF/\nqI5u2IhgnMXsDd7o2pXDo9kMFqrw7IWEBP3aSyvC/fvJha1SJZqqr1CxUhMzGIZRIgTwBz4FAGxE\nGKqE3TSzRHaEOiVlwOAHjHUjrQh37DCBGAxju7x8CXz8MbAakRiIpfCFhJ2GYRiTIj19olIlWooW\npbckxcIwjCSrVgGenjQx/ntMwBIMBv97GMaykFaEW7ZQcicfH3LNqVwZaNvW+JIxjBWTilKIiAD6\n9qX3yJgYYAJ+ZCXIMEbmyBFg2TJaf/QIiI+XbiOtCL/9ltL+Vq1KR4yJARo31lNUxmLhXr/e7EIr\nVMNVLF0KfPUVcPgwBWhiGMa4TJlCud+nTaPtrCx6GZVCWhEWKkTBDuVyWpo1o1RMDMO8QTQ64kNs\nRQXcw5kzwMyZQLFi5paKYeyDqCgyYrq60naFCjS/UAppZxk3N+DZM+D994E+fYCyZXnmL8PkgxDA\nRHyHariKQ3gfJQOfmFskhrErChfObdDSdqqkdI8wOpocZWbPBtq0AapUoaimDMMouX6dMshfQBA+\nwwKURIa5RWIYu6N7d2DIECA9HfjzT4oC+skn0u04MS+TG54/pTMHD1JMw7Q04EvMws8YDUcInptm\nKRhyHqGhys2MNs/O7GxKO+vpCWzdSi4iPXsCqakUJP7vv6kHlhMhgMhIYPt2wMWF4ujWqUP7VqwA\nfviB1idMAMLDaf30aWDAAJpm1K4dMGeOfi4Ke/ZQwiQhgNatgZYtpdtI9wg3bSKv0ZIlgRIlKEV2\niRIFl5JhbISsLJofGBpKYxKnTwOzMIqUIMNYOXPmADVqqLbHjgW++IKsH6VKUUravOzYQfuvXwcW\nLQI++4zKU1PJkSU2lsJWT5lCL44A1Vm0SNVu586Cyzx7Nsn88880Pq+NEgS0UYRjxtDo45MnQEYG\njTxmaGH2efWKAnQHBVEi30mTqDw+nnLM+PsDPXoAmZlU/vo1bfv50X59Y+YwjBFJSyO/sSVLyDP0\n6lXVmy/DWDt37wLbttGLHkC9q337KKA1QL25zZvfbBcdTemPHBwoOmd6OpCcDOzaRUrJ3Z2UaMuW\npPCSk0mdvPsutenfP//jaktGBvUCmzQB5s8HHjzQrp2010u5crlfC7SlSBG6csWK0avze+/R/MNf\nfqHXip49KRX3kiX0SrBkCV2hGzcol8bYscC6dRpP4e7ujgMHDuguG6OemTNV63xt8yUlpTDGjAnE\n3bsumDDhClq0eIi4uP/fqen68bU1D+que0HulaHKzYxM5oaQENV2RAQtCkaOpBRhCo/Lx4/Jb1Lh\nJ+nlRWmP8pKUBHh7q7YV9TSVe3m9WV5QJk2i5cIFUh9Nm9Ix9+7V3E69Ity0iT5DQqin9tFHuRPx\ndumi+cgODiq/8awsWhwcSDmuXk3l4eHA5MmkCKOjaR2g145hw1RxTdWQmpqK0NBQzXIwutGsmWrd\ngsY08sXE4y9yOfDrr/Qu9+QJmYFatAgAEKCqpOn6WdO1tSXUXfeC3CtDlZsZZ+d0nDqV/76tW2ly\nQN26Kt2dn+j5PZrV1dO1XF/KlgXKl6eZfw8fStdXrwhzeoa6uNDoowIHB2lFCNBoa9261Mv7/HPy\nOFX3WpHzlcHZmcYkHz8GypTJfcxFi2gBAJlMWgaGMQByOb0lz51L1v4tW9gUytgmR4/S73v7dhrh\nysig3356Oj1ynZ3JdJpfWlovLyAxUbWtqOfllbtDfPcuja17edF63voFZcEC6gk+ekT9qT//BAIC\npNtBmIK0NCFCQ4U4dEiIKlVU5XfuCFGrFq0HBAiRmKja5+srREqKxsO6uLgYQVg7h17SaLF0TCTr\n06dCfPABnWbkSCHk8gLKZE3X1pZQd90Lcq8MVW5mtH127t8vRPv2tB4WJsSaNbQ+ZIgQ8+fT+qZN\nQnz9Na1v3SpEmzb0Hzl+XIh69aj88WMhKlcWIjWVlsqVqUwIIUJCqK5cTm23bSv49xo7VoizZ3Vv\nJ+0sEx5OrwIK0tKAQYN0U9NubqT+T5xQvVYAudV/zlcJmYxsT1JphRnGyCQn0/D2vn3AH3+QWZSj\nz1kgHBrQ6MyYQb9/Pz8y1g0eTOU3b6omErRrB/j6Up1PPgF+/53K3d0pWme9erRMnKh6vC9YQE45\nfn5kNCxIKGuF/+aYMUDFiuSlmnORQnoe4TvvAGfPSpfl5dEjCs/m5kYTRFq1IgeYFSuArl1VzjKB\ngcDQoeTi87//0dNm7Voao1y/XuMpeB6hEbDQeU/5YmRZr18nD7SHD4GNGymehF4yWdO1tTYMOSew\nIG1scB6htvTtS9MW3n7bIIcrEB9+SGObPj5vjj0aJkO9XE69wFKlaDs1VbuxueRk6k1mZ9Mxuncn\naQMCSAlOmEAKVfFaMXgw0K8fvRa4u5MyZBgzkJkJLF1Kb61CUG7qevXMLRXDWCYrV5pbAlKCgHaZ\nJvJDukf4118UylsxgWTDBmD8eFJaZoZ7hEbAQt9g88UIsr5+TT/tDRuAWrWoJ1itmoFksqZra21Y\nY4/QjL8HW312tmhBCZKkyvIi3SPs35+mUOzbRzdr0yYt3XAYxrqIjwd69aLoF99/D4wbBzhKj6Iz\nDGNmXr0CXrwAUlLIgKl4r8jIAO7dk24vrQhv3qQRzIAA8n/du5ccXNzc9JOcYSyIs2dpGPv1a4rt\noKs/GMMw5mPhQprje+8ezdhTKMISJWjmnhTSptHgYODUKQp51qYN0KEDxZPavl1/6fXEVrv3ZsWa\nzHcGkvXAAaBTJ/rT7Nqlp8HDQk1hNg+bRnXCVp+dc+cCw4fr3k66R+joSDMoN22isOLDh5OTC8NY\nOa9ekVPMl1+S0WPHDnK9Zhizwi9MBWb4cODiReDyZfp/K+jfX3M7aUVYqBCwZg05zSiizWRl6SEq\nw5ifZ8+A9u2BQ4donmBU1JtBjBiGsS6mTCELz+XLNKdxxw76f0spQmlXgGXLgOPHyVPUx4c8Cvr2\nNYzUDGMGDh6kcYQjR2jC74EDrAQZC4EDAujFxo3kIVq+PKmu8+dp3F8K6R5hQADw22+qbR8f4Ouv\n9RCVYczHrl0Uz6FcORrmbt3a3BIxjAbYTKoTRYuqRvMyMij4ttRkekCTIuzenSK71K6d/1vKhQt6\niMtYFTbyZzx0iKbDKsYD9QnuyzCM5RESQlE8P/mErD7FilFaXCnUe40mJwMeHsDt2/m3rFRJD3EN\ng616PpmV/JSepSpCHeTatQvo3Jl+tjExRlSCFuolaPPYiteoJtOoAX8z9vDsTEigXmFgoHRd9T1C\nDw/6LFmSgi4CQNWqtM0wVsLLlzQ1Ys8e+kPs2UPmEoPC4zoMY1bOnNG8TyplmnpFmJlJKYs3b6Zx\nQSGod9i5MwXGLly4gCIzjGmIiaGpERcu0LD2+PGqXNEMw9gOX32lfp8iH7wm1CvCH36gaRKJiUDx\n4lT29ClN0//+e1oYxpAYMIL/4sX0Hle5Mr3LdepkGBEZOyBvD597/BbP/v36tVc/RlirFhAXR9np\nc/LsGdCwIc1aNDP2YOc2KXn/8KYeIzTQ+Mvs2dQTbNMG+OefN3/CBkfdg5LHCE2Hscb7NGHqMUJ1\n5y4Atvrs/Ouv/MsLPqHe0TH/J0ixYvyGxFgkcjlNqP3uO/IOXbWKLfhWg76KrCDH0qZcE4o2BTkH\nvwgZhZMnVeuvXtHwSJ06+ihCB4fcYbxzwiH5bQcbeam5cAHo0oVixA8YAPz5J80lYhjGfpg7N/f2\nkyfaZQxU/6h48iR3GO+c2MjDk7F+5HDArxiJSY3JWLF8Ob398U+UMRn8Y7NYXFxUkx40oV4RJiQY\nThqGMQLZcMTHWIzlGIhWjSiAtqenuaViGMZcdOigei+RyynmaPfu0u3YeMRYFw4OgBAQApiKcViO\ngRiNnzBj55j8X8x5XMY6JpRrc+6c+7S9l9xbsytGjVKtOztTAA0vL+l20vkILRhb9XwyOAV5GGmD\nISJ46NoeQLZM4MsvKQRuN6zHWvSEo5DrdkxDYuleo9amCHMiRP711JVbC3nlZ69Rg5KRAchkqm13\nd831uUfIWBX7EYrPalJu6K8wEz9hDBxRgN6B9b7/MQyjhkWLgG+/VQXfVrxvSAXelnb/HDUKuHTJ\nQGIyJsXBQbP5yZhv1NocWyFDfnXzlGfBGd9jAlpiD15evY1V6I2ZGK1SgpqOJXXu/CZQ61Je0O/I\nMIxB+flnUlcJCaT84uO1yz4hrQirV6cQHQ0aUGi1J0+0kygxEWjWDKhRA6hZE5gzh8pTU4GWLQF/\nf/pMS6NyIYARIwA/PwoKqSl4HGNXvMRb6Ip/MBHfoyO24H+ojd5Yo74BKx+GsUuqVClYAA3txwiv\nXqVMh2vWAI0bU56LZs3U109OpqVOHQrNVrcuxbpavpwMtl9/DUyfTopwxgxKDjd3Ln3GxgKRkfSp\nAVu3c+uNurEVdft0xZDjNGqO9SRdoKPbQRxGE8zDMAzFAsOcL7/zKzBUJBJN14fHCHmM0JDj8zpi\nq8/Os2eBgQOp31akiKo8Z0rd/NBujDA7G7hyhZYyZYCgIOCXX4CFC4G1a/Nv4+GhymBRvDj1DJOS\ngOhoSgkOAOHhQGgoKcLoaNUEsIYNKamUIhUUY1is5AHyEG+jbXPgAhphFfqgF9T81hiGYQAMGQI0\nb05pdHWJ+yKtCL/8EtiyBWjRAhg3TpXlcOxYoFo17c6SkECqukED4MEDlXLz8AAePqT1pCTA21vV\nxsuLyvIqwkWLaAFyuwUxNoMA8CtG4nt8i1f/AdHohHbYYW6xGIaxcJydqY+mK9I6s1Ytil+1cOGb\nqX7j4qTP8OwZ0LUr8OuvQIkS6utpG8EmIgI4dYoWjqFlc9xHOXTHenyJ2QjAZRw9CtMqQSvpLVs0\nhnTQMmRsUMbmadaM+knJyeSOolikUK9JFM4qwcFkEs1LnTrSSXqzskgJ9ulDgSABoFw5lckzOVmV\nJdXLixxsFNy9a8Q04owlcg3+aI1dSIQ3puIbfI3pcHjHRGNp/MBlGKtn9Wr6nDZNVabN9An1ilDf\nTIdCAIMH09jgl1+qyjt2BFasIGeZFStUieI6dgTmzQN69iQnmZIleXzQjjh9GmiLIxBwwAk0RAhO\nm1skhmGsjPj4grUzXmSZI0eAJk1yj1pOnUrjhN27A3fuABUrAhs2kBepEMCwYcDOneT/umwZEBKi\n8RS26vlkMIztbWegY8WgOT4qFoPSzxKwG61QFTmi5FqqdyB7jWq+Bpq8kw3puWytsNeoUShoPkLt\nFOGxY+TwktM5RerIJsBWb6bBsHBFmI6SGIKF2IBuCKjpiF2XPOGJewY9h9HQRi5WhKwI1cGK0CgM\nH65az5mPcONGze2knWX69aPoMkeOUNbDkyfJUYVh9GA72qI+4rAJXTAUv+PoUbypBBmGMQu6xkPJ\ny7RpFBulWjVg1y5V+c6dVObnR9PIFcTHk7HQ3x/o0QPIzCyY3HPnqpY//6TJClodS0hRvboQcrlk\nNXPg4uJibhEsG3pvzL2oKy/IUsBjzUakAISoguviIJpoPpYh5TXkoo1cmuqYEnXn1SSPrm3UfT9N\n393c99DSfz9S7fVA6tl5754Qp0/TekaGEP7+Qly6JMTo0UJMm0bl06YJMWbMm20vXRIiMFCIV6+E\nuHVLCF9fIWQyWnx9hbh5U4jXr6nOpUvUpls3IdasofUhQ4T4/Xe9v6IQQojMTFJhUkjPP6hVC7h/\nnx1XDElBTFKWiI6mHTkc8D2+xWRMwYf4F+vRHUXxSvOxLNV8pq9cupitNZXreix19aX2Gapcah9j\nEegaDyUn0dHk81ikCODjQ70/xUw7Pz/A15fWe/akujVqkO+lwuMzPByYPBn47DPd5S5oPkL1Y4SK\nIz59Cpw7R3MIc8as2bJFdykNjLe3N/7++29zi6E7p3N4RNatq/0+fc5jZrJkjvh5fWvsOROAlnUv\nYUz33XB2UpM6yVaoW1e3e6CuvqZyQPc2xj4WI42+107PZ0Pr1n1Qu3aScjsigpb8SEgA3n8fuHiR\n/BvT01X7SpV60zw6bBgFB+vbl7YHDwbatqX1nTuBxYtp/e+/aYLA5MlU/8YNKk9MpPoXL+r+vQ4e\nVK3rko9QfY8wZ4ZDCyU1NRWhoaHmFkN3csZozfseom5fQRweNMWCNSHH8C7CsQZ3UAnfYwLGn/4R\nDvbw/BRCt3ugrr6mckD3NsY+FiONvtdOT2uRs3O6Vq4e2sZDkRLNwYF6aPmVq6uvCzduUNCypk1z\nlx8+DLx+TcG4NaHeWaZpU1q2b1et5yxjGAleoQgmYxKaYT8EHPAPumACfgQbxhjG8tEUDwXIHQ8l\nJ+pio6grL1OGepmKSQkFiaUyciSZcPNStCjtk0Laa3TPnjfLdnDcR60wVjogQ+bLMxIZKI6PsBlT\nMBmdEI1zCEYXRJlbLNNiAfeBYQqCEJrjoQC546HExalm1HXsSLkYXr8mb9Dr12lkrV49Wo+PJ0/O\ntWuproMDdY4VUxxyHldbEhIoe19eQkJonxTqTaMLFgC//06xaXKe4elToFEj3aRk7IoHKIvuWI8j\neA8L8Ck+xUJzi8QwjA4cPUpjeLVrU5RNgOKhfP01OZ8sWaKKhwJQfJSiRWm9Zk2qExBA43Tz5wNO\nTrRv3jygdWtKaDRoENUFyOGmZ09gwgTgnXdICevCq1fq9718Kd1evbPMkyc0CvrNN7knfBQvTpFg\nLACLnxRqrInM2qDJa9CIPMTbeBfHkQwPLMFgTp3EMPmh7/9TzzFCQz87R4+mKef59cpMQa9elH7p\nk09yly9ZAuzeDaxbp7m9dpFlsrNpJDJnZJmKFQsgrmExiiK0pGSlVqYI16IHRmEmUuGOfWiOhtCc\nWJlh7BYbU4Tm5sEDoHNnoHBhlUPtqVNkgo2KAsqX19xeWhHOm0f+reXKqWKGOjhQaiYzw4pQAyZU\nhI/hjkmYgvkYhpq4iDmIRAtIBGVnGHuGFaFR2L9fNe2iZk3qJWqDtCL086PJHqVL6ymi4WFFqAET\nKcIHKIsWiMF/qIGh+B2z8BUKI8vo52UYq4YVoUUhHVnG21s67yBjeZhACd5GRXTEFsTDBzvQFq2Q\nj4cxwzCMhSOtCH19KY5O+/a5I8vk9Kll7I6TCEEH/ItXeAvR6IQPEGNukRiGYQqEtCKsWJGWzMyC\nhwRnbAYZnNAPf2MteqEy4rEfzVADV8wtFsMwTIHRPjHv06dkbitWzMgiaQ+PEZqWRyiDCCzCZnRG\nBBZiKsahNFLNLRbDWB88RmhRSPcIL16kCSKp///AK1OG0gArZkIydsFtVERz7EM8fDAbIzESc8wt\nEsMwjEGQVoQREcAvv6gCxB44QLMWjx0zrmSMxRCPymiG/UiHGw6jCRqD7z3DMLaDtCJ8/jx3lPTQ\nUCpj7IKLqIm22IEXcME+NEcdnDW3SAzDMAZFOui2ry/w/fcUuTQhAfjhB8q2yNg0MjjhNwxHIxxD\nNpxYCTIMY7NIK8KlS4FHjygPR+fOtL5smQlEY8xFCkrjI2xGJH5DPZxELBogCOaPJMQwDGMMtPca\n1ZVBg4CtWylhlSLmTWoq0KMH9SwrVwbWr6cUx0IAkZGU59DFBVi+HKhTR/IU7DVqeO6jHOojDg9R\nFlMwCWPxk0nOyzB2BXuNWhTqFWHHjppbbtmief+hQzTVon9/lSIcM4YyV3z9NWW0SEuj/BvbtwNz\n59JnbCwpxVjpgM2sCA3LaxRGF2zCTrTBYTRBIxw3+jkZxi5hRWhRqHeWOX6cwqv16gU0aKD7hX//\n/TczIkZHk9cpAISHk+PNjBlU3r8//TAaNqR0xcnJgIeHbudkCsxLvPX/SrAt5mMoK0GGYewG9WOE\n9+9TJsaLF6mHtmcPzSFs2pSWgvDggUq5eXgADx/SelISKV0FXl5UxpiE8whEC8RgF1rjT3yMoVhg\nbpEYhmFMhnpF6OQEtGkDrFgBnDhBWShCQ8mEaWjy622qMxssWgSEhNCSMz+iLeLgoFqMwHO4YAj+\nQDDO4zyCsBFh+BhLjHIuhmEYS0XzPMLXr4Ft24A1a8jMOWIEeY8WlHLlVCbP5GRypAGoB5iYqKp3\n9y5QoUL+x4iIoAUAXF0LLoudsxct0Aer8BDlMADLMAtfwR1p5haLYRjG5KjvEYaHA40aAWfOAJMm\nASdPAt9+C3h6FvxsHTtSDxOgz06dVOV//UU9wxMnKO0Tjw8aBQHgL/RDe2yDG9JxBI2xDINYCTIM\nY7eo9xp1dFT1uPJ6Mjo4ABkZmo/cqxc5xqSkUE9wyhTgo4+A7t2BO3coo8WGDeRFKgQwbBiwcydN\nn1i2jEyfEtiU16gJvEKz4YhIzMF8DENDHMe/6IAyeGz08zIMY2DYa9SgGG8eoQlgRag9C/ApFuAz\n/A+B+AozMRXjOJM8w1grrAgNinRkGcaq2Yr2CMFJDMUCyOGIv9EXMzGalSDDMMz/w4rQRkmDGybg\ne3yEzUiHG+ZgBM7iHfTFKnOLxjAMY1FIZ59grIqDeB9LMQir0AfZcEYHbMEKhKMU0s0tGsMwjEXC\nitDcODjobe8XAPbiA5Iv+28AAA9NSURBVPyEMdiLliiM1xiEpeiPv9AIx+AIqx0GZhiGMTqsCI2B\nOscXXcsleIISOIwmmI6vcRTvoQKSMAmTMQozUQw8EM4wDKMNrAitDAEgFg0wH5/jX3TAE7jBBc8x\nD5/jYyxGEWSaW0SGYRirghWhlZAFZ8zFcMzCV7gHT7jiGdpjGwZjCRrjKFzxwtwiMgzDWCWsCA2N\nAc2fj1AGe/EBNuMjbEA3CDiiOWLwHSaiPbahPB7oKSzDMAzDitDCeIISmI/PsQa98B9qIBvOcEMa\nPsUf+AB70QVR5haRYRjGpmBFaGZeoCjOIRinEIJodEIc6uMZiqMpDmAsZqAjtqAOzqAQbDzTBsMw\njJlgRWhiXqMw4uGDNeiFrfgQ5xAMOZwAAFVxFZ0RhQFYjubYb2ZJGYZh7ANWhEbmEJrgCN5DAirj\nCqrjPIKQgZIAgHdwBt9gGuojDnVxGhVwD8YPvc0wjNVjgPnHjApWhAZAADiNujiKxrgBPyTDA5cR\ngIcoi8coAwAoh/vwQTx6YB2a4DD8cR31EceT3RmGsUh27gQiI4HsbODjj4Gvvza3RMaDFWE+pMEN\nSfDE69OUm/jFC3r5uoFPcRSNkYIyyGgEPH1K5alIQjIokXBJpMMDyfDDDTTBYVTEHXyGBZzvj2EY\nqyE7G/j8c2DPHsqbXq8epY0NCDC3ZMbBrhVhvXqk5HIicAlXUB0CjsAbKREXwB2PUQU3UdIVKF+e\n0jbi0jHURxz6YBU8cc9E0jMMwxiHuDjAzw/w9aXtnj2B6GhWhBaJu7s7Dhw4UOD2pUpVh6trngQc\ngU6oVOQy6vjfhktARRQqJPDWW9lwcBBwun4FVb0ewNlJDtTNoUHfvQ2gHK7jS1wvsDQMwzA6oMez\nTyZzy5X7PCKCFgVJSYC3t2rbywuIjS3w6Sweq1aEqampCA0NLXD7fJs6lKPPk8gnMW9d1XrOfc2a\nFVgGhmGYAqGHs4yzczpOndLt0EbOHW5WOB8hwzAMkwsvLyAxUbV99y5QoYL55DE2rAg14eCgWnTZ\nxzAMY8XUqwdcvw7ExwOZmcDateQsY6tYtWmUYRiGMTzOzsC8eUDr1uRBOmgQULOmuaUyHg5CWO+s\nTFdXVzx/buC8e+p6eEJw749hGMtBj0e3UZ6dVgybRhmGYRi7xrIU4c6dQLVqNIFl+nRzS8MwDMPY\nAZajCBWhDHbsAC5fBtasoU+GYRiGMSKWowhzhjIoXFgVyoBhGIZhjIjleI1qG8pg0SJaALx88QKu\nrq56nVYmk8HZOcdlcHHJv6Krq/p9RuANuSwAS5QJYLl0wRJlAlguXVDKpMez7+XLlwaUyPqxnDus\nbSiDHLGA5AY4bUhICE5pCrFgJixRLkuUCWC5dMESZQJYLl2wRJmsHcsxjdpbKAOGYRjGIrAcRWhv\noQwYhmEYi8Bp8uTJk80tBADKZ+TvD/TtC8ydS59du5rk1HXr1pWuZAYsUS5LlAlguXTBEmUCWC5d\nsESZrBmrjizDMAzDMPpiOaZRhmEYhjEDrAgZhmEYu8bmFOGgQYNQtmxZ1KpVS1l2/vx5vPvuu6hd\nuzY6dOiAjIwMAMCqVasQHBysXBwdHXHu3DkAwOnTp1G7dm34+flhxIgR0NeCrItcWVlZCA8PR+3a\ntVGjRg1MmzZN2Wbnzp2oVq0a/Pz8MN0AYeh0kSszMxMDBw5E7dq1ERQUhAM5MmQb8nolJiaiWbNm\nqFGjBmrWrIk5c+YAoETMLVu2hL+/P1q2bIm0tDQAgBACI0aMgJ+fHwIDA3HmzBnlsVasWAF/f3/4\n+/tjxYoVBZapIHJduXIF7777LooUKYKZM2fmOpah7qOuMq1atQqBgYEIDAxEo0aNcP78eYPLVBC5\noqOjERgYiODgYISEhODIkSPKY5nzHio4efIknJycsHHjRouQ68CBAyhZsqTy2fXdd98pj2XoZ4Rd\nIGyMgwcPitOnT4uaNWsqy0JCQsSBAweEEEIsWbJETJgw4Y12Fy5cED4+PsrtevXqiWPHjgm5XC7a\ntGkjtm/fbjK5Vq1aJXr06CGEEOL58+eiUqVKIj4+XshkMuHr6ytu3rwpXr9+LQIDA8WlS5dMJte8\nefPEgAEDhBBCPHjwQNSpU0dkZ2cLIQx7ve7duydOnz4thBAiIyND+Pv7i0uXLonRo0eLadOmCSGE\nmDZtmhgzZowQQoht27aJNm3aCLlcLo4fPy7q168vhBDi8ePHwsfHRzx+/FikpqYKHx8fkZqaajK5\nHjx4IOLi4sS4cePEzz//rDyOIe+jrjIdPXpUeQ22b9+uvFaG/m3pKtfTp0+FXC4XQghx/vx5Ua1a\nNSGE+e+hEHRtmjVrJtq2bSs2bNhgEXLt379ftG/f/o3jGOMZYQ/YnCIUQoj4+PhcD/bixYsr/2R3\n7twRNWrUeKPNN998I8aNGyeEoB+l4o8ohBCrV68WERERJpNr9erV4sMPPxRZWVkiJSVF+Pv7i8eP\nH4tjx46JVq1aKdtPnTpVTJ061WRyDR06VPz999/Kes2bNxexsbFGu14KOnbsKHbv3i2qVq0q7t27\nJ4Sge1S1alUhhBARERFi9erVyvqKennlyFvP2HIpmDRpUi5FaKz7qItMQgiRmpoqKlSoYHSZdJXr\n2LFjonr16kKIN39L5riHs2fPFvPmzRPh4eFKRWhuudQpQmPfR1vF5kyj+VGrVi1s2bIFALBhwwYk\n5py4//+sW7cOvXr1AgAkJSXBy8tLuc/LywtJSUkmkyssLAyurq7w8PBAxYoVMWrUKLi7uyMpKQne\nOcLQmVquoKAgREdHQyaTIT4+HqdPn0ZiYqJRr1dCQgLOnj2LBg0a4MGDB/Dw8AAAeHh44OHDhwCg\n9roY83ppI5c6jCWXrjItWbIEbdu2NapMusgVFRWF6tWro3379li6dKlFyJWUlISoqCh8+umnudqa\nWy4AOH78OIKCgtC2bVtcunTJ6HLZMnahCJcuXYr58+ejbt26ePr0KQoXLpxrf2xsLFxcXJTjZCKf\n8S0HIyTlVSdXXFwcnJyccO/ePcTHx2PWrFm4deuW2eUaNGgQvLy8EBISgpEjR6JRo0ZwdnY2mlzP\nnj1D165d8euvv6JEiRJq66k7v7nlUocx5NJVpv3792PJkiWYMWOG0WTSVa7OnTvjypUr2Lx5M779\n9luLkGvkyJGYMWMGnJyccpWbW646derg9u3bOH/+PIYPH46PPvrIqHLZOpYTa9SIVK9eHbt37wYA\nXLt2Ddu2bcu1f+3atcreIEBvUXfv3lVu3717FxWMEO5NnVyrV69GmzZtUKhQIZQtWxaNGzfGqVOn\n4O3tnas3a2q5nJ2dMXv2bGW9Ro0awd/fH6VKlTL49crKykLXrl3Rp08fdOnSBQBQrlw5JCcnw8PD\nA8nJyShbtiwAul/5XRcvL69cDj13795FaGioyeRShzp5TSXThQsX8PHHH2PHjh0oXbq0UWQqiFwK\n3n//fdy8eRMpKSlmv4enTp1Cz549AQApKSnYvn07nJ2dzS5XTiXZrl07DB06VHm9TPGMsDnMZZM1\nJnnHvB48eCCEECI7O1v069dPLFmyRLkvOztbeHp6ips3b+Y6RkhIiDh+/LjS+WPbtm0mk2v69Oli\nwIABQi6Xi2fPnokaNWqI8+fPi6ysLOHj4yNu3bqlHAi/ePGiyeR6/vy5ePbsmRBCiN27d4smTZoo\n2xjyesnlctGvXz8RGRmZq3zUqFG5HAdGjx4thBBi69atuZxl6tWrJ4Qgh4bKlSuL1NRUkZqaKipX\nriweP35sMrkU5B0jNOR91FWm27dviypVqoijR4/mqm/o35aucl2/fl05Ln369GlRoUIFIZfLLeYe\nCiFyjRGaW67k5GTl9YqNjRXe3t5CLpcb7Rlh69icIuzZs6coX768cHZ2Fp6enmLx4sXi119/Ff7+\n/sLf31+MHTtW+QMSggadGzRo8MZxTp48KWrWrCl8fX3F559/nquNseV6+vSpCAsLEwEBAaJGjRri\np59+Uh5n27Ztwt/fX/j6+ooffvhBL5l0lSs+Pl5UrVpVVK9eXbRo0UIkJCQoj2PI63X48GEBQNSu\nXVsEBQWJoKAgsW3bNpGSkiKaN28u/Pz8RPPmzZUPHrlcLoYOHSp8fX1FrVq1xMmTJ5XHWrJkiahS\npYqoUqWKWLp0aYFlKohcycnJwtPTUxQvXlyULFlSeHp6iidPngghDHcfdZVp8ODBws3NTVm3bt26\nymMZ8relq1zTp08XAQEBIigoSDRs2FAcPnxYeSxz3sOc5FSE5pZr7ty5IiAgQAQGBooGDRrkerEx\n9DPCHuAQawzDMIxdYxfOMgzDMAyjDlaEDMMwjF3DipBhGIaxa1gRMgzDMHYNK0KGYRjGrmFFyDAS\nCCHw3nvvYceOHcqy9evXo02bNmaUimEYQ8HTJxhGCy5evIhu3brh7NmzyM7ORnBwMHbu3IkqVaoU\n+JgymQzOznYR3IlhLBpWhAyjJWPGjIGrqyueP3+O4sWL49tvv8WKFSswf/58ZGZmolGjRpg3bx4c\nHR0RERGBM2fO4OXLl+jRowcmTpwIgEKZDRkyBDt37sTIkSNx9+5d/PnnnyhUqBBq166NlStXmvlb\nMoz9wa+jDKMlkyZNQp06dVC4cGGcOnUKFy9eRFRUFI4dOwZnZ2dERERg7dq16N27N6ZPnw53d3fI\nZDI0a9YMYWFhCAgIAAC4urri6NGjACijwO3bt1G4cGGkp6eb8+sxjN3CipBhtMTV1fX/2rtbFgWi\nMAzDDwNi0BF/g9GPNs02TDcOyCT/hNGgzWSzmIVpxgGLVatYbCIa1KKghnHcsDCwrGWFtZz7SgfO\nB5z0cA4vvPJ9X/l8XtlsVtPpVIvFQo7jSJJut1vaAmc8Hms0GimOY+12O61WqzQIfd9Pz6xUKgqC\nQI1GI+0gAOCzCELgDyzLkmV915g9n0+1Wi11u90fa9brtQaDgebzuYrFooIg0P1+T+dzuVw6jqJI\ns9lMk8lEvV5Py+XyV8sfAP+LqlHgTZ7nKQxDHY9HSdLpdNJms9H5fJZt2yoUCtrv94qi6OX+x+Oh\n7XYr13XV7/d1OBx0vV4/eQUA4kUIvK1Wq6nT6cjzPCVJokwmo+FwKMdxVC6XVa1WVSqVVK/XX+6P\n41jNZlOXy0VJkqjdbsu27Q/fAgBVowAAo/E1CgAwGkEIADAaQQgAMBpBCAAwGkEIADAaQQgAMBpB\nCAAw2he3G2o+0RQFngAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display natural U demand  vs Time\n",
-    "from IPython.display import Image\n",
-    "Image(filename='cum_mass_snf_discharge.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JeRv2e6pXr8p86JAhMHCgNTnCjRuiUPv2hS5dMh4vXVoUKsiY0o4dsh0SYu21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\niEzyD+dE5p/A+1CGAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display spent fuel vs Time\n",
-    "from IPython.display import Image\n",
-    "Image(filename='tails_tailings.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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qc3Jy1O1yc3NxdXWt0re2bTp37kxJSQllZWXV+v6xT1lZGRcvXqw2DS2EuDu5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\ngQSqEEIIYQQSqEIIIYQR/H9i7ey4/wBKhwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display total fuel usage  vs Time\n",
-    "from IPython.display import Image\n",
-    "Image(filename='total_fuel.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 9f595a466f4fc281e676ff839c7bc93102cff8cc Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 12:19:13 -0500
Subject: [PATCH 32/35] remove unnecessary files

---
 input/master_us/input_fourty.xml     | 706 ---------------------------
 input/master_us/rebus_dep_fourty.xml | 366 --------------
 2 files changed, 1072 deletions(-)
 delete mode 100644 input/master_us/input_fourty.xml
 delete mode 100644 input/master_us/rebus_dep_fourty.xml

diff --git a/input/master_us/input_fourty.xml b/input/master_us/input_fourty.xml
deleted file mode 100644
index 3bc53f0c..00000000
--- a/input/master_us/input_fourty.xml
+++ /dev/null
@@ -1,706 +0,0 @@
-
-<simulation xmlns:xi="http://www.w3.org/2001/XInclude">
-  <control>
-    <duration>1764</duration>
-    <startmonth>5</startmonth>
-    <startyear>2013</startyear>
-    <decay>lazy</decay>
-  </control>
-
-  <archetypes>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Source</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Sink</name>
-    </spec>
-    <spec>
-        <lib>cycamore</lib>
-        <name>Reactor</name>
-    </spec>
-    <spec>
-      <lib>agents</lib>
-      <name>NullRegion</name>
-    </spec>
-    <spec>
-      <lib>agents</lib>
-      <name>NullInst</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Separations</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>DeployInst</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Separations</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Enrichment</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Mixer</name>
-    </spec>
-    <spec>
-      <lib>cycamore</lib>
-      <name>Storage</name>
-    </spec>
-    <spec>
-      <lib>saltproc_reactor.saltproc_reactor</lib>
-      <name>saltproc_reactor</name>
-    </spec>
-  </archetypes>
-
-
-
-
-<!-- La Hague Model from Schneider & Marignac -->
-  <facility>
-    <name>reprocessing</name>
-    <config>
-      <Separations>
-         <feed_commods>   <val>cooled_uox</val> <val>pre_2013_unf</val> </feed_commods>
-         <feed_recipe>uox_used_fuel_recipe</feed_recipe>
-         <feedbuf_size>1e100</feedbuf_size>
-         <!-- 1100 tons/year 91.6 tons/timestep -->
-         <throughput>1e100</throughput>
-         <leftover_commod>reprocess_waste</leftover_commod>
-         <leftoverbuf_size>1e100</leftoverbuf_size>
-         <streams>
-          <item>
-            <commod>lwr_pu</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>Pu</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-          <item>
-            <commod>lwr_u</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>U</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-          <item>
-            <commod>lwr_np</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>Np</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-          <item>
-            <commod>lwr_am</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>Am</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-          <item>
-            <commod>lwr_cm</commod>
-            <info>
-              <buf_size>1e100</buf_size>
-              <efficiencies>
-                <item>
-                  <comp>Cm</comp> <eff>.99</eff>
-                </item>
-                </efficiencies>
-            </info>
-          </item>
-        </streams>
-      </Separations>
-    </config>
-  </facility>
-
-
-
-<!-- La Hague Model from Schneider & Marignac -->
-<facility>
-  <name>salt_reprocessing</name>
-  <config>
-    <Separations>
-      <feed_commods>
-        <val>fuel_out</val>
-      </feed_commods>
-      <feed_recipe>init_salt_recipe</feed_recipe>
-      <feedbuf_size>1e100</feedbuf_size>
-      <!-- 1100 tons/year 91.6 tons/timestep -->
-      <throughput>1e100</throughput>
-      <leftover_commod>salt_reprocess_waste</leftover_commod>
-      <leftoverbuf_size>1e100</leftoverbuf_size>
-      <streams>
-        <item>
-          <commod>salt_pu</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Pu</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_u</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>U</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_np</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Np</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_am</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Am</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_cm</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Cm</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_na</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Na</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-        <item>
-          <commod>salt_cl</commod>
-          <info>
-            <buf_size>1e100</buf_size>
-            <efficiencies>
-              <item>
-                <comp>Cl</comp>
-                <eff>.99</eff>
-              </item>
-            </efficiencies>
-          </info>
-        </item>
-      </streams>
-    </Separations>
-  </config>
-</facility>
-
-
-
-<!-- Infinite UOX_Pu to MOX mixer -->
-<facility>
-    <name>rebus_fuel_fab_lwr</name>
-    <config>
-      <Mixer>
-        <in_streams>
-          <stream>
-            <info>
-              <mixing_ratio>0.067524083</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>na</commodity>
-                <pref>1.0</pref>
-              </item>
-              <item>
-                <commodity>salt_na</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.359795664</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>cl</commodity>
-                <pref>1.0</pref>
-              </item>
-              <item>
-                <commodity>salt_cl</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.477050824</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>dep_u</commodity>
-                <pref>1.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.00452453985</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>lwr_np</commodity>
-                <pref>1.0</pref>
-              </item>
-              <item>
-                <commodity>salt_np</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.00195838076</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>lwr_am</commodity>
-                <pref>1.0</pref>
-              </item>
-              <item>
-                <commodity>salt_am</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.00088198336</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>lwr_cm</commodity>
-                <pref>1.0</pref>
-              </item>
-              <item>
-                <commodity>salt_cm</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-          <stream>
-            <info>
-              <mixing_ratio>0.08827330133</mixing_ratio>
-              <buf_size>1e100</buf_size>
-            </info>
-            <commodities>
-              <item>
-                <commodity>lwr_pu</commodity>
-                <pref>1.0</pref>
-              </item>
-              <item>
-                <commodity>salt_pu</commodity>
-                <pref>2.0</pref>
-              </item>
-            </commodities>
-          </stream>
-        </in_streams>
-        <out_commod>init_fuel</out_commod>
-        <out_buf_size>1e100</out_buf_size>
-        <throughput>1e100</throughput>
-      </Mixer>
-    </config>
-</facility>
-
-
-
- <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>lwr</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1110</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
-   <facility>
-    <!-- us -->
-    <!-- PWR -->
-    <name>fake_rebus</name>
-    <config>
-      <Reactor>
-        <fuel_inrecipes>  <val>uox_fuel_recipe</val>       </fuel_inrecipes>
-        <fuel_outrecipes> <val>uox_used_fuel_recipe</val>  </fuel_outrecipes>
-        <fuel_incommods>  <val>uox</val>                   </fuel_incommods>
-        <fuel_outcommods> <val>uox_waste</val>             </fuel_outcommods>
-        <cycle_time>18</cycle_time>
-        <refuel_time>1</refuel_time>
-        <assem_size>29565</assem_size>
-        <n_assem_core>3</n_assem_core>
-        <n_assem_batch>1</n_assem_batch>
-        <power_cap>1628</power_cap>
-      </Reactor>
-    </config>
-  </facility>
-
-
-<facility>
-  <name>rebus</name>
-  <config>
-    <saltproc_reactor>
-      <init_fuel_commod>init_fuel</init_fuel_commod>
-      <final_fuel_commod>fuel_out</final_fuel_commod>
-      <fill_commod>dep_u</fill_commod>
-      <fissile_out_commod>fissile_out</fissile_out_commod>
-      <waste_commod>waste</waste_commod>
-      <db_path>./30_rebus_tru.hdf5</db_path>
-      <power_cap>1628</power_cap>
-    </saltproc_reactor>
-  </config>
-</facility>
-
-<facility>
-        <!-- Stores uox unf for 48 months (4years) -->
-    <name>uox_storage</name>
-    <config>
-        <Storage>
-            <in_commods>
-                <val>uox_waste</val>
-            </in_commods>
-            <residence_time>72</residence_time>
-            <out_commods>
-                <val>cooled_uox</val>
-            </out_commods>
-        </Storage>
-    </config>
-</facility>
-
-
-  <facility>
-    <name>enrichment</name>
-    <config>
-      <Enrichment>
-        <feed_commod>natl_u</feed_commod>
-        <feed_recipe>natl_u_recipe</feed_recipe>
-        <product_commod>uox</product_commod>
-        <tails_assay>0.003</tails_assay>
-        <tails_commod>tailings</tails_commod>
-        <swu_capacity>1e100</swu_capacity>
-        <initial_feed>1e100</initial_feed>
-      </Enrichment>
-    </config>
-  </facility>
-
-
-  <facility>
-    <name>nat_u_source</name>
-    <config>
-        <Source>
-        <outcommod>natl_u</outcommod>
-        <outrecipe>natl_u_recipe</outrecipe>
-        <throughput>1e100</throughput>
-      </Source>
-    </config>
-  </facility>
-
-<facility>
-  <name>tails_storage</name>
-  <config>
-    <Storage>
-      <in_commods>
-        <val>tailings</val>
-      </in_commods>
-      <out_commods>
-        <val>dep_u</val>
-      </out_commods>
-    </Storage>
-  </config>
-</facility>
-
-
-  <facility>
-    <name>unf_source</name>
-    <config>
-      <Source>
-        <outcommod>pre_2013_unf</outcommod>
-        <outrecipe>udb_precise</outrecipe>
-        <throughput>1e100</throughput>
-        <inventory_size>68072030</inventory_size>
-      </Source>
-    </config>
-  </facility>
-
-  <facility>
-  <name>fill_commod_source</name>
-  <config>
-    <Source>
-      <outcommod>dep_u</outcommod>
-      <outrecipe>depleted_u</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-  <facility>
-    <name>SomeSink</name>
-    <config>
-      <Sink>
-        <in_commods>
-          <val>waste</val>
-          <val>fissile_out</val>
-          <val>reprocess_waste</val>
-          <val>salt_reprocess_waste</val>
-          <val>lwr_u</val>
-          <val>salt_u</val>
-        </in_commods>
-        <capacity>1e100</capacity>
-      </Sink>
-    </config>
-  </facility>
-
-  <facility>
-    <name>na_source</name>
-    <config>
-      <Source>
-        <outcommod>na</outcommod>
-        <outrecipe>na_recipe</outrecipe>
-        <throughput>1e100</throughput>
-      </Source>
-    </config>
-  </facility>
-
-<facility>
-  <name>cl_source</name>
-  <config>
-    <Source>
-      <outcommod>cl</outcommod>
-      <outrecipe>cl_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-<facility>
-  <name>na_source2</name>
-  <config>
-    <Source>
-      <outcommod>na2</outcommod>
-      <outrecipe>na_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-<facility>
-  <name>cl_source2</name>
-  <config>
-    <Source>
-      <outcommod>cl2</outcommod>
-      <outrecipe>cl_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-<facility>
-  <name>init_fuel_source</name>
-  <config>
-    <Source>
-      <outcommod>init_fuel</outcommod>
-      <outrecipe>init_salt_recipe</outrecipe>
-      <throughput>1e100</throughput>
-    </Source>
-  </config>
-</facility>
-
-  <xi:include href='./reactor_with_coordinates.xml' xpointer='xpointer(/root/facility)'/>
-
-  <region>
-    <name>us</name>
-    <config><NullRegion/></config>
-    <institution>
-      <name>us_government</name>
-      <config>
-        <DeployInst>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/prototypes)'/>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/build_times)'/>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/n_build)'/>
-            <xi:include href='./depinst.xml' xpointer='xpointer(/root/lifetimes)'/>
-        </DeployInst>
-      </config>
-    </institution>
-
-    <institution>
-    <name>lwr_inst</name>
-    <config>
-      <DeployInst>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/prototypes)'/>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/build_times)'/>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/n_build)'/>
-        <xi:include href='./lwr_dep.xml' xpointer='xpointer(/root/lifetimes)'/>
-      </DeployInst>
-    </config>
-  </institution>
-
-  <institution>
-  <name>rebus_inst</name>
-    <config>
-    <DeployInst>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/prototypes)'/>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/build_times)'/>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/n_build)'/>
-      <xi:include href='./rebus_dep_fourty.xml' xpointer='xpointer(/root/lifetimes)'/>
-    </DeployInst>
-    </config>
-  </institution>
-
- </region>
-
-
-<region>
-    <name>Sink_Source</name>
-    <config><NullRegion/></config>
-        <institution>
-         <name>Sink_Source_Inst</name>
-         <config><NullInst/></config>
-        <initialfacilitylist>
-          <entry>
-            <number>1</number>
-            <prototype>nat_u_source</prototype>
-          </entry>
-          
-          <entry>
-            <number>1</number>
-            <prototype>enrichment</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>SomeSink</prototype>
-          </entry>
-
-
-          <entry>
-            <number>1</number>
-            <prototype>salt_reprocessing</prototype>
-          </entry>
-
-          
-          <entry>
-            <number>1</number>
-            <prototype>reprocessing</prototype>
-          </entry>
-
-          <entry>
-            <number>1</number>
-            <prototype>rebus_fuel_fab_lwr</prototype>
-          </entry>
-
-          <entry>
-            <number>1</number>
-            <prototype>uox_storage</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>unf_source</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>tails_storage</prototype>
-          </entry>
-
-<!--
-          <entry>
-            <number>1</number>
-            <prototype>init_fuel_source</prototype>
-          </entry>
-
-          <entry>
-            <number>1</number>
-            <prototype>fill_commod_source</prototype>
-          </entry>
--->
-          <entry>
-            <number>1</number>
-            <prototype>cl_source</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>na_source</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>cl_source2</prototype>
-          </entry>
-          <entry>
-            <number>1</number>
-            <prototype>na_source2</prototype>
-          </entry>
-
-        </initialfacilitylist>
-        </institution>
-</region>
-<xi:include href='recipes.xml' xpointer='xpointer(/root/recipe)'/>
-</simulation>
\ No newline at end of file
diff --git a/input/master_us/rebus_dep_fourty.xml b/input/master_us/rebus_dep_fourty.xml
deleted file mode 100644
index 95069f3f..00000000
--- a/input/master_us/rebus_dep_fourty.xml
+++ /dev/null
@@ -1,366 +0,0 @@
-<root>
-<prototypes>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-		<val>rebus</val>
-</prototypes>
-<build_times>
-		<val>443</val>
-		<val>444</val>
-		<val>513</val>
-		<val>757</val>
-		<val>923</val>
-		<val>924</val>
-		<val>993</val>
-		<val>1159</val>
-		<val>1161</val>
-		<val>1173</val>
-		<val>1178</val>
-		<val>1189</val>
-		<val>1190</val>
-		<val>1199</val>
-		<val>1201</val>
-		<val>1202</val>
-		<val>1205</val>
-		<val>1207</val>
-		<val>1212</val>
-		<val>1214</val>
-		<val>1216</val>
-		<val>1217</val>
-		<val>1218</val>
-		<val>1221</val>
-		<val>1224</val>
-		<val>1236</val>
-		<val>1237</val>
-		<val>1240</val>
-		<val>1244</val>
-		<val>1252</val>
-		<val>1260</val>
-		<val>1263</val>
-		<val>1286</val>
-		<val>1287</val>
-		<val>1297</val>
-		<val>1300</val>
-		<val>1311</val>
-		<val>1313</val>
-		<val>1315</val>
-		<val>1321</val>
-		<val>1330</val>
-		<val>1331</val>
-		<val>1333</val>
-		<val>1338</val>
-		<val>1341</val>
-		<val>1342</val>
-		<val>1343</val>
-		<val>1346</val>
-		<val>1347</val>
-		<val>1351</val>
-		<val>1354</val>
-		<val>1357</val>
-		<val>1359</val>
-		<val>1365</val>
-		<val>1367</val>
-		<val>1370</val>
-		<val>1372</val>
-		<val>1375</val>
-		<val>1379</val>
-		<val>1391</val>
-		<val>1396</val>
-		<val>1403</val>
-		<val>1404</val>
-		<val>1473</val>
-		<val>1639</val>
-		<val>1641</val>
-		<val>1653</val>
-		<val>1658</val>
-		<val>1669</val>
-		<val>1670</val>
-		<val>1679</val>
-		<val>1681</val>
-		<val>1682</val>
-		<val>1685</val>
-		<val>1687</val>
-		<val>1692</val>
-		<val>1694</val>
-		<val>1696</val>
-		<val>1697</val>
-		<val>1698</val>
-		<val>1701</val>
-		<val>1704</val>
-		<val>1716</val>
-		<val>1717</val>
-		<val>1720</val>
-		<val>1724</val>
-		<val>1732</val>
-		<val>1740</val>
-		<val>1743</val>
-</build_times>
-<n_build>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>2</val>
-		<val>1</val>
-		<val>1</val>
-		<val>1</val>
-</n_build>
-<lifetimes>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-		<val>480</val>
-</lifetimes>
-</root>

From f7f32ff552a3457b40218332b78427b497954c46 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Thu, 25 Oct 2018 13:40:02 -0500
Subject: [PATCH 33/35] write_input -> cyclus_input_gen

---
 input/master_us/get_deployment.ipynb |  6 +--
 scripts/tests/test_write_input.py    | 81 ----------------------------
 2 files changed, 3 insertions(+), 84 deletions(-)
 delete mode 100644 scripts/tests/test_write_input.py

diff --git a/input/master_us/get_deployment.ipynb b/input/master_us/get_deployment.ipynb
index 4eacbd1b..281acae0 100644
--- a/input/master_us/get_deployment.ipynb
+++ b/input/master_us/get_deployment.ipynb
@@ -39,13 +39,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Usage: write_input.main([csv],[init_date], [duration], [country_list], [output_file_name])\n"
+      "Usage: cyclus_input_gen.main([csv],[init_date], [duration], [country_list], [output_file_name])\n"
      ]
     }
    ],
    "source": [
-    "import write_input.demand_deploy as dd\n",
-    "import write_input.from_pris as fp\n",
+    "import cyclus_input_gen.demand_deploy as dd\n",
+    "import cyclus_input_gen.from_pris as fp\n",
     "import analysis as an"
    ]
   },
diff --git a/scripts/tests/test_write_input.py b/scripts/tests/test_write_input.py
deleted file mode 100644
index 1fcedf39..00000000
--- a/scripts/tests/test_write_input.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import numpy as np
-import collections
-import os
-import sys
-path = os.path.realpath(__file__)
-sys.path.append(os.path.dirname(os.path.dirname(path)))
-import write_input as wi
-
-dir = os.path.dirname(__file__)
-test_database_path = os.path.join(dir, 'test_database.csv')
-
-
-def test_read_csv():
-    """Test if read_csv returns the correct list with key"""
-    reactor_array = wi.read_csv(test_database_path)
-    print(type(reactor_array))
-    test_dict = {}
-    test_dict['country1'] = reactor_array[0]['country'].decode('utf-8')
-    test_dict['country3'] = reactor_array[2]['country'].decode('utf-8')
-    # makes sure the test reactor is filtered out
-    test_dict['capacity1'] = reactor_array[0]['net_elec_capacity']
-    test_dict['capacity3'] = reactor_array[2]['net_elec_capacity']
-
-    answer_dict = {}
-    answer_dict['country1'] = 'France'
-    answer_dict['country3'] = 'Czech_Republic'
-    answer_dict['capacity1'] = 1495
-    answer_dict['capacity3'] = 1200
-    assert test_dict == answer_dict
-
-
-def test_filter_test_reactors():
-    """Test if filter__reactors filters reactors with
-       net electricity capacity less than 100Mwe"""
-    test = np.array([('foo', 85), ('bar', 1200)],
-                    dtype=[('name', 'S10'), ('net_elec_capacity', 'i4')])
-    answer = np.array([('bar', 1200)],
-                      dtype=[('name', 'S10'), ('net_elec_capacity', 'i4')])
-    assert wi.filter_test_reactors(test) == answer
-
-
-def test_get_ymd_ceil():
-    """Test if get_ymd gets the correct year, month, and day"""
-    ceil = wi.get_ymd(20180225)
-    answer = (2018, 3)
-    assert ceil == answer
-
-
-def test_get_ymd_floor():
-    """Test if get_ymd gets the correct year, month, and day"""
-    floor = wi.get_ymd(20180301)
-    answer = (2018, 3)
-    assert floor == answer
-
-
-def test_get_lifetime():
-    """Test if get_lifetime calculates the right lifetime"""
-    start_date = 19700101
-    end_date = 20070225
-    lifetime = 446
-    assert wi.get_lifetime(start_date, end_date) == lifetime
-
-
-def test_get_lifetime_no_endtime():
-    """Test if get_lifetime calculates the right lifetime
-       given no endtime"""
-    assert wi.get_lifetime(19700101, -1) == 720
-
-
-def test_get_entrytime():
-    """Test if get_entrytime calculates the correct entrytime"""
-    init_date = 19700101
-    start_date = 20070225
-    assert wi.get_entrytime(init_date, start_date) == 446
-
-
-def test_refine_name():
-    """Test if name is refined correctly"""
-    string = 'Charles(Bukowski)'
-    string = string.encode('utf-8')
-    assert wi.refine_name(string) == 'Charles'

From a7a4c8debe8be1c0b1d0b19e9b8e702af3c4d7d1 Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Mon, 5 Nov 2018 13:13:56 -0600
Subject: [PATCH 34/35] Change directory name

---
 input/{master_us => us_transition_to_msr}/analysis.ipynb          | 0
 input/{master_us => us_transition_to_msr}/depinst.xml             | 0
 input/{master_us => us_transition_to_msr}/get_deployment.ipynb    | 0
 input/{master_us => us_transition_to_msr}/input.xml               | 0
 input/{master_us => us_transition_to_msr}/lwr_dep.xml             | 0
 .../reactor_with_coordinates.xml                                  | 0
 input/{master_us => us_transition_to_msr}/rebus_dep.xml           | 0
 .../{master_us => us_transition_to_msr}/rebus_init_mass_comp.csv  | 0
 input/{master_us => us_transition_to_msr}/recipes.xml             | 0
 9 files changed, 0 insertions(+), 0 deletions(-)
 rename input/{master_us => us_transition_to_msr}/analysis.ipynb (100%)
 rename input/{master_us => us_transition_to_msr}/depinst.xml (100%)
 rename input/{master_us => us_transition_to_msr}/get_deployment.ipynb (100%)
 rename input/{master_us => us_transition_to_msr}/input.xml (100%)
 rename input/{master_us => us_transition_to_msr}/lwr_dep.xml (100%)
 rename input/{master_us => us_transition_to_msr}/reactor_with_coordinates.xml (100%)
 rename input/{master_us => us_transition_to_msr}/rebus_dep.xml (100%)
 rename input/{master_us => us_transition_to_msr}/rebus_init_mass_comp.csv (100%)
 rename input/{master_us => us_transition_to_msr}/recipes.xml (100%)

diff --git a/input/master_us/analysis.ipynb b/input/us_transition_to_msr/analysis.ipynb
similarity index 100%
rename from input/master_us/analysis.ipynb
rename to input/us_transition_to_msr/analysis.ipynb
diff --git a/input/master_us/depinst.xml b/input/us_transition_to_msr/depinst.xml
similarity index 100%
rename from input/master_us/depinst.xml
rename to input/us_transition_to_msr/depinst.xml
diff --git a/input/master_us/get_deployment.ipynb b/input/us_transition_to_msr/get_deployment.ipynb
similarity index 100%
rename from input/master_us/get_deployment.ipynb
rename to input/us_transition_to_msr/get_deployment.ipynb
diff --git a/input/master_us/input.xml b/input/us_transition_to_msr/input.xml
similarity index 100%
rename from input/master_us/input.xml
rename to input/us_transition_to_msr/input.xml
diff --git a/input/master_us/lwr_dep.xml b/input/us_transition_to_msr/lwr_dep.xml
similarity index 100%
rename from input/master_us/lwr_dep.xml
rename to input/us_transition_to_msr/lwr_dep.xml
diff --git a/input/master_us/reactor_with_coordinates.xml b/input/us_transition_to_msr/reactor_with_coordinates.xml
similarity index 100%
rename from input/master_us/reactor_with_coordinates.xml
rename to input/us_transition_to_msr/reactor_with_coordinates.xml
diff --git a/input/master_us/rebus_dep.xml b/input/us_transition_to_msr/rebus_dep.xml
similarity index 100%
rename from input/master_us/rebus_dep.xml
rename to input/us_transition_to_msr/rebus_dep.xml
diff --git a/input/master_us/rebus_init_mass_comp.csv b/input/us_transition_to_msr/rebus_init_mass_comp.csv
similarity index 100%
rename from input/master_us/rebus_init_mass_comp.csv
rename to input/us_transition_to_msr/rebus_init_mass_comp.csv
diff --git a/input/master_us/recipes.xml b/input/us_transition_to_msr/recipes.xml
similarity index 100%
rename from input/master_us/recipes.xml
rename to input/us_transition_to_msr/recipes.xml

From 7e91395983259fcd7c453bd280d7611792a95e5e Mon Sep 17 00:00:00 2001
From: jbae11 <jbae11@illinois.edu>
Date: Mon, 5 Nov 2018 13:16:33 -0600
Subject: [PATCH 35/35] change ipynb name

---
 .../{get_deployment.ipynb => get_input_file.ipynb}                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename input/us_transition_to_msr/{get_deployment.ipynb => get_input_file.ipynb} (100%)

diff --git a/input/us_transition_to_msr/get_deployment.ipynb b/input/us_transition_to_msr/get_input_file.ipynb
similarity index 100%
rename from input/us_transition_to_msr/get_deployment.ipynb
rename to input/us_transition_to_msr/get_input_file.ipynb