diff --git a/doc/ipynb/standard_problem_4.ipynb b/doc/ipynb/standard_problem_4.ipynb
index 4973a90c..c5c58132 100644
--- a/doc/ipynb/standard_problem_4.ipynb
+++ b/doc/ipynb/standard_problem_4.ipynb
@@ -25,7 +25,8 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from math import sqrt"
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
    ]
   },
   {
@@ -126,6 +127,8 @@
     "from fidimag.micro import Sim, UniformExchange, Demag, Zeeman, TimeZeeman\n",
     "\n",
     "sim = Sim(mesh)  # create simulation object\n",
+    "\n",
+    "sim.set_tols(rtol=1e-10, atol=1e-10)\n",
     "sim.Ms = Ms\n",
     "sim.alpha = 0.5  # large value since the magnetisation dynamics is not important in the relexation stage\n",
     "sim.gamma = gamma\n",
@@ -133,787 +136,524 @@
     "\n",
     "\n",
     "# Starting magnetisation.\n",
-    "sim.set_m((1, 0.25, 0.1))"
+    "sim.set_m((1, 0.25, 0.1))\n",
+    "sim.add(UniformExchange(A=A))\n",
+    "sim.add(Demag())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The energies are added to the simulation. We use `TimeZeeman` to model a decaying applied field."
+    "We have ignored the decaying external field. Finally, the system can be relaxed and the obtained equilibrium configuration saved, so that it can be used as an initial state for simulating magnetisation dynamics."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": false,
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step=1, time=1e-13, max_dmdt=517 ode_step=0\n",
+      "step=2, time=2e-13, max_dmdt=510 ode_step=1.8e-14\n",
+      "step=3, time=3e-13, max_dmdt=502 ode_step=4.38e-14\n",
+      "step=4, time=4e-13, max_dmdt=495 ode_step=4.38e-14\n",
+      "step=5, time=5e-13, max_dmdt=489 ode_step=7.03e-14\n",
+      "step=6, time=6e-13, max_dmdt=482 ode_step=7.03e-14\n",
+      "step=7, time=7e-13, max_dmdt=476 ode_step=7.03e-14\n",
+      "step=8, time=8e-13, max_dmdt=470 ode_step=7.03e-14\n",
+      "step=9, time=9e-13, max_dmdt=464 ode_step=7.03e-14\n",
+      "step=10, time=1e-12, max_dmdt=459 ode_step=7.03e-14\n",
+      "step=11, time=1.14e-12, max_dmdt=453 ode_step=1.43e-13\n",
+      "step=12, time=1.29e-12, max_dmdt=445 ode_step=1.43e-13\n",
+      "step=13, time=1.43e-12, max_dmdt=439 ode_step=1.43e-13\n",
+      "step=14, time=1.57e-12, max_dmdt=433 ode_step=1.43e-13\n",
+      "step=15, time=1.72e-12, max_dmdt=428 ode_step=1.43e-13\n",
+      "step=16, time=1.86e-12, max_dmdt=422 ode_step=1.43e-13\n",
+      "step=17, time=2e-12, max_dmdt=417 ode_step=1.43e-13\n",
+      "step=18, time=2.15e-12, max_dmdt=412 ode_step=1.43e-13\n",
+      "step=19, time=2.29e-12, max_dmdt=407 ode_step=1.43e-13\n",
+      "step=20, time=2.51e-12, max_dmdt=401 ode_step=2.15e-13\n",
+      "step=21, time=2.72e-12, max_dmdt=394 ode_step=2.15e-13\n",
+      "step=22, time=2.94e-12, max_dmdt=387 ode_step=2.15e-13\n",
+      "step=23, time=3.15e-12, max_dmdt=381 ode_step=2.15e-13\n",
+      "step=24, time=3.37e-12, max_dmdt=375 ode_step=2.15e-13\n",
+      "step=25, time=3.58e-12, max_dmdt=369 ode_step=2.15e-13\n",
+      "step=26, time=3.8e-12, max_dmdt=364 ode_step=2.15e-13\n",
+      "step=27, time=4.01e-12, max_dmdt=358 ode_step=2.15e-13\n",
+      "step=28, time=4.23e-12, max_dmdt=353 ode_step=2.15e-13\n",
+      "step=29, time=4.44e-12, max_dmdt=348 ode_step=2.15e-13\n",
+      "step=30, time=4.66e-12, max_dmdt=343 ode_step=2.15e-13\n",
+      "step=31, time=4.87e-12, max_dmdt=338 ode_step=2.15e-13\n",
+      "step=32, time=5.09e-12, max_dmdt=333 ode_step=2.15e-13\n",
+      "step=33, time=5.3e-12, max_dmdt=328 ode_step=2.15e-13\n",
+      "step=34, time=5.52e-12, max_dmdt=324 ode_step=2.15e-13\n",
+      "step=35, time=5.73e-12, max_dmdt=320 ode_step=2.15e-13\n",
+      "step=36, time=5.95e-12, max_dmdt=315 ode_step=2.15e-13\n",
+      "step=37, time=6.16e-12, max_dmdt=311 ode_step=2.15e-13\n",
+      "step=38, time=6.38e-12, max_dmdt=307 ode_step=2.15e-13\n",
+      "step=39, time=6.59e-12, max_dmdt=303 ode_step=2.15e-13\n",
+      "step=40, time=6.81e-12, max_dmdt=299 ode_step=2.15e-13\n",
+      "step=41, time=7.02e-12, max_dmdt=295 ode_step=2.15e-13\n",
+      "step=42, time=7.24e-12, max_dmdt=291 ode_step=2.15e-13\n",
+      "step=43, time=7.45e-12, max_dmdt=288 ode_step=2.15e-13\n",
+      "step=44, time=7.78e-12, max_dmdt=283 ode_step=3.29e-13\n",
+      "step=45, time=8.11e-12, max_dmdt=278 ode_step=3.29e-13\n",
+      "step=46, time=8.44e-12, max_dmdt=273 ode_step=3.29e-13\n",
+      "step=47, time=8.77e-12, max_dmdt=268 ode_step=3.29e-13\n",
+      "step=48, time=9.09e-12, max_dmdt=263 ode_step=3.29e-13\n",
+      "step=49, time=9.42e-12, max_dmdt=258 ode_step=3.29e-13\n",
+      "step=50, time=9.75e-12, max_dmdt=254 ode_step=3.29e-13\n",
+      "step=51, time=1.01e-11, max_dmdt=249 ode_step=3.29e-13\n",
+      "step=52, time=1.04e-11, max_dmdt=245 ode_step=3.29e-13\n",
+      "step=53, time=1.07e-11, max_dmdt=240 ode_step=3.29e-13\n",
+      "step=54, time=1.11e-11, max_dmdt=236 ode_step=3.29e-13\n",
+      "step=55, time=1.14e-11, max_dmdt=232 ode_step=3.29e-13\n",
+      "step=56, time=1.17e-11, max_dmdt=229 ode_step=3.29e-13\n",
+      "step=57, time=1.21e-11, max_dmdt=225 ode_step=3.29e-13\n",
+      "step=58, time=1.24e-11, max_dmdt=221 ode_step=3.29e-13\n",
+      "step=59, time=1.27e-11, max_dmdt=218 ode_step=3.29e-13\n",
+      "step=60, time=1.3e-11, max_dmdt=214 ode_step=3.29e-13\n",
+      "step=61, time=1.34e-11, max_dmdt=211 ode_step=3.29e-13\n",
+      "step=62, time=1.37e-11, max_dmdt=208 ode_step=3.29e-13\n",
+      "step=63, time=1.4e-11, max_dmdt=204 ode_step=3.29e-13\n",
+      "step=64, time=1.45e-11, max_dmdt=201 ode_step=4.94e-13\n",
+      "step=65, time=1.5e-11, max_dmdt=196 ode_step=4.94e-13\n",
+      "step=66, time=1.55e-11, max_dmdt=192 ode_step=4.94e-13\n",
+      "step=67, time=1.6e-11, max_dmdt=188 ode_step=4.94e-13\n",
+      "step=68, time=1.65e-11, max_dmdt=184 ode_step=4.94e-13\n",
+      "step=69, time=1.7e-11, max_dmdt=180 ode_step=4.94e-13\n",
+      "step=70, time=1.75e-11, max_dmdt=177 ode_step=4.94e-13\n",
+      "step=71, time=1.8e-11, max_dmdt=173 ode_step=4.94e-13\n",
+      "step=72, time=1.85e-11, max_dmdt=170 ode_step=4.94e-13\n",
+      "step=73, time=1.9e-11, max_dmdt=167 ode_step=4.94e-13\n",
+      "step=74, time=1.95e-11, max_dmdt=164 ode_step=4.94e-13\n",
+      "step=75, time=2e-11, max_dmdt=161 ode_step=4.94e-13\n",
+      "step=76, time=2.04e-11, max_dmdt=159 ode_step=4.94e-13\n",
+      "step=77, time=2.09e-11, max_dmdt=156 ode_step=4.94e-13\n",
+      "step=78, time=2.17e-11, max_dmdt=153 ode_step=7.48e-13\n",
+      "step=79, time=2.24e-11, max_dmdt=150 ode_step=7.48e-13\n",
+      "step=80, time=2.32e-11, max_dmdt=147 ode_step=7.48e-13\n",
+      "step=81, time=2.39e-11, max_dmdt=144 ode_step=7.48e-13\n",
+      "step=82, time=2.47e-11, max_dmdt=141 ode_step=7.48e-13\n",
+      "step=83, time=2.54e-11, max_dmdt=138 ode_step=7.48e-13\n",
+      "step=84, time=2.62e-11, max_dmdt=136 ode_step=7.48e-13\n",
+      "step=85, time=2.69e-11, max_dmdt=134 ode_step=7.48e-13\n",
+      "step=86, time=2.77e-11, max_dmdt=131 ode_step=7.48e-13\n",
+      "step=87, time=2.84e-11, max_dmdt=129 ode_step=7.48e-13\n",
+      "step=88, time=2.92e-11, max_dmdt=127 ode_step=7.48e-13\n",
+      "step=89, time=2.99e-11, max_dmdt=125 ode_step=7.48e-13\n",
+      "step=90, time=3.07e-11, max_dmdt=124 ode_step=7.48e-13\n",
+      "step=91, time=3.14e-11, max_dmdt=122 ode_step=7.48e-13\n",
+      "step=92, time=3.22e-11, max_dmdt=120 ode_step=7.48e-13\n",
+      "step=93, time=3.29e-11, max_dmdt=119 ode_step=7.48e-13\n",
+      "step=94, time=3.37e-11, max_dmdt=118 ode_step=7.48e-13\n",
+      "step=95, time=3.44e-11, max_dmdt=116 ode_step=7.48e-13\n",
+      "step=96, time=3.52e-11, max_dmdt=115 ode_step=7.48e-13\n",
+      "step=97, time=3.59e-11, max_dmdt=114 ode_step=7.48e-13\n",
+      "step=98, time=3.67e-11, max_dmdt=113 ode_step=7.48e-13\n",
+      "step=99, time=3.74e-11, max_dmdt=112 ode_step=7.48e-13\n",
+      "step=100, time=3.82e-11, max_dmdt=111 ode_step=7.48e-13\n",
+      "step=101, time=3.89e-11, max_dmdt=110 ode_step=7.48e-13\n",
+      "step=102, time=3.97e-11, max_dmdt=109 ode_step=7.48e-13\n",
+      "step=103, time=4.04e-11, max_dmdt=108 ode_step=7.48e-13\n",
+      "step=104, time=4.12e-11, max_dmdt=107 ode_step=7.48e-13\n",
+      "step=105, time=4.19e-11, max_dmdt=107 ode_step=7.48e-13\n",
+      "step=106, time=4.26e-11, max_dmdt=106 ode_step=7.48e-13\n",
+      "step=107, time=4.34e-11, max_dmdt=105 ode_step=7.48e-13\n",
+      "step=108, time=4.41e-11, max_dmdt=104 ode_step=7.48e-13\n",
+      "step=109, time=4.49e-11, max_dmdt=104 ode_step=7.48e-13\n",
+      "step=110, time=4.56e-11, max_dmdt=103 ode_step=7.48e-13\n",
+      "step=111, time=4.64e-11, max_dmdt=102 ode_step=7.48e-13\n",
+      "step=112, time=4.71e-11, max_dmdt=102 ode_step=7.48e-13\n",
+      "step=113, time=4.79e-11, max_dmdt=101 ode_step=7.48e-13\n",
+      "step=114, time=4.86e-11, max_dmdt=101 ode_step=7.48e-13\n",
+      "step=115, time=4.94e-11, max_dmdt=100 ode_step=7.48e-13\n",
+      "step=116, time=5.01e-11, max_dmdt=99.6 ode_step=7.48e-13\n",
+      "step=117, time=5.09e-11, max_dmdt=99.1 ode_step=7.48e-13\n",
+      "step=118, time=5.16e-11, max_dmdt=98.6 ode_step=7.48e-13\n",
+      "step=119, time=5.24e-11, max_dmdt=98.2 ode_step=7.48e-13\n",
+      "step=120, time=5.31e-11, max_dmdt=97.7 ode_step=7.48e-13\n",
+      "step=121, time=5.39e-11, max_dmdt=97.2 ode_step=7.48e-13\n",
+      "step=122, time=5.46e-11, max_dmdt=96.8 ode_step=7.48e-13\n",
+      "step=123, time=5.54e-11, max_dmdt=96.3 ode_step=7.48e-13\n",
+      "step=124, time=5.61e-11, max_dmdt=95.9 ode_step=7.48e-13\n",
+      "step=125, time=5.69e-11, max_dmdt=95.5 ode_step=7.48e-13\n",
+      "step=126, time=5.8e-11, max_dmdt=95 ode_step=1.14e-12\n",
+      "step=127, time=5.91e-11, max_dmdt=94.4 ode_step=1.14e-12\n",
+      "step=128, time=6.03e-11, max_dmdt=93.8 ode_step=1.14e-12\n",
+      "step=129, time=6.14e-11, max_dmdt=93.2 ode_step=1.14e-12\n",
+      "step=130, time=6.26e-11, max_dmdt=92.7 ode_step=1.14e-12\n",
+      "step=131, time=6.37e-11, max_dmdt=92.2 ode_step=1.14e-12\n",
+      "step=132, time=6.48e-11, max_dmdt=91.6 ode_step=1.14e-12\n",
+      "step=133, time=6.6e-11, max_dmdt=91.1 ode_step=1.14e-12\n",
+      "step=134, time=6.71e-11, max_dmdt=90.6 ode_step=1.14e-12\n",
+      "step=135, time=6.82e-11, max_dmdt=90.1 ode_step=1.14e-12\n",
+      "step=136, time=6.94e-11, max_dmdt=89.6 ode_step=1.14e-12\n",
+      "step=137, time=7.05e-11, max_dmdt=89.2 ode_step=1.14e-12\n",
+      "step=138, time=7.17e-11, max_dmdt=88.7 ode_step=1.14e-12\n",
+      "step=139, time=7.28e-11, max_dmdt=88.2 ode_step=1.14e-12\n",
+      "step=140, time=7.39e-11, max_dmdt=87.8 ode_step=1.14e-12\n",
+      "step=141, time=7.51e-11, max_dmdt=87.3 ode_step=1.14e-12\n",
+      "step=142, time=7.62e-11, max_dmdt=86.9 ode_step=1.14e-12\n",
+      "step=143, time=7.74e-11, max_dmdt=86.4 ode_step=1.14e-12\n",
+      "step=144, time=7.85e-11, max_dmdt=86 ode_step=1.14e-12\n",
+      "step=145, time=7.96e-11, max_dmdt=85.6 ode_step=1.14e-12\n",
+      "step=146, time=8.08e-11, max_dmdt=85.2 ode_step=1.14e-12\n",
+      "step=147, time=8.19e-11, max_dmdt=84.8 ode_step=1.14e-12\n",
+      "step=148, time=8.3e-11, max_dmdt=84.4 ode_step=1.14e-12\n",
+      "step=149, time=8.42e-11, max_dmdt=84 ode_step=1.14e-12\n",
+      "step=150, time=8.53e-11, max_dmdt=83.5 ode_step=1.14e-12\n",
+      "step=151, time=8.65e-11, max_dmdt=83.1 ode_step=1.14e-12\n",
+      "step=152, time=8.76e-11, max_dmdt=82.7 ode_step=1.14e-12\n",
+      "step=153, time=8.87e-11, max_dmdt=82.3 ode_step=1.14e-12\n",
+      "step=154, time=8.99e-11, max_dmdt=82 ode_step=1.14e-12\n",
+      "step=155, time=9.1e-11, max_dmdt=81.6 ode_step=1.14e-12\n",
+      "step=156, time=9.21e-11, max_dmdt=81.2 ode_step=1.14e-12\n",
+      "step=157, time=9.33e-11, max_dmdt=80.8 ode_step=1.14e-12\n",
+      "step=158, time=9.44e-11, max_dmdt=80.4 ode_step=1.14e-12\n",
+      "step=159, time=9.56e-11, max_dmdt=80 ode_step=1.14e-12\n",
+      "step=160, time=9.67e-11, max_dmdt=79.6 ode_step=1.14e-12\n",
+      "step=161, time=9.78e-11, max_dmdt=79.3 ode_step=1.14e-12\n",
+      "step=162, time=9.9e-11, max_dmdt=78.9 ode_step=1.14e-12\n",
+      "step=163, time=1.01e-10, max_dmdt=78.4 ode_step=1.75e-12\n",
+      "step=164, time=1.02e-10, max_dmdt=77.8 ode_step=1.75e-12\n",
+      "step=165, time=1.04e-10, max_dmdt=77.3 ode_step=1.75e-12\n",
+      "step=166, time=1.06e-10, max_dmdt=76.7 ode_step=1.75e-12\n",
+      "step=167, time=1.08e-10, max_dmdt=76.1 ode_step=1.75e-12\n",
+      "step=168, time=1.09e-10, max_dmdt=75.6 ode_step=1.75e-12\n",
+      "step=169, time=1.11e-10, max_dmdt=75 ode_step=1.75e-12\n",
+      "step=170, time=1.13e-10, max_dmdt=74.5 ode_step=1.75e-12\n",
+      "step=171, time=1.15e-10, max_dmdt=74 ode_step=1.75e-12\n",
+      "step=172, time=1.16e-10, max_dmdt=73.4 ode_step=1.75e-12\n",
+      "step=173, time=1.18e-10, max_dmdt=72.9 ode_step=1.75e-12\n",
+      "step=174, time=1.2e-10, max_dmdt=72.4 ode_step=1.75e-12\n",
+      "step=175, time=1.22e-10, max_dmdt=71.8 ode_step=1.75e-12\n",
+      "step=176, time=1.23e-10, max_dmdt=71.3 ode_step=1.75e-12\n",
+      "step=177, time=1.25e-10, max_dmdt=70.8 ode_step=1.75e-12\n",
+      "step=178, time=1.27e-10, max_dmdt=70.3 ode_step=1.75e-12\n",
+      "step=179, time=1.29e-10, max_dmdt=69.8 ode_step=1.75e-12\n",
+      "step=180, time=1.3e-10, max_dmdt=69.3 ode_step=1.75e-12\n",
+      "step=181, time=1.32e-10, max_dmdt=68.8 ode_step=1.75e-12\n",
+      "step=182, time=1.34e-10, max_dmdt=68.3 ode_step=1.75e-12\n",
+      "step=183, time=1.36e-10, max_dmdt=67.8 ode_step=1.75e-12\n",
+      "step=184, time=1.37e-10, max_dmdt=67.3 ode_step=1.75e-12\n",
+      "step=185, time=1.39e-10, max_dmdt=66.8 ode_step=1.75e-12\n",
+      "step=186, time=1.41e-10, max_dmdt=66.3 ode_step=1.75e-12\n",
+      "step=187, time=1.43e-10, max_dmdt=65.8 ode_step=1.75e-12\n",
+      "step=188, time=1.44e-10, max_dmdt=65.3 ode_step=1.75e-12\n",
+      "step=189, time=1.46e-10, max_dmdt=64.8 ode_step=1.75e-12\n",
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+      "step=386, time=2.85e-09, max_dmdt=0.468 ode_step=3.51e-11\n",
+      "step=387, time=2.89e-09, max_dmdt=0.443 ode_step=3.51e-11\n",
+      "step=388, time=2.92e-09, max_dmdt=0.42 ode_step=3.51e-11\n",
+      "step=389, time=2.96e-09, max_dmdt=0.397 ode_step=3.51e-11\n",
+      "step=390, time=2.99e-09, max_dmdt=0.376 ode_step=3.51e-11\n",
+      "step=391, time=3.03e-09, max_dmdt=0.357 ode_step=3.51e-11\n",
+      "step=392, time=3.06e-09, max_dmdt=0.338 ode_step=3.51e-11\n",
+      "step=393, time=3.1e-09, max_dmdt=0.32 ode_step=3.51e-11\n",
+      "step=394, time=3.13e-09, max_dmdt=0.303 ode_step=3.51e-11\n",
+      "step=395, time=3.17e-09, max_dmdt=0.287 ode_step=3.51e-11\n",
+      "step=396, time=3.2e-09, max_dmdt=0.272 ode_step=3.51e-11\n",
+      "step=397, time=3.24e-09, max_dmdt=0.258 ode_step=3.51e-11\n",
+      "step=398, time=3.27e-09, max_dmdt=0.245 ode_step=3.51e-11\n",
+      "step=399, time=3.31e-09, max_dmdt=0.232 ode_step=3.51e-11\n",
+      "step=400, time=3.34e-09, max_dmdt=0.22 ode_step=3.51e-11\n",
+      "step=401, time=3.38e-09, max_dmdt=0.208 ode_step=3.51e-11\n",
+      "step=402, time=3.42e-09, max_dmdt=0.197 ode_step=3.51e-11\n",
+      "step=403, time=3.45e-09, max_dmdt=0.187 ode_step=3.51e-11\n",
+      "step=404, time=3.49e-09, max_dmdt=0.177 ode_step=3.51e-11\n",
+      "step=405, time=3.52e-09, max_dmdt=0.168 ode_step=3.51e-11\n",
+      "step=406, time=3.56e-09, max_dmdt=0.159 ode_step=3.51e-11\n",
+      "step=407, time=3.59e-09, max_dmdt=0.151 ode_step=3.51e-11\n",
+      "step=408, time=3.63e-09, max_dmdt=0.143 ode_step=3.51e-11\n",
+      "step=409, time=3.66e-09, max_dmdt=0.136 ode_step=3.51e-11\n",
+      "step=410, time=3.7e-09, max_dmdt=0.129 ode_step=3.51e-11\n",
+      "step=411, time=3.76e-09, max_dmdt=0.12 ode_step=5.91e-11\n",
+      "step=412, time=3.81e-09, max_dmdt=0.11 ode_step=5.91e-11\n",
+      "step=413, time=3.87e-09, max_dmdt=0.101 ode_step=5.91e-11\n",
+      "step=414, time=3.93e-09, max_dmdt=0.092 ode_step=5.91e-11\n",
+      "step=415, time=3.99e-09, max_dmdt=0.0841 ode_step=5.91e-11\n",
+      "step=416, time=4.05e-09, max_dmdt=0.077 ode_step=5.91e-11\n",
+      "step=417, time=4.11e-09, max_dmdt=0.0705 ode_step=5.91e-11\n",
+      "step=418, time=4.17e-09, max_dmdt=0.0645 ode_step=5.91e-11\n",
+      "step=419, time=4.23e-09, max_dmdt=0.059 ode_step=5.91e-11\n",
+      "step=420, time=4.29e-09, max_dmdt=0.054 ode_step=5.91e-11\n",
+      "step=421, time=4.35e-09, max_dmdt=0.0494 ode_step=5.91e-11\n",
+      "step=422, time=4.41e-09, max_dmdt=0.0453 ode_step=5.91e-11\n",
+      "step=423, time=4.47e-09, max_dmdt=0.0414 ode_step=5.91e-11\n",
+      "step=424, time=4.52e-09, max_dmdt=0.0379 ode_step=5.91e-11\n",
+      "step=425, time=4.58e-09, max_dmdt=0.0347 ode_step=5.91e-11\n",
+      "step=426, time=4.64e-09, max_dmdt=0.0318 ode_step=5.91e-11\n",
+      "step=427, time=4.7e-09, max_dmdt=0.0291 ode_step=5.91e-11\n",
+      "step=428, time=4.76e-09, max_dmdt=0.0267 ode_step=5.91e-11\n",
+      "step=429, time=4.82e-09, max_dmdt=0.0244 ode_step=5.91e-11\n",
+      "step=430, time=4.88e-09, max_dmdt=0.0224 ode_step=5.91e-11\n",
+      "step=431, time=4.94e-09, max_dmdt=0.0205 ode_step=5.91e-11\n",
+      "step=432, time=5e-09, max_dmdt=0.0187 ode_step=5.91e-11\n",
+      "step=433, time=5.09e-09, max_dmdt=0.0168 ode_step=8.9e-11\n",
+      "step=434, time=5.18e-09, max_dmdt=0.0147 ode_step=8.9e-11\n",
+      "step=435, time=5.26e-09, max_dmdt=0.0129 ode_step=8.9e-11\n",
+      "step=436, time=5.35e-09, max_dmdt=0.0113 ode_step=8.9e-11\n",
+      "step=437, time=5.44e-09, max_dmdt=0.0099 ode_step=8.9e-11\n"
+     ]
+    }
+   ],
    "source": [
-    "sim.add(UniformExchange(A=A))\n",
-    "sim.add(Demag())\n",
-    "\n",
-    "H0 = [Ms/sqrt(3) for _ in range(3)]\n",
-    "Ht = lambda t: 1 - t / 0.5e-9 if t < 0.5e-9 else 0\n",
-    "sim.add(TimeZeeman(H0, Ht))  # saturating field which is reduced over 0.5 nanoseconds"
+    "# PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "sim.relax(dt=1e-13, stopping_dmdt=0.01, max_steps=5000, save_m_steps=None, save_vtk_steps=None);\n",
+    "np.save(\"m0.npy\", sim.spin)  # save equilibrium configuration"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, the system can be relaxed and the obtained equilibrium configuration saved, so that it can be used as an initial state for simulating magnetisation dynamics."
+    "We not plot the magnetisation configuration,"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false,
-    "scrolled": true
+    "collapsed": false
    },
    "outputs": [
+    {
+     "data": {
+      "image/png": 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aB+yq0+MU1tSMprQ01c0pvPXWD/nss0NoNJJLS51Ce7t9wK5+PU5hdXUWJSWp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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fc3e0ada050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step=1, time=1e-13, max_dmdt=2617.79 ode_step=0\n",
-      "step=2, time=2e-13, max_dmdt=2587.93 ode_step=2.40116e-14\n",
-      "step=3, time=3e-13, max_dmdt=2558.71 ode_step=3.70756e-14\n",
-      "step=4, time=4e-13, max_dmdt=2530.12 ode_step=5.57677e-14\n",
-      "step=5, time=5e-13, max_dmdt=2508.12 ode_step=5.57677e-14\n",
-      "step=6, time=6e-13, max_dmdt=2486.88 ode_step=5.57677e-14\n",
-      "step=7, time=7e-13, max_dmdt=2465.86 ode_step=5.57677e-14\n",
-      "step=8, time=8e-13, max_dmdt=2445.06 ode_step=9.07016e-14\n",
-      "step=9, time=9e-13, max_dmdt=2424.46 ode_step=9.07016e-14\n",
-      "step=10, time=1e-12, max_dmdt=2404.07 ode_step=9.07016e-14\n",
-      "step=11, time=1.1e-12, max_dmdt=2383.88 ode_step=9.07016e-14\n",
-      "step=12, time=1.2e-12, max_dmdt=2363.89 ode_step=9.07016e-14\n",
-      "step=13, time=1.3e-12, max_dmdt=2344.09 ode_step=9.07016e-14\n",
-      "step=14, time=1.4e-12, max_dmdt=2324.49 ode_step=9.07016e-14\n",
-      "step=15, time=1.55947e-12, max_dmdt=2299.36 ode_step=1.59474e-13\n",
-      "step=16, time=1.71895e-12, max_dmdt=2268.88 ode_step=1.59474e-13\n",
-      "step=17, time=1.87842e-12, max_dmdt=2238.87 ode_step=1.59474e-13\n",
-      "step=18, time=2.0379e-12, max_dmdt=2209.32 ode_step=1.59474e-13\n",
-      "step=19, time=2.19737e-12, max_dmdt=2180.21 ode_step=1.59474e-13\n",
-      "step=20, time=2.35684e-12, max_dmdt=2151.54 ode_step=1.59474e-13\n",
-      "step=21, time=2.51632e-12, max_dmdt=2123.31 ode_step=1.59474e-13\n",
-      "step=22, time=2.67579e-12, max_dmdt=2095.49 ode_step=1.59474e-13\n",
-      "step=23, time=2.83527e-12, max_dmdt=2068.1 ode_step=1.59474e-13\n",
-      "step=24, time=2.99474e-12, max_dmdt=2041.11 ode_step=1.59474e-13\n",
-      "step=25, time=3.15421e-12, max_dmdt=2014.53 ode_step=1.59474e-13\n",
-      "step=26, time=3.31369e-12, max_dmdt=1988.34 ode_step=1.59474e-13\n",
-      "step=27, time=3.47316e-12, max_dmdt=1962.54 ode_step=1.59474e-13\n",
-      "step=28, time=3.63264e-12, max_dmdt=1937.12 ode_step=1.59474e-13\n",
-      "step=29, time=3.79211e-12, max_dmdt=1912.08 ode_step=1.59474e-13\n",
-      "step=30, time=3.95158e-12, max_dmdt=1887.41 ode_step=1.59474e-13\n",
-      "step=31, time=4.11106e-12, max_dmdt=1863.1 ode_step=1.59474e-13\n",
-      "step=32, time=4.27053e-12, max_dmdt=1839.16 ode_step=1.59474e-13\n",
-      "step=33, time=4.43001e-12, max_dmdt=1815.57 ode_step=1.59474e-13\n",
-      "step=34, time=4.58948e-12, max_dmdt=1792.32 ode_step=1.59474e-13\n",
-      "step=35, time=4.74895e-12, max_dmdt=1769.42 ode_step=1.59474e-13\n",
-      "step=36, time=4.9909e-12, max_dmdt=1741.09 ode_step=2.41946e-13\n",
-      "step=37, time=5.23285e-12, max_dmdt=1707.62 ode_step=2.41946e-13\n",
-      "step=38, time=5.47479e-12, max_dmdt=1674.9 ode_step=2.41946e-13\n",
-      "step=39, time=5.71674e-12, max_dmdt=1642.9 ode_step=2.41946e-13\n",
-      "step=40, time=5.95869e-12, max_dmdt=1611.61 ode_step=2.41946e-13\n",
-      "step=41, time=6.20063e-12, max_dmdt=1581.01 ode_step=2.41946e-13\n",
-      "step=42, time=6.44258e-12, max_dmdt=1551.1 ode_step=2.41946e-13\n",
-      "step=43, time=6.68452e-12, max_dmdt=1521.84 ode_step=2.41946e-13\n",
-      "step=44, time=6.92647e-12, max_dmdt=1493.23 ode_step=2.41946e-13\n",
-      "step=45, time=7.16842e-12, max_dmdt=1465.24 ode_step=2.41946e-13\n",
-      "step=46, time=7.41036e-12, max_dmdt=1437.88 ode_step=2.41946e-13\n",
-      "step=47, time=7.65231e-12, max_dmdt=1411.11 ode_step=2.41946e-13\n",
-      "step=48, time=7.89425e-12, max_dmdt=1384.93 ode_step=2.41946e-13\n",
-      "step=49, time=8.1362e-12, max_dmdt=1359.32 ode_step=2.41946e-13\n",
-      "step=50, time=8.37815e-12, max_dmdt=1334.27 ode_step=2.41946e-13\n",
-      "step=51, time=8.62009e-12, max_dmdt=1309.76 ode_step=2.41946e-13\n",
-      "step=52, time=8.86204e-12, max_dmdt=1285.79 ode_step=2.41946e-13\n",
-      "step=53, time=9.10398e-12, max_dmdt=1262.33 ode_step=2.41946e-13\n",
-      "step=54, time=9.34593e-12, max_dmdt=1240.62 ode_step=2.41946e-13\n",
-      "step=55, time=9.58788e-12, max_dmdt=1220.06 ode_step=2.41946e-13\n",
-      "step=56, time=9.82982e-12, max_dmdt=1199.89 ode_step=2.41946e-13\n",
-      "step=57, time=1.00718e-11, max_dmdt=1180.11 ode_step=2.41946e-13\n",
-      "step=58, time=1.03137e-11, max_dmdt=1160.7 ode_step=2.41946e-13\n",
-      "step=59, time=1.05557e-11, max_dmdt=1141.65 ode_step=2.41946e-13\n",
-      "step=60, time=1.07976e-11, max_dmdt=1122.96 ode_step=2.41946e-13\n",
-      "step=61, time=1.10396e-11, max_dmdt=1104.62 ode_step=2.41946e-13\n",
-      "step=62, time=1.12815e-11, max_dmdt=1086.62 ode_step=2.41946e-13\n",
-      "step=63, time=1.15234e-11, max_dmdt=1068.95 ode_step=2.41946e-13\n",
-      "step=64, time=1.17654e-11, max_dmdt=1051.61 ode_step=2.41946e-13\n",
-      "step=65, time=1.20073e-11, max_dmdt=1034.58 ode_step=2.41946e-13\n",
-      "step=66, time=1.22493e-11, max_dmdt=1017.87 ode_step=2.41946e-13\n",
-      "step=67, time=1.24912e-11, max_dmdt=1001.45 ode_step=2.41946e-13\n",
-      "step=68, time=1.27332e-11, max_dmdt=985.337 ode_step=2.41946e-13\n",
-      "step=69, time=1.29751e-11, max_dmdt=969.51 ode_step=2.41946e-13\n",
-      "step=70, time=1.32171e-11, max_dmdt=953.967 ode_step=2.41946e-13\n",
-      "step=71, time=1.3459e-11, max_dmdt=938.701 ode_step=2.41946e-13\n",
-      "step=72, time=1.3701e-11, max_dmdt=923.707 ode_step=2.41946e-13\n",
-      "step=73, time=1.39429e-11, max_dmdt=908.978 ode_step=2.41946e-13\n",
-      "step=74, time=1.41849e-11, max_dmdt=894.51 ode_step=2.41946e-13\n",
-      "step=75, time=1.44268e-11, max_dmdt=880.297 ode_step=2.41946e-13\n",
-      "step=76, time=1.46687e-11, max_dmdt=866.333 ode_step=2.41946e-13\n",
-      "step=77, time=1.49107e-11, max_dmdt=852.613 ode_step=2.41946e-13\n",
-      "step=78, time=1.51526e-11, max_dmdt=839.132 ode_step=2.41946e-13\n",
-      "step=79, time=1.53946e-11, max_dmdt=825.886 ode_step=2.41946e-13\n",
-      "step=80, time=1.56365e-11, max_dmdt=812.869 ode_step=2.41946e-13\n",
-      "step=81, time=1.58785e-11, max_dmdt=800.077 ode_step=2.41946e-13\n",
-      "step=82, time=1.61204e-11, max_dmdt=787.506 ode_step=2.41946e-13\n",
-      "step=83, time=1.63624e-11, max_dmdt=775.15 ode_step=2.41946e-13\n",
-      "step=84, time=1.66043e-11, max_dmdt=763.007 ode_step=2.41946e-13\n",
-      "step=85, time=1.69692e-11, max_dmdt=748.081 ode_step=3.64864e-13\n",
-      "step=86, time=1.7334e-11, max_dmdt=730.541 ode_step=3.64864e-13\n",
-      "step=87, time=1.76989e-11, max_dmdt=713.448 ode_step=3.64864e-13\n",
-      "step=88, time=1.80638e-11, max_dmdt=696.789 ode_step=3.64864e-13\n",
-      "step=89, time=1.84286e-11, max_dmdt=680.552 ode_step=3.64864e-13\n",
-      "step=90, time=1.87935e-11, max_dmdt=664.724 ode_step=3.64864e-13\n",
-      "step=91, time=1.91584e-11, max_dmdt=649.295 ode_step=3.64864e-13\n",
-      "step=92, time=1.95232e-11, max_dmdt=634.251 ode_step=3.64864e-13\n",
-      "step=93, time=1.98881e-11, max_dmdt=619.663 ode_step=3.64864e-13\n",
-      "step=94, time=2.0253e-11, max_dmdt=605.444 ode_step=3.64864e-13\n",
-      "step=95, time=2.06178e-11, max_dmdt=591.577 ode_step=3.64864e-13\n",
-      "step=96, time=2.09827e-11, max_dmdt=578.053 ode_step=3.64864e-13\n",
-      "step=97, time=2.13475e-11, max_dmdt=564.862 ode_step=3.64864e-13\n",
-      "step=98, time=2.17124e-11, max_dmdt=551.997 ode_step=3.64864e-13\n",
-      "step=99, time=2.20773e-11, max_dmdt=539.447 ode_step=3.64864e-13\n",
-      "step=100, time=2.24421e-11, max_dmdt=527.204 ode_step=3.64864e-13\n",
-      "step=101, time=2.2807e-11, max_dmdt=515.259 ode_step=3.64864e-13\n",
-      "step=102, time=2.31719e-11, max_dmdt=503.606 ode_step=3.64864e-13\n",
-      "step=103, time=2.35367e-11, max_dmdt=492.235 ode_step=3.64864e-13\n",
-      "step=104, time=2.39016e-11, max_dmdt=481.14 ode_step=3.64864e-13\n",
-      "step=105, time=2.42665e-11, max_dmdt=470.313 ode_step=3.64864e-13\n",
-      "step=106, time=2.46313e-11, max_dmdt=459.746 ode_step=3.64864e-13\n",
-      "step=107, time=2.49962e-11, max_dmdt=449.434 ode_step=3.64864e-13\n",
-      "step=108, time=2.5361e-11, max_dmdt=439.37 ode_step=3.64864e-13\n",
-      "step=109, time=2.57259e-11, max_dmdt=429.547 ode_step=3.64864e-13\n",
-      "step=110, time=2.60908e-11, max_dmdt=419.958 ode_step=3.64864e-13\n",
-      "step=111, time=2.64556e-11, max_dmdt=410.598 ode_step=3.64864e-13\n",
-      "step=112, time=2.68205e-11, max_dmdt=401.461 ode_step=3.64864e-13\n",
-      "step=113, time=2.71854e-11, max_dmdt=392.541 ode_step=3.64864e-13\n",
-      "step=114, time=2.75502e-11, max_dmdt=383.832 ode_step=3.64864e-13\n",
-      "step=115, time=2.79151e-11, max_dmdt=375.33 ode_step=3.64864e-13\n",
-      "step=116, time=2.828e-11, max_dmdt=367.029 ode_step=3.64864e-13\n",
-      "step=117, time=2.86448e-11, max_dmdt=358.923 ode_step=3.64864e-13\n",
-      "step=118, time=2.90097e-11, max_dmdt=351.009 ode_step=3.64864e-13\n",
-      "step=119, time=2.93745e-11, max_dmdt=343.28 ode_step=3.64864e-13\n",
-      "step=120, time=2.97394e-11, max_dmdt=335.733 ode_step=3.64864e-13\n",
-      "step=121, time=3.03081e-11, max_dmdt=326.345 ode_step=5.68718e-13\n",
-      "step=122, time=3.08768e-11, max_dmdt=315.273 ode_step=5.68718e-13\n",
-      "step=123, time=3.14456e-11, max_dmdt=304.6 ode_step=5.68718e-13\n",
-      "step=124, time=3.20143e-11, max_dmdt=294.313 ode_step=5.68718e-13\n",
-      "step=125, time=3.2583e-11, max_dmdt=284.396 ode_step=5.68718e-13\n",
-      "step=126, time=3.31517e-11, max_dmdt=274.835 ode_step=5.68718e-13\n",
-      "step=127, time=3.37204e-11, max_dmdt=265.617 ode_step=5.68718e-13\n",
-      "step=128, time=3.42892e-11, max_dmdt=256.729 ode_step=5.68718e-13\n",
-      "step=129, time=3.48579e-11, max_dmdt=248.159 ode_step=5.68718e-13\n",
-      "step=130, time=3.54266e-11, max_dmdt=239.894 ode_step=5.68718e-13\n",
-      "step=131, time=3.59953e-11, max_dmdt=231.923 ode_step=5.68718e-13\n",
-      "step=132, time=3.6564e-11, max_dmdt=224.236 ode_step=5.68718e-13\n",
-      "step=133, time=3.71327e-11, max_dmdt=216.838 ode_step=5.68718e-13\n",
-      "step=134, time=3.77015e-11, max_dmdt=209.704 ode_step=5.68718e-13\n",
-      "step=135, time=3.82702e-11, max_dmdt=202.822 ode_step=5.68718e-13\n",
-      "step=136, time=3.88389e-11, max_dmdt=196.182 ode_step=5.68718e-13\n",
-      "step=137, time=3.94076e-11, max_dmdt=189.776 ode_step=5.68718e-13\n",
-      "step=138, time=3.99763e-11, max_dmdt=183.606 ode_step=5.68718e-13\n",
-      "step=139, time=4.05451e-11, max_dmdt=177.655 ode_step=5.68718e-13\n",
-      "step=140, time=4.11138e-11, max_dmdt=171.912 ode_step=5.68718e-13\n",
-      "step=141, time=4.16825e-11, max_dmdt=166.37 ode_step=5.68718e-13\n",
-      "step=142, time=4.22512e-11, max_dmdt=161.022 ode_step=5.68718e-13\n",
-      "step=143, time=4.28199e-11, max_dmdt=155.861 ode_step=5.68718e-13\n",
-      "step=144, time=4.33887e-11, max_dmdt=150.88 ode_step=5.68718e-13\n",
-      "step=145, time=4.39574e-11, max_dmdt=146.073 ode_step=5.68718e-13\n",
-      "step=146, time=4.45261e-11, max_dmdt=141.433 ode_step=5.68718e-13\n",
-      "step=147, time=4.50948e-11, max_dmdt=136.956 ode_step=5.68718e-13\n",
-      "step=148, time=4.56635e-11, max_dmdt=132.635 ode_step=5.68718e-13\n",
-      "step=149, time=4.62322e-11, max_dmdt=128.47 ode_step=5.68718e-13\n",
-      "step=150, time=4.6801e-11, max_dmdt=124.451 ode_step=5.68718e-13\n",
-      "step=151, time=4.73697e-11, max_dmdt=120.572 ode_step=5.68718e-13\n",
-      "step=152, time=4.79384e-11, max_dmdt=116.828 ode_step=5.68718e-13\n",
-      "step=153, time=4.85071e-11, max_dmdt=113.215 ode_step=5.68718e-13\n",
-      "step=154, time=4.90758e-11, max_dmdt=109.728 ode_step=5.68718e-13\n",
-      "step=155, time=4.96446e-11, max_dmdt=106.363 ode_step=5.68718e-13\n",
-      "step=156, time=5.02133e-11, max_dmdt=103.115 ode_step=5.68718e-13\n",
-      "step=157, time=5.0782e-11, max_dmdt=99.9809 ode_step=5.68718e-13\n",
-      "step=158, time=5.13507e-11, max_dmdt=96.9565 ode_step=5.68718e-13\n",
-      "step=159, time=5.19194e-11, max_dmdt=94.0382 ode_step=5.68718e-13\n",
-      "step=160, time=5.24881e-11, max_dmdt=91.2223 ode_step=5.68718e-13\n",
-      "step=161, time=5.30569e-11, max_dmdt=88.5054 ode_step=5.68718e-13\n",
-      "step=162, time=5.39135e-11, max_dmdt=85.2401 ode_step=8.56596e-13\n",
-      "step=163, time=5.47701e-11, max_dmdt=81.4997 ode_step=8.56596e-13\n",
-      "step=164, time=5.56267e-11, max_dmdt=77.957 ode_step=8.56596e-13\n",
-      "step=165, time=5.64832e-11, max_dmdt=74.6023 ode_step=8.56596e-13\n",
-      "step=166, time=5.73398e-11, max_dmdt=71.4264 ode_step=8.56596e-13\n",
-      "step=167, time=5.81964e-11, max_dmdt=68.4204 ode_step=8.56596e-13\n",
-      "step=168, time=5.9053e-11, max_dmdt=65.5761 ode_step=8.56596e-13\n",
-      "step=169, time=5.99096e-11, max_dmdt=62.8856 ode_step=8.56596e-13\n",
-      "step=170, time=6.07662e-11, max_dmdt=60.3416 ode_step=8.56596e-13\n",
-      "step=171, time=6.16228e-11, max_dmdt=57.937 ode_step=8.56596e-13\n",
-      "step=172, time=6.24794e-11, max_dmdt=55.665 ode_step=8.56596e-13\n",
-      "step=173, time=6.3336e-11, max_dmdt=53.5194 ode_step=8.56596e-13\n",
-      "step=174, time=6.41926e-11, max_dmdt=51.4941 ode_step=8.56596e-13\n",
-      "step=175, time=6.50492e-11, max_dmdt=49.5835 ode_step=8.56596e-13\n",
-      "step=176, time=6.59058e-11, max_dmdt=47.7819 ode_step=8.56596e-13\n",
-      "step=177, time=6.67624e-11, max_dmdt=46.0843 ode_step=8.56596e-13\n",
-      "step=178, time=6.7619e-11, max_dmdt=44.4857 ode_step=8.56596e-13\n",
-      "step=179, time=6.84756e-11, max_dmdt=42.9814 ode_step=8.56596e-13\n",
-      "step=180, time=6.93322e-11, max_dmdt=41.696 ode_step=8.56596e-13\n",
-      "step=181, time=7.01888e-11, max_dmdt=40.5047 ode_step=8.56596e-13\n",
-      "step=182, time=7.10454e-11, max_dmdt=39.3892 ode_step=8.56596e-13\n",
-      "step=183, time=7.1902e-11, max_dmdt=38.3456 ode_step=8.56596e-13\n",
-      "step=184, time=7.27586e-11, max_dmdt=37.3703 ode_step=8.56596e-13\n",
-      "step=185, time=7.36152e-11, max_dmdt=36.4597 ode_step=8.56596e-13\n",
-      "step=186, time=7.44718e-11, max_dmdt=35.6102 ode_step=8.56596e-13\n",
-      "step=187, time=7.53284e-11, max_dmdt=34.8187 ode_step=8.56596e-13\n",
-      "step=188, time=7.61849e-11, max_dmdt=34.0931 ode_step=8.56596e-13\n",
-      "step=189, time=7.70415e-11, max_dmdt=33.438 ode_step=8.56596e-13\n",
-      "step=190, time=7.78981e-11, max_dmdt=32.8313 ode_step=8.56596e-13\n",
-      "step=191, time=7.87547e-11, max_dmdt=32.2703 ode_step=8.56596e-13\n",
-      "step=192, time=7.96113e-11, max_dmdt=31.7521 ode_step=8.56596e-13\n",
-      "step=193, time=8.04679e-11, max_dmdt=31.2742 ode_step=8.56596e-13\n",
-      "step=194, time=8.13245e-11, max_dmdt=30.8339 ode_step=8.56596e-13\n",
-      "step=195, time=8.21811e-11, max_dmdt=30.429 ode_step=8.56596e-13\n",
-      "step=196, time=8.30377e-11, max_dmdt=30.0571 ode_step=8.56596e-13\n",
-      "step=197, time=8.38943e-11, max_dmdt=29.7161 ode_step=8.56596e-13\n",
-      "step=198, time=8.47509e-11, max_dmdt=29.4038 ode_step=8.56596e-13\n",
-      "step=199, time=8.56075e-11, max_dmdt=29.1243 ode_step=8.56596e-13\n",
-      "step=200, time=8.69351e-11, max_dmdt=28.8881 ode_step=1.32761e-12\n",
-      "step=201, time=8.82627e-11, max_dmdt=28.641 ode_step=1.32761e-12\n",
-      "step=202, time=8.95903e-11, max_dmdt=28.4341 ode_step=1.32761e-12\n",
-      "step=203, time=9.0918e-11, max_dmdt=28.2625 ode_step=1.32761e-12\n",
-      "step=204, time=9.22456e-11, max_dmdt=28.1221 ode_step=1.32761e-12\n",
-      "step=205, time=9.35732e-11, max_dmdt=28.009 ode_step=1.32761e-12\n",
-      "step=206, time=9.49008e-11, max_dmdt=27.9201 ode_step=1.32761e-12\n",
-      "step=207, time=9.62284e-11, max_dmdt=27.8524 ode_step=1.32761e-12\n",
-      "step=208, time=9.7556e-11, max_dmdt=27.8033 ode_step=1.32761e-12\n",
-      "step=209, time=9.88836e-11, max_dmdt=27.7704 ode_step=1.32761e-12\n",
-      "step=210, time=1.00211e-10, max_dmdt=27.752 ode_step=1.32761e-12\n",
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-      "step=212, time=1.02866e-10, max_dmdt=27.7513 ode_step=1.32761e-12\n",
-      "step=213, time=1.04194e-10, max_dmdt=27.7662 ode_step=1.32761e-12\n",
-      "step=214, time=1.05522e-10, max_dmdt=27.7896 ode_step=1.32761e-12\n",
-      "step=215, time=1.06849e-10, max_dmdt=27.8206 ode_step=1.32761e-12\n",
-      "step=216, time=1.08177e-10, max_dmdt=27.8582 ode_step=1.32761e-12\n",
-      "step=217, time=1.09505e-10, max_dmdt=27.9017 ode_step=1.32761e-12\n",
-      "step=218, time=1.10832e-10, max_dmdt=27.9504 ode_step=1.32761e-12\n",
-      "step=219, time=1.1216e-10, max_dmdt=28.0036 ode_step=1.32761e-12\n",
-      "step=220, time=1.13487e-10, max_dmdt=28.0609 ode_step=1.32761e-12\n",
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-      "step=223, time=1.1747e-10, max_dmdt=28.253 ode_step=1.32761e-12\n",
-      "step=224, time=1.18798e-10, max_dmdt=28.3226 ode_step=1.32761e-12\n",
-      "step=225, time=1.20125e-10, max_dmdt=28.3945 ode_step=1.32761e-12\n",
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-      "step=228, time=1.24108e-10, max_dmdt=28.622 ode_step=1.32761e-12\n",
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-      "step=231, time=1.28091e-10, max_dmdt=28.8635 ode_step=1.32761e-12\n",
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-      "step=233, time=1.30746e-10, max_dmdt=29.0306 ode_step=1.32761e-12\n",
-      "step=234, time=1.32074e-10, max_dmdt=29.1158 ode_step=1.32761e-12\n",
-      "step=235, time=1.33402e-10, max_dmdt=29.2019 ode_step=1.32761e-12\n",
-      "step=236, time=1.34729e-10, max_dmdt=29.2889 ode_step=1.32761e-12\n",
-      "step=237, time=1.36057e-10, max_dmdt=29.3768 ode_step=1.32761e-12\n",
-      "step=238, time=1.38051e-10, max_dmdt=29.4879 ode_step=1.99398e-12\n",
-      "step=239, time=1.40045e-10, max_dmdt=29.6227 ode_step=1.99398e-12\n",
-      "step=240, time=1.42039e-10, max_dmdt=29.7591 ode_step=1.99398e-12\n",
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-      "step=242, time=1.46027e-10, max_dmdt=30.0362 ode_step=1.99398e-12\n",
-      "step=243, time=1.48021e-10, max_dmdt=30.1768 ode_step=1.99398e-12\n",
-      "step=244, time=1.50015e-10, max_dmdt=30.3187 ode_step=1.99398e-12\n",
-      "step=245, time=1.52009e-10, max_dmdt=30.4619 ode_step=1.99398e-12\n",
-      "step=246, time=1.54003e-10, max_dmdt=30.6062 ode_step=1.99398e-12\n",
-      "step=247, time=1.55997e-10, max_dmdt=30.7517 ode_step=1.99398e-12\n",
-      "step=248, time=1.57991e-10, max_dmdt=30.8984 ode_step=1.99398e-12\n",
-      "step=249, time=1.59985e-10, max_dmdt=31.0462 ode_step=1.99398e-12\n",
-      "step=250, time=1.61979e-10, max_dmdt=31.1951 ode_step=1.99398e-12\n",
-      "step=251, time=1.63973e-10, max_dmdt=31.3452 ode_step=1.99398e-12\n",
-      "step=252, time=1.65967e-10, max_dmdt=31.4963 ode_step=1.99398e-12\n",
-      "step=253, time=1.67961e-10, max_dmdt=31.6486 ode_step=1.99398e-12\n",
-      "step=254, time=1.69955e-10, max_dmdt=31.802 ode_step=1.99398e-12\n",
-      "step=255, time=1.71948e-10, max_dmdt=31.9564 ode_step=1.99398e-12\n",
-      "step=256, time=1.73942e-10, max_dmdt=32.112 ode_step=1.99398e-12\n",
-      "step=257, time=1.75936e-10, max_dmdt=32.2686 ode_step=1.99398e-12\n",
-      "step=258, time=1.7793e-10, max_dmdt=32.4264 ode_step=1.99398e-12\n",
-      "step=259, time=1.79924e-10, max_dmdt=32.5853 ode_step=1.99398e-12\n",
-      "step=260, time=1.81918e-10, max_dmdt=32.7453 ode_step=1.99398e-12\n",
-      "step=261, time=1.83912e-10, max_dmdt=32.9064 ode_step=1.99398e-12\n",
-      "step=262, time=1.85906e-10, max_dmdt=33.0686 ode_step=1.99398e-12\n",
-      "step=263, time=1.879e-10, max_dmdt=33.232 ode_step=1.99398e-12\n",
-      "step=264, time=1.89894e-10, max_dmdt=33.3965 ode_step=1.99398e-12\n",
-      "step=265, time=1.91888e-10, max_dmdt=33.5621 ode_step=1.99398e-12\n",
-      "step=266, time=1.94986e-10, max_dmdt=33.7754 ode_step=3.09762e-12\n",
-      "step=267, time=1.98084e-10, max_dmdt=34.0373 ode_step=3.09762e-12\n",
-      "step=268, time=2.01181e-10, max_dmdt=34.3021 ode_step=3.09762e-12\n",
-      "step=269, time=2.04279e-10, max_dmdt=34.5698 ode_step=3.09762e-12\n",
-      "step=270, time=2.07376e-10, max_dmdt=34.8404 ode_step=3.09762e-12\n",
-      "step=271, time=2.10474e-10, max_dmdt=35.1139 ode_step=3.09762e-12\n",
-      "step=272, time=2.13572e-10, max_dmdt=35.3905 ode_step=3.09762e-12\n",
-      "step=273, time=2.16669e-10, max_dmdt=35.67 ode_step=3.09762e-12\n",
-      "step=274, time=2.19767e-10, max_dmdt=35.9527 ode_step=3.09762e-12\n",
-      "step=275, time=2.22864e-10, max_dmdt=36.2385 ode_step=3.09762e-12\n",
-      "step=276, time=2.25962e-10, max_dmdt=36.5275 ode_step=3.09762e-12\n",
-      "step=277, time=2.2906e-10, max_dmdt=36.8197 ode_step=3.09762e-12\n",
-      "step=278, time=2.32157e-10, max_dmdt=37.1151 ode_step=3.09762e-12\n",
-      "step=279, time=2.3835e-10, max_dmdt=37.5649 ode_step=6.19311e-12\n",
-      "step=280, time=2.44544e-10, max_dmdt=38.1759 ode_step=6.19311e-12\n",
-      "step=281, time=2.50737e-10, max_dmdt=38.8009 ode_step=6.19311e-12\n",
-      "step=282, time=2.55016e-10, max_dmdt=39.3403 ode_step=4.27961e-12\n",
-      "step=283, time=2.59296e-10, max_dmdt=39.7893 ode_step=4.27961e-12\n",
-      "step=284, time=2.63575e-10, max_dmdt=40.2455 ode_step=4.27961e-12\n",
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-      "step=298, time=3.2349e-10, max_dmdt=48.0069 ode_step=4.27961e-12\n",
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-      "step=311, time=3.79125e-10, max_dmdt=59.7215 ode_step=4.27961e-12\n",
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-      "step=313, time=3.87684e-10, max_dmdt=61.8713 ode_step=4.27961e-12\n",
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-      "step=316, time=4.00523e-10, max_dmdt=65.3134 ode_step=4.27961e-12\n",
-      "step=317, time=4.04803e-10, max_dmdt=66.5241 ode_step=4.27961e-12\n",
-      "step=318, time=4.09082e-10, max_dmdt=67.7689 ode_step=4.27961e-12\n",
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-      "step=320, time=4.17642e-10, max_dmdt=70.368 ode_step=4.27961e-12\n",
-      "step=321, time=4.21921e-10, max_dmdt=71.7263 ode_step=4.27961e-12\n",
-      "step=322, time=4.26201e-10, max_dmdt=73.1265 ode_step=4.27961e-12\n",
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-      "step=329, time=4.56158e-10, max_dmdt=86.376 ode_step=4.27961e-12\n",
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-      "step=331, time=4.64717e-10, max_dmdt=91.2625 ode_step=4.27961e-12\n",
-      "step=332, time=4.67852e-10, max_dmdt=93.54 ode_step=3.13426e-12\n",
-      "step=333, time=4.70986e-10, max_dmdt=95.5516 ode_step=3.13426e-12\n",
-      "step=334, time=4.7412e-10, max_dmdt=97.6446 ode_step=3.13426e-12\n",
-      "step=335, time=4.77254e-10, max_dmdt=99.8235 ode_step=3.13426e-12\n",
-      "step=336, time=4.80389e-10, max_dmdt=102.093 ode_step=3.13426e-12\n",
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-      "step=338, time=4.86657e-10, max_dmdt=106.924 ode_step=3.13426e-12\n",
-      "step=339, time=4.89791e-10, max_dmdt=109.496 ode_step=3.13426e-12\n",
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-      "step=341, time=4.9606e-10, max_dmdt=114.997 ode_step=3.13426e-12\n",
-      "step=342, time=4.99194e-10, max_dmdt=117.965 ode_step=3.13426e-12\n",
-      "step=343, time=5.00634e-10, max_dmdt=119.64 ode_step=1.4399e-12\n",
-      "step=344, time=5.0074e-10, max_dmdt=118.193 ode_step=1.06379e-13\n",
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-      "step=346, time=5.00953e-10, max_dmdt=117.56 ode_step=1.06379e-13\n",
-      "step=347, time=5.01176e-10, max_dmdt=117.077 ode_step=2.22609e-13\n",
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-      "step=350, time=5.01844e-10, max_dmdt=115.17 ode_step=2.22609e-13\n",
-      "step=351, time=5.02066e-10, max_dmdt=114.553 ode_step=2.22609e-13\n",
-      "step=352, time=5.02431e-10, max_dmdt=113.755 ode_step=3.64657e-13\n",
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-      "step=362, time=5.06751e-10, max_dmdt=103.984 ode_step=5.89088e-13\n",
-      "step=363, time=5.0734e-10, max_dmdt=102.841 ode_step=5.89088e-13\n",
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-      "step=379, time=5.18606e-10, max_dmdt=89.2682 ode_step=8.95867e-13\n",
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-      "step=381, time=5.20398e-10, max_dmdt=87.769 ode_step=8.95867e-13\n",
-      "step=382, time=5.21294e-10, max_dmdt=87.0598 ode_step=8.95867e-13\n",
-      "step=383, time=5.22189e-10, max_dmdt=86.3745 ode_step=8.95867e-13\n",
-      "step=384, time=5.23085e-10, max_dmdt=85.7112 ode_step=8.95867e-13\n",
-      "step=385, time=5.23981e-10, max_dmdt=85.0681 ode_step=8.95867e-13\n",
-      "step=386, time=5.24877e-10, max_dmdt=84.4436 ode_step=8.95867e-13\n",
-      "step=387, time=5.25773e-10, max_dmdt=83.8883 ode_step=8.95867e-13\n",
-      "step=388, time=5.26669e-10, max_dmdt=83.3988 ode_step=8.95867e-13\n",
-      "step=389, time=5.27565e-10, max_dmdt=82.9282 ode_step=8.95867e-13\n",
-      "step=390, time=5.2846e-10, max_dmdt=82.4752 ode_step=8.95867e-13\n",
-      "step=391, time=5.29356e-10, max_dmdt=82.0488 ode_step=8.95867e-13\n",
-      "step=392, time=5.30252e-10, max_dmdt=81.6546 ode_step=8.95867e-13\n",
-      "step=393, time=5.31148e-10, max_dmdt=81.2728 ode_step=8.95867e-13\n",
-      "step=394, time=5.32044e-10, max_dmdt=80.9024 ode_step=8.95867e-13\n",
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-      "step=397, time=5.34732e-10, max_dmdt=79.8498 ode_step=8.95867e-13\n",
-      "step=398, time=5.35627e-10, max_dmdt=79.5158 ode_step=8.95867e-13\n",
-      "step=399, time=5.36523e-10, max_dmdt=79.1891 ode_step=8.95867e-13\n",
-      "step=400, time=5.37419e-10, max_dmdt=78.8692 ode_step=8.95867e-13\n",
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-      "step=409, time=5.46253e-10, max_dmdt=76.0998 ode_step=1.66699e-12\n",
-      "step=410, time=5.4792e-10, max_dmdt=75.5925 ode_step=1.66699e-12\n",
-      "step=411, time=5.49587e-10, max_dmdt=75.0947 ode_step=1.66699e-12\n",
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-      "step=416, time=5.55562e-10, max_dmdt=73.3115 ode_step=1.19494e-12\n",
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-      "step=420, time=5.60342e-10, max_dmdt=72.0047 ode_step=1.19494e-12\n",
-      "step=421, time=5.61536e-10, max_dmdt=71.6851 ode_step=1.19494e-12\n",
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-      "step=424, time=5.65121e-10, max_dmdt=70.7645 ode_step=1.19494e-12\n",
-      "step=425, time=5.66316e-10, max_dmdt=70.4695 ode_step=1.19494e-12\n",
-      "step=426, time=5.67511e-10, max_dmdt=70.1763 ode_step=1.19494e-12\n",
-      "step=427, time=5.68706e-10, max_dmdt=69.885 ode_step=1.19494e-12\n",
-      "step=428, time=5.69901e-10, max_dmdt=69.5954 ode_step=1.19494e-12\n",
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-      "step=431, time=5.73486e-10, max_dmdt=68.7374 ode_step=1.19494e-12\n",
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-      "step=434, time=5.77071e-10, max_dmdt=67.8952 ode_step=1.19494e-12\n",
-      "step=435, time=5.78895e-10, max_dmdt=67.5453 ode_step=1.82456e-12\n",
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-      "step=564, time=1.42129e-09, max_dmdt=16.3467 ode_step=1.26721e-11\n",
-      "step=565, time=1.43397e-09, max_dmdt=15.9487 ode_step=1.26721e-11\n",
-      "step=566, time=1.44664e-09, max_dmdt=15.5604 ode_step=1.26721e-11\n",
-      "step=567, time=1.45931e-09, max_dmdt=15.1815 ode_step=1.26721e-11\n",
-      "step=568, time=1.47198e-09, max_dmdt=14.8118 ode_step=1.26721e-11\n",
-      "step=569, time=1.48465e-09, max_dmdt=14.4512 ode_step=1.26721e-11\n",
-      "step=570, time=1.49733e-09, max_dmdt=14.0994 ode_step=1.26721e-11\n",
-      "step=571, time=1.51e-09, max_dmdt=13.7564 ode_step=1.26721e-11\n",
-      "step=572, time=1.52267e-09, max_dmdt=13.4218 ode_step=1.26721e-11\n",
-      "step=573, time=1.53534e-09, max_dmdt=13.0955 ode_step=1.26721e-11\n",
-      "step=574, time=1.54802e-09, max_dmdt=12.7774 ode_step=1.26721e-11\n",
-      "step=575, time=1.56069e-09, max_dmdt=12.4672 ode_step=1.26721e-11\n",
-      "step=576, time=1.57991e-09, max_dmdt=12.0883 ode_step=1.92267e-11\n",
-      "step=577, time=1.59914e-09, max_dmdt=11.6483 ode_step=1.92267e-11\n",
-      "step=578, time=1.61837e-09, max_dmdt=11.2259 ode_step=1.92267e-11\n",
-      "step=579, time=1.63759e-09, max_dmdt=10.8196 ode_step=1.92267e-11\n",
-      "step=580, time=1.65682e-09, max_dmdt=10.4287 ode_step=1.92267e-11\n",
-      "step=581, time=1.67605e-09, max_dmdt=10.0528 ode_step=1.92267e-11\n",
-      "step=582, time=1.69527e-09, max_dmdt=9.69122 ode_step=1.92267e-11\n",
-      "step=583, time=1.7145e-09, max_dmdt=9.34347 ode_step=1.92267e-11\n",
-      "step=584, time=1.73373e-09, max_dmdt=9.00904 ode_step=1.92267e-11\n",
-      "step=585, time=1.75295e-09, max_dmdt=8.68739 ode_step=1.92267e-11\n",
-      "step=586, time=1.77218e-09, max_dmdt=8.37806 ode_step=1.92267e-11\n",
-      "step=587, time=1.79141e-09, max_dmdt=8.08056 ode_step=1.92267e-11\n",
-      "step=588, time=1.81063e-09, max_dmdt=7.79444 ode_step=1.92267e-11\n",
-      "step=589, time=1.82986e-09, max_dmdt=7.51925 ode_step=1.92267e-11\n",
-      "step=590, time=1.84909e-09, max_dmdt=7.25456 ode_step=1.92267e-11\n",
-      "step=591, time=1.86831e-09, max_dmdt=6.99997 ode_step=1.92267e-11\n",
-      "step=592, time=1.88754e-09, max_dmdt=6.75507 ode_step=1.92267e-11\n",
-      "step=593, time=1.90677e-09, max_dmdt=6.51948 ode_step=1.92267e-11\n",
-      "step=594, time=1.92599e-09, max_dmdt=6.29284 ode_step=1.92267e-11\n",
-      "step=595, time=1.94522e-09, max_dmdt=6.07478 ode_step=1.92267e-11\n",
-      "step=596, time=1.96445e-09, max_dmdt=5.86539 ode_step=1.92267e-11\n",
-      "step=597, time=1.98367e-09, max_dmdt=5.66488 ode_step=1.92267e-11\n",
-      "step=598, time=2.0029e-09, max_dmdt=5.4719 ode_step=1.92267e-11\n",
-      "step=599, time=2.02213e-09, max_dmdt=5.28614 ode_step=1.92267e-11\n",
-      "step=600, time=2.04135e-09, max_dmdt=5.10731 ode_step=1.92267e-11\n",
-      "step=601, time=2.06058e-09, max_dmdt=4.93513 ode_step=1.92267e-11\n",
-      "step=602, time=2.07981e-09, max_dmdt=4.76934 ode_step=1.92267e-11\n",
-      "step=603, time=2.09903e-09, max_dmdt=4.60968 ode_step=1.92267e-11\n",
-      "step=604, time=2.11826e-09, max_dmdt=4.45591 ode_step=1.92267e-11\n",
-      "step=605, time=2.13749e-09, max_dmdt=4.30779 ode_step=1.92267e-11\n",
-      "step=606, time=2.15672e-09, max_dmdt=4.1651 ode_step=1.92267e-11\n",
-      "step=607, time=2.17594e-09, max_dmdt=4.02762 ode_step=1.92267e-11\n",
-      "step=608, time=2.19517e-09, max_dmdt=3.89514 ode_step=1.92267e-11\n",
-      "step=609, time=2.2144e-09, max_dmdt=3.76746 ode_step=1.92267e-11\n",
-      "step=610, time=2.23362e-09, max_dmdt=3.6444 ode_step=1.92267e-11\n",
-      "step=611, time=2.25285e-09, max_dmdt=3.52578 ode_step=1.92267e-11\n",
-      "step=612, time=2.27208e-09, max_dmdt=3.4114 ode_step=1.92267e-11\n",
-      "step=613, time=2.2913e-09, max_dmdt=3.30112 ode_step=1.92267e-11\n",
-      "step=614, time=2.31053e-09, max_dmdt=3.21413 ode_step=1.92267e-11\n",
-      "step=615, time=2.32976e-09, max_dmdt=3.13492 ode_step=1.92267e-11\n",
-      "step=616, time=2.34898e-09, max_dmdt=3.0575 ode_step=1.92267e-11\n",
-      "step=617, time=2.36821e-09, max_dmdt=2.98182 ode_step=1.92267e-11\n",
-      "step=618, time=2.38744e-09, max_dmdt=2.90786 ode_step=1.92267e-11\n",
-      "step=619, time=2.40666e-09, max_dmdt=2.83558 ode_step=1.92267e-11\n",
-      "step=620, time=2.42589e-09, max_dmdt=2.76496 ode_step=1.92267e-11\n",
-      "step=621, time=2.44512e-09, max_dmdt=2.69595 ode_step=1.92267e-11\n",
-      "step=622, time=2.46434e-09, max_dmdt=2.62853 ode_step=1.92267e-11\n",
-      "step=623, time=2.48357e-09, max_dmdt=2.56267 ode_step=1.92267e-11\n",
-      "step=624, time=2.5028e-09, max_dmdt=2.49834 ode_step=1.92267e-11\n",
-      "step=625, time=2.52202e-09, max_dmdt=2.4355 ode_step=1.92267e-11\n",
-      "step=626, time=2.54125e-09, max_dmdt=2.37413 ode_step=1.92267e-11\n",
-      "step=627, time=2.56048e-09, max_dmdt=2.3142 ode_step=1.92267e-11\n",
-      "step=628, time=2.5797e-09, max_dmdt=2.25568 ode_step=1.92267e-11\n",
-      "step=629, time=2.59893e-09, max_dmdt=2.19854 ode_step=1.92267e-11\n",
-      "step=630, time=2.61816e-09, max_dmdt=2.14274 ode_step=1.92267e-11\n",
-      "step=631, time=2.63738e-09, max_dmdt=2.08827 ode_step=1.92267e-11\n",
-      "step=632, time=2.65661e-09, max_dmdt=2.0351 ode_step=1.92267e-11\n",
-      "step=633, time=2.67584e-09, max_dmdt=1.9832 ode_step=1.92267e-11\n",
-      "step=634, time=2.69506e-09, max_dmdt=1.93253 ode_step=1.92267e-11\n",
-      "step=635, time=2.71429e-09, max_dmdt=1.88309 ode_step=1.92267e-11\n",
-      "step=636, time=2.73352e-09, max_dmdt=1.83483 ode_step=1.92267e-11\n",
-      "step=637, time=2.75274e-09, max_dmdt=1.78773 ode_step=1.92267e-11\n",
-      "step=638, time=2.77197e-09, max_dmdt=1.74178 ode_step=1.92267e-11\n",
-      "step=639, time=2.7912e-09, max_dmdt=1.69694 ode_step=1.92267e-11\n",
-      "step=640, time=2.81042e-09, max_dmdt=1.65319 ode_step=1.92267e-11\n",
-      "step=641, time=2.82965e-09, max_dmdt=1.61051 ode_step=1.92267e-11\n",
-      "step=642, time=2.85884e-09, max_dmdt=1.55831 ode_step=2.91866e-11\n",
-      "step=643, time=2.88802e-09, max_dmdt=1.49743 ode_step=2.91866e-11\n",
-      "step=644, time=2.91721e-09, max_dmdt=1.43881 ode_step=2.91866e-11\n",
-      "step=645, time=2.9464e-09, max_dmdt=1.38238 ode_step=2.91866e-11\n",
-      "step=646, time=2.97558e-09, max_dmdt=1.32805 ode_step=2.91866e-11\n",
-      "step=647, time=3.00477e-09, max_dmdt=1.27576 ode_step=2.91866e-11\n",
-      "step=648, time=3.03396e-09, max_dmdt=1.22543 ode_step=2.91866e-11\n",
-      "step=649, time=3.06314e-09, max_dmdt=1.17701 ode_step=2.91866e-11\n",
-      "step=650, time=3.09233e-09, max_dmdt=1.13041 ode_step=2.91866e-11\n",
-      "step=651, time=3.12152e-09, max_dmdt=1.08558 ode_step=2.91866e-11\n",
-      "step=652, time=3.1507e-09, max_dmdt=1.04246 ode_step=2.91866e-11\n",
-      "step=653, time=3.17989e-09, max_dmdt=1.00098 ode_step=2.91866e-11\n",
-      "step=654, time=3.20908e-09, max_dmdt=0.961095 ode_step=2.91866e-11\n",
-      "step=655, time=3.23826e-09, max_dmdt=0.922736 ode_step=2.91866e-11\n",
-      "step=656, time=3.26745e-09, max_dmdt=0.885852 ode_step=2.91866e-11\n",
-      "step=657, time=3.29664e-09, max_dmdt=0.850391 ode_step=2.91866e-11\n",
-      "step=658, time=3.32582e-09, max_dmdt=0.8163 ode_step=2.91866e-11\n",
-      "step=659, time=3.35501e-09, max_dmdt=0.783531 ode_step=2.91866e-11\n",
-      "step=660, time=3.3842e-09, max_dmdt=0.752034 ode_step=2.91866e-11\n",
-      "step=661, time=3.41338e-09, max_dmdt=0.721763 ode_step=2.91866e-11\n",
-      "step=662, time=3.44257e-09, max_dmdt=0.692673 ode_step=2.91866e-11\n",
-      "step=663, time=3.47176e-09, max_dmdt=0.66472 ode_step=2.91866e-11\n",
-      "step=664, time=3.50094e-09, max_dmdt=0.637863 ode_step=2.91866e-11\n",
-      "step=665, time=3.53013e-09, max_dmdt=0.612059 ode_step=2.91866e-11\n",
-      "step=666, time=3.55932e-09, max_dmdt=0.587271 ode_step=2.91866e-11\n",
-      "step=667, time=3.5885e-09, max_dmdt=0.56346 ode_step=2.91866e-11\n",
-      "step=668, time=3.61769e-09, max_dmdt=0.540589 ode_step=2.91866e-11\n",
-      "step=669, time=3.64688e-09, max_dmdt=0.518623 ode_step=2.91866e-11\n",
-      "step=670, time=3.67606e-09, max_dmdt=0.497528 ode_step=2.91866e-11\n",
-      "step=671, time=3.70525e-09, max_dmdt=0.477269 ode_step=2.91866e-11\n",
-      "step=672, time=3.73444e-09, max_dmdt=0.457817 ode_step=2.91866e-11\n",
-      "step=673, time=3.76362e-09, max_dmdt=0.439139 ode_step=2.91866e-11\n",
-      "step=674, time=3.79281e-09, max_dmdt=0.421207 ode_step=2.91866e-11\n",
-      "step=675, time=3.82199e-09, max_dmdt=0.403991 ode_step=2.91866e-11\n",
-      "step=676, time=3.85118e-09, max_dmdt=0.387464 ode_step=2.91866e-11\n",
-      "step=677, time=3.88037e-09, max_dmdt=0.371599 ode_step=2.91866e-11\n",
-      "step=678, time=3.90955e-09, max_dmdt=0.356372 ode_step=2.91866e-11\n",
-      "step=679, time=3.93874e-09, max_dmdt=0.341756 ode_step=2.91866e-11\n",
-      "step=680, time=3.96793e-09, max_dmdt=0.327728 ode_step=2.91866e-11\n",
-      "step=681, time=3.99711e-09, max_dmdt=0.314266 ode_step=2.91866e-11\n",
-      "step=682, time=4.0263e-09, max_dmdt=0.301347 ode_step=2.91866e-11\n",
-      "step=683, time=4.05549e-09, max_dmdt=0.28895 ode_step=2.91866e-11\n",
-      "step=684, time=4.09967e-09, max_dmdt=0.274103 ode_step=4.41835e-11\n",
-      "step=685, time=4.14386e-09, max_dmdt=0.257185 ode_step=4.41835e-11\n",
-      "step=686, time=4.18804e-09, max_dmdt=0.241295 ode_step=4.41835e-11\n",
-      "step=687, time=4.23222e-09, max_dmdt=0.226373 ode_step=4.41835e-11\n",
-      "step=688, time=4.27641e-09, max_dmdt=0.212361 ode_step=4.41835e-11\n",
-      "step=689, time=4.32059e-09, max_dmdt=0.199205 ode_step=4.41835e-11\n",
-      "step=690, time=4.36477e-09, max_dmdt=0.186853 ode_step=4.41835e-11\n",
-      "step=691, time=4.40896e-09, max_dmdt=0.175258 ode_step=4.41835e-11\n",
-      "step=692, time=4.45314e-09, max_dmdt=0.164374 ode_step=4.41835e-11\n",
-      "step=693, time=4.49732e-09, max_dmdt=0.154159 ode_step=4.41835e-11\n",
-      "step=694, time=4.54151e-09, max_dmdt=0.144571 ode_step=4.41835e-11\n",
-      "step=695, time=4.58569e-09, max_dmdt=0.135575 ode_step=4.41835e-11\n",
-      "step=696, time=4.62987e-09, max_dmdt=0.127134 ode_step=4.41835e-11\n",
-      "step=697, time=4.67406e-09, max_dmdt=0.119214 ode_step=4.41835e-11\n",
-      "step=698, time=4.71824e-09, max_dmdt=0.111782 ode_step=4.41835e-11\n",
-      "step=699, time=4.76242e-09, max_dmdt=0.10481 ode_step=4.41835e-11\n",
-      "step=700, time=4.80661e-09, max_dmdt=0.098269 ode_step=4.41835e-11\n",
-      "step=701, time=4.85079e-09, max_dmdt=0.0921327 ode_step=4.41835e-11\n",
-      "step=702, time=4.89497e-09, max_dmdt=0.0863766 ode_step=4.41835e-11\n",
-      "step=703, time=4.93916e-09, max_dmdt=0.0809775 ode_step=4.41835e-11\n",
-      "step=704, time=4.98334e-09, max_dmdt=0.0759134 ode_step=4.41835e-11\n",
-      "step=705, time=5.02752e-09, max_dmdt=0.0711638 ode_step=4.41835e-11\n",
-      "step=706, time=5.07171e-09, max_dmdt=0.0667094 ode_step=4.41835e-11\n",
-      "step=707, time=5.11589e-09, max_dmdt=0.0625321 ode_step=4.41835e-11\n",
-      "step=708, time=5.18986e-09, max_dmdt=0.0573679 ode_step=7.39726e-11\n",
-      "step=709, time=5.26384e-09, max_dmdt=0.0514753 ode_step=7.39726e-11\n",
-      "step=710, time=5.33781e-09, max_dmdt=0.0461849 ode_step=7.39726e-11\n",
-      "step=711, time=5.41178e-09, max_dmdt=0.0414358 ode_step=7.39726e-11\n",
-      "step=712, time=5.48575e-09, max_dmdt=0.0371729 ode_step=7.39726e-11\n",
-      "step=713, time=5.55973e-09, max_dmdt=0.0333467 ode_step=7.39726e-11\n",
-      "step=714, time=5.6337e-09, max_dmdt=0.0299127 ode_step=7.39726e-11\n",
-      "step=715, time=5.70767e-09, max_dmdt=0.0268312 ode_step=7.39726e-11\n",
-      "step=716, time=5.78164e-09, max_dmdt=0.024066 ode_step=7.39726e-11\n",
-      "step=717, time=5.85562e-09, max_dmdt=0.021585 ode_step=7.39726e-11\n",
-      "step=718, time=5.92959e-09, max_dmdt=0.019359 ode_step=7.39726e-11\n",
-      "step=719, time=6.00356e-09, max_dmdt=0.0173619 ode_step=7.39726e-11\n",
-      "step=720, time=6.07754e-09, max_dmdt=0.0155703 ode_step=7.39726e-11\n",
-      "step=721, time=6.15151e-09, max_dmdt=0.0139632 ode_step=7.39726e-11\n",
-      "step=722, time=6.22548e-09, max_dmdt=0.0125216 ode_step=7.39726e-11\n",
-      "step=723, time=6.29945e-09, max_dmdt=0.0112285 ode_step=7.39726e-11\n",
-      "step=724, time=6.37343e-09, max_dmdt=0.0100687 ode_step=7.39726e-11\n",
-      "step=725, time=6.4474e-09, max_dmdt=0.00902843 ode_step=7.39726e-11\n",
-      "CPU times: user 59.4 s, sys: 4.15 s, total: 1min 3s\n",
-      "Wall time: 33.3 s\n"
+      "[  9.66821767e-01   1.25543432e-01   3.11232500e-18]\n"
      ]
     }
    ],
    "source": [
     "# PYTEST_VALIDATE_IGNORE_OUTPUT\n",
-    "sim.set_m((1.0, 0.25, 0.1))\n",
-    "sim.set_tols(rtol=1e-10, atol=1e-10)\n",
-    "sim.relax(dt=1e-13, stopping_dmdt=0.01, max_steps=5000, save_m_steps=None, save_vtk_steps=None);\n",
-    "np.save(\"m0.npy\", sim.spin)  # save equilibrium configuration"
+    "m = sim.spin\n",
+    "m.shape = (-1,3)\n",
+    "mx = m[:,0]\n",
+    "my = m[:,1]\n",
+    "mx.shape = (40, 160)\n",
+    "my.shape = (40, 160)\n",
+    "fig = plt.figure(figsize=(15,5))\n",
+    "plt.axes().set_aspect('equal')\n",
+    "plt.quiver(mx[1::4,1::4], my[::4,::4], my[::4,::4], pivot='mid', alpha=0.9, scale=45, cmap=plt.get_cmap('jet'), edgecolors='None' )\n",
+    "plt.xlim([-0.5,39.5])\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "plt.show()\n",
+    "print(np.average(m[:,:], axis=0))"
    ]
   },
   {
@@ -942,7 +682,7 @@
     "    sim.add(Zeeman(H))\n",
     "    sim.set_m(np.load('m0.npy'))  # load the equilibrium magnetisation from the previous step\n",
     "    \n",
-    "    timesteps = np.linspace(0, 1e-9, 101)\n",
+    "    timesteps = np.linspace(0, 0.2e-9, 21)\n",
     "    for i, t in enumerate(timesteps):\n",
     "        sim.run_until(t)\n",
     "        if i % 10 == 0:\n",
@@ -953,7 +693,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the created field_simulation function, we obtain the average magnetisation components time evolutions."
+    "Using the created field_simulation function, we obtain the average magnetisation components time evolutions. Note that we only run the simulation for a short time, a full version of this standard problem 4 can be found in folder examples/micromagnetic/std4."
    ]
   },
   {
@@ -971,29 +711,7 @@
       "field_1\n",
       "\tsimulated 0.0 s\n",
       "\tsimulated 1e-10 s\n",
-      "\tsimulated 2e-10 s\n",
-      "\tsimulated 3e-10 s\n",
-      "\tsimulated 4e-10 s\n",
-      "\tsimulated 5e-10 s\n",
-      "\tsimulated 6e-10 s\n",
-      "\tsimulated 7e-10 s\n",
-      "\tsimulated 8e-10 s\n",
-      "\tsimulated 9e-10 s\n",
-      "\tsimulated 1e-09 s\n",
-      "field_2\n",
-      "\tsimulated 0.0 s\n",
-      "\tsimulated 1e-10 s\n",
-      "\tsimulated 2e-10 s\n",
-      "\tsimulated 3e-10 s\n",
-      "\tsimulated 4e-10 s\n",
-      "\tsimulated 5e-10 s\n",
-      "\tsimulated 6e-10 s\n",
-      "\tsimulated 7e-10 s\n",
-      "\tsimulated 8e-10 s\n",
-      "\tsimulated 9e-10 s\n",
-      "\tsimulated 1e-09 s\n",
-      "CPU times: user 8min 51s, sys: 35.5 s, total: 9min 26s\n",
-      "Wall time: 4min 33s\n"
+      "\tsimulated 2e-10 s\n"
      ]
     }
    ],
@@ -1003,7 +721,7 @@
     "mT = 1e-3 / mu0  # millitesla\n",
     "\n",
     "field_simulation([-24.6 * mT,  4.3 * mT, 0], \"field_1\")\n",
-    "field_simulation([-35.5 * mT, -6.3 * mT, 0], \"field_2\")"
+    "#field_simulation([-35.5 * mT, -6.3 * mT, 0], \"field_2\")"
    ]
   },
   {
@@ -1020,29 +738,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "//anaconda/envs/p2/lib/python2.7/site-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
-      "  \"matplotlib is currently using a non-GUI backend, \"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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msI2oKFOGd/VqhzocOzb0CpZYLSoKHnkk6HZzZCo52e0IAnPmjNmB4NZixMaN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\n1u8opf4PKKy1/r8rrskPRGmtTymlrgKmA69qrad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gGjU6cfTsaf/PGzaMdC2Dx3X6cfA+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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114ecdd10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
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ULVPX67Ai1zffZNnEpEj+Irxz/TuM7TiWZ2c/y3WjrmN1/Gr7xYcfhtGjHQlh\nwfYFdBrXidfnve7I+dySkpZCmSJluHbUtXT4pgMrd690/iJjxkCbNrab3HPP5W5E9dprsDukuonn\nSER4Y94bvPnrm/6/+dAhu5qmeXPeW/gea/esdTa4Y8fsDVmkmjLFbnc6fDj85z9nfv3882HDBvt4\nr317SEoKfoy+EpFsX0BfoBTQAdid/no5p/cF82X/GQ5KSnL2fA5ZuOS4nN8gzdFzHk8+LsOXD5e0\nNGfPG/aOHBEpUUJkz54cDz2RckI+WPSB/PeH/9pPJCWJlC4t8tdfAV161+Fd8u6Cd+XCjy+UGu/W\nkHcXvCsHjx8M6FzBdvTEUXlnwTtSYVAFuenrm2TFrhXOnXzKFJFVq5w517PPirRtKxImP/epaanS\ne1pvaTC0gew4uMP/E8ybJ9KunYiIjF49Ws5+82z5adtPzgW4Y4f9mU9Nde6cDjt0/JAkpQTwu/34\ncZHatUWmTcv52JQUkVtuEenUyf7dRel5z/986dfBUBA4K5ALuflyNOn/+adInTr2hzjIth7YKu8v\nfF/uG39fpl8/ckSkUCFnf5YOHj8osR/FyguzX3DupB5ZuWuldPmuizM3MN98I9KqVeDvnzxZZO9e\nv9+27cA2Kfl6Sen6fVeZvmm6pKS6+4vDLYknEuX9he/LmDVjvA4lc8ePizRqJDJihNeR5Oh48nHp\nPK6zNBveTA4cOxD4iTL8fzFr8ywpO7CszP9rvgMRpjv3XJFly5w7Xy4lpybLqFWj5IrhV0iZN8pI\n4VcKy6IdizI9dvCiwbLh7w2Zn2jAAHuD6Ktjx0RatBAZPjyAqH0XaNLPsXeoMeZW4AcROQQ8CcQa\nY14RkeWOTzuEgqpVbS/166+3TT1KlXL1cnO2zmHG5hlM2TSFXUd2cWPdG2lbty0iZ661LloUypWz\n2ws41WG4RMESTO8ynWYjmlGmcBl6Nw20z28WXnrJro+uXNnZ855m+a7ltBndhsFtBme6Rt1v2Uzt\n++SGG/7569OznqZKiSoUzl+YvCYvefPkJY/JQ+fzO5M3T95T3latZDV29dlFoXyFTj9jWCmcvzC9\nLgnh3dcKFrTPZq++Gq66KmRrew4nHab92PaUKFiCH7r8kLsltxn+v7i65tV8ftPndPimAwvuX+BM\nrc/VV8OsWcFrApaNyRsn02taL6qeVZUnLnuCiytdTPmi5bP83fB34t9cMfwKmlZuyn+a/ocW1Vv8\ne2z58vANS9J0AAAgAElEQVTOO75fvFAhuz9HqNab5HRXAKxJ//MKYC5wI3Z7Xc9H+BlidOTO6R9p\naSK9e4tcf73r01V3fHuHPPfjc/LrX7/6NKpr1Upk6lQ/LtCtm0+zFtsObJMqb1eRL1Z+4cfJfdCu\nnci4cc6e8zSLdyyWs988W75b951zJ42LEzl0yJFTfb7ic+k+sbvcM/4eueu7u+SOb++QzuM6y96j\n/s8ERIKklCR5auZTsnr36jO/ePCgHVkNGRKcYF58UaRr1+BcKwC7Du+SvrP7ujbj89b8t6TtV36M\nYrMzfnzuZscctGLXClmwfYFf7zl64qgMWzpMzh18rsR8GCMTfp/gUnTOIMCRvi9L9laKSGNjzOvp\nNwCjjTErRCTWxXsRv+RqyV5WkpOheXPo0AH69AnoFPFH4lmycwnzt8+n0/mdMm2A4q9evewoP7Na\nkjMcP25nKg4csHefOVi/dz0tv2jJmI5jaFG9Ra5jBexI/9gx2xHNBQt3LKTd1+34rN1ntK3X1pVr\nKGcdTjrMwF8H8vmqzzmn2Dnc1/g+Op7TknIfj4YPP7SzbM8+ayul3fb33/DMM7YBRhjuyZFbIsKh\npEOcVciB/QkSEmxjqr17ffp9E6rSJI3pf0znz4Q/efiih70OJ0turtOfAsQBrYBY4Dh2nX7IdMRw\nJemD7eTVqpVdf1nctw5u438fz8jVI1kSt4QjJ45wYcULaVq5KXfH3E2t0rVyHdKQIbBmje3/kqOl\nS23nwVWrfD7/b3t+o0apGhTJ79D2ClOn2qmxmTOdOd9p7p9wPx3O60CbOm1cOb9yT2paKrM2/cCI\nL//DdNnEgyca8cbD39oljyo8vfgiPPJIWHQ5DXduJv2iwPXAahHZZIypADQUkRmBhXrKua8H3gXy\nAp+KyBuZHPM+0BpIBO4RkTO2c3It6YPdQjV9r3QRIf5oPL///TulC5emUfkz23rO3jqb+CPxXFTp\nImqVquXM8+UMZs2CV16BuXN9OPjjj2H+fPj8c0dj8Et8fPD7/ocSETvjEqrP90LB0KEk3Xg9+0oV\nomLximd8OU3S/t3pUPlv8WL7/6CPA5dwMnr1aOZvn8+QG4Z4HUr2/vzT7kjYtatjp3Rjl72THd4L\nAnOAfcaY0kASp27AExBjTF5gMPaG4jzgdmPMuacd0waoLSJ1gAeAD3N7XX/NifuVW8beQuywWEq+\nUZKGHzbkhTkvsGp35qPnq2pcxe0Nb6d26dqOJ3ywy0A3bPDx4GXL7MZCXipfHooUsc0rotFrr9lH\nHCprjzxCwao1M034AN0mdqPpp01589c32X5we5CDC56tB7by2i+v4fgApnNn/3bqCgMiwis/v8IL\nc17gwQsfdPrktidESsoZX5q6aSoPTX6Ig8cP+nfOfPlsp85FixwKMheyetgPTEn/cxuw9fRXIAUE\np53/UmB6ho+fBp4+7ZiPgM4ZPv4dKJ/JuXwuftiyf4t8uORD6Tennzw06SG5eczNcumnl8qTM57M\n9Pj1e9fLN2u/kSVxS2Rf4j6fr+OWtDSRokVFEhJ8OLhJE5Fff3U9phz99JOPAYeA1audXRO5YoVI\n9ephsx7cNQcOiIweHdBbjyUfkxl/zJBuE7pJ6TdKyxXDr5Chi4fKkaQjDgfpnQXbF0iFQRXk/YXv\nO3vi3btFSpb0uSA5LS1N+s/tL/FH4p2Nw0EpqSnSY0oPifkwRnYe2un8BebNE6lbN9P/Zw8cOyAP\nTHxAKr9d2f9Cv+++E6lVy7ECYQIs5MtypC8iN6T/WV1Eapz+cuB+oxKQ8bZ9R/rncjom07VfX6z8\nggG/DKDX1F50/KYjfef0zfSie47uYcWuFaRKKg3LN+SuRncx6NpBPHrJo5keX79sfW49/1YurHgh\npQuX9vGf5h5j7Fb1Po32R4zI9fKZIyeO8PWar3N1Dq68Es7KXaGQiPD5ys85euJo7mLJzsGDtne2\nk200Y2Ls8rBQuMP3yoIFdreohQvtKMpPhfIVolWtVnzS7hN2Pr6TJy57gvk75iO49EgvyMb9No62\nX7fl47YfO7/McdEiuOQSyOPb4xFjDCdST9BpXCeSU5OdjcUBSSlJ3PHdHazds5af7vmJCsUrOH+R\nTz6B7t0zfRxZslBJhrUdxsj2I+kzow/tvm7H+r3rfTtv+/a2Te+jmeeaYPFlnf6PInJ1Tp8LgK//\nx57+nc/0fW8PeJviBYtTvEBxLr78Ym658pZMT3ZJ5Uu4pPIlfoSZQVqa7V/dpo2nz6fr1YPff7ct\nyLPVsGGur5WYnMizs58lVVLp0qhLrs8XiOTUZHpO7cmiuEVcX/t6R3eQO8XEidCiBZQs6dw5jYHb\nb7fThU2bOnfecDFkiH28MWyY3SQnlwrmK0i7eu1oV6+dA8Gd5oMPbA1PgKt1/CUiDPx1IIOXDGZG\nlxnEVnBhQdTChX7/3PVv0Z92Y9rRZ0Yf3m/9vvMx5UKqpFK/TH2+uPkLd3pZJCTA+PHwZvatjltU\nb8Hah9cyZMkQ7p1wL/Pvn+9b3ck779gtnqdMOaWXhy/mzp3LXJ+KuXKQ1RQAdme9MsBqoHSGV3Xg\n90CmFU47f1NOnd5/BnjqtGM+Am7L8HGup/dzJSlJ5LzzRP73v+BcLwv9+4s880zwrrc2fq2c/ebZ\nMn3T9OBdNN2BYwfkupHXSetRreXQcWemxbJ0++0in3zi/HnXrxepWDGkW5S6YuJEkUqVRLZuDdol\n1+1ZF/jPyaJFIjVrBu2/07HkY9JpXCfZfnC7exdp2dLPxh7WgWMHpM77dWTEihH+X3PvXpEePfx/\nXygYMkTk1lv9eovfHUDnzMn175ltB7Y534YXeAz7/D6JU5/nrwZ6BnKx086fD9icfhNRAFgJnHva\nMW2AqfLvTcLCLM6Vq2+gX37+2f4i8/AZ9Zgxtr1zMM37c56UHVg2yzaWbpizdY5Ufaeq9J7WW5JT\nk929WGqqSLlyItu2uXP+zp1F4kP3OanjUlNFrrhCZIF/DVJy64XZL0iVt6vIt+u+9f+XcVqaSGys\nyA8/uBOcF/7zH5F9gdUirduzTsoNLOd/j/6UFFtHsNOF5+1uSksTadxYZMYMR063Nn6tJJ5IzNU5\nsnr/gWMHXG3O86iIuDLHY4xpzb9L9j4TkQHGmAfTs/iw9GNOVvgfBe6VTNr/urpkLzPdu9vmEx7t\nS71qFdxxB/z2W3CvO3njZLpN7Mb8++dTs5RDfYCz8cSMJ7iqxlW0rhOE7UBXrYJbb4WNG92/VrRI\nS/P5WbKTftr2Ew9PeZiapWryQesPqFHKjxKkjz+GH36w26gqlu5cSq1StShV2M925O3bQ8eOcOed\nubp+cmoy+fPmz9U5fCYCP/5o2zI78HPbbWI3vv/9e66oegWNyzem8TmNia0QS7WzqmW6smvz/s1s\nPrCZzfs3s3bPWubvmM9fB/9id5/dmX4P3F6n/zhQVUS6G2PqAPVEZLK/F3NL0JP+/v12K8UJE3x4\nsO68xEQoU8bWm+XLrCpDxLWag7nb5nJZlcsokLeAf28cNco2O3r+eVfiyrVly+zWoA895HUkygEn\nUk/w1vy3eGvBWwy4egDdm3T37Y2HD9uucn/8AWXLOhKLiDBhwwSuqXkNxQoUc+ScIW/wYFixAj77\nLKC3Hz1xlCdmPkEek4fBbQY7HFzwxB2KY+GOhazYvYKVu1eyYvcKlj2wjHOKnbk7fZOPm1CyUElq\nlapF/bL1uazKZVxQ4YIsf9e6mfS/AZYBXUXk/PSbgPkSDR35sjNqlL0rHDEiuNdNV726bdRTu3Ym\nX1y2zK4J/fHHYIeVtWnTYNCg0IpJRbzN+zcTdziOK6td6fubHnrIjlCbNcv19ZfELeHxGY9zKOkQ\n424dR90ydXN9zrCwbp0teN661e8ByJSNU+gxtQfNqjXjg9YfULKQg4W1EcTNpL9MRJpk7LdvjFkV\n9UlfxE5f5s2b87EuuP5624c/0wLQYcNs1a5HNySZ2rPHLjvYv/+UXwJpksbIVSOJOSfGkb0JlAoF\na/esZcC8AczdNpeXW77M3TF3n7GjYkQTgUqV4JdffG6rvOvwLnpP783yXcv56MaPuKbmNS4HGSIm\nTbJ5pI1/rcQd78iXQZIx5p8eosaYWtjivuhmjGcJH/5dtpepIHfiiz8Sn/P6+bPPtm1AN28GbLKf\nsnEKlw+/nKFLh2qb1XB34AA89VRA6/C9cPTEUXYe3unKudfuWct1o67j3LLnsqHnBu6LvS+iEn6P\nKT34as1X2R9kjJ3V82NL7U+Wf0Kd0nVY8/Ca6En4YDdFu+8+2OnOz+PpfPlN2w+YDlQ2xnwFzAae\ncjMolbNs2/EuWwYXXhi0WL5Y9QUXfXIRE36fwJETR7I+8MILObroF95d+C71Btfjxbkv0vOiniy4\nf0Gm+xhEpGeesU2AIk2fPnD0aNjsr7A4bjENhjbg4ckPsyZ+DU7OFJ5f7ny29d7G81c+790z/JUr\nYfRoV07d7YJuvPTTS3T9viuHk7JpZHXuubYxlY/6Nu/Lq1e/SuH8Qd6n4u+/7R4hXrniCvtI6e67\n7eyxy3JM+mI31ukA3At8BTQRkTluB6ayV78+rM+sEVRSkv1CTPCevjx5+ZO8cOULvLfoPSq8VYFr\nvryGt+a/deYvhKZNSVm6hOW7lvPlzV+ypPsS7mx0Z3SN8letstXhkeSHH2D2bNe2T3ZDyxot2dBz\nA2WKlOHmsTdT6e1K3DP+HlbsOmM/r3+ICHuP7mX5ruV8veZrun7flY37zlztYYwJXsV5ViZP9mt3\nTX/EVohl2QPLKJSvELHDYpm/fb7PN02JyYlM2jDJ0ZusXBs61O6R4aXnn7dbkL/9tuuXyvGZPoAx\nphJ2PX0+0jviicjPrkbmB0+e6Z/u8OGg7mK1ezc0aGBvUk+xdq3dyWn5GSsbg+Jw0mFmb53ND5t/\n4K1r3zr1rv3oUbtjYX6PfyFmtH8/9O1rq42D4aOPYN48WwgaCQ4dsp0fP/3UbkMdpjbv38yMzTO4\ntMqlmdaWdJvYjdFrRlMkfxEql6hMjZI1uK7WdXRu0Dkk2nOf4cYb4d57oUMHVy/z7bpveeyHx3jh\nyhd4oMkDZ3x979G97Dy8k3V71/Hd798xY/MMLq50MWM7jg2d79v559vWu5dd5m0cf/4JF11ki559\neDzrZiHfG0BnYB2QevLzItLW34u5xfOkL2KX7r3+Olyd2+7Evl+ydGm7rLxcudO+mJrqab1BWPnf\n/2D4cJg6NTjX27HDzsLs3h1aNz+BeuQR27r200+9jsR506bZfhwtW7Ln6B6K5i/qXgtoJ4nYGpoV\nK/x6ph745YTktORMl5Z1+KYDm/ZtokapGtxU7yba1WtH2SLOLIV0xNq1toBu2zZPekqcYfJkqFjR\npz1TAk36OfbeB9pj1+Vr8V5WjLFFTI8/bkfYQUi4xthHZuvXZ5L0NeH7bubM4I5QK1eGGjXg119t\nn/9wlpZmt032emrULbt32x7pLVtydtGzvY7Gd9u22Rm1ICR8sMknq7Xk33b61s7wFQ3Rm6UxY6BT\np9BI+GBnaFzmy790M7ZNrspOhw5QogR8/nnQLnky6atcmDUr+NPS7drZzX3CXZ48tveCkxsUhZJ2\n7exN4VEXd3Z0w+LFdpo4FCQk2JuP5NDbsQ8RGDsWbrvN60iCypeR/jFgpTHmR/5dqici4u3+gKHG\nGFuEcdNN0LkzFHO/avfcc20PDBWgLVtse8Pzzw/udR98EFJSgntN5b8yZey2tNOnu/5s3FEXXmi7\nd4WCkiWhWjVYuhQuvdTraE51/Lgd5QdxeXMo8GWkPxF4GZiP7cx38qVOd9FF0Ly53T4xCM47L0xH\n+rt2hcZ67pkz4Zprgr/MrHx527hEhb4OHWzdRzipVcverISKq68OzU6chQvDq6+G/jLTxYsdPZ1P\n1fuhzvNCvoy2bbMl9UFYJ791K1x5JWzfnv6JnTvts7OzznL92rlyslNXTfc37clWQoKtPq9a1ds4\nwsUPP9gb29IhUnUdDPHxthPW7t22qE/5b8oU+xhojq709tvRoxAbCz16QO/ep3zJzer9NdhlehlP\nfhBYArwiIvv8vajTQirpB1Faml0luGuXLSege3f7A/LII16Hlr2OHe0uXLncgUu56OBBW2y4fLl9\nLVtmn8tOnw6NoqSR0knLlkHjxlogG6gjR6BCBfuLKgiPPSPOn3/aot927ewKscJ2GbSbbXinA1OA\nO4A7gUnAUiAe+NzfCyrn5MkDdetmaMe7eHFQO/EF7NJLYf58r6NQ2RkxAt56y/af6NzZTs/u2BF9\nCR/sM19N+IErVgxuvtlOTSr/Vatmbzz37LFL+ZYsydXpfBnp/7PRzumfM8asEZGGuYrAAdE60ge4\n4w647jq4++oddjSye3cW++2GkPnzoWdPzxoIhYzUVPuLMNOtEpVS6jRjxsAHH8DPP2Py5XNtpJ/X\nGPNPVYgx5uIM79MSZI/9U8w3aZLdci/UEz7Yu9UNG8JvKZTTtmyx27empuZ8rFuOHg1Kv28VJJ06\nwaZNXkcR2ubNg/vv9zqKwNx2m40/FzNPviT9+4HPjDHbjDHbgM+A7saYokD4NNv2wqFDdorUxVmI\nf9bqT5hgn/mEg0KF4JZbIC7Om+snJdk+116rU8euYZ4925vr79ljV5uMG+fN9ZWzkpJsRzddGZK9\nsWO9LyLOjVyuNvBlw50lItIAaAzEiEhDEVksIkdF5JtcXT3SFS4MH3/s6gYr554L69eJTR7XXuva\ndRw3cqQtSPDCxIn2OXUouPNO13ZDy9a2bXZ3rzZt7OhQ5WzXLnsjH6pWrbI3kkWKeB1J6EpNhW+/\nhVtv9ToSz/jUe9AYcyPwANDbGNPXGNPX3bAiRP78MHCg3XbUpWYstWvDX9sNxwd/GtQNf8La9Ol2\nfX4o6NzZztIEc+Zh3Tqb8Hv1gpdeCv11yqGiTx9vbtB8tWSJ3QNEZe3XX23fcq8GHCEgx6RvjBkG\ndAIexS7b6wRUczmuyNGunW3G8sknrpy+QAHbyl0f4/lIxM68XHed15FYFSrYFReTJwfnegkJ9mfy\n5Zdt0le+u+OO0E/6odJ+NzMi8O673najHDcu6me2fBnpXyYiXYH9ItIfaArUczesCHKyPW///vYX\nrgu0B78f1q2zxY6hdKffp096o4UgSEmx17v33uBcL5Jcd50tQN22zetIMhfqSd8YuzeJwx3m/LJw\nYVRP7YNvSf/kvGOiMaYStmL/HPdCikCNG9t1qi4VbGnS98PJUX4oTWlff33wZh7KloWHHw7OtSJN\n/vy2sdSYMV5Hkrm5c6Gh5yuos3fdda7WOOVo0aLQuuH3gC9Jf5IxphTwJrbn/jbgazeDikgffmgr\n1l0Qtkl/+3b7fD2Yjh2zmyIpFYhQnuIvVy70l+xee623ST9UttD1kF+9940xBYFCInLQvZD8F7XN\neUTgwQdZecdAuj5aktWrvQ7IT0uXwl132Sn3UBp5K5WVtDR44QXo2xcKFvQ6mvCTlGRvTrZti649\nHFzgZu/9fMANQHUgL7aYT0Tk7QDidEXUJv116+D66zm67k/KljMcORJm3ULT0ux62QkTICbG62gi\n04kTdnQT6iNAFT1uuAHuuSfqn63nlpu99ycBdwOlgeJAsfQ/ldcmToR27ShazFC+fBi2ts6TB26/\nHb76yutIQsfhw8526Hv8cXjlFefOp1Ru9e2rN/ke8iXpVxKRW0TkRRHpf/LlemSRzokK4PSkD2H8\nXP/OO+Hrr7UV7ElONusZO9bWTDz2mDPnU6HpxAk7bR4uLrkkuMV0qakweLD+jknn0y57xpgQWdQc\nIY4csTvNLVoU+Dni4+30fvPmgE3669Y5FF8wNWgAJUvaftIKnnjCjoRy+0t8wwa7qdG4cfb7qyLX\nrFnh04LbC7/+Cp9+qkV86Xx50LcA+M4YkxdITv+ciEiQFhZHoGLF7E5Jd90FK1cG1jbzyy/taoD0\nYqJzz7U/22Hpo4+genV3rzFjhv1epd8kBWr3brvL5dGj9t7t6FG7xL5rV4dqEZs1szdCH30EvXsH\ndo7ERLu07NVXITY25+NV4Pbv974gbcGC8NhS2yvjxmn9QAa+FPJtA9oBa0UkJOdHwraQr0sXKFXK\n3gD46+SmMemjuF9/tY9vczN5ENGuuAKefDJXI6K1a+2S+gYNbKIvWtS+fv3VdvUdONChxL96NbRq\nZdssBtK05+23YcUKe2OoqyLc8/vvdgnapk3eVvJfdZWdIWrd2rsYQlVKClSpAj/9FHHr892s3v8Z\naCkiHu7/mb2wTfoHDtiCls8+s7/kc2H/ftuONyFBf8+fYcsW+xwxLs72LQ7AwoV2ef+779raw4z2\n74cWLWx3z+efz324gJ0FqlnTdnL0V2oqJCfb3QyVu2680SbbHj28uX5ysp1p2L49PB/jpKa6u+Ro\n4kR4440wngbNWqBJ35fp/a3AHGPMNOBE+udCasle2CpVyib8vn3tUDEX2bp0afs7Pi7ObrinMhg9\n2m5sE2DCnzXLJvrPP7erjU5XurR9etCsmd3zKNBZ+VO88grs2BHYe/PmDbO1m2GsXz/bbfP++725\nyVq50t7th2PCf/VV++dzz7l3jWHD4IEH3Dt/GPKlsmErMBsogF2up0v2nNSqFcyZ48jwPCbGzuqq\nDERg1Cg7cg7Ad9/ZJmzffZd5wj/pnHPszcHbb8OIEQHGmlG1anD55Q6cSLnqwguhSRO7hbYX4uLs\nbEM4atYMvnF5d/b+/fV5/mn86sgXqsJ2et8fqan/Vo1l4YUX7J8vvxykmJwmYp9PlCrl3DmXLLHL\n4DZs8PvGavx4eOghmDoVLrjAt/ds2AAtW9pZgWuv9T9cvyUm2voOJ79nyj8rVtjE+8cfULiw19GE\nj9RU+7x9zhyop3u4+cvN5jzKSydO2AqyZ56BBx/M9tCLLw7zQr7vv7fDaifFxNhta/1M+HPm2FnB\nKVN8T/hgf3d9+ik8+qh93Oq4+Hj7fHLYMLsk76KLYMgQFy6kfBYbC2+9pevA/ZU3L3ToYKvrVdDo\nSD8UidhK86lTYfNmO9XbsCG8+aZ9fpeFPXts0tm3L0yXpB49CpUqwcaNcPbZnoWxdCm0aWNnHlu0\n8P/9IvapTYcODm9od3KZQMOG9tWggb2padEiTP+Dq6j3yy/25nXVKq8jCTtuVu9fISLzTvvc5SIS\nMuWQEZf0k5Jg0iSoU8dmcT8KhGrUsE3Ywna27JFH7AggkGWMDli/3k7PDxuWu834VqywNw4bN9ri\nPqVUJtLS4Oqr7SxfOBYjesjNpL9CRGJz+pyXIi7p50LnzrbgrGtXryMJ0IEDcP758O23tmthEG3Z\nYgfNL78Md9+d+/N17WonacK2xkKpcLRnj93DolYtryNxlePP9I0xlxpj+gDljDGPG2P6pL/6Zfc+\n5a1LLoHFi72OIhdKlYJ33oHu3W09QxAcPmxXDV10ETz7rDMJH+yqu6FDbYG1Uo773//Cq+d+sAwd\nan+HqExll7wLYJfm5eXf3fWKAYeAju6HpgIR9sV8YLvcDBgQ8Hawx47BH1M3smdjAtlNAKWk2Gn8\nunXtkvhVq2y1vlOqVrX3LidXVagoERdnf4ZTUty7xv79cN992o/hdCkptvdJDkXP0cyX6f1qIvJn\nkOIJiE7v/ysxEcqVs8V80dKQbfNmu4nW77/b1+5dws+mOUPy9eZ/0oGaNe1MX8mS//bMP3IE/vrL\n1kC89ZZ/Ffr+OHjQ3lTMnAmNGrlzDRViRGzB5bXXwlNPuXONyZPhvffsD5b61/jxtgPfggVeR+I6\nx5/pG2PeE5HexphJmXxZRCRktnXSpH+qCy6wM1xNm3odifu2brV76Nxxh22vX78+1Jj9GXkHvwdL\nlnDweEG2bLHP6xMSbFFdsWL2VaqULYB3u23xBx/YpX/Tp7t7HRVCtm61jXtWrXKnRebTT9ueAC++\n6Py5w9Xx4/bO+p13su+kFSHcSPpNRGSZMaZFZl8Xkbn+XswtmvRP9dBDcN55dq14JNu+3Sb8Pn0y\ntD7fuNF2spszx2b0EHDihF2IMXZsdNyIqXTPPgu7djnUovE0zZrZhH/NNc6f2yuTJsG2bdCrV2Dv\nf/llu2zmu+8cDStUuVa9Hw406Z9qxAjbEnb0aK8jcdCePaes3d+1C6680q6Df/zx9E+eOAGXXQb3\n3uvdBihZGDoUpk2zv9dUlDh0yD7bmT4dGjd27rxJSVCmjP2fIJLWg/7xh70rXrcusD4dcXF22q5i\nRedjC0GudeQzxlxhjJlpjNlkjNma/toSWJgqGC6+OMwr+E+XmmrX8g4cCCLs2WM/vPfeDAkfbDOj\nChXsWv8Qc999sGyZ7o0QVUqUsNsyHj7s7HkTE+1GP5GU8AFq17bbjQf6yKJSpahJ+LnhSyHfBuAx\nYDnwz/a6IvK3u6H5Tkf6p0pNtc+rt261A4KIsGMHtG6NtLyKK5e+TYur82a+/j05GfLnD3p4vnj7\nbVtfpF1HlcrC/v22s9hPP9lnlCpLbvbeTxCRaSISLyJ/n3wFEKMKkrx5bQ3RkiVeR+KgypXhl184\nMHcVL/zWmf6Nv8+8uX2IJnywq4h+/tnOXiqlMlG6tN1n5MknvY4kYvmS9OcYY95Mb9ZzwcmX65Gp\nXLnkkghYr3+6kiW5tcQPVLmsKnlGfenSjjbuKVoUHnsMXnvN60iUCmE9etipyqNHsz9OxD7qUH7x\nZXp/LnDGQSLS0qWY/KbT+2caP95u8T11qteROOeXX+Cee+z2tQH27fHcoUNQsyYsXGgfYSqlAhAX\nB9262Q2nXn/d62g8odX7EfDvcNLOnXbJ6t697q9DD5bWraF9e7vlbTh78UX7O+vTT72ORAVVYqKd\nfmsZMuOl8CMCo0bZdbo9e9pHASH8SM9Nbm648yJ2pG/IMOIXkZf8vZhbNOlnrkoVmDs3MvadWL4c\n2nwe0JEAAB1cSURBVLWz3fcKFvQ6mtzZv9+u21++3G7Io6LE9u126V6g/+F377a7OP3wQ+Tcyftj\n5kzbenPLFvjiC/faaIYJNwv5jqa/jgBpQBugur8XUsEXSUv3XnsN/vvf8E/4YGuVHnwQXn3V60hU\nUFWpYjtmBVqk9vXXdllaNCZ8sDtwNm0KS5dGfcLPDb+n940xBYEZItLcnZD8pyP9zA0caFe6vf++\n15Hkzvr1dsvbLVtsMVwk2L/f9m3RZ/tRJjHR9ooePdp21fNHbKzdKOKqq9yJTYUVN0f6pysKVArg\nfSrIbrrJtn49dszrSHLn9dftAClSEj7Y0f6jj8JLIfOQTAVFkSJ2Q5jHHoO0NN/ft2YN/P23vftV\nKhd86ci3JsPrN2AD8J77oancqlfPLt378kuvIwncn3/aDcVCrKuuIx57zHZo1XX7Uea22+wWmDNm\n+P6ekSNtt7o8gYzTlPqXL4V81TN8mALEi0hILZDW6f2s/fSTrXZfvz48f1+89pqtdB8yxOtI3DFw\noH1E+c03XkeigurwYf/a6DZrBsOGaZc69Q9dshcB/w43iNiCvhdesNXv4SY21rYvbx4yFSTOSky0\nz/SnTnV2TxYVYUSit4BPZSqYz/RVGDHGLml96y2vI/Hfpk12ldIVV3gdiXuKFLFLjV94wetIVEjT\nhK8cokk/CnTsaJ+Nh1sv/nHj4JZb7F4CkeyBB2DVKlvJr5RSbtKkHwXy5YPevcNvtD9uHNx6q9dR\nuK9gQTvSf/ppO4urotCKFeF3V67Ckib9KNGtm21otW2b15H45o8/YNcu/5cyh6t774UjR2DECK8j\nUZ6Ii7NFNxs3Qnw8dO8OP/7odVQqAmnSjxLFi9vE/16YLLYcNw46dIj8qf2T8uWD4cPtaH/nTq+j\nUUF3443wyiu2L//550PJktCkiddRqQik1ftRZMcOuwnPjBlw4YVeR5O92Fh4553o60XSty+sXg3f\nf6+1W1Hp+++hQQO7OYNS2dDqfZWjypXtdrs33GB3egvV7eijbWo/o+eeszO8//uf15EoT7Rvrwlf\nuUqTfpTp2PHfmqFLLoG1a72O6EzRUrWfmYIF4bPPbIveffu8jkYpFWk06UehihVhyhR45BH7CPGV\nV0KrP/+4cdCpk9dReOfSS6FzZ3j8ca8jUUpFGk36UcoYW9i3ZIkd+Z93nk22XpdG/PGHLWSLxqn9\njF59FX75BcaP9zoSpVQk0UI+BcCcOfCf/9gq/3ff9a5weMAA2L4dhg715vqhZNEiaNvWNu2pWdPr\naJRSoUQL+VSutGwJy5bB3XdD69bejTC/+87WHShbc/Hcc7ZB0fHjXkejlIoEOtJXZ1i2zCb+sWPt\nzUCw7NxpVyvFx0P+/MG7bigTsUn/7LN19kMp9S8d6SvHNGlit3rt3NneAATL5Mlw/fWa8DMyxlbz\nz5wJX33ldTRKqXCnSV9lqkULu6b/xhthw4bgXHPSJPsMW53qrLNskWXv3rB+vdfRKKXCmSfT+8aY\n0sBYoBqwDegkIgmZHLcNOASkAskicnEW59PpfZeMGAH9+8O8eba5j1sSE+Gcc+xugKVKuXedcPbx\nxzBsGCxeHJ09DJRS/wq36f2ngZkiUhf4Mf3jzAjQQkRis0r4yl333mv3/rjzTkhLc+86s2bZ1sCa\n8LPWvbtt3vPll15HopQKV14l/XbAF+l//wK4OZtjtQO5x55+2rbs/egj964xcaLdZExlzRi7YdJz\nz8Hhw15Ho5QKR15N7x8QkVLpfzfA/pMfn3bcFuAgdnp/mIh8ksX5dHrfZevX24Y5S5dC9erOnjst\nDSpVso8QatVy9tyR6O677ffrtde8jkQp5ZVAp/ddS/rGmJnAOZl86Tngi4xJ3hizX0RKZ3KOCiKy\nyxhTDpgJ9BKRXzI5TpN+EAwYYJv4/PCDszvALV4M99wD69Y5d85IFhcHMTG2m2KNGl5Ho5TyQqBJ\nP58bwQCISKusvmaMiTfGnCMiu40xFYA9WZxjV/qfe40x3wMXA2ckfYB+/fr98/cWLVrQItr2ZA2C\n//7X7v42YgTcd59z59Wpff9UqgSPPQZPPmmr+pVSkW/u3LnMnTs31+fxanp/ILBPRN4wxjwNlBSR\np087pgiQV0QOG2OKAjOA/iIyI5Pz6Ug/SFatglatYOVKu3GPE2JibOOZyy935nzR4NgxqF/fFvU1\nb+51NEqpYAu36v3XgVbGmI3AVekfY4ypaIyZkn7MOcAvxpiVwCJgcmYJXwVXTAw8/DA89JAzm/P8\n+Sfs2gVNm+b+XNGkcGEYONCO+FNTvY5GKRUutA2v8tuJExAbCy++mPstcAcPtsWBn3/uSGhRRcTe\nLD31FNxyi9fRKKWCKdxG+iqMFSgAn3xiR5kHDuTuXNqFL3DGwOOPw/vvex2JUipc6EhfBaxHDzvq\n/yTThZQ5O3TIdvmLi7Nb+ir/JSfbCv7Jk6FxY6+jUUoFi470VdANGADTp8NPPwX2/i++sEWBmvAD\nlz8/PPKIjvaVUr7Rkb7KlfHj7TPlVaugUCHf33fiBNSuDd9+Cxdd5F580eDvv6FOHdi4EcqV8zoa\npVQw6EhfeeLmm6FBA3j1Vf/e99VXUK+eJnwnlC0LHTrYDXmUUio7OtJXubZzp13KN3s2NGyY8/Gp\nqXD++XZt/lVXuR9fNFi9Glq3hm3b7JS/Uiqy6UhfeaZiRXjrLTva9KWa//vvoWRJaNnS/diiRaNG\nULeu7ZiolFJZ0aSvHNG1K7RpA507Q0pK1seJ2ALAZ55xtn+/gt69taBPKZU9TfrKMYMG2aT+5JNZ\nHzNjBiQl6dp8N7RtC7t32w2MlFIqM5r0lWPy5YOxY23DnREjMj/mtdfsKD+P/uQ5Lm9e6NkT3nvP\n60iUUqFKC/mU49avt5vAjB8Pl1327+d//RXuussuLcvn2v6O0S0hwTbr+e035zZEUkqFnkAL+TTp\nK1dMmQIdO9qOcSKQlmaf4Y8YAXff7XV0ka1nT1so+corXkeilHKLJv0I+HdEmpQUm/CNsdP5xmjx\nXjBs2ABXXml3MPSnYZJSKnzokj0VcvLls2vG8+X7N+kr99WrB02a2AZISimVkSZ9pSLQY4/Bu+/a\nmRallDpJk75SEahVK/t4Ze5cryNRSoUSTfpKRSBjbLMeXb6nlMpIC/mUilCJiVCtGixcCLVqeR2N\nUspJWsinlDpFkSJw//0weLDXkSilQoWO9JWKYNu32x0Qt2yxa/eVUpFBR/pKqTNUqWJ3P3ztNa8j\nUUqFAh3pKxXhdu2CBg1g2TKoXt3raJRSTtCRvlIqUxUq2Er+Z57xOhKllNd0pK9UFDh61Hbq+/Zb\nuOQSr6NRSuWWjvSVUlkqWhRefhn69NEufUpFM036SkWJrl3h8GH47juvI1FKeUWn95WKIjNnwsMP\nw7p1UKCA19EopQKl0/tKqRy1agV169rNeJRS0UdH+kpFmS1b4NJL4ZtvoHlzr6NRSgVCR/pKKZ/U\nrAkjR8Jtt8Fff3kdjVIqmDTpKxWFrr0WHn8cbrkFjh3zOhqlVLDo9L5SUUr+396dx0dVn3sc/zwC\ngvgqImq1gIq1KmqFohfqhVrRUkWroAgXERDrUmtxrVdQrOKCWrRWrVpaRYq2KtUCCohYLGsRLVtI\nkEVAKBC0VXAjEMnyu388k5obQnImmSUz832/XvNiZnLmnGd+xjzntwe45BJo3Biee8634xWRzKDm\nfRGJixk88wysWAGPPZbuaEQkFRqnOwARSZ/mzWHyZB/Y164dXHBBuiMSkWRS0hfJce3awbRpcM45\n0KIFnHlmuiMSkWRR876IcMopPoXv4oth0aJ0R1O7khIoLk53FCKZR0lfRADo3h3GjoXzz/cV+xqa\n4mKYOhUuuwwOOwyOOaZhxlmd8nKYMwe2bEl3JJLrlPRF5D969YKHHoKzz4aNG9MdjSsrg+uu80T/\nq1/BySfD8uVw//3eFfHOO+mOcO+KimDMGDjhBBg6FDp29O+ydWu6I5NcpaQvIv/P4MFw/fUwYIAn\n3HQKwZPkypWwejXMneuxtW3rcVa0TMycmd44q9q1C4YPhyOP9NieespnSaxaBU2bwre/DTfeCB9+\nmO5IJddonr6I7KG8HHr0gJ49Ydiw9MVx333w8sswb54PMqzO/PnQty888QT065fa+Pbmhhtg7Vp4\n8kk46qg9f/7hh/DAAzBlit/Q7Ldf6mOUzFbXefpK+iJSrY0boXNn74s+8cTUX/8Pf4B77oG33oJv\nfKPmY5cv9y6JiumH6TRzJlx+ucfUqlXNx/brByedBHfemZrYJHso6WfB9xBpaJ56yh8LF0KTJqm7\n7vTpnjjnzoXjjov2mWef9f7zhQvTt7rg9u3ebz9unO9oWJt//tNnTixbBocfnvz4JHtoRT4RSbir\nroKDDoLRo1N3zbw8GDLEa+1REz54H39pKUyYkLzYahIC/Oxnvp9BlIQP3uc/dGh6u1Akt6imLyI1\n2rzZa6MzZ3otNpk+/9yvdc89PpAwXvPnw6BBPugv1f3kL7wAo0bBkiXxXXvnTmjfHp5/Hk47LXnx\nSXZR834WfA+Rhmr8eHj0UV+4J1nN/BUbAH3ta96lUFd9+0KnTnD77YmLrTYVN0YzZviUwnhNmOCt\nKYsXQ6NGiY9Pso+a90UkaYYMga9/3UfIJ8vYsfDuu/Xf/OfBB+GRR+CDDxITVxRDh/pUwrokfID+\n/f1mZ9y4xMYlUpVq+iISyZo10K0b5OdD69aJPXd+PvzgB9483759/c83bBhs2+a7CCbbggXeQrFm\nDTRrVvfzLFvm+x+sXg0tWyYuPslOat7Pgu8h0tDdfju8/z68+GLizrljh08NvO02uPTSxJzzs898\nEODrr3tTf7KE4MsXDxnisw3q68or4ZBDfA6/SE2U9LPge4g0dDt3+pKy48YlZje+8nIfeNe0qc/L\nT6QxY2DSpOSu1jdjBtx0ExQUQOME7FlaWAgdOnjLR5s29T+fZC/16YtI0jVv7gP6hg6F3bvrd64Q\n4Oc/h02bfOW6RLvyStiwAWbPTvy5wW9YRoyAe+9NTMIHT/Q/+QmMHJmY84lUpaQvInHp3Ru++U0f\nLFcfo0Z5Qp42zW8mEq1JE7j7bu+SSEZD4F/+AvvsAxddlNjzDh/+1fK8Iomm5n0Ridv69fDd7/qc\n9COPjP/zTz7pNw1//7vvnpcsZWW+tsAvfwnnnZe485aW+tLEjz8OZ52VuPNWePhhH9T4yiuJP7dk\nBzXvi0jKHH003HGHLybz9tvxffaFF3yg2syZyU344HPeR43y2n55eeLOO368z2CIuvJevIYO9dH8\nCxYk5/ySu5T0RaRObrjB5+336uU199oa2woLvQ//ppt8AFx1u88lQ+/ePlDw5ZcTc75du3zFwAce\nSN4a/82a+TWGD09O14TkLiV9EamzXr18g5unn4aBA336XVXr18PVV/tucgBLl/p+8qliBvff7y0T\npaX1P9/o0d61ceqp9T9XTQYN8qmHU6cm9zqSW9SnLyL1tmuXN0lPnw6HHuqD6Jo08YT73ntwzTXe\nMnDwwemJLwRf/GfgQLjiirqf5/33oUuX1O2KN2OGb+KTlwctWiT/epI5NE8/C76HSKZbuxaKiqCk\n5KvHd74DBxyQ7si8RaJ/f1i1Cvbfv27n6NULunaFW29NbGw1ufpqKC72rYNz2datPmNi+/avHjt3\n+uqLyW51aYiU9LPge4hIcg0e7Gvc//a38X922jS4+WZfiGfffRMf294UFfma/nffDRdfnLrrNiSL\nFsGFF0KPHj5bpFUrf+zcCXfe6a1MI0Ykbr2ETKCknwXfQ0SS69NPfQrf737n69xHVVzsU/TGjEnO\nFL3aLFni8S5eDEcckfrrp9NLL8G11/q4kd699/x5YaEvg1xcDH/6E7Rrl/IQ00JT9kREatGypTeT\nX3klfPxx9M89+KCv4Z+OhA++be/NN3tLRVlZemJItRC8deOWW3x6Z3UJH3wVw7/+1VsCOneGyZNT\nG2emUU1fRHLOLbf4rIKJE2ufdrdhgyeTpUvTW8suK/Pm7bPO8s2JsllJCfz4x7BunS9QFHU9hyVL\nfBGm3/wG+vVLbozpppq+iEhEo0Z50h8/vubj3nvPk8itt6a/Wb1RI/jjH33vg+efT28syVRS4lsV\nf/qpL9MczwJOp5ziMx6uu85v6GRPqumLSE4qKPCdAhcsgGOP3fPnU6Z4N8CoUb4JTkNRUAB9+sDZ\nZ8Ovf52YQYWFhb7A0tq1PtPigAN8imDbtt6lkKqBiyUlPlhx924fqd+0ad3Ok5cHPXv6GIwLL0xs\njA2FavoiInE46SRftKdLFzj/fE8yxcW+XO/IkT4ifMqUhpXwweNevBi2bIHTT/d/6yovDy691M9Z\nVAR9+/r0t9atvRxeeumrro1k273bp1SWltYv4YNPE50+HX76U/9vKF9RTV9EctqOHTBpkg/wy8vz\n5YH3398T3qGHpju6vSsv9wGGjz3mtfTevb0LoDYhwKxZfsOzZg1cfz1cdRUceGD1xz7/vA8ivOoq\nX9WwPsl4b7780hM+eLknqmVhyRI491wvo2yb7qgpe1nwPUQkvTZt8kV8+vTxFQUzwaxZPke9sBAu\nv9xXHKxu/EEI8NprcN993l9+222eCKMk2A8/9FUV166FceO8dSRR3nrLu1E6dvQbr0R3JRQUeOK/\n6Sbf+yFbKOlnwfcQEamr/Hyfy/7CC76YT9u2nkD33ddvYGbP9sR/++1+UxOlVaCyEODPf4Ybb/Qu\ngbvvhv32q3u8n3/uNyuTJ/to+z59kreB0aZN3sd/zjnw0EOwTxZ0bCvpZ8H3EBGpr5074Y034JNP\nfGDc7t3+OP54T3r1Taz//rcvlpOf77X+rl3j+3xxsU/DGzbMByM++GD1XQuJtn27L6N8xBE+ayOV\nqyomg5J+FnwPEZFMMXGiT43r1csHAHbrtveaf0kJvPmmtxS8+qo35Y8cCWeckdqYd+3y6YAffeSx\ntGmTmuuWlfneARs3+s1Yy5Z+o1OxnHBdWkyU9LPge4iIZJJt27xp/s03vebfubNPg9xvPx9jsHWr\n/7tqlU+LvPhiv0Fo3Tp9MZeX+yDGJ5+E556DH/4w8edfscJXEfzb32D1ai+Dgw7yJYJbtfItk7dv\n9xuAbdv8Zyec8NXjxBN9RkXLlnu/jpJ+FnwPEZFM9cUXMG+ejx0oK/PE3qaNP771rdTVqqOaNQsG\nDfJdDH/xi/jHOFRWXOxTBCdO9BugFi189cQePaBDB9+GuVm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UBkQC9vvfw1dfucX9Tj4Zvv++Em+yfDn06AE33ADjxrmEceqpoS5VAlW9uhtU+MEHLuXl\n5npdkSSAp556irlz5zJlyhSvSzlAYUCkAmrWdH/I33KLm7E2eXIl3qR/f7cWsrbhjQ4NG8KcOZCT\n44La9u1eVyRRzhz0/+3Bnx/OtGnTWL16NY0bNz4wq2D8+PGhLLHCNJtApJKWLnVbApx5ptskMCXF\n64okKEVFcOutbpvkzz9XUPNYNM8m8IL2JgiQwoB4YfduGDnSbaD3+uslxqHl5kJ2NmRkeFqfVMLG\njTG+a1V8UBgoTVMLRaJYWhpMmwZXXw2nnw6ff1oEr7wCbdq4/gSJPQoCEgJTp0490AVQfKSmptK2\nbVuvS/NLLQMiIfLN/XNI+r9baN7cUPfZB9w+AiJSKWoZKE0tAyLRzlq49FI6TbqKOvf8mbb7vuZf\nS7t5XZWE0kcfuZ0QReKUWgZEQmHOHOjSBWrUYM0aOP98t1ne+PFuzxyJYUVF7j9mQYHbKyItzeuK\nEoJaBkrTAMIAKQxINNm2Dfr2dfsavPiiVhaOeQUFMGaMm2Uwc6b7DythpTBQmroJRKLJ7t0BnVa/\nvmtZzstzrQQ7d4a5LgmvatXgiSdg+HDXAhS2jSqkWPPmzTHG6PAdzZs3D+v9VsuASCCKiuC55+DO\nO12XwLHHBvSywkK46SaYPRtmzdIflHHhzTfh7rtdIKim7V3EW+omOIjCgITN4sVw3XVuoOCTT0KH\nDhV6ubXw8MPwyCOuhdnjzckkFPLy3LrUIh5TN4FIuO3d6/6s794drrjC9RdXMAiAW8ju5pvhgQfg\n3HNdK4HEOAUBiTNq4xIpjzFQtSp89x00aBD0211yiVvPZtAg11IwdGgIahQRCQF1E4hE2HffQc+e\nrufhttu0BH7c2LMHatf2ugpJMOomEIlRxx3n9sJ55RW4/no3yFBi3JdfQqdOsGOH15WIVIrCgMhH\nH8F557lBYRHSpAl8+qnb4Oiii2DfvohdWsKhSxfX3NO/v9ukSiTGKAxI4tq6FYYNg1Gj4IYbIj4o\nLC0N3n8fUlPhnHNcORLDHnjALTBx5ZVuColIDFEYkMRjLbz6KrRt6wYGfvst9OrlSSnJyfDyy3DW\nWXDqqfDjj56UIaFQpQpMmQI//QRjx3pdjUiFeB4GjDHnG2OWG2NWGmNu8/P14caYzcaYBb7jSi/q\nlDjy1Vfwt7/B22+7Yf21anlajjFw331w443QtSvMn+9pORKMlBSYPh2++Sbg1SpFooGnswmMMVWA\nlcA5wAbgG+ASa+3yEucMB0601t5wmPfSbAIJXEFBVK4e9847cM01brXCjh29rkZEol2oZhN4/a/h\nycAqa+1aAGPMNKAvsPyg8zT5SkIrCoMAQL9+rhejZ0/48EOtVigikeF1N0ET4JcSn6/zPXewAcaY\nRcaY14wxTSNTmsS8ggLXJRBj+veHxx5zExy+/97rakQkEXgdBgIxA2hhre0A/Bd4yeN6JBYsXepG\n5I0b5zYZijGDBrnB6d27w4oVXlcjQbM2olNXRSrK67bS9cCRJT5v6nvuAGttyVU8ngXuL+/Nxo0b\nd+Djbt260a1bt1DUKLEkN9eNxnviCfd49dUxu8TfZZdBfr7bz2DOHGjd2uuKpNJefRVeegnefTdq\nu6gkNmRlZZGVlRXy9/V6AGFVYAVuAOFGYB4wxFq7rMQ5jay1v/o+7g/cYq091c97aQBholu61G0A\n0Lq1CwNN/PU4xZ6nn4Z774W5c6FFC6+rkUopKIDeveHII2HSpJgNqBJ94mYLY2PM+cCjuC6L56y1\n440xdwPfWGvfM8bcB/QB8oHtwHXW2pV+3kdhINGtXevGCAwaFHf/2E6c6GZBzp0LzZp5XY1USna2\nmzs6ZIjblEIkBOImDISKwoDEu4cfhiefdIGgcWOvq5FKWb/eLV18//2uFUskSNqoSCTB3HQTXHUV\nnH02bNrkdTVSKU2awHvvwVNPaYcqiSpqGZDY8+678NZb8MILXlfiiXvugddec4MKGzTwuhqpFGvj\nritLvBEviw6JBG7bNvjjH912sc8953U1nhk71s1S694dPv4Y6tXzuiKpMAUBiTLqJpDY8NZbbmOh\njAxYsgQSeNqoMW5rhe7doUcP2LnT64pEJNapm0Ci35tvwl/+As8/D6ed5nU1UcNa+NOf4Ouv3dLF\naWleVySVpm4DqSTNJjiIwkAcy89387RTUryuJOpYC9df7xpL/vMfqF3b64qkUu64A44/HoYO9boS\niTEKAwdRGJBEVVQEo0bBjz/CzJlQs6bXFUmFLVzo+ny+/hpatfK6Gokhmloo8cdaWLfO6ypiTpUq\nbpXCZs2gb1/Yv9/riqTCTjjBdYUNHepawUQiTGFAosO6dXDhhW4vAamwKlXcTMuMDBgwwG3RIDHm\nj390Az/uucfrSiQBKQyIt6x10wRPOAFOPhlmzPC6ophVtSq8/LLrJhg4UJvkxZwqVdxmRs8846bP\nikSQxgyId9audZ3dW7e6P2vbtfO6oriQlwcXXeTWH3jxRQ1SjzmLF8PvfqcBsxIQjRmQ2Ld6NZx5\npttcSEEgZJKTYdo0+PZbGD/e62qkwtq3VxCQiFPLgEic2rABTjnFbXB08cVeVyMi4aCphQdRGBAp\nq3jG2vvvQ6dOXlcjIqGmbgKJHQsWwL/+5XUVCemEE+DZZ6FfP/j5Z6+rkUrJztZoUAk7hQEJn337\n4NZb4fzzoU4dr6tJWH37wo03Qu/e7veKxJjbbnO7U4mEkboJJDw+/tjNFOjUCR55BBo29LqihGat\n+8/x66/wzjtuGqLEiK1boUMHN+3wnHO8rkaijLoJJHo9+yyMGOFCwCuvKAhEAWPgiSdcY80tt3hd\njVRIRoabejt8uAsGImGglgEJve3bISkJUlO9rkQOsmOHm2Fw001wzTVeVyMVcsstsGoVvP22Fo+Q\nAzSb4CAKAyKB+eEHOP10mDIFzj3X62okYHl50KULPPggnHWW19VIlFAYOIjCgAeKilwrQEaG15VI\nBX3yiVuyOCsLjjnG62okYHv3Qq1aXlchUURjBsRbK1ZAt24a5RyjzjgD7r/f7Q2lbugYoiAgYaIw\nIBWTnw/33gunneb+tJw40euKpJKGD4fBg6F/f+1yKJLo1E0ggfvmG7jqKmjaFJ58Epo397oiCVJR\nEQwa5HY6fOkljUsTiTUaM3AQhYEIePFFtwvOkCH6rRFH9u1z+0X16wf/939eVyMVsmgRNGrkDklI\nCgMHURgQqbyNG6FzZzdQfdAgr6uRgI0dC/Pnw8yZUEW9volIAwhFJGQyM+Hdd2H0aPj6a6+rkYDd\ndZdbPOKxx7yuRGLcIcOAMaaLMeZxY8wSY8wWY8zPxpj3jTHXG2O02Hw82rABhg51k9AlobRvD889\nBwMGaFOjmJGUBFOnukG9ixZ5XY3EsHLDgDFmFnA18AFwPpAJHAvcCdQAphtj+kSiSImAvDx44AFo\n1w5atHBDzCXh9O4NN9/sphxqU6MY0aoV/POfcOmlbgCISCWUO2bAGJNhrT3kDORAzokUjRkIwgcf\nwB//CK1bu/0EWrf2uiLxkLVw7bWwfj1Mn65NjWLGH/4Aw4a5VQolYWgA4UEUBiqpeG7ZiBHuz0ER\n3HISPXvC8ce7PzpFJDp5GgaMMUuttW2DvXgoKQyIhNbOnW5To9tvd1lRRKJPqMJAtUNcYEB5XwI0\nqVUkzqWnw5tvulWnO3WC447zuiIRCZdDjRnIB/4N+DvhYmttVO1Pq5aBw1i+3E1DmjQJ6tXzuhqJ\nIS++6MaWzpunpfFFok0k1hlYAjxorb3i4APYGeyFJUI2bYIbb4SuXeHUUyE1qjKcxIARI1zLwOjR\nXlciASsqgrfecqNBRQJwqDDwJ2B3OV/TvLNot3Ur3HorHHus+4fh22/hT39y85JFKujxx91iRC++\n6HUlEpCiIrjvPv0Hk4BpNkG8mjfP7Txzxx1uYyGRIH37LZx1FmRlafxATFi4EM47D5YuhYYNva5G\nwiTsswmMMXcCT1hrt5fz9bOBmtba94ItIhQUBkTC74UX3P4FGj8QI269FX75BV55xetKJEwiEQb6\nArcC+4EFwBbcyoNHAx2A/wL3WWu3BFtEKCRsGNi1CwoKoH59ryuRBGCtG0NQtSo8/7zX1chh7dsH\nbdvCv/7lFo6QuBP2AYTW2unW2tOAa4HvgKq4MQRTgJOttTdGSxBISNnZbj3y1q3djmUiEWAMPPEE\nfPWV64WSKFezJjz1lGvSETkEjRmINXv2uNFcDz0EPXq46YK/+53XVUmCKR4/MHeuG6MqUc5al+Qk\n7oR90SGJQnv3Qps2bprg3LlwzDFeVyQJ6vjjYcIEGDhQ4wdigoKAHIZaBmLNhg3QuLHXVYhgLQwf\nDtWqafyAiFciseiQeKm8YKMgIFGiePzAl19q/IBIrDtsy4AxpgEwEmhBiW4Fa+2VYa2sguKiZaCw\n0G0n/OyzkJamBUMkJmj8QIzZsgVycuDII72uREIgkmMGpgOf4qYSFgZ7QfFjzRrXzvrCC+4v/6uv\nhsGDva5KJCDF4wcGDXLjB2rW9LoiOaRp0+Dtt2H2bI0lkAMCaRlYZK3tEKF6Ki1mWwb273cDAfv0\ngauugnbtvK5IpMKKxw8kJcFzz3ldjRxSYaHbm/q66+DKqGrglUoI+6JDJS70d+ALa+37wV4snGI2\nDIBbR7yKhm9IbNuzx21odMcdcPnlXlcjh7R4MXTvDkuWQCPtSB/LIhkGsoFaQB6Q73vaWmvTgr14\nKEV1GNizB157zfXRnXuu19WIhM3SpXD22fDJJ5r5GvXuuAN++glefdXrSiQIEZtNYK1NtdZWsdbW\n8H2cGm1BICpZ6zpQR46EZs1g+nR1pkrca9sWxo936w/s2+d1NXJId93lWgi+/97rSiQKBLTOgDGm\nD3CG79OsaNmcqKSoahlYuxZ693b/Gl51letM1ZRASRDWum6C5GSNH4h6OTmQkuJ1FRKESHYTjAc6\nAf/2PTUEmG+tvSPYi4eSJ2Fg/36oUaPs8zk5bvP3M87QWABJSHv2wEknwV/+ovEDIuEUyTCwBOhg\nrS3yfV4VWGitjaph7xEJA3v2wOefuwnVWVlugvX69ZCaGt7risQgjR8QCb9Ir0CYXuLjOsFeNCYN\nHOhG3d53n1t/9e9/h19/VRAQKYfGD4jEjkBaBoYA44E5gMGNHbjdWhtVQ1CDahkoKoLt22HTJte3\nX7du2XNWrHCzAdS/JhIwjR+IMdr7JOZErJvAd7FM3LgBgHnW2l+DvXColQkDe/fC5s1uWd/69cu+\n4IEHYPJkd862be68hg3dX/wDBkSucJE4t2cPnHii+19r4ECvq5Fy7dzptkP/9FP4/e+9rkYCFPYw\nYIxpY61dbozp6O/r1toFwV48lIwx1nbu7H65b9rk/tpv2BDuvReGDi37glWrXNvlEUdARoZbOk1E\nwmLePDfBZtEiyMz0uhop16OPwltvwZw5GvwcIyIRBp621o4yxszx82VrrT072IuHkjHG2i++cL/c\njzgCatfWutsiUeSvf4VvvoGZM/W/ZtQqLIQuXdxSxVdc4XU1EoBIziaoYa3df7jnvBZV6wyISBn5\n+e73zKhR7pAo9c03bq+UZcsgPf3w54unIhkGFlhrOx7uOa8pDIhEv2XL3PIbX30FRx3ldTVSrlGj\noF49Nx1EolokugkaAU2AKcCluJkEAGnAJGttm2AvHkoKAyKx4ZFH4PXX3foDVat6XY34tWOHe/Q3\ns0qiSiTCwHBgBHASML/El7KBF621bwV78VBSGBCJDUVFbr+uHj3g9tu9rkYktkWym+Aia+2bwV4o\n3BQGRGLHzz+75Yo//BA6dPC6GpHYFel1BnoBxwEHFuK31t4T7MVDSWFAJLa8/LJb7mP+fKhe3etq\nRGJTxJYjNsZMAgYDY3DjBgYCzYO9sIgktmHD4OijYexYryuRw9IfWnEvkFUlTrXWXg7ssNbeDXQB\nfhfeskQk3hkDTz0FU6a4Re8kSlkL3bvD8uVeVyJhFEgYyPE97jPGNAbyAa0hJiJBa9DABYLhwyE7\n2+tqxC9joFcvuOEGtRDEsUDCwHvGmHTgAWABsAZ4JZxFiUji6N3bbXV8001eVyLlGj3abdf+zjte\nVyJhEtDnPTcdAAAgAElEQVQAwgMnG1MdqGGt3RW+kipHAwhFYld2NrRv75bG793b62rEr48/hiuv\nhO+/h5o1va5GfCI5gHCgMSbV9+ktwAvGmBOCvbCISLHUVHjpJbjmGtiyxetqxK+zz4bOnWHCBK8r\nkTAIpJtgrLU22xhzOnAu8BwwKbxliUii6doVLrsMrr1WXdNR68EH3TbHEncCCQOFvsdewNPW2plA\ncvhKEpFEdc89sHIlTJ7sdSXiV7Nm/reEl5gXyAqE7wHrge5AR9zsgnnW2vbhLy9wGjMgEh8WLXIz\n2f73PzjySK+rEYluERszAAwCPgDOs9buBOrhxg6EhDHmfGPMcmPMSmPMbX6+nmyMmWaMWWWM+dIY\no38eROJYhw5uZsGIEW4fAxEJv3LDgDEmzfdhDSAL2GaMqQfkUnrjokozxlQBJgLn4ZY7HmKMOXg3\nxKuA7dbao4FHgPtDcW0RiV633gq5ufCvf3ldiUhiOFTLwFTf4/9wv/z/V+IISRgATgZWWWvXWmvz\ngWlA34PO6Qu85Pv4DeCcEF1bRKJU1apudsHf/w7LlnldjZRr0iS365TEvHLDgLX2Qt9jS2ttK99j\n8dEqRNdvAvxS4vN1vuf8nmOtLQR2+looytiesx2NGxCJD61buzAwbBjk53tdjfi1aRP8+c9eVyEh\nUO1wJxhjZltrzznccxFU7kCJxr0bU2SLSKueRosOLehwSgdGdBjB6UeeHsn6RCRERo2C6dNdKLj7\nbq+rkTJuvRWOPRZmz4Zz1GgbCVlZWWRlZYX8fcudTWCMqQHUBOYA3fjtl3Aa8B9r7cF9+xW/uDGn\nAOOstef7Pr8dsNbaCSXOmeU752tjTFVgo7X2CD/vZa217M7dzS+7fuGX3b/w866f6dykM+0blZ34\n8Le5f+ObDd/QLK0Zzeo048g6R9IsrRltG7YlvUZ6sN+aiITIxo1uUOG778LJJ3tdjZTxzjvwl7/A\n4sWQlOR1NQknVLMJDhUG/gj8CWgMbCjxpd3AM9baiUFf3P1yX4EbB7ARmAcMsdYuK3HOH4DjrbV/\nMMZcAvSz1l7i570qNLVw5baVfL/l+1LB4Zfdv3DXGXdxXuvzypw/ffl0duXuomlaU5qmNaVJahNq\nJdeq8PcsIhX3+utuq+MFC7QSbtSxFi64AHr00AYTHgh7GChxoTHW2rCN6TXGnA88ihu/8Jy1drwx\n5m7gG2vte779ECYDJwDbgEustWv8vE9Y1xl4av5TfPrzp6zbve7AUTOpJjOGzPDbDfHzrp+pnVyb\nujXqYkzQ/51EEt7QoVCvnmYYRKWVK+G22+Ctt9wuhxIxkQwDtYAbgSOttaOMMUcDv7fWvhfsxUMp\n0osOWWvZlrONWkm1SElKKfP1Qa8P4sMfP2R/wX4yUzNpnNqYzNqZTDh3AkfVOypidYrEix07oF07\nePllOOssr6sRiQ6RDAOv4qYTXm6tPd4YUxP4wlrbIdiLh1K0rkC4L38fG7M3snHPRjZkb+DcVudS\nL6XsZIiTnj6J7TnbXWhIzaRx7cY0Tm3MyBNH+j1fJBHNnAljxsCSJVC7ttfViHgvkmFgvrX2JGPM\nQmvtCb7nFms54tAqDg0bsjeUOm4//Xbq16xf5vwek3tQUFRAw9oNaVjLHUfUOoJLjr9EYxkkro0Y\n4YLAxKBHLYnEvkiGgS9wA/w+t9Z2NMYcBbxirY2qcb2xHgYq6rvN3/Hrnl/ZtHcTm/ZsYtPeTWze\nu5lHz3+U1OqpZc7vNbUXSVWSqJ9Sn4yaGdSv6R6Hth1K9WrVPfgORCpnxw5o2xamTIFu3byuRsRb\nkQwD3YE7gWOBD4HTgBHW2qxgLx5KiRYGKuqznz9jy94tbMvZxtZ9W9m2bxtbc7by1IVPkVy17CaU\nHSZ1oFZyLRccigNESn1uPvVmqlUpuzxFQVGB3+dFwmHmTLjhBjebTd0FUWjjRkhOhvplWzUltCIW\nBnwXqw+cgltr4Ctr7dZgLxxqCgOhY61l+dblB4LDgfCwbyv3d7+/zOyIIltE9b9XJ6VaCuk10qmb\nUtc91qjL24PfLnO+tZa5a+eSVj2t1FG9anXNvJCADR8OaWmaXRCVbr3VNeE884zXlcS9SIeBJkBz\nSqxYaK39JNiLh5LCgLeKbBHZudns3L+Tnft3smP/Dnbn7qbP7/uUOTevMI8ek3uwO3d3qSOpahLZ\nd2SXOT+/MJ8xs8aQmpxaKjyk10in/zH9I/HtSRQq7i7497/hzDO9rkZK2bXLbS5xww1eVxL3ItlN\nMAEYDHwHFG8oaq21Zf+V95DCQOzLLcj1O34hrzCPFxa+wK7cXaXCQ0FRAVMGTClz/q79u2jwQANS\nq6eSmpxK7eTapFZPpVHtRrw9+G2/7z916VTSqqcdCByp1VOpU70OTdIO3ipDosm778Kf/uRmF9TS\nuFlJQJEMAyuAdtba3GAvFk4KA1LMWkteYR7Zedlk52azJ28P2XnZ5BbkclbLshPUs3OzGT1rNNm5\n2ezO3U12nntMrprM4msXlzl/R84OLnrtogNhozhENKrdiBu73Fjm/CJbRF5hHjWq1QjL95voLr8c\n0tPhsce8rkQk8iIZBmYBA621e4K9WDgpDEik5Bbk8tnPnx0IDdm52WTnZWMw3Hb6bWXOX7d7HUc9\ndhQGQ3qN9APjKo6qe5Tflo39BfuZt34eDWo2oEGtBtRLqUcVc6jdxhPbjh1w/PEwdaq6CyTxRDIM\nvAm0B2YDB1oHrLVR1RmkMCDRLic/p9SYivzCfM5sUfa317rd6xjy5hC27N3C5r2byc7Lpl5KPU7M\nPJH3h75f5vy9eXuZv2E+DWo1oEFNFx6qVqkaiW8parz7Ltx4o5tdoO4CSSSRDAPD/T1vrX0p2IuH\nksKAxKv8wny25WxjT94eWtdrXebrP+34iRHvjGDLPhcedu3fRd2UupzZ/EzeGPRGmfNzC3LJKcih\nTvU6cTV7Y9gwt3fBo496XYlI5ER0NkEsUBgQcQqKCti2bxt78/fSqm6rMl+ft34e3Sd3p6Co4MAO\nnE3TmtKlaReu63SdBxWHxvbtbnbBK6/AGWd4XY1IZESyZWApcPBJu4D5wN+ttduCLSIUFAZEKmZ3\n7m7W717P+uz1rNu9jlpJtRh43MAy53269lPu+eQeWqa3dEdd99i6Xmu/S2V7acYMt4uuugskUUQy\nDNwPFAJTfU9dAtQEfgVOt9b2DraIUFAYEAmP7Tnbmbd+Hqt3rGb1Tt+xYzWdm3Tm8V6Plzl/Y/ZG\ntuzbQsv0ln6Xxg63yy6DjAx45JGIX1ok4iIZBhZYazv6e84Ys9Ra2zbYIkJBYUAkOry97G3unHMn\na3auoWZSTVqmt6RV3VYMOm4QA44ZEPbrF3cXTJsGXbuG/XIingpVGAhkMfmqxpiTrbXzfBfuBBQP\nVS4ItgARiS/9j+lP/2P6Y61l897NrN65mp92/MQRtY7we/7r373OJ2s/4ah6R3FU3aNoVbcVreq2\nIiUppVLXr1cPnngCrrzSdRfUrBnMdyOSGAJpGegEPA/Uxu1NsBu4GrciYS9r7WvhLjIQahkQiU2L\nfl1E1posftz+Iz/ucMfanWt5+LyH+UOnP5Q5Pyc/hxrVahx2JsTQoXDEEfDPf4archHvRXw2gTGm\nDoC1dlewFw0HhQGR+FFYVEh+Ub7fVRuve+86Xlr8Es3Tm9O8TnNapLegRXoLLjrmIo6uf/SB87Zt\nc90Fr70Gp58eyepFIifSGxX1Ao4DDvyfaa29J9iLh5LCgEji2JO3h7U717Jm5xrW7nKPg48bzImN\nTyx13jvvwKinnub6W7bSOuO34JCZmqlVHSUuRHIA4STc7IGzgGeBi4F51tqrgr14KCkMiIg/XUe9\nzf76X3PUiWsOhIdt+7Yx+/LZdG1edoThvPXzSKqSRGZqJg1qNki41RwltkQyDCyx1rYr8VgbmGWt\njapxugoDIuJPcXfB66/Daae553ILcqliqpBUNanM+aPfH81nP3/GhuwN7Ni/gyNqHUFm7Uz+PeDf\n/D7j92XOX7d7HTWq1SC9RjrVqgQyJju2rdu9zu8xZcCUhPj+o00kZxPk+B73GWMaA9uAzGAvLCIS\nCfXrw+OPwxVXwKJFbnaBv62yi03sOfHAx/mF+Wzau4kN2RtomtbU7/lXTr+S/238H7v276JmUk3q\nptSlbo26vHPJO7RIb1Hm/P/88B+qmCrUrVH3wLm1k2uTXDXZk+WhN2ZvZE/eHvYX7CenIId9+fvY\ntGcT/dr083ufekzuQe3k2jRNa3rg6JjZkSJb5OfdJVYE0jIwFvgXcA7wOG41wmettWPDX17g1DIg\nIocyZAg0bgwPPRSe9y+yRWTnZrNj/w525OzgmAbH+B0A+YeZf2DV9lXsyNlx4Ny9+XtZMXqF3/Bw\n5otnsmnPJpKrJpc6pl08jUa1G5U5f+SMkfy8+2dy8nPIKcg58PjpFZ/SOLVxmfM7PdOJ7TnbqZlU\nk5RqKaQkpdCwVkOe6PUEGTUzQnJvJHw82ZvAGFMdqBGNMwoUBkTkULZuhXbt4I034NRTva4mcGt2\nriEnP4e8wjzyi/LJK8wjrzCPU5qe4jdsfPDDBxTZIlKSUg78ck+plkKL9BZ+u0UktkVyzEBVoBfQ\nghLdCtbah4O9eCgpDIjI4bz1Ftxxh+suSKncmkYiUSWSYeB9YD+wFDjQKWStvTvYi4eSwoCIBOKS\nS6BJk/B1F4hEUsRnEwR7oXBTGBCRQGzd6mYXvPHGb7MLRGJVqMJAIKtuzDLG9Aj2QiIi0SAjw+1d\nMGIE7NvndTUi0SGQloH+wBRccMjH7U9grbVp4S8vcGoZEJGKGDoUGjTQVscS2yLZTbAa6Assjebf\ntgoDIlIR27a52QVTp8KZZ3pdjUjlRLKb4BfgW/2mFZF4Ur8+TJrktjres8frakS8FUjLwItAK2AW\nkFv8vKYWikg8GDECateGiRMPe6pI1Ilky8BqYDaQDKSWOEREYt4jj8D06fDxx15XIuKdCq1AGM3U\nMiAilTVrFlx3HSxZAmlRNTRa5NA8WY44mikMiEgwrr4aqlaFp57yuhKRwCkMHERhQESCsWuXm13w\nzDPQQyurSIyI2JgBY0yZNbr8PSciEsvq1IFnn3UtBLuibis2kfAKZDbBAmttx8M95zW1DIhIKFx7\nLeTnw3PPeV2JyOGFqmWgWnlfMMZ0AU4FGhhjbirxpTSgarAXFhGJRg884LoL3n8fevb0uhqRyDhU\nN0EyUBsXGEpOKdwNXBz+0kREIi811bUKjBoFO3Z4XY1IZATSTdDcWrs2QvVUmroJRCSURo+G7Gx4\n6SWvKxEpX9hnExhjHrHW/skY8y5Q5iRrbZ9gLx5KCgMiEkp790L79vDww9Anqv61E/lNJMLAidba\n/xlj/G7hYa2dG+zFQ0lhQERC7dNPYfBgWLrU7WUgEm20zsBBFAZEJBxuvBE2bXK7G4pEm4iuM2CM\n+cgYs9IY85MxZrUx5qdgLywiEgvuvRfmz4c33/S6EpHwCWQA4XLgRuB/QGHx89babeEtrWLUMiAi\n4fLFF3DRRW7vggYNvK5G5DcR6yYwxnxtre0c7IXCTWFARMLplltg7Vp47TWvKxH5TSTDwHjcIkNv\nAbnFz1trFwR78VBSGBCRcMrJgY4d4e67YdAgr6sRcSIZBub4edpaa88O9uKhpDAgIuH29dfQty8s\nXgwNG3pdjYhmE5ShMCAikXDHHbBihRtQaIL+J1gkOJFsGbjL3/PW2nuCvXgoKQyISCTk5sKJJ7pQ\nMHSo19VIoovY1EJgb4mjELgAaBHshUVEYlH16m6J4htvhA0bvK5GJDQq3E1gjKkOfGCt7RaWiipJ\nLQMiEkl33eXWH5g5U90F4p1ItgwcrCbQNNgLi4jEsrFjYetWmDjR60pEglftcCcYY5by20ZFVYEG\nQFSNFxARibSkJLdEcZcucOaZ0K6d1xWJVF5AWxiX+LQA2GStLQhrVZWgbgIR8cJLL8H997sug5QU\nr6uRRKOphQdRGBARL1gLQ4ZARoa6DCTyvBwzICIiPsbApEnw3nvw7rteVyNSOWoZEBEJgc8+g4sv\nhgULoHFjr6uRRKGWARGRKHL66XDttTB8OBQVeV2NSMUoDIiIhMidd8K+ffDww15XIlIx6iYQEQmh\nNWugUyf44AO3y6FIOKmbQEQkCrVoAY895mYY7N3rdTUigVHLgIhIGAwfDsnJ8MwzXlci8UwtAyIi\nUWziRJgzx211LBLt1DIgIhImX38Nffq41QmbNfO6GolHahkQEYlynTvDH/8Iw4ZBYaHX1YiUT2FA\nRCSMbrvNPU6Y4G0dIoeibgIRkTD75Rc46SSYMcO1FoiEiroJRERiRLNm8MQTMHQoZGd7XY1IWWoZ\nEBGJkJEjIS/PbXssEgpqGRARiTGPPAJffQVTp3pdiUhpnrUMGGPqAq8CzYE1wCBr7S4/5xUCiwED\nrLXW9ivn/dQyICJRb8ECOO88mDcPWrb0uhqJdaFqGfAyDEwAtllr7zfG3AbUtdbe7ue83dbatADe\nT2FARGLCQw+5xYg++QSqVfO6Goll8RAGlgNnWms3GWMaAVnW2jZ+zsu21qYG8H4KAyISE4qK4Pzz\n4dRTYdw4r6uRWBYPYWC7tbZeeZ+XeD4PWAQUABOstdPLeT+FARGJGRs3wgknwBtvwOmne12NxKpQ\nhYGwNlAZYz4CGpZ8CrDAnX5OL+83eXNr7UZjTEvgY2PMEmvt6hCXKiISUZmZbhOjyy6DRYsgPd3r\niiSRhTUMWGu7l/c1Y8wmY0zDEt0Em8t5j42+x9XGmCzgBMBvGBhXor2tW7dudOvWrdK1i4iEW+/e\n8OGHbofDt9+GKprfJYeRlZVFVlZWyN/X6wGE2621E8obQGiMSQf2WWvzjDEZwOdAX2vtcj/vp24C\nEYk5eXlw7rnQtSvce6/X1UisiYd1BiYA3Y0xK4BzgPEAxpgTjTFP+845BphvjFkIzAb+4S8IiIjE\nquRkN7Ng6lStPyDe0QqEIiJRYOlSOPtsmDkTTj7Z62okVsRDy4CIiPi0bQvPPgsDBsD69V5XI4lG\ny12IiESJvn3h+++hXz+YOxdq1vS6IkkU6iYQEYki1rrphoWF8MorYIJuAJZ4pm4CEZE4ZIzrLli9\nWrMLJHLUTSAiEmVSUuCdd9xAwuOOg/79va5I4p26CUREotT8+XDBBfDRR9Chg9fVSDRSN4GISJw7\n6SSYONENKNzsd41WkdBQGBARiWKDB8OwYW7KYW6u19VIvFI3gYhIlCsqgosvdpsZPfecZhjIb9RN\nICKSIKpUgZdfhgUL4J//9LoaiUeaTSAiEgNq14bp0+GUU+CYY9zAQpFQUcuAiEiMaN4c3njDbXm8\nbJnX1Ug8URgQEYkhp50G998PvXvDtm1eVyPxQgMIRURi0J//DAsXwn/+A0lJXlcjXgnVAEKFARGR\nGFRY6FoHWrVyaxFIYtJsAhGRBFa1qtvIaPZsePJJr6uRWKfZBCIiMapOHXj3XTeOoE0bOOssryuS\nWKWWARGRGNa6tWshGDIEVq70uhqJVQoDIiIx7uyz4R//cI+aciiVoW4CEZE4cMUVbhzBOee4GQbt\n2nldkcQShQERkThx+eVQvTr06AEzZ8KJJ3pdkcQKhQERkTgyeLALBD17/rZ8scjhKAyIiMSZfv0g\nORn69HHLF59xhtcVSbTTAEIRkTjUs6ebZXDRRfDf/3pdjUQ7hQERkTh1zjnw1ltw6aXw/vteVyPR\nTGFARCSOde0KM2a42QZvv+11NRKtNGZARCTOnXIKzJrlug7y8twgQ5GSFAZERBJAx47w0Udw3nmw\nfz8MH+51RRJNFAZERBJE27bw8cdw7rmQmwujRnldkUQLhQERkQTSpg1kZblAsH8/3HCD1xVJNFAY\nEBFJMK1bw9y5bi+D/fvh1lu9rki8pjAgIpKAmjeHTz5x0w/374exY8EYr6sSrygMiIgkqCZNSncZ\n3HuvAkGi0joDIiIJrFEjFwhmzYKbbgJrva5IvKAwICKS4DIy3CyDL76A66+HoiKvK5JIUxgQERHq\n1nXrECxb5sYRrFnjdUUSSQoDIiICQFqa29ToggugUyd44QV1GyQKY+Pkv7QxxsbL9yIi4rUlS2DY\nMDfr4JlnoGFDrysSf4wxWGuDHvaplgERESmjXTv45hu3amH79vDmm15XJOGklgERETmkL7+Eyy+H\nLl3gsccgPd3riqSYWgZERCQiunSBRYsgNdW1GPz3v15XJKGmlgEREQnYhx/CVVdBv34wYQLUrOl1\nRYlNLQMiIhJxPXq4wYU7dsAJJ8DXX3tdkYSCWgZERKRS3njDLVI0ciTcdRckJ3tdUeJRy4CIiHjq\n4oth8WJ3dO4M337rdUVSWQoDIiJSaY0awYwZMHo0nHUWPPAAFBZ6XZVUlLoJREQkJFavhhEj3N4G\nL70ErVp5XVH8UzeBiIhElZYtYc4c6N/fTUfcutXriiRQahkQEZGQ27rV7YYo4RWqlgGFARERkRil\nbgIREREJCYUBERGRBKcwICIikuAUBkRERBKcwoCIiEiCUxgQERFJcAoDIiIiCU5hQEREJMEpDIiI\niCQ4hQEREZEEpzAgIiKS4BQGREREEpzCgIiISIJTGBAREUlwCgMiIiIJTmFAREQkwSkMiIiIJDiF\nARERkQSnMCAiIpLgFAZEREQSnMKAiIhIglMYEBERSXAKAyIiIgnOszBgjLnYGPOtMabQGNPxEOed\nb4xZboxZaYy5LZI1ioiIJAIvWwaWAv2BueWdYIypAkwEzgOOA4YYY9pEpjwREZHEUM2rC1trVwAY\nY8whTjsZWGWtXes7dxrQF1ge/gpFREQSQ7SPGWgC/FLi83W+50RERCREwtoyYIz5CGhY8inAAv9n\nrX03nNcWERGRwIQ1DFhruwf5FuuBI0t83tT3nF/jxo078HG3bt3o1q1bkJcXERGJHllZWWRlZYX8\nfY21NuRvWqECjJkD/Nla+z8/X6sKrADOATYC84Ah1tplfs61Xn8vIiIikWSMwVp7qLF3AfFyamE/\nY8wvwCnAe8aYWb7nM40x7wFYawuB0cCHwHfANH9BQERERCrP85aBUFHLgIiIJJqYbxmQ2BSOviop\nTfc4/HSPI0P3OXYoDEiF6H/u8NM9Dj/d48jQfY4dCgMiIiIJTmFAREQkwcXVAEKvaxAREYm0UAwg\njJswICIiIpWjbgIREZEEpzAgIiKS4GIiDBhjzjfGLDfGrDTG3Obn68nGmGnGmFXGmC+NMUeW+Nod\nvueXGWN6RLby2FHZe2yMaW6M2WeMWeA7noh89bEjgPvc1RjzP2NMvjFmwEFfG+573QpjzOWRqzq2\nBHmPC30/xwuNMe9ErurYEsA9vtEY850xZpEx5iNjTLMSX9PPcQCCvMcV/zm21kb1gQssPwDNgSRg\nEdDmoHOuA57wfTwYt2wxwLHAQtyGTC1872O8/p6i7QjyHjcHlnj9PcTCEeB9PhI4HngRGFDi+brA\nj0AdIL34Y6+/p2g7grnHvq/t9vp7iPYjwHt8JlDD9/G1Jf690M9xmO+x7/MK/xzHQsvAycAqa+1a\na20+MA3oe9A5fYGXfB+/AZzt+7gP7gYVWGvXAKt87yelVeYen1Pia0GPZE0Qh73P1tqfrbXf4rb6\nLuk84ENr7S5r7U7cfh3nR6LoGBPMPQb9LAcikHs811q73/fpV0AT38f6OQ5MMPcYKvFzHAthoAnw\nS4nP11H6my51jnWbG+0yxtTz89r1fl4rlbvHO333GKCFr9l1jjHm9LBXG7sCuc+BvlY/y/4Fc48B\nqhtj5hljvjDGHByIxanoPb4KmFXOa/Vz7F8w9xgq8XNcreI1xgSl+/ArvscbgSOttTuMMR2Bd4wx\nx1pr93hYm0hlNbfWbjTGtAQ+NsYssdau9rqoWGWMuQw4EdekLWFQzj2u8M9xLLQMrMf18RVr6nuu\npHVAMwBjTFUgzVq73Xdes8O8VoK4x9baPGvtDgBr7QJcH+Dvwl9yTArkPofjtYkkqPtkrd3oe1wN\nZAEnhLK4OBHQPTbGnAvcAfT2NXUH/FoJ6h5X6uc4FsLAN0Br36j1ZOASYMZB57wLDPd9PBD42Pfx\nDOAS30j4lkBrYF4Eao41lb7HxpgMY0wV38etcPf4p4hUHXsCuc8llWzh+gDoboypY4ypC3T3PSel\nVfoeG2PSfa/BGJMBnAp8H85iY9Rh77Ex5gRgEtDHWrutxJf0cxyYSt/jSv8cez1qMsCRlecDK3AD\nAG/3PXc3cKHv4+rAa76vfwW0KPHaO3CjMpcBPbz+XqL1qOw9BgYA3wILgPlAT6+/l2g+ArjPJ+H6\nCrOBLcDSEq8d4XvdSuByr7+XaD0qe4+BLsAS3AykxcAIr7+XaD0CuMcf4boQF/ju5zslXquf4zDe\n48r+HGs5YhERkQQXC90EIiIiEkYKAyIiIglOYUBERCTBKQyIiIgkOIUBERGRBKcwICIikuAUBkRE\nRBKcwoBIgvCt+nZdic8zjTGvhelafY0xd1bytR8ZY+qEuiYRKZ8WHRJJEMaYFsC71tq2EbjW57j1\n0rdX4rXDgGbW2vtCX5mI+KOWAZHE8Q+glTFmgTFmgm/d86UAxpjhxpi3jTEfGmN+MsZcb4y50Xfu\nF8aYdN95rYwxs4wx3xhj5hpjymxKZYw5GthfHASMMS8YYx41xnxujPnBGDPA93wj33ssMMYsMcac\n5nuLd4EhkbghIuIoDIgkjtuBH621Ha21t/meK9k0eBzQDzgZuBfYY63tiNuL4nLfOU8Do621nYBb\ngCf9XOc03HrpJTWy1p4G9AYm+J67FPiP7xrtgUUA1tqdQLJvIxsRiYBqXhcgIlFjjrV2H7DPGLMT\neM/3/FKgrTGmFm4HtNeNMcW7/SX5eZ9M3AZAJb0DYK1dZow5wvfcN8BzxpgkYLq1dnGJ87cAjYEd\nwVyiqDUAAAEQSURBVH5TInJ4ahkQkWK5JT62JT4vwv3hUAXY4WtZOMF3HO/nfXKAGod4bwNgrf0U\nOAO3T/uLxpjLSpxTw/c+IhIBCgMiiSMbSK3si6212cBqY8zFxc8ZY9r5OXUZcPQh3sr4XnsksNla\n+xzwLNCxxDkNgTWVrVVEKkZhQCRB+Ab0fe4brDfhcKeX8/xlwFXGmEXGmG+BPn7O+QTocIj3Kv68\nG7DYGLMAGAQ8CmCMORH4ylpbdJgaRSRENLVQRELOGPNP3DTGjyvx2kdwYwjmhL4yEfFHLQMiEg73\nATUr+dqlCgIikaWWARERkQSnlgEREZEEpzAgIiKS4BQGREREEpzCgIiISIJTGBAREUlw/w89vFSA\nocHyMwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11170f690>"
+       "<matplotlib.figure.Figure at 0x7fc3e148f110>"
       ]
      },
      "metadata": {},
@@ -1055,8 +755,8 @@
     "%matplotlib inline\n",
     "from fidimag.common.fileio import DataReader\n",
     "\n",
-    "for f in (1, 2):\n",
-    "    dynamics = DataReader(\"field_{}.txt\".format(f))\n",
+    "def do_plot(data_name):\n",
+    "    dynamics = DataReader(data_name)\n",
     "    # we could load the data with np.loadtxt, but using the DataReader gives \n",
     "    # us the possibility to use the column headers to access our data\n",
     "    fig = plt.figure(figsize=(8, 6))\n",
@@ -1065,12 +765,13 @@
     "    axes.plot(dynamics[\"time\"] * 1e9, dynamics[\"m_y\"], \"r--\", label=\"m_y\")\n",
     "    axes.plot(dynamics[\"time\"] * 1e9, dynamics[\"m_z\"], \"g--\", label=\"m_z\")\n",
     "    axes.set_xlabel(\"time (ns)\")                                             \n",
-    "    axes.set_xlim((0, 1))\n",
+    "    axes.set_xlim((0, 0.25))\n",
     "    axes.set_ylabel(\"unit magnetisation (1)\")                                \n",
     "    axes.set_ylim((-1.05, 1))                                                \n",
     "    axes.legend()\n",
-    "    axes.set_title(\"field {}\".format(f))\n",
-    "    fig.show()"
+    "    plt.show()\n",
+    "    \n",
+    "do_plot('field_1.txt')"
    ]
   },
   {
@@ -1106,7 +807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,