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"# Visualizing orbits\n",
"\n",
"
\n",
"\n",
"This example shows how to collect and visualize marker orbits.\n",
"\n",
"## Detailed description of the orbit collection options\n",
"\n",
"The orbit data collection is one of the diagnostics that can be active in any simulation.\n",
"Simply put, it records the marker position in phase-space (and some other quantities) at fixed time intervals or when a marker crosses a specific surface.\n",
"The latter is used to generate Poincaré plots, while the former can be used to evaluate orbit-averaged quantities or just to visualize orbits.\n",
"In simulations with large number of markers $(\\gtrsim 10^4)$ the orbit data is rarely collected because it may use up a lot of memory and disk space.\n",
"\n",
"The orbit data collection is enabled in options by setting ``ENABLE_ORBITWRITE = 1``.\n",
"The next option, ``ORBITWRITE_MODE``, selects whether the marker trajectory is recorded when **i)** a given amount of time has passed or **ii)** marker crosses a predetermined surface.\n",
"The latter is used for Poincaré plots, so choose ``ORBITWRITE_MODE=1`` to collect the data in time intervals.\n",
"The time interval is set by ``ORBITWRITE_INTERVAL`` which, if set to zero, collects the data at each integration time-step.\n",
"\n",
"> **_NOTE:_** The data is not collected exactly at ``ORBITWRITE_INTERVAL`` intervals, but on the first time step when at least the set amount of time has passed from the last time orbit data was recorded.\n",
"If desired, the only way to collect the data at fixed intervals is to use a fixed time-step and choose ``ORBITWRITE_INTERVAL`` so that it is a multiple of the time-step.\n",
"\n",
"Next we have ``ORBITWRITE_NPOINT`` which sets a **maximum** number of points kept in record for each marker.\n",
"Roughly speaking, there is a fixed-size array in simulation for each marker that has length equal to this value, which is then filled one slot at a time every time the marker position is recorded.\n",
"If this array becomes full, then the earliest record is overwritten, i.e, the array contains last ``ORBITWRITE_NPOINT`` records.\n",
"If the array is not full at the end of the marker's simulation, the unused slots are pruned before the data is written to disk.\n",
"\n",
"Preferably ``ORBITWRITE_NPOINT`` is equal to the simulation time divided by ``ORBITWRITE_INTERVAL`` as then all the recorded points are kept.\n",
"One can of course choose to keep only the last $N$ values, which could be useful for debugging simulations or seeing how exactly markers ended up on the wall.\n",
"Just keep in mind to not use a large value for ``ORBITWRITE_NPOINT`` in simulations with a large number of markers as that will easily eat all available RAM.\n",
"It is a good practice to check how much data was allocated for the diagnostics when using the orbit collection.\n",
"This value is printed at the beginning of the simulation after all inputs have been initialized.\n",
"\n",
"## Running a simulation where orbit data is being collected\n",
"\n",
"First, initialize a test case where markers can be traced:"
]
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inputs created"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import numpy as np\n",
"from a5py import Ascot\n",
"\n",
"a5 = Ascot(\"ascot.h5\", create=True)\n",
"a5.data.create_input(\"bfield analytical iter circular\")\n",
"a5.data.create_input(\"wall rectangular\")\n",
"a5.data.create_input(\"plasma_1D\")\n",
"a5.data.create_input(\"E_TC\")\n",
"a5.data.create_input(\"N0_1D\")\n",
"a5.data.create_input(\"Boozer\")\n",
"a5.data.create_input(\"MHD_STAT\")\n",
"a5.data.create_input(\"asigma_loc\")\n",
"\n",
"print(\"Inputs created\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As for markers, we create two alpha particles, one of which will not be confined in the simulation (for demonstration purposes):"
]
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"outputs": [
{
"data": {
"text/plain": [
"'gc_0621731983'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from a5py.ascot5io.marker import Marker\n",
"mrk = Marker.generate(\"gc\", n=2, species=\"alpha\")\n",
"mrk[\"energy\"][:] = 3.5e6\n",
"\n",
"# Passing particle in the core = confined\n",
"mrk[\"ids\"][0] = 1\n",
"mrk[\"pitch\"][0] = 0.9\n",
"mrk[\"r\"][0] = 7.2\n",
"\n",
"# Banana on outward excursion near the edge = unconfined\n",
"mrk[\"ids\"][1] = 404\n",
"mrk[\"pitch\"][1] = 0.5\n",
"mrk[\"r\"][1] = 8.1\n",
"\n",
"a5.data.create_input(\"gc\", **mrk)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we create options where the orbit data collection is enabled, and the data is collected at each time-step.\n",
"Since we only have two markers, we set ``ORBITWRITE_NPOINT`` large enough to hold all recorded values.\n",
"As for the simulation, we trace markers in hybrid mode (again for demonstration purposes) and enable orbit-following and set maximum mileage and wall collisions as end conditions."
]
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"outputs": [
{
"data": {
"text/plain": [
"'opt_3609305668'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from a5py.ascot5io.options import Opt\n",
"\n",
"opt = Opt.get_default()\n",
"opt.update({\n",
" # Settings specific for this tutorial\n",
" \"SIM_MODE\":3, \"ENABLE_ADAPTIVE\":1,\n",
" \"ENDCOND_SIMTIMELIM\":1, \"ENDCOND_MAX_MILEAGE\":1e-5,\n",
" \"ENDCOND_WALLHIT\":1, \"ENDCOND_MAX_RHO\":1.0,\n",
" \"ENABLE_ORBIT_FOLLOWING\":1,\n",
" # Orbit diagnostics\n",
" \"ENABLE_ORBITWRITE\":1, \"ORBITWRITE_MODE\":1,\n",
" \"ORBITWRITE_INTERVAL\":0.0, \"ORBITWRITE_NPOINT\":10**4,\n",
"})\n",
"a5.data.create_input(\"opt\", **opt, desc=\"HYBRID\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now run the simulation:"
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASCOT5_MAIN\n",
"Tag e37fd83\n",
"Branch docs\n",
"\n",
"Initialized MPI, rank 0, size 1.\n",
"\n",
"Reading and initializing input.\n",
"\n",
"Input file is ascot.h5.\n",
"\n",
"Reading options input.\n",
"Active QID is 3609305668\n",
"Options read and initialized.\n",
"\n",
"Reading magnetic field input.\n",
"Active QID is 2881489678\n",
"\n",
"Analytical tokamak magnetic field (B_GS)\n",
"Psi at magnetic axis (6.618 m, -0.000 m)\n",
"-5.410 (evaluated)\n",
"-5.410 (given)\n",
"Magnetic field on axis:\n",
"B_R = 0.000 B_phi = 4.965 B_z = -0.000\n",
"Number of toroidal field coils 0\n",
"Magnetic field read and initialized.\n",
"\n",
"Reading electric field input.\n",
"Active QID is 1513424353\n",
"\n",
"Trivial Cartesian electric field (E_TC)\n",
"E_x = 0.000000e+00, E_y = 0.000000e+00, E_z = 0.000000e+00\n",
"Electric field read and initialized.\n",
"\n",
"Reading plasma input.\n",
"Active QID is 0483527035\n",
"\n",
"1D plasma profiles (P_1D)\n",
"Min rho = 0.00e+00, Max rho = 1.00e+02, Number of rho grid points = 3, Number of ion species = 1\n",
"Species Z/A charge [e]/mass [amu] Density [m^-3] at Min/Max rho Temperature [eV] at Min/Max rho\n",
" 1 / 1 1 / 1.000 1.00e+20/1.00e+20 1.00e+20/1.00e+20 \n",
"[electrons] -1 / 0.001 1.00e+20/1.00e+20 1.00e+03/1.00e+03 \n",
"Toroidal rotation [rad/s] at Min/Max rho: 0.00e+00/0.00e+00\n",
"Quasi-neutrality is (electron / ion charge density) 1.00\n",
"Toroidal rotation [rad/s] at Min/Max rho: 0.00e+00/0.00e+00\n",
"Plasma data read and initialized.\n",
"\n",
"Reading neutral input.\n",
"Active QID is 1488189872\n",
"\n",
"1D neutral density and temperature (N0_1D)\n",
"Grid: nrho = 3 rhomin = 0.000 rhomax = 2.000\n",
" Number of neutral species = 1\n",
"Species Z/A (Maxwellian)\n",
" 1/ 1 (1) \n",
"Neutral data read and initialized.\n",
"\n",
"Reading wall input.\n",
"Active QID is 2524205529\n",
"\n",
"2D wall model (wall_2D)\n",
"Number of wall elements = 20, R extend = [4.10, 8.40], z extend = [-3.90, 3.90]\n",
"Wall data read and initialized.\n",
"\n",
"Reading boozer input.\n",
"Active QID is 0826758554\n",
"\n",
"Boozer input\n",
"psi grid: n = 6 min = 0.000 max = 1.000\n",
"thetageo grid: n = 18\n",
"thetabzr grid: n = 10\n",
"Boozer data read and initialized.\n",
"\n",
"Reading MHD input.\n",
"Active QID is 0826113073\n",
"\n",
"MHD (stationary) input\n",
"Grid: nrho = 6 rhomin = 0.000 rhomax = 1.000\n",
"\n",
"Modes:\n",
"(n,m) = ( 1, 3) Amplitude = 0.1 Frequency = 1 Phase = 0\n",
"(n,m) = ( 2, 4) Amplitude = 2 Frequency = 1.5 Phase = 0.785\n",
"MHD data read and initialized.\n",
"\n",
"Reading atomic reaction input.\n",
"Active QID is 1596986108\n",
"\n",
"Found data for 1 atomic reactions:\n",
"Reaction number / Total number of reactions = 1 / 1\n",
" Reactant species Z_1 / A_1, Z_2 / A_2 = 0 / 0, 0 / 0\n",
" Min/Max energy = 1.00e+03 / 1.00e+04\n",
" Min/Max density = 1.00e+18 / 1.00e+20\n",
" Min/Max temperature = 1.00e+03 / 1.00e+04\n",
" Number of energy grid points = 3\n",
" Number of density grid points = 4\n",
" Number of temperature grid points = 5\n",
"Atomic reaction data read and initialized.\n",
"\n",
"Reading marker input.\n",
"Active QID is 0621731983\n",
"\n",
"Loaded 2 guiding centers.\n",
"Marker data read and initialized.\n",
"\n",
"All input read and initialized.\n",
"\n",
"Initializing marker states.\n",
"Estimated memory usage 0.0 MB.\n",
"Marker states initialized.\n",
"\n",
"Preparing output.\n",
"Note: Output file ascot.h5 is already present.\n",
"\n",
"The qid of this run is 0507697446\n",
"\n",
"Inistate written.\n",
"Simulation begins; 4 threads.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulation complete.\n",
"Simulation finished in 0.014431 s\n",
"Endstate written.\n",
"\n",
"Combining and writing diagnostics.\n",
"\n",
"Writing diagnostics output.\n",
"Writing orbit diagnostics.\n",
"\n",
"Diagnostics output written.\n",
"Diagnostics written.\n",
"\n",
"Summary of results:\n",
" 1 markers had end condition Sim time limit\n",
" 1 markers had end condition Wall collision\n",
"\n",
" No markers were aborted.\n",
"\n",
"Done.\n",
"Simulation completed"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import subprocess\n",
"subprocess.run([\"./../../build/ascot5_main\"])\n",
"print(\"Simulation completed\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The orbit data is accessed by using the run group's ``getorbit`` method:"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"outputs": [],
"source": [
"a5 = Ascot(\"ascot.h5\")\n",
"\n",
"# Retrieve mileage\n",
"mil = a5.data.active.getorbit(\"ekin\")\n",
"\n",
"# Multiple quantities can be accessed simultaneously\n",
"# (which is more efficient than accessing individually)\n",
"r, z = a5.data.active.getorbit(\"r\", \"z\")\n",
"\n",
"# Data can be parsed by marker ID\n",
"rho = a5.data.active.getorbit(\"rho\", ids=1)\n",
"rho = a5.data.active.getorbit(\"rho\", ids=[1,404])\n",
"\n",
"# ...or by end condition\n",
"rho = a5.data.active.getorbit(\"rho\", endcond=\"NOT WALL\")\n",
"rho = a5.data.active.getorbit(\"rho\", endcond=[\"TLIM\", \"WALL\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some quantities are evaluated run-time and some of those requires access to input data, or otherwise exception is raised:"
]
},
{
"cell_type": "code",
"execution_count": 6,
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},
"outputs": [],
"source": [
"a5.input_init(bfield=True)\n",
"psi = a5.data.active.getorbit(\"psi\", ids=404)\n",
"a5.input_free(bfield=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Orbits can also be plotted easily with the ``plotorbit_trajectory`` method:"
]
},
{
"cell_type": "code",
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{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a5.data.active.plotorbit_trajectory(\"r\", \"z\", endcond=\"TLIM\")\n",
"a5.data.active.plotorbit_trajectory(\"mileage\", \"ekin\", ids=404)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the orbit data it is not possible to choose if the quantities are evaluated in particle or guiding-center coordinates with the reason being that only one these are stored.\n",
"So which one?\n",
"\n",
"By default, the one corresponding to the simulation mode: particle coordinates in GO simulation and guiding-center coordinates in GC simulation.\n",
"In hybrid mode, the stored quantity **corresponds to simulation mode that was active at the moment**.\n",
"This is why there is a \"jump\" of 500 eV in energy in the plot above, which corresponds to time instant when the simulation changed from guiding center to hybrid.\n",
"This is more clearly visible if we plot the orbit poloidal trajectory:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-01T08:58:24.043557Z",
"iopub.status.busy": "2025-04-01T08:58:24.043341Z",
"iopub.status.idle": "2025-04-01T08:58:24.155303Z",
"shell.execute_reply": "2025-04-01T08:58:24.154767Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a5.data.active.plotorbit_trajectory(\"r\", \"z\", ids=404)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is possible to force the code to collect only the guiding center data (even in GO mode):"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-01T08:58:24.157110Z",
"iopub.status.busy": "2025-04-01T08:58:24.156921Z",
"iopub.status.idle": "2025-04-01T08:58:24.277817Z",
"shell.execute_reply": "2025-04-01T08:58:24.277205Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/runner/work/ascot5/ascot5/a5py/ascotpy/libsimulate.py:298: AscotUnitWarning: Argument(s) r, phi, z, time, mass, charge, energy, zeta given without dimensions (assumed m, degree, m, s, amu, e, eV, rad)\n",
" r, phi, z, t, m, q, energy, pitch, zeta, anum, znum, w, ids = parse(\n"
]
},
{
"data": {
"image/png": 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np8k/Dut/NN1o2+eVnI+IM2OLut0OUfHW1SdiAYUhF1IYEqkm9kLYuQg2TIENP8GR7Sc8aYPE80yLUeshENvMqio9z6EtJhSt/KJkir5fgFnluvs9ZqVxER+gMORCCkMiNaB4AcLiYLR3Zenn67Y2oajVELOvmqaVn1lBHqRMgUXvQerCkvONekLP+6HlIL2P4tUUhlxIYUjEAhm7TDfahp9Mt9qJaxtFxpuutNZDzAy1gCDr6vQUe1bAH+/Cmokli2jWagzn3AldhkNItKXliVQHhSEXUhgSsdixdNg00wSjzb9AXnbJc8FRZj+v1kPMDDVtdFq+zD2mpWjZR2b8FkBQJHS9BbrfC9ENrK1PxIUUhlxIYUjEjRTkwra5JhhtmApH95c85xcITfoUdacN1oDh8uQdNStc//EuHFhvzvkFQPtrTBda/Q7W1ifiAgpDLqQwJOKm7HazPciGn8xYoxM3OgUzULj1EGg/VJvLno7DYVrdFrxVehZa037Q6wFzr01ixUMpDLmQwpCIhziw0QwY3jAFdi0p/VyTPpA83OztFRhqTX3ubvdyWPAfWDe5ZC2o+h2h7+OmpU2hSDyMwpALKQyJeKCsNDMAe+13pluNol9zwdHQ/moTjBp00R/4shzZAX+8YxZzzM8x5+q1hZ4PmFY2DVgXD6Ew5EIKQyIeLj0VVn4JKyeY42J125itKzpeDxF1ravPXeUchoVvw6JxJXuhRSaY7rOut0JgiLX1iZyBwpALKQyJeAm73YyLWTEB1v8ABcfN+eLNTpNvMjPS/AOsrdPdHEs3s8/+GAvZ+8y5yAToPcq0sCkUiZtSGHIhhSERL3QsHdZOMsFo97KS8xFx0GkYdL4J6ra0rDy3VJBrVrWe+0/I3G3ORcRBj/vMdh/aV07cjMKQCykMiXi5fetg5eew6n+Qc7DkfMNzTWtRu6u0ftGJ8o+b8US/vwmZu8y58LrQ51HTfaYxReImFIZcSGFIxEcU5MGmGaa1aNMMcBSa84Fh0PZKE4ySemrQdbGCPPjza5j7GhzZZs5FJ8L5D5v3KiDY2vrE5ykMuZDCkIgPykozLUUrJpRev6hWE0j+C3S6Uas1FyvMhxWfwexXIDvNnItqYFqKkodrDJZYRmHIhRSGRHyYw2HWLFrxGayZVLIViM0Pml1oWkBaDVYrCED+MdN9Nv91yNprztVpBRc9D60GqUVNapzCkAspDIkIYLawWPe9aS3a8XvJ+dBa0OE6E4ziO1pXn7vIP25mn815FY4dNucadIP+z0DTvpaWJr5FYciFFIZE5BSHtphB1yu/hKw9JefjO0H3+8zCjv6B1tXnDo6lw+9vmI1hixdvbNoXLhkDcW0tLEx8hcKQCykMichp2QthyyzTjbZhCtjzzfnoRmbKeZfhEBRubY1Wy9oH8/5lWosK80wXY5eb4YLHtZmuVCuFIRdSGBKRCjl6CJZ9aHaCL56iH1obzr3L3MJjra3Pake2w4xnzIKXAAGh0H0k9HoIQmMsLEy8lcKQCykMiUil5B8zXWgL/mMCAJjp+V1uNq1FMY0sLc9yOxbAL8/DzkXmcVgs9H/OzDzz87O0NPEuCkMupDAkIlVSWADrv4f5b0DaanPO5m82O+31INRvb2l5lnI4YOPPMPM5OJhizsV3ggEvQZPe1tYmXkNhyIUUhkTkrDgcsHWWCUXb5pScb34xnP8QJPXy3Wnnhfmw+H2YPQZyM825lgPh4r9rOxQ5awpDLqQwJCIus2eFCUXrfwCH3Zxr0M2EolZDfLebKPsAzHkFln5oVv72C4ReD0DvRyAozOrqxEMpDLmQwpCIuNyhLWZM0covoDDXnIttYQJAx+t9dxHHg5tg+lOwabp5HN0ILv6b2R/OV1vPpMoUhlxIYUhEqk3WPlj0LiwZD7kZ5lxkPHS/x2x66osbxDockDIVpo2GjJ3mXKMeMPAfkNDZ0tLEsygMuZDCkIhUu+OZsOxj+OOdkq0sgqPhnNvgvHsgMs7S8iyRl2Naz35/wyzaaPOD7vdCvye1dpNUiMKQCykMiUiNKciF1V/Dgrfg4EZzzj8YOt8APR+A2GbW1meFjN0w42lYO8k8jm4Eg/5h9oRT15mUQ2HIhRSGRKTG2e2wcZoZbL1rcdFJG7S93CxS2KCLhcVZZOMMmDKqpOus+cUw6BXfDIhSIQpDLqQwJCKWcTggdaEJRcWDigGa9IG+T0BST8tKs0TeUZj7muk+s+dDQAhc+IwZY+Xnb3V14mYUhlxIYUhE3MK+tfD7W/DnN2b6OUCby81sq9pNra2tph3cBFP+r2TdpobnwKVv+PZClnIKhSEXUhgSEbeSnmo2Pl3+qVmryC8Qzrsb+jwCobWsrq7mOBzmPZjxtFmw0eZvWoj6Pg7BkVZXJ25AYciFFIZExC3tW2eCwJZfzePQWqbrrNtt4B9obW01KXMP/Pw4rPvePI5uBFf+13Qlik9TGHIhhSERcWubfoEZT8GBDeZxbAsY8CK0vMS3ZlttmmkGWKenmsfn3g0XPadp+D5MYciFFIZExO0VFsDyT2DWy5Bz0Jxr0sdsfBrf0draalJuFsx4BpZ9ZB7XagyXvQVNL7C0LLFGZf5+e9RGOHPnzuWyyy4jISEBm83G5MmTy71+9uzZ2Gy2U24bNmyomYJFRGqCfwCcczs8sNxMvfcPhm1z4b0+8P19kJVmdYU1IzgSLnsDbpoIUQ3hyHb49HL48UEzE03kNDwqDB09epROnTrx9ttvV+p1KSkp7N2713lr0aJFNVUoImKhkGgzu+z+JdB+KOCAFRPgrS4w51WzqrMvaH4R3LsQut1uHi/72ATDPSssLUvcl8d2k9lsNr777juuvPLK014ze/Zs+vXrx5EjR4iJianS91E3mYh4rJ2LYfqTsGuJeRyZYMbRdLgO/Dzq38JVt20uTLobsvaAXwD0/j/o/QgEBFldmVQzr+0mq6rk5GTi4+Pp378/s2bNKvfa3NxcMjMzS91ERDxS4rlw+0y45kMzyyprD3x3N7zfD7b/bnV1NaNJH7jnd2h7BdgLYM4r5uffu8rqysSNeHUYio+PZ9y4cUycOJFJkybRqlUr+vfvz9y5c0/7mjFjxhAdHe28JSYm1mDFIiIuZrOZLrP7l8BFz0NQJOxdCR8Phq9ugkNbrK6w+oXVhus+hWs/hrBY2LcGPrgIFr1n1isSn+fV3WRlueyyy7DZbPzwww9lPp+bm0tubq7zcWZmJomJieomExHvkH0AZr9sxtH44qKNRw+aAdUbfjKPW18Kl70J4XWsrUtcTt1k5ejevTubNm067fPBwcFERUWVuomIeI2IunDp63DPArPZqT0fFr4NbyWblpLCfKsrrF7hdeD6CTDwFRMEN/wE73SHDVOsrkws5HNhaMWKFcTHx1tdhoiIteq1gZu+NdPQ67aBY0dg2mMmGKRM8+7uI5sNuo+EO3+Fem3h6AH4343ww18h/5jV1YkFAqwuoDKys7PZvHmz8/G2bdtYuXIltWvXplGjRjzxxBPs3r2bTz/9FIA33niDxo0b065dO/Ly8pgwYQITJ05k4sSJVv0IIiLupflF0KQvrPgMZr0EhzbDl8N8Y9HG+E5w12zzc//+ltnrbPdyuPYTqNPc6uqkBnlUy9DSpUtJTk4mOTkZgFGjRpGcnMyzzz4LwN69e0lNTXVen5eXxyOPPELHjh3p3bs38+fPZ8qUKVx99dWW1C8i4pb8A6DbrfDX5XD+qJJFG8ddAL++AAV5VldYfQKC4eIXYPh3EF7XDK4edwH8+a3VlUkN8tgB1DVF6wyJiM9JT4WZz8La78zjuPZw1XtQv721dVW3zL0w8Q7YMd887joCBv4DAkMtLUuqRgOoRUSk6mIamWno131WMhV9XF+Y9y+zD5q3ioqHm7+HPo8CNjPj7oOLzLYe4tUUhkREpGxtL4d7F5np5/Z802X20UA4uPnMr/VU/gFw4dMndZv1g62zra5MqpHCkIiInF5EXTMV/ar3IDjabO3x7vnwx7tgt1tdXfVp1g/umgMJyXDsMHx6JcwaA/ZCqyuTaqAwJCIi5bPZoNMwuHcBNO0HBcfg59FmR/j01DO/3lNFN4Bbp0HycMABc/4Bn14BWWlWVyYupjAkIiIVE93QdB8N+RcEhsH2efBOT1j+mfeuSxQYCle8DVe/D4Hh5mcep73NvI3CkIiIVJzNBufcASPnQ2J3yMuCH+43axN5c4tJx+vg7jlQp5XZ8PbDgbCu7G2dxPMoDImISOXFNoNbp5o1evyDYOPPZvXqNZOsrqz61GkBd8yEZhdCfg58PRxmPO39W5j4AIUhERGpGj9/6PUg3D3XrOZ87Ah8eyt8cyvkHLa6uuoREg03fgM97jePF/wHPrkcsvdbW5ecFYUhERE5O/XawB2/wgWPg80f1k4yrUQbp1tdWfXwD4BLXoLrPoWgSEhdAB/0h/0brK5MqkhhSEREzp5/IPR7Au74xYyryd4HX1wH398HxzOtrq56tL0C7vwNajUxs+rGD9B6RB5KYUhERFynQRcz0LjH/YANVkyAsb3MXmfeqG5L0yqW2B1yM2DCUFjygdVVSSUpDImIiGsFhppupBFTICYJMlLhk8tg2mjIy7G6OtcLjzXbeHS4DuwFMOX/4KdR3r11iZdRGBIRkerRuBfcswC63moeL3oX3usNO5dYW1d1CAyBq8dB/+cAGywdb2ab5R+zujKpAIUhERGpPsERcNkb8JeJEBkPhzbDhwPMPmcFeVZX51o2G/QeZbYvCQiBlKnw2VVmlp24NYUhERGpfi0ugnsXmq4khx3m/QvevxAObbG6Mtdrc6lZqTs4GlIXwgcXe+fP6UUUhkREpGaE1oKh75sp6WGxsO9PeL8fbP7V6spcL6kn3DYNohrCoU1m6v32+VZXJaehMCQiIjWr7RVmLFHDc+F4Bnx+jVm80Nv2N4trZ6beN+hquso+uxpSplldlZRBYUhERGpeZH0Y8RMk32S6zWY8Dd/d7X0DjiPjzKy61pdCYS58dRP8+a3VVclJFIZERMQaAcFw+dsw6J9m5erVX8FHgyBjt9WVuVZgKFz7CXS83ky9n3gHLBpndVVyAoUhERGxjs0G591lBhyH1oY9K2BcX0hdZHVlruUfAFe+C+fcAThg2qMw4xmw262uTFAYEhERd9D0ArhrFsS1h6P74eMhsPxTq6tyLT8/GPxa0VpEwIK3zHYl9kJr6xKFIRERcRO1GsNt06HN5WDPhx/+ClMfhcJ8qytzneK1iK56z3QNrvrCjJXSatWWUhgSERH3ERxhpt73e9o8XjzOLFx49JC1dblap2Fw7UfgFwB/fgMTb/e+RSg9iMKQiIi4F5sNLngUhn0BQRGwfR683xfS/rS6MtdqewVc9xn4BcK6yWammbfNpvMQCkMiIuKeWg+BO36BWk0gPRXGD4C131ldlWu1Hgw3/A8CQmHTdJhwDeRmW12Vz1EYEhER91WvjVm4sNmFkJ8D34yA3170rllYLS4q2r4jCnbMhy+HqYWohikMiYiIewurDTd+Az3/ah7P/Sf870Y4nmltXa6U1AOGT4agSNMt+L+/QEGu1VX5DIUhERFxf/4BMOBFuGoc+AfDxmnwwUXetQFqw67wl28gMAy2/GoCn1qIaoTCkIiIeI5O15sNUCMT4GCK2QB17yqrq3KdpB5mDFFgGGz+Bb64DvKOWl2V11MYEhERz9KgK9w1u2QD1E8ug93LrK7KdZpeADdNNDPpts2FL29Ql1k1UxgSERHPExlnxtgkdofjGfDplbBzidVVuU5Sz6IxRBGwbY7Zz0wrVVcbhSEREfFMIVGmBSWpF+RmmsUZU/+wuirXSTwHhn0O/kGw/gf48QHvmkXnRhSGRETEcwVHmEHHTfpAXhZ8djVsn291Va7TtC8MHQ82P1gxAaaMUiCqBgpDIiLi2YLC4YavoGk/yD9qFi7cOtvqqlyn7eVw5VjABss+gmmPgcNhdVVeRWFIREQ8X1CYmYXVYgAUHIMvrjezsbxFp2Fw5TuADZa8D3Nfs7oir6IwJCIi3iEwBK6fAK0GQ8FxMwtr4wyrq3KdzjfCoFfN8awXYfln1tbjRRSGRETEewQEw7WfQJvLoDDPLFy4YarVVbnOeXfB+Q+b4x8fhI3Tra3HSygMiYiIdwkIgms+grZXgj0fvh4O6763uirX6f8cdLoBHIXw9S2wc7HVFXk8hSEREfE+/oFmFlaHa8FeAN/cCmsmWl2Va9hscPl/oPnFZnzU59fCgY1WV+XRFIZERMQ7+QfAVe9BpxtNK8rEO2D111ZX5Rr+gXDdJ9DwHDiebna6P3bE6qo8lsKQiIh4Lz9/uOK/kDwcHHaYdBes/MLqqlwjKByGfQnRiXB4C3wzAgoLrK7KIykMiYiId/Pzg8vegm63AQ6YfC8s+8Tqqlwjoi7c8CUEhpu1lX56UGsQVYHCkIiIeD8/Pxjybzj3bsBhtrZY8oHVVblG/Q4w9IOSVap//ZvVFXkchSEREfENNhsMegV63G8eT/k/WPSetTW5SuvBcNmb5nj+67D0Q2vr8TAKQyIi4jtsNhjwIvR6yDye9hgseNvSklymy83Q7ylzPPVR2LHA2no8iMKQiIj4FpsNLnoe+jxqHs94Cub929KSXKbPo9DuKrOcwFfDIX2n1RV5BIUhERHxPTYbXPg09H3SPP71bzDnVWtrcgWbzcyeq98Bcg6aFbjzcqyuyu15VBiaO3cul112GQkJCdhsNiZPnnzG18yZM4euXbsSEhJC06ZNeffdd6u/UBER8Qx9R0P/Z83xrJfgt5c8fzZWUDgM+wLC6kDaajNY3NN/pmrmUWHo6NGjdOrUibffrlj/7rZt2xg8eDC9e/dmxYoVPPnkkzzwwANMnOglq5CKiMjZ6/1/cPHfzfHcV+GPd6ytxxViGsF1n4JfAPz5Dfwx1uqK3JrN4fDMuGiz2fjuu++48sorT3vN6NGj+eGHH1i/fr3z3MiRI1m1ahULFy6s0PfJzMwkOjqajIwMoqKizrZsERFxV/PfgF+eM1PUb/wGWlxkdUVnb9E4mPYo+AXC7dOhQVerK6oxlfn77VEtQ5W1cOFCBgwYUOrcJZdcwtKlS8nPzy/zNbm5uWRmZpa6iYiID+j1ICTfZFaq/vZWOJBidUVn79w7oe0VZsPab2+D4xlWV+SWvDoMpaWlERcXV+pcXFwcBQUFHDx4sMzXjBkzhujoaOctMTGxJkoVERGr2Www5HVo1BNyM+GL6yHnsNVVnR2bzay+Hd0IjmyHiXeCvdDqqtyOV4chMN1pJyruFTz5fLEnnniCjIwM523nTk1LFBHxGQFBcP1nReFhG3xzCxSW3ZPgMUJjzKauASGwaTr89nerK3I7Xh2G6tevT1paWqlz+/fvJyAggNjY2DJfExwcTFRUVKmbiIj4kPA6cOP/ICgCts2Fnx+3uqKz16ALXF40+Wj+67B2sqXluBuvDkM9evRg5syZpc7NmDGDbt26ERgYaFFVIiLi9uLawdXvAzazh9ni962u6Ox1vNaMiwIz3V4LMjp5VBjKzs5m5cqVrFy5EjBT51euXElqaipgurhuvvlm5/UjR45kx44djBo1ivXr1/Phhx8yfvx4HnnkESvKFxERT9J6MFz0nDmeNtrsCu/pLnwGGnQzA6kn3aXxQ0U8KgwtXbqU5ORkkpOTARg1ahTJyck8+6xZMGvv3r3OYATQpEkTpk6dyuzZs+ncuTN///vfeeuttxg6dKgl9YuIiIfp9RB0vB4chfD1LXBoi9UVnR3/QLPDfVAkpC6Aua9ZXZFb8Nh1hmqK1hkSEfFx+cfh4yGweynEtoA7fjGDkj3Zqq/gu7vMmkojpkBST6srcjmtMyQiIuIqgSFme4uoBnBok1mvp7DA6qrOTqfrodMNRWsq3e75SwicJYUhERGRM4mMgxu+hMAw2PIrzHzG6orO3uDXILY5ZO0xY6J8mMKQiIhIRcR3giuL9vj64x1Y9om19Zyt4Ai4apzpKvvza0iZZnVFllEYEhERqah2V0LfJ83xlP+D7b9bWs5Za9gVetxvjn98yGe7yxSGREREKuOCx6DdVWa/r6+Hm20uPFm/J83A8Ow0mHwv+OC8KoUhERGRyrDZ4Ip3IL4z5ByCL2+A3Cyrq6q6wFC45kPwD4KN07xjgclKUhgSERGprKAwM6A6oj7sXwffjfTsFpX4jjDgJXP8y/OQscvScmqawpCIiEhVRCWYKff+QbDhJ1j9tdUVnZ1z7oBGPSH/qM/NLlMYEhERqaqGXeGCouDw82jIPmBtPWfDzw8u/Tf4BZhw50OzyxSGREREzkavB6F+Bzh2BKY9anU1Z6dem5LZZVMfg7yj1tZTQxSGREREzoZ/IFzxX7D5w9rvYP1PVld0di54DKIbQUYqzHnV6mpqhMKQiIjI2YrvZFqIAKaMMq1EniooHAYXhaCFb8P+9dbWUwMUhkRERFzhgtFQpyVk74PpT1tdzdlpNQhaXwr2AvjpYbDbra6oWikMiYiIuEJgCFz+H8AGKyfAlt+srujsDPwHBIZD6kJY9YXV1VSrCoWhzMzMSt9ERER8TqPucO5d5viHByE329p6zkZMIvR7whzPeMart+qoUBiKiYmhVq1aFb7Vrl2brVu3VnftIiIi7qf/sxBTNAD51xesrubsnDcS6rWDY4dh5rNWV1NtAip64bfffkvt2rXPeJ3D4WDw4MFnVZSIiIjHCo6Ay96Ez66CxeOg/dWmxcgT+QfCpa/DhwNgxWeQfJPn/izlqFAYSkpKok+fPsTGxlboizZt2pTAwMCzKkxERMRjNbvQBIcVE+D7+2HkfDOmyBM1Og+63ALLPzGDqe+eB/4VbkvxCBXqJtu2bVuFgxDAmjVrSExMrHJRIiIiHm/AS2bvskObYM4rVldzdi56HkJrm33YVv/P6mpcTrPJREREqkNoDAz5lzn+/U3Ys9LKas5OWG3oPcocz/4H5OVYW4+LVamda/HixcyePZv9+/djP2ntgX//+98uKUzEG9jtDrJyCzieX4jNBpHBgYQE+mGz2awuTURqQptLod1VZmXqH+6HO2eZcTie6Jw7YNF7kLETfn8D+j1pdUUuU+kw9PLLL/P000/TqlUr4uLiSv1S1y948Vb5hXb2pB9jx6EcdhzOYcfBo6QeziH1cA47DuVwLL+wWr5vcIAfdSKCqRMZTJ3wIGIjgoiNCKZJnXC6JtWiaZ1w/X8n4u4G/RO2zoG0P02I6OOh+5cFhsIlL8HXN8P8N6DTDVC7idVVuYTN4XA4KvOCuLg4XnnlFUaMGFFNJbmXzMxMoqOjycjIICoqyupypJpk5OQzY10a36/cw/zNB60up0rqRgYzoG0cl7SrT/emsQQFqBdcxG2s/hom3Qn+QWYAcr3WVldUNQ4HfHYlbJ0NLQfBje47fqgyf78rHYbi4+OZO3cuLVq0OKsiPYXCkHc5nl/Iqp3pfL9qD5NX7CYnz3UtOuFB/jSKDSepdhhJsWE0ig0jqXY4jWqHERkSQKHDwZGjeRzMzuNgdi6HsnM5VMbjQ9l5ZOcWuKwugH6t6jKgXX0uahNH3chgl35tEakAhwO+uA42zYCG58JtP4Ofv9VVVc2BFBjb02zVcdsMM9vMDVVrGHr11VfZs2cPb7zxxtnU6DEUhjyT3e5gy4Fsftuwn+9W7GZDWlalXt8uIYrOiTE0jg2nUWwYjWqbW3iw+0wnPZCVy6yU/cxYu49f1u+r0tdoFRfJ4A7x3HBuIvWiPHTar4inyNgF/+0OeVlmq4vu91hdUdX98FdY/ik0vxhu+tbqaspUrWHIbrczZMgQNm7cSNu2bU9ZT2jSpEmVr9iNKQx5huP5hcxOOcD787aybEfFd4uODg3kys4JXJHcgE4NY/D38/zxN/mFdpZsO8yMdfuYsTaNPRnHK/xafz8bTw1uw03dk9TNJlIdln5o1uoJDIN7FnjumJvDW+E/3cBRCLf/AonnWF3RKao1DN13332MHz+efv36nTKAGuCjjz6qfMVuTGHIfaUeyuGFn9byy/r9Fbq+b6u6XNm5AX1a1qV2eFA1V+d+HA4HOw7lMGNdGjPW7mNpBUJjl0YxPDWkLV2TatVAhSI+wG6HTy+H7fOgyQVw8/fgqZMgJt9nNqRteA7cPtPtfo5qDUORkZH873//Y8iQIWdVpKdQGHIfeQV2fl6bxjOT15BxLL/ca4edk8iN5zWidf0otXCcgcPhYMn2I7w8dT0rd6aXe+2tvRrzwIUtqOWDYVLEZQ5tgbG9oOAYXPYWdL3F6oqqJnMv/Kcr5B+Fq9+HjtdZXVEp1RqGkpKSmD59Oq1be+hI+EpSGLKOw+Fg8/5sJvyxg08W7ij32kcGtOTmno2JCvHQ9TvcSG5BIZ8t3MGLU9aXe13dyGCeHtKGyzom4OcF3YsiNWrBf2DG0xAcBfctgqgEqyuqmrmvwW9/h9jmcP9St2odqtYw9NFHH/Hzzz/z0UcfERYWdlaFegKFoZqXnVvAqz9v4NNyAlCLehH8/cr2dG9a8W1ipGpSD+Xw6vQN/LR6b7nXXdw2jr9d3o6EmNAaqkzEg9kLYfzFsHsZtL4Uhn1udUVVk5sF/2oNedmmy69pX6srcqrWMJScnMyWLVtwOBw0btz4lAHUy5cvr3zFbkxhqGbY7Q4+X5zKM5PXnPaaG85txIP9W1A/WrOerOJwOJixbh8vTlnHzsPHTnvdo5e04p4LmqnFSKQ8+9bBu73AYTcbudbvYHVFVTPl/2DJB9C0H9w82epqnKo1DP3tb38r9/nnnnuuMl/O7SkMVZ+CQjuzUw5w3xfLyS2wl3nN00PacE3XhsSEaYyKO8o4ls+7c7YwdvaWMp9vUS+CT28/l/hotRaJlOnb22DNROhwHQx93+pqqubIdjN2yF4AI6ZA4/Otrgio5jDkaxSGXG/HoaOM+nrVaafAD++exOhBrYlwozV95MwcDgdfLdnJ45P+LPP5f1/Xiau7NKzhqkTc3N5V8F4fsPnDAyugVpLVFVXNTw+bZQMangu3z3CLsUMKQy6kMOQ6y3YcYejYBWU+16NpLP++vpNaELzEriM53Dx+MVsPHj3luT4t6/L2jcka7C5S7NMrYessOG8kDHrF6mqqJnMvvJVsZsgN+xJaD7a6IteHodq1a7Nx40bq1KlToQIaNWrEvHnzSEry0IR7AoWhszfhjx08XcZYoJiwQN4alsz5zetobImXstsd/Oe3zbz+y8Yyn//s9nPp3aJuDVcl4ma2zDL7fQWGwcNrIay21RVVzcxn4fc33WbsUGX+fleoHyI9PZ1p06YRHR1doQIOHTpEYWH17OItnuFYXiGPfLuKKWXMQLq8UwLPXNpWe2T5AD8/Gw9e1IIHL2rB2j0ZDB27gOP5JePDho9fDMD13RJ54cp2BAd46F5NImejaV+I72S6zBaPg76PW11R1XS91YShrbMhPRViGlldUYVVqGXIz6/yi9Zt3ryZpk2bVqkod6KWocpJScviLx/8wcHsvFOee/HK9tx4biO1Avm43IJCnp28lq+W7jzluZBAP6Y92IcmdcItqEzEQmsmmsHUobVN61CQhy5d88llsG0u9HwABvzd0lI0ZsiFFIYqZn/mcbqP+RV7GZ+mn/56Pu0bVKxVUXzL3I0HuPnDxaecD/L3Y9GT/bXStfiOwgJ4u6uZmTXon3DeXVZXVDUp0+DLYWYxyYdWQ6h1W/koDLmQwlD5so7nc8V/f2frgdIDZbsm1eL9m7v55B5gUnkZx/K5/4vlzNt0sNT5S9rFMfYvXdWaKL5h8fsw9RHTvfTXFeDvgTNq7XZ493zYvxb6PmFpl5/CkAspDJXtyNE8Rn29klkpB0qdv6RdHK9f35mwIA/8n1gsV2h3cM+EZcxYt6/U+RevbM9N3T1/QoZIufKPwevtIecgDB0PHa6xuqKqWfsdfDMCQqLhoT/NvQUq8/dbO1hKpTgcDt6bs4Xkv88sFYRa149k3QuX8N7wbgpCUmX+fjbG3dyNFc9cTJB/ya+npyevofHjU/hzV4aF1YlUs8BQOO9uc/z7G+CpbRVtroA6LeF4Bqz6yupqKkQtQ2eglqESaRlmXNCJggP8mPNoP22RIdVi5c50rvzv76XOhQb688cT/YkO0zpF4oVyDsPr7SA/B26aBM37W11R1SwaB9Mehbpt4N6FlizCqJYhcbmXpqw7JQh9eWd3Ul4cpCAk1aZzYgzb/zGE5y9r6zx3LL+QTi/M4L7Pl6N/y4nXCasNXW4xx7+/aW0tZ6PT9WbdpAPrIfUPq6s5o0qHoQsvvLDM/cmOHDnChRde6JKixH1sPZBN48en8P68bc5zw85JZPNLg+jRTDvGS80Y0asJW18ezIWt6znPTflzL02emMr/FqdaWJlINehxn9meY9sc2LPC6mqqJiQa2g81x8s+sraWCqh0N5mfnx+xsbH06tWLzz//nPBwsx7Ivn37SEhI8LrFFn21m6zQ7uDuz5byy/r9pc4vePxCEmK0ZYZY51B2Ll1f/OWU81MeOJ92CVrCQbzEpLtg9VfQ9kq47hOrq6ma3cvh/X7gHwT/l1LjK2tXezfZL7/8QlpaGt27d2f79u1V+RLixn7ffJBmT04tFYSeHtKG7f8YoiAklouNCGb7P4Yw8Z4epc4PeWs+Pcb8Sl6B/TSvFPEgvR409+t/gENbrK2lqhp0gfodoTAPVv3P6mrKVaUwFB8fz5w5c+jYsSPnnHMOs2fPdnFZYoXDR/Po+89Z/OWDRc5zAX421v7tEu7o7fmriYt36ZpUm+3/GMLTQ9o4z+3NOE7Lp6exIS3TwspEXCCuHTS/GBx2WPi21dVUXdei8U8rP7e2jjOodBiyFY0IDw4O5vPPP+fBBx9k4MCBvPPOOy4vTmqGw+Hgw/nb6PL3mWw/lOM8/98bu7D55cGEB2uqvLivO3o3ZfNLg+jUsKSLbOAb83hn9mYLqxJxgfMfMvcrPofs/eVe6rbaXQ1+AbBvDRwoe8Nmd1DpMHTyEKOnn36azz//nH/9618uK6o877zzDk2aNCEkJISuXbsyb9680147e/ZsbDbbKbcNGzbUSK2eILegkAv/NYcXflrnPNcyLoI1f7uEIR3jLaxMpOIC/P34/v7zefemLs5zr/6cwnkv/0JhWXvEiHiCpF7QoCsU5sKi96yupmrCakOzoslV6yZbWkp5Kh2Gtm3bRt26dUudGzp0KIsWLeLDDz90WWFl+eqrr3jooYd46qmnWLFiBb1792bQoEGkppY/myQlJYW9e/c6by1atKjWOj1FWsZxWj39M9sOlmylMf6Wbsx4+AIi1BokHmhg+3iWPHWR8/G+zFyaPTmVnYdzynmViJuy2aDXQ+Z4yfuQm21pOVXW7ipzv/ort11I0qMWXTzvvPPo0qULY8eOdZ5r06YNV155JWPGjDnl+tmzZ9OvXz+OHDlCTExMlb6nt84m+2HVHh74smTKZp2IYH4ddYEWshOv4HA4GPjGPFL2ZTnPvTq0I9edk2hhVSJVYC+Et8+Bw1vgkpfNtHtPk5sF/2oNedlw8w/Q9IIa+bZeuehiXl4ey5YtY8CAAaXODxgwgAULFpT72uTkZOLj4+nfvz+zZs0q99rc3FwyMzNL3bxJQaGd699bWCoIPTKgJUufvkhBSLyGzWZj+sN9eOGKds5zj01czdCxCygo1Gwz8SB+/tDrAXO88L9QmG9tPVURHAmdhpnjpeOtreU0PCYMHTx4kMLCQuLi4kqdj4uLIy0trczXxMfHM27cOCZOnMikSZNo1aoV/fv3Z+7cuaf9PmPGjCE6Otp5S0z0nn9JZh7Pp/lT01i07bDz3MyH+3D/heo2FO90c4/G/DKq5F+hy3YcoflT00jLOG5hVSKV1HEYhNeDzN3w57dWV1M1ycPN/aZfoCDP2lrK4DFhqJjtpP1NHA7HKeeKtWrVijvvvJMuXbrQo0cP3nnnHYYMGcJrr7122q//xBNPkJGR4bzt3LnTpfVb5VB2Lh2fn1HqXMqLA2kRF2lRRSI1o3m9CDb8fSDBASW/7rqP+ZVNJ3Shibi1wBDofo85/v1NsHtg62b9jhAWC/lHYfdSq6s5hceEoTp16uDv739KK9D+/ftPaS0qT/fu3dm0adNpnw8ODiYqKqrUzdPtOHS01Iq9QzrGs23MYIID/C2sSqTmhAT6s+HvA7n9/CbOcxe/Ppf5mw5aWJVIJXS7DYIizV5fO+ZbXU3l+flBs6JNZzdMsbaWMnhMGAoKCqJr167MnDmz1PmZM2fSs2fPCn+dFStWEB/vO1PGl+04wgX/nO18/PBFLfnvjV1O25om4q1sNhvPXNqWt29Mdp67afwiPl243bqiRCoqNAbaXmGO1/9kaSlV1vZyc79mkhkY7kY8JgwBjBo1ig8++IAPP/yQ9evX8/DDD5OamsrIkSMB08V18803O69/4403mDx5Mps2bWLt2rU88cQTTJw4kfvvv9+qH6FG/bR6D0PHlgwuf3NYZx68SOODxLdd2jGB7+/r5Xz87PdreWnKunJeIeImWg829ylT3XaKerlaDICQGMjaYzahdSMetZjM9ddfz6FDh3jhhRfYu3cv7du3Z+rUqSQlJQGwd+/eUmsO5eXl8cgjj7B7925CQ0Np164dU6ZMYfDgwVb9CDXmv7M288/pKc7HX9/dg3Ob1OwmeSLuqlNiDPMe60fvV83s0vfnbSMmLIj7+jW3uDKRcjTtBwGhkLET0v6E+I5WV1Q5AcFmJ/ul42HllyWLMboBj1pnyAqeuM7Q6zM38uavJeOifhl1Ac3rRVhYkYh72ptxjB5jfnM+fumq9vzlvCQLKxI5gy9vMC1DfZ+Avo9bXU3l7VoKH/SHwHAYvc0EpGrilesMScV8sSi1VBD644n+CkIipxEfHcrvj5f86/Sp79bw46o9FlYkcgath5h7NxyEXCEJXSAizswqS11odTVOCkNeZNqfe3nyuz+dj5c8dRH1o0MsrEjE/TWICWXeY/2cj//65QpmpXjoppji/VoOBJsfpK2GdA9c+sXPD5pfbI43zSz/2hqkMOQl5mw8wD2fL3c+XvjEhdSNrL7mRxFvklg7jF//r2Rxxls/WsKS7YfLeYWIRcLrQOJ55jhlmrW1VFWL4jA0o/zrapDCkBdYuv0wt3y42Pn41/+7gPjoUAsrEvE8zepGMPWB3s7H1767kDW7MyysSOQ0WhXPKvPQrrJm/cDmDwc3wuFtVlcDKAx5vPScPK55t6TfdeI9PWhWV2OERKqibUIUk+4tWbfs0v/M10rV4n6Kxw1tnw/H0i0tpUpCoqFRd3O8+Zfyr60hCkMezOFw0PmFkj7Xt25IpmuSps+LnI0ujWrx+R3nOR9f/Ppcth88amFFIieJbQZ1WoG9wK3G3VRK86LVqLf8Vv51NURhyINdP+4P5/F13RpyeacEC6sR8R69mtfh3Zu6Oh/3fW02B7NzLaxI5CStPbyrrEnRGL2di9xiAUmFIQ/12R87WFy0+7yfDV69ppPFFYl4l4Ht6/PqNSWL2nV78Re0LJu4jVZFXWWbfoECDwzq9TuCfzDkHILDW62uRmHIE21Iy+SZyWucjze+OMjCakS813XdErmnbzPn4/Ne/tXCakRO0KCrWa8nLwu2z7O6msoLCIL4on/E71xkbS0oDHmcrOP5DHyj5IO/4PELCfDXf0aR6jJ6YGsa1Q4DYH9WLh/Od4/ZL+Lj/PzMmkMAG6ZaW0tVJRVNVnCDcUP6K+pBCgrtdHi+ZF2GsX/pQkKMptCLVLfZj/R1Hr/w0zoyjuVbV4xIseJZZSnT3GLcTaUVh7lNM6GwwNJSFIY8yCPfrHIeX94pgUEd4i2sRsR3+PnZmHVCIOr0N/dZLE58WJMLzB5fWXtgzwqrq6m8xHMhtBYcT4e0VWe8vDopDHmIbQePMnllyZ5Jbw7rbF0xIj6oSZ1whp2T6Hz8wTzrB32KjwsMKZminuKBXWV+/hDX3hwf3FT+tdVdiqXfXSrE4XDQ77XZzsfLn7kYm81mXUEiPuofQ0tml704ZT0HsjxwFo94F2dX2c/W1lFVsc3N/aHNlpahMOQBnvyuZObY00PaUDs8yMJqRHzbid1l57yk6fZiseL1evavhTwPXBw08Vwzdqh2szNfW40Uhtzc9oNH+XJxqvPxHb2bWliNiDSpE8513Ro6H78+c6OF1YjPi4qHiPrgsEPan1ZXU3mdb4Qbv4LON1hahsKQm7vwX7Odx4ue7G9dISLi9PJVHZzHb/22mexca2fCiI9r0MXc715ubR0eTGHIja3bk4m9qAV+UPv6xEWFWFuQiAAQ4O/H5Pt6OR+P/GyZhdWIz0tINveeOKPMTSgMubHBb5UsrvjmsGQLKxGRk3VOjCEyJACA+ZsPkp6TZ3FF4rMSilqG9qhlqKoUhtzU/E0Hncf39WtGUID+U4m4m3mP9XMen//KLAsrEZ9W3DJ0aDMcz7C2Fg+lv7Bu6qbxJXu1PHpJawsrEZHTiQkLok/LugBk5xawZrf+EIkFwmMhppE53mvt4oWeSmHIDX2xqGT22D+u7lDOlSJitfdv7uo8vvQ/8zXVXqxR3DqkQdRVojDkZvIK7Dz5Xcn0yGHnNrKwGhE5k+AAf+7s3cT5eP7mg+VcLVJNnOOGNIi6KhSG3Mzni3Y4j98b3rWcK0XEXYy6uJXzePj4xRZWIj7LOaNMLUNVoTDkZv724zrn8YC2cRZWIiIVFRrkz5COJRsnH8rWNh1SwxI6m/v0VDh6yNJSPJHCkBvZuC/LefzMpW21/5iIB3nlhH3L7vh0qYWViE8KiS7Z50tdZZWmMORGrvzv787j23o1tq4QEam0iOAA6kYGA7AiNR27XQOppYY5d4DXFjGVpTDkJo4czSMnrxCAXs1j1Sok4oG+u7en8/iV6RssrER8Uq0kc5+eWv51cgqFITfx0YLtzuN/X9fZsjpEpOoa1gpzHr83Z6um2UvNKl5rSGGo0hSG3MRbv25yHmsPMhHPNe6EWaCLth22sBLxOTFqGaoqhSE3UHjC2IJh5yRaWImInK2LT5gF+tQJa4aJVDu1DFWZwpAb+OKEtYWeGNTGwkpE5GzZbDbqRAQBsOXAUYurEZ8S3dDc52bAsXRLS/E0CkNu4Jnv1zqPo8MCLaxERFxhzNUl0+y1X5nUmKBwCKtjjjN2WluLh1EYslhBod15fLEWWRTxChcUbd4KMH7+NgsrEZ+jrrIqURiy2JQ/9zqPnx6iLjIRbxAUUPKr9bsVuy2sRHyOwlCVKAxZ7J/TU5zHSbHhFlYiIq50UZt6zuNCLcAoNUVhqEoUhiy268gxAAL9tciiiDe5uUdj53FKWtbpLxRxJYWhKlEYstCJC7LdcsIvThHxfOc0ru08/n6lusqkhjjD0I7yr5NSFIYstH5vyb8Wh53byMJKRMTVQoP8ncfvzd1qYSXiU8KLBu/nHLG2Dg+jMGShr5eWTH1sWkfjhUS8TceG0VaXIL4mpOgzd1xLOlSGwpCFFm455Dz289OYIRFvc1efps7jA1m5FlYiPqM4DOVlgb3Q2lo8iMKQhVL2aVCliDfr3aJkvaFlO7RPmdSA4KiS49xM6+rwMApDbqB2eJDVJYhINYgOLVlR/ttlGkQtNSAgCAKKNvs+rjBUUQpDbqBbUi2rSxCRavbbhn1WlyC+QuOGKk1hyA10a6wwJOLttO6i1JjirjJ1k1WYwpBFjuYWOI+7qmVIxGuFnzDFXqRGhBSFIXWTVZjCkEU27892HjetE2FhJSJSnU4cNyRSI4rHDBXmWVuHB/G4MPTOO+/QpEkTQkJC6Nq1K/PmzSv3+jlz5tC1a1dCQkJo2rQp7777bg1VWr5lO0oWxNIvSxHvFRmi/7+lhvkVtUbaC8q/Tpw8Kgx99dVXPPTQQzz11FOsWLGC3r17M2jQIFJTy96DZdu2bQwePJjevXuzYsUKnnzySR544AEmTpxYw5Wfaseho85jrTEk4r0iQgKsLkF8jV/RZ05hqMI8Kgz9+9//5vbbb+eOO+6gTZs2vPHGGyQmJjJ27Ngyr3/33Xdp1KgRb7zxBm3atOGOO+7gtttu47XXXqvhyk+VV6jRlCK+QJswS43zK2qNLMy3tg4P4jFhKC8vj2XLljFgwIBS5wcMGMCCBQvKfM3ChQtPuf6SSy5h6dKl5OeX/SHJzc0lMzOz1K065Bfaq+Xrioh78VfLr9Q0tQxVmseEoYMHD1JYWEhcXFyp83FxcaSlpZX5mrS0tDKvLygo4ODBg2W+ZsyYMURHRztviYmJrvkBTpJXoDAk4gv8bApDUsP8A4tah9QDUVEe15ltO+kXi8PhOOXcma4v63yxJ554glGjRjkfZ2ZmVksgCg/WdFsRX1AnIhiApwa3sbgS8RnXfWJ1BR7HY1qG6tSpg7+//ymtQPv37z+l9adY/fr1y7w+ICCA2NjYMl8THBxMVFRUqVt1SE7U2kIivqDkH2AWFyIip+UxYSgoKIiuXbsyc+bMUudnzpxJz549y3xNjx49Trl+xowZdOvWjcBAa6e7dj1h1enM4xrkJuKtileeLq8FW0Ss5TFhCGDUqFF88MEHfPjhh6xfv56HH36Y1NRURo4cCZgurptvvtl5/ciRI9mxYwejRo1i/fr1fPjhh4wfP55HHnnEqh/BqXFsuPN4ZWq6dYWISLWyF7UMaRy1iPvyqDFD119/PYcOHeKFF15g7969tG/fnqlTp5KUlATA3r17S6051KRJE6ZOncrDDz/Mf//7XxISEnjrrbcYOnSoVT+C04kzTJbuOEKflnUtrEZEqkvxEFYNpBZxXx4VhgDuvfde7r333jKf+/jjj085d8EFF7B8+fJqrursLD9hNWoR8S4aMyTi/jyqm8xbLVMYEvFa9qJVNDRmSMR9KQy5gWP5hVaXICLV5EiO2SwzNFDLaYi4K4UhC3VKjHEeZxzTjDIRb5NfaGf1rgwAOjWMtrgaETkdhSELdW9a23m8aV+WhZWISHXYsDeLY/mFRIUE0KxuhNXliMhpKAxZqGezOs7jDWkKQyLeZtmOwwB0SaqFn+bWi7gthSELnXPCwou/rt9nYSUiUh2WFa0h1rWRVpwXcWcKQxYKCypZ2WBWygELKxGR6lC8bEbXJIUhEXemMGSx/q3rOY+L1yMREc+3N+MYu9OP4WcrPVlCRNyPwpDFhvdIch4fyM61sBIRcaXlO9IBaBMfRXiwx61vK+JTFIYs1qNZrPN46uq9FlYiIq60TF1kIh5DYchiwQElC7F9tGC7dYWIiEstS1UYEvEUCkNuIKBoyu2OQzkWVyIirpBxLJ+1u81ii100k0zE7SkMuYERPRs7jwvtGkQt4um+WpJKgd1By7gIGtYKtbocETkDhSE3cFefps7jzxZut64QETlr+YV2Pv59OwC3n99EG7SKeACFITdQLyrEefz8j+ssrEREzta0NWnsyThObHgQV3RuYHU5IlIBCkNu4oELmzuP1+3JtLASEakqh8PB+HlbAbipexIh2qlexCMoDLmJB/q3cB4PfmuehZWISFUt23GEVbsyCArw46buSWd+gYi4BYUhNxHg78elHeOdj/dnHbewGhGpivHztwFwVecG1I0MtrgaEakohSE38q/rOjmPrxm70MJKRKSyUg/lMH1tGgC3925icTUiUhkKQ24kOMCfdglRAKQezuFYXqHFFYlIRX20YBt2B/RuUYeWcZFWlyMilaAw5Ga+uKO783jkhGUWViIiFZV5PJ+vl+wE4I7eTc9wtYi4G4UhNxMdFkhYkJmBMmfjAexahFHE7X21eCdH8wppUS+CPi3qWF2OiFSSwpAbmv5QH+fxCz9p3SERd1ZQaOfjon0FtciiiGdSGHJDibXDnMcfL9iOw6HWIRF39fPaNHanHyM2PIgrk7XIoognUhhyUxPv6ek8/nThDgsrEZHyfDDPTKfXIosinkthyE11TSrZ6fq5H9ZaWImInM6MtWms3JlOkL8WWRTxZApDbuy/N3ZxHr/68wYLKxGRk6VlHOexiasBuLVXYy2yKOLBFIbc2JATVqR+Z/YWth88amE1IlKs0O7g4a9Wkp6TT/sGUfzfgFZWlyQiZ0FhyM0tfqq/87jva7M1mFrEDbw7ZwsLtx4iLMift4YlExSgX6Uinkz/B7u5epEhvHxVB+fjq8cusLAaEVmReoR/z9wIwN8ub0fTuhEWVyQiZ0thyAPceF4jaocHAbAiNZ2Z6/ZZXJGIb8o8ns8D/1tBod3BZZ0SuKZrQ6tLEhEXUBjyEIufLOkuu/PTpWTnFlhYjYjvcTgcPDN5DTsPH6NhrVBeuqq9FlgU8RIKQx4iwN+P7+/r5Xzc/rnpFlYj4nsmLd/N9yv34O9n481hyUSFBFpdkoi4iMKQB+mUGFOqWX7M1PUWViPiO7YfPMqz368B4KH+LUqtAyYink9hyMP885qOzuP35m5l474sC6sR8X55BXYe+N8KjuYVcl6T2tzbr7nVJYmIiykMeRibzcbSpy9yPh7w+lzyC+0WViTi3f41M4XVuzKIDg3k9es74++ncUIi3kZhyAPViQjm1aElLUSD35xnYTUi3mvepgO8N2crAK8M7UhCTKjFFYlIdVAY8lDXnZNIg6JfzJv2Z/P3n9ZZXJGIdzmUncuor1cB8JfzGjGwfX2LKxKR6qIw5MF+e+QC5/H4+dv4dOF264oR8SLZuQXcM2E5B7JyaVEvgqeHtLW6JBGpRgpDHiw4wJ81f7vE+fjZ79fy7bJdFlYk4vnSc/L4yweLWLz9MJHBAfznxmRCg/ytLktEqpHCkIeLCA5gxsN9nI8f+WYVnyzYbl1BIh7sQFYuw8b9waqd6dQKC+SLO7vTun6U1WWJSDVTGPICLeMi2fryYG7t1RiA535Yy39nbba2KBEPsyf9GNe/t5ANaVnUjQzmq7t70KFhtNVliUgNUBjyEn5+Np69tC0P9G8BwD+np/CPaRu0y71IBWw/eJRr313I1oNHaRATyjd396BlXKTVZYlIDVEY8iI2m41RF7fkycGtAXh3zhae+X4NdrsCkcjpbNyXxXXvLWR3+jGa1Annm5E9aFwn3OqyRKQGKQx5obv6NOPlqzpgs8GEP1J55JtVFGhhRpFT/Lkrg+vfW8j+rFxa14/kq7u7ay0hER+kMOSlbjyvEW8UrZY7acVu7vtiObkFhVaXJeI2lm4/zI3v/8GRnHw6NYzmf3d1p15kiNVliYgFFIa82BWdGzD2L10I8vdj+tp93PHJUnLyCqwuS8Ry8zcdZPj4xWTlFnBuk9pMuOM8YsKCrC5LRCyiMOTlBrSrz4cjziE00J95mw5y8/jFZB7Pt7osEcvMXLeP2z5ewrH8Qi5oWZdPbj2XyJBAq8sSEQt5TBg6cuQIw4cPJzo6mujoaIYPH056enq5rxkxYgQ2m63UrXv37jVTsBs5v0UdJtxxLpEhASzdcYQb3/+DQ9m5VpclUuO+X7mbkROWkVdoZ2C7+oy7uasWVBQRzwlDN954IytXruTnn3/m559/ZuXKlQwfPvyMrxs4cCB79+513qZOnVoD1bqfrkm1+d9d3YkND2LN7kyuH/cHaRnHrS5LpMZ8sSiVh75aSaHdwdXJDXj7xmSCAxSERAQCrC6gItavX8/PP//MH3/8wXnnnQfA+++/T48ePUhJSaFVq1anfW1wcDD161d8g8Xc3Fxyc0taTTIzM6teuJtplxDNV3f34KYPFrF5fzbXvreAL+7oTmLtMKtLE6k22bkFPPf9WiYuN1vV/OW8Rvz9ivb4+dksrkxE3IVHtAwtXLiQ6OhoZxAC6N69O9HR0SxYsKDc186ePZt69erRsmVL7rzzTvbv31/u9WPGjHF2xUVHR5OYmOiSn8FdNK8XwTcje9Codhg7Dx/jmncXsHl/ltVliVSLlTvTGfLWPCYu34WfDUZd3JIXr1QQEpHSPCIMpaWlUa9evVPO16tXj7S0tNO+btCgQXz++ef89ttv/Otf/2LJkiVceOGFpVp+TvbEE0+QkZHhvO3cudMlP4M7Sawdxjcje9CiXgT7MnMZOnYh363YpdWqxWvY7Q7emb2Za8YuYMehHBrEhPLV3T14oH8LbDYFIREpzdIw9Pzzz58ywPnk29KlSwHK/AXmcDjK/cV2/fXXM2TIENq3b89ll13GtGnT2LhxI1OmTDnta4KDg4mKiip180ZxUSF8dXcPOiXGkHEsn4e/WsVtHy9hT/oxq0sTOStpGce5afwiXv05hQK7gyEd4pn6QG/OaVzb6tJExE1ZOmbo/vvvZ9iwYeVe07hxY1avXs2+fftOee7AgQPExcVV+PvFx8eTlJTEpk2bKl2rN6odHsS3I3swbu5W3vxlE7NSDjDg9bmMHtSav5zbSF0J4nGmr01j9MTVpOfkExbkz/OXt+Parg3VGiQi5bI0DNWpU4c6deqc8boePXqQkZHB4sWLOffccwFYtGgRGRkZ9OzZs8Lf79ChQ+zcuZP4+Pgq1+xtAv39uK9fcy5pF8dj365meWo6z0xew4+r9vDK0I400R5N4gGO5RXy4pR1fL4oFYD2DaJ4a1gyTetGWFyZiHgCm8NDBooMGjSIPXv28N577wFw1113kZSUxI8//ui8pnXr1owZM4arrrqK7Oxsnn/+eYYOHUp8fDzbt2/nySefJDU1lfXr1xMZWbEdqTMzM4mOjiYjI8Nru8yKFdodfLpwO6/+nMKx/EKCA/x4+OKW3HF+EwL8PWJ4mfigZTsO8/jEP9m0PxuAu/s05f8GtCIoQJ9ZEV9Wmb/fHvPb4vPPP6dDhw4MGDCAAQMG0LFjRz777LNS16SkpJCRkQGAv78/f/75J1dccQUtW7bklltuoWXLlixcuLDCQcjX+PvZuLVXE2Y83Ifzm9cht8DOP6Zt4Kp3FrBuj/csMSDeYfP+LO76dClDxy5k0/5s6kYG89nt5/LE4DYKQiJSKR7TMmQVX2oZOpHD4eCbZbt48ad1ZB4vIMDPxj19m3H/hc21UJ1YKi3jOG/8spGvl+7E7gA/G1zbNZHHBrYiNiLY6vJExE1U5u+3wtAZ+GoYKrY/8zjPfL+G6WvNAPbm9SJ4ZWhHuibVsrgy8TWZx/N5d/YWPvx9G8fz7QAMaBvHYwNb0byeWntFpDSFIRfy9TAEppVo6p9pPPfDGg5m52GzwYiejXn0klaEBXnEIubiwXILCvls4Q7enrWZ9ByzyXDXpFo8Mag13TRdXkROQ2HIhRSGShw5msffp6xj0vLdAMSGB3Hb+U24qXsS0aHa9Vtcy2538P2q3bw2fSO7i9a/al4vgtEDW3NRm3qaLi8i5VIYciGFoVPNStnPs9+vYedh8wcqIjiAv3RvxO29mlAvKsTi6sTTORwO5m46yD+mbWD9XjNwPy4qmFEXt2Rol4aa2SgiFaIw5EIKQ2XLL7Tz0+o9jJ29hY37zJTmoAA/runakLt6N6Wx1ieSKli9K51/TNvAgi2HAIgMCeCevs24tWcTQoM0cF9EKk5hyIUUhspntzv4bcN+3pm9meWp6YCZ3TO4QzwjL2hG+wbR1hYoHmH7waP8c0YKU1bvBSDI349beiZxb9/m1AoPsrg6EfFECkMupDBUMQ6HgyXbjzB29mZmpRxwnr+gZV3u6duM85rU1hgPOcXB7Fz+8+smPl+USoHdgc0GVyU3YNTFLWlYK8zq8kTEgykMuZDCUOWt25PJu3O28NPqPdiLPl3JjWK454JmXNQmTnue+bj8QjtzUg4wacUuflm/n7wCM02+b6u6jB7Ymjbx+v9MRM6ewpALKQxVXeqhHMbN28LXS3c5/+C1qBfByAuacXnnBAI1ENZnOBwOVu3K4Lvlu/hx9V4OH81zPtcpMYbRA1vRs9mZ9ykUEakohSEXUhg6e/uzjvPR79uZsHAHWbkFADSICeWO3k24rlsi4cFaq8hb7Tycw+QVu/luxW62HjzqPF83MpgrOiVwVZcGtI2PUheqiLicwpALKQy5TubxfD7/I5Xx87dxMDsXMDPQ+rSoy6D29bmoTRzRYVqvyNNlHMtn2p97mbR8N4u3H3aeDwn0Y2C7+lzVpSG9msVqiryIVCuFIRdSGHK94/mFfLtsF+Pnb2PbCa0FAX42ejSLZWD7+lzcNo56kVqzyFMUjwP6bsVuZq7f5+wWtdmgZ7NYrkpuyMD29YlQK6CI1BCFIRdSGKo+DoeDDWlZTFuTxvQ1aaTsy3I+Z7PBOUm1uaR9fQa2r0+DmFALK5WyOBwOVu/KYFIZ44BaxkVwdZeGXNE5gfho/bcTkZqnMORCCkM1Z+uBbH5ea4LRql0ZpZ7r2DCaS9rVZ1D7+jStG2FRhQKw64gZBzRpxW62Hihp2asTEcwVnRO4KrkB7RI0DkhErKUw5EIKQ9bYnX6M6WvS+HltGku2H+bET2nLuAgGtqvPwPbxtImP1B/danYgK5eVO9NZtTOdRdsOsWT7EedzIYF+XNKuPlclN+D85nU0DkhE3IbCkAspDFnvQFYuM9ftY9qavSzccogCe8lHNik2jAFt4+iUGEOruEia1AnXH+SzcCyvkDV7MliZms7KneZWvElqMZsNejSN5arkBgzqEK9xQCLilhSGXEhhyL1k5OTz64Z9TFuTxtyNB8gtGqhbLMjfj2b1ImgVF0Gr+lG0qm/uE6JD1IJ0kkK7gy0Hsk3w2ZXOytR0UvZlUWgv/SvBZoPmdSPonBhD50YxXNi6nsYBiYjbUxhyIYUh93U0t4A5Gw8wd+MBUvZlsTEti6N5hWVeGxkcQMv6kbSqH0mrOHPfun4kMWG+s+/Vvszjztaelanp/Lk7g+yidZ9OVC8y2Bl8OjeMoUPDaCJDtOSBiHgWhSEXUhjyHHa7g93px0hJyyJlX5a5T8tiy4HsUl1rJ6oXGXxSQIqieb0Ij98h/WhuAX/uznCO9Vm5M529GcdPuS4syJ8ODaJN+CkKQPWj1IomIp5PYciFFIY8X16BnW0Hj7IhLZONRSFpQ1oWu44cK/N6mw0ax4bTsqirrXX9SFrGRdI4NsyS8UgOh4OjeYUcOZpHek4+h3PySM/J4/DRPI7k5DuP03Pyi+7zSMs8zsn5z88GLeMiSwWf5nUjNMZKRLySwpALKQx5r+zcAjYWda9tKGpFStmXVWq9nBMFBfgRGx5EaJA/YUH+hAUGOI+d54ICCA0sPvYnJNCcK32NP/5+fqTn5HEkJ48jR/PNfY4JN0eOlj6fnpNPXqG9zJrKEx8d4gw+nRJj6NAgWlufiIjPqMzfb/1mFJ8VERxAl0a16NKoVqnzB7Jy2bivOCBlkrIvm41pWRzLLyyzq6mmBAf4USssiFrhQdQKCyy5DwsqOl9yHB8dQr0oreAtIlIRCkMiJ6kbGUzdyGB6NS/ZRb14PFJ6Tj45eQXk5BdyLK+QnLxC87jo+FjRsfO5/DLO5RVgd0B0aGCpAFM7PIiY4nBzYtAJD6J2WJDHj2MSEXFXCkMiFeDnZyOxdhiJta2uREREXE0jJ0VERMSnKQyJiIiIT1MYEhEREZ+mMCQiIiI+TWFIREREfJrCkIiIiPg0hSERERHxaQpDIiIi4tMUhkRERMSnKQyJiIiIT1MYEhEREZ+mMCQiIiI+TWFIREREfJrCkIiIiPi0AKsLcHcOhwOAzMxMiysRERGRiir+u138d7w8CkNnkJWVBUBiYqLFlYiIiEhlZWVlER0dXe41NkdFIpMPs9vt7Nmzh8jISGw2m9XlWCIzM5PExER27txJVFSU1eV4Fb231UfvbfXRe1t99N66jsPhICsri4SEBPz8yh8VpJahM/Dz86Nhw4ZWl+EWoqKi9D9nNdF7W3303lYfvbfVR++ta5ypRaiYBlCLiIiIT1MYEhEREZ+mMCRnFBwczHPPPUdwcLDVpXgdvbfVR+9t9dF7W3303lpDA6hFRETEp6llSERERHyawpCIiIj4NIUhERER8WkKQyIiIuLTFIaE3bt3c9NNNxEbG0tYWBidO3dm2bJl5b5mzpw5dO3alZCQEJo2bcq7775bQ9V6lsq+t7Nnz8Zms51y27BhQw1W7f4aN25c5vt03333nfY1+sxWTGXfW31mK66goICnn36aJk2aEBoaStOmTXnhhRew2+3lvk6f3eqnFah93JEjR+jVqxf9+vVj2rRp1KtXjy1bthATE3Pa12zbto3Bgwdz5513MmHCBH7//Xfuvfde6taty9ChQ2uueDdXlfe2WEpKSqnVZ+vWrVuNlXqeJUuWUFhY6Hy8Zs0aLr74Yq699toyr9dntuIq+94W02f2zF555RXeffddPvnkE9q1a8fSpUu59dZbiY6O5sEHHyzzNfrs1hCH+LTRo0c7zj///Eq95rHHHnO0bt261Lm7777b0b17d1eW5vGq8t7OmjXLATiOHDlSPUV5qQcffNDRrFkzh91uL/N5fWar7kzvrT6zFTdkyBDHbbfdVurc1Vdf7bjppptO+xp9dmuGusl83A8//EC3bt249tprqVevHsnJybz//vvlvmbhwoUMGDCg1LlLLrmEpUuXkp+fX53lepSqvLfFkpOTiY+Pp3///syaNauaK/VseXl5TJgwgdtuu+20mynrM1s1FXlvi+kze2bnn38+v/76Kxs3bgRg1apVzJ8/n8GDB5/2Nfrs1gyFIR+3detWxo4dS4sWLZg+fTojR47kgQce4NNPPz3ta9LS0oiLiyt1Li4ujoKCAg4ePFjdJXuMqry38fHxjBs3jokTJzJp0iRatWpF//79mTt3bg1W7lkmT55Meno6I0aMOO01+sxWTUXeW31mK2706NHccMMNtG7dmsDAQJKTk3nooYe44YYbTvsafXZrhsYM+Ti73U63bt14+eWXAfOvu7Vr1zJ27Fhuvvnm077u5H8lOooWMj/Tvx59SVXe21atWtGqVSvn4x49erBz505ee+01+vTpUyN1e5rx48czaNAgEhISyr1On9nKq8h7q89sxX311VdMmDCBL774gnbt2rFy5UoeeughEhISuOWWW077On12q59ahnxcfHw8bdu2LXWuTZs2pKamnvY19evXJy0trdS5/fv3ExAQQGxsbLXU6Ymq8t6WpXv37mzatMmVpXmNHTt28Msvv3DHHXeUe50+s5VX0fe2LPrMlu3RRx/l8ccfZ9iwYXTo0IHhw4fz8MMPM2bMmNO+Rp/dmqEw5ON69epFSkpKqXMbN24kKSnptK/p0aMHM2fOLHVuxowZdOvWjcDAwGqp0xNV5b0ty4oVK4iPj3dlaV7jo48+ol69egwZMqTc6/SZrbyKvrdl0We2bDk5Ofj5lf6z6+/vX+7Uen12a4jFA7jFYosXL3YEBAQ4XnrpJcemTZscn3/+uSMsLMwxYcIE5zWPP/64Y/jw4c7HW7dudYSFhTkefvhhx7p16xzjx493BAYGOr799lsrfgS3VZX39vXXX3d89913jo0bNzrWrFnjePzxxx2AY+LEiVb8CG6tsLDQ0ahRI8fo0aNPeU6f2bNTmfdWn9mKu+WWWxwNGjRw/PTTT45t27Y5Jk2a5KhTp47jsccec16jz641FIbE8eOPPzrat2/vCA4OdrRu3doxbty4Us/fcsstjgsuuKDUudmzZzuSk5MdQUFBjsaNGzvGjh1bgxV7jsq+t6+88oqjWbNmjpCQEEetWrUc559/vmPKlCk1XLVnmD59ugNwpKSknPKcPrNnpzLvrT6zFZeZmel48MEHHY0aNXKEhIQ4mjZt6njqqaccubm5zmv02bWGzeEoGoklIiIi4oM0ZkhERER8msKQiIiI+DSFIREREfFpCkMiIiLi0xSGRERExKcpDImIiIhPUxgSERERn6YwJCIiIj5NYUhEfMKIESOw2WzYbDYmT558Vl/r+eefd36tN954wyX1iYh1FIZExGcMHDiQvXv3MmjQoLP6Oo888gh79+6lYcOGLqpMRKwUYHUBIiKulJeXR1BQUJnPBQcHU79+/bP+HhEREURERODv73/WX0tErKeWIRHxaH379uX+++9n1KhR1KlTh4svvrjCr92+fTs2m42vv/6a3r17ExoayjnnnMPGjRtZsmQJ3bp1IyIigoEDB3LgwIFq/ClExEoKQyLi8T755BMCAgL4/fffee+99yr9+ueee46nn36a5cuXExAQwA033MBjjz3Gm2++ybx589iyZQvPPvtsNVQuIu5A3WQi4vGaN2/Oq6++WuXXP/LII1xyySUAPPjgg9xwww38+uuv9OrVC4Dbb7+djz/+2BWliogbUsuQiHi8bt26ndXrO3bs6DyOi4sDoEOHDqXO7d+//6y+h4i4L4UhEfF44eHhZ/X6wMBA57HNZivznN1uP6vvISLuS2FIREREfJrCkIiIiPg0hSERERHxaZpNJiIebfbs2VV+bePGjXE4HKXO9e3b95RzI0aMYMSIEVX+PiLi3tQyJCI+46effiIiIoKffvrprL7Oyy+/TEREBKmpqS6qTESsZHOc/E8gEREvtH//fjIzMwGIj48/qxlohw8f5vDhwwDUrVuX6Ohol9QoItZQGBIRERGfpm4yERER8WkKQyIiIuLTFIZERETEpykMiYiIiE9TGBIRERGfpjAkIiIiPk1hSERERHyawpCIiIj4tP8HntnMn2NY17IAAAAASUVORK5CYII=",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"opt = a5.data.options.active.read()\n",
"opt.update({\"RECORD_MODE\":1})\n",
"\n",
"a5.simulation_initinputs()\n",
"a5.simulation_initoptions(**opt)\n",
"\n",
"mrk = a5.data.marker.active.read()\n",
"a5.simulation_initmarkers(**mrk)\n",
"\n",
"vrun = a5.simulation_run(printsummary=False)\n",
"\n",
"vrun.plotorbit_trajectory(\"r\", \"z\")\n",
"a5.simulation_free()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}