diff --git a/docs/changes/newsfragments/7851.improved_driver b/docs/changes/newsfragments/7851.improved_driver new file mode 100644 index 00000000000..cc932deb198 --- /dev/null +++ b/docs/changes/newsfragments/7851.improved_driver @@ -0,0 +1,3 @@ +Refactored ``Keithley2600`` driver to use `ParameterWithSetpoints` instead +of relying on deprecated `qcodes_loop` code. Contains breaking change since +method `doFastSweep` has been replaced with `fastweep`. diff --git a/docs/examples/driver_examples/Qcodes example with Keithley 2600.ipynb b/docs/examples/driver_examples/Qcodes example with Keithley 2600.ipynb index 7c7673da651..89af5ec33e8 100644 --- a/docs/examples/driver_examples/Qcodes example with Keithley 2600.ipynb +++ b/docs/examples/driver_examples/Qcodes example with Keithley 2600.ipynb @@ -15,7 +15,7 @@ "outputs": [], "source": [ "import qcodes as qc\n", - "from qcodes.dataset import do0d, initialise_database, new_experiment, plot_dataset\n", + "from qcodes.dataset import initialise_database, new_experiment, plot_dataset\n", "from qcodes.instrument_drivers.Keithley import Keithley2614B" ] }, @@ -188,7 +188,7 @@ "source": [ "## Fast IV or VI curves\n", "\n", - "Onboard the Keithley 2600 sits a small computer that can interpret `Lua` scripts. This can be used to make fast IV- or VI-curves and is supported by the QCoDeS driver. To make IV- or VI-curves the driver has the ArrayParameter fastsweep, which have 3 modes: 'IV','VI' and 'VIfourprobe'. The Modes 'IV' and 'VI'are two probe measurements, while the mode 'VIfourprobe' makes a four probe measuremnt." + "Onboard the Keithley 2600 sits a small computer that can interpret `Lua` scripts. This can be used to make fast IV- or VI-curves and is supported by the QCoDeS driver. To make IV- or VI-curves the driver has the ParameterWithSetpoints fastsweep, which has 3 modes: 'IV', 'VI' and 'VIfourprobe'. The Modes 'IV' and 'VI'are two probe measurements, while the mode 'VIfourprobe' makes a four probe measurement." ] }, { @@ -203,164 +203,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to prepare the sweep by spesifyring the start, stop, number of points and the mode " + "First we need to prepare the sweep by specifying the start, stop, number of points and the mode " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "keith.smua.fastsweep.prepareSweep(1, 2, 500, mode=\"IV\")" + "keith.smua.fastsweep_start(1)\n", + "keith.smua.fastsweep_stop(2)\n", + "keith.smua.fastsweep_npts(500)\n", + "keith.smua.fastsweep_mode(\"IV\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "When the sweep parameters are set, we can use a \"do0d\" to preform the sweep " + "Next we want to register our parameter" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting experimental run with id: 154. \n" - ] - }, - { - "data": { - "text/plain": [ - "(results #154@C:\\Users\\Farzad\\experiments.db\n", - " -------------------------------------------\n", - " keithley_smua_Voltage - array\n", - " keithley_smua_iv_sweep - array,\n", - " [],\n", - " [None])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "do0d(keith.smua.fastsweep, do_plot=True)" + "fastsweep_meas = qc.Measurement()\n", + "fastsweep_meas.register_parameter(keith.smua.fastsweep)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can likewise do a VI two- or four-probe measurement " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting experimental run with id: 155. \n" - ] - }, - { - "data": { - "text/plain": [ - "(results #155@C:\\Users\\Farzad\\experiments.db\n", - " -------------------------------------------\n", - " keithley_smua_Current - array\n", - " keithley_smua_vi_sweep - array,\n", - " [],\n", - " [None])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEXCAYAAACK4bLWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAB4WElEQVR4nO19d7weRbn/93nf95T0nhBSOJAEY6iB0FHpUlQULFdQwItwEQsoyi/Y4Ir1quD1KgqiAgoqVZAAUqQjgVBDQkkILaT3fsr7Pr8/dmZ3dnZm21vOyTnz/XzO57y7OzszOzv7PPPUIWaGg4ODg4NDtSh0dwccHBwcHHoHHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENx6FMgoolEtImIit3dl54CMR67pCjXRkRMRKVG9CsNiOgvRPTR7u5HPUBEZxDRYynL/pyIvlDvPiXBMZQ6gojeJKKt4oNdRkTXENHABrT7LhH1I6IjiOhW7dqlRDSXiLqI6BLt2mFEVBH9lX+nK9cfIqJtyrVXM/RJv3cTEf2j6ofNCGZ+m5kHMnO50W0LYjw55npqApK2zjQQ47Gomjq6A0S0J4C9ANze3X3pAfgZgG8SUXN3dsIxlPrjw8w8EMDeAKYDuKiejRHRBACrmXkrgH0BPKsVWQjgQgCzLFUsEQRG/l2rXf+Scu09Gbv3Ja3uD2e8vyr0pJV1T0AvGI//AnA9u+hsMPNSAK8A+Eh39sMxlAaBmZcB+Cc8xiKlgcVqGSHRHCV+X0JENxLRdUS0kYjmEdGMFE3NAPCM8jvEUJj5Wma+G8DG6p6odiCi/0dEsyWBI6IviOdtVdQsZxPREiJaSkRfV+4tENFMInqdiFaLMRsursl7zySitwH8S1fbCMnp+0T0hJSaiGgEEV1PRBuI6GkialPam0pE9xHRGiJ6lYg+qVy7hoh+TUSzxDubTUSTxLVHRLEXRDuf0sbgvQB+C+AgcX2d0r/PK+V8KcZWJxGdRUQLRR/vIKIdlfuZiL5IRAsALFDOTRa/TyCi58Szv6NLsSnf5xAi+r14V++K8S0SUTMRPU9EXxblikT0OBF9VxxfQkQ3E9HfxPg9S0R7xTR1HICH9bEhop8R0VoieoOIjlOu7yjGY40Yn7NinuF4Ipov+vGunHNENIyI7iSilaKNO4lovHJf1vnERPQVIlpERKuI6KdEZKTLcXNP4CEAJ8SMV/3BzO6vTn8A3gRwlPg9HsBcAP8rjg8DsDim/CUAtgE4HkARwI8APBnT1sUA1ol7tojfZQDrxe+iVv7PAC7Rzh0GoAPAcgBvALgcwADl+kMAVgJYBeBxAIdlGIuHAHzecq0A4BHxzFMArAUwXVxrA8AA/gJgAIA9RB/kOJ0H4Ekxvi0ArgTwF+3e68S9/ZRzJaVfCwFMAjAEwHwArwE4CkBJ3PtHUXYAgHcAfE5cmy7GYpq4fg2A1QD2F9evB/BX5TkZwOSYMToDwGNx46aX0esEcITo0z5iPP4PwCNa+fsADAfQT69DzIE9xDvZU8yFj2rjWUp417eJ9zAAwGgATwH4L3Ftd/F+3wvgW+LdFZU53wng4wCaAHwd3jxsMrQxQPRllDY2nQDOgvfNfAHAEgAkrj8C4AoArfAWdisBHGF5hqUA3id+DwOwj/g9AsDJAPoDGATgJgB/195XqvmkjP2D4n1MFGU/r79rJMw9UeYkAM92K83rzsZ7+x88BrEJnjTAAB4AMFRcOwzJDOV+5do0AFsT2isBeBnAGAAHA5gVU9bEUHYQ7RQA7Cw+wCuV6weIj6gFwOniuSalHIuHEDA6+Xepcr0NwBrR/4u08wxgqnLufwD8Xvx+GcCRyrWx8IhKSbl3F0N9KkP5lnL95wDuVo4/DOB58ftTAB7VnutKABeL39cAuFq5djyAV5TjRjCU3wP4H+V4oBiPNqX8EVob1n4B+AWAy01jZyk/BkA7BLMS5z4N4EHl+AIAr8JjLFOU85dAWTTBm4c+YdfaGSf60qqNzULluL8oswOACfAWWIOU6z8CcI3lOd6Gp1IbnDCv9wawVntfqeaTMvbHKsfnAnhAf9dJc08cHw1gUZrvsV5/TuVVf3yUmQfBYyBTAYzMcO8y5fcWAK1k0HsT0d5CRbIWwGR4H+uDAA4jonVEdFKaxph5GTPPZ+YKM78Bz9ZysnJ9NjNvZOZ29mwrj8MjmmnxFWYeqvx9R6n7TdHnNgC/Ntz7jvL7LQBSjbMTgNvEc66Dx2DK8Aib6V4Tliu/txqOpSPFTgAOkG2J9k6FR7Ak9HdWdycMDTvCGx8AADNvgic1jVPKWMeDiA4gogeFSmc9gHOQbc7uBE+6WKqM0ZXwJBWJa0W5u5h5gXa/3zdmrgBYjOBdq1gn/g/Szvvjz8xbxM+Boo41zKyqet9CeFxUnAxvbr9FRA8T0UEAQET9iehKInqLiDbAW3QNpbDXYNr5JGGb2yrSzL1BCMalW+AYSoPAzA/DW8H+TJzaDG8FBcDTJwMYlbPu55l5KIAfAPiu+D0fwF6CcN8ad39c1YifIwyActYdAhGdAOAgeFLcTw1FJii/J8JTZQDex3icxqhamfldrZ+1wDsAHtbaGsjMtXLXNPUzNE8QJiAmLIFHfAAARDQAnpom7XjcAOAOABOYeQg8u06Wd/wOPAllpDJGg5l5N6XMFQDuBPBBIjpUu99/z8KWMB7Buw4egHkzgNcB7JqyX0sADCcilQFNRHhc1PqfZuYT4THCvwO4UVy6AMB7ABzAzIMBvF92N2U/TLDNbRVp5t57AbxQRT+qhmMojcUvABwtDI2vwZM4TiCiJgDfhqdKqgb7AniWPNfBHZl5oV6AiJqIqBXeuy+RZ/guimuHE9FO5GECgB9DuGQS0VAi+qAoXyKiU+F9TPeI69LY3Za100Q0EsDVAD4PT5X2YSLSJZ/viNXhbvD0yH8T538L4AdEtJOoaxQRnZi1DylxJ4BdieizYhybiGg/8gzqabAcQFy8x3IA4yns+vk8gJPEs08GcGZCnX8B8DkhtbYA+CGA2UICTINB8Fby24hofwCnpLwPgO9tdC+AnxPRYPKcJiYR0QcAgIg+C2+engHgKwCupbAr/b5EdJKQxM+Hx5yetDR3F4APpOzXOwCeAPAjMYf3hDeWf9bLkuc8cCoRDWHmTgAbAFTE5UHwpIx15Dl/XJym/QR8Qxj7J8CzCf7NUCbN3PsAgLtr0J/ccAylgWDmlfCMct9l5vXw9KVXw1slbYYn3lcD6Sa8B4CXLGV+B++D+DQ8o+hWAJ8V16bD++g2i/9z4X30gKfG+D4Co/yX4anzXhPXJ8AT140rPoFfUTgORXqjXQXgdma+i5lXw/vQryaiEcq9D8Mzdj4A4GfMfK84/7/wVtT3EtFGeMTngJg+5IZQlxwD4D/grSKXAfgJ0i8ELoFHQNcZPHQA4F8A5gFYRkSrxLnLEThKXAvP0G+tk5nvB/AdALfAsz9MEv1Ni3MBfE+M5XcRrMyz4DQAzfCk5LUAbgYwlogmwltUncbMm5j5BgBzxDNK3A7PXrAW3rw8SRB1E64CcCoRpZUOPg1PpboEnuPAxWK8TPgsgDeFWusceOoliP73g/cNPAmxoKoSt8PzzHwenjv/7/UCSXOPiMbCs3/+vQb9yQ3p/eDgUBWI6NsAVjLzlTWutw2Bp09XLet26Fkgz0V5MjN/JsM9NwC4kZn/Xq9+1RNExPAcEyLahIz1/BzA68x8RW16lg/be2CTQw8BM3+/u/vg0PfAzJlUcr0VzHxBd/cBcAzFwcEhB4hok+XSccz8aEM749Bj4FReDg4ODg41gTPKOzg4ODjUBH1a5TVy5Ehua2vr7m44ODg4bFd45plnVjFzJG6uTzOUtrY2zJkzp7u74eDg4LBdgYjeMp13Ki8HBwcHh5rAMRQHBwcHh5rAMRQHBwcHh5rAMRQHBwcHh5rAMRQHBwcHh5rAMRQHBwcHh5rAMRQHBwcHh5rAMRSHmuLVZRuxeO0W6/WX3l2P5Ru2NbBHDg4OjUKfDmx0qD0++ItHAABv/vgE4/UP/d9jaCkV8Or3j2tktxwcHBoAJ6E4NBztXZXkQg4ODtsdHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHEPpwXjqjTWY+p27sW5LR8Pa3NZZxl7/fS/un7+8YW06ODj0DjiG0oPx6wcXYltnBc+9s65hbb61egvWb+3ET+55pWFtOjg49A44hrI9gBvXVGfZc+ktFd3UcHBwyAZHNXowiBrfZlfF415NxW5o3MHBYbuGYyg9GJKkcwNFlC4poRQcQ3FwcMgGx1C2A3BDVV5eY07l5eDgkBWOatQQazd34PGFq2pWH9VJ57WlowsPvrLCeK0sVF61llAWrtiIl5duqGmdDg4OPQsuOWQNcfofn8KLi9fj1e8fi5ZSsWb11lpCmXnLXNzxwhLc/7UPYPLogaFrnZX6GOWPuuyRmtbn4ODQ8+AklBrilaUbAdSOAdTLirFo1SYAnqSio0uovJqcDcXBwSEjHEOpIeplPK91rZLhFQwqNd8o77y8HBwcMsIxlDqgUiMRpV5uw3Hdk27Dzijv4OCQFY5q1BCSUFca6JWVB7J7JobVVXFuww4ODvngGEoNIQl1rSQUv9461WdSefluwwU3NRwcHLLBUY0aQhJqNmxIeMYfn8KXbng2Y431kRIkfzJKKGUXKe/g4JAPjqHUEHESykOvrsSdLy6tqt5aQToPkIFh+Sovx1AcHBwywjGUGiKwofRso3wlhYTiVF4ODg5Z4ahGHVBro3ytAxulas7Er5xR3sHBIS8cQ6kDamVErxdJj/Pycrm8HBwc8sJRjTqgVhJKQPBrLaKIf4ZqqzHK19obzcHBYfuCYyh1QO3dhmtaneI8EL0mVV7FHCqvnh5/4+DgUF84hlIjLFu/zf9dM6N8nZResn+mVDEyUj7PI3SnhPLa8o1Yt6Wj29p3cHCoI0MhoglE9CARzSeieUR0njg/nIjuI6IF4v8wy/2nizILiOh05fy+RDSXiBYS0S9J5HgnokuJ6EUiep6I7iWiHev1bCYc+KMH/N86Xa1UuXSvVy6viiFeRubyytNmd0oox1z+CD7668e7rwMODg51lVC6AFzAzNMAHAjgi0Q0DcBMAA8w8xQAD4jjEIhoOICLARwAYH8AFyuM5zcAzgIwRfwdK87/lJn3ZOa9AdwJ4Lv1erAk6BJKR9lAuVOgbrm8YJdQpFE+j4hSa1VfVry5eku3tu/g0NdRN4bCzEuZ+VnxeyOAlwGMA3AigGtFsWsBfNRw+wcB3MfMa5h5LYD7ABxLRGMBDGbmJ9nTr1wn72dmdfemAaj9wj419JV6e2d1DKXWdFpKJqZ6O6qQUJxN3sGhb6MhG2wRURuA6QBmAxjDzDJkfBmAMYZbxgF4RzleLM6NE7/187KdHwA4DcB6AIdb+nI2gLMBYOLEidkfJgX0lXp7uVyXdqqFiQF0dlWs15LQ3RKKg4ND96LuRnkiGgjgFgDna1IEhJRRMyrEzN9i5gkArgfwJUuZq5h5BjPPGDVqVK2a1toIH+eVUOoFTmOUz/FaHENxcOjbqCtDIaImeMzkema+VZxeLlRXEP9Nm5u/C2CCcjxenHtX/NbP67gewMnV9T4/dG+nvDYUv74aa+8qMWYSX+WVx8urij45ODhs/6inlxcB+D2Al5n5MuXSHQCk19bpAG433P5PAMcQ0TBhjD8GwD+FqmwDER0o6j9N3k9EU5T7TwTwSk0fKANqZkPJ6DZ85jVP49zrnwmdO+byh/Gju18OnZMMyiRR+CqvTC2LehskiH3297Mx85YXG9OYQ4/Hzc8sxq7fvhudVS7cHKpHPSWUQwB8FsARwpX3eSI6HsCPARxNRAsAHCWOQUQziOhqAGDmNQAuBfC0+PueOAcA5wK4GsBCAK8DuFuc/zERvUREL8JjQOfV8dliUSsvL4m00sIDr6zAXXOXhc69tnwTrnx4kbE+U7XlKuJQGqXyenTBKvz16XeSCzr0CVx653x0dFWwcVtXd3elz6NuRnlmfgz2dFRHGsrPAfB55fgPAP5gKbe74Xy3qbh0RIzynYFRvlzh9FHodXMbFv8NDCAu6DEJzobi0B2ol3u9Q3a4SPk6IGKU7woklCxiufxOah/YaJdCyj63yV6vS73i0J0ouwnY7XAMpQ6IqLwUhtITJn2cyqvie3nlqbexz7ZNSH4uKaUDEOShc+g+OIZSB0SM8gpD6SqnJ34iq0zt95QX/00pYXyVV442G03WV2/2cnc5ftK3Id9/lm/LoT5wDKUOiBrlAxtKZ45V1LotnXjhnXXVdstHEIcSRT2M8vWSIFZubI9t16FvoUtZIK3a1I55S9Z3Y2/6JhoSKd/XEIlDyanykjaUi++YBwB488cnVN03QE1fH2eUzw7bo9VLy7dWSij1qd5hO4E0yncp9smjL3sYa7d01uybcUgHJ6HUAToBVY+r8ZXvqpGfva/qMlDiuKDH1PVG6qwPyZf1OgnFAVASmwJYu6WzG3vSd+EYSh2gE1aV3mWzoYSPq41n8fuj/VdRriL1io2u18sRoRrm59D74Izy3Q/HUOqAqIQSnOiqgrjWLCeY3A8lTuVVQxtKvSUUx1D6NuT773RG+W6HYyh1gG5DUY+qWUWp3mLVII4QV0P8bXfWy4YSl+TSoe+hJ7jk93U4hlIH6PNaZTCZVF7acXtXbdLgxxnlq9Gq2ZhRvVVejo70bZiM8g7dA8dQ6gCdsIZsKNWovGokoaQKbMwTh2JTecU883f+/hIO/cm/MrcFVBczs3pTO9pmzsL985dnvvfqRxehbeasPh1QuWD5RrTNnIW5i3uOa25nhm/rghtfwBE/eyhXO/94YQnaZs7Chm3da/g/7KcP4sKbX+jWPuhwDKUO0BlKyIaSJfWKZpWvlQ3FVxHFqLxq6zZsr+1PT76FxWu35mitOgnlpSXe1jzX/vvNzPd+f5aXvbkvq1jue9ljxHfOXdLNPQmQ5du65dnFWLRqc652fvPQ6wCAt7t5y+k3V2/BjXMWJxdsIBxDqQN0+lnJKaFUq/KyraDZJ8QGlVcdjPLlOq3kOU7USoDsq860M7Wf+06HWqLRRvl6bc3dG+AYSh0QVXnls6HoyKrysvEunw6bJJRqdmy0dC+NH0KcWsxWrqo4FHFL2sTPxr44itKj0CiJ0WcobkkRgWModUDUKB/8zpR6RSN2WSUUayoU2NVa1cR22D6wNIR3a2e6Z1OlHTmU+dRz3l2FaiQUR096BHyjfIPiUOTGd+79R+EYSh0QkVAUkleuRkLJaEOx59ayXw8CG7OjmsDGzR3pNkcq10hCkdVUs5WGIyjZdxWtJxqu8mpIa9sXHEOpA3TbRdiGkmU/lPDHus0ioSxbv82YPFLtxuMLV/leKWy4HvTVbENZvsHchuneuH7YsKU9+mxd5Qrun788NJ5qG3GquyTUwobSl1VePfHR87gNP75wVeqyzIz75i9X5l3PGIQnF63G+h6SasYxlDogLlK+mlWUTUI57GcP4sRfPx45r873U6+ejQtvelGct7vbBn0NXzvspw8Z2wjfaz6fxihvklB+89Dr+Px1c/CvV1YEdRkklGpcnJ0NpTr0hN0SfaN8DhvKqVfPxrL121KVvfmZxTjrujmY+67nKt1T3v5/XPUkPn/d093dDQCOodQFcXEoWSah/rHajPLbLIxG78fSDdvEeXtf5CJPp5VpbBzVBDZu6YjWv2S95068fEN70IbyqHHPkQTZ1WpsKH3Ya7hHopwzsHHd1o5U5fK6tzcCryzd2N1dAOAYSl0QFymf1psJqN5tWCfwowY2a/2y35PLKG/1KrNXJun55vaohFIU4kNZ4SIho3wNbCiFKr6AnqLy6OsIjPLR95HmHW01LGZM0NXV7vVH4RhKHRDJ5aUcVqMmyW6UDx+PGNCiXY8zyudXI0XqjHnmlpI3BU0SSklQe5VQqNIOV8H8ahKH4ghKj4JJnZxm/ZbWwzBafw+aAD1A9Qg4hlIXRCPlg9/V+MpnjUPRCfzIQZqEYrinumzD5vNxz9zaVASQJKGYjfJBpHweCaV6t2FnQ+lZMBnl00go21IzlHD9TuUZhWModYDuyGXyTMqD7Cqv8HH/5vAGnUajfBVuw3m8vGwSSle54i+66iGhyHuqWdg5gtIzFsZxRvlUEkpHuoWa27M+GY6h1AHROBT7tTikNcqn7Ufcxl8SdUm9EvNVNwuGonp5rd/SiWnf/SeufuwN//49L/knzvvrc6G67hBJ+lZtakdaXHLHPLTNnKVIKN75qx55HW0zZ2FbZxltM2fh6kcXJdaV1YYik0p2dFVw67OL0TZzFtZvze/u2TZzFr5/5/zc96dt43v/yN7G5G/ehW/eNjdy/vCfPYTT/vBULbrmQ86JssEl/9t/n4vv3v5S7P3pVV49x4aSxRbbSDiGUgfoE00lPNXktcqaqThO9QaY7SSB51QeG4r5fNwzy1gbNeBz9eb20O6UneUKNmzrwu3PLwk907NvrwMALFi+KXUfr3nizVBfpcrrF/cvEG17Hj+/fGBBYl1Zv2nZxrauMq4V/Xh9Zfq+myCZbj3xh8ejbSQx064K44bZb0fOv7FqMx55bWXN+gYEhN4kQby8dCNeWRbvAZWWoeg7pnanU0ZPVbc6hlIHxLkNZyFCemBj1lWJPueiEkv0nrj95rO2F5y3VxZ4aqnnwmVUqcTEVPMwaZtRPosKJ+tHLZ+jQITB/ZoAABuqkFAcPMg5YTLKlyucaLfcmjJLg15/d5L0eiVcrRaOodQBcYGNWVY1usorKwGLU72ZjgFF5ZWpJXN7fp0xmjrJwMLG9nA9KhMxMdU8jg6BhBI+zlJT1lbVQMzBrR5DqUbl5eCNpXz/piwUaRiKycPQBN3o370qr/BxT7BlAY6h1AVxhLwaL6+scVtx8TDedV2S4qrSSqS1oaiMsqwQWVs96v2mlVmeMdW9vKSKL4sUmFViVKWxQa2eg8SqTemC6qptOw/SjGt3R8qrUoNRQmFOXIilZShRCaX7OIqTUPoQ4gh3JpWX9rFmJfI60Yl8WBFJynopFdKqvNTHKlcCIuv3Q2Ocqm7cROTy7IJpC2zUDa9xyPpN+zE+zL679MqN6R0KQnU1gKBkGYvuQmixYZBQKikkFJPLugmR8ehGmt5TN3dzDCUnHl+4Cis2mHMAxaWvz7Ky1GmGTkQ2bOvEAy9Ht7BlZixYvhEvvbteO6+V076I2YtWW8umgU21p/dbtVt0pVJ5BR+yye5jIiQ6Nm7rxH3Kdr9BG2GubWNOd89dGolXSKuC7CpXcOeLS0I7TEqPvdwMJWEevb16C559e22uuiXiGLV89EcXrMLqDF52tcI9Ly3Dlo6u0HYQJqN8WajEnnpjDZasM6dOSa3y0iPlM/S3Gjzx+ios12iNTkc2bOvCwhXVOXjUAo6h5EC5wjj16tk49erZxutxG2xlsYNEGIo2ic77y3M489o5xvuOvvwRfOH6Z7V+6f0MH5+iPE8+G0rybx3SuyskHWnlOxR3abPKK7lvF98xD2ddF4yVnhzSj2UwVPbkotX4wvXP4if3vBI6n/Zd/vbh1/GlG54L3SdjitanzCOlI0kqe/9PH8RJVzyRq26/jRQD++Li9Tjld+bvoF546d31OOfPz+C7t88LeQea4lC6yh5D+eSV/8bhP3vIWN+WtEb5Lv27Tt/nanDK72bjQ//3WOic6Ts46rKHG9OhGDiGkgOrN3srsqVKltL+zUV8asYEAGbCLaO+s0iqugShz6E3LHti2wMM038Q1WTw1X/rqylVJuhS1EB+ea1tNf7GtDJPQ9jXbA4Tbl/lpekVTatcee/SddoqMeUQLVmv38f+M+VVXVSzr05apFUlvrq8sYkJ5TYM76zZkujwUmH2ia8tjiutZk/fHK+RNhRdknVxKL0I8uWOVJItVphRLMqd3KKEO2AotZNQbHmobHNNPx33QdiuxLsAm++PNcobVF766kvNEGAavzQRzMMHhNPOSGLpSyjaeRWyj/L9Bsj3UTMHedny8oVG7E7YYyPDlcUAR0+HUK5wIvFN+032pNQrzijfi7BCMJQRA4NkixUGimRmGhVmNEmGksWLKMGGYvOwsX0gUSN9TOM2ppRSqomziajxNZIwhlVeGkPpjJdQ0thQRg4MJ8aU6hzStt8zqXlkmyVt85S8BEVVeeUNjmuEUbaeRvlqggJVhwqOmTdeWU4kvmklMZ3BdqeU4IzyvQgmCQUxai1mRqEmKi+76kiFlaFERBRFKtDTslg4Slz3bXaQSH9ExysVNiZ41PupqipMH3EagjC0f1PoWBLLiMorpv4i6Qwl30etGuXzEoY8nm21bKPa1vWo8yzwg1JBmmo1WrarwonqwbSMISqh1P8d2PrWAAE1FxxDyYGVRgmFrWothpI5N8skrLHKq8KsSRHBtbR5iuI+oqyBjepYxHnCqSovo1E+xZjqWQc6BJEJBBTv2LQql/0p6hJKyo9a716lUgMbSiMYSh0llKx56VTIJydKzpNXriRLKGnHUo9DaYSQYOt7FjqyrbMccmypJ+rGUIhoAhE9SETziWgeEZ0nzg8novuIaIH4P8xy/+mizAIiOl05vy8RzSWihUT0SxJUlYh+SkSvENGLRHQbEQ2t17NJhtJcDIZPZRr/c8+rmPqdu/1rFWYUiFCgbKK+XlKfwFklFAC4/P4gR5Xal7QMJa77artTv3OPvT/iMCktvUSiUV45d8iP/5XYNyB43t8/9gbaZs7yiYXJbiBX6qVibSQUz4ZS9n/f/vy7aJs5C8s3bEPbzFm48OYXAHiJGc/50zPGOqplKJ/87b+x3w/uD53b+3v34pTfPQnAczs++vJHUtd30a1z0TZzVuT8Ppfeh0nfvAv/eU14i9ose/vc+PQ7aJs5y3dPVoNSQ0Z5w71epHx8/WnHUmewsu2jL3sYbTNnGZ8/CT+/91W0zZxl30tI6VvbzFm47bnFmfq8qb0L0757D/b7wf0NUdGlYihENIyIdiOiXYgoLRPqAnABM08DcCCALxLRNAAzATzAzFMAPCCO9faGA7gYwAEA9gdwscJ4fgPgLABTxN+x4vx9AHZn5j0BvAbgopT9zIxjd98hck4yDQl1W15mz/irfwBJSIpst9lQ2PIBVZixeO2WoJxyLW0UcDV7j+h1d1kYiv7cNsYTXA9+v2uJNdA/wE7Lis2cvsOsHsuLCrO/Yiwz429PvwMgSHJ545zFftl75i2z9LM64vDUm2sinkPrtnTiide9WKTZb6w23WbFX54KEkGq72/N5g6UK4x/vbIiVD7LVgzXi7rfXrMlVD9Rsg2lXEmOlE+72o9qjL0zC6qI//i/fy0EYH+fet//+tQ7xvM2rNrYjgp7KX4aoaKzMgciGkJE3ySiuQCeBHAlgBsBvEVENxHR4XEVM/NSZn5W/N4I4GUA4wCcCOBaUexaAB813P5BAPcx8xpmXguPWRxLRGMBDGbmJ9l7m9fJ+5n5XmaWDuVPAhif+PQ5ceAuIzC0f5NGBKNGWwlvrhAKRJnSp+ivP6LyssgodhuK5/Ei7Ql5VF5xSLsfijxWddtxcSvqsWn80hjl9bGzfcC+G7Ph3qhRPu0gRRcGUury1JDe+Sz8KpQwsw6qqY3b0sVm6OgsV1KpgvKovGS1/nghXrKFuJ60mk/LnPUFRS0W/HJK2RwgorZN83kb1G0hGqGiK8VcuxkewX4fM69TLxDRvgA+S0S7MPPvkxohojYA0wHMBjCGmZeKS8sAjDHcMg7AO8rxYnFunPitn9fxnwD+ZunL2QDOBoCJEycmdd2KAlEk75WuY5dgZk9CKWRUeUUIa7p74wh7RWF8al90HautpVgbioVG6NKOrEOVBuIi60PXchrl9X7bjMJx9Rdq5uUVrNArFfbHJ4v8o45de1cFpWJttdcy1sMG2zTY0lHGgOZiYv1ZVF76uMhxJ+Ub9PpkllCSiG9eVZCpXmbOtK00CTGrs4uB5uh1/ZuSz5iWoahZABohoVgZCjMfHXPtGQBm5a4GIhoI4BYA5zPzBnWwmZmJqKZPSUTfgqduu950nZmvAnAVAMyYMSN32wUKXpAtSC5oE8KGQpl033rJSIZRy7y1ifAV9q4VfYYSXKu1DSV03pICKaTKUsrEJYe0GV6TkFblJVV/pnxj+SWUMJhZiUMJJJQsHEV9nvauCga0xBRO6AsRRVN5bM0noWzp6EK/phQMJePuoyrULAdhb8Jo2Qonz4+89ijbXNRtbXGQJa0LnMjiyt424Emr6uJCzVPWAH4Sq/KaT0TfJqJJeSsnoiZ4zOR6Zr5VnF4uVFcQ/1cYbn0XwATleLw49y7Cqix5XrZ3BoAPATiV6777DfkTWJ3gKs1hn+EwiDy300xuwwk2FPt99vqY2Xd/VSWHaKbWdDrdNO3a9odJbUNJkFBMWWZ16B+mTcVgjEPxJdDw55J2ikUlzUDlU6koXksZOEpXiKHkJ86SkOmbTCVKKJb5sbm9nGqe1sbLi0L9sHoZ1sjLK9IPCwPLAsqo8pLPaOuzzpgaLaHEycmfBjAAwL1E9BQRfZWIdkxbsfC++j2Al5n5MuXSHQBOF79PB3C74fZ/AjhGOAMMA3AMgH8KVdkGIjpQ1H+avJ+IjgVwIYCPMPMWQ501hcc4whIKUVhKueOFJZClCkQgAh56bUXIMG7DO2u24LGFq0Ln5i/dEEr4Z3cbtqu8yhU1oj+4ZpJQXl+5KZQwUj6Lin+8sARrN3fgpjnvGFVPi9duwcOGHfrumrsUq5XU7SEbivZtJUkoSQF4i1ZuwhMLw89hY0KmfFDS1qNrlfKqvLoqFf/DV125s6yB1DG5ac5ibEqZMVfHlnaP4GzW8llFUtWkVrF0pVoJ3zRnceQ7uP35d2MZma5iJqRbdSdKKDGVdJYruPmZxSJmKkrcdVVxVqItFxG2rAS2rTBsj7Rmcwduf95fY4cklO5Web0A4AUAFxHRgQA+BeBJInodwA3M/LuEug8B8FkAc4noeXHumwB+DOBGIjoTwFsAPgkARDQDwDnM/HlmXkNElwKQvobfY+Y14ve5AK4B0A/A3eIPAH4FoAXAfYLQPsnM5yQPQT4QBYTP14ELtZZ87ef99XkcuMuIwM2xQFi0cjOOvuwRvHzpsaZqffz+sTewbkv449rSUcZJVzyBN398gtee5d64OJRyBWgSK221XGR7UwBH/txLNifbA8IeZAtXbMSX//Kcf7z3hKGRNo+67OGQx5vEudc/i3FD+wX1sp1pJKevj2coR/w8mjQvr4SiEta836fuBu07KWSoUB2Ty+57Da8u24hfn7pP5r5s7ujCsAHNPmORWLclzFA6KxW0FAJVlk2a2txeTpXj6pZnF2PHoa244Jj3AABeW74R5/31eZyw51j8+pTwc0Q3mvP+616Taffj0RHHLH/70Ov4+X2voVQgo6S5VctUnFnaEc+W1qaXpPL61m0v4eHXVmLc0H6Y0TZck1CydS0P4ozyPpj5SXjM5HYAl8Mj3rEMhZkfg53mHWkoPwfA55XjPwD4g6Xc7obzk+P6U2sUFHGblQmuT/6uCgPsGeSlqinNHtZ6qnQTrKlXrC6I4aj9kMpLN8rbpBzlHj3ttykVu4mZSKguvnHeOupHZQrQ6ujK/qWkVTGo50oFsqrpskA1SFeYjavOJGlF7+ciS6LQJGy2SCgRm1OZ0aJQCxvT2NLRFUu4Rg5swTG7jcHNcxaHxlLuXPn26mTp3d9KuaAFNubUosU5dawSsS/rtnQYJZRN2rhll1A8pPbySlB5LV3vfVOrhYSpvte6WwGQgqEQ0X7w1F8nA3gDnvvwTXXuV4+H57Lo/fZTQVDUMF8usx+jksX7I01qCmscimXeMHtRwyWjUd4sWusIE73wtWoSFobdhjUJRTNA68jTrlXlFRPYCCSr30zQi4WTXSrOHaG64+vUnznNAsQESXD0xYFOZDu7Kp78n1hfvA2FmUEQEr5SLsueKvIdRFKv5CSYaaUKvRgzY4umasw6FZNsKFaVl6XPkv7I+aBKnt0qoRDRD+GpudYA+CuAQ5h5sa18X4PqshhIKGGjPOARjwp7DMjiVWxEGkOzTe1gU50wwl4o+SLlFZWPdq2a7LRxqotyyEXW+0DUoLY87doYtknlpe57H3Z1ztwsAC03meLlpZ5PYpL6GOmql7TwbSiCMLaUPHVoVEKJnx/yfWxpj7ehMIKFl1ou3UZj3g2S2UUCG1PUYELeeKJyxWOgoXM5bSi2792m8rK1Ixetcj6E41C6V0LZBuBYZl4QU6bPQo0pUZPV6RJKe1clZJRPC5tbq4rM2YYF8ZLeSmoxkw3FXIe9P9Vkp41z/zSpvJoKBb/Pedq13WMyysuPXXdBzfuBdug2FEOfEoPxNAK0Laenly6h9BMxJHr7SRKzHIrNHeVY1YonoXhpiNRVtszgbVI/6dNczVyQRU1oQ9rARr1YxSShZOxDUmCjSc0G2OeHrG+rUUKpP0OJ8/L6VxwzIaLBRBSxZfQVEAKDoHxNRFEi70konoiiT/6VG9uxfmun786rIg2RtPEn24clCWLJkPk4ugK1STlsLVNNOpC0qVdWCc8w1dc/D0OxSTWqhCIJnqy/UmHNhhL01zpeHPUOkuqIpqIX/yHv7QhJKOHxkOWCPkUlFFM/kois3K1QSigyhsRkQwnVa6lvc3tXYpZiKaGoxaSEsmZzu3U8fYlUlVAQfR9ZUdHGWra9qb0rZO80xUfpEkpXmUPqO/WdmSAlCtMcZo7mIUsyyhc0O23YhmLtRs0QJ6GcTET/A+AeeEGMKwG0ApgM4HAAOwG4oO497KEoKJlOpedTgSgSTd3e6QUa6CL+zhfdFSr3jQ++B188PPArSJXeO+sGW4K4meNQ0hHlOBVD3VReyvE1T7wJAGhtKvqr6jyMzOrlJerasK0Lu3zzLvzxc/sFDIU5dJ8kPDtfdBemTxyK2849JFLfJXfMw03PhDXF37j5RQBAS6koHCWifVLT0ux80V0ikNY7fvgbh+GLNzwbqrO9q4ITf/04Xnp3PRb9KPDKqzAQF2cnjfKSALUKhhKxodgom4bL7nsNv7j/NWt7zIG0rr5nafxetakDH7viCTz/zrqQd6F8FkC1oYRtFrn3llHuu+CmF3Drs+/ixUuOwZ6X3Bvpu4qL75iH4/cI5/X7+k0v4LGFq/Dbz+yDY3cfi6seWYQf3f0KXrzkGAxuDW+hoMI0H/e85F5stEhAts9VkoRt4tvoMXEozPxVeEGCSwF8AsClAL4GLyHjlcz8fmZ+2nZ/bwcpK6w4o3x7uSKM8vET/obZb4eOa0WcVTB714yR8in3yw4xFK1MNXtchFReMXEoxQLhj2fsh8GtwVooT2puuw0l/FD3z1/uly0zh+Iz1D4/9/Y6Y33X/vstax9aSgUvUt6QOl/Xkav0/ZVl5i13X1y83qiW0aGOp2xTPrdcD9kC6kz90RF/TbRTCBvUZe6wcoXx/DvrjPfKPsm+RgMb7e3GQWXetz7rxXCs3xKNhzGN5V1zw8k7F6zw3s0yse3zn2d773+tFtcjERjlo3XrzCTUZ8vDShuYXCCoc6rb3YZF7MfvkOAi3BdBCoOQ70mmqFfR3ukxFFVFZkItVV62ySYT5bWUDLm8IjYUex1qqThkWTGm9fKaNGoADp86GpfeOd94PS3sEkr4PFHwsTOHjcfVrvham4rY1lk2GuXjbChZosxNXVSfvcu3D+ljrgXsaU3mfnY2q7ziXOmlWihIdaSovEILnOolFP+cYfzTVO9LUXKxqWgvTEhyG7b1Ia3KK5zeqBslFId4qCosdYLrrsHtXR7B0DcD0qFfS8VQbLm8LBPHk1C81WFB609qL6/EXgXIMn/j3D/Vw6H9vQx66rPnMspbYlf0lWKByHeQqFTYt+F4/czcbAgtpULIy0ttO45JbkxIi6IiKbOAn11ZK1aNhBIHBgKjvFLnlo4yRg2K90sOkopKaUpLDpmvS8axNjGZNEzUpwmKZyAQTSwqEWdDMSHJKC+vb+0QkmfIPpSqiargGEpOeHEo4Y+RTBJKl5fO25SAT0U0G27y27etemwuiBWWMTFSZRdVfUjYVn5ZYiWyrGKT0pBLDO3n6aHVZ8+jHkwbKU9K2bJwpAj6Wd0X2tJUDO3XEfL+inkmPYNCHMwSisK4FPsQEBA4ncjq9eSVBrxklGGVMeA5Few0vH/svZKIqjaUNLm8kmD6LvNKKLprs6xH3z5awld5pQzOlaWsySFFe9tMEkp32lAc4mFKX08w2FC6KgCkDcVen36tGpWX3QVR5PIiiuRBittgy0bgkwh5NoaS7r5hQkJRxzmP7SbJKC9BRH79usqr2shjT0IJjjvKgdonLg4lXcyGhyQJpdNfSYfL6AzN5r6aFZ6EgohNcUtHFyaOMDMU+aZ9CcW39wRMqVig3JHyaVVeaZ5Zt6v6noAW+Uk+W9o5nBQpL89LlVctMjtkQSJDIaL+RPQdIvqdOJ5CRB+qe896OFQvlftf9hImyxT1Kjp8CSVZ5fXa8o1+IsU4hvIPkXTSpvKy3et5eUmVF4EB/HPeMryzZkt0PxSls/ateuMnaNr5WywQnly0GnMXrxf12ssOHeBJKOqzx20wZSP6NinOpP5QvbxWhlxCEUrEBwBPv7kGLy5eZ+2PijiVV5wNZfmGbbH1qn26ac47oWvvrNmCO19c6h/LmI44u5V+feXGdtz+/JLYPthQYfbnn1rn5o4yRg1sQX9tL5WFKzZizltrvXsrwL3zluGNVcEOif5eRBl2Q426VgMPajtKmt1409dd9hl1mLFUKozfPvy6v5WvpBdpN0mTXbDND8lsZWBjFo1CLZBGQvkjgHYAB4njdwF8v2492k5AgiADwDdvmwsA/iZaKjwbCidu/8vMuOqRRbjoFs+lNC6w8ct/eQ5bO8rWSHmb5MDwJliBIOJiGP/1p2dw3P8+GvqAxg/rF86RpPTbxmhMSPuBlwqE9q4KLhfupvKjHNgS9Rn5xL7e7gVpVV42W4Qt5XtE5aUY5csVxvqtnT7RW7GxHef99flw/377b3zkV49b+6OiNUblFWdD0TMB61D7dMk/5mOhskXtx654IuzQ4DscBFI24D2ryrTV7nz+2qf9QMSsYA4kedUNuKOrgv7NJV+lKXHUZcG+9mVmnP2nZ/B3wczKYoEERL+7pD7o+Jy2572RoaSw0sj+6CovSdjfWL0ZP777FXz1by8AiPfyMkKTgHTI9uT81uOZ6o00r2ESM/8PgE4AEKnhM8R89054KiNdPQI0iRznpx20EwDp5RW1Wehg9oiZTEOeZEORwZImxEXdSrfhgqeABuAFcJWZ0Vwq4M0fn4Dxw/qFxCmb2JzMUGIv+5CBlpuE66j8+B698HCceejOfrmff2IvTB49CECYgHTG6DpsLsW2vpmM8oGdwXtH0jWzmswAANDaVABzMNSq2iNubLO2q+b5WqXlzOryV9LQzlfwpcMn409n7g8gPNffXRcvIcWBAYDCEr4MruzfXLQar/U+eH1mWSNKhULqBUyacronHSPdfNYZiPwvm0y7j4kNSXEoUlVa9ttrrNtwGobSQUT9IOeCt+FWvuVJL0KhEH1BRIRmwVCKBUJzqSCM8py4d0OFGWX2vF2YOZFoxK3K7UZ5b1VHRCBQWMVSDiLoCWH/fvUjUGuulYQi42JkVK/slu6GrTIRVUKJ2H+UdrNu5KTvT09K/ZUKo6vsMV6vnUxVR9BSKopVdjYJJfVq1gB910ndKA9A7P3hvRcpBdeMGHGQokg2uVVJ+2LbRhuIElHZT0DuoJquC2nKmcY4zXz243qkhCIddxCWWAJk8/LyVV4JEopkZF0VRlMx7HZdT6RhKBfDi5afQETXA3gA3kZWfRreBxFOEUEIUoKUCoSWUsEXPXU3XR3eCshL7dFRriQzlErF6jVmM+jKNBBFEamsGoHLHETQ6/79emoK9Z44cEpaLqU6SVh89UshzDjU36p7tv6RhrfHzZbjyuhCqujDuyoVv79pVCBxaCkVjEGGXpv2wcsqoaivSUpXfl0GCUW+11IhYOgqMcqSky7SF7CIQ1ElFO8d9W8uWj0XgehzVzh4tqIWKBmHVIzBsBBJU32XMlcAde5E21bpR1pPRd8mY/32o+3L+drdySEBAMx8HxE9C+BAeDTzPGZelXBbr4fnNhwmQAUi/+UVfIYiJJQEqzxzMEm2dpRTMBS2EnSbmocZ/sqzQBQqV6kEOznqXbX5stdLQpH3FbWU/xRiLnHtBr+zRtHr414okK9Sk+9bSqFR19psH2xLU1jSCTMU+31ZvdrU99DaVAzlnyprNhQZ/Ap4SUTlmNeKFjHLrNyBDUVlKHHMyrQ7oj9XDBtgxfUhCXmzPuiuzbItPcRAlpHvvFaBjXr7XWXJUMo1e4dxSLMfitw+TbqGTCSiIQDeYuZ8+472AkijvDoRCoVgtV0kQkupiI6uiv8RxRFY9UPe1N4VEbmbimEVVWfZLqHExaFIYytBT5euSCgx+0xUtA8iDlmM8kBAWAI1hqbyCv22Ux613ewqr3CfCeFo8k5F5aWvKrNG7LeWZN4sr49hlVcNJRS1zaawF1Wn5uXFHN5QTI55rQy6MmuEakPZ2ikSUzaXrPEagJfGSK+LlblSSxuKSRrKAp+w++MalhwAsSgU5dK7DYfr1yHnoB871dMkFABXANgHwIvwvq/dAcwDMISIvsDM98bd3FshU6+oOxISAhtKySChxL5QDibthq1RPl0qFNCpqqhiJBS7ysubVEXBUUISCgc7OcZKKBbbigmpjfJizKT9yJYbrZBSQkna4TEOJmYcUnmVK0ECRY0IZG1L1iOlhLSpV9IGwUmozKC1KazyCpilKIvgfRcLQbLTWtlQGOp+KNlUXu1aepZKJZiPxQKlVkCmIaxRN/psA6BLCoHKK1ymU2MASTAxprh2wzaUDA+QE2lsKEsATGfmGcy8L4DpABYBOBrA/9Szcz0ZBSI8umAV9rn0Pv8cUaC+KQij/D9eWIInF62B4lRlBCOYJHI7VBW6sbKzHE1trV4zIezlRZF9OVSDrc2GcuwvHsWKDdvwxOurcP7fno95IuDEXz0We11CtlsW9iN11UkWCcXmMg0Ae3/vXt+VO7sNJTyoVz6yyN+quCJW776Eon2hujR06tVPxrbVotVjSoliQlYJRa1Kl1D0eIk3Vm3GXv/trRGLFhtKNZDOiQUKAhH9vViaElReNZBQ2mbOwm3PvZtYTm/r+7NeTrxHRTS5p1lCkYuStIsRebfVy8tXoUnGUvHtuj3FKL8rM8+TB8w8H8BUZl5Uv271fJhWyGrqlSIRWpSPV887pENVea3fGo0z0JuLM8rbAxu9iUi+UT5MwOTqUI2xAaKr5affXIub5iRv3rlkfTr3UpVZbmkv+88lU8RIhH9H6xkxwIui7yyzn705q0dUnHG0UmF0VipWG4pOFB5fuDq2rWZtd8S0qVfke/vMgRNj6w+gSijBnBzUUvLnimluFguBDaumxEhLXy/HvLlUiPXyMtlQVKN82kj5Kx9OJl1pNriLQyTTgKhO3yVVTqG1KdPpyNttgZCBZBJ4m/lOJD2Eocwjot8Q0QfE3xUA5hNRC0RsSl+EaYUsjY0AUCxSyKPGRACbimGJQH5gxlxN2v1d5RiVV0wcCjOjKLyn2nWjvO82jBCF0Qnnms219RqXKi/AM8zL54raUMzqL4mPTR8XOZd2v3CJuNW/p/JiNFlsKFmlITnegYRiH3NTHw/bdXSqdsISSjDWOwxpDbyCDO2VCkHmh1rQIvbfa9goL4mfKdOEiihDURw4au3lVYVrNmCXUNTppX5/adPp6AkydZgCKpuLYeePeiINQzkDwEIA54u/ReJcJ7yNtvokTPNe3WDLM8oXQtd0qKsxRmA7WGdQeen3d1XsO8HZgiIZ3kQviFxeIZ09B6on3Yaif4CrEyK1s0JVtW3tKCvRz2S1m5gio4uGnaSyGsrjyku37uaiZARhApfVAUCu/o0SSkw/JCMrxe2cZSgPeLEvEv1bStbARqD2Ki/ZBoGMW2iXCtEN6lQYJRSlr9XaUOK2c8gK/ds0eWepAadpGYq822YnLWuLE1VCaYQNJY3b8FYAPxd/OjYZzvUJmBgKIcgqWiykkFAKBWxD4JIq54jJhqLf31WuWCWUuEj5slBtEekErBIY5RHvHrx6U40ZikIY5Z7kkq5kkVD0oD0gPp7DhDiVV7nijXuzHykfr/JKgnxs38srpIK01yWZQFMxzXow/P5UCaWpQMbARolSMWDo6hTIy1v8+CIK2zzkmKsMzASdYatpa9RcXkmSio2wVuNursOWCy3kgSgceooFwspN7SITc/wiISluRbbbFfLy6kE2FJEM8mYimk9Ei+Rf3XvWw2EiaETkr5w9hlIMXdMRWmFyICabVF763Z5RPqvKyyMGUjfe0RX2GgsCG+2R8kByLqmsUBnBlo4uz+NM6YuEOoSm8SwaxJasqe3jCDlLlZe0oZR1CSWfyiurhCJhYqAmqM/UrDChUpEiubzC/Sv4Y14LYiRrIITtdKraKtbLSxtf5qDSQkHdQTWhHwkeUkD1aXVsKi/19DbxPDsObUVHVwUbtiVHYcj7k1RyXRVPvR2WUHoAQ4GXHPI3ALrgqbiuA/DnenZqe4CZoQSETpdQTN++SgA7yhW8vGQDAItRXmuvHKPysk42X0KJxqE8/ebakA0lVkKpsQ0lYpRnKAwlKJfkNqwT2JUb21N59KiIlVB8lVc6L68k6HuPqETsb0+/Y7xHRSmHhBK6v1DwmY2pSNiGwnj4tZVYuMK8/XAayDkVRMp7x6qbshqHohN+XWoISSgFr/51WzpwyzPxDiN2CUVReVUpoVQqjFeWbfCPdeYJAE8s9OLDxw7pByCd2itJ5QX/epCWRk8V9MTrqzBvyfrEtvIgzYzsx8wPACBmfouZLwFwQl16sx3BZkMJqbya4odXpwdyD2ld5dVSKqBN2yuisxJWeU0bO9j/bdL/7jJqgOf2ykH6cPWjWbO5I2AoFGYoOuE0xclUgybNKO/F7XjH9jgU7/cgJSOx7iH0+evm4O6Xwnt+JyHeKO99yM2W5JBZGMrUHQb5/ZVjrb632W+sSayjKaUNJbz9gPf/vCOneBKK5jasQpUYKgyc/oencNRlj1hde+M8tIAgZoRI5vIKDMdA1E1cZ4RxbsNFUd+X//IcLhQZu22wrdTV89VKKF0VxrG/eNQ/DuxFQZk/PfkWAOCgXUYASMdQZB+TJBQ1Cl+XUE753Wyc8Mt0Lv1ZkYahtBNRAcACIvoSEX0MwMC69GY7gknl4vnXe79lpLyEOpE+vb/n7mkT71WV145DWvHq94/D9InDQmW6tDiUw6eOwsPfOAxAdHU1/3sfxM4jBgReXuLD1QlgQBDCBk5dtVNr0Vkdy60dZZFi3ztnj5T3/qtMW5dQlq3fGjoe0q8Jj3wj3o8k1ihf8SLl5QcaySGWQb1293nvi0hZWV1VSylztqv9LDNjl1ED8NWjdxXBsnajfKlAmVReOkM5fo8dQsdRCSUwHMv21G9C71N7Z3h8mIMVu6fyYry1ektiP22SfSggtsZGeV8dF1KrMWbsNAwf3mssAIT22rFBjmEa26D8vntaYON5APoD+AqAfQF8BsBp9ezU9gATKygUgr2jZWCjhDqRpJdQGoZiEpUBb0Kp5wpE/getMwpv5Ud+Wg3p5aUznrCEEtStE9msrrhJUOnQ5g6p8gr6LmGKSVGZtq4C0sfXywYQ35f4LM7eeNmM8llWtXKVriIrEWsupbWhBP2UCwrAI+CSMJltKKqXV3I7TRpD0W1aPkMBiS2xvWM5nwoFCjElfc7r46Nmai4Km8y2zmQ7lo03qjS6I2M2Ah0Ro7xBEmzvLKNQIIwa2AogpcorwSivQmYWKPUwG0obM29i5sXM/DlmPhlA2oiqXgt7YKO4wOHMrurLlATJpjrYoKi85G36XOgsc6hOIvJXrB2a8VJdEVbY+3DVrW0lglxeYUQ+5BozFLW9rdIo7zM3s93El1BKdgklwrDZzsQl4nTTchwCG0rFeD0tTNkPsiCPhFKpBGOgGuWNXl6h5JDZJRT9fQQqr7CEoroNq69Hb1JfKEUDGzmV2rERKi+9DZPDQEe5giIRBvcroblYwIqNyYHA8vbOFN+gTAvV3MMCGy9Kea5PIUnlVWFOZCg24iZtKUDwEeqToatSCRGKAgUftC55yIAxfyVI3ketMwarDaXKJIhJUMfScxtWjPJKOTVGQV5XpcBkHX6yYT/u0Tp8FYI5sDHN6lhFEnNLQuo4lJANJbBPNRULoUzKkf4p2wekoUW6G7P+PgIJJZw5ImSUj5NQDIGN/hbABa++NJ52tnes2iSrNcrr35YpB1dnmVEqeou7UYNa0sWiyDFLwfDkWPgqr+oeKRWscShEdByA4wGMI6JfKpcGw/P46tMwESNV7VRmDttQlJfZXCxa69Bh+5C7ymEvrwKRvyI0q7yCSV60RCQHXl5ht2F98tZXQikLt2HZ9+CaKSZFTW8TkVAMyyX1XLFAqGSQCiSRqYVR3utLdQwlfRyKkhFBYdbFAvl2H5uEkiWwUWcgeuZgWUMQhyKYmcJQwjaUcJsRCaUSDmyscHUSypb2gBnVwigfbtP7ry8M5fOmZShpjfKAakPpGdmGlwB4BsBHxH+JjQC+Ws9ObQ8wSigUTJAKh71wskgoKlj7L6Hvh1KgIFJcX13JFWGXoqs2eqlZJBRdlWNSCxULlJvRqH3Z3N6FlqZCYJRXiJQpJqWlaJdQ9PFlJb4lDzp8rxmvjqRcXkmIS9WeBunjUMI2FMlUm4qEzgrjrOvm4L75y6P9K5gDG9P2R89cEKRe8ebfknVb0TZzFnbb0fNQ9BY6QXm9TV2VO/fd9Tj3+mf9OhnpJClbmff/9MGgrZob5Rm3P/8uzvvr86HzcsxGDmzxk5DGQdaa5DYMBBKzZChn/+kZ7DC4NfG+amBd4jDzC8x8Dbw95a9V/m5l5rV17dV2AKNRXln5VyrhqNeygaGkoSfyNn110VWuhKQeUiSUbdqHJxmIlDSkUV6HLfVKROWl2W8AL/V4fgS92SKM8nLswjaU6G9V7aOrgCIMBdDiHMy92XnkAFz84WmR87qEojOU7CqvTMXxm1P3wYydhvnHuWwoClMtFbwdI03MxLuezctLd4rQGYw6XAUiP3noPBF/pafa0YlyHJEvFdLvh6JK38P6N+Gw94yKlKla5WUIbPyzcBNWIRdMLU2FkLrOZu8IcoKll1BUtfCyDekStuaFdUYS0VwiehHAs0T0ov5X115tB7AFNtpUBOr7zyKhwLehhM92askhSbGhbO2IEjZSJJRiwdx2YLcIJ9ozGZ91JjOgOc3WOjYEdW3p6BJuw7JPav+iv1WpRPcqitjkUxjlAWDSqIH43CE7R877H6g1Uj6cbDMJSSqv9yqxRQBw3B5jQ2quPLm8ygqzLhYoVrUTklBCUo69fNyxfM1E0RQrkvnEqrw67X1VI+WToJYjIpywx9hImaoj5SM2FPO4yecuEoXG2PYsQaR8FgmlOkk4C+KowIca1ovtECa6JN0hAW9CqNEcKoGWapo0xM3XvWrnyxXWjPKBl9c2w4dXUGwoBSKjiOXTKk1C0ZNNdpYrkdVi/5b8Eor6oW0WNpSigcCYfqsSTJKXF4NBBfXYjFGDWoznO3UJJZJtWM3FlYKhJLx/9Xl0BqouIJJQ1lRekr40KV5exvaVXF5pvIpSe3kh+uyBu31wLqLyimN+ulgdA51Rmd6DzUZRKlCqd6szFDWRZahtyVAKFFogWhNYiv9p3IYlHUhra6sFrAyFmX35jIjGANhPHD7FzCvq3bGeDtMkVAmhukc7EJ5gqq0iCZIRRdJQ6HufK9LRVoPqpUDBatSu8hKqOCD0ceor8c4yR1ZI1Ugo6pNt7dBSryjX1PGSjESlWTpB05+RWVd5mT9KE0MpFsjfglaqdjr1bMPKuKczYsdfNzkhqNms09qDdC+vQF1YiFWdqNsHqHPA1mzEhqJJjLItomgdJgklKfVKuK0MKi+tmIkx2yQANbtAHMwSisnxIXhudTolZUROw+ADLy/zREuTjDIr0iSH/CSApwB8AsAnAcwmoo+nuG8CET0okkrOI6LzxPnhRHQfES0Q/4dZ7j9dlFlARKcr5/cV6riFRPRLEiNCRJ8Q7VSIaEa6x88P03soVxSGEqPyku83k1Femz+24MVSgcwqLyheXgUzMVJjP+JsKOUKR+wF1dhQ1LFKn3olei5ZQtEIlqU/ow0MpblYCNyGRcBfnISSRsed9P5N9iO5RikU4jPzqlDHNxSHUqAIU1TRWa74fUijYtFVcDodkytwQtSppUhRhhKJlI9hKIUMDEUFKzFPKmzSUFq7lSnbsKl3QaomXZK09VfUn0rlFVbR6qh2zxcT0ozOtwDsx8ynM/NpAPYH8J0U93UBuICZpwE4EMAXiWgagJkAHmDmKQAeEMchENFwABcDOEC0d7HCeH4D4CwAU8TfseL8SwBOAvBIir5VDRNnLyuEMGKU09RTgNmtVYfNKK/726t6cZOEotpQZByKDkkPCOHVlInobNGY1oCWKiQU0VRzqYDl67fh1meDhI5qP40qL6WeiIQSEVHSSYUmCUU1bJaKXsCf/lGrTLYWKi/1cXQGm0lCUQiHyqxLhUKsV1R7V8XvQxrioxNbXUKR08jEDKU0HxeHEseki5Q/tYiJMS9audncTkouri/qKhYbiknl9eLidbj0zvnGerOpvGSkvLnPWbNjp0EahlLQVFyr09zHzEuZ+VnxeyOAlwGMA3AigGtFsWsBfNRw+wcB3MfMa4RH2X0AjiWisQAGM/OT7FG86+T9zPwyM7+a4nlqAjmvVO5fqTA+se8ENBU9Q9/RuwW5jCrM+M6HpmHK6IFG+4CESriAgLCfdlBb6LxuoJQ12VxJC4SQ27At/b73P97LC/Dce1VICSXOADiguYgvHDYpcl4SjgN2Hu6fW7x2q+h3VCqRz6D22WvbnnplzOAW/PCkPVIRhKH9mgAAFx77Hv9csxaRr46nRFYJJakvJglFJUBptRVqHIrqmCDddXU0FQmDWkqYMnpgYENJsSJOsqFI5w5CdO4XlbkX9Ds9h8gb00NEmdy31Wc6bvcdrOX0RR1bJJSwyssr8btH38D1YgtrHUH+syy5vMzkOmvcVBqkYSj3ENE/iegMIjoDwCwAd2VphIjaAEwHMBvAGGZeKi4tAzDGcMs4AGr+7sXi3DjxWz/fcMhpddpBO+Go93qP0FVhTB49EAt+cDx2GjEA44b2w7X/uT8AbyKceejOuO9rH7CmOAHC2XOBgLDvPm4I7v/a+/3zmzSCbvJ6Cl+nUGCjCayoJMKR8ikkFGFDGTukH/529oH++RP2DDxo5n3vWHx0b/vr+vT+E/G//zE93G9lhqoENsiHFlyPxKEo987+5lH4+L7jNR29uR+SeZx72GRMnzgUQJiQyOhmneCpH2gat9MsEop8tkA1ZJaSTVAZX1mJQzlq2hjspGWxBoBDJ4/E3P/+IAa1Nvl9TLMi1ueV/j58GmiQrkyLrCwarGpierLYEeRq/6BdRuDHJ+/pn3/l0mND5eSC6/SDdgIgpCfDA5kklJUxKVjU7AI2VZaElEBs5erBUNLs2PgNIjoJwKHi1FXMfFvaBohoIIBbAJzPzBsoNGGYiaj2irz4/pwN4GwAmDgxf0oyNdpYvq84o1vIB9+wupYY2FoKb7EbqjIov6VDYyiiTtseGbrqyNS27KO+wZZpS+GIhCK8vCqaTlqfzCb7hBy2AlHEW8wmoTQZPOX0FbHJ9SDNQlZd0cn6VXWWjCDXV+1qmTQr+qS+mAI5iwoBSgtbHIrXB7uk6h14/zo1KccEXYUbJ6HozeoMU/Y1LbKMRzX3+o4rFJbG9eeRxLq1OfgukiQU+Z7SRMx3lRnNpUKs55vv5WVJItqeMW4qDdIY5b8GYDYzf038ZWEmTfCYyfXMfKs4vVyoriD+mzzG3gUwQTkeL869K37r51ODma9i5hnMPGPUqGhAU1qogXfBznvRciYjvUm8lxhokVD0o00iTYTc0lW1ocT1F7AbdGXteSQUqfLyVCrBeZ2oDBHqJBVqcsA4bzGV+DXJWB4tlUq4fLSONKtRVb0lS6vPK91p41ReaVZ/SYQs1ssrAxEMe3mFx9E0HOqpwMtLNRibCb1N6gjaDry8Im7DBrti4xhKlrIBAwjFQFnmVb8mhaGYbCjKd1vJwFA6la2obUjy8uouldcgAPcS0aNiPxSTiioC4X31ewAvM/NlyqU7AEivrdMB3G64/Z8AjiGiYcIYfwyAfwpV2QYiOlDUf5rl/rojMJIGk8Kk1yz5zEYxyotRN6q8WjWGwuqHHJyXEkJ/SYAVomxCmDiZCYn/AVO4LdNqWyemraXgwyED4ffbNvRPtlUsEAbESihRyUeVQqJG+XxERv0AZRXqxye95HQ1UEdGlVdS/9Tn8VWa/rzLJ6GwkictTT0mG4rN4UCvS5+LsgrVHdkvW4xK7VmM7NUwlDwqLyKgqRCVZHVIhsIcjtD369NUXts6y6m2Ava29jW36WfMSIhD6RaGwsz/zcy7AfgigLEAHiai+1PUfQiAzwI4goieF3/HA/gxgKOJaAGAo8QxiGgGEV0t2lwD4FIAT4u/74lzAHAugKsBLATwOoC7xf0fI6LFAA4CMIuI/plqBHJCtVnYXIXldSBMoE1BeRIDW8IreLXGioGhyAmrugSboDZlcxsObCjhaxtTTHC50ZWuUknS8wLBh1YoUMAgBWyrabk604M7w/cmNm1EWJURrWRLexmEsBqIKLvKK1lCiTLTPBJKZ7mCtUKNGlV5RcubPOtULy+bsVxfLOh9TGOUTxMnZGy7ChtKXqN8wfJbRb/meAlFlXgqFWBVik22gHgJRZ6XKi2rDaUOKq8svp4r4BnRVwMYnVSYmR+DeREOAEcays8B8Hnl+A8A/mApt7vh/G0AUqvjqoUkukSEXUZ6G1gOHxC1D0idq/oRxhGDqIQS/FZX75LIS1WT/N6jwWXBhJWwBTb6AgqFP+ZbU+zLPrjVY4RTdxis2TqSP9aK0ndd5WXy7FLrVaXCpOSQKiYM74d31piT8akf4K5jBuIpbTve5lIhkkBzQHMJC1Zs8o9P/PXj1rb9/iYQMpWZTVMSKALZCOj1s9/G9bPfxsUfnoZKxZ4fTWlZ6YP3PyyhmJllxBXY4jZMZIhD8eepUj6Gn+gR63klFMp4b7EQVjEDXu43wOu73udAck9yG/ZiXw79yYOJfWibOQuAPaNDc6mALR1l39PM7jbcDRIKEZ1LRA/BixkZAeAsZt4z/q6+gyIRvnj4JFzzuf3wgV2jNpk4G4oJEYaiyCjjh/XHDWcdgCmjB/qb8UiGUrFIKDaGIlcx+0wcipOme55XisYrlbrh+D128OsfNagFfz37QPzqlOlhW4dhdfTohYfj8k/tpT6k369+WoBkyDCtnPf3JImRUGzDfNu5B+Pv5x7iH3/vxN1ww1kHGPv87ROm4U9n7o9xQ/sBAL58xGTsv/PwEHP7wxkzIrYviX2VZI46knTgsomDJ43AFafuAyCfUV5i8dqtoa0BALMEZpJQVDuaVUJJUnkpEnCU+USl9ji34aS9V7Igi3BT0hjfrecejJvPOUjUE61IldzjjPJ5vNRskseIAc0AgkSQ/ZqiQcdHTB2NSaNrv5N7GhvKBHgeWrsx8yXMbI646WPw1TTkeVYd9h6z0CZXByYvLxMkYZITTV/VHDxpJHYa0d9XQfTzJRRpQ9E+NMNELRaCrXP7N5dw+NTRoo+B0VTWJwmpCe/dYbC/OmsqFnDgLiMwqLUp9IGaGMqE4f1DiQ/9bVwLFCGyNn2/aV93vSmbbnz6xGEYMTBY3R3+ntE4eNLIoG6lD61NRbxvyih/tSfdoNW+HLTLSGuQ2BFT7cJ8i4GhqERY/jpgZ29cgXwqL4n2rnI6lZfyW15XU33YCL1eld5HPx6G0klYcUb5pMzSWZBJ5SVtKOJ4H2UumWrx3zGbVXi6KjMLTPMHAIb1b0ZTkfxYrlYDQznj4LbYbzsv0rgN9/ndGeOQNBGMEkrMPaViAS2lgifSi90LdaiirrQ56DYUqZIx5UgqEPmTsalIvvoo8PIKPE7iVFb9W0pKObOBMslwCARjk+SVpV4PMv5Wb0PR36Gpz9I+IlVyYVWc3QgfN35y9aqitanoxxgFtrbguhqHkhXbOiuosG7sTyehdKqZlFOaNnSiXw74ScTF2GRzijOhRFW76fpkQhZiHmzxED9uEi2Ks0pcfXkkFJuxvVggjBzYgqViewBd6gfSZ6rOisaloexlUGMn4lAyMJS4O4pEGNBS8nciNAnKo5TVdT9N5SUnimQYRUMAYFeFfWLWVAw2s2KDhBKXqXRAc9FXOTWXzETKdr8pp5ZpKG3qGelbH2eUT2vT1T/mJkNOHCmhSBWj2pcikTUeIG781B095XO2KkzGRLSqUXm1d1VCqVfUdm2QZfNs+xyVUOTCIRoHZao/TkKJqnbzk7Is0k2wq6kdKrOT36Ft+IIsyzlUXhYJpUAUWnSaVF71ykDsGEpOyPmRNA+CGJV0H2Sx4BEtORETJZSmsMpLtieJlbpplsTqzR3+9aZSwFCCwMagv3ETr39LyVgubJQ336+q5tSASh1JgY1xRvm0Y66/wzjXZpmzTC3h7StibiueoQTXJHEwMZmwCioqcaZFe2c5sidMkssskZfiJc/+IFG3YTUOJVzWFImfiaFUo/LKQMzl+zQ1J11d1HcesqEYHqdUxQLBylAK2qLTwFDS7vaZFY6h5IQaOxEHSTjTrpaJKMxQDGXCKi9NQvEZSiHUP5UerN7U7l9vLhZ89UOg4yXfi0yPI1ExoLloZChxcSgSoQWlkvJFh0n9oranrmz1jzwtEcwShyDHTVVHxd0f5zYdYiiinKoGM6q8CvJ/fgklbJeKltN9AAnAowtWZW5PZ3plVhlKWHI2vatrn3jTWnc0EWXm7il9SV/e5DwQVCb6pqiT5ALBy+VlDyuopcpLl1BMNhQnofQw+JsFJUyEuBgVE4iAo6eN8T3GTIa8kDgrbSi6hCIIk/zwOsqB0fik6eNDNhRJQEyqp+Y4G0pzyW9XXS0VUtyvEoSgXa/sZw6ciDMObhN1mVVpqg3lhx/bAzN2GhYh3qaUMSakISj/8/E9sc/EoX4fpWoqiRDY0l4A4TQ50hYWklAMX2ftjfLJtoC8WXx1Pb1c9HheXt41KfGZpMm/P7/EWvfeIseaRB6V0fABzfjhx/awSnvH7xFN/uhL/IbysgvqPFRVXrFxKDn6P2X0QLQZcrEREYYLT6/mUsE4j6rxiouDYyh5ISWUhPdiipQ34dP7e3nFmIFvfHAqzv7AJLWZEEYNbPV/+3EoFdleWHUiJ05nl1fT907cDRNH9PdtNE3Fgv91qG7DErE2lJaiTySarUb5ZAlFN8p//6N74JKP7BapS91tUfXyOuWAibj5Cwejv+a6m1ZCSaM++uSMCbhVcTWWTD2JEKRdCcqUNCYbim6vievzdf+5Pw437JEOCAklRRxKmsXyTz+eHDmQZoMt6dWYVaV2zLQxePpbR/nHeVQ4j1x4OI7ZbQcrcf3Rx6LPqOby0lHwFxvBoqBVTb0Sqie8MMhjI58wvD8e+sbhmKy5/xYoYNS2bQ7qtS2wYyhVItHLS3pQJazydLpT0Ii8ipGDmsU9gZttRZNQJCGTE1cajVt8XX3UKK+6DUvE2lCaS77KyeblZUtWGY6I9v6bwi3tKi/vt2pD6a+J9mnSn+j1poVkKIkSSkaG0lIyj6OEzSg/rH+Tf972PO2dFbGraHAur+khzf430eSQgVSfRkKJg55LK887bPIdVsz3Glf2vtuw4d2IelSvqrCEEjxjSWu7mEMFFRdzFiw02fh8aTcKywrHUHJCV9PY4EsoCRzFlizPhP7NJQxsKaGlVPAncVmzoci5VowwlKL4r6yExX8TYU8rodgyr6ZSeSnOADpsHknSNmPaWlkitYSS4yuQRs8k1UGa1DMAMNiXUKJGeRW67UFCqjhsmaSBDCqvWB8mD2l26LQ5SRCC/g8Q9XRWoouZOBCFmXkuhlKQdkbzdTMhlioqQ3lxTTWCh20o0barcxvWaUbwX7q3d1Uqxnnk3IZ7GORqI2kipLWhmFx34zBqUAtaSgV/EutxKGoWUyCII2jRvIlYITCBXShopznGBqDaUFTjezhFSnqVV5waQf/dbDDK62iEhJLEtNJ+uHESStgob14hy+A6PfmjCs8or6m8TK8nRZfzSCgmo3w1EoqqAs1KIEsFClx2Le/ftFiwjb9Xj/dfZShSoql42SEj59XUK1mhSxmqijTYTsK86HVG+R6GtG7Dab289CSSSUTOYyhFX/fqp4DX4k7ksZRQmhVjPBDetlgJZPaRNg4lnHk1+X6TyG3+SM2ST5NilLchbq8IWxtpIRlKUj6ktCtPmXKnWTHKm261SUQy3caaLR12lVdXJVW24TQ9TiOh6BKjapQnC0NJIx3JPqaVUExjZlPRhtowrexNvtxaO63NUSlT4yf+/DcFHqeFH5Cs2SBVCcUGZ5TvYfAJf8KLkZf3njDUPze0v/fxT91hkH/OZyj+ffH1jhvaD4P7lTBhuOflMVH8l4ZQuWLdfcchAIKVdLPmTlxhYMRArz/vEf2J21YXgG8ELBULmC6eS10hZjXKy8SHg/tFPwIb8QtsKNVLKHnsCHpiPn2nTYm03jvSOK3q2XcRaW3GDws8eWx93WfiMAAegbaVae8sG/ZDMTCUFAOSRLCA+DgU3SifFaMHt4SeI45AmgzQ6jnTvROG97PYsIRR3tCOHLd+BtfviA1FN8rnIPCSKe0+zvvGZYJW1YZiQ70klHxv08FHEuEnIvzjS4diouLeN3n0QNx8zkHYY/wQf+/oQOUl641vd+ZxU7FhayemjBmEG846AAfuPAJAsAqcPmEozj1sEvYYLxhKV9h4Tn57jKk7DMZN5xyEvcYPjbRjmng3n3OQnyfod6fPwJurNhv3EAHsBEOVUH74sT1w6gETQ4TTVJdKoAIvLzvT6CxXMG3sYPzhjP2sZYB8q8PRg1pDxw98/QNYsaEdry3fiK/d+IKxzwBw/9fe7+flUqHueCnx6f0nYp+dhuGgXUb45+SqfLC2UdmZh+6M3XYcjIMmjcDNcxaHrt1w1gF48JUVuO7fbwmDeHAt70JV31nThIgNRYk3kvNU3//GhKPeOxrDBzTjxjmLcfI+4/HJGeOx707DQ/nT4iTBpkIB2xCeJ6FN1JRb/3r2gRjYUsK4of2MdcYZ8n2jfFNUyox4eRWrZyhSbfaTk/fEaQe14dI752PFxnYUiBJVkrYFULVwDCUn0qq8APhEXcWMtuGh44iXV0LFYwa3Ysxgj6ipiQ3XbvH2vRg1qCXUhlT/SELsSyjiO9tPKauupExG9aH9m30pa3BrE/bUGJH6sdkIhvp4rU1F7LvTcGM5sqymJUGIk1AqDEwZMxA7DGm1ltH7kha6hDJ6UCtGD2pNbGvy6EHG89JDTVXhlQqF0LsFgvc2UkiVEoUC4eDJXlmd1h08aSSeXLQG7V0VEciaTlUUh3QSis1tONjuNk09e44f6udS69dcwAGCwYbsazEv0WRfURdAKjE/UGHephgwNVee7Vq/kMorWLip1enG+GqcCrzvZ1iQWYGSVZJ54l7SwKm8csI3ytfoxeiG8bwf+prNAUNRIdU/0pgtu21yFlCJdB7RWO17iyFKF0gfnW4bh2ZfQok3TqWxYeR5h9KrSodOINPmwGo2JBEkw9BvFVsR29oHzGMrjf3tXRVrOhv//hT9NaXz0BHdYCtQeUlpJY1xv0CBE0lIzRVSednvN7muhxiK1YYSPR8X2CiLhz31FJWXIqPIPlUTqGrLuFwgyq1KrBZOQsmJtG7DaRGooLzjvHxKlVBU6DYUUia6DpVIx6VesUHterX81nZ/UwoJBUi3EsvzDm2MtlXLIBznNKCixfA8JkK3WiwY4hiK6ZFl/R3lsBtp2qy5kTZSjKuu7qtUoiqvNMZ9IkJLU/j70PsQtwBrMhrl4+1INpQ0lbEKX0IxuH7rubzk2NTCKB+0JetCJMi3UXASSl5USfh1yGrSGuVtWLu5E4Cdoeg6YJNYX0sJpZp9KuLu943yCZ5c1SQNzINoFt10jgFptjQGAgl0xMA4hqISS+9/i2HV7P2O3l+rEbPGoRD5Cxk9waFNldSiBfDqiJtniRJKho84PvWKyYaiSCgqQ4m4DeeQUCJuw0E/0kiQ9YCTUHLCvP9afvjfQ8o4FBukrWTEALPKqymFykslanG5vGxQVTXV0nPb/VJ/nKRRqpeuOC3SSii+TUjd38XAy9f4Eop5+1cgPGbyZyiQtUqGn5b46eUqilG+7Lu6JtdVoIAp2N53XDUmG4rKyLLtKW+XUPw4FIPU9csHFhjrCby9onUlzW3bltdEFPuO0kiFeeEklJyISxeSB3o9eVf2V5y6D4567+jIyu+nn9gL0ycO9Y3GR0wdjak7DMKXjpgcqaPcgyQU2/gWCoSDJ43Abz+zb+j8bz+zb0jVEtf9X50yHYdODozeJ+8zHt/90LTUfbvouKl+EksTxgxuwQG7DDcmGZT4ycl74IQ9xuLgSSMwbexgXHjse/xrprH7f8dOxV7jh2C/tmHWOk1uwS2W5J3mXF7x70xGt3/vxN3wkb12tJYb1r8Z+7cN99WAMmanUAhUXgUiHDNtDH72ib1in0cSfZNErT/HyfuMxwVH74pjpo3BXhOGYsch0Z0JQ16JGaa4v2OjRZICzNl9I/UkGOXTpEbRv015i3y/x+2+A36ujevBk0bgys+Gv5lawkkoVaJWGhVZT7Uqr+P3GIvj9xgbOX/I5JE4RCGeQ/s3457z32+so3qGEvyudnjivqsbzjowcu7Y3XfAPee/H0dd9jCA+NXnh/bcER/aMyCIP/+knaiZ8F8igacN93/tAxjU2oQrTt0XbTNnGct8ar+J+NR+XmLQu857Xyjy3tT3PcYPwe1fOjS2XTL8Du+zElWJ2e43QcY4nXZQG047qA13vGDOCtxcKuDGcw7CvCXrccIvH/N3oiSQktkBuOq0GfHPo7g621Vewe/vnbhbyNj/X3+aEykfikPJ8K3FbbBlUnnZoEtNxrxc5l2lrXXoe+X85jNRxmH6ZmoJJ6HkhCkzbzXQNF41s83kgar3z5PzpxE2lDio9qPuVHnlYcbFBGKfBqYxC+2zkmTMTmhXJqJMgqxGer5tFgwFlF3lpW8CZ2/NnjRRRZpIeRNMG9bp9dg2vgrX45WROcx0phaOubLYEa2pVxKbrxscQ8kJU96r2tbbfbNClVDy9ELtevUMJfs9g1tLQUaAbhzHXNJdQWUo+fqu0hnfKG9ReZn5SXy7cfabUD2iGhkEuWmblFAClZdO7G0rfzVA0AS1Gt27zDQHQ9stZHhNeq4807U0Nia5UJOBufrCp6gwkdaSWeKJSije/+6kHY6h5ETaHRjTwld51bjePMizf7gK2x4meZDn4yCi1NmA64nuajsUDIqoXr9aCXL4gJQSiqhbSii+ykvx8kozRgWK5rqLlrFLKKa9UtLEoZgQuA0b+uAzmxT1SAmlbGasoX3pLSo0u8oruf16wTGUnKhuDR9Fd64qdKgSSh7W0t0qLyD9Bli9EWQ4yCShJAxZWglFQtoUNncEEkoWlZe6f4pNQgl5tml1muaALfdcEgJCb5BQMkgIgdu7eRxUBtNiUaHphvsgOaSTULZb1OrdtY3wEgHuLBICSuwmEic2EllTieuQE3v/tuGJq6Wk6/Jy1h3m0m6AVQ/o77DRUAnKATt7KW1aStHYCL2sfz2h/j0NqYRi+1PwkhVuai/7bVpVXobGiwXCuGGep9Z7djCnronNNmy4pmY0kAwnznNO7Yutn7pRPM49VyZ0HDPYLEmrzEJNLBsqU9THLl5CqVf+LhXOyysnam2UP36PHXDruQf72XsB4L6vvh9jEnJD1QOqyiuPCo6I8M/z349xw/rF6uMf/sZhiak3fENnRntEd0oofz/3EKzctK0hbT150ZGRzdvkI//HfhPwHeEGbdsJMsvw3P7FQ9BVYey7Uzzh/cWn9vYzSEsMaClh0zYv6JYoWLTojMA03wrk5Zq77dyDjQlMZZ02qMR6xIBmXPrR3UO56wD481XHvy74ALoqjGMufwSAGhhs6Kei8nro64f5WxJI3PD5A3DK1bMBAF/4wCQcPGkEposs0TrTk/zE6+swzJq7NNKebpSPs6E8+PXDMLjVMZQejNoaz4nIT0EuMWWMeTVWb6gSSl5pRa4kZVI/E3YakbySl8ObNQXMSJGaxBa3UE8M6d+EISk9oaqFKRmlnJO7jhnkM2zVy0vlzeY4FHNbu48bksrmMbClhF21uTuguYjNQkIhRDeEi4N8nukT7YwsTkJRFxUTR/Q3utXbJJ9dRnlbNQxqKWFje5eSvj7anqpyajNIqbuNCyS7QoFCz6M7Bsjn2Wfi0JB0qSLJbVhFo6Rmp/LKiZ5gPK8XumrAUCSqtqGIrzSrx5TMdSVzm/UlmDyi7HEo6d9PNcJe/+aS0W1Yf61xqqQ4xEbKKx0fOTCb/cdWV1xgo60vNluIeq+EPCoQWdW9Nm82Z5TfDuEnh+zWXtQH6h4j1Xt8VdcXqWvPqvIaLgiHTFXSl6DvrQPEpV6J3m9TU6ZlPqZiA1qK2NQRDWxMG4eSXCZGQqEaMBRRRZrARhvi5nBE5aVIG7a4Fv19yMPudERxDCUn0u79vj1CzbcYt4FVGlQrofhbDGc0ysstcVdv6osMxfuv5pvLlnql9n3q31wKBe3atro258iqTkIphiQUe1LN2PrF/yY/9Yq9n1bX5hhCH7nmq8/SL6aCXF6pitcFjqHkRJC+vlu7URfUUkKpdnz0pJZpIVVefVlCUV+dzbPLKKHUYU6rG62pG2yls6Ek1x/n/KEb5fNAjp9vQ4lReeVRE9skFCD93O8JbsPOKF8lapUcsidBMpHhA5pxygET8Yv7vUypJ+w5Fh/be1ymuqp1Wthl1AC8b8pIXHDMe5ILK5g8eiCOeu9ofOmIKVW1Xytc8uFpWLqhMZ5fQcr05Khy8/vJ9s6+fMRkDG5twr8XrQZgXqGrbrpEwMUfmYb/vmN+xGPsPw/ZGU+9sQYA8Pw760R/k/szefRATJ84FNMnRA3375syEq+v2IStnWV8bPr4tI8VguxCkL7ebpS3jXscbDaUCocZyqf3n4C/PPWOsQ4ZJNldqesBx1Byg3uxEUWusG79wsGhvdN/fco+De9LU7GAP515QK77rj49fi/5RuKMQ3ZuWFtJWReqTV+vQzJ7yVBMGNwv8HojAFN3GIy/nB1NVLjDkFb8/YuHAICfUDONFDNqUAtuO/cQ47X3TRmF900ZlVhHGqSJQ8nDUHSVl1qXqu790Ul7WhnK6s3tAIDRg6pzPKgGTuWVE72Yn/jRu92ZtsQhP3wbikbYgr036qPyirtNjcnIWn9PmIayC3FGeXktj5ZYV3mpnnpppXxpL9Q312skHEPJie6Ib2gU5AorT6Zhh+6HLTOvvrkakC99fR4MblXjcrK10BPSEulR6CYDuxz3PDYU/RGTDPwmyO2hHUPZjtETJnut0ZXBYOrQ8xDo38PUyOSh1Kj5q6q8sk6r7jQy6/BTxBuuFXwJJTtD0W9JUluasMYxlOwgoglE9CARzSeieUR0njg/nIjuI6IF4n/UOleP/jSikQZDrrDS7Brn0PNAFglFxjOEAhuN99e+T2GVV7YGesK6RnbBHzujDcX7n4uhaGlYfQklR3pWffvvRmJ7pBhdAC5g5mkADgTwRSKaBmAmgAeYeQqAB8Rx3SATto0ZXF2urR60+PJx0C4jAMDfurUW2H1c45Nc9lUE6pIwMRo/rD8Ae9LCIUKKeO/YfO9qL/FNmNLBqCqvrFO+J0goB4vdTuXYmby8ir7KK3v9/ZvC/lEzRKLKYf3TuzlPFelj0mzwVS9sd15ezLwUwFLxeyMRvQxgHIATARwmil0L4CEA/69e/Tj/qF1xwp5jrTmA0uKZbx+N9q6EvT4bjJ9/ci987Zhd0V+4ej550ZGZAwtVPPyNw/xtYx3qj4JFXfKbz+yDV5dtDG0FLXHSPuPww4/tgUUrN+O9Y/PN6S8ePhnH7DYGU3eIMqQ8Rvn3TRmJRxeswgaRVLI78dOP74nzjpyCgSI3WlxyyEqMDeWpbx5plNCG9G/CP750KD78q8cAAN88/r349P4TMWF4/0jZZ759lO8irOLGcw7C+i3dO1bbHUNRQURtAKYDmA1gjGA2ALAMwBjLPWcDOBsAJk6cmLvtYoGMH05WDM8ZaFVPtDYVMUkkxQPMK84sSJME0qF2OO3gNsxbsgFnHhp2VR47pB/GDolm1AWAKaMHobWpGMkSnAVx38SQkNtwOo5y2Sf3xiV3zMPBk6IMsNFobSpi8uiBWLHRiyUyMcULP/gebOnowof2iiaflBgdo9GYMib45pqKhUiCTQnb4mxwa5Pm/NB4bLcMhYgGArgFwPnMvEHl+szMRGRcJjDzVQCuAoAZM2b0Xlcthz6LIf2a8NvP7pvpnjip4Z/nvx+defQ4CvJIKKMGteDXpzY+9ikOfnoTA1McPbgVV5xqHveJBklDR29wgtkuGQoRNcFjJtcz863i9HIiGsvMS4loLIAV3ddDB4f8+Poxu2Jtg1UXcXbkatW6AHz1KdAz7YZpIe0kWZ7hlUuPTWUH6o7N4GqN7Y6hkCeK/B7Ay8x8mXLpDgCnA/ix+H97N3TPwaFqNDRdTINomGqD257TFRX8SPn0z9CaMhVKL+An2x9DAXAIgM8CmEtEz4tz34THSG4kojMBvAXgk93TPQcHBx3h2Jdu7EiV8HdFrEPdcUzqa0fvWnWi1kZgu2MozPwY7O/zyEb2xcHBITu2Z4aStIlWvfCVI3tGktMkbI9xKA4ODtsxtmuVV4xR3sExFAcHhwZje3Zm6gmbWPVkbHcqLwcHh9ojT4qPvKglMb7ys/s2dFfOpiLh3MMm4cj3jm5Ym9sTHENxcOjD6B7VTe3a/OBuO9SsrjQgIlx47NSGtrk9wam8HBwcGgqnLuq9cAzFwcGhoXD8pPfCMRQHB4eGoidkD3aoDxxDcXBwaCgcP+m9cAzFwcEh086A1cLFcPReOIbi4NCH0R3SgpNQei8cQ3FwcHBwqAkcQ3FwcGgonITSe+EYioODQ0MgGYnz8uq9cJHyDg4ODUFToYCOcsVJKDE4/6gpmDAseXfHngrHUBwcHBqCpiKho+y8vOJw/lG7dncXqoJTeTk49GE0krSXih65cRJK74VjKA4ODg1Bk2Qo3dwPh/rBMRQHB4eGQO4rvz1sZeuQD46hODg4NAQlyVDKjqH0VjiG4uDgAG5A7pV9Jw4DALQ0ObLTW+G8vBwcHBqCH5+8Jz53yM4YM7i1u7viUCe4pYKDQx9GIz2uWpuK2GvC0MY16NBwOIbi4ODg4FATOIbi4ODg4FATOIbi4NCH0ch9UBx6PxxDcXBwcHCoCZyXl4NDH8Z/fWASlq7fhtMObuvurjj0AjiG4uDQhzGkXxMu/9Te3d0Nh14Cp/JycHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJqBEb6/RUENFKAG9luGUkgFV16k5PRl987r74zEDffO6++MxAdc+9EzOP0k/2aYaSFUQ0h5lndHc/Go2++Nx98ZmBvvncffGZgfo8t1N5OTg4ODjUBI6hODg4ODjUBI6hZMNV3d2BbkJffO6++MxA33zuvvjMQB2e29lQHBwcHBxqAiehODg4ODjUBI6hODg4ODjUBI6hpAQRHUtErxLRQiKa2d39qQeIaAIRPUhE84loHhGdJ84PJ6L7iGiB+D+su/taaxBRkYieI6I7xfHORDRbvO+/EVFzd/ex1iCioUR0MxG9QkQvE9FBvf1dE9FXxdx+iYj+QkStvfFdE9EfiGgFEb2knDO+W/LwS/H8LxLRPnnbdQwlBYioCODXAI4DMA3Ap4loWvf2qi7oAnABM08DcCCAL4rnnAngAWaeAuABcdzbcB6Al5XjnwC4nJknA1gL4Mxu6VV98b8A7mHmqQD2gvf8vfZdE9E4AF8BMIOZdwdQBPAf6J3v+hoAx2rnbO/2OABTxN/ZAH6Tt1HHUNJhfwALmXkRM3cA+CuAE7u5TzUHMy9l5mfF743wCMw4eM96rSh2LYCPdksH6wQiGg/gBABXi2MCcASAm0WR3vjMQwC8H8DvAYCZO5h5HXr5u4a37Xk/IioB6A9gKXrhu2bmRwCs0U7b3u2JAK5jD08CGEpEY/O06xhKOowD8I5yvFic67UgojYA0wHMBjCGmZeKS8sAjOmuftUJvwBwIYCKOB4BYB0zd4nj3vi+dwawEsAfharvaiIagF78rpn5XQA/A/A2PEayHsAz6P3vWsL2bmtG3xxDcYiAiAYCuAXA+cy8Qb3Gnp95r/E1J6IPAVjBzM90d18ajBKAfQD8hpmnA9gMTb3VC9/1MHir8Z0B7AhgAKJqoT6Ber1bx1DS4V0AE5Tj8eJcrwMRNcFjJtcz863i9HIpAov/K7qrf3XAIQA+QkRvwlNlHgHPtjBUqEWA3vm+FwNYzMyzxfHN8BhMb37XRwF4g5lXMnMngFvhvf/e/q4lbO+2ZvTNMZR0eBrAFOEN0gzPkHdHN/ep5hC2g98DeJmZL1Mu3QHgdPH7dAC3N7pv9QIzX8TM45m5Dd57/RcznwrgQQAfF8V61TMDADMvA/AOEb1HnDoSwHz04ncNT9V1IBH1F3NdPnOvftcKbO/2DgCnCW+vAwGsV1RjmeAi5VOCiI6Hp2svAvgDM/+ge3tUexDRoQAeBTAXgT3hm/DsKDcCmAgv3f8nmVk3+G33IKLDAHydmT9ERLvAk1iGA3gOwGeYub0bu1dzENHe8BwRmgEsAvA5eIvMXvuuiei/AXwKnkfjcwA+D89e0KveNRH9BcBh8FLULwdwMYC/w/BuBXP9FTz13xYAn2PmObnadQzFwcHBwaEWcCovBwcHB4eawDEUBwcHB4eawDEUBwcHB4eawDEUBwcHB4eawDEUBwcHB4eawDEUBwcHB4eawDEUh24DEbWp6bVTlD+HiE4Tv88goh2Va28S0UjDPWcQ0a9q0+OeASLalYjuEmnInyWiG4mooTm3iOijcRm3ieh8+a5y1MtENFU5N4qI7snbV4fGwTEUh+0GzPxbZr5OHJ4BLx9TnwIRtQKYBS8H1xRm3gfAFQBGZaijGHecEh+Ft5WDqf4SgP8EcEOOej8N4DHxHwDAzCsBLCWiQ3LU59BAOIbi0CNARLuIrLf7EdEkIrqHiJ4hokflapWILiGirxPRxwHMAHA9ET1PRP1ENV8WK/a56gpXaWMUEd1CRE+Lv0OIqCBW+qNEmYLYaMhIoInoE+RtzvQCET0izp1BRH8Xmxa9SURfIqKvied5koiGi3IPEdEM8XukyB8mJbVHRd+fJaKDY4bqFAD/ZuZ/yBPM/BAzv6RLY0R0p4j+BxFtIqKfE9ELAA4yHH+GiJ4S43mlZDKi3A/E8z5JRGNE/z4C4Kei/CStj0cAeFZm8BXPfTkRzSFvI6/9iOhWMe7fV/o7EMCh8PYj+Q+tzr8DODVmXBx6ABxDceh2kJdP6hYAZzDz0wCuAvBlZt4XwNfhrcB9MPPNAOYAOJWZ92bmreLSKrFi/424T8f/wttIaT8AJwO4mpkrAP6MgFgdBeAFsSo24bsAPsjMe8EjqhK7AzgJwH4AfgBgi8ji+28ASaqfFQCOFn3/FIBfxpTdHV7K9awYAGA2M+/FzI+pxwBWi3YPYea9AZQRjMcAAE+Kco8AOIuZn4CX/+kbYvxf19o6xNDHDmaeAeC38HJIfVE8yxlENEKUORHehl+vAVhNRPsq988B8L4cz+3QQJSSizg41BWj4BGYk5h5vlilHgzgJi/FEACgJWVdMjvyM/CIu46jAExT6h0s2vuD6MMv4Klq/hjTxuMAriGiG5X2AOBBsSnZRiJaD0BKEHMB7JnQ7yYAvxK5tcoAdk0onwdleEzbdHwkgH0BPC3Gph+CTLQdAO4Uv58BcHSKtsYivPslECRTnQtgnkw+SESL4GW6XQ1PzfW/otxfxbFkTCvQB1Wc2xscQ3HobqyHlwX2UHiZXwvwNjzaO0ddMqFfGea5XQBwIDNv085vIqLlRHQEvN05raoVZj6HiA6At8PjM8oqWk0mWFGOK0pfuhBoBVqV8l+Fl8BvL3Fd75+KeQA+YLmm1q+3sY2Zy5ZjAnAtM19kqLOTg4R/tnHVsVVrGwiPhz5WJaEWPALAHkTE8JKwMhF9Q7TfKup16MFwKi+H7kYHgI/BS599itjQ6w0i+gTgpdQnor0M920EMChjW/cC+LI8EBKBxNXwVF83aYQ3BCKaxMyzmfm78HY8nGAra8Cb8CQBIEiXDgBDACwV6rfPwiOmNtwA4GAiOkHp0/uJaHdR/97CDjQBHnNMgwcAfJyIRov6hhPRTgn3xI3/ywAmp2xb4uMA/sTMOzFzGzNPAPAGAjXXrgBSewQ6dA8cQ3HodjDzZgAfAvBVIvoIPAnhTGEwngdPt67jGgC/1YzySfgKgBlE9CIRzQdwjnLtDgADEa/uAjxD9Fzy3J2fAPBCyrYBb/vZLxDRc/DSiktcAeB08bxT4e2eaISwF30IngPCAvEc58Jjbo/DI8Lz4dlhnk3TKWaeD+DbAO4lohcB3AdPbRWHvwL4hnA80I3yd8Pbrz4LPg3gNu3cLQi8vQ6H593m0IPh0tc7OAAQ3leXM7Mz/NYARHQbgAuZeUGN6nsEwInMvLYW9TnUB05CcejzIKKZ8FbDJhuCQz7MRLKUkwrChfsyx0x6PpyE4uBgABF9C8AntNM3NWqnTiLaA8CftNPtzHxAI9p3cMgDx1AcHBwcHGoCp/JycHBwcKgJHENxcHBwcKgJHENxcHBwcKgJHENxcHBwcKgJ/j9ud8xIfqKXYwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "keith.smua.fastsweep.prepareSweep(0.001, 0.1, 500, mode=\"VI\")\n", - "do0d(keith.smua.fastsweep, do_plot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting experimental run with id: 156. \n" - ] - }, - { - "data": { - "text/plain": [ - "(results #156@C:\\Users\\Farzad\\experiments.db\n", - " -------------------------------------------\n", - " keithley_smua_Current - array\n", - " keithley_smua_vi_sweep_four_probe - array,\n", - " [],\n", - " [None])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "keith.smua.fastsweep.prepareSweep(0.001, 0.1, 500, mode=\"VIfourprobe\")\n", - "do0d(keith.smua.fastsweep, do_plot=True)" + "And finally we can perform the measurement" ] }, { @@ -369,7 +248,18 @@ "metadata": {}, "outputs": [], "source": [ - "do0d(keith.smua.fastsweep, do_plot=True)" + "initialise_database()\n", + "new_experiment(name=\"fastweep_exp\", sample_name=\"no sample\")\n", + "\n", + "with fastsweep_meas.run() as datasaver:\n", + " somenumbers = keith.smua.fastsweep.get()\n", + " datasaver.add_result(\n", + " (keith.smua.fastsweep, somenumbers),\n", + " (keith.smua.fastweep_setpoints, keith.smua.fastweep_setpoints.get()),\n", + " )\n", + "\n", + "data = datasaver.dataset\n", + "plot_dataset(data)" ] }, { diff --git a/src/qcodes/instrument_drivers/Keithley/_Keithley_2600.py b/src/qcodes/instrument_drivers/Keithley/_Keithley_2600.py index fcba7c7ae53..26c2f2d7d4a 100644 --- a/src/qcodes/instrument_drivers/Keithley/_Keithley_2600.py +++ b/src/qcodes/instrument_drivers/Keithley/_Keithley_2600.py @@ -4,7 +4,7 @@ import struct import warnings from enum import StrEnum -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING, Any, Literal, Self import numpy as np import numpy.typing as npt @@ -17,7 +17,6 @@ VisaInstrumentKWArgs, ) from qcodes.parameters import ( - ArrayParameter, Parameter, ParameterWithSetpoints, ParamRawDataType, @@ -27,84 +26,131 @@ if TYPE_CHECKING: from collections.abc import Sequence - from qcodes_loop.data.data_set import DataSet from typing_extensions import Unpack log = logging.getLogger(__name__) -class LuaSweepParameter(ArrayParameter): +class LuaSweepParameter(ParameterWithSetpoints[npt.NDArray, "Keithley2600Channel"]): """ Parameter class to hold the data from a deployed Lua script sweep. + + For more information on writing Lua scripts for the Keithley2600, please see + https://www.tek.com/en/documents/application-note/how-to-write-scripts-for-test-script-processing-(tsp) """ - def __init__(self, name: str, instrument: Instrument, **kwargs: Any) -> None: - super().__init__( - name=name, - shape=(1,), - docstring="Holds a sweep", - instrument=instrument, - **kwargs, - ) + def _set_mode(self, mode: Literal["IV", "VI", "VIfourprobe"]) -> None: + match mode: + case "IV": + self.unit = "A" + self.setpoint_names = ("Voltage",) + self.setpoint_units = ("V",) + self.label = "current" + self._short_name = "iv_sweep" + case "VI": + self.unit = "V" + self.setpoint_names = ("Current",) + self.setpoint_units = ("A",) + self.label = "voltage" + self._short_name = "vi_sweep" + case "VIfourprobe": + self.unit = "V" + self.setpoint_names = ("Current",) + self.setpoint_units = ("A",) + self.label = "voltage" + self._short_name = "vi_sweep_four_probe" + + def _fast_sweep(self) -> npt.NDArray: + if self.instrument is None: + raise RuntimeError("No instrument attached to Parameter.") - def prepareSweep(self, start: float, stop: float, steps: int, mode: str) -> None: - """ - Builds setpoints and labels + channel = self.instrument.channel - Args: - start: Starting point of the sweep - stop: Endpoint of the sweep - steps: No. of sweep steps - mode: Type of sweep, either 'IV' (voltage sweep), - 'VI' (current sweep two probe setup) or - 'VIfourprobe' (current sweep four probe setup) + # an extra visa query, a necessary precaution + # to avoid timing out when waiting for long + # measurements + nplc = self.instrument.nplc() - """ + mode = self.instrument.fastsweep_mode() + start = self.instrument.fastsweep_start() + stop = self.instrument.fastsweep_stop() + steps = self.instrument.fastsweep_npts() - if mode not in ["IV", "VI", "VIfourprobe"]: - raise ValueError('mode must be either "VI", "IV" or "VIfourprobe"') + dV = (stop - start) / (steps - 1) - self.shape = (steps,) + match mode: + case "IV": + meas = "i" + source = "v" + func = "1" + sense_mode = "0" + case "VI": + meas = "v" + source = "i" + func = "0" + sense_mode = "0" + case "VIfourprobe": + meas = "v" + source = "i" + func = "0" + sense_mode = "1" + case _: + raise ValueError(f"Invalid fastsweep mode {mode}") - if mode == "IV": - self.unit = "A" - self.setpoint_names = ("Voltage",) - self.setpoint_units = ("V",) - self.label = "current" - self._short_name = "iv_sweep" + script = [ + f"{channel}.measure.nplc = {nplc:.12f}", + f"{channel}.source.output = 1", + f"startX = {start:.12f}", + f"dX = {dV:.12f}", + f"{channel}.sense = {sense_mode}", + f"{channel}.source.output = 1", + f"{channel}.source.func = {func}", + f"{channel}.measure.count = 1", + f"{channel}.nvbuffer1.clear()", + f"{channel}.nvbuffer1.appendmode = 1", + f"for index = 1, {steps} do", + " target = startX + (index-1)*dX", + f" {channel}.source.level{source} = target", + ] - if mode == "VI": - self.unit = "V" - self.setpoint_names = ("Current",) - self.setpoint_units = ("A",) - self.label = "voltage" - self._short_name = "vi_sweep" + # Only add delay code to lua script if greater than 0 + inter_delay = self.instrument.fastsweep_inter_delay.get_latest() + if inter_delay > 0: + script.append(f" delay({inter_delay})") + + script.extend( + [ + f" {channel}.measure.{meas}({channel}.nvbuffer1)", + "end", + "format.data = format.REAL32", + "format.byteorder = format.LITTLEENDIAN", + f"printbuffer(1, {steps}, {channel}.nvbuffer1.readings)", + ] + ) - if mode == "VIfourprobe": - self.unit = "V" - self.setpoint_names = ("Current",) - self.setpoint_units = ("A",) - self.label = "voltage" - self._short_name = "vi_sweep_four_probe" + return self.instrument._execute_lua(script, steps) - self.setpoints = (tuple(np.linspace(start, stop, steps)),) + def get_raw(self) -> npt.NDArray: + data = self._fast_sweep() - self.start = start - self.stop = stop - self.steps = steps - self.mode = mode + return data + + +class FastSweepSetpoints(Parameter[npt.NDArray, "Keithley2600Channel"]): + """ + A simple :class:`Parameter` that holds all the setpoints for a fastsweep + """ def get_raw(self) -> npt.NDArray: - if self.instrument is not None: - data = self.instrument._fast_sweep( - self.start, self.stop, self.steps, self.mode - ) - else: + if self.instrument is None: raise RuntimeError("No instrument attached to Parameter.") - return data + npts = self.instrument.fastsweep_npts() + start = self.instrument.fastsweep_start() + stop = self.instrument.fastsweep_stop() + return np.linspace(start, stop, npts) class TimeTrace(ParameterWithSetpoints): @@ -576,10 +622,64 @@ def __init__(self, parent: Instrument, name: str, channel: str) -> None: ) """Current limit e.g. the maximum current allowed in voltage mode. If exceeded the voltage will be clipped.""" + self.fastsweep_npts: Parameter[int, Self] = self.add_parameter( + "fastsweep_npts", + initial_value=20, + label="Number of fastweep points", + get_cmd=None, + set_cmd=None, + ) + """Parameter fastweep_npts""" + + self.fastsweep_start: Parameter[float, Self] = self.add_parameter( + "fastsweep_start", label="fastsweep start", get_cmd=None, set_cmd=None + ) + """Starting value of fastsweep. Can be current or voltage.""" + + self.fastsweep_stop: Parameter[float, Self] = self.add_parameter( + "fastsweep_stop", label="fastsweep stop", get_cmd=None, set_cmd=None + ) + """Stopping value of fastsweep. Can be current or voltage.""" + + self.fastsweep_inter_delay: Parameter[float, Self] = self.add_parameter( + name="fastsweep_inter_delay", + label="Fastsweep Inter Delay", + initial_value=0, + vals=vals.Numbers(min_value=0), + unit="s", + get_cmd=None, + set_cmd=None, + ) + """Time in seconds to wait between setting a target value and taking a measurement.""" + + self.fastsweep_setpoints: FastSweepSetpoints = self.add_parameter( + name="fastsweep_setpoints", + label="Fastsweep setpoints", + snapshot_value=False, + vals=vals.Arrays(shape=(self.fastsweep_npts,)), + parameter_class=FastSweepSetpoints, + ) + """Holds array of setpoints for doing a fastsweep. Can + be of units V or I depending on `Keithley2600Channel.fastsweep_mode`""" + self.fastsweep: LuaSweepParameter = self.add_parameter( - "fastsweep", parameter_class=LuaSweepParameter + "fastsweep", + vals=vals.Arrays(shape=(self.fastsweep_npts,)), + setpoints=(self.fastsweep_setpoints,), + parameter_class=LuaSweepParameter, + ) + """Performs buffered readout of desired sweep mode.""" + + self.fastsweep_mode: Parameter[Literal["IV", "VI", "VIfourprobe"], Self] = ( + self.add_parameter( + "fastsweep_mode", + initial_value="IV", + get_cmd=None, + set_cmd=self.fastsweep._set_mode, + vals=vals.Enum("IV", "VI", "VIfourprobe"), + ) ) - """Parameter fastsweep""" + """Parameter fastsweep_mode""" self.timetrace_npts: Parameter = self.add_parameter( "timetrace_npts", @@ -645,107 +745,6 @@ def reset(self) -> None: log.debug(f"Reset channel {self.channel}. Updating settings...") self.snapshot(update=True) - def doFastSweep(self, start: float, stop: float, steps: int, mode: str) -> DataSet: - """ - Perform a fast sweep using a deployed lua script and - return a QCoDeS DataSet with the sweep. - - Args: - start: starting sweep value (V or A) - stop: end sweep value (V or A) - steps: number of steps - mode: Type of sweep, either 'IV' (voltage sweep), - 'VI' (current sweep two probe setup) or - 'VIfourprobe' (current sweep four probe setup) - - """ - try: - # lazy import to avoid a geneal dependency on qcodes_loop - from qcodes_loop.measure import Measure - except ImportError as e: - raise ImportError( - "The doFastSweep method requires the " - "qcodes_loop package to be installed." - ) from e - # prepare setpoints, units, name - self.fastsweep.prepareSweep(start, stop, steps, mode) - - data = Measure(self.fastsweep).run() - - return data - - def _fast_sweep( - self, - start: float, - stop: float, - steps: int, - mode: Literal["IV", "VI", "VIfourprobe"] = "IV", - ) -> npt.NDArray: - """ - Perform a fast sweep using a deployed Lua script. - This is the engine that forms the script, uploads it, - runs it, collects the data, and casts the data correctly. - - Args: - start: starting voltage - stop: end voltage - steps: number of steps - mode: Type of sweep, either 'IV' (voltage sweep), - 'VI' (current sweep two probe setup) or - 'VIfourprobe' (current sweep four probe setup) - - """ - - channel = self.channel - - # an extra visa query, a necessary precaution - # to avoid timing out when waiting for long - # measurements - nplc = self.nplc() - - dV = (stop - start) / (steps - 1) - - if mode == "IV": - meas = "i" - sour = "v" - func = "1" - sense_mode = "0" - elif mode == "VI": - meas = "v" - sour = "i" - func = "0" - sense_mode = "0" - elif mode == "VIfourprobe": - meas = "v" - sour = "i" - func = "0" - sense_mode = "1" - else: - raise ValueError(f"Invalid mode {mode}") - - script = [ - f"{channel}.measure.nplc = {nplc:.12f}", - f"{channel}.source.output = 1", - f"startX = {start:.12f}", - f"dX = {dV:.12f}", - f"{channel}.sense = {sense_mode}", - f"{channel}.source.output = 1", - f"{channel}.source.func = {func}", - f"{channel}.measure.count = 1", - f"{channel}.nvbuffer1.clear()", - f"{channel}.nvbuffer1.appendmode = 1", - f"for index = 1, {steps} do", - " target = startX + (index-1)*dX", - f" {channel}.source.level{sour} = target", - f" {channel}.measure.{meas}({channel}.nvbuffer1)", - "end", - "format.data = format.REAL32", - "format.byteorder = format.LITTLEENDIAN", - f"printbuffer(1, {steps}, {channel}.nvbuffer1.readings)", - ] - - return self._execute_lua(script, steps) - def _execute_lua(self, _script: list[str], steps: int) -> npt.NDArray: """ This is the function that sends the Lua script to be executed and diff --git a/tests/drivers/test_keithley_26xx.py b/tests/drivers/test_keithley_26xx.py index 5d935728af8..18bd8bbd4ca 100644 --- a/tests/drivers/test_keithley_26xx.py +++ b/tests/drivers/test_keithley_26xx.py @@ -1,9 +1,12 @@ from collections import Counter +from typing import cast +from unittest.mock import patch import numpy as np import pytest from qcodes.instrument_drivers.Keithley import ( + Keithley2600Channel, Keithley2600MeasurementStatus, Keithley2614B, ) @@ -176,3 +179,60 @@ def test_setting_measure_current_range_disables_autorange(smus) -> None: some_valid_measurerange_i = smu.root_instrument._iranges[smu.model][2] smu.measurerange_i(some_valid_measurerange_i) assert smu.measure_autorange_i_enabled() is False + + +def test_fast_sweep_parameters(smus) -> None: + for smu in smus: + smu = cast("Keithley2600Channel", smu) + + start_val = 0.0001 + stop_val = 0.001 + npts = 50 + + # Test IV mode + smu.fastsweep_mode("IV") + assert smu.fastsweep.unit == "A" + assert smu.fastsweep.label == "current" + + # Test VI mode + smu.fastsweep_mode("VI") + assert smu.fastsweep.unit == "V" + assert smu.fastsweep.label == "voltage" + + # Test VIfourprobe mode + smu.fastsweep_mode("VIfourprobe") + assert smu.fastsweep.unit == "V" + assert smu.fastsweep.label == "voltage" + + smu.fastsweep_start(start_val) + assert smu.fastsweep_start() == start_val + + smu.fastsweep_stop(stop_val) + assert smu.fastsweep_stop() == stop_val + + smu.fastsweep_npts(npts) + assert smu.fastsweep_npts() == npts + + # Test the setpoints (fastsweep_setpoints) + setpoints = smu.fastsweep_setpoints() + expected_setpoints = np.linspace(start_val, stop_val, npts) + np.testing.assert_array_almost_equal(setpoints, expected_setpoints) + + +def test_fastsweep(driver) -> None: + smu = driver.smua + + # Configure the fastsweep + smu.fastsweep_mode("IV") + smu.fastsweep_start(0.0) + smu.fastsweep_stop(1.0) + smu.fastsweep_npts(10) + + # Mock _execute_lua to return fake measurement data + fake_data = np.linspace(0, 0.001, 10) # Fake current readings + + with patch.object(smu, "_execute_lua", return_value=fake_data): + result = smu.fastsweep() + + assert len(result) == 10 + np.testing.assert_array_equal(result, fake_data)