diff --git a/examples/cancer-vitamins.ipynb b/examples/cancer-vitamins.ipynb
index 1cba027..fdc88e3 100644
--- a/examples/cancer-vitamins.ipynb
+++ b/examples/cancer-vitamins.ipynb
@@ -217,7 +217,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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581m/fj1nz561qWzu4r81Kmn6E8JnKIrtzW8REbYf56kmvaCgIFquIdvU1NTquJCQEKvHiqK0uc1kMtn1+lF2vNGITv4Hnj592lxjayklJaXT86elpbFnzx7ef/993nvvPW688UYee+wxNm/e3Oq9eor/1qik6U8I4SKJiYkcPnzYKqx27tzp9HmzsrL46quvqK2tNW/75JNPzE2HjrjgggswmUxs3ry5zf0jR47k22+/JSMjg/79+1vdbA3EiIgIZsyYwYoVKygsLGTLli3s2rXLofK6gv8GVbAsniiEP+rRQ7tOqiPh4dpxnpKTk8PRo0d59NFH2b9/P08//TTvvPOO0+edPXs24eHhzJkzh2+++YZNmzZx880385vf/MbcP2WvjIwM5syZw7x583jttdcoKSmhsLCQtWvXArBgwQJOnDjBlVdeybZt29i/fz8bN27kf//3fzEajZ2ef/Xq1Tz33HN88803/PDDD7z44otERETQu3dvh8rrCn4cVFKjEsIfpadrF/Nu397+zdUX+3YmKyuLv/71rzz99NMMGzaMrVu3snjxYqfPGxkZycaNGzlx4gRjxowhLy+PSZMm8dRTTzl13pUrV5KXl8eNN97IoEGDmD9/vrnW1qtXLz755BOMRiNTpkzhggsuYNGiRcTHx1v1r7UnPj6eZ599lgkTJjB06FDef/993njjDbp37+5UmZ2hqC0bZt2surqauLg4qqqqiI2NdfxEux+BnXdA37nwk1WdHi6EcJ36+npKSkro06cP4Z1Vj0SX1dHviT1Z4Mc1Kmn6E0KIrsCPg0qa/oQQgaeoqMhqaHnLW1fkv8PTZfFEIUQAGj16tEtGHAYS/w0qmetPCBGAIiIi6N+/v7eL4VMCoOlPalRCCBHI/Deo9KY/qVEJIURA8/+mPy/XqIwmI0VlRVTUVJASk0J2ejaGIINXyySEEIHEf4NKr1EZz4DJCF4Ih4LiAhZuWMjB6oPmbamxqSyftpzcrFyPl0cIIQKR/zb9BVsM0/RCraqguIC8tXlWIQVwqPoQeWvzKCgu8HiZhBAiEPlvUAWFgnKuQujhoDKajCzcsBCV1pN66NsWbViE0dT5vFpCCN+xdOlShg8f7rXXVxSF1157zWuv76v8t+lPUbTmv8aTHh9QUVRW1KomZUlFpby6nKKyInIycjxXMCH8RW05NBxtf394T4hMdelLzpgxg6amJjZs2NBqX1FRERdeeCFfffUVN998s3n73LlzOXXqlMfCo6Kigm7dunnktfyJ/wYVaM1/jSc9XqOqqKlw6XFCdCnGBtg4BuqPtH9MeDLMLAVDmMte9tprr+Xyyy/n4MGDpKZah+CqVasYPXo0Q4cOddnrOSI5Odmrr++r/LfpD7w2O0VKTOeLj9lznBBdSlAoRKbT/p+fIIhM045zoUsuuYTExERWr15ttf306dO8+uqrXHvttVZNf0uXLuX555/n9ddfR1EUFEWhsLAQgD/84Q8MHDiQyMhI+vbty91332210KJ+nn/+85+kp6cTHR3NjTfeiNFo5NFHHyU5OZmePXvy4IMPWpXFsumvtLQURVEoKChg4sSJREZGMmzYMLZs2WI+/sCBA8yYMYNu3boRFRXFkCFDePvtt136/80X+H+NCjze9Jednk1qbCqHqg+12U8FkBiZyE9Tf+rRcgnhNaoKxjrbjz//Lvjo0nZ2mrT9tp7PEKl1BXQiODiYa665htWrV3PXXXehnHvOq6++itFo5Morr+SJJ54wH7948WKKi4uprq5m1SpthYaEhAQAYmJiWL16Nb169WLXrl3Mnz+fmJgY/u///s/8/P379/POO++wYcMG9u/fT15eHj/88AMDBw5k8+bNfPrpp8ybN4/Jkyczbty4dst911138fjjjzNgwADuuusurrzySvbt20dwcDALFiygsbGRjz76iKioKHbv3h2Q8wEGRlB5uEZlCDKwfNpyLl97ebvHHK07Sr8n+8lQddE1GOtgrQv/QLYbYm2YdRqCbVu5dt68eeZl1XNycgCt2e/yyy8nLi7O6tjo6GgiIiJoaGho1ST3xz/+0Xw/IyODxYsXs2bNGqugMplM/POf/yQmJobBgwczceJE9uzZw9tvv21e4feRRx5h06ZNHQbV4sWL+eUvfwnAfffdx5AhQ9i3bx+DBg2irKyMyy+/nAsuuACAvn372vT/wd8ESNOf52enuGzQZaREd9y0J0PVhfAtgwYN4qc//Sn//Oc/Adi3bx9FRUVce+21dp3nlVdeYcKECSQnJxMdHc0f//hHysrKrI7JyMggJibG/DgpKYnBgwdbLV6YlJREZWVlh69l2W+WkqL9zdGfc8stt/CnP/2JCRMmcO+99/L111/b9T78RWDUqDy4JpU+E0XRgSIqTlcQFhRGTHgMx+qOtTpWRUVBYdGGRczMnCkzVojAZYjUajb2UFV4/yI49RWoRlAMED8MJm+2qSnP6rXtcO2113LzzTfz9NNPs2rVKvr168dFF11k8/O3bNnC7Nmzue+++5g6dSpxcXGsWbOGP//5z1bHhYSEWD1WFKXNbSaTqcPXs3yO3lypP+e6665j6tSpvPXWW7z77rvk5+fz5z//2WrkYiDw7xpVsGcHUxQUF5CxPIOJz0/knsJ7tI0KbYaUznKouhABS1G05jd7biHRMOwhLaRA+znsIW27PeexJ9SAWbNmERQUxEsvvcS//vUv5s2bZw6AlkJDQzEara+H/PTTT+nduzd33XUXo0ePZsCAARw4cMCh/22ukJaWxg033EBBQQG33347zz77rNfK4i7+XaMK8dziifpMFC0HTzQYG2x6vgxVF6INKVMgYQyc2Kb9TJni9peMjo7miiuuYMmSJVRXVzN37tx2j83IyGDjxo3s2bOH7t27ExcXx4ABAygrK2PNmjWMGTOGt956i/Xr17u93G1ZtGgR06dPZ+DAgZw8eZJNmzaRlZXllbK4k5/XqDzT9NfRTBS2kqHqQrRBUWD4QxCbpf20s3bkqGuvvZaTJ08ydepUevXq1e5x8+fPJzMzk9GjR5OYmMgnn3zC//zP/3Drrbdy0003MXz4cD799FPuvvtuj5S7JaPRyIIFC8jKymLatGkMHDiQv/71r14pizspqqo6/tfXAdXV1cTFxVFVVUVsbKxzJyv+C3x5O2TMhp++6JoCtqGwtJCJz0906LkKCqmxqZQsLJE+KhEw6uvrKSkpoU+fPoSHh3u7OMJHdfR7Yk8W+HeNKsQzw9MdbbZT0L4dLpu2TEJKCCEc5N9BpQ+mcHPTn63NdomRiVaPk6KTWDdrnVxHJYQQTvDzoPLMYAp9Jgq9htSSgkJabBoHbz3Ipjmb6B3XG4CVv1gpISWEEE7y76DyUNOfPhNFWyyb90KDQ8nJyGFcqnaV+f6T+91aLiGE6Ar8O6jMTX/uH56em5XLulnriA61niYmNTa1VfNeZvdMAPYe3+v2cgnhTR4eiyX8jKt+P/w8qDw7119uVi5je40F4PqR17NpziZKFpa0at4b2H0gAHuO7/FIuYTwNH22hLo6OyaiFV2O/vvRckYOe/n5Bb8WNSpV9cg1GMXHigGYN2KeuYmvJT2opEYlApXBYCA+Pt4851xkZGS7szuIrkdVVerq6qisrCQ+Ph6DwblRz/4dVHqNSj0LpkaXLrLWlpNnTlJxWhuqPjhxcLvH6UFVcbqCmoYaYsJi2j1WCH+lzyje2aSqouuKj493yWKQfh5UFlP7nz3t9qD69ui3AKTFpnUYPvHh8fSM6kllbSV7j+9lVK9Rbi2XEN6gKAopKSn07NnTatFAIUBr7nO2JqXz76AKCgZDBBjPaM1/Yd3d+nK7j+4GYEjPIZ0eO7D7QAkq0SUYDAaX/UESoi3+PZgCPDqg4ttKrUY1uEf7zX46feSfDKgQQgjn+H9QhXhuiPruY/bVqEAGVAghhLP8P6i8UKMakihBJYQQniJBZSPLEX9ZiZ2v92LZ9CcXRQohhOP8P6g81PSnD6RIi00jNqzz5Un6dutLkBLE6cbTHD592K1lE0KIQOb/QeWhGpU+NL2j66cshQWHNU9Ou20lhaWFGE3GTp4lhBCiJf8PKr1G5cYZ1I0mI+/ufxeA2NBYmwKnoLjAvI7VA0UPMPH5iWQsz6CguMBt5RRCiEDkVFA9/PDDKIrCokWLXFQcB7h5OfqC4gIylmfwn+L/APBq8audBk5BcQF5a/OoN9ZbbT9UfYi8tXkSVkIIYQeHg2rbtm387W9/Y+jQoa4sj/30GdTd0PSnB87B6oNW2zsKHKPJyMINC1FpPYBC37ZowyJpBhRCCBs5FFSnT59m9uzZPPvss3Tr1s3VZbJNbTmc2AFNVecel2iP9VvdwY6f3wlHA6eorKhVsLV8bnl1OUVlRU6VTwghugqHgmrBggX88pe/ZPLkya4uj22MDbBxDGwYBd//Vdt28HXtsfk2RjvOQY4Gjt4v1RlbjxNCiK7O7rn+1qxZw44dO9i2bZtNxzc0NNDQ0BwY1dXV9r5ka0GhEJkO9UcBU1sHQGSadpyDHA2clJgUm55n63FCCNHV2VWjKi8vZ+HChfz73/8mPDzcpufk5+cTFxdnvqWlpTlUUCuKAkMfoO2QQts+9AGn1qdyNHCy07NJjU01L1HfkoJCWmwa2enZDpdNCCG6EkW1Y9qE1157jcsuu8xqpmSj0YiiKAQFBdHQ0NBqFuW2alRpaWlUVVURG9v5hbPtUlXYOA5ObMcqsBQDdBsJUz93KqiMJiMZyzM4VH2ozX4qBYXU2FRKFpZgCLJ+z/ogDMDquXp4tVy6Xgghuprq6mri4uJsygK7alSTJk1i165d7Ny503wbPXo0s2fPZufOnW1O9R8WFkZsbKzVzSXaq1WpRqdrUwCGIAPLpy1v+6XPBc6yactahRRoS9avm7WO82LPs9qeGpsqISWEEHayK6hiYmI4//zzrW5RUVF0796d888/311lbF/KFIizeF3FAAljtO0uoAeOQbEOI1sCJzcrl9KFpUzMmAjAjaNvpGRhiYSUEELYyb8XTlQUGHInfHqV9thFtSlLlwy8BKOqDUFf+YuVDEocRHZ6dps1qZYMQQaGJg1lU+kmokOjbXqOEEIIa04HVWFhoQuK4YTUS5vvdxvhstqUrryqHIDw4HB+O/q3KHaGYHJ0MgBHao+4tFxCCNFV+P9cf8EREBSi3R90m0trUwClp0oByIjPsDukoDmoZAZ1IYRwjP8HFUBYD+1nXOcLGtrLMqgcIUElhBDOCYygCj03jVPTKZef2hxUcRkOPV+CSgghnBMYQRUSr/1sPOnyU5dWlQLO16iO1h2ViWiFEMIBgRFUeo3KHUHlZNNfj8geKCiYVBNH6466rmBCCNFFBEhQxWs/G0+5/NR6UPXp1seh5wcHBZMYlQjAkdMy8k8IIewVIEHlnhpVo7GRQ9WHAMdrVCD9VEII4YwAC6pTLj1teVU5KioRwREkRiY6fB4JKiGEcFxgBJWbBlOUnCoBHL+GSidBJYQQjguMoHJT05+zAyl0SVFJgASVEEI4IkCCKl776eLrqFwVVOYaVa0ElRBC2CtAgsq3a1Tm+f5k1J8QQtgtwILqlEtP6/IalTT9CSGE3QIkqOK1nz5eo5KgEkII+wVIUJ2rUZka4OwZl5yy4WwDP9b8CLguqE7Wn6ThbIOzRRNCiC4lMIIqOBqUc2/FRQMqyqtdcw0VQLfwboScW4pE1qUSQgj7BEZQKUEuv5bK2XWoLCmKQlK0NkRdBlQIIYR9AiOowKUDKowmIxv3bQQgJjTGJbOeSz+VEEI4JoCCKl776WSNqqC4gIzlGTy+5XEAtv64lYzlGRQUFzh1XgkqIYRwTAAFlfPXUhUUF5C3No+D1Qetth+qPkTe2jynwio5SoJKCCEcEThBZe6jOuXQ040mIws3LERFbbVP37ZowyKHmwGlRiWEEI4JnKByskZVVFbUqiZlSUWlvLqcorIih86vD6aQaZSEEMI+gRdUDg5Pr6ipcOlxLck0SkII4ZgACqp47aeDNaqUmBSXHteSNP0JIYRjAiionGv6y07PJjU2FYW2r5lSUEiLTSM7Pduh80tQCSGEYwInqJwcTGEIMrB82vI29+nhtWzaMgxBBofOr89uUdtUyzvfv+OSa7OEEKIrCJygcsHw9NysXNbNWkdcWJzV9tTYVNbNWkduVq5D5y0oLuD8leebH//ipV+45NosIYToCgIoqOK1n07O9ZeblcvVQ68GYMbAGWyas4mShSVOhZS7rs0SQoiuIICCynWLJx6qOQTA1H5TycnIcbi5z93XZgkhRFcQeEHVVA1O/uEvryoHIC0uzanzuPvaLCGE6AoCKKjim+83VTl1qvLqc0EV61xQufvaLCGE6AoCJ6iCQiA4SrvvRPNfw9kGKmsrAedrVO6+NksIIbqCwAkqaB6i7sSACr1/Kjw4nO4R3Z0qjruvzRJCiK4gsILKBQMq9P6p1NhUpxdMtLw2q2VYueLaLCGE6AokqFpwVf+UTr8267zY86y2O3ttlhBCdBUBFlTx2k8nVvl11Yg/S7lZuZQuLOXvl/wdgG7h3Zy6NksIIbqSAAsq52tU+nByV9WodIYgA9P6TwOgprGGICWw/tcLIYS7BNZfSyfn+4Pmpr/U2FTny9NCj8geAJw1naW6odrl5xdCiEAUWEHlg31UliJCIogK0YbQH6s75vLzCyFEIJKgasEdfVSW9FqVBJUQQtgmwIIqXvvp4HVUZ5rOcPzMccA9NSqQoBJCCHsFWFA5V6PSB1JEhUQRHx7vokJZk6ASQgj7BFhQxWs/HRxMYTmQwtmLfdsjQSWEEPYJsKByrkbl7v4pkKASQgh7BW5Qqa3XgOqMO0f86SSohBDCPoEVVPp1VOpZMNbZ/XR3XexryRxUZySohBDCFsHeLoBL1JZDw9FztSgDYITKjyA8Sdsf3hMiO7+A11yjkqY/IYTwGf4fVMYG2DgG6o9Yby/8RfP98GSYWQqGsA5PZTlzurtIUAkhhH38v+kvKBQi02n/rQRBZJp2XAeMJiM/nPwB0FbcNTq5nH179KA6WnvULecXQohA4/9BpSgw9AHA1M4BJm1/B8PNC4oL6L2sN7VNtQDM++88MpZnUFBc4PLi6kF14swJt4WhEEIEEv8PKoCUKZAwBpQWCxAqBm17ypR2n1pQXEDe2jzzyr66Q9WHyFub5/Kw0lcNVlE5We/4VE9CCNFVBEZQ6bUqtUUNRTV2WJsymows3LAQldZD2fVtizYscmnNJ8QQQlxYHCD9VEIIYYvACCporlWZl3xXOq1NFZUVmYekt0VFpby6nKKyIpcWVQZUCCGE7QInqMx9VXrtSO20b6qipsKmU9t6nK0kqIQQwnaBE1Sg1Z6iMrT7IbEd1qYAUmJSbDutjcfZSoJKCCFsF1hBpSjQ7zrtfljPDmtTANnp2doEtLR9nIJCWmwa2enZLi2mBJUQQtgusIIKIGWq9tN4ptNDDUEGlk9b3uY+PbyWTVuGIcjQ5jGOkqASQgjbBV5QhffUfjZU2jQxbW5WLutmrWtVq0qNTWXdrHXkZuW6vIiJkYmABJUQQtjC/6dQailMCwFMTdBU1bxGVQem9JtiHo7+3P88R99ufclOz3Z5TUonNSohhLBd4AVVcAQEx8DZGqivtCmo9CHqsWGxzBsxz80FlKASQgh7BF7THzTPml5fadPhelC5czJaSxJUQghhO7uCauXKlQwdOpTY2FhiY2MZP34877zzjrvK5ji9n6rljOrtMK/s68Z1qCxJUAkhhO3sCqrU1FQefvhhtm/fzhdffMHFF1/MzJkz+fbbb91VPsdYDqiwgbdqVFUNVTQZmzzymkII4a/sCqoZM2bwi1/8ggEDBjBw4EAefPBBoqOj+eyzz9xVPseYa1S+GVTx4fEEKdr/+uNnjnvkNYUQwl85PJjCaDTy6quvUltby/jx49s9rqGhgYaGBvPj6upqR1/SdmF2BlWN+5egt2QIMpAQkcCxumMcqztGcnSyR15XCCH8kd2DKXbt2kV0dDRhYWHccMMNrF+/nsGDB7d7fH5+PnFxceZbWpoHwsDOwRSeWNm3JemnEkII29gdVJmZmezcuZPPP/+c3/3ud8yZM4fdu3e3e/ySJUuoqqoy38rLy50qsE3sHEzh6aY/kKASQghb2d30FxoaSv/+/QEYNWoU27ZtY/ny5fztb39r8/iwsDDCwsKcK6W97BhMUdtYa17AUIJKCCF8j9PXUZlMJqs+KJ9gx2AKvTYVExpDXHicO0tlpUeEFlRHa4967DWFEMIf2VWjWrJkCdOnTyc9PZ2amhpeeuklCgsL2bhxo7vK5xh9MEXjSTA2giG03UO90ewHUqMSQghb2RVUlZWVXHPNNVRUVBAXF8fQoUPZuHEjP//5z91VPseEJYBi0JaibzgGkb3aPdTrQXVGgkoIITpiV1A999xz7iqHaylB2uS09Ye1ARU2BJWnhqbrEiISANh1ZBeFpYVunQRXCCH8WWDO9Qc291OVV3t+aHpBcQH/997/AbCrchcTn59IxvIMCooLPFYGIYTwF4EfVJ2M/PN0019BcQF5a/NaNfkdqj5E3to8CSshhGghcIPKxtkpPBlURpORhRsWmte+sqRvW7RhEUaT0e1lEUIIfxG4QWXj7BR6019anPv7qIrKiszB2BYVlfLqcorKitxeFiGE8BcBHFSdz05R11THiTMnAM/UqCpqKlx6nBBCdAVdIKjar1Edqj4EQFRIFHFh7r/YNyUmxaXHCSFEVxD4QdXBYArz0PS4NBRFcXuRstOzSY1NRaHt11JQSItNIzs92+1lEUIIfxG4QWXDYApPD003BBlYPm05QKuw0h8vm7ZMrqcSQggLgRtUERaDKdTWo+zAO7NS5Gblsm7WOs6LPc9qe2psKutmrSM3K9djZRFCCH8QuEEVlqj9NDVAU+vFGo0mI58f/ByAs8azHh0SnpuVS+nCUi7NvBSAqy+4mpKFJRJSQgjRhsANquBICI7W7rdo/isoLiBjeQb/3ftfAF7c9aLHZ4YwBBkYljwMgKjQKGnuE0KIdgRuUEGbAyr0mSFaXs/kjZkhEiO1Wt/ROlnqQwgh2hPYQdViQIWvzQwhS30IIUTnAjuoWlxL5WszQyRGaTUqCSohhGhfgAeVPvJPm53C12aG0GtUssqvEEK0z671qPxGbTk0HNUWTgQ49S2c2MEApYYRYVBphENn23+6p2aG0Puojp85jkk1EaQE9vcGIYRwROAFlbEBNo6xnuOvfC2Ur2U0sCMdKs5CRik0tuiqUlBIjU312MwQ3SO7A2BSTZw8c9L8WAghRLPA+wofFAqR6bT31lQUys+2HVLg2ZkhQg2h5jkGpZ9KCCHaFnhBpSgw9AHA1PZuVBqH/JGokCir7d6aGcLcTyVD1IUQok2BF1QAKVMgYQyt3p5igIQx/Gzs/UxImwDA9SOvZ9OcTV6bGUJG/gkhRMcCM6jaq1WpRm27onCwRhum/qshvyInI8drM0PIyD8hhOhYYAYVWNSqdEHa45QpqKpKWVUZAGmx7l/ZtyNy0a8QQnQscIPKXKvSmcy1qaqGKk43ngY8swR9R/Qh6hJUQgjRtsANKtBqVSHnVu6NytAeA+VV2jpU3SO6ExkS6aXCaWQwhRBCdCywg0pRIPFn2v3ECdpjmhdM9HZtCqRGJYQQnQnsoAJI/Kn2MyjEvMlX+qdAalRCCNGZwA+qyHNhVFtm3qQ3/aXHpXujRFZkeLoQQnSsCwTVuTCyDCq96c+XalQyPF0IIdoU+EEVdS6o6spB1eZN0pv+fKJGda6PqrapljNNZ7xcGiGE8D2BH1QR5wEKmBq0GdXxrcEUsWGxhJzrP5PmPyGEaC3wg8oQChHJ2v3aMkyqybx4oi80/SmKIhf9CiFEBwI/qKC5n6qujMraShqNjQQpQfSK6eXdcp0jQSWEEO3rGkGl91PVlptH/KVEpxBiCOngSZ6jj/yTIepCCNFa4C2c2BZ9iHpdGeWqdt8X+qd0UqMSQoj2dZGgah6iXtaoBZQvjPjT9YiQIepCCNGerhFUFkPUy/Gda6h0ctGvEEK0r4sFVRnlZ7X7vhRUMo2SEEK0r2sEld5HdaaCirpSwLea/mRiWiGEaF/XGPUXlghBYQCcrT0A+OZgCqlRCSFEa10jqBTF3PwX1lAJ+FiNSvqohBCiXV0jqAA1IhWAtGAICQohITzByyVqpteojtcdx6SavFwaIYTwLV0iqAqKC3j1wOcApAdDk6mJPiv6UFBc4OWSafSgMqpGTtWf8m5hhBDCxwR8UBUUF5C3No/iujoA0s9NRnGo+hB5a/N8IqxCDaHEhsUC0vwnhBAtBXRQGU1GFm5YiIpK2VltW9q5cY4q2pIfizYswmgyeqmEzfSLfl/a9RKFpYU+USYhhPAFAR1URWVF5pnSy88FVbrFgHwVlfLqcorKirxQumYFxQXmct63+T4mPj+RjOUZPlHbE0IIbwvooKqoqSA1GEaEQbSibcs491i/nResHectetNko6nRarsvNU0KIYQ3BfQFv72iurMtDZIt3mW0AXZYjEyvOAt7o7p7vnBYN022pKKioLBowyJmZs7EEGTwQgmFEML7ArpG9bPeF3NYDcHYOgcAMKpwRA3hZ70v9mzBzrFsmmyLrzRNCiGENwV0UBkMwZwe9AcMSjv7FbT9Bu9ULG1tcvRm06QQQnhbQAcVwM/G3s+JiH6cbVGrOqvCiYh+/Gzs/d4pGJASk+LS44QQIhAFfFChKCSMe5rgFrWqYAUSxj2tTa/kJdnp2aTGpqLQdhkUFNJi08hOz/ZwyYQQwncEflABpEzhcGiadV9VwhhImeK1IgEYggwsn7YcoFVY6Y+XTVsmAymEEF1a1wgqReE/IcOt+6qGPuDV2pQuNyuXdbPWkRydbLU9NTaVdbPWkZuV66WSCSGEb+gaQQVsqDWxs95iQ8+LvFaWlnKzcildWGquRa371TpKFpZISAkhBF0oqA5UlbH4GKh6E1vtD94tUAuhwaEkRScB0LdbX2nuE0KIc7pEUKmqSumpUj44A/WxQ7SN1Xu8W6g26M1/h08f9nJJhBDCd3SJoDpZf5KaxhoAQuIv0DZWf+fFErVNgkoIIVrrEkFVeqoUgKSoJILj9RqVBJUQQviDLhVUGfEZEJupbfTFoIrSgupI7REvl0QIIXxHlwiqA6cOAHpQDdI2Vu8BtZ1JAL1EalRCCNGaXUGVn5/PmDFjiImJoWfPnlx66aXs2eN7gxJasqpRxfQHJQiaqqDet2ou+qg/CSohhGhmV1Bt3ryZBQsW8Nlnn/Hee+/R1NTElClTqK2tdVf5XKK0qhSA3nG9wRAOUX20HT7W/Cc1KiGEaM2uacM3bNhg9Xj16tX07NmT7du3c+GFF7q0YK5kVaMCrZ/q9H4tqJJyvFWsViSohBCiNaf6qKqqqgBISEhwSWHcxaqPCqz7qXyIHlRVDVWcaTrj5dIIIYRvcHghJpPJxKJFi5gwYQLnn39+u8c1NDTQ0NBgflxdXe3oSzrkVP0pqhq0QO0d31vbaA4q32r6iwuLI8wQRoOxgSO1R5qDVQghujCHa1QLFizgm2++Yc2aNR0el5+fT1xcnPmWlpbm6Es6RG/2S4xMJDIkUtvoo0GlKIo0/wkhRAsOBdVNN93Em2++yaZNm0hNTe3w2CVLllBVVWW+lZeXO1RQR7Xqn4LmoKo9AGd9q4lNgkoIIazZ1fSnqio333wz69evp7CwkD59+nT6nLCwMMLCwhwuoLNa9U8BhPWA0G7QeBJqvoduQ71TuDboQXXktG8NnRdCCG+xK6gWLFjASy+9xOuvv05MTAyHD2vf+uPi4oiIiHBLAZ1hNBkpKisCIEgJwmgyYjjzIzQchchULah+fBvUs81PCu+p7fOSpCi5lkoIISzZFVQrV64EICcnx2r7qlWrmDt3rqvK5BIFxQUs3LCQg9UHAXjl21fYVv4x3553mvCzVc0HfrVEu+nCk2FmKRi8UwuUpj8hhLBmd9OfPygoLiBvbR4q1uUtqT7E13EwJlxBoa33EgSRaRAU6pmCtsEcVLUSVEIIAQE415/RZGThhoWtQgpABe45TjshBWDy+hL1UqMSQghrARdURWVF5ua+tmysg631oLZ864oBEsZAyhQ3l7BjElRCCGEt4IKqoqai02PuPg4KJuuNqtHrtSmwDip/aWoVQgh3CrigSolJ6fSYd+ugOirTYkuQT9SmoHkG9fqz9VQ3eHYWDyGE8EUBF1TZ6dmkxqai0HbNSEEhLTaNqNF/sdjq/b4pXWRIJLFhsYA0/wkhBARgUBmCDCyftrzNfXp4LZu2DEOv6c3LfQRH+0RtSqdfSyUr/QohRAAGFUBuVi7rZq0jrMW1UKmxqaybtY7crFyt9nT+H7Udpkatj8pHyIAKIYRo5vDs6b4uNyuXxMhEDtYcZOlFS7ko4yKy07MxBBmaD+o7F3bcCk3VUPUtdBvmtfJakqASQohmAVmjAm0wwqGaQwDcOOZGcjJyrEMKtCXpE0Zp949v83AJ2ydBJYQQzQI2qEpOlqCiEhMaQ4/IHu0f2H2s9vOEBJUQQviigA2qfSf2AdA/oT9KR6P5EsZoP6VGJYQQPilgg2r/yf2AFlQd6n4uqE7tAmO9m0tlm8TIRAB2H91NYWkhRpPvDPQQQghPC9ig0mtU/br16/jAyDRtaQ/1LJzc6f6CdaKguIDr3rgOgANVB5j4/EQylmdQUFzg5ZIJIYR3BHxQdVqjUhRIONdPdXyrm0vVMX3W98raSqvth6oPkbc2T8JKCNElSVDVlkPkedr9HzfAiR3Wt7r2J7h1pY5nfde2LdqwSJoBhRBdTkBeR9VkbOJAlbYEfYdBZWyAjWOg/twMEBXvaDdLHlpIsbNZ31VUyqvLKSorIicjx61lEUIIXxKQQVVWVcZZ01nCg8M7nqQ2KBQi06G+Ery8kGKbs76fSoM666H1n35eT+wJ7X6PHpCe7vaiCSGEVwVkUFkOpAhSOmjdVBRtMtrCae0c4LnJalNiUqyD6XQyvLIejNY1ubv+Dnedux8eDnv2SFgJIQJbQAdVp/1ToE1GmzAGTnyBVa1KMUC3kR6brLY32fDUXjgbbvNz6uvh2DEJKiFEYAvIwRQ2X0MFzbWqlk1/Hl5I8eQJg10hpSsuhh07oKzMDYUSQggfENA1qk6vodKlTIG4IdrEtODx2pQzrr5a+ynNgEKIQBWQNSq7mv5AqzWNeLz5sQdrU2VlWo2ouNi58+jNgEIIEWgCrkZlUk38cPIHwI6gAkiZCqHdofE4hCd5pDZVVgaZmVrIuIJl2MmIQCFEoAiooDKajPxn939oMDYQpATRK6aX7U9WFBhwA3z7IBgiPFKbOnbMdSEFzc2AIE2BQojAETBNfwXFBWQsz+CK/1wBaDWr/k/2t2/aoUG3AgrUlkLdIbeU01OkKVAIESgCIqj0OfJazuxg9xx5Yd2bZ1OveNfFpRRCCOEIvw8ql8+RlzJV+1mx0VVFFEII4QS/Dyp75sjrVG05RGVo9398G45t88oEtS2Nmu3YrOlyjZUQIhD4/WCKNufIc+S4lhPUnq2Bd8daH+OhCWpbMiXvIDw81+6BF3KNlRAiEPh9jarDSWftOU6foLbd/yWem6C2pZPBxezZA9u3a7cXX7Tv+TKwQgjhz/y+RpWdnk1qbCqHqg+12U+loJAam0p2enbHJ/LQBLVlZc2hUVEBBgMYO+o+Cz5DhXEXaWkq6ememc5JCCF8id8HlSHIwPJpy8lbm9dqn4L2h33ZtGUYggydn8w8Qe0OwCI9XDSlki0X+IaGQkEBpKRA49lGxr+cSUN0OcfPHKdHpDazeo8eWnOeK6/BEkIIX+X3TX8AuVm5rJu1jtjQWKvtqbGprJu1jtysXNtOZJ6gtkUVx0VTKtlygW9joxZSI0fCT8aGkpzaBGhrbOnS0zE3BdraDKgPrJDBFUIIfxMQQQVaWE3pp9V4rjr/KjbN2UTJwhLbQ0qn16qwCKWEMV6boDY9ThsBceDUAevt6VqYZWXZdp6rr4ZRo7RbZqaElRDCfwRMUAEUH9Mmu7t66NXkZOTY1tzXUlvLfvT/rceW+2ipd1xvwLpG5SwZXCGE8CcBE1RNxib2Ht8LwODEwc6dzFyrOuf0D86dzwnmGlXVgU6OFEKIwBQwQbXvxD6aTE1Eh0ab/7g7TFFg+EMQcZ72uPQFUE3OF9IBndWo9IEVQggRqPx+1J/u26PaoodZPbJQXNFMlzwZJm+Cd0ZCXTns+zt0b3kBcE+ITHX+tTqgh257QaUPrNCb8oqLrWdRb4++JIgsByKE8HUBE1S7j+4GYEjPIa45obEB3suGs6e1x9t+1/oYD8xU0Tteq1F11PSXnm5/2MisFUIIfxEwTX96jWpwDyf7p3TmmSraq53ZP1OFLc104eHacTq9RlVZW8mZpjM2v5atZGCFEMLXBUxQubxG1dboPyv2z1ShN9MNH649vuOO5mmR9FvL2k238G5EhUQBUF5d7tBbEUIIfxYQTX9Nxib2HNsDuGDEnyXzTBVfYBVYTsxUERMDX3+t3f/tbyEjo+PjFUUhPS6d4mPFrN65min9ppCdnt3u0HuZtUIIEWgCIqj2n9xPk6mJqJAo50f8WWpv/j87ZqqwnNsP4N13wWTSAurECQgK6rh/qKC4gB9OasPj8z/OJ//jfFJjU1k+bXmbFzNbDq6wd2AFyOAKIYTvCYig+rby3Ii/xCyCFBe3ZrZVq0oYbVNtqqO5/UpLtVkiOhrMoK9c3HKyXX3l4vamh7J3cIVlmMngCiGEr/H7Piqjychbe98CoHtEd9tX8rVVW31VCWPh5JedLqpoy9x+7Q1mcPnKxTaSwRVCCF/j10FVUFxAxvIMVn21CoCN+zeSsTyDgmLHVsRtV8+LQLGofO77K2wY1eI2RhvS7iIuXblYCCH8mN8Gld4s1vKPud4s5tKwMoRBdN8ODnD9ooquWLlYZq0QQgQCvwwqjzeLKQqMWtHBAa5ZVNGSK1YutlwORJYEEUL4K78cTGFPs1hORo5rXlQfVHFyhzbqT+eiRRVbctXKxc7MWgEyuEIIRxlNRorKiqioqSAlJqXDy0pEx/wyqFzRLGa3joaq97lGG1xhKbwn4Pg8gJYrFysoVmFl98rFTtAHV0hQCdE5PZxe/+51/r3r3xytO2red17MeVw/6noGJAygZ1RPQJtxRkKsc34ZVK5oFnPshfWh6tsBi9nUt9/c+tjwZJT0UsDxeQD1lYsXblhoVYNMjU1l2bRl9i8KKYRwKcta0/cnvufZ7c9ysKbt1p5DNYe4t/DeNvd1dG2k8NOgclWzmN3aq1W1og2u6J4Y2uksES3n9mspNyuXmZkz+f17v+eJz57gJ+f9hI/nfezQty9HZ62QmdaFaK2guKDVl0hHHaw+yOVrL2fRTxYxM3Om1LBaUFRVbW8yO7eorq4mLi6OqqoqYmNjHT5PexfD6s1i7V0M6zRVhY3j4MS2jo/L2QC9plJWBocPw0UXaQHx8sswcGDzYbb+8d9cupmc53Po260v+2/Z73DxLWfKsHXmCp30V4muzrJpb9nny9z2Oj0ie3D10KsDOrTsyQK/DSqAP330J+7edLfVtrTYNPc3ix1+H7bdDBih5vsWO4MgdhCM/5d5FOCX3/Vk5IRU4uPh+HFt2iR7HTl9hOQ/J6OgUHtnLREhEc6+C3bs0GbHsMf27TBypNMvLYTfcWUNyh6B2ixoTxb4ZdOfLiQoBNCaAn83+nee65RMngwziuHHjW00A5qgejdsHG3eMsCYTGhwKdnZYQ6FFEDPqJ50C+/GyfqT7D2+l2HJwxwvvxDCZkaTkQeLHmy3f8ndOpsyrSvw66D69OCnAMzMnMmVF1zp+QKkTIFuo+HkFx0cFMSPp9JoPBvKRRc5/lKKopCVmMWn5Z9SfKzYa0ElE9iKrqSguIBb3rmFQzWHvFYGvXtj/hvziQuLIycjJyCbAjvilxf8AqiqypbyLQCMTxvvnUIoCgz7UycHmfj7e9cwIuNLpo9rf15AW2T1yAKg+GhxJ0faxpGZK66+WmsuHDVKm3BXLggWgUrvB/dmSFk6ceYEk1+Y7J5p4nyc3wbV/pP7OVp3lFBDKCNTvNhpoteqOvD4r29mx4OjGFymzQtofMuxeQH1oPru+HcOFbUly5krbJ21wpJMYCsCkdFk5IMfPmD+G/PbHFXsbW6ZJs7H+W1Q6bWpUSmjCA/24oR2igIj8rW5/mxgNCnsKU2g4rtv7K5dZSW6tkYFWliNHAlZWS47pRB+S5/oevILkzlx5oTT50uMTGTRTxZxX859pMY4PgGAJfXcfze8eQONZxtdck5f57d9VJ+Wa/1T41O91OxnKXkyzDwAG8ehntiBQvtzDBqCVAaftxu+Gg1fAeHJMLNUm/i2E4N6DAJg7/G9GE1Gn2inlmusRKBo75IXe6TGpDJ/1HwGJAxoNbjrruy7zBcH6zNTvLn3TYeHuR+tO0rqE6k8c8kzAT/Iwm+CSr/+x2gy8uXhL3ltUxmcHkFi9VR27PCBP5TnLgZWLEYBqmpn89QqEJoAp75pPjC8J0S2/c2rd1xvwoPDqT9bT8mpEvon9HdZ8R29GFi/DkuusRL+rPFsIze8eYPDIWXLhbqGIEOruUcn9Z1Edu9sh4e9H6072iVGBNp9HdVHH33EY489xvbt26moqGD9+vVceumlNj/fkeuoOlopV+cTfyhVldr144iq38aeHweS2Wuv/ecI7Q4XvQmGc0uGtAiu4c8M56sjX/HfX/+XGZkzXFRwjTMXA4NcYyX8U0FxAb9987ccq7O/w9VV1zh1NEegLRIiElibt9avRgTakwV291HV1tYybNgwnn76aYcLaC9nVsr1KEXhx8SH2H0wiwWrn2Lr/jGcNdr5v7jxOLw3vnlBxreHw9HPzP1Z5n6qY67rp9Lp/VWO9lnJ8iDC3+jNfY6E1H0591G6sNQlNRm9tvXEtCeouL2C93/zPgkRCTY/P9BHBNrd9Dd9+nSmT5/ujrK0S1tXqvNvCbYe5041UZMZ9YfdANz9qomNd3Q2L2An9OACCI7jV9EXoURDw75VfB2pMiTtYu0bVAdNhp4iy4MIf2E0GSksLXRoZJ+7Z78xBBmY1HcSz8541u4+s0C9ONjtfVQNDQ00NDQPxa6urrb7HF8e/hLoeAi4ftwYG47zlHd3TWHr/jGM7beN0/WRhIc0EGww2tB31Y6zVeSe+i+5KQDfQfEdoFesguNg1HIICoaG49oLhCZY39eFJUB4kna/nYBztM9KJ8uDCF/l6FRInm5e01dPsKdZMlAvDnZ7UOXn53Pfffc5dY5jtbZ9SLYe5zkKd77yECuuuYV/Ff2Gh6+8U9vquoWAm52tgs/n2v+8kHgYuaxVwKUDBz88Tm2tQtmRBNb86zioCidqtcBLiG5+bHnfcl+3qgQoQVuzSzn3j6VlcHYUqq7Yh7x+wL++Ha+xs+xDPvzyn2SbgGhICAIUOGG0vg/Wj7sHwbUjL2W4chgOrPHYe8wNUpiR+zj3bLyRmsa6NssGEKSASbXcd4Ln1k9mfUQ3rhtyGcOTh7v2MwabvvC6ilOT0iqK0ulgirZqVGlpaXYNpvj7m1/w2xmd15T+9sYXXH+Jd2tU7Q/8UPn8/nGM7beNL0pGc8EFEFb7pfVqwUII4Y/suMxG59bBFPYKCwsjNjbW6mavEckjXHqcO+mzPUw5tzL99ddro+G2b1folvMQZ0KzSJuRT9joP1mHVMwA/Pj6ayFEl6Wtv0dQqNtewS+uo7K1jdVX2mLT0mDXLu3+lVdaDtmeDOwmArSLrBLGaOtaJYyBofdDoeUglSCsVhEWQgifZNIWlHVLn4bG7q/wp0+fZufOnezcuROAkpISdu7cSZkbxyPbMnlqZyvletL330NFBYSGwrhx7RykKDD8IYjN0n6mTNUCCyB6IJYhVReeytlzDbSqqt2guU1aCCG8QjFof7dSprj1ZewOqi+++IIRI0YwYoTWzHbbbbcxYsQI7rnnHpcXTmc5eerWbUbufeobACKjm9i6zcj27b41FHrzZu3nuHEQ0dH6hsmT4ZLd2k/L4BrzVHNoJYwhbOzfCD73ZUVRmr+4BCmwp5HmELM6uQJB4UhzohDCbVSj22tT4EDTX05ODh5eFBjQQkgLIgNZg87nvpug7nQIAwdAXJzHi2PFckYHgPXrtZ+Zmdg3vZMeXKB98F/cAsMfwpA0iRMR/Ug4s5+t5wZpjA2HrfVwz3HYcN65p1idTIXz/whf/7Ht14oZCKf3n+snCwJDOJga2hjc0Xqfeu61zhqDqG8Ktxp2rxe95b42j1W086gEodjx+o7tc9V55PV99/WtH1v9PqpQr0K4AsEKHe7z7fdovU81hKMa6wnCZNd7tNzn8OsrBug20u21qXOl8D/R0c3NfKWlXi2KeZSfvkbTqFHwzjvavn/8w4l1m1rUthLGP0N1WCpPnOnBncdgdwPceQy+UBM4EdFPe0630ZBwbtRjwhgYvKS5ZtZy36jlFr94JhhyZzsjEFvv0/8tBxtM7Au7k2CDts+ythdsMPHQ6837Wj5WlObzKJg4lWr76zu2z1Xnkdf33de3fmz1+6jAQydos2UiWIEVNZFthJQvvkfrfcqQOwk6103Q2Xt8qDSlzX1gYufZ9l+j3X2qke8jH2DHl4rbZ6Lxy6ACyMjQfno7qDw2vVPyZGIvL+fF3x3mj7M2cVvIVD44A1eefxUJ45/RmgxH5MPw/OZ+r6Cg5ubElvss+8Q6C7UO9pkyl7B1v7Zv6/7RbN0/+tz9MeT/13Jfy8fWx/4Q5tjr27zPVeeR1/fd108YQ+PA2/myUWso2lqPuQViaz3kn7R+rN//sjGYxdce94/32MG+ExH9eLYhga2nQ7T3WB3O1lPaKOut+8eQf/fBdv/9jbi6/X+bHe0beNEUzyyiqnpYVVWVCqhVVVVOnScvTxtWsGyZiwrmoO3b9eENHd+2b3ft67709UsqS1HHPjvW8ZNUvKeqb2RpP1s+tnHf9u2qOmnIe+q3j2Spk4a8Z3Uf1A4fW95/8UVV3fvRe2pDgX2vb9c+V51HXt8nX/+jz+5RezzaQ530COq3/0Sd9AhW91lq/XjyufsffXaP37zH9vY1FGSpez96T33t9UZ10tC3bPr36Kp9jv6NsycLnLrg1xGOzJ7elv/7P3jsMVi0CJ54wnXls9eOHVrzXmdcPbN46alS+izvQ0hQCNVLqr22eKQtM9vbQ+YIFI5wZC0pd8/Z506W/eIVFZCbC41eXkPR3r9x9mSBX1xH1Ra96a+kxKvF8Jrecb1JikriSO0RdlTs4KdpP/VKOfQRmc4sD2Kpvh6Kippnb/f6OmPC5xlNRhZuWGhXSD0x9QluHnuzz1x72RlfDCZP8vug8nYflbcoisJPUn/C63te57ODn3ktqMByRKZryCzswlZGk5Entz5p8wSzCgqpsal+EVJ6OHXFYGpJgsqPWQaVr3B21vWWLGtYUrsSlhydBX3ZtGV+EVKubFL3d34bVL17az+rquDUKYiP92ZpvOMnqT8BYFPpJl7e9TIpMSkdLoXtCa5uCgRZ7l605kifVGJkIs9c8ozP9km1XGFbQqqZ3wZVVBT07AmVlVo/1QgvzUdrSw3CXdM7/VjzIwDH6o5xVcFVgOuWxnaGZVOgK2tY0n8lHF3wMDEykYO3HiQ02H0TpzpCmvds47dBBVrzX2Wl1vznraDSaxAvvwx33KGV6T//sT7GHX9QC4oLuLqgdVXF11b4tKxhubJ2BVLD6mocaepTzl1W/swlz/hkSAVK856751r1+6DautX7/VT6H2PQvhW5chh6Wzoa5aSioqCwaMMiZmbO9Im2eFcPttBJ/1XX4UhTH2gtDL40BN0nmveCGmDK7yHyGNR117ZFHre+Dy329WDeiHlcPGS4+TTx8ZCSot13978/vw8q8E5QWf7CqSq88YZ2v18/O+f3c0BRWVGH3ypVVMqryykqKyInI8c9hXCAqwdagPRfdQWODD8H3xuC7s0aVGgoFBRowbLt5If86ev1dg9CeS3i31w1fq1Xlrf366Dq00f76emg6ugXbsEC7ac7/3BW1FS49DhPccdAC530XwWuwtJCu5v7fGkIuv6l1pM1KMtgAut/DyOZznUTSyksLWTWulmcOHPCpnOeOHOCyS9M9ko/uF8Hlbcu+rVnfj93/LFMiUlx6XGe5K6BFiD9V4GooLiA+W/Mt/l4vU/KV4age6oW1VEwtcUQZGBS30k8O+NZu5tUD1Yf5PK1l3Nfzn3clX2XR/4/++2ktGDd9OeFlUe8Jjs9m9TYVPM/ypYUFNJi08hOz/Zwyexjuc7Yiy+69tx6DWvHDjdPlincRu+XsvUbP2h9Ut4eSFRWpv3e7dih/Q66K6RCQ+HNN7V/P99/D7/8pdY/PnKk7V/QcrNyWTdrHT0i7R8JcW/hvWQsy6CguMDu59rLb+f6AzhzBiIjtfvHj0NCggsKaANvze9nSf9HDFh9G9LDy9v/WO3lzm+e9n7bFN6lD0G3p1kqISKBtXne6T+x5O4a1Isvuqd5u/FsI6lPpHK07qjdz1VQHPp70yXm+tPbfRMS4MQJbQ0o/QOEwP9jpH8Tajlct0dkD5++qLE97uy/amyESy5pfizB5bscHYL+7IxnmdR3khtL1jFP9EOFh0N2tnt+V0ODQ3nmkmfa/PJrC3ePMvbLGpUt31rc2UfhCzUqndFkpKisiPsL72fTgU0sGLOAp37xlHtf1AM8OUJK+rN8gyND0LtHdOfvM/7u8S9mnpok1vJLlSe+UDk6LRXApjmb7BplHPA1Km8PZvAlhiADORk51IyvYdOBTby5902enP4kitJ2/5W/cPWFwh2R67G8r/FsIze8eYPd3+RfyXvF4zUpf23es0VuVi4zM2fa3fQK7h1l7JdBJVqb1HcS4YZwDlQd4NFPHmVc6jivz/vnLH2EoDuuv2pJD0JpFvS8guICfvvmbzlWZ/sy2PoQdE9dJ+ipC3Xd2bxnK0dHBLpzlLEElQO8Ob9fezbs24A+CPCOD+4AfGPeP1dwZ/9VSx31Z0louZ4jzX2eHoLuiWZovRblS79jej/4Le/cwqGaQ+0ep39pcOcoYwkqG1l+owJ4/HG46SYwGODppyE1tflbOHj2F669f+y+Nu+fM9x5/VVHLINLaluu4+jksuC5aZE8daGuL9Si2qM3BT5Y9CD3Ft7bar+nvjT45WAKTw9m8PbgjY4YTUYylme02/mpf9spWVji182ALVn+EXFn/1VHpLblGEc77N09BN2Tq+h6sx/KUW19bmmxaQ5/aQj4wRSe5suDN/x13j9nebL/qj1S27Kfo5PLumsIujeW2fDlGlRH9NpVUVkRFTUVHl3/ToLKz/nrvH+u0rL/ylvr+kjfVuccHdnnrgUPPT1JrC/2Q9lLH2XsaX4ZVL44mMFb/HneP1dpuYzI99/7VnBJbcuxkX3g+gUPvbXMhr/WonyFXwZVy2/RADNnwsGD2iCHiRO7zh8Dfd6/Q9WH2vym6okROb6mveDyVn9Wy9pWcLD2e6p/kfLkuj6epF+M/vp3r7Ps82V2PdeVCx56o3lPvpy4ll8GFbT+Y3TZZfDkk1qA3X6798rlaYYgA8unLSdvbR4KSquwUlG5fPDlFJUV+f11VY7yhf4sS2fPwqJFbe8LlD9wzsxwAM6N7PPkoAhL/jhAwl/45ai/tmzYANOna8PEy8rAFRMz2DuyzBNTJrXHlj8MgXJdlTO89UfMUZa1L8uaF/juH0NHB0yA4yP7vFFrsiTTcNnPniwIiKAqK9Oa/S6+GBoaYM0aGDCgeb8j/6Ad6Wj1ZlBB500t/jqzujt5+w+cM3wtxByZ9dxSR7Nwt7yOsaICTp3S7h87Br//PTQ1OVhwJwTCAAlv6VJB5a5rnGy9VsuZ13CHrnpdlSv4W22rI5Yh1tioNSnq3BFqzjb1JUYm8sDI5xnTbbp5mx5G3gyijvjKv3l/1aWuo/L2NU6+9o2qq15X5QrtDcLwx9DqqB+sJT3UdPogDz20e/Swvm+5L6G7kZc+38iGfZsg8tyAnbru2s/I49pPYwgYmtreV9edqJBo7ph4PzdPD/G5MLIUKP2H/sjvg8rbsrK829zXUle/rsqVLIPLF4a8u4s9odaaAfjFuZtjaoHb33D46W4l18P5BgkqCy2vsfBHtl4v1TOqp5tLElgCqbYlOiaj93yPBNU5nr5K3V06u65KN/e1uSyf3rVHADqjq9S2uhq5MNc3dZmgsqwhtfUtyZa+Ln/Q2XVVukM1gTOzurf54swYwnbSvOf7ukxQWV4HZTlax/JaqUBhyzoyKioKCos2LGJm5kwZAehCnQWXLwyr7spkUIT/6TJBZam+Hv71L4iJce4PhS/PJ5iblUtcWByTX5jc7jEyAtAzWgaXpcsuk74uT5Bak3/z+6BydFqcu+927PX8qaO1srbSpuM++OGDLju9kre119cFvn8dkS+TWlNg8fsLfqH1aD13Tjzq7dkn7FFYWsjE5yfadKxMr+Tb2puZoSuHmK/NzCHs06Uu+IWOm1a6MltHAEJgLVsfiGxpPtRZ9oNB88wU/hhqEkYCAiSoRNtsHQEImPfNf2M+cWFxblvuW7ierV/UjCYjyaO38t8vP+Xt79/mVP1JOJ0M9fHaAW3MGmF+3Mm+CzNyyM7MIrNHJieOa783ev+t5RRO7c1w0daSJ/p2CSMhQWUHXx480R59BKCt87CdOHOCyS9MlqbAANNqLr4E15xXfk+EJwR5uwD+4MUXtb4pf52AMjcrl9KFpfwx+482P0dvCiwoLnBjyYQ76bOZ37rhVi5fe7nDE8a2576c+yhdWCohJdwu4GpU7lgcz9fm83OEIcjApL6T+FPRn2w6Xm8KvOHNG7hkwCUuWwpcuI++zEtFTQXfn/ieZ7c/y8Ea14YTQFpsmsOLGgrhiIALKstl6l0xAtAfm/vaY8/gCt3RuqOkPpHKM5c8I3+YfJDlGmT/3vVvjtYdddtrLfqJdnG4XMogPC0ghqe3x5H5+wL9+gt99VXA7hVY5Q+V93mq1mRJalDCHbrUwomd6WhlUOiao4ycXeSuR2QPrh56tYSWm1iGUUpMCj9N/SmfHvzUI7UmXWJkIrOHzpbPWLiNBJXolLPLhuvOizmP60ddz4CEAaTEpMgfNQd11IRnUAwYVaNHyiG1ZuEpElTCZnpToL3NgO2R2lb7LGtK+npglbWVHmvC64g07wlPk6ASdikoLuC3b/6WY3XHOj/YDl2tttVek50n+5PsIc17wpskqITdGs82kvpEqlv7PyyDy7JG4esh1l5NqLP+I0822dlDmveEL5CgEg5xZkSgszoKMcuaiaP7HD1PZzUhXw2jtkjznvAlElTCYc6OCHSHjsLAnn2OnsdfpcakMn/U/C7T9Cr8iwSVcIrlCLRlny/zdnGEHaTfSfiLLrfMh3AtQ5CBnIwccjJyyO6d7XM1LNFMak2iK5AaleiUJ6fpEa21bJaUWpMIBNL0J9zGG1P4dEWWYdRyUIiEkwgEElTCY6S25RrShCe6Ggkq4RVS27LWssnOMoz86ToyIdzB7UH19NNP89hjj3H48GGGDRvGk08+ydixY11eOOHfukJwddR/JE12QrTPrUH1yiuvcM011/DMM88wbtw4li1bxquvvsqePXvo2bOnSwsnAoujc915+zqqjmpCEkZCOMatQTVu3DjGjBnDU089BYDJZCItLY2bb76ZO+64w6WFE12HLdMUeWNmCgkfIdzDbUHV2NhIZGQk69at49JLLzVvnzNnDqdOneL11193aeGEEEIEJrdd8Hvs2DGMRiNJSUlW25OSkvjuu+/afE5DQwMNDQ1WhRNCCCFsFeTuF8jPzycuLs58S0tLc/dLCiGECCB2BVWPHj0wGAwcOXLEavuRI0dITk5u8zlLliyhqqrKfCsvL3e8tEIIIbocu4IqNDSUUaNG8cEHH5i3mUwmPvjgA8aPH9/mc8LCwoiNjbW6CSGEELaye1La2267jTlz5jB69GjGjh3LsmXLqK2t5X//93/dUT4hhBBdnN1BdcUVV3D06FHuueceDh8+zPDhw9mwYUOrARZCCCGEK8gUSkIIITzOnixw+6g/IYQQwhkSVEIIIXyaBJUQQgif5vGl6PUuMZmhQgghui49A2wZJuHxoKqpqQGQGSqEEEJQU1NDXFxch8d4fNSfyWTixx9/JCYmBkVRHDpHdXU1aWlplJeXB/zIQXmvgaervE+Q9xqIXPU+VVWlpqaGXr16ERTUcS+Ux2tUQUFBpKamuuRcXWmmC3mvgaervE+Q9xqIXPE+O6tJ6WQwhRBCCJ8mQSWEEMKn+WVQhYWFce+99xIWFubtoridvNfA01XeJ8h7DUTeeJ8eH0whhBBC2MMva1RCCCG6DgkqIYQQPk2CSgghhE+ToBJCCOHT/DKonn76aTIyMggPD2fcuHFs3brV20VyuaVLl6IoitVt0KBB3i6W0z766CNmzJhBr169UBSF1157zWq/qqrcc889pKSkEBERweTJk/n++++9U1gndfZe586d2+oznjZtmncK64T8/HzGjBlDTEwMPXv25NJLL2XPnj1Wx9TX17NgwQK6d+9OdHQ0l19+OUeOHPFSiR1ny3vNyclp9bnecMMNXiqx41auXMnQoUPNF/aOHz+ed955x7zfk5+p3wXVK6+8wm233ca9997Ljh07GDZsGFOnTqWystLbRXO5IUOGUFFRYb59/PHH3i6S02praxk2bBhPP/10m/sfffRRVqxYwTPPPMPnn39OVFQUU6dOpb6+3sMldV5n7xVg2rRpVp/xyy+/7MESusbmzZtZsGABn332Ge+99x5NTU1MmTKF2tpa8zG33norb7zxBq+++iqbN2/mxx9/JDc314uldowt7xVg/vz5Vp/ro48+6qUSOy41NZWHH36Y7du388UXX3DxxRczc+ZMvv32W8DDn6nqZ8aOHasuWLDA/NhoNKq9evVS8/PzvVgq17v33nvVYcOGebsYbgWo69evNz82mUxqcnKy+thjj5m3nTp1Sg0LC1NffvllL5TQdVq+V1VV1Tlz5qgzZ870SnncqbKyUgXUzZs3q6qqfYYhISHqq6++aj6muLhYBdQtW7Z4q5gu0fK9qqqqXnTRRerChQu9Vyg36tatm/qPf/zD45+pX9WoGhsb2b59O5MnTzZvCwoKYvLkyWzZssWLJXOP77//nl69etG3b19mz55NWVmZt4vkViUlJRw+fNjq842Li2PcuHEB+fkCFBYW0rNnTzIzM/nd737H8ePHvV0kp1VVVQGQkJAAwPbt22lqarL6XAcNGkR6errff64t36vu3//+Nz169OD8889nyZIl1NXVeaN4LmM0GlmzZg21tbWMHz/e45+pxyeldcaxY8cwGo0kJSVZbU9KSuK7777zUqncY9y4caxevZrMzEwqKiq47777yM7O5ptvviEmJsbbxXOLw4cPA7T5+er7Asm0adPIzc2lT58+7N+/nzvvvJPp06ezZcsWDAaDt4vnEJPJxKJFi5gwYQLnn38+oH2uoaGhxMfHWx3r759rW+8V4KqrrqJ379706tWLr7/+mj/84Q/s2bOHgoICL5bWMbt27WL8+PHU19cTHR3N+vXrGTx4MDt37vToZ+pXQdWVTJ8+3Xx/6NChjBs3jt69e7N27VquvfZaL5ZMuMqvf/1r8/0LLriAoUOH0q9fPwoLC5k0aZIXS+a4BQsW8M033wREf2pn2nuv119/vfn+BRdcQEpKCpMmTWL//v3069fP08V0SmZmJjt37qSqqop169YxZ84cNm/e7PFy+FXTX48ePTAYDK1Glhw5coTk5GQvlcoz4uPjGThwIPv27fN2UdxG/wy74ucL0LdvX3r06OG3n/FNN93Em2++yaZNm6yW8klOTqaxsZFTp05ZHe/Pn2t777Ut48aNA/DLzzU0NJT+/fszatQo8vPzGTZsGMuXL/f4Z+pXQRUaGsqoUaP44IMPzNtMJhMffPAB48eP92LJ3O/06dPs37+flJQUbxfFbfr06UNycrLV51tdXc3nn38e8J8vwMGDBzl+/LjffcaqqnLTTTexfv16PvzwQ/r06WO1f9SoUYSEhFh9rnv27KGsrMzvPtfO3mtbdu7cCeB3n2tbTCYTDQ0Nnv9MXT48w83WrFmjhoWFqatXr1Z3796tXn/99Wp8fLx6+PBhbxfNpW6//Xa1sLBQLSkpUT/55BN18uTJao8ePdTKykpvF80pNTU16pdffql++eWXKqD+5S9/Ub/88kv1wIEDqqqq6sMPP6zGx8err7/+uvr111+rM2fOVPv06aOeOXPGyyW3X0fvtaamRl28eLG6ZcsWtaSkRH3//ffVkSNHqgMGDFDr6+u9XXS7/O53v1Pj4uLUwsJCtaKiwnyrq6szH3PDDTeo6enp6ocffqh+8cUX6vjx49Xx48d7sdSO6ey97tu3T73//vvVL774Qi0pKVFff/11tW/fvuqFF17o5ZLb74477lA3b96slpSUqF9//bV6xx13qIqiqO+++66qqp79TP0uqFRVVZ988kk1PT1dDQ0NVceOHat+9tln3i6Sy11xxRVqSkqKGhoaqp533nnqFVdcoe7bt8/bxXLapk2bVKDVbc6cOaqqakPU7777bjUpKUkNCwtTJ02apO7Zs8e7hXZQR++1rq5OnTJlipqYmKiGhISovXv3VufPn++XX7jaeo+AumrVKvMxZ86cUW+88Ua1W7duamRkpHrZZZepFRUV3iu0gzp7r2VlZeqFF16oJiQkqGFhYWr//v3V3//+92pVVZV3C+6AefPmqb1791ZDQ0PVxMREddKkSeaQUlXPfqayzIcQQgif5ld9VEIIIboeCSohhBA+TYJKCCGET5OgEkII4dMkqIQQQvg0CSohhBA+TYJKCCGET5OgEsKFCgsLURSl1RxoQgjHyQW/QjghJyeH4cOHs2zZMkBbM+3EiRMkJSWhKIp3CydEgJBlPoRwodDQUL+dEVwIXyVNf0I4aO7cuWzevJnly5ejKAqKorB69Wqrpr/Vq1cTHx/Pm2++SWZmJpGRkeTl5VFXV8fzzz9PRkYG3bp145ZbbsFoNJrP3dDQwOLFiznvvPOIiopi3LhxFBYWeueNCuFlUqMSwkHLly9n7969nH/++dx///0AfPvtt62Oq6urY8WKFaxZs4aamhpyc3O57LLLiI+P5+233+aHH37g8ssvZ8KECVxxxRWAtt7R7t27WbNmDb169WL9+vVMmzaNXbt2MWDAAI++TyG8TYJKCAfFxcURGhpKZGSkubnvu+++a3VcU1MTK1euNK/umpeXxwsvvMCRI0eIjo5m8ODBTJw4kU2bNnHFFVdQVlbGqlWrKCsro1evXgAsXryYDRs2sGrVKh566CHPvUkhfIAElRBuFhkZabUEeVJSEhkZGURHR1ttq6ysBGDXrl0YjUYGDhxodZ6Ghga6d+/umUIL4UMkqIRws5CQEKvHiqK0uc1kMgHaas4Gg4Ht27djMBisjrMMNyG6CgkqIZwQGhpqNQjCFUaMGIHRaKSyspLs7GyXnlsIfySj/oRwQkZGBp9//jmlpaUcO3bMXCtyxsCBA5k9ezbXXHMNBQUFlJSUsHXrVvLz83nrrbdcUGoh/IsElRBOWLx4MQaDgcGDB5OYmEhZWZlLzrtq1SquueYabr/9djIzM7n00kvZtm0b6enpLjm/EP5EZqYQQgjh06RGJYQQwqdJUAkhhPBpElRCCCF8mgSVEEIInyZBJYQQwqdJUAkhhPBpElRCCCF8mgSVEEIInyZBJYQQwqdJUAkhhPBpElRCCCF8mgSVEEIIn/b/4NibWNWICagAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -228,8 +228,8 @@
    ],
    "source": [
     "url = \"https://www.ebi.ac.uk/biomodels/services/download/get-files/MODEL2108260003/3/Alharbi2019%20TNVM.xml\"\n",
-    "ctlsb = csbml.ControlSBML(url, times=np.linspace(0, 30, 100), input_names=[\"Vitamins\"], output_names=[\"Normal_cells\", \"Tumor_cells\"], is_fixed_input_species=True)\n",
-    "ts = ctlsb.plotModel()"
+    "ctlsb = csbml.ControlSBML(url, times=np.linspace(0, 30, 100), is_fixed_input_species=True)\n",
+    "ts = ctlsb.plotModel(figsize=FIGSIZE)"
    ]
   },
   {
@@ -270,8 +270,9 @@
     }
    ],
    "source": [
-    "ctlsb = csbml.ControlSBML(url, times=np.linspace(0, 30, 300), input_names=[\"Vitamins\"], output_names=[\"Normal_cells\"], is_fixed_input_species=True)\n",
-    "_ = ctlsb.plotStaircaseResponse(initial_value=0, final_value=10)"
+    "ctlsb = csbml.ControlSBML(url, times=np.linspace(0, 30, 300), input_name=\"Vitamins\",\n",
+    "                          output_name=\"Normal_cells\", is_fixed_input_species=True)\n",
+    "_ = ctlsb.plotStaircaseResponse(initial_value=0, final_value=10, figsize=FIGSIZE)"
    ]
   },
   {
@@ -313,7 +314,7 @@
     }
    ],
    "source": [
-    "ctlsb = csbml.ControlSBML(url, times=TIMES, input_names=[INPUT], output_names=[OUTPUT], is_fixed_input_species=True)\n",
+    "ctlsb = csbml.ControlSBML(url, times=TIMES, input_name=INPUT, output_name=OUTPUT, is_fixed_input_species=True)\n",
     "_ = ctlsb.plotStaircaseResponse(initial_value=INITIAL_VALUE, final_value=FINAL_VALUE, figsize=FIGSIZE,\n",
     "                               times=TIMES)"
    ]
@@ -367,7 +368,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 2 Axes>"
       ]
@@ -392,12 +393,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x500 with 2 Axes>"
       ]
@@ -500,9 +501,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████| 1/1 [00:02<00:00,  2.33s/it]\n",
-      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  2.35it/s]\n",
-      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  2.36it/s]\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:02<00:00,  2.46s/it]\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  1.53it/s]\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  1.72it/s]\n",
       "/Users/jlheller/home/Technical/repos/controlSBML/src/controlSBML/msgs.py:13: UserWarning:\n",
       "\n",
       "\n",
@@ -526,15 +527,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/9 [00:00<?, ?it/s]\u001b[35mError: CVODE Error: CV_CONV_FAILURE, Module: CVODES, Function: CVode, Message: At t = 33.3051 and h = 1.11778e-155, the corrector convergence test failed repeatedly or with |h| = hmin.\u001b[0m\n",
-      "100%|██████████| 9/9 [00:03<00:00,  2.82it/s]\n",
-      "\u001b[35mError: CVODE Error: CV_TOO_MUCH_WORK, Module: CVODES, Function: CVode, Message: At t = 44.4362, mxstep steps taken before reaching tout.\u001b[0m\n",
-      "  0%|          | 0/9 [00:00<?, ?it/s]\u001b[35mError: CVODE Error: CV_CONV_FAILURE, Module: CVODES, Function: CVode, Message: At t = 33.3051 and h = 1.11778e-155, the corrector convergence test failed repeatedly or with |h| = hmin.\u001b[0m\n",
-      "100%|██████████| 9/9 [00:03<00:00,  3.00it/s]\n",
-      "\u001b[35mError: CVODE Error: CV_TOO_MUCH_WORK, Module: CVODES, Function: CVode, Message: At t = 44.4362, mxstep steps taken before reaching tout.\u001b[0m\n",
       "  0%|          | 0/9 [00:00<?, ?it/s]\u001b[35mError: CVODE Error: CV_TOO_MUCH_WORK, Module: CVODES, Function: CVode, Message: At t = 44.4362, mxstep steps taken before reaching tout.\u001b[0m\n",
+      "100%|██████████| 9/9 [00:04<00:00,  1.85it/s]\n",
+      "\u001b[35mError: CVODE Error: CV_CONV_FAILURE, Module: CVODES, Function: CVode, Message: At t = 33.3051 and h = 1.11778e-155, the corrector convergence test failed repeatedly or with |h| = hmin.\u001b[0m\n",
+      "100%|██████████| 9/9 [00:03<00:00,  2.25it/s]\n",
+      "\u001b[35mError: CVODE Error: CV_TOO_MUCH_WORK, Module: CVODES, Function: CVode, Message: At t = 44.4362, mxstep steps taken before reaching tout.\u001b[0m\n",
       "\u001b[35mError: CVODE Error: CV_CONV_FAILURE, Module: CVODES, Function: CVode, Message: At t = 33.3051 and h = 1.11778e-155, the corrector convergence test failed repeatedly or with |h| = hmin.\u001b[0m\n",
-      "100%|██████████| 9/9 [00:03<00:00,  2.82it/s]\n"
+      "100%|██████████| 9/9 [00:03<00:00,  2.33it/s]\n",
+      "\u001b[35mError: CVODE Error: CV_TOO_MUCH_WORK, Module: CVODES, Function: CVode, Message: At t = 44.4362, mxstep steps taken before reaching tout.\u001b[0m\n",
+      "\u001b[35mError: CVODE Error: CV_CONV_FAILURE, Module: CVODES, Function: CVode, Message: At t = 33.3051 and h = 1.11778e-155, the corrector convergence test failed repeatedly or with |h| = hmin.\u001b[0m\n"
      ]
     },
     {
@@ -570,6 +571,42 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████████| 1/1 [00:02<00:00,  2.53s/it]\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  1.91it/s]\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  1.83it/s]\n",
+      "/Users/jlheller/home/Technical/repos/controlSBML/src/controlSBML/msgs.py:13: UserWarning:\n",
+      "\n",
+      "\n",
+      "\n",
+      "***Warning*** No design found!\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None, None)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ctlsb.plotDesign(setpoint=2, kP_spec=0, kI_spec=0, figsize=FIGSIZE, times=np.linspace(0, 100, 1000), min_parameter_value=1,\n",
+    "                max_parameter_value=100, title=\"No control\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "ename": "NameError",
@@ -578,7 +615,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m _ \u001b[38;5;241m=\u001b[39m plotModel(\u001b[43mMODEL_CL\u001b[49m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel with no control\u001b[39m\u001b[38;5;124m\"\u001b[39m, selections\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msetpoint\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTumor_cells\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNormal_cells\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVitamins\u001b[39m\u001b[38;5;124m\"\u001b[39m], \n\u001b[1;32m      2\u001b[0m               setpoint\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, kP\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, kI\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m _ \u001b[38;5;241m=\u001b[39m plotModel(\u001b[43mMODEL_CL\u001b[49m, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel with no control\u001b[39m\u001b[38;5;124m\"\u001b[39m, selections\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msetpoint\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTumor_cells\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNormal_cells\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVitamins\u001b[39m\u001b[38;5;124m\"\u001b[39m], \n\u001b[1;32m      2\u001b[0m               setpoint\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, kP\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, kI\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
       "\u001b[0;31mNameError\u001b[0m: name 'MODEL_CL' is not defined"
      ]
     }
diff --git a/src/controlSBML/constants.py b/src/controlSBML/constants.py
index 9a8ea55..50c39f4 100644
--- a/src/controlSBML/constants.py
+++ b/src/controlSBML/constants.py
@@ -93,10 +93,15 @@ def equals(self, other):
 # Keyword options
 O_AX = "ax"
 O_AX2 = "ax2"
+O_SELECTIONS = "selections"   # Selections for the plot
 O_END_TIME = "end_time"
 O_FIGURE = "figure"
 O_FIGSIZE = "figsize"
 O_FINAL_VALUE = "final_value"
+O_FITTER_METHOD = "fitter_method"
+O_KP_SPEC = "kP_spec"
+O_KI_SPEC = "kI_spec"
+O_KF_SPEC = "kF_spec"
 O_STEP_VAL = "step_val"
 O_INITIAL_VALUE = "initial_value"
 O_INPUT_NAME = "input_name"
@@ -109,6 +114,8 @@ def equals(self, other):
 O_LEGEND_SPEC = "legend_spec"
 O_MARKERS = "markers"
 O_NUM_POINT = "num_point"
+O_NUM_PROCESS = "num_process"
+O_NUM_RESTART = "num_restart"
 O_NUM_STEP = "num_step"
 O_OUTPUT_NAME = "output_name"
 O_OUTPUT_NAMES = "output_names"
diff --git a/src/controlSBML/control_sbml.py b/src/controlSBML/control_sbml.py
index af978f5..768ad06 100644
--- a/src/controlSBML/control_sbml.py
+++ b/src/controlSBML/control_sbml.py
@@ -54,42 +54,77 @@
 from controlSBML import util
 import controlSBML.constants as cn
 import controlSBML.msgs as msgs
-from controlSBML.option_set import OptionSet
-from controlSBML.grid import Grid, Point
+from controlSBML.grid import Grid
 
 import os
 import control  # type: ignore
 import numpy as np
-from typing import List, Dict, Tuple, Optional
+from typing import List, Tuple, Optional
 
-PLOT_KWARGS = list(set(cn.PLOT_KWARGS).union(cn.FIG_KWARGS))
 SETPOINT = 1
-STAIRCASE_OPTIONS = [cn.O_INITIAL_VALUE, cn.O_FINAL_VALUE, cn.O_NUM_STEP]
-TIMES_OPTIONS = [cn.O_TIMES]
-CLOSED_LOOP_PARAMETERS = [cn.CP_KP, cn.CP_KI, cn.CP_KF, cn.O_SETPOINT, cn.O_SIGN]
-SYSTEM_SPECIFICATIONS = [cn.O_INPUT_NAME, cn.O_OUTPUT_NAME, cn.O_IS_FIXED_INPUT_SPECIES, cn.O_IS_STEADY_STATE,
-                        cn.FITTER_METHOD]
-PLOT_OPTIONS = list(cn.PLOT_KWARGS)
-PLOT_OPTIONS.extend(cn.FIG_KWARGS)
-PLOT_OPTIONS.append(cn.O_MARKERS)
-OPTIONS = STAIRCASE_OPTIONS + TIMES_OPTIONS + CLOSED_LOOP_PARAMETERS + PLOT_OPTIONS + SYSTEM_SPECIFICATIONS
-CONTROL_PARAMETERS = [cn.CP_KP, cn.CP_KI, cn.CP_KF]
 FIGSIZE = (5, 5)
-INITIAL_PLOT_OPTION_DCT = {cn.O_TITLE: "", cn.O_SUPTITLE: "", cn.O_WRITEFIG: False,
-                        cn.O_XLABEL: "time", cn.O_YLABEL: "concentration",
-                        cn.O_FIGSIZE: FIGSIZE,
-                        cn.O_IS_PLOT: True,
-                        cn.O_LEGEND_SPEC: None,
-                        cn.O_LEGEND_CRD: None,
-                        cn.O_YLIM: None,
-                        cn.O_XLIM: None,
-                        cn.O_XTICKLABELS: None,
-                        cn.O_YTICKLABELS: None,
-                        cn.O_AX: None,
-                        cn.O_AX2: None,
-                        cn.O_FIGURE: None,
-                        cn.O_MARKERS: False,
-                        }
+# Optional dictionaries
+TIMES_DCT = {
+    cn.O_TIMES: cn.TIMES,
+}
+STAIRCASE_DCT = {
+    cn.O_INITIAL_VALUE: 0,
+    cn.O_FINAL_VALUE: 10,
+    cn.O_NUM_STEP: 5,
+}
+SIMULATION_DCT = {
+    cn.O_END_TIME: cn.TIMES[-1],
+    cn.O_START_TIME: cn.TIMES[0],
+    cn.O_NUM_POINT: len(cn.TIMES),
+    cn.O_SELECTIONS: None,
+}
+CLOSED_LOOP_DCT = {
+    cn.CP_KP: 0, 
+    cn.CP_KI: 0, 
+    cn.CP_KF: 0, 
+    cn.O_SETPOINT: 1, 
+    cn.O_SIGN: -1,
+    cn.O_NUM_PROCESS: -1,
+    cn.O_NUM_RESTART: 3,
+    cn.O_KP_SPEC: False,
+    cn.O_KI_SPEC: False,
+    cn.O_KF_SPEC: False,
+    }
+SYSTEM_OPTION_DCT = {
+    cn.O_FITTER_METHOD: cn.DEFAULT_FITTER_METHOD,
+    cn.O_IS_FIXED_INPUT_SPECIES: True,
+    cn.O_IS_STEADY_STATE: False,
+    cn.O_IS_GREEDY: False,
+    cn.O_INPUT_NAME: None, 
+    cn.O_OUTPUT_NAME: None,
+    }
+PLOT_OPTION_DCT = { 
+    cn.O_AX: None,
+    cn.O_AX2: None,
+    cn.O_FIGURE: None,
+    cn.O_FIGSIZE: FIGSIZE,
+    cn.O_IS_PLOT: True,
+    cn.O_LEGEND_CRD: None,
+    cn.O_LEGEND_SPEC: None,
+    cn.O_MARKERS: False,
+    cn.O_SUPTITLE: "",
+    cn.O_TITLE: "",
+    cn.O_XLABEL: "time", cn.O_YLABEL: "concentration",
+    cn.O_XTICKLABELS: None,
+    cn.O_XLIM: None,
+    cn.O_YLIM: None,
+    cn.O_YTICKLABELS: None,
+    cn.O_WRITEFIG: False,
+    }
+OPTION_DCT = {**STAIRCASE_DCT, **TIMES_DCT, **CLOSED_LOOP_DCT, **SYSTEM_OPTION_DCT, **PLOT_OPTION_DCT,
+               **SIMULATION_DCT}
+# Option keywords
+PLOT_KEYS = list(PLOT_OPTION_DCT.keys())
+STAIRCASE_KEYS = list(STAIRCASE_DCT.keys())
+TIMES_KEYS = list(TIMES_DCT.keys())
+OPTION_KEYS = list(OPTION_DCT.keys())
+SIMULATION_KEYS = list(SIMULATION_DCT.keys())
+#
 SAVE_PATH = os.path.join(cn.DATA_DIR, "control_sbml.csv")
 
 
@@ -99,13 +134,13 @@ def __init__(self, model_reference:str,
                  roadrunner=None,
                  input_name:Optional[str]=None,
                  output_name:Optional[str]=None,
-                 is_fixed_input_species:Optional[bool]=False,
-                 is_steady_state:Optional[bool]=False,
-                 fitter_method:Optional[str]=cn.DEFAULT_FITTER_METHOD,
-                 setpoint:Optional[float]=SETPOINT,
-                 sign:Optional[int]=-1,
-                 times:Optional[np.ndarray[float]]=None,
-                 save_path:Optional[str]=None,
+                 is_fixed_input_species:Optional[bool]=OPTION_DCT[cn.O_IS_FIXED_INPUT_SPECIES],
+                 is_steady_state:Optional[bool]=OPTION_DCT[cn.O_IS_STEADY_STATE],
+                 fitter_method:Optional[str]=OPTION_DCT[cn.O_FITTER_METHOD],
+                 setpoint:Optional[float]=OPTION_DCT[cn.O_SETPOINT],
+                 sign:Optional[int]=OPTION_DCT[cn.O_SIGN],
+                 times:Optional[np.ndarray[float]]=OPTION_DCT[cn.O_TIMES],
+                 save_path:Optional[str]=SAVE_PATH,
                  **kwargs):
         """
         model_reference: str
@@ -116,89 +151,86 @@ def __init__(self, model_reference:str,
         is_fixed_input_species: bool
         is_steady_state: bool
         save_path: str (path to file where results are saved after a design)
-        Plot options:
-            ax: axis for plot
-            figure: figure object
-            figsize: figure size (width, height)
-            is_plot: bool (plot if True)
-            markers: list-str (markers for plot lines; False, no markers)                                                                                       
-            suptitle: str (subtitle)
-            title: str (title)
-            xlabel: str (x label)
-            xlim: tupe-float (x lower limit, x upper limit)
-            xticklabels: list-float (labels for x ticks)
-            ylabel: str (y label)
-            ylim: tupe-float (y lower limit, y upper limit)
-            yticklabels: list-float (labels for y ticks)
-        System options:
-            input_name: str
-            is_steady_state: bool (start system in steady state; default: False)
-            is_fixed_input_species: bool (concentration of input species are controlled externally; default: False)
-            output_name: str
-        Closed loop options:
-            kF: float (filter constant)
-            kI: float (integral control)
-            kP: float (proportional control)
-            setpoint: float (regulation point)
-            sign: -1/+1 (direction of feedback: default: -1) 
-        Staircase options:
-            final_value: float (last value of input in staircase; default: maximum input value in SBML model)
-            initial_value: float (first value of input in staircase; default: minimum input value in SBML model)
-            num_step: int (number of steps in staircase; default: 5)
-        Miscellaneous options
-            times: list-float (times of simulation; default: np.linspace(0, 5, 50))
-
-        """
-        self._checkKwargs(PLOT_OPTIONS, **kwargs)
+        kwargs: dict with options listed below. These are the default options used unless overridden.
+            Plot options:
+                ax: axis for plot
+                figure: figure object
+                figsize: figure size (width, height)
+                is_plot: bool (plot if True)
+                markers: list-str (markers for plot lines; False, no markers)                                                                                       
+                suptitle: str (subtitle)
+                title: str (title)
+                xlabel: str (x label)
+                xlim: tupe-float (x lower limit, x upper limit)
+                xticklabels: list-float (labels for x ticks)
+                ylabel: str (y label)
+                ylim: tupe-float (y lower limit, y upper limit)
+                yticklabels: list-float (labels for y ticks)
+            System options:
+                input_name: str
+                is_steady_state: bool (start system in steady state; default: False)
+                is_fixed_input_species: bool (concentration of input species are controlled externally; default: False)
+                output_name: str
+            Closed loop options:
+                kF: float (filter constant)
+                kI: float (integral control)
+                kP: float (proportional control)
+                setpoint: float (regulation point)
+                sign: -1/+1 (direction of feedback: default: -1) 
+            Staircase options:
+                final_value: float (last value of input in staircase; default: maximum input value in SBML model)
+                initial_value: float (first value of input in staircase; default: minimum input value in SBML model)
+                num_step: int (number of steps in staircase; default: 5)
+            Miscellaneous options
+                times: list-float (times of simulation; default: np.linspace(0, 5, 50))
+
+        """
+        self._checkKwargs(OPTION_KEYS, **kwargs)
+        # Set initial values of all options
+        for key, value in OPTION_DCT.items():  # type: ignore
+            new_value = kwargs.get(key, value)
+            setattr(self, key, new_value)
         # Initializations
         self.model_reference = model_reference
         if roadrunner is None:
             roadrunner = makeRoadrunner(model_reference)
         self._roadrunner = roadrunner
-        self.setpoint = setpoint
-        self.times = cn.TIMES if times is None else times
-        self.sign = sign
-        self.fitter_method = fitter_method
-        # Input and output names
+        # Other assignments
         self.input_name = input_name
         self.output_name = output_name
         self.is_fixed_input_species = is_fixed_input_species
         self.is_steady_state = is_steady_state
         self.save_path = save_path
+        self.fitter_method = OPTION_DCT[cn.O_FITTER_METHOD] if fitter_method is None else fitter_method
+        self.setpoint = OPTION_DCT[cn.O_SETPOINT] if setpoint is None else setpoint
+        self.sign = OPTION_DCT[cn.O_SIGN] if sign is None else sign
+        self.times = OPTION_DCT[cn.O_TIMES] if times is None else times
         # Internal state
         self._sbml_system, self._transfer_function_builder =  self.setSystem(input_name=input_name, output_name=output_name,
                        is_fixed_input_species=is_fixed_input_species, is_steady_state=is_steady_state)  # type: ignore
         self._fitter_result = cn.FitterResult()
-        # Options
-        for key, value in INITIAL_PLOT_OPTION_DCT.items():
-            setattr(self, key, value)
-        # Set initial values of options
-        self.ax = None
-        self.initial_value, self.final_value, self.num_step = self._initializeStaircaseOptions()
-        self.figure = kwargs.get(cn.O_FIGURE, None)
-        self.figsize = kwargs.get(cn.O_FIGSIZE, FIGSIZE)
-        self.is_greedy = kwargs.get(cn.O_IS_GREEDY, False)
-        self.is_plot = kwargs.get(cn.O_IS_PLOT, True)
-        self.is_fixed_input_species = kwargs.get(cn.O_IS_FIXED_INPUT_SPECIES, True)    
-        self.is_steady_state = kwargs.get(cn.O_IS_STEADY_STATE, False)
-        self.markers = kwargs.get(cn.O_MARKERS, False)
-        self.sign = kwargs.get(cn.O_SIGN, -1)
-        self.setpoint = kwargs.get(cn.O_SETPOINT, SETPOINT)
-        self.suptitle = kwargs.get(cn.O_SUPTITLE, "")
-        self.title = kwargs.get(cn.O_TITLE, "")
-        self.writefig = kwargs.get(cn.O_WRITEFIG, False)
-        # Outputs
-        self.kP = None
-        self.kI = None
-        self.kF = None
+        if False:
+            self.ax = None
+            self.figure = kwargs.get(cn.O_FIGURE, None)
+            self.figsize = kwargs.get(cn.O_FIGSIZE, FIGSIZE)
+            self.initial_value, self.final_value, self.num_step = self._initializeStaircaseOptions()
+            self.is_greedy = kwargs.get(cn.O_IS_GREEDY, False)
+            self.is_plot = kwargs.get(cn.O_IS_PLOT, True)
+            self.is_fixed_input_species = kwargs.get(cn.O_IS_FIXED_INPUT_SPECIES, True)    
+            self.is_steady_state = kwargs.get(cn.O_IS_STEADY_STATE, False)
+            self.markers = kwargs.get(cn.O_MARKERS, False)
+            self.setpoint = kwargs.get(cn.O_SETPOINT, SETPOINT)
+            self.sign = kwargs.get(cn.O_SIGN, -1)
+            self.suptitle = kwargs.get(cn.O_SUPTITLE, "")
+            self.title = kwargs.get(cn.O_TITLE, "")
+            self.writefig = kwargs.get(cn.O_WRITEFIG, False)
+            # Outputs
+            self.kP = None
+            self.kI = None
+            self.kF = None
         
     def copy(self):
-        options = list(PLOT_OPTIONS)
-        options.extend(["kP", "kI", "kF"])
-        kwargs = {}
-        for key in options:
-            kwargs[key] = getattr(self, key)
-        return ControlSBML(self.model_reference,
+        ctlsb = ControlSBML(self.model_reference,
                 roadrunner=self._roadrunner,
                 input_name=self.input_name,
                 output_name=self.output_name,
@@ -208,10 +240,12 @@ def copy(self):
                 setpoint=self.setpoint,
                 sign=self.sign,
                 times=self.times,
-                save_path=self.save_path,
-                    **kwargs)
+                save_path=self.save_path)
+        for key in cn.CONTROL_PARAMETERS:
+            setattr(ctlsb, key, getattr(self, key))
+        return ctlsb
     
-    def _checkKwargs(self, valids:Optional[List[str]]=OPTIONS, invalids:Optional[List[str]]=None,
+    def _checkKwargs(self, valids:Optional[List[str]]=OPTION_KEYS, invalids:Optional[List[str]]=None,
                      **kwargs):
         """
         Checks that the kwargs are valid.
@@ -233,7 +267,7 @@ def equals(self, other:object):
             return False
         if not self.model_reference == other.model_reference:
             return False
-        if not self._getOptions() == other._getOptions():
+        if not self.getOptions() == other.getOptions():
             return False
         if not self._fitter_result.equals(other._fitter_result):
             return False
@@ -276,21 +310,6 @@ def getPossibleInputs(self):
     def getPossibleOutputs(self):
         return self._sbml_system.getValidOutputs()
     
-    def _getOptions(self, options:Optional[dict]=None):
-        """
-        Gets current values of the options
-
-        Returns: dict. Keys are listed below by category.
-            STAIRCASE_OPTIONS: initial_value, final_value, num_step
-            TIMES_OPTIONS: times
-            CLOSED_LOOP_PARAMETERS: kP, kI, kF, setpoint, sign
-            PLOT_OPTIONS: ax ax2 end_time figure figsize is_plot
-                            suptitle title writefig xlabel xlim xticklabels ylabel ylim yticklabels 
-        """
-        if options is None:
-            option_lst = OPTIONS
-        return {k: getattr(self, k) for k in option_lst}
-    
     def _getTimes(self, **kwargs)->np.ndarray:
         times = kwargs.get(cn.O_TIMES, self.times)
         if times is None:
@@ -302,9 +321,11 @@ def getOpenLoopTransferFunction(self):
 
     def _getStaircase(self, initial_value:Optional[float]=None,
                       final_value:Optional[float]=None, num_step:Optional[int]=None):
-        initial_value = self.initial_value if initial_value is None else initial_value
-        final_value = self.final_value if final_value is None else final_value
-        num_step = self.num_step if num_step is None else num_step
+        dct = self.getOptions(keys=STAIRCASE_KEYS, 
+                              initial_value=initial_value, final_value=final_value, num_step=num_step)
+        initial_value = dct[cn.O_INITIAL_VALUE]
+        final_value = dct[cn.O_FINAL_VALUE]
+        num_step = dct[cn.O_NUM_STEP]
         return Staircase(initial_value=initial_value, final_value=final_value,
                          num_step=num_step, num_point=len(self.times))
     
@@ -324,7 +345,7 @@ def getClosedLoopTransferFunction(self, sign:Optional[int]=None)->control.Transf
         kP = 0
         kI = 0
         kF = 0
-        for parameter_name in CONTROL_PARAMETERS:
+        for parameter_name in cn.CONTROL_PARAMETERS:
             parameter = getattr(self, parameter_name)
             if parameter is not None:
                 if not util.isNumber(parameter):
@@ -349,14 +370,6 @@ def _getParameterStr(self, parameters:List[str], **kwargs):
         return stg
 
     ############ SETTERS ##############
-    def _setOptions(self, **kwargs):
-        """
-        Sets values of options.
-
-        Args:
-            kwargs: dict of options
-        """
-        self.setOptionSet(**kwargs)
 
     def setSystem(self, input_name:Optional[str]=None, output_name:Optional[str]=None,
                   is_fixed_input_species:bool=True,
@@ -386,6 +399,16 @@ def setSystem(self, input_name:Optional[str]=None, output_name:Optional[str]=Non
         self._sbml_system = sbml_system
         self._transfer_function_builder = builder
         return sbml_system, builder
+    
+    def setOption(self, key:str, value:object):
+        """
+        Sets an option.
+
+        Args:
+            key: str
+            value: object
+        """
+        setattr(self, key, value)
 
     def _initializeStaircaseOptions(self, initial_value=None, final_value=None, num_step=cn.DEFAULT_NUM_STEP):
         """
@@ -405,7 +428,7 @@ def _initializeStaircaseOptions(self, initial_value=None, final_value=None, num_
         # Calculate defaults if required
         if initial_value is None:
             if is_assign_from_simulation:
-                ts = self.plotModel(is_plot=False)
+                ts = self.plotModel(is_plot=False, selections=[input_name])
                 initial_value = ts[input_name].min()
             else:
                 initial_value = cn.DEFAULT_INITIAL_VALUE
@@ -417,39 +440,38 @@ def _initializeStaircaseOptions(self, initial_value=None, final_value=None, num_
         if num_step is None:
             num_step = cn.DEFAULT_NUM_STEP
         return initial_value, final_value, num_step
+  
 
     ############ PLOTTERS ##############
     def plotModel(self, 
                   times:Optional[np.ndarray[float]]=None,
+                  selections:Optional[List[str]]=None,
                   **kwargs)->Timeseries:
         """
         Plots the SBML model without modification.
 
         Args:
             times: numpy array (times of simulation)
+            selections: list-str (selections for simulation)
             kwargs: dict (plot options)
         
         Returns:
             Timeseries
         """
-        options = list(PLOT_OPTIONS)
-        options.append(cn.O_IS_PLOT)
-        options.extend(TIMES_OPTIONS)
+        options = list(PLOT_KEYS)
+        options.extend(TIMES_KEYS)
         self._checkKwargs(options, **kwargs)
-        is_plot = kwargs.get(cn.O_IS_PLOT, True)
+        plot_dct = self.getOptions(keys=PLOT_KEYS, **kwargs)
         times = kwargs.get(cn.O_TIMES, self.times)
+        #
         self._roadrunner.reset()
-        if (self.input_name is None) and (not "input_name" in kwargs.keys()):
-            selections = None
-        else:
-            selections = [cn.TIME, self.input_name, self.output_name]
+        self._roadrunner.resetSelectionLists()
+        if selections is not None:
+            selections.extend([cn.TIME])    # type: ignore
+            selections = list(set(selections))
         data = self._roadrunner.simulate(times[0], times[-1], len(times), selections=selections)  # type: ignore
         ts = Timeseries(data)
-        new_kwargs = dict(kwargs)
-        if cn.O_TIMES in new_kwargs:
-            new_kwargs.pop(cn.O_TIMES)
-        if is_plot:
-            util.plotOneTS(ts, markers=self.markers, **kwargs)
+        util.plotOneTS(ts, **plot_dct)
         return ts
     
     def plotStaircaseResponse(self,
@@ -474,7 +496,7 @@ def plotStaircaseResponse(self,
                 columns: <output_name>, staircase
             AntimonyBuilder
         """
-        self._checkKwargs(**kwargs, valids=PLOT_OPTIONS)
+        self._checkKwargs(**kwargs, valids=PLOT_KEYS)
         times = self._getTimes(times=times)
         initial_value, final_value, num_step = self._initializeStaircaseOptions(initial_value=initial_value,
             final_value=final_value, num_step=num_step)
@@ -517,7 +539,7 @@ def plotTransferFunctionFit(self,
             AntimonyBuilder
         """
         # Check the options
-        self._checkKwargs(valids=PLOT_OPTIONS, **kwargs)
+        self._checkKwargs(valids=PLOT_KEYS, **kwargs)
         # Setup values
         times = self.times if times is None else times
         fitter_method = self.fitter_method if fitter_method is None else fitter_method
@@ -549,6 +571,7 @@ def _plotClosedLoop(self,
                         kF:Optional[float]=None, 
                         sign:Optional[int]=None,
                         setpoint:Optional[float]=None,
+                        selections:Optional[List[str]]=None,
                         times:Optional[np.ndarray]=None,
                         **kwargs):
         """
@@ -561,22 +584,27 @@ def _plotClosedLoop(self,
             sign: int (direction of feedback: -1 or 1)
             setpoint: float (regulation point)
             times: numpy array (times of simulation)
+            selections: list-str (selections for simulation)
             kwargs: plot options
         Returns:
             Timeseries
             AntimonyBuilder
         """
-        self._checkKwargs(PLOT_OPTIONS, **kwargs)
+        self._checkKwargs(PLOT_KEYS, **kwargs)
         #
         # Construct the SBML system
-        sign = self.sign if sign is None else sign
-        kP = self.kP if kP is None else kP
-        kI = self.kI if kI is None else kI
-        kF = self.kF if kF is None else kF
-        setpoint = self.setpoint if setpoint is None else setpoint
+        dct = self.getOptions(sign=sign, setpoint=setpoint, kP=kP, kI=kI, kF=kF,
+                              times=times, selections=selections)
+        sign = dct[cn.O_SIGN]
+        setpoint = dct[cn.O_SETPOINT]
+        kP = dct[cn.CP_KP]
+        kI = dct[cn.CP_KI]
+        kF = dct[cn.CP_KF]
+        times = dct[cn.O_TIMES]
+        # Plot the response
         response_ts, builder = self._sbml_system.simulateSISOClosedLoop(input_name=self.input_name,
                 output_name=self.output_name, sign=sign,
-                kP=kP, kI=kI, kF=kF, setpoint=setpoint,
+                kP=kP, kI=kI, kF=kF, setpoint=setpoint, selections=selections,
                 times=times,
                 )
         if (not cn.O_TITLE in kwargs) or (len(kwargs[cn.O_TITLE]) == 0):
@@ -592,6 +620,7 @@ def plotGridDesign(self,
                        times:Optional[np.ndarray]=None,
                        num_process:Optional[int]=-1,
                        num_restart:Optional[int]=3,
+                       selections:Optional[List[str]]=None,
                        **kwargs):
         """
         Plots the results of a closed loop design based a grid of values for the control parameters.
@@ -603,6 +632,7 @@ def plotGridDesign(self,
             sign: float (direction of feedback: -1 or 1)
             times: numpy array (times of simulation)
             num_process: int (number of processes to use; -1 means use all available)
+            selections: list-str (selections for the simulation)
             kwargs: dict (plot options)
         Returns:
             Timeseries
@@ -610,11 +640,16 @@ def plotGridDesign(self,
         """
         save_path = None   # Disable "save_path" feature
         #
-        self._checkKwargs(PLOT_OPTIONS, **kwargs)
+        self._checkKwargs(PLOT_KEYS, **kwargs)
+        option_dct = self.getOptions(sign=sign, setpoint=setpoint, times=times, selections=selections,
+                                     num_process=num_process, num_restart=num_restart)
         # Initialize parameters
-        setpoint = self.setpoint if setpoint is None else setpoint
-        sign = self.sign if sign is None else sign
-        times = self._getTimes(**kwargs)
+        setpoint = option_dct[cn.O_SETPOINT]
+        sign = option_dct[cn.O_SIGN]
+        times = option_dct[cn.O_TIMES]
+        selections = option_dct[cn.O_SELECTIONS]
+        num_process = option_dct[cn.O_NUM_PROCESS]
+        num_restart = option_dct[cn.O_NUM_RESTART]
         if save_path is not None:
             if len(save_path) == 0:
                 save_path = self.save_path
@@ -636,18 +671,20 @@ def plotGridDesign(self,
             msgs.warn("No design found!")
             return None, None
         # Persist the design parameters
-        self.kP = designer.kP
-        self.kI = designer.kI
-        self.kF = designer.kF
-        options = dict(kwargs)
+        self.setOption(cn.CP_KP, designer.kP)
+        self.setOption(cn.CP_KI, designer.kI)
+        self.setOption(cn.CP_KF, designer.kF)
+        # Plot the results 
+        options = self.getOptions(keys=PLOT_KEYS, **kwargs)
         options[cn.O_YLABEL] = self.output_name if not cn.O_YLABEL in kwargs else kwargs[cn.O_YLABEL]
         response_ts, antimony_builder = self._plotClosedLoop(
                 times=times,
                 setpoint=setpoint,
                 sign=self.sign,
-                kP=self.kP,
-                kI=self.kI,
-                kF=self.kF,
+                kP=designer.kP,
+                kI=designer.kI,
+                kF=designer.kF,
+                selections=selections,
                 **options)
         return response_ts, antimony_builder
 
@@ -663,7 +700,8 @@ def plotDesign(self,
                    num_coordinate:int=3,
                    is_report:bool=False, 
                    num_process:int=-1,
-                   times:Optional[np.ndarray]=None, 
+                   times:Optional[np.ndarray]=None,
+                   selections:Optional[List[str]]=None,
                    **kwargs)->Tuple[Timeseries, AntimonyBuilder]:
         """
         Plots the results of a closed loop design. The design is specified by the parameters kP_spec, kI_spec, and kF_spec.
@@ -683,6 +721,8 @@ def plotDesign(self,
             num_coordinate: int (number of coordinate descent iterations)
             is_report: bool (report progress on the design search)
             times: numpy array (times of simulation)
+            num_process: int (number of processes to use; -1 means use all available)
+            selections: list-str (selections for the simulation)
             kwargs: dict (plot options)
         Returns:
             Timeseries
@@ -693,10 +733,23 @@ def setValue(val):
                 return val
             return 0.0
         #
-        times = self.times if times is None else times
-        setpoint = self.setpoint if setpoint is None else setpoint
-        sign = self.sign if sign is None else sign
-        self._checkKwargs(PLOT_OPTIONS, **kwargs)
+        self._checkKwargs(PLOT_KEYS, **kwargs)
+        option_dct = self.getOptions(kP_spec=kP_spec, kI_spec=kI_spec, kF_spec=kF_spec,
+                                     setpoint=setpoint, sign=sign,
+                                     is_report=is_report,
+                                     num_process=num_process,
+                                     times=times,
+                                     selections=selections)
+        times = option_dct[cn.O_TIMES]
+        setpoint = option_dct[cn.O_SETPOINT]
+        sign = option_dct[cn.O_SIGN]
+        is_greedy = option_dct[cn.O_IS_GREEDY]
+        sign=option_dct[cn.O_SIGN]
+        kP_spec = option_dct[cn.O_KP_SPEC]
+        kI_spec = option_dct[cn.O_KI_SPEC]
+        kF_spec = option_dct[cn.O_KF_SPEC]
+        num_process = option_dct[cn.O_NUM_PROCESS]
+        selections = option_dct[cn.O_SELECTIONS]
         #
         designer = SISOClosedLoopDesigner(self._sbml_system, self.getOpenLoopTransferFunction(),
                 is_steady_state=self.is_steady_state,
@@ -708,14 +761,14 @@ def setValue(val):
                 save_path=self.save_path)
         designer.design(kP_spec=kP_spec, kI_spec=kI_spec, kF_spec=kF_spec,
                 num_restart=num_restart, min_value=min_parameter_value, max_value=max_parameter_value,
-            num_coordinate=num_coordinate, is_greedy=self.is_greedy, is_report=is_report, num_process=num_process)
+            num_coordinate=num_coordinate, is_greedy=is_greedy, is_report=is_report, num_process=num_process)
         if designer.residual_mse is None:
             msgs.warn("No design found!")
             return None, None  # type: ignore
         # Persist the design parameters
-        self.kP = designer.kP
-        self.kI = designer.kI
-        self.kF = designer.kF
+        self.setOption(cn.CP_KP, designer.kP)
+        self.setOption(cn.CP_KI, designer.kI)
+        self.setOption(cn.CP_KF, designer.kF)
         # Plot the results
         title = "" if not cn.O_TITLE in kwargs else kwargs[cn.O_TITLE]
         if len(title) == 0:
@@ -726,9 +779,10 @@ def setValue(val):
                 times=times,
                 setpoint=setpoint,
                 sign=sign,  # type: ignore
-                kP=self.kP,
-                kI=self.kI,
-                kF=self.kF,
+                kP=designer.kP,
+                kI=designer.kI,
+                kF=designer.kF,
+                selections=selections,
                 **new_kwargs)
         return response_ts, antimony_builder
     
@@ -742,7 +796,7 @@ def _plotDesignResult(self, save_path:Optional[str]=None, **kwargs):
             AntimonyBuilder
         """
         valids = ["save_path"]
-        valids = list(set(valids).union(PLOT_OPTIONS))
+        valids = list(set(valids).union(PLOT_KEYS))
         self._checkKwargs(**kwargs)
         if save_path is None:
             save_path = self.save_path
@@ -750,4 +804,27 @@ def _plotDesignResult(self, save_path:Optional[str]=None, **kwargs):
         if designer.design_result_df is None:
             msgs.warn("No design found!")
             return None, None
-        return designer.plotDesignResult(**kwargs)
\ No newline at end of file
+        return designer.plotDesignResult(**kwargs)
+    
+    ############## MISC ################
+    def getOptions(self, keys=None, **kwargs)->dict:
+        """
+        Optons the options in the object.
+            STAIRCASE_OPTIONS: initial_value, final_value, num_step
+            TIMES_OPTIONS: times
+            CLOSED_LOOP_PARAMETERS: kP, kI, kF, setpoint, sign
+            PLOT_OPTIONS: ax ax2 end_time figure figsize is_plot
+                            suptitle title writefig xlabel xlim xticklabels ylabel ylim yticklabels 
+        Args:
+            keys: list-str (keys in the dictionary returned)
+            kwargs: dict (plot options)
+        Returns:
+            dict
+        """
+        keys = OPTION_KEYS if keys is None else keys
+        new_kwargs = {}
+        # FIXME: Doesn't allow user to change option to None
+        for key in keys:
+            new_kwargs[key] = getattr(self, key) if not key in kwargs.keys() else kwargs[key]
+            new_kwargs[key] = getattr(self, key) if new_kwargs[key] is None else new_kwargs[key]
+        return new_kwargs
\ No newline at end of file
diff --git a/src/controlSBML/sbml_system.py b/src/controlSBML/sbml_system.py
index b5d9917..7492d0c 100644
--- a/src/controlSBML/sbml_system.py
+++ b/src/controlSBML/sbml_system.py
@@ -12,6 +12,7 @@
 import pandas as pd  # type: ignore
 import numpy as np  # type: ignore
 import tellurium as te  # type: ignore
+from typing import Optional, List
 
 
 class SBMLSystem(object):
@@ -320,7 +321,10 @@ def simulate(self, start_time=cn.START_TIME, end_time=cn.END_TIME, num_point=Non
             self.setSteadyState()
         return self._simulate(start_time, end_time, num_point, is_steady_state, is_reload=False)
     
-    def _simulate(self, start_time, end_time, num_point, antimony_builder=None, is_steady_state=False, is_reload=False):
+    def _simulate(self, start_time:float, end_time:float, num_point:int, 
+                  antimony_builder:Optional[AntimonyBuilder]=None,
+                  selections:Optional[list]=None,
+                  is_steady_state:Optional[bool]=False, is_reload:Optional[bool]=False):
         """
         Simulates the system the roadrunner object.
 
@@ -330,17 +334,20 @@ def _simulate(self, start_time, end_time, num_point, antimony_builder=None, is_s
         end_time: float
         num_point: int
         antimoney_builder: AntimonyBuilder
+        selections: list-str (names of species to be selected)
         is_steady_state: bool (start the simulation at steady state)
 
         Returns
         -------
         DataFrame
         """
-        if antimony_builder is None:
-            antimony_builder = self.antimony_builder
-        antimony = str(antimony_builder)
-        if antimony[-1] == "\n":
-            antimony = antimony[:-1]
+        # Set defaults
+        selections = [] if selections is None else selections
+        antimony_builder = self.antimony_builder if antimony_builder is None else antimony_builder
+        # Initializations
+        antimony_str = str(antimony_builder)
+        if antimony_str[-1] == "\n":
+            antimony_str = antimony_str[:-1]
         if is_reload:
             try:
                 self._roadrunner = te.loada(str(antimony_builder))
@@ -350,7 +357,7 @@ def _simulate(self, start_time, end_time, num_point, antimony_builder=None, is_s
             self.roadrunner.reset()
         if is_steady_state:
             self.setSteadyState()
-        selections = list(self.input_names)
+        selections.extend(self.input_names)
         selections.extend(self.output_names)
         selections.insert(0, cn.TIME)
         data = self.roadrunner.simulate(float(start_time), float(end_time), num_point, selections=selections)
@@ -370,6 +377,7 @@ def isInitialized(self)->bool:
     def simulateSISOClosedLoop(self, input_name=None, output_name=None, kP=None, kI=None, kF=None, setpoint=1,
                                start_time=cn.START_TIME, end_time=cn.END_TIME, times=None, num_point=None,
                                is_steady_state=False, inplace=False, initial_input_value=None,
+                               selections:Optional[list[str]]=None,
                                sign=-1):
         """
         Simulates a closed loop system.
@@ -387,6 +395,7 @@ def simulateSISOClosedLoop(self, input_name=None, output_name=None, kP=None, kI=
             num_point: int (overridden by times)
             inplace: bool (update the existing model with the closed loop statements)
             initial_input_value: float (initial value of the input)
+            selections: list-str (names of species to be selected)
             sign: float (sign of the feedback)
         Returns:
             Timeseries
@@ -418,11 +427,13 @@ def simulateSISOClosedLoop(self, input_name=None, output_name=None, kP=None, kI=
                                          initial_output_value=initial_input_value, sign=sign)
         # Run the simulation
         result = self._simulate(start_time, end_time, num_point, is_steady_state=is_steady_state,
-                            antimony_builder=builder, is_reload=True), builder
+                            antimony_builder=builder, is_reload=True, selections=selections), builder
         return result
     
     def simulateStaircase(self, input_name, output_name, times=cn.TIMES, initial_value=cn.DEFAULT_INITIAL_VALUE,
-                 num_step=cn.DEFAULT_NUM_STEP, final_value=cn.DEFAULT_FINAL_VALUE, is_steady_state=True, inplace=True):
+                 num_step=cn.DEFAULT_NUM_STEP, final_value=cn.DEFAULT_FINAL_VALUE,
+                 selections:Optional[List[str]]=None,
+                 is_steady_state=True, inplace=True):
         """
         Adds events for the staircase.
         Args:
@@ -432,6 +443,8 @@ def simulateStaircase(self, input_name, output_name, times=cn.TIMES, initial_val
             final_value: float (value for final step)
             num_step: int (number of steps in staircase)
             num_point_in_step: int (number of points in each step)
+            selections: list-str (names of species to be selected)
+            is_steady_state: bool (start the simulation at steady state)
             inplace: bool (update the existing model with the Staircase statements)
         Returns:
             Timeseries
@@ -446,7 +459,8 @@ def simulateStaircase(self, input_name, output_name, times=cn.TIMES, initial_val
         builder.makeStaircase(input_name, times=times, initial_value=initial_value,
                                             num_step=num_step, final_value=final_value)
         ts = self._simulate(start_time=times[0], antimony_builder=builder, end_time=times[-1], num_point=len(times),
-                            is_steady_state=is_steady_state, is_reload=True)
+                            is_steady_state=is_steady_state, is_reload=True,
+                            selections=selections)
         return ts, builder
     
     @staticmethod 
diff --git a/src/controlSBML/siso_transfer_function_fitter.py b/src/controlSBML/siso_transfer_function_fitter.py
index 9ede353..4252e1c 100644
--- a/src/controlSBML/siso_transfer_function_fitter.py
+++ b/src/controlSBML/siso_transfer_function_fitter.py
@@ -212,7 +212,7 @@ def plot(self, **kwargs):
         self._setYAxColor(ax, "left", cn.SIMULATED_COLOR)
         self._setYAxColor(ax2, "right", cn.INPUT_COLOR)
         ax.set_title(title)
-        ax.set_title(latex, y=0.2, x=0.5, pad=-14, fontsize=14, loc="right")
+        ax.set_title(latex, y=0.3, x=0.8, fontsize=14, loc="right")
         ax.legend([self.output_name, cn.O_PREDICTED], loc="upper left")
         mgr.doPlotOpts()
         mgr.doFigOpts()
diff --git a/src/controlSBML/util.py b/src/controlSBML/util.py
index 23862da..317f637 100644
--- a/src/controlSBML/util.py
+++ b/src/controlSBML/util.py
@@ -623,13 +623,14 @@ def roundToDigits(number:float, num_digits:int=2):
     required_decimal = -int(log_number) + num_digits
     return np.round(number, required_decimal)
 
-def subsetDct(dct, keys):
+def subsetDct(dct:dict, keys:List[str], default:Optional[dict]=None)->dict:
     """
     Returns a subset of a dictionary.
 
     Args:
         dct: dict
         keys: list-str
+        default: dict (default values of subsetted keys)
     Returns:
         dict
     """
@@ -637,6 +638,8 @@ def subsetDct(dct, keys):
     for key in keys:
         if key in dct.keys():
             new_dct[key] = dct[key]
+        elif (default is not None) and (key in default.keys()):
+            new_dct[key] = default[key]
     return new_dct
 
 def differenceDct(dct1, dct2):
diff --git a/tests/test_control_sbml.py b/tests/test_control_sbml.py
index 25ea32c..a27ebd6 100644
--- a/tests/test_control_sbml.py
+++ b/tests/test_control_sbml.py
@@ -13,9 +13,9 @@
 import unittest
 
 
-IGNORE_TEST = True
+IGNORE_TEST = False
 IS_PLOT = False
-TIMES = np.linspace(0, 1000, 10000)
+TIMES = cn.TIMES
 FIGSIZE = (5, 5)
 SAVE1_PATH = os.path.join(cn.TEST_DIR, "control_sbml_save_path.csv")
 LINEAR_MDL = """
@@ -91,7 +91,9 @@ def testSetSystem(self):
     def testPlotModel(self):
         if IGNORE_TEST:
             return
-        ts = self.ctlsb.plotModel(is_plot=IS_PLOT, figsize=FIGSIZE)
+        self.ctlsb = CTLSB.copy()
+        ts = self.ctlsb.plotModel(is_plot=IS_PLOT, figsize=FIGSIZE, selections=["S1", "S2", "S3"])
+        ts = self.ctlsb.plotModel(is_plot=IS_PLOT, figsize=FIGSIZE, selections=["S2"])
         ts = self.ctlsb.plotModel(is_plot=IS_PLOT, figsize=FIGSIZE, times=np.linspace(0, 100, 1000))
         ts = self.ctlsb.plotModel(is_plot=IS_PLOT, figsize=FIGSIZE, markers=False)
         self.assertTrue(isinstance(ts, Timeseries))
@@ -99,6 +101,7 @@ def testPlotModel(self):
     def testPlotStaircaseResponse(self):
         if IGNORE_TEST:
             return
+        self.ctlsb = CTLSB.copy()
         self.ctlsb.setSystem(input_name="S1", output_name="S3")
         ts, builder = self.ctlsb.plotStaircaseResponse(is_plot=IS_PLOT, figsize=FIGSIZE,
                                                        times=np.linspace(0, 100, 1000))
@@ -118,8 +121,9 @@ def testPlotTransferFunctionFit(self):
     def testPlotSISOClosedLoop(self):
         if IGNORE_TEST:
             return
-        self.ctlsb.setSystem(input_name="S1", output_name="S3")
-        ts, builder = self.ctlsb._plotClosedLoop(setpoint=3, is_plot=IS_PLOT, kP=1, figsize=FIGSIZE,
+        ctlsb = CTLSB.copy()
+        ctlsb.setSystem(input_name="S1", output_name="S3")
+        ts, builder = ctlsb._plotClosedLoop(setpoint=3, is_plot=IS_PLOT, kP=1, figsize=FIGSIZE,
                                                           times=np.linspace(0, 100, 1000))
         self.assertTrue(isinstance(ts, Timeseries))
         self.assertTrue(isinstance(builder, AntimonyBuilder))
@@ -127,13 +131,14 @@ def testPlotSISOClosedLoop(self):
     def testPlotDesign(self):
         if IGNORE_TEST:
             return
+        ctlsb = CTLSB.copy()
         setpoint = 5
-        self.ctlsb.setSystem(input_name="S1", output_name="S3")
-        ts, builder = self.ctlsb.plotDesign(setpoint=setpoint, kP_spec=True, kI_spec=True, figsize=FIGSIZE, is_plot=IS_PLOT,
+        ctlsb.setSystem(input_name="S1", output_name="S3")
+        ts, builder = ctlsb.plotDesign(setpoint=setpoint, kP_spec=True, kI_spec=True, figsize=FIGSIZE, is_plot=IS_PLOT,
                                             min_parameter_value=0.001, max_parameter_value=10, num_restart=2,
-                                            num_coordinate=5, num_process=1)
+                                            num_coordinate=5, num_process=10)
         # Show that kP, kI are now the defaults
-        _ = self.ctlsb._plotClosedLoop(setpoint=setpoint, is_plot=IS_PLOT, kP=1, figsize=FIGSIZE,
+        _ = ctlsb._plotClosedLoop(setpoint=setpoint, is_plot=IS_PLOT, kP=1, figsize=FIGSIZE,
                                                           times=np.linspace(0, 100, 1000))
         self.assertTrue(isinstance(ts, Timeseries))
         self.assertTrue(isinstance(builder, AntimonyBuilder))
@@ -183,11 +188,11 @@ def testGetters(self):
         self.assertTrue(isinstance(self.ctlsb._transfer_function_builder, SISOTransferFunctionBuilder))
         self.assertTrue(isinstance(self.ctlsb.getAntimony(), str))
         self.assertTrue(isinstance(self.ctlsb.getClosedLoopTransferFunction(), control.TransferFunction))
-        self.assertTrue(isinstance(self.ctlsb._getOptions(), dict))
+        self.assertTrue(isinstance(self.ctlsb.getOptions(), dict))
 
     def testFullAPI(self):
-        #if IGNORE_TEST:
-        #    return
+        if IGNORE_TEST:
+            return
         INPUT_NAME = "pIRS"
         OUTPUT_NAME = "pmTORC1"
         INPUT_NAME = "pIRS"
@@ -346,9 +351,10 @@ def testBug6(self):
             end
             """
         try:
-            ctlsb = ControlSBML(model, input_name="S0", output_name="S4") 
+            _ = ControlSBML(model, input_name="S0", output_name="S4") 
             self.assertTrue(True)
-        except:
+        except Exception as e:
+            print(e)
             self.assertTrue(False)
 
     def testBug7(self):
diff --git a/tests/test_util.py b/tests/test_util.py
index 1d8502a..33d26af 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -72,6 +72,15 @@ def testSubsetDct(self):
         self.assertTrue(len(subset) == 2)
         self.assertTrue("b" not in subset)
 
+    def testSubsetDct2(self):
+        if IGNORE_TEST:
+          return
+        dct = {"a": 1, "b": 2}
+        subset = util.subsetDct(dct, ["a", "c"], {"c": 3})
+        self.assertTrue(len(subset) == 2)
+        self.assertTrue("b" not in subset)
+        self.assertTrue(subset["c"] == 3)
+
     def testDifferenceDct(self):
         if IGNORE_TEST:
           return