diff --git a/notebooks/DCR Pseudo-section Simulation.ipynb b/notebooks/DCR Pseudo-section Simulation.ipynb
index a443897e..216e1de2 100644
--- a/notebooks/DCR Pseudo-section Simulation.ipynb	
+++ b/notebooks/DCR Pseudo-section Simulation.ipynb	
@@ -45,7 +45,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transmitter 8 of 9 -> Time:0.976000070572 sec Transmitter 8 of 9\n",
+      "Transmitter 8 of 9 -> Time:0.957999944687 sec Transmitter 8 of 9\n",
       "Forward completed\n"
      ]
     },
@@ -53,7 +53,7 @@
      "data": {
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NLUtzicwncfd7pG0bkdkVklFkDAaDoak0l8h8GZe3P8lTk2nzcTIGg8FgaBp5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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x154fecc0>"
+       "<matplotlib.figure.Figure at 0x38afc88>"
       ]
      },
      "metadata": {},
@@ -95,7 +95,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transmitter 8 of 9 -> Time:0.976999998093 sec Transmitter 8 of 9\n",
+      "Transmitter 8 of 9 -> Time:0.968999862671 sec Transmitter 8 of 9\n",
       "Forward completed\n"
      ]
     },
@@ -103,7 +103,7 @@
      "data": {
       "image/png": 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BEemsqlvMfDLJh/HJNA3jkzHURTL6ZBJxxP/VwD3OvDGo6hYn3cwnk2C2NGSbmU+mqeWW\nOD4ZM59MYvlk9sj5ZETkQxE5PSbt0dYziSLgGBH5TERmicihTrqZT8ZgMBiSjAaby5xIyGuBD1R1\nkpP2taoObfJF659P5i7gQ1W9XkQOA/6nqv1E5J/AZ6r6vJPH48DbqvpqTN4m1H+CYJrLmoZpLjOE\n2RNC/TemuawEOB54QETeAC5p7kVV9aS69onI1cCrznHzRMQSkU7Y88r0ch0anmumFpfNijbRNJeZ\n5jLTXGaay5LJtnDa6KfOikq7U5IvPnCjepepalBVxwKvAB8BnVvRptewRQ0RGQikqupWYBpwgYik\nikhf7Ga1L1rRDoPBYDA0k8bUZB4Or6jqUyKyGPht65nEk8CTznX8wKXOtc18MgaDwZBkJFwX5uZi\nujAnDsYn0zSMT8ZQF6YLc4JgujAniG1E34u2DDMT0luSttwgerstw8zEhpFpyNZE2k4kW5prW11p\ne2QXZoPBYDAYmooRGYPBYDC0GsYnY2gz2tJHk0w+mIZoSx+N8cEkNsYnkyAYn0yC2kbr+WhifTC7\nbVsCl1tr+mga8sHsrq2JXI7JZFtdacYnYzAYDAaDCyMyBoPBYGg1Es4nIyLDgQeBFGoGXc5z9o0D\nrgRCwHWq+l6c841PJkloSR/NnuSDaYiW9NEYH0xyYXwyLcO9wG2qOl1ERjnbxznzyYwB9seZT0ZE\nas0nA8YnkzS20XbxxBKpnJprW6yPJpFtNbYZn0wiNpdtBPKc9XxqgmBG5pNR1VVAeD4Zg8FgMCQo\niViTuRn4WETuwxbBI5z07kRPUGbmkzEYDIYEp11EpoH5ZK7D9rdMFZHzsQNm1jU1QFyH0uyJcyLr\nfUb2aZ6xBoPB0E6smrWa1bNWt7cZzSIRHf+7VDXXWRegRFXzRORmAFX9s7PvXWCCqn4ec75x/BsM\nhj0S4/hvGVaIyLGqOht7XpnlTvo04AURmYzdTFbnfDLG8W9sM7btfbYmki3Nta2utGR0/CeiyPwK\n+JeIpAGVzraZT8ZgMBiSkIQTGVX9EhhRx767geSbf9RgMBj2UhKxC7PBYDAY9hCMyBgMBoOh1TAi\nYzAYDIZWI+G6MDcX04XZYDDsqZguzAmC6cJsbDO27X22JpItzbWtrrRk7MJsmssMBoPB0Gq0i8iI\nyPki8q2IhETkkJh940SkWESWisjJrvRhIrLY2fePtrfaYDAYDLtLe9VkFgPnAHPciTHh/E8FHnJC\nywD8G7hKVYuAIhE5tQ3tNRgMBkMTaBeRUdWlqro8zq544fxHiEg3IEdVw2FkngHObhtrDQaDwdBU\nEs0n0x07hH+YcDj/2PT1mDD/BoPBkPC0Wu+yesL536Kqb7TWdQ0Gg8GQOLTrOBkRmQn8XlW/crbj\nhvMHVgMzVXU/J/3nwLGq+ps4eWrvY3tFtvML88gvzG/tr2IwGAwtTsmqEkpW7Yxsr5m91oyTaQLu\nAosbzl9VVUR2icgI7PD+lwAP1JXhZbMuido242SMbYlkSzLZlky2JpItzbUtnDb6qbOi0u6U5IsP\n3F5dmM8RkbXA4cBbIvIO2OH8gXA4/3eIDuc/FngcKAZWqOq7bW+5wWAwGHaHdqnJqOpUYGod++KG\n81fV+cCBrWyawWAwGFqQROtdZjAYDIY9CCMyBoPBYGg1jMgYDAaDodUwImMwGAyGVsOIjMFgMBha\nDSMyBoPBYGg1jMgYDAaDodVIhPlkhrnSTxKRL0VkkfN5nGufmU/GYDAYkox2iV0mIoMAC3iE6Nhl\nQ4BNqrpJRAYD01W1p7PvC+AaVf1CRN4GHog36l9E9JgJR7XZdzEYDIa2Ys6kj03sssagqksBauYj\ni6QvcG1+B2SISArQifjzycQNLdOYubKTJeZRMtmaSLYY2/Y+WxPJlubaVlfanEkfk2wksk/mXGC+\nqgawg2Wa+WQMBoMhyUjI+WScprI/Ayc15dqzJ9bM6txnZJ+mZNEqrJq1msIEsseNsa1pGNuahrGt\ncayatZrVs1a3txnNotVERlWbJBAi0hN4FbhEVX9wktcDPV2H9XTS4hJbxUyUm7Q6gX68sRjbmoax\nrWkY2xpH4cg+UbaY5rKmEXHMiEg+8BZwk6p+Gk5X1Y3ALhEZIbYj5xLgtTa31GAwGAy7RULNJwNc\nA/QHJojI187Sydln5pMxGAyGJKNdp19uDURkz/pCBoPB4CLZujDvcSJjMBgMhsQhEXwyBoPBYNhD\nMSJjMBgMhlbDiIzBYDAYWo2kFRkR+auILBGRhSLyqojkufaNcwJpLhWRk13pbRJkMyYA6CGu9EIR\nqXT1nHsoUWxz9rVrucXYMlFE1rnKalRDdrYlInKqc/1iEbmpPWyIsWeVE1j2ayfOHyJSICIzRGS5\niLznDBFoC1ueFJHNIrLYlVanLW15P+uwLSF+ayLSS0RmOv/Pb0TkOic9IcquyahqUi7Y0QA8zvqf\ngT876/sDC4AUoBBYQU0Hhy+A4c7628CprWTbIGAgMBM4xJVeCCyu45z2tq3dyy3GzgnADXHS49np\naePfnte5bqFjxwJgv7a0IY5NPwAFMWn3Ajc66zeF/yNtYMvRwFD3b70uW9r6ftZhW0L81rAjpAxx\n1rOBZcB+iVJ2TV2StiajqjNU1XI2P6cmIsBo4EVVDajqKuyCHyEi3YgfZLM1bFuqqssbe3yC2Nbu\n5RaHeF0149k5vI3sCTMce6zWKrVj6/3Xsau9iS2vs4CnnfWnaaP7pqofATsaaUub3s86bIME+K2p\n6iZ1ggSrahmwBDtGY0KUXVNJWpGJ4UrsN2yA7kQH01yHfaNi09sryGZfp0o+S0TCcxIkQgDQRCy3\na53m0CdcTQR12dmW9ADWtrMNsSjwvtjzMP3SSeuqqpud9c1A1/YxrV5bEuF+QoL91kSkELvG9TmJ\nX3b10i6h/huLNCLIpojcCvhV9YVEsy0OG4BeqrrD8Ye8JnYw0ESwrc2px85bgX8DdzjbdwL3A1fV\nkVVbD/ZKxMFlR6rqRhHpDMwQkaXunaqqiTJQuRG2tLWdCfVbE5Fs4BXgelUtFdeUKAlYdg2S0CKj\nDQTZFJHLgdOAE1zJ64Feru2e2Aq/W0E2m2tbHef4Ab+z/pWIrASKEsE22qjc3DTWThF5HAiLYzw7\nW8Se3SDWhl5Ev1G2OWrH90NVt4jIVOxmk80iso/akwB2A35sRxPrsqXd76eqRsqlvX9rYs+f9Qrw\nrKqG4zMmbNk1hqRtLhORU4E/AqNVtcq1axpwgYikikhf7If4F6q6ifYJsukOANpJRLzOej/Htu+1\n/QKAutuhE6rcnD9TmHOAcG+guHa2tj0xfAkUid1bMBUY49jVLohIpojkOOtZwMnY5TUNuMw57DLa\nN6hsXba0+/1MlN+a8/96AvhOVf/u2pWwZdco2rvnQVMX7ECZq4GvneUh175bsJ1gS4FTXOnDsH9A\nK7Cnb24t287BbrOvBDYB7zjp5wLfOPbOB05PFNsSodxi7HwGWAQsxP5TdW3Izjb+/Y3C7v2zAhjX\nHja4bOmL3ctogfP7GuekFwDvA8uB94D8NrLnReymYb/zW7uiPlva8n7Gse3KRPmtAUdhT0u/wPVc\nOzVRyq6pi4ldZjAYDIZWI2mbywwGg8GQ+BiRMRgMBkOrYUTGYDAY9iJEZJCIfCoiVSLy+3qO6ysi\nnztha/7r9Hxr9PlhjMgYDAbD3sU24FrgvgaO+wtwv6oWYUdJCI8dauz5gBEZg8Fg2KtQ1S2q+iUQ\nqOsYpzv1ccDLTlIknE1jzndjRMZgMBgMsXQESrQmPmSTw0klhMhIgoVNNxgMhmRBRDTe0t52hWn3\nsDLOCPgHgROx1XKeiExT1SWuYxKmwAwGg6ElUdV4EaB3i7s90fWFWywraltExgLhwKmj1I7kUR/b\ngHwR8Ti1mSaHrEmEmkyjwqa396hVVaW6uprOPTrbY4QnAscCV0DnHp3x+/0tn//Epuc/YcKEqO1p\n06YxcGAW06fDzJn2Mn06FBVlMW3atGbbPn78eHr2pFb+PXrA+PHj67WtJfNurt2XXdZyebe07bHl\n1pbl0lDee8s9ben/UUuRbllRS5zn50OqOtRZwgJTp7ipbdxM4HwnKV5YokaJYyKITCKGTY9Lamoq\nd024i6xPsiJpWZ9mcffEu0lJSUno/F966TlGjSonNdV9PRg1qpyXX36+WXkDTJnyFOeeS638zz0X\nXnrp6bpP3IPzbu38kzXv1s6/NfNu7f9RU8n1eKKW+hCRfURkLfB/wHgRWeNEfkZE3hKRcGT0m4Ab\nRKQY6IAdV63e8+NeryXVtCmIyLnYMy3+0tm+GBihqte6jtEJEyZEzhk5ciQjR45sa1MB8Pv99OzX\nky2nbIEn28UEg8GQQMycCVOnQknJGJ5++r/1Hjtr1ixmzZoV2Z40aRLazOYyEdFYebuIlmmGaxGa\n2xzQ3AU4HHjXtT0OuCnmGE0kHn30Uc0alKXpvdL1sccea7X8swZlNTn/mTNnRm1PmzZNi4qydPp0\ndOZMe5k+HS0qytJp06Y12+bx48drjx7Uyr9HD3T8+PH12taSeTfX7r/9reXybmnbY8utLculobz3\nlnva0v8j59nW3GeovuHxRC0tkW9LLYlQk/FhR7M9ATs66hfAzzXG8d/edroJ12YA1v+wvkWaylo7\nf8uyuOii85k/fzqjRpUD8M47WQwbdgrPP/8Sngaq2A0RDAYZNKgPVVUbOPdcO+2VVyA9vTtLl67G\n52t6H5NkzTuZbTflEp+W/h+JSIvUZN6PSTsRU5OJVeJ6w6aTYDUZVdV3331X33333aTKPxQK6bRp\n0/TSS8fopZeO0WnTpmkoFGqx/AOBgI4fP1733beX7rtvLx0/frwGAoG9Ou/Wzj9Z827t/Fsz75b8\nH9FCNZnPPZ6opSXybaml3WsyjSHRajIGg8HQErRUTWZRTNpBJE5Npt3HyRgMBoOheeS2twH1YETG\nYDAYkpzc1BhfkL/2WJn2woiMwWAwJDkZqTGi4m8fO+JhRMZgMBiSnPTsmJpMmanJGAwGg6GlSEsc\nUYnFiIzBYDAkO7E1GRJHdIzIGAwGQ7IT65NJIFo9QKaI/FVElojIQhF5VUTyXPvGOXPILBWRk1vb\nFoPBYNgjyfZELwlEW1jzHjBYVQ8GlmPHJkNE9gfGAPsDpwIPiUhilY7BYDAkA2lW9JJAtPpDXVVn\naM0Unp9jT34D9pwxL6pqQFVXYYeUGd7a9hgMBsMex15ek3FzJfC2s94de+6YMAk7j4zBYDAkNKlW\n9JJAtIjjX0RmAPvE2XWLqr7hHHMr4FfVF+rJqs4AZRMnToyst+d8MgaDwdBUYueTaTESuHdZmwTI\nFJHLseeXPkFVq5y0mwFU9c/O9rvABFX9PM75JkCmwWDY42ipAJl6dUzav/eiAJkicirwR+DYsMA4\nTANeEJHJ2M1kRdhzyRgMhhZm69atzJ8/n0WLFlFaWkp1dTUpKSlkZWWx3377MWzYMHr27IlIQjyX\nDLtLVuLWZNpinMw/gVRghvMD/lRVx6rqdyIyBfgOCAJjTXWlaWzfvp0NGzbQs2dP8vPzWzTvQCBA\ncXExWVlZ9OnTp0XzBli9ejXl5eUMGDCAVPfE6S1ARUUFq1atYp999qGgoKBF8w4GgxQXF5Oenk5h\nYWGLP5zXrVvHzp07GTBgAGlpabt9vmVZfPDBBzzxxBN8+umnrFmzpsFzOnfuzPDhw7nkkks455xz\nmnQ/QqEQxcXF+Hw++vfv3+LlsmHDBnbs2EG/fv3IyMho0byTmgTrURZFe09o09hJeQy12b59u55z\nzgWalpajOTn9NC0tRy+88HLdtWtXs/O2LEsnT/6H5uV10ezsPpqR0VEHDz5M582b1wKWq86bN08H\nDz5cMzK6aHZ2kebl7aP33/93tSyrRfIPs3NnqT700CN6zTV/1O3bt7dIno888pgWFHR3yqWzFhUN\n0Y8++qhF8v7mm2/0kEOO0PT0PM3J6aU5OQV6xx13NbpcduzYoZMnT9aioiLF9nE2aenatauOHz9e\n165d22jbn3vuee3SpZdmZXXTzMzOWlg4SN97772mFkUUxcXFesQRx2t6er7m5PTTrKwOetNN4zUY\nDLZI/u1DgPwKAAAgAElEQVQFLTRpmd7jiVpaIt+WWtrdgEYXoiGKYDCoBxxwqKamjlaYqjBD4RVN\nSztVDz98ZLMf1n/962TNzOyv8KiT97sKN2l2dkddvnx5s/Jevny5Zmd3VnhSoULBr/C1ZmYO1nvv\nndysvOv63sFgSB977KlmP5Qee+wJzczso/CcwnyFeQr3amZmgS5cuLBZea9du1bz8jorXKbwmMJ/\nFO7RzMwivfXW2xs8/7XXXtOuXbvGFY20tDTNy8tXr7dA4ScKJygcrR5PD01Pz9CcnJy452VmZuoD\nDzzQ4MyPU6ZM0czMrgp3KPxX4UWFmzQjo4N+8sknzSqXLVu2aMeO3dXjuU7hQ4WPFaZoZuYwHTv2\nd83Ku71pMZGZHL0kksgkVodqQ6OZPn06q1aV4/f/Fsh2UnOprv4dixf/wNy5c5ucd3V1NXfccTcV\nFeOAvk6qFziRysrTuPvuvzbL9nvumUxl5S+Bi6lpsR1MKPRffD5f+I/TJObOnct++43kuussli6t\nSfd6PfziF5dRUVHR5LxDoRC33DKJioo7gP2cVA9wApWVlzFhwj1Nzhvg739/gMrKQ4CR1JTLPlRU\n/JrJk/9OaWlp3PO2bdvGRRddxNlnn83mzZsj6bm5uVx33XV8+eWXfPLJJ/j9HkKhscDJwNHACVjW\nL/D5evPQQw+xZMkSJk6cSPfu3SN5VFRUcN1113H88cezcuXKuNdXVW688XYqKq4CBjqpAgylsvJ8\nxo2b1Jxi4eGHH6WiYiiW9TPslneA7lRU3MGTTz7Jli1bmpX/HkGWJ3pJIEzssiTl/fc/pKzscOw/\nsxsvFRWHc889r3HKKb2bkLOH9eu/w+/PAWr7YEKho5g69V6GDv0xcnyNDV7nU1zp7v0CCFOnziYU\neiYq3y5d4N13BzN06AGRNL8fvvwSvv0WKisbZ/17781m2bIRLF3m4Z8PwnHHwW3j7U+AnJwcLAtm\nzYJvvnGf6X6Bt2LW7f3btn3Pzp1+4ABiUT2B6dOv4oEHNjkpNefV9My36t333HPv4PefGedbdUS1\nG7fe+jIDBhyG26m7YcNyHn54LDt31jxoc3M7MmrUrxg2bBRpaRnMnWsxZ87/CAQGUPsvL5SV9ecf\n/5jCJZfsS4cOp/GHP5zM4sUf8c47j7Bp0/cAzJ49m/33P4Arr7yf/fY7yjnXfpiVl+9k7do1wIFx\nbB/O3LnP8sADK2LSYx+EdW8//vi7VFaeESfvPFJTD+TTTz/lrLPOirN/LyKBfTJGZJKUzMxMvN51\nhELx9pazaJGwefMuZ9tLbTGqm6qqEIFAOfbDLPbPX0ZVVTrPPBN08g0/JH3ONbx1bNdcv7IyE9gV\n2e7RA2bO9FFUVHPM51/Cn/4MG8PPbA+1v0KsaQKb12WArI2YNXOmLSi//jX89d5qsrPT8Hjg+ONh\n8VJ49gXna6rYnyGJuVCNEAQCeQQClUAASIm5eCmBQDrPPFMFhIgWlvADIOh8huLsC1FamgLEq2kp\nfn8ZM2aU8skn6yLHl5evoLj4NkKhmhpOQcEx9Op1BStW5LBixcbI+Vu37sSy6lLqKlav9vHMM8XU\n3K8+dOv2J+BlNm16DbDw+6t45JHr6dv3Rjp0OIrwS0MoVI1lhbBnyortpFCBZaXxzDM/Yt+w8E0T\n1xLejn0hsY/dvt0HlNdhexmZmZl17NuLSOBxMkZkkpSf/3wMkycfR2XlGCDHtWcHPt8cOna8Heja\npLzT0grw+Tri98/FblYJo4hMo2PHC7HH3orzTHAeDLUrVbUrOh7o2P18Nq5+ELWOolMn4f33awQm\nGAxy/xNeXnrbOakj7udNdN5xrpeffS7rVx4CegdIF9tqhYf/XcL0905h2tuzOGCg3Svp+rFQ6YGX\n33TOt8TWhpDUPP/DehCClNRepGccQGXFO0D0m7PIFDp2PA8oILomFM4gLDDu2pFbjIJ07HgOlZUv\no3pQzBdegsdjkZl5WCSPysofKC6eEBEYjyeTvn1vJj//SKJrY/ZnXt4JrFnzPFACuHsg+vF4FlJQ\ncLOTHi5sDx6P0KPHb8nPP5Hvv5+I3/8jqkF++OFePJ6O5OUdAXjweoWsrEMpK/sAOC3mpkynQ4dT\nnXJx4370xL6IRG936nQe5eWPYFnHx5y3FJGNHHPMMez1JHBNpk0GYzYXMxgzPtdeewP/+c8blJdf\nCAwAlpGV9TzXX38pXbqczrPPDm1y3qWlH7NixblY1rnAkcAuRF4nNW0b+x34Ed7UnJqKSlhIwg/+\n8Atr7LZzXEjLWPreSPwV/fhgxqOMHGk/9Px+P7/78wq+WL1/tDFeam/HEzSH9Z/dwY8LXsAKTgIO\nAxbg8U2i434nMujk+5n8Kzh0YM3xv/krfPkdNc/lEDUi415CUFHyFcs+ORnLOh/0RKACkZdJSVnO\nfgfMxefpYKtaVPMb1IhNPP+6fQHLqmLZsrOprAyhejKQCyxC5B3697+fvLwjAYtQqIzvvjsHv3+9\n/dW9eRQV/YusrEGu67nFzM5/06b/sXHjC1jWMUAvYAsez8fk5R1I377jnO7GsTfSLly//0eWL/81\n1dWrAfB4Mth//9dIS+sNeKisXMnSpRc7eR8JBBH5EK93Cfvv/wYpKeGAIO58pRHboGqxfPlPqara\nRTB4MfYLzudkZj7LU0/9i/PPP59kpcUGY34Yk3Z84gzGbDOREZHfA38FOqnqdidtHHY8sxBwnaq+\nV8e5RmTioKpMmTKFe+99kDVrVtG//wBuvvl6zj77bACef34+f/vnsKZlLlBZ9S0b1/2F0p2z8Xiz\n6NjjIrr2vw5vSo79QhkWGLeghB/27m23e8ZZQv5STh/4CX+64RQAQiGLP/x7HR+t6R39Eh+vmSxW\ndOIcU7L0dTZ99CBVO4pJzS1kn6OupsPgnyEiZKTAQ5fCgU6o1vXb4YK/QWU10TUYjfl09lVtK2bj\nwnvZtfF9xJNOxx5j6NrnenzSwW4xildpcbeeaTgh1lcDllXJjz/+iy1bnsWydpGVNZzu3a8jM/MA\nJ2NlzZqb2LLF9ml5PFkMHPg8WVkHui5kxXyGr2Gxc+dcNm16hqqq70lJ6ULXrhdQUHAWNQHQJWap\nERy/fxPLlp2P32832WVnj2DgwFcRsW9ydfVaNm16iJKSGYj4KCgYTdeu15KS0pWamxrbJBaP2v6Z\ns8/+gszMuTz88DPs2LGNww47lNtu+yNHHHFEHXkkBy0mMp9Hl5mMsPYukRGRXsBjwL7AMFXd7oT6\nfwH7VbMH8D4wUGsiNrvPNyLTBCzL4pnnv+bBZ5sgND7XIs5nCjWi4d7niVncAhP9Qhx5xvQogP/+\nCjKczkJPfAL//ohoAWmMwMRrSmsEHdNhykWQl25vT/ka7p2B/Ux2u07CIhMWn2DMEnCtV8fsc7eW\nud0wsX0KYvdF/Gzu37y9Y9eu9ykurmmSKix8mI4dxzhbQdfx7nV3s1w86mo1D9/AmreH8vLFLF16\nXMTIXr3up0uX3xJ9kyH6xjXwrItXU3Wdetwxi7lr0r4tPlg3EWgxkVkQkzZkL6vJiMhLwJ3A69SI\nzDjAUtW/OMe8C0xU1c/inG9Ephnc99BX/G/OUCK/udg/dTzcIhMrKvEEKCwuUFt0YpvOgH+dCSOc\nmsSK7XDJ6xCwiG4KixWZ2O2mCIzr5X5UIdzpehG+8m1YtInoGky4VmMRLShukfHHSQ/vixWZ2IpG\neF9sM10o5lPBCvn5dvG++P2rAMjLO4v+/V9FxP3fcPcEcddk3DTm2eN2ynujPtevH8+mTXcDti9o\n8AHLSE3rWft096nu2ixx1mPvpXP8sP2W88hkV9vmHkaLiUxxTE2mKHFqMm0Ru2w0sE5VF8WEmOgO\nuAXFhPpvJf4w9hDSs+bz1OeNqNHEq6nEE5XYdXfTmbsW424ucx4kRXk1AhO0YOIiCIT9wu5fpPu8\n2E5f8Wo5DeF+sAPvlMGJP8Kxdv8ALhwKi74ifk0mnrDEEx137aaaaAGJtcEtQG7fT5DoJjtnu2Tj\n1IjAeH0d6NP/YcTnemNQiBSgFZUYQ5yCiy3fug73QLfet1Oy83WqKr/FsirYUvIIPQrvjL5fseJS\nl+iE16l9zOmHLWTSHw+uwyiDG01PXMd/a4f6vxV7Jkz31Mr1PRpMdaWVuOayYeTlzefeGZ0oWfYK\nVqCCnN4jyep5pO3wdQuCl/i1GE+cbde6Faxgx+evUr15JWnd+tHh6HPxZGTWerCc53ox/WArLE0F\nOkcfE/umqx6lfOEnlM6biSctg/yTzyOtR+1xPHGp64Ue+Nf2GpEZ2cXCmv0PynofTM6Q4xCk5mEf\nrq0EiC8u9ey3qqspWTqVqu3LSM3pTYcB5+NNya7VqaDWp1t8grBlwUMRu7sUXk9Kp24QhPKd89i1\n/T1EUsjv+FPSMwa4hE0aL8ixxwgEApvZsWUKweAOsvKGk9vxZDyeNLoPnMT3C88DYOumx+h2wG14\nvKl1C0xsTcYLagUoWTuNyl3fkJLZnYK+P8ObmgceOOOgBfzxqqJGGG0A0D09QKaqnhQvXUQOwB4y\nvtCpxfQE5ovICGA9dheXMD2dtLiY+WSaz7IFb7H0mb8TGn4+mt8Bz4yryMjtTdElr+LNyml805i7\neczZLlvyGSsmnk3KIYPxHTaYHfPmsPbpPzDgvtfJPvhw2wCfPRh5lKtn9ZQKoJOzEft266yHyktZ\nOXY0/o0/kH/OUfh3lrPkorvocuVv6X7DhPq/tCWgdbepfQ988sNaftK3Nz6vhzEn92XC9b/Bl9GR\nAQ+9hS+noLavxV1jaaC5rGLVQpY/eQa6zyCsAYfjWTuNtXNvpP+5/yO3z/G2EIT9N7E1GZfYVG77\nhrJtc2yjxUen/X+J5alixecXUL5zEVbP8yBYwYaFP6FTj0vpte9f6w9O2Yh//pbVj7F2yY3QYzSa\n2QPPqltIWXUL+x75Nvn9RpOyvDuByg0E/ZspKZlKQeGY2Ja1OgWmascylr1zGlZeL6zCo/FsncG6\nV26m78lPM/LwfZl43ZCGDUxCWms+mWrThdm5mMgP1Hb8D6fG8T8gnvPF+GSaz2uvvcbF191M+fg5\nkOe8ulsh5OFfkG956PerJ+LXYuraDq+nQKi6nMVn9iP3P3eTfvrxkWtWvT2TXVeM46CPl+PNzEK8\nyukZwoQ8+wlXHLS4uMQPXrvdyCOKeOz7LKKIWCBQ/NtrCLGDPk/chHjtp1fgxx0sP+ZaBtwxgc6j\nT6/1fRWx/etW/a/wO2bNYfAb7/LiQ48AsJYqRukiNl43GV1TxYBHXkKDggYFggJBT/zmspBr29lv\nVflZ/PsigufeCyPG1Fx02Ww8D53HgX9cii+9Y7S4hGsgMUKzbvZNbJ5/LwAd+p9PvxOmsGbu79la\nshod8SJ4nMGh/hI8c46n177X0anf5TXX3E03TPnWeSyfPRrrhI8gp79TqAqLbyNr+xcMGvUeGxbc\nwcavbJHP7X0aRae/FS0qEN13wNmnarH4P4MJ/OQGOPSXNddf9wW+50fx/dIF9Orlfv/cc2kpn8w2\nzYpK6yjle49PJoaIUqgJ9d+m3PP3f1F+zqQagQHweNFL7qPk+v4E0+/Hl5tf41+JFZVwU1pUD7MQ\n+JSSl14k5fCDowQGIP2046gacRDlHz7NPpdfhFdCDPFmE/7ZfSzl5OWXRhzXHlGXE1vxiEWgZBfb\nXnudA1b+LyIwACldOtDtjivY+OgjFP3syKjrKoLVSIfNsscfZtdpI6jGIg0PvUgnX3zon8eyvPdZ\npFQvwde1J8Ggj1DAiwZ8WAEPGvRAwOMID9EC46zvnPUmVud+0QIDsO+x6IGj2Pbds3Q97nfxHf5u\nwQlB+baaqZY6HDgGK6uKrSv+g56wsEZgAFLzsQ64m01Lb6PTkMtr0uuqzNVRTJvn/Rtr3xtqBAZA\nBAbfTsUbfagKLafgoDERkanY8gWao4g3pnNJrF/NA6UrPySUmgHDfhF90Z7D8R58AY8+/iR3Tmqg\nhmqIorpWBIrEoU1FRlX7xWzfDdzdljbsrXy/ohh+eljtHTkdkYLuBPzr8HXIr6cWo+C1wBfC47UQ\nj+L1BfH6QoQ2fYfvsNrxvAC8ww8iuHoZ2WlleFAGkRvZt8ZbShbleJz2Y0HxuAYTerDYuf570rp1\nwtcxr1beWYcOYtOND5MRN+SIU5NpoCtdxYpVZB9yCcup4EAn0Oj+ZPFpVoi0gb2RdUvJ7pNHUH0E\nQimEgj6CQS+hoBcr6MMKeiHotQUnplmtensxVuGhca+rfQ+jcutSO1iDy7kfz/mvIYuKTV9Fzs3s\nfyhBtiDeDDQzzht/h8Pw71qBq6hrcItNPTpcVbYCelxae4c3FU+Hg6iuXklu0Sl4UnOw/KUEK7cS\nCK4lNbd3g36Z6vIVaPdDbdGKoXqf4Xyz9P26DTPEpZKsmJSSdrEjHiaszF5Crz6FbF29ALr2i95R\nXoLu2EBK/+52VBGfOjUXRTwWpARtURELry+IzxfC47Wbsny+EB5C5A3oyrbps+Je11rwHfknH0EW\nFXiBvtSMdVjPTjIIAGGBscXG6+qG6+2WhX/jVoIlpfjyc6Lyrli4guzCfUjD3+D3t+p4lc8p3Ieq\nhcV8OzRaZOZW/kh18RoKeheQRiVB8RLyeQn6fAQ0lVDIFpqw4ISCPsjwogEvVHsgCKl9C/F89EFc\nF6ysXUB60X6QR01zW1hsXDUYQlC9cSVWlR3rzZvVkdQ+vdHKKjRUAZUbIaNbdOYlC0jNL7SDczel\nFx6Q1rGQypKF0HVkTEEGsXZ+Q2q3QiTTQ2bPYZR9PwuA8u3zSe3Vu+Z64adLjNCkditEPv1P3L4Y\nqT8uYN9jCnff4L2cRK7JNGEYmyEZufHa35A1dSKUu95wVEl9aTz7HXEUKYW5ePOr8eZXkppfRlpe\nGRkdSsnOLSUnZxe5OaXkZJeRnV5KTkopub5d5LCLXHZRNGYkgdmfUz0reohT9azPCMz+nAEXHE8a\n1fQkRGo46CF+gpSRQSWZVJBBJRlUkUkFaVSTThWZVJFXkE6fMw5n462PRk0BECwpZfPEJzn4N2eS\nTjXpVJOGv84lgyoyqCKdKtKoJo1qUvFz0G/OYttdT/Ldrpooxv01nS13PEHXnxxIp165ZFBBprNk\nUUG2lJHhKycjvYLMzEoysyrJzK4gNbsCX04FntxqyA6Rf+aZyIZvYOGbUeXC91/AgtfoOPpSWwhy\nsGsd2diik01Neh5UlSyJnJpZOBTJETydMigYfiHy7U2grrExwTJk6Xi6HPsbO68cV37xlhzXkof9\nopEPXU/4NZ7l90HFOqJYeh8ZnQeQUbgf5EBmn5rQRVXbv4Ms7CUHyHCukemkOeu5I07CU70VFr8Y\nnffmxXgXP8uvf3klht2jkqyoJZEwNZm9hDFjxjD38y954sb9CBx5CcGMfLK/eoXC3BTmzHiHV4q/\n4rkh/fB6gvg8IQTF6wnhJYQHCw8WPkJ4CSIoPiw8BPGgeHKVU6ZMZPr5Y6k+8Ug8hxyI9dVi/O9/\nwgn/u5PcXB9COTmuH38FfjKojHhPJKrJLLwOHixO/eeveem02yk+9Cpyzh2JVVLKjmenM+jC49lv\nzNF4qUaw0HCsK1ejW30oQtGooWy75ESW/+5eePJpAKxZCwi88wmnTr+PdKpQPATxEsLnfHpJdUrD\n70kh5PER8nkJpfoIWV5CQT/BgI9gpo+iZ15i+SXnwqdHYhUejmz8Bln4Jn3vfpqU3l1q+1/c205z\nmSU1zYG+/AL7AW5Bryv+QuVfRlM1cwjWPj8DqxLPumfJHzKKzif9Mv4rZLwBrXHIPvhIup1+Axum\nDUF7/xzSe+L58R28wU30/8N7tmgI+PI7RM6xqLQFxd2bzN2rzOkCL3gpuu1VfphwKt6lz1Pe7VjS\ndy5DlrzKE4/+m759+zbi7hncVJO40RBMgMy9jG+//Zb/TXmJsvIKTjrhOE455RQ8Hg+hUIhHF37C\ne4d0Q7DwYuEl6IgKrser7TTwEcLntPH4CKEIFSUVLH3hQ3as3EhO/17se+HxpOdnR44rJIcbsP1C\na9jJP/gMT8RFb/cHswVHY0RHCVpC8fSFrJ65CG9mBgPPP5rOg/vgJRTjyyEiNkCjOgCE8NJrQwq/\n6253XPhsUzHPdFqDz+fB9upYWIgjseFvHi4dL0FSnU87PYSXkPoIhbwE/ClUbatk68tTKV9aTFq3\nPnQcfQm+/I5Ro/nrGoBJCLa9/TSr7rocgIITL6bvH56NnKdBpXTBh+z86j08Kal0GHEumX1d3X8t\n6h9o2QDVm39g+0cvEty1neyBI8g/9GzEV9M0s2naX1j/ws0AdD37j/S87N7aIgO1YtoNqSzmvqM6\nM3XqVBYs+pbePbtz8cUX0bVr0yKHJyst1bvsA43u/HKCzN1re5cZ2pnBgwdzx6TBtdK9Xi9XH3I0\nlQs/YMHB+ZFHqcd5iIcfn4I6j9FgZBvs2kNqvpfDxo5ypEicZ0wVHtQ5vubnlgpkYs9vYtdmnB5m\nrpGTHkcuQnjwejzsN+ogBo4aGhERwU+43uI+Lx7uSWBjRUhQOnbvHElL3yePVHBqamERs1w1Gm+k\nZmPXbgIEIutOjcfx4aT6vKSmppB59RjHf+Oz5TRYhYY8YHnsbtaW2FMMhLDntnHVaiSnZo4W1eoa\nP46CqJB7zAnkHnlCzZdtKFRZXYQH20KkA0Jadl+69bulzrF9llRH1iUjzW4SixWYGNHZv/wHHj/D\nHmh5xRVXNMFQQyyJXJMxImOI4oaDT+DJr2aw8BBPlKj4nPd0IPKYFaf2EXLqPfbbfrghzeukWBER\nsFy9wHJII50qp8aizvNHCfcqE8ItRh68eLDw4iUYkT7AyTssUhbeqMjGdSERkQk3BHpQ8lx/BT8B\nUgjgIeQInfs7eCK1lWBEbOwSCuAjSEpkOyI4Pj9BXwpBy0vQ8qHqwQp5sFSwQh7U8mBZgga9jgB5\nwRI0JKCCt1NNN7FAySb7Qe6e7yx67rPacTVxHQeN62Hm1mx3AM+YJrhgec10z9687JrmsvB1YgTm\nxG2L+dNx+2FoWSpI3InbjMgYanHpQcfx8vwZLB1WEREZWzbU9YgNAeLUdbyuh28QCw8+PBGfSnhv\nFRVU4yeNVLJIpzNQRpXTBGcTrtUIdi3Dvmo4Xx9eQoSctpewyIV9RnbTVuNe4UN48DjfKIhFT1d/\n383sdETGcvKPFptw01nI1TwWbi4LOkJTIz6uJjaPl4AnxZZEny3BqkJIvagKlnrQkIdQ0FMjRJaH\nzGE14VUqli1As0J2aP7wQFN312eoLQ5NIRRnPY5IVayYH1nPGHxQxFfjHt0fFrJjf/yGcYf2wecz\nj52Wxr+312RE5FpgLPbP9S1VvclJb9R8Moa2xefzccGwUdz35oNMe+9VfpizhLTsVIaOGcHRvzic\njAz7IR9+0GrEX+F1Hse24IQfzDU+HostbKMndpfbkrfW8Og9/6K6rJqiY4o44frj6Ny/E14sgo40\n2euWU5sJOTWkIIoXj+Mvqun6HBYoixUffsecf33IluItFBR25Ce/OY7Bp9lzrigeBK8jWD5A6eGa\nLXIz2/ERRLCcTyLfQVBK1/7IRw/MoPj9b/ClpzDo/KMZ8qtRpGVnR8ohQGrER+NuYkvFZ3u7xBY4\nS2rqYBYeghpk3QtT2Pj8qwRKdpE7fDjdr7malE6dCGzdilVWSnDrQtIHDEItwbI8TpXP4zSz1TEu\nKGQ/6cu++JjNzz5A1YpvSdmnJ53H/Ib8U862Q9DEC+YcL6hzeOR+MEhF8cLI7tQ+PVn3yC3s/Pgd\nxOejw4nn0PnisfgK8uizbQP3HxN/LJWh+exOjzIRGQT8BxgK3Kqq99dx3BPAMOxXhpXA5aq6U0Q6\nAc9hx6v0Afep6lN1Xa8tojAfhz1X7UGqGhCRzk76/sAYYH+csDIiEnc+GUPb8+WXX3LPlbdy/FWd\nGf14H8q2B5j2jzksnvIpN733S7wZ6U4twK6rhPCR4ohNuMtAuIbhiTQ3hdjGpojIdCxL5/zbepDd\nMZV5Uzdx7xH3ccNbV1J4WCEeQs5jPejIQVh0Qk4jmT2+pqa2oZFa0IeTZzDrgQ847dbBFN7Wm7UL\nd/Da755l/ReHc+bEMwg6tZiwdKWRRmenJqMoW9iCD79zRcvJ25bPH79dy8Mn/pVDLurPzx4ZQVVp\ngE8e+oTnn5/FZTMnkZGb49Rw/PhJcXUGiO4sEG7scy/BQIi5o68m4K+g141nk9qtgG1vfsGik04n\no38hga1bAQh8M5eOQ3uiIfvbW5ZdbbBCNY4Qe90m3Gdm8zNPse7euygYdyW5d5yHf8kPrP/TTZQv\nnEGfu+6rCcHjnKsqtniB3XTnblsTperbr9HqKgBSunZj+a9PJf2nJ5DzyC3g91Py6Ets+9kzXPCv\nx3nqhKNa8udpiKF69x7l24BrgbMbOO53qloKICL3O+f8CbgG+FpVxzmCs0xEnlPVYLxM2qImczVw\nj6oGAFQ1PCBhNPCik75KRFZgxzGrNZ+Moe351dgruOxvfRh5Uc1Av4NP7Mhdoxcx++GPOe3/jsTC\nhy/ii/E6/hipQ2TsZQdrsV+g4IKf/Zw3ZTaKRf9DO9BrcDbPjH2FP827xhEVu4ks3H3a3aFaEUdg\nwn4Z23+0c30J7/zpXSYsOo0OPe23u15DCjjwtB5MPOAtjrjwEDoP7IqFOnLlYX8G4nHad7ZQglJJ\nqiNa0Ve0eP13z3Hy7UP5ydU1b+VFx/fg+Ys/ZP7fpnL8hPMivphUvARcQuMWnHDNpUbCvBQ/9waB\nylIO+uDuSAidnEMGkDNsAEsv+1vkelue/y/9rjgTfHYe6gQAtRDUiu1rZ4tMYEcJayfcSq8vniO1\nyI5enT5kEFmnHcWaA8+n+8U/JWeYfV8spzZkWbZPyF73ENvBc/vUp2o2vJB5zYXkjvt1JCnt2BHs\n/I/91SEAACAASURBVM3tpE19EYzItCq7U5NxnsFbRKR20L/o48ICI9jetmJn10bgIGc9F9hWl8BA\n2wzGLAKOEZHPRGSWiITjbHTHnkMmjJlPJkFYuXIla9et4egLomdv8HiE0f/Xk09eXOgMcKwkncrI\n4MZ0KkmnilSqnX1VkSUNP+lUsYVv2b5jGwBZ0plu1Mxxc8SYnuxYv5OtKzaT6tQDUp1Bkyn4nXXb\nKZ/qDLJMJUiKc2wafha8PJ9DftorIjBhcjqnc/jFhXz54jznsW/n4SPIwQyKHPcNy/A510hx9Rnz\nEaTqx+2s/XINw6+MdlyLCMfecCALXvjEGbBZSVZk8Ga5M5CzJi2bskhaeMminB9eeIvuvxsdFaMN\noOC0w/DlpoPHfuBv/eATrKWLnLzKyZYysqTM/vSWk+UtI9tZMr0VZPkq2PXmNLJOHBERmDDevBxy\nrzqb7a/8j6y0CtLSqsjIKLeXrEoysipIz6okI6uSzOyaJU22svXF5yL5BLdtI/uai2r9ljJ/fyVT\nXnmlwd+coXlUkxK1tBQi8h9sUTkQeNxJfgwYLCIbgIXA9fXl0RbzyfiADqp6uIgcBkwB+sU5Furp\nGmRC/bcdpaWl5HVMx+ut3fUor0sqVaW2iITw4Yu4xT0R53cKQUJ4nE4DVqRGEB5T88LLz3DNL/8P\ngCJGsYF5AHi8Qk6nNIKl5aSSR8jpTRZ29tsNbm7HP1FOeYBAaQXZneM7QXO6pFG1pZJUAgSd5rh9\n6EYnZ66BAAGWs5RUQq7GLI30pAuVlZGem4ovrbbfI7tLBtWlVWRSGeX0d/cyCzefhetI9vjLcK3G\nS7C0jNTOtWO0iQjpPTqR270zm+cuBmD1v59nxD9+D0TXiGpGG0V31faWbsPTuUOtvAG8XQrQ9ctJ\np8LuOiHhUUseey4fBMsT/VtY+9L/CO60Q91k9u1F1bYdSHbtt2lvl45UlpbFve7eSGuF+v9u1hZW\nz1rd4vmq6hUi4gEexH6eTwJuARao6kgR6Q/MEJGDwzWfWFp1PhkAEbkaeNU5bp6IWE47XpPnkzG0\nLvvuuy87NlWxcWUF3fpHd438/LWtHHRkV9KodpqzxHHShwUnEPHLSOQhHXI1bSnzVr6DpdfjEQ/d\nGEo3hrGR+WxeWU7Jxir6DcrBRyDi7A/hxRdx+ttCA9G9y8KNRIOP7M5j137FOXcPjZpPRVVZ+Pp6\nfnrjcfgIIHgB5QiOjhyzgmVYVJCCJyIyYaH0EKJr7xywYP2CrfQY0imqXL59fRWDjupFJuW1Bmy6\nnf4BUlz91TzUdL4W+h61L5umfUbekdHjmKo3bafsm1Wc8sTveNMRmaUPT2XIr0+lw/59Y0rB9Z1d\nDRW9j9qfb+9/Bg3ehMT07qp8fSb9f34KmVQSjvHmFquwgIXx79hJ8YTJke3+v72Alf/f3nmHSU3t\nb/xzkpnZvgsL7C4dpINeRKqigBWxUa3X3lHv1WsvVxEV4fqzF/R6rVcF7BULRcGCSJEqdSmylKUt\n28vMJOf3R5KZJDtLuS67g+R9njyTnGSSs2eyefNt7/nPBwR/mE/CgD6uc8+gV/9j8WDA/YI8duzY\nWjlv9qAuZA+KWtg/jP3RsV8IcQNgzaswREqZv7/nllLqQogpwJ1m03HAOHPfOnMKl07Agljfrwt3\n2SfASQBCiI5AQEq5C/gMuEAIERBCtMVwq82r+TQe6gpJSUnceuvtPHXBGrZvNAompZT88tkOvnwm\nnxGnX+Jyg0XdYwlURTTCjKWCBJtmWCIVnHRpJu9/8G7ker0ZTfEmwXMXLODcW48mNUmSQBV+050V\nIISPEAkRt1nQdJEZLq8AIXOp4uhBzUlrkMSUv8+nstRIDghWhPnk3sWEyyV9z2oXcYV1ozeNzSQE\njTArmG+6yKLuMmvbR4hEn8Y59wxk8sUz2bmmMDIuq6fnMfPhhZx3Z1/DfUUZyZTZNM8MRSnLfZZk\ntiVFXGnG8YNuGsTON6azfcospG5YZlVbdrH6/PH0uv5Ujh7Wnaa92gOgB0N8d8UEksLFpFBGKqXm\ntUsj7rgUSiNL62Oa06RbK3ZeMxatyHjh1KuCFIx7Gbk+j87nHR/RjrO05CxXXlRfztj/2z/GUbl1\nBwCJWZl0vXIIPf55BUVX3k1o+ZrI71r1w3yCdz3OI/fcdzBvVw8YxZj2xQ0p5UQpZQ9zsQhmr4oA\nQoj25qfASN5aZO5aBZxi7svGIJj1NZ7nYMu1CCH8wGvA0RhTPd0mpZxl7rsXI4U5DNwspfymhnN4\nsjJ1DCkljzz6EE8++Tg5bVMpKagiNbkhL098nf79+zNr+dM06L40ErS2FmkWZGqmzaE4LAINQwU+\nzMqfyzmz47M0bmTMbzNpytt8t+4ZLrm3Jwh7Plo09K5FXE3Wu5GM1NgI2zVK91TwzPUz+HX6JnI6\nprM9t4Su/Ztx08tn0jDbcOkk05SB3IClQrCYWSzjF8LmtpUK7YvU35iWmAzz5TNz+fDRH2nQIoWq\nkiCqojD62ZPpM7gVlsdXM6M+1eRmzHVJ1CEX3RZsXJDHpGvfomRXGQnZDShft41+o0/i9IdGgOon\nf3kezx/zT7SQYc2dMv5Cjr97uM1VFrVELFjSOlUlFXx503/I/WweiZ1aU7l+C1k92jHklVtIbZlj\ns4Si8/FYrklr/fcvfmLa2XdG2k778GGOGDEQgHX/9yHzn5yCyMpErwqSGNSY+MQTjBg+ohbuyD8n\naktW5ir5gqPtVXFjjecVQuQA8zGC9jpQAnSVUpYKIaYCVwHbgR+IThixALhRSllheqJeB1phGCrj\npZSTauzfofDw9kim/lBeXs7SpUtJTU2lW7duDhfUrN8eonG3la4yzWhxpjsmY081BkiTvWknojHD\nTXzAVr6JHCFt5KJHKCtaDRONQliPaaPiXyCRKOzeVsbWDSU0bpVGkxZp6CaBJJBBP24gGSNGUcAW\nZvKa2XdLBSBKjtHYTNTtF64MsXFJPgmJKu3+0gifiLoDjd4bvQoSqFawGY0iqbbcOCWyHpaQv2oH\n5UVV5HRrRmJaYmTkAL4e9xVT//kpAEIRXPLeDRw1spfNreV2m1mfxl9TtKOEgnU7SGnWiIzW2bYE\nbSUyihaR20kmf2EuH554B8GScgA6XTCIMybfA0DmhiD/bHsWwWCQJUuW4PP56N69O4riCb3vDbVF\nMhfLlx1tb4tr40a7zCMZD38Isxc9R06PWZE4jBYhGx+WSkBUliWMAo435ubcSEOi6a15vEs+39jo\nQrERV1S6BqKEpUQei8Y9Yj2scV1LQyGRBvRkNCkYWmUaQb7nRQrZFem/jMRknJIywnGEXu0Y1VSl\nNq5kCSv7CEZiND7slpkeebAT+dv0CEkojjGA6Iyf4bDOhP7PsWHeJgBUv8oVk6+gx8geDjLC7IOM\njJZRtqpXS6EmQniyhmtvWbCOyYPHUllguNpSmzbk2qVPk9w4nazfdC5vdSppac75fjzsG7VFMiPl\nO462D8VfPZI5EHgkE7+orKxk/sqXaNHj28jbum5aHFY2mfXOjo0ELOeWwE8L7iGVaN3JTmawhSmE\nCduOjwraRAUy7bIyMuLiiaqfOR+UaXSkGxeTaFowOhqLeI18ciGSwGCQQLQQM6oiYNXrWNvW32Sf\nCsG+bYl+Wu4yy6qxCM/KyLOL6dj7a2m02SVEpTluhTtKGXvCK+SvMQo0hSIYOe50Tr99IIoZ2LdP\n1GYnkDCW4lvUerKrzNkJSJewdMrPfHrda1SVGPG5pIYpXDl7DE2Pakn6WslFjQbTKDPzj95KhyVq\ni2TOku852r4Q53kkcyDwSCb+MXfF3TTvupTiEo3t+SEa5iSTnhaVljFUjJ0PUGuPIJHm3Ecy0ayq\nKvLJ4z+UshrroRzWYPPvFSQk+clsmmJzZ8nIOcE5n4yhspxEW4bSjBMj59cJs5w32MlypEkwu7dX\nUFEaplHrhvh9whHnsa6j2tKbo7pu1kTPUaee3VUIgpIyybatQdKz0kjOSHC4/XTTJRW1YoQZvbKs\nMWu/EvmUwM68Yh44ZTJb1xRE/q72fZoz+o3htOjSxDYWwmZ/EXEbOunTioFFSacwv5T3R7/Nsk8W\nR86fnJnC1V/fSqvebVGL4ZZ0T0X5j6C2SOZkOdXRNlOc6ZHMgcAjmfhHaWkpl155LDOnraBRI0FB\ngeSsc7N58MnOpKZG3TKW2wYgGvEQQAJZ3EoqznTXQn5gN98w6fXpvPTQUnRNp6JMp23XDG59+li6\n9bRPmqXazitQSaIRx9GU00gkmnIcopyVvMoeViFRWP9bAc/fPIu1C3eSkuEjHBacd3dfht7YHZ+I\nusOE7VEcdW45LRe7M0wgCVWGeOKuJXzy3zwaZgoKdksGDW3OP54+lrSGiRFXXtSSsaQ+jSqgaBpx\ndL9l0egoTH15KRP/PotQVVSNyRdQOGN0D864oSdNOzbC/hfYXZDWde2JCBKFPduKmf3KfKY9OYuK\nworIeYUiOOmWgYx4fCQJuclc1cGbwfKPorZIpr+c7mj7SZzqkcyBwCOZ+IaUklNOPpZmOYv518NV\nZDWBHTvh7gcEG7em8tG3R4NwxhzcpGM9RFM4hUyuRiXVcY2lyxYSSnuPzDZLKA2tYeqkYv7vjmJe\n++E02nQyEmCMWp00EmlLBj1pxLGoJDrOs4el5PI2VRQBCvl5ZVzX533++kBzBl+ZTSBBYd2SUv51\ncS6nX9GdC27t7rDGnCF7zUYy9mOiEZabRswhQS1k/DMqTZsJ9hRIxt2vsWBBEu/MORVFtVxW4IyF\nVLdq7K4sEMx8fz3P3/4TD7/RmMU/VfLvh3YTDjl/m2NOackpl3alc9+m5LTPBCU67Zw1/mEp2LW5\nhNwF+fw45Tfmf7QCLeyUEDzx2rYMuroNb964lO69+/DyQ2/SuFETPPwx1BbJ9JQ/OdoWiv4eyRwI\nPJKJb8yaNYsbRp/Nojml2BVRNA2OPlbhsRe7cMKJGREHkvvtPfqINtoFTWjI30imT8zr6VRSxUpW\nrdhIcZFO92OzUUkigdYEaBzzO2HK2MRkdjPH4VZ79vafCVPItY+3cRy/ZW0Ftxy3jI83/ZWkJBXh\nIhLV4fBzimharrIViwq4fthcFub68fudhaGnHatx/T1HcerQnBriMFQjYnvWly4Fw7p+zn0vNqT3\nIKNgdu3yKh64PJ8VC6MTidmRkh6gfY/GNMxOxpfgIxzUKdlTRe6iHRTtrIj5nUatkrnm1WM48hRj\nxso9Wyu4t9ts8jZuISOjujqBhwNDbZFMNznf0fab6B03JFMXKsx9MCQJ/Bj1MDdIaYyIJ/X/58DM\nmTMYfraTYABUFUYM1flxegEnnpAKQkNX1cjDUzEtGEtZWY+4iPIp4H5K6IxeOJiGiSeSmJgUOa9C\nIkn0oEfXHvvsWwV57GIGe/iJEEEUjJkwjQe7zsJvN3PTi62rfa95hySyWiWyYckO/tKvCZa+tJHM\nEJ2+wHDSSZdVY1g5c2fu4IxhioNgwHiwDD1XMm/mVoYNTXe4v8wjbIF+y5WFjZwVtm+rpHBXFb0G\nRselw5EJvDOvFV+/W8L9l21H10DXoy9nZcVBlszeus8xA0jOCHDy6LaMeLALfpuMTsNmSbQ9ujG/\n/PILp5122n6dy8PBR+VhPmnZY8D9UspvhBBDzO0TPan/Pw8SE5PYvtsHVBdiLSmBRlkCX1hDCoGQ\nEqEoIAQ6OoqIFv0p5uMfDOeQxko2Ff7KwCFXMPu3G0hV+pLIkfhpUWNfdIJUspEK1lPEXMpZHXE3\nGfluCjJCMgoJiSoVJdVn95JSUlESJikRMzU56gKz0petQk230rRFQEmJgtKYak5QUgQpfkli2FRU\nEKY7TIm6zXRhT12OBv0B0hNChIKScAj8tgJvRRF0PSaRRll+3p1zIh+9uYlFPxewYmEhu3fEtnAA\nUtL9dDqmEV16ZzH4kg48d+cvtO3V0EEwFiqKgyQmJsY4i4f6QqU8vCct24YxKzlAA6L6ZJ7U/58E\no0aNYsCAcdx/V5iGNg3GPXtg8vs+Zn3TEDUskUIipEBRjWRmRYBUFDRFwZiITHGQjI5Om9Z+ElPK\neO+j5xk86nUAVDJJoCvvvqDQtFlTThmejU6IIPlUkRdxMRnnEag2d5Ql8G+VbJ48qi1fTNxIj5Mz\nHIWmv84oRBEKXf6SFpnAzF7rI1yk4y7eVNE5a1gm//dALjvGq2RlR89dWip5978677/ekOTSMFI1\nSUaYk12K6DaArpoBe8Xst4DmjRSO7pXC528VM+Iqp9vq3YmFnHVuNu1aSW6/vw06bdGlYOvmKlYt\nK6GkRKeqUsMX8JGQ7OOIrhm0aJcOSvQvGTyqJZ++uI5ew5uh2MQxc38poDg/xLHHenpk8YQqLWnf\nB9UT6oJk7gZ+FEI8jiFBYN2dzXASiif1f4iiU6dOXHzxVZxy9uuMva+MHt1h0RIYMy6FSy65HFXV\n8GnzjYeolGjSsBykEEhFgmoQi1QkurBkYlQEAkUIHn26A5cNX8aWjWEGj0qhuDCft5/LZdlcjfd/\nPJ5yEiJZWGrkrV9HR0VFmo9+8xoYhZZWfOO8a4/gm0nrGH/RGkbd1owGWX5+/qyASQ9vZvx/++FX\ndEcmmZXOHE1rtsdhLNkcY1/rFgo33tKMcwZt495HVHr1U1i5XGfC/RqnDWhAn84pUGmNogSrhlQA\nitlf05CwJr40xs3YfvyR5pw5dA27t4U5/cI0KiskH7xUxM9fVfD1T51IpjJCg1IIjmip0KZluiN1\nwUoPxxTHtPaNvLgpU99cy3Mj5nLGXR3JbJHEkq+28+mYXF56/lX8/tqTk/fwx1FRmVDfXagRtRL4\n34fU/9+BF6SUHwshzgWulVKeKoR4DpgrpVGqKoR4BfhSSvlRjPN7gf84h5SSyZMnM/GFCaxb/ztH\ntG3FDTfezUUXXYQQgjWLr6Njq4VIxTZDsPmw1FUz1qAYb+lSCHRFMd1Fxlv8smXlPPtYHj99t4fE\nJIWzz2/KNbe2o0FmwJUK7XYvWVaMcOy3z6VZWhLinadW8uWkjZSXhOnRvxFX39mJo3o1tJ056haz\ndAeitTBWaN6W2iwlqqYhJHz+wS5eeDGftWuraNncz/WXZHP5qEYoujA8jDpRggEiSi72NsX2aa5L\nFVblVvKv57Yx/dsS/AHB8OENufnWZmRl+Y3xRYAQaMKS8DcIJ+rYi6pA27P8dFQ2rWrOnE9a8sZb\nr1C4p4jefXpzzx33079//4NxCx2WqK3Av7rNOZ2C1jQ1bgL/dSGQWSylTDfXBVAopcwQQtwNIKWc\nYO77GhgjpfwlxjnkmDFjItvefDKHHqSULPllLEe3/wKzyN2IaCigR97WTZeRzyQIIaKE4xCVtD8c\nIVoS6dTtctaeONOC7Zla7uoTizyssLtxjF1/ze4ic1b/q7qG0I0zK2EdVZMoOqgaBqHYP0PmYg9l\n2X0LdstGUJ2IhPmpGvukuVjQbcRkjKPxqanCHH/VHHOBLtTImOgI8lZ3p3Xm3TRpkv0//NoeaoJ7\nPpmxY8fWCsmwwZW73tZ/WJHMr8A/pJSzhRAnAxOklL3NwP8kjDhMc2AG0D6WyeJZMn8OlJaWsn75\nOP7SbrrjwRkhGXO2X8180ErFfOtWFXTrbdxhV1RPf7YEHZ1ij87aHGO/QVqWRWJ0xXDV2WvxjW3D\nveeu8o9Sl4aqW9aLjtAlqiYREtQwCItUJAahuBcNQ58cnIQCsS0ca7+NYBykA85JPCw3m2kFSQUz\nDmQQua5aJGQcuDW/LQ2Tn/PqYOoAtWXJKMudz0f9yD9+3tpCXcRkrgVeEEIkABXmNlLKFUKI94AV\nRFObPSb5EyM1NZW/9BvPhsVbaJuzIvKwNMMPKGbcwfrP0BU9EodAEeYbuZH0rJsUIcyYizCjI1Y6\ntECLWDEKMrLPogZL99jSF7OmDFAihBKVjTS27dQWdY8JKVF0HVXTUTQdoRt/j6qZ5KLjtF4soqmi\nulVjHW//r3S7zqxFNY8TtuN9tuMtRNVpML2Pxqb1XSFNopJIAYWV6XRoOWW/fk8P8QM9dplTXMAr\nxvRQL/ht7j9olPgtH329h9JynQH9UunbOwWhiOhbtmIGvBUMl5lquHY0RUUKSxqFmFZNVVAy/fPd\nbFhbRvM2yZw2LIdAoj9iydjLQd2uMrt8pLNslAixLPu1hNkzCklKEAw9qwEtm/lRNCLWCzpR0rB/\nut1mFrnYyGZXUZgPf9xDYbnGcd1SOP6oVISV4aXaFotgLEsmlnvNDgFhTfLlD0WsyK2kRTM/I4Y0\nJDlZARXWbT+KnE7Pk5JSfRplDwcHtWbJzHJZMoPix5LxSMZDveBfE8Yx/tExDDtRJzNd8tn3Cke0\nTuLD1zuQlqFG3tYtotEsl47PpARFIaw4k4kxnV/Ll5Rx6dmLadMOuvcWrFgiWblU8tLHvTimn5Fj\nrTi+EbVU7KQTlZSUhsUidaoqda66cDWLF5Yw7DSdkjLBp9Ph79dnM+aO5gZRuK0X3bVuJxbX8p+p\nO7nz1TzOaCdomqwzdYNC48wEPn2gI5kZvqgFYpGK3ZqxyMaCK2lg9cZKzrxpDTlpGse101mxTWHe\nepjyVDtadzyO5LaP0LRpzTVIHmoftUUyfOV6Pg7xSOaA4JHMnwvTpk3juquG8/0r5bQ0cxI1Da55\nSKD7M3jj+XbRLCphxA+sFN6wzyAYK2CtqXaFYoXyKkmf9nN54HEfQ8+PFqjN/DLELVcE+SF3AOlp\nqiMlwDl7inGfWW4zVdNAgip1kHDHPzawfeNuJj0lsbJ4d+yCgRcqPHxvG0ad0TBKGpZrLBbxuAkm\nBHOXlTJy3BpmXyBpb9YbSQm3fAt5Mp2P7ukQHRd7PMZONpb14nKbaZqky0XLuGNwiGtOjLbPXgkj\nn1eYt2gtRxxxxP/yc3r4A6g1S+YDlyUzKn5Ixpu2zkOd4/lnJ3D/1VGCAUOC5onbJJ98U8TunWHj\nQWwuQgPFjGf4wqBoOmpYxxfW8YXDqLqOKg0J/q8+3kG7TsJBMAAnn+Gn7wkqn0/ajN+c4cVPyJxe\nLYyPMH4ZQpVhAuEqfOEwgWAIX1gnEAyjhnSCe0JMmrSbZ+6PEgxAVmMYd5vOcy9vM2ItFnHY14O2\nbSurzGqrMtZf+Hw7d/SKEgyAEDB+AHy/soRNm4JGVLMS49NaqoBys73Stm1rm/ZTMQ0SNQfBAAzs\nAuf0TODDD97/n35LD3GCUtcSR/BIxkOdY82a1fQ7qnp7w3Ro21yw8feqKMkY00siNPBpRkBd1UDR\nJMIWcFc1DVVqbFhdzjH9Yr/A9ewnWL+61CAUgqhSM4lFM8hK0wgEQwaBhXTUkPkZBl8QdmwJ0SBd\n0CxGVm+/HrB6XVWUUCyXWFWMbXebuazeXEG/ZtXPneyHI5sIcvMqDVIpJ0owlUTJxGord62Xw5oN\nlfRrF9sb0K9NBatXLIm5z8OhAb3CucQTPJLxUOdo1bIly3Ort5dVwO9bJc2zAtVTfM24htCNwLrP\ntG7UsETRdBRNooY1Wrb0s3p57IfpymWS1q0C+PUQPi1MQAvhC2v4q4xPX1BHDUl8YYk/JPGFwBcy\nrqeEITvDT0GhZPee6udethpaNws4rRSLRKztoO3vCdqOMfe3ykxg+a7q5w5qsHq3pFVywEki5a51\ni3TchFMJLTMCLN8cm3yXbQ3Qqk37mPs8HBpQypxLPMEjGQ91jmtH387Dr6ZQWBxtkxIe/o+Pvj2y\nyMn0Y2YQx4xhCN1YfGY2ly9s1KeoYY2Rwxow/yeNObOcYp2L5oWZ8UWIv57XADWs4Q+GUUMa/ioN\nX1jiC0kCIYlPM6wWJQiKRRImGaQnqgwf3ID7nxTYQ4Rl5TDmKYVrR2UZhGF3i9ndZHa3mZ2AzPXr\nTspmwjyFneXO8XpiHnTNSqJ9WqJBGlU43WWWJVMOlJlLpW0ph7M6Z7B6q+CLRc5zL8+DSXNVLr/y\n6gP4BT3EG/RK5xJPqIs6GQ8eHBg5ciQ//zSLbue+xuVnB2mUofHxrDSKKpswfeYc1ub+kw7ZixwS\nKkiMYLbpQrNqPlRpFhlKo6gzI1Hw9lvt+OuoXE4a4qN7b4XfFul881mYV19uS3aGihI0gviKbpAU\nGO44M/4fJTjTVRe5vgZP3deKwZdV0G9EkHPP0CkphTc/Uji1bwOuOLOxQRj2AL97sYjTOs7mMju1\nfTqX9M3iyNe2c/mRkpwUmLpeYVOxyswr2xmEEivw7/60GyxmOnMAhQ+vbc85L61j0JEBjj+inBX5\nCbw3T2HiS6/QqlWrWvltPdQP3NZLPEnZ11p2malL9iDQGegtpfzVti/mvDFCiJ7AG0Aihm7ZzTWc\n28su+xNiyZIlvDvlHUpLixgw8FSGDh2K3++nrKyM9Qsf5qicGbaiQapXtyu2dgGaVc0uoGBXmLen\n7CZ3XSVtWyVw6fmNyGrij9YmWqRirUP0P1OzfdqJx2zXgpKp3xUx8+ciEgMKo07OpHfnFOP7duvL\nnV1m1cu4a2NsGWZosGJzBZN/3U1RhUa/FmmM7NyABFWJHm+Xk3HXy7gr/W2EsyG5K5X9b+fbWd+z\nasUSmrdsy6WXXU6zZjECQR7qBLWWwnyL6/n4dPxkl9UmyXTG+Bf6N3CbRTI2+ZjeROVjOkgppRBi\nHnCTlHKeEOJL4Fkp5dcxzu2RzGGG4uJiti6+m84586IPTzfBuEgGS6lY2p6tlkUSa9uyWMBJNHZr\nRqe6ZWN9WmnJmu1Y+/6wa91ONi5XWbW0530RlkUgVpW/nYRjOMGLAg0IXvYOTbI8LbJ4Qq2lMF/t\nSmF+JX5IptbcZVLKVYBjTg4TseaN6SuE+B1Ik1LOM4/7LzAMqEYyHg4/pKenkz5gItvnnUN2xlYn\nIdhdaCapYKj7G+Ri/b+5fQZ268W+X4+x31q3k4xFRHYS0GNsu4nGflws3bK9kYzbzWYnN7d1rkEb\nmQAAEKJJREFUZycYW51McaABGXfMwMOfF/EWh7GjLmIyNc0bEzLXLWzBm0/GgwvZfT5j5ey/0SXr\nZ+NuDWM8QK23eXvRod0KAaeri32sxyIY+7b9oR/r000wscjFTjw1EU0sK8hNMtK2H6oTjQ0bG3cm\n+crHSa8+tB7+RIjnmMwBkcxe5o25V0r5ee10KTYefPDByLon9X94oWn3h5gx/S5mTvuU734pJilR\ncN6ZjbnigsYkJijGg9VNGhD9T3Nvu9qDVTpvf17ApM93UlKm0b9HOn+7IJu2zRKcVozbsrA/7O11\nPa7jfl5VygvfbGfN1gpaZga4dkA2gztkRInFfd5Yrjb3uvm5vTTExJU7mJZXSEAVjOzYiKu6NCYl\noLKlQRvUCx8iKyfWv6yH+oBb6r+2oJfv+5j6Qq3LygghvsMZk4k5bwzwO/CdlLKL2X4hMFBKeX2M\nc3oxmcMYy5cv55ST+nP+wFLOHahTWArPfywoCyfyzRudSU4yX99rIpVY1ou5HazSGfr3tVSUlHHL\nmZIm6fDZAnj9O4UvnuhIn64pTguiJneWm2hMEnhl5k7GfJjHnUdJ+mXD8gIYv0Th4t5NePDUFrGt\nE7ubzX1OKzM7DOsKqxj45UrO9utckCCpkPBSpSDPH+DbM7ug/vsHUlNT//D4ezh4qLWYzAmumMwP\nf8KYjAv2P+4zYJIQ4kkMd1gHYJ4Z+C8WQvQF5gGXAM8epP54OIRx0+jLGHtpMdedE207o59k+P2V\nvPDmDu64KsfpH3BbNXuJzbzx0W6qSsuZMUbiM11v/bvAMW11rhu/gV9f7mbEGd0ZYm5Lxk0WwK49\nYe6Yksf84ZL2GUZb32w4p43OUe/v4LyujejaOKmadbK/JHPrz79zc4LGHTYeGZwguaQ4xIRmpzDe\nI5jDBvFsydRaMaYQYrgQIg/oB0wVQnwFxrwxgDVvzFc45425AXgFWAvkxsos83B4Y8uWLSxfvoIr\nz3C2Kwrccb7knc93Vqucj2zbq+xjFUaG4J2pO7ntbD1CMBbOPQ72FIdYub4yKgPjPncl0eJLe7sp\nH/PR/D2c0UpECMZCkyS4vJNk0qLdzpiMO14Tq91cLywP892OUm5Idp5bCLgzWefdt/67/4Ps4ZCH\nUu5c4gm1mV32MfBxDfseBR6N0b4QiKFi5cGDgaKiIho18OP3VU+fycmEohLdeLhbqCmDrIYkgKJS\njRybIKUFRYGsDCgq0iCL6sF9d9DfboWY+wpLNXKSYodgc5JgXUW4eqzHHZupwYoprdRJUSAlxmti\njgJFpXGmkujhoCLe9Mrs8GRlPMQ12rVrR2EprN5Ufd/nP8OxR6ZEhScty8IuRBlLS8wmFXNs1zS+\nWFD93FsLIHeb5MimSc6aFrtVE3at268XhuNapjJ1k4IeI5z4xSaFY5unV58CwL3YLRui600T/CSo\nKguDMc5dBf169aphRD38GeFpl3nw8D8iISGBW2+9k0smpLBpu9EmJcxcCOMnJXP+2QOd5GJ/6Nu3\n3dL6JhncclYOz3+l8MUCInpk+XvgwicFo0/LIs2vVneF2QnHRliO9RD0b5ZCVloCt8wRlJvWVlCD\n8b9CXpnKyA4Nqsdf3GRjj/fYstFUKbi7Y1MuK1ZYZxEQMCcI94WSueuhh2vnB/BwSCCeVZi9Scs8\nxD2klDw89n6efvpJOrcJUFiiUaWn8PzE1+nbtw/5c8bSNTDHPNj8kttNhq3d5Tb7/rcSrp24AV1q\nZGUIfsvTue7kJow7rwUqonrm2N5qZVwutILyMNd8uoHZv5fQLVOwulDSrXESb5zRjpZpAaerzV1b\nEyuhwGbRyLBkzM4qXlixnvaJfip0SaEvwFMvvsTIUaNqY+g9HGTUWnaZ4sou0+Mnu8wjGQ+HDEpK\nSli0aBFJSUn07NkTRTEM8aLCQgq/HU3rwNrowZrry/btGBlnMixZtL6CkgqN7i2TaJDkqy4x466F\nqYkgrOOs6+qQVxxkXUEVLVL9tG+YGCULPcb33ZX9MQjGat/x2hekpqezcOFCAoEAPXv2xOfzdG8P\nFdSadlm1FErFI5kDgUcyHvYHuz48g8Zixz4tl5gpznuTlrHLy8SyNmqycOySNPa+2M+tx/hurKJM\nV0ymKLUBaVOmRYjWw6GJ2rNknIkxup7okcyBwCMZD/uDyspKfv/0FjrJBc4HOsR2n7kJxC2S6f60\n1muq8o/Vbr+e/Tz2a9nXa5KTsRHb5uZHoN/1KK3aexONHeqoPUvGPZNew7ghmdqskzlXCPGbEEIz\nJfyt9lOFEAuEEEvNzxNt+3oKIZYJIdYKIZ6prb54ODyRmJhIw0EPskbrCWGoLNfJyw9SUapXC8pH\nssTs2WLu7LFgjOPMJANZJdlaEGR3cbj6ea0kAHd2mD05IJaGmY1gCsvDbC4JooWkI9OsIK0x4dsf\n9gjGgwOKUuJY4gl1IfV/NJAvpcwXQnQDvpFStjD3eVL/HmodhYWF3DfqSCbP2UqSCmVhuKh3Jo+N\nbElqQK3uvnK7tdxuMns78P7SAh74djO7ysMEdeiZlcTjg1pzTHZybPVmnN+v0XLSYF1JFf+Y/zvf\n7SwlVYGAonB3x2Zc36IJQhOUfTyblJSU2hwuD/WI2rNkVrlaO//5LBkp5Sop5ZoY7YullPnm5gog\nSQjhF0I0JbbUvwcPfwiXXXgu2zfvZslIyZaLJSvPlZTtKODsZ9egV8rYFoy1Xunatls4YXh30W5u\n/+p3JjYPsaOXZGdvycXJ5Zz+4WpW5Vc6LRZ7KrJdaSDs2m9uby8NMWjaSk4oKyG/kWRbpuSjFI2J\nq7cwNr+MXW9/6RGMhxpQ4lriB3UdNRwJLDTnlmmOJ/XvoZYxf/58ls6fw+QBlbQ0pbuapsDrgyS7\niyv5dmVJdXdYLBdZDJeaXiG5/7stvN1O58QGhoRLQIErs+GWHJ3HFmx1Eox7Bkx3m1XAaRLTxDU7\nOMunc0cypJjvoD398Gm6znNrNpHsaZF5qAHx7C47IJIRQkw3Yyju5ez9+G43YAJw3f/aWQ8e9oXp\n06czqlUVfpcWmSLg/LY601YWVScVN7mEqK5LFoRNBUHKgmGOjzE5y4WNYdrm4ppjMG4rxh23CcH0\nbYVcEKjuFj5ChY6JARYsiCFN4MEDoOvljmVvEEJ0FkL8LISoFELctpfjvhdCLDKXLUKIj832QUKI\nItu+f+7tegeUUC+lPPVAjrd1tgXwEXCJlHKD2bwFaGE7rIXZFhPxOJ/MrFmz4qIfsXC49i0QCFCu\nq1QvlIHyMAQCIvpwh2qZY7M2ljCoZZqzNsY8LhAWVGlGs+o+tw4BIaKpxlC9FsadzgyO9OeAEJTX\nEHos13WWL1/OgAED9mMU6h6H6/12oDhY88m4rRd977OW7Qb+xj7CE1LKyM0mhPgA+MS2e7aU8pzq\n34rRt/056H9AJOAkhGgATAXuklL+bLVLKbcBxUKIvsKYs/kSnH+EAw8++GBkiacbJl5xuPZt2LBh\nvL9BZU+Vs700BP/NVRnVKTNqsVRSzWU2a1OJ04Vmyxhr5gvQIT2BD3ZVv+5L+TCqVWbUUqkkdvaY\nPQ5jl6PRYGR2I16qErhzXOaEYI8vQH5+PvGKw/V+O1AMGjTI8SyrLRyIJSOl3CmlXIBTWrZGCCHS\ngZNwPp/3O6ngoEv9AzcB7YAxNvOqsbnPk/r3UKto3749l115NSd9k8JXm2BXBczYDKdOS2HQkGGk\ndDiuRndYzGkBXOtPHNOamzYqPL0VtlbB6nK4eT18WeTjtg45+3aL2d1nJuHIIMgwXJ7TmC1qAheX\nKiwJwXYdXquAkcFknn7p317hpYcaoSiljqWWMQyYIaW0TiyB44QQS4QQXwohuu7tywdd6l9K+Qjw\nSA3f8aT+PdQ6HnvyGd7q0Ysxjz9K7g95tGnRjOvvu52rr7mG4qIiCsZdSGZoV7XU5Ih7rNK27Uo1\nPi4jlekndGLCyq2MW1JCoio4t3kmPx3dlGyfP1o46VYUcKdIm/ukDppmiHP6pcqXf+nMhJ7HM+L9\n9ygqK6Nfr168O/YhBgwYwNJlyw7OgHn4E+CgqmJeCLxs2/4VaCmlLBdCDMGwcDrW9OVDpuK/vvvg\nwYMHDwcDtVMns/fzCiFuAK4xN4eYdYtjgFIp5RN7OXdjjCKcZlLKGBNLgBBiA9BTSlkQa/8hoaQX\nL0VFHjx48BBv2J/no5RyIjDR1bw/z9VRwOd2ghFCZAM7pJRSCNEHw1iJSTBwiJCMBw8ePHioHQgh\ncoD5QDqgCyFuBrpKKUuFEFOBq2wF9OcD412nGAWMFkKEgXLggr1e71Bwl3nw4MGDh0MTcZuuIoR4\nUAix2ZaRNsS27x5TVHOVEOK0eurf6eb11woh7qqPPrj6s9EUIV1kasIhhMg0C2jXCCGmmenkddGX\n14QQ24UQy2xtNfalLn/PGvpW7/eaEKKlEOI7U2R2uRDi72Z7vY/bXvoWD+OWKIT4RQixWAixQggx\n3myPh3GrqW/1Pm51CillXC7AGODWGO1dgcWAH2gD5AJKHfdNNa/bxuzHYqBLPY/XBiDT1fYYcKe5\nfhcwoY76cgLQA1i2r77U9e9ZQ9/q/V4DcoCjzfVUYDXQJR7GbS99q/dxM6+XbH76gLnA8fEwbnvp\nW1yMW10tcWvJmIgVmBoKTJZShqSUGzF+iD512ivjerlSyo3S0GGbYvarvuEer3OAN831N6kjAVIp\n5Q9Un+Cipr7U6e9ZQ9+gnu81KWW+lHKxuV4KrMTQ8qv3cdtL3yAO/kellFb1YQDjBXAPcTBue+kb\nxMG41RXinWT+Zhb8vGozd5vhFNbcTN0LazYH8uq5D25IYIYw5uyxUhWzpZTbzfXtQHb9dG2vfYmH\n3xPi6F4TQrTBsLZ+Ic7Gzda3uWZTvY+bEEIRQizGGJ/vpJS/ESfjVkPfIA7Gra5QryQjahbcPAd4\nEWgLHA1sA2rM5cY5B2JdIB6zJfpLKXsAQ4AbhRAn2HdKwx6Pi37vR1/qup9xc68JIVKBD4GbpZQO\nQar6Hjezbx+YfSslTsZNSqlLKY/G0D8cIGwTI5r7623cYvRtEHEybnWFek1hlvspuCmEeAX43Nzc\nArS07d6rsOZBgrsPLXG+gdQ5pKEFh5RypzDUUvsA24UQOdIovGoK7KjHLtbUl3r/PaWUkXGpz3tN\nCOHHIJi3pJSWTlRcjJutb29bfYuXcbMgpSwSRgpuT+Jk3GL0rZeUcpbVHg/jdrARt+4y88awMByw\nsoE+Ay4QQgSEEG2BDsA89/cPMhYAHYQQbYQQAYxc8s/quA8RCCGShRBp5noKcBrGeH0GXGYedhl7\nESCtA9TUl3r/PePhXhNCCOBVYIWU8mnbrnoft5r6Fifj1thyNwkhkoBTgUXEx7jF7Jsw6lQsxNuz\nrfZR35kHNS0YM2UuBZZg3CDZtn33YgTFVgGD66l/QzCybHKBe+p5rNpiZKUsBpZb/QEygRnAGmAa\n0KCO+jMZ2IohBZkHXLG3vtTl7xmjb1fGw72GkXWkm7/hInM5PR7GrYa+DYmTcTsKQ0trsdmXO/Z1\n78dB3+p93Opy8YoxPXjw4MHDQUPcuss8ePDgwcOhD49kPHjw4MHDQYNHMh48ePDg4aDBIxkPHjx4\n8HDQ4JGMBw8ePHg4aPBIxoMHDx48HDR4JOPBgwcPHg4aPJLx4MGDBw8HDf8P/T2c+UAyH7UAAAAA\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2183a90>"
+       "<matplotlib.figure.Figure at 0x38c8400>"
       ]
      },
      "metadata": {},
@@ -112,8 +112,8 @@
     {
      "data": {
       "text/plain": [
-       "(<matplotlib.figure.Figure at 0x2183a90>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x187ccba8>)"
+       "(<matplotlib.figure.Figure at 0x38c8400>,\n",
+       " <matplotlib.axes._subplots.AxesSubplot at 0x187e9828>)"
       ]
      },
      "metadata": {},
@@ -133,6 +133,15 @@
     "                       "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/SimPEG Tutorial - DC Fwr Problem.ipynb b/notebooks/SimPEG Tutorial - DC Fwr Problem.ipynb
deleted file mode 100644
index b7769364..00000000
--- a/notebooks/SimPEG Tutorial - DC Fwr Problem.ipynb	
+++ /dev/null
@@ -1,441 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Objective:** \n",
-    "\n",
-    "In this tutorial we will create a simple two-sphere model and simulate DC Resistivity data for various transmitter-receiver configurations.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Efficiency Warning: Interpolation will be slow, use setup.py!\n",
-      "\n",
-      "            python setup.py build_ext --inplace\n",
-      "    \n"
-     ]
-    }
-   ],
-   "source": [
-    "from SimPEG import *\n",
-    "import simpegDCIP as DC\n",
-    "import scipy.interpolate as interpolation\n",
-    "import time\n",
-    "import re"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
-      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n",
-      "WARNING: "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "pylab import has clobbered these variables: ['linalg']\n",
-      "`%matplotlib` prevents importing * from pylab and numpy\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-200, 200)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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RDA2upGdwZQsqOM4oQw2GkG6ETkVInA18CZhlZn+JZS0CLpN0PpF7vyuwzMxM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\n4jiO42TCDYnjOI6TCTckjuM4Tib+P03R8VBVbPvSAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15e3dd30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# First we need to create a mesh and a model.\n",
-    "\n",
-    "# This is our mesh\n",
-    "dx    = 5.\n",
-    "\n",
-    "hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]\n",
-    "hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]\n",
-    "hzind = [(dx,15,-1.3),(dx, 15)]\n",
-    "\n",
-    "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')\n",
-    "\n",
-    "# Define our model\n",
-    "bckgr = 1e-2\n",
-    "cond  = 1e-1\n",
-    "resis = 1e-3\n",
-    "zloc  = -50.\n",
-    "xloc  = 50.\n",
-    "yloc  = 0.\n",
-    "radi  = 25.\n",
-    "\n",
-    "# Set background conductivity\n",
-    "model = np.ones(mesh.nC) * bckgr\n",
-    "\n",
-    "# First anomaly (conductor)\n",
-    "ind = Utils.ModelBuilder.getIndicesSphere([-xloc,yloc,zloc],radi,mesh.gridCC)\n",
-    "model[ind] = cond\n",
-    "\n",
-    "# Second anomaly (resistor)\n",
-    "ind = Utils.ModelBuilder.getIndicesSphere([xloc,yloc,zloc],radi,mesh.gridCC)\n",
-    "model[ind] = resis\n",
-    "\n",
-    "# Get index of the center\n",
-    "indy = int(mesh.nCy/2)\n",
-    "indz = int(np.argmin( np.abs(mesh.vectorCCz - zloc) ))\n",
-    "\n",
-    "# Plot the model for reference\n",
-    "# Define core mesh extent\n",
-    "xlim = 200\n",
-    "zlim = 200\n",
-    "\n",
-    "plt.figure()\n",
-    "ax = plt.subplot(1,2,1, aspect='equal')\n",
-    "mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)\n",
-    "ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')\n",
-    "plt.gca().set_aspect('equal', adjustable='box')\n",
-    "plt.xlim([-xlim,xlim])\n",
-    "plt.ylim([-zlim,0])\n",
-    "\n",
-    "ax = plt.subplot(1,2,2, aspect='equal')\n",
-    "mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
-    "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
-    "plt.gca().set_aspect('equal', adjustable='box')\n",
-    "plt.xlim([-xlim,xlim])\n",
-    "plt.ylim([-xlim,xlim])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have model in 3D, we can define a survey.\n",
-    "There area various ground DC configurations used in the field.\n",
-    "We can explore two survey types here: pole-dipole (pdp) and dipole-dipole (dpdp).\n",
-    "In both cases we need to specify three important parameter.\n",
-    "\n",
-    "a: Seperation (m) between the transmitter and receivers\n",
-    "\n",
-    "b: Dipole seperation (m) of the receiver (and transmitter for dpdp)\n",
-    "\n",
-    "n: Number of receiver dipoles along line\n",
-    "\n",
-    "We also need to give a starting and end point for the survey.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-200, 200)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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afm2K5RfpP1lXY6Hk1yW9SYedDQyfaW4YV6e3X+dBijvA9Uk6kOKOcSsyb3MX\nSQdJOjgtvxw4neL1WAGcn1Y7nzzviUaabbfrv5uy3ifpvX89sDYi/qamqzOvTafO2nX7B1hHcWnu\nQ+nnmpq+yymK048B76hpP4HiF7ceuLqDsZxNUfN7HtgM3Jna/wT4WYrvx8A7y4qljNelLq6bgJ8C\nj6Q36+S9xZX5/XMGxZnl9cD8Lr93D6c4Y/1wen/MT+2TgLspbh16FzAxw7aXUZS0Xkjvkw+OtN2c\nv5sGsXyorPcJcDLwu/Q7Gc4pczr12viCBTOzzMZt6cDMbLxwojUzy8yJ1swsMydaM7PMnGjNzDJz\nojUzy8yJ1swsMydaM7PMnGhtnyPpjenuUC+V9PJ0o+dZZcdlvctXhtk+SdIi4GUU30G3MSKuKjkk\n62FOtLZPSndqepDinhBvCv9HsIxcOrB91SHAyym+tuT3So7FepyPaG2fJGkFsBR4LTAlIi4uOSTr\nYZW+8bdZDpI+APy/iPimpJcA/1vSaRFxT8mhWY/yEa2ZWWau0ZqZZeZEa2aWmROtmVlmTrRmZpk5\n0ZqZZeZEa2aWmROtmVlmTrRmZpn9f2OrPHcOE6ghAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15e3d240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Survey parameters\n",
-    "a = 30.\n",
-    "b = 30.\n",
-    "n = 20 # Integer number of rx dipoles\n",
-    "\n",
-    "# Specify the survey type: \"pdp\" | \"dpdp\"\n",
-    "stype = 'dpdp'\n",
-    "\n",
-    "# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh\n",
-    "ends = [(-175,0),(175,0)]\n",
-    "ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]\n",
-    "\n",
-    "# Snap the endpoints to the grid. Easier to create 2D section.\n",
-    "indx = Utils.closestPoints(mesh, ends )\n",
-    "locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]\n",
-    "\n",
-    "# We will handle the geometry of the survey for you and create all the combination of tx-rx along line\n",
-    "[Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, a, b, n)\n",
-    "\n",
-    "# Here is an example for the first tx-rx array\n",
-    "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
-    "mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
-    "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
-    "plt.gca().set_aspect('equal', adjustable='box')\n",
-    "\n",
-    "plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')\n",
-    "plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')\n",
-    "plt.xlim([-xlim,xlim])\n",
-    "plt.ylim([-xlim,xlim])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next section will specify all the parameters used to forward model the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "#Set boundary conditions\n",
-    "mesh.setCellGradBC('neumann')\n",
-    "\n",
-    "# Define the differential operators needed for the DC problem\n",
-    "Div = mesh.faceDiv\n",
-    "Grad = mesh.cellGrad\n",
-    "Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))\n",
-    "\n",
-    "A = Div*Msig*Grad\n",
-    "\n",
-    "# Change one corner to deal with nullspace\n",
-    "A[0,0] = 1\n",
-    "A = sp.csc_matrix(A)\n",
-    "\n",
-    "# We will solve the system iteratively, so a pre-conditioner is helpful\n",
-    "# This is simply a Jacobi preconditioner (inverse of the main diagonal)\n",
-    "dA = A.diagonal()\n",
-    "P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transmitter 8 of 9 -> Time:0.990999937057 sec Transmitter 8 of 9\n",
-      "Forward completed\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Now we can solve the system for all the transmitters\n",
-    "# We want to store the data\n",
-    "data = []\n",
-    "\n",
-    "# There is probably a more elegant way to do this, but we can just for-loop through the transmitters\n",
-    "for ii in range(len(Tx)):\n",
-    "    \n",
-    "    start_time = time.time() # Let's time the calculations\n",
-    "    \n",
-    "    #print(\"Transmitter %i / %i\\r\" % (ii+1,len(Tx)))\n",
-    "    \n",
-    "    # Select dipole locations for receiver\n",
-    "    rxloc_M = np.asarray(Rx[ii][:,0:3])\n",
-    "    rxloc_N = np.asarray(Rx[ii][:,3:])\n",
-    "    \n",
-    "    # Number of receivers\n",
-    "    nrx = rxloc_M.shape[0]\n",
-    "  \n",
-    "    # For usual cases \"dpdp\" or \"gradient\"\n",
-    "    if not re.match(stype,'pdp'): \n",
-    "        inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )\n",
-    "        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )   \n",
-    "        \n",
-    "    else: \n",
-    "        \n",
-    "        # Create an \"inifinity\" pole\n",
-    "        tx =  np.squeeze(Tx[ii][:,0:1])\n",
-    "        tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2\n",
-    "        inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)\n",
-    "        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )  \n",
-    "\n",
-    "\n",
-    "    # Iterative Solve\n",
-    "    Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)\n",
-    "\n",
-    "    # We now have the potential everywhere\n",
-    "    phi = mkvc(Ainvb[0])\n",
-    "    \n",
-    "    # Solve for phi on pole locations\n",
-    "    P1 = mesh.getInterpolationMat(rxloc_M, 'CC')\n",
-    "    P2 = mesh.getInterpolationMat(rxloc_N, 'CC')\n",
-    "        \n",
-    "    # Compute the potential difference\n",
-    "    dtemp = (P1*phi - P2*phi)*np.pi\n",
-    "    \n",
-    "    data.append( dtemp )     \n",
-    "    print '\\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),\n",
-    "    \n",
-    "print 'Transmitter {0} of {1}'.format(ii,len(Tx))\n",
-    "print 'Forward completed'\n",
-    " \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once we have our problem, we can use the inversion tools in SimPEG to run our inversion:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JhCOYJFk08UyqVo7numvHcd21J+Ta9faXcOMTYAS/wkLu3q0kBgBSmiz77FLWz3oUoY0C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\nxYGiKIqiKAn8f1A9ajP5f0M7AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x18d372e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Let's just convert the 3D format into 2D (distance along line) and plot\n",
-    "[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)\n",
-    "\n",
-    "fig, ax = plt.subplots(1,1, figsize = (8,7))\n",
-    "plt.gca().set_aspect('equal', adjustable='box')\n",
-    "\n",
-    "# Plot the location of the spheres for reference\n",
-    "circle1=plt.Circle((-xloc-Tx[0][0,0],zloc),radi,color='w',fill=False, lw=3)\n",
-    "circle2=plt.Circle((xloc-Tx[0][0,0],zloc),radi,color='k',fill=False, lw=3)\n",
-    "ax.add_artist(circle1)\n",
-    "ax.add_artist(circle2)\n",
-    "\n",
-    "# Add the speudo section\n",
-    "DC.plot_pseudoSection(Tx2d,Rx2d,data,mesh.vectorNz[-1],stype)\n",
-    "\n",
-    "plt.xlim([0,2*xlim])\n",
-    "plt.ylim([-zlim,0])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Back in the days (not so long ago really), geophysicists used to interpret DCR data directly from pseudo-section. Hopefully this example will convince you that interpretating speudo-section is really tricky, arguably impossible.\n",
-    "Fortunately for us, we now have inversion techniques to make sense of the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "125.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print -xloc-Tx[0][0,0]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/ThreeMeshDC.ipynb b/notebooks/ThreeMeshDC.ipynb
deleted file mode 100644
index 4797e7e3..00000000
--- a/notebooks/ThreeMeshDC.ipynb
+++ /dev/null
@@ -1,295 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:189621fc59a92a7c23842dcbc46e2decfeef63b14613f47a605c97b622f48fc0"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from SimPEG import Mesh, Utils, np, SolverLU\n",
-      "import matplotlib.pyplot as plt\n",
-      "# %pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "Vendor:  Continuum Analytics, Inc.\n",
-        "Package: mkl\n",
-        "Message: trial mode expires in 20 days\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib\n",
-      "from matplotlib.mlab import griddata"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True})"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sz = [15,15]\n",
-      "tM = Mesh.TensorMesh(sz)\n",
-      "qM = Mesh.TreeMesh(sz)\n",
-      "qM.refine(lambda X: 1 if np.sqrt(((X-0.5)**2).sum()) < 0.45 else 0)\n",
-      "rM = Mesh.CurvilinearMesh(Utils.meshutils.exampleLrmGrid(sz,'rotate'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fun = lambda X: 1 if np.sqrt(((X-0.5)**2).sum()) < 0.45 else 0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fun(np.ones(30))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "0"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def DCfun(mesh, pts):\n",
-      "    D = mesh.faceDiv\n",
-      "    G = D.T\n",
-      "    sigma = 1e-2*np.ones(mesh.nC)\n",
-      "    Msigi = mesh.getFaceInnerProduct(1./sigma)\n",
-      "    MsigI = Utils.sdInv(Msigi)\n",
-      "    A = D*MsigI*G\n",
-      "    A[-1,-1] /= mesh.vol[-1] # Remove null space\n",
-      "    rhs = np.zeros(mesh.nC)\n",
-      "    txind = Utils.meshutils.closestPoints(mesh, pts)\n",
-      "    rhs[txind] = np.r_[1,-1]\n",
-      "    return A, rhs"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tM.vectorCCy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "array([ 0.03333333,  0.1       ,  0.16666667,  0.23333333,  0.3       ,\n",
-        "        0.36666667,  0.43333333,  0.5       ,  0.56666667,  0.63333333,\n",
-        "        0.7       ,  0.76666667,  0.83333333,  0.9       ,  0.96666667])"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pts = np.vstack((np.r_[0.25, 0.5], np.r_[0.75, 0.5]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "AtM, rhstM = DCfun(tM, pts)\n",
-      "AinvtM = SolverLU(AtM)\n",
-      "phitM = AinvtM*rhstM"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "AqM, rhsqM = DCfun(qM, pts)\n",
-      "AinvqM = SolverLU(AqM)\n",
-      "phiqM = AinvqM*rhsqM"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ArM, rhsrM = DCfun(rM, pts)\n",
-      "AinvrM = SolverLU(ArM)\n",
-      "phirM = AinvrM*rhsrM"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "coreind = (qM.gridCC[:,0]-0.5)**2+(qM.gridCC[:,1]-0.5)**2 >0.43**2\n",
-      "phiqM[coreind] = 0."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Xi = tM.gridCC[:,0].reshape(sz[0], sz[1], order='F')\n",
-      "Yi = tM.gridCC[:,1].reshape(sz[0], sz[1], order='F')\n",
-      "PHItM = griddata(tM.gridCC[:,0], tM.gridCC[:,1], phitM, Xi, Yi, interp='linear')\n",
-      "PHIqM = griddata(qM.gridCC[:,0], qM.gridCC[:,1], phiqM, Xi, Yi, interp='linear')\n",
-      "PHIrM = griddata(rM.gridCC[:,0], rM.gridCC[:,1], phirM, Xi, Yi, interp='linear')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Users/sgkang/anaconda/lib/python2.7/site-packages/matplotlib/tri/triangulation.py:110: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
-        "  self._neighbors)\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, axes = plt.subplots(1,3,figsize=(14*1.2,4*1.2))\n",
-      "label = [\"(a)\", \"(b)\", \"(c)\"]\n",
-      "opts = {}\n",
-      "vmin, vmax = PHItM.min(), PHItM.max()\n",
-      "dat = axes[0].contourf(Xi, Yi, PHItM, 100)\n",
-      "tM.plotGrid(ax=axes[0], **opts)\n",
-      "axes[0].set_title('TensorMesh')\n",
-      "axes[1].contourf(Xi, Yi, PHIqM, 100)\n",
-      "qM.plotGrid(ax=axes[1], **opts)\n",
-      "axes[1].set_title('TreeMesh')\n",
-      "axes[2].contourf(Xi, Yi, PHIrM, 100)\n",
-      "rM.plotGrid(ax=axes[2], **opts)\n",
-      "axes[2].set_title('CurvilinearMesh')\n",
-      "for i in range(3):\n",
-      "    axes[i].set_xlim(0.025, 0.975)\n",
-      "    axes[i].set_ylim(0.025, 0.975)\n",
-      "    axes[i].text(0., 1.0, label[i], fontsize=24)\n",
-      "    if i==0:        \n",
-      "        axes[i].set_ylabel(\"y\")\n",
-      "    else:\n",
-      "        axes[i].set_ylabel(\" \")\n",
-      "    axes[i].set_xlabel(\"x\")\n",
-      "plt.show()\n",
-      "# fig.savefig(\"./ThreeMesh.png\", dpi=100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 68
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/simpegDCIP/Dev/DC2D_Demo_HTML_anim.py b/simpegDCIP/Dev/DC2D_Demo_HTML_anim.py
deleted file mode 100644
index b41d4574..00000000
--- a/simpegDCIP/Dev/DC2D_Demo_HTML_anim.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import os
-from SimPEG import *
-import simpegDCIP as DC
-import pylab as plt
-from matplotlib import animation
-from JSAnimation import HTMLWriter
-import time
-import re
-#from readUBC_DC2DMesh import readUBC_DC2DMesh
-#from readUBC_DC2DModel import readUBC_DC2DModel
-#from readUBC_DC2DLoc import readUBC_DC2DLoc
-#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
-#from readUBC_DC3Dobs import readUBC_DC3Dobs
-
-#%%
-home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
-msh_file = 'Mesh_2D.msh'
-mod_file = 'Model_2D.con'
-obs_file = 'FWR_data3D.dat'
-
-dsep = '\\'
-
-# Forward solver
-slvr = 'BiCGStab' #'LU'
-
-# Preconditioner
-pcdr = 'Jacobi' #'Gauss-Seidel'#
-
-# Number of padding cells to remove from plotting
-padc = 15
-
-# Load UBC mesh 2D
-mesh = DC.readUBC_DC2DMesh(home_dir + dsep + msh_file)
-
-# Load model
-model = DC.readUBC_DC2DModel(home_dir + dsep + mod_file)
-
-# load obs file
-[Tx,Rx,d,wd] = DC.readUBC_DC3Dobs(home_dir + dsep + obs_file)
-[Tx, Rx] = DC.convertObs_DC3D_to_2D(Tx,Rx)
-#%% Create system
-#Set boundary conditions
-mesh.setCellGradBC('neumann')
-
-Div = mesh.faceDiv
-Grad = mesh.cellGrad
-Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
-
-A = Div*Msig*Grad
-
-# Change one corner to deal with nullspace
-A[0,0] = 1
-A = sp.csc_matrix(A)
-
-start_time = time.time()
-
-if re.match(slvr,'BiCGStab'):
-    # Create Jacobi Preconditioner  
-    if re.match(pcdr,'Jacobi'):
-        dA = A.diagonal()
-        P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-    # Create Gauss-Seidel Preconditioner
-    elif re.match(pcdr,'Gauss-Seidel'):
-        LD = sp.tril(A,k=0)
-        #LDinv = sp.linalg.splu(LD)
-
-elif re.match(slvr,'LU'):
-    # Factor A matrix
-    Ainv = sp.linalg.splu(A)    
-    print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
-    
-#%% Create SimPEG objects
-
-
-# Create sub-mesh for plotting
-hx = mesh.hx
-hy = mesh.hy
-
-hx_sub = hx[padc:-padc]
-hy_sub = hy[padc:]
-mesh_sub = Mesh.TensorMesh([hx_sub,hy_sub],(mesh.vectorNx[padc], mesh.vectorNy[padc]))
-model_sub = model.reshape(mesh.nCy,mesh.nCx)
-model_sub = mkvc(model_sub[padc:,padc:-padc].T)
-
-xx = mesh_sub.vectorCCx
-yy = mesh_sub.vectorCCy
-
-#%% Solve
-#txii = range(50,1950,100)
-#jx_CC_sub = np.zeros((len(txii),mesh_sub.nCx,mesh_sub.nCy))
-#jy_CC_sub = np.zeros((len(txii),mesh_sub.nCx,mesh_sub.nCy))
-
-fig = plt.figure(figsize=(10,5))
-axs = plt.axes(ylim = (yy[0],yy[-1]+mesh.hy[-1]*2), xlim = (xx[0],xx[-1]))#
-plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
-plt.ylim(yy[0],yy[-1]+mesh.hy[-1]*2)
-plt.xlim(xx[0],xx[-1])
-#im1 = axs.pcolormesh([],[],[], alpha=0.75,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',vmin=-1e-2, vmax=1e-2)
-#im2 = axs.pcolormesh([],[],[],alpha=0.2,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',cmap='gray')
-im1 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,np.zeros((mesh_sub.nCy,mesh_sub.nCx)), alpha=0.75,vmin=-1e-2, vmax=1e-2)
-im2 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,np.zeros((mesh_sub.nCy,mesh_sub.nCx)), alpha=0.75,vmin=-1e-2, vmax=1e-2)
-im3 = axs.streamplot(xx, yy, np.zeros((mesh_sub.nCy,mesh_sub.nCx)), np.zeros((mesh_sub.nCy,mesh_sub.nCx)),color='k')
-im4 = axs.scatter([],[], c='r', s=200)
-im5 = axs.scatter([],[], c='r', s=200)
-
-
-
-#==============================================================================
-# def init():
-#     im1.set_data([[],[],[]])
-#     im2.set_data([[],[],[]])
-#     
-#     return [im1]+[im2]
-#==============================================================================
-
-def animate(ii):
-    
-    
-
-#for ii in range(len(txii)):
-    
-    removeStream()
-    tx = np.asarray(np.c_[Tx[ii],np.ones(Tx[ii].shape[0])*mesh.vectorNy[-1]-1])
-    inds = Utils.closestPoints(mesh, tx )
-    RHS = mesh.getInterpolationMat( tx , 'CC').T*( [-1,1] / mesh.vol[inds] )
-    
-    if re.match(slvr,'BiCGStab'):
-        
-        if re.match(pcdr,'Jacobi'):
-            dA = A.diagonal()
-            P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-            # Iterative Solve
-            phi = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
-            phi = mkvc(phi[0])
-               
-    elif re.match(slvr,'LU'):
-        #Direct Solve
-        phi = Ainv.solve(RHS)
-    
-    
-    j = -Msig*Grad*phi
-    j_CC = mesh.aveF2CCV*j
-    
-    # Compute charge density solving div*grad*phi
-    Q = -mesh.faceDiv*mesh.cellGrad*phi
-    
-    jx_CC = j_CC[0:mesh.nC].reshape(mesh.nCy,mesh.nCx)
-    jy_CC = j_CC[mesh.nC:].reshape(mesh.nCy,mesh.nCx)
-    
-    #%% Grab only the core for presentation
-    jx_CC_sub = jx_CC[padc:,padc:-padc]
-
-    jy_CC_sub = jy_CC[padc:,padc:-padc]
-
-    Q_sub = Q.reshape(mesh.nCy,mesh.nCx)   
-    Q_sub = Q_sub[padc:,padc:-padc]
-    
-    J_rho = np.sqrt(jx_CC_sub**2 + jy_CC_sub**2)
-    lw = np.log10(J_rho/J_rho.min())
-    
-
-    #axs.imshow(Q_sub,alpha=0.75,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',vmin=-1e-2, vmax=1e-2)    
-    #axs.imshow(np.log10(model_sub.reshape(mesh_sub.nCy,mesh_sub.nCx)),alpha=0.2,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',cmap='gray')     
-    global im1
-    im1 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,Q_sub, alpha=0.75,vmin=-1e-2, vmax=1e-2)
-    
-    global im2
-    im2 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,np.log10(model_sub.reshape(mesh_sub.nCy,mesh_sub.nCx)), alpha=0.25)
-
-    global im3
-    im3 = axs.streamplot(xx, yy, jx_CC_sub, jy_CC_sub,color='k',linewidth = lw,density=0.5)
-    
-    global im4
-    im4 = axs.scatter(tx[0,0],mesh.vectorNy[-1], c='r', s=75, marker='v' )
-    
-    global im5
-    im5 = axs.scatter(tx[1,0],mesh.vectorNy[-1], c='b', s=75, marker='v' )
-    
-
-    #plt.show()
-    #im1.set_array(Q_sub)
-    #im2.set_array(np.log10(model_sub.reshape(mesh_sub.nCy,mesh_sub.nCx)))
-    #im2.set_array(mesh_sub.vectorCCx, mesh_sub.vectorCCy,jx_CC_sub.T,jy_CC_sub.T)
-
-    #return [im1] + [im2]
-#%% Create widget
-def removeStream():
-    global im1
-    im1.remove()    
-    
-    global im2
-    im2.remove()  
-    
-    global im3
-    im3.lines.remove()
-    axs.patches = []
-    
-    global im4
-    im4.remove()
-    
-    global im5
-    im5.remove()
-#def viewInv(msh,iteration):
-
-
-
-#, linewidth=lw.T
-#%%   
-#interact(viewInv,msh = mesh_sub, iteration = IntSlider(min=0, max=len(txii)-1 ,step=1, value=0))
-# set embed_frames=True to embed base64-encoded frames directly in the HTML
-anim = animation.FuncAnimation(fig, animate,
-                               frames=len(Tx), interval=5)
-                               
-anim.save(home_dir + '\\animation.html', writer=HTMLWriter(embed_frames=True))
diff --git a/simpegDCIP/Dev/DC3D_Demo_TwoSpheres.py b/simpegDCIP/Dev/DC3D_Demo_TwoSpheres.py
deleted file mode 100644
index a041aedd..00000000
--- a/simpegDCIP/Dev/DC3D_Demo_TwoSpheres.py
+++ /dev/null
@@ -1,478 +0,0 @@
-"""
-        Experimental script for the forward modeling of DC resistivity data 
-        along survey lines defined by the user. The program loads in a 3D mesh
-        and model which is used to design pole-dipole or dipole-dipole survey
-        lines.
-        
-        Uses SimPEG to generate the forward problem and compute the LU
-        factorization.
-        
-        Calls DCIP2D for the inversion of a projected 2D section from the full
-        3D model.
-
-        Assumes flat topo for now...
-            
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-"""
-
-
-#%%
-from SimPEG import *
-import simpegDCIP as DC
-import pylab as plt
-from pylab import get_current_fig_manager
-from scipy.interpolate import griddata
-import time
-import re
-import numpy.matlib as npm
-import scipy.interpolate as interpolation
-#==============================================================================
-# from readUBC_DC3Dobs import readUBC_DC3Dobs
-# from readUBC_DC2DModel import readUBC_DC2DModel
-# from writeUBC_DCobs import writeUBC_DCobs
-
-# from plot_pseudoSection import plot_pseudoSection
-# from gen_DCIPsurvey import gen_DCIPsurvey
-# from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
-#==============================================================================
-from matplotlib.colors import LogNorm
-import os
-
-home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
-dsep = '\\'
-#from scipy.linalg import solve_banded
-
-# Load UBC mesh 3D
-mesh = Mesh.TensorMesh.readUBC(home_dir + '\Mesh_5m.msh')
-#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
-#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
-
-# Load model
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
-model = Mesh.TensorMesh.readModelUBC(mesh,home_dir + '\TwoSpheres.con')
-
-#model = model**0 * 1e-2
-# Specify survey type
-stype = 'dpdp'
-
-# Survey parameters
-a = 30
-b = 30
-n = 20
-
-# Forward solver
-slvr = 'BiCGStab' #'LU'
-
-# Preconditioner
-pcdr = 'Jacobi'#
-
-# Inversion parameter
-pct = 0.01
-flr = 1e-4
-chifact = 100
-ref_mod = 1e-2
-
-# DOI threshold
-cutoff = 0.8
-
-#%% Create system
-#Set boundary conditions
-mesh.setCellGradBC('neumann')
-
-Div = mesh.faceDiv
-Grad = mesh.cellGrad
-Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
-
-A = Div*Msig*Grad
-
-# Change one corner to deal with nullspace
-A[0,0] = 1
-A = sp.csc_matrix(A)
-
-start_time = time.time()
-
-if re.match(slvr,'BiCGStab'):
-    # Create Jacobi Preconditioner  
-    if re.match(pcdr,'Jacobi'):
-        dA = A.diagonal()
-        P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-        #LDinv = sp.linalg.splu(LD)
-
-elif re.match(slvr,'LU'):
-    # Factor A matrix
-    Ainv = sp.linalg.splu(A)    
-    print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
-
-#%% Create survey
-# Display top section 
-top = int(mesh.nCz)-1
-
-plt.figure()
-ax_prim = plt.subplot(1,1,1)
-mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
-plt.xlim([423200,423750])
-plt.ylim([546350,546650])
-plt.gca().set_aspect('equal', adjustable='box')
-    
-plt.show()
-cfm1=get_current_fig_manager().window
-gin=[1]
-
-# Keep creating sections until returns an empty ginput (press enter on figure)
-#while bool(gin)==True:
-    
-# Bring back the plan view figure and pick points     
-cfm1.activateWindow()
-plt.sca(ax_prim)
-    
-# Takes two points from ginput and create survey
-#if re.match(stype,'gradient'):
-gin = [(423230.  ,  546440.), (423715.  ,  546440.)]
-#else:
-#gin = plt.ginput(2, timeout = 0)
-
-
-
-#==============================================================================
-# if not gin:
-#     print 'SimPED - Simulation has ended with return'
-#     break
-#==============================================================================
-
-# Add z coordinate to all survey... assume flat
-nz = mesh.vectorNz
-var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
-
-# Snap the endpoints to the grid. Easier to create 2D section.
-indx = Utils.closestPoints(mesh, var )
-endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
-      
-[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
- 
-dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) ) 
-dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
-dl_y = ( Tx[-1][1,1] - Tx[0][1,0]  ) / dl_len
-azm =  np.arctan(dl_y/dl_x)
-  
-# Plot stations along line   
-plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
-plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
-
-#%% Forward model data
-data = []#np.zeros( nstn*nrx )
-unct = []
-problem = DC.ProblemDC_CC(mesh)
-    
-for ii in range(len(Tx)):
-    start_time = time.time()
-    
-    # Select dipole locations for receiver
-    rxloc_M = np.asarray(Rx[ii][:,0:3])
-    rxloc_N = np.asarray(Rx[ii][:,3:])
-    
-    # Number of receivers
-    nrx = rxloc_M.shape[0]
-
-    
-    
-    if not re.match(stype,'pdp'):
-        inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
-        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )   
-        
-    else: 
-        
-        # Create an "inifinity" pole
-        tx =  np.squeeze(Tx[ii][:,0:1])
-        tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
-        inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
-        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )  
-    
-    # Solve for phi on pole locations
-    P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
-    P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
-
-    if re.match(slvr,'BiCGStab'):
-        
-        if re.match(pcdr,'Jacobi'):
-            dA = A.diagonal()
-            P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-            # Iterative Solve
-            Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
-            
-
-        phi = mkvc(Ainvb[0])
-               
-    elif re.match(slvr,'LU'):
-        #Direct Solve
-        phi = Ainv.solve(RHS)        
-    
-    
-    
-    # Compute potential at each electrode
-    dtemp = (P1*phi - P2*phi)*np.pi
-    
-    data.append( dtemp )     
-    unct.append( np.abs(dtemp) * pct + flr)
-   
-    print("--- %s seconds ---" % (time.time() - start_time))  
-    
-
-#%% Run 2D inversion if pdp or dpdp survey
-# Otherwise just plot and apparent susceptibility map
-if not re.match(stype,'gradient'):
-    
-    #%% Write data file in UBC-DCIP3D format
-    DC.writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')     
-    
-    
-    #%% Load 3D data
-    [Tx, Rx, data, wd] = DC.readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
-    
-    
-    #%% Convert 3D obs to 2D and write to file
-    [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
-    
-    DC.writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')        
-    
-    #%% Create a 2D mesh along axis of Tx end points and keep z-discretization    
-    dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
-    nc = np.ceil(dl_len/dx)+3
-    
-    padx = dx*np.power(1.4,range(1,15))
-    
-    # Creating padding cells
-    h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
-    
-    # Create mesh with 0 coordinate centerer on the ginput points in cell center
-    mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
-    
-    # Create array of points for interpolating from 3D to 2D mesh
-    xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
-    yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
-    zz = mesh2d.vectorCCy
-    
-    [XX,ZZ] = np.meshgrid(xx,zz)
-    [YY,ZZ] = np.meshgrid(yy,zz)
-    
-    xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
-    
-    #plt.scatter(xx,yy,s=20,c='y')
-    
-    
-    F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
-    m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
-    
-     
-    #==============================================================================
-    # mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
-    # m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
-    # m2D = m3D[:,1,:]
-    #==============================================================================
-    #%%   
-    plt.figure()
-    axs = plt.subplot(1,1,1)
-    
-    plt.xlim([-dx,nc*dx+dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
-    circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
-    axs.add_artist(circle1)
-    axs.add_artist(circle2)
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D))#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
-    cbar = plt.colorbar(format = '%.2f',fraction=0.02)
-    cmin,cmax = cbar.get_clim()
-    ticks = np.linspace(cmin,cmax,3)
-    cbar.set_ticks(ticks) 
-    
-    # Plot poles
-    plt.scatter(Tx2d[0][0],mesh2d.vectorNy[-1]+dx,s=50,c='r',marker='v')
-    plt.scatter(Tx2d[0][1],mesh2d.vectorNy[-1]+dx,s=50,c='b',marker='v')
-    plt.scatter(Rx2d[0][:,0],np.ones(Rx2d[0].shape[0])*mesh2d.vectorNy[-1]+dx,s=50,c='g')    
-    #mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
-    
-    #%% Plot pseudo section
-    plt.figure()
-    axs = plt.subplot(1,1,1)
-    plt.xlim([-dx,nc*dx+dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
-    circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
-    axs.add_artist(circle1)
-    axs.add_artist(circle2)
-    
-    DC.plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
-    plt.show()
-
-    #%% Run two inversions with different reference models and compute a DOI
-
-    invmod = []
-    refmod = []
-    plt.figure()
-    
-    for jj in range(2):
- 
-        # Create dcin2d inversion files and run
-        inv_dir = home_dir + '\Inv2D' 
-        if not os.path.exists(inv_dir):
-            os.makedirs(inv_dir)
-            
-        mshfile2d = 'Mesh_2D.msh'
-        modfile2d = 'Model_2D.con'
-        obsfile2d = 'FWR_3D_2_2D.dat'
-        inp_file = 'dcinv2d.inp'
-        
-        
-        # Export 2D mesh
-        fid = open(inv_dir + dsep + mshfile2d,'w')
-        fid.write('%i\n'% mesh2d.nCx)
-        fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))  
-        np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
-        fid.write('\n')
-        fid.write('%i\n'% mesh2d.nCy)
-        fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))   
-        np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
-        fid.close()
-        
-        # Export 2D model
-        fid = open(inv_dir + dsep + modfile2d,'w')
-        fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
-        np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
-        fid.close()
-        
-        # Export data file
-        DC.writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D') 
-        
-        # Write input file
-        fid = open(inv_dir + dsep + inp_file,'w')
-        fid.write('OBS LOC_X %s \n'% obsfile2d)
-        fid.write('MESH FILE %s \n'% mshfile2d)
-        fid.write('CHIFACT 1 %f\n'% chifact)
-        fid.write('TOPO DEFAULT  %s \n')
-        fid.write('INIT_MOD DEFAULT\n')
-        fid.write('REF_MOD VALUE %e\n'% (ref_mod*(jj+1)))
-        fid.write('ALPHA DEFAULT\n')
-        fid.write('WEIGHT DEFAULT\n')
-        fid.write('STORE_ALL_MODELS FALSE\n')
-        fid.write('INVMODE SVD\n')
-        fid.write('USE_MREF TRUE\n')
-        fid.close()
-        
-        os.chdir(inv_dir)
-        os.system('dcinv2d ' + inp_file)
-        
-        
-        #Load model
-        minv = DC.readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
-        
-        axs = plt.subplot(2,1,jj+1)
-        
-        plt.xlim([-dx,nc*dx+dx])
-        plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
-        plt.gca().set_aspect('equal', adjustable='box')
-        
-        minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
-        #plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
-        
-        circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
-        circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
-        axs.add_artist(circle1)
-        axs.add_artist(circle2)
-        
-        
-        axp = plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=1,vmin = -2.25, vmax = -1.5)
-            
-        plt.show()
-        
-        if jj == 1:
- 
-            plt.ylabel('(b)',rotation=360)
-            plt.xlabel('Distance (m)')
-            
-        else:
-            plt.ylabel('(a)',rotation=360)
-            
-            
-        cbar = plt.colorbar(format = '%.2f',fraction=0.05,orientation='vertical',pad=0.02)
-        cmin,cmax = cbar.get_clim()
-        ticks = np.linspace(cmin,cmax,3)
-        cbar.set_ticks(ticks)
-        #cbar.set_ticklabels('%.2f')
-        
-        invmod.append(minv)
-        refmod.append(ref_mod*(jj+1))
-        
-    #%% Compute DOI
-    DOI = np.abs(invmod[0] - invmod[1]) / np.abs(refmod[0] - refmod[1])
-    # Normalize between [0 1]
-    DOI = DOI - np.min(DOI)
-    DOI = (1.- DOI/np.max(DOI))
-    DOI[DOI > cutoff] = 1     
-    
-    plt.figure()
-    plt.xlim([-dx,nc*dx+dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,DOI,alpha=1)
-    cbar = plt.colorbar(format = '%.2f',fraction=0.02)
-    
-    #%% Replace alpha values from inversion
-    #rgba_plt = axp.get_facecolor()
-    #rgba_plt[:,3] = mkvc(DOI)/2    
-    plt.figure()
-    axs = plt.subplot(1,1,1)
-    
-    plt.xlim([-dx,nc*dx+dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
-    circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
-    axs.add_artist(circle1)
-    axs.add_artist(circle2)
-    
-    axs = plt.pcolor(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(invmod[0]),edgecolor="none")
-    plt.draw()
-    cbar = plt.colorbar(format = '%.2f',fraction=0.02)
-    aa = axs.get_facecolors()
-    aa[:,3] = mkvc(DOI.T)
-    axs.set_facecolor(aa)
-    
-    plt.draw()    
-    
-#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
-elif re.match(stype,'gradient'):
-    
-    rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
-    rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
-    rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
-    rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 )) 
-    
-    rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
-    rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
-    
-    rho = np.abs(data[0]) * np.pi *((rC1P1)**2 / rP1P2)#/ ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )
-
-    Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2  
- 
-    # Grid points
-    grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
-    grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
-    
-    
-    #plt.subplot(2,1,2)
-    plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z))  ,origin='lower')
-    var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
-    plt.title(var)
-    plt.colorbar()
diff --git a/simpegDCIP/Dev/DC3D_fwr_MtIsa.py b/simpegDCIP/Dev/DC3D_fwr_MtIsa.py
deleted file mode 100644
index 854a32ce..00000000
--- a/simpegDCIP/Dev/DC3D_fwr_MtIsa.py
+++ /dev/null
@@ -1,403 +0,0 @@
-"""
-        Experimental script for the forward modeling of DC resistivity data 
-        along survey lines defined by the user. The program loads in a 3D mesh
-        and model which is used to design pole-dipole or dipole-dipole survey
-        lines.
-        
-        Uses SimPEG to generate the forward problem and compute the LU
-        factorization.
-        
-        Calls DCIP2D for the inversion of a projected 2D section from the full
-        3D model.
-
-        Assumes flat topo for now...
-            
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-"""
-
-
-#%%
-from SimPEG import np, Utils, Mesh, mkvc, sp
-import simpegDCIP as DC
-import pylab as plt
-from pylab import get_current_fig_manager
-from scipy.interpolate import griddata
-import time
-import re
-import numpy.matlib as npm
-#from readUBC_DC3Dobs import readUBC_DC3Dobs
-#from readUBC_DC2DModel import readUBC_DC2DModel
-#from writeUBC_DCobs import writeUBC_DCobs
-import scipy.interpolate as interpolation
-#from plot_pseudoSection import plot_pseudoSection
-#from gen_DCIPsurvey import gen_DCIPsurvey
-#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
-import os
-
-#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
-home_dir ='C:\Users\dominiquef.MIRAGEOSCIENCE\ownCloud\Research\MtIsa\Modeling'
-dsep = '\\'
-#from scipy.linalg import solve_banded
-
-# Load UBC mesh 3D
-#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_10m.msh')
-mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
-#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
-
-# Load model
-model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_20m.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
-
-#model[model>1] = 0.08
-
-#model = model**0 * 1e-2
-# Specify survey type
-stype = 'pdp'
-
-# Survey parameters
-a = 100
-b = 100
-n = 15
-
-# Forward solver
-slvr = 'BiCGStab' #'LU'
-
-# Preconditioner
-pcdr = 'Jacobi'#'Gauss-Seidel'#
-
-# Inversion parameter
-pct = 0.01
-flr = 1e-4
-chifact = 100
-ref_mod = 1e-2
-
-#%% Create system
-#Set boundary conditions
-mesh.setCellGradBC('neumann')
-
-Div = mesh.faceDiv
-Grad = mesh.cellGrad
-Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
-
-A = Div*Msig*Grad
-
-# Change one corner to deal with nullspace
-A[0,0] = 1
-A = sp.csc_matrix(A)
-
-start_time = time.time()
-
-if re.match(slvr,'BiCGStab'):
-    # Create Jacobi Preconditioner  
-    if re.match(pcdr,'Jacobi'):
-        dA = A.diagonal()
-        P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-    # Create Gauss-Seidel Preconditioner
-    elif re.match(pcdr,'Gauss-Seidel'):
-        LD = sp.tril(A,k=0)
-        #LDinv = sp.linalg.splu(LD)
-
-elif re.match(slvr,'LU'):
-    # Factor A matrix
-    Ainv = sp.linalg.splu(A)    
-    print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
-
-#%% Create survey
-# Display top section 
-top = int(mesh.nCz)-1
-
-plt.figure()
-ax_prim = plt.subplot(1,1,1)
-mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
-#plt.xlim([423000,424000])
-#plt.ylim([546200,547000])
-plt.gca().set_aspect('equal', adjustable='box')
-    
-plt.show()
-cfm1=get_current_fig_manager().window
-gin=[1]
-
-# Keep creating sections until returns an empty ginput (press enter on figure)
-#while bool(gin)==True:
-    
-# Bring back the plan view figure and pick points     
-cfm1.activateWindow()
-plt.sca(ax_prim)
-    
-# Takes two points from ginput and create survey
-#if re.match(stype,'gradient'):
-gin = [(400.,12200.), (1800.,12200.)]
-#else:
-#gin = plt.ginput(2, timeout = 0)
-
-
-
-#==============================================================================
-# if not gin:
-#     print 'SimPED - Simulation has ended with return'
-#     break
-#==============================================================================
-
-# Add z coordinate to all survey... assume flat
-nz = mesh.vectorNz
-var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
-
-# Snap the endpoints to the grid. Easier to create 2D section.
-indx = Utils.closestPoints(mesh, var )
-endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
-      
-[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
- 
-dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) ) 
-dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
-dl_y = ( Tx[-1][1,1] - Tx[0][1,0]  ) / dl_len
-azm =  np.arctan(dl_y/dl_x)
-  
-# Plot stations along line   
-plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
-plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
-
-#%% Forward model data
-data = []#np.zeros( nstn*nrx )
-unct = []
-problem = DC.ProblemDC_CC(mesh)
-    
-for ii in range(len(Tx)):
-    start_time = time.time()
-    
-    # Select dipole locations for receiver
-    rxloc_M = np.asarray(Rx[ii][:,0:3])
-    rxloc_N = np.asarray(Rx[ii][:,3:])
-    
-    # Number of receivers
-    nrx = rxloc_M.shape[0]
-
-    
-    
-    if not re.match(stype,'pdp'):
-        inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
-        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )   
-        
-    else: 
-        
-        # Create an "inifinity" pole
-        tx =  np.squeeze(Tx[ii][:,0:1])
-        tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
-        inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
-        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )  
-    
-    # Solve for phi on pole locations
-    P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
-    P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
-
-    if re.match(slvr,'BiCGStab'):
-        
-        if re.match(pcdr,'Jacobi'):
-            dA = A.diagonal()
-            P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-            # Iterative Solve
-            Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
-            
-        # Create Gauss-Seidel Preconditioner
-        elif re.match(pcdr,'Gauss-Seidel'):
-            LD = sp.tril(A,k=0)
-            
-
-        phi = mkvc(Ainvb[0])
-               
-    elif re.match(slvr,'LU'):
-        #Direct Solve
-        phi = Ainv.solve(RHS)        
-    
-    
-    
-    # Compute potential at each electrode
-    dtemp = (P1*phi - P2*phi)*np.pi
-    
-    data.append( dtemp )     
-    unct.append( np.abs(dtemp) * pct + flr)
-   
-    print("--- %s seconds ---" % (time.time() - start_time))  
-    
-
-#%% Run 2D inversion if pdp or dpdp survey
-# Otherwise just plot and apparent susceptibility map
-if not re.match(stype,'gradient'):
-    
-    #%% Write data file in UBC-DCIP3D format
-    DC.writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')     
-    
-    
-    #%% Load 3D data
-    [Tx, Rx, data, wd] = DC.readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
-    
-    
-    #%% Convert 3D obs to 2D and write to file
-    [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
-    
-    DC.writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')        
-    
-    #%% Create a 2D mesh along axis of Tx end points and keep z-discretization    
-    dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
-    nc = np.ceil(dl_len/dx)+3
-    
-    padx = dx*np.power(1.4,range(1,15))
-    
-    # Creating padding cells
-    h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
-    
-    # Create mesh with 0 coordinate centerer on the ginput points in cell center
-    mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
-    
-    # Create array of points for interpolating from 3D to 2D mesh
-    xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
-    yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
-    zz = mesh2d.vectorCCy
-    
-    [XX,ZZ] = np.meshgrid(xx,zz)
-    [YY,ZZ] = np.meshgrid(yy,zz)
-    
-    xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
-    
-    #plt.scatter(xx,yy,s=20,c='y')
-    
-    
-    F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
-    m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
-    
-     
-    #==============================================================================
-    # mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
-    # m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
-    # m2D = m3D[:,1,:]
-    #==============================================================================
-    
-    plt.figure()
-    axs = plt.subplot(2,1,1)
-    
-    plt.xlim([0,nc*dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
-    #mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
-    
-    #%% Plot pseudo section
-    
-    DC.plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
-    plt.colorbar
-    plt.show()
-
-    #%% Create dcin2d inversion files and run
-    inv_dir = home_dir + '\Inv2D' 
-    if not os.path.exists(inv_dir):
-        os.makedirs(inv_dir)
-        
-    mshfile2d = 'Mesh_2D.msh'
-    modfile2d = 'MtIsa_2D.con'
-    obsfile2d = 'FWR_3D_2_2D.dat'
-    inp_file = 'dcinv2d.inp'
-    
-    
-    # Export 2D mesh
-    fid = open(inv_dir + dsep + mshfile2d,'w')
-    fid.write('%i\n'% mesh2d.nCx)
-    fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))  
-    np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
-    fid.write('\n')
-    fid.write('%i\n'% mesh2d.nCy)
-    fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))   
-    np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
-    fid.close()
-    
-    # Export 2D model
-    fid = open(inv_dir + dsep + modfile2d,'w')
-    fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
-    np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
-    fid.close()
-    
-    # Export data file
-    DC.writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D') 
-    
-    # Write input file
-    fid = open(inv_dir + dsep + inp_file,'w')
-    fid.write('OBS LOC_X %s \n'% obsfile2d)
-    fid.write('MESH FILE %s \n'% mshfile2d)
-    fid.write('CHIFACT 1 %f\n'% chifact)
-    fid.write('TOPO DEFAULT  %s \n')
-    fid.write('INIT_MOD DEFAULT\n')
-    fid.write('REF_MOD VALUE %e\n'% ref_mod)
-    fid.write('ALPHA DEFAULT\n')
-    fid.write('WEIGHT DEFAULT\n')
-    fid.write('STORE_ALL_MODELS FALSE\n')
-    fid.write('INVMODE SVD\n')
-    fid.write('USE_MREF TRUE\n')
-    fid.close()
-    
-    os.chdir(inv_dir)
-    os.system('dcinv2d ' + inp_file)
-    
-    #%%
-    #Load model
-    minv = DC.readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
-    #plt.figure()
-    axs = plt.subplot(2,1,2)
-    
-    plt.xlim([0,nc*dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=0.5, clim=(np.min(np.log10(m2D)),np.max(np.log10(m2D))))
-    cbar = plt.colorbar(format = '%.2f',fraction=0.02)
-    cmin,cmax = cbar.get_clim()
-    ticks = np.linspace(cmin,cmax,3)
-    cbar.set_ticks(ticks)
-
-#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
-elif re.match(stype,'gradient'):
-    
-    rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
-    rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
-    rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
-    rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 )) 
-    
-    rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
-    rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
-    
-    rho = np.abs(data[0]) *np.pi *2. / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )#*((rC1P1)**2 / rP1P2)#
-
-    Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2  
- 
-    # Grid points
-    grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
-    grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
-    
-    
-    #plt.subplot(2,1,2)
-    plt.figure()
-    plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z))  ,origin='lower')
-    
-    var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
-    plt.title(var)
-    plt.colorbar()
-    plt.contour(grid_x,grid_z,grid_rho, colors='k')
-    
-#%% Load tight model and plot
-mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_5m.msh')
-
-# Load model
-model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_5m.con',mesh)
-model = model.reshape((mesh.nCz,mesh.nCx))
-plt.figure()
-plt.imshow(np.log10(model),extent = (125,375,0,75),origin='lower')
-plt.colorbar(fraction=0.015)
-    
\ No newline at end of file
diff --git a/simpegDCIP/Dev/DC3D_fwr_live.py b/simpegDCIP/Dev/DC3D_fwr_live.py
deleted file mode 100644
index 90e1d0a9..00000000
--- a/simpegDCIP/Dev/DC3D_fwr_live.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-
-home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
-
-os.chdir(home_dir)
-
-
-#%%
-from SimPEG import np, Utils, Mesh, mkvc, SolverLU
-import simpegDCIP as DC
-import pylab as plt
-
-# Load UBC mesh 3D
-mesh = Utils.meshutils.readUBCTensorMesh('Mesh_40m.msh')
-
-# Load model
-model = Utils.meshutils.readUBCTensorModel('Synthetic.con',mesh)
-
-#%%
-# Display top section 
-top = int(mesh.nCz)-1
-mesh.plotSlice(model, ind=top, normal='Z', grid=True, pcolorOpts={'alpha':0.8})
-ylim=(546000,546750)
-xlim=(422900,423675)
-# Takes two points from ginput and create survey
-temp = plt.ginput(2)
-
-# Add z coordinate
-nz = mesh.vectorNz
-endp = np.c_[np.asarray(temp),np.ones(2).T*nz[-1]]
-
-# Create dipole survey receivers and plot
-nrx = 10
-ab = 40
-a = 20
-
-# Evenly distribute transmitters for now and put on surface
-dplen = np.sqrt( np.sum((endp[1,:] - endp[0,:])**2) ) 
-dp_x = ( endp[1,0] - endp[0,0] ) / dplen
-dp_y = ( endp[1,1] - endp[0,1] ) / dplen
-
-nstn = np.floor( dplen / ab )
-
-stn_x = endp[0,0] + np.cumsum( np.ones(nstn)*dp_x*ab )
-stn_y = endp[0,1] + np.cumsum( np.ones(nstn)*dp_y*ab )
-
-plt.scatter(stn_x,stn_y,s=100, c='w')
-
-M = np.c_[stn_x-a*dp_x, stn_y-a*dp_y, np.ones(nstn).T*nz[-1]]
-N = np.c_[stn_x+a*dp_x, stn_y+a*dp_y, np.ones(nstn).T*nz[-1]]
-
-plt.scatter(M[:,0],M[:,1],s=10,c='r')
-plt.scatter(N[:,0],N[:,1],s=10,c='b')
-
-#%% Create inversion parameter
-
-Rx = DC.RxDipole(M,N)
-Tx = DC.SrcDipole([Rx], tx[0,:],tx[1,:])
-survey = DC.SurveyDC([Tx])
-
-problem = DC.ProblemDC_CC(mesh)
-problem.pair(survey)
-
-problem.Solver = SolverLU
-
-data = survey.dpred(model)
-
-#Set boundary conditions
-mesh.setCellGradBC('neumann')
-
-Div = mesh.faceDiv
-Grad = mesh.cellGradBC
-Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
-
-A = Div*Msig*Grad
-
-# Change one corner to deal with nullspace
-A[0,0] = 1.
-
-# Get the righthand side
-RHS = problem.getRHS
-
-# Solve for phi
-phi = SolverLU(A)*-RHS
-
diff --git a/simpegDCIP/Dev/DC3D_fwr_live_test.py b/simpegDCIP/Dev/DC3D_fwr_live_test.py
deleted file mode 100644
index 6aad24dd..00000000
--- a/simpegDCIP/Dev/DC3D_fwr_live_test.py
+++ /dev/null
@@ -1,390 +0,0 @@
-"""
-        Experimental script for the forward modeling of DC resistivity data 
-        along survey lines defined by the user. The program loads in a 3D mesh
-        and model which is used to design pole-dipole or dipole-dipole survey
-        lines.
-        
-        Uses SimPEG to generate the forward problem and compute the LU
-        factorization.
-        
-        Calls DCIP2D for the inversion of a projected 2D section from the full
-        3D model.
-
-        Assumes flat topo for now...
-            
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-"""
-
-
-#%%
-from SimPEG import np, Utils, Mesh, mkvc, sp
-import simpegDCIP as DC
-import pylab as plt
-from pylab import get_current_fig_manager
-from scipy.interpolate import griddata
-import time
-import re
-import numpy.matlib as npm
-#from readUBC_DC3Dobs import readUBC_DC3Dobs
-#from readUBC_DC2DModel import readUBC_DC2DModel
-#from writeUBC_DCobs import writeUBC_DCobs
-import scipy.interpolate as interpolation
-#from plot_pseudoSection import plot_pseudoSection
-#from gen_DCIPsurvey import gen_DCIPsurvey
-#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
-import os
-
-#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
-home_dir ='C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
-dsep = '\\'
-#from scipy.linalg import solve_banded
-
-# Load UBC mesh 3D
-#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_10m.msh')
-mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
-#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
-
-# Load model
-model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
-#model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
-
-#model = model**0 * 1e-2
-# Specify survey type
-stype = 'pdp'
-
-# Survey parameters
-a = 150
-b = 150
-n = 40
-
-# Forward solver
-slvr = 'BiCGStab' #'LU'
-
-# Preconditioner
-pcdr = 'Jacobi'#'Gauss-Seidel'#
-
-# Inversion parameter
-pct = 0.01
-flr = 1e-4
-chifact = 100
-ref_mod = 1e-3
-
-#%% Create system
-#Set boundary conditions
-mesh.setCellGradBC('neumann')
-
-Div = mesh.faceDiv
-Grad = mesh.cellGrad
-Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
-
-A = Div*Msig*Grad
-
-# Change one corner to deal with nullspace
-A[0,0] = 1
-A = sp.csc_matrix(A)
-
-start_time = time.time()
-
-if re.match(slvr,'BiCGStab'):
-    # Create Jacobi Preconditioner  
-    if re.match(pcdr,'Jacobi'):
-        dA = A.diagonal()
-        P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-    # Create Gauss-Seidel Preconditioner
-    elif re.match(pcdr,'Gauss-Seidel'):
-        LD = sp.tril(A,k=0)
-        #LDinv = sp.linalg.splu(LD)
-
-elif re.match(slvr,'LU'):
-    # Factor A matrix
-    Ainv = sp.linalg.splu(A)    
-    print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
-
-#%% Create survey
-# Display top section 
-top = int(mesh.nCz)-1
-
-plt.figure()
-ax_prim = plt.subplot(1,1,1)
-mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
-#plt.xlim([423000,424000])
-#plt.ylim([546200,547000])
-plt.gca().set_aspect('equal', adjustable='box')
-    
-plt.show()
-cfm1=get_current_fig_manager().window
-gin=[1]
-
-# Keep creating sections until returns an empty ginput (press enter on figure)
-#while bool(gin)==True:
-    
-# Bring back the plan view figure and pick points     
-cfm1.activateWindow()
-plt.sca(ax_prim)
-    
-# Takes two points from ginput and create survey
-#if re.match(stype,'gradient'):
-#gin = [(425347, 6079766), (427792, 6081806)]
-#else:
-gin = plt.ginput(2, timeout = 0)
-
-
-
-#==============================================================================
-# if not gin:
-#     print 'SimPED - Simulation has ended with return'
-#     break
-#==============================================================================
-
-# Add z coordinate to all survey... assume flat
-nz = mesh.vectorNz
-var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
-
-# Snap the endpoints to the grid. Easier to create 2D section.
-indx = Utils.closestPoints(mesh, var )
-endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
-      
-[Tx, Rx] = gen_DCIPsurvey(endl, mesh, stype, a, b, n)
- 
-dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) ) 
-dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
-dl_y = ( Tx[-1][1,1] - Tx[0][1,0]  ) / dl_len
-azm =  np.arctan(dl_y/dl_x)
-  
-# Plot stations along line   
-plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
-plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
-
-#%% Forward model data
-data = []#np.zeros( nstn*nrx )
-unct = []
-problem = DC.ProblemDC_CC(mesh)
-    
-for ii in range(len(Tx)):
-    start_time = time.time()
-    
-    # Select dipole locations for receiver
-    rxloc_M = np.asarray(Rx[ii][:,0:3])
-    rxloc_N = np.asarray(Rx[ii][:,3:])
-    
-    # Number of receivers
-    nrx = rxloc_M.shape[0]
-
-    
-    
-    if not re.match(stype,'pdp'):
-        inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
-        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )   
-        
-    else: 
-        
-        # Create an "inifinity" pole
-        tx =  np.squeeze(Tx[ii][:,0:1])
-        tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
-        inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
-        RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )  
-    
-    # Solve for phi on pole locations
-    P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
-    P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
-
-    if re.match(slvr,'BiCGStab'):
-        
-        if re.match(pcdr,'Jacobi'):
-            dA = A.diagonal()
-            P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
-    
-            # Iterative Solve
-            Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
-            
-        # Create Gauss-Seidel Preconditioner
-        elif re.match(pcdr,'Gauss-Seidel'):
-            LD = sp.tril(A,k=0)
-            
-
-        phi = mkvc(Ainvb[0])
-               
-    elif re.match(slvr,'LU'):
-        #Direct Solve
-        phi = Ainv.solve(RHS)        
-    
-    
-    
-    # Compute potential at each electrode
-    dtemp = (P1*phi - P2*phi)*np.pi
-    
-    data.append( dtemp )     
-    unct.append( np.abs(dtemp) * pct + flr)
-   
-    print("--- %s seconds ---" % (time.time() - start_time))  
-    
-
-#%% Run 2D inversion if pdp or dpdp survey
-# Otherwise just plot and apparent susceptibility map
-if not re.match(stype,'gradient'):
-    
-    #%% Write data file in UBC-DCIP3D format
-    writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')     
-    
-    
-    #%% Load 3D data
-    [Tx, Rx, data, wd] = readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
-    
-    
-    #%% Convert 3D obs to 2D and write to file
-    [Tx2d, Rx2d] = convertObs_DC3D_to_2D(Tx,Rx)
-    
-    writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')        
-    
-    #%% Create a 2D mesh along axis of Tx end points and keep z-discretization    
-    dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
-    nc = np.ceil(dl_len/dx)+3
-    
-    padx = dx*np.power(1.4,range(1,15))
-    
-    # Creating padding cells
-    h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
-    
-    # Create mesh with 0 coordinate centerer on the ginput points in cell center
-    mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
-    
-    # Create array of points for interpolating from 3D to 2D mesh
-    xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
-    yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
-    zz = mesh2d.vectorCCy
-    
-    [XX,ZZ] = np.meshgrid(xx,zz)
-    [YY,ZZ] = np.meshgrid(yy,zz)
-    
-    xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
-    
-    #plt.scatter(xx,yy,s=20,c='y')
-    
-    
-    F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
-    m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
-    
-     
-    #==============================================================================
-    # mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
-    # m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
-    # m2D = m3D[:,1,:]
-    #==============================================================================
-    
-    plt.figure()
-    axs = plt.subplot(2,1,1)
-    
-    plt.xlim([0,nc*dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
-    #mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
-    
-    #%% Plot pseudo section
-    
-    plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
-    plt.colorbar
-    plt.show()
-
-    #%% Create dcin2d inversion files and run
-    inv_dir = home_dir + '\Inv2D' 
-    if not os.path.exists(inv_dir):
-        os.makedirs(inv_dir)
-        
-    mshfile2d = 'Mesh_2D.msh'
-    modfile2d = 'MtIsa_2D.con'
-    obsfile2d = 'FWR_3D_2_2D.dat'
-    inp_file = 'dcinv2d.inp'
-    
-    
-    # Export 2D mesh
-    fid = open(inv_dir + dsep + mshfile2d,'w')
-    fid.write('%i\n'% mesh2d.nCx)
-    fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))  
-    np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
-    fid.write('\n')
-    fid.write('%i\n'% mesh2d.nCy)
-    fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))   
-    np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
-    fid.close()
-    
-    # Export 2D model
-    fid = open(inv_dir + dsep + modfile2d,'w')
-    fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
-    np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
-    fid.close()
-    
-    # Export data file
-    writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D') 
-    
-    # Write input file
-    fid = open(inv_dir + dsep + inp_file,'w')
-    fid.write('OBS LOC_X %s \n'% obsfile2d)
-    fid.write('MESH FILE %s \n'% mshfile2d)
-    fid.write('CHIFACT 1 %f\n'% chifact)
-    fid.write('TOPO DEFAULT  %s \n')
-    fid.write('INIT_MOD DEFAULT\n')
-    fid.write('REF_MOD VALUE %e\n'% ref_mod)
-    fid.write('ALPHA DEFAULT\n')
-    fid.write('WEIGHT DEFAULT\n')
-    fid.write('STORE_ALL_MODELS FALSE\n')
-    fid.write('INVMODE SVD\n')
-    fid.write('USE_MREF TRUE\n')
-    fid.close()
-    
-    os.chdir(inv_dir)
-    os.system('dcinv2d ' + inp_file)
-    
-    #%%
-    #Load model
-    minv = readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
-    #plt.figure()
-    axs = plt.subplot(2,1,2)
-    
-    plt.xlim([0,nc*dx])
-    plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
-    plt.gca().set_aspect('equal', adjustable='box')
-    
-    minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
-    plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=0.5, clim=(np.min(np.log10(m2D)),np.max(np.log10(m2D))))
-    cbar = plt.colorbar(format = '%.2f',fraction=0.02)
-    cmin,cmax = cbar.get_clim()
-    ticks = np.linspace(cmin,cmax,3)
-    cbar.set_ticks(ticks)
-
-#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
-elif re.match(stype,'gradient'):
-    
-    rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
-    rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
-    rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
-    rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 )) 
-    
-    rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
-    rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
-    
-    rho = np.abs(data[0]) *np.pi *2. / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )#*((rC1P1)**2 / rP1P2)#
-
-    Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2  
- 
-    # Grid points
-    grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
-    grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
-    
-    
-    #plt.subplot(2,1,2)
-    plt.figure()
-    plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z))  ,origin='lower')
-    
-    var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
-    plt.title(var)
-    plt.colorbar()
-    plt.contour(grid_x,grid_z,grid_rho, colors='k')
\ No newline at end of file
diff --git a/simpegDCIP/Dev/DCIP2D_FWR/PDP/FWR_3D_2_2D.dat b/simpegDCIP/Dev/DCIP2D_FWR/PDP/FWR_3D_2_2D.dat
deleted file mode 100644
index 58df56f7..00000000
--- a/simpegDCIP/Dev/DCIP2D_FWR/PDP/FWR_3D_2_2D.dat
+++ /dev/null
@@ -1,245 +0,0 @@
-! GENERAL FORMAT
-
-0.000000e+00 0.000000e+00 4.000000e+01 8.000000e+01 4.536103e-01 4.636103e-03
-0.000000e+00 0.000000e+00 8.000000e+01 1.200000e+02 1.956283e-01 2.056283e-03
-0.000000e+00 0.000000e+00 1.200000e+02 1.600000e+02 9.661533e-02 1.066153e-03
-0.000000e+00 0.000000e+00 1.600000e+02 2.000000e+02 5.443205e-03 1.544321e-04
-0.000000e+00 0.000000e+00 2.000000e+02 2.400000e+02 2.977518e-03 1.297752e-04
-0.000000e+00 0.000000e+00 2.400000e+02 2.800000e+02 3.113318e-03 1.311332e-04
-0.000000e+00 0.000000e+00 2.800000e+02 3.200000e+02 7.216380e-03 1.721638e-04
-0.000000e+00 0.000000e+00 3.200000e+02 3.600000e+02 6.475000e-03 1.647500e-04
-0.000000e+00 0.000000e+00 3.600000e+02 4.000000e+02 4.750858e-03 1.475086e-04
-
-4.000000e+01 4.000000e+01 8.000000e+01 1.200000e+02 4.737093e-01 4.837093e-03
-4.000000e+01 4.000000e+01 1.200000e+02 1.600000e+02 1.933017e-01 2.033017e-03
-4.000000e+01 4.000000e+01 1.600000e+02 2.000000e+02 9.625111e-03 1.962511e-04
-4.000000e+01 4.000000e+01 2.000000e+02 2.400000e+02 4.710170e-03 1.471017e-04
-4.000000e+01 4.000000e+01 2.400000e+02 2.800000e+02 4.146907e-03 1.414691e-04
-4.000000e+01 4.000000e+01 2.800000e+02 3.200000e+02 9.014465e-03 1.901446e-04
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-4.000000e+01 4.000000e+01 3.600000e+02 4.000000e+02 5.711976e-03 1.571198e-04
-4.000000e+01 4.000000e+01 4.000000e+02 4.400000e+02 6.259850e-04 1.062599e-04
-
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-8.000000e+01 8.000000e+01 4.400000e+02 4.800000e+02 6.193333e-04 1.061933e-04
-
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-
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diff --git a/simpegDCIP/Dev/DCIP2D_FWR/PDP/Mesh_2D.msh b/simpegDCIP/Dev/DCIP2D_FWR/PDP/Mesh_2D.msh
deleted file mode 100644
index 2c3b3079..00000000
--- a/simpegDCIP/Dev/DCIP2D_FWR/PDP/Mesh_2D.msh
+++ /dev/null
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diff --git a/simpegDCIP/Dev/DCIP2D_FWR/PDP/MtIsa_2D.con b/simpegDCIP/Dev/DCIP2D_FWR/PDP/MtIsa_2D.con
deleted file mode 100644
index 7b8246af..00000000
--- a/simpegDCIP/Dev/DCIP2D_FWR/PDP/MtIsa_2D.con
+++ /dev/null
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-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
-5.000000e-03
diff --git a/simpegDCIP/Dev/DCIP2D_FWR/PDP/dcipf2d.log b/simpegDCIP/Dev/DCIP2D_FWR/PDP/dcipf2d.log
deleted file mode 100644
index f67bc687..00000000
--- a/simpegDCIP/Dev/DCIP2D_FWR/PDP/dcipf2d.log
+++ /dev/null
@@ -1,48 +0,0 @@
-
-Parallelized with OpenMP.  # of threads:  4
-
-DCIP2D - Version 5  (BETA) 20110811:  DCIPF2D
-
-Developed by University of British Columbia 
-Geophysical Inversion Facility  (UBC-GIF)
-
-(C) Copyright 1992 - 2011, UBC-GIF,
-Department of Earth and Ocean Sciences, UBC
-http://www.eos.ubc.ca/research/ubcgif/
-
-Distributed by:
-Mira Geoscience Ltd.
-
-
- DCIPF2D started on:12/09/2015  17:56:14
-
-
-Reading input file: dcipf2d.inp
-
-----------------------------------------------
-FWD DC
-MESH FILE Mesh_2D.msh
-LOC LOC_X FWR_3D_2_2D.dat
-TOPO DEFAULT
-COND FILE MtIsa_2D.con
-----------------------------------------------
-
-electrode locations were read from: FWR_3D_2_2D.dat
-       # of current locations:   28
-                    # of data:  216
-
-mesh was read from: Mesh_2D.msh
-                      # of cells:   85 x   45
-                total # of cells:  3825
-               # of active cells:  3825
-
-# of wave values:  13
-  2.5000E-04  4.9901E-04  9.9606E-04  1.9882E-03  3.9685E-03  7.9213E-03  1.5811E-02  3.1560E-02  6.2996E-02  1.2574E-01  2.5099E-01  5.0099E-01  1.0000E+00
-
-conductivity was read from file: MtIsa_2D.con
-dc fwd cpu time:   0:00:00.19
-
-total cpu time:   0:00:00.19
-
- DCIPF2D ended on:12/09/2015  17:56:14
-
diff --git a/simpegDCIP/Dev/DCIP2D_FWR/PDP/obs_dc.dat b/simpegDCIP/Dev/DCIP2D_FWR/PDP/obs_dc.dat
deleted file mode 100644
index 34f73203..00000000
--- a/simpegDCIP/Dev/DCIP2D_FWR/PDP/obs_dc.dat
+++ /dev/null
@@ -1,217 +0,0 @@
-! Predicted data ! GENERAL FORMAT Last column are apparent conductivities.
-  0.0000000E+00  0.0000000E+00  4.0000000E+01  8.0000000E+01   4.37963E-01   4.54248E-03
-  0.0000000E+00  0.0000000E+00  8.0000000E+01  1.2000000E+02   1.62622E-01   4.07785E-03
-  0.0000000E+00  0.0000000E+00  1.2000000E+02  1.6000000E+02   7.69824E-02   4.30712E-03
-  0.0000000E+00  0.0000000E+00  1.6000000E+02  2.0000000E+02   4.38528E-03   4.53662E-02
-  0.0000000E+00  0.0000000E+00  2.0000000E+02  2.4000000E+02   2.46637E-03   5.37751E-02
-  0.0000000E+00  0.0000000E+00  2.4000000E+02  2.8000000E+02   2.74581E-03   3.45017E-02
-  0.0000000E+00  0.0000000E+00  2.8000000E+02  3.2000000E+02   6.51619E-03   1.09038E-02
-  0.0000000E+00  0.0000000E+00  3.2000000E+02  3.6000000E+02   5.91274E-03   9.34628E-03
-  0.0000000E+00  0.0000000E+00  3.6000000E+02  4.0000000E+02   4.36195E-03   1.01353E-02
-  4.0000000E+01  4.0000000E+01  8.0000000E+01  1.2000000E+02   4.63636E-01   4.29095E-03
-  4.0000000E+01  4.0000000E+01  1.2000000E+02  1.6000000E+02   1.61950E-01   4.09476E-03
-  4.0000000E+01  4.0000000E+01  1.6000000E+02  2.0000000E+02   7.91654E-03   4.18836E-02
-  4.0000000E+01  4.0000000E+01  2.0000000E+02  2.4000000E+02   3.92652E-03   5.06667E-02
-  4.0000000E+01  4.0000000E+01  2.4000000E+02  2.8000000E+02   3.64355E-03   3.64011E-02
-  4.0000000E+01  4.0000000E+01  2.8000000E+02  3.2000000E+02   8.11826E-03   1.16694E-02
-  4.0000000E+01  4.0000000E+01  3.2000000E+02  3.6000000E+02   7.18120E-03   9.89407E-03
-  4.0000000E+01  4.0000000E+01  3.6000000E+02  4.0000000E+02   5.23964E-03   1.05469E-02
-  4.0000000E+01  4.0000000E+01  4.0000000E+02  4.4000000E+02   5.79728E-04   7.62594E-02
-  8.0000000E+01  8.0000000E+01  1.2000000E+02  1.6000000E+02   4.82045E-01   4.12707E-03
-  8.0000000E+01  8.0000000E+01  1.6000000E+02  2.0000000E+02   1.74835E-02   3.79299E-02
-  8.0000000E+01  8.0000000E+01  2.0000000E+02  2.4000000E+02   7.15442E-03   4.63451E-02
-  8.0000000E+01  8.0000000E+01  2.4000000E+02  2.8000000E+02   5.20669E-03   3.82092E-02
-  8.0000000E+01  8.0000000E+01  2.8000000E+02  3.2000000E+02   1.05211E-02   1.26060E-02
-  8.0000000E+01  8.0000000E+01  3.2000000E+02  3.6000000E+02   8.95319E-03   1.05812E-02
-  8.0000000E+01  8.0000000E+01  3.6000000E+02  4.0000000E+02   6.42718E-03   1.10548E-02
-  8.0000000E+01  8.0000000E+01  4.0000000E+02  4.4000000E+02   6.98004E-04   7.91716E-02
-  8.0000000E+01  8.0000000E+01  4.4000000E+02  4.8000000E+02   5.71181E-04   7.74005E-02
-  1.2000000E+02  1.2000000E+02  1.6000000E+02  2.0000000E+02   5.63584E-02   3.52997E-02
-  1.2000000E+02  1.2000000E+02  2.0000000E+02  2.4000000E+02   1.61965E-02   4.09437E-02
-  1.2000000E+02  1.2000000E+02  2.4000000E+02  2.8000000E+02   8.43582E-03   3.93053E-02
-  1.2000000E+02  1.2000000E+02  2.8000000E+02  3.2000000E+02   1.45236E-02   1.36979E-02
-  1.2000000E+02  1.2000000E+02  3.2000000E+02  3.6000000E+02   1.15992E-02   1.14344E-02
-  1.2000000E+02  1.2000000E+02  3.6000000E+02  4.0000000E+02   8.11343E-03   1.16763E-02
-  1.2000000E+02  1.2000000E+02  4.0000000E+02  4.4000000E+02   8.58536E-04   8.27587E-02
-  1.2000000E+02  1.2000000E+02  4.4000000E+02  4.8000000E+02   6.86451E-04   8.05041E-02
-  1.2000000E+02  1.2000000E+02  4.8000000E+02  5.2000000E+02   5.56629E-04   7.94241E-02
-  1.6000000E+02  1.6000000E+02  2.0000000E+02  2.4000000E+02   4.17530E-02   4.76478E-02
-  1.6000000E+02  1.6000000E+02  2.4000000E+02  2.8000000E+02   1.45259E-02   4.56525E-02
-  1.6000000E+02  1.6000000E+02  2.8000000E+02  3.2000000E+02   2.02746E-02   1.63541E-02
-  1.6000000E+02  1.6000000E+02  3.2000000E+02  3.6000000E+02   1.48931E-02   1.33581E-02
-  1.6000000E+02  1.6000000E+02  3.6000000E+02  4.0000000E+02   1.00734E-02   1.31663E-02
-  1.6000000E+02  1.6000000E+02  4.0000000E+02  4.4000000E+02   1.03480E-03   9.15490E-02
-  1.6000000E+02  1.6000000E+02  4.4000000E+02  4.8000000E+02   8.06769E-04   8.80689E-02
-  1.6000000E+02  1.6000000E+02  4.8000000E+02  5.2000000E+02   6.42732E-04   8.59801E-02
-  1.6000000E+02  1.6000000E+02  5.2000000E+02  5.6000000E+02   5.16345E-04   8.56205E-02
-  2.0000000E+02  2.0000000E+02  2.4000000E+02  2.8000000E+02   3.10109E-02   6.41528E-02
-  2.0000000E+02  2.0000000E+02  2.8000000E+02  3.2000000E+02   2.84303E-02   2.33253E-02
-  2.0000000E+02  2.0000000E+02  3.2000000E+02  3.6000000E+02   1.81787E-02   1.82396E-02
-  2.0000000E+02  2.0000000E+02  3.6000000E+02  4.0000000E+02   1.17012E-02   1.70020E-02
-  2.0000000E+02  2.0000000E+02  4.0000000E+02  4.4000000E+02   1.16142E-03   1.14196E-01
-  2.0000000E+02  2.0000000E+02  4.4000000E+02  4.8000000E+02   8.82426E-04   1.07358E-01
-  2.0000000E+02  2.0000000E+02  4.8000000E+02  5.2000000E+02   6.91684E-04   1.02722E-01
-  2.0000000E+02  2.0000000E+02  5.2000000E+02  5.6000000E+02   5.49788E-04   1.00515E-01
-  2.0000000E+02  2.0000000E+02  5.6000000E+02  6.0000000E+02   4.38101E-04   1.00912E-01
-  2.4000000E+02  2.4000000E+02  2.8000000E+02  3.2000000E+02   6.63054E-02   3.00041E-02
-  2.4000000E+02  2.4000000E+02  3.2000000E+02  3.6000000E+02   2.86962E-02   2.31092E-02
-  2.4000000E+02  2.4000000E+02  3.6000000E+02  4.0000000E+02   1.61554E-02   2.05239E-02
-  2.4000000E+02  2.4000000E+02  4.0000000E+02  4.4000000E+02   1.47767E-03   1.34634E-01
-  2.4000000E+02  2.4000000E+02  4.4000000E+02  4.8000000E+02   1.05797E-03   1.25361E-01
-  2.4000000E+02  2.4000000E+02  4.8000000E+02  5.2000000E+02   7.99625E-04   1.18474E-01
-  2.4000000E+02  2.4000000E+02  5.2000000E+02  5.6000000E+02   6.20964E-04   1.14421E-01
-  2.4000000E+02  2.4000000E+02  5.6000000E+02  6.0000000E+02   4.87210E-04   1.13426E-01
-  2.4000000E+02  2.4000000E+02  6.0000000E+02  6.4000000E+02   3.82964E-04   1.15441E-01
-  2.8000000E+02  2.8000000E+02  3.2000000E+02  3.6000000E+02   1.70137E-01   1.16932E-02
-  2.8000000E+02  2.8000000E+02  3.6000000E+02  4.0000000E+02   5.51339E-02   1.20279E-02
-  2.8000000E+02  2.8000000E+02  4.0000000E+02  4.4000000E+02   3.74082E-03   8.86365E-02
-  2.8000000E+02  2.8000000E+02  4.4000000E+02  4.8000000E+02   2.14407E-03   9.27878E-02
-  2.8000000E+02  2.8000000E+02  4.8000000E+02  5.2000000E+02   1.40564E-03   9.43551E-02
-  2.8000000E+02  2.8000000E+02  5.2000000E+02  5.6000000E+02   9.95293E-04   9.51831E-02
-  2.8000000E+02  2.8000000E+02  5.6000000E+02  6.0000000E+02   7.34406E-04   9.67466E-02
-  2.8000000E+02  2.8000000E+02  6.0000000E+02  6.4000000E+02   5.53238E-04   9.98885E-02
-  2.8000000E+02  2.8000000E+02  6.4000000E+02  6.8000000E+02   4.21795E-04   1.04813E-01
-  3.2000000E+02  3.2000000E+02  3.6000000E+02  4.0000000E+02   2.84337E-01   6.99676E-03
-  3.2000000E+02  3.2000000E+02  4.0000000E+02  4.4000000E+02   1.39919E-02   4.73949E-02
-  3.2000000E+02  3.2000000E+02  4.4000000E+02  4.8000000E+02   6.43218E-03   5.15490E-02
-  3.2000000E+02  3.2000000E+02  4.8000000E+02  5.2000000E+02   3.62113E-03   5.49396E-02
-  3.2000000E+02  3.2000000E+02  5.2000000E+02  5.6000000E+02   2.30061E-03   5.76495E-02
-  3.2000000E+02  3.2000000E+02  5.6000000E+02  6.0000000E+02   1.57121E-03   6.02942E-02
-  3.2000000E+02  3.2000000E+02  6.0000000E+02  6.4000000E+02   1.11891E-03   6.35005E-02
-  3.2000000E+02  3.2000000E+02  6.4000000E+02  6.8000000E+02   8.17331E-04   6.76129E-02
-  3.2000000E+02  3.2000000E+02  6.8000000E+02  7.2000000E+02   6.08827E-04   7.26146E-02
-  3.6000000E+02  3.6000000E+02  4.0000000E+02  4.4000000E+02   4.74032E-02   4.19684E-02
-  3.6000000E+02  3.6000000E+02  4.4000000E+02  4.8000000E+02   1.54783E-02   4.28435E-02
-  3.6000000E+02  3.6000000E+02  4.8000000E+02  5.2000000E+02   7.33839E-03   4.51833E-02
-  3.6000000E+02  3.6000000E+02  5.2000000E+02  5.6000000E+02   4.20683E-03   4.72907E-02
-  3.6000000E+02  3.6000000E+02  5.6000000E+02  6.0000000E+02   2.68996E-03   4.93053E-02
-  3.6000000E+02  3.6000000E+02  6.0000000E+02  6.4000000E+02   1.83372E-03   5.16629E-02
-  3.6000000E+02  3.6000000E+02  6.4000000E+02  6.8000000E+02   1.29930E-03   5.46843E-02
-  3.6000000E+02  3.6000000E+02  6.8000000E+02  7.2000000E+02   9.45999E-04   5.84167E-02
-  3.6000000E+02  3.6000000E+02  7.2000000E+02  7.6000000E+02   7.05763E-04   6.26410E-02
-  4.0000000E+02  4.0000000E+02  4.4000000E+02  4.8000000E+02   3.81851E-02   5.20998E-02
-  4.0000000E+02  4.0000000E+02  4.8000000E+02  5.2000000E+02   1.36233E-02   4.86774E-02
-  4.0000000E+02  4.0000000E+02  5.2000000E+02  5.6000000E+02   6.80313E-03   4.87383E-02
-  4.0000000E+02  4.0000000E+02  5.6000000E+02  6.0000000E+02   4.01999E-03   4.94886E-02
-  4.0000000E+02  4.0000000E+02  6.0000000E+02  6.4000000E+02   2.61132E-03   5.07900E-02
-  4.0000000E+02  4.0000000E+02  6.4000000E+02  6.8000000E+02   1.79405E-03   5.28052E-02
-  4.0000000E+02  4.0000000E+02  6.8000000E+02  7.2000000E+02   1.27904E-03   5.55507E-02
-  4.0000000E+02  4.0000000E+02  7.2000000E+02  7.6000000E+02   9.39590E-04   5.88152E-02
-  4.0000000E+02  4.0000000E+02  7.6000000E+02  8.0000000E+02   7.10076E-04   6.22605E-02
-  4.4000000E+02  4.4000000E+02  4.8000000E+02  5.2000000E+02   2.91377E-02   6.82771E-02
-  4.4000000E+02  4.4000000E+02  5.2000000E+02  5.6000000E+02   1.04151E-02   6.36715E-02
-  4.4000000E+02  4.4000000E+02  5.6000000E+02  6.0000000E+02   5.29883E-03   6.25747E-02
-  4.4000000E+02  4.4000000E+02  6.0000000E+02  6.4000000E+02   3.18206E-03   6.25204E-02
-  4.4000000E+02  4.4000000E+02  6.4000000E+02  6.8000000E+02   2.09086E-03   6.34329E-02
-  4.4000000E+02  4.4000000E+02  6.8000000E+02  7.2000000E+02   1.45130E-03   6.52762E-02
-  4.4000000E+02  4.4000000E+02  7.2000000E+02  7.6000000E+02   1.04806E-03   6.77931E-02
-  4.4000000E+02  4.4000000E+02  7.6000000E+02  8.0000000E+02   7.82930E-04   7.05837E-02
-  4.4000000E+02  4.4000000E+02  8.0000000E+02  8.4000000E+02   6.02044E-04   7.34326E-02
-  4.8000000E+02  4.8000000E+02  5.2000000E+02  5.6000000E+02   2.76526E-02   7.19439E-02
-  4.8000000E+02  4.8000000E+02  5.6000000E+02  6.0000000E+02   9.58946E-03   6.91536E-02
-  4.8000000E+02  4.8000000E+02  6.0000000E+02  6.4000000E+02   4.78028E-03   6.93627E-02
-  4.8000000E+02  4.8000000E+02  6.4000000E+02  6.8000000E+02   2.83237E-03   7.02394E-02
-  4.8000000E+02  4.8000000E+02  6.8000000E+02  7.2000000E+02   1.84884E-03   7.17363E-02
-  4.8000000E+02  4.8000000E+02  7.2000000E+02  7.6000000E+02   1.28476E-03   7.37377E-02
-  4.8000000E+02  4.8000000E+02  7.6000000E+02  8.0000000E+02   9.35719E-04   7.59323E-02
-  4.8000000E+02  4.8000000E+02  8.0000000E+02  8.4000000E+02   7.07359E-04   7.81246E-02
-  4.8000000E+02  4.8000000E+02  8.4000000E+02  8.8000000E+02   5.44824E-04   8.11450E-02
-  5.2000000E+02  5.2000000E+02  5.6000000E+02  6.0000000E+02   2.71482E-02   7.32807E-02
-  5.2000000E+02  5.2000000E+02  6.0000000E+02  6.4000000E+02   9.24381E-03   7.17394E-02
-  5.2000000E+02  5.2000000E+02  6.4000000E+02  6.8000000E+02   4.53338E-03   7.31403E-02
-  5.2000000E+02  5.2000000E+02  6.8000000E+02  7.2000000E+02   2.65663E-03   7.48858E-02
-  5.2000000E+02  5.2000000E+02  7.2000000E+02  7.6000000E+02   1.72839E-03   7.67354E-02
-  5.2000000E+02  5.2000000E+02  7.6000000E+02  8.0000000E+02   1.20700E-03   7.84879E-02
-  5.2000000E+02  5.2000000E+02  8.0000000E+02  8.4000000E+02   8.87817E-04   8.00292E-02
-  5.2000000E+02  5.2000000E+02  8.4000000E+02  8.8000000E+02   6.72287E-04   8.22002E-02
-  5.2000000E+02  5.2000000E+02  8.8000000E+02  9.2000000E+02   4.38246E-04   1.00879E-01
-  5.6000000E+02  5.6000000E+02  6.0000000E+02  6.4000000E+02   2.69080E-02   7.39347E-02
-  5.6000000E+02  5.6000000E+02  6.4000000E+02  6.8000000E+02   9.06610E-03   7.31457E-02
-  5.6000000E+02  5.6000000E+02  6.8000000E+02  7.2000000E+02   4.40651E-03   7.52461E-02
-  5.6000000E+02  5.6000000E+02  7.2000000E+02  7.6000000E+02   2.57356E-03   7.73028E-02
-  5.6000000E+02  5.6000000E+02  7.6000000E+02  8.0000000E+02   1.68174E-03   7.88643E-02
-  5.6000000E+02  5.6000000E+02  8.0000000E+02  8.4000000E+02   1.18662E-03   7.98363E-02
-  5.6000000E+02  5.6000000E+02  8.4000000E+02  8.8000000E+02   8.76426E-04   8.10694E-02
-  5.6000000E+02  5.6000000E+02  8.8000000E+02  9.2000000E+02   5.64302E-04   9.79301E-02
-  5.6000000E+02  5.6000000E+02  9.2000000E+02  9.6000000E+02   9.98730E-05   4.42659E-01
-  6.0000000E+02  6.0000000E+02  6.4000000E+02  6.8000000E+02   2.67823E-02   7.42819E-02
-  6.0000000E+02  6.0000000E+02  6.8000000E+02  7.2000000E+02   8.97982E-03   7.38484E-02
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diff --git a/simpegDCIP/Dev/DCIP2D_FWR/dcipf2d.inp b/simpegDCIP/Dev/DCIP2D_FWR/dcipf2d.inp
deleted file mode 100644
index 4a7e0bd5..00000000
--- a/simpegDCIP/Dev/DCIP2D_FWR/dcipf2d.inp
+++ /dev/null
@@ -1,5 +0,0 @@
-FWD DC
-MESH FILE Mesh_2D.msh
-LOC LOC_X FWR_3D_2_2D.dat
-TOPO DEFAULT
-COND FILE MtIsa_2D.con
\ No newline at end of file
diff --git a/simpegDCIP/Dev/DCIP3D_FWR/Mesh_20m.msh b/simpegDCIP/Dev/DCIP3D_FWR/Mesh_20m.msh
deleted file mode 100644
index 78995ad2..00000000
--- a/simpegDCIP/Dev/DCIP3D_FWR/Mesh_20m.msh
+++ /dev/null
@@ -1,5 +0,0 @@
-104 21 45
--1565.00 10725 0
-350 300 250 200 175 150 103.00 86.00 72.00 60.00 50.00 40.00 35.00 30.00 24.00 74*20.00 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
-300 250 200 175 150 103.00 86.00 72.00 5*60 72.00 86.00 103.00 150 175 200 250 300
-30*20 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
\ No newline at end of file
diff --git a/simpegDCIP/Dev/DCIP3D_FWR/MtIsa_3D.con b/simpegDCIP/Dev/DCIP3D_FWR/MtIsa_3D.con
deleted file mode 100644
index a627a009..00000000
--- a/simpegDCIP/Dev/DCIP3D_FWR/MtIsa_3D.con
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diff --git a/simpegDCIP/Dev/DCIP3D_FWR/PDP/FWR_UBC3D_2_2D.dat b/simpegDCIP/Dev/DCIP3D_FWR/PDP/FWR_UBC3D_2_2D.dat
deleted file mode 100644
index d1a4c44f..00000000
--- a/simpegDCIP/Dev/DCIP3D_FWR/PDP/FWR_UBC3D_2_2D.dat
+++ /dev/null
@@ -1,245 +0,0 @@
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diff --git a/simpegDCIP/Dev/DCIP3D_FWR/PDP/FWR_data3D.dat b/simpegDCIP/Dev/DCIP3D_FWR/PDP/FWR_data3D.dat
deleted file mode 100644
index ae98e33a..00000000
--- a/simpegDCIP/Dev/DCIP3D_FWR/PDP/FWR_data3D.dat
+++ /dev/null
@@ -1,301 +0,0 @@
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diff --git a/simpegDCIP/Dev/DCIP3D_FWR/PDP/dc3d.dat b/simpegDCIP/Dev/DCIP3D_FWR/PDP/dc3d.dat
deleted file mode 100644
index 7cd14e1b..00000000
--- a/simpegDCIP/Dev/DCIP3D_FWR/PDP/dc3d.dat
+++ /dev/null
@@ -1,273 +0,0 @@
-! dc data
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-
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-  1.230000E+03  1.215100E+04  0.000000E+00   1.270000E+03  1.215100E+04  0.000000E+00    1.86182E-02  1.06854E-01
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-
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-
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-  1.310000E+03  1.215100E+04  0.000000E+00   1.350000E+03  1.215100E+04  0.000000E+00    1.90718E-02  1.04313E-01
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-
-  1.310000E+03  1.215100E+04  0.000000E+00   1.310000E+03  1.215100E+04  0.000000E+00    8
-  1.350000E+03  1.215100E+04  0.000000E+00   1.390000E+03  1.215100E+04  0.000000E+00    1.98653E-02  1.00147E-01
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-
-  1.350000E+03  1.215100E+04  0.000000E+00   1.350000E+03  1.215100E+04  0.000000E+00    7
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-
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-
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diff --git a/simpegDCIP/Dev/DCIP3D_FWR/fwr.inp b/simpegDCIP/Dev/DCIP3D_FWR/fwr.inp
deleted file mode 100644
index 89065a72..00000000
--- a/simpegDCIP/Dev/DCIP3D_FWR/fwr.inp
+++ /dev/null
@@ -1,9 +0,0 @@
-dc
-Mesh_20m.msh
-OBS_LOC_3D.dat
-MtIsa_3D.con
-VALUE 0.0
-null
-0
-1e-5
--1
\ No newline at end of file
diff --git a/simpegDCIP/Dev/FWR_3D_2_2D.dat b/simpegDCIP/Dev/FWR_3D_2_2D.dat
deleted file mode 100644
index b03242e9..00000000
--- a/simpegDCIP/Dev/FWR_3D_2_2D.dat
+++ /dev/null
@@ -1,55 +0,0 @@
-! GENERAL FORMAT
-
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-
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-
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-
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diff --git a/simpegDCIP/Dev/FWR_data3D.dat b/simpegDCIP/Dev/FWR_data3D.dat
deleted file mode 100644
index fa27cf90..00000000
--- a/simpegDCIP/Dev/FWR_data3D.dat
+++ /dev/null
@@ -1,73 +0,0 @@
-! GENERAL FORMAT
-
-
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-
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diff --git a/simpegDCIP/Dev/Inv2D/FWR_3D_2_2D.dat b/simpegDCIP/Dev/Inv2D/FWR_3D_2_2D.dat
deleted file mode 100644
index b03242e9..00000000
--- a/simpegDCIP/Dev/Inv2D/FWR_3D_2_2D.dat
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diff --git a/simpegDCIP/Dev/Inv2D/Mesh_2D.msh b/simpegDCIP/Dev/Inv2D/Mesh_2D.msh
deleted file mode 100644
index 611ee4c6..00000000
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+++ /dev/null
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diff --git a/simpegDCIP/Dev/Inv2D/MtIsa_2D.con b/simpegDCIP/Dev/Inv2D/MtIsa_2D.con
deleted file mode 100644
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diff --git a/simpegDCIP/Dev/Inv2D/dcinv2d.con b/simpegDCIP/Dev/Inv2D/dcinv2d.con
deleted file mode 100644
index 14839d1d..00000000
--- a/simpegDCIP/Dev/Inv2D/dcinv2d.con
+++ /dev/null
@@ -1,46 +0,0 @@
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-  1.00031E-02  1.00040E-02  1.00060E-02  1.00099E-02  1.00162E-02  1.00243E-02  1.00328E-02  1.00402E-02  1.00458E-02  1.00497E-02  1.00521E-02  1.00537E-02  1.00546E-02  1.00552E-02  1.00556E-02  1.00559E-02  1.00561E-02  1.00563E-02  1.00565E-02  1.00566E-02  1.00567E-02  1.00568E-02  1.00568E-02  1.00568E-02  1.00567E-02  1.00567E-02  1.00565E-02  1.00563E-02  1.00561E-02  1.00558E-02  1.00555E-02  1.00551E-02  1.00547E-02  1.00542E-02  1.00537E-02  1.00532E-02  1.00526E-02  1.00519E-02  1.00512E-02  1.00505E-02  1.00497E-02  1.00489E-02  1.00480E-02  1.00471E-02  1.00461E-02  1.00451E-02  1.00441E-02  1.00431E-02  1.00420E-02  1.00408E-02  1.00396E-02  1.00384E-02  1.00372E-02  1.00359E-02  1.00346E-02  1.00333E-02  1.00320E-02  1.00307E-02  1.00293E-02  1.00280E-02  1.00266E-02  1.00252E-02  1.00239E-02  1.00225E-02  1.00211E-02  1.00197E-02  1.00184E-02  1.00168E-02  1.00145E-02  1.00114E-02  1.00074E-02  1.00021E-02  9.99590E-03  9.98953E-03  9.98484E-03  9.98408E-03  9.98784E-03  9.99362E-03  9.99826E-03  1.00008E-02  1.00019E-02
-  1.00031E-02  1.00039E-02  1.00056E-02  1.00086E-02  1.00126E-02  1.00170E-02  1.00210E-02  1.00241E-02  1.00261E-02  1.00274E-02  1.00281E-02  1.00285E-02  1.00288E-02  1.00289E-02  1.00289E-02  1.00290E-02  1.00290E-02  1.00290E-02  1.00290E-02  1.00290E-02  1.00290E-02  1.00289E-02  1.00289E-02  1.00288E-02  1.00287E-02  1.00286E-02  1.00285E-02  1.00283E-02  1.00282E-02  1.00280E-02  1.00278E-02  1.00276E-02  1.00274E-02  1.00271E-02  1.00269E-02  1.00266E-02  1.00263E-02  1.00260E-02  1.00257E-02  1.00253E-02  1.00250E-02  1.00247E-02  1.00243E-02  1.00238E-02  1.00234E-02  1.00230E-02  1.00226E-02  1.00221E-02  1.00217E-02  1.00212E-02  1.00207E-02  1.00202E-02  1.00197E-02  1.00191E-02  1.00186E-02  1.00180E-02  1.00175E-02  1.00169E-02  1.00164E-02  1.00158E-02  1.00152E-02  1.00147E-02  1.00141E-02  1.00135E-02  1.00129E-02  1.00123E-02  1.00117E-02  1.00110E-02  1.00100E-02  1.00087E-02  1.00068E-02  1.00044E-02  1.00013E-02  9.99777E-03  9.99449E-03  9.99262E-03  9.99320E-03  9.99588E-03  9.99890E-03  1.00010E-02  1.00020E-02
-  1.00031E-02  1.00038E-02  1.00053E-02  1.00074E-02  1.00099E-02  1.00120E-02  1.00136E-02  1.00146E-02  1.00152E-02  1.00155E-02  1.00156E-02  1.00156E-02  1.00157E-02  1.00157E-02  1.00156E-02  1.00156E-02  1.00156E-02  1.00156E-02  1.00156E-02  1.00155E-02  1.00155E-02  1.00155E-02  1.00154E-02  1.00154E-02  1.00153E-02  1.00152E-02  1.00152E-02  1.00151E-02  1.00150E-02  1.00149E-02  1.00149E-02  1.00148E-02  1.00147E-02  1.00146E-02  1.00145E-02  1.00144E-02  1.00143E-02  1.00142E-02  1.00140E-02  1.00139E-02  1.00138E-02  1.00136E-02  1.00135E-02  1.00133E-02  1.00132E-02  1.00130E-02  1.00129E-02  1.00127E-02  1.00125E-02  1.00123E-02  1.00122E-02  1.00120E-02  1.00118E-02  1.00116E-02  1.00114E-02  1.00112E-02  1.00110E-02  1.00108E-02  1.00106E-02  1.00103E-02  1.00101E-02  1.00099E-02  1.00097E-02  1.00094E-02  1.00092E-02  1.00090E-02  1.00087E-02  1.00084E-02  1.00080E-02  1.00075E-02  1.00067E-02  1.00056E-02  1.00040E-02  1.00021E-02  9.99987E-03  9.99783E-03  9.99688E-03  9.99765E-03  9.99947E-03  1.00011E-02  1.00020E-02
diff --git a/simpegDCIP/Dev/Inv2D/dcinv2d.inp b/simpegDCIP/Dev/Inv2D/dcinv2d.inp
deleted file mode 100644
index 9d685f9f..00000000
--- a/simpegDCIP/Dev/Inv2D/dcinv2d.inp
+++ /dev/null
@@ -1,11 +0,0 @@
-OBS LOC_X FWR_3D_2_2D.dat 
-MESH FILE Mesh_2D.msh 
-CHIFACT 1 100.000000
-TOPO DEFAULT  %s 
-INIT_MOD DEFAULT
-REF_MOD VALUE 1.000000e-02
-ALPHA DEFAULT
-WEIGHT DEFAULT
-STORE_ALL_MODELS FALSE
-INVMODE SVD
-USE_MREF TRUE
diff --git a/simpegDCIP/Dev/Inv2D/dcinv2d.log b/simpegDCIP/Dev/Inv2D/dcinv2d.log
deleted file mode 100644
index 5b30b7f7..00000000
--- a/simpegDCIP/Dev/Inv2D/dcinv2d.log
+++ /dev/null
@@ -1,410 +0,0 @@
-
-Parallelized with OpenMP.  # of threads:  4
-
-DCIP2D - Version 5  (BETA) 20110811:  DCINV2D
-
-Developed by University of British Columbia 
-Geophysical Inversion Facility  (UBC-GIF)
-
-(C) Copyright 1992 - 2011, UBC-GIF,
-Department of Earth and Ocean Sciences, UBC
-http://www.eos.ubc.ca/research/ubcgif/
-
-Distributed by:
-Mira Geoscience Ltd.
-
-
- DCINV2D started on: 1/12/2016  21:07:02
-
-
-Reading input file: dcinv2d.inp
-
-----------------------------------------------
-OBS LOC_X FWR_3D_2_2D.dat
-MESH FILE Mesh_2D.msh
-CHIFACT 1 100.000000
-TOPO DEFAULT  %s
-INIT_MOD DEFAULT
-REF_MOD VALUE 1.000000e-02
-ALPHA DEFAULT
-WEIGHT DEFAULT
-STORE_ALL_MODELS FALSE
-INVMODE SVD
-USE_MREF TRUE
-----------------------------------------------
-
-      maximum # of iterations: 100
-data were read from: FWR_3D_2_2D.dat
-       # of current locations:    9
-                    # of data:   45
-                      chifact: 1.00000E+00
-                target misfit: 4.50000E+01
-
-mesh was read from: Mesh_2D.msh
-                      # of cells:   81 x   45
-                total # of cells:  3645
-               # of active cells:  3645
-
-   # of unique data locations:    9
-# of wave values:  13
-  2.5000E-04  4.9901E-04  9.9606E-04  1.9882E-03  3.9685E-03  7.9213E-03  1.5811E-02  3.1560E-02  6.2996E-02  1.2574E-01  2.5099E-01  5.0099E-01  1.0000E+00
-
-reference conductivity model is set to a constant: 1.000000E-02
-initial model is set to the reference model.
-
-using default length scales (Lx, Lz): ( 8.00000E+01, 8.00000E+01)
-corresponding alpha (a_s, a_x, a_z): ( 1.56250E-04, 1.0000E+00, 1.0000E+00)
-
-Using basis vectors and SVD.
-reference model will be used in the derivative terms.
-number of basis vectors:  17 + 3 + 1 =   21
-
-init cpu time:   0:00:00.18
-
- 
-   initial misfit =  2.57080E+05
- init. model norm =  0.00000E+00
-    norm comp Ws  =  0.00000E+00
-    norm comp Wx  =  0.00000E+00
-    norm comp Wz  =  0.00000E+00
-
-
-Iteration    1
-
-  beta vs. misfit:
-     beta         misfit
-  1.11493E+03  4.74485E+04
-  2.22985E+03  5.68364E+04
-  5.57463E+03  8.02516E+04
-  1.39366E+04  1.21526E+05
-  1.57750E+04  1.28279E+05
-  1.58488E+04  1.28536E+05
-  1.58499E+04  1.28540E+05
-  3.96247E+04  1.79392E+05
-
-      chosen beta =  1.58499E+04
-    target misfit =  1.28540E+05
-  achieved misfit =  1.28540E+05
-       model norm =  3.63297E+00
-    misfit change =  5.00000E-01
-model norm change =  0.00000E+00
-
-    norm comp Ws  =  2.88514E+00
-    norm comp Wx  =  3.70226E-01
-    norm comp Wz  =  3.77603E-01
-
-iter cpu time:   0:00:01.81
-
-
-Iteration    2
-
-  beta vs. misfit:
-     beta         misfit
-  3.96247E+03  6.15053E+04
-  7.92494E+03  9.46707E+04
-
-      chosen beta =  4.25263E+03
-    target misfit =  6.42701E+04
-  achieved misfit =  6.42774E+04
-       model norm =  1.37169E+01
-    misfit change =  4.99943E-01
-model norm change =  2.77567E+00
-
-    norm comp Ws  =  1.06979E+01
-    norm comp Wx  =  1.61088E+00
-    norm comp Wz  =  1.40814E+00
-
-iter cpu time:   0:00:00.83
-
-
-Iteration    3
-
-  beta vs. misfit:
-     beta         misfit
-  1.06316E+03  2.49833E+04
-  2.12631E+03  4.10044E+04
-
-      chosen beta =  1.51222E+03
-    target misfit =  3.21387E+04
-  achieved misfit =  3.17674E+04
-       model norm =  2.91306E+01
-    misfit change =  5.05777E-01
-model norm change =  1.12370E+00
-
-    norm comp Ws  =  2.19928E+01
-    norm comp Wx  =  4.11507E+00
-    norm comp Wz  =  3.02271E+00
-
-iter cpu time:   0:00:00.64
-
-
-Iteration    4
-
-  beta vs. misfit:
-     beta         misfit
-  3.78054E+02  1.14051E+04
-  7.56108E+02  1.87488E+04
-
-      chosen beta =  6.00000E+02
-    target misfit =  1.58837E+04
-  achieved misfit =  1.56569E+04
-       model norm =  4.84312E+01
-    misfit change =  5.07137E-01
-model norm change =  6.62554E-01
-
-    norm comp Ws  =  3.49213E+01
-    norm comp Wx  =  8.32898E+00
-    norm comp Wz  =  5.18098E+00
-
-iter cpu time:   0:00:01.47
-
-
-Iteration    5
-
-  beta vs. misfit:
-     beta         misfit
-  1.50000E+02  5.05151E+03
-  3.00000E+02  8.76679E+03
-
-      chosen beta =  2.60199E+02
-    target misfit =  7.82847E+03
-  achieved misfit =  7.79602E+03
-       model norm =  6.97577E+01
-    misfit change =  5.02073E-01
-model norm change =  4.40344E-01
-
-    norm comp Ws  =  4.74916E+01
-    norm comp Wx  =  1.43993E+01
-    norm comp Wz  =  7.86671E+00
-
-iter cpu time:   0:00:01.41
-
-
-Iteration    6
-
-  beta vs. misfit:
-     beta         misfit
-  6.50498E+01  1.83324E+03
-  1.30100E+02  3.68412E+03
-  1.37599E+02  3.90387E+03
-
-      chosen beta =  1.37400E+02
-    target misfit =  3.89801E+03
-  achieved misfit =  3.89800E+03
-       model norm =  8.95751E+01
-    misfit change =  5.00001E-01
-model norm change =  2.84090E-01
-
-    norm comp Ws  =  5.75580E+01
-    norm comp Wx  =  2.11440E+01
-    norm comp Wz  =  1.08731E+01
-
-iter cpu time:   0:00:01.02
-
-
-Iteration    7
-
-  beta vs. misfit:
-     beta         misfit
-  3.43499E+01  8.99667E+02
-  6.86999E+01  1.65819E+03
-  8.25106E+01  2.00613E+03
-
-      chosen beta =  8.02499E+01
-    target misfit =  1.94900E+03
-  achieved misfit =  1.94758E+03
-       model norm =  1.06566E+02
-    misfit change =  5.00364E-01
-model norm change =  1.89686E-01
-
-    norm comp Ws  =  6.57720E+01
-    norm comp Wx  =  2.69488E+01
-    norm comp Wz  =  1.38454E+01
-
-iter cpu time:   0:00:01.00
-
-
-Iteration    8
-
-  beta vs. misfit:
-     beta         misfit
-  2.00625E+01  5.63946E+02
-  4.01250E+01  9.43655E+02
-  4.18598E+01  9.78372E+02
-
-      chosen beta =  4.16303E+01
-    target misfit =  9.73791E+02
-  achieved misfit =  9.73755E+02
-       model norm =  1.22904E+02
-    misfit change =  5.00019E-01
-model norm change =  1.53314E-01
-
-    norm comp Ws  =  7.39851E+01
-    norm comp Wx  =  3.20341E+01
-    norm comp Wz  =  1.68852E+01
-
-iter cpu time:   0:00:01.08
-
-
-Iteration    9
-
-  beta vs. misfit:
-     beta         misfit
-  1.04076E+01  3.54740E+02
-  2.08152E+01  5.47348E+02
-
-      chosen beta =  1.72632E+01
-    target misfit =  4.86877E+02
-  achieved misfit =  4.81217E+02
-       model norm =  1.41185E+02
-    misfit change =  5.05813E-01
-model norm change =  1.48742E-01
-
-    norm comp Ws  =  8.27069E+01
-    norm comp Wx  =  3.78831E+01
-    norm comp Wz  =  2.05954E+01
-
-iter cpu time:   0:00:00.62
-
-
-Iteration   10
-
-  beta vs. misfit:
-     beta         misfit
-  4.31579E+00  2.09965E+02
-  8.63158E+00  2.93092E+02
-
-      chosen beta =  5.72809E+00
-    target misfit =  2.40608E+02
-  achieved misfit =  2.34998E+02
-       model norm =  1.66836E+02
-    misfit change =  5.11659E-01
-model norm change =  1.81682E-01
-
-    norm comp Ws  =  9.43748E+01
-    norm comp Wx  =  4.67077E+01
-    norm comp Wz  =  2.57538E+01
-
-iter cpu time:   0:00:01.14
-
-
-Iteration   11
-
-  beta vs. misfit:
-     beta         misfit
-  1.43202E+00  1.08765E+02
-  2.86405E+00  1.48776E+02
-
-      chosen beta =  1.69894E+00
-    target misfit =  1.17499E+02
-  achieved misfit =  1.13855E+02
-       model norm =  2.07214E+02
-    misfit change =  5.15505E-01
-model norm change =  2.42021E-01
-
-    norm comp Ws  =  1.10709E+02
-    norm comp Wx  =  6.50107E+01
-    norm comp Wz  =  3.14941E+01
-
-iter cpu time:   0:00:01.18
-
-
-Iteration   12
-
-  beta vs. misfit:
-     beta         misfit
-  4.24735E-01  4.69288E+01
-  8.49470E-01  5.82083E+01
-
-      chosen beta =  7.90778E-01
-    target misfit =  5.69276E+01
-  achieved misfit =  5.61534E+01
-       model norm =  2.39426E+02
-    misfit change =  5.06799E-01
-model norm change =  1.55454E-01
-
-    norm comp Ws  =  1.22208E+02
-    norm comp Wx  =  7.90861E+01
-    norm comp Wz  =  3.81319E+01
-
-iter cpu time:   0:00:01.10
-
-
-Iteration   13
-
-  beta vs. misfit:
-     beta         misfit
-  5.07840E-01  2.85576E+01
-  6.33710E-01  3.23824E+01
-  1.13145E+00  5.17204E+01
-
-      chosen beta =  9.52357E-01
-    target misfit =  4.50000E+01
-  achieved misfit =  4.40177E+01
-       model norm =  2.37754E+02
-    misfit change =  2.16117E-01
-model norm change = -6.98309E-03
-
-    norm comp Ws  =  1.26074E+02
-    norm comp Wx  =  7.63269E+01
-    norm comp Wz  =  3.53536E+01
-
-iter cpu time:   0:00:00.88
-
-
-Iteration   14
-
-  beta vs. misfit:
-     beta         misfit
-  9.73609E-01  3.64390E+01
-  9.95336E-01  3.71506E+01
-  1.23871E+00  4.57613E+01
-
-      chosen beta =  1.21709E+00
-    target misfit =  4.50000E+01
-  achieved misfit =  4.49504E+01
-       model norm =  2.30537E+02
-    misfit change = -2.11895E-02
-model norm change = -3.03571E-02
-
-    norm comp Ws  =  1.26466E+02
-    norm comp Wx  =  7.21243E+01
-    norm comp Wz  =  3.19461E+01
-
-iter cpu time:   0:00:00.89
-
-
-Target misfit achieved.  Minimizing model norm.
-
-
-Iteration   15
-
-  beta vs. misfit:
-     beta         misfit
-  1.21843E+00  3.86348E+01
-  1.21978E+00  3.86736E+01
-  1.44018E+00  4.53382E+01
-
-      chosen beta =  1.42896E+00
-    target misfit =  4.50000E+01
-  achieved misfit =  4.49850E+01
-       model norm =  2.28950E+02
-    misfit change = -7.69307E-04
-model norm change = -6.88525E-03
-
-    norm comp Ws  =  1.25759E+02
-    norm comp Wx  =  6.90749E+01
-    norm comp Wz  =  3.41159E+01
-
-iter cpu time:   0:00:00.95
-
-
-   Exit at convergence.
-
-
- Iterations performed:   15
-
-total cpu time:   0:00:16.25
-
- DCINV2D ended on: 1/12/2016  21:07:18
-
diff --git a/simpegDCIP/Dev/Inv2D/dcinv2d.out b/simpegDCIP/Dev/Inv2D/dcinv2d.out
deleted file mode 100644
index f42bd34d..00000000
--- a/simpegDCIP/Dev/Inv2D/dcinv2d.out
+++ /dev/null
@@ -1,19 +0,0 @@
-   15  iter    data misfit       model norm              beta
-       0       2.57080E+05       0.00000E+00       0.00000E+00
-       1       1.28540E+05       3.63297E+00       1.58499E+04
-       2       6.42774E+04       1.37169E+01       4.25263E+03
-       3       3.17674E+04       2.91306E+01       1.51222E+03
-       4       1.56569E+04       4.84312E+01       6.00000E+02
-       5       7.79602E+03       6.97577E+01       2.60199E+02
-       6       3.89800E+03       8.95751E+01       1.37400E+02
-       7       1.94758E+03       1.06566E+02       8.02499E+01
-       8       9.73755E+02       1.22904E+02       4.16303E+01
-       9       4.81217E+02       1.41185E+02       1.72632E+01
-      10       2.34998E+02       1.66836E+02       5.72809E+00
-      11       1.13855E+02       2.07214E+02       1.69894E+00
-      12       5.61534E+01       2.39426E+02       7.90778E-01
-      13       4.40177E+01       2.37754E+02       9.52357E-01
-      14       4.49504E+01       2.30537E+02       1.21709E+00
-      15       4.49850E+01       2.28950E+02       1.42896E+00
- 4.50000E+01 target misfit
-          45 number of data
diff --git a/simpegDCIP/Dev/Inv2D/dcinv2d.pre b/simpegDCIP/Dev/Inv2D/dcinv2d.pre
deleted file mode 100644
index a321a074..00000000
--- a/simpegDCIP/Dev/Inv2D/dcinv2d.pre
+++ /dev/null
@@ -1,46 +0,0 @@
-! Predicted data ! GENERAL FORMAT
-  0.0000000E+00  0.0000000E+00  2.0000000E+02  3.0000000E+02   8.31819E-02
-  0.0000000E+00  0.0000000E+00  3.0000000E+02  4.0000000E+02   4.41288E-03
-  0.0000000E+00  0.0000000E+00  4.0000000E+02  5.0000000E+02   1.23515E-02
-  0.0000000E+00  0.0000000E+00  5.0000000E+02  6.0000000E+02   2.09881E-03
-  0.0000000E+00  0.0000000E+00  6.0000000E+02  7.0000000E+02   7.28316E-04
-  0.0000000E+00  0.0000000E+00  7.0000000E+02  8.0000000E+02   5.24885E-04
-  0.0000000E+00  0.0000000E+00  8.0000000E+02  9.0000000E+02   3.56979E-04
-  0.0000000E+00  0.0000000E+00  9.0000000E+02  1.0000000E+03   2.71240E-04
-  0.0000000E+00  0.0000000E+00  1.0000000E+03  1.1000000E+03   1.71849E-04
-  1.0000000E+02  1.0000000E+02  3.0000000E+02  4.0000000E+02   8.00012E-03
-  1.0000000E+02  1.0000000E+02  4.0000000E+02  5.0000000E+02   1.75080E-02
-  1.0000000E+02  1.0000000E+02  5.0000000E+02  6.0000000E+02   2.81046E-03
-  1.0000000E+02  1.0000000E+02  6.0000000E+02  7.0000000E+02   9.20650E-04
-  1.0000000E+02  1.0000000E+02  7.0000000E+02  8.0000000E+02   6.40887E-04
-  1.0000000E+02  1.0000000E+02  8.0000000E+02  9.0000000E+02   4.28101E-04
-  1.0000000E+02  1.0000000E+02  9.0000000E+02  1.0000000E+03   3.19325E-04
-  1.0000000E+02  1.0000000E+02  1.0000000E+03  1.1000000E+03   1.97133E-04
-  2.0000000E+02  2.0000000E+02  4.0000000E+02  5.0000000E+02   3.00094E-02
-  2.0000000E+02  2.0000000E+02  5.0000000E+02  6.0000000E+02   4.17179E-03
-  2.0000000E+02  2.0000000E+02  6.0000000E+02  7.0000000E+02   1.23020E-03
-  2.0000000E+02  2.0000000E+02  7.0000000E+02  8.0000000E+02   8.09705E-04
-  2.0000000E+02  2.0000000E+02  8.0000000E+02  9.0000000E+02   5.26566E-04
-  2.0000000E+02  2.0000000E+02  9.0000000E+02  1.0000000E+03   3.82914E-04
-  2.0000000E+02  2.0000000E+02  1.0000000E+03  1.1000000E+03   2.28587E-04
-  3.0000000E+02  3.0000000E+02  5.0000000E+02  6.0000000E+02   7.27481E-03
-  3.0000000E+02  3.0000000E+02  6.0000000E+02  7.0000000E+02   1.78177E-03
-  3.0000000E+02  3.0000000E+02  7.0000000E+02  8.0000000E+02   1.07707E-03
-  3.0000000E+02  3.0000000E+02  8.0000000E+02  9.0000000E+02   6.74696E-04
-  3.0000000E+02  3.0000000E+02  9.0000000E+02  1.0000000E+03   4.74671E-04
-  3.0000000E+02  3.0000000E+02  1.0000000E+03  1.1000000E+03   2.71808E-04
-  4.0000000E+02  4.0000000E+02  6.0000000E+02  7.0000000E+02   3.37547E-03
-  4.0000000E+02  4.0000000E+02  7.0000000E+02  8.0000000E+02   1.52487E-03
-  4.0000000E+02  4.0000000E+02  8.0000000E+02  9.0000000E+02   8.67080E-04
-  4.0000000E+02  4.0000000E+02  9.0000000E+02  1.0000000E+03   5.71693E-04
-  4.0000000E+02  4.0000000E+02  1.0000000E+03  1.1000000E+03   3.07672E-04
-  5.0000000E+02  5.0000000E+02  7.0000000E+02  8.0000000E+02   6.64946E-03
-  5.0000000E+02  5.0000000E+02  8.0000000E+02  9.0000000E+02   2.83925E-03
-  5.0000000E+02  5.0000000E+02  9.0000000E+02  1.0000000E+03   1.50341E-03
-  5.0000000E+02  5.0000000E+02  1.0000000E+03  1.1000000E+03   6.31242E-04
-  6.0000000E+02  6.0000000E+02  8.0000000E+02  9.0000000E+02   4.28770E-03
-  6.0000000E+02  6.0000000E+02  9.0000000E+02  1.0000000E+03   2.02291E-03
-  6.0000000E+02  6.0000000E+02  1.0000000E+03  1.1000000E+03   7.75855E-04
-  7.0000000E+02  7.0000000E+02  9.0000000E+02  1.0000000E+03   3.85090E-03
-  7.0000000E+02  7.0000000E+02  1.0000000E+03  1.1000000E+03   1.15357E-03
-  8.0000000E+02  8.0000000E+02  1.0000000E+03  1.1000000E+03   2.22667E-03
diff --git a/simpegDCIP/Dev/Inv2D/dcinv2d_0.pre b/simpegDCIP/Dev/Inv2D/dcinv2d_0.pre
deleted file mode 100644
index 4498ec87..00000000
--- a/simpegDCIP/Dev/Inv2D/dcinv2d_0.pre
+++ /dev/null
@@ -1,46 +0,0 @@
-! Predicted data ! GENERAL FORMAT Last column are apparent conductivities.
-  0.0000000E+00  0.0000000E+00  2.0000000E+02  3.0000000E+02   2.63497E-02   1.00668E-02
-  0.0000000E+00  0.0000000E+00  3.0000000E+02  4.0000000E+02   1.31837E-02   1.00601E-02
-  0.0000000E+00  0.0000000E+00  4.0000000E+02  5.0000000E+02   7.93784E-03   1.00251E-02
-  0.0000000E+00  0.0000000E+00  5.0000000E+02  6.0000000E+02   5.30187E-03   1.00062E-02
-  0.0000000E+00  0.0000000E+00  6.0000000E+02  7.0000000E+02   3.79075E-03   9.99646E-03
-  0.0000000E+00  0.0000000E+00  7.0000000E+02  8.0000000E+02   2.84529E-03   9.98863E-03
-  0.0000000E+00  0.0000000E+00  8.0000000E+02  9.0000000E+02   2.21518E-03   9.97881E-03
-  0.0000000E+00  0.0000000E+00  9.0000000E+02  1.0000000E+03   1.77462E-03   9.96491E-03
-  0.0000000E+00  0.0000000E+00  1.0000000E+03  1.1000000E+03   1.45107E-03   9.97103E-03
-  1.0000000E+02  1.0000000E+02  3.0000000E+02  4.0000000E+02   2.64919E-02   1.00128E-02
-  1.0000000E+02  1.0000000E+02  4.0000000E+02  5.0000000E+02   1.32352E-02   1.00209E-02
-  1.0000000E+02  1.0000000E+02  5.0000000E+02  6.0000000E+02   7.96229E-03   9.99429E-03
-  1.0000000E+02  1.0000000E+02  6.0000000E+02  7.0000000E+02   5.31543E-03   9.98068E-03
-  1.0000000E+02  1.0000000E+02  7.0000000E+02  8.0000000E+02   3.79921E-03   9.97417E-03
-  1.0000000E+02  1.0000000E+02  8.0000000E+02  9.0000000E+02   2.85122E-03   9.96784E-03
-  1.0000000E+02  1.0000000E+02  9.0000000E+02  1.0000000E+03   2.21994E-03   9.95742E-03
-  1.0000000E+02  1.0000000E+02  1.0000000E+03  1.1000000E+03   1.77401E-03   9.96831E-03
-  2.0000000E+02  2.0000000E+02  4.0000000E+02  5.0000000E+02   2.65164E-02   1.00036E-02
-  2.0000000E+02  2.0000000E+02  5.0000000E+02  6.0000000E+02   1.32488E-02   1.00106E-02
-  2.0000000E+02  2.0000000E+02  6.0000000E+02  7.0000000E+02   7.97076E-03   9.98368E-03
-  2.0000000E+02  2.0000000E+02  7.0000000E+02  8.0000000E+02   5.32136E-03   9.96956E-03
-  2.0000000E+02  2.0000000E+02  8.0000000E+02  9.0000000E+02   3.80396E-03   9.96173E-03
-  2.0000000E+02  2.0000000E+02  9.0000000E+02  1.0000000E+03   2.85567E-03   9.95231E-03
-  2.0000000E+02  2.0000000E+02  1.0000000E+03  1.1000000E+03   2.21779E-03   9.96706E-03
-  3.0000000E+02  3.0000000E+02  5.0000000E+02  6.0000000E+02   2.65249E-02   1.00004E-02
-  3.0000000E+02  3.0000000E+02  6.0000000E+02  7.0000000E+02   1.32547E-02   1.00062E-02
-  3.0000000E+02  3.0000000E+02  7.0000000E+02  8.0000000E+02   7.97549E-03   9.97775E-03
-  3.0000000E+02  3.0000000E+02  8.0000000E+02  9.0000000E+02   5.32580E-03   9.96125E-03
-  3.0000000E+02  3.0000000E+02  9.0000000E+02  1.0000000E+03   3.80888E-03   9.94888E-03
-  3.0000000E+02  3.0000000E+02  1.0000000E+03  1.1000000E+03   2.85172E-03   9.96611E-03
-  4.0000000E+02  4.0000000E+02  6.0000000E+02  7.0000000E+02   2.65296E-02   9.99858E-03
-  4.0000000E+02  4.0000000E+02  7.0000000E+02  8.0000000E+02   1.32592E-02   1.00028E-02
-  4.0000000E+02  4.0000000E+02  8.0000000E+02  9.0000000E+02   7.98040E-03   9.97162E-03
-  4.0000000E+02  4.0000000E+02  9.0000000E+02  1.0000000E+03   5.33212E-03   9.94944E-03
-  4.0000000E+02  4.0000000E+02  1.0000000E+03  1.1000000E+03   3.80236E-03   9.96593E-03
-  5.0000000E+02  5.0000000E+02  7.0000000E+02  8.0000000E+02   2.65345E-02   9.99673E-03
-  5.0000000E+02  5.0000000E+02  8.0000000E+02  9.0000000E+02   1.32655E-02   9.99807E-03
-  5.0000000E+02  5.0000000E+02  9.0000000E+02  1.0000000E+03   7.98964E-03   9.96008E-03
-  5.0000000E+02  5.0000000E+02  1.0000000E+03  1.1000000E+03   5.32134E-03   9.96960E-03
-  6.0000000E+02  6.0000000E+02  8.0000000E+02  9.0000000E+02   2.65437E-02   9.99326E-03
-  6.0000000E+02  6.0000000E+02  9.0000000E+02  1.0000000E+03   1.32806E-02   9.98668E-03
-  6.0000000E+02  6.0000000E+02  1.0000000E+03  1.1000000E+03   7.97063E-03   9.98384E-03
-  7.0000000E+02  7.0000000E+02  9.0000000E+02  1.0000000E+03   2.65717E-02   9.98273E-03
-  7.0000000E+02  7.0000000E+02  1.0000000E+03  1.1000000E+03   1.32427E-02   1.00153E-02
-  8.0000000E+02  8.0000000E+02  1.0000000E+03  1.1000000E+03   2.64774E-02   1.00183E-02
diff --git a/simpegDCIP/Dev/Inv2D/sensitivity.txt b/simpegDCIP/Dev/Inv2D/sensitivity.txt
deleted file mode 100644
index ba0fdaac..00000000
--- a/simpegDCIP/Dev/Inv2D/sensitivity.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-   81   45
-  1.00411E-13  3.10408E-13  9.74717E-13  3.09564E-12  9.68504E-12  2.91605E-11  8.35615E-11  2.28263E-10  6.04123E-10  1.61401E-09  4.67374E-09  1.58647E-08  6.23317E-08  2.35358E-07  1.71041E-08  2.08590E-07  1.56029E-07  1.46650E-07  1.72752E-07  1.54926E-07  1.77390E-07  2.03028E-07  2.69140E-07  4.10395E-07  1.29687E-07  7.93598E-07  2.97657E-07  1.28094E-07  7.80834E-08  7.94358E-08  1.06544E-07  6.15537E-08  7.23303E-08  1.38411E-07  8.59111E-08  1.68916E-07  1.64240E-07  1.58717E-07  1.44871E-07  1.00497E-07  1.53571E-07  1.14875E-07  1.03632E-07  9.31407E-08  4.54508E-08  5.91715E-08  3.66679E-08  6.10459E-08  1.23422E-07  6.11907E-08  8.25315E-08  6.19628E-08  8.25261E-08  1.49285E-07  7.23998E-08  1.14387E-07  8.11571E-08  6.93066E-08  7.27982E-08  5.00166E-08  1.01158E-07  5.96975E-08  4.37654E-08  4.14493E-08  3.33156E-08  6.30583E-08  3.86673E-08  2.63025E-08  1.28371E-08  7.03143E-09  2.55569E-09  6.86395E-10  2.47006E-10  1.07284E-10  4.64772E-11  1.87670E-11  6.99064E-12  2.43083E-12  8.12035E-13  2.70167E-13  9.04729E-14
-  1.00408E-13  3.10389E-13  9.74598E-13  3.09493E-12  9.68107E-12  2.91394E-11  8.34535E-11  2.27725E-10  6.01411E-10  1.59939E-09  4.58076E-09  1.50821E-08  5.37357E-08  1.45015E-07  9.49187E-08  3.64320E-08  2.63448E-08  4.94770E-08  6.85655E-08  7.00921E-08  5.92742E-08  6.09382E-08  4.59596E-08  9.87180E-08  2.28836E-07  4.66832E-07  1.96472E-07  6.17388E-08  3.68928E-08  7.81857E-08  6.83590E-08  3.70845E-08  2.61880E-08  3.78832E-08  3.43796E-08  8.14050E-08  1.25510E-07  1.28215E-07  8.53512E-08  2.97777E-08  6.74204E-08  7.99932E-08  7.90362E-08  5.79363E-08  2.71734E-08  3.44196E-08  2.90110E-08  4.29354E-08  7.81656E-08  3.22896E-08  2.56389E-08  2.96368E-08  4.22860E-08  6.50714E-08  3.97379E-08  3.15747E-08  3.02920E-08  2.68593E-08  2.63743E-08  3.31479E-08  4.64930E-08  3.35157E-08  2.40778E-08  1.98269E-08  2.25180E-08  2.99855E-08  2.16250E-08  1.19611E-08  3.79368E-09  5.30861E-09  2.42064E-09  6.74309E-10  2.45568E-10  1.07010E-10  4.64139E-11  1.87527E-11  6.98765E-12  2.43026E-12  8.11935E-13  2.70151E-13  9.04706E-14
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-  8.36871E-14  2.11997E-13  4.60601E-13  8.15381E-13  1.17142E-12  1.43328E-12  1.58758E-12  1.66071E-12  1.68239E-12  1.67669E-12  1.65967E-12  1.64019E-12  1.62233E-12  1.60750E-12  1.59582E-12  1.58528E-12  1.57415E-12  1.56245E-12  1.55015E-12  1.53727E-12  1.52380E-12  1.50973E-12  1.49507E-12  1.47981E-12  1.46395E-12  1.44749E-12  1.43044E-12  1.41279E-12  1.39454E-12  1.37571E-12  1.35628E-12  1.33627E-12  1.31567E-12  1.29450E-12  1.27277E-12  1.25046E-12  1.22761E-12  1.20420E-12  1.18027E-12  1.15580E-12  1.13082E-12  1.10534E-12  1.07936E-12  1.05292E-12  1.02601E-12  1.00195E-12  9.78502E-13  9.54632E-13  9.30349E-13  9.05668E-13  8.80605E-13  8.55175E-13  8.29397E-13  8.03290E-13  7.76874E-13  7.50167E-13  7.23191E-13  6.95967E-13  6.68518E-13  6.40865E-13  6.13031E-13  5.85039E-13  5.56915E-13  5.28682E-13  5.00433E-13  4.73117E-13  4.45755E-13  4.13820E-13  3.84114E-13  3.45355E-13  3.18491E-13  3.24929E-13  3.61710E-13  4.47700E-13  6.15733E-13  7.72106E-13  7.87687E-13  6.27249E-13  3.83284E-13  1.85351E-13  7.57304E-14
diff --git a/simpegDCIP/Dev/JSAnimation/IPython_display.py b/simpegDCIP/Dev/JSAnimation/IPython_display.py
deleted file mode 100644
index 70dcb869..00000000
--- a/simpegDCIP/Dev/JSAnimation/IPython_display.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from .html_writer import HTMLWriter
-from matplotlib.animation import Animation
-import matplotlib.pyplot as plt
-import tempfile
-import random
-import os
-
-
-__all__ = ['anim_to_html', 'display_animation']
-
-
-class _NameOnlyTemporaryFile(object):
-    """A context-managed temporary file which is not opened.
-
-    The file should be accessible by name on any system.
-
-    Parameters
-    ----------
-    suffix : string
-        The suffix of the temporary file (default = '')
-    prefix : string
-        The prefix of the temporary file (default = '_tmp_')
-    hash_length : string
-        The length of the random hash.  The size of the hash space will
-        be 16 ** hash_length (default=8)
-    seed : integer
-        the seed for the random number generator.  If not specified, the
-        system time will be used as a seed.
-    absolute : boolean
-        If true, return an absolute path to a temporary file in the current
-        working directory.
-
-    Example
-    -------
-
-    >>> with _NameOnlyTemporaryFile(seed=0, absolute=False) as f:
-    ...     print(f)
-    ...
-    _tmp_d82c07cd
-    >>> os.path.exists('_tmp_d82c07cd')  # file removed after context
-    False
-
-    """
-    def __init__(self, prefix='_tmp_', suffix='', hash_length=8,
-                 seed=None, absolute=True):
-        rng = random.Random(seed)
-        self.name = '%s%0*x%s' % (prefix, hash_length,
-                                  rng.getrandbits(4 * hash_length), suffix)
-        if absolute:
-            self.name = os.path.abspath(self.name)
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *exc_info):
-        if os.path.exists(self.name):
-            os.remove(self.name)
-
-
-def anim_to_html(anim, fps=None, embed_frames=True, default_mode='loop'):
-    """Generate HTML representation of the animation"""
-    if fps is None and hasattr(anim, '_interval'):
-        # Convert interval in ms to frames per second
-        fps = 1000. / anim._interval
-
-    plt.close(anim._fig)
-    if hasattr(anim, "_html_representation"):
-        return anim._html_representation
-    else:
-        # tempfile can't be used here: we need a filename, and this
-        # fails on windows.  Instead, we use a custom filename generator
-        #with tempfile.NamedTemporaryFile(suffix='.html') as f:
-        with _NameOnlyTemporaryFile(suffix='.html') as f:
-            anim.save(f.name,  writer=HTMLWriter(fps=fps,
-                                                 embed_frames=embed_frames,
-                                                 default_mode=default_mode))
-            html = open(f.name).read()
-
-        anim._html_representation = html
-        return html
-
-
-def display_animation(anim, **kwargs):
-    """Display the animation with an IPython HTML object"""
-    from IPython.display import HTML
-    return HTML(anim_to_html(anim, **kwargs))
-
-
-# This is the magic that makes animations display automatically in the
-# IPython notebook.  The _repr_html_ method is a special method recognized
-# by IPython.
-Animation._repr_html_ = anim_to_html
diff --git a/simpegDCIP/Dev/JSAnimation/__init__.py b/simpegDCIP/Dev/JSAnimation/__init__.py
deleted file mode 100644
index a684391d..00000000
--- a/simpegDCIP/Dev/JSAnimation/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .html_writer import HTMLWriter
diff --git a/simpegDCIP/Dev/JSAnimation/examples.py b/simpegDCIP/Dev/JSAnimation/examples.py
deleted file mode 100644
index 066ebdf4..00000000
--- a/simpegDCIP/Dev/JSAnimation/examples.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import numpy as np
-from matplotlib import pyplot as plt
-from matplotlib import animation
-from JSAnimation import IPython_display
-
-def basic_animation(frames=100, interval=30):
-    """Plot a basic sine wave with oscillating amplitude"""
-    fig = plt.figure()
-    ax = plt.axes(xlim=(0, 10), ylim=(-2, 2))
-    line, = ax.plot([], [], lw=2)
-
-    x = np.linspace(0, 10, 1000)
-
-    def init():
-        line.set_data([], [])
-        return line,
-
-    def animate(i):
-        y = np.cos(i * 0.02 * np.pi) * np.sin(x - i * 0.02 * np.pi)
-        line.set_data(x, y)
-        return line,
-
-    return animation.FuncAnimation(fig, animate, init_func=init,
-                                   frames=frames, interval=interval)
-
-
-def lorenz_animation(N_trajectories=20, rseed=1, frames=200, interval=30):
-    """Plot a 3D visualization of the dynamics of the Lorenz system"""
-    from scipy import integrate
-    from mpl_toolkits.mplot3d import Axes3D
-    from matplotlib.colors import cnames
-
-    def lorentz_deriv(coords, t0, sigma=10., beta=8./3, rho=28.0):
-        """Compute the time-derivative of a Lorentz system."""
-        x, y, z = coords
-        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]
-
-    # Choose random starting points, uniformly distributed from -15 to 15
-    np.random.seed(rseed)
-    x0 = -15 + 30 * np.random.random((N_trajectories, 3))
-
-    # Solve for the trajectories
-    t = np.linspace(0, 2, 500)
-    x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t)
-                      for x0i in x0])
-
-    # Set up figure & 3D axis for animation
-    fig = plt.figure()
-    ax = fig.add_axes([0, 0, 1, 1], projection='3d')
-    ax.axis('off')
-
-    # choose a different color for each trajectory
-    colors = plt.cm.jet(np.linspace(0, 1, N_trajectories))
-
-    # set up lines and points
-    lines = sum([ax.plot([], [], [], '-', c=c)
-                 for c in colors], [])
-    pts = sum([ax.plot([], [], [], 'o', c=c, ms=4)
-               for c in colors], [])
-
-    # prepare the axes limits
-    ax.set_xlim((-25, 25))
-    ax.set_ylim((-35, 35))
-    ax.set_zlim((5, 55))
-
-    # set point-of-view: specified by (altitude degrees, azimuth degrees)
-    ax.view_init(30, 0)
-
-    # initialization function: plot the background of each frame
-    def init():
-        for line, pt in zip(lines, pts):
-            line.set_data([], [])
-            line.set_3d_properties([])
-
-            pt.set_data([], [])
-            pt.set_3d_properties([])
-        return lines + pts
-
-    # animation function: called sequentially
-    def animate(i):
-        # we'll step two time-steps per frame.  This leads to nice results.
-        i = (2 * i) % x_t.shape[1]
-
-        for line, pt, xi in zip(lines, pts, x_t):
-            x, y, z = xi[:i + 1].T
-            line.set_data(x, y)
-            line.set_3d_properties(z)
-
-            pt.set_data(x[-1:], y[-1:])
-            pt.set_3d_properties(z[-1:])
-
-        ax.view_init(30, 0.3 * i)
-        fig.canvas.draw()
-        return lines + pts
-
-    return animation.FuncAnimation(fig, animate, init_func=init,
-                                   frames=frames, interval=interval)
diff --git a/simpegDCIP/Dev/JSAnimation/html_writer.py b/simpegDCIP/Dev/JSAnimation/html_writer.py
deleted file mode 100644
index 063d7aa7..00000000
--- a/simpegDCIP/Dev/JSAnimation/html_writer.py
+++ /dev/null
@@ -1,327 +0,0 @@
-import os
-import sys
-import random
-import string
-import warnings
-if sys.version_info < (3, 0):
-    from cStringIO import StringIO as InMemory
-else:
-    from io import BytesIO as InMemory
-from matplotlib.animation import writers, FileMovieWriter
-from base64 import b64encode
-
-
-ICON_DIR = os.path.join(os.path.dirname(__file__), 'icons')
-
-
-class _Icons(object):
-    """This class is a container for base64 representations of the icons"""
-    icons = ['first', 'prev', 'reverse', 'pause', 'play', 'next', 'last']
-
-    def __init__(self, icon_dir=ICON_DIR, extension='png'):
-        self.icon_dir = icon_dir
-        self.extension = extension
-        for icon in self.icons:
-            setattr(self, icon,
-                    self._load_base64('{0}.{1}'.format(icon, extension)))
-
-    def _load_base64(self, filename):
-        data = open(os.path.join(self.icon_dir, filename), 'rb').read()
-        return 'data:image/{0};base64,{1}'.format(self.extension,
-                                                  b64encode(data).decode('ascii'))
-
-
-JS_INCLUDE = """
-<script language="javascript">
-  /* Define the Animation class */
-  function Animation(frames, img_id, slider_id, interval, loop_select_id){
-    this.img_id = img_id;
-    this.slider_id = slider_id;
-    this.loop_select_id = loop_select_id;
-    this.interval = interval;
-    this.current_frame = 0;
-    this.direction = 0;
-    this.timer = null;
-    this.frames = new Array(frames.length);
-
-    for (var i=0; i<frames.length; i++)
-    {
-     this.frames[i] = new Image();
-     this.frames[i].src = frames[i];
-    }
-    document.getElementById(this.slider_id).max = this.frames.length - 1;
-    this.set_frame(this.current_frame);
-  }
-
-  Animation.prototype.get_loop_state = function(){
-    var button_group = document[this.loop_select_id].state;
-    for (var i = 0; i < button_group.length; i++) {
-        var button = button_group[i];
-        if (button.checked) {
-            return button.value;
-        }
-    }
-    return undefined;
-  }
-
-  Animation.prototype.set_frame = function(frame){
-    this.current_frame = frame;
-    document.getElementById(this.img_id).src = this.frames[this.current_frame].src;
-    document.getElementById(this.slider_id).value = this.current_frame;
-  }
-
-  Animation.prototype.next_frame = function()
-  {
-    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));
-  }
-
-  Animation.prototype.previous_frame = function()
-  {
-    this.set_frame(Math.max(0, this.current_frame - 1));
-  }
-
-  Animation.prototype.first_frame = function()
-  {
-    this.set_frame(0);
-  }
-
-  Animation.prototype.last_frame = function()
-  {
-    this.set_frame(this.frames.length - 1);
-  }
-
-  Animation.prototype.slower = function()
-  {
-    this.interval /= 0.7;
-    if(this.direction > 0){this.play_animation();}
-    else if(this.direction < 0){this.reverse_animation();}
-  }
-
-  Animation.prototype.faster = function()
-  {
-    this.interval *= 0.7;
-    if(this.direction > 0){this.play_animation();}
-    else if(this.direction < 0){this.reverse_animation();}
-  }
-
-  Animation.prototype.anim_step_forward = function()
-  {
-    this.current_frame += 1;
-    if(this.current_frame < this.frames.length){
-      this.set_frame(this.current_frame);
-    }else{
-      var loop_state = this.get_loop_state();
-      if(loop_state == "loop"){
-        this.first_frame();
-      }else if(loop_state == "reflect"){
-        this.last_frame();
-        this.reverse_animation();
-      }else{
-        this.pause_animation();
-        this.last_frame();
-      }
-    }
-  }
-
-  Animation.prototype.anim_step_reverse = function()
-  {
-    this.current_frame -= 1;
-    if(this.current_frame >= 0){
-      this.set_frame(this.current_frame);
-    }else{
-      var loop_state = this.get_loop_state();
-      if(loop_state == "loop"){
-        this.last_frame();
-      }else if(loop_state == "reflect"){
-        this.first_frame();
-        this.play_animation();
-      }else{
-        this.pause_animation();
-        this.first_frame();
-      }
-    }
-  }
-
-  Animation.prototype.pause_animation = function()
-  {
-    this.direction = 0;
-    if (this.timer){
-      clearInterval(this.timer);
-      this.timer = null;
-    }
-  }
-
-  Animation.prototype.play_animation = function()
-  {
-    this.pause_animation();
-    this.direction = 1;
-    var t = this;
-    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);
-  }
-
-  Animation.prototype.reverse_animation = function()
-  {
-    this.pause_animation();
-    this.direction = -1;
-    var t = this;
-    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);
-  }
-</script>
-"""
-
-
-DISPLAY_TEMPLATE = """
-<div class="animation" align="center">
-    <img id="_anim_img{id}">
-    <br>
-    <input id="_anim_slider{id}" type="range" style="width:350px" name="points" min="0" max="1" step="1" value="0" onchange="anim{id}.set_frame(parseInt(this.value));"></input>
-    <br>
-    <button onclick="anim{id}.slower()">&#8211;</button>
-    <button onclick="anim{id}.first_frame()"><img class="anim_icon" src="{icons.first}"></button>
-    <button onclick="anim{id}.previous_frame()"><img class="anim_icon" src="{icons.prev}"></button>
-    <button onclick="anim{id}.reverse_animation()"><img class="anim_icon" src="{icons.reverse}"></button>
-    <button onclick="anim{id}.pause_animation()"><img class="anim_icon" src="{icons.pause}"></button>
-    <button onclick="anim{id}.play_animation()"><img class="anim_icon" src="{icons.play}"></button>
-    <button onclick="anim{id}.next_frame()"><img class="anim_icon" src="{icons.next}"></button>
-    <button onclick="anim{id}.last_frame()"><img class="anim_icon" src="{icons.last}"></button>
-    <button onclick="anim{id}.faster()">+</button>
-  <form action="#n" name="_anim_loop_select{id}" class="anim_control">
-    <input type="radio" name="state" value="once" {once_checked}> Once </input>
-    <input type="radio" name="state" value="loop" {loop_checked}> Loop </input>
-    <input type="radio" name="state" value="reflect" {reflect_checked}> Reflect </input>
-  </form>
-</div>
-
-
-<script language="javascript">
-  /* Instantiate the Animation class. */
-  /* The IDs given should match those used in the template above. */
-  (function() {{
-    var img_id = "_anim_img{id}";
-    var slider_id = "_anim_slider{id}";
-    var loop_select_id = "_anim_loop_select{id}";
-    var frames = new Array({Nframes});
-    {fill_frames}
-
-    /* set a timeout to make sure all the above elements are created before
-       the object is initialized. */
-    setTimeout(function() {{
-        anim{id} = new Animation(frames, img_id, slider_id, {interval}, loop_select_id);
-    }}, 0);
-  }})()
-</script>
-"""
-
-INCLUDED_FRAMES = """
-  for (var i=0; i<{Nframes}; i++){{
-    frames[i] = "{frame_dir}/frame" + ("0000000" + i).slice(-7) + ".{frame_format}";
-  }}
-"""
-
-
-def _included_frames(frame_list, frame_format):
-    """frame_list should be a list of filenames"""
-    return INCLUDED_FRAMES.format(Nframes=len(frame_list),
-                                  frame_dir=os.path.dirname(frame_list[0]),
-                                  frame_format=frame_format)
-
-
-def _embedded_frames(frame_list, frame_format):
-    """frame_list should be a list of base64-encoded png files"""
-    template = '  frames[{0}] = "data:image/{1};base64,{2}"\n'
-    embedded = "\n"
-    for i, frame_data in enumerate(frame_list):
-        embedded += template.format(i, frame_format,
-                                    frame_data.replace('\n', '\\\n'))
-    return embedded
-
-
-@writers.register('html')
-class HTMLWriter(FileMovieWriter):
-    # we start the animation id count at a random number: this way, if two
-    # animations are meant to be included on one HTML page, there is a
-    # very small chance of conflict.
-    rng = random.Random()
-    exec_key = 'animation.ffmpeg_path'
-    args_key = 'animation.ffmpeg_args'
-    supported_formats = ['png', 'jpeg', 'tiff', 'svg']
-
-    @classmethod
-    def new_id(cls):
-        #return '%16x' % cls.rng.getrandbits(64)
-        return ''.join(cls.rng.choice(string.ascii_uppercase)
-                       for x in range(16))
-
-    def __init__(self, fps=30, codec=None, bitrate=None, extra_args=None,
-                 metadata=None, embed_frames=False, default_mode='loop'):
-        self.embed_frames = embed_frames
-        self.default_mode = default_mode.lower()
-
-        if self.default_mode not in ['loop', 'once', 'reflect']:
-            self.default_mode = 'loop'
-            warnings.warn("unrecognized default_mode: using 'loop'")
-
-        self._saved_frames = list()
-        super(HTMLWriter, self).__init__(fps, codec, bitrate,
-                                         extra_args, metadata)
-
-    def setup(self, fig, outfile, dpi, frame_dir=None):
-        if os.path.splitext(outfile)[-1] not in ['.html', '.htm']:
-            raise ValueError("outfile must be *.htm or *.html")
-
-        if not self.embed_frames:
-            if frame_dir is None:
-                frame_dir = outfile.rstrip('.html') + '_frames'
-            if not os.path.exists(frame_dir):
-                os.makedirs(frame_dir)
-            frame_prefix = os.path.join(frame_dir, 'frame')
-        else:
-            frame_prefix = None
-
-        super(HTMLWriter, self).setup(fig, outfile, dpi,
-                                      frame_prefix, clear_temp=False)
-
-    def grab_frame(self, **savefig_kwargs):
-        if self.embed_frames:
-            suffix = '.' + self.frame_format
-            f = InMemory()
-            self.fig.savefig(f, format=self.frame_format,
-                             dpi=self.dpi, **savefig_kwargs)
-            f.seek(0)
-            self._saved_frames.append(b64encode(f.read()).decode('ascii'))
-        else:
-            return super(HTMLWriter, self).grab_frame(**savefig_kwargs)
-
-    def _run(self):
-        # make a ducktyped subprocess standin
-        # this is called by the MovieWriter base class, but not used here.
-        class ProcessStandin(object):
-            returncode = 0
-            def communicate(self):
-                return ('', '')
-        self._proc = ProcessStandin()
-
-        # save the frames to an html file
-        if self.embed_frames:
-            fill_frames = _embedded_frames(self._saved_frames,
-                                           self.frame_format)
-        else:
-            # temp names is filled by FileMovieWriter
-            fill_frames = _included_frames(self._temp_names,
-                                           self.frame_format)
-
-        mode_dict = dict(once_checked='',
-                         loop_checked='',
-                         reflect_checked='')
-        mode_dict[self.default_mode + '_checked'] = 'checked'
-
-        interval = int(1000. / self.fps)
-
-        with open(self.outfile, 'w') as of:
-            of.write(JS_INCLUDE)
-            of.write(DISPLAY_TEMPLATE.format(id=self.new_id(),
-                                             Nframes=len(self._temp_names),
-                                             fill_frames=fill_frames,
-                                             interval=interval,
-                                             icons=_Icons(),
-                                             **mode_dict))
diff --git a/simpegDCIP/Dev/JSAnimation/icons/first.png b/simpegDCIP/Dev/JSAnimation/icons/first.png
deleted file mode 100644
index 059e426e..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/first.png and /dev/null differ
diff --git a/simpegDCIP/Dev/JSAnimation/icons/last.png b/simpegDCIP/Dev/JSAnimation/icons/last.png
deleted file mode 100644
index b77efb42..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/last.png and /dev/null differ
diff --git a/simpegDCIP/Dev/JSAnimation/icons/next.png b/simpegDCIP/Dev/JSAnimation/icons/next.png
deleted file mode 100644
index b01d3697..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/next.png and /dev/null differ
diff --git a/simpegDCIP/Dev/JSAnimation/icons/pause.png b/simpegDCIP/Dev/JSAnimation/icons/pause.png
deleted file mode 100644
index 5274532d..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/pause.png and /dev/null differ
diff --git a/simpegDCIP/Dev/JSAnimation/icons/play.png b/simpegDCIP/Dev/JSAnimation/icons/play.png
deleted file mode 100644
index 705863c0..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/play.png and /dev/null differ
diff --git a/simpegDCIP/Dev/JSAnimation/icons/prev.png b/simpegDCIP/Dev/JSAnimation/icons/prev.png
deleted file mode 100644
index bbfbb005..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/prev.png and /dev/null differ
diff --git a/simpegDCIP/Dev/JSAnimation/icons/reverse.png b/simpegDCIP/Dev/JSAnimation/icons/reverse.png
deleted file mode 100644
index 0003c250..00000000
Binary files a/simpegDCIP/Dev/JSAnimation/icons/reverse.png and /dev/null differ
diff --git a/simpegDCIP/Dev/Mesh_20m.msh b/simpegDCIP/Dev/Mesh_20m.msh
deleted file mode 100644
index 431ac5b7..00000000
--- a/simpegDCIP/Dev/Mesh_20m.msh
+++ /dev/null
@@ -1,5 +0,0 @@
-53 53 30
-422045.00 545169.00 1584.88
-300.00 250.00 193.00 114.00 88.00 67.00 52.00 40.00 28.00 35*20.00 28.00 40.00 52.00 67.00 88.00 114.00 193.00 250.00 300.00 
-300.00 250.00 193.00 114.00 88.00 67.00 52.00 40.00 28.00 35*20.00 28.00 40.00 52.00 67.00 88.00 114.00 193.00 250.00 300.00 
-19*20.00 28.00 40.00 52.00 67.00 52.00 67.00 88.00 114.00 193.00 250.00 300.00 
diff --git a/simpegDCIP/Dev/MtIsa_20m.msh b/simpegDCIP/Dev/MtIsa_20m.msh
deleted file mode 100644
index 78995ad2..00000000
--- a/simpegDCIP/Dev/MtIsa_20m.msh
+++ /dev/null
@@ -1,5 +0,0 @@
-104 21 45
--1565.00 10725 0
-350 300 250 200 175 150 103.00 86.00 72.00 60.00 50.00 40.00 35.00 30.00 24.00 74*20.00 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
-300 250 200 175 150 103.00 86.00 72.00 5*60 72.00 86.00 103.00 150 175 200 250 300
-30*20 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
\ No newline at end of file
diff --git a/simpegDCIP/Dev/MtIsa_3D.con b/simpegDCIP/Dev/MtIsa_3D.con
deleted file mode 100644
index a627a009..00000000
--- a/simpegDCIP/Dev/MtIsa_3D.con
+++ /dev/null
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diff --git a/simpegDCIP/Dev/SimPEG_FWR/FWR_3D_2_2D.dat b/simpegDCIP/Dev/SimPEG_FWR/FWR_3D_2_2D.dat
deleted file mode 100644
index e6663fb5..00000000
--- a/simpegDCIP/Dev/SimPEG_FWR/FWR_3D_2_2D.dat
+++ /dev/null
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diff --git a/simpegDCIP/Dev/SimPEG_FWR/FWR_data_2D.dat b/simpegDCIP/Dev/SimPEG_FWR/FWR_data_2D.dat
deleted file mode 100644
index cca0d5f3..00000000
--- a/simpegDCIP/Dev/SimPEG_FWR/FWR_data_2D.dat
+++ /dev/null
@@ -1,183 +0,0 @@
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diff --git a/simpegDCIP/Dev/SimPEG_FWR/FWR_data_3D.dat b/simpegDCIP/Dev/SimPEG_FWR/FWR_data_3D.dat
deleted file mode 100644
index 9a7aa3b9..00000000
--- a/simpegDCIP/Dev/SimPEG_FWR/FWR_data_3D.dat
+++ /dev/null
@@ -1,212 +0,0 @@
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diff --git a/simpegDCIP/Dev/SimPEG_FWR/Mesh_20m.msh b/simpegDCIP/Dev/SimPEG_FWR/Mesh_20m.msh
deleted file mode 100644
index 7e6b661b..00000000
--- a/simpegDCIP/Dev/SimPEG_FWR/Mesh_20m.msh
+++ /dev/null
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diff --git a/simpegDCIP/Dev/SimPEG_FWR/MtIsa_3D.con b/simpegDCIP/Dev/SimPEG_FWR/MtIsa_3D.con
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diff --git a/simpegDCIP/Dev/Synthetic.con b/simpegDCIP/Dev/Synthetic.con
deleted file mode 100644
index f48693ae..00000000
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+++ /dev/null
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diff --git a/simpegDCIP/Dev/Workspace/convertObs_DC3D_to_2D.py b/simpegDCIP/Dev/Workspace/convertObs_DC3D_to_2D.py
deleted file mode 100644
index 73325fa1..00000000
--- a/simpegDCIP/Dev/Workspace/convertObs_DC3D_to_2D.py
+++ /dev/null
@@ -1,44 +0,0 @@
-def convertObs_DC3D_to_2D(Tx,Rx):
-    
-    from SimPEG import np
-    import numpy.matlib as npm
-    """
-        Read list of 3D Tx Rx location and change coordinate system to distance
-        along line assuming all data is acquired along line
-        First transmitter pole is assumed to be at the origin
-
-        Assumes flat topo for now...
-    
-        Input:
-        :param Tx, Rx
-        
-        Output:
-        :figure Tx2d, Rx2d
-        
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-    """
-    
-                
-    Tx2d = []
-    Rx2d = []
-
-    for ii in range(len(Tx)):
-        
-        if ii == 0:
-            endp = Tx[0][0:2,0]
-        
-        nrx = Rx[ii].shape[0]
-                  
-        rP1 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,0] )**2 , axis=0))
-        rP2 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,1] )**2 , axis=0))
-        rC1 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,0:2] )**2 , axis=1))
-        rC2 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,3:5] )**2 , axis=1))
-        
-        Tx2d.append( np.r_[rP1, rP2] )
-        Rx2d.append( np.c_[rC1, rC2] )
-            #np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n')
-
-    return Tx2d, Rx2d
\ No newline at end of file
diff --git a/simpegDCIP/Dev/Workspace/gen_DCIPsurvey.py b/simpegDCIP/Dev/Workspace/gen_DCIPsurvey.py
deleted file mode 100644
index 49ff71d9..00000000
--- a/simpegDCIP/Dev/Workspace/gen_DCIPsurvey.py
+++ /dev/null
@@ -1,149 +0,0 @@
-def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
-    
-    from SimPEG import np
-    import re
-    """
-        Load in endpoints and survey specifications to generate Tx, Rx location
-        stations.
-        
-        Assumes flat topo for now...
-    
-        Input:
-        :param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
-        :object mesh -> SimPEG mesh object
-        :switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole)
-        : param a, n -> pole seperation, number of rx dipoles per tx
-        
-        Output:
-        :param Tx, Rx -> List objects for each tx location
-            Lines: P1x, P1y, P1z, P2x, P2y, P2z
-        
-        Created on Wed December 9th, 2015
-    
-        @author: dominiquef
-    
-    """
-    def xy_2_r(x1,x2,y1,y2):
-        r = np.sqrt( np.sum((x2 - x1)**2 + (y2 - y1)**2) )
-        return r 
-        
-    ## Evenly distribute electrodes and put on surface
-    # Mesure survey length and direction
-    dl_len = xy_2_r(endl[0,0],endl[1,0],endl[0,1],endl[1,1])
-    
-    dl_x = ( endl[1,0] - endl[0,0] ) / dl_len
-    dl_y = ( endl[1,1] - endl[0,1] ) / dl_len
-       
-    nstn = np.floor( dl_len / a )
-    
-    # Compute discrete pole location along line
-    stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*a
-    stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*a
-    
-    # Create line of P1 locations
-    M = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
-    
-    # Create line of P2 locations
-    N = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
-    
-    ## Build list of Tx-Rx locations depending on survey type
-    # Dipole-dipole: Moving tx with [a] spacing -> [AB a MN1 a MN2 ... a MNn]
-    # Pole-dipole: Moving pole on one end -> [A a MN1 a MN2 ... MNn a B]
-    Tx = []
-    Rx = []
-    
-    if not re.match(stype,'gradient'):
-        
-        for ii in range(0, int(nstn)-1): 
-            
-            
-            if re.match(stype,'dpdp'):
-                tx = np.c_[M[ii,:],N[ii,:]]
-            elif re.match(stype,'pdp'):
-                tx = np.c_[M[ii,:],M[ii,:]]
-                
-            #Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
-            
-            # Current elctrode seperation
-            AB = xy_2_r(tx[0,1],endl[1,0],tx[1,1],endl[1,1])
-            
-            # Number of receivers to fit
-            nstn = np.min([np.floor( (AB - b) / a ) , n])
-            
-            # Check if there is enough space, else break the loop
-            if nstn <= 0:
-                continue
-            
-            # Compute discrete pole location along line
-            stn_x = N[ii,0] + dl_x*b + np.array(range(int(nstn)))*dl_x*a
-            stn_y = N[ii,1] + dl_y*b + np.array(range(int(nstn)))*dl_y*a
-            
-            # Create receiver poles
-            # Create line of P1 locations
-            P1 = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
-            
-            # Create line of P2 locations
-            P2 = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
-            
-            Rx.append(np.c_[P1,P2])
-            Tx.append(tx)            
-            
-#==============================================================================
-#     elif re.match(stype,'dpdp'):
-#         
-#         for ii in range(0, int(nstn)-2):  
-#             
-#             indx = np.min([ii+n+1,nstn])
-#             Tx.append(np.c_[M[ii,:],N[ii,:]])
-#             Rx.append(np.c_[M[ii+2:indx,:],N[ii+2:indx,:]])
-#==============================================================================
-            
-    elif re.match(stype,'gradient'):
-        
-        # Gradient survey only requires Tx at end of line and creates a square
-        # grid of receivers at in the middle at a pre-set minimum distance
-        Tx.append(np.c_[M[0,:],N[-1,:]])
-              
-        # Get the edge limit of survey area
-        min_x = endl[0,0] + dl_x * b
-        min_y = endl[0,1] + dl_y * b
-            
-        max_x = endl[1,0] - dl_x * b
-        max_y = endl[1,1] - dl_y * b
-        
-        box_l = np.sqrt( (min_x - max_x)**2 + (min_y - max_y)**2 )
-        box_w = box_l/2.
-        
-        nstn = np.floor( box_l / a )
-        
-        # Compute discrete pole location along line
-        stn_x = min_x + np.array(range(int(nstn)))*dl_x*a
-        stn_y = min_y + np.array(range(int(nstn)))*dl_y*a
-        
-        # Define number of cross lines
-        nlin = int(np.floor( box_w / a ))
-        lind = range(-nlin,nlin+1) 
-        
-        ngrad = nstn * len(lind)
-        
-        rx = np.zeros([ngrad,6])
-        for ii in range( len(lind) ):
-            
-            # Move line in perpendicular direction by dipole spacing
-            lxx = stn_x - lind[ii]*a*dl_y
-            lyy = stn_y + lind[ii]*a*dl_x
-            
-            
-            M = np.c_[ lxx, lyy , np.ones(nstn).T*mesh.vectorNz[-1]]
-            N = np.c_[ lxx+a*dl_x, lyy+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
-            
-            rx[(ii*nstn):((ii+1)*nstn),:] = np.c_[M,N]
-            
-        Rx.append(rx)
-        
-    else:
-        print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
-
-        
-   
-    return Tx, Rx             
diff --git a/simpegDCIP/Dev/Workspace/plot_pseudoSection.py b/simpegDCIP/Dev/Workspace/plot_pseudoSection.py
deleted file mode 100644
index cfff120e..00000000
--- a/simpegDCIP/Dev/Workspace/plot_pseudoSection.py
+++ /dev/null
@@ -1,68 +0,0 @@
-def plot_pseudoSection(Tx,Rx,data,z0, stype):
-    
-    from SimPEG import np, mkvc
-    from scipy.interpolate import griddata
-    from matplotlib.colors import LogNorm
-    import pylab as plt
-    import re
-    """
-        Read list of 2D tx-rx location and plot a speudo-section of apparent
-        resistivity.
-        
-        Assumes flat topo for now...
-    
-        Input:
-        :param d2D, z0
-        :switch stype -> Either 'pdp' (pole-dipole) | 'dpdp' (dipole-dipole)
-    
-        Output:
-        :figure scatter plot overlayed on image
-        
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-    """
-    #d2D = np.asarray(d2D)
-    
-    midl = []
-    midz = []
-    rho = []
-    
-    for ii in range(len(Tx)):
-        # Get distances between each poles
-        rC1P1 = np.abs(Tx[ii][0] - Rx[ii][:,0]) 
-        rC2P1 = np.abs(Tx[ii][1] - Rx[ii][:,0])
-        rC1P2 = np.abs(Tx[ii][1] - Rx[ii][:,1])
-        rC2P2 = np.abs(Tx[ii][0] - Rx[ii][:,1])
-        rP1P2 = np.abs(Rx[ii][:,1] - Rx[ii][:,0])    
-    
-        # Compute apparent resistivity
-        if re.match(stype,'pdp'):
-            rho = np.hstack([rho, data[ii] * 2*np.pi  * rC1P1 * ( rC1P1 + rP1P2 ) / rP1P2] )
-            
-        elif re.match(stype,'dpdp'):
-            rho = np.hstack([rho, data[ii] * 2*np.pi / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 ) ])
-    
-        Cmid = (Tx[ii][0] + Tx[ii][1])/2
-        Pmid = (Rx[ii][:,0] + Rx[ii][:,1])/2
-    
-        midl = np.hstack([midl, ( Cmid + Pmid )/2 ])
-        midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + z0 ])
-    
-   
-    # Grid points
-    grid_x, grid_z = np.mgrid[np.min(midl):np.max(midl), np.min(midz):np.max(midz)]
-    grid_rho = griddata(np.c_[midl,midz], np.log10(abs(1/rho.T)), (grid_x, grid_z), method='linear')
-    
-    
-    #plt.subplot(2,1,2)
-    plt.imshow(grid_rho.T, extent = (np.min(midl),np.max(midl),np.min(midz),np.max(midz)), origin='lower', alpha=0.8)
-    cbar = plt.colorbar(format = '%.2f',fraction=0.02)
-    cmin,cmax = cbar.get_clim()
-    ticks = np.linspace(cmin,cmax,3)
-    cbar.set_ticks(ticks)
-    
-    # Plot apparent resistivity
-    plt.scatter(midl,midz,s=50,c=np.log10(abs(1/rho.T)))
-                
diff --git a/simpegDCIP/Dev/Workspace/readUBC_DC2DLoc.py b/simpegDCIP/Dev/Workspace/readUBC_DC2DLoc.py
deleted file mode 100644
index e1ba265d..00000000
--- a/simpegDCIP/Dev/Workspace/readUBC_DC2DLoc.py
+++ /dev/null
@@ -1,57 +0,0 @@
-def readUBC_DC2DLoc(fileName):
-
-    from SimPEG import np
-    """
-        Read UBC GIF 2D observation file and generate arrays for tx-rx location
-
-        Input:
-        :param fileName, path to the UBC GIF 2D model file
-
-        Output:
-        :param rx, tx
-        :return
-        
-        Created on Thu Nov 12 13:14:10 2015
-
-        @author: dominiquef
-
-    """
-    
-    # Open fileand skip header... assume that we know the mesh already
-#==============================================================================
-#     fopen = open(fileName,'r')
-#     lines = fopen.readlines()
-#     fopen.close()
-#==============================================================================
-
-    # Load file
-    obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
-    
-    # Check first line and figure out if 2D or 3D file format
-    line = np.array(obsfile[0].split(),dtype=float)   
-    
-    tx_A  = []
-    tx_B  = []
-    rx_M  = []
-    rx_N  = []
-    d   = []
-    wd  = []
-    
-    for ii in range(obsfile.shape[0]):
-        
-        # If len==3, then simple format where tx-rx is listed on each line
-        if len(line) == 4:
-        
-            temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
-            tx_A = np.hstack((tx_A,temp[0]))
-            tx_B = np.hstack((tx_B,temp[1]))
-            rx_M = np.hstack((rx_M,temp[2]))
-            rx_N = np.hstack((rx_N,temp[3]))
-            
-        
-    rx = np.transpose(np.array((rx_M,rx_N)))    
-    tx = np.transpose(np.array((tx_A,tx_B)))
-    
-    return tx, rx, d, wd
-
-
diff --git a/simpegDCIP/Dev/Workspace/readUBC_DC2DMesh.py b/simpegDCIP/Dev/Workspace/readUBC_DC2DMesh.py
deleted file mode 100644
index f71a7bb3..00000000
--- a/simpegDCIP/Dev/Workspace/readUBC_DC2DMesh.py
+++ /dev/null
@@ -1,70 +0,0 @@
-def readUBC_DC2DMesh(fileName):
-
-    from SimPEG import np
-    """
-        Read UBC GIF 2DTensor mesh and generate 2D Tensor mesh in simpeg
-
-        Input:
-        :param fileName, path to the UBC GIF mesh file
-
-        Output:
-        :param SimPEG TensorMesh 2D object
-        :return
-        
-        Created on Thu Nov 12 13:14:10 2015
-
-        @author: dominiquef
-
-    """
-    
-    # Open file 
-    fopen = open(fileName,'r')
-    
-    # Read down the file and unpack dx vector
-    def unpackdx(fid,nrows):
-        for ii in range(nrows):
-            
-            line = fid.readline()
-            var = np.array(line.split(),dtype=float)
-            
-            if ii==0:
-                x0= var[0]
-                xvec = np.ones(int(var[2])) * (var[1] - var[0]) / int(var[2])
-                xend = var[1]            
-                
-            else:
-                xvec = np.hstack((xvec,np.ones(int(var[1])) * (var[0] - xend) / int(var[1])))
-                xend = var[0] 
-                
-        return x0, xvec
-        
-    #%% Start with dx block
-    # First line specifies the number of rows for x-cells
-    line = fopen.readline()
-    nl = np.array(line.split(),dtype=float)
-
-    [x0, dx] = unpackdx(fopen,nl) 
-    
-
-    #%% Move down the file until reaching the z-block
-    line = fopen.readline()
-    if not line:
-        line = fopen.readline()
-        
-    #%% End with dz block
-    # First line specifies the number of rows for z-cells
-    line = fopen.readline()
-    nl = np.array(line.split(),dtype=float)
-
-    [z0, dz] = unpackdx(fopen,nl) 
-    
-    # Flip z0 to be the bottom of the mesh for SimPEG
-    z0 = z0 - sum(dz)
-    dz = dz[::-1]
-    #%% Make the mesh using SimPEG
-    
-    from SimPEG import Mesh
-    tensMsh = Mesh.TensorMesh([dx,dz],(x0, z0))
-    return tensMsh
-
-
diff --git a/simpegDCIP/Dev/Workspace/readUBC_DC2DModel.py b/simpegDCIP/Dev/Workspace/readUBC_DC2DModel.py
deleted file mode 100644
index 999daed6..00000000
--- a/simpegDCIP/Dev/Workspace/readUBC_DC2DModel.py
+++ /dev/null
@@ -1,56 +0,0 @@
-def readUBC_DC2DModel(fileName):
-
-    from SimPEG import np, mkvc
-    """
-        Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
-
-        Input:
-        :param fileName, path to the UBC GIF 2D model file
-
-        Output:
-        :param SimPEG TensorMesh 2D object
-        :return
-        
-        Created on Thu Nov 12 13:14:10 2015
-
-        @author: dominiquef
-
-    """
-    
-    # Open fileand skip header... assume that we know the mesh already
-
-    obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
-    
-    dim = np.array(obsfile[0].split(),dtype=float)
-    
-    temp = np.array(obsfile[1].split(),dtype=float)
-    
-    if len(temp) > 1:
-        model = np.zeros(dim)
-        
-        for ii in range(len(obsfile)-1):
-            mm = np.array(obsfile[ii+1].split(),dtype=float)
-            model[:,ii] = mm
-            
-        model = model[:,::-1]
-        
-    else:
-        
-        if len(obsfile[1:])==1:
-            mm = np.array(obsfile[1:].split(),dtype=float)
-            
-        else:
-            mm = np.array(obsfile[1:],dtype=float)
-            
-        # Permute the second dimension to flip the order
-        model = mm.reshape(dim[1],dim[0])
-    
-        model = model[::-1,:]
-        model = np.transpose(model, (1, 0))
-        
-    model = mkvc(model)
-
-
-    return model
-
-
diff --git a/simpegDCIP/Dev/Workspace/readUBC_DC3Dobs.py b/simpegDCIP/Dev/Workspace/readUBC_DC3Dobs.py
deleted file mode 100644
index ca83513b..00000000
--- a/simpegDCIP/Dev/Workspace/readUBC_DC3Dobs.py
+++ /dev/null
@@ -1,69 +0,0 @@
-def readUBC_DC3Dobs(fileName):
-    
-    from SimPEG import np
-    """
-        Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
-    
-        Input:
-        :param fileName, path to the UBC GIF 3D obs file
-    
-        Output:
-        :param rx, tx, d, wd
-        :return
-        
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-    """
-       
-    # Load file
-    obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
-    
-    # Pre-allocate
-    Tx = []
-    Rx = []
-    d = []
-    wd = []
-    
-    # Countdown for number of obs/tx
-    count = 0
-    for ii in range(obsfile.shape[0]):
-        
-        if not obsfile[ii]:
-            continue
-        
-        # First line is transmitter with number of receivers
-        if count==0:
-    
-            temp = (np.fromstring(obsfile[ii], dtype=float,sep=' ').T)
-            count = int(temp[-1])
-            temp = np.reshape(temp[0:-1],[2,3]).T
-            
-            Tx.append(temp)
-            rx = []
-            continue
-        
-        temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
-        
-            
-        rx.append(temp)          
-        
-        count = count -1        
-        
-        # Reach the end of  
-        if count == 0:
-            temp = np.asarray(rx)
-            Rx.append(temp[:,0:6])
-            
-            # Check for data + uncertainties
-            if temp.shape[1]==8:
-                d.append(temp[:,6])
-                wd.append(temp[:,7])
-                
-            # Check for data only    
-            elif temp.shape[1]==7:
-                d.append(temp[:,6])
-            
-    return Tx, Rx, d, wd
-                
diff --git a/simpegDCIP/Dev/Workspace/writeUBC_DCobs.py b/simpegDCIP/Dev/Workspace/writeUBC_DCobs.py
deleted file mode 100644
index c559b166..00000000
--- a/simpegDCIP/Dev/Workspace/writeUBC_DCobs.py
+++ /dev/null
@@ -1,49 +0,0 @@
-def writeUBC_DCobs(fileName,Tx,Rx,d,wd, dtype):
-    
-    from SimPEG import np, mkvc
-    import re
-    """
-        Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
-    
-        Input:
-        :param fileName, path to the UBC GIF 3D obs file
-    
-        Output:
-        :param rx, tx, d, wd
-        :return
-        
-        Created on Mon December 7th, 2015
-    
-        @author: dominiquef
-    
-    """
-    fid = open(fileName,'w')
-    fid.write('! GENERAL FORMAT\n')  
-    
-    for ii in range(len(Tx)):
-        
-        tx = np.asarray(Tx[ii])
-        rx = np.asarray(Rx[ii])
-        nrx = rx.shape[0]
-        
-        fid.write('\n')
-        
-        if re.match(dtype,'2D'):
-            
-            for jj in range(nrx):
-                
-                fid.writelines("%e " % ii for ii in mkvc(tx))
-                fid.writelines("%e " % ii for ii in mkvc(rx[jj]))
-                fid.write('%e %e\n'% (d[ii][jj],wd[ii][jj]))
-                #np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n') 
-        
-        elif re.match(dtype,'3D'):
-                                
-            fid.write('\n')
-            fid.writelines("%e " % ii for ii in mkvc(tx))
-            fid.write('%i\n'% nrx)
-            np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n') 
-        
-        
-    fid.close()  
-            
diff --git a/simpegDCIP/Dev/animation.html b/simpegDCIP/Dev/animation.html
deleted file mode 100644
index 22cc20f8..00000000
--- a/simpegDCIP/Dev/animation.html
+++ /dev/null
@@ -1,191 +0,0 @@
-
-<script language="javascript">
-  /* Define the Animation class */
-  function Animation(frames, img_id, slider_id, interval, loop_select_id){
-    this.img_id = img_id;
-    this.slider_id = slider_id;
-    this.loop_select_id = loop_select_id;
-    this.interval = interval;
-    this.current_frame = 0;
-    this.direction = 0;
-    this.timer = null;
-    this.frames = new Array(frames.length);
-
-    for (var i=0; i<frames.length; i++)
-    {
-     this.frames[i] = new Image();
-     this.frames[i].src = frames[i];
-    }
-    document.getElementById(this.slider_id).max = this.frames.length - 1;
-    this.set_frame(this.current_frame);
-  }
-
-  Animation.prototype.get_loop_state = function(){
-    var button_group = document[this.loop_select_id].state;
-    for (var i = 0; i < button_group.length; i++) {
-        var button = button_group[i];
-        if (button.checked) {
-            return button.value;
-        }
-    }
-    return undefined;
-  }
-
-  Animation.prototype.set_frame = function(frame){
-    this.current_frame = frame;
-    document.getElementById(this.img_id).src = this.frames[this.current_frame].src;
-    document.getElementById(this.slider_id).value = this.current_frame;
-  }
-
-  Animation.prototype.next_frame = function()
-  {
-    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));
-  }
-
-  Animation.prototype.previous_frame = function()
-  {
-    this.set_frame(Math.max(0, this.current_frame - 1));
-  }
-
-  Animation.prototype.first_frame = function()
-  {
-    this.set_frame(0);
-  }
-
-  Animation.prototype.last_frame = function()
-  {
-    this.set_frame(this.frames.length - 1);
-  }
-
-  Animation.prototype.slower = function()
-  {
-    this.interval /= 0.7;
-    if(this.direction > 0){this.play_animation();}
-    else if(this.direction < 0){this.reverse_animation();}
-  }
-
-  Animation.prototype.faster = function()
-  {
-    this.interval *= 0.7;
-    if(this.direction > 0){this.play_animation();}
-    else if(this.direction < 0){this.reverse_animation();}
-  }
-
-  Animation.prototype.anim_step_forward = function()
-  {
-    this.current_frame += 1;
-    if(this.current_frame < this.frames.length){
-      this.set_frame(this.current_frame);
-    }else{
-      var loop_state = this.get_loop_state();
-      if(loop_state == "loop"){
-        this.first_frame();
-      }else if(loop_state == "reflect"){
-        this.last_frame();
-        this.reverse_animation();
-      }else{
-        this.pause_animation();
-        this.last_frame();
-      }
-    }
-  }
-
-  Animation.prototype.anim_step_reverse = function()
-  {
-    this.current_frame -= 1;
-    if(this.current_frame >= 0){
-      this.set_frame(this.current_frame);
-    }else{
-      var loop_state = this.get_loop_state();
-      if(loop_state == "loop"){
-        this.last_frame();
-      }else if(loop_state == "reflect"){
-        this.first_frame();
-        this.play_animation();
-      }else{
-        this.pause_animation();
-        this.first_frame();
-      }
-    }
-  }
-
-  Animation.prototype.pause_animation = function()
-  {
-    this.direction = 0;
-    if (this.timer){
-      clearInterval(this.timer);
-      this.timer = null;
-    }
-  }
-
-  Animation.prototype.play_animation = function()
-  {
-    this.pause_animation();
-    this.direction = 1;
-    var t = this;
-    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);
-  }
-
-  Animation.prototype.reverse_animation = function()
-  {
-    this.pause_animation();
-    this.direction = -1;
-    var t = this;
-    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);
-  }
-</script>
-
-<div class="animation" align="center">
-    <img id="_anim_imgBYBDWVXULSOEHXUW">
-    <br>
-    <input id="_anim_sliderBYBDWVXULSOEHXUW" type="range" style="width:350px" name="points" min="0" max="1" step="1" value="0" onchange="animBYBDWVXULSOEHXUW.set_frame(parseInt(this.value));"></input>
-    <br>
-    <button onclick="animBYBDWVXULSOEHXUW.slower()">&#8211;</button>
-    <button onclick="animBYBDWVXULSOEHXUW.first_frame()"><img class="anim_icon" src="data:image/png;base64,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"></button>
-    <button onclick="animBYBDWVXULSOEHXUW.previous_frame()"><img class="anim_icon" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAgyTCyQ6wAAANRJREFUKM9jYBjO4AiUfgzFGGAp4+yayUvX6jMwMDCsYmBgOCS4OAOrSYmMgcc8/pd5Q3irC+Neh/1AlmeBMVgZmP8yMLD8/c/cqv9r90whzv/MX7Eq/MfAwMDIwCuZdfSV8U8WDgZGRmYGrAoZGRgY/jO8b3sj/J2F6T8j4z80pzEhmIwMjAxsSbqqlkeZGP//Z8SlkJnhPwMjwx/Guoe1NhmRwk+YGH5jV8jOwMPHzcDBysAwh8FrxQwtPU99HrwBXsnAwMDAsJiBgYGBoZ1xmKYqALHhMpn1o7igAAAAAElFTkSuQmCC"></button>
-    <button onclick="animBYBDWVXULSOEHXUW.reverse_animation()"><img class="anim_icon" src="data:image/png;base64,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"></button>
-    <button onclick="animBYBDWVXULSOEHXUW.pause_animation()"><img class="anim_icon" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkR91DQ2AAAAKtJREFUKM9jYCANTEVib2K4jcRbzQihGWEC00JuNjN8Z2Q0Zo3VYWA4lL005venH9+c3ZK5IfIsMIXMBtc12Bj+MMgxMDAwMPzWe2TBzPCf4SLcZCYY4/9/RgZGBiaYFf8gljFhKiQERhUOeoX/Gf8y/GX4y/APmlj+Mfxj+MfwH64Qnnq0zr9fyfLrPzP3eQYGBobvk5x4GX4xMIij23gdib0cRWYHiVmAAQDK5ircshCbHQAAAABJRU5ErkJggg=="></button>
-    <button onclick="animBYBDWVXULSOEHXUW.play_animation()"><img class="anim_icon" src="data:image/png;base64,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"></button>
-    <button onclick="animBYBDWVXULSOEHXUW.next_frame()"><img class="anim_icon" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkd/uac8wAAAMhJREFUKM9jYBie4DEUQ8B+fEq3+3UrMzAwMFxjYGBgYJizYubaOUxYFUaXh/6vWfRfEMIL/+//P5gZJoei4/f/7wxnY1PeNUXdE2RgYGZgYoCrY2BBVsjKwMDAwvCS4f3SG/dXxm5gYESSQ1HIwvCPgZmB8f8Pxv+Kxxb/YfiPJIdi9T8GJgaG/38ZFd4Fx0xUYsZt4h8GBgb2D2bLy7KnMTAwMEIxFoVCXIYr1IoDnkF4XAysqNIwUMDAwMDAsADKS2NkGL4AAIARMlfNIfZMAAAAAElFTkSuQmCC"></button>
-    <button onclick="animBYBDWVXULSOEHXUW.last_frame()"><img class="anim_icon" src="data:image/png;base64,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"></button>
-    <button onclick="animBYBDWVXULSOEHXUW.faster()">+</button>
-  <form action="#n" name="_anim_loop_selectBYBDWVXULSOEHXUW" class="anim_control">
-    <input type="radio" name="state" value="once" > Once </input>
-    <input type="radio" name="state" value="loop" checked> Loop </input>
-    <input type="radio" name="state" value="reflect" > Reflect </input>
-  </form>
-</div>
-
-
-<script language="javascript">
-  /* Instantiate the Animation class. */
-  /* The IDs given should match those used in the template above. */
-  (function() {
-    var img_id = "_anim_imgBYBDWVXULSOEHXUW";
-    var slider_id = "_anim_sliderBYBDWVXULSOEHXUW";
-    var loop_select_id = "_anim_loop_selectBYBDWVXULSOEHXUW";
-    var frames = new Array(0);
-    
-  frames[0] = 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"
-  frames[1] = 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"
-  frames[2] = 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"
-  frames[3] = 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-  frames[4] = 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49n1OMjKc0t5bWh/zY+UACwsDbnuQ3z8Ar1vL43FMhIyOC7l1dRW11L2sURAi5+Lkx5bTJpcWnsX3mAkpwSALzCPOnQpQPDHh56xaXnWkNybDJfPvE1VaVVTcLzb1naGaYAKFVKzMzNUFuosbCxwNLWAmsHa2wcbbBztUVppjKE64t/P4VSgUKhQKlSUJ5fTllBORVFFVSVVlNTUYPaUk1xVnGTkRjugW7YutjReUg4fSb3wbWDyw18J24sCehCCCGEEOJWIwG9lVwa0BUKBXq9nkkv38WI2cObPX7jol/Z+OEmNJWGyujenbx4aes/mhxXkF7A/+at4NSu081ex8HTgdKcUlz9XLCwsTCpkK5UKY29tQqFgvHzbmfUYyNQmak4tfs0ix9YgrO3E47eTrgHuHHHvNv54rGvyEjMNA6TH/vUGG7/+zhjz2ydpo6f3lnPzqU7sfdwwL+rH/ctnEHizlN8+uBnxnsrlIaQOfZvoxn9+Khrmpd+Kb1ez5Gfj7L8+ZVUlVRh42yDo4cDk16eSMf+YSTuPMWxX49zaHWsceRAcO8ghj00lG5jIo0F9lpTTlIO6xf+go2jzcWQbY21gzXWTtbG0G3tYKhYb25l3ur3b6DX68lJyuX07tOkHj1P9tkcqsuqKMxonCJhZmGGm78rPW7vweBZA7Fzsbth7WltEtCFEEIIIcStRgJ6K/ltD3qP8d2ZseBerOytmj1+93/3sOGDX/AM9SQnKZeS7BKe/eFpQqJDmhyr1+vZu3w/q1/9ARQYe20VCgV69HiGelKcWYymUoN3J29KskuoKq2i94RexG88Rl1NHWF9Qzm7/xzdx0XxwEeziF1zmG+eWWa8R3DvIP6+Zg7aei2LH1hC4s5TBHYPIDk2han/vpvBswaZtCnpUDJfPbGUosxieozvzvQF03hj9FsUXijitzyCPZj+zjRCm3ltLVWUWcxXTy6lvKCc3OQ8lGZK7nt/BtGT+gBQWVLFvhX72PHVLmNxO2cfJ+547nZ63dELlbr1g3pbpdVqObklgbj1cSQfTqUoswilQkF9nRZrBytc/Fzpd08M9u72ZJ3OIv1kBllnsrn71Ul0Hdn1ZjffSAK6EEIIIYS41fzRAb1drIN+PRrWq2vQd2oMHft3vOzxPuHeHN90wjA/WVOLR7AHZ/edJXpKH1Qq0zCpUCjw69qBPhN7k30um7zkPJP9FYUVWNhYoFQpKckuoU5Th5W9FQ8veZDoSX3IS8nj7L5zAOScy6Ewo5D6Oi1luWXU19ajrdfSeWgEkSO7olQq6TY6ksSdp0g9eh6FQoGVvRVhfUNNeoOdfZzpfVdv47WPbzqBd0dvsi/OYQdQminw6+aPlZ0l69//hfzz+QR2D8DS7toLnVnZWxEzJZqy/HKSDiaj1+nJS8nDztUOrzAvzC3VBPcKYsj9g/Dt7EtpXim5yXmc2XOO3f/djUqtwruj9y0R1JVKJZ4hHkSNjWLYg0O47ZmxdB8XhVarIycpl4K0AhK2JXL0pziSDiSTm5xrfKDjEexxs5tvJOugCyGEEEKIW42sg95KLu1Bdw9yZ/6ul5oscfZbC6f8nyE4X6zq7hrgSkjvYGa+P/2yxb70ej0v9X2FwnTTnmq1lRq9Vk99bb2hDYFuvLrnFQAqiytZNPNjzselYe1gjXMHZyoKKgyV2VUKArr5c9c/JhAa09jDfeFEOp8+tAQzczPyUvKZ8MIdjH5iVJP26HQ6Vr/6I9s+346VgxU15TXG+e+2rrb4dPJm1GMjWTV/NdlncwjtG0pIn2CGPzwUGyeblr25lzgfn0bSoWS2LtlGbU0tmgoNj339VyIGhzc5NjMxkz3/28ve5fupq6nDztWO4Y8MY9B9A7Cya35kwx+htrqWvNR88lLyyD6XTdbpbKpKKqkqq6GmogZrByvK8sows1CjtjDD1d8VSztLfDp5ExIdgm+ED2qLxikDOp2O3OQ8MhIzCO0TgqPXlSvoV5ZWsuShz40PbRo4+zrTZVhndDod2WdzyD+fT4/borjnktoIfyTpQRdCCCGEELcaGeLeShreSJWZilf3vYyLz5WLc1UUVfBc9xeN64k3sHe3J6C7P9Pfnoa9m32z5276aDM/vrHW8MslS7aZW6lx8HBAr9Pj182Phxc/aDynpqKGxQ8uIScpl9KcUpPrmVmY8e7Jt7GwtjDZ/uUTS4n98TBgmOv+r/2vXnZJsy2Lt7Jr2W4K0gpx9nYyzIPGMBf97fg3sLa3Zv/3B9jwwUaKM4uxtLVk5GMjiJ7UBxfflq1prtPqeKnvK5QXVhDYM4Bz+5PQ6/RYWJvz7Jo5dOjcdDk5gLL8MrZ9vp2dS3dTU1GDlYMVwx8ayqjHR5oE3Wul0+nY9fVuosZ2a7KsXHV5NYUXCim4UEjBhQIKLhSSfSab8qIKss9kX/G6ZhZm1Gvqjb+r1CpjlXy1hRl1tfVY2loaqtt39iX9ZLpx2sPwh4cyef6kFrV/2xc7+P7lVc3eEwx/u7FPjWH8329r0fVamwR0IYQQQghxq5GA3koa3sghDwzmntenXPX4lMMpvHPn+83uU6qUWNiY4+rvRteRXQjrG4oCBRdOXuDYL8dJOpSMtaMVVSXVzZ4PMHBGf+59e5rJNk2VhvcmfkD6CUNVeBsnG8yt1HiFefHkt48DsH52YwCfWbKMe/bsMf5+f1QU0b6mIVj/6Rjjz6teXc3WJduNv7t0cMHR25FxT40x9nDXaerYv/IAe5fvI/NUFrp6HRFDwxk4oz8RgyOuWExOW68lbn08u/67m3P7GyvXu3RwwdrRijmrnr7iOuGVJVXsXLqLbZ9vxyvMk6KMIsY+PZa+U6Kva+j7uYNJvD/xg8YNFwdMKBQKPEM9mw3iCqWhwr7STImdqx2Ono7YOVoZKrnb2GHtYI2ZjTVqSzU6FJiZq9GUV1GQVUpRag6a/AIyT2cZRyn81uRXJhLaN5SizCIsrM0JH9R0ZMGlDnx/kG/mLOPet6bSeVgEb4x6m8riSho+ph7B7ox6bAQxd8f84Uu4SUAXQgghhBC3GgnoraThjXx2zTOE9A6+6vFH1h3l80e/xD3InbyUvKseD+Dk7URxVjEuHVwozCjEys7KZGmzBmpLNb0n9GTmezOa7CvJLuHVwa9Tc7F6PMDfVj5J+ADDfPlLA/pb/Mysffs4WVZGB3t7FAoFz8TEmAzdvzSgr13wE7v/u4eq4iou/TPf8dx4xv5ttEk7tPVaTm5LYM+yvSRsT8TFz4Xy/HK6DO9Mj9u703loRJMe/Utln8th93/3sHvZHmPPr0ewO4989jDeHa88V0NTpeH4phNs+GAjOedycPV3ZdzTY+gzsfc1VX3X6XSc3XuONW+uJT0hA129DpW5Cm2tFoVKgV7buNSd0kyJk5cTBWkFzV4rMMqP1PgLJtss7KzQlBv+vt7dgtHr9HQf2RHPEA/MLdT8+OZacpMu/29HoVDwn9QPrvrwoSizGEcvB5RKJSU5Jbxzx3sUZRXj6OGAZ6inYRk/PQT1CmTMU6OJuEroby0S0IUQQgghxK1GAnoraXgjX9j4HH5dO1z1+C2Lt7L69R+NS7JdOoz5cmydbagsriJsQBhndp+h06CO5KfkG4eTm1DCPa9NIXpSH5NK8oXphfxr5Jsm63f7+Q0kMnI6AN7e3sbtb/EzG86f58GtW9FoDW17bdw4oi7pRc/KalzW7ZPYWI7l5qI0U2JhY0F1aTVqSzX+3fyZ+M8J+HfzQ6lq2gtbnF3CkXVHiNtwjJTDKQT1CuTC8XRCooOJGBJOxJAIvDt6NTunv6ayhg+mfGhcJx0gNCaEgTMHEDW22xWHsOu0Og6vPcL69zeQl5qPe5A7tz87jp7jezTbzsvR6/U8HfYs5pZqbJxsjcvbqS3NAAXW9laYW5njGeaBpqoeGycbMqx7YO7khLmjExbm2Zg72hFsdgFtvY7aetDWaamv06Gr11FVVIFFXjKZZwqpKawifs8FzG0siRjbB2sXew58vh6dtunHyi/Sjxd+mdfi19GgJLuE9yZ9wOBZAxnxyHD0ej2xaw6zdck20k9moLZUEz4onNufHYdvhM81X7+lJKALIYQQQohbjQT0VnKtAX3t2z+x8cNfW3Rta0cr9HqoLq0mpE8wOUm53DHvdrqN7cYnsz7lfNz5y56rUChwD3an04COmFmYcXrXGYqzi1Fbqo1z0VUqc2JinsHJKbBJQK/X6YhYvpyCykoAevn58fKYxl7zhoBeq9Xyxu7d5FZUoAesHaypKq0CDHPja2vqsHGwJrhPMG7+rvhE+GDnaoernysuvs7Goe0l2SWc2XeWYxuPc3rPGeMIAUdPx4thPZxOAzth42htbEN1WTVv3fYO5pZq8s7no9PqqNfU03lYBGF9Q+l7T8wV1//W1ms5tDqW9R/8QuGFQrw6enH7nHFEjevW4mHdmirNFXv8jffC0Ju9uWKkcZui3DDPPwxD0bY6DKMY9DTeO4BUwzEe1iTGZrLyxyJO/rSf6pIKAnoEkJmYiZ2rnXGJuQbdxkYy853p11yQT6fToVAomjwUqautY8cXO9n97V7yz+dj42hD93FRjHtmDE5eTtd0j6uRgC6EEEIIIW41EtBbybUG9JNbE/jovk+Mv1vaWdJzfA+6juyCnbMdqXGpFKYXUVFYgbOvE6BAoVTQfVwU7oFuxrnWcyOfp6KwokVtdPRyRFevxdrRBks7S7LPZqPGhfr6aszNbRgw4AV8fRvb/hY/AzDn2DGWHjxo3P7J3Xfj42goitYQ0A9kZLA0Ph4LlYrggWFY2FoS93McAH0m9qbL8M4kH07h9O4z5KfmE9QzgKRDKcZrOng64NrBBd/Ovti62OIe4IZLBxeqyqpIPZJK4o5TXDiejl6vR6FU0HN8D3w7+xIxOBzfzj4kx6bw3l0LARj9xEicvJyIXXuY5EMpqNQquo+LYuDMAYTGhFy2ur62Tsv+7y4WsssqJmZyHwbeN5CgnoEten9b4vcG9E5ehqB9iGjqa+tJ3nWMhBUbif/l2GXvqVKruOdfUxg4Y0CrvQ6AiuJKNn74K7FrDlOWX4aTtxMxU6IZ9dgILG2ufSm935KALoQQQgghbjUS0FvJtQZ0gPhfjpF2LA2fcB8iR3U1WWe8JWoqanim49+NvzcMrb4cF5eOKJVm6PU6LC0d8fLqRWzsf4z7o6Pv54UD45ucV1FcwYO+D2NhbYFvuA+d+odz35uG+e3K2RsBeHn9euIzM43n9OvQgX3p6ZgrlUTP6Me9b0417qvT1JGbnEteaj6F6YUUpBVSkF5I4YVCbJysSTmcanJ/Kwcr3APccPRyxNLGguLsUpQqBad3nwHA3s2O8EHh5J/PJ+VIKpa2lrx+4FVsnWzIPJXJ7mV7ObjqEDUVNXiGeDBw5gCiJ0eb9MJfqk5Tx74V+9nz7V4yEjLpOrILd8y9Hd/LVIm/HA2NIXW3xhCOtUXxALiTb9znQQ4tVY3VxXMa554HejmTlVLIs6OWUFetMTleqVKi1+vR6/R0GxvJg4vuv2IhvuuVl5rP+vc3cHzzCWqra/GP9GPoQ0PpOb77dReXk4AuhBBCCCFuNRLQW8n1BPTfS6fT8Wz4PGoqaq5+MBAUNIqKimw8PLri5zcAhUJJSso6EhM3oFKp8fLqwqsHHmt2ibjVb//A8pdXUF9bj5WdFZ+nL8HGwQbl7I2czMrixZ9/xsfBASdrawrKy4l0d2dTiqGHvKWV7RteU2lOKfnn88lLzTd8P59PfmoB+efzUZopjfPnrR2tsXOxpb5OS0l2iXEOv5mFGWF9Q5n8ykQ8Qz1RKBTUVGo4vPYIe5btIe3YBdSWanqO78Gox0fiFerZbFsahr7//P4GijKKGDhjAGOfGo2Td8uGcv+RAR3ghL4zqx//gJNr9xr3hfYN4YGP7+ebp5dxaucp7FztePbHZ/AIcm/xPa/VhRPp7PhyB8c3n0BTVUtAlD/DHx5K5OjIawrrEtCFEEIIIcStRgJ6K7lcQD+16zRVpVX0HN/0za0srmT1az8w6eWJV50shrFBAAAgAElEQVQjXFOp4ddFmxj+8FBsnW2N209uTWDjok3YOhnmAifuOsXxTSfwCfcmOTbFuEY6wKBBf6NTpxEm1x28oJS3Ji4g61wWhemF9L+7H3NX/p3fKs4p5pGAv1KnqQPgvrdmMP7p8Wjrtbw6+jVO7T1tPPa5ESMoqqxkb2oqZTU1BNjbMyMy0qTi+/VoCO8ZCRmkn8wgPdHwvfBCISgwVmC/tNiee5A7UWMi6TY6koAeASiVStKOpbF72V5SDqeQfTaHiKERDL1/MBFDw5sNkHWaOvYu38epnadJ3HGKflP7MvqJkTj7NF2/fdMnWzi0L5/Q6RNxCLTF3N7wd7WjDAAfMpuc83tVYPj34EIh9XVa/vfUj5w9dB4wPKyY9uZU+t0Tw68fbWbtm+tQmin561eP0GVo51Zvy29lnclm44cbObktkfraegJ7BjLikWF0HhZx1bAuAV0IIYQQQtxqJKC3kuYCen5aAe/c8R7lBeUMmN6PyfMnGQuJFWcV85/pH5F9NoewfqH89ctHsLKzanJdvV5P3Pp4vpmzDE2lhrD+oTz+9aOXHQ5fklfKkgc/I/XoeZPtCoUKX98o+vefjb19Y4/xuE/rObj2EG9OeMu47dUt8+k2PLLJtT+e/QmblmwGIHJEJDYO1lw4cQGUCrLPZaPT6vAO9eKjwbfxv8OH+T7e0Fsc5enJX3v1+t0B/XKqSqvISMwk/UQ6J7clGIe+A5iZq9DW69Dr9Ni52NJtTDdi7o4mqGcgtdW1HP05ju1f7iT9RDrugW4Mvn8wfe+ObvZvodPqOLzuCL98sBE7Nzs8gj0Y8dfheAQ29kZr67Ss3a+m5HQSJUmnUKqUhN47BndXw8ODGxnQHevz0FTU4uvhwvzbF+Hu54JzkCe7vt7NAx/PovedvTgfn8ZXTy4lLzWfqf+6m8GzBrV6ey4n7fgFNv5nE6d3nUKn1RHcJ4QRs4fSaWCnZsO6BHQhhBBCCHGrkYDeSn4b0CuLK/m/qf+htqaWkqxSNFUaJrx4J6MfH4ler+fdCQtJOZyCs48TVaXVOPs68eAnD+AdZrqGt6ZKw8v9X6Usr8y4rf+M/sx4e9pl29KwhJuZmRX19YYq6GZmFtTXa+jYtyNv7PqXyXrfer2ef9/xJvGb4uk8KAJLO0te+OH5JtfNOpfFgsnvAHD+eJrJPjd/N3w7+TBs1jAGTh3Atq+3s2nJJvSAc5aGF0aNwv+SJdn23aCwDrDk4c8ozCjC3MoCR08HKooqDKFdAWoLNXU1dTh6OhA9JZroib3xDPUk5XAq27/cQdz6eNSWasY+NZq+9/TF3rVp9XedVsfpvWc4+lMch9ccJnxwOH5dO1DedQy2vp7UmWkws7KkO0cpyy1j26LtRN7RjaDeATfsNQPU19YTtzaeurIKwvoH4RHkQjcfS957ci1pqeXc+9Y0AqL8qamsYcHt75J9NodhDw9lyvxJN7Rdv6XX60mOTWHTx5s5u+8c6PWExoQy4tHhhPUNNRbxk4AuhBBCCCFuNX90QFfNnz9//g2/y02QnZ3NkiVLGDhjAHW1dfxn+sfUVmnIS8nHI8SD8EEdmfDCnShVShQKBYE9Asg+m01VSTUVhRU4eDiyZfFWbByt6dDF1xhSzNRmFGcVk3Y8DTO1GTqtjoLUAjoPi8Dezb7ZtgR0D6CsoJzz8Y1V0nU6w7DvwoxClColXYZ0Me5TKBSE9A5h57e7uHDyApmnM+k6vCvu/m4m17VzsSN+y3FO7kgAQGmmRK8zPG+pKq3Czc+VWe/8BYCCjEJ+XLCGwvRCCisrmRwVhWNFY7X59PEhLXpfywrKOfpzHJ7BHiYPFa5IoWD7FzupLKlk7to59L0nhl539sK1gws1lRqKs4qpKa8hOTaZnUt3c3jtERzc7Rn71BiGzBqEQqXk+KaTrHv7J4qzivEI9jCZgqBQKnDzdyNyVFeGPTwUF19n6jT1ZOXrKTqWQOHxUxQdP4OyIBufzt50GtqRk7+cpOB8Id4RXldo+O+jVCnxDPOgvryKHV8e4MKxDMK7eTDkri78uvwk+1bsp9edPbF1tmXgzAGkHUvj0OpYLhy/QM87e1y2un1rUygUOPs403tCL0Y/MYqAKH+SYpPZungrv360mZTYFKydrPHw86S7/Y3/j5IQQgghhBBtRUOunD17Nl5eNy47NPjT96CP+dsoYtccoSS7BJWZEs8QT6rLa3j2x2dwcDcN1DlJufxn+kdYO1qTfTYbba0WBw8HY/EzG0cbYqb0Yctn2zi3LwkAhUqBjaMNddV1PPjxLLqO7GpyzfWzzdDptMTGfkR+fiKgQGmmQFffWN3dPcCNlze+hG9H06rkix/7lI2fGNZm7zG2By9v+GeT13nop1jeuOPNJtvNzM1w9nbita2v4hnkSW5qLrODHjXuTzpzL+XPNo4CONDCHvSPZn7CyW0JPLHsMToPjWjROdVl1fy963Po6nX8bfkThA/q1GT/qV2nSdiewNH18dSU16C2VKOt19JlaGdGPDqcwB4BxG2IZ/MnW8k4mcGI2cMYOHMAbgGmDy1KcWx8TdmG5dG6cgKAnDM5JO1LxiPUg5D+wRxff4LK4ir6zoi+oWHYHcNa8F+9uIUTu9J54L0pBMRE8Na4dwjpE8ysD+8zTpH47uVV7P7vbsIHh/PoV7P/sJDeHJ1WR+LOU2z9bDuZiZno6nXkZeZhbd18tX0hhBBCCCH+bKQHvZU0POkoyiymKL2IkOgQ8lPz8Qn34a9fPIyjp0OTc2ydbek9oSf5qfmcj0uj04COZJ7KRKfVkXMul5xzORxee4Si9CJsnW0wtzLHwtqCisIKtHVaYtceAb2esH5hxmueWacjIeE7srIO4+ISRHV1MXqdHrWFGjNzM3Q6HfU1dZw/nsbQ+4ZQVlBG4u5EvEK86BDRgQ2LfkGv15Ofls/AaQOwczYd4l1dUcOWL7aiUDYGOaVKiU6vo7K4koNrDhI9oQ9u/m6seXetsWDbyJG+2B9tnGec0cIe9OryGk5uTcDK3oouw1tW1ExtoebM3jOgB7cAVwJ7BDbZ7xXmRbfRkYx6dAQd+4Wi0+nJS84jJymHfSv2s3/FfgJ7BHL3q5PpOKAjybHJLH9hJflpBQT1CjTWEri0UntGheH1NVRYt3W1xa+7HzVl1cStice/px/WjtYc+PYgAb0DUKqub/mxq7GhCoVSQY+RwSgtHFj52s9UlVVz14sTWP/+BjJPZdH99igUCgWdh0bg4GkYvXF2/zliptzYhwdXolAqcA90J2ZyH4Y+OITw6AjGRo69KW0RQgghhBDiZvije9D/9AG9uswwnzwzMZPbnh3H5FcmYufSdB5zAwsbC7oM74yrvytn9p6lLL8MW2db41JiDWqr6wxfVbUm28/tT2LLkq0cWHWQ45tPcHz/FioqcrG396aoKJmgoIHozEsJjAogJykH9Iblw/JS8yjOLubrud+w9ctt9L6jF76dfMk6m4WFjQVKMyWaKg1Ro6IAKM4t5vt/reLr576huryahoEQZmoznls9F1dfV07vNVSsP7jmEDF3xZCXlo+9qx2h3kqCAu3xPWdhbHdLA7q9mz3bPttOeUE5wx4a0uLwWJhexPFNx1EqlfSZ2PuyxymUClw6uNBtdCQjHxtBWL8wNBUa0k9mcHzTCbZ9th0LWwsm/mMCvSf0Ii85l/89twKVSklAd39qFY3F5H4b0I2vwd0e/55+ZJ7MovhCMQG9AohfE49XhBdm5mYtej3XwoYq489RY6IJjQ5g9b9/5vTuM9zx3Hi2fr4NGydbAqL8AfDr0gGvUE+2frqNxJ2n6HtPzE3tSQfDQx8vf2+ibKNuajuEEEIIIYT4I8kQ91bSMBQBoM+k3kSO6EqP8d2vKejUVGo4tOoQ6xb8RGVJ1dVP+A0bJxv0GmusrFzQamvxDh1N7xFv0Peh5bw09GXqNHWU5JaanKO2NBRNcw9054P498lJzmFOD8Mya44ejsx69z7iNsZz4IeD6PQ66mrqCIj0NxaJ8wjy4NPkT9Dr9Syd+zVr31uHR6A7HkGeuPg4s/2bHQCMe2Isr9c2Lj+nuVgw7lgLhrr/e+SbZCRm8s8tL+AT7tOi9+LYpuMsvn8Jzr7O/Pvgay0651KaKg2HVsdyYNVBUo6kolQp6TWhJ0P+Mpg6TS37VhykvLAMz7lfYe1tmCrgWL8RgEBSL3tdbZ2W2JWH2fv1PjRVGmydbXALdiNsUBg9J7b+EBZzDMvi1VdZ8J97P0Kv0xEzJZpfP9rM8+vn0aFL4zSH45tPsPiBJQyY3o9737p8EcI/ihSJE0IIIYQQt5o/eoj7jRnT24ZY2FgQMTjcOIT4WljaWNBxQBhWDk2X+GqJyuJKqqryKS5OxtzcDg+/AWSmbCElLpVh9w+lTlPf5Jy6GkOAy0vNY7rjTF4c9E+UZoY/U0luCUse/4yd3+5CU60hLNowlD7rXLbxteWm5FKSW4JCoWDWO39h0gsTqSiu4PjW4yTuOWW8T8ap619erMft3QnuHcT5+AstPqehGn5RRhE1FTVXObopC2sLBs4cwNy1z/LS1heJGBzOoVWxLBj/Lgsnf8jBVQdJ3H6Kbbf3ozDuUIuvq1KriJkRjX9PP7QaLaXZZSTtSWbHJztZ+vA3xr9Ha/MM9uD59XOp09SRcjgV747ebPl0C5c+L4sc2ZUXNj5HcmwK6xb8zJ/0WZoQQgghhBDiotYfz9vGOHo6sPRv37B1yTb6Te1L1NhuOHo6XvEcnVZHUmwye7/dx7GNx6mrbRrSLO0ssbSxoCSnsQdcaaY0Kf5mvJ6ujvLyNLavno6tky1bVhqqp9teUom8OXq9vkmYrSqrxtHDkZLcEvLS8giMCmTMo6OJ3xRPfnoB1WXVpCem4+jhiEKhYMa/p1OSW8rWL7eSm5KLrbMtvuG+WDtYk35Jb7lytqG3+Y7Nhu/rRl6+J73n+B6sW/Azdq529J/W94qvoYGLnwvBfYJRmSkpzS3F0tby6iddhndHbx7/5lFyU/OYP+A3vfF6PYefuo+Yj1+he6eSFl/zzvl3UJReTO6ZXABqymvw7OiJ2lJ93e1sTi2G62VlZwMw7/OpvDLpSybNn8Sql1cRMyWa8EHhxuM7dPblme+fYtGMj6mpqGHKq5Nu+nB3IYQQQgghxI3xpw/oucmG+ccqtRkr//k93720iqBegbgHuuHk44yzjxMAtdW1lOaWkXkqi/Px56korKBD1w5oqjT4dvYhI8HQ42zvbs/oJ0Yx4N5+7F62l1XzVxvv1Vw4b2Bp6UB1dQlW9lZUFBsCuqWtFRXFlS16HdYO1lSVGobZdxnSmdK8Uob+ZSiD7h2ImdqMzDOZ7F99AIC0kxfoOtRQTV6hUPDo4tkUZRYS92u8YQ3yvaexdrj+StzuQe64Bbhxatdp6mvrWzRvW6lUUpxVTFFG0XVNF2iOR6A7z62fy9u3vWPcplCr6fTkDKpzCjiWlEDnkZ0xs7h6+9SWakb8bTgr53xHfW09Tr5O2LraUF1WjZX99Y2gaIlOffzx6+TOzq920vuuXqx//xeTgA6G4oVPrXyST2Z9yrK5/2P629NuWEE7IYQQQgghxM3zpw/ofpF+hgJsWcUAqMxVpBxOJTk2BWcfJ4oyDdu9OnqRfcbQq+kZ6klFYQVgGFKsUquY+PJdeAS6Ez6ok7FXddB9A8g5l0NGYibugW74d/PDv5sf7kHufP+YN+eTllJVcZbKikwcXXpTXJyGrZMt+Wn5AMah67+lNFPiE+aNhY0lKjMVHkEeJOxMMAb0CfPuJKSHaVE3306Nc5czT5sOXzdTmzHv+7nc5zaLOo1hNEBVaRUfPfIx4x4fS2C3QMa/ZTg2K7tl72vnYRHs+HInybEpdOwfdvUTACs7Q6/59Qxxv5yAKH/GzRnLhvd/MdzD1pyh4/1QmqlQFodw+Psj2LnbETEi3KTSfXOCogOZ8PqdxK6IZfrH93J21zlObDiJW5Arfj38Wr7uews4YBh5UY8XE58dwf/NXs7MhdP59tnlZCRm4hthOrffys6KJ5Y9xqcPf86XTyzl/g//gkrdeu0RQgghhBBC3Hx/+oA+fcE0LKzM2f7VTuI2xOMZ4sHZfecAcPZxNgZ0Cxtz4znugW4Uphfi0sGFya9MIiQ6GKWyaZhWW6iZvqD54l0e3iPw8B6Bt5c31VX5VFVlM+WtQCxtLFAoFLj4unDuUBL/eWBR4/Us1VjZWRHSK5gHFt5vsi760rlfs+bdtQCkHk1tEtB9OhkCnYO7A9p6bZP2WNlZ8dQ3T/HuPe8at23+bAubP9tC12FdGfxFDxwdLJqcdzldhnVmx5c7ObktocUBvWFY+28r4v9et88ZR3ZaKXGr96FQq1BeDNI2TjbEzIimKL2Ifd/sx7OTB0HRQVccIh4+rBPhwzoZf06NPU9VcSUX4tIxt1Lj3dm71YeY9x4TgbO3Ewe/O4RXR092Lt3V7L8rcytzHv3yEb57eRWleaU4+zi3ajuEEEIIIYQQN9efPqADeIR4MPXfd3P3a5PJTc4l6VAyOedysXawMg5/9u7kjVeoF56hngT3CuKBj2YZ19a+LpEXv3uBFW5Y4Ua/SabztX06+VBbU0tuah4xd0VzZv8Zvnp2KUd/iSO0TyjT5k81Htt1WFfWvLsWlZmKzLNZTW7n7u8GQGleKalxzVctH3B3P96f3nSe/IltJ9hxLpihI/0oq27ZcO7QmBDUlmoStiUw6aW7WnSOvZs9nmGemFubX/3ga6BQKJj84RP0mj6C1c99xuEV+4iaOtC437mDM/1n9SPnTA77/3sAr3BPAnsHXuGKjQJ7B5CVkEVecj5hg0NJOZCCg5cjrgEuv6vN+ov1GXUYHiYMum8g6xb8zKNLZ/PFo18x6eW7mp2nb2Zuxr1vTW2yXQghhBBCCNH+3RIBvYFSpcQrzAuvsBu/fl1LqFQqxj7aWIzNM8iDr+d+g06nY8+KvUx95R5jb234gE54h3lTkF7A8S3Hm1zr0rXdq8qqL3tPM7UZtfWma7f3ur0XA4f6XuaM5plbmdOxXxgntyVQmFGEi+/Ve3PL8svIOZuD6jJD+3+vgOhO3LnoYXa89QNlGYUMmNIFF//GIO3Z0RPPjp7knsvl2E/H8Ar3wj3E/arX9e7sjYWtBQUpBQT3DaY4s4Tk/cl4dvTExvnKhf5aatgjQ/nhX2tY8eJKAqL8Ob37DFFju7XKtYUQQgghhBDtg1Sauor62no0VRpyknLYvHjLDb2Xo4cjXYd1ASDzTCap8Y094dZ21uh1emqra8k4nYlOZ9oLbmFtYRyGX11++YD+26rko2eP4oUfn8PsOkJz5+ERACRsSzBuO77lBNu+2IG2zjDMvmHOO0BNhcbQVpvrr+B+NTYu9oxb8Be8ewSxd+k+Nr7zK4nbTqHTNr5fHqEedBvfjXpNPfHrjlGSdfVq7y7+Lvh19wPAyceR4L7BlOWVkXIghfpmlsu7VgoUKBSQf76A7KQcdi7d9buvKYQQQgghhGhfbqke9Gulrdfy1ZNfk3Umm+KsYjSVGmwcbeg3tQVLi3lf/H5JZ/3K176jsrSS6Dv70Klfp2aLjg2YOoAT208S1D2Qs4fOEdQ9yLjPN9yH7KRsaqtrKUgvwN2/sfdXoVAQFhOGUqXEwcOh2SbV1tRSeUnV+DF/Hc3sjx9BoVBQgS0AFbS8RzhyZFeOrDvK6T1nGHSfYUj5j/9aS865HLZ+upUxfxvNL//3KzPfu5fwQeHG4nC/Z4m1llAoFIQMi6TXUF8yT2ZyausZUval0KF7BzqPiGic1tDZG68IL9KOpJF2JI2Q/iHX1CPu1ckLnVZHenw6SpUS326+1z0/vbqsGjNLNXXVdZTmlFCSVULy4VSCe7VsKL4QQgghhBCi/Wv1HvScnByef/55hg4dip2dHUqlkp07d172+H379jFgwABsbGzw8vLiqaeeorKy6dJjer2eBQsWEBgYiJWVFd26dWPFihWt3XyT+/332W85+nMcOedyqK2qxdnHiTVvrCX+l2PXfj2djo2fbGTd+z/xj8EvcWL7iWaPi76zD2oLNUmHk9m7cq/JPt9wwzB0Kzsr8tMKmpybGp9K4u5Ekg6da/bauSm5Jr9vXPwrK1/77ppfSwNnH2f0Oj3HN50wVpiPntQbpUpJUWYx/3tuBeUF5Sya+QkHvj+ISq3EL7IDDh72133Pa6FQKPDt6svIp4fTb1ZfKgoq2LRwM3Fr4tFc7M1XKBQE9Aog8rZIMhOyOLb+OJVFLVv6DgzTJvx7+uMR5kHKwVRKs0uvq62VJVXGcK/T6lFbqTnw/YHrupYQQgghhBCifWr1gH769GkWLFhAdnY2kZGGSmmX61WMj49n+PDh1NTUsHDhQh566CGWLFnClClTmhz74osv8vzzzzN69GgWLVqEn58f9957LytXrmztl2Bss1+kYUhzx/5h6PV6irNKKC+sYO3b69j59S70ev3lL+Bk+pWdtpviHMNQamt7azoP7tzsafau9vh17gDAmf1nTYaIewR5YGlrSXV5NQUZpgFdq9WiqTKETiu75gu9XUi40GRbaJ9QAGoxv/hlQS0tL47X686eaOu0HNtomBc/5snR/Pvga0RP6gMKwxQBXb2Or5/+L3kp+RRnlWDj2Drztq+Fs68z/f/Sj/6z+lNTUcO2j7cT+/1hqksN0wEUSgVhA0PpMqozq57/gfdHfcAnUz7l02mf8eX9S5utjH8pc2tzgmOCMLcxJz8l3/gA4HJ0KNGhRI8CPQrqNHXUa+oJ7Wuozq+t0xK3Ps5kaL4QQgghhBDiz63VA3qvXr0oKiri9OnTPPPMM1c89sUXX8TFxYUdO3bwyCOP8Prrr7No0SI2btzI5s2bjcdlZmby3nvv8cQTT7B48WIefPBBfvrpJwYOHMjcuXObzMduLcMeHMIT3z5G8uEUwFANHCA3OY8VL37HCz3/ye5le64c1C9K3vodrn6uRI6IZPj9w1Cbq5scU1NZw69LNhmL2NXW1HJ6/xnjfkd3B+Mw8coS017eS9cWv1xAP73vDFx8VuIZ4smYR0fTc2yPq7b9Srrf1h2FUsHhn442ttPLkVkf3sdru18hsKfpEG2VWtWi9+tGcfC0p++MGPr/pR/aei2HVx3mxC8n0VRqjO2bvmgaDt4OFKQWkHcujw6Rvi1eA93K3gq3IDdqKmrIS85vccD27ujFwjPv8tAnD2BpZ4m2TktVaTUL7njvijUFhBBCCCGEEH8erR7QbW1tcXR0vOpxZWVlbNmyhRkzZmBra2vcft9992Fra8t33zUOvV67di319fU89thjJtd49NFHycjIYP/+/a33An6j85AI/r5mDn0m9abwQiEoQK8zBMzS3FL+99wKnu/xD07tOm16ot3FL3uoVZaR9Ms3FFwwVGDveVtjKC7NL2Xfqn3836z/MDf6OT6ZvZjssznG/Z8+tsT486U9z6X5pkOpywrKsbSxpEOELxGDIpq8juqKarZ/swMuZuPKkkqmzr/HuL+xB11NLU0fHlyOg7s9oTGhnN59moqiCpN9boFuzFv3LLM+vA+FyvBkoCS7hGVz/9fi698o9h72xEyLJurOKPQ6HUdWHyVhcyJ1NXWYmZsxdu5obF1tcPJ1wquLN4Vphdd0fQdPB9yCXClKL2q2CJ0OBToUaFGiRYlCocDcyhxzK3NjsT+9Tk/6yXTDvzshhBBCCCHEn95NKxJ34sQJ6uvr6dWrl8l2tVpNVFQUcXFxxm1xcXHY2trSqVMnk2N79+4NGIbK9+/f/4a11T/Sj3vfmoqjhwM5Sbkc32Q6f7wsr4wPpy0CBZipVXS/rTsV7rmYWdtR7uZO6qbVmNs7oaAWcwtz9n63j9Vv/kDW2Sxqa+ooLywHICwmDICkI0nGa2ecyqCqrApre2t0usae59h1h5n2SuN62NnnsqiprCE9MYNet5u+p7U1tbxx51uNAVoBf18+B0f3qz9IaYled/bg7L6zxG2IZ+CMAU32R0/qQ3pCBvtX7KeqtJp9y/eTciSVl7f947qLqrUWO1c7Im+LpOB8IZknMjn6Yxx2bnaEDQql9z190Gt1dBkZQWFaIWd3n8O3qw/WjtYturZCocA1wJXa6lryU/KxdrLGxunKw/vVlmo0VRqs7K1QqVVUlVZh7/7HzNkXQgghhBBC3Fw3bZm17OxsALy8mq5J7unpSVZWlsmxHh4eTY5rOPfSY28UC2sL7vrHBKa8OomQmJDmD9JDfa2W2B8Pc+rT1zmxcB5HP3mdCzt+ojInndqqWiqKK9j8+RZyknMozCyivLAcW2fDCIKcpGwcPRyJuSva5LLvTn0fbb2W0rzGXvOUoykmx2SeaXwPfDv5GH+uLq/mjTvfJPNMpnFb12Fd6Tai9dbY7j4uCqVKyZF1Ry97TPKhZKpKqxk8axAAOWdzeMz3SZ7r/iJfPPYVJ7clUFdTd9nzbzTXABe6jY+kQ6QvtVUa4tcdw7uzF/0f6AcYllkLHRBC0YUikvYlX3VO+qXMrcxxC/p/9u47ruqy/+P46yy2gCACCgooKO6B5sq9cmSW27JhVrbMus3Rr7rrttSsNDOblqXlaKgN0xy5t7hScW9wsgRZB87vD+IogQoKSPV+Ph487sP1vb7X9fke7PG4P99r+WDLspFwNoEs67WnvZvMJnq+dA//Xf0yb2x6HaPRyOzRxbPPgoiIiIiIlC7XHUG32WykpV1/s6scTk6FOzorJSV7Xa2jY94NyZycnOzXc+peq97VbZWEcpXKUbt9LQ5tPHTjykDymZPXaMeH8yeyN3qr2bIGBoOBiG4RODo7MH34F7nqRv4ayefPf4GLx5W15QajgbPHzuEblH3U2oWTF6hUM5C4s/FUrJadoJ87cY4J907k8LbDGIxXRqpbD2yZJ57MP/8pZCkCguIAACAASURBVN7EpAo3Lzeq31mNfaujSDyfiLtP7hHf1KRUTuw6iaunC33+1wuTg4kVn/wOZM8+2LpwG1sXbsPV05W390wodP8Atj/fNWVRsLXi15Jz9NqxrcdJOJNA1IooygZ44V/dL3tX+DoBZFozObblGI6ujlSsXbHAswDcvLNfxKQkpJBlS8fV05msfN6RtRvSxv551JIXmdBlIss/WUG7x9re0rOJiIiIiEjpdt1sbNWqVbRtW7CkICoqirCwsAJ37OycnWzm9wIgNTUVFxeXXHVTU1PzrXd1WyUlM92ap8wrwAsHZwfSU9JxdHEgNhbSLpwhKyPdXqd8cHlqtqyBZ3lPAmsF0OP57gTWCMSvih8ms4lzx8/xfP3/kBSXlKf9X95fhKfflSnptiwbX474khe/HQHAjt92cmLPSZzLOBPSMISN8zfywaPT8Pkzgc9ZN+/o4kizXs2K9PsAiLi7IXtX7iPy5+20frhVrmtrZ68nKzMLv2p+GI1Ger96H2tmriUjJfeIuatXwaaOFzeDwUBwoyCyrFkcWn+I2BMXSTybSPkqPpQNKIvJbKJK0ypcjr/M0c1H8a5cDg+/gk9Dd/ZwxpJlIOFcIt4eXji6XHvX/IphFXjpt1GM6/wWWVlZdHiifVE8ooiIiIiIlELXTdDDw8OZMWNGgRry8/MrVMc509NzprpfLSYmhgoVKuSqu3LlynzrAbnq/tW3r36Ps3vu0f1G90TQ6J6Ia9xxYzXb1uS3actIS06jTqfatB3cmtCmoRgMBi4nXGbz/C38+MFmHH38saWmkH4pHoPFgbTkVFbPWoPNYMPDx4PnZg6zj3ZfTrzMxD7v4OnvmW+CDhB/JvdmY+u/28DulX9QqWYgx3cfByCwRgDvP/wBp/ef5lJsEpdikwgIDyAjLQOTxURY49B8d3lPpAwA1pscga7TqTZhTUPZtWR3rgQ9MyOT+f+bD8CFqzZau3Ngc1Z8tjJXG8PmPnNTfWdlZZGYZsDi7Eg4++zl5/C5qfZyGM1GwlqGkZGawbGtx4nZf4bzR84TUCcAF08XXDxdCLkjhLhTcRyPPEGFGv5YnAq2wV4ZYwplypuJXH6IiO71r1vXN8SXUYtH8vbd75CSmMLdL3a/pecSEREREZG8Zs+ezezZs3OVJSQkXKN28bhugu7r68ugQYOKpeNatWphNpvZsmULvXr1spenp6ezY8cO+vW7sgFa/fr1mT59Ovv27SM8PNxevmnTJgDq1at3zX56v3YflWoHFmnslWoHMnbT6xgM4OLhQmZGJntW7GXL/K3sWbmX5LhkXCoGYctIx+zsil/DllhTLxO9cZm9jbiYOP7b4TX6vtqH7s93Z3zP8RzcfBCT2YSrp2uuY9TcfdxxKePMmSNn88Ty3ZvfU63JlZkLZ4+d48Cmg1icLJQPLo/ZYib+TBxJcckYjAZGLxhVpN9FDldPV9y83Ij8ZTsXT17EO9Abm83G7DFzycrMHr2vXLeyvX6T3nfkSdAndn+HV1e9jJNbwZdL7PxtFzOe/YrGj/fkzuF9b3zDTbA4WQhtUZWEM4mcPXCGkztPYjKbCGoUhNnBTNmAsnhW9CR6TzRGswn/6gV/WeXq6cyxnSeodNV3kx+/kPKM+nUEE7q8zYndJ3nyyyfsu72LiIiIiMit69+/P/37989VFhkZScOGDUsshtv2//A9PDxo3749s2bNIinpyojxzJkzSU5Opnfv3vayHj16YLFYmDZtmr3MZrPx0UcfERAQQLNm156yvfqrNVw8FVvk8Tu5OnJi90kWjFvIqAYv8cGgD9k8fwu+VbI3s6tc1Qn/yq60/HIid379OvXGPk5Q29xx2mw25vx3LoO8H+T0gSubvN3/5kDu7HdlN/THPxiCf2jezfQA0pJTmfe/b+2/W9OsmC0mMtMz6fxEJ2q0DCcpLjvZr9+pPoHVA+x1Xbhs/zkcncDh6ASOEsxRgvP0UxCN78veVX/z/K0ALP94Beu+WW+/fvbQlRcMATUDaDukDR5+HjQf2JRyQeWIP5PA2A7jSLtcsH0PALwqeJF6KZXYyF0EcIoa/s72nyTcSMLtxo0UkIefO2Etw3D39SArM4sjm44SvTf772YwGKhYqyJeAWU5HnmChDP5v2krz3nKc57K/uWo7F+Oqk3D2L+hYPsZlAssx9jNrxF/JoHX27xBcvzlIns2ERERERG5/YrlmLWxY8cCsGfPHgC++uorVq9eDcD//d//2eu98cYbNGvWjFatWjFkyBBOnTrFu+++S6dOnejYsaO9XsWKFXnuueeYOHEiGRkZREREsGDBAtauXcs333xz3U261n2znvWzNxDRoyHBDYOofmd1/Kr6kpGawfw3FpCWnEajnhFUa14No+na7yuyMrOI3h/D0e3H2L92P3tX7iMlMYWg+pVznf/t6OJAh6HtaXxvIyqGV2CdIftM8rLhodw770PeLZd39/TMjExiT8ViMBjwCvDidNRpylUqZ79+dOcx2j/SjjOHzxJzKHtav19VP84fP8/h7bl3c0+OT+bu57vT9sE2OLu78HjwE/ZrfV/pTXGq2aYGrp4ubP5+M9WahfLD2Pm5rp89fJak2CTcvNwwGAz0/u999P7vfUD2RnL/bfU/Yk/FkhSXfN112VerUN0fB2cHjkYexWazQQmc2uZf3Q/f0PIc23oca6qV49uO41HBE09/DxzdHKncoBJxp+M4ueMk/uH+mB2v/Z+ZyWwi8XwiGakZBZoe7+jsyEu/jWLlF6t5r+8Uer/ei9A7rnGqgIiIiIiI/K0YbDab7cbVCsdoNGIwGLDZbPb/hexRxszM3MdTrVu3jpEjRxIZGYm7uzt9+vRh3LhxuLrmPi/aZrMxYcIEPv74Y2JiYggLC2P06NF5piDk+OtUhIrhFTi9L3u0M7hBEFZrJid3Xdlh3c3Llep3VqOMdxnKViyLo4sjiecTSTibQPSBM5zec4q0y9kbvlWsUZHTe7OPLXN0c8TF3YXa7WvR+N4IQiJCcr0wWJHRzv45ecNJFvW/8uLhejx9PYk/m73mvKyfJ3FXrT83GA32Td+u/gzw/DfDadn/TuLOxPFMjWfto+cAC2w/AOBL9kj2vugrI7BpFCwhvpFvRs1hzcy1PPz+g8x9+Vsu/2WU96mZQ6nVtma+9yZeuMTZQ2cIbRJaqD7nvvIdkT9uY9jcZzBUu7K3gHvMCgAS8CjkUxRcalIq5w6ew2gxkZlmxT/cHwcXByD732zM3hhMl+Op0qAiRpORav7ZI/qnubJvwtHIo5w7ep477mtcqL7jY+L5cvhMQiJC6PJcZ0zmW9vB/kacTa486PtQsfYhIiIiIlKa5OSV27Zto0GDBsXeX7GMoGdlXfuc579q3rw5a9euvWE9g8HAqFGjGDWqcGuoXT1dMZoMnDl0xl5mdjBzNPJYrnpJsclsXZj/Od5Xv2QAcPcpQ0awD7U71KZOh1qENArBbLnxV2mz5f+9WBwtODg5cPnSZWxZNkxmE+mpV3Z/dy7jnCtBd3BysE8Dvzo5B0hPSWf377uZ0GtiruS858ieN4yvoI7tOE6Fatkj1391x32NWDNzLSd2neDxz4Ywqdd7BNQKoKyfJ7GnYykfdO2N29zLlcG9XJlCxxNYoyIrp69kx6+7qF/t5jf/uxlObk5Uql+JiydiSY5N4vyR8xjNRvyqZR/LVqFmBSxJDvzx+2Gc3Z0Iu7t6nhkfwQ2C2TB3Iw27N8DsUPD/JD39PXnm66dY8envJMUm41G+4DvJi4iIiIhI6VMsCXppkpaahjXVil+oH3cOasGhTYcwGg2YzCYyrZk3boDskVCD0UCLgc0IbRpKlYYheAV4FToW75r18QgJI+HIAXtZUJ3K3DfqXs4dP8/u33dTLqAc9TvXJ2pDFD9N+hnIPjO9Uu1KbJq/GZODibod6gCweeGWPH0s+3w5+zfsz5O4V2kQUuh487NvdRQfDPqQep3rMvjDh/MkmyERIXgHerNl4TbiYrJfKvR65V6qNS/4EXyFVadjbYwmI9sX7aD+c8XWzXV5V/KibM5GcSYjMfticPVyxcPPA2c3R+q0DyU5PoVFX27DZDYR2tMbJ9crsxZa3N+cX6csoft/uhaqX6PJSPsn2t24ooiIiIiIlHr/+G2gywVmr+U+c/AMqz5fxfafdxBz4AxjfhuZp67BaMDiZLGvRbc4mjGYDLh6ufLYJ48yYHx/GvWIKFRy3taynLaW5VTc/CHfd6qNxfvKWd8VQv05tus4P7w1n5mjZ7Hjt50c2X6E5r2b0XPEPfZ6jbpH0O3ZbthsNqxpVhp0bkBw3fw3cotaH5UnOYfsNfQ5UnAhBRfcuWT/KajghsH4VfVl20+RLJ6yJM91g8FA43sbkXguke2LdlChuj9hzQo3Zb2w3LzcaNi9Po6uDpiP78KfaPyJJsTfixB/LzxIwIPiPx7BaDISUCcAVy9XDEYDmdZMovfFcDn+MgaycPN0JOKhroR0bMbaWWtZ9vFyzp+4AECl2pUwO5iJWhNV7HGKiIiIiEjp9I9P0AdO7E/dTnUIrB3IuaPnAYiOiuHzp2bk2pSrcr3KtHu0DZ7+nvbp7BlpVtx93Bkwvj/17sq7uduNbP5hC8+Hj+C11mOZ9eI3pJw5x4UtO7C4ZifpDn9uhBZ/7kryeHz3CVKSUnIl1AajgX3rrpzvHd6iOs37XNkR3uJkubI52jV2FLjeRnqF4eTqyNAvHsfNy40f3/qZffkklI3va4RXxbLYsmy0fqhVkfV9PfW71ufw5iNsmLuxwPecOXCWYtiCATdvN/yr+2MwGnAq48SF0/FcPJXA3jVH2bMqilP7oqnVriYRPSNYO3MtX7/4DeeOnKPTUx3YMG8jp/7c30BERERERP5d/vFT3B2cHHjs00f54Y0FnNx9ZVO40/uicS3rileAF2UrlCX2dCzLPsneVKxcUDms6Va8K3pxeMsRPntiOnfc15hOT3fEr6pvgfq12Wxs/HYTKYkppCSm2MvNjmYyki9jcnbk+L5oLGXcMHn706xlTYLrhlCvXU0yMzJzTb83moxErctOhF09XakQVoEpD71vn6bvWd6T8yfO5w7AQO5k3d0bF7I3bDsWnT1qm0Y5boZ3oDePfvgwP72ziM+fmsHLK17KtXbc09eD+DMJGM1GIu4pmTMD63SojYevO+tmr6fr8LswWUycpTwAfv7ZL0QyY67sQ+BBPB7lU1n9y1nqdK1dpC8RckbrPSoYyczMIinDneTzlzFbLUQfPs/BjYdITUolMz2T+LMJuPu6M/flb/EJ8qHtkLb8+NZP9B3bB++bWEYhIiIiIiJ/X//4BD3LmonRZCQjJSPPteS4ZJLjkjl76CzBDYLs5YnnEnFycyIkIhhPP0+S45PZ+O0mNn6/iYbdGtC0TxPCW1bP91i2rMws9q7ax7rZ69m3Ou/osjXNCkBmShqOXp6kxcZzYfdeEg87sn7OWr7+s57ZwYzZwYyjqyM/jJ/PxdMXAUhPTWeA+/1kpGVgNGf3f+HkBVw9XXKdiz30oyfwDvBm8YdLcPL2IrxtfeDWz4NPu5zG1yNmE3Mghrqd63B482G+eXE2j08fYk9yV3y20j4D4OTuk4Q1K7715zlMFhPN+zdjxWe/s3v5H9TrfOMZD26eToS28GfXL7uLPEm3x2UyUrNZCFGbj9G4Sy3i8KTNI63t1xPOJvDHij3sWLyTTT9sZuuP26jWohoLJ/xEjxe74R3oXeQxiYiIiIhI6VQsx6yVBjnb4RvNRrwDvIk9HUtmxvU3hStXuRzpKemUD/bh0KbD9vOrywf7cOlCEpcuXFmr7RPsQ0B4RUKbVsUv1I+ki0nsX3+AfaujiD0Zi9nRbE/G82NxNOMR6MuFQ6exuDqRkZxa4Gczmoy5psD7hvhy7tg5+9rze155gMdfu8t+/VR09uh6MtlH1zlS8L7+6qPBn7Bz8S4A6naqQ+KFRI5uO8YD7w6kWd+mJMdf5uWmr+Lg7EDC2QRaP9ySvmP73HR/hZF0MYmx7cdRrrI3L8wfnifhDuSU/XM8ngCcjDnL5YRUDq+JolHXMAwGAxcpeFLsRRwABq78PYL8s++PJXsEPA1H9vy+h6D6wbh6uuRt5E+pyaksnbaMdbM3kBSXhEd5D56b+ww+19n5viTpmDURERER+bcp6WPW/vEJ+nX9dRo44OLpwuX4y5gcTLiVdSPhbPZ0ZUcXR/vRZoVtM4ejmyMt+jejzaNt8A7w4ix+ZKSkkRgTy7F9Z0k+H0t6qoXks+dJ2beDY9uP4OjqwOX4FDLSsmcAuJZ1JfnP49OqRFRh0PgH+PipT7Bl2Yg5GEPLRzoz+LMXKG+4MuW9KBP0U3tOMfGed0n/80z4to+2Yd036zAYjfzfstGs/Xodi9//jQfeHcivU5aQFJvE239MwGQq3jO6cyyc8BOLpyzh2dlPE96yeq5r10rQAWyJ8exeeZSGd1UlwVKwZQxQ8AT9/PELHNp4iKZ9m9ywTZvNxsovVjH/jYVY06x0eeEuug3vUuCYiosSdBERERH5t1GCXkSul6DX61KPDo+3Jah+EFmZWfyxYg+bvttMfEw8x3Yct9fzCfLh/LHs5NZgNOS7O/pf2Ue3DeAd4M3Fkxfx8POg6/Au3Hl/8xvef/HPdeGbf9zBj0PHA+BVNYjYQ8fwCvDBmmHFw9cTW5aN+6c8xccPTCDu9AVcPVxoc38LuozogsliIunPZBywrz0vKruX/cG0hz6yv4ho9VBLdi/7g9AmoRzefJhKdQJ55IOH+G/L/3Hh+AVaPXQn/d7oW6QxXEtyXDL/1/RVKlSrwH8W5B1F/ysfstfjJ1KG+HOJLHx7CU0HN8HZ3QlXkgCw/bmXYtZVeyrmvOSo6p+d6J/jyih3GleOT7vaT2//Uuhj1Ba+9ROL31+Ci7sLT301lJCG+e/eXxKUoIuIiIjIv01JJ+j/+DXoV+s8rBN3j+iWK2kzmozU61yXep3rkpKUwqENhzi0+TCHtxzJtblb2QpliT2V/xpuB2cH/EJ9qVynMtXvDKNS7UqUq5ydaCfFJuHi6YLRWLgN86O376d8zRDKVKrE4V9XZscQ4EN8zEVO7jpKt9H9+GX8XNL+nBrfpEcET0x5mNNnLhSqn5tRu30t+rzei3kvf4ezuzP71+3HO8CLTd9tAuDB9x7AZDZRo1U4q79aw5qZ62jcsxEhEUVzFvv1uJZ1pcPQ9uxbtY8jW49SpVHB+/Qs7859o7owd+IvNLi7Dq5B+SfaNx2bpwuXLl6ijHeZG1f+U48Xu9Pt+S5MvX8ab/d4l4q1KvL0l0Px8PUo0thEREREROT2+9ck6L1evZd2j7W9bh1nN2dqd6hN7Q617WUpl1KIj4ln9/I9zB+7wF7u4ulCr1fvpWmf609ZdvNyK3SsNpuNkxt2c27PEYyOV9YsH90ShcFopOG9LUiOTeLwpn2U8fEg4t4WPPfJg4V+CXAr2jzSmviYeH6btoyUxBQcnLOTWWd3Z/uGexan7H9eWZlZTBnwAc/NfYag+kHFHlvHoe3Z9N1mfhi7oECj6Fdz83Kl3ZOt2PJdJOunn8DZ3ZFKdSqQkZZJcnwq6SkZtHowAkenwsdVp0NtVn+5hq7PF266uslsYticZzi55yRT+n/AmEYvE3F3Qwa81Q9Hl6J9iSAiIiIiIrfPvyJBr9m2xg2T82txLuNs//lxwk9kZmTS8O4G9B3bu1AjoQXlzQUObDjA2d2HqFyvMhmZKXiH+GO0mHB2c+LEtkMENAhh06wVWFwcqdw4jLte68+Bs9mj/S5/Hohe1NPa89Ptha78sWIv0VHRnNh1AqPZSEpiCmtmrqX1w604e/icvW5achrv9ZvK6F9fpHxI+WKNy+xgptcrPZn20Mds+zGSiB7X3ovgfD5HzdUIdKfG8NZEH43lpbs+YO3c3fZrjq4OeJf3omygL2UreHI+zoDRZMRoisOjvAfO7s7X7Ktc5XLEn4knPSUdB2eHQj9XYM1AJux4k18nL2bpR8vZuWQnvf57H80HNCuRs+ZFRERERKR4ldyQ622UdDGJea98l2vn88Ly9Pdk9OKRjPxlBI9++EixJOc5fv9iFZB9BFfMH0e5eCSGhFMXOLHtENXa1uXCkRhiT5ynYp0gek54uERHzq9mcbLw6IcPYzBmJ4cmkxHnMk78/PYv2cfXXZWgQ/Yu5bGn40oktlrtaxHesjrz31xIekr6TbXhF+zNexv+Q/nKf55HboDA6n7s23CEnyYv4Zf3fmNynym83fNdJnR7m93L/7hhm3f0uoNN322+qXgAjEYjXZ/vwsvLxxDUIIgfxs7n9TZvcObw2ZtuU0RERERESod//Ah6kz53sOnbzRzfeYLzx84zeNrDOLndxPxkoGL1CkUcXV4XT8XajzGrXLeS/bOTi4XAGhVo1rMeM1/4GmuGlTv7NMTTeOl6zRW7zIxMDAYDNmxkpFmpGF6R88cvsH7uRjIzrPbk3T/Mj2e+fgpPP89iiSP+TDyxp2Lt69wNBgP3vXovb3QYx/JPf+euZzsVuK04roqxrAdPfDmU9/pOxWCAJ756+rpHpd1IlUYhbP1xGxmpGVicLDfdjnegN8/NfZb0lHRmjfiGcZ0n0OieCAa+1V+j6SIiIiIif1P/+BH0Vg+2pN1jbQD4Y/kePn96Bif/OHWDu26f1V+uxpZlw8HZAbPFjIefBx5+HiSev8SxHcdx83alfEh5arQMJ6xp6G2NNT0lnc+f+RJnd2dqtqmBm7cbx3efwNHNkV/eXcSwOc8wYceb1Gpbk4snY29qPX5BzXj2Kz4e8hlJsUn2sorVK9BiYHN2/baLy4k3P+XfP9SfAeP7Ue+uureUnEP2i4Oqjaqwbs76W2onh4OzA49MfYgXvn8OpzJOSs5FRERERP7G/vEJutFk5L5X7mXgxAEENQhiz4q9jO/6Fj+9/QvWdOvtDi+X9JR0ju88gU+wD7U71GLbT5EknEnAxSM7KazauAoH1h8kOioaa7rVXn4jly5eInp/TJHHu2Dcj8Tsj+H+iQN48L0HsKZZsWXayLJmkpacxpaF2yjjXYYarcNJS07jyNYjRR5Djt6v3cfl+MvMGTMvV/k9Y+4mLjqeX99bckvt1+lYm75j+9xSGzkadKvPrt9228+2LwqV6lSi1yv3Fll7IiIiIiJS8v7xCXqOFgOa0f/NPvhWKU+WNYtFk35ly/yt9us2m43ln6zINQJb0jZ+u4n96w5w4cQFHF2zd+d2dHPEmppBteZhNLy7IbuWZq9zrtOx9vWasotau58X64zm08enF2msUWui+H36Spr1a0q9u+pSxrsMPV/qAUB8TAKObo4snrKYhPOJ1GgVDsDeVfuKNIarVQyvSPcRXdn2UyRbFl75u7q4u9DzpR6s+Ox3ovdHF1v/hWE0GWnatwnrZ2+43aGIiIiIiEgp8q9J0AEq1a7E6MUj6fxMR8KahXFH78b2azsX7+K7137g9TZvEPlTJDabrURjy7RmsvSj5QCEtw5n68JtAAT8uab78JYj+FUtT1pyGuEtq1OnU8ESdP9QPwDOHTlXZCO28Wfi+eLZr6jftR69X7vPXt5iYHMq1qgIgDXNijU9k88en45PsA/elbzZu7J4EvS9K/eyY/FO2j7ahiqNQpgzZh7xZ+Lt1xvf2wi3sq5M7j2lWPq/GQ27NWD74h2kpaTd7lBERERERKSU+Fcl6AAWRws9Rt3NsDlP59r9vGbbGtw1rDPJ8cks/mApk3tP4diO4yUWV+Qv27lw/AIA+1fvJ/1yOlUahxAXk51oNuhWn80/bCX2VCwYwKeyT4HadS/vjmtZF7Iyszhz8NZ3+ramW/n0semkp6TTY2T3XBvuGU1G7p84AMjePA7g0KbDLP1wGY3uaYjZwUxqUuotx3A1m83GL5MWM/3JL3i312RCGgaTmW5l8fu/2esYDAbuGX03ly4m8c59k0m5lFKkMdwMo8mILTOLMREv39ZZGyIiIiIiUnr86xL0HEZT7ke3OFq4+8Vu/N+y0bi4O3Ngw0E+GfIp0x76iP3rDhTriHpSfBK/Tl6M0WzE2d0ZW1Z2XzabDWd3ZwJrB9KwRwO2LMieut3mkdYFbnvOmHkkx2VvkBYddetTvOe98h1Hth3lkfcfxLeKb57rQfUq07Rvk+zv98/9yua/sZAD6w5yNPIYRyOP3XIMVzMYDDw+fQgevh6c3H2KpR8vp83g1qyasTrXcWZN+jQB4NDGQ7za4nX+WLGnSOO4GWHNqnE5/jLjOk/gwIaDtzscERERERG5zf61Cfq1+If6M2zuMzw960nCmoWxe+kfTO4zhU+GfMaSqb9x/s9RbsgeJV78/hIOrC98Ap+VmcWB9Qf46vlZjGn4f8QcOEOWNYvA2oHZ57UbIDk2mdN7T+Pq6cKRLUdwcHagUc8IaratUeB+3Lyzd053dncmOT65UDH+1brZ61kzcy1dX+hC7Q7XnmJ/70v3YHG2wFVfyZFtRwGIWrf/lmLIj3u5Mjz79VM4uDhgdjCzYvpKIno0ZPboOZz983xwg8FgX9d/6cIlPnjgQ+a8NLfIYykMoyn7DUbs6Tg+fWw6iyYvvq3xiIiIiIjI7fWPPwf9ZhgMBmq2qUGN1uG0GNic1V+uyV7n/OtOFoz7kap3VME/1A9nd2d+m7YMgKD6len8TCdqd6iVa+p8juT4y5w7co7jO4+zf90B9q8/QIVQfw5vPYLJbAKg/l31uHDqIpXrVcZoMnL0z6S29SOt+OyJz7GmWWnQrX6+7V+LT+VyAKQkphB7Ou6mv5PDW46wbvYGGt7dgC7Pdb5uXTdvNzo91YEfJ/yc59qhjYduOobrKR9Snqe+Gsqk3u9hTbdyeOthvAK8mP7kF4z48QUsjhacyjiRlnxlzffVn4ta2uU0LE6WAv2tjCYjLp7OrYG6zwAAIABJREFU+IXmnZEgIiIiIiL/Hv/4BD0uOo6oNVE06dME93JlCnWvwWCgauMqVG1cheS4ZHYu2UXUuv3s/HUXhzYdzlX32PbjfPTIJwD4BPvgVdGLzAwrZbzdOLjpMEkXs9cZ+4f5EXPgDAE1A6jdqRaV6gby+/RVmB3MOLo5cnL3SRxcHHAt60pY01DKVijLvlVRYIPQJlWp26lOoZ7BJ6ic/XNODIV15tBZPnzoI3yCfXjgnYEFSjrbPtomV4JeoXoFylX25sC6g2RlZuVZYlAUQhoG88A7A/ni6S+JOx1PpTqBnDl4lgVv/kjv1+7Dxd2ZhDMJGE1GWj3Ukj6v9yryGHJs/n4L6+duZMD4fgTWCsi3jtnBglfFstRoXYNKtQNp0LV+scUjIiIiIiKl3z8+Qf/imRmkJadz4fgFBkzof9PtuJZ1pVm/pjTr15RMaybRUdFs+n4Lyz9Zkafu+aPnuXjyIhYHM8ENg/H086Ra8zB8Q8oT3CCIyvUqU8a7DPEx8bzRaTxhzULxDvRi47fZa6YDwityZNtRUhJSaNijAbNHzaV8sA99x/bGYDAUKm6fIB/KVihLXHQcSXGFT9ATziUy9f5puHi6MHTGEzi6OBboPkcXRyrWqMjpvacBiD0dS/P+Tdm1ZDfRUdEE1Mw/ab1VjXs24si2o6z6YjUndp0ktGlVVnz2Oyd2naDN4NbYsmwYjAZ+fmcRPUZ1L/DzFNadD7TAK8CLz4Z+Tu32tej2n644uebuq92QNrR7rA22TBvv3z+NZv2b2mdTiIiIiIjIv88/PkFPS04HYMO3m+gxugeuni633KbJbCKwViCZ1qxcCXr1O6vR+dlOVL2jCibT9RMtm83GnJfmkXQxifPHL5CemkGl2oEkxSVxcs8pADo904Efx/8EwB33NaZieMVCx1qmXBniYuLsnwsjNSmVDx6YRtrlNEYsfKHQMxCem/cMI2qNym7rUioXT8UC2dPliytBB+g3tg/x0XHsXLKbgxuyp9Qf2nyYs0fO0WNUdxrdE0HUmv1smb+VFgObF1sc4a2qM3rxiyyZupQvn/uKTk92IKh+kP26fRaBEep3qce2HyNpfG+jYotHRERERERKt3/NJnHWNCtrZq4t0ja9A71wcHGgUp1KPDv7aYbNeYZqzcJumJwDRP68nZ1LdgEQUCOAY5HHOLXvNKmX0ijj7UaTPnewe+kf+Ff3p0rjKnR6puNNxWgwGHByzT4KrTDHrGWkZjDz+VlkpGbw1JdPUD64YMe6Xc2trBsOLg7231d89ju+Vctz4cSF69xVNB6f/hghjUJylV26cIlZI77htVb/I7h+EL9NW5q9IV8xyMzI5Jd3FjGu81vUaB3OXc925qvnZ7Hs4+X5bijYrG8TNszbSFZW8cQjIiIiIiKl378mQQf48a2fiDtz8xul/VUZ7zJM2v82oxaNILxl9QLfl3jhEmu/XofFyUJok6rsXrobAE9fD5LjksnKzCL9cjpHth7l3OFzDJ728C1NfXZwtmR/KOBO8xlpGXw85FN2L99Dvzf75hr1LSynMlfOSccGZw+dY9tPkTfdXkEZDAZe+OE5fKuUz12OgaTYJEIah5CVZWPH4p3F0v+5Y+dZ+cUqzh87z/sDPmDR5F95/NMhnNh9khnPfkVKYu6z2C1OFmq2rsGuJbuLJR4RERERESn9/vEJerP+TanftR5GsxFblo23ur1DUuzNbZaWH6PRWKh14VmZWXzx9Ayi1uzHP9SPk3tOEdqkKp7+nnj4eWKymKh+ZzXOHDqL2dHME58/Tll/z1uKMWcUOyM944Z1relWPn18OvvXHWDoF49TrXnYLfVtIO93k3AusURGio1GIy8tHY2Lh7O9zGaz8cgHjxDSIJj2j7dl6YfLiuWMe/9QP97cOpZOT3ckKzOLnUt2MbbjOIIbBhPaLJRJvd+zT/nP0eKBFqyZtbZY4hERERERkdLvH5+gH9lyFGu6lcEfPUzEPQ2Jj4lnfNeJnN4XfVviWTxlCVFr9mMwGYg9HUfqpVSio6KJj4nnyJYj1OlUm43fbubC8fM8/tmjBDcIuuU+ndyzE9QbPXNmRiZfPPsl+1ZF8cT0xwo1K+BaDMbcCbqbtxtZ1ixi/5KcFheLo4UXf/5PrrJ5r8wj7XIazfo2JS46nmM7jhVL344ujvQY2Z3xkW/QpPcdWJwt/PC/+az5cg0dh7bn3fsm8/3rP5Acl30+vZOrI1UiQti3al+xxCMiIiIiIqXbPz5Bt2ZY2b30Dz59dDoHNxykTsfaZKZbmXr/B2yYt7FERyv3rtrHpu+zd2r3D/UjKTYJRxcHjH9OXw9rGsrRrccwmowMevcBarWtVST9Xjx+MfuDLXt6fX7SU9L5aPAnxEfH89gng6nZpkaR9H3nAy3wq+pLxZrZG9zlHPVWki9IfEN8Gf79s/bfY0/F8WLd0Wz6bjPN+zdl/eyNxdq/m5cbD056gDG/jsTvz1kTXz3/NWFNq7Ls4xWMjvg/xjR+ma+en0VgnUB2/qZp7iIiIiIi/0b/+AS9+3+60vLBO3F2d8ZkNrHrt93En0kg/mwCXw2fxedPzbCPYBan01HRfPr4dC6ejsXVy5XoqBg8fN1Ju5yO0WzEL9SXAxsO4ujqwJBPBtPw7gZF0u+li5dIuXRlvXN+x8JdTrjMlP5TObTpMD1Gdad2h9pF0jdAl2GdeXXVywye+vCVQkPJJugAYU3Ccp1Hnn45nW9GzWHnkl1s/XEb6SnpxR5DuUrlGLN4JAPf6o/NZmPT91uwOJnJSM0g7nQcm7/fzLYfIzGZTcQcPFPs8YiIiIiISOnyj0/Q/UL96P9mX8ZHvkG/N/vSqGcEbl5uYIPK9SqReD6R11qPZeO3m4ptND3+TDwfPPAhqZdSMZqMJMcmY7KYSDx/CYPRgNliJv5MAu4+ZRg06QHqda5bZH0v/XA5XPVYK79YlWsNfsLZBN697z3OHjnH8O+GEdbs1tacX4t/mB/lQ7J3gnd0ceRywuVi6ed6Ino0zFMWc+AMPkHl2FVCo9YGg4Hm/ZsxfvubVG9ZncyrdpH3rFCW+ycOoNWDd7JqxuoSiUdEREREREqPf3yCnsPB2YHa7WvxyNSHmLDzTUYtGsGQjwbT7T9dKVPOjS+fm8mMYV9xZOuRIu03OS6ZOS/NI/5sPAaTATcvVwxGA5kZmfgElQPg4smL1OlYh5G/jCCkYXCR9r3yi1W5ytIvp/P759llp/ed5sOHP8ZkMfGf+cOpVDuwyPrOT49RdwOQlpxW5N9zQeQ3bT+wVgCtHmzJhm83lWgsrp4uPPv1Uzwy9SF7WXpKOjsX78K3ii+Xzl/Ks9O7iIiIiIj8s/1rEvSrGY1GKtetjHegN6F3VGXM4lHc9+q9HN12jIk93mXqA9M4GnnslvtJjkvmvX7vs3PxLmxZNmyZNi5dSMKWZcNkMZGVacNkNtJl+F0MemcgXhW9bv3hrpKRmv+u7WlJqez8bRcTe7yL0WRk6IzH8a3iW6R956feXXUxWbLX2587er7Y+/urCtUrENY0FADXsi4AHN95gqS4ZFISL3PpYv7r84tTw24NGDTpfhycHLh0/hKfDf2c/2vyCmZHM0s/Wl7i8YiIiIiIyO1T5An68uXLeeSRRwgLC8PV1ZUqVaowZMgQzpzJf03t+vXradGiBa6urvj7+zNs2DCSk/OuCbfZbLz11lsEBwfj7OxM3bp1mTNnTpHEbLKYaP9YW0YtGkGX5zpzNPIYHw3+hLe6v83mH7aQkXbj48n+KvH8JSb1eY+Tf5wCsB/FZk2zYnY0k5mRiYuHMyN/HkH3/3S1J65FydPfk+CrRuR9q/oydtNrlPEpw8ePfEq9u+oy/NthePp6FHnf+TEajVS/sxqQ/fLiZr7XW2EwGBj+3TCe//45Xlv7X1o91BKABW8uxMXDhag1+0s0nhxN+zRh0sG37f8GLp6MZfMPW/j1vcWM6/LWbYlJRERERERKnrmoGxw5ciTx8fH07t2b0NBQDh8+zNSpU/n555/ZsWMHvr5XRmp37NhBu3btqFmzJpMmTeLkyZO8/fbbHDx4kEWLFuVqd8yYMUyYMIHHHnuMRo0asWDBAgYMGIDBYKBv375FEruLhwvdR3Sj7ZA2rJu9gdVfreGLZ75k+acrCKgRQP2u9ajeohpmh+t/bRdOXODdXu+ReD7RXmYym7CmWwGo0boGdTrUomnfJhiNxTeJIWrNfg6sO2D//eyhs3z+1JfYbFncM+ZuOgxtX6gz3ItCj9F3s2fFXgDiouMpH+xTov0DhDapCkC/N/pw7ug59q2KImrtflw8XGh0T0SJxwPZLy9GLHye8V0m5iqv1qJ49gQQEREREZHSp8gT9MmTJ9OiRYtcZZ07d6ZVq1ZMnTqV//3vf/byMWPG4O3tzcqVK3FzcwMgKCiIIUOGsHTpUjp06ADA6dOneeedd3j66aeZMmUKAIMHD6ZVq1aMGDGC3r17F2mi6+rpSseh7Wn/WFui1u1n47xNbPspkvVzNlCtRTUsjmaqNAqhSuMqBNWtjMXJYr/3j9/38OFDH5NlzcrVppuXGwajgY5PtadZ36Y4ODsUWbzX8tPEn/OUndh9gme+fso+1bukBdYIwOxgxppu5UjkkduSoF/t2W+eZkSdUSRdTGLLwq088M5ALI6WG99YDCrXrYy7rzuJZ6+82DmyueTX6ouIiIiIyO1R5An6X5NzgDvvvBMvLy+ioqLsZYmJiSxbtoznn3/enpwDDBo0iOHDhzNv3jx7gr5w4UKsVitPPvlkrnaHDh3KgAED2LBhA82bN883HgNGDIabmz5uMpuo2aoWNVvVIiM1g72r9nJ0+zF2LdnJH8v3AFD1jqrEn4mnXKVyuJcvw+bvt+Tu32Cg+p3VaPlgK+p0qI3JXPRT2QvDr4of1ZpVv60xhDUPZe/v+1j+0e807dXstsYCMGHHeJ6q9DRkwfPhI2j/eHtaDGxBuUDvEo/lqS+fZFzn8ZQpVwaDycDhLUdY+tFyOg7tWOKx/JXR8K/cskJEREREpMQUeYKen6SkJC5dukS5cuXsZbt378ZqtRIRkXtKscVioV69emzfvt1etn37dtzc3KhePXdi2ahRIyB7qvy1EvT7fHrRwL9ozhQnGHgo+2NsbCzr16/n0KFDREZGcvjwYY6sO4KLiwuXL18mKCiIp59+mkGDBuHjc3tGiYdsfow//viD8ePHk5KSwrhx46hcuTLOzs63JZ4cnb+4i+7duzN58mTa+re9rbHksHzswGOPPYY1zcryD5ez5P0lvPbaa7z88sslG4g/3LGgCa1atcLT05PGjRtzb7X7GOg/sGTjEBERERGRElciCfrkyZPJyMjItVY8JiYGAH9//zz1/fz8WLt2ba66V69dz5Fzb3R0dFGHfENeXl5069YtT/mxY8fw8vLC3d29xGP6K6PRSJ06dfjmm29udyi5VK5cmV27dt3uMHK5+m9ptVoxm815Xh6VlB49etg/b968+bbEICIiIiIiJe+6CbrNZiMtLa1ADTk5OeVbvnr1al577TX69u1L69at7eUpKdlnPDs6OubbVs71nLrXqnd1W6VBUFDQ7Q5BboK/vz/VqlVj//79uLm5sWjRonyXa4iIiIiIiBSX6y4qXbVqFS4uLgX6OXDgQJ77o6Ki6NmzJ3Xq1OGzzz7LdS1nmnV+LwBSU1NxcXHJVTc1NTXfele3JXIr+vbti7+/Pxs3blRyLiIiIiIiJe66I+jh4eHMmDGjQA35+fnl+v3kyZN07NiRsmXLsmjRIlxdXXNdz5menjPV/WoxMTFUqFAhV92VK1fmWw/IVfevhg8fjodH7nO++/fvT//+/a//QPKv88orr/DKK69gMt3ejfxERERERKTkzZ49m9mzZ+cqS0hIKNEYrpug+/r6MmjQoEI3evHiRTp27EhGRga///57vuvHa9WqhdlsZsuWLfTq1ctenp6ezo4dO+jXr5+9rH79+kyfPp19+/YRHh5uL9+0aRMA9erVu2YskyZNokGDItokTv7RlJiLiIiIiPx75TeQGxkZScOGDUsshiI/Nyk5OZkuXboQExPDokWLqFKlSr71PDw8aN++PbNmzSIpKclePnPmTJKTk+ndu7e9rEePHlgsFqZNm2Yvs9lsfPTRRwQEBNCs2e0/qktERERERETkVhT5Lu4DBw5ky5YtPPLII+zZs4c9e/bYr5UpUybXDtVvvPEGzZo1o1WrVgwZMoRTp07x7rvv0qlTJzp2vHLuc8WKFXnuueeYOHEiGRkZREREsGDBAtauXcs333yDwWAo6scQERERERERKVEGm81mK8oGg4ODOXHiBPk1GxQUxJEjR3KVrVu3jpEjRxIZGYm7uzt9+vRh3Lhxedas22w2JkyYwMcff0xMTAxhYWGMHj36mmvJc6YibNu2TVPcRUREREREpNBKOq8s8hH0o0ePFqp+8+bNc515fi0Gg4FRo0YxatSomw1NREREREREpNQq8jXoIiIiIiIiIlJ4StBFRERERERESgEl6CIiIiIiIiKlgBJ0ERERERERkVJACbqIiIiIiIhIKaAEXURERERERKQUUIIuIiIiIiIiUgooQRcREREREREpBZSgi4iIiIiIiJQCStBFRERERERESgEl6CIiIiIiIiKlgBJ0ERERERERkVJACbqIiIiIiIhIKaAEXURERERERKQUUIIuIiIiIiIiUgooQRcREREREREpBZSgi4iIiIiIiJQCStBFRERERERESgEl6CIiIiIiIiKlgBJ0ERERERERkVJACbqIiIiIiIhIKaAEXURERERERKQUUIIuIiIiIiIiUgooQRcREREREREpBZSgi4iIiIiIiJQCStBFRERERERESgEl6CIiIiIiIiKlgBJ0ERERERERkVJACbqIiIiIiIhIKaAEXURERERERKQUUIIuIiIiIiIiUgooQRcREREREREpBYo8QV+9ejV33303lSpVwtnZGV9fX9q1a8evv/6ab/3169fTokULXF1d8ff3Z9iwYSQnJ+epZ7PZeOuttwgODsbZ2Zm6desyZ86cog5fRERERERE5LYo8gT94MGDmM1mhg4dyrRp03jxxReJjY2la9eufP3117nq7tixg3bt2pGamsqkSZN49NFH+eSTT+jdu3eedseMGcOoUaPo1KkTU6dOpVKlSgwYMIC5c+cW9SOIiIiIiIiIlDiDzWazFXcnKSkphISEEBYWxqpVq+zlXbp0YdeuXURFReHm5gbA9OnTGTJkCEuWLKFDhw4AnD59muDgYJ544gmmTJliv79Vq1YcPXqUY8eOYTTmftcQGRlJw4YN2bZtGw0aNCjuRxQREREREZF/mJLOK0tkDbqzszPlypXDYrHYyxITE1m2bBn333+/PTkHGDRoEG5ubsybN89etnDhQqxWK08++WSudocOHcqpU6fYsGFD8T+EiIiIiIiISDEqtgQ9MTGRCxcuEBUVxZgxYzhw4AAvvPCC/fru3buxWq1ERETkus9isVCvXj22b99uL9u+fTtubm5Ur149V91GjRoB2VPlRURERERERP7OzMXVcJ8+ffjtt98AcHV1Zd68edx111326zExMQD4+/vnudfPz4+1a9fmquvr65unXs690dHRRRq7iIiIiIiISEm77gi6zWYjNTW1QD9/NWHCBJYuXcr06dOpUaMG/fr1Y+nSpfbrKSkpADg6Oua518nJyX49p+616l3dloiIiIiIiMjf1XVH0FetWkXbtm0L1FBUVBRhYWH23+vWrWv/fP/999OgQQOeeuopDhw4AGSvSwdIS0vL01ZqaiouLi72352dnfN9CZBTltOWiIiIiIiIyN/VdRP08PBwZsyYUaCG/Pz8rnnNYrHQvXt3xo8fT3x8PJ6envbp6TlT3a8WExNDhQoV7L/7+/uzcuXKfOsBuer+1fDhw/Hw8MhV1r9/f/r373/d5xEREREREZF/j9mzZzN79uxcZQkJCSUaw3UTdF9fXwYNGlQkHeVMQ885Dq1WrVqYzWa2bNlCr1697PXS09PZsWMH/fr1s5fVr1+f6dOns2/fPsLDw+3lmzZtAqBevXrX7HfSpEk6Zk1ERERERESuK7+B3Jxj1kpKke/ifu7cuTxl8fHxfP/999SuXRt3d3cAPDw8aN++PbNmzSIpKcled+bMmSQnJ9O7d297WY8ePbBYLEybNs1eZrPZ+OijjwgICKBZs2ZF/RgiIiIiIiIiJarId3G/6667CAwMpHHjxpQvX54TJ07wxRdfcP78+TzT5d944w2aNWtGq1atGDJkCKdOneLdd9+lU6dOdOzY0V6vYsWKPPfcc0ycOJGMjAwiIiJYsGABa9eu5ZtvvsFgMBT1Y4iIiIiIiIiUqCJP0AcPHsycOXOYPHky8fHxeHt706JFC0aPHp1nqnn9+vVZtmwZI0eO5Pnnn8fd3Z1HH32UcePG5Wl3/PjxlC1blo8//pgZM2YQFhbG119/nWsqvIiIiIiIiMjflcFms9ludxDFIWetwLZt27QGXURERERERAqtpPPKIl+DLiIiIiIiIiKFpwRdREREREREpBRQgi4iIiIiIiJSCihBFxERERERESkFlKCLiIiIiIiIlAJK0EVERERERERKASXoIiIiIiIiIqWAEnQRERERERGRUkAJuoiIiIiIiEgpoARdREREREREpBRQgi4iIiIiIiJSCihBFxERERERESkFlKCLiIiIiIiIlAJK0EVERERERERKASXoIiIiIiIiIqWAEnQRERERERGRUkAJuoiIiIiIiEgpoARdREREREREpBRQgi4iIiIiIiJSCihBFxERERERESkFlKCLiIiIiIiIlAJK0EVERERERERKASXoIiIiIiIiIqWAEnQRERERERGRUkAJuoiIiIiIiEgpoARdREREREREpBRQgi4iIiIiIiJSCihBFxERERERESkFlKCLiIiIiIiIlAJK0EVERERERERKASXoIiIiIiIiIqWAEnQRERERERGRUkAJuoiIiIiIiEgpUOwJ+pAhQzAajXTv3j3f6+vXr6dFixa4urri7+/PsGHDSE5OzlPPZrPx1ltvERwcjLOzM3Xr1mXOnDnFHb6IiIiIiIhIiSjWBH3r1q18+eWXODk5YTAY8lzfsWMH7dq1IzU1lUmTJvHoo4/yySef0Lt37zx1x4wZw6hRo+jUqRNTp06lUqVKDBgwgLlz5xbnI4iIiIiIiIiUCHNxNWyz2Xj22Wd58MEHWbZsWb51xowZg7e3NytXrsTNzQ2AoKAghgwZwtKlS+nQoQMAp0+f5p133uHpp59mypQpAAwePJhWrVoxYsQIevfujdGo2foiIiIiIiLy91VsWe3MmTPZu3cvY8eOxWaz5bmemJjIsmXLuP/+++3JOcCgQYNwc3Nj3rx59rKFCxditVp58sknc7UxdOhQTp06xYYNG4rrMURERERERERKRLEk6JcuXWLkyJGMGTMGX1/ffOvs3r0bq9VKRERErnKLxUK9evXYvn27vWz79u24ublRvXr1XHUbNWoEZE+VFxEREREREfk7K5YE/fXXX8fV1ZXhw4dfs05MTAwA/v7+ea75+fkRHR2dq25+iX7OvVfXFREREREREfk7uu4adJvNRlpaWoEacnJyAuDAgQNMmTKFOXPmYLFYrlk/JSUFAEdHx3zbyrmeU/da9a5uS0REREREROTv6roj6KtWrcLFxaVAPwcOHABg2LBhNG/enJ49e163Y2dnZ4B8XwCkpqbi4uKSq25qamq+9a5uS0REREREROTv6roj6OHh4cyYMaNADfn5+bFixQqWLFnCDz/8wLFjx+zXrFYrly9f5vjx43h5eVGmTBn79PScqe5Xi4mJoUKFCvbf/f39WblyZb71gFx1/2r48OF4eHjkKuvfvz/9+/cv0HOJiIiIiIjIP9/s2bOZPXt2rrKEhIQSjeG6Cbqvry+DBg0qcGMnTpwA4N57781zLTo6muDgYCZPnsyzzz5LrVq1MJvNbNmyhV69etnrpaens2PHDvr162cvq1+/PtOnT2ffvn2Eh4fbyzdt2gRAvXr1rhnTpEmTaNCgQYGfQURERERERP598hvIjYyMpGHDhiUWQ5FuEteuXTsWLFiQ62f+/Pn4+PjQqFEjFixYQLdu3QDw8PCgffv2zJo1i6SkJHsbM2fOJDk5md69e9vLevTo8f/t3X1Y1fX9x/HXQRGOnEBFBdRKKxlMl4AwL9HCvAGdoVuDvMloLbG8WajLNO3arutX5m1yZc67jUWXCmo3w66ymrRwI81MoNwmUyub6FnpzDsEBf3+/ujiO08Hyeoc+lDPx3VxXZ73930+fD9d14fT63w/33MUGBiolStX2jXLsrR69Wp169ZNycnJvpwGAAAAAADNrskr6F/Vtddeq2uvvdarnpOTo4iICI0aNcqjPn/+fCUnJyslJUXZ2dmqqqrSsmXLlJaWptTUVLuva9eumj59upYsWaK6ujolJiaqqKhIpaWlKigokMPh8OU0AAAAAABodj4N6FdypQAdHx+v4uJizZ49WzNnzlRoaKgmTpyoBQsWePUuXLhQ7du315o1a5Sfn6/o6Ght2LDBYys8AAAAAAAtVbME9I8++uiKxwYMGKDS0tIvHcPhcGjOnDmaM2eOL08NAAAAAAAj+PQedAAAAAAA8PUQ0AEAAAAAMAABHQAAAAAAAxDQAQAAAAAwAAEdAAAAAAADENABAAAAADAAAR0AAAAAAAMQ0AEAAAAAMAABHQAAAAAAAxDQAQAAAAAwAAEdAAAAAAADENABAAAAADAAAR0AAAAAAAMQ0AEAAAAAMAABHQAAAAAAAxDQAQAAAAAwAAEdAAAAAAADENABAAAAADAAAR0AAAAAAAMQ0AEAAAAAMAABHQAAAAAAAxDQAQAAAAAwAAEdAAAAAADKuhWWAAAfUklEQVQDENABAAAAADAAAR0AAAAAAAMQ0AEAAAAAMAABHQAAAAAAAxDQAQAAAAAwAAEdAAAAAAADENABAAAAADAAAR0AAAAAAAMQ0AEAAAAAMIDPA3p+fr4CAgIa/fn000+9+nfs2KGBAwcqJCREUVFRysnJUXV1tVefZVlavHixevToIafTqT59+mjjxo2+Pn0AAAAAAL4Vrf018GOPPaYePXp41MLCwjweV1RUaMiQIerVq5dyc3N1+PBhLV26VAcOHNDWrVs9eufOnatFixZp0qRJSkpKUlFRkcaPHy+Hw6ExY8b4axoAAAAAADQLvwX0ESNGKCEhocmeuXPnKjw8XCUlJXK5XJKk7t27Kzs7W9u2bdOwYcMkSUeOHNGTTz6padOmafny5ZKk++67TykpKZo1a5YyMzMVEMBufQAAAABAy+W3VGtZls6cOaOLFy82evz06dMqLi7WhAkT7HAuSVlZWXK5XNq8ebNd27Jli+rr6zVlyhSPMSZPnqyqqirt3LnTP5MAAAAAAKCZ+C2g33bbbQoLC1NISIhGjx6tgwcPehzfu3ev6uvrlZiY6FEPDAxUXFycysvL7Vp5eblcLpdiYmI8epOSkiR9vlUeAAAAAICWzOdb3ENCQnTvvffqtttuU2hoqN59910tW7ZMycnJKisrU7du3SRJbrdbkhQVFeU1RmRkpEpLS+3HbrdbERERXn0Nzz169KivpwEAAAAAQLNqMqBblqXz589f1UDBwcGSpMzMTGVmZtr1UaNGKS0tTbfeeqvmz5+vVatWSZJqamokSUFBQY2O1XC8ofdKfZePBQAAAABAS9XkFvft27erbdu2V/Wzf//+K44zYMAA9evXT8XFxXbN6XRKUqNvANTW1qpt27YevbW1tY32XT4WAAAAAAAtVZNX0GNjY5Wfn39VA0VGRjZ5vFu3bh4hvmF7esNW98u53W516dLFo7ekpKTRPkkevV80Y8YMr693GzdunMaNG9fk+QIAAAAAvj8KCwtVWFjoUTt16lSznkOTAT0iIkJZWVk++UUffvihOnXqZD/u3bu3Wrdurd27dysjI8OuX7hwQRUVFRo7dqxdi4+PV15envbt26fY2Fi7vmvXLklSXFzcFX9vbm7ul37dGwAAAADg+62xC7llZWXq27dvs52Dzz/F/dixY161rVu3qqysTMOHD7drYWFhGjp0qNavX6+zZ8/a9XXr1qm6utrjPvbRo0crMDBQK1eutGuWZWn16tXq1q2bkpOTfT0NAAAAAACalc8/xT05OVkJCQnq27evwsLCVFZWpj/+8Y+67rrrNHfuXI/e+fPnKzk5WSkpKcrOzlZVVZWWLVumtLQ0paam2n1du3bV9OnTtWTJEtXV1SkxMVFFRUUqLS1VQUGBHA6Hr6cBAAAAAECz8nlAHzt2rF555RX9+c9/1rlz59SlSxfdf//9+u1vf+uxxV36fOt6cXGxZs+erZkzZyo0NFQTJ07UggULvMZduHCh2rdvrzVr1ig/P1/R0dHasGGDx1Z4AAAAAABaKodlWda3fRL+0HCvwJ49e7gHHQAAAADwlTV3rvT5PegAAAAAAOCrI6ADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAbwW0AvLi7W4MGD1a5dO4WGhioxMVGbN2/26tuxY4cGDhyokJAQRUVFKScnR9XV1V59lmVp8eLF6tGjh5xOp/r06aONGzf66/QBAAAAAGhWrf0x6DPPPKOJEycqNTVVCxYsUKtWrVRZWamqqiqPvoqKCg0ZMkS9evVSbm6uDh8+rKVLl+rAgQPaunWrR+/cuXO1aNEiTZo0SUlJSSoqKtL48ePlcDg0ZswYf0wDAAAAAIBm4/OAfujQIU2dOlUPPvigcnNzm+ydO3euwsPDVVJSIpfLJUnq3r27srOztW3bNg0bNkySdOTIET355JOaNm2ali9fLkm67777lJKSolmzZikzM1MBAezWBwAAAAC0XD5PtatXr5ZlWfq///s/SdLZs2dlWZZX3+nTp1VcXKwJEybY4VySsrKy5HK5PLbDb9myRfX19ZoyZYrHGJMnT1ZVVZV27tzp62kAAAAAANCsfB7Qi4uLFRMTo5dfflndunVTaGioOnbsqN/85jceQX3v3r2qr69XYmKix/MDAwMVFxen8vJyu1ZeXi6Xy6WYmBiP3qSkJEmfb5UHAAAAAKAl83lAP3DggP7973/rl7/8pSZOnKgXXnhBI0aM0OOPP6558+bZfW63W5IUFRXlNUZkZKSOHj3q0RsREeHV1/Dcy3sBAAAAAGiJmrwH3bIsnT9//qoGCg4OlvS/Le2LFi3SrFmzJEk/+9nPdOLECT311FOaN2+eQkJCVFNTI0kKCgpqdKyG45JUU1Nzxb6G4wAAAAAAtGRNXkHfvn272rZte1U/+/fvlyQ5nU45HA6NGzfOY6yxY8eqpqbG3rrudDolqdE3AGpra9W2bVv7sdPpVG1tbaN9l48FAAAAAEBL1eQV9NjYWOXn51/VQJGRkZKkLl266IMPPvDakt65c2dJ0meffSbpf9vTG7a6X87tdqtLly7246ioKJWUlDTa1/A7r2TGjBkKCwvzqI0bN87rDQQAAAAAwPdXYWGhCgsLPWqnTp1q1nNoMqBHREQoKyvrKw2YmJiogwcPqqqqSj169LDrDfeJd+rUSZLUu3dvtW7dWrt371ZGRobdd+HCBVVUVGjs2LF2LT4+Xnl5edq3b59iY2Pt+q5duyRJcXFxVzyf3NxcJSQkfKU5AAAAAAC+Xxq7kFtWVqa+ffs22zn4/EPixowZI0nKy8uza5cuXdIzzzyj8PBwe3JhYWEaOnSo1q9fr7Nnz9q969atU3V1tTIzM+3a6NGjFRgYqJUrV9o1y7K0evVqdevWTcnJyb6eBgAAAAAAzarJK+hfx+jRozVkyBAtWLBAx48f180336yioiK99dZbWrt2rQIDA+3e+fPnKzk5WSkpKcrOzlZVVZWWLVumtLQ0paam2n1du3bV9OnTtWTJEtXV1SkxMVFFRUUqLS1VQUGBHA6Hr6cBAAAAAECz8nlAl6SioiI9+uij2rRpk/Lz8xUTE6MNGzZ4bReIj49XcXGxZs+erZkzZyo0NFQTJ07UggULvMZcuHCh2rdvrzVr1ig/P1/R0dHasGGDx1Z4AAAAAABaKodlWda3fRL+0HCvwJ49e7gHHQAAAADwlTV3rvT5PegAAAAAAOCrI6ADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAnwf0QYMGKSAgoNGfNm3aePXv2LFDAwcOVEhIiKKiopSTk6Pq6mqvPsuytHjxYvXo0UNOp1N9+vTRxo0bfX36AAAAAAB8K1r7esBHH31Un376qUft7NmzeuCBB5SWluZRr6io0JAhQ9SrVy/l5ubq8OHDWrp0qQ4cOKCtW7d69M6dO1eLFi3SpEmTlJSUpKKiIo0fP14Oh0Njxozx9TQAAAAAAGhWPg/oQ4cO9aqtX79eknTXXXd51OfOnavw8HCVlJTI5XJJkrp3767s7Gxt27ZNw4YNkyQdOXJETz75pKZNm6bly5dLku677z6lpKRo1qxZyszMVEAAu/UBAAAAAC1Xs6TagoICuVwujR492q6dPn1axcXFmjBhgh3OJSkrK0sul0ubN2+2a1u2bFF9fb2mTJniMe7kyZNVVVWlnTt3+n8SAAAAAAD4kd8D+rFjx7Rt2zb99Kc/ldPptOt79+5VfX29EhMTPfoDAwMVFxen8vJyu1ZeXi6Xy6WYmBiP3qSkJEmfb5UHAAAAAKAl83tA37Rpky5evOi1vd3tdkuSoqKivJ4TGRmpo0ePevRGRER49TU89/JeAAAAAABaoibvQbcsS+fPn7+qgYKDgxutFxQUqHPnzvb95A1qamokSUFBQY2O1XC8ofdKfZePBQAAAABAS9XkFfTt27erbdu2V/Wzf/9+r+d/+OGHevvttzVmzBivD3Fr2O7e2BsAtbW1atu2rUdvbW1to32XjwUAAAAAQEvV5BX02NhY5efnX9VAkZGRXrWCggJJ3p/eLv1ve3rDVvfLud1udenSxaO3pKSk0T5JHr1fNGPGDIWFhXnUxo0bp3Hjxl3xOQAAAACA75fCwkIVFhZ61E6dOtWs59BkQI+IiFBWVtbXHrygoEA33XSTfvzjH3sd6927t1q3bq3du3crIyPDrl+4cEEVFRUaO3asXYuPj1deXp727dun2NhYu75r1y5JUlxc3BXPITc3VwkJCV97DgAAAACA777GLuSWlZWpb9++zXYOfvuQuPLyclVWVmr8+PGNHg8LC9PQoUO1fv16nT171q6vW7dO1dXVyszMtGujR49WYGCgVq5cadcsy9Lq1avVrVs3JScn+2saAAAAAAA0iyavoH8TGzZskNT49vYG8+fPV3JyslJSUpSdna2qqiotW7ZMaWlpSk1Ntfu6du2q6dOna8mSJaqrq1NiYqKKiopUWlqqgoICORwOf00DAAAAAIBm4ZeAfunSJW3atEl9+/ZVz549r9gXHx+v4uJizZ49WzNnzlRoaKgmTpyoBQsWePUuXLhQ7du315o1a5Sfn6/o6Ght2LDBYys8AAAAAAAtlV8CekBAgA4fPnxVvQMGDFBpaemX9jkcDs2ZM0dz5sz5pqcHAAAAAIBx/HYPOgAAAAAAuHoEdAAAAAAADEBABwAAAADAAAR0AAAAAAAMQEAHAAAAAMAABHQAAAAAAAxAQAcAAAAAwAAEdAAAAAAADEBABwAAAADAAAR0AAAAAAAMQEAHAAAAAMAABHQAAAAAAAxAQAcAAAAAwAAEdAAAAAAADEBABwAAAADAAAR0AAAAAAAMQEAHAAAAAMAABHQAAAAAAAxAQAcAAAAAwAAEdAAAAAAADEBABwAAAADAAAR0AAAAAAAMQEAHAAAAAMAABHQAAAAAAAxAQAcAAAAAwAAEdAAAAAAADEBABwAAAADAAAR0AAAAAAAMQEAHAAAAAMAABHQAAAAAAAxAQAcAAAAAwAAEdAAAAAAADEBABwAAAADAAH4J6MXFxRoyZIg6d+6sa665Rn369NHTTz+tS5cuefXu2LFDAwcOVEhIiKKiopSTk6Pq6mqvPsuytHjxYvXo0UNOp1N9+vTRxo0b/XH6AAAAAAA0O58H9Ndee02pqak6duyY5s2bp2XLlumGG25QTk6OZs6c6dFbUVGhIUOGqLa2Vrm5uZo4caLWrl2rzMxMr3Hnzp2rOXPmKC0tTStWrNB1112n8ePHa9OmTb6eAgAAAAAAzc5hWZblywHvuusuvfjii3K73WrXrp1dHzRokCoqKnTy5Em79pOf/ETvv/++Kisr5XK5JEl5eXnKzs7W66+/rmHDhkmSjhw5oh49euiBBx7Q8uXL7eenpKToo48+0qFDhxQQ4PleQ1lZmfr27as9e/YoISHBl1MEcAWFhYUaN27ct30aABrB+gTMxhoFzNTcudLnV9CdTqeCgoIUFhbmUY+MjFTbtm3tx6dPn1ZxcbEmTJhgh3NJysrKksvl0ubNm+3ali1bVF9frylTpniMOXnyZFVVVWnnzp2+ngaAr6GwsPDbPgUAV8D6BMzGGgUg+SGg/+pXv9KlS5d0//33q7KyUh9//LFWr16tP/3pT3rkkUfsvr1796q+vl6JiYkezw8MDFRcXJzKy8vtWnl5uVwul2JiYjx6k5KSJH2+VR4AAAAAgJasta8H7NOnj/7yl78oPT1df/jDHyRJrVq10u9+9ztNmjTJ7nO73ZKkqKgorzEiIyNVWlrq0RsREeHV1/Dco0eP+nQOAAAAAAA0tyYDumVZOn/+/FUNFBwcLEmqrKzUyJEjdf3112vJkiUKDg5WQUGBpk2bpoiICI0ePVqSVFNTI0kKCgpqdKyG4w29V+q7fCwAAAAAAFqqJgP69u3bNXjw4KsaqLKyUtHR0XrooYfUunVrlZSU2PecZ2RkaPDgwZo6darS09MVEBAgp9MpSY2+AVBbW+txv7rT6VRtbW2jfQ3Hv6ghtO/bt++qzh/AN3fq1CmVlZV926cBoBGsT8BsrFHATA15srkuCjcZ0GNjY5Wfn39VAzVsNy8tLVV6erpHwJak9PR0/frXv9ahQ4d0ww032P0NW90v53a71aVLF4+xS0pKGu2T5NHb4NChQ5KkCRMmXNX5A/CNvn37ftunAOAKWJ+A2VijgLkOHTqkAQMG+P33NBnQIyIilJWV9ZUGrK+v18WLF73qdXV19nFJ6t27t1q3bq3du3crIyPD7rtw4YIqKio0duxYuxYfH6+8vDzt27dPsbGxdn3Xrl2SpLi4OK/fl5aWpvXr16t79+6NXmEHAAAAAKApNTU1OnTokNLS0prl9/n8e9BvueUW7du3T/v371eHDh0kSRcvXlS/fv108OBB/fe//1WrVq0kff496O+9957+9a9/eX0P+muvvabU1FRJn38P+g033KBJkybp6aeflvT5/fEpKSk6dOiQPv74YzkcDl9OAwAAAACAZuXzT3GfN2+eRo4cqX79+mnSpEkKDg5WYWGhysrKNH/+fDucS9L8+fOVnJyslJQUZWdnq6qqSsuWLVNaWpodziWpa9eumj59upYsWaK6ujolJiaqqKhIpaWlKigoIJwDAAAAAFo8n19Bl6TXX39d8+fPV1lZmerr6xUTE6OpU6cqOzvbq/ett97S7NmzVVZWptDQUN15551asGCBQkJCPPosy9KiRYu0Zs0aud1uRUdH65FHHtG4ceN8ffoAAAAAADQ7vwR0AAAAAADw1QR82ycAwCwlJSUKCAho9Oedd97x6N23b5+GDx+ua665RuHh4crKytLx48cbHTcvL0+xsbFyOp2Kjo7WihUrmmM6QItWXV2t3/72txo+fLg6dOiggIAAPfvss432+mM9njx5UpMmTVKnTp3kcrk0ePBglZeX+2x+QEt2tevzF7/4RaOvqZd/8PHlWJ/AN7d7925NmzZNvXr1ksvl0vXXX68xY8bowIEDXr2mvX76/B50AN8NOTk5SkpK8qjdeOON9r+rqqp06623qn379lqwYIHOnDmjpUuXau/evXrnnXcUGBho965Zs0aTJ09WRkaGHnroIf31r3/Vgw8+qHPnzunhhx9utjkBLc2xY8f02GOP6frrr1dcXJxKSkoa/dwVf6zHS5cuaeTIkXr//ff18MMPKzw8XCtXrtSgQYO0Z88e3XTTTc3y3wAw1dWuT0kKCgpSXl6eRy0sLMyrj/UJ+MaiRYu0c+dOZWZm6uabb5bb7daKFSuUkJCgt99+W7169ZJk6OunBQCXefPNNy2Hw2G98MILTfZNnjzZCgkJsQ4fPmzXiouLLYfDYa1du9aunTt3zgoPD7fS09M9nj9hwgTL5XJZn332mW8nAHyHnD9/3vrkk08sy7Ksd99913I4HNazzz7r1eeP9bhp0yavvwXHjh2z2rdvb40fP95ncwRaqqtdn/fcc491zTXXfOl4rE/Ad3bs2GHV1dV51A4cOGAFBwdbEyZMsGsmvn6yxR1AoyzL0pkzZ1RfX9/o8RdeeEG33367unXrZteGDBmi6Ohobd682a69+eabOnHihKZMmeLx/KlTp6q6ulqvvPKKfyYAfAe0adNGnTt3lvT5mrwSf6zH559/XpGRkbrjjjvsWseOHXXnnXdqy5Ytqqur+8bzA1qyq12fDccvXbqk06dPX7GH9Qn4Tv/+/dW6tedm8Ztuukk//OEPVVlZaddMfP0koANo1L333quwsDA5nU4NHjxYe/bssY8dOXJEx44dU2JiotfzkpKSPO6xafj3F3sTEhIUEBCgiooKP80A+H7w13osLy9XQkJCo2OeO3dO+/fv99UUgO+8c+fOKTQ0VO3atVN4eLimTZum6upqjx7WJ+BflmXpk08+UceOHSWZ+/pJQAfgISgoSBkZGVq+fLleeuklPf7449q7d69uueUW+4+P2+2WJEVFRXk9PyoqSidOnLDfHXS73WrVqpX9x7BBmzZtFB4erqNHj/p5RsB3m7/Wo9vtvuKYkli7wFXq0qWLZs+erfz8fG3cuFGjRo3SypUrNXz4cF28eNHuY30C/rVhwwYdPXpUY8aMkWTu6ycfEgfAQ//+/dW/f3/78e23366MjAzdfPPNeuSRR/Tqq6+qpqZG0udh/ouCg4MlSTU1NQoMDFRNTY3atGnT6O8KCgqyxwLw9fhrPdbW1n7pmAC+3BNPPOHx+M4771R0dLTmzZun559/3g4LrE/AfyorKzV16lQlJyfrnnvukWTu6ydX0AF8qRtvvFGjRo3Sm2++Kcuy5HQ6JUnnz5/36q2trZUku8fpdOrChQuNjltbW2v3Afh6/LUenU7nVY0J4KubMWOGAgIC9MYbb9g11ifgH//5z380cuRItW/fXs8//7z9bQumvn4S0AFclWuvvVYXLlxQdXW1vUWnYWvQ5dxut8LDw+2vpYiKitLFixe9vk/ywoULOnHihLp06eL/kwe+w/y1HqOiohrdhtfwe1i7wNcXHBysDh066MSJE3aN9Qn43qlTpzRixAidPn1ar732miIjI+1jpr5+EtABXJUPP/xQTqdTLpdLXbt2VadOnbR7926vvnfeeUdxcXH24/j4eEny6n333Xd16dIlj14AX52/1mNcXJzKysq8Pp16165dCgkJUXR0tC+nAXyvnDlzRsePH1enTp3sGusT8K3a2lqlp6fr4MGDevnllxUTE+Nx3NTXTwI6AA/Hjh3zqr333nt66aWXlJqaatd+/vOf6+WXX1ZVVZVde+ONN3TgwAFlZmbatcGDB6tDhw5atWqVx5irVq1SSEiIRo4c6YdZAN8v/liPGRkZ+uSTT/Tiiy/atePHj+u5555Tenq6fVUBwJWdP39eZ86c8ao/9thjkqThw4fbNdYn4DsXL17UmDFjtGvXLj333HPq169fo30mvn46rC/74kYA3yuDBw9W27Zt1b9/f3Xu3Fn//Oc/tXbtWgUFBWnnzp36wQ9+IEmqqqpSfHy82rVrp5ycHJ05c0ZLlizRddddp927d3v88Vm1apWmTp2qjIwMpaam6m9/+5vWrVunJ554QnPmzPm2pgq0CCtWrNDJkyd19OhRrV69WnfccYf9Tv2DDz6o0NBQv6zHS5cuaeDAgfr73/+uWbNmKTw8XCtXrlRVVZV2796tnj17Nvt/C8A0X7Y+T5w4ofj4eI0fP95+/Xz99df16quvasSIER7fnSyxPgFfmT59upYvX6709HSPoN1gwoQJkvzz/7PfeH1aAHCZ5cuXW/369bPCw8OtwMBAq2vXrlZWVpb1wQcfePX+4x//sNLS0qyQkBCrQ4cO1t133219+umnjY77+9//3oqJibGCgoKsnj17Wk899ZS/pwJ8J3Tv3t1yOByWw+GwAgICrICAAPvfH3/8sd3nj/X42WefWRMnTrQ6duxohYSEWLfddpu1Z88ev8wTaIm+bH2ePHnSuvvuu62ePXtaISEhVnBwsPWjH/3IWrhwoVVfX9/omKxP4JsbNGiQvR6/+BMQEODRa9rrJ1fQAQAAAAAwAPegAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABgAAI6AAAAAAAGIKADAAAAAGAAAjoAAAAAAAYgoAMAAAAAYAACOgAAAAAABiCgAwAAAABggP8HnY2JjpAQEyMAAAAASUVORK5CYII="
-  frames[5] = 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"
-  frames[6] = 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"
-  frames[7] = 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"
-  frames[8] = 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"
-  frames[9] = 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"
-  frames[10] = 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"
-  frames[11] = 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"
-  frames[12] = 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-  frames[13] = 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"
-
-
-    /* set a timeout to make sure all the above elements are created before
-       the object is initialized. */
-    setTimeout(function() {
-        animBYBDWVXULSOEHXUW = new Animation(frames, img_id, slider_id, 33, loop_select_id);
-    }, 0);
-  })()
-</script>
diff --git a/simpegDCIP/Dev/dcipf2d.inp b/simpegDCIP/Dev/dcipf2d.inp
deleted file mode 100644
index 80dbf471..00000000
--- a/simpegDCIP/Dev/dcipf2d.inp
+++ /dev/null
@@ -1,5 +0,0 @@
-FWD DC
-MESH FILE Mesh_2D.msh
-LOC LOC_X OBS_LOC.dat
-TOPO DEFAULT
-COND FILE MtIsa_2D.con
\ No newline at end of file
diff --git a/simpegDCIP/Dev/obs2d_East.loc b/simpegDCIP/Dev/obs2d_East.loc
deleted file mode 100644
index 6738aef9..00000000
--- a/simpegDCIP/Dev/obs2d_East.loc
+++ /dev/null
@@ -1,325 +0,0 @@
-0 0  75 150
-0 0  150 225
-0 0  225 300
-0 0  300 375
-0 0  375 450
-0 0  450 525
-0 0  525 600
-0 0  600 675
-0 0  675 750
-0 0  750 825
-0 0  825 900
-0 0  900 975
-0 0  975 1050
-0 0  1050 1125
-0 0  1125 1200
-0 0  1200 1275
-0 0  1275 1350
-0 0  1350 1425
-0 0  1425 1500
-0 0  1500 1575
-0 0  1575 1650
-0 0  1650 1725
-0 0  1725 1800
-0 0  1800 1875
-0 0  1875 1950
-75 75  150 225
-75 75  225 300
-75 75  300 375
-75 75  375 450
-75 75  450 525
-75 75  525 600
-75 75  600 675
-75 75  675 750
-75 75  750 825
-75 75  825 900
-75 75  900 975
-75 75  975 1050
-75 75  1050 1125
-75 75  1125 1200
-75 75  1200 1275
-75 75  1275 1350
-75 75  1350 1425
-75 75  1425 1500
-75 75  1500 1575
-75 75  1575 1650
-75 75  1650 1725
-75 75  1725 1800
-75 75  1800 1875
-75 75  1875 1950
-150 150  225 300
-150 150  300 375
-150 150  375 450
-150 150  450 525
-150 150  525 600
-150 150  600 675
-150 150  675 750
-150 150  750 825
-150 150  825 900
-150 150  900 975
-150 150  975 1050
-150 150  1050 1125
-150 150  1125 1200
-150 150  1200 1275
-150 150  1275 1350
-150 150  1350 1425
-150 150  1425 1500
-150 150  1500 1575
-150 150  1575 1650
-150 150  1650 1725
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diff --git a/simpegDCIP/Examples/DC_PseudoSection_Simulation.py b/simpegDCIP/Examples/DC_PseudoSection_Simulation.py
new file mode 100644
index 00000000..c3517360
--- /dev/null
+++ b/simpegDCIP/Examples/DC_PseudoSection_Simulation.py
@@ -0,0 +1,179 @@
+from SimPEG import *
+import simpegDCIP as DC
+import scipy.interpolate as interpolation
+import matplotlib.pyplot as plt
+import time
+import re
+
+def run(loc=np.c_[[-50.,0.,-50.],[50.,0.,-50.]], sig=np.r_[1e-2,1e-1,1e-3], radi=np.r_[25.,25.], param = np.r_[30.,30.,5], stype = 'dpdp',  plotIt=True):
+    """
+    DC Forward Simulation
+    
+    Forward model conductive spheres in a half-space and plot a pseudo-section
+    
+    Created on Mon Feb 01 19:28:06 2016
+    
+    @fourndo
+    """
+    
+    # First we need to create a mesh and a model.
+    
+    # This is our mesh
+    dx    = 5.
+    
+    hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]
+    hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]
+    hzind = [(dx,15,-1.3),(dx, 15)]
+    
+    mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
+    
+    
+    # Set background conductivity
+    model = np.ones(mesh.nC) * sig[0]
+    
+    # First anomaly
+    ind = Utils.ModelBuilder.getIndicesSphere(loc[:,0],radi[0],mesh.gridCC)
+    model[ind] = sig[1]
+    
+    # Second anomaly
+    ind = Utils.ModelBuilder.getIndicesSphere(loc[:,1],radi[1],mesh.gridCC)
+    model[ind] = sig[2]
+    
+    # Get index of the center
+    indy = int(mesh.nCy/2)
+
+    
+    # Plot the model for reference
+    # Define core mesh extent
+    xlim = 200
+    zlim = 125
+    
+    # Specify the survey type: "pdp" | "dpdp"
+    
+    
+    # Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh
+    ends = [(-175,0),(175,0)]
+    ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]
+    
+    # Snap the endpoints to the grid. Easier to create 2D section.
+    indx = Utils.closestPoints(mesh, ends )
+    locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]
+    
+    # We will handle the geometry of the survey for you and create all the combination of tx-rx along line
+    [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
+    
+    # Define some global geometry
+    dl_len = np.sqrt( np.sum((locs[0,:] - locs[1,:])**2) ) 
+    dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
+    dl_y = ( Tx[-1][1,1] - Tx[0][1,0]  ) / dl_len
+    azm =  np.arctan(dl_y/dl_x)
+
+    #Set boundary conditions
+    mesh.setCellGradBC('neumann')
+    
+    # Define the differential operators needed for the DC problem
+    Div = mesh.faceDiv
+    Grad = mesh.cellGrad
+    Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
+    
+    A = Div*Msig*Grad
+    
+    # Change one corner to deal with nullspace
+    A[0,0] = 1
+    A = sp.csc_matrix(A)
+    
+    # We will solve the system iteratively, so a pre-conditioner is helpful
+    # This is simply a Jacobi preconditioner (inverse of the main diagonal)
+    dA = A.diagonal()
+    P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
+
+    # Now we can solve the system for all the transmitters
+    # We want to store the data
+    data = []
+    
+    # There is probably a more elegant way to do this, but we can just for-loop through the transmitters
+    for ii in range(len(Tx)):
+        
+        start_time = time.time() # Let's time the calculations
+        
+        #print("Transmitter %i / %i\r" % (ii+1,len(Tx)))
+        
+        # Select dipole locations for receiver
+        rxloc_M = np.asarray(Rx[ii][:,0:3])
+        rxloc_N = np.asarray(Rx[ii][:,3:])
+
+      
+        # For usual cases "dpdp" or "gradient"
+        if not re.match(stype,'pdp'): 
+            inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
+            RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )   
+            
+        else: 
+            
+            # Create an "inifinity" pole
+            tx =  np.squeeze(Tx[ii][:,0:1])
+            tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
+            inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
+            RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )  
+    
+    
+        # Iterative Solve
+        Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
+    
+        # We now have the potential everywhere
+        phi = mkvc(Ainvb[0])
+        
+        # Solve for phi on pole locations
+        P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
+        P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
+            
+        # Compute the potential difference
+        dtemp = (P1*phi - P2*phi)*np.pi
+        
+        data.append( dtemp )     
+        print '\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),
+        
+    print 'Transmitter {0} of {1}'.format(ii,len(Tx))
+    print 'Forward completed'
+    
+    
+    # Let's just convert the 3D format into 2D (distance along line) and plot
+    [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
+    
+
+    # Here is an example for the first tx-rx array
+    if plotIt:
+        fig = plt.figure()
+        ax = plt.subplot(2,1,1, aspect='equal')
+        mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)
+        ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')
+        plt.gca().set_aspect('equal', adjustable='box')
+
+        plt.scatter(Tx[0][0,:],Tx[0][2,:],s=40,c='g', marker='v')
+        plt.scatter(Rx[0][:,0::3],Rx[0][:,2::3],s=40,c='y')
+        plt.xlim([-xlim,xlim])
+        plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
+        
+        
+        ax = plt.subplot(2,1,2, aspect='equal')   
+        
+        # Plot the location of the spheres for reference
+        circle1=plt.Circle((loc[0,0]-Tx[0][0,0],loc[2,0]),radi[0],color='w',fill=False, lw=3)
+        circle2=plt.Circle((loc[0,1]-Tx[0][0,0],loc[2,1]),radi[1],color='k',fill=False, lw=3)
+        ax.add_artist(circle1)
+        ax.add_artist(circle2)  
+        
+        # Add the speudo section
+        DC.plot_pseudoSection(Tx2d,Rx2d,data,mesh.vectorNz[-1],stype)
+        
+        plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
+        plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y')
+        plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k')
+        plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
+        
+        plt.show()
+        
+        return fig, ax
+        
+if __name__ == '__main__':
+    run()
\ No newline at end of file