diff --git a/Luminescent_AI_docs.ipynb b/Luminescent_AI_docs.ipynb
index d0b970e..33302b0 100644
--- a/Luminescent_AI_docs.ipynb
+++ b/Luminescent_AI_docs.ipynb
@@ -25,6 +25,30 @@
     "[Follow us](https://www.linkedin.com/company/luminescent-ai/about) for updates and bug fixes!"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "94918b9d-e6b1-4e80-9d0d-502a0fd119ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/n_O1r6wggLU?si=x2tKzw4hzrIz6h7C\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%HTML\n",
+    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/n_O1r6wggLU?si=x2tKzw4hzrIz6h7C\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4da41568-e209-4ebc-88b8-98ca7bdb12e0",
@@ -158,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "78ed9a44-53f9-481e-9b92-43664b283ed0",
    "metadata": {},
    "outputs": [
@@ -166,85 +190,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-09-24 22:08:15.844\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mkfactory.conf\u001b[0m:\u001b[36m_validate_layout_cache\u001b[0m:\u001b[36m254\u001b[0m - \u001b[33m\u001b[1m'cell_layout_cache' has been set to True. This might cause when as any cell names generated automatically are loaded from the layout instead of created. This could happen e.g. after reading a gds file into the layout.\u001b[0m\n",
+      "\u001b[32m2024-09-29 13:22:16.951\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mkfactory.conf\u001b[0m:\u001b[36m_validate_layout_cache\u001b[0m:\u001b[36m254\u001b[0m - \u001b[33m\u001b[1m'cell_layout_cache' has been set to True. This might cause when as any cell names generated automatically are loaded from the layout instead of created. This could happen e.g. after reading a gds file into the layout.\u001b[0m\n",
       "showing solution from C:\\Users\\pxshe\\OneDrive\\Desktop\\Luminescent.jl\\runs\\mode_converter\n",
-      "loading solution from C:\\Users\\pxshe\\OneDrive\\Desktop\\Luminescent.jl\\runs\\mode_converter\n",
-      "Converting an image file to a GDS file..\n",
-      "width:100\n",
-      "height:60\n",
-      "\u001b[32m2024-09-24 22:08:22.695\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgdsfactory.component\u001b[0m:\u001b[36m__setattr__\u001b[0m:\u001b[36m185\u001b[0m - \u001b[33m\u001b[1mSetting `Unnamed_11_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_11_0_0.dxmin` instead. For further information, please consult the migration guide: \u001b[0m\n",
-      "\u001b[32m2024-09-24 22:08:22.711\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgdsfactory.component\u001b[0m:\u001b[36m__setattr__\u001b[0m:\u001b[36m185\u001b[0m - \u001b[33m\u001b[1mSetting `Unnamed_11_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_11_0_0.dymin` instead. For further information, please consult the migration guide: \u001b[0m\n",
-      "{'path': 'C:\\\\Users\\\\pxshe\\\\OneDrive\\\\Desktop\\\\Luminescent.jl\\\\runs\\\\mode_converter',\n",
-      " 'sparams': {'1.55': {'o1@0,o1@1': (0.05592486+0.0098690735j),\n",
-      "                      'o1@1,o1@1': (-0.0887472-0.07247121j),\n",
-      "                      'o2@0,o1@1': (0.951611+0.31702667j),\n",
-      "                      'o2@1,o1@1': (-0.034790922+0.020795016j)}},\n",
-      " 'tparams': {'1.55': {'o1@0,o1@1': 0.0032249885,\n",
-      "                      'o1@1,o1@1': 0.013128143,\n",
-      "                      'o2@0,o1@1': 1.0060694,\n",
-      "                      'o2@1,o1@1': 0.0016428409}}}\n"
+      "loading solution from C:\\Users\\pxshe\\OneDrive\\Desktop\\Luminescent.jl\\runs\\mode_converter\n"
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:163: UserWarning: Setting `Unnamed_11_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_11_0_0.dxmin` instead.\n",
-      "  g.xmin = x0\n",
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:164: UserWarning: Setting `Unnamed_11_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_11_0_0.dymin` instead.\n",
-      "  g.ymin = y0\n"
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\pxshe\\\\OneDrive\\\\Desktop\\\\Luminescent.jl\\\\runs\\\\mode_converter\\\\prob.bson'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mluminescent\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mlumi\u001b[39;00m \n\u001b[0;32m      2\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode_converter\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mlumi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_solution\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\sol.py:130\u001b[0m, in \u001b[0;36mshow_solution\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m    128\u001b[0m path \u001b[38;5;241m=\u001b[39m lastrun(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    129\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshowing solution from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 130\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[43mload_solution\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    131\u001b[0m sol \u001b[38;5;241m=\u001b[39m {k: sol[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparams\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtparams\u001b[39m\u001b[38;5;124m\"\u001b[39m, ]}\n\u001b[0;32m    132\u001b[0m pprint(sol)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\sol.py:103\u001b[0m, in \u001b[0;36mload_solution\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m    101\u001b[0m path \u001b[38;5;241m=\u001b[39m lastrun(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    102\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloading solution from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 103\u001b[0m prob \u001b[38;5;241m=\u001b[39m bson\u001b[38;5;241m.\u001b[39mloads(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprob.bson\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mread())\n\u001b[0;32m    104\u001b[0m p \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msol.json\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    105\u001b[0m \u001b[38;5;66;03m# sol = bson.loads(p, \"rb\").read())[\"sol\"]\u001b[39;00m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\pxshe\\\\OneDrive\\\\Desktop\\\\Luminescent.jl\\\\runs\\\\mode_converter\\\\prob.bson'"
      ]
-    },
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",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1200x900>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading solution from C:\\Users\\pxshe\\OneDrive\\Desktop\\Luminescent.jl\\runs\\mode_converter\n",
-      "Converting an image file to a GDS file..\n",
-      "width:100\n",
-      "height:60\n",
-      "\u001b[32m2024-09-24 22:08:24.513\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgdsfactory.component\u001b[0m:\u001b[36m__setattr__\u001b[0m:\u001b[36m185\u001b[0m - \u001b[33m\u001b[1mSetting `Unnamed_16_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_16_0_0.dxmin` instead. For further information, please consult the migration guide: \u001b[0m\n",
-      "\u001b[32m2024-09-24 22:08:24.515\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mgdsfactory.component\u001b[0m:\u001b[36m__setattr__\u001b[0m:\u001b[36m185\u001b[0m - \u001b[33m\u001b[1mSetting `Unnamed_16_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_16_0_0.dymin` instead. For further information, please consult the migration guide: \u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:163: UserWarning: Setting `Unnamed_16_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_16_0_0.dxmin` instead.\n",
-      "  g.xmin = x0\n",
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:164: UserWarning: Setting `Unnamed_16_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_16_0_0.dymin` instead.\n",
-      "  g.ymin = y0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
     "import luminescent as lumi \n",
     "name=\"mode_converter\"\n",
-    "lumi.show_solution(name=name)\n",
-    "sol=lumi.load_solution(name=name)\n",
-    "sol[\"optimized_component\"].plot()"
+    "lumi.show_solution(name=name)"
    ]
   },
   {
@@ -266,20 +234,28 @@
     "from pprint import pprint\n",
     "import luminescent as lumi\n",
     "\n",
-    "name=\"1x2_splitter\"\n",
-    "c = lumi.gcells.mimo(west=1, east=2, l=3.0, w=3.0, wwg=.5, name=name)\n",
-    "targets = {\"tparams\":{1.55: {\"2,1\": 0.5}}}\n",
+    "name = \"1x2_splitter\"\n",
+    "c = lumi.gcells.mimo(west=1, east=2, l=4.0, w=2.0, wwg=.5, name=name)\n",
+    "targets = {\"tparams\": {1.55: {\"2,1\": 0.5, \"3,1\": 0.5}}}\n",
     "\n",
     "prob = lumi.gcell_problem(\n",
-    "    c, targets, \n",
-    "    symmetries=[1], lmin=0.15, dx=0.05, \n",
-    "    approx_2D=True, iters=10)\n",
-    "sol = lumi.solve(prob)"
+    "    c, targets,\n",
+    "    symmetries=[1], lvoid=0.1, dx=0.05,\n",
+    "    approx_2D=True, iters=30, stoploss=.03)\n",
+    "sol = lumi.solve(prob)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f032cac-144f-4297-878b-a133c4c64d76",
+   "metadata": {},
+   "source": [
+    "In an anticlimatic turn of events, optimization stops after a couple iterations as `stoploss` threshold is reached. Turns out the slab initial condition is already a good splitter  :D"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "2b23b10b-9edf-4b31-9eb6-5485e149bdce",
    "metadata": {
     "scrolled": true
@@ -295,28 +271,34 @@
       "width:80\n",
       "height:40\n",
       "{'path': 'C:\\\\Users\\\\pxshe\\\\OneDrive\\\\Desktop\\\\Luminescent.jl\\\\runs\\\\1x2_splitter',\n",
-      " 'sparams': {'1.55': {'o1@0,o1@0': (-0.041301988-0.10184847j),\n",
-      "                      'o2@0,o1@0': (0.42761898+0.56001j),\n",
-      "                      'o2@1,o1@0': (0.012352835-0.0011391708j)}},\n",
-      " 'tparams': {'1.55': {'o1@0,o1@0': 0.012078965,\n",
-      "                      'o2@0,o1@0': 0.4964692,\n",
-      "                      'o2@1,o1@0': 0.00015389026}}}\n"
+      " 'sparams': {'1.55': {'o1@0,o1@0': (-0.02497092-0.025129166j),\n",
+      "                      'o1@1,o1@0': (0.0045466204-0.0023630492j),\n",
+      "                      'o2@0,o1@0': (-0.50640124+0.50496256j),\n",
+      "                      'o2@1,o1@0': (0.0054302337+0.044088982j),\n",
+      "                      'o3@0,o1@0': (-0.48066247+0.5202666j),\n",
+      "                      'o3@1,o1@0': (0.039007787-0.10546609j)}},\n",
+      " 'tparams': {'1.55': {'o1@0,o1@0': 0.0012550219,\n",
+      "                      'o1@1,o1@0': 2.6255759e-05,\n",
+      "                      'o2@0,o1@0': 0.5114294,\n",
+      "                      'o2@1,o1@0': 0.0019733259,\n",
+      "                      'o3@0,o1@0': 0.50171375,\n",
+      "                      'o3@1,o1@0': 0.012644704}}}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:163: UserWarning: Setting `Unnamed_143_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_143_0_0.dxmin` instead.\n",
+      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:163: UserWarning: Setting `Unnamed_28_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_28_0_0.dxmin` instead.\n",
       "  g.xmin = x0\n",
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:164: UserWarning: Setting `Unnamed_143_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_143_0_0.dymin` instead.\n",
+      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:164: UserWarning: Setting `Unnamed_28_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_28_0_0.dymin` instead.\n",
       "  g.ymin = y0\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1200x900>"
       ]
@@ -326,13 +308,13 @@
     }
    ],
    "source": [
-    "# pprint(sol[\"tparams\"])\n",
+    "# import luminescent as lumi \n",
     "lumi.show_solution()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "7af8f2bd-bc33-481c-b34e-b4310f5379c2",
    "metadata": {},
    "outputs": [
@@ -342,7 +324,7 @@
      "text": [
       "loading solution from C:\\Users\\pxshe\\OneDrive\\Desktop\\Luminescent.jl\\runs\\1x2_splitter\n",
       "Converting an image file to a GDS file..\n",
-      "width:70\n",
+      "width:80\n",
       "height:40\n"
      ]
     },
@@ -350,15 +332,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:163: UserWarning: Setting `Unnamed_138_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_138_0_0.dxmin` instead.\n",
+      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:163: UserWarning: Setting `Unnamed_33_0_0.xmin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_33_0_0.dxmin` instead.\n",
       "  g.xmin = x0\n",
-      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:164: UserWarning: Setting `Unnamed_138_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_138_0_0.dymin` instead.\n",
+      "C:\\Users\\pxshe\\AppData\\Roaming\\Python\\Python311\\site-packages\\luminescent\\gplugins\\luminescent\\inverse_design.py:164: UserWarning: Setting `Unnamed_33_0_0.ymin` in um is deprecated and will change to DataBaseUnits in gdsfactory9. Please use `Unnamed_33_0_0.dymin` instead.\n",
       "  g.ymin = y0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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2rft16Iz3wY5V9R8VJzfygJnwplLt+BDrfh2qqqqa192r6j9SfraRB4yqqqrqu3at+3Wo/sr7dMeqbnDA7P6FqU6v96uqqqq+a9eq3vSA2bnPv7Ad6/6Lr6qqqvld634dqqff91UOmOP7/KGza91vJp1R7wdVVVW9Q3fMAdO414djSFVV9W5d634dqqff91UOGPvfv5gfew+iCW/2u9TvW1VVVe/QHXPA2D+/OT4cQ6qqqru71v06VE+/76s2fUd1wNipff7Q3rX1503vu/3Po6qqqvqj7pgDxmL3+nAMqapqXte6X4fq6fd91abvgA4Ys//b54fKrq0/71f1MFNVVdU7dMccMGbftNeHvwOkqqp/71r361A9/b6v2vQdywFj1j8PM1VVVb1Dd8wBYzZg3R8mqqp6rmvdr0P19Pu+atP3JgeMWf88zFRVVfUO3TEHjNmAdX+YqKrqua51vw7V0+/7qk3fmxwwZv3zMFNVVdU7dMccMGYD1v1hoqqq57rW/TpUT7/vqzZ9b3LAmPXPw0xVVVXv0B1zwJgNWPeHiaqqnuta9+tQPf2+r9r0vckBY9Y/DzNVVVW9Q3fMAWM2YN0fJqqqeq5r3a9D9fT7vmrT9yYHjFn/PMxUVVX1Dt0xB4zZgHV/mKiq6rmudb8O1dPv+6pN35scMGb98zBTVVXVO3THHDBmA9b9YaKqque61v06VE+/76s2fW9ywJj1z8NMVVVV79Adc8CYDVj3h4mqqp7rWvfrUD39vq/a9L3JAWPWPw8zVVVVvUN3zAFjNmDdHyaqqnqua92vQ/X0+75q0/cmB4xZ/zzMVFVV9Q7dMQeM2YB1f5ioquq5rnW/DtXT7/uqTd+bHDBm/fMwU1VV1Tt0xxwwZgPW/WGiqqrnutb9OlRPv++rNn1vcsCY9c/DTFVVVe/QHXPAmA1Y94eJqqqe61r361A9/b6v2vS9yQFj1j8PM1VVVb1Dd8wBYzZg3R8mqqp6rmvdr0P19Pu+atP3JgeMWf88zFRVVfUO3TEHjNmAdX+YqKrqua51vw7V0+/7qk3fmxwwZv3zMFNVVdU7dMccMGYD1v1hoqqq57rW/TpUT7/vqzZ9b3LAmPXPw0xVVVXv0B1zwJgNWPeHiaqqnuta9+tQPf2+r9r0vckBY9Y/DzNVVVW9Q3fMAWM2YN0fJqqqeq5r3a9D9fT7vmrT9yYHjFn/PMxUVVX1Dt0xB4zZgHV/mKiq6rmudb8O1dPv+6pN35scMGb98zBTVVXVO3THHDBmA9b9YaKqque61v06VE+8z//3Pb9jDhizAfMwU1VV1a7uXtU3f29ywJj1b8KHl6qqnula9+vQ7O5cVf93oX8zB4zZgHmYqaqqvm93r6r/u0vnHDBmAzbhw1VVVc90rft16K/9ddqxqv7vGu80B4zZgHmYqaqqXu/uVfV/N7CffG9ywJj1b8KHv6qqnula9+vo7s5V9T/L7eD3JgeMWf88zFRVdXJ3r6r/2Wu5c8CYDdiEh5Oqqp7p2ql/nR2r6n9Wmq05YMwG7NTDTFVVZ3b3qvqfbWbfNQeM2YBNeHiqquqZ/mhV/c8is5Q5YMwG7PMDTFVV71EzuzYHjNmAdT9EVVX1XNfPZnZtDhizAfv8QFNV1XvUzK7NAWM2YN0PUVVVPdf1s5ldmwPGbMA+P9BUVfUeNbNrc8CYDVj3Q1RVVc91/Wxm1+aAMRuwzw80VVW9R83s2hwwZgPW/RBVVdVzXT+b2bU5YMwG7PMDTVVV71EzuzYHjNmAdT9EVVX1XNfPZnZtDhizAfv8QFNV1XvUzK7NAWM2YN0PUVVVPdf1s5ldmwPGbMA+P9BUVfUeNbNr+8rjryPlHz0ej5/9l/+15/NZz+dz65+TX+N33+f18dH9EgA46PHnn90v4ZZ81+mz83f/xXlSxw8YAACAf/LVAfOfQ68DAADgtzlgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACCGAwYAAIjhgAEAAGIcP2Cez+fpf0n+4nffx+++j999H7/7Xn7/ffzu+/jd9zn5u3+8Xq/XT/+Ax+PUawEAAG7ui/PEP0IGAADkcMAAAAAxHDAAAEAMBwwAABDDAQMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABADAcMAAAQwwEDAADEcMAAAAAxHDAAAEAMBwwAABDDAQMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABADAcMAAAQwwEDAADEcMAAAAAxHDAAAEAMBwwAABDDAQMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABADAcMAAAQwwEDAADEcMAAAAAxHDAAAEAMBwwAABDDAQMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABADAcMAAAQwwEDAADEcMAAAAAxHDAAAEAMBwwAABDDAQMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABADAcMAAAQwwEDAADEcMAAAAAxHDAAAEAMBwwAABDDAQMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABAjD+6XwCc9Hw+f/ofAwAwmwOG23g+nz88YBwxAAA5/CNk3MbvHio/+u///J9bx9D6zzmOAAD283dgYKMfHS8OGQCAffwdGG7LYQEAkMffgeF2Pv+dEQAAsjhguBV/1wUAIJt/hIzb2H28OIQAAM7zd2C4la/+L4n9m/9+fzcHAOC8x+v1ev30D3g8Tr0WGM3BAgDw/b44T/wjZAAAQA4HDAAAEMM/QgYAAIzhHyEDAADehgMGAACIcfz/jLL/K07clfc+AMDv8/8H5mZ8ie7h9w4AsId/hAwAAIjhgAEAAGI4YAAAgBgOGAAAIIb/Jf6b8b9MDgBAtNcXqmrrns/n9j+n+d1Pn9+93/0d53fv93/X+d373d9xO3/3X3n8daT8o8fj8bP/MgAAwDZfnCf+d2AAAIAcDhgAACCGAwYAAIjhgAEAAGI4YAAAgBgOGAAAIIYDBgAAiOGAAQAAYjhgAACAGA4YAAAghgMGAACI4YABAABiOGAAAIAYDhgAACDGH1/9Aa/X68TrAAAA+JK/AwMAAMRwwAAAADEcMAAAQAwHDAAAEMMBAwAAxHDAAAAAMRwwAABAjP8C7KU7q9IQ1oEAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -393,7 +375,7 @@
     "import luminescent as lumi\n",
     "\n",
     "name=\"1x4_splitter\"\n",
-    "c = lumi.gcells.mimo(west=1, east=4, l=6.0, w=6.0, wwg=.5, name=name)\n",
+    "c = lumi.gcells.mimo(west=1, east=4, l=6.0, w=4.0, wwg=.5, name=name)\n",
     "targets = {\n",
     "    \"tparams\":{1.55: {\"2,1\": 0.25, \"3,1\":0.25}},\n",
     "    \"phasediff\":{1.55: {\"2,3\": 0.0}},\n",
@@ -401,8 +383,8 @@
     "\n",
     "prob = lumi.gcell_problem(\n",
     "    c, targets, \n",
-    "    symmetries=[1], lmin=0.15, dx=0.05, \n",
-    "    approx_2D=True, iters=40)\n",
+    "    symmetries=[1], lvoid=0.2,lsolid=0.1, dx=0.05, \n",
+    "    approx_2D=True, stoploss=.05, iters=40)\n",
     "sol = lumi.solve(prob)"
    ]
   },
@@ -419,21 +401,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "f5435e48-aa6d-4661-ab36-8c787d5c2d20",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "c_opt=sol[\"optimized_component\"]\n",
     "c_opt.plot()"
@@ -467,7 +438,7 @@
     "\n",
     "prob = lumi.gcell_problem(\n",
     "    c, targets,\n",
-    "    lmin=0.15, dx=0.05, \n",
+    "    lvoid=0.2,lsolid=0.1, dx=0.05, \n",
     "    approx_2D=True, iters=50)\n",
     "sol = lumi.solve(prob)"
    ]
@@ -513,7 +484,7 @@
     "\n",
     "prob = lumi.gcell_problem(\n",
     "    c, targets,\n",
-    "    lmin=0.15, dx=0.05, symmetries=[0,\"diag\"],\n",
+    "    lvoid=0.2,lsolid=0.1, dx=0.05, symmetries=[0,\"diag\"],\n",
     "    approx_2D=True, iters=40)\n",
     "sol = lumi.solve(prob)"
    ]
diff --git a/Manifest.toml b/Manifest.toml
index f4798fa..845a0eb 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -224,10 +224,10 @@ uuid = "4e9b3aee-d8a1-5a3d-ad8b-7d824db253f0"
 version = "1.0.1+0"
 
 [[deps.CUDA]]
-deps = ["AbstractFFTs", "Adapt", "BFloat16s", "CEnum", "CUDA_Driver_jll", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "Crayons", "DataFrames", "ExprTools", "GPUArrays", "GPUCompiler", "KernelAbstractions", "LLVM", "LLVMLoopInfo", "LazyArtifacts", "Libdl", "LinearAlgebra", "Logging", "NVTX", "Preferences", "PrettyTables", "Printf", "Random", "Random123", "RandomNumbers", "Reexport", "Requires", "SparseArrays", "StaticArrays", "Statistics"]
-git-tree-sha1 = "cdbdca28f19c2c7fcf34ffb48bfdaca404dcd18a"
+deps = ["AbstractFFTs", "Adapt", "BFloat16s", "CEnum", "CUDA_Driver_jll", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "Crayons", "DataFrames", "ExprTools", "GPUArrays", "GPUCompiler", "KernelAbstractions", "LLVM", "LLVMLoopInfo", "LazyArtifacts", "Libdl", "LinearAlgebra", "Logging", "NVTX", "Preferences", "PrettyTables", "Printf", "Random", "Random123", "RandomNumbers", "Reexport", "Requires", "SparseArrays", "StaticArrays", "Statistics", "demumble_jll"]
+git-tree-sha1 = "e0725a467822697171af4dae15cec10b4fc19053"
 uuid = "052768ef-5323-5732-b1bb-66c8b64840ba"
-version = "5.5.0"
+version = "5.5.2"
 
     [deps.CUDA.extensions]
     ChainRulesCoreExt = "ChainRulesCore"
@@ -265,9 +265,9 @@ version = "1.1.0"
 
 [[deps.CairoMakie]]
 deps = ["CRC32c", "Cairo", "Cairo_jll", "Colors", "FileIO", "FreeType", "GeometryBasics", "LinearAlgebra", "Makie", "PrecompileTools"]
-git-tree-sha1 = "4f827b38d3d9ffe6e3b01fbcf866c625fa259ca5"
+git-tree-sha1 = "0852b8edf4da66cc44861b12d7d6c69693fc620f"
 uuid = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
-version = "0.12.11"
+version = "0.12.12"
 
 [[deps.Cairo_jll]]
 deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
@@ -433,10 +433,10 @@ uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
 version = "1.16.0"
 
 [[deps.DataFrames]]
-deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
-git-tree-sha1 = "04c738083f29f86e62c8afc341f0967d8717bdb8"
+deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
+git-tree-sha1 = "fb61b4812c49343d7ef0b533ba982c46021938a6"
 uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-version = "1.6.1"
+version = "1.7.0"
 
 [[deps.DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
@@ -460,9 +460,9 @@ version = "0.1.2"
 
 [[deps.DelaunayTriangulation]]
 deps = ["AdaptivePredicates", "EnumX", "ExactPredicates", "Random"]
-git-tree-sha1 = "94eb20e6621600f4315813b1d1fc9b8a5a6a34db"
+git-tree-sha1 = "90fe18ca4b73bdd2320fbbccec727a75069455f6"
 uuid = "927a84f5-c5f4-47a5-9785-b46e178433df"
-version = "1.4.0"
+version = "1.5.1"
 
 [[deps.DelimitedFiles]]
 deps = ["Mmap"]
@@ -499,9 +499,9 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 
 [[deps.Distributions]]
 deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "e6c693a0e4394f8fda0e51a5bdf5aef26f8235e9"
+git-tree-sha1 = "d7477ecdafb813ddee2ae727afa94e9dcb5f3fb0"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.111"
+version = "0.25.112"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"
@@ -559,9 +559,9 @@ version = "0.1.4"
 
 [[deps.FFMPEG]]
 deps = ["FFMPEG_jll"]
-git-tree-sha1 = "b57e3acbe22f8484b4b5ff66a7499717fe1a9cc8"
+git-tree-sha1 = "53ebe7511fa11d33bec688a9178fac4e49eeee00"
 uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
-version = "0.4.1"
+version = "0.4.2"
 
 [[deps.FFMPEG_jll]]
 deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
@@ -583,9 +583,9 @@ version = "1.8.0"
 
 [[deps.FFTW_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "c6033cc3892d0ef5bb9cd29b7f2f0331ea5184ea"
+git-tree-sha1 = "4d81ed14783ec49ce9f2e168208a12ce1815aa25"
 uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a"
-version = "3.3.10+0"
+version = "3.3.10+1"
 
 [[deps.FLoops]]
 deps = ["BangBang", "Compat", "FLoopsBase", "InitialValues", "JuliaVariables", "MLStyle", "Serialization", "Setfield", "Transducers"]
@@ -644,23 +644,27 @@ uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
 version = "0.8.5"
 
 [[deps.Flux]]
-deps = ["Adapt", "ChainRulesCore", "Compat", "Functors", "LinearAlgebra", "MLUtils", "MacroTools", "NNlib", "OneHotArrays", "Optimisers", "Preferences", "ProgressLogging", "Random", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "Zygote"]
-git-tree-sha1 = "fbf100b4bed74c9b6fac0ebd1031e04977d35b3b"
+deps = ["Adapt", "ChainRulesCore", "Compat", "Functors", "LinearAlgebra", "MLUtils", "MacroTools", "NNlib", "OneHotArrays", "Optimisers", "Preferences", "ProgressLogging", "Random", "Reexport", "Setfield", "SparseArrays", "SpecialFunctions", "Statistics", "Zygote"]
+git-tree-sha1 = "d7d0a182089d9d3ff0cd0b761d21020fea2b1035"
 uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-version = "0.14.19"
+version = "0.14.20"
 
     [deps.Flux.extensions]
     FluxAMDGPUExt = "AMDGPU"
     FluxCUDAExt = "CUDA"
     FluxCUDAcuDNNExt = ["CUDA", "cuDNN"]
     FluxEnzymeExt = "Enzyme"
+    FluxMPIExt = "MPI"
+    FluxMPINCCLExt = ["CUDA", "MPI", "NCCL"]
     FluxMetalExt = "Metal"
 
     [deps.Flux.weakdeps]
     AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
     CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
     Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+    MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
     Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
+    NCCL = "3fe64909-d7a1-4096-9b7d-7a0f12cf0f6b"
     cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [[deps.Fontconfig_jll]]
@@ -732,9 +736,9 @@ version = "0.1.6"
 
 [[deps.GPUCompiler]]
 deps = ["ExprTools", "InteractiveUtils", "LLVM", "Libdl", "Logging", "PrecompileTools", "Preferences", "Scratch", "Serialization", "TOML", "TimerOutputs", "UUIDs"]
-git-tree-sha1 = "fd8e483d0921ab300fd7c5a144f08332a8fbb745"
+git-tree-sha1 = "1d6f290a5eb1201cd63574fbc4440c788d5cb38f"
 uuid = "61eb1bfa-7361-4325-ad38-22787b887f55"
-version = "0.27.7"
+version = "0.27.8"
 
 [[deps.GeoFormatTypes]]
 git-tree-sha1 = "59107c179a586f0fe667024c5eb7033e81333271"
@@ -743,9 +747,9 @@ version = "0.4.2"
 
 [[deps.GeoInterface]]
 deps = ["Extents", "GeoFormatTypes"]
-git-tree-sha1 = "5921fc0704e40c024571eca551800c699f86ceb4"
+git-tree-sha1 = "2f6fce56cdb8373637a6614e14a5768a88450de2"
 uuid = "cf35fbd7-0cd7-5166-be24-54bfbe79505f"
-version = "1.3.6"
+version = "1.3.7"
 
 [[deps.GeometryBasics]]
 deps = ["EarCut_jll", "Extents", "GeoInterface", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"]
@@ -790,9 +794,9 @@ version = "1.3.14+0"
 
 [[deps.Graphs]]
 deps = ["ArnoldiMethod", "Compat", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"]
-git-tree-sha1 = "ebd18c326fa6cee1efb7da9a3b45cf69da2ed4d9"
+git-tree-sha1 = "1dc470db8b1131cfc7fb4c115de89fe391b9e780"
 uuid = "86223c79-3864-5bf0-83f7-82e725a168b6"
-version = "1.11.2"
+version = "1.12.0"
 
 [[deps.GridLayoutBase]]
 deps = ["GeometryBasics", "InteractiveUtils", "Observables"]
@@ -1040,9 +1044,9 @@ weakdeps = ["Random", "RecipesBase", "Statistics"]
     IntervalSetsStatisticsExt = "Statistics"
 
 [[deps.InverseFunctions]]
-git-tree-sha1 = "2787db24f4e03daf859c6509ff87764e4182f7d1"
+git-tree-sha1 = "a779299d77cd080bf77b97535acecd73e1c5e5cb"
 uuid = "3587e190-3f89-42d0-90ee-14403ec27112"
-version = "0.1.16"
+version = "0.1.17"
 weakdeps = ["Dates", "Test"]
 
     [deps.InverseFunctions.extensions]
@@ -1097,7 +1101,7 @@ version = "0.21.4"
 deps = ["ArrayPadding", "ChainRulesCore", "FFTW", "Flux", "Functors", "ImageMorphology", "ImageTransformations", "LinearAlgebra", "NNlib", "Porcupine", "Random", "SparseArrays", "Statistics", "UnPack", "Zygote"]
 path = "C:\\Users\\pxshe\\OneDrive\\Desktop\\Jello.jl"
 uuid = "6872b481-e419-48a0-81d2-be4ee5684529"
-version = "0.1.16"
+version = "0.1.17"
 
 [[deps.JpegTurbo]]
 deps = ["CEnum", "FileIO", "ImageCore", "JpegTurbo_jll", "TOML"]
@@ -1107,9 +1111,9 @@ version = "0.1.5"
 
 [[deps.JpegTurbo_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "c84a835e1a09b289ffcd2271bf2a337bbdda6637"
+git-tree-sha1 = "25ee0be4d43d0269027024d75a24c24d6c6e590c"
 uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
-version = "3.0.3+0"
+version = "3.0.4+0"
 
 [[deps.JuliaNVTXCallbacks_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -1125,9 +1129,9 @@ version = "0.2.4"
 
 [[deps.KernelAbstractions]]
 deps = ["Adapt", "Atomix", "InteractiveUtils", "MacroTools", "PrecompileTools", "Requires", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"]
-git-tree-sha1 = "045d41a364a81e357757c566b5a69fd4a2a2c445"
+git-tree-sha1 = "5126765c5847f74758c411c994312052eb7117ef"
 uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
-version = "0.9.26"
+version = "0.9.27"
 
     [deps.KernelAbstractions.extensions]
     EnzymeExt = "EnzymeCore"
@@ -1186,9 +1190,9 @@ version = "18.1.7+0"
 
 [[deps.LZO_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "70c5da094887fd2cae843b8db33920bac4b6f07d"
+git-tree-sha1 = "854a9c268c43b77b0a27f22d7fab8d33cdb3a731"
 uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac"
-version = "2.10.2+0"
+version = "2.10.2+1"
 
 [[deps.LaTeXStrings]]
 git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec"
@@ -1350,9 +1354,9 @@ version = "0.5.13"
 
 [[deps.Makie]]
 deps = ["Animations", "Base64", "CRC32c", "ColorBrewer", "ColorSchemes", "ColorTypes", "Colors", "Contour", "Dates", "DelaunayTriangulation", "Distributions", "DocStringExtensions", "Downloads", "FFMPEG_jll", "FileIO", "FilePaths", "FixedPointNumbers", "Format", "FreeType", "FreeTypeAbstraction", "GeometryBasics", "GridLayoutBase", "ImageBase", "ImageIO", "InteractiveUtils", "Interpolations", "IntervalSets", "Isoband", "KernelDensity", "LaTeXStrings", "LinearAlgebra", "MacroTools", "MakieCore", "Markdown", "MathTeXEngine", "Observables", "OffsetArrays", "Packing", "PlotUtils", "PolygonOps", "PrecompileTools", "Printf", "REPL", "Random", "RelocatableFolders", "Scratch", "ShaderAbstractions", "Showoff", "SignedDistanceFields", "SparseArrays", "Statistics", "StatsBase", "StatsFuns", "StructArrays", "TriplotBase", "UnicodeFun", "Unitful"]
-git-tree-sha1 = "2281aaf0685e5e8a559982d32f17d617a949b9cd"
+git-tree-sha1 = "e08a87ca672b6f26a6f7237000554d2a093d3495"
 uuid = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
-version = "0.21.11"
+version = "0.21.12"
 
 [[deps.MakieCore]]
 deps = ["ColorTypes", "GeometryBasics", "IntervalSets", "Observables"]
@@ -1464,9 +1468,9 @@ version = "0.1.5"
 
 [[deps.NearestNeighbors]]
 deps = ["Distances", "StaticArrays"]
-git-tree-sha1 = "91a67b4d73842da90b526011fa85c5c4c9343fe0"
+git-tree-sha1 = "3cebfc94a0754cc329ebc3bab1e6c89621e791ad"
 uuid = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
-version = "0.4.18"
+version = "0.4.20"
 
 [[deps.Netpbm]]
 deps = ["FileIO", "ImageCore", "ImageMetadata"]
@@ -1534,9 +1538,9 @@ version = "0.8.1+2"
 
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "1b35263570443fdd9e76c76b7062116e2f374ab8"
+git-tree-sha1 = "7493f61f55a6cce7325f197443aa80d32554ba10"
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "3.0.15+0"
+version = "3.0.15+1"
 
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
@@ -1658,7 +1662,7 @@ version = "1.4.3"
 deps = ["ArrayPadding", "ChainRulesCore", "DataStructures", "Functors", "LinearAlgebra", "Statistics", "UnPack", "Zygote"]
 path = "C:\\Users\\pxshe\\OneDrive\\Desktop\\Porcupine.jl"
 uuid = "3b53a3d7-3f34-4bba-8df2-4717a8b1e972"
-version = "0.1.43"
+version = "0.1.44"
 
 [[deps.PrecompileTools]]
 deps = ["Preferences"]
@@ -1679,9 +1683,9 @@ version = "0.2.0"
 
 [[deps.PrettyTables]]
 deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "Reexport", "StringManipulation", "Tables"]
-git-tree-sha1 = "66b20dd35966a748321d3b2537c4584cf40387c7"
+git-tree-sha1 = "1101cd475833706e4d0e7b122218257178f48f34"
 uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-version = "2.3.2"
+version = "2.4.0"
 
 [[deps.Printf]]
 deps = ["Unicode"]
@@ -2017,9 +2021,9 @@ weakdeps = ["ChainRulesCore", "InverseFunctions"]
 
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
-git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5"
+git-tree-sha1 = "a6b1675a536c5ad1a60e5a5153e1fee12eb146e3"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
-version = "0.3.4"
+version = "0.4.0"
 
 [[deps.StructArrays]]
 deps = ["ConstructionBase", "DataAPI", "Tables"]
@@ -2254,15 +2258,15 @@ version = "1.2.13+1"
 
 [[deps.Zstd_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "e678132f07ddb5bfa46857f0d7620fb9be675d3b"
+git-tree-sha1 = "555d1076590a6cc2fdee2ef1469451f872d8b41b"
 uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
-version = "1.5.6+0"
+version = "1.5.6+1"
 
 [[deps.Zygote]]
 deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "19c586905e78a26f7e4e97f81716057bd6b1bc54"
+git-tree-sha1 = "f2f85ad73ca67b5d3c94239b0fde005e0fe2d900"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.6.70"
+version = "0.6.71"
 
     [deps.Zygote.extensions]
     ZygoteColorsExt = "Colors"
@@ -2280,6 +2284,12 @@ git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00"
 uuid = "700de1a5-db45-46bc-99cf-38207098b444"
 version = "0.2.5"
 
+[[deps.demumble_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "6498e3581023f8e530f34760d18f75a69e3a4ea8"
+uuid = "1e29f10c-031c-5a83-9565-69cddfc27673"
+version = "1.3.0+0"
+
 [[deps.isoband_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
 git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
@@ -2311,15 +2321,15 @@ version = "2.0.3+0"
 
 [[deps.libpng_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
-git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4"
+git-tree-sha1 = "b70c870239dc3d7bc094eb2d6be9b73d27bef280"
 uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
-version = "1.6.43+1"
+version = "1.6.44+0"
 
 [[deps.libsixel_jll]]
 deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Pkg", "libpng_jll"]
-git-tree-sha1 = "d4f63314c8aa1e48cd22aa0c17ed76cd1ae48c3c"
+git-tree-sha1 = "7dfa0fd9c783d3d0cc43ea1af53d69ba45c447df"
 uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
-version = "1.10.3+0"
+version = "1.10.3+1"
 
 [[deps.libvorbis_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
diff --git a/image.bmp b/image.bmp
index 91d0510..59b3bab 100644
Binary files a/image.bmp and b/image.bmp differ
diff --git a/lumi/Manifest.toml b/lumi/Manifest.toml
index b727f87..34b7b7e 100644
--- a/lumi/Manifest.toml
+++ b/lumi/Manifest.toml
@@ -2,7 +2,7 @@
 
 julia_version = "1.10.5"
 manifest_format = "2.0"
-project_hash = "b66d9039fab8516706fadfa77bab72d8b6d65583"
+project_hash = "c4f8d06fd3acb921540668be25a0817f3075f53f"
 
 [[deps.AbstractFFTs]]
 deps = ["LinearAlgebra"]
@@ -15,11 +15,6 @@ weakdeps = ["ChainRulesCore", "Test"]
     AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
     AbstractFFTsTestExt = "Test"
 
-[[deps.AbstractTrees]]
-git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177"
-uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
-version = "0.4.5"
-
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "InverseFunctions", "LinearAlgebra", "MacroTools", "Markdown"]
 git-tree-sha1 = "b392ede862e506d451fc1616e79aa6f4c673dab8"
@@ -55,23 +50,6 @@ weakdeps = ["StaticArrays"]
     [deps.Adapt.extensions]
     AdaptStaticArraysExt = "StaticArrays"
 
-[[deps.AdaptivePredicates]]
-git-tree-sha1 = "7e651ea8d262d2d74ce75fdf47c4d63c07dba7a6"
-uuid = "35492f91-a3bd-45ad-95db-fcad7dcfedb7"
-version = "1.2.0"
-
-[[deps.AliasTables]]
-deps = ["PtrArrays", "Random"]
-git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff"
-uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"
-version = "1.1.3"
-
-[[deps.Animations]]
-deps = ["Colors"]
-git-tree-sha1 = "e81c509d2c8e49592413bfb0bb3b08150056c79d"
-uuid = "27a7e980-b3e6-11e9-2bcd-0b925532e340"
-version = "0.4.1"
-
 [[deps.ArgCheck]]
 git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4"
 uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197"
@@ -81,12 +59,6 @@ version = "2.3.0"
 uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
 version = "1.1.1"
 
-[[deps.ArnoldiMethod]]
-deps = ["LinearAlgebra", "Random", "StaticArrays"]
-git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
-uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
-version = "0.4.0"
-
 [[deps.ArrayInterface]]
 deps = ["Adapt", "LinearAlgebra"]
 git-tree-sha1 = "3640d077b6dafd64ceb8fd5c1ec76f7ca53bcf76"
@@ -132,35 +104,18 @@ git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be"
 uuid = "a9b6321e-bd34-4604-b9c9-b65b8de01458"
 version = "0.1.0"
 
-[[deps.Automa]]
-deps = ["PrecompileTools", "TranscodingStreams"]
-git-tree-sha1 = "014bc22d6c400a7703c0f5dc1fdc302440cf88be"
-uuid = "67c07d97-cdcb-5c2c-af73-a7f9c32a568b"
-version = "1.0.4"
-
 [[deps.AxisAlgorithms]]
 deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"]
 git-tree-sha1 = "01b8ccb13d68535d73d2b0c23e39bd23155fb712"
 uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
 version = "1.1.0"
 
-[[deps.AxisArrays]]
-deps = ["Dates", "IntervalSets", "IterTools", "RangeArrays"]
-git-tree-sha1 = "16351be62963a67ac4083f748fdb3cca58bfd52f"
-uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
-version = "0.4.7"
-
 [[deps.BFloat16s]]
 deps = ["LinearAlgebra", "Printf", "Random", "Test"]
 git-tree-sha1 = "2c7cc21e8678eff479978a0a2ef5ce2f51b63dff"
 uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b"
 version = "0.5.0"
 
-[[deps.BSON]]
-git-tree-sha1 = "4c3e506685c527ac6a54ccc0c8c76fd6f91b42fb"
-uuid = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
-version = "0.3.9"
-
 [[deps.BangBang]]
 deps = ["Accessors", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires"]
 git-tree-sha1 = "e2144b631226d9eeab2d746ca8880b7ccff504ae"
@@ -214,20 +169,11 @@ git-tree-sha1 = "5a97e67919535d6841172016c9530fd69494e5ec"
 uuid = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
 version = "0.2.6"
 
-[[deps.CRC32c]]
-uuid = "8bf52ea8-c179-5cab-976a-9e18b702a9bc"
-
-[[deps.CRlibm_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "e329286945d0cfc04456972ea732551869af1cfc"
-uuid = "4e9b3aee-d8a1-5a3d-ad8b-7d824db253f0"
-version = "1.0.1+0"
-
 [[deps.CUDA]]
-deps = ["AbstractFFTs", "Adapt", "BFloat16s", "CEnum", "CUDA_Driver_jll", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "Crayons", "DataFrames", "ExprTools", "GPUArrays", "GPUCompiler", "KernelAbstractions", "LLVM", "LLVMLoopInfo", "LazyArtifacts", "Libdl", "LinearAlgebra", "Logging", "NVTX", "Preferences", "PrettyTables", "Printf", "Random", "Random123", "RandomNumbers", "Reexport", "Requires", "SparseArrays", "StaticArrays", "Statistics"]
-git-tree-sha1 = "fdd9dfb67dfefd548f51000cc400bb51003de247"
+deps = ["AbstractFFTs", "Adapt", "BFloat16s", "CEnum", "CUDA_Driver_jll", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "Crayons", "DataFrames", "ExprTools", "GPUArrays", "GPUCompiler", "KernelAbstractions", "LLVM", "LLVMLoopInfo", "LazyArtifacts", "Libdl", "LinearAlgebra", "Logging", "NVTX", "Preferences", "PrettyTables", "Printf", "Random", "Random123", "RandomNumbers", "Reexport", "Requires", "SparseArrays", "StaticArrays", "Statistics", "demumble_jll"]
+git-tree-sha1 = "e0725a467822697171af4dae15cec10b4fc19053"
 uuid = "052768ef-5323-5732-b1bb-66c8b64840ba"
-version = "5.4.3"
+version = "5.5.2"
 
     [deps.CUDA.extensions]
     ChainRulesCoreExt = "ChainRulesCore"
@@ -241,9 +187,9 @@ version = "5.4.3"
 
 [[deps.CUDA_Driver_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "325058b426c2b421e3d2df3d5fa646d72d2e3e7e"
+git-tree-sha1 = "e2d9695d60b647e8803f1b5771412a7bf980e971"
 uuid = "4ee394cb-3365-5eb0-8335-949819d2adfc"
-version = "0.9.2+0"
+version = "0.10.2+0"
 
 [[deps.CUDA_Runtime_Discovery]]
 deps = ["Libdl"]
@@ -253,27 +199,15 @@ version = "0.3.5"
 
 [[deps.CUDA_Runtime_jll]]
 deps = ["Artifacts", "CUDA_Driver_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"]
-git-tree-sha1 = "afea94249b821dc754a8ca6695d3daed851e1f5a"
+git-tree-sha1 = "1dce783060fb871658a6a4852c853293126eef8c"
 uuid = "76a88914-d11a-5bdc-97e0-2f5a05c973a2"
-version = "0.14.1+0"
+version = "0.15.2+0"
 
 [[deps.CUDNN_jll]]
 deps = ["Artifacts", "CUDA_Runtime_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"]
-git-tree-sha1 = "cbf7d75f8c58b147bdf6acea2e5bc96cececa6d4"
+git-tree-sha1 = "9851af16a2f357a793daa0f13634c82bc7e40419"
 uuid = "62b44479-cb7b-5706-934f-f13b2eb2e645"
-version = "9.0.0+1"
-
-[[deps.Cairo]]
-deps = ["Cairo_jll", "Colors", "Glib_jll", "Graphics", "Libdl", "Pango_jll"]
-git-tree-sha1 = "7b6ad8c35f4bc3bca8eb78127c8b99719506a5fb"
-uuid = "159f3aea-2a34-519c-b102-8c37f9878175"
-version = "1.1.0"
-
-[[deps.CairoMakie]]
-deps = ["CRC32c", "Cairo", "Cairo_jll", "Colors", "FileIO", "FreeType", "GeometryBasics", "LinearAlgebra", "Makie", "PrecompileTools"]
-git-tree-sha1 = "361dec06290d76b6d70d0c7dc888038eec9df63a"
-uuid = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
-version = "0.12.9"
+version = "9.4.0+0"
 
 [[deps.Cairo_jll]]
 deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
@@ -281,12 +215,6 @@ git-tree-sha1 = "a2f1c8c668c8e3cb4cca4e57a8efdb09067bb3fd"
 uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a"
 version = "1.18.0+2"
 
-[[deps.CatIndices]]
-deps = ["CustomUnitRanges", "OffsetArrays"]
-git-tree-sha1 = "a0f80a09780eed9b1d106a1bf62041c2efc995bc"
-uuid = "aafaddc9-749c-510e-ac4f-586e18779b91"
-version = "0.2.2"
-
 [[deps.ChainRules]]
 deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"]
 git-tree-sha1 = "be227d253d132a6d57f9ccf5f67c0fb6488afd87"
@@ -309,24 +237,6 @@ git-tree-sha1 = "05ba0d07cd4fd8b7a39541e31a7b0254704ea581"
 uuid = "fb6a15b2-703c-40df-9091-08a04967cfa9"
 version = "0.1.13"
 
-[[deps.Clustering]]
-deps = ["Distances", "LinearAlgebra", "NearestNeighbors", "Printf", "Random", "SparseArrays", "Statistics", "StatsBase"]
-git-tree-sha1 = "9ebb045901e9bbf58767a9f34ff89831ed711aae"
-uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
-version = "0.15.7"
-
-[[deps.ColorBrewer]]
-deps = ["Colors", "JSON", "Test"]
-git-tree-sha1 = "61c5334f33d91e570e1d0c3eb5465835242582c4"
-uuid = "a2cac450-b92f-5266-8821-25eda20663c8"
-version = "0.4.0"
-
-[[deps.ColorSchemes]]
-deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"]
-git-tree-sha1 = "b5278586822443594ff615963b0c09755771b3e0"
-uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
-version = "3.26.0"
-
 [[deps.ColorTypes]]
 deps = ["FixedPointNumbers", "Random"]
 git-tree-sha1 = "b10d0b65641d57b8b4d5e234446582de5047050d"
@@ -334,10 +244,14 @@ uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
 version = "0.11.5"
 
 [[deps.ColorVectorSpace]]
-deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "SpecialFunctions", "Statistics", "TensorCore"]
-git-tree-sha1 = "600cc5508d66b78aae350f7accdb58763ac18589"
+deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "Requires", "Statistics", "TensorCore"]
+git-tree-sha1 = "a1f44953f2382ebb937d60dafbe2deea4bd23249"
 uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4"
-version = "0.9.10"
+version = "0.10.0"
+weakdeps = ["SpecialFunctions"]
+
+    [deps.ColorVectorSpace.extensions]
+    SpecialFunctionsExt = "SpecialFunctions"
 
 [[deps.Colors]]
 deps = ["ColorTypes", "FixedPointNumbers", "Reexport"]
@@ -380,33 +294,27 @@ weakdeps = ["InverseFunctions"]
     [deps.CompositionsBase.extensions]
     CompositionsBaseInverseFunctionsExt = "InverseFunctions"
 
-[[deps.ComputationalResources]]
-git-tree-sha1 = "52cb3ec90e8a8bea0e62e275ba577ad0f74821f7"
-uuid = "ed09eef8-17a6-5b46-8889-db040fac31e3"
-version = "0.3.2"
-
 [[deps.ConstructionBase]]
 git-tree-sha1 = "76219f1ed5771adbb096743bff43fb5fdd4c1157"
 uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 version = "1.5.8"
-weakdeps = ["IntervalSets", "LinearAlgebra", "StaticArrays"]
 
     [deps.ConstructionBase.extensions]
     ConstructionBaseIntervalSetsExt = "IntervalSets"
     ConstructionBaseLinearAlgebraExt = "LinearAlgebra"
     ConstructionBaseStaticArraysExt = "StaticArrays"
 
+    [deps.ConstructionBase.weakdeps]
+    IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
+    LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+
 [[deps.ContextVariablesX]]
 deps = ["Compat", "Logging", "UUIDs"]
 git-tree-sha1 = "25cc3803f1030ab855e383129dcd3dc294e322cc"
 uuid = "6add18c4-b38d-439d-96f6-d6bc489c04c5"
 version = "0.1.3"
 
-[[deps.Contour]]
-git-tree-sha1 = "439e35b0b36e2e5881738abc8857bd92ad6ff9a8"
-uuid = "d38c429a-6771-53c6-b99e-75d170b6e991"
-version = "0.6.3"
-
 [[deps.CoordinateTransformations]]
 deps = ["LinearAlgebra", "StaticArrays"]
 git-tree-sha1 = "f9d7112bfff8a19a3a4ea4e03a8e6a91fe8456bf"
@@ -424,21 +332,16 @@ git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15"
 uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
 version = "4.1.1"
 
-[[deps.CustomUnitRanges]]
-git-tree-sha1 = "1a3f97f907e6dd8983b744d2642651bb162a3f7a"
-uuid = "dc8bdbbb-1ca9-579f-8c36-e416f6a65cce"
-version = "1.0.2"
-
 [[deps.DataAPI]]
 git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
 version = "1.16.0"
 
 [[deps.DataFrames]]
-deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
-git-tree-sha1 = "04c738083f29f86e62c8afc341f0967d8717bdb8"
+deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
+git-tree-sha1 = "fb61b4812c49343d7ef0b533ba982c46021938a6"
 uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-version = "1.6.1"
+version = "1.7.0"
 
 [[deps.DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
@@ -460,12 +363,6 @@ git-tree-sha1 = "0fba8b706d0178b4dc7fd44a96a92382c9065c2c"
 uuid = "244e2a9f-e319-4986-a169-4d1fe445cd52"
 version = "0.1.2"
 
-[[deps.DelaunayTriangulation]]
-deps = ["AdaptivePredicates", "EnumX", "ExactPredicates", "Random"]
-git-tree-sha1 = "94eb20e6621600f4315813b1d1fc9b8a5a6a34db"
-uuid = "927a84f5-c5f4-47a5-9785-b46e178433df"
-version = "1.4.0"
-
 [[deps.DelimitedFiles]]
 deps = ["Mmap"]
 git-tree-sha1 = "9e2f36d3c96a820c678f2f1f1782582fcf685bae"
@@ -484,37 +381,10 @@ git-tree-sha1 = "23163d55f885173722d1e4cf0f6110cdbaf7e272"
 uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
 version = "1.15.1"
 
-[[deps.Distances]]
-deps = ["LinearAlgebra", "Statistics", "StatsAPI"]
-git-tree-sha1 = "66c4c81f259586e8f002eacebc177e1fb06363b0"
-uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-version = "0.10.11"
-weakdeps = ["ChainRulesCore", "SparseArrays"]
-
-    [deps.Distances.extensions]
-    DistancesChainRulesCoreExt = "ChainRulesCore"
-    DistancesSparseArraysExt = "SparseArrays"
-
 [[deps.Distributed]]
 deps = ["Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 
-[[deps.Distributions]]
-deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "e6c693a0e4394f8fda0e51a5bdf5aef26f8235e9"
-uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.111"
-
-    [deps.Distributions.extensions]
-    DistributionsChainRulesCoreExt = "ChainRulesCore"
-    DistributionsDensityInterfaceExt = "DensityInterface"
-    DistributionsTestExt = "Test"
-
-    [deps.Distributions.weakdeps]
-    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-    DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
-    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
 [[deps.DocStringExtensions]]
 deps = ["LibGit2"]
 git-tree-sha1 = "2fb1e02f2b635d0845df5d7c167fec4dd739b00d"
@@ -526,23 +396,6 @@ deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"]
 uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 version = "1.6.0"
 
-[[deps.EarCut_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "e3290f2d49e661fbd94046d7e3726ffcb2d41053"
-uuid = "5ae413db-bbd1-5e63-b57d-d24a61df00f5"
-version = "2.2.4+0"
-
-[[deps.EnumX]]
-git-tree-sha1 = "bdb1942cd4c45e3c678fd11569d5cccd80976237"
-uuid = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
-version = "1.0.4"
-
-[[deps.ExactPredicates]]
-deps = ["IntervalArithmetic", "Random", "StaticArrays"]
-git-tree-sha1 = "b3f2ff58735b5f024c392fde763f29b057e4b025"
-uuid = "429591f6-91af-11e9-00e2-59fbe8cec110"
-version = "2.2.8"
-
 [[deps.Expat_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
 git-tree-sha1 = "1c6317308b9dc757616f0b5cb379db10494443a7"
@@ -554,16 +407,11 @@ git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec"
 uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
 version = "0.1.10"
 
-[[deps.Extents]]
-git-tree-sha1 = "81023caa0021a41712685887db1fc03db26f41f5"
-uuid = "411431e0-e8b7-467b-b5e0-f676ba4f2910"
-version = "0.1.4"
-
 [[deps.FFMPEG]]
 deps = ["FFMPEG_jll"]
-git-tree-sha1 = "b57e3acbe22f8484b4b5ff66a7499717fe1a9cc8"
+git-tree-sha1 = "53ebe7511fa11d33bec688a9178fac4e49eeee00"
 uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
-version = "0.4.1"
+version = "0.4.2"
 
 [[deps.FFMPEG_jll]]
 deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"]
@@ -571,12 +419,6 @@ git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e"
 uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5"
 version = "4.4.4+1"
 
-[[deps.FFTViews]]
-deps = ["CustomUnitRanges", "FFTW"]
-git-tree-sha1 = "cbdf14d1e8c7c8aacbe8b19862e0179fd08321c2"
-uuid = "4f61f5a4-77b1-5117-aa51-3ab5ef4ef0cd"
-version = "0.3.2"
-
 [[deps.FFTW]]
 deps = ["AbstractFFTs", "FFTW_jll", "LinearAlgebra", "MKL_jll", "Preferences", "Reexport"]
 git-tree-sha1 = "4820348781ae578893311153d69049a93d05f39d"
@@ -585,9 +427,9 @@ version = "1.8.0"
 
 [[deps.FFTW_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "c6033cc3892d0ef5bb9cd29b7f2f0331ea5184ea"
+git-tree-sha1 = "4d81ed14783ec49ce9f2e168208a12ce1815aa25"
 uuid = "f5851436-0d7a-5f13-b9de-f02708fd171a"
-version = "3.3.10+0"
+version = "3.3.10+1"
 
 [[deps.FLoops]]
 deps = ["BangBang", "Compat", "FLoopsBase", "InitialValues", "JuliaVariables", "MLStyle", "Serialization", "Setfield", "Transducers"]
@@ -607,23 +449,6 @@ git-tree-sha1 = "82d8afa92ecf4b52d78d869f038ebfb881267322"
 uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
 version = "1.16.3"
 
-[[deps.FilePaths]]
-deps = ["FilePathsBase", "MacroTools", "Reexport", "Requires"]
-git-tree-sha1 = "919d9412dbf53a2e6fe74af62a73ceed0bce0629"
-uuid = "8fc22ac5-c921-52a6-82fd-178b2807b824"
-version = "0.8.3"
-
-[[deps.FilePathsBase]]
-deps = ["Compat", "Dates"]
-git-tree-sha1 = "7878ff7172a8e6beedd1dea14bd27c3c6340d361"
-uuid = "48062228-2e41-5def-b9a4-89aafe57970f"
-version = "0.9.22"
-weakdeps = ["Mmap", "Test"]
-
-    [deps.FilePathsBase.extensions]
-    FilePathsBaseMmapExt = "Mmap"
-    FilePathsBaseTestExt = "Test"
-
 [[deps.FileWatching]]
 uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
 
@@ -632,13 +457,17 @@ deps = ["LinearAlgebra"]
 git-tree-sha1 = "6a70198746448456524cb442b8af316927ff3e1a"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
 version = "1.13.0"
-weakdeps = ["PDMats", "SparseArrays", "Statistics"]
 
     [deps.FillArrays.extensions]
     FillArraysPDMatsExt = "PDMats"
     FillArraysSparseArraysExt = "SparseArrays"
     FillArraysStatisticsExt = "Statistics"
 
+    [deps.FillArrays.weakdeps]
+    PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
+    SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+    Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+
 [[deps.FixedPointNumbers]]
 deps = ["Statistics"]
 git-tree-sha1 = "05882d6995ae5c12bb5f36dd2ed3f61c98cbb172"
@@ -675,11 +504,6 @@ git-tree-sha1 = "db16beca600632c95fc8aca29890d83788dd8b23"
 uuid = "a3f928ae-7b40-5064-980b-68af3947d34b"
 version = "2.13.96+0"
 
-[[deps.Format]]
-git-tree-sha1 = "9c68794ef81b08086aeb32eeaf33531668d5f5fc"
-uuid = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8"
-version = "1.3.7"
-
 [[deps.ForwardDiff]]
 deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions"]
 git-tree-sha1 = "cf0fe81336da9fb90944683b8c41984b08793dad"
@@ -690,24 +514,12 @@ weakdeps = ["StaticArrays"]
     [deps.ForwardDiff.extensions]
     ForwardDiffStaticArraysExt = "StaticArrays"
 
-[[deps.FreeType]]
-deps = ["CEnum", "FreeType2_jll"]
-git-tree-sha1 = "907369da0f8e80728ab49c1c7e09327bf0d6d999"
-uuid = "b38be410-82b0-50bf-ab77-7b57e271db43"
-version = "4.1.1"
-
 [[deps.FreeType2_jll]]
 deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Zlib_jll"]
 git-tree-sha1 = "5c1d8ae0efc6c2e7b1fc502cbe25def8f661b7bc"
 uuid = "d7e528f0-a631-5988-bf34-fe36492bcfd7"
 version = "2.13.2+0"
 
-[[deps.FreeTypeAbstraction]]
-deps = ["ColorVectorSpace", "Colors", "FreeType", "GeometryBasics"]
-git-tree-sha1 = "2493cdfd0740015955a8e46de4ef28f49460d8bc"
-uuid = "663a7486-cb36-511b-a19d-713bb74d65c9"
-version = "0.10.3"
-
 [[deps.FriBidi_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
 git-tree-sha1 = "1ed150b39aebcc805c26b93a8d0122c940f64ce2"
@@ -737,27 +549,10 @@ uuid = "46192b85-c4d5-4398-a991-12ede77f4527"
 version = "0.1.6"
 
 [[deps.GPUCompiler]]
-deps = ["ExprTools", "InteractiveUtils", "LLVM", "Libdl", "Logging", "Preferences", "Scratch", "Serialization", "TOML", "TimerOutputs", "UUIDs"]
-git-tree-sha1 = "ab29216184312f99ff957b32cd63c2fe9c928b91"
+deps = ["ExprTools", "InteractiveUtils", "LLVM", "Libdl", "Logging", "PrecompileTools", "Preferences", "Scratch", "Serialization", "TOML", "TimerOutputs", "UUIDs"]
+git-tree-sha1 = "1d6f290a5eb1201cd63574fbc4440c788d5cb38f"
 uuid = "61eb1bfa-7361-4325-ad38-22787b887f55"
-version = "0.26.7"
-
-[[deps.GeoFormatTypes]]
-git-tree-sha1 = "59107c179a586f0fe667024c5eb7033e81333271"
-uuid = "68eda718-8dee-11e9-39e7-89f7f65f511f"
-version = "0.4.2"
-
-[[deps.GeoInterface]]
-deps = ["Extents", "GeoFormatTypes"]
-git-tree-sha1 = "5921fc0704e40c024571eca551800c699f86ceb4"
-uuid = "cf35fbd7-0cd7-5166-be24-54bfbe79505f"
-version = "1.3.6"
-
-[[deps.GeometryBasics]]
-deps = ["EarCut_jll", "Extents", "GeoInterface", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"]
-git-tree-sha1 = "b62f2b2d76cee0d61a2ef2b3118cd2a3215d3134"
-uuid = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
-version = "0.4.11"
+version = "0.27.8"
 
 [[deps.Gettext_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"]
@@ -765,12 +560,6 @@ git-tree-sha1 = "9b02998aba7bf074d14de89f9d37ca24a1a0b046"
 uuid = "78b55507-aeef-58d4-861c-77aaff3498b1"
 version = "0.21.0+0"
 
-[[deps.Ghostscript_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "43ba3d3c82c18d88471cfd2924931658838c9d8f"
-uuid = "61579ee1-b43e-5ca0-a5da-69d92c66a64b"
-version = "9.55.0+4"
-
 [[deps.Glib_jll]]
 deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE2_jll", "Zlib_jll"]
 git-tree-sha1 = "7c82e6a6cd34e9d935e9aa4051b66c6ff3af59ba"
@@ -782,47 +571,18 @@ git-tree-sha1 = "97285bbd5230dd766e9ef6749b80fc617126d496"
 uuid = "c27321d9-0574-5035-807b-f59d2c89b15c"
 version = "1.3.1"
 
-[[deps.Graphics]]
-deps = ["Colors", "LinearAlgebra", "NaNMath"]
-git-tree-sha1 = "d61890399bc535850c4bf08e4e0d3a7ad0f21cbd"
-uuid = "a2bd30eb-e257-5431-a919-1863eab51364"
-version = "1.1.2"
-
 [[deps.Graphite2_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
 git-tree-sha1 = "344bf40dcab1073aca04aa0df4fb092f920e4011"
 uuid = "3b182d85-2403-5c21-9c21-1e1f0cc25472"
 version = "1.3.14+0"
 
-[[deps.Graphs]]
-deps = ["ArnoldiMethod", "Compat", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"]
-git-tree-sha1 = "ebd18c326fa6cee1efb7da9a3b45cf69da2ed4d9"
-uuid = "86223c79-3864-5bf0-83f7-82e725a168b6"
-version = "1.11.2"
-
-[[deps.GridLayoutBase]]
-deps = ["GeometryBasics", "InteractiveUtils", "Observables"]
-git-tree-sha1 = "fc713f007cff99ff9e50accba6373624ddd33588"
-uuid = "3955a311-db13-416c-9275-1d80ed98e5e9"
-version = "0.11.0"
-
-[[deps.Grisu]]
-git-tree-sha1 = "53bb909d1151e57e2484c3d1b53e19552b887fb2"
-uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe"
-version = "1.0.2"
-
 [[deps.HarfBuzz_jll]]
 deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll"]
 git-tree-sha1 = "401e4f3f30f43af2c8478fc008da50096ea5240f"
 uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566"
 version = "8.3.1+0"
 
-[[deps.HistogramThresholding]]
-deps = ["ImageBase", "LinearAlgebra", "MappedArrays"]
-git-tree-sha1 = "7194dfbb2f8d945abdaf68fa9480a965d6661e69"
-uuid = "2c695a8d-9458-5d45-9878-1b8a99cf7853"
-version = "0.3.1"
-
 [[deps.HostCPUFeatures]]
 deps = ["BitTwiddlingConvenienceFunctions", "IfElse", "Libdl", "Static"]
 git-tree-sha1 = "8e070b599339d622e9a081d17230d74a5c473293"
@@ -835,12 +595,6 @@ git-tree-sha1 = "f7f2a0a920ad015da58d016587aaf4af4fa6ad5e"
 uuid = "7ec9b9c5-1998-51e1-b7fc-fc3590c18259"
 version = "1.0.0"
 
-[[deps.HypergeometricFunctions]]
-deps = ["LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"]
-git-tree-sha1 = "7c4195be1649ae622304031ed46a2f4df989f1eb"
-uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
-version = "0.3.24"
-
 [[deps.IRTools]]
 deps = ["InteractiveUtils", "MacroTools"]
 git-tree-sha1 = "950c3717af761bc3ff906c2e8e52bd83390b6ec2"
@@ -852,77 +606,17 @@ git-tree-sha1 = "debdd00ffef04665ccbb3e150747a77560e8fad1"
 uuid = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
 version = "0.1.1"
 
-[[deps.ImageAxes]]
-deps = ["AxisArrays", "ImageBase", "ImageCore", "Reexport", "SimpleTraits"]
-git-tree-sha1 = "2e4520d67b0cef90865b3ef727594d2a58e0e1f8"
-uuid = "2803e5a7-5153-5ecf-9a86-9b4c37f5f5ac"
-version = "0.6.11"
-
 [[deps.ImageBase]]
 deps = ["ImageCore", "Reexport"]
-git-tree-sha1 = "b51bb8cae22c66d0f6357e3bcb6363145ef20835"
+git-tree-sha1 = "eb49b82c172811fd2c86759fa0553a2221feb909"
 uuid = "c817782e-172a-44cc-b673-b171935fbb9e"
-version = "0.1.5"
-
-[[deps.ImageBinarization]]
-deps = ["HistogramThresholding", "ImageCore", "LinearAlgebra", "Polynomials", "Reexport", "Statistics"]
-git-tree-sha1 = "f5356e7203c4a9954962e3757c08033f2efe578a"
-uuid = "cbc4b850-ae4b-5111-9e64-df94c024a13d"
-version = "0.3.0"
-
-[[deps.ImageContrastAdjustment]]
-deps = ["ImageBase", "ImageCore", "ImageTransformations", "Parameters"]
-git-tree-sha1 = "eb3d4365a10e3f3ecb3b115e9d12db131d28a386"
-uuid = "f332f351-ec65-5f6a-b3d1-319c6670881a"
-version = "0.3.12"
+version = "0.1.7"
 
 [[deps.ImageCore]]
-deps = ["AbstractFFTs", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Graphics", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "Reexport"]
-git-tree-sha1 = "acf614720ef026d38400b3817614c45882d75500"
+deps = ["ColorVectorSpace", "Colors", "FixedPointNumbers", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "PrecompileTools", "Reexport"]
+git-tree-sha1 = "b2a7eaa169c13f5bcae8131a83bc30eff8f71be0"
 uuid = "a09fc81d-aa75-5fe9-8630-4744c3626534"
-version = "0.9.4"
-
-[[deps.ImageCorners]]
-deps = ["ImageCore", "ImageFiltering", "PrecompileTools", "StaticArrays", "StatsBase"]
-git-tree-sha1 = "24c52de051293745a9bad7d73497708954562b79"
-uuid = "89d5987c-236e-4e32-acd0-25bd6bd87b70"
-version = "0.1.3"
-
-[[deps.ImageDistances]]
-deps = ["Distances", "ImageCore", "ImageMorphology", "LinearAlgebra", "Statistics"]
-git-tree-sha1 = "08b0e6354b21ef5dd5e49026028e41831401aca8"
-uuid = "51556ac3-7006-55f5-8cb3-34580c88182d"
-version = "0.2.17"
-
-[[deps.ImageFiltering]]
-deps = ["CatIndices", "ComputationalResources", "DataStructures", "FFTViews", "FFTW", "ImageBase", "ImageCore", "LinearAlgebra", "OffsetArrays", "PrecompileTools", "Reexport", "SparseArrays", "StaticArrays", "Statistics", "TiledIteration"]
-git-tree-sha1 = "3447781d4c80dbe6d71d239f7cfb1f8049d4c84f"
-uuid = "6a3955dd-da59-5b1f-98d4-e7296123deb5"
-version = "0.7.6"
-
-[[deps.ImageIO]]
-deps = ["FileIO", "IndirectArrays", "JpegTurbo", "LazyModules", "Netpbm", "OpenEXR", "PNGFiles", "QOI", "Sixel", "TiffImages", "UUIDs"]
-git-tree-sha1 = "437abb322a41d527c197fa800455f79d414f0a3c"
-uuid = "82e4d734-157c-48bb-816b-45c225c6df19"
-version = "0.6.8"
-
-[[deps.ImageMagick]]
-deps = ["FileIO", "ImageCore", "ImageMagick_jll", "InteractiveUtils", "Libdl", "Pkg", "Random"]
-git-tree-sha1 = "5bc1cb62e0c5f1005868358db0692c994c3a13c6"
-uuid = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
-version = "1.2.1"
-
-[[deps.ImageMagick_jll]]
-deps = ["Artifacts", "Ghostscript_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "OpenJpeg_jll", "Zlib_jll", "libpng_jll"]
-git-tree-sha1 = "d65554bad8b16d9562050c67e7223abf91eaba2f"
-uuid = "c73af94c-d91f-53ed-93a7-00f77d67a9d7"
-version = "6.9.13+0"
-
-[[deps.ImageMetadata]]
-deps = ["AxisArrays", "ImageAxes", "ImageBase", "ImageCore"]
-git-tree-sha1 = "355e2b974f2e3212a75dfb60519de21361ad3cb7"
-uuid = "bc367c6b-8a6b-528e-b4bd-a4b897500b49"
-version = "0.9.9"
+version = "0.10.2"
 
 [[deps.ImageMorphology]]
 deps = ["DataStructures", "ImageCore", "LinearAlgebra", "LoopVectorization", "OffsetArrays", "Requires", "TiledIteration"]
@@ -930,52 +624,12 @@ git-tree-sha1 = "6f0a801136cb9c229aebea0df296cdcd471dbcd1"
 uuid = "787d08f9-d448-5407-9aad-5290dd7ab264"
 version = "0.4.5"
 
-[[deps.ImageQualityIndexes]]
-deps = ["ImageContrastAdjustment", "ImageCore", "ImageDistances", "ImageFiltering", "LazyModules", "OffsetArrays", "PrecompileTools", "Statistics"]
-git-tree-sha1 = "783b70725ed326340adf225be4889906c96b8fd1"
-uuid = "2996bd0c-7a13-11e9-2da2-2f5ce47296a9"
-version = "0.3.7"
-
-[[deps.ImageSegmentation]]
-deps = ["Clustering", "DataStructures", "Distances", "Graphs", "ImageCore", "ImageFiltering", "ImageMorphology", "LinearAlgebra", "MetaGraphs", "RegionTrees", "SimpleWeightedGraphs", "StaticArrays", "Statistics"]
-git-tree-sha1 = "44664eea5408828c03e5addb84fa4f916132fc26"
-uuid = "80713f31-8817-5129-9cf8-209ff8fb23e1"
-version = "1.8.1"
-
-[[deps.ImageShow]]
-deps = ["Base64", "ColorSchemes", "FileIO", "ImageBase", "ImageCore", "OffsetArrays", "StackViews"]
-git-tree-sha1 = "3b5344bcdbdc11ad58f3b1956709b5b9345355de"
-uuid = "4e3cecfd-b093-5904-9786-8bbb286a6a31"
-version = "0.3.8"
-
 [[deps.ImageTransformations]]
 deps = ["AxisAlgorithms", "CoordinateTransformations", "ImageBase", "ImageCore", "Interpolations", "OffsetArrays", "Rotations", "StaticArrays"]
 git-tree-sha1 = "e0884bdf01bbbb111aea77c348368a86fb4b5ab6"
 uuid = "02fcd773-0e25-5acc-982a-7f6622650795"
 version = "0.10.1"
 
-[[deps.Images]]
-deps = ["Base64", "FileIO", "Graphics", "ImageAxes", "ImageBase", "ImageBinarization", "ImageContrastAdjustment", "ImageCore", "ImageCorners", "ImageDistances", "ImageFiltering", "ImageIO", "ImageMagick", "ImageMetadata", "ImageMorphology", "ImageQualityIndexes", "ImageSegmentation", "ImageShow", "ImageTransformations", "IndirectArrays", "IntegralArrays", "Random", "Reexport", "SparseArrays", "StaticArrays", "Statistics", "StatsBase", "TiledIteration"]
-git-tree-sha1 = "12fdd617c7fe25dc4a6cc804d657cc4b2230302b"
-uuid = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
-version = "0.26.1"
-
-[[deps.Imath_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "0936ba688c6d201805a83da835b55c61a180db52"
-uuid = "905a6f67-0a94-5f89-b386-d35d92009cd1"
-version = "3.1.11+0"
-
-[[deps.IndirectArrays]]
-git-tree-sha1 = "012e604e1c7458645cb8b436f8fba789a51b257f"
-uuid = "9b13fd28-a010-5f03-acff-a1bbcff69959"
-version = "1.0.0"
-
-[[deps.Inflate]]
-git-tree-sha1 = "d1b1b796e47d94588b3757fe84fbf65a5ec4a80d"
-uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9"
-version = "0.1.5"
-
 [[deps.InitialValues]]
 git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3"
 uuid = "22cec73e-a1b8-11e9-2c92-598750a2cf9c"
@@ -994,12 +648,6 @@ version = "1.4.2"
     ArrowTypes = "31f734f8-188a-4ce0-8406-c8a06bd891cd"
     Parsers = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
 
-[[deps.IntegralArrays]]
-deps = ["ColorTypes", "FixedPointNumbers", "IntervalSets"]
-git-tree-sha1 = "be8e690c3973443bec584db3346ddc904d4884eb"
-uuid = "1d092043-8f09-5a30-832f-7509e371ab51"
-version = "0.1.5"
-
 [[deps.IntelOpenMP_jll]]
 deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"]
 git-tree-sha1 = "10bd689145d2c3b2a9844005d01087cc1194e79e"
@@ -1015,40 +663,17 @@ deps = ["Adapt", "AxisAlgorithms", "ChainRulesCore", "LinearAlgebra", "OffsetArr
 git-tree-sha1 = "88a101217d7cb38a7b481ccd50d21876e1d1b0e0"
 uuid = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 version = "0.15.1"
-weakdeps = ["Unitful"]
 
     [deps.Interpolations.extensions]
     InterpolationsUnitfulExt = "Unitful"
 
-[[deps.IntervalArithmetic]]
-deps = ["CRlibm_jll", "MacroTools", "RoundingEmulator"]
-git-tree-sha1 = "fe30dec78e68f27fc416901629c6e24e9d5f057b"
-uuid = "d1acc4aa-44c8-5952-acd4-ba5d80a2a253"
-version = "0.22.16"
-weakdeps = ["DiffRules", "ForwardDiff", "IntervalSets", "LinearAlgebra", "RecipesBase"]
-
-    [deps.IntervalArithmetic.extensions]
-    IntervalArithmeticDiffRulesExt = "DiffRules"
-    IntervalArithmeticForwardDiffExt = "ForwardDiff"
-    IntervalArithmeticIntervalSetsExt = "IntervalSets"
-    IntervalArithmeticLinearAlgebraExt = "LinearAlgebra"
-    IntervalArithmeticRecipesBaseExt = "RecipesBase"
-
-[[deps.IntervalSets]]
-git-tree-sha1 = "dba9ddf07f77f60450fe5d2e2beb9854d9a49bd0"
-uuid = "8197267c-284f-5f27-9208-e0e47529a953"
-version = "0.7.10"
-weakdeps = ["Random", "RecipesBase", "Statistics"]
-
-    [deps.IntervalSets.extensions]
-    IntervalSetsRandomExt = "Random"
-    IntervalSetsRecipesBaseExt = "RecipesBase"
-    IntervalSetsStatisticsExt = "Statistics"
+    [deps.Interpolations.weakdeps]
+    Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [[deps.InverseFunctions]]
-git-tree-sha1 = "2787db24f4e03daf859c6509ff87764e4182f7d1"
+git-tree-sha1 = "a779299d77cd080bf77b97535acecd73e1c5e5cb"
 uuid = "3587e190-3f89-42d0-90ee-14403ec27112"
-version = "0.1.16"
+version = "0.1.17"
 weakdeps = ["Dates", "Test"]
 
     [deps.InverseFunctions.extensions]
@@ -1065,57 +690,22 @@ git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2"
 uuid = "92d709cd-6900-40b7-9082-c6be49f344b6"
 version = "0.2.2"
 
-[[deps.Isoband]]
-deps = ["isoband_jll"]
-git-tree-sha1 = "f9b6d97355599074dc867318950adaa6f9946137"
-uuid = "f1662d9f-8043-43de-a69a-05efc1cc6ff4"
-version = "0.1.1"
-
-[[deps.IterTools]]
-git-tree-sha1 = "42d5f897009e7ff2cf88db414a389e5ed1bdd023"
-uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
-version = "1.10.0"
-
 [[deps.IteratorInterfaceExtensions]]
 git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856"
 uuid = "82899510-4779-5014-852e-03e436cf321d"
 version = "1.0.0"
 
-[[deps.JLD2]]
-deps = ["FileIO", "MacroTools", "Mmap", "OrderedCollections", "PrecompileTools", "Requires", "TranscodingStreams"]
-git-tree-sha1 = "a0746c21bdc986d0dc293efa6b1faee112c37c28"
-uuid = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
-version = "0.4.53"
-
 [[deps.JLLWrappers]]
 deps = ["Artifacts", "Preferences"]
 git-tree-sha1 = "f389674c99bfcde17dc57454011aa44d5a260a40"
 uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210"
 version = "1.6.0"
 
-[[deps.JSON]]
-deps = ["Dates", "Mmap", "Parsers", "Unicode"]
-git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a"
-uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
-version = "0.21.4"
-
 [[deps.Jello]]
 deps = ["ArrayPadding", "ChainRulesCore", "FFTW", "Flux", "Functors", "ImageMorphology", "ImageTransformations", "LinearAlgebra", "NNlib", "Porcupine", "Random", "SparseArrays", "Statistics", "UnPack", "Zygote"]
 path = "C:\\Users\\pxshe\\OneDrive\\Desktop\\Jello.jl"
 uuid = "6872b481-e419-48a0-81d2-be4ee5684529"
-version = "0.1.16"
-
-[[deps.JpegTurbo]]
-deps = ["CEnum", "FileIO", "ImageCore", "JpegTurbo_jll", "TOML"]
-git-tree-sha1 = "fa6d0bcff8583bac20f1ffa708c3913ca605c611"
-uuid = "b835a17e-a41a-41e7-81f0-2f016b05efe0"
-version = "0.1.5"
-
-[[deps.JpegTurbo_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "c84a835e1a09b289ffcd2271bf2a337bbdda6637"
-uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8"
-version = "3.0.3+0"
+version = "0.1.17"
 
 [[deps.JuliaNVTXCallbacks_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -1145,29 +735,17 @@ version = "0.9.27"
     LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
     SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
-[[deps.KernelDensity]]
-deps = ["Distributions", "DocStringExtensions", "FFTW", "Interpolations", "StatsBase"]
-git-tree-sha1 = "7d703202e65efa1369de1279c162b915e245eed1"
-uuid = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
-version = "0.6.9"
-
 [[deps.LAME_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
 git-tree-sha1 = "170b660facf5df5de098d866564877e119141cbd"
 uuid = "c1c5ebd0-6772-5130-a774-d5fcae4a789d"
 version = "3.100.2+0"
 
-[[deps.LERC_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "bf36f528eec6634efc60d7ec062008f171071434"
-uuid = "88015f11-f218-50d7-93a8-a6af411a945d"
-version = "3.0.0+1"
-
 [[deps.LLVM]]
 deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Preferences", "Printf", "Requires", "Unicode"]
-git-tree-sha1 = "2470e69781ddd70b8878491233cd09bc1bd7fc96"
+git-tree-sha1 = "4ad43cb0a4bb5e5b1506e1d1f48646d7e0c80363"
 uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
-version = "8.1.0"
+version = "9.1.2"
 weakdeps = ["BFloat16s"]
 
     [deps.LLVM.extensions]
@@ -1175,9 +753,9 @@ weakdeps = ["BFloat16s"]
 
 [[deps.LLVMExtra_jll]]
 deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"]
-git-tree-sha1 = "597d1c758c9ae5d985ba4202386a607c675ee700"
+git-tree-sha1 = "05a8bd5a42309a9ec82f700876903abce1017dd3"
 uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab"
-version = "0.0.31+0"
+version = "0.0.34+0"
 
 [[deps.LLVMLoopInfo]]
 git-tree-sha1 = "2e5c102cfc41f48ae4740c7eca7743cc7e7b75ea"
@@ -1192,9 +770,9 @@ version = "18.1.7+0"
 
 [[deps.LZO_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "70c5da094887fd2cae843b8db33920bac4b6f07d"
+git-tree-sha1 = "854a9c268c43b77b0a27f22d7fab8d33cdb3a731"
 uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac"
-version = "2.10.2+0"
+version = "2.10.2+1"
 
 [[deps.LaTeXStrings]]
 git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec"
@@ -1217,11 +795,6 @@ version = "0.15.1"
 deps = ["Artifacts", "Pkg"]
 uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
 
-[[deps.LazyModules]]
-git-tree-sha1 = "a560dd966b386ac9ae60bdd3a3d3a326062d3c3e"
-uuid = "8cdb02fc-e678-4876-92c5-9defec4f444e"
-version = "0.3.1"
-
 [[deps.LibCURL]]
 deps = ["LibCURL_jll", "MozillaCACerts_jll"]
 uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
@@ -1279,12 +852,6 @@ git-tree-sha1 = "0c4f9c4f1a50d8f35048fa0532dabbadf702f81e"
 uuid = "4b2f31a3-9ecc-558c-b454-b3730dcb73e9"
 version = "2.40.1+0"
 
-[[deps.Libtiff_jll]]
-deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "LERC_jll", "Libdl", "XZ_jll", "Zlib_jll", "Zstd_jll"]
-git-tree-sha1 = "6355fb9a4d22d867318db186fd09b09b35bd2ed7"
-uuid = "89763e89-9b03-5906-acba-b20f662cd828"
-version = "4.6.0+0"
-
 [[deps.Libuuid_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
 git-tree-sha1 = "5ee6203157c120d79034c748a2acba45b82b8807"
@@ -1295,12 +862,6 @@ version = "2.40.1+0"
 deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"]
 uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 
-[[deps.LittleCMS_jll]]
-deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll"]
-git-tree-sha1 = "fa7fd067dca76cadd880f1ca937b4f387975a9f5"
-uuid = "d3a379c0-f9a3-5b72-a4c0-6bf4d2e8af0f"
-version = "2.16.0+0"
-
 [[deps.LogExpFunctions]]
 deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
 git-tree-sha1 = "a2d09619db4e765091ee5c6ffe8872849de0feea"
@@ -1331,12 +892,6 @@ weakdeps = ["ChainRulesCore", "ForwardDiff", "SpecialFunctions"]
     ForwardDiffExt = ["ChainRulesCore", "ForwardDiff"]
     SpecialFunctionsExt = "SpecialFunctions"
 
-[[deps.Luminescent]]
-deps = ["ArrayPadding", "BSON", "CUDA", "CairoMakie", "ChainRulesCore", "DataStructures", "Dates", "Flux", "Functors", "GPUArraysCore", "Humanize", "Images", "JSON", "Jello", "LinearAlgebra", "Optimisers", "Porcupine", "Random", "SparseArrays", "Statistics", "UnPack", "VideoIO", "Zygote"]
-path = "C:\\Users\\pxshe\\OneDrive\\Desktop\\Luminescent.jl"
-uuid = "60a98398-2d19-4130-ae20-63172a4a42f2"
-version = "0.1.0"
-
 [[deps.MKL_jll]]
 deps = ["Artifacts", "IntelOpenMP_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "oneTBB_jll"]
 git-tree-sha1 = "f046ccd0c6db2832a9f639e2c669c6fe867e5f4f"
@@ -1360,18 +915,6 @@ git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 version = "0.5.13"
 
-[[deps.Makie]]
-deps = ["Animations", "Base64", "CRC32c", "ColorBrewer", "ColorSchemes", "ColorTypes", "Colors", "Contour", "Dates", "DelaunayTriangulation", "Distributions", "DocStringExtensions", "Downloads", "FFMPEG_jll", "FileIO", "FilePaths", "FixedPointNumbers", "Format", "FreeType", "FreeTypeAbstraction", "GeometryBasics", "GridLayoutBase", "ImageIO", "InteractiveUtils", "IntervalSets", "Isoband", "KernelDensity", "LaTeXStrings", "LinearAlgebra", "MacroTools", "MakieCore", "Markdown", "MathTeXEngine", "Observables", "OffsetArrays", "Packing", "PlotUtils", "PolygonOps", "PrecompileTools", "Printf", "REPL", "Random", "RelocatableFolders", "Scratch", "ShaderAbstractions", "Showoff", "SignedDistanceFields", "SparseArrays", "Statistics", "StatsBase", "StatsFuns", "StructArrays", "TriplotBase", "UnicodeFun", "Unitful"]
-git-tree-sha1 = "204f06860af9008fa08b3a4842f48116e1209a2c"
-uuid = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
-version = "0.21.9"
-
-[[deps.MakieCore]]
-deps = ["ColorTypes", "GeometryBasics", "IntervalSets", "Observables"]
-git-tree-sha1 = "b0e2e3473af351011e598f9219afb521121edd2b"
-uuid = "20f20a25-4f0e-4fdf-b5d1-57303727442b"
-version = "0.8.6"
-
 [[deps.ManualMemory]]
 git-tree-sha1 = "bcaef4fc7a0cfe2cba636d84cda54b5e4e4ca3cd"
 uuid = "d125e4d3-2237-4719-b19c-fa641b8a4667"
@@ -1386,23 +929,11 @@ version = "0.4.2"
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
-[[deps.MathTeXEngine]]
-deps = ["AbstractTrees", "Automa", "DataStructures", "FreeTypeAbstraction", "GeometryBasics", "LaTeXStrings", "REPL", "RelocatableFolders", "UnicodeFun"]
-git-tree-sha1 = "e1641f32ae592e415e3dbae7f4a188b5316d4b62"
-uuid = "0a4f8689-d25c-4efe-a92b-7142dfc1aa53"
-version = "0.6.1"
-
 [[deps.MbedTLS_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
 version = "2.28.2+1"
 
-[[deps.MetaGraphs]]
-deps = ["Graphs", "JLD2", "Random"]
-git-tree-sha1 = "1130dbe1d5276cb656f6e1094ce97466ed700e5a"
-uuid = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
-version = "0.7.2"
-
 [[deps.MicroCollections]]
 deps = ["Accessors", "BangBang", "InitialValues"]
 git-tree-sha1 = "44d32db644e84c75dab479f1bc15ee76a1a3618f"
@@ -1474,27 +1005,10 @@ git-tree-sha1 = "1a0fa0e9613f46c9b8c11eee38ebb4f590013c5e"
 uuid = "71a1bf82-56d0-4bbc-8a3c-48b961074391"
 version = "0.1.5"
 
-[[deps.NearestNeighbors]]
-deps = ["Distances", "StaticArrays"]
-git-tree-sha1 = "f3b409a8844a2afa56b0aa7992417b2c9ae90845"
-uuid = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
-version = "0.4.19"
-
-[[deps.Netpbm]]
-deps = ["FileIO", "ImageCore", "ImageMetadata"]
-git-tree-sha1 = "d92b107dbb887293622df7697a2223f9f8176fcd"
-uuid = "f09324ee-3d7c-5217-9330-fc30815ba969"
-version = "1.1.1"
-
 [[deps.NetworkOptions]]
 uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
 version = "1.2.0"
 
-[[deps.Observables]]
-git-tree-sha1 = "7438a59546cf62428fc9d1bc94729146d37a7225"
-uuid = "510215fc-4207-5dde-b226-833fc4488ee2"
-version = "0.5.5"
-
 [[deps.OffsetArrays]]
 git-tree-sha1 = "1a27764e945a152f7ca7efa04de513d473e9542e"
 uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
@@ -1521,24 +1035,6 @@ deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
 uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
 version = "0.3.23+4"
 
-[[deps.OpenEXR]]
-deps = ["Colors", "FileIO", "OpenEXR_jll"]
-git-tree-sha1 = "327f53360fdb54df7ecd01e96ef1983536d1e633"
-uuid = "52e1d378-f018-4a11-a4be-720524705ac7"
-version = "0.3.2"
-
-[[deps.OpenEXR_jll]]
-deps = ["Artifacts", "Imath_jll", "JLLWrappers", "Libdl", "Zlib_jll"]
-git-tree-sha1 = "8292dd5c8a38257111ada2174000a33745b06d4e"
-uuid = "18a262bb-aa17-5467-a713-aee519bc75cb"
-version = "3.2.4+0"
-
-[[deps.OpenJpeg_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Libtiff_jll", "LittleCMS_jll", "libpng_jll"]
-git-tree-sha1 = "f4cb457ffac5f5cf695699f82c537073958a6a6c"
-uuid = "643b3616-a352-519d-856d-80112ee9badc"
-version = "2.5.2+0"
-
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
@@ -1546,9 +1042,9 @@ version = "0.8.1+2"
 
 [[deps.OpenSSL_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "1b35263570443fdd9e76c76b7062116e2f374ab8"
+git-tree-sha1 = "7493f61f55a6cce7325f197443aa80d32554ba10"
 uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
-version = "3.0.15+0"
+version = "3.0.15+1"
 
 [[deps.OpenSpecFun_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
@@ -1578,48 +1074,12 @@ deps = ["Artifacts", "Libdl"]
 uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15"
 version = "10.42.0+1"
 
-[[deps.PDMats]]
-deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65"
-uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.31"
-
-[[deps.PNGFiles]]
-deps = ["Base64", "CEnum", "ImageCore", "IndirectArrays", "OffsetArrays", "libpng_jll"]
-git-tree-sha1 = "67186a2bc9a90f9f85ff3cc8277868961fb57cbd"
-uuid = "f57f5aa1-a3ce-4bc8-8ab9-96f992907883"
-version = "0.4.3"
-
-[[deps.Packing]]
-deps = ["GeometryBasics"]
-git-tree-sha1 = "ec3edfe723df33528e085e632414499f26650501"
-uuid = "19eb6ba3-879d-56ad-ad62-d5c202156566"
-version = "0.5.0"
-
 [[deps.PaddedViews]]
 deps = ["OffsetArrays"]
 git-tree-sha1 = "0fac6313486baae819364c52b4f483450a9d793f"
 uuid = "5432bcbf-9aad-5242-b902-cca2824c8663"
 version = "0.5.12"
 
-[[deps.Pango_jll]]
-deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "FriBidi_jll", "Glib_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "e127b609fb9ecba6f201ba7ab753d5a605d53801"
-uuid = "36c8627f-9965-5494-a995-c6b170f724f3"
-version = "1.54.1+0"
-
-[[deps.Parameters]]
-deps = ["OrderedCollections", "UnPack"]
-git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe"
-uuid = "d96e819e-fc66-5662-9728-84c9c7592b0a"
-version = "0.12.3"
-
-[[deps.Parsers]]
-deps = ["Dates", "PrecompileTools", "UUIDs"]
-git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821"
-uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "2.8.1"
-
 [[deps.Pixman_jll]]
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"]
 git-tree-sha1 = "35621f10a7531bc8fa58f74610b1bfb70a3cfc6b"
@@ -1631,35 +1091,12 @@ deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 version = "1.10.0"
 
-[[deps.PkgVersion]]
-deps = ["Pkg"]
-git-tree-sha1 = "f9501cc0430a26bc3d156ae1b5b0c1b47af4d6da"
-uuid = "eebad327-c553-4316-9ea0-9fa01ccd7688"
-version = "0.3.3"
-
-[[deps.PlotUtils]]
-deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"]
-git-tree-sha1 = "7b1a9df27f072ac4c9c7cbe5efb198489258d1f5"
-uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043"
-version = "1.4.1"
-
 [[deps.PolyesterWeave]]
 deps = ["BitTwiddlingConvenienceFunctions", "CPUSummary", "IfElse", "Static", "ThreadingUtilities"]
 git-tree-sha1 = "645bed98cd47f72f67316fd42fc47dee771aefcd"
 uuid = "1d0040c9-8b98-4ee7-8388-3f51789ca0ad"
 version = "0.2.2"
 
-[[deps.PolygonOps]]
-git-tree-sha1 = "77b3d3605fc1cd0b42d95eba87dfcd2bf67d5ff6"
-uuid = "647866c9-e3ac-4575-94e7-e3d426903924"
-version = "0.1.2"
-
-[[deps.Polynomials]]
-deps = ["LinearAlgebra", "RecipesBase"]
-git-tree-sha1 = "a14a99e430e42a105c898fcc7f212334bc7be887"
-uuid = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"
-version = "3.2.4"
-
 [[deps.PooledArrays]]
 deps = ["DataAPI", "Future"]
 git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3"
@@ -1670,7 +1107,7 @@ version = "1.4.3"
 deps = ["ArrayPadding", "ChainRulesCore", "DataStructures", "Functors", "LinearAlgebra", "Statistics", "UnPack", "Zygote"]
 path = "C:\\Users\\pxshe\\OneDrive\\Desktop\\Porcupine.jl"
 uuid = "3b53a3d7-3f34-4bba-8df2-4717a8b1e972"
-version = "0.1.43"
+version = "0.1.44"
 
 [[deps.PrecompileTools]]
 deps = ["Preferences"]
@@ -1691,9 +1128,9 @@ version = "0.2.0"
 
 [[deps.PrettyTables]]
 deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "Reexport", "StringManipulation", "Tables"]
-git-tree-sha1 = "66b20dd35966a748321d3b2537c4584cf40387c7"
+git-tree-sha1 = "1101cd475833706e4d0e7b122218257178f48f34"
 uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-version = "2.3.2"
+version = "2.4.0"
 
 [[deps.Printf]]
 deps = ["Unicode"]
@@ -1705,35 +1142,6 @@ git-tree-sha1 = "80d919dee55b9c50e8d9e2da5eeafff3fe58b539"
 uuid = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
 version = "0.1.4"
 
-[[deps.ProgressMeter]]
-deps = ["Distributed", "Printf"]
-git-tree-sha1 = "8f6bc219586aef8baf0ff9a5fe16ee9c70cb65e4"
-uuid = "92933f4c-e287-5a05-a399-4b506db050ca"
-version = "1.10.2"
-
-[[deps.PtrArrays]]
-git-tree-sha1 = "77a42d78b6a92df47ab37e177b2deac405e1c88f"
-uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d"
-version = "1.2.1"
-
-[[deps.QOI]]
-deps = ["ColorTypes", "FileIO", "FixedPointNumbers"]
-git-tree-sha1 = "18e8f4d1426e965c7b532ddd260599e1510d26ce"
-uuid = "4b34888f-f399-49d4-9bb3-47ed5cae4e65"
-version = "1.0.0"
-
-[[deps.QuadGK]]
-deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "cda3b045cf9ef07a08ad46731f5a3165e56cf3da"
-uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.11.1"
-
-    [deps.QuadGK.extensions]
-    QuadGKEnzymeExt = "Enzyme"
-
-    [deps.QuadGK.weakdeps]
-    Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
-
 [[deps.Quaternions]]
 deps = ["LinearAlgebra", "Random", "RealDot"]
 git-tree-sha1 = "994cc27cdacca10e68feb291673ec3a76aa2fae9"
@@ -1760,11 +1168,6 @@ git-tree-sha1 = "c6ec94d2aaba1ab2ff983052cf6a606ca5985902"
 uuid = "e6cf234a-135c-5ec9-84dd-332b85af5143"
 version = "1.6.0"
 
-[[deps.RangeArrays]]
-git-tree-sha1 = "b9039e93773ddcfc828f12aadf7115b4b4d225f5"
-uuid = "b3c3ace0-ae52-54e7-9d0b-2c1406fd6b9d"
-version = "0.3.2"
-
 [[deps.Ratios]]
 deps = ["Requires"]
 git-tree-sha1 = "1342a47bf3260ee108163042310d26f2be5ec90b"
@@ -1781,72 +1184,33 @@ git-tree-sha1 = "9f0a1b71baaf7650f4fa8a1d168c7fb6ee41f0c9"
 uuid = "c1ae055f-0cd5-4b69-90a6-9a35b1a98df9"
 version = "0.1.0"
 
-[[deps.RecipesBase]]
-deps = ["PrecompileTools"]
-git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff"
-uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
-version = "1.3.4"
-
 [[deps.Reexport]]
 git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
 uuid = "189a3867-3050-52da-a836-e630ba90ab69"
 version = "1.2.2"
 
-[[deps.RegionTrees]]
-deps = ["IterTools", "LinearAlgebra", "StaticArrays"]
-git-tree-sha1 = "4618ed0da7a251c7f92e869ae1a19c74a7d2a7f9"
-uuid = "dee08c22-ab7f-5625-9660-a9af2021b33f"
-version = "0.3.2"
-
-[[deps.RelocatableFolders]]
-deps = ["SHA", "Scratch"]
-git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864"
-uuid = "05181044-ff0b-4ac5-8273-598c1e38db00"
-version = "1.0.1"
-
 [[deps.Requires]]
 deps = ["UUIDs"]
 git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7"
 uuid = "ae029012-a4dd-5104-9daa-d747884805df"
 version = "1.3.0"
 
-[[deps.Rmath]]
-deps = ["Random", "Rmath_jll"]
-git-tree-sha1 = "852bd0f55565a9e973fcfee83a84413270224dc4"
-uuid = "79098fc4-a85e-5d69-aa6a-4863f24498fa"
-version = "0.8.0"
-
-[[deps.Rmath_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "58cdd8fb2201a6267e1db87ff148dd6c1dbd8ad8"
-uuid = "f50d1b31-88e8-58de-be2c-1cc44531875f"
-version = "0.5.1+0"
-
 [[deps.Rotations]]
 deps = ["LinearAlgebra", "Quaternions", "Random", "StaticArrays"]
 git-tree-sha1 = "5680a9276685d392c87407df00d57c9924d9f11e"
 uuid = "6038ab10-8711-5258-84ad-4b1120ba62dc"
 version = "1.7.1"
-weakdeps = ["RecipesBase"]
 
     [deps.Rotations.extensions]
     RotationsRecipesBaseExt = "RecipesBase"
 
-[[deps.RoundingEmulator]]
-git-tree-sha1 = "40b9edad2e5287e05bd413a38f61a8ff55b9557b"
-uuid = "5eaf0fd0-dfba-4ccb-bf02-d820a40db705"
-version = "0.2.1"
+    [deps.Rotations.weakdeps]
+    RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 
 [[deps.SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 version = "0.7.0"
 
-[[deps.SIMD]]
-deps = ["PrecompileTools"]
-git-tree-sha1 = "98ca7c29edd6fc79cd74c61accb7010a4e7aee33"
-uuid = "fdea26ae-647d-5447-a871-4b548cad5224"
-version = "3.6.0"
-
 [[deps.SIMDTypes]]
 git-tree-sha1 = "330289636fb8107c5f32088d2741e9fd7a061a5c"
 uuid = "94e857df-77ce-4151-89e5-788b33177be4"
@@ -1879,12 +1243,6 @@ git-tree-sha1 = "e2cc6d8c88613c05e1defb55170bf5ff211fbeac"
 uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 version = "1.1.1"
 
-[[deps.ShaderAbstractions]]
-deps = ["ColorTypes", "FixedPointNumbers", "GeometryBasics", "LinearAlgebra", "Observables", "StaticArrays", "StructArrays", "Tables"]
-git-tree-sha1 = "79123bc60c5507f035e6d1d9e563bb2971954ec8"
-uuid = "65257c39-d410-5151-9873-9b3e5be5013e"
-version = "0.4.1"
-
 [[deps.SharedArrays]]
 deps = ["Distributed", "Mmap", "Random", "Serialization"]
 uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
@@ -1894,36 +1252,12 @@ git-tree-sha1 = "7f534ad62ab2bd48591bdeac81994ea8c445e4a5"
 uuid = "605ecd9f-84a6-4c9e-81e2-4798472b76a3"
 version = "0.1.0"
 
-[[deps.Showoff]]
-deps = ["Dates", "Grisu"]
-git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de"
-uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f"
-version = "1.0.3"
-
-[[deps.SignedDistanceFields]]
-deps = ["Random", "Statistics", "Test"]
-git-tree-sha1 = "d263a08ec505853a5ff1c1ebde2070419e3f28e9"
-uuid = "73760f76-fbc4-59ce-8f25-708e95d2df96"
-version = "0.4.0"
-
 [[deps.SimpleTraits]]
 deps = ["InteractiveUtils", "MacroTools"]
 git-tree-sha1 = "5d7e3f4e11935503d3ecaf7186eac40602e7d231"
 uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
 version = "0.9.4"
 
-[[deps.SimpleWeightedGraphs]]
-deps = ["Graphs", "LinearAlgebra", "Markdown", "SparseArrays"]
-git-tree-sha1 = "4b33e0e081a825dbfaf314decf58fa47e53d6acb"
-uuid = "47aef6b3-ad0c-573a-a1e2-d07658019622"
-version = "1.4.0"
-
-[[deps.Sixel]]
-deps = ["Dates", "FileIO", "ImageCore", "IndirectArrays", "OffsetArrays", "REPL", "libsixel_jll"]
-git-tree-sha1 = "2da10356e31327c7096832eb9cd86307a50b1eb6"
-uuid = "45858cf5-a6b0-47a3-bbea-62219f50df47"
-version = "0.1.3"
-
 [[deps.Sockets]]
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
@@ -2016,22 +1350,11 @@ git-tree-sha1 = "5cf7606d6cef84b543b483848d4ae08ad9832b21"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 version = "0.34.3"
 
-[[deps.StatsFuns]]
-deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
-git-tree-sha1 = "b423576adc27097764a90e163157bcfc9acf0f46"
-uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-version = "1.3.2"
-weakdeps = ["ChainRulesCore", "InverseFunctions"]
-
-    [deps.StatsFuns.extensions]
-    StatsFunsChainRulesCoreExt = "ChainRulesCore"
-    StatsFunsInverseFunctionsExt = "InverseFunctions"
-
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
-git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5"
+git-tree-sha1 = "a6b1675a536c5ad1a60e5a5153e1fee12eb146e3"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
-version = "0.3.4"
+version = "0.4.0"
 
 [[deps.StructArrays]]
 deps = ["ConstructionBase", "DataAPI", "Tables"]
@@ -2093,12 +1416,6 @@ git-tree-sha1 = "eda08f7e9818eb53661b3deb74e3159460dfbc27"
 uuid = "8290d209-cae3-49c0-8002-c8c24d57dab5"
 version = "0.5.2"
 
-[[deps.TiffImages]]
-deps = ["ColorTypes", "DataStructures", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "Mmap", "OffsetArrays", "PkgVersion", "ProgressMeter", "SIMD", "UUIDs"]
-git-tree-sha1 = "bc7fd5c91041f44636b2c134041f7e5263ce58ae"
-uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69"
-version = "0.10.0"
-
 [[deps.TiledIteration]]
 deps = ["OffsetArrays", "StaticArrayInterface"]
 git-tree-sha1 = "1176cc31e867217b06928e2f140c90bd1bc88283"
@@ -2111,11 +1428,6 @@ git-tree-sha1 = "5a13ae8a41237cff5ecf34f73eb1b8f42fff6531"
 uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 version = "0.5.24"
 
-[[deps.TranscodingStreams]]
-git-tree-sha1 = "e84b3a11b9bece70d14cce63406bbc79ed3464d2"
-uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.11.2"
-
 [[deps.Transducers]]
 deps = ["Accessors", "Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "SplittablesBase", "Tables"]
 git-tree-sha1 = "5215a069867476fc8e3469602006b9670e68da23"
@@ -2136,11 +1448,6 @@ version = "0.4.82"
     OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338"
     Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e"
 
-[[deps.TriplotBase]]
-git-tree-sha1 = "4d4ed7f294cda19382ff7de4c137d24d16adc89b"
-uuid = "981d1d27-644d-49a2-9326-4793e63143c3"
-version = "0.1.0"
-
 [[deps.UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
@@ -2153,23 +1460,6 @@ version = "1.0.2"
 [[deps.Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 
-[[deps.UnicodeFun]]
-deps = ["REPL"]
-git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
-uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
-version = "0.4.1"
-
-[[deps.Unitful]]
-deps = ["Dates", "LinearAlgebra", "Random"]
-git-tree-sha1 = "d95fe458f26209c66a187b1114df96fd70839efd"
-uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
-version = "1.21.0"
-weakdeps = ["ConstructionBase", "InverseFunctions"]
-
-    [deps.Unitful.extensions]
-    ConstructionBaseUnitfulExt = "ConstructionBase"
-    InverseFunctionsUnitfulExt = "InverseFunctions"
-
 [[deps.UnsafeAtomics]]
 git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278"
 uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f"
@@ -2211,12 +1501,6 @@ git-tree-sha1 = "a54ee957f4c86b526460a720dbc882fa5edcbefc"
 uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
 version = "1.1.41+0"
 
-[[deps.XZ_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "ac88fb95ae6447c8dda6a5503f3bafd496ae8632"
-uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800"
-version = "5.4.6+0"
-
 [[deps.Xorg_libX11_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
 git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495"
@@ -2270,17 +1554,11 @@ deps = ["Libdl"]
 uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
 version = "1.2.13+1"
 
-[[deps.Zstd_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "e678132f07ddb5bfa46857f0d7620fb9be675d3b"
-uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
-version = "1.5.6+0"
-
 [[deps.Zygote]]
 deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "19c586905e78a26f7e4e97f81716057bd6b1bc54"
+git-tree-sha1 = "f2f85ad73ca67b5d3c94239b0fde005e0fe2d900"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.6.70"
+version = "0.6.71"
 
     [deps.Zygote.extensions]
     ZygoteColorsExt = "Colors"
@@ -2300,15 +1578,15 @@ version = "0.2.5"
 
 [[deps.cuDNN]]
 deps = ["CEnum", "CUDA", "CUDA_Runtime_Discovery", "CUDNN_jll"]
-git-tree-sha1 = "4909e87d6d62c29a897d54d9001c63932e41cb0e"
+git-tree-sha1 = "4b3ac62501ca73263eaa0d034c772f13c647fba6"
 uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
-version = "1.3.2"
+version = "1.4.0"
 
-[[deps.isoband_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
-uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
-version = "0.2.3+0"
+[[deps.demumble_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "6498e3581023f8e530f34760d18f75a69e3a4ea8"
+uuid = "1e29f10c-031c-5a83-9565-69cddfc27673"
+version = "1.3.0+0"
 
 [[deps.libaom_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]
@@ -2335,15 +1613,9 @@ version = "2.0.3+0"
 
 [[deps.libpng_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
-git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4"
+git-tree-sha1 = "b70c870239dc3d7bc094eb2d6be9b73d27bef280"
 uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
-version = "1.6.43+1"
-
-[[deps.libsixel_jll]]
-deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Pkg", "libpng_jll"]
-git-tree-sha1 = "d4f63314c8aa1e48cd22aa0c17ed76cd1ae48c3c"
-uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
-version = "1.10.3+0"
+version = "1.6.44+0"
 
 [[deps.libvorbis_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
diff --git a/lumi/Project.toml b/lumi/Project.toml
index a0c1f97..eef91b9 100644
--- a/lumi/Project.toml
+++ b/lumi/Project.toml
@@ -4,7 +4,6 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 FFMPEG = "c87230d0-a227-11e9-1b43-d7ebe4e7570a"
 Humanize = "7ec9b9c5-1998-51e1-b7fc-fc3590c18259"
 Jello = "6872b481-e419-48a0-81d2-be4ee5684529"
-Luminescent = "60a98398-2d19-4130-ae20-63172a4a42f2"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Porcupine = "3b53a3d7-3f34-4bba-8df2-4717a8b1e972"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
diff --git a/lumi/src/luminescent/__init__.py b/lumi/src/luminescent/__init__.py
index 7f01761..d50f2d3 100644
--- a/lumi/src/luminescent/__init__.py
+++ b/lumi/src/luminescent/__init__.py
@@ -8,14 +8,17 @@
     "gcell_problem",
     "sparams_problem",
     "solve",
-    "finetune",
     "apply_design",
     "add_bbox",
+    "XYMARGIN",
     "XMARGIN",
     "YMARGIN",
     "ZMARGIN",
-    "XYMARGIN",
     "load_solution",
+    "finetune",
+    "load_problem",
     "show_solution",
+    "make_training_movie",
+    "make_simulation_movie",
     "gcells",
 ]
diff --git a/lumi/src/luminescent/__pycache__/__init__.cpython-311.pyc b/lumi/src/luminescent/__pycache__/__init__.cpython-311.pyc
index 05240a2..55a7f01 100644
Binary files a/lumi/src/luminescent/__pycache__/__init__.cpython-311.pyc and b/lumi/src/luminescent/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lumi/src/luminescent/gplugins/luminescent/__init__.py b/lumi/src/luminescent/gplugins/luminescent/__init__.py
index ca70cb6..2c0b7cf 100644
--- a/lumi/src/luminescent/gplugins/luminescent/__init__.py
+++ b/lumi/src/luminescent/gplugins/luminescent/__init__.py
@@ -7,6 +7,7 @@
 from .sparams import *
 from .sol import *
 from .setup import *
+from .runs_utils import *
 from gdsfactory.generic_tech import *
 
 __all__ = [
@@ -18,7 +19,6 @@
     "gcell_problem",
     "sparams_problem",
     "solve",
-    "finetune",
     "apply_design",
     "add_bbox",
     "XYMARGIN",
@@ -26,6 +26,10 @@
     "YMARGIN",
     "ZMARGIN",
     "load_solution",
+    "finetune",
+    "load_problem",
     "show_solution",
+    "make_training_movie",
+    "make_simulation_movie",
     "gcells",
 ]
diff --git a/lumi/src/luminescent/gplugins/luminescent/__pycache__/__init__.cpython-311.pyc b/lumi/src/luminescent/gplugins/luminescent/__pycache__/__init__.cpython-311.pyc
index f3a330d..3ff2544 100644
Binary files a/lumi/src/luminescent/gplugins/luminescent/__pycache__/__init__.cpython-311.pyc and b/lumi/src/luminescent/gplugins/luminescent/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lumi/src/luminescent/gplugins/luminescent/__pycache__/gcells.cpython-311.pyc b/lumi/src/luminescent/gplugins/luminescent/__pycache__/gcells.cpython-311.pyc
index 5fe8933..ab4c354 100644
Binary files a/lumi/src/luminescent/gplugins/luminescent/__pycache__/gcells.cpython-311.pyc and b/lumi/src/luminescent/gplugins/luminescent/__pycache__/gcells.cpython-311.pyc differ
diff --git a/lumi/src/luminescent/gplugins/luminescent/__pycache__/inverse_design.cpython-311.pyc b/lumi/src/luminescent/gplugins/luminescent/__pycache__/inverse_design.cpython-311.pyc
index e032a5e..d53259c 100644
Binary files a/lumi/src/luminescent/gplugins/luminescent/__pycache__/inverse_design.cpython-311.pyc and b/lumi/src/luminescent/gplugins/luminescent/__pycache__/inverse_design.cpython-311.pyc differ
diff --git a/lumi/src/luminescent/gplugins/luminescent/__pycache__/setup.cpython-311.pyc b/lumi/src/luminescent/gplugins/luminescent/__pycache__/setup.cpython-311.pyc
index 7e14541..41bcfbe 100644
Binary files a/lumi/src/luminescent/gplugins/luminescent/__pycache__/setup.cpython-311.pyc and b/lumi/src/luminescent/gplugins/luminescent/__pycache__/setup.cpython-311.pyc differ
diff --git a/lumi/src/luminescent/gplugins/luminescent/__pycache__/sol.cpython-311.pyc b/lumi/src/luminescent/gplugins/luminescent/__pycache__/sol.cpython-311.pyc
index c3bb453..90aee5f 100644
Binary files a/lumi/src/luminescent/gplugins/luminescent/__pycache__/sol.cpython-311.pyc and b/lumi/src/luminescent/gplugins/luminescent/__pycache__/sol.cpython-311.pyc differ
diff --git a/lumi/src/luminescent/gplugins/luminescent/gcells.py b/lumi/src/luminescent/gplugins/luminescent/gcells.py
index aba3a25..d3b74e2 100644
--- a/lumi/src/luminescent/gplugins/luminescent/gcells.py
+++ b/lumi/src/luminescent/gplugins/luminescent/gcells.py
@@ -13,7 +13,7 @@
 
 
 def mimo(west=0, east=0, south=0, north=0,
-         l=2.0, w=2.0, wwg=.5, lwg=None,
+         l=2.0, w=2.0, wwg=.5, lwg=None, taper=0,
          wwg_west=None, wwg_east=None, wwg_south=None, wwg_north=None,
          wwg_layer=LAYER.WG,  # bbox_layer=LAYER.WAFER,
          design_layer=DESIGN_LAYER,
@@ -51,7 +51,7 @@ def mimo(west=0, east=0, south=0, north=0,
     ):
         for wwg, v in zip(wwg, d):
             center = (x, y+v) if i in [0, 1] else (x+v, y)
-            wwg2 = wwg+.1*lwg
+            wwg2 = wwg+2*taper*lwg
             name = "o"+str(n+1)
             design.add_port(name=name, center=center, width=wwg2,
                             orientation=a, layer=wwg_layer)
diff --git a/lumi/src/luminescent/gplugins/luminescent/inverse_design.py b/lumi/src/luminescent/gplugins/luminescent/inverse_design.py
index 019d5db..ea66ad1 100644
--- a/lumi/src/luminescent/gplugins/luminescent/inverse_design.py
+++ b/lumi/src/luminescent/gplugins/luminescent/inverse_design.py
@@ -16,7 +16,7 @@
 def gcell_problem(c,  targets, iters,
                   lvoid=0, lsolid=0, symmetries=[],
                   weights=dict(),
-                  eta=.1, init=1,   stoploss=.03,
+                  eta=.4, init=1,   stoploss=.03,
                   design_region_layer=DESIGN_LAYER,
                   #    design_guess_layer=LAYER.GUESS,
                   fill_layer=LAYER.WG,
@@ -49,6 +49,7 @@ def gcell_problem(c,  targets, iters,
                         _k = f"o{po}@{_mo},o{pi}@{mi}"
                         if _k not in targets["tparams"][wl]:
                             d[_k] = 0
+
                     _k = f"o{pi}@0,o{pi}@{mi}"
                     if _k not in targets["tparams"][wl]:
                         d[_k] = 0
diff --git a/lumi/src/luminescent/gplugins/luminescent/run copy 2.jl b/lumi/src/luminescent/gplugins/luminescent/run copy 2.jl
index d7188f4..09f9278 100644
--- a/lumi/src/luminescent/gplugins/luminescent/run copy 2.jl	
+++ b/lumi/src/luminescent/gplugins/luminescent/run copy 2.jl	
@@ -124,7 +124,7 @@ using Porcupine: values, keys, fmap
 using CUDA: @allowscalar
 println("setting up simulation...")
 # do something based on ARGS?
-PROB_PATH = joinpath(path, "prob.bson")
+PROB_PATH = joinpath(path, "problem.bson")
 
 calibrate = true
 model_name = nothing # if load saved model
@@ -472,8 +472,8 @@ elseif study == "inverse_design"
     )
 end
 sol = (; sol..., path, dx, study,)
-# @save "$path/sol.json" sol
-open("$(path)/sol.json", "w") do f
+# @save "$path/solution.json" sol
+open("$(path)/solution.json", "w") do f
     write(f, json(sol))
 end
 sol
\ No newline at end of file
diff --git a/lumi/src/luminescent/gplugins/luminescent/run copy.jl b/lumi/src/luminescent/gplugins/luminescent/run copy.jl
index 5d1d879..b58a19a 100644
--- a/lumi/src/luminescent/gplugins/luminescent/run copy.jl	
+++ b/lumi/src/luminescent/gplugins/luminescent/run copy.jl	
@@ -22,7 +22,7 @@ include("$(pwd())/../LuminescentVisualization.jl/src/main.jl") # hide
 if isempty(ARGS)
     path = joinpath(pwd(), "runs")
     path = filter(isdir, readdir(path, join=true)) |> sort |> last
-    PROB_PATH = joinpath(path, "prob.bson")
+    PROB_PATH = joinpath(path, "problem.bson")
 else
     PROB_PATH = ARGS[1]
     path = dirname(PROB_PATH)
@@ -407,7 +407,7 @@ elseif study == "inverse_design"
         # designs=[m() for m in model],
     )
 end
-# @save "$path/sol.json" sol
-open("$(path)/sol.json", "w") do f
+# @save "$path/solution.json" sol
+open("$(path)/solution.json", "w") do f
     write(f, json(sol))
 end
\ No newline at end of file
diff --git a/lumi/src/luminescent/gplugins/luminescent/run.jl b/lumi/src/luminescent/gplugins/luminescent/run.jl
deleted file mode 100644
index 67424c5..0000000
--- a/lumi/src/luminescent/gplugins/luminescent/run.jl
+++ /dev/null
@@ -1,20 +0,0 @@
-using Luminescent
-if isempty(ARGS)
-    path = lastrun()
-    println("path: ", path)
-else
-    path = ARGS[1]
-end
-sol = gfrun(path)
-
-# @show sol.tparams
-
-# using Pkg
-# pkg"add Porcupine,Jello,ArrayPadding;up"
-
-
-# using Pkg
-# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Porcupine.jl;dev C:\Users\pxshe\OneDrive\Desktop\ArrayPadding.jl; dev C:\Users\pxshe\OneDrive\Desktop\Jello.jl;dev C:\Users\pxshe\OneDrive\Desktop\Luminescent.jl;up"
-
-# using Pkg
-# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Porcupine.jl;dev C:\Users\pxshe\OneDrive\Desktop\ArrayPadding.jl; dev C:\Users\pxshe\OneDrive\Desktop\Jello.jl;up"
diff --git a/lumi/src/luminescent/gplugins/luminescent/runs_utils.py b/lumi/src/luminescent/gplugins/luminescent/runs_utils.py
new file mode 100644
index 0000000..61d8e9c
--- /dev/null
+++ b/lumi/src/luminescent/gplugins/luminescent/runs_utils.py
@@ -0,0 +1,138 @@
+# import dill
+import cv2
+from pprint import pprint
+from PIL import Image
+import os
+import subprocess
+import time
+import json
+import bson
+import numpy as np
+from .inverse_design import *
+from .sparams import *
+from .utils import *
+from .layers import *
+from .constants import *
+from .sol import *
+
+
+def lastrun(wd=os.path.join(os.getcwd(), "runs"), name="", study="",  **kwargs):
+    if name:
+        return os.path.join(wd, name)
+    l = [os.path.join(wd, x) for x in os.listdir(wd)]
+    l = [x for x in l if os.path.isfile(os.path.join(x, "problem.bson"))]
+    l = sorted(l, key=lambda x: os.path.getmtime(x), reverse=True)
+    if study:
+        for x in l:
+            try:
+                if bson.loads(open(os.path.join(x, "problem.bson"), "rb").read())["study"] == study:
+                    return x
+            except:
+                pass
+    return l[0]
+
+
+def finetune(iters, **kwargs):
+    path = lastrun(study="inverse_design", **kwargs)
+
+    prob = bson.loads(open(os.path.join(path, "problem.bson"), "rb").read())
+    prob["iters"] = iters
+    # prob["eta"] = eta
+    prob["restart"] = False
+    prob = {**prob, **kwargs}
+    return solve(prob)
+
+
+def load_problem(**kwargs):
+    path = lastrun(**kwargs)
+    print(f"loading problem from {path}")
+    return bson.loads(open(os.path.join(path, "problem.bson"), "rb").read())
+
+
+def load_solution(**kwargs):
+    path = lastrun(**kwargs)
+    print(f"loading solution from {path}")
+    prob = bson.loads(open(os.path.join(path, "problem.bson"), "rb").read())
+    p = os.path.join(path, "solution.json")
+    # sol = bson.loads(p, "rb").read())["sol"]
+    sol = json.loads(open(p).read())
+    sol["sparams"] = load_sparams(sol["sparams"])
+    sol["component"] = gf.import_gds(os.path.join(path, "component.gds"))
+    if prob["study"] == "sparams":
+        pass
+    elif prob["study"] == "inverse_design":
+        l = [np.array(d) for d in sol["optimized_designs"]]
+        sol["optimized_designs"] = l
+        for i, a in enumerate(l):
+            name = f"optimized_design_region_{i+1}.png"
+            Image.fromarray(np.uint8((1-a) * 255),
+                            'L').save(os.path.join(path, name))
+            pic2gds(os.path.join(
+                path, name), sol["dx"])
+        c = apply_design(sol["component"],  sol)
+        # sol["optimized_component"] = copy.deepcopy(c)
+        sol["optimized_component"] = c
+        c.write_gds(os.path.join(path, "optimized_component.gds"))
+    return sol
+
+
+def show_solution(**kwargs):
+    path = lastrun(**kwargs)
+    print(f"showing solution from {path}")
+    sol = load_solution(**kwargs)
+    sol = {k: sol[k] for k in ["path", "sparams", "tparams", ]}
+    pprint(sol)
+
+    i = 1
+    while True:
+        p = os.path.join(path, f"run_{i}.png")
+        if os.path.exists(p):
+            img = Image.open(p)
+            img.show()
+            try:
+                display(img)
+            except:
+                pass
+            i += 1
+        else:
+            break
+
+
+def make_simulation_movie(framerate=30, **kwargs):
+    path = lastrun(**kwargs)
+
+    f = os.path.join(path, "temp")
+    imgs = sorted(os.listdir(f), key=lambda x: float(x[0:-4]))
+    frame = cv2.imread(os.path.join(f, imgs[0]))
+    height, width, layers = frame.shape
+
+    video = cv2.VideoWriter(os.path.join(
+        path, "simulation_video.mp4"), 0x7634706d, framerate, (width, height))
+
+    for img in imgs:
+        video.write(cv2.imread(os.path.join(f, img)))
+
+    cv2.destroyAllWindows()
+    video.release()
+
+
+def make_training_movie(framerate=2, **kwargs):
+    path = lastrun(**kwargs)
+
+    f = os.path.join(path, "checkpoints")
+    ckpts = sorted(os.listdir(f))
+    frame = cv2.imread(os.path.join(f, ckpts[0], "run_1.png"))
+    height, width, layers = frame.shape
+
+    video = cv2.VideoWriter(os.path.join(
+        path, "training_video.mp4"), 0x7634706d, framerate, (width, height))
+
+    for ckpt in ckpts:
+        video.write(cv2.imread(os.path.join(f, ckpt, "run_1.png")))
+
+    cv2.destroyAllWindows()
+    video.release()
+
+
+def write_sparams(*args, run=True, **kwargs):
+    return solve(sparams_problem(*args, **kwargs), run=run)
diff --git a/lumi/src/luminescent/gplugins/luminescent/setup.py b/lumi/src/luminescent/gplugins/luminescent/setup.py
index 3200eea..2fc3628 100644
--- a/lumi/src/luminescent/gplugins/luminescent/setup.py
+++ b/lumi/src/luminescent/gplugins/luminescent/setup.py
@@ -8,18 +8,11 @@
 from copy import deepcopy
 # from time import time
 import datetime
-import json
-import subprocess
-import sys
-import time
-from functools import partial
 from math import cos, pi, sin
 import os
 import numpy as np
 
-from gdsfactory.cross_section import Section
 from sortedcontainers import SortedDict, SortedSet
-from sympy import N
 from gdsfactory.generic_tech import LAYER_STACK, LAYER
 
 
@@ -32,11 +25,12 @@ def setup(c, study, dx, margin,
           exclude_layers=[
               DESIGN_LAYER, GUESS], approx_2D=False, Courant=None,
           gpu=None, dtype=np.float32,
-          plot=False, magic="", wd=os.path.join(os.getcwd(), "runs"), name=None, **kwargs):
+          plot=False, framerate=0,
+          magic="", wd=os.path.join(os.getcwd(), "runs"), name=None, **kwargs):
     if name is None:
         name = c.name
     if name.startswith("Unnamed"):
-        name = ""
+        name = None
     if type(bbox_layer[0]) is int:
         bbox_layer = (bbox_layer,)
     prob = dict()
@@ -44,8 +38,8 @@ def setup(c, study, dx, margin,
     prob["dtype"] = str(dtype)
     prob["timestamp"] = datetime.datetime.now().isoformat(
         timespec="seconds").replace(":", "-")
-    prob["name"] = name
     prob["magic"] = magic
+    prob["framerate"] = framerate
     prob["gpu_backend"] = gpu if gpu else ""
     ports = {
         p.name: {
@@ -105,6 +99,7 @@ def setup(c, study, dx, margin,
         name = "#".join(l)
     path = os.path.join(wd, name)
     prob["path"] = path
+    prob["name"] = name
 
     prob["eps_3D"] = eps.tolist()
     prob["eps"] = prob["eps_3D"]
diff --git a/lumi/src/luminescent/gplugins/luminescent/sol.py b/lumi/src/luminescent/gplugins/luminescent/sol.py
index 79fb1d7..17dcb2a 100644
--- a/lumi/src/luminescent/gplugins/luminescent/sol.py
+++ b/lumi/src/luminescent/gplugins/luminescent/sol.py
@@ -29,7 +29,7 @@ def solve(prob, dev=False, run=True):
           started julia process
           compiling julia code...
           """)
-    prob_path = os.path.join(path, "prob.bson")
+    prob_path = os.path.join(path, "problem.bson")
     with open(prob_path, "wb") as f:
         # Write the BSON data to the file
         f.write(bson_data)
@@ -63,88 +63,8 @@ def run(cmd):
     return sol
 
 
-def lastrun(wd=os.path.join(os.getcwd(), "runs"), name="", study="",  **kwargs):
-    if name:
-        return os.path.join(wd, name)
-    l = [os.path.join(wd, x) for x in os.listdir(wd)]
-    l = [x for x in l if os.path.isfile(os.path.join(x, "prob.bson"))]
-    l = sorted(l, key=lambda x: os.path.getmtime(x), reverse=True)
-    if study:
-        for x in l:
-            try:
-                if bson.loads(open(os.path.join(x, "prob.bson"), "rb").read())["study"] == study:
-                    return x
-            except:
-                pass
-    return l[0]
-
-
-def finetune(iters, eta=0.2, **kwargs):
-    path = lastrun(study="inverse_design", **kwargs)
-
-    prob = bson.loads(open(os.path.join(path, "prob.bson"), "rb").read())
-    prob["iters"] = iters
-    prob["eta"] = eta
-    prob["restart"] = False
-    prob = {**prob, **kwargs}
-    return solve(prob)
-
-
 def load_sparams(sparams):
     if "re" in list(sparams.values())[0]:
         return {k: v["re"]+1j*v["im"] for k, v in sparams.items()}
     return {wl: {k: (v["re"]+1j*v["im"])
                  for k, v in d.items()} for wl, d in sparams.items()}
-
-
-def load_solution(**kwargs):
-    path = lastrun(**kwargs)
-    print(f"loading solution from {path}")
-    prob = bson.loads(open(os.path.join(path, "prob.bson"), "rb").read())
-    p = os.path.join(path, "sol.json")
-    # sol = bson.loads(p, "rb").read())["sol"]
-    sol = json.loads(open(p).read())
-    sol["sparams"] = load_sparams(sol["sparams"])
-    sol["component"] = gf.import_gds(os.path.join(path, "component.gds"))
-    if prob["study"] == "sparams":
-        pass
-    elif prob["study"] == "inverse_design":
-        l = [np.array(d) for d in sol["optimized_designs"]]
-        sol["optimized_designs"] = l
-        for i, a in enumerate(l):
-            name = f"optimized_design_region_{i+1}.png"
-            Image.fromarray(np.uint8((1-a) * 255),
-                            'L').save(os.path.join(path, name))
-            pic2gds(os.path.join(
-                path, name), sol["dx"])
-        c = apply_design(sol["component"],  sol)
-        # sol["optimized_component"] = copy.deepcopy(c)
-        sol["optimized_component"] = c
-        c.write_gds(os.path.join(path, "optimized_component.gds"))
-    return sol
-
-
-def show_solution(**kwargs):
-    path = lastrun(**kwargs)
-    print(f"showing solution from {path}")
-    sol = load_solution(**kwargs)
-    sol = {k: sol[k] for k in ["path", "sparams", "tparams", ]}
-    pprint(sol)
-
-    i = 1
-    while True:
-        p = os.path.join(path, f"run_{i}.png")
-        if os.path.exists(p):
-            img = Image.open(p)
-            img.show()
-            try:
-                display(img)
-            except:
-                pass
-            i += 1
-        else:
-            break
-
-
-def write_sparams(*args, run=True, **kwargs):
-    return solve(sparams_problem(*args, **kwargs), run=run)
diff --git a/lumi/src/maketest.py b/lumi/src/maketest.py
index e69f8b5..0fab1f6 100644
--- a/lumi/src/maketest.py
+++ b/lumi/src/maketest.py
@@ -15,17 +15,6 @@
     elif os.path.isdir(file_path):
         shutil.rmtree(file_path)
 
-c = gf.components.straight(.5,)
-for (approx_2D, gpu, dtype, wavelengths) in itertools.product(
-    [True, False],
-    [None, "CUDA"],
-    ["f32"],
-        [[1.55], ]):
-    lumi.write_sparams(c, name="",
-                       wavelength=wavelengths, keys=["2,1"], dx=0.1,
-                       approx_2D=approx_2D, gpu=gpu, dtype=dtype,
-                       run=False, wd=BUILD_RUNS)
-    sleep(1)
 
 c = lumi.gcells.mimo(west=1, east=1, l=1, w=1,  wwg=.5)
 targets = {"tparams": {1.55: {"2,1": 1.0}}}
@@ -39,9 +28,23 @@
 ):
     prob = lumi.gcell_problem(
         c, targets,
-        bbox_layer=LAYER.WAFER,
-        lmin=0.2, dx=0.1, iters=2,
+        # bbox_layer=LAYER.WAFER,
+        lvoid=0.2, lsolid=.2, dx=0.1, iters=2,
         approx_2D=approx_2D, gpu=gpu, dtype=dtype, save_memory=save_memory,
         run=False, wd=BUILD_RUNS)
     sol = lumi.solve(prob, run=False)
     sleep(1)
+
+c = gf.components.straight(.5,)
+i = 1
+for (approx_2D, gpu, dtype, wavelengths) in itertools.product(
+    [False, True],
+    [None, "CUDA"],
+    ["f32"],
+        [[1.55], ]):
+    lumi.write_sparams(c, name=f"{i}",
+                       wavelength=wavelengths, keys=["2,1"], dx=0.1,
+                       approx_2D=approx_2D, gpu=gpu, dtype=dtype,
+                       run=False, wd=BUILD_RUNS)
+    i += 1
+    sleep(1)
diff --git a/lumi/src/mode_converter_inverse_design_test.py b/lumi/src/mode_converter_inverse_design_test.py
index 940dc63..097bb7c 100644
--- a/lumi/src/mode_converter_inverse_design_test.py
+++ b/lumi/src/mode_converter_inverse_design_test.py
@@ -3,11 +3,12 @@
 import luminescent as lumi
 
 name = "mode_converter"  # can be any string
-c = lumi.gcells.mimo(west=1, east=1, l=3.0, w=3.0, wwg=.5, name=name)
-targets = {"tparams": {1.55: {"o2@1,o1@0": 1.0}}}
+c = lumi.gcells.mimo(west=1, east=1, l=6.0, w=3.0,
+                     wwg=.5, taper=.05, name=name)
+targets = {"tparams": {1.55: {"o2@0,o1@1": 1.0}}}
 
 prob = lumi.gcell_problem(
     c, targets,
-    lmin=0.15, dx=0.05,
-    approx_2D=True, iters=50)
+    lvoid=0.2, lsolid=.1, dx=0.05,
+    approx_2D=True, iters=60, stoploss=.03)
 sol = lumi.solve(prob)
diff --git a/lumi/src/scratch.py b/lumi/src/scratch.py
index 778d427..7d73349 100644
--- a/lumi/src/scratch.py
+++ b/lumi/src/scratch.py
@@ -1,12 +1,23 @@
+import itertools
 from pprint import pprint
 import luminescent as lumi
-
-name = "1x2_splitter"
-c = lumi.gcells.mimo(west=1, east=2, l=4.0, w=2.0, wwg=.5, name=name)
-targets = {"tparams": {1.55: {"2,1": 0.5, "3,1": 0.5}}}
-
-prob = lumi.gcell_problem(
-    c, targets,
-    symmetries=[1], lvoid=0.1, dx=0.05,
-    approx_2D=True, iters=30, stoploss=.03)
-sol = lumi.solve(prob)
+# lumi.make_training_movie(name="mode_converter")
+# lumi.make_simulation_movie(name="mode_converter")
+c = lumi.gcells.mimo(west=1, east=1, l=1, w=1,  wwg=.5)
+targets = {"tparams": {1.55: {"2,1": 1.0}}}
+for (approx_2D, gpu, dtype, save_memory) in itertools.product(
+    [True,],
+    # [None, "CUDA"],
+    [None, ],
+    ["f32"],
+    # ["f32", "f16"],
+    [False],
+):
+    prob = lumi.gcell_problem(
+        c, targets,
+        # bbox_layer=LAYER.WAFER,
+        lvoid=0.2, lsolid=.2, dx=0.1, iters=2,
+        approx_2D=approx_2D, gpu=gpu, dtype=dtype, save_memory=save_memory,
+        run=False)
+    sol = lumi.solve(prob, run=False)
+    sleep(1)
diff --git a/src/gf copy.jl b/src/gf copy.jl
index e43ab03..4e6b054 100644
--- a/src/gf copy.jl	
+++ b/src/gf copy.jl	
@@ -22,7 +22,7 @@ function lastrun(s=nothing, path=joinpath(pwd(), "runs"))
     end
     for p = reverse(l)
         try
-            open(joinpath(p, "sol.json")) do f
+            open(joinpath(p, "solution.json")) do f
                 JSON.parse(f)["study"]
             end == s && return p
         catch e
@@ -156,8 +156,8 @@ global virgin, stop, best, best0, sparams0
 
 println("setting up simulation...")
 # do something based on ARGS?
-PROB_PATH = joinpath(path, "prob.bson")
-SOL_PATH = joinpath(path, "sol.json")
+PROB_PATH = joinpath(path, "problem.bson")
+SOL_PATH = joinpath(path, "solution.json")
 
 calibrate = true
 model_name = nothing # if load saved model
@@ -544,7 +544,7 @@ elseif study == "inverse_design"
     )
 end
 sol = (; sol..., path, dx, study,) |> cpu
-# @save "$path/sol.json" sol
+# @save "$path/solution.json" sol
 open(SOL_PATH, "w") do f
     write(f, json(sol))
 end
diff --git a/src/gf.jl b/src/gf.jl
index 966da36..8b631c3 100644
--- a/src/gf.jl
+++ b/src/gf.jl
@@ -21,7 +21,7 @@ function lastrun(; name=nothing, study=nothing, wd="runs")
     if !isnothing(study)
         for p = l
             try
-                open(joinpath(p, "sol.json")) do f
+                open(joinpath(p, "solution.json")) do f
                     JSON.parse(f)["study"]
                 end == study && return p
             catch e
@@ -34,7 +34,7 @@ end
 
 function write_sparams(runs, run_probs, geometry, origin, dx,
     designs=nothing, design_config=nothing, models=nothing;
-    autodiff=false, save_memory=false, verbose=false, perturb=nothing, ulims=nothing, kw...)
+    autodiff=false, save_memory=false, verbose=false, perturb=nothing, ulims=nothing, framerate=0, path="", kw...)
     F = run_probs[1].F
     geometry = make_geometry(models, origin, dx, geometry, designs, design_config; F, perturb)
 
@@ -42,7 +42,7 @@ function write_sparams(runs, run_probs, geometry, origin, dx,
         begin
             prob[:geometry] = geometry
             #@debug typeof(prob.u0.E.Ex), typeof(prob.geometry.ϵ)
-            sol = solve(prob; autodiff, ulims, save_memory, verbose)
+            sol = solve(prob; autodiff, ulims, save_memory, verbose, framerate, path)
         end for (i, prob) in enumerate(run_probs)
         # end for (i, prob) in enumerate(run_probs)
     ]
@@ -75,7 +75,7 @@ function write_sparams(runs, run_probs, geometry, origin, dx,
     end
     # return coeffs(1)(1)[1] |> abs2
 
-    global sparams = OrderedDict([λ => OrderedDict([k => begin
+    sparams = OrderedDict([λ => OrderedDict([k => begin
         s = ignore() do
             split(string(k), ",")[2]
         end
@@ -160,7 +160,14 @@ end
 function plotsols(sols, probs, path)
     for (i, (prob, sol)) in enumerate(zip(probs, sols))
         try
-            CairoMakie.save(joinpath(path, "run_$i.png"), quickie(sol |> cpu, cpu(prob)),)
+            @unpack u, p = sol |> cpu
+            @unpack monitor_instances, source_instances = prob |> cpu
+            a = u.Hz
+            g = p.ϵxx
+
+            plt = quickie(a, g; monitor_instances, source_instances)
+            display(plt)
+            CairoMakie.save(joinpath(path, "run_$i.png"), plt,)
         catch e
             println("plot failed")
             println(e)
@@ -173,8 +180,8 @@ end
 function gfrun(path; kw...)
     Random.seed!(1)
     println("setting up simulation...")
-    PROB_PATH = joinpath(path, "prob.bson")
-    SOL_PATH = joinpath(path, "sol.json")
+    PROB_PATH = joinpath(path, "problem.bson")
+    SOL_PATH = joinpath(path, "solution.json")
 
     calibrate = true
     model_name = nothing # if load saved model
@@ -183,7 +190,7 @@ function gfrun(path; kw...)
     verbose = false
 
     prob = load(PROB_PATH)
-    @load PROB_PATH name dtype margin zmargin source_margin Courant port_source_offset source_portsides nonsource_portsides runs ports dx components study mode_solutions eps_2D eps_3D mode_height zmin thickness zcore gpu_backend d magic
+    @load PROB_PATH name dtype margin zmargin source_margin Courant port_source_offset source_portsides nonsource_portsides runs ports dx components study mode_solutions eps_2D eps_3D mode_height zmin thickness zcore gpu_backend d magic framerate
     F = Float32
     if contains(dtype, "16")
         F = Float16
@@ -251,12 +258,12 @@ function gfrun(path; kw...)
                 #     init = nothing
                 # end
 
-                lvoid = round(d.lvoid / dx)
-                lsolid = round(d.lsolid / dx)
-                lmin = max(lvoid, lsolid, 1)
-                frame = eps_2D[range.(o - lmin, o + szd + lmin - 1)...]
+                lvoid = d.lvoid / dx
+                lsolid = d.lsolid / dx
+                margin = maximum(round.((lvoid, lsolid)))
+                frame = eps_2D[range.(o - margin, o + szd + margin - 1)...]
                 frame = frame .== maximum(frame)
-                display(heatmap(frame))
+                # display(heatmap(frame))
                 b = Blob(szd;
                     init, lvoid, lsolid, symmetries, F, frame)
 
@@ -291,9 +298,9 @@ function gfrun(path; kw...)
             push!(ms[:calibrated_modes], mode)
         end
     end
-    # global a = mode_solutions
+    #  a = mode_solutions
     # modal source
-    global runs_sources = [
+    runs_sources = [
         begin
             d = run.d
             sources = []
@@ -369,7 +376,7 @@ function gfrun(path; kw...)
         end
         for (port, m) = run.monitors |> pairs] for run in runs]
 
-    global run_probs =
+    run_probs =
         [
             begin
                 ϵ = if run.d == 2
@@ -418,9 +425,9 @@ function gfrun(path; kw...)
         println("Computing s-parameters...")
         # sparams = write_sparams(img="", autodiff=false, verbose=true)
         @unpack sparams, sols = write_sparams(runs, run_probs, g0, origin, dx;
-            F, verbose=true)
+            F, verbose=true, framerate, path)
         plotsols(sols, run_probs, path)
-        global sol = (; sparam_family(sparams)...,
+        sol = (; sparam_family(sparams)...,
             path, dx, study)
         open(SOL_PATH, "w") do f
             write(f, json(cpu(sol)))
@@ -434,17 +441,17 @@ function gfrun(path; kw...)
         end
         autodiff = true
         # save_memory = true
-        global sparams = sparams0 = 0
+        sparams = sparams0 = 0
         opt = Adam(eta)
         opt_state = Flux.setup(opt, models)
         println("starting optimization... first iter will be slow due to adjoint compilation.")
-        global img = nothing
+        img = nothing
         best = best0 = 0
         S, ulims = write_sparams(runs, run_probs, g0, origin, dx,
             designs, design_config, models;
-            F, img, autodiff, with=true)
+            F, img, autodiff, with=true,)
         # heatmap(_as[3])
-        # global ass = gradient(models) do models
+        #  ass = gradient(models) do models
         #     write_sparams(runs, run_probs, g0, path, origin, dx,
         #         designs, design_config, models;
         #         F, img, autodiff, save_memory)
@@ -452,7 +459,7 @@ function gfrun(path; kw...)
         # error()
 
         # prob = run_probs[1]
-        # global aaaaa = gradient(g0) do geometry
+        #  aaaaa = gradient(g0) do geometry
         #     # solve(prob, geometry; autodiff, save_memory, verbose).forward_mode_powers[1][1][1]
         #     solve(prob, geometry; autodiff, save_memory, verbose)
         # end
@@ -518,19 +525,25 @@ function gfrun(path; kw...)
                             elseif :tparams == k
                                 ŷ = fmap(abs2, S)
                             end
-                            ignore_derivatives() do
-                                println(json(ŷ))
-                            end
+
+                            # global a1 = ŷ
+                            # global a2 = y
+                            # println("ŷ: $ŷ")
+                            # println("y: $y")
+
                             ŷ = [[ŷ(λ)(k) for k = keys(y[λ])] for λ = keys(y)]
                             ŷ = flatten(ŷ)
                             y = flatten(y)
                             Z = sum(abs, y)
                         end
                         _l = sum(abs, err.(ŷ, y),) * weights(k) / Z
-                        print("$(k) loss: $_l ")
+                        println("$(k) loss: $_l ")
+                        # ignore_derivatives() do
+                        #     println(json(ŷ))
+                        # end
                         l += _l
                     end
-                    println("\n weighted total loss $l")
+                    println("    weighted total loss $l")
                     l
                 end
             end
@@ -559,6 +572,7 @@ function gfrun(path; kw...)
             if i % 5 == 0 || stop
                 println("saving checkpoint...")
                 ckptpath = joinpath(path, "checkpoints", replace(string(now()), ':' => '_', '.' => '_'))
+
                 mkpath(ckptpath)
                 for (i, (m, d)) = enumerate(zip(models, designs))
                     a = Gray.(m() .< 0.5)
@@ -569,7 +583,7 @@ function gfrun(path; kw...)
                 plotsols(sols, run_probs, path)
                 plotsols(sols, run_probs, ckptpath)
 
-                global sol = (;
+                sol = (;
                     sparam_family(sparams)...,
                     optimized_designs=[m() .> 0.5 for m in models],
                     params=getfield.(models, :a),
@@ -579,10 +593,10 @@ function gfrun(path; kw...)
                     study,
                 )
 
-                open(joinpath(ckptpath, "sol.json"), "w") do f
+                open(joinpath(ckptpath, "solution.json"), "w") do f
                     write(f, json(cpu(sol)))
                 end
-                open(joinpath(path, "sol.json"), "w") do f
+                open(joinpath(path, "solution.json"), "w") do f
                     write(f, json(cpu(sol)))
                 end
             end
@@ -591,6 +605,11 @@ function gfrun(path; kw...)
             end
             Flux.update!(opt_state, models, dldm)# |> gpu)
         end
+        if framerate > 0
+            write_sparams(runs, run_probs, g0, origin, dx,
+                designs, design_config, models;
+                F, img, autodiff, framerate, path)
+        end
         println("Done in $(time() - t0) .")
 
     end
diff --git a/src/run.jl b/src/run.jl
index 956fcbd..ba8737f 100644
--- a/src/run.jl
+++ b/src/run.jl
@@ -1,2 +1,7 @@
 include("main.jl")
 gfrun(lastrun())
+# using Pkg
+# pkg"add Porcupine,Jello,ArrayPadding;up"
+
+# using Pkg
+# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Porcupine.jl;dev C:\Users\pxshe\OneDrive\Desktop\ArrayPadding.jl; dev C:\Users\pxshe\OneDrive\Desktop\Jello.jl;up"
diff --git a/src/snapshot.jl b/src/snapshot.jl
index 4deaf15..ba2512b 100644
--- a/src/snapshot.jl
+++ b/src/snapshot.jl
@@ -1,12 +1,105 @@
-# try
-#     using GLMakie
-#     using GLMakie: volume
-#     global gl = true
-# catch e
-# using CairoMakie
-global gl = false
 
 ° = π / 180
+function quickie(u, g=nothing; monitor_instances=[], source_instances=[], ulims=nothing, kw...)
+    fig = Figure()
+    N = ndims(u)
+    if ulims == nothing
+        colorrange = (-1, 1) .* maximum(abs, u)
+    else
+        colorrange = ulims
+    end
+    colormap = :seismic
+    algorithm = :absorption
+    labels = []
+    for (i, m) = enumerate(monitor_instances)
+        text = isempty(m.label) ? "o$i" : m.label
+        push!(labels, (m.center, text))
+    end
+    for (i, s) = enumerate(source_instances)
+        text = isempty(s.label) ? "s$i" : s.label
+        push!(labels, (s.center, text))
+    end
+
+    a = u
+    grid = fig[1, 1]
+    aspect = size(a, 1) / size(a, 2)
+    title = "Hz"
+    if N == 3
+        # println("3D array: plotting middle slice")
+        title *= " (middle slice of 3D array)"
+
+        a1 = a[:, :, round(Int, size(a, 3) / 2)]
+        ax, plt = heatmap(grid[1, 1], real(a1); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
+
+        a1 = a[round(Int, size(a, 1) / 2), :, :]
+        ax, plt = heatmap(grid[1, 2], real(a1); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
+    else
+        title *= " (2D array)"
+        axis = (; kw..., title, aspect)
+        ax, plt = heatmap(grid[1, 1], a; axis, colormap, colorrange)
+        if diff(collect(extrema(a)))[1] > 0
+            Colorbar(grid[1, 2], plt)
+            contour = g .> 0.99maximum(g)
+            contour = morpholaplace(contour,)
+            heatmap!(ax, contour; colormap=[(:gray, 0), :black], colorrange=(0, 1))
+        end
+    end
+    for (pos, text) in labels
+        text!(grid[1, 1], pos..., ; text, align=(:center, :center))
+        # annotate!(g[1, 1], pos, text; fontsize=10, color=:black)
+    end
+    if isnothing(g)
+        return fig
+    end
+
+    a = g
+    grid = fig[2, 1]
+    aspect = size(a, 1) / size(a, 2)
+    title = "ϵ"
+    colormap = [:white, :gray]
+    if N == 3
+        # println("3D array: plotting middle slice")
+        title *= " (middle slice of 3D array)"
+
+        a1 = a[:, :, round(Int, size(a, 3) / 2)]
+        ax, plt = heatmap(grid[1, 1], real(a1); axis=(; kw..., title, aspect), colormap)
+
+        a1 = a[round(Int, size(a, 1) / 2), :, :]
+        ax, plt = heatmap(grid[1, 2], real(a1); axis=(; kw..., title, aspect), colormap,)
+    else
+        title *= " (2D array)"
+        axis = (; kw..., title, aspect)
+        ax, plt = heatmap(grid[1, 1], a; axis, colormap)
+        if diff(collect(extrema(a)))[1] > 0
+            Colorbar(grid[1, 2], plt)
+        end
+    end
+
+    # i = 1
+    # for k = keys(fields)
+    #     j = 1
+    #     # for (k2, a) = pairs(fields[k1])
+    #     a = fields[k]
+    #     g = fig[i, j]
+    #     title = string(k)
+    #     _plot!(g, a, ; title, colormap, colorrange, algorithm, labels, kw...)
+
+    #     # j += 1
+    #     i += 1
+    # end
+    # if !isnothing(geometry)
+    #     j = 1
+    #     for (k2, a) = pairs(geometry)
+    #         g = fig[i, j]
+    #         title = string(k2)
+    #         _plot!(g, a, ; title, algorithm=:mip, kw...)
+
+    #         j += 1
+    #     end
+    # end
+    return fig
+end
+
 function _plot!(g, a, ; colorrange=nothing, title="", labels=[], colormap=:seismic, algorithm=nothing,
     azimuth=75°, elevation=75°,
     kw...)
@@ -21,13 +114,13 @@ function _plot!(g, a, ; colorrange=nothing, title="", labels=[], colormap=:seism
             title *= " (middle slice of 3D array)"
 
             a1 = a[:, :, round(Int, size(a, 3) / 2)]
-            ax, pl = heatmap(g[1, 1], real(a1); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
+            ax, plt = heatmap(g[1, 1], real(a1); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
 
             a1 = a[round(Int, size(a, 1) / 2), :, :]
-            ax, pl = heatmap(g[1, 2], real(a1); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
+            ax, plt = heatmap(g[1, 2], real(a1); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
         else
             title *= " (2D array)"
-            ax, pl = heatmap(g[1, 1], real(a); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
+            ax, plt = heatmap(g[1, 1], real(a); axis=(; kw..., title, aspect), colormap, colorrange=colorrange)
         end
         for (pos, text) in labels
             text!(g[1, 1], pos..., ; text, align=(:center, :center))
@@ -37,54 +130,11 @@ function _plot!(g, a, ; colorrange=nothing, title="", labels=[], colormap=:seism
         if isnothing(colorrange)
             colorrange = extrema(a) * 0.1
         end
-        ax, pl = volume(g[1, 1], real(a), ; axis=(; kw..., type=Axis3, title,), colormap, colorrange, algorithm)
+        ax, plt = volume(g[1, 1], real(a), ; axis=(; kw..., type=Axis3, title,), colormap, colorrange, algorithm)
         ax.elevation[] = elevation
         ax.azimuth[] = azimuth
     end
     if diff(collect(extrema(a)))[1] > 0
-        Colorbar(g[1, d], pl)
+        Colorbar(g[1, d], plt)
     end
 end
-function quickie(sol, prob; kw...)
-    @unpack u, p = sol |> cpu
-    @unpack monitor_instances, source_instances = prob |> cpu
-    fig = Figure()
-    fields = (; Hz=u.Hz)
-    geometry = (; ϵ=p.ϵxx)
-    colorrange = (-1, 1) .* maximum(maximum.(a -> abs.(real(a)), leaves(fields)))
-    colormap = :seismic
-    algorithm = :absorption
-    labels = []
-    for (i, m) = enumerate(monitor_instances)
-        text = isempty(m.label) ? "o$i" : m.label
-        push!(labels, (m.center, text))
-    end
-    for (i, s) = enumerate(source_instances)
-        text = isempty(s.label) ? "s$i" : s.label
-        push!(labels, (s.center, text))
-    end
-
-    i = 1
-    for k = keys(fields)
-        j = 1
-        # for (k2, a) = pairs(fields[k1])
-        a = fields[k]
-        g = fig[i, j]
-        title = string(k)
-        _plot!(g, a, ; title, colormap, colorrange, algorithm, labels, kw...)
-
-        # j += 1
-        i += 1
-    end
-    if !isnothing(geometry)
-        j = 1
-        for (k2, a) = pairs(geometry)
-            g = fig[i, j]
-            title = string(k2)
-            _plot!(g, a, ; title, algorithm=:mip, kw...)
-
-            j += 1
-        end
-    end
-    return fig
-end
\ No newline at end of file
diff --git a/src/solve.jl b/src/solve.jl
index 904b6ad..ab575d2 100644
--- a/src/solve.jl
+++ b/src/solve.jl
@@ -17,8 +17,9 @@ function f2(((u, mf), p, (dx, dt, field_padding, source_instances, autodiff), (T
     ((u, mf), p, (dx, dt, field_padding, source_instances, autodiff), (T, monitor_instances))
 end
 
-function solve(prob, ; autodiff=false, save_memory=false, ulims=nothing, verbose=false, kwargs...)
-    # global _prob = prob
+function solve(prob, ;
+    autodiff=false, save_memory=false, ulims=(-3, 3),
+    verbose=false, framerate=0, path="", kwargs...)
     @unpack dx, dt, u0, geometry, field_padding, geometry_padding, subpixel_averaging, source_instances, monitor_instances, transient_duration, F, polarization, steady_state_duration, d, n = prob
 
     p = apply_geometry_padding(geometry_padding, geometry)
@@ -53,7 +54,23 @@ function solve(prob, ; autodiff=false, save_memory=false, ulims=nothing, verbose
     if save_memory
         (u,), = adjoint_reduce(f1, 0:dt:T[1], init, ulims)
     else
-        (u,), = reduce(f1, 0:dt:T[1]; init)
+        (u,), = reduce(0:dt:T[1]; init) do us, t
+            ignore() do
+                if framerate > 0 && t > 0
+                    if t % (1 / framerate) < dt
+                        (u,), p, = us
+                        a = u.Hz
+                        g = p.ϵxx
+
+                        _path = joinpath(path, "temp")
+                        mkpath(_path)
+                        CairoMakie.save(joinpath(_path, "$t.png"), quickie(a, g; monitor_instances, source_instances, ulims),)
+                        # quickie(a, g; monitor_instances, source_instances)
+                    end
+                end
+            end
+            f1(us, t)
+        end
     end
     # return sum.(u) |> sum
 
diff --git a/src/sources.jl b/src/sources.jl
index 218daa1..6564830 100644
--- a/src/sources.jl
+++ b/src/sources.jl
@@ -26,27 +26,27 @@ struct ModalSource
 
 end
 """
-    function PlaneWave(f, dims; fields...)
+    function PlaneWave(f, dims; mode...)
 
 Constructs plane wave source
 
 Args
 - f: time function
 - dims: eg -1 for wave coming from -x face
-- fields: which fields to excite & their scaling constants (typically a current source, eg Jz=1)
+- mode: which mode to excite & their scaling constants (typically a current source, eg Jz=1)
 """
 struct PlaneWave
     f
-    fields
+    mode
     dims
     label
-    function PlaneWave(f, dims, label=""; fields...)
-        new(f, fields, dims, label)
+    function PlaneWave(f, dims, label=""; mode...)
+        new(f, mode, dims, label)
     end
 end
 @functor PlaneWave
 """
-    function GaussianBeam(f, σ, c, dims; fields...)
+    function GaussianBeam(f, σ, c, dims; mode...)
 
 Constructs gaussian beam source
 
@@ -54,23 +54,23 @@ Args
 - f: time function
 - σ: std dev length
 - dims: eg 1 for x direction
-- fields: which fields to excite & their scaling constants (typically a current source, eg Jz=1)
+- mode: which mode to excite & their scaling constants (typically a current source, eg Jz=1)
 """
 struct GaussianBeam
     f
     σ
-    fields
+    mode
     c
     dims
-    function GaussianBeam(f, σ, c, dims; fields...)
-        new(f, σ, fields, c, dims)
+    function GaussianBeam(f, σ, c, dims; mode...)
+        new(f, σ, mode, c, dims)
     end
 end
 @functor GaussianBeam
 
 """
-    function Source(f, c, lb, ub, label=""; fields...)
-    function Source(f, c, L, label=""; fields...)
+    function Source(f, c, lb, ub, label=""; mode...)
+    function Source(f, c, L, label=""; mode...)
         
 Constructs custom  source. Can be used to specify uniform or modal sources
 
@@ -80,33 +80,33 @@ Args
 - lb: lower bounds wrt to c
 - ub: upper bounds wrt to c
 - L: source dimensions in [wavelengths]
-- fields: which fields to excite & their scaling constants (typically a current source, eg Jz=1)
+- mode: which mode to excite & their scaling constants (typically a current source, eg Jz=1)
 """
 struct Source
     f
-    fields
+    mode
     center
     lb
     ub
     label
     meta
-    function Source(f, fields, c, lb, ub, label::String=""; kw...)
-        new(f, fields, c, lb, ub, label, kw)
+    function Source(f, mode, c, lb, ub, label::String=""; kw...)
+        new(f, mode, c, lb, ub, label, kw)
     end
-    function Source(f, fields, c, L, label::String=""; kw...)
-        new(f, fields, c, -L / 2, L / 2, label, kw)
+    function Source(f, mode, c, L, label::String=""; kw...)
+        new(f, mode, c, -L / 2, L / 2, label, kw)
     end
 end
-# fields(m::Source) = m.fields
+# mode(m::Source) = m.mode
 Base.string(m::Union{Source,ModalSource}) =
     """
     $(m.label): $(count((m.ub.-m.lb).!=0))-dimensional source, centered at $(m.center|>d2), spanning from $(m.lb|>d2) to $(m.ub|>d2) relative to center,"""
-#  exciting $(join(keys(m.fields),", "))"""
+#  exciting $(join(keys(m.mode),", "))"""
 
-function Source(f, c, L, label::AbstractString=""; fields...)
+function Source(f, c, L, label::AbstractString=""; mode...)
     # Source
-    # function Source(f, c, L::Union{AbstractVector{<:Real},Tuple{<:Real}}; fields...)
-    Source(f, c, -L / 2, L / 2, label; fields...)
+    # function Source(f, c, L::Union{AbstractVector{<:Real},Tuple{<:Real}}; mode...)
+    Source(f, c, -L / 2, L / 2, label; mode...)
 end
 Source = Source
 
@@ -164,69 +164,70 @@ function SourceInstance(s::ModalSource, dx, sizes, common_left_pad_amount, fl, s
 
     Jx, Jy, Jz = J
     if D == 2
-        fields = (; Jx, Jy)
+        mode = (; Jx, Jy)
     else
-        fields = (; Jx, Jy, Jz)
+        mode = (; Jx, Jy, Jz)
     end
     v = zip(lb, ub)
     lb = minimum.(v)
     ub = maximum.(v)
-    SourceInstance(Source(f, fields / dx^(D - d), center, lb, ub; meta...), dx, sizes, common_left_pad_amount, fl, sz0; F)
+    SourceInstance(Source(f, mode / dx^(D - d), center, lb, ub; meta...), dx, sizes, common_left_pad_amount, fl, sz0; F)
 end
 function SourceInstance(s::PlaneWave, dx, sizes, common_left_pad_amount, fl, sz0; F=Float32)
-    @unpack f, fields, dims, label, meta = s
+    @unpack f, mode, dims, label, meta = s
     _F(x::Real) = F(x)
     _F(x::Complex) = complex(F)(x)
     f = _F ∘ f
     d = length(common_left_pad_amount)
-    g = Dict([k => _F(fields[k]) * ones([i == abs(dims) ? 1 : sz0[i] for i = 1:d]...) / dx for k = keys(fields)])
+    g = Dict([k => _F(mode[k]) * ones([i == abs(dims) ? 1 : sz0[i] for i = 1:d]...) / dx for k = keys(mode)])
     o = NamedTuple([k =>
         1 .+ fl[k] .+ (dims < 0 ? 0 : [i == abs(dims) ? sizes[k][i] - 1 : 0 for i = 1:d])
                     for k = keys(fl)])
-    _g = Dict([k => place(zeros(F, sizes[k]), o[k], g[k],) for k = keys(fields)])
+    _g = Dict([k => place(zeros(F, sizes[k]), o[k], g[k],) for k = keys(mode)])
     c = first(values(sizes)) .÷ 2
-    SourceInstance(f, keys(fields), g, _g, o, c, label, meta)
+    SourceInstance(f, keys(mode), g, _g, o, c, label, meta)
 end
 
 function SourceInstance(s::GaussianBeam, dx, sizes, fl, stop; F=Float32)
     _F(x::Real) = F(x)
     _F(x::Complex) = complex(F)(x)
     f = _F ∘ f
-    @unpack f, σ, fields, c, dims = s
+    @unpack f, σ, mode, c, dims = s
     n = round(Int, 2σ / dx)
     r = n * dx
     r = [i == abs(dims) ? (0:0) : range(-r, r, length=(2n + 1)) for i = 1:length(c)]
     g = [gaussian(norm(F.(collect(v)))) for v = Iterators.product(r...)] / dx
     fl = fl .- 1 .+ index(c, dx) .- round.(Int, (size(g) .- 1) ./ 2)
     _g = place(zeros(F, sz), g, fl)
-    SourceInstance(f, keys(fields), g, _g, fl, c, label)
+    SourceInstance(f, keys(mode), g, _g, fl, c, label)
 end
 
 function SourceInstance(s::Source, dx, sizes, field_origin, common_left_pad_amount, stop; F=Float32)
-    @unpack f, fields, center, lb, ub, label, meta = s
+    @unpack f, mode, center, lb, ub, label, meta = s
     _F(x::Real) = F(x)
     _F(x::Complex) = complex(F)(x)
 
     f = _F ∘ f
     g = Dict([k =>
         begin
-            if isa(fields[k], AbstractArray)
-                # imresize(fields[k], ratio=1)
-                sz0 = size(fields[k])
+            if isa(mode[k], AbstractArray)
+                sz0 = size(mode[k])
                 sz = max.(1, round(abs.(ub - lb) ./ dx)) |> Tuple
                 # sz0 != sz && @warn "source array size$sz0 not same  as domain size $sz. source will be interpolated"
-                imresize(_F.(fields[k]), sz, method=ImageTransformations.Lanczos4OpenCV())
+                imresize(_F.(mode[k]), sz, method=ImageTransformations.Lanczos4OpenCV())
             else
-                r = [a == b ? (a:a) : (a+dx/2*sign(b - a):dx*sign(b - a):b) for (a, b) = zip(lb, ub)]
-                [_F.(fields[k](v...)) for v = Iterators.product(r...)]
+                # r = [a == b ? (a:a) : (a+dx/2*sign(b - a):dx*sign(b - a):b) for (a, b) = zip(lb, ub)]
+                # [_F.(mode[k](v...)) for v = Iterators.product(r...)]
             end
-        end
-
-              for k = keys(fields)])
+        end for k = keys(mode)])
     o = NamedTuple([k => F((center + lb - o) / dx .+ 1.5) for (k, o) = pairs(field_origin)])
-    _g = Dict([k => place(zeros(F, sizes[k]), o[k], g[k],) for k = keys(fields)])
+    _g = Dict([k => begin
+        a = zeros(complex(F), sizes[k])
+        setindexf!(a, g[k], range.(o[k], o[k] + size(g[k]) - 1)...)
+        a
+    end for k = keys(mode)])
     _center = round(center / dx) + 1 + common_left_pad_amount
-    SourceInstance(f, keys(fields), g, _g, o, _center, label, meta)
+    SourceInstance(f, keys(mode), g, _g, o, _center, label, meta)
 end
 
 # Complex