diff --git a/examples/simulations/reference/tortuosity_gdd.ipynb b/examples/simulations/reference/tortuosity_gdd.ipynb
index 0610e53f1..a511d0464 100644
--- a/examples/simulations/reference/tortuosity_gdd.ipynb
+++ b/examples/simulations/reference/tortuosity_gdd.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# `tortuosity_gdd`\n",
+    "# `tortuosity_bt`\n",
     "Calculation of tortuosity via geometric domain decomposition method"
    ]
   },
@@ -18,6 +18,7 @@
     "import numpy as np\n",
     "import porespy as ps\n",
     "from porespy import beta\n",
+    "\n",
     "ps.visualization.set_mpl_style()"
    ]
   },
@@ -29,7 +30,7 @@
     {
      "data": {
       "text/plain": [
-       "<Signature (im, scale_factor=3, use_dask=True)>"
+       "<Signature (im, block_size=None, use_dask=True)>"
       ]
      },
      "execution_count": 2,
@@ -39,7 +40,7 @@
    ],
    "source": [
     "import inspect\n",
-    "inspect.signature(beta.tortuosity_gdd)"
+    "inspect.signature(beta.tortuosity_bt)"
    ]
   },
   {
@@ -49,6 +50,11 @@
     "# im\n",
     "Can be a 3D image:"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -67,7 +73,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/simulations/tutorials/finding_tortuosity_from_image.ipynb b/examples/simulations/tutorials/finding_tortuosity_from_image.ipynb
index 60a8764fd..0a9dd0b37 100644
--- a/examples/simulations/tutorials/finding_tortuosity_from_image.ipynb
+++ b/examples/simulations/tutorials/finding_tortuosity_from_image.ipynb
@@ -29,7 +29,7 @@
    "id": "6d938e7e",
    "metadata": {},
    "source": [
-    "1. ``tortuoisty_fd`` starts by calculating the porosity of the image. \n",
+    "1. ``tortuosity_fd`` starts by calculating the porosity of the image. \n",
     "<br><br>\n",
     "$$ \\epsilon_{orginal} = \\frac{\\sum_{i = 0}^{N_{x}}\\sum_{j = 0}^{N_{y}}im_{ij}}{N_{x}\\cdot N_{y}} $$\n",
     "<br><br>\n",
@@ -97,7 +97,7 @@
    "id": "e0b6f5a8",
    "metadata": {},
    "source": [
-    "For the purposes of this tutorial, we will generate a 200 x 200 pixel image with a target porosity of 0.65."
+    "For the purposes of this tutorial, we will generate a 200 x 200 pixel image with a target porosity of 0.5."
    ]
   },
   {
@@ -125,29 +125,148 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 276,
-       "width": 276
-      },
-      "needs_background": "light"
+       "height": 463,
+       "width": 463
+      }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
     "np.random.seed(2)\n",
-    "im = ps.generators.overlapping_spheres([200, 200], r=10, porosity=0.65)\n",
+    "im = ps.generators.overlapping_spheres([200, 200], r=10, porosity=0.5)\n",
     "fig, ax = plt.subplots()\n",
     "ax.imshow(im, origin='lower', interpolation='none')\n",
     "ax.axis(False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4c0fb466",
+   "metadata": {},
+   "source": [
+    "### Trimming non-percolating pores\n",
+    "\n",
+    "Non-percolating pores should be trimmed to reduce calculation requirements. The image must then be checked to ensure precolation is still occuring. This can be done using `check_percolating`. The returned result is the trimmed image. If the image no longer percolates, an error is thrown instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edebc474",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[10:41:30] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <a href=\"file://D:\\CS\\porespy\\src\\porespy\\simulations\\_dns.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://D:\\CS\\porespy\\src\\porespy\\simulations\\_dns.py#47\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[10:41:30]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b]8;id=32123;file://D:\\CS\\porespy\\src\\porespy\\simulations\\_dns.py\u001b\\\u001b[2m_dns.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=849741;file://D:\\CS\\porespy\\src\\porespy\\simulations\\_dns.py#47\u001b\\\u001b[2m47\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.5, 199.5, -0.5, 199.5)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 463,
+       "width": 463
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trimmed_im = ps.simulations.check_percolating(im, 1)\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(trimmed_im + (im != trimmed_im) * 0.5)\n",
+    "ax.axis(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69883f0d",
+   "metadata": {},
+   "source": [
+    "### Performing Fickian Diffusion\n",
+    "\n",
+    "openpnm's Fickian diffusion algorithm is applied on the specified axis.\n",
+    "\n",
+    "The result from `fickian_diffusion` contains two attributes:\n",
+    "\n",
+    "|Attribute| Description |\n",
+    "|-|-|\n",
+    "|`r_in` |Molar Flowrate into the image |\n",
+    "|`concentration_map` |Concentration map of the image |\n",
+    "\n",
+    "The concentration map can be accessed by using `object.attribute`, or in this case, `diffusion_results.concentration_map`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "70a01dc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x16c4f8763d0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 463,
+       "width": 539
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diffusion_results = ps.simulations.fickian_diffusion(im=trimmed_im, axis=1, cL=5, cR=2)\n",
+    "\n",
+    "plt.imshow(diffusion_results.concentration_map, origin='lower', interpolation='none', cmap=plt.cm.plasma)\n",
+    "plt.colorbar()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dfed0b5a",
@@ -161,12 +280,12 @@
    "id": "e79a24ab",
    "metadata": {},
    "source": [
-    "As mentioned at the start of the tutorial, the only two inputs for the function are the image and the axis along which to run the calculation. For the x-axis we assign axis a value of 1 and for the y-axis we assign axis a value of 0. "
+    "As mentioned at the start of the tutorial, the only two inputs for the function are the image and the axis along which to run the calculation. For the x-axis we assign axis a value of 1 and for the y-axis we assign axis a value of 0."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "13d39345",
    "metadata": {
     "execution": {
@@ -182,20 +301,18 @@
      "output_type": "stream",
      "text": [
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
-      "Results of tortuosity_fd generated at Fri Mar 17 18:20:54 2023\n",
+      "Results of tortuosity_fd generated at Mon Jul  8 10:41:32 2024\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
       "im                        Array of size (200, 200)\n",
-      "tortuosity                1.8776185599936215\n",
-      "formation_factor          2.9043946942938574\n",
-      "original_porosity         0.646575\n",
-      "effective_porosity        0.646475\n",
-      "concentration             Array of size (200, 200)\n",
+      "tortuosity                1.345433860454569\n",
+      "formation_factor          2.692887386448975\n",
+      "porosity                  0.499625\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
      ]
     }
    ],
    "source": [
-    "results = ps.simulations.tortuosity_fd(im=im, axis=1)\n",
+    "results = ps.simulations.tortuosity_fd(im=trimmed_im, axis=1, r_in=diffusion_results.r_in)\n",
     "print(results)"
    ]
   },
@@ -209,11 +326,10 @@
     "\n",
     "|Attribute| Description |\n",
     "|-|-|\n",
+    "|``im``|The image supplied to the function\n",
     "|``tortuosity``|The **calculated tortuosity** is given by the equation:<br> <br>$$\\tau = \\frac{D_{AB}}{D_{Eff}} \\cdot \\varepsilon $$<br><br> where $\\varepsilon$ is the ``effective_porosity``|\n",
-    "|``effective_porosity``|The effective porosity of the image after removing disconnected voxels |\n",
-    "|``original_porosity``|The porosity of the image as inputted|\n",
     "|``formation_factor``|The formation factor is given by the equation: <br><br>$$\\mathscr{F}=\\frac{D_{AB}}{D_{Eff}}$$|\n",
-    "|``concentration``| Returns an image containing the concentration values from the simulation|"
+    "|``porosity``|The porosity of the image as inputted|"
    ]
   },
   {
@@ -234,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "0cd027c6",
    "metadata": {
     "execution": {
@@ -249,34 +365,16 @@
     {
      "data": {
       "text/plain": [
-       "2.9043946942938574"
+       "1.345433860454569"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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0B2oJrr3LguMa6ElxFh7zMgDAg6ddDGDI/FBLGCdneKOsIPmubQ6xwDekUj8U0PA5ngDi+aPpuaPAeCmMNiQxN7bYufgqrnWpyGlF9C8u3b7S9sl4kFcFzRoH2ILdN0necQsVVTINcDBKCCGEEFIUPi+UZgxcwQkhhBBCyKRCZXRK0a7lOcTirOSbf75fmKpj7Ku2NlJ12ojLBN5qZyKLE1cjjlo5ncmmEUl/HaZQably9p0AgOds3KHhnuSjqBm9CacsUg8y5ln62kAlyqiIbCkiR4rIx0XkWyJyn4ho+NrTsd8Ko17W6wzL/j/w2O/zVZwjIYQQQgipnqqU0UMAXFhgvzUA7naUzwYQRbb+paPeWgCPOspIGtss+rzKlWeAe9c26745+9E6RXLMcKql4aYqHKSsxzYehyPHsoO+ubiy9seVtFJKBpkkB2YfdZXiW4uhyF2aKs309wC4BsDPAdwB4NSsHVT1eADHDysXkXcD+AyADQDOcTR1vKquyNNZQgghhBDSPFUNRr+hqhdFKyKypKJ23xi+f1NV76+oTUIIIYSQaqBsXZpKBqOq2q2iHRMReSqAp4arK6pun4S4cs8b5QkTruOLZzP1VmJGpxlkpKRN90/76pJajnP1kXfFywdeMl6OK6QefjS7/5kYN2emsoxbTNHumPWXtJc2e9NHqui9AL416oMv2uxWr3rz59bcEUIIIWTMuX/D+5vuQn1M0gTmhmjlYFREZgE4Olw9R1U3ZuzyXhE5AcC2CJyifg3gfABnqOpj9fV0jMlSRH2acGVRylJDU/vkVVTb4rTUln44MTPQWPK/23LCx4roV5bU2rUDLp4u5cvkHRse33QXWk+kkpoKad6/+53ws2zLxBR9e82STrj16lku39ri7NFdFPRnzFRFXxV03M6LtIO2Br3/GwCPC5dXeNTfB4HX/cMIBqQHA/gCgKtFZNc6OkgIIYQQAq34NYW0dTAameh/rarXOer9AMAbAOwAYJ6qLgKwPYDlANYDeDKAS0VkTn1dJYQQQsjU0pNqX1NI68z0IrI1gCPC1TNddVX1I5Zt9wA4UUR+DeASBKrpMniEmjJ58LGlXvV2WZCn1THBZnq2ODVZyencVMV0AZIkignqex0j8/yTz9m9tj6RPnWb56P2vzDnj7UeZ1K5ataqWtu/eeZBAMATuwud9Yqau6swk9dhat9mzqfQlcFJFptSx1q3fnnlxybtp43K6GsAzAGwCcD/FG1EVb8J4Mpw9QhXXUIIIYSQQqhU+5pCWqeMom+i/3aocpbhZwCeC4CeAgXxcdDJrOOTeSmjjb6z1Oi/qGWU2tY5ODFP/NRgKrDjrpJW4azcsXzmf2KEkRo1t8ysHti2tLvV6DtCSAto1WBURPYCcEC46jTRE0IIIYQ0jWX2AclJqwaj6KuiDwC4uIL2Dgzf/YKGThNVKGSOaUX2+aGe9TzazzxmHKzf2NSguto6jDA3Tz77CQ12ZLJh+KZq+IklEP60/f3vGL9lt86sreUYC3TQ1zcd0ilvoHvbPFFC0rRmMCoiHQB/G65+WVU3ZNQXVR36rRCRFyIw0QPAN6vpJSGEEEKIAZ1uS1PZYFREtjVWFxnLC1NlD6iq7VHpBQB2Cpd9TPQfEJEnAjgXwFWq+lDYj+0AHAPgI2G9mwF8yaM9QgghhBAyYqpURu8dsv2nqfWlAFZa6kUm+t+q6tUex5uLYNB5DAAVkbUIDLsLjTq/AXCkqq73aC8X0mtjIIIcRN3v9cVlCbclHhXymrR9MymVzK6UqFPwqbSIc9FwLR5jZf6nab4+2mian8RwT7ZfYF+DcJOOSy5M83vamaku07zJGgkMkjZzPXEwpYHqq6QVZnoR2QrAy8JVX8el8xD0/1kAdgewDYDZAO4CcB2CdKBn1zEQJYQQQggh1VDZYFRLxLBR1bUANs+5zw0A/qnoMYtgU0P//MAZ8fLOWx8zyu4U4sHTvw4A0G54Lh3jtnUH60cB0RWO22tTJn3COQF9R6MsdTN2SKpHfWwkBJOvilyQvMHvSTHaqIQC7QwiXTeuc/5RS9XQYYxCCR1GpJCazNPhw4Wpz0fP39jSTOPvFSGEEEIIaQmtMNMTQgghhIwlY+Qv0FY4GK2QyGTfNnP96hUXxsvaDb40fROuUTH8Pomhl0f1TVxm3yrji/qal+O2jDadjkbOxnIe07fZUlmcCu5H09FIcDkF+ZrwaaIihEwzHIwSQgghhBSFcf1Lw8GoB9YwTjq4TdqqRBmpO6IeavjtEUOTidU+iyNTXnzDOUXqXVZ9TTsw+aqmZr2cTlBFlcW6naFal++eDCVSTW0KKdVQMk48KpsAuB2ZCCkKP1WEEEIIIUWhQFAaDkbzkKGGRil477x7RWIdAHbYYVmNHeuz5sJz+yvhfE/dMGNsC85BwnNxZFTNxDZX01rPpn6m97HlkK8Zb4W0ykD3NYdzAjhXtI3Y5pX+XUtDQrWdKERTlKOejJZIISV9+JtbHlqKCCGEEEJIY1AZJYQQQggpCs30peFgtCCReV4SedyR3GaUrbp9BQBg8S7LaunPmm/9d7DQNcXu0CQ/u9+R2CQcfXfEsEFHy2W+V5G5wjRtF3RcUosDExwmf7WZ+n1/JDwzQY0qpJNtaoDr2DQTER/MqQGfn6A89RFS6seLENIUHIwSQgghhBSFoZ1Kw8HoiIhEx3v+2M9lL1EIpZ4m1wFEc8QT28Jl2Wg8/UfLGyJF1PhWRIum+DkTOi51w42WoPbWjnuiFoXRHpQ+qaDalL2EEphWRAuYRQbCQ2XWL9Z+qxmHPk4JpjJJZ6b8RI5Mz924Y8M9IYSUhYNRQgghhJCi8CG/NPSmJ4QQQgghjUFltA5C87jUNI9EzYxK0ePETHgwM1tUL7KBG/bmWYGtf+FrXllL31Z97IfhMS0ORolsSEhus5nwPVGbqd/HJF8mD31eZyLP+KKVOi7xaX1siEz2NNfn58rZd8bLNNmTRqADaWmojBJCCCGEkMagMloW0znIpYQWVUmNxwVNh44C4jBMOhOVGR2KnJRm+tsWvOQ1BTvix+J/fh4A4M5//vFAWSJUU0oRdYVxSmxzhXHKwEdhHAenpczznWBF9Ooj74qXD7iYGXjKEKmwkxTiqcPQTqQJJvg3d1RQGSWEEEIIIY3BwSghhBBCSFF6Uu2rICKyWEROFpE/iMhjInK3iHxDRA4pc3oi8jIRuURE7hKRjSLykIhcJyKfFJHty7QdH0Pz2iUnFJFkQM3Vj+zeX9Ewk5HxIRFbDM9U5iXTnB7HCDXM6M44oz0MbEM3WZbYtinsmxE3dOFzl6FN3P7+q/orKfO8M6Zoon64muEkZHcOSpn6rbFNLfWHHCNdx8dxqYzTUlFnJee1APDULy8d3m5LMM3zPoyrCf+dng5MdfxqT4K5/vkbdxpa9oPZd4ywJ6Qo69YvT6xrCwM4p8cL3fOfUWn7M6/4RWLd5xqIyFMAXAFgm3DTWgDzEYiOCmC5qn4yTz9EpAPgLABHG5sfArA5gHByIB4A8EJV/XmettNQGSWEEEIIKYpW/MqJiMwDcDGCgei1APZV1QUAFgH4NwTeJSeIyGE5mz4O/YHovwPYXlW3ArAZgBcC+BOArQGcGw5cC0MHphrJmbwo+WgQqp/m85BEwp5RL1ZJ420tVrotyqWPs1KwLbvdLAXQrR5mtz+0XQ+qCONkbzhf32xl1716JQDgaV9Zku/YNZNXDZ0morvY4m976zjYUE2voEpKJou3AtgNwDoAR6jqHQCgqmsBvFdEdgfwUgAnArgsR7uvC9+vUNV3RxtVdROA74jIGwF8H8DuAJ4C4LqiJ0BllBBCCCGkINqTSl8FiNTLc6KBaIqTwvf9RGSPHO1G80GvHVJuzifYIke7A1AZbZBI4bSFhIrLEhvDd6O+zkoWLvqLN1XXwYrZ5dMHxsu3vecaAHYVNMIZvinn/NBEmePLnlvx9AxwXwbXucebSswZJeNLGYU0/bPzDmO+6hfGdP6or7oSqaRUSEklNPh7KiJbAogmrX5nSLWrAKwBsADAIQBu9mx+JYA9ADx9SHl03PUAbvRs0woHo4QQQgghNTPrNaV8fIaxF/rPpTfYKqhqT0RuBnAAgL1ztH0agL8GcLCIfBbAiap6j4jMQjCoPTWs9zFVfbBQ70NopieEEEIIKUrNIZsyMEOH3Dm0Vr/MO9SIqn4NwP9FELfnXQDuFpG1AB4D8G0EnvXHqOoJeTpsg8poDdSWkz78LNt81hbt117zvI3dPvNMAMDKd/0SwBBnHocJvIjpOd1e3jBOmcfwqF8qjFMlTlO2doO3a19x20DR08/fLdcxy0CHJaAT+fPRM4kQ4oc5V/NRR71Hwvf5Ods/EYHX/H+Fx9oydextRaSjqqVGPlRGCSGEEEKKouL3GjPC+ajfAPDfCLzmDwSwFYAlAN4WLp8E4Oyyx6IyWiWF889HQdiNgPjhY4IpkMTOCjUpr03Q65ZTGodt8w1sX/iY9ZlccuFzPVxqaFa7v3z5nwAA+31t12IdzIBqqJ2O5Za51FKzuuvWTtBPRyUcbAmSPyqnpgM2DSauuXrW3SM5NmmGTWcfmF0JwKyjf5an2YeN5XkITOc2Ng/f1+Vo+zMADgfwPVU9wtj+EIBTROQmBIPU14jIWar6rRxtJ6AySgghhBBSlGbTgZrzRHd01IvKvBQAEdkKwDHh6r/b6qjqDwH8Mlx9iU+7w+BglBBCCCFkPLkJfYPIPrYKYXakKL6obwimJ6Kf8vNWR70oDtwSz3at0Ew/Rlhjj447VcTH9DSjp83zmQ5PPu16TyUo2L7lGL7Xxdc8Hx3T1cY1L/tzRrujiWU6rjnnq8A03dPByY6Ev45aIjdVlN++rlz2B21aPLCtF/bXNN3TZD8+OKd+1X5sfUhErgGwP4BDAVxgqXYgghijAHC5Z9PmrJ5dMXwQG3m5Dpse4AWVUUIIIYSQ8eWc8P1oEbE9sb83fP+FqvoGvL8ZQTB7IMhRP4CI7Adgv3A110TXNFRG244tX/0EPUL4qmcD9QqqoUOPWVNOeB811jeMU2FF1DdsVka7TTItiqg4FD01bCJ5Q0C5HJeaVHXqQiz2o7xq6fMN56Zeal9bW7Zr3JPBehN4uUnzv5enIIgDuhuAS0Tk9ap6Y+gN/yEAR4X1lqd3FIk/pB9V1Y9E21X1ERE5E8BbABwlIqchCG5/u4hshiAY/ucQjCPXAlhR5gQ4GCWEEEIIGVNU9VEReQkCE/x+AG4Ig9PPRyBpKYDlqnpZzqb/AUHGpmcDeDOAN4vIwwi89iNZ7CEAr1TV+8qcAwejJXEGuDfKJJIw1NyW82DRrZ+g+CzeCpxrbmlRRXTMwjhVgf1a5Zu32y+sZ57otKigJv+waSkA+8e8rvmh6e+I+bPy1vVBnvpT5rY/R/2hG3aOl11fVVMtLTOndFQ8c9PjEuvXzLqnoZ6QTFrwN0JVfyUi+wL4IIAXA9gJwP0ArgbwWVX1nStqtrlORJ4P4A0AXoMgR/0iBMH1bwXwXQAnq+pgxpSccDBKCCGEEDLmqOoqAMeHL999nCNpVe0COCN81QYHo4QQQgghBWnbHPtxhIPRIey86NjSbdy9ckXhfeM89DZrksWpaWzJm2/dYQ4p46xUbZglv34UdVwqE8YpdxseZb5MUuawKnhfd2l/JeWQZN66vrNSf2vkzFRFLnvbbYmae0torgeAU1tmsj9swy4Akib3juU6tMCCCmDw65h2ihq2LdryDMNs/wua7MmEwcEoIYQQQkhR2vLEM8ZwMFoj2y9ZFi/f8wfHdIsyssYEhXlK4+s4VFQRrT7MUnY/ag/jBAxIMEXCOFXpuOSriB54yXQ5LplXLLplNqWzSlcbVxgnW9E4iNlFHJOqCI4/akzV9Kmbth0oj76CZr200mpXwQevwc0zDxbr5LRCM31pKhnKiMiWInKkiHxcRL4lIveJiIavPTP2VY/XKzLaeKaIfFlE7hSRx0TkTyJyuog8oYrzI4QQQggh9VCVMnoIgAtLtnEfgO6QsseG7SQibwRwOoJzUQTBV3cBcCyA14jIkap6Rcm+EUIIIYQMQAem8lRppr8HwDUAfg7gDgCn5tx/f1VdmWcHEXkKgNMQnMfZAN6tqveKyG7h9kMBfE1EnqSq9+bsT6U8bvdjBrbde/OXhta3Oi5NIHljeDrNjDU7K9n2LeWsVDNVxBQt49TkY56fFtP8ciyJl6PPjBpf8p5GZuNBoquczE2vYf3in6vo9rjM82bZMaEzk7ltxYicml4UOiuZ9FLvgN38Hjk1jdu0Pp8/AWXGQK4pCk/sLoyXb5lZXfwghHhS1WD0G6p6UbQiIksqajeLjwGYjWAQ/MYwHhZU9TYROQrAjQhU0g8gyCRACCGEEFId4zC5uuVUMhiNBoGjREQWAnhRuPqZdB/CzAH/BeBfALxWRN6rOolZmCePvHepqCLqq4bWRkEnpbxhnKyHzlI1PbJVWfez/ChPswnrQ7IbAMD86enJ4PXohDcwUkiTKmiNHTQwb506t/U79Lfrg/BU/zP31lr6dPiGXQe2pRU9W6Q7m1OTGfYp+uqPoyNTndhCSxEyCsbZF/vZCFRRABiWb/U74fsOAPaqvUeEEEIImS5Uqn1NIW0K7fRVEXkigM0B3AvgZwC+pKrfHFJ/7/B9lareP6TOjan6Nw6pN8C69cu96s2fe4Jvk4QQQshU4vs3lUwnbRqM7g/gIQAbAewE4CgAR4nIeQD+VlU3pOpHng93DmtQVR8VkdUAFhr1W8N2e7wJQN+RqYzT0n03BW1su+ebSvdrFPzuLdcDqMgkHxfmjJdZ4piFHZdqjilqO2Zuk3yi0NcBLF+74266/9jsvvm4lzp387MRmdhNy3wnrOBjrgf6JvuTZuoxhb/Z4phkc2BymXBfG5rrbfXdDlI6sC0xNSD8UbRNyYvMelVP16urXReua1uF6dx1LlmZoJZ2t0qU3TqztnR/Jo0mHFQnjTaY6c8E8EIAi1R1K1Wdj8CkHkWJfyWAz1v22yJ8fzSj/UfC9/llO0oIIYQQQqqlcWVUVZdZtt0E4E0ich+A9wF4s4j8m6rePOr+jYJIIY3UzUklUkN98VbPcmcEqi7LUpkwUU1QVK3MUkNzZ3GaIDrhI32kkCZUUIvSGZV3LE5N0aaOYSb5ZGdltR1OcXoYnulYIw+9C5tTU90knZSGOx3ZVM0qnJQ6YRttdvCpRkF1KLTh53aX3pbxtts7D5U+5kQwJb91ddL4YDSDjwL4OwDzALwYgDkYfTh8n5fRxubh+7o8B+ZcUEIIIaQafP+mcm7pdNLqwaiqPiwi1yOYT5p+bI/miu44bH8RmYdgvigA3FV5B1vEuMwVjSitmhXYv/j8TUtbFQS2LzxX1GOe6LC2fMqCCi5l2bEbFdLEHNJYBTXnRobXwzaP9ITObbX20cUXLQHs3xDOBQXcQfLrQqJkAI4J9bbQTlmMayD8sviqw4n89pLcZpbt0NtiYNum8C70ZLB+N1zuJuYKh9tkfIN1TstvXJ20Yc5oUSLP+MUiss2QOnsby96e9IQQQgghZDS0ejAqIlsA2DdcTbuS/hiB5z0AvGBIE4eF73cC+G21vSOEEELI1NOTal9TSKNmehGRjKxIH0IwJ1QBXGoWqOoaEbkUwEsAvEdEzlPtGxDDgezbwtVzJzX70jiZ55906r7x8s3H3VCskdxONtU5K9nqZZrmPbIn1W3iKRPaybcsWp4WM71YzMbReXaMR3yXU5NJ5JTTNv+YLGelOkz3vmZ3l2NSXtO9LWNTlfWrwNdBqep6o26LTCeVKaMism30ArDIKFpolomIecyvisi/iMgzRWSO0dYeInIagPeHm85UVZuZ/cMI1NEDAKwIjw0R2RXABQB2BbAawKeqOk9CCCGEkBhmYCpNlcrovUO2/zS1vhTAynB5OwCvALAcQFdE1gCYi34MUQA4H32FM4Gq/kpEjgNwOoDXA/hbEVkLYEFY5WEAL1fVYX0j40INimgZZyUnFaiDRQPcFzmWV38sYZxsyqi1H1OslnY6g+cZqaXmZ+3j2pzjkgsz5/zRoTNTXW4mvsHmXfVcZU2omeNAz+JMVGTfaWYy7a6jpek5oycA+ByAnwO4B8EgtINgfui5AP5aVV+pquuHNaCqZwL4CwBfBXA3ArP+7QC+BOBpqnpFrWdACCGEEEIKU5kyqgWkDlW9DMBlFRz7GgCvLtsOIYQQQkguptTpqEpaHWd06hjfMGv1UEVu84pjicZlJRyj3P1o0Jkpw+yeu/2UOX+S8jdb0srHmJ8lm+l+ZiZ4/9CG2yvuVb2cHZrsX2vEHh2VdVLMz6Yj5mheXPFGi8QvbRtF+21ej3R8UZrmSR1wMEoIIYQQUpBJnAM/ajgYJRND/gxItm0ZYZAKZnFylWVnQ8roQ9F2PdDY2cYdxsnmrBRt63U74Xrp7rSGjkOd62Gy/zC5bmNe1awKp6KsNnwdo9qA7xXI7VNJNZO0HA5GCSGEEEKKQmW0NE170xNCCCGEkCmGyihpH1U8ZVZgPo/LanJWcjJuVjWL6T5tnu/1+s++Ub3vPWddvO0FP5pfdy9L86nNdwIA9CxzDuIMTBkONj2qKN4UjSma5Xzkyt7kzuwUTj0Zuy8oqZNJcs5sCiqjhBBCCCGkMaiMtoht9x7MM3/fjV9qoCejY4/T9qml3d8uuxmAO4yTd+amRIXsLE6+5b5ZluKilj19Z12/tCKaUE3DbZF6CgDfPugxAMALr9qs8r6W4dNb7GisBSfVsTgpudQy89yzlNNxxrwu0fUwr5TG9aI6tjb6WFXNSIVv2XUch1BQVHRrgtaO0lAZJYQQQgghjUFldAyRtj5256AuRTRirxV7AABufOPv4m2FFdEq559W/ATdRHw7VxgpVwgoc85opIgmtvXa+WxsBriPPkNmMPv+XNHBfXuW+qQ+3PM9+6R/QjtG9ZYZIJyUUTqpklYH44yWp52//oQQQgghZCqgMkoIIYQQUpRxktNbCgejLcd0arr/+sl2ZqqDvc980sC2619/CwD/kE2J4jrM8xnWsiodlyJzsdmf/S/aqbL2Ta543loAdmelbncmLBsMBXXRUzWxHwAc9ZvRmRQ/u3AxADP7VP/Y9uxKyb4lnZUGa/fG3DqafQUCbGGQxFG/sIndUVYV45TFKS8015M2wMEoIYQQQkhBJinVcVNwMDpGbLNvoJLe/2sqpGWoO4i992T2gmGcqpgs/8wLdy7dRhYH/3ArAMBlz3oEQNpZKZm33ixPvwPAV/cMll9108Za+vofWz8uXo6U0F4kaxpSZqyQGfcgcnByOTeZ2NTScceleLrIUjVdiqRviCdnGw41dhqhtbkYdGAqDx2YCCGEEEJIY1AZJYQQQggpCiXl0nAwOoaYVqkHfhWY7Ld+6mD2JmLnyWc/AQDwm9f9YaCs8oxKcaFf3+pwVtrva7tW1mYRDvvfzQe2Xfz0LgB7nFGbw1O07X92n9ffZotVmoppat6TbncwFmo3ruc2rUdE1zRhXg7rp831Zv1JMuO5TOv2bEvDszK5HJmC8mLm81G24WvqLzqVoQno1ERGDQejhBBCCCEFmaSHzabgYHRCoEKanyefs3u8/OvX/nGg3OcHJrOOh8Dgq4ZO0g+ezUkpVjN7gwpmr6Zzj/LEdxMOSUn1s2d4HKnFCyZqw9XHLKemcSXtdGBenrxKoG/4JJ+89mXaIEkilZRqKakTDkYJIYQQQooyQQ+YTcHBKCHwV6u86hUM2VSkPy6anivq4qW/Cs7v/H0G89VH5941VNM4z71x/WzXaFSqo0vptOWyt/GpzfvJBt7/yB2V9a0u3rB+KQC7mtifH9rHrlwmA+Hb5pjacM5TNa6/K8yTb3D8KE+966tqmwdbBb7Xo0nM86VaSqqCg1FCCCGEkIJM0tSbpmCcUUIIIYQQ0hhURsedlL3pgWsHszNt/XQ6NfmS+wm34pBNRZ+wzf2eccEuhdpoglfc0I2Xv/yk4OcocgRKODBZwjLFZRWoEpJoInlTE+bdTjRdoPQhE0Qm+7aZ649Z//h4WS1L0WWwOSvZnITS4Z6yzLzjniGpY36GMzJFjTMJ0/0En+dQGGe0NFRGCSGEEEJIY1AZHUd8VZnwAfWBX/bV0q33q14lXf3TM+LlhX9xTOXtj4Iq8snHVWpSQSd9XlI/AH2wbnNWMq+Bz/XojTB2j0+Ip2H0xkBMis6qY3GzSSuk/RJ7G0WdlWz1sm6xj7rqG+CeIaH8idTSrnE9u5YwUV0JruamMVW/AbejIvGDyighhBBCCGkMKqOEEEIIIQWZdKvVKOBgdMp48JqUg5MraKCBTOME7ZymF5d5nib5bCInpYgi18Bmzm8rNtP8CZvtHJb1+/9P628fSX/eanFWsjkfmds6KcO7LQOTy+xui9eZla++YzlWmqpN/YSQeuFglBBCCCGkKGPw8Nt2OBglfaZ5EnaFKmiiXhX57aeE9HXIyrBkCwHVVrL6GJX3z6lf9vE5QaiuD22oRyF9x4ZAETW/Ar5K4aBT02DYJ5O0Epn1tcurXEbZmFyZmIqQN8RU3n77hroi7cT37wEZDh2YCCGEEEJIY1AZJYQQQggpyDhYZ9oOB6PjiO/sfBee+3k5Lk3R7H8fc4zrh6mMU86k43Pdqsi2VDUSmoR971PemKJRux/u7Gq0EWzrWsz60XI3MaUh2mbUC03DUQ6sfi6sPrafmjLOQe74ooOmat94pD79yeobIaQ5OBglhBBCCClKCx+Sxw0ORseI+38dhmXKSgA9KiZcVsg7Kb2oIjotyqcvvtdj3K+b3RnLUjai75kYl7MT9iMZxil7my1Uk428qmminwXz1ftmWYroGFV8fgpyt+95repug5A2wMEoIYQQQkhBxv3BuA1wMNpy7r/eCFIfygjm5z6OYDJKhXQCFdGioTmywg/lKStSL2L/i3bKVb+NuM7ZNVd0Gv8QFM2F3TEuVTR/1Bac3jZ/tCi2Oab+KujwNpz7GZ+JjmQHyU8eM28YJ4ZlIqQsHIwSQgghhBREJ1CgGTWMM0oIIYQQQhqDymjLUeNxITY8VfkU5puH3jOH/eofnwEAWPjsY8r1awRc+4rbCu9bpXl+Gs3MPthM876ZjEg2En+BB69Z5MyUcOIJ313hnmx5633N14PZnPKbvovOVqoiWp4vg7mqCrQRObrx494OeCNKU4kyKiJbisiRIvJxEfmWiNwnIhq+9nTst5mIvFxETheRX4vIOhFZLyJ/EpGviMjzM477A+M4w16fr+IcCSGEEEJI9VSljB4C4MIC+30DwAuM9fUANgLYJXy9SkROVtV3ZbSzFsCjjrLc3L7mtHh5lwXHFWkiN/feckZ/JYpSnQhSHWA+g0VzVSpOxWz0w7ItfSyLkrr6yhXx8sLnLquyR6Upo4jmxSffun1HPmm3GTEe49s6X8z3ExQ5NXVH4H8zGAqqT97LGDkadYwfpKKKaJuV1P4xGcaprdAiU54qzfT3ALgGwM8B3AHgVI99ZgO4BcBpAL6hqjcBgIjsDuBEAK8EcLyI/E5Vv+Bo53hVXVGi74QQQgghpAGqGox+Q1UvilZEZInnfssB/ExVE5FEVPUPIvJqANsAOBjAewG4BqOEEEIIISOHymh5KhmMpgeTOfb7X0eZishZCAajS0Vka1V9oGgfyxCZ7Osy19/zx8A8b8YOjGPdGYms6/64O/PQ26xCrvotNV0CwNPP362ytn5x1O0D27ydm3L+gE3iD96KXbdsugtjgyvWahWYrcfm65xZmezOTeXNy9a+FWopaq9YFqcydML718s5p4pxTMk00HZv+vuN5ZlRHnjRZrdati4f2DJ/7gn1d4YQQggZY1ZteF/TXaiPCRQKRk3bB6PPC9/vBnCfo957ReQEANsCWAPg1wDOB3CGqj5WZwf//EDf6ShSFsXh9GOWyaawKPwgy6b+k28U0klcMkRdZCWMTiuilv6YD/9rvr9iYL8Fh7yxaO9axTMu2CVevuZlfx4oH1AzC4QrIqQso3RSykOW4lmlKliF01H+7Ezlj0naT9EMfqRPa4Pei8hOAN4Wrq5QdSbA2wfA1gAeRjAgPRjBHNOrRWTXWjtKCCGEEEIK08rBqIjMAnA2gPkA/oTAs97GDwC8AcAOAOap6iIA2yOwp68H8GQAl4rInLr7TAghhJDpQ1UqfU0jbTXTfw6BiX4DgNep6hpbJVX9iGXbPQBOFJFfA7gEgWq6DH6hpmIefGzpwDbpmcEFh4/jrSZ2V71utN7/EErouKSz+tu22736rEarf2rGNnVU9MzKZJ2bb9l3zeVnApgccz0APPPCnQe2/fwldybWfX9o2hq7ctrphB/wup2JTJw2oZwkzOI1m+5dZvdo/k9dH/OEeTy8V1oiGHPsvDXCcUIVmZraxOI5J6Eb3/f+WXXDP5Sbwm3r1g/6ZpDJp3XKaDj3820AugCOVtWfFGlHVb8J4Mpw9YiKukcIIYQQ0kcrfk0hrVJGReT/AvgggttxnKqeX7LJnwF4LoDHl+3bAJHs6VBIfYlElkRWl/C5ePsly0q372LhX/TV1tU/OSM6eB8PRdRXDU0QnvSa752V2b4vCw57Q7EdR4A13JPneU6y2UaMD0/6PDtGWaRuueqb5VVes6xj1kFvhMp4lKO+I/1ziyLKJRRGj3z1Wb6PVdDPYR8ds9/vTs2Kqy9FQ0cxyxKZVlozGBWRdwP4RLh6vKqe4apPCCGEENI0kywYjIpWDEZF5O0APhOufkBVP1dR0weG77agoaMl/KyaQmp6TtDinZaNsEODLPzLwTmpq3+wYmCbc+qVM3B+PV/YNiui+399RwDA1UfeBcCuhmb9kE3LD11RVbNq5bIvEFavsmYxLfc6PXcUqD9wfl7EOKaPwlmmj6MKbN/JSJ1CNXa8EZHFCKzLLwawE4JQl1cD+HdVvbxk2zsBeCeAwwHshuCreheCFPBfUdWvl2m/8cGoiLwRwP8LVz+mqp/y3E9c4Z5E5IUITPQA8M1yvSSEEEIIGaQND5Ei8hQAVyBIow4AaxGEunwxgMNFZLmqfrJg268E8EUAUcq8RxA8ST4hfD0OQKnBaGUOTCKybfQCsMgoWmiWifRnRorIyxGcoAA4SVU/nOOQHxCRL4nIoSIS5xQUke1E5B8BXBBuuhnAlwqfGCGEEEJISxGReQAuRjAQvRbAvqq6AMFY7N8QjLFOEJHDCrT9IgDnIhiIfgnAnqq6harORzDYfTmAS8ueQ5XK6L1Dtv80tb4UwMpw+ST003y+QURc9tajUrns5wI4JnypiKxFMFJfaNT5DYAjVXV9Zu8rQMOZ/VEmpoRJ3maejUI71d2xCsjtpOT7pDiBzko2Drh4BwDAVYevGijzDvfUgqfvqrGZ2G3m+rpCKkXHsoUCGiVVhm9qEruJfXBbm093VInuzE9ZFdfDboKPHLr8HKpi5zQZ/bSIcaYFGZjeisB0vg7AEap6BwCo6loEGSp3B/BSBDHbL/NtVES2AnA6gnHaCar6f81yVb0ffeGvFE2b6c3fqe0z6qYD15+HoP/PArA7gieC2QjmMFyHIB3o2aMaiBJCCCGENMDR4fs50UA0xUkIBqP7icgeqnqzZ7vHIEgq9GcAHynbSReVDUa1gGyjqktKHO8GAP9UdP/GsDwO77DDsiZ64sXC5y8DYOSXBwYV0ZpV0EmiCmclW52f/PV98fJffmfb/B0bMf+z+zwAgPb8VJfonBPhnlJlWYhF3m9SbW5CgS1DrBga3Y6DwYfr06ih5VVSO+F975UJwh85PBmS+ph9nCYLz4u/62nVBwkKpyk+I1z9zpBqVyFwZloA4BAE0xd9iAa556vqxsKd9KB1Qe8JIYQQQogXe6Evc91gq6CqPfQHoHv7NCoimwF4Wrh6rYjsKSLnisg9IvKYiPxRRP5TRJYU73ofDkYJIYQQQgrScM75HYzlO4fW6pft4KhjsgTB1EcAeBKAXwJ4DYAtAGxE4P/zNgC/EpHne7Y5lKbnjI4/pmdS6JGUdmQyihLstO2yOntWKQv+alm8bM2aFFGzKX7cHJfSVOKsZMvmZGz70QseAAA853tb5+tczZz9hHnxcmRdFEs6nyoyK8WOSR3DEaMl00Ta5ogWXSLT5BtlYzJnURQ1KptnmzZpdyzzlnqO+uY+4+ZYU9Qxqp9xqhmnonG93lPEFsbyo456j4Tv8z3bXWgsfxDAKgTzTr+rqioizwJwBoKB6nnhXNQHPNsegINRQgghhJCC+D5grjz2WK96S774xTLdqYpOavkNZuB8Vf1fEXkFAofxbQG8GcCnix6Mg9EaUUP1keZDP1RH3kd7ny+qZTL/uKugNgqpYql97Hnu2//5SjoQhaFmjE2S9oYxsJ1zHO7Jcqy2qY+eflq1YwjFcZ+qCSsUtpmxrQ6SoaMGMzs1SZksThFUJNtPw6HZHjaW5wF4aEi9zcP3dZ7tmvVusGVwUtXfiMj3AByGwDGq8GCUc0YJIYQQQsYTc57ojo56UdldBdp1ed9HZbt4tmuFymgOtNN/xpeeZRwfzR+1TBC1zSMdW+pQnVqmZI2UEvNIbYpoVO+HBz84sN/zv7+wQAeLcd5esxLH7xnyoPFNipfS6oItFJNJ29TPNlOlXphQIsOG8ypDrrmgtnz1ebEF2m8z7dJzSV4a/i26CcFHRwDsA8vAMcx8uUe4eqNPo6p6n4jcjewY8PEunvWsUBklhBBCCBlDVPUhANeEq4cOqXYgghijADBgbnfwvfB9D0edPcP3lTnaHYCDUUIIIYSQovSk2ld+zgnfjxYRW+im94bvv8iRfQkAotA5+4jIC9KFIvJkBHNFgZL56WmmrwOLuX7nrY9pqDM1UIfdy3gsWvDXr6/hAC2hgtBOpmnearrXZJlZ54rnrQUAHPzDrbz6kZev7du/kb1ussw0u9udW8J+hlNaijhluUz7UZinbndolcpp0nyXyFZVsB9lHJ76IYmMNpCvjbgfxnLe2xft67tfFT9vPuGQOsY9KZONya8/4XGMaTH9bfUcuxu2201Mu+BEhJo4BcC7EOSnv0REXq+qN4bZmT4E4Kiw3vL0jtL/0fyoqn7ELFPVy0TkuwgU1zNFZBmA74Whnf4CwAoEH6VbEYR5KgwHo4QQQgghBWl6/rqqPioiL0Fggt8PwA0ishZBTNEOgue/5ap6WYHmXwvg+wCeDOAyAI+ISBfAlmH5nQCOVNVHhuzvBQejBYmcmayOTBEyDlPn28FEq6EZeAfCdyiFpgNJWhE124+Wv/ecftSOXrczUK/XS24z23fVN52UXKGaIuyOJkmFNFcbHtcyyzFqqp3pQiRUsNQSashaP98tc6qmtkD4pqo5qrvjCsWUN2STiW/Iq3SYp67ncfKGh/rDzBqvdvMyX2cPLevyb2PlqOqvRGRfBAHqXwxgJwD3A7gawGdtoZk8271fRPYHcDyCDExPRDB2vAHA1wF8RlXvL9t/DkYJIYQQQgrStDIaoaqrEAwaj8+xT2bnVXU9ghiiheOIZkEHJkIIIYQQ0hhURkviij268yK/1F9jxyTESm2Iakzyw2NzevdjBPcwNodb7JJtURJsRI4/3Rb3sQ5szkqZ+0T1PcvK5mcHgHPn3ppz72wO21AqXrcXkWnfZtaPnJlMR6Z0LfMaFM0Xb/6FunHmwVz75mWdbAQAzNWZWo/TBtr8ezYuUBklhBBCCCGNQWW0QiKVdJcFxzXck5rhU2Bt+CqiecqK1CuKzTnIdkxbvUHVzNjPwxnK3oa7Hy4ip5y86nPDeaprZ9C9yCgzCm3XIe24lKWaRgrgWXP/mLebubhszu3x8qEbdh4o9zEkZDljRSpmpJB2MjJN9a9VsHT1rLuzO9FC1stgUK1ZxnXZNAHhnqiMlofKKCGEEEIIaQwqo4QQQgghRaEyWhoORgkZAT96wQMAijkOpU1AWWZgV5zRuojM7k6TvMWOOUrzVtSPjuGp45ONyZxSMInmuOiMsoyl0WWzOTf5ODLZym1lZ9Rsks/iu3P+PLDtkI2Dpvsqia+DJSvTVbNW1XrsJpgE0zypFg5GCSGEEEIKoozhXxoORkl++MXzJlJEJ4kos5J2/RyTXGqinyMTUDQrU1Z4prz9nTYEZqghy/2Oyyz7WhzAfBXUccfljJV2ZDL5yew7a+4ZqQP+ZpRnkr7/hBBCCCFkzKAySrxY8/Vz+it8CqydpkM1ueaApuvkqRfXH1GG8VEqn72c7ZqKYdSnXm+wrG24guPb5oDawj3ZlMNTG54r6uLy2cE8UtvcUWcwe2PZFu6pX9biG04yoTJaHiqjhBBCCCGkMaiMEkIIIYQUhMpoeTgYJU7WXHhusGALSdQpYVpifvuJwsesn6jvkVHJHhLILyuT/7QBhxNWhX9gzP7kNef34hBdlXWncmyOTDZTvKRO/ZQ57TXN2/Ax1wPZJnsgeV2upOMSmXI4GCWEEEIIKQiV0fJwMEqKQ3Uzk+d8b2sA4xviKbfi2ZJQSXGAe2NbpESZ/emEFXyC35vt+qqmtgDxVdDzcADLq8CauALcu7Dlrbc5Jkx6dDhb2Ka0WvqD2XeMqjuEtB4ORgkhhBBCikJltDT0pieEEEIIIY1BZZTErLng3MGN1tQqjkZa7GTRJHnN9XljeNZtCrfFivTf17GDK3G5pVrerEy262K7trZtnXBbVhanoiScmgrarUfp1BRlYzJjxBY154+7CpL4yHl+PGymezIZcM5oecb9N4EQQgghhIwxVEZJfoqqMQ4JYc15X4mXF7zy1QUPQEZBrCIaj7K2PPVtwFQ87aGGovJ6wjhFmM5ELjUzKvOt32ZsV3RMT8VJmQh3ZDKgMloeKqOEEEIIIaQxqIySmAVHvRbAkLmjvuQN92R5olzzla8G/Xn1q4r3Y4JIzwvNmr/pnKMZYZmrWebpvmhIp8R+9omhCcoEwre1oeHx37pqtaubhTlhs8Hg6BHmPFEf9dMVzilr/7rFO9+5o9SPyCSikx6rbARQGSWEEEIIIY1BZZQQQgghpCCcM1oeDkZr4PY1pwEAdllwXMM9qYmimZdcX9gpyeYkBbwdtJcMP+Qbrih33ypoY1h7w8h7nCyHpIFwTwlT+OCxjv3zmlzHz8vyx4Jc5h+fs4uzHxG+eejH1alpUigTpsmWt56MNxyMlodmekIIIYQQ0hhURskgVaiUWU+KrmOE+64+57yBooWve2WZXjXOsy/bJl7+8WH3e+0TqalphTQsDcoqFltGFUzfemyHE5KtPx3jegw4NRlK9BtvXVdNBwvwoQ23D2z7cGfXeNmVRz6v41KZnPSkfhj8fvKgMlqeSpRREdlSRI4UkY+LyLdE5D4R0fC1p8f+HRF5i4j8VERWi8hDInKtiLxPROZ47P9MEfmyiNwpIo+JyJ9E5HQReUIV50cIIYQQQuqhKmX0EAAXFtlRRGYDuAjAi8JNGwB0ATwtfL1SRA5WVausISJvBHA6gnNRAGsB7ALgWACvEZEjVfWKIn0jhBBCCHFBZbQ8Vc4ZvQfApQA+CuAtOfb7BIKB6GMAlgHYHMAWAI4A8ACA/QGcYttRRJ4C4DQEA9GzAWyvqgsBLAHw3bCdr4nIdnlPhuRAZfBloyf9l2Nf7Q2P27b67POx+uzzqz+HBnj2Zdvg2ZdtAxF1viKkowMOUP16MF7u9rzikKbar4u8fcuq3xFFJ1Hevy5to6cSvyJUg1cPEr/SZU07Lwk0fqXpSP81yXQ8X4QQP6r6vnxDVbdX1cNV9SMIBoKZiMhiAMeHq+9X1TNVtasBlwB4U1j22nDgmeZjAGYDuAbAG1X1XgBQ1dsAHAXgdgALAXyg4HkRQgghhAxFVSp9TSOVmOlVtVtw15cDmAtgDYBTLe1+XUR+B+BJAF4H4NdRmYgsRN+0/5l0H1R1nYj8F4B/QTCYfa/qaDQF6U3JM3GZUE2pfTMzWEzyFzRLDUxvMHPCF3Q2S4Rx8nCoqCvsk6uthApsyRLlUlGjS/SaWx4r3Me6+bjeFi//E3YDYHdWsv1qpbfZnJZsVycrQxLxw54JzF2PEDKcpr8rfxW+X6mqw/5qXBa+H5za/mwEqqhZJ813wvcdAOxVqIeEEEIIIUOgMlqepkM77R2+3+Coc2P4vpeIiKFuRvuuUtVhMXJuNJb3Tq07WbTZrV71HnxsqW+ThBBCyFSybv3yprtAWkzTg9Edwvc7HXWisvnh6yHffVX1URFZjWDe6A7D6pVhakzyVWAzJdpsXFOWqSnLaSddmrgC1jREUY3Bdq3Zm6I4pt16r62vqT9vpinb9Xv1zRsK97MJPoHAZL8cSwCM1kkp76F8pnX48s4Nj4+XPzfnj5W1WxeHbwhiw5pZlGx/AbJmHZEJYwL/Lo2apkdSW4TvjzrqPGIsz8+5r7n/fGctQgghhBAycppWRsk4kle59FFEbW1OydNm3tBJg/mXMMSjYjA7UxX57aMwSaPM+pTOQmXWnyRc19Q321KTVyWvg5RZ/e9ClfTzLVRIj9iwa2I9EXLLFuIqrjecF27YJV7+9pzBDF1kfJjWeZ5V0vRg9GEEJvR5jjqbG8tm4PuHw3fXvub+uXIBZs0FpXmeEEII8WP+3BO86nFu6XTS9GD0TgSD0R0ddaKydar6kLH9zlT5ACIyL2wfAO4q1kWSSVE1dEg9n/ZWr+gn/Fq47GVZPWw1ZRS+aE/bPNJoDmjWPMu2PtVb+92xzIOdIAX9RFkJAPigLom3VZF/Pq9iWXcIqHHTtCW83mr5rka55l0KKeCnklIhHU/a+hs6TjQt70Xe7fs46kRe878dsu9iEdkmY1+zPiGEEEIIaQlND0a/H74/R0Q2G1Ln0PD98tT2HwPYGC6/YMi+h4Xvd2JwMEsIIYQQUgozVW8Vr2mkaTP9BQA+g8CU/mYAnzcLReQIAHsgsOqca5ap6hoRuRTASwC8R0TOU+0bgkVkCwBvC1fPrSL7UuY8UW16bF8Ricw3NZkf0maNLNN8utzo4uozApP9wmPG01zfmbGZnvvLPtmKbE5NfTP3oNPPuJmVfJybJonIXA8AHzBM9sMokm2piavmc8x3GOGeIr4wQqeml69fAiBpdo++jtH0iI4MlmXh49RExpNx+z1tI5WNnkRk2+gFYJFRtNAsE5H4mKq6CsDJ4eqnReT1IjITtvciAGeEZeeq6q8xyIcRqKMHAFgRHhsisiuCge6uAFYD+FRV50kIIYQQQqqjSmX03iHbf5paXwpgpbH+TwD2RZBn/iwAp4lIF30v+J+jr3AmUNVfichxAE4H8HoAfysiawEsCKs8DODlqjqsb+WxqKESqnh33Lci3rbTtstq60JVrLkwFJ9N+SJSnUoopLHK53JW8nZksrU/3k+l0jG0kshRZ8bYFF2bUOoyHXt6FrU+1kPje9cve/73Fw3Ur4KLn94Njp1TIXA5V5UJPzVJfLKzcmDb+7qTl/nNpRi+fX1fLf3PudWrpK8K1VCg//0xwzd1Uj88F835U+V9aANzdSa7kkFXsnXeTWPnrpafafktqpPG7cqquhHAEQgGnFcBWI/g9+A6AO8H8OyUF316/zMB/AWArwK4G0Gop9sBfAnA01T1ijr7TwghhBBCilOZMqolHg3CuZ6nhK8i+18D4NVFj08IIYQQUgQqo+Vp2oFpLLA6LjnM8/31/vKd96wAAOz4uGUV9qw8a75+Tn8l6r95GjYLS2QqtX0Bfb+ULtN6bLq3NO8Z03ScSMQZtcUcjc3XUUYl03zYG9gWm+5D8/+zL9uuyu5aOfLawLz39acNN9tV/YNti8/KPwpuxyWXwTTb4Wn4ta0rLqnN7fStocm+b0436ju3DTodRVu6Rv2oXjJGaHDuF81d6dPtsSKvaZ6QOuBglBBCCCGkIHwILg8HozVgndPd1jncproUxx4p/8VKZFtyfVFj5xyjjksRLaPGtpSZmb4uE/2omT9uAw49ibLB/PMHffNxdXU1k5dcN2gxuPApxdoq4qw0ifnqbZw0cyuAviNT3RmTyuBUY0fWCzfmp6sTrn157q3NdGYI87SeP9e91v5xItMEB6OEEEIIIQUZ94gubaBxb3pCCCGEEDK9UBktSdppKYHplxLao+66Y8XAtsW7LKu8X8NY853/DhYi+1jXeB6JbH2Jmfvh+VWRlSlhireY51OorX5kxrbs98AXLomXt37Hi4v1cYT8+rVBvEQzVqiE9nbb+dlM+NG+k/5cPq6Zo+omMtf/wyZ33FEaYv2Ztk9YxzjjOkz2s4z2JzXmKH+XykNllBBCCCGENAaV0Txk5J6XVLyQhCNTb3Bb5Gtx98oVAIDtlywr20Mra7531kA/4uf/hMNHg093RjeszkppRTSRtx4D2+7/3KUAgG3e+aKqe1qK619/S7wcnYv5qYqfsC23wqWaPuOCXSrrY9W8LEzkW9SRiRTDV4Nyh4Jy/yY06TgV9cz8/pxaQ3amtvCobIqX63JmIsWgMloeKqOEEEIIIaQx+HhVEOdc0dxtBfLCvbec0d/WDfOQbzIqRuqqEaE5Xg7fZZPRr264vNHY1kkFlDfV2zi0U3G11JqH3qpmJvvhmh+aKI/nmmKgvrWNliG2UFrGyYhlXmiETTV9xvntVUTTRAopUH+4p6/tG1zcl1/fluBB08eoQjpNsho6jEglrVohjeaP+s4dnQmthT456icZKqPloTJKCCGEEEIag8ooIYQQQkhBqIyWh4PRkvhaJ6J6CX+hUVk2zLBMkeleLGGcupYvVLSvrSyiii+iy1nJLLdMA7A5NWkYsuref/2u0Ubwtt37Di3fX09uPu6G8Nih+csIpaVhWCa1mZ4t5z7p4Y3qOL/IXA9Mj8l+MoPn9DllCs3yw5gx5ux0J/7Ot5dJ/U0eJTTTE0IIIYSQxqAyWiXpkE6+QkyVD7TJOEHBm6Gy9R1ArLGDhpeVwZF/3plGzRG+yaaaqhnAP3aCMjaF2+458QoAwOM+eLBX9/Py+7f/pn/MsJ+9SBnvDErjYiq60XVxOepMeOq5SVeAxx13KCjSFDOW3+2iamndgfAnDf5WlYfKKCGEEEIIaQwqo4QQQgghBaEyWh4ORhtEqkxfkve7EGniNoeqhFOTow2XuTjDlDzgdGRzSErsMDwDkzraSPxIpLat+tgP+/VDE78aeeIjZyPtmu12HPVlYFt0MWOre2cwsKuaWblsS7HZOlyfodkM8DPnm2Xn7zMTbDM+X6/87aaBfcad6OzKfEqyMi8VJa8LmYTd+MIcOi0VIW26p5MTaSscjBJCCCGEFMTp+0C84GB01LikAYtSqobIZgsjFZU7Q0xZnJqcoZrKYFOpUtmWbPWdYZxs9W2Kp8VByhYiqVIHIMPRSGK12XA+CstFLHqVJbxWpJLaezhdP3i+2ZZs+NY/b6/gJ3ASFdJRUpfeRkW0WoqEgvLJyjRj/KFyZWOaFba1iQotScHBKCGEEEJIQThntDwcjI4jjjmdaoQOEh8F0JYrPadqqg4FM1nPkTveEcbJ3NcVFD7RnGOb2tTYvPgkA4Ch7kX1jWPGqmmi3bi0X6+XDHpfSZKBhhEZrozY7l0V4Z5s9ztq76t7zo63veqmjYWP0SbMs61Chyo6xd07wh3FspESqaScR0raAAejhBBCCCEF4YNUeRhnlBBCCCGENAaV0RHhsEr26RiGtW7OR63YxG4rc5h8q3aKsYZlstRzmcpdTk2WdbU5SFlCQLkclyqd82Pe7Khdy/WOTc/mrpb+RFmb4vP0+jCNL3VnYJrE+V3mT4fNnF5FuKe8+JjnqSiVg7np20FvAn9TRg2VUUIIIYQQ0hhURscJm/qZfiAz111qad30bMrlcBXUFcYJGOK4FBem6phF1mPaOpzq6xBcqp1YBNp0/URu+t6gXuXUqyfo0dHmwJS+pmVCO8X7eTqpme1/+UlzAQCv+d36QsccJf/YXQog+ZmLVNIqc2oMo+ghqIhWjy03fYRLNfVVV31CPE0rk2htGTUT9OeNEEIIIYSMG1RGCSGEEEIKQmW0PByMtpTY78VRZpbbMjFFMUcT8UbTWYKKZGJK54I3O6QWW3W6DIb51CemKDDgkGQ1v+aNPVrBD4jNlGya4qO89lZnpU6yzrB6UT/rduwZJa44oxGueKPDyn3z1Ke32crOeeJmAIDX3fJYZl9HzQd6SwD0vz62mKJZTk11kOW05GOez5u/nmTjG1O0itijUTYmVyamSWMSfpObhmZ6QgghhBDSGFRG2475uGB70Ew7KdmyM7ly0zuyORXCmifeEarJJ4yTrZ4ZPcmlkNmy7jjCSVWOR9YkMU89pyBx07G/BQDs+cW98vZs5FzyzH5mI5v67XYOq04VNtuwtddzqKVtQeKvb3BdzNAyrjBOebMy2RRV234uDcz1mZ4e7axaFuncoWXOHPIVhILqGG3QmSmgLb8VIrIYwAcBvBjATgDWALgawL+r6uUVHWMGwM8APCPc9FFV/UjZdqmMEkIIIYSMMSLyFADXA/h7AI8HsB7AtggGpt8VkQ9UdKh3oj8QrQwORgkhhBBCCqI9qfSVFxGZB+BiANsAuBbAvqq6AMAiAP+GwChygogcVuY8RWRnAB8HcBuAu8u0lYZm+hHhckjybiNyUjI3RqZ1SdYB+s5MajjURM5MC/5qWYmeDGf1GReGBx1ukgcQ2+dsMUhzxxR1xDFN1MtrUcpb32GS94mrOax+vOSo/9tjboqX9zpjT3c/R8ylB2wAkDLXRp9T0+Eu5ahlUrcZrGf5/EX9PWvJFgP137Dy4Vr7Y/Ih2S3RH2Dwq9ExPqxtyQZjM897ZWWqvCfTha8Z3eWsxMxOY8dbAewGYB2AI1T1DgBQ1bUA3isiuwN4KYATAVxW4jifAzAfwNEA/qNMh9NQGSWEEEIIKYiqVPoqwNHh+znRQDTFSeH7fiKyR5EDiMiRCAa0l6jqxUXacEFldNQYw38Nnz4lbx56g1hxtShNNvVp4V8eU/hYPiw85mUAgAdP639WB8I4AdBuFNcoO4xTog0brjYc1KW2ia8YGyqAknBSS6pyk0Bf6bS4z1gc9CpxUrI4SNnCONmclaJrb/vMRfW+tMtW8bY33b62dH/TfGz2rvFyN/x96BnXrxN2sutpa/HJyqQZbfk4LuVVQyfoYz5SOjbHO4tFoZO6p3Q4apYjb3h35W2KyJboz+H8zpBqVyFwZloA4BAAN+c8xhYAPg/gUQRzRiuHyighhBBCSEFqVDx92Av9J/0b7P3THvoD0L0LHOPjAHYBcIKqriywfyZURtuAbb4nolBGxnzPSEwc3BXaS9YBgEVPf1O1/czBouOOHNh2/+cu7a+kFanMUFAYXt8T2/zUovSD07vbigLga5HkAh7YzunGN/4u2BRes33OelItx7bxveesG+hbr9dJvAdYAhDZ1P0UowyhYlNSbdtO23ERAKDbTZ4vAHR7lm3dwTaieptCi0HX/N6HUrs5L7Qbz68d7Lct3FPdepivIurqB8M8lcNXLU2TNT+0ikD4pFZ2MJbvdNSLynZw1BlARJ6OwEP/dwA+na9r/nAwSgghhBBSkIbjjJoelo866j0Svs/3bVhEOgBOATAD4O9UdUP+7vnBwSghhBBCSM1ctNfJXvVe+tvja+6JN/8HwP4Avqqq363zQI0PRsUnSXWf56vqD419lwC41WO//VX1mrx9GwfS4Z60zbYuV/gmm7OSLcuSZyioeFtOy1JtT7gemZiqQlNTGn5z9O/jsief/YRajvn9568BkDLFR5/NsEP2r/qg45CLrNz0RTGP7XKWqyI7UzzdxpLxLDo/MezvnbB+t2vWizpbuBtOXI5Orp8YX9O8b71j1j8eAHDG3D86jjodbN/bPF62OSLZtkWm+8hcX3X2pKg9W1vTlKO+4XBqZpy5eQAeGlIv+gCtG1KeQER2BPCJsL3qPa9SND4YRXbg1K0QXOANCLILFGlno6OMEEIIIWQcMeeJ7ojhnvI7hu93ebZ7IoLx14cArBWRtHk/GoHPicpU1Wuga6PxwaiqLnaVi8h1AJ6KILbV/UXbmQQGwjgBAxLDNk9pzmkpi23+/m/i5fs+G0SgiEM8meQMbF8FRbJeAACMhALwcFKqPKC7Q6mLwxsZauV1r14ZbDP6anMwivbpWZxy+mWmU06wbDu/vtrnPve4PFYOM5zDCuartwW4t2FzVrK2NyLhJ+GsFF+qfCGe8pKlnbnUbK9QUBl1ovJloUIKACumTCXdoTeYdCHCVCRdKqUNW/1ROSvNMj6vmybAMarhOaM3IfiqCIB9YBmMhnM/o/iiN3q2u1v4/vHwNYwPhi+gRF6fVod2EpGnIRiIAsCZDXaFEEIIIaRVqOpDAKJpiIcOqXYgghijAHB57Z0qQKsHowDeGL7fA+BSV0VCCCGEkFHTggxM54TvR4uILXTTe8P3X6iqV8B7VX2+qsqwF4L89ADwUWNbYRo30w9DRGYBeF24eo6qbmqyP43RMU3VFnNG+Dixzd7tNc/b2Pbdfw0AuPekwEHP+jm2xB6NVy0mfFseerXFL42dm6o1rRQ1GyNhvs7eN2tKQRMmI5spPs41H05lEItNVyznHlWrwlnJFiPUWs/m/JbRXh4S55mzDXsGq9GT13HJVeYyz9vLxt+UWxSbOT2dWckstzkppR2ZisB4o8NpgePwKQDehcC0fomIvF5VbwyzM30IwFFhveXpHQ0n8o+q6kdG0FcrrR2MAvgbAI8LlzNN9CLyUwTzJWYDWAXgJwD+S1V/XOTgizbzcdIH1q4ZXUBxQgghZBxZvf6D2ZVIIVT1URF5CQIT/H4AbhCRtQhiinYQPOMtV9XLGuymkzYPRpeF779S1es86h+EIPcqACwJX0eLyMkA3q06Xtm+45BNlieuZudKV4szo5LNKadEBqaRYagPcSYt310LZmyyfrorCENkJVJBDectiTMCmZmDkp2yqqaObWZ2q8KqcwY+vwq2sC1Wh7EK+hYZQtS4Ll1Lu52w3JaJydepKDK0RNfZNLy4LovZvsshyUcRtTkrJct1oN7R65cmtp071084GAeWdreKl6OfOFcYJ5dCapanFdKg/eF3OSsrE0nSsANT2Af9lYjsi8CZ6MUAdgJwP4CrAXxWVVs5VzSilYNREdkawcUE3KroYwC+AODLAK5V1XUSBOh7OoCPADgCwPEI5pyeUFuHCSGEEEIaRFVXIRjzeEfNLzrXU1WXFNlvGG11YHotgDkANgE4e1glVV2lqv9HVX8UxbfSgF+q6pEAzgurLheRhXV3mhBCCCHTRU+l0tc00kplFH0v+m+p6j0l2nk/gFciyN16CICv+e744GNL42WJYixqx9gW21BIGTzjh7rM87ZsS4XjhtZEbH5P2CWH99HX7JobWwxXD3ydiWxm+rQjE9B3ZrLFGa3C5FXGCck3vmiVRPe7rslEroxKzv2M5bwxRV1lvs5Ktnrpba9avyQu++rclY6eFOfgjTsBAK6YfUct7T+xuxBAyiTf9+Qbup8161LOLEtVZ2UaxsK5J1q3p+OMrls/4GNDpoDWDUZFZC8EuVCBkrFFVfVWEbkXwHYAHp9VnxBCCCEkD22YMzrutG4wir7j0gMAvtFgP8go8HUESSmd3l/+Fv9I1OaUk1MVriJcke1c0uGeXGW2fphKal6HLhsu85fvNfBRhZPnGe03vH5QIfv4HWNSVdeS1z6Nr8bVd2TyrO84RrZDkqts0Fkpr0738lAlVWPPKCag6RTW78fgMSOHnURUuHDbczfuONB+tG830b4m2gr60Qv7kVxP10u34SLLgSldb5pDZJF206rBqIjMAPjbcPVcVd1Qsr2lCFRRAJgcl0tCCCGEtAIqo+Vp1WAUwAsA7Bgu+8QWlYyQTZEH/aMArijZt5FiC+lkrTfmD7rOL7FrfmhiYwXHapAqgrvbqKItl6qZ2BYHtjeDxyfrJ5TAKHSZ8fXt10sGvy+DPQSTsexQkXs1zR2NwzJVGMQ+61pFxcnwTa65iIPtVhn0vl82qDBmtRGfCzSxPs5IfC8sge0dc0ezlE4qomRcaNtgNHJculFVf+5R/wci8m0E5vzfqmo3DO30NAD/DOClYb1PqeoDVXeWEEIIIdNNW4WOcaI1g1ER2Qr9waOv49JuCNTPEwBsDDMObA5gnlHncwA+VlE3CSGEEEJIhbRmMArgVQgGkT0A/+O5z/sAHArgAACLAWwNYAOAmxGkAz1VVX9WfVdHiCUmy7ib5q3kzbduuQbejjsVmq8TzVqyZkW1YjOc6ZQT20KN/ljykJd1dKrN2cvigJN0akpVz8jA5HXIjCkNrs+AK2uSbw57H2yOSdZzN6M8VxgiLppekBXOKe24VCSMU9EMTC6zcVYbPpS5nDaTeUSnRH52n32rCLPk20YV+epJwLTGBq2S1gxGVfV0AKfn3Oc89APbE0IIIYSQMaM1g1GSjfUBdtyD7udUsryp60k1ugkVtm9VBysOaxS31avHKceeTz4+aqIOAHQ6wQe3ZySBe+FPNit49N6QZeDsJ/Rn7ERq3ygTIqRzyAP962AKo+9bd2e5A5mnNBO8vWdTP3FHOg+9L3bHoUF8yoLy5GfdN4yTrQ2rojtGjjpZCmZe56NxclZKB7ofd+pKVjFNtDUdKCGEEEIImQKojBJCCCGEFKRt6afHEQ5GW06Wad43Huk44TQhl3FcKrhfIhOQzz5mDvbw/gw4Mhn1rGZ448Y/6dQne/UzD9e87M/xso/J3uqwZdnPvFb9z+lwZ6UX/Gh+5rHLcPTvH42X/3vp5gPlNgemOuKLJp25guvw9w/cU1n7Nj4zq5/n4/iNQTbkZJzRAFdM0Szro69pPU3emKJNYH6UK5wpk78f6XijQKGYo6OmO4l/nIbA0E7loZmeEEIIIYQ0BpXRFpF4kLTFZenZ6tXZo9FR15PlKJ9YC4dgMsMhhe+7/7+nVtQrO8+8cOd4+ecvCZxnEuGNIkXXIxc7YCq/BtGjbtjW87+/qGBvq+H1tz4ysG3FrlsCqCZvvStM1d/de59XG3Vx8uw/Dmx7+/rHJ9bzOivZ9rWXDc+y5BvGyWyjCf0vHe4pYeFomSLpokzoqCLhrKYFhnYqD5VRQgghhBDSGFRG24Dn5KpIEZWuUR4uP3j1lwAAiw54U6Vdq4u7P/EDAHkCsqdWbXM3G3g6zcwrH+VsT80dDfYN3peevF9NvfNj/6/vOLDtZy++K3M/6zxSszxaaPEjr03RcH0m86reeYP6N4FN1axaEfVpy6fMduwyRApnx3LU7sCWAbF/YvFVQds2T7UpGNqpPC3+M0EIIYQQQiYdKqOEEEIIIQWhN315OBgtiRracmyKjXKUmxVz2nWcIZ1M0/2m8D2MPbL6x2fEZQuffUy+g9ZMZJofG4anqHZmYrKa7lPm+kTZmGFz1HKZ7J/9nW1H0a1CvOn2tQCAL+68YKCs6P0xr8VbVz1YrGMj4D/nBk5NbwkdmbJCKvmY7m1mW1e7WT+LPmbgIlmXfMztppNSJ7TD9sK5NVfOviP3MX14YnehV70o7NRtMw/V0o/5OruWdgkZBgejhBBCCCEFoTd9eTgYLYjGSle9H0JTSYsFFzP2cRSNeWMYWNw2677FOMMhjdmkcJdimFZIAWDJvz5zdJ3LyYGX7AAAuOrwVUPrZDnnPOtbj6u0T3Vy7J/XxMun77QwUZZXIW2zGmrj1FAhPdYI9eRSDH2dlXwD27va9SkbBZFKevns22s9zi0zq+Pl3btJtd4My/SHmTWok3WyMV6ep8lhQpZaPU3B7kl1cDBKCCGEEFIQetOXh970hBBCCCGkMaiM5iHhfeIYx8tglR12WVZLl1ZfuSI4ZGimx6b+Qdd8/ZxgYWN/24JXvKaWfqS5+4TvD26seUpDJhXM64lyr8dxTk1TtQ7mYNfUtl1POqB0H0bJQd9cDMAv7uikMK6OZWXJazoP9tHUer428sYnHXYMnzIxM4ylppgknJXC4397Tr0m+SzqNsX78mjkJRsyV2ca6kl7mdbfjCqhMkoIIYQQQhqDymhJtGM+YYcqWPh4vuP2y2o//sLnBsdYc9lZwYZNxhNa5Ny0qf8ku/q/vxbs9/qX19Kfe068YmCbzXmnqEpqzbzUYsYhA48PkvHYGuWyn5TznUbyqqDJMr/2fPPP+/apLLYQT8m888QHX6elTY27otUDvenLQ2WUEEIIIYQ0BpVRQgghhJCCKKNZlYaDUQ+0E3zSpOdItwRgp22XjbBXSRYc9oaBbavPOS9Y6Br9DpcfPO1iAIAazk2RA44a9WMTu7ktMpVrqg7QN7+P0mxhOVYjE8odWZkmnSwz/qTQm+A/Oj7ZjoJ6wymauanIMYtkXhqG+fH9xpw/VdbuJLLeCGY9i1MaANCBqQqm5E8IIYQQQghpI1RGy9LmbBORmmmqmmHoJ5sjkNU5qAKHIWeWpTFlIMRTonAw3NO4M82OSZOshuahDkW0CaclG1RDixE5JLkU0kl1WjKhA1N5qIwSQgghhJDGoDKag2juKNCfP7rzomOb6k4mUfimB794Ubwtng+qw+d2TpKC2TiTMo90ipXRaaGIADyodGbXCer5KaI+c0VtdShmk1HCdKDloTJKCCGEEEIag8ooIYQQQkhBOGe0PByMFqTN5vk0i459abz8wBcuCRaiME6mAw6/UHbMy1LUHDPmZu5pdGDydVyalmktrk8AzeKEkDJwMEoIIYQQUhDOGS0PB6NThhnQPtjQV3Vihceilo5bTnhSLVnK6CSqg53wq2JTSCfxfE18/7bmD/ekHnVsbQ3WzOu4pFOo7hMyLnAwSgghhBBSkEl/OB0F9KYnhBBCCCGNQWV02kg9wVnzyme2UWF/xoEqznfCn5yn0cEpYlJUkVGZ5ofXs7XnZ553QfM8qZseP2KloTJKCCGEEEIag8rolDGg4tjUUNOpyaGWTooiVCsTco2KKJ+/fHmQ73u/r+1adXcq54s7L4iXNXSNtX2+o22uss9vu1287e/uu7fSftbB0euXlm7DVxGtA6uiSjWUjBB605eHyighhBBCCGkMKqPThmteqEvFc0a8dqt/U6egOs73T++7Ol7e9aQDRtGbUlz3qpUD22xqueset1khXbHrlgCAnkXa8P3cRvV6sWraLzt50fYAgOMfvLtMN2vhb1OKaJa4U0Vge79c9sXDOLkUUQbmb4ZZRtaQTRPqcMAMTOXhYJQQQgghpCA005eHZnpCCCGEENIYVEanFYdZofFsS5anzIE+NWwWyXuNbCbfyGTfRnP9r15zKwCj38Y9kU7o4GNcA5uD0zhMz4j6bT6Vd8N+98/JfR5p83yv1xkom1QGze71h3FKb8tyVnKHoqKkRcpDZbQ8VEYJIYQQQkhjUBmdNipQRJ2KV04lqEr1rHElrsFzr4tIHVSLOhgppIDfZ+cXR90+sO0ZF+xSonf5+MoecwAAXUO57G4KdDNTzYzPObw/HeORvdtFoixYTm4z1dBeN1j+1OY79beF9T/46B2FzyUvy9Y/Pji2oQT6OPS0xemnCmclqqDNEzkzTZoj06RbQEZBK5RREVkmIprxWufYvyMibxGRn4rIahF5SESuFZH3icicUZ4LIYQQQgjxp23K6EYADwwpe9i2UURmA7gIwIvCTRsAdAE8LXy9UkQOVtWhg1lCCCGEkCJMls7bDG0bjP6vqj4/5z6fQDAQfQzA2wD8DwIrzeEAzgSwP4BTABxdXTeniCq/ZU04RjXt6JQ6vs00f9t7rhkoX/LZZ9TbMYPrX39LcGzz/nhkGjJxOTW52vj5S/um6v0v2mmgXlm+tq/hTNTtDfSnF/ZbDA+ETtRvR/9txNmZDBtxLzbd97dF0wQ+Nrsfd/WfN/7J6xh5OCY0zQP2r3F0ZXLnerdsK+q4VEXu+ax9fY5x2Ib+dJHL5gxOJyF25urMwLautGVyBxknWmGmL4qILAZwfLj6flU9U1W7GnAJgDeFZa8Vkac000tCCCGETCo9rfY1jbRNGc3LywHMBbAGwKnpQlX9uoj8DsCTALwOwK9H2732UyqM05jmrW8idJX1evSGq48r3/XLeHnJv+9XeX9ueMPv3H1LYQ3dZDo1VfADevWRdwHoO/10u33VRUM1sdc1nI/CZbPepo2zEmUbN8weOI7prNSJFV2/E7A5KQ1mYDKUV8s9jkTYyBkKAJZjSdD/6NxNJTWqb7aRKjO1qG6qjrmtY9yzUeWOt1FllqUqwkSZ9Q/dsDMA4Ltz/mzZg8zT/rDB9hma0fB7SYWU5GDcB6N/Fb5fqaqPDalzGYLB6MF5Gl602a0ZNZYDAObPPSFPs4QQQsjUsXr9B5vuQm3Yoo2QfLTNTL+PiNwgIo+GHvHXi8hnRWTpkPp7h+83ONq8MXzfS0T4iSGEEEIIaRFtU0a3BbANgAcBbAVgn/D1VhF5s6qek6q/Q/h+p6PNqGx++Hqouu6OH9u+5zAAwL3/+t3RHdRiFh8wDftkXWojng4+edszM3r88Z3XpsoMc21ocjavVTrupdnHyMytPfM5NNzB2BQ54UR7Zp5TWJw39qjLwcycGuBrUE5PJzDX42xLnb75MHImMvstOSdtpR2XusZ5R02ZZv1u12bqz3XIwT5klMf3sdxhBkibaX2zLbnbHMQWUzSved67/hj87LSFaNrHtJvrp3WeZ5W0RRm9E8CHAewLYDNV3QbBwPFwBMrmPABnishzU/ttEb4/6mj7EWN5fjXdJYQQQgghVdAKZVRVL0Mwt9Pcth7ApSLyEwDXAHgCgE8CeNYo+vTgY8NmBqQ5LV7aZcFx9XSmBrZ776G1tHv3Cd+vpd26sSp/dai1LZFdkgqiRTcLH1PTCukw6nZYszlQRdts6mc0IcdUQXu9wWfvKIxTz3GGPYuwYyqZaccltTk3WZVrS7up96F9yiivgyrEn7QSWeQ8qggPNdAPGSx7/kYja1a47crZLiNctezRXTS07OaZB0fWjwUaZi6zXOdom49j3MK5J1q3p7MxrVu/vFA/m4TCaHnaoowORVXXAIi8hA4SkW2N4igQ/jxHE5sbywx8TwghhBDSIlqhjHrws/BdACwFcF+4fieAhQB2dOwbla1T1ameLzoKtl8eBDi4+xM/GCjzVR+dtDhklA2XYthE+Ks4OH23pmMbzXody1Q8U9fDNmc0qYLatiXV0sRcUNu2ovNDE/N2g/dormjPopqa6mq8DYNqqfWYYb02qy9554oWDeOUPGbx8E1xmQyW9eeYDtZ/zsbATeFHs+8a2q8y7NndOl4eVJH760/sLgQA3DKzupZ+LNK5A8edCT+HNoXURTR3FJjc+aOcM1qe1iujGUSe8vs46kQe97+tuS+EEEIImTK04tc0Mi6D0QON5ZXGcjRB8TkistmQfaPJkZdX3SlCCCGEEFKOxs30IiKqw41UIrIVgA+Eq1er6r1G8QUAPoPAVP9mAJ9P7XsEgD0QPGycW2G3Y8bJaWmUbP9Pzx/YturjP+yvuMySLXHyGRkVhvjxxjRtW5bS4Z7MfOuuu2PNW+8ytRr1085HibZsJvawPGGm7yTDN5mOQzaHp37Z0C5mkjbd2zMwmfUxsC2dw97X7FeX0dNtYs85tSFvfc8wTq4yc5uved7ZJ0SObgEHbVpsHEsT70F9DGyLlqOPh63M935G9XfvLhhsQwbr2fpou4/Rtk2GOd0ZvsliunfVn1Ropi9PG5TR3UTkKhE5VkR2jTaKyBwReSGAnyDIoNQDkEjhoKqrAJwcrn5aRF4vIjPh/i8CcEZYdq6qMhUoIYQQQkjLaFwZDTkwfEFEHkPgJb8VgCix9CMA3qaqV1j2/ScE8UlfBOAsAKeJSBd9L/qfA3hbFZ2kCloSPj16460Ox4qhsW9dzkk1kHBSipTO8FzEkA7jehlB7GN1shO8z8z0r8XhP48ukqk/bSjVfwD49BaBj2TkwJQIem8JhB/lmDe3pUM6mekFY7XUOKbb4clSP95W75cwS9lLl+etD7jVTKeC6vha9BLLOnCc2KnJofI3QV33s2Oq+6lznjE+m3mdmSYVXoXytEEZvRvA3wP4KoCbEQw8F4Tv1wD4FIC9VfW/bTur6kYARyAYcF4FYD2Cz8Z1AN4P4Nn0oieEEEIIaSeNK6Oq+iiAz4Wvom30AJwSvgghhBBCRsJkBqwaLY0PRsn0sPifnxcvr/rYDxNlk+C05IwbOsLz6zvjqPF/WBaXuPuTbsOVt34Y0TFipyNrJiOLk9JAHwyHJGPfqEuqM0a/Q9P9TFDz0J9sgbr5x4eDrDyfmLtL2B/DjBnFHrXEFDXvSzfluGQ6RLhieNpM8r747JNlfnSVV+G45GrL1f+sY6cdkkg2Po5MwGBWpmlyZCLF4WCUEEIIIaQgHG6Xh4NRD+i4VD2RSnrXR65suCfTiRkiyaZ0jio7lC3MUrRFOmaMGktYplAtnTF+xZ57+fB83nXzT+tvH9j2QV0CIOlw1E2FcTKXbdmWYhW0pr94vuqqS+GqItuST5mtvIowTsmwTIOqaeTEM0mDjiaUyygbk5mJaVbYj3SOejJdcDBKCCGEEFIQTvcoTxu86QkhhBBCyJRCZXQINM23jBGZjYmdpHk8ePPOypR2ZAL8pITOoLtSx9ix1+tY6rWLyIHJ5awUlKcyMBn11bYNyW1Ws35iW96sSUXLqnNaymqvaExRc9+iJmprZqUh5eNOFHPUFmPVloFpGhlZ5rwJhsooIYQQQsiYIyKLReRkEfmDiDwmIneLyDdE5JCC7W0nIm8VkfOMNh8Wkd+KyOdF5AlV9Z3KKBkPoqfymhTSSPkbleNOm4gUS7vSOZhb3dmWMzyP0aoj3JOVUP2URBL5YNuBl+zg2cjo+fTMrQCA92xaGm+zOTANKJ0WETlL/fTBpq6667trjcpxqYowTr51qtTZJ0khJcNpg21GRJ4C4AoA24Sb1gLYFsCLARwuIstV9ZM5m70TyXHiOgBzAOwZvo4VkTep6rmlOg8qo4QQQgghY4uIzANwMYKB6LUA9lXVBQAWAfg3BKrCCSJyWM6mZwG4EsAbAeygqlsiSLX+bARZLjcDcFY4EC4FlVEydfSVwAwVtB8h3q/dcVBXU4Hlgfz9jdVPSyB8l3KdyEOfcx6p7doe+PWd/TrcAj4z69Z4+e82PH6gPK2IuuaH2iiTh95X1WkywL1JlfnnncdJfF7LU/fPQlvyhkzjPNIWnOlbAeyGQLk8QlXvAABVXQvgvSKyO4CXAjgRwGU52n2eqibiL6pqF8BPwoHt9QAeB+DdAI4pcwJURgkhhBBCCtKr+FWAo8P3c6KBaIqTwvf9RGQP30bTA9FU2b0ALg1Xn+Hb5jA4GCWEEEIIGUNEZEv0B4PfGVLtKgBrwuVCzkxDuD98n3HW8oBmejLZmObANpvPUyQzJBXr90B+eZQwyVtyq9t36Bemj2QeOza7O5yaEmb9cF+Xg9S40LWcQloNsTkr2cp9HZl86tV1ZavMtmTiMuf7ThvIO70gMu+XcUwaB6emjvG9TPfXVTat+KqZX8Br6jj8Xuj/3N5gq6CqPRG5GcABAPau8NjPC9+vL9sQlVFCCCGEkPHEDCdyp6NeVFZJ+BEReQmAZ4arZ5Rtj8ooGS8cIZ5sSlrlh/d1fkoTq371q7NF+2jLV+/rkNQPhG/UtzhLxe1blM7YqWkmTtTebwoFr3sL8VEps0Iwudroq3d+/bCXVZeH3qSo41LdAe5teeh9qVsbpPrYfhq+Q1sYy4866j0Svs8ve0AR2QnAqeHqxar67bJtUhklhBBCCCGZiMh8ABch8KK/DcCxVbRLZZQQQgghpCC+c0bfii971Tsl39zSh43leQAeGlJv8/B9XZ7GTURkMwBfR2CevxfAX6vqfUXbM+FglDRKIs5kyCSYYosSOR3lznWc11HL4mjkmtqQdBwanpXJ5XiV1/lILQb+2IQ/xnQ96rhM87byZK50v/bKlgXl1ZvzM039I3Jcyp/dqpgzFCElMeeJ7gjg5iH1dgzf7ypyEBGZA+B8AAcDWA3gMFUddqzc0ExPCCGEEFIQrfhfTm5C/9lpH1sFEekAiOKL3pj3ACIyC8C5AA5HoKy+SFWvy9uOCw5GSeuQjloV02Ql7b+8G07Wj45jHktE41euNofUz9VWAfK271s/vi7OczNfg+262rDWT20z74XtNa6ox8sWCNssj+hBhyps9vqD7aXbGtaeKyC3q0xF49dgH91/gJ3HlOHOS8lrV+yckvWGt1FF/UmmA4lfpHpU9SEA14Srhw6pdiCABeHy5XnaDweyZwI4CoGD1JGq+tMCXXXCwSghhBBCSEFakIHpnPD9aBGxhW56b/j+izymdRERBF7zrwOwAcBRqvr9Yl10w8EoaS025dJ73yrVMzFedZBT5c2rDpoKZhXtuurY2hhQSI1Xlvo5icqozx8ju1qqltfwfW3HtPdnUMWztdEvG65mZv1BLayuOo7pa9509ls0frmoS/EsMQghBABOQeDZviWAS0RkbyDIziQin0agagLA8vSOIqLh6yOWdj+LwFt+E4BXVRHCaRh0YCKEEEIIKUjTj8aq+mgYhP5yAPsBuEFE1iKIKdpB0MXlqnqZb5sisiuA46NDADhFRE5x9GFx0f4DHIwSQgghhIw1qvorEdkXwAcBvBjATghyx18N4LOqmmuuKJKW89kAtq+ko0PgYJQ0wqqP/3B4oSNkkDXskyMrk5W89W1NOPrjmwkqzs9eU7Yob3yuhyV0VFa4p/T5WcM+ZbY7eficXZYp2JXLflidrGP7tuFbXke2JcCdccnX1O3OeZ+N7ye0yq82HaLaS1umWKjqKgRq5vFZdY19rJ9SVV2J+ianDcA5o4QQQgghpDGojA7h9jWnxcu7LDiuwZ74cd9nBqeCbPuewxroyXDu/sQPBrZZH8riKOyWIh+FFLCqbE6V0tJua5TLiJyKbtEc9ZltOPphOklFKqntOtqTHTg60pZ7UAH5g6MPp4wimrcf4xTg3jw3Zz2Heut7faquR8YPlxWgWIPVNjcOcDBKCCGEEFKQtpjpxxma6QkhhBBCSGNQGSWN4jSFm5tSZgtXDvRacUwh8DKjm+Zpz37nNrd79CNr+oKX85GjfrAtan/wmLZjp0331qkBE0AVKootjmhWnaCeLU6nrd5wijotBccqaOpv2GnJx8RehRm+QCrIWvpB8kFltDxURgkhhBBCSGNQGR3CODgtmUTOSqYjU5NOTfeceMXANqv62fN0NHIpkjYs6uCAemdzeLIogK6++Sq0rXOGsmG5Hnnr265VWiFNNOF5bW1c//pbAAD7/vcTs/vaMEevX5qrvu/HPG/4pvxt5QsxVUWZTyal4e3WqyxWoTlSuZw8qlC0px0qo4QQQgghpDGojBJCCCGEFIRzRsvDweiUEZnu6zLX3/NJS8YxV8zPyBTrG9/TYq7P6+BjbT93FqfBfgy0ZVbLaZ63xeusApfJ3F5/eIzQrNijA05Qlktgy9jUL/O7ZpG5Hmifyd5mnq/SjO7bpq/jUuHjtyzbUub0gpIOcVmm9rpn49DUTyYNDkYJIYQQQgrCOaPl4WB0SrE5N0VkKoyp753LMWkYA4qbJeSR06nJEvbJN/e5L3mzMhXNeJQ4z3ihxY5OLhxOTfYQUkb1dPiuDPVqn7OeVKyPI8Sl3tkm7Oc197muUJkwTnX9cS2qZpZxWiqqKNetPmaqwmM0wJkxfpC7Y9Rv0h44GCWEEEIIKQjnjJaHg1EyQOUB5fPOx8w7j9R2SI95jVbl1TPcU79NY9khCFgDvkfS2CiD9lvwmUNrDdmU9TlxtJFuKyhPl/n1vy28Nmf4pipV0GS7zSl6VQSgz/o6FFVXy+ShL6qgjpO6WQaqoaQsHIwSQgghhBSk8llVUzi2Z5xRQgghhBDSGK1QRkVkVwBHATgEwFMBbA9gA4A/AvgWgJNV9S7LfksA3OpxiP1V9ZrKOtxmbKaoqh/bUmGNnCGbgL7dLadzi/XQVpNysj+jwMeRCQA0simOq0OSBe9wT3Hh8GkazgxMlqb2PrO9Tkvnzg1+ivKa623kD7uU38ycNnfbTNxZZveiIZ186xR1XMrst6OMZneSF34uytP4YFREdgGwEsnZd2sBbAHgKeHrLSLyclX9vqOpux1lG8v2kxBCCCGEVE/jg1EAM+H7NwGsAHC5qj4oInMQKKX/D8BSABeJyB6qusrWiKouHkVnx5IS4Y1qw8eZyKauOts0ln3CPbny1g+r51QAk8dujCrvcU7nM7ezl/tz6ArltNeKPbyO3wYihRQAXlOBSuqi7jzqVeDt1OQZ4D59zmUC3FepaGV9Rdz3qqAC3JKf82mH3vTlacNg9EEAT1fVX5kbVXUDgG+JyIsAXAtgKwBvBfDR0XeREEIIIWQQBr0vT+MOTKq6Jj0QTZXfBOCqcPUZo+kVIYQQQggZBW1QRn24P3yfcdYi2aRNVhm2pbxmaau52xI31HlMjzac2ZmGtZs+F4dDVaJdV+xRwz7Tr2875kARdj3pAK9+5+X3b/9Nof3yXtOs+l5OTSZtmULSUnxNym7nnDLHL99u7M/nabLOG1O0aLalvPXHxWFlXPo57tBMX57WD0ZFZBaAvwxXr3fU+ymAfQDMBrAKwE8A/Jeq/rjIcdetX+5Vb/7cE4o0TwghhEwN9274x6a7QFpM6wejAP4PgMUIHj7OdNQ7CMCacHlJ+DpaRE4G8G7VccvnMiI8nZvyZmXKHe6p6uxMeZ2JCjo1SeKcMFg/2i2st/OJB3l2qDhP+M8nAwBueevQZ7f8eDof5VZIhx1jyiirYLXlyuWdO2d+JKoM4+RyWrK1l53fPud5lQg7RcYPKtDlaXzOqAsReQqAE8PVz6vqjakqjwH4AoDnAthSVRcC2BzB3NJvhHWOB/DB+ntLCCGEEELy0trBqIjsAOAiAPMA/ALA+9N1VHWVqv4fVf2Rqq4Lt6mq/lJVjwRwXlh1uYgsHE3PCSGEEDItqFT7mkZaaaYXka0BXIYgvugtAA5X1ccKNPV+AK9EEED/EABf891xKueC5jWZx/sZy77WirS53deZyNUNq/PRYN+8HWt8nJosMUhNO9xOJzwrs9918cRT9h3Y9ru3+JnunZmximZUyjnVYxKo23xXV+tVmJKLtpGdPSnfWVfpuGStX+FHeZTm3lGblreb8+n4mF0ZvMubwjJffw0yWbRuMCoiCwB8B8C+AP4E4AWq6squNBRVvVVE7gWwHYDHV9dLQgghhBDOGa2CVg1GRWQLAJcCeCYCj/gXqOqfmujL7WtOi5d3WXBcE11ojixnlSrCPfkc3+VMlDc7k6VvmUqdTz9cYZ8mnQqcmkymRS0tSplPVZNOM+axI1UzutWZ2ZMqdFyytVWlY1IV9UfVFiFtozWDURGZh8Dp6FkI4oq+QFVvKdnmUgSqKADc6qpLCCGEEJIXZmAqTysGo2Ee+gsA/BWA1QAOU9UbPPaTjJBN0cTPRwFcUbR/kUo6Dgrptu/+63j5vs9+J1hwKXW+4XaKKqRAX4l0hXuqMCD+0HrpvpnqbcF5pLawTzt+7NlZp9EYTzo1mEdaydzRikmrpdOolLblT9pFc2+rpd1DNu48tKwXv1tUc0cgfJsaWsU80bpVUA5gCOnTuDe9iMwAOAfACwE8BOBvVPWXnrv/QEQ+KCL7hu1AAp4uIhcCeE1Y71Oq+kDlnSeEEELIVNOr+DWNtEEZ/UsALw+XZwO4SGSoInK7qu5vrO+GQP08AcBGEVmLIM7oPKPO5wB8rNIeE0IIIYSQSmjDYNRUZzcLX8NIh3d6H4BDARyAIEvT1gA2ALgZQTrQU1X1Z9V1dbyITPb3nfzt4ZVsJvycucSLhOzxckjyzA4VY2nDGR7KEfYJ8MupbtbY4aPPye5jS4jM9UA/U5NrwktmmC2PcE95Tf2T4AjW5BnkVVguqMk0b3L57D8DAA7euBOAZB+zzPNpfM3zVTguDRynhTNI6ODUHLz25Wl8MKqqP0Dyb3qefc9DP7A9IYQQQggZMxofjJL6EUe4E6talVcttQR+T6iKefPE20jnhM8ZEN/cJ69Tk8mAWjpB+dRts2NsaqlXQHxgQD4qcs9IfnwV0a/NXVlnN5xcMfuOgW3P2bjD0PoJBbXm71wdSmrQRvl+jAoqffng1SoPB6OEEEIIIQWp+wFpGmjcm54QQgghhEwvVEanHJsJP9N07+GkZHUEcsQezYob6jxWzja8zPVGHxPFE+BUkweX6Z5m9+aY5PAveTMrlXFaquvbTDP3dMH7XR4qo4QQQgghpDGojE4DrvksjlA8iWou1TFvCCZgME+8KzuT7Vi+x3EoutnhipJ9nFRcaq8tvFWklprOTXnDN+Wtf9OxvwUA7PnFvbzqN8nL1y+Jl9umYDbptJTFj2bfBQD4S8ORqW2KqO3jWo1zU85sTzkNEVTu6oVXtzxURgkhhBBCSGNQGZ0CnIHcHcHsE9V888r7hnsqiyNffKKPJlXMI40P4N3T9uMKWO8Ib2XOJ41U0j1O26fy7gF9ZTR6B9qnkpqKaNtosyKa5iehQgoAB21anFm/LmWyLjWROeknDyrP5aEySgghhBBCGoPKKCGEEEJIQaiMloeD0SnDy1klw3RfNK+8M9xTljNMFeGecuyXue8kRjLK6ehmYua6r4PIJG+a6cl04xoAjDKMk4u2ObAR0lY4GCWEEEIIKQgfOsrDweg04Kl4OXOxOxyGMhVMH6cmR0D8xDE823fiqbIWVVfHDVsor4j43I06TzzlyXV3aSg2p6Wj1u8WL18w97aR9MM8JqmP3CGPKqhvVVUlu05dplqagMk0wMEoIYQQQkhBGCGhPPSmJ4QQQgghjUFldBpwPXL0MpyVbDFCU2bx1uQozxt71JGdKdFsW86vJlxm+nEjMp/XZa53medt6ohU4OlG1cVNlhl72q4ezfqjh9e8PFRGCSGEEEJIY1AZnQJkpu/rN5AFqWNLtuwblsnhOJQz3FNSeY3qGJt8wkllMC0OSXmJPh/WPPTh++M//7TRdSgnpgoaKZdtcTCiqlkfZdSotipZTfSrrddinOhNkHWpKTgYJYQQQggpCEM7lYdmekIIIYQQ0hhURnPw5we/GC/vvOjYBnvix4NfvChYSDgkJeuo+UgXm8CNbb1BU7zVtB6VVZA9qRIaiD266uM/BAAs/tDz/I7ZICvf/Yv+SjRlYsZ01Arel5789FF2qzSRyb4tZnpSDXlNyXmNpjSP+zOu/a4TXpPyUBklhBBCCCGNQWXUA+mFY3btj93//MAZAICdtz6miS4NZfVZF/RXQpVPu0aFdFgm43EkVkkTqmbUlsv5CINlWeGQBvrhyFsPDGRlaiJvvS+RQgq0TyW97T3XDGyzPdMLfbxIC6hSEc3dls230zPzkm0OYR3ObOOgyI1DH8tCR8XyUBklhBBCCCGNQWW0IDLK+Y95MENMhI8aiZ5GgqTl0T1SSa3zSG3HyFtWIBzTyBmHPpbBoiJPUtD7aYNevIQ0zzSov3VDZZQQQgghhDQGlVFCCCGEkIJQGS0PB6NDiJ2WgNhxyTTNS2gfu+uuFf164bYddlpWb+cM1pz3lfDYkbOSYVruhueQCNWUcj4y64fmWqtTk5mpKWUbtDofmeUuhyGLWd8VOio3lqxPo2TVx36YWF/8z6NzaLr9H382sC26B0qbyNhAU3x9cBBBSDvgYJQQQgghpCB8qCkPB6NFsXz2IrX07pUrAADbL1lWy6HXXHhufyVSNiNnJUP90yhyeUJaSamDRZTDdLgnl+I5rDxvIPxUvnpr6Ki2OB85fpfu+siV8fIOH3luLYe//f1Xhf0Yfg1sTkt1hboi+alCDTXbODJMAnBxmBSgzRywaft42eeP/LQMAybRn5KQCA5GCSGEEEIKQmW0PJw5RgghhBBCGoPKaA7EZjsztkWWTwnN1/fe/KW4rLMxLNto1O9G20L7i5Epqb/NeF6Itm00nyE6yX1N86skqwDoJx1POTIBJWOPxvULOh814GiUmSWqQmzX464P/2iwXtQPS39sbfTr244ZLbhjiqolaxYZLVU6Kbnaisz1QPtM9qZ5PqIT/oiVUZ6qyLxEiAtOoSgPlVFCCCGEENIYVEY9sGVbilTShFoaK1FhWQmhKXo6tz5w2ZROTa2j78xkzU0ftdGt4JHOU9UcpRJJDDLuD52ZmqFqJyUflOr3VMO5jfXA61oeDkYJIYQQQgrCwWh5OBjNg+fnzTq3tApiNdOzfjyJ1dbGoPTan+9pacoVCD/rfF356l272YLpp0I85WuwWD/GAvOUottum7/reQ04f7Q+RhXEPksFPXzDron1b875U53dSXDQpsVDy9r4h72JPrXxOhBSFxyMEkIIIYQUpMsHh9LQgYkQQgghhDQGldEhLNz8D1j9yO7DK0QOTGYkpV6xpyMNzani60xkC98kMliGKHyTcayBEFANm6zzZmKyEJmUE043Q9rd/N2XJ9Yf+cwLCh93gHF6OG4glNaoWLd+Oc4y1t+AsxvrS0TVpvmi7dn20/CD+6INu8TbLp1ze8EjuPmLlHne9yvTMX6nrugemyh73szpZbtFSCk4paI8VEYJIYQQQkhjUBkdFUWlDIvS6U2kDroUVzOfexVhnkKszkfjRgWqbRvIvBcOR5coz/0unzqo8n7VwVHrd0uook0zqmD2JkXDN5ntH2aopHG7ofpjfoTibXEbljBhxra43pg7yFWthFFZG294/8ozMcqoiCwWkZNF5A8i8piI3C0i3xCRQ5ruGyGEEEIIsTMRg1EReQqA6wH8PYDHA1gPYFsALwbwXRH5QIPdI4QQQsiE0pVepa+i1CXKichWIvIJEfmtiDwiIveLyOUi8ooy7ZqMvZleROYBuBjANgCuBfB6Vb1BRLYC8M8A/gHACSLyS1W9rMGulsfH6SjL7O5qowprdBWxPCcoHujYTlFIEZnrgfaZ7I8y8q0TOy7HpSaxhMdNOCuNk/lzVPFjCbERinJXIBgLAcBa9EW5w0Vkuap+skC7OwO4EsDScNM6AFsBOBjAwSLyn6r6jrL9nwRl9K0AdkNwgY5Q1RsAQFXXqup7AVyE4DfvxMZ6SAghhJCJpAut9JUXiyi3r6ouALAIwL8hGAOdICKH5WxXAJyPYCC6EsBfquqWALYE8I8InsHeLiLH5e50ikkYjB4dvp+jqndYyk8K3/cTkT1G1Kd66ajxQvjSpCpaAulo/KoLEZ3OLD8q/Ve0SSV+VYKg8Yhd00zPeI07avybJHrQxlTXJo9NJpa6RLmXADgQwc/Zy1T1f8N2H1PVkwD8R1jvYyIyp8wJjLWZXkS2BPCMcPU7Q6pdBWANgAUADgFws2/7Czf/Q2adh+9+km9zhBBCyFRy/4b3N92F2mhBBiYfUe6lCEU5VfUdB0Xtfk9Vr7OU/yuA4wEsRmC2/7Z3j1OMuzK6F/oa0A22CqraQ38AuvcoOkUIIYQQUjc5RTkgEOV8+StXu+HANxp7HZyj3QHGWhkFsIOxfKejXlS2g6NOIbbY/ndVN0lGyObv+V7TXRhDLm66Awmy4oqeFT/cTwl1izSNi0BJfth9c9NdIFPO6vWNBuzxEuVE5GYAB8BTlBORx6HvDGVtN+RGAPv6tjuMcVdGtzCWH3XUeyR8n19jXwghhBBCRkldotxIxb5xH4wSQgghhEwrdYlyIxX7xn0w+rCxPM9Rb/PwfV2NfSGEEEIIITkZ9zmjpnS8I4Z7yu8Yvt81rCGtLK4OIYQQQiaVlo0X0qLcQ0Pq5RXlRir2jbsyehP60+n3sVUQkQ6AKL7ojaPoFCGEEELICEiLcsPIFOVG1K6VsR6MqupDAK4JVw8dUu1ABDFGAeDy2jtFCCGEEDIaahHlVPVeAPe52g2JvOhLiX1jPRgNOSd8P1pEbN5c7w3ff5Ej0CshhBBCSKupWZT7vqtdEdkJ/YFqKbFvEgajpwC4DUGu1EtEZG8gCAQrIp8GcFRYb3lD/SOEEEIIqYu6RLmo3cNE5KmW8vcgiHF6F/oD10KM/WBUVR9FkD/1fgD7AbhBRNYAWA3gfQjk6w+q6mWNdZIQQgghpB4Ki3IiouHrI5Z2vw7gZwjGiheKyEHhPnNF5B8AvCus92FV3VDmBMbdmx4AoKq/EpF9AXwQwIsB7IRgcHo1gM+qKueKEkIIIWTiUNVHReQlCEzlkSi3FkHszw4CUW55XlFOVVVEXgHgSgBLAfxURNYB2Az98eN/qeppZc9h7JXRCFVdparHq+ruqrqZqj5OVV/sOxAVkcUicrKI/EFEHhORu0XkGyKSJ48raQARWWY83Q17DQ07ISIdEXmLiPxURFaLyEMicq2IvE9E5ozyXKaV8An+SBH5uIh8S0TuM+7dnh77l7qHIvJMEfmyiNwZfv//JCKni8gTqjlDUuYee3y/oz+arjZ4j8nEoqq/QpCW8z8A/BHAXASi3DcBHKqqnyzY7p8BPA3ACQicpWYhCB/1fQCvUtW3l+48AFFtWaLhBhCRpwC4Av08rLYnikI3ktSPiCwDcAaAjQAeGFLtYVXd3bLvbAAXAXhRuGkDgC76cdV+DuBgVWXChBoRkZcCuHBI8V6qepNj31L3UETeCOB0BD+yiuD7H032fxjAkap6he+5EDsl73H0h+o+BPfWxptV9ZIh+/MeE9JiJkYZLYqIzANwMYKB6LUA9lXVBQAWAfg3BJNzTxCRw5rrJfHkf1V18ZDXwEA05BMIBjGPAViGIIDvFgCOQDCw3R/BfBxSP/cAuBTARwG8Jcd+he9h+CB6GoJBytkAtlfVhQCWAPhu2M7XRGS7vCdDrBS9xxH7O77jwwaivMeEtJypV0ZF5F0APosge8CeqnpHqvxCAC8F8EtVfcbIO0gyMZTRH6rq83PstxjASgTmjONV9T9S5S9BoLgpgKep6q+r6TFJIyIzqto11pcAuDVcHaqalb2HInIRAgfIawAclOrDfASx83YB8BlV/YfiZ0iK3uOwbvSHaqmqrsx53IvAe0xIq5l6ZRTA0eH7OemBaMhJ4ft+IrKHpZyMLy9HMIhZA+DUdKGqfh3A7xCo468bbdemC3OAkJPC91BEFqJv2v9Mug+hWf+/wtXXikibUgCOHSXucWF4jwkZD6Z6MCoiWwKI1M7vDKl2FYI/dABAZ6bJ4q/C9ytV9bEhdSLvw4NH0B+SnzL38NkAZqfqpIl+F3YAsFehHpIm4T0mZAyY6sEogh+e6En4BlsFVe0BiILE7m2rQ1rDPiJyg4g8GnpTXy8inxWRpUPqR/fTeu9DohRne1E1aSVl7mG07ypVvT9jX7M+aY6visiDIrJeRP4sIl8TkcMd9XmPCRkDpn0wamYquNNRLyqzZTYg7WFbBA8YjyCIg7YPgqC8N4iIzcwe3U+fez8/fJF2UeYeZu4bJtVYnapPmmN/ADMIImfshCCY9yUi8tUhIbx4jwkZA6Z9MLqFsfyoo94j4TsHI+3kTgAfRhBjbTNV3QbBvTocgeoxD8CZIvLc1H7R/fe59wDvfxspcw999jX35/1vjjMBvBDAIlXdSlXnI3jwPCMsfyWAz1v24z0mZAyY9sEomQBU9TJV/Ziq3hClJFPV9ap6KYBnAfg9grAujBVLyBiiqstU9TuqutrYdpOqvgl9J9M308mUkPFk2gejDxvL84bWCuIWAkH4JzJGqOoaBJkjAOAgEdnWKI7uv8+9B3j/20iZe+izr7k/7387+SgC5VMQpIM24T0mZAyY9sGoOY9oR0e9qOyuGvtC6uNn4bsgyK8bEd1/n3u/TlUfqrpjpDRl7mHmvmFSjIXhKr//LURVHwZwfbj6+FQx7zEhY8C0D0ZvQhAMGwicXQYQkQ6AyPRzo60OGVui+2m99yGRd+1va+4LKUaZexjtu1hEtoEd07ua3//xg/eYkDFgqgejoUpyTbh66JBqB6Kfw/jy2jtF6uBAY3mlsfz98P05IrLZkH2jzwXvfTspcw9/jMArGwBeMGTfKA3wneADSSsRkS0QOC8C/YxOEbzHhIwBUz0YDTknfD9aRGxhPd4bvv9CVW+2lJMGyYr9KSJbAfhAuHq1qt5rFF8AYD0CE92bLfsegUAVVwDnVtFfUjmF72E4n/jScPU9oRXE3HcLAG8LV8/Vac+d3BAe8X0/hGBOqKJ/PwHwHhMyLnAwCpwC4DYAWyKIV7c3EGRnEpFPI4hjBwDLG+ofcbObiFwlIseKyK7RRhGZIyIvBPATAE8C0APwQXNHVV0F4ORw9dMi8noRmQn3fxH6YWPOZV76+hGRbaMXgEVG0UKzzBxQVHAPP4xAOTsAwIrIwS38LF0AYFcEMSg/VdmJTjFF7jGCQPf/IiLPNGOJisgeInIagPeHm85UVZuZnfeYkJYjfBAEROSpCEx40ZyitQjizXUQPG0vV1WGBWohIrIESdPcYwg8aLdCPw3gIwDepqr/bdl/NoCL0M9fvR5AF33v2p8DOITOS/UjIr4/RktVdaWxX6l7KCJvBHA6gvBfiuD7H03NeRjAkap6hfeJkKEUucci8gMAzwu3dxGkZ56LZJzo8wH8raquH3Jc3mNCWgwHoyEishiBcvZiBJk91gK4GsBnVZXzBVtK6An7ZgQ5qJ8KYDsEA9GHAdyC4CHjP1X1NkcbHQDHAViGwJlhJtz3XAD/HsUuJfVSdDAa7lvqHorIMwG8D8BzAWwN4G4A3wVwoqr+3v8siIuCg9HDEPwuHwRgZwT3pwdgFYCrAKxQ1WF5581j8x4T0lI4GCWEEEIIIY3BOaOEEEIIIaQxOBglhBBCCCGNwcEoIYQQQghpDA5GCSGEEEJIY3AwSgghhBBCGoODUUIIIYQQ0hgcjBJCCCGEkMbgYJQQQgghhDQGB6OEEEIIIaQxOBglhBBCCCGNwcEoIYQQQghpDA5GCSGEEEJIY3AwSgghhBBCGoODUUIIIYQQ0hgcjBJCCCGEkMbgYJQQQgghhDQGB6OEEEIIIaQx/j8e0tiG4d3uEgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 276,
-       "width": 337
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.imshow(results.concentration,origin='lower', interpolation='none', cmap=plt.cm.plasma)\n",
-    "plt.colorbar()\n",
-    "results.formation_factor"
+    "results.tortuosity"
    ]
   }
  ],
@@ -296,7 +394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/examples/simulations/tutorials/using_tortuosity_gdd.ipynb b/examples/simulations/tutorials/using_tortuosity_gdd.ipynb
index 7dbd1ac38..1d2d48511 100644
--- a/examples/simulations/tutorials/using_tortuosity_gdd.ipynb
+++ b/examples/simulations/tutorials/using_tortuosity_gdd.ipynb
@@ -18,16 +18,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jeff\\anaconda3\\envs\\dev\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import porespy as ps\n",
@@ -50,17 +41,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(-0.5, 162.5, 130.5, -0.5)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -68,7 +49,7 @@
      "metadata": {
       "image/png": {
        "height": 463,
-       "width": 572
+       "width": 463
       }
      },
      "output_type": "display_data"
@@ -77,9 +58,9 @@
    "source": [
     "# im = ps.generators.fractal_noise(shape=[100,100,100], seed=1)<0.8\n",
     "np.random.seed(1)\n",
-    "im = ps.generators.blobs(shape=[100,100,100], porosity=0.7)\n",
-    "plt.imshow(ps.visualization.show_3D(im))\n",
-    "plt.axis('off')"
+    "im = ps.generators.blobs(shape=[100, 100, 100], porosity=0.7)\n",
+    "plt.imshow(ps.visualization.sem(~im))\n",
+    "plt.axis('off');"
    ]
   },
   {
@@ -91,44 +72,528 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:18:34] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:18:34]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:18:38] </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.2810e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.2802e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:18:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.2810e+01\u001b[0m vs. \u001b[1;36m-2.2802e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.1896e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.1890e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.1896e+01\u001b[0m vs. \u001b[1;36m-2.1890e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.6630e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.6619e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.6630e+01\u001b[0m vs. \u001b[1;36m-2.6619e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4781e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.4769e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.4781e+01\u001b[0m vs. \u001b[1;36m-2.4769e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:18:39] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:18:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5437e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5431e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5437e+01\u001b[0m vs. \u001b[1;36m-2.5431e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.7020e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.7012e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.7020e+01\u001b[0m vs. \u001b[1;36m-2.7012e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.3387e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.3378e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.3387e+01\u001b[0m vs. \u001b[1;36m-2.3378e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5927e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5921e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5927e+01\u001b[0m vs. \u001b[1;36m-2.5921e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:18:41] </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.6571e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.6564e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:18:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.6571e+01\u001b[0m vs. \u001b[1;36m-2.6564e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5440e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5433e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5440e+01\u001b[0m vs. \u001b[1;36m-2.5433e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5395e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5391e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5395e+01\u001b[0m vs. \u001b[1;36m-2.5391e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:18:42] </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.8140e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.8126e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:18:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.8140e+01\u001b[0m vs. \u001b[1;36m-2.8126e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Max distance transform found: 9.164999961853027\n",
-      "[3 3 3] <= [3,3,3], using 33 as chunk size.\n",
-      "[1.3940749221735982, 1.4540195658662034, 1.4319709358246486]\n"
+      "[1.3925109029722786, 1.3999150512069598, 1.3606684244878253]\n"
      ]
     }
    ],
    "source": [
-    "from porespy.beta import tortuosity_gdd\n",
-    "out = tortuosity_gdd(im, scale_factor=3)\n",
-    "print(out.tau)"
+    "from porespy.beta import tortuosity_bt\n",
+    "out = tortuosity_bt(im, block_size=50)\n",
+    "print(out)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first three results in the returned object are the tortuosity values in the x, y, and z-direction respectively. The following results are time stamps on  However, there is a more useful form of this function."
+    "The three values in the returned list are the tortuosity values in the x, y, and z-direction respectively. However, there is a more useful form of this function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max distance transform found: 9.164999961853027\n",
-      "[3 3 3] <= [3,3,3], using 33 as chunk size.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:19:51] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:19:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:19:56] </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.1896e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.1890e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:19:56]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.1896e+01\u001b[0m vs. \u001b[1;36m-2.1890e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.3387e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.3378e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.3387e+01\u001b[0m vs. \u001b[1;36m-2.3378e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.7020e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.7012e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.7020e+01\u001b[0m vs. \u001b[1;36m-2.7012e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.6571e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.6564e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.6571e+01\u001b[0m vs. \u001b[1;36m-2.6564e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5927e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5921e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5927e+01\u001b[0m vs. \u001b[1;36m-2.5921e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.6630e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.6619e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.6630e+01\u001b[0m vs. \u001b[1;36m-2.6619e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5440e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5433e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5440e+01\u001b[0m vs. \u001b[1;36m-2.5433e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.8140e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.8126e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.8140e+01\u001b[0m vs. \u001b[1;36m-2.8126e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:19:58] </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.2810e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.2802e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:19:58]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.2810e+01\u001b[0m vs. \u001b[1;36m-2.2802e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5437e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5431e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5437e+01\u001b[0m vs. \u001b[1;36m-2.5431e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4781e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.4769e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.4781e+01\u001b[0m vs. \u001b[1;36m-2.4769e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">ERROR   </span> Inlet/outlet rates don't match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.5395e+01</span> vs. <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-2.5391e+01</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:109</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR   \u001b[0m Inlet/outlet rates don't match: \u001b[1;36m2.5395e+01\u001b[0m vs. \u001b[1;36m-2.5391e+01\u001b[0m                          \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m109\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
@@ -151,109 +616,153 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Throat Number</th>\n",
-       "      <th>Tortuosity</th>\n",
-       "      <th>Diffusive Conductance</th>\n",
-       "      <th>Porosity</th>\n",
+       "      <th>eps_orig</th>\n",
+       "      <th>eps_perc</th>\n",
+       "      <th>g</th>\n",
+       "      <th>tau</th>\n",
+       "      <th>volume</th>\n",
+       "      <th>length</th>\n",
+       "      <th>axis</th>\n",
+       "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>0.705176</td>\n",
+       "      <td>0.705176</td>\n",
+       "      <td>25.439633</td>\n",
+       "      <td>1.414264</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.242083</td>\n",
-       "      <td>19.555253</td>\n",
-       "      <td>0.736038</td>\n",
+       "      <td>4.177472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1.404705</td>\n",
-       "      <td>15.727675</td>\n",
-       "      <td>0.669477</td>\n",
+       "      <td>0.721312</td>\n",
+       "      <td>0.721312</td>\n",
+       "      <td>26.571451</td>\n",
+       "      <td>1.385007</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.947818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1.268947</td>\n",
-       "      <td>19.731037</td>\n",
-       "      <td>0.758717</td>\n",
+       "      <td>0.705616</td>\n",
+       "      <td>0.705592</td>\n",
+       "      <td>26.630394</td>\n",
+       "      <td>1.351823</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.038600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.520043</td>\n",
-       "      <td>15.566115</td>\n",
-       "      <td>0.717005</td>\n",
+       "      <td>0.651352</td>\n",
+       "      <td>0.651344</td>\n",
+       "      <td>21.896334</td>\n",
+       "      <td>1.517690</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.931128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1.350561</td>\n",
-       "      <td>18.242258</td>\n",
-       "      <td>0.746584</td>\n",
+       "      <td>0.706072</td>\n",
+       "      <td>0.706024</td>\n",
+       "      <td>25.394748</td>\n",
+       "      <td>1.418468</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.053686</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1.405354</td>\n",
-       "      <td>15.285239</td>\n",
-       "      <td>0.650945</td>\n",
+       "      <td>0.717600</td>\n",
+       "      <td>0.717600</td>\n",
+       "      <td>27.020315</td>\n",
+       "      <td>1.354990</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.961196</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>1.634790</td>\n",
-       "      <td>13.765760</td>\n",
-       "      <td>0.681943</td>\n",
+       "      <td>0.691104</td>\n",
+       "      <td>0.691016</td>\n",
+       "      <td>24.780591</td>\n",
+       "      <td>1.422723</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.012183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>1.388642</td>\n",
-       "      <td>17.317457</td>\n",
-       "      <td>0.728720</td>\n",
+       "      <td>0.679584</td>\n",
+       "      <td>0.679256</td>\n",
+       "      <td>22.810414</td>\n",
+       "      <td>1.519303</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.946066</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>8</td>\n",
-       "      <td>1.528631</td>\n",
-       "      <td>14.278425</td>\n",
-       "      <td>0.661407</td>\n",
+       "      <td>0.695480</td>\n",
+       "      <td>0.695480</td>\n",
+       "      <td>25.926987</td>\n",
+       "      <td>1.368600</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.006884</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>1.481147</td>\n",
-       "      <td>14.914736</td>\n",
-       "      <td>0.669421</td>\n",
+       "      <td>0.683104</td>\n",
+       "      <td>0.683104</td>\n",
+       "      <td>23.386926</td>\n",
+       "      <td>1.490245</td>\n",
+       "      <td>125000</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.937272</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Throat Number  Tortuosity  Diffusive Conductance  Porosity\n",
-       "0              0    1.242083              19.555253  0.736038\n",
-       "1              1    1.404705              15.727675  0.669477\n",
-       "2              2    1.268947              19.731037  0.758717\n",
-       "3              3    1.520043              15.566115  0.717005\n",
-       "4              4    1.350561              18.242258  0.746584\n",
-       "5              5    1.405354              15.285239  0.650945\n",
-       "6              6    1.634790              13.765760  0.681943\n",
-       "7              7    1.388642              17.317457  0.728720\n",
-       "8              8    1.528631              14.278425  0.661407\n",
-       "9              9    1.481147              14.914736  0.669421"
+       "   eps_orig  eps_perc          g       tau  volume  length  axis      time\n",
+       "0  0.705176  0.705176  25.439633  1.414264  125000    50.0     0  4.177472\n",
+       "1  0.721312  0.721312  26.571451  1.385007  125000    50.0     0  3.947818\n",
+       "2  0.705616  0.705592  26.630394  1.351823  125000    50.0     0  4.038600\n",
+       "3  0.651352  0.651344  21.896334  1.517690  125000    50.0     0  3.931128\n",
+       "4  0.706072  0.706024  25.394748  1.418468  125000    50.0     1  2.053686\n",
+       "5  0.717600  0.717600  27.020315  1.354990  125000    50.0     1  3.961196\n",
+       "6  0.691104  0.691016  24.780591  1.422723  125000    50.0     1  2.012183\n",
+       "7  0.679584  0.679256  22.810414  1.519303  125000    50.0     1  1.946066\n",
+       "8  0.695480  0.695480  25.926987  1.368600  125000    50.0     2  4.006884\n",
+       "9  0.683104  0.683104  23.386926  1.490245  125000    50.0     2  3.937272"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from porespy.beta import chunks_to_dataframe\n",
-    "out2 = chunks_to_dataframe(im, scale_factor=3)\n",
+    "from porespy.beta import analyze_blocks\n",
+    "out2 = analyze_blocks(im, block_size=50)\n",
     "out2.iloc[:10,:]"
    ]
   },
@@ -261,44 +770,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ``chunks_to_dataframe`` function returns a DataFrame containing the tortuosity, diffusive conductance, and porosity values of each slice, which can be used to obtain the previous results from OpenPNM.\n",
-    "\n",
-    "Assign the diffusive conductance values to the network and run the simulation."
+    "The ``analyze_blocks`` function returns a `DataFrame` containing the tortuosity, diffusive conductance, and porosity values of each block, which can be used to obtain the tortuosity by using OpenPNM to solve the resistor network as follows.  Start by assigning the diffusive conductance values to the network, then run the simulation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.3940749221735982"
+       "1.3606684244878253"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "net = op.network.Cubic([3,3,3])\n",
+    "net = op.network.Cubic([2, 2, 2])\n",
     "air = op.phase.Phase(network=net)\n",
     "\n",
     "air['throat.diffusive_conductance']=np.array(out2.iloc[:,2]).flatten()\n",
     "\n",
-    "fd=op.algorithms.FickianDiffusion(network=net, phase=air)\n",
+    "fd = op.algorithms.FickianDiffusion(network=net, phase=air)\n",
     "fd.set_value_BC(pores=net.pores('left'), values=1)\n",
     "fd.set_value_BC(pores=net.pores('right'), values=0)\n",
     "fd.run()\n",
     "\n",
     "rate_inlet = fd.rate(pores=net.pores('left'))[0]\n",
     "\n",
-    "# the length of one slice is removed from the total length since the network edge begins\n",
+    "# the length of one block is removed from the total length since the network edge begins\n",
     "# in the center of the first slice and ends in the center of the last slice, so the image\n",
     "# length is decreased\n",
-    "L = im.shape[1] - 33\n",
+    "L = im.shape[1] - 50\n",
     "A = im.shape[0] * im.shape[2]\n",
     "d_eff = rate_inlet * L /(A * (1-0))\n",
     "\n",
@@ -319,16 +826,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[22:22:49] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Found non-percolating regions, were filled to percolate                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">_dns.py:74</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[22:22:49]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Found non-percolating regions, were filled to percolate                              \u001b[2m_dns.py\u001b[0m\u001b[2m:\u001b[0m\u001b[2m74\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/plain": [
-       "1.396708006475956"
+       "1.4041847226215034"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -343,53 +863,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With similar results, the main benefit to using the GDD method is the time save on larger images. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting info from the DataFrame\n",
-    "A graph representing the tortuosity distribution can be created from the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 684,
-       "width": 983
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tau_values = np.array(out2.iloc[:, 1])\n",
-    "# the array of tau values is split up into thirds, where each third describes the throats in\n",
-    "# orthogonal directions of x, y, and z\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=[10,7])\n",
-    "ax.set_title(r\"$\\tau$ Distribution for x Throats\")\n",
-    "ax.hist(tau_values[:len(tau_values)//3], edgecolor='k')\n",
-    "ax.axvline(np.mean(tau_values[:len(tau_values)//3]), color='red', label='Mean', linestyle='--')\n",
-    "ax.axvline(tau_direct, color='lime', label='Direct', linestyle='--')\n",
-    "ax.axvline(tau_gdd, color='yellow', label='GDD', linestyle='--')\n",
-    "\n",
-    "ax.set_xlabel(r'Tau Value')\n",
-    "ax.set_ylabel(r'Relative Frequency')\n",
-    "ax.legend();"
+    "With similar results, the main benefit to using the \"block and tackle\" method is the time save on larger images. "
    ]
   }
  ],
@@ -409,7 +883,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/src/porespy/beta/__init__.py b/src/porespy/beta/__init__.py
index 4c233d41c..ba67b431e 100644
--- a/src/porespy/beta/__init__.py
+++ b/src/porespy/beta/__init__.py
@@ -1,5 +1,5 @@
 from ._dns_tools import *
 from ._drainage2 import *
-from ._gdd import *
+from ._tortuosity_bt_funcs import *
 from ._generators import *
 from ._poly_cylinders import *
diff --git a/src/porespy/beta/_gdd.py b/src/porespy/beta/_gdd.py
deleted file mode 100644
index fef1907b7..000000000
--- a/src/porespy/beta/_gdd.py
+++ /dev/null
@@ -1,361 +0,0 @@
-import time
-
-import dask
-import dask.delayed
-import edt
-import numpy as np
-import openpnm as op
-from pandas import DataFrame
-
-from porespy import settings, simulations
-from porespy.tools import Results
-
-__all__ = ['tortuosity_gdd', 'chunks_to_dataframe']
-settings.loglevel = 50
-
-
-@dask.delayed
-def calc_g(image, axis):
-    r'''Calculates diffusive conductance of an image.
-
-    Parameters
-    ----------
-    image : np.ndarray
-        The binary image to analyze with ``True`` indicating phase of interest.
-    axis : int
-        0 for x-axis, 1 for y-axis, 2 for z-axis.
-    result: int
-        0 for diffusive conductance, 1 for both diffusive conductance
-        and results object from Porespy.
-    '''
-    try:
-        # if tortuosity_fd fails, throat is closed off from whichever axis was specified
-        results = simulations.tortuosity_fd(im=image, axis=axis)
-
-    except Exception:
-        # a is diffusive conductance, b is tortuosity
-        a, b = (0, 99)
-
-        return (a, b)
-
-    L = image.shape[axis]
-    A = np.prod(image.shape)/image.shape[axis]
-
-    return ((results.effective_porosity * A) / (results.tortuosity * L), results)
-
-
-def network_calc(image, chunk_size, network, phase, bc, axis):
-    r'''Calculates the resistor network tortuosity.
-
-    Parameters
-    ----------
-    image : np.ndarray
-        The binary image to analyze with ``True`` indicating phase of interest.
-    chunk_size : np.ndarray
-        Contains the size of a chunk in each direction.
-    bc : tuple
-        Contains the first and second boundary conditions.
-    axis : int
-        The axis to calculate on.
-
-    Returns
-    -------
-    tau : Tortuosity of the network in the given dimension
-    '''
-    fd=op.algorithms.FickianDiffusion(network=network, phase=phase)
-
-    fd.set_value_BC(pores=network.pores(bc[0]), values=1)
-    fd.set_value_BC(pores=network.pores(bc[1]), values=0)
-    fd.run()
-
-    rate_inlet = fd.rate(pores=network.pores(bc[0]))[0]
-    L = image.shape[axis] - chunk_size[axis]
-    A = np.prod(image.shape) / image.shape[axis]
-    d_eff = rate_inlet * L / (A * (1 - 0))
-
-    e = image.sum() / image.size
-    D_AB = 1
-    tau = e * D_AB / d_eff
-
-    return tau
-
-
-def chunking(spacing, divs):
-    r'''Returns slices given the number of chunks and chunk sizes.
-
-    Parameters
-    ----------
-    spacing : float
-        Size of each chunk.
-    divs : list
-        Number of chunks in each direction.
-
-    Returns
-    -------
-    slices : list
-        Contains lists of image slices corresponding to chunks
-    '''
-
-    slices = [[
-    (int(i*spacing[0]), int((i+1)*spacing[0])),
-    (int(j*spacing[1]), int((j+1)*spacing[1])),
-    (int(k*spacing[2]), int((k+1)*spacing[2]))]
-    for i in range(divs[0])
-    for j in range(divs[1])
-    for k in range(divs[2])]
-
-    return np.array(slices, dtype=int)
-
-
-def tortuosity_gdd(im, scale_factor=3, use_dask=True):
-    r'''Calculates the resistor network tortuosity.
-
-    Parameters
-    ----------
-    im : np.ndarray
-        The binary image to analyze with ``True`` indicating phase of interest
-
-    chunk_shape : list
-        Contains the number of chunks to be made in the x,y,z directions.
-
-    Returns
-    -------
-    results : list
-        Contains tau values for three directions, time stamps, tau values for each chunk
-    '''
-    t0 = time.perf_counter()
-
-    dt = edt.edt(im)
-    print(f'Max distance transform found: {np.round(dt.max(), 3)}')
-
-    # determining the number of chunks in each direction, minimum of 3 is required
-    if np.all(im.shape//(scale_factor*dt.max())>np.array([3, 3, 3])):
-
-        # if the minimum is exceeded, then chunk number is validated
-        # integer division is required for defining chunk shapes
-        chunk_shape=np.array(im.shape//(dt.max()*scale_factor), dtype=int)
-        print(f"{chunk_shape} > [3,3,3], using {(im.shape//chunk_shape)} as chunk size.")
-
-    # otherwise, the minimum of 3 in all directions is used
-    else:
-        chunk_shape=np.array([3, 3, 3])
-        print(f"{np.array(im.shape//(dt.max()*scale_factor), dtype=int)} <= [3,3,3], \
-using {im.shape[0]//3} as chunk size.")
-
-    t1 = time.perf_counter() - t0
-
-    # determines chunk size
-    chunk_size = np.floor(im.shape/np.array(chunk_shape))
-
-    # creates the masked images - removes half of a chunk from both ends of one axis
-    x_image = im[int(chunk_size[0]//2): int(im.shape[0] - chunk_size[0] //2), :, :]
-    y_image = im[:, int(chunk_size[1]//2): int(im.shape[1] - chunk_size[1] //2), :]
-    z_image = im[:, :, int(chunk_size[2]//2): int(im.shape[2] - chunk_size[2] //2)]
-
-    t2 = time.perf_counter()- t0
-
-    # creates the chunks for each masked image
-    x_slices = chunking(spacing=chunk_size,
-                        divs=[chunk_shape[0]-1, chunk_shape[1], chunk_shape[2]])
-    y_slices = chunking(spacing=chunk_size,
-                        divs=[chunk_shape[0], chunk_shape[1]-1, chunk_shape[2]])
-    z_slices = chunking(spacing=chunk_size,
-                        divs=[chunk_shape[0], chunk_shape[1], chunk_shape[2]-1])
-
-    t3 = time.perf_counter()- t0
-    # queues up dask delayed function to be run in parallel
-
-    x_gD = [calc_g(x_image[x_slice[0, 0]:x_slice[0, 1],
-                           x_slice[1, 0]:x_slice[1, 1],
-                           x_slice[2, 0]:x_slice[2, 1],],
-                           axis=0) for x_slice in x_slices]
-
-    y_gD = [calc_g(y_image[y_slice[0, 0]:y_slice[0, 1],
-                           y_slice[1, 0]:y_slice[1, 1],
-                           y_slice[2, 0]:y_slice[2, 1],],
-                           axis=1) for y_slice in y_slices]
-
-    z_gD = [calc_g(z_image[z_slice[0, 0]:z_slice[0, 1],
-                           z_slice[1, 0]:z_slice[1, 1],
-                           z_slice[2, 0]:z_slice[2, 1],],
-                           axis=2) for z_slice in z_slices]
-
-    # order of throat creation
-    all_values = [z_gD, y_gD, x_gD]
-
-    if use_dask:
-        all_results = np.array(dask.compute(all_values), dtype=object).flatten()
-
-    else:
-        all_values = np.array(all_values).flatten()
-        all_results = []
-        for item in all_values:
-            all_results.append(item.compute())
-
-        all_results = np.array(all_results).flatten()
-
-    # THIS DOESNT WORK FOR SOME REASON
-    # all_gD = all_results[::2]
-    # all_tau_unfiltered = all_results[1::2]
-
-    all_gD = [result for result in all_results[::2]]
-    all_tau_unfiltered = [result for result in all_results[1::2]]
-
-    all_tau = [result.tortuosity if not isinstance(result, int)
-               else result for result in all_tau_unfiltered]
-
-    t4 = time.perf_counter()- t0
-
-    # creates opnepnm network to calculate image tortuosity
-    net = op.network.Cubic(chunk_shape)
-    air = op.phase.Phase(network=net)
-
-    air['throat.diffusive_conductance']=np.array(all_gD).flatten()
-
-    # calculates throat tau in x, y, z directions
-    throat_tau = [
-    # x direction
-    network_calc(image=im,
-                 chunk_size=chunk_size,
-                 network=net,
-                 phase=air,
-                 bc=['left', 'right'],
-                 axis=1),
-
-    # y direction
-    network_calc(image=im,
-                 chunk_size=chunk_size,
-                 network=net,
-                 phase=air,
-                 bc=['front', 'back'],
-                 axis=2),
-
-    # z direction
-    network_calc(image=im,
-                 chunk_size=chunk_size,
-                 network=net,
-                 phase=air,
-                 bc=['top', 'bottom'],
-                 axis=0)]
-
-    t5 = time.perf_counter()- t0
-
-    output = Results()
-    output.__setitem__('tau', throat_tau)
-    output.__setitem__('time_stamps', [t1, t2, t3, t4, t5])
-    output.__setitem__('all_tau', all_tau)
-
-    return output
-
-
-def chunks_to_dataframe(im, scale_factor=3, use_dask=True):
-    r'''Calculates the resistor network tortuosity.
-
-    Parameters
-    ----------
-    im : np.ndarray
-        The binary image to analyze with ``True`` indicating phase of interest
-
-    chunk_shape : list
-        Contains the number of chunks to be made in the x, y, z directions.
-
-    Returns
-    -------
-    df : pandas.DataFrame
-        Contains throat numbers, tau values, diffusive conductance values, and porosity
-
-    '''
-    dt = edt.edt(im)
-    print(f'Max distance transform found: {np.round(dt.max(), 3)}')
-
-    # determining the number of chunks in each direction, minimum of 3 is required
-    if np.all(im.shape//(scale_factor*dt.max())>np.array([3, 3, 3])):
-
-        # if the minimum is exceeded, then chunk number is validated
-        # integer division is required for defining chunk shapes
-        chunk_shape=np.array(im.shape//(dt.max()*scale_factor), dtype=int)
-        print(f"{chunk_shape} > [3,3,3], using {(im.shape//chunk_shape)} as chunk size.")
-
-    # otherwise, the minimum of 3 in all directions is used
-    else:
-        chunk_shape=np.array([3, 3, 3])
-        print(f"{np.array(im.shape//(dt.max()*scale_factor), dtype=int)} <= [3,3,3], \
-using {im.shape[0]//3} as chunk size.")
-
-    # determines chunk size
-    chunk_size = np.floor(im.shape/np.array(chunk_shape))
-
-    # creates the masked images - removes half of a chunk from both ends of one axis
-    x_image = im[int(chunk_size[0]//2): int(im.shape[0] - chunk_size[0] //2), :, :]
-    y_image = im[:, int(chunk_size[1]//2): int(im.shape[1] - chunk_size[1] //2), :]
-    z_image = im[:, :, int(chunk_size[2]//2): int(im.shape[2] - chunk_size[2] //2)]
-
-    # creates the chunks for each masked image
-    x_slices = chunking(spacing=chunk_size,
-                        divs=[chunk_shape[0]-1, chunk_shape[1], chunk_shape[2]])
-    y_slices = chunking(spacing=chunk_size,
-                        divs=[chunk_shape[0], chunk_shape[1]-1, chunk_shape[2]])
-    z_slices = chunking(spacing=chunk_size,
-                        divs=[chunk_shape[0], chunk_shape[1], chunk_shape[2]-1])
-
-    # queues up dask delayed function to be run in parallel
-    x_gD = [calc_g(x_image[x_slice[0, 0]:x_slice[0, 1],
-                           x_slice[1, 0]:x_slice[1, 1],
-                           x_slice[2, 0]:x_slice[2, 1],],
-                           axis=0) for x_slice in x_slices]
-
-    y_gD = [calc_g(y_image[y_slice[0, 0]:y_slice[0, 1],
-                           y_slice[1, 0]:y_slice[1, 1],
-                           y_slice[2, 0]:y_slice[2, 1],],
-                           axis=1) for y_slice in y_slices]
-
-    z_gD = [calc_g(z_image[z_slice[0, 0]:z_slice[0, 1],
-                           z_slice[1, 0]:z_slice[1, 1],
-                           z_slice[2, 0]:z_slice[2, 1],],
-                           axis=2) for z_slice in z_slices]
-
-    # order of throat creation
-    all_values = [z_gD, y_gD, x_gD]
-
-    if use_dask:
-        all_results = np.array(dask.compute(all_values), dtype=object).flatten()
-
-    else:
-        all_values = np.array(all_values).flatten()
-        all_results = []
-        for item in all_values:
-            all_results.append(item.compute())
-
-        all_results = np.array(all_results).flatten()
-
-    all_gD = [result for result in all_results[::2]]
-    all_tau_unfiltered = [result for result in all_results[1::2]]
-
-    all_porosity = [result.effective_porosity if not isinstance(result, int)
-                    else result for result in all_tau_unfiltered]
-    all_tau = [result.tortuosity if not isinstance(result, int)
-               else result for result in all_tau_unfiltered]
-
-    # creates opnepnm network to calculate image tortuosity
-    net = op.network.Cubic(chunk_shape)
-
-    df = DataFrame(list(zip(np.arange(net.Nt), all_tau, all_gD, all_porosity)),
-                        columns=['Throat Number', 'Tortuosity',
-                                 'Diffusive Conductance', 'Porosity'])
-
-    return df
-
-
-if __name__ =="__main__":
-    import numpy as np
-
-    import porespy as ps
-    np.random.seed(1)
-    im = ps.generators.blobs(shape=[100, 100, 100], porosity=0.7)
-    res = ps.simulations.tortuosity_gdd(im=im, scale_factor=3, use_dask=True)
-    print(res)
-
-    # np.random.seed(2)
-    # im = ps.generators.blobs(shape=[100, 100, 100], porosity=0.7)
-    # df = ps.simulations.chunks_to_dataframe(im=im, scale_factor=3)
-    # print(df)
diff --git a/src/porespy/beta/_tortuosity_bt_funcs.py b/src/porespy/beta/_tortuosity_bt_funcs.py
new file mode 100644
index 000000000..f5e3567a2
--- /dev/null
+++ b/src/porespy/beta/_tortuosity_bt_funcs.py
@@ -0,0 +1,341 @@
+import time
+import porespy as ps
+from porespy import tools
+from porespy.tools import Results
+import logging
+import numpy as np
+import openpnm as op
+import pandas as pd
+import dask
+from dask.diagnostics import ProgressBar
+try:
+    from pyedt import edt
+except ModuleNotFoundError:
+    from edt import edt
+
+__all__ = [
+    'tortuosity_bt',
+    'get_block_sizes',
+    'df_to_tortuosity',
+    'rev_tortuosity',
+    'analyze_blocks',
+]
+
+
+def calc_g(im, axis, solver_args={}):
+    r"""
+    Calculates diffusive conductance of an image in the direction specified
+
+    Parameters
+    ----------
+    im : ndarray
+        The binary image to analyze with ``True`` indicating phase of interest.
+    axis : int
+        0 for x-axis, 1 for y-axis, 2 for z-axis.
+    solver_args : dict
+        Dicionary of keyword arguments to pass on to the solver.  The most
+        relevant one being `'tol'` which is 1e-6 by default. Using larger values
+        might improve speed at the cost of accuracy.
+
+    Returns
+    -------
+    results : dataclass-like
+        An object with the results of the calculation as attributes.
+
+    Notes
+    -----
+    This is intended to receive blocks of a larger image and is used by
+    `tortuosity_bt`.
+    """
+    from porespy.simulations import tortuosity_fd
+    solver_args = {'tol': 1e-6} | solver_args
+    solver = solver_args.pop('solver', None)
+    t0 = time.perf_counter()
+
+    try:
+        solver = op.solvers.PyamgRugeStubenSolver(**solver_args)
+        results = tortuosity_fd(im=im, axis=axis, solver=solver)
+    except Exception:
+        results = Results()
+        results.effective_porosity = 0.0
+        results.original_porosity = im.sum()/im.size
+        results.tortuosity = np.inf
+        results.time = time.perf_counter() - t0
+    L = im.shape[axis]
+    A = np.prod(im.shape)/im.shape[axis]
+    g = (results.effective_porosity * A) / (results.tortuosity * (L - 1))
+    results.diffusive_conductance = g
+    results.volume = np.prod(im.shape)
+    results.axis = axis
+    results.time = time.perf_counter() - t0
+    return results
+
+
+def get_block_sizes(im, block_size_range=[10, 100]):
+    """
+    Finds all viable block sizes between lower and upper limits
+
+    Parameters
+    ----------
+    im : np.array
+        The binary image to analyze with ``True`` indicating phase of interest.
+    block_size_range : sequence of 2 ints
+        The [lower, upper] range of the desired block sizes. Default is [10, 100]
+
+    Returns
+    -------
+    sizes : ndarray
+        All the viable block sizes in the specified range
+
+    Notes
+    -----
+    This is called by `rev_tortuosity` to determine what size blocks to use.
+    """
+    shape = im.shape
+    Lmin, Lmax = block_size_range
+    a = np.ceil(min(shape)/Lmax).astype(int)
+    block_sizes = min(shape) // np.arange(a, 9999)  # Generate WAY more than needed
+    block_sizes = np.unique(block_sizes[block_sizes >= Lmin])
+    return block_sizes
+
+
+def rev_tortuosity(im, block_sizes=None, use_dask=True):
+    """
+    Generates the data for creating an REV plot based on tortuosity
+
+    Parameters
+    ----------
+    im : ndarray
+        The binary image to analyze with ``True`` indicating phase of interest
+    block_sizes : np.ndarray
+        An array containing integers of block sizes to be calculated
+    use_dask : bool
+        A boolean determining the usage of `dask`.
+
+    Returns
+    -------
+    df : DataFrame
+        A `pandas` data frame with the properties for each block on a given row
+    """
+    if block_sizes is None:
+        block_sizes = get_block_sizes(im)
+    block_sizes = np.array(block_sizes, dtype=int)
+    tau = []
+    for s in block_sizes:
+        tau.append(analyze_blocks(im, block_size=s, use_dask=use_dask))
+    df = pd.concat(tau)
+    return df
+
+
+def block_size_to_divs(shape, block_size):
+    r"""
+    Finds the number of blocks in each direction given the size of the blocks
+
+    Parameters
+    ----------
+    shape : sequence of ints
+        The [x, y, z] shape of the image
+    block_size : int or sequence of ints
+        The size of the blocks
+
+    Returns
+    -------
+    divs : list of ints
+        The number of blocks to divide the image into along each axis. The minimum
+        number of blocks is 2.
+    """
+    shape = np.array(shape)
+    divs = shape // np.array(block_size)
+    # scraps = shape % np.array(block_size)
+    divs = np.clip(divs, a_min=2, a_max=shape)
+    return divs
+
+
+def analyze_blocks(im, block_size=None, method="chords", use_dask=True):
+    r'''
+    Computes structural and transport properties of each block
+
+    Parameters
+    ----------
+    im : np.ndarray
+        The binary image to analyze with ``True`` indicating phase of interest
+    block_size : int
+        The size of the blocks to use. Only cubic blocks are supported so an integer
+        must be given, or an exception is raised. If the image is not evenly
+        divisible by the given `block_size` any extra voxels are removed from the
+        end of each axis before all processing occcurs. Block size will be prioritized
+        if use_chords is also provided.
+    method : string
+        The method to use to determine block sizes if `block_size` is not provided.
+        =========== ==================================================================
+        method      description
+        =========== ==================================================================
+        'chords'    Uses `apply_chords_3D` from Porespy to determine the longest chord
+                    possible in the image as the length of each block.
+        'dt'        Uses the maximum length of the distance transform to determine
+                    the length of each block.
+        ========== ==================================================================
+    use_dask : bool
+        A boolean determining the usage of `dask`.
+
+    Returns
+    -------
+    df_out : DataFrame
+        A `pandas` data frame with the properties for each block on a given row.
+    '''
+
+    # determines block size, trimmed to fit in the image
+    if block_size is None:
+        if method == "chords":
+            tmp = ps.filters.apply_chords_3D(im)
+
+            # find max chord length in each direction
+            block_size = np.int_(np.amax(ps.filters.region_size(im = tmp>0)))
+            block_size = min(block_size, min(np.array(im.shape)/2))
+
+        elif method == "dt":
+            scale_factor = 3
+            dt = edt(im)
+            # TODO: Is the following supposed to be over 2 or over im.ndim?
+            block_size = min(dt.max() * scale_factor, min(np.array(im.shape)/2))
+        
+        else:
+            print("Provide a valid method")
+            raise Exception
+
+    results = []
+    offset = int(block_size/2)
+
+    # create blocks and queues them for calculation
+    for ax in range(im.ndim):
+
+        # creates the masked images - removes half of a chunk from both ends of one axis
+        tmp = np.swapaxes(im, 0, ax)
+        tmp = tmp[offset:-offset, ...]
+        tmp = np.swapaxes(tmp, 0, ax)
+        slices = tools.subdivide(tmp, block_size=block_size, mode='whole')
+        if use_dask:
+                for s in slices:
+                    results.append(dask.delayed(calc_g)(tmp[s], axis=ax))
+
+        # or do it the regular way
+        else:
+            for s in slices:
+                results.append(calc_g(tmp[s], axis=ax))
+
+    with ProgressBar():
+    # collect all the results and calculate if needed
+        results = np.asarray(dask.compute(results), dtype=object).flatten()
+
+    # format results to be returned as a single dataframe
+    df_out = pd.DataFrame()
+
+    df_out['eps_orig'] = [r.original_porosity for r in results]
+    df_out['eps_perc'] = [r.effective_porosity for r in results]
+    df_out['g'] = [r.diffusive_conductance for r in results]
+    df_out['tau'] = [r.tortuosity for r in results]
+    df_out['volume'] = [r.volume for r in results]
+    df_out['length'] = [block_size for r in results]
+    df_out['axis'] = [r.axis for r in results]
+    df_out['time'] = [r.time for r in results]
+
+    return df_out
+
+
+def df_to_tortuosity(im, df):
+    """
+    Compute the tortuosity of a network populated with diffusive conductance values
+    from the given dataframe.
+
+    Parameters
+    ----------
+    im : ndarray
+        The boolean image of the materials with `True` indicating the void space
+    df : dataframe
+        The dataframe returned by the `blocks_to_dataframe` function
+    block_size : int
+        The size of the blocks used to compute the conductance values in `df`
+
+    Returns
+    -------
+    tau : list of floats
+        The tortuosity in all three principal directions
+    """
+
+    block_size = list(df['length'])[0]
+    divs = block_size_to_divs(shape=im.shape, block_size=block_size)
+
+    net = op.network.Cubic(shape=divs)
+    air = op.phase.Phase(network=net)
+    gx = df['g'][df['axis']==0]
+    gy = df['g'][df['axis']==1]
+    gz = df['g'][df['axis']==2]
+
+    g = np.hstack([gz, gy, gx])
+
+    air['throat.diffusive_conductance'] = g
+
+    bcs = {0: {'in': 'left', 'out': 'right'},
+           1: {'in': 'front', 'out': 'back'},
+           2: {'in': 'top', 'out': 'bottom'}}
+
+    e = np.sum(im, dtype=np.int64) / im.size
+    D_AB = 1
+    tau = []
+
+    for ax in range(im.ndim):
+        fick = op.algorithms.FickianDiffusion(network=net, phase=air)
+        fick.set_value_BC(pores=net.pores(bcs[ax]['in']), values=1.0)
+        fick.set_value_BC(pores=net.pores(bcs[ax]['out']), values=0.0)
+        fick.run()
+        rate_inlet = fick.rate(pores=net.pores(bcs[ax]['in']))[0]
+        L = (divs[ax] - 1) * block_size
+        A = (np.prod(divs) / divs[ax]) * (block_size**2)
+        D_eff = rate_inlet * L / (A * (1 - 0))
+        tau.append(e * D_AB / D_eff)
+
+    ws = op.Workspace()
+    ws.clear()
+    return tau
+
+
+def tortuosity_bt(im, block_size=None, method="chords", use_dask=True):
+    r"""
+    Computes the tortuosity of an image in all directions
+    
+    Parameters
+    ----------
+    im : ndarray
+        The boolean image of the materials with `True` indicating the void space
+    block_size : int
+        The size of the blocks which the image will be split into. If not provided,
+    it will be determined by the provided method in `method`
+    method : str
+        The method to use to determine block sizes if `block_size` is not provided.
+        =========== ==================================================================
+        method      description
+        =========== ==================================================================
+        'chords'    Uses `apply_chords_3D` from Porespy to determine the longest chord
+                    possible in the image as the length of each block.
+        'dt'        Uses the maximum length of the distance transform to determine
+                    the length of each block.
+        ========== ==================================================================
+    use_dask : bool
+        A boolean determining the usage of `dask` for parallel processing.
+    """
+    df = analyze_blocks(im, block_size, method, use_dask)
+    tau = df_to_tortuosity(im, df)
+    return tau
+
+
+if __name__ =="__main__":
+    import porespy as ps
+    import numpy as np
+    
+    np.random.seed(1)
+
+    im = ps.generators.blobs([100, 100, 100])
+    # df = analyze_blocks(im, method="dt")
+    # tau = df_to_tortuosity(im, df)
+    r1 = tortuosity_bt(im, method="chords")
+    print(r1)
\ No newline at end of file
diff --git a/src/porespy/simulations/_dns.py b/src/porespy/simulations/_dns.py
index f506d7bda..d1df4e743 100644
--- a/src/porespy/simulations/_dns.py
+++ b/src/porespy/simulations/_dns.py
@@ -1,5 +1,5 @@
 import logging
-
+import time
 import numpy as np
 import openpnm as op
 
@@ -89,6 +89,7 @@ def tortuosity_fd(im, axis, solver=None):
     cL, cR = 1.0, 0.0
     fd.set_value_BC(pores=inlets, values=cL)
     fd.set_value_BC(pores=outlets, values=cR)
+    t = time.perf_counter_ns()
     if openpnm_v3:
         if solver is None:
             solver = op.solvers.PyamgRugeStubenSolver(tol=1e-8)
@@ -99,6 +100,7 @@ def tortuosity_fd(im, axis, solver=None):
     else:
         fd.settings.update({"solver_family": "scipy", "solver_type": "cg"})
         fd.run()
+    t = time.perf_counter_ns() - t
 
     # Calculate molar flow rate, effective diffusivity and tortuosity
     r_in = fd.rate(pores=inlets)[0]
@@ -122,7 +124,7 @@ def tortuosity_fd(im, axis, solver=None):
     conc = np.zeros(im.size, dtype=float)
     conc[net["pore.template_indices"]] = fd["pore.concentration"]
     result.concentration = conc.reshape(im.shape)
-    result.sys = fd.A, fd.b
+    result.time = t/1e9
 
     # Free memory
     ws.close_project(net.project)
diff --git a/src/porespy/tools/_funcs.py b/src/porespy/tools/_funcs.py
index 8443316f0..46ab0e5c2 100644
--- a/src/porespy/tools/_funcs.py
+++ b/src/porespy/tools/_funcs.py
@@ -260,7 +260,7 @@ def align_image_with_openpnm(im):
     return im
 
 
-def subdivide(im, divs=2, overlap=0):
+def subdivide(im, divs=2, block_size=None, overlap=0, mode='offset'):
     r"""
     Returns slices into an image describing the specified number of sub-arrays.
 
@@ -274,20 +274,39 @@ def subdivide(im, divs=2, overlap=0):
         The image of the porous media
     divs : scalar or array_like
         The number of sub-divisions to create in each axis of the image.  If a
-        scalar is given it is assumed this value applies in all dimensions.
+        scalar is given it is assumed this value applies in all dimensions. If
+        `block_size` is given this is ignored.
+    block_size : scalar or array_like
+        The size of the divisions to create. If a scalar is given then cubic
+        blocks are created. If this argument is given then `divs` is ignored.
     overlap : scalar or array_like
         The amount of overlap to use when dividing along each axis.  If a
         scalar is given it is assumed this value applies in all dimensions.
+    mode : str
+        This argument is only used if `block_size` is given and it controls how
+        to handle the situation when block sizes is not a clean multiple of
+        the image shape. The options are:
+
+        ========== ==================================================================
+        mode       description
+        ========== ==================================================================
+        'whole'    Blocks start at the beginning of each axis, and only "whole"
+                   blocks (that fit within the image) are included in the returned
+                   list of slice objects.
+        'offset'   Only whole blocks are included, but an offset is applied to the
+                   start of each axis so that an equal amount of voxels are missed
+                   at the start and end of each axis.
+        'partial'  Blocks start at the beginning of each axis, and any blocks which
+                   partially extend beyond the end of the image are returned.
+        'strict'   Raises an Exception of the image cannot be evenly divided by the
+                   given block size.
+        ========== ==================================================================
 
     Returns
     -------
     slices : ndarray
         An ndarray containing sets of slice objects for indexing into ``im``
-        that extract subdivisions of an image.  If ``flatten`` was ``True``,
-        then this array is suitable for iterating.  If ``flatten`` was
-        ``False`` then the slice objects must be accessed by row, col, layer
-        indices.  An ndarray is the preferred container since its shape can
-        be easily queried.
+        that extract subdivisions of an image.
 
     See Also
     --------
@@ -307,33 +326,57 @@ def subdivide(im, divs=2, overlap=0):
     to view online example.
 
     """
-    divs = np.ones((im.ndim,), dtype=int) * np.array(divs)
-    overlap = overlap * (divs > 1)
-
+    offset = np.zeros(im.ndim, dtype=int)
+    shape = np.array(im.shape, dtype=int)
+    if block_size is None:
+        divs = np.ones((im.ndim,), dtype=int) * np.array(divs)
+        overlap = overlap * (divs > 1)
+        spacing = np.round(shape/divs, decimals=0).astype(int)
+    else:
+        block_size = np.array(block_size, dtype=int)
+        spacing = np.ones((im.ndim,), dtype=int) * block_size
+        divs = shape/spacing
+        if mode == 'offset':
+            divs = np.array(divs, dtype=int)
+            offset = ((shape - block_size*divs)/2).astype(int)
+        elif mode == 'whole':
+            divs = np.array(divs, dtype=int)
+        elif mode == 'partial':
+            divs = np.ceil(divs).astype(int)
+        elif mode == 'strict':
+            if np.any(shape % block_size):
+                m = 'The image cannot be evenly divided by the given block_size'
+                raise Exception(m)
+            divs = np.array(divs).astype(int)
+        else:
+            raise Exception('Unsupported mode')
     s = np.zeros(shape=divs, dtype=object)
-    spacing = np.round(np.array(im.shape)/divs, decimals=0).astype(int)
     for i in range(s.shape[0]):
         x = spacing[0]
-        sx = slice(x*i, min(im.shape[0], x*(i+1)), None)
+        o = offset[0]
+        sx = slice(x*i + o, min(im.shape[0], x*(i+1)) + o, None)
         for j in range(s.shape[1]):
             y = spacing[1]
-            sy = slice(y*j, min(im.shape[1], y*(j+1)), None)
+            o = offset[1]
+            sy = slice(y*j + o, min(im.shape[1], y*(j+1)) + o, None)
             if im.ndim == 3:
                 for k in range(s.shape[2]):
                     z = spacing[2]
-                    sz = slice(z*k, min(im.shape[2], z*(k+1)), None)
+                    o = offset[2]
+                    sz = slice(z*k + o, min(im.shape[2], z*(k+1)) + o, None)
                     s[i, j, k] = tuple([sx, sy, sz])
             else:
                 s[i, j] = tuple([sx, sy])
     s = s.flatten().tolist()
-    for i, item in enumerate(s):
-        s[i] = extend_slice(slices=item, shape=im.shape, pad=overlap)
+    if np.any(overlap):
+        for i, item in enumerate(s):
+            s[i] = extend_slice(slices=item, shape=im.shape, pad=overlap)
     return s
 
 
 def recombine(ims, slices, overlap):
     r"""
-    Recombines image chunks back into full image of original shape
+    Recombines image chunks back into full image
 
     Parameters
     ----------
@@ -348,8 +391,7 @@ def recombine(ims, slices, overlap):
     Returns
     -------
     im : ndarray
-        An image constituted from the chunks in ``ims`` of the same shape
-        as the original image.
+        An image constituted from the chunks in ``ims``
 
     See Also
     --------
diff --git a/test/unit/GenericTest.py b/test/unit/GenericTest.py
new file mode 100644
index 000000000..d435a1e07
--- /dev/null
+++ b/test/unit/GenericTest.py
@@ -0,0 +1,22 @@
+
+
+class GenericTest:
+
+    hr = '―' * 78
+
+    def __init__(self):
+        print(self.hr)
+
+    def setup_class(self):
+        pass
+
+    def teardown_class(self):
+        pass
+
+    def run_all(self):
+        self.setup_class()
+        for item in self.__dir__():
+            if item.startswith('test'):
+                print(f"Running test: {item}")
+                self.__getattribute__(item)()
+        self.teardown_class()
diff --git a/test/unit/test_simulations_block_and_tackle.py b/test/unit/test_simulations_block_and_tackle.py
new file mode 100644
index 000000000..f0bbeab12
--- /dev/null
+++ b/test/unit/test_simulations_block_and_tackle.py
@@ -0,0 +1,66 @@
+import numpy as np
+from porespy.tools import subdivide
+import openpnm as op
+from porespy import beta
+from porespy import generators
+from GenericTest import GenericTest
+
+
+class TestBlockAndTackle(GenericTest):
+
+    def test_blocks_on_ideal_image(self):
+
+        block_size = 20
+        im = np.arange(120).reshape(4, 5, 6)
+        im = np.repeat(im, block_size, axis=0)
+        im = np.repeat(im, block_size, axis=1)
+        im = np.repeat(im, block_size, axis=2)
+        offset = int(block_size/2)
+        queue = [[], [], []]
+        for ax in range(im.ndim):
+            im_temp = np.swapaxes(im, 0, ax)
+            im_temp = im_temp[offset:-offset, ...]
+            im_temp = np.swapaxes(im_temp, 0, ax)
+            slices = subdivide(im_temp, block_size=block_size, mode='strict')
+            for s in slices:
+                queue[ax].append(np.unique(im_temp[s]))
+        queue.reverse()
+        conns = np.vstack(queue)
+        shape = np.array(im.shape)//block_size
+        pn = op.network.Cubic(shape)
+        assert np.all(pn.conns == conns)
+
+    def test_analyze_blocks_on_empty_image(self):
+        im = np.ones([100, 100, 100], dtype=bool)
+        df = beta.rev_tortuosity(im, [25], dask_args={'enable': False})
+        assert len(df) == 144
+        assert np.all(df['volume'] == 25**3)
+        assert np.all(df['length'] == 25)
+        assert np.all(np.around(df['tau'], decimals=4) == 1.0000)
+
+    def test_analyze_block_on_lattice_spheres(self):
+        im = generators.lattice_spheres(
+            shape=[100, 100, 100], r=10, offset=25, spacing=50)
+        df = beta.rev_tortuosity(im, [25], dask_args={'enable': False})
+        assert np.all(df['volume'] == 25**3)
+        assert np.all(df['length'] == 25)
+        assert np.all(df['tau'] > 1.0)
+
+    def test_analyze_blocks_on_asymmetric_image(self):
+        im1 = np.ones([100, 75, 50], dtype=bool)
+        im2 = np.ones([100, 80, 60], dtype=bool)  # Not multiple of block size
+        df1 = beta.rev_tortuosity(im1, [25], dask_args={'enable': False})
+        df2 = beta.rev_tortuosity(im2, [25], dask_args={'enable': False})
+        assert len(df1) == 46
+        assert np.all(df1['volume'] == 25**3)
+        assert np.all(df1['length'] == 25)
+        assert np.all(np.around(df1['tau'], decimals=4) == 1.0000)
+        assert np.sum(df1['axis'] == 0) == 18
+        assert np.sum(df1['axis'] == 1) == 16
+        assert np.sum(df1['axis'] == 2) == 12
+        assert np.all(df2 == df1)
+
+
+if __name__ == "__main__":
+    t = TestBlockAndTackle()
+    t.run_all()
diff --git a/test/unit/test_tools.py b/test/unit/test_tools.py
index 6ed02ad87..2d38f28df 100644
--- a/test/unit/test_tools.py
+++ b/test/unit/test_tools.py
@@ -298,6 +298,46 @@ def test_recombine_3d_odd_shape_vector_overlap(self):
         im2 = ps.tools.recombine(ims=ims, slices=s, overlap=[10, 20, 25])
         assert np.all(im == im2)
 
+    def test_subdivide_with_mode_offset(self):
+        im = im = np.random.rand(143, 177, 111)
+        s = ps.tools.subdivide(im, block_size=10, mode='offset')
+        assert s[0][0].start > 0
+        assert s[0][1].start > 0
+        assert s[0][2].start == 0  # If only 1 remainder, the start is 0
+        assert s[-1][0].stop < im.shape[0]
+        assert s[-1][1].stop < im.shape[1]
+        assert s[-1][2].stop < im.shape[2]
+
+    def test_subdivide_with_mode_unsupported(self):
+        im = im = np.random.rand(143, 177, 111)
+        with pytest.raises(Exception):
+            ps.tools.subdivide(im, block_size=10, mode='blah')
+
+    def test_subdivide_with_mode_strict(self):
+        im = im = np.random.rand(143, 177, 111)
+        with pytest.raises(Exception):
+            ps.tools.subdivide(im, block_size=10, mode='strict')
+
+    def test_subdivide_with_mode_partial(self):
+        im = im = np.random.rand(143, 177, 111)
+        s = ps.tools.subdivide(im, block_size=10, mode='partial')
+        assert s[0][0].start == 0
+        assert s[0][1].start == 0
+        assert s[0][2].start == 0  # If only 1 remainder, the start is 0
+        assert s[-1][0].stop == im.shape[0]
+        assert s[-1][1].stop == im.shape[1]
+        assert s[-1][2].stop == im.shape[2]
+
+    def test_subdivide_with_mode_whole(self):
+        im = im = np.random.rand(143, 177, 111)
+        s = ps.tools.subdivide(im, block_size=10, mode='whole')
+        assert s[0][0].start == 0
+        assert s[0][1].start == 0
+        assert s[0][2].start == 0  # If only 1 remainder, the start is 0
+        assert s[-1][0].stop < im.shape[0]
+        assert s[-1][1].stop < im.shape[1]
+        assert s[-1][2].stop < im.shape[2]
+
     def test_sanitize_filename(self):
         fname = "test.stl.stl"
         assert ps.tools.sanitize_filename(fname, "stl") == "test.stl.stl"