diff --git a/src/python_routing_v01/Florence_Test.ipynb b/src/python_routing_v01/Florence_Test.ipynb
index 1c1a68bcb..e5337afaa 100644
--- a/src/python_routing_v01/Florence_Test.ipynb
+++ b/src/python_routing_v01/Florence_Test.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,17 +20,18 @@
     "from tqdm import tqdm\n",
     "import multiprocessing\n",
     "\n",
-    "root = pathlib.Path(\"../../..\").resolve()\n",
+    "root = pathlib.Path(\"../../\").resolve()\n",
     "sys.path.append(str(root.joinpath(\"src\", \"python_framework_v02\")))\n",
     "sys.path.append(str(root.joinpath(\"src\", \"python_framework_v01\")))\n",
+    "sys.path.append(str(root.joinpath(\"src\", \"python_routing_v01\")))\n",
     "sys.path.append(\".\")\n",
     "import nhd_io as nio\n",
     "import compute_nhd_routing_SingleSeg as tr\n",
     "import nhd_network_utilities_v01 as nnu\n",
     "import nhd_reach_utilities as nru\n",
     "\n",
-    "custom_input_folder = root.joinpath(\"test\", \"input\", \"json\")\n",
-    "custom_input_file = \"florence_933020089.json\"\n",
+    "custom_input_folder = root.joinpath(\"test\", \"input\", \"yaml\")\n",
+    "custom_input_file = \"florence_933020089_dt60.yaml\"\n",
     "run_pocono2_test = None\n"
    ]
   },
@@ -43,9 +44,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jacob.hreha/github/t-route/src/python_framework_v02/nhd_io.py:53: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
+      "  data = yaml.load(custom_file)\n"
+     ]
+    }
+   ],
    "source": [
     "supernetwork_parameters = None\n",
     "waterbody_parameters = None\n",
@@ -128,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,9 +161,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "begin program t-route ...\n",
+      "creating supernetwork connections set\n",
+      "supernetwork connections set complete\n",
+      "... in 0.03888821601867676 seconds.\n"
+     ]
+    }
+   ],
    "source": [
     "if showtiming:\n",
     "    program_start_time = time.time()\n",
@@ -199,9 +220,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "organizing connections into reaches ...\n",
+      "reach organization complete\n",
+      "... in 0.004379987716674805 seconds.\n"
+     ]
+    }
+   ],
    "source": [
     "# STEP 2: Separate the networks and build the sub-graph of reaches within each network\n",
     "if showtiming:\n",
@@ -225,9 +256,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reading waterbody parameter file ...\n",
+      "waterbodies complete\n",
+      "... in 0.017271041870117188 seconds.\n",
+      "ordering waterbody subnetworks ...\n",
+      "ordering waterbody subnetworks complete\n",
+      "... in 3.7670135498046875e-05 seconds.\n",
+      "setting waterbody initial states ...\n",
+      "waterbody initial states complete\n",
+      "... in 0.01991748809814453 seconds.\n"
+     ]
+    }
+   ],
    "source": [
     "# STEP 3: Organize Network for Waterbodies\n",
     "if break_network_at_waterbodies:\n",
@@ -327,9 +374,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setting channel initial states ...\n",
+      "channel initial states complete\n",
+      "... in 0.02246999740600586 seconds.\n"
+     ]
+    }
+   ],
    "source": [
     "# STEP 4: Handle Channel Initial States\n",
     "if showtiming:\n",
@@ -370,9 +427,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "creating qlateral array ...\n",
+      "qlateral array complete\n",
+      "... in 6.396091938018799 seconds.\n"
+     ]
+    }
+   ],
    "source": [
     "# STEP 5: Read (or set) QLateral Inputs\n",
     "if showtiming:\n",
@@ -413,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -436,9 +503,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "writing segment output to --> ../../test/output/text/933020089.csv\n"
+     ]
+    }
+   ],
    "source": [
     "# Define the pool after we create the static global objects (and collect the garbage)\n",
     "if parallel_compute:\n",
@@ -454,9 +529,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  0%|          | 0/1108 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executing routing computation ...\n",
+      "routing ordered reaches for networks of order 2 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " 42%|████▏     | 467/1108 [00:35<00:48, 13.19it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 35.58297324180603 seconds.\n",
+      "max_courant: [5.51265717]\n",
+      "routing ordered reaches for networks of order 1 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " 42%|████▏     | 467/1108 [00:48<00:48, 13.19it/s]\u001b[A\n",
+      " 45%|████▌     | 499/1108 [00:49<01:53,  5.37it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 49.79691505432129 seconds.\n",
+      "max_courant: [0.]\n",
+      "routing ordered reaches for networks of order 0 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "100%|██████████| 1108/1108 [01:50<00:00,  6.22it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 111.17425584793091 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1108/1108 [01:54<00:00,  9.68it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_courant: [25.36383247]\n",
+      "ordered reach computation complete\n",
+      "... in 114.53386664390564 seconds.\n",
+      "program complete\n",
+      "... in 121.2260513305664 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "################### Main Execution Loop across ordered networks\n",
     "if showtiming:\n",
@@ -466,12 +633,12 @@
     "\n",
     "compute_network_func = tr.compute_network\n",
     "\n",
-    "tr.connections = connections\n",
-    "tr.networks = networks\n",
-    "tr.qlateral = qlateral\n",
-    "tr.waterbodies_df = waterbodies_df\n",
-    "tr.waterbody_initial_states_df = waterbody_initial_states_df\n",
-    "tr.channel_initial_states_df = channel_initial_states_df\n",
+    "tr.connections_g = connections\n",
+    "tr.networks_g = networks\n",
+    "tr.qlateral_g = qlateral\n",
+    "tr.waterbodies_df_g = waterbodies_df\n",
+    "tr.waterbody_initial_states_df_g = waterbody_initial_states_df\n",
+    "tr.channel_initial_states_df_g = channel_initial_states_df\n",
     "\n",
     "progress_count = 0\n",
     "percentage_complete = True\n",
@@ -609,17 +776,2364 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# creation of the main dataframe\n"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# print(results)\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "import pandas as pd\n",
+    "\n",
+    "all_results = {}\n",
+    "seg_courant_maxes = []\n",
+    "time = []\n",
+    "flowval = []  # flowval\n",
+    "velval_list = []  # velval\n",
+    "depthval = []  # depthval\n",
+    "qlatval = []  # qlatval\n",
+    "storageval = []  # storageval\n",
+    "qlatCumval = []  # qlatCumval\n",
+    "kinCelerity = []  # ck\n",
+    "courant = []  # cn\n",
+    "X = []  # X\n",
+    "\n",
+    "for result in results:\n",
+    "    all_results.update(result)\n",
+    "\n",
+    "# print(all_results[8780801][1][0])\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "# print(all_results)\n",
+    "\n",
+    "for key, data in all_results.items():\n",
+    "    time.extend(data[:, 0])  # time\n",
+    "    flowval.extend(data[:, 1])  # flowval\n",
+    "    velval_list.extend(data[:, 2])  # velval\n",
+    "    depthval.extend(data[:, 3])  # depthval\n",
+    "    qlatval.extend(data[:, 4])  # qlatval\n",
+    "    storageval.extend(data[:, 5])  # storageval\n",
+    "    qlatCumval.extend(data[:, 6])  # qlatCumval\n",
+    "    kinCelerity.extend(data[:, 7])  # ck\n",
+    "    courant.extend(data[:, 8])  # cn\n",
+    "    X.extend(data[:, 9])  # X\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8798400"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "single_seg_length = len(all_results[933020089][:, 0])\n",
+    "single_seg_length * 611\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8798400"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(time)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
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+      ],
+      "text/plain": [
+       "               time   flowval  velval_list  depthval   qlatval    storageval  \\\n",
+       "key_index                                                                      \n",
+       "8780801         0.0  0.000180     0.034005  0.005051  0.000180 -3.929017e-08   \n",
+       "8780801        60.0  0.000180     0.034037  0.005058  0.000180 -7.508788e-08   \n",
+       "8780801       120.0  0.000180     0.034044  0.005060  0.000180 -1.082662e-07   \n",
+       "8780801       180.0  0.000180     0.034046  0.005060  0.000180 -1.388253e-07   \n",
+       "8780801       240.0  0.000180     0.034046  0.005060  0.000180 -1.667649e-07   \n",
+       "...             ...       ...          ...       ...       ...           ...   \n",
+       "8778465    863700.0  0.000036     0.000000  0.000000  0.149393  8.135146e+06   \n",
+       "8778465    863760.0  0.000036     0.000000  0.000000  0.149393  8.135199e+06   \n",
+       "8778465    863820.0  0.000036     0.000000  0.000000  0.149393  8.135252e+06   \n",
+       "8778465    863880.0  0.000036     0.000000  0.000000  0.149393  8.135305e+06   \n",
+       "8778465    863940.0  0.000036     0.000000  0.000000  0.149393  8.135357e+06   \n",
+       "\n",
+       "             qlatCumval  kinCelerity   courant         X  \n",
+       "key_index                                                 \n",
+       "8780801    1.079874e-02     0.056269  0.017676  0.499495  \n",
+       "8780801    2.159748e-02     0.056322  0.017693  0.499496  \n",
+       "8780801    3.239622e-02     0.056334  0.017696  0.499496  \n",
+       "8780801    4.319496e-02     0.056336  0.017697  0.499496  \n",
+       "8780801    5.399370e-02     0.056337  0.017697  0.499496  \n",
+       "...                 ...          ...       ...       ...  \n",
+       "8778465    1.368751e+06     0.000000  0.000000  0.000000  \n",
+       "8778465    1.368760e+06     0.000000  0.000000  0.000000  \n",
+       "8778465    1.368769e+06     0.000000  0.000000  0.000000  \n",
+       "8778465    1.368778e+06     0.000000  0.000000  0.000000  \n",
+       "8778465    1.368787e+06     0.000000  0.000000  0.000000  \n",
+       "\n",
+       "[8798400 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key_index = all_results.keys()\n",
+    "key_index = list(\n",
+    "    itertools.chain.from_iterable(\n",
+    "        itertools.repeat(x, single_seg_length) for x in key_index\n",
+    "    )\n",
+    ")\n",
+    "data = {\n",
+    "    \"key_index\": list(key_index),\n",
+    "    \"time\": list(time),\n",
+    "    \"flowval\": list(flowval),\n",
+    "    \"velval_list\": list(velval_list),\n",
+    "    \"depthval\": list(depthval),\n",
+    "    \"qlatval\": list(qlatval),\n",
+    "    \"storageval\": list(storageval),\n",
+    "    \"qlatCumval\": list(qlatCumval),\n",
+    "    \"kinCelerity\": list(kinCelerity),\n",
+    "    \"courant\": list(courant),\n",
+    "    \"X\": list(X),\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "df = df.set_index(\"key_index\")\n",
+    "df = df.reset_index()\n",
+    "df = df.set_index(\"key_index\")\n",
+    "\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting plotly\n",
+      "  Downloading plotly-4.12.0-py2.py3-none-any.whl (13.1 MB)\n",
+      "\u001b[K     |████████████████████████████████| 13.1 MB 6.9 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting retrying>=1.3.3\n",
+      "  Using cached retrying-1.3.3.tar.gz (10 kB)\n",
+      "Requirement already satisfied: six in /home/jacob.hreha/anaconda3/lib/python3.7/site-packages (from plotly) (1.14.0)\n",
+      "Building wheels for collected packages: retrying\n",
+      "  Building wheel for retrying (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11430 sha256=955fe8c56e0da1a48727c21349a3bdf87e4a3b4377f30b8a6947f02fff9ed733\n",
+      "  Stored in directory: /home/jacob.hreha/.cache/pip/wheels/f9/8d/8d/f6af3f7f9eea3553bc2fe6d53e4b287dad18b06a861ac56ddf\n",
+      "Successfully built retrying\n",
+      "Installing collected packages: retrying, plotly\n",
+      "Successfully installed plotly-4.12.0 retrying-1.3.3\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\n",
+      "You should consider upgrading via the '/home/jacob.hreha/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# top 25 courants based on our work this morning and their segment IDs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(933020027, 25.363832473754883), (8777735, 14.950478553771973), (933020017, 9.564001083374023), (8778581, 8.574037551879883), (933020059, 7.254691123962402), (933020061, 4.696855068206787), (8780597, 3.591726064682007), (933020020, 2.8707876205444336), (933020058, 2.4734983444213867), (933020023, 2.1059963703155518), (8777485, 1.860101580619812), (933020036, 1.6248382329940796), (8778795, 1.6150635480880737), (8778021, 1.4462398290634155), (8777339, 1.393180251121521), (8777867, 1.353600263595581), (8777477, 1.3393661975860596), (8780495, 1.3077224493026733), (8777861, 1.1583272218704224), (8777481, 1.149632215499878), (8780607, 1.0738232135772705), (8779003, 1.0580987930297852), (933020050, 1.0441739559173584), (8780751, 0.9917784929275513), (8778947, 0.9753884673118591)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "seg_list = []\n",
+    "seg_courant_maxes = []\n",
+    "for seg in all_results:\n",
+    "    seg_list.append(seg)\n",
+    "    seg_courant_maxes.append(max(all_results[seg][:, tr.courant_index]))\n",
+    "zipped = zip(seg_list, seg_courant_maxes)\n",
+    "zipped = list(zipped)\n",
+    "res = sorted(zipped, key=lambda x: x[1], reverse=True)\n",
+    "# A.sort(reverse=True)\n",
+    "res = (res)[:25]\n",
+    "print((res)[:25])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A list of those IDs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[933020027,\n",
+       " 8777735,\n",
+       " 933020017,\n",
+       " 8778581,\n",
+       " 933020059,\n",
+       " 933020061,\n",
+       " 8780597,\n",
+       " 933020020,\n",
+       " 933020058,\n",
+       " 933020023,\n",
+       " 8777485,\n",
+       " 933020036,\n",
+       " 8778795,\n",
+       " 8778021,\n",
+       " 8777339,\n",
+       " 8777867,\n",
+       " 8777477,\n",
+       " 8780495,\n",
+       " 8777861,\n",
+       " 8777481,\n",
+       " 8780607,\n",
+       " 8779003,\n",
+       " 933020050,\n",
+       " 8780751,\n",
+       " 8778947]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "major_segments = []\n",
+    "for x, y in res[:25]:\n",
+    "    major_segments.append(x)\n",
+    "major_segments\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filtering out the above segment IDs from the original df.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>flowval</th>\n",
+       "      <th>velval_list</th>\n",
+       "      <th>depthval</th>\n",
+       "      <th>qlatval</th>\n",
+       "      <th>storageval</th>\n",
+       "      <th>qlatCumval</th>\n",
+       "      <th>kinCelerity</th>\n",
+       "      <th>courant</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <td>8777735</td>\n",
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+       "      <td>8777735</td>\n",
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+       "      <td>0.498455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8777735</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>0.004196</td>\n",
+       "      <td>0.185567</td>\n",
+       "      <td>0.009794</td>\n",
+       "      <td>0.000035</td>\n",
+       "      <td>-7.770723e-08</td>\n",
+       "      <td>0.010456</td>\n",
+       "      <td>0.307085</td>\n",
+       "      <td>1.675010</td>\n",
+       "      <td>0.498455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>0.145244</td>\n",
+       "      <td>0.492948</td>\n",
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+       "    <tr>\n",
+       "      <td>8780751</td>\n",
+       "      <td>863760.0</td>\n",
+       "      <td>0.008624</td>\n",
+       "      <td>0.134757</td>\n",
+       "      <td>0.029788</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.558343e+01</td>\n",
+       "      <td>50.799377</td>\n",
+       "      <td>0.220284</td>\n",
+       "      <td>0.145242</td>\n",
+       "      <td>0.492948</td>\n",
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+       "    <tr>\n",
+       "      <td>8780751</td>\n",
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+       "      <td>8780751</td>\n",
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+       "      <td>50.799377</td>\n",
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+       "      <td>0.492949</td>\n",
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+       "    <tr>\n",
+       "      <td>8780751</td>\n",
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+       "      <td>0.492949</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>360000 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               time   flowval  velval_list  depthval   qlatval    storageval  \\\n",
+       "key_index                                                                      \n",
+       "8777735         0.0  0.004196     0.185580  0.009796  0.000035 -6.722985e-08   \n",
+       "8777735        60.0  0.004196     0.185576  0.009795  0.000035 -4.190952e-08   \n",
+       "8777735       120.0  0.004196     0.185573  0.009795  0.000035 -4.627509e-08   \n",
+       "8777735       180.0  0.004196     0.185570  0.009795  0.000035 -6.810296e-08   \n",
+       "8777735       240.0  0.004196     0.185567  0.009794  0.000035 -7.770723e-08   \n",
+       "...             ...       ...          ...       ...       ...           ...   \n",
+       "8780751    863700.0  0.008624     0.134758  0.029789  0.000000 -1.558339e+01   \n",
+       "8780751    863760.0  0.008624     0.134757  0.029788  0.000000 -1.558343e+01   \n",
+       "8780751    863820.0  0.008623     0.134755  0.029788  0.000000 -1.558348e+01   \n",
+       "8780751    863880.0  0.008623     0.134754  0.029787  0.000000 -1.558353e+01   \n",
+       "8780751    863940.0  0.008623     0.134752  0.029786  0.000000 -1.558357e+01   \n",
+       "\n",
+       "           qlatCumval  kinCelerity   courant         X  \n",
+       "key_index                                               \n",
+       "8777735      0.002091     0.307107  1.675127  0.498455  \n",
+       "8777735      0.004183     0.307099  1.675087  0.498455  \n",
+       "8777735      0.006274     0.307094  1.675061  0.498455  \n",
+       "8777735      0.008365     0.307090  1.675035  0.498455  \n",
+       "8777735      0.010456     0.307085  1.675010  0.498455  \n",
+       "...               ...          ...       ...       ...  \n",
+       "8780751     50.799377     0.220286  0.145244  0.492948  \n",
+       "8780751     50.799377     0.220284  0.145242  0.492948  \n",
+       "8780751     50.799377     0.220281  0.145240  0.492948  \n",
+       "8780751     50.799377     0.220279  0.145239  0.492949  \n",
+       "8780751     50.799377     0.220276  0.145237  0.492949  \n",
+       "\n",
+       "[360000 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtered = df.loc[df.index.isin(major_segments), :]\n",
+    "df_filtered\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>flowval</th>\n",
+       "      <th>velval_list</th>\n",
+       "      <th>depthval</th>\n",
+       "      <th>qlatval</th>\n",
+       "      <th>storageval</th>\n",
+       "      <th>qlatCumval</th>\n",
+       "      <th>kinCelerity</th>\n",
+       "      <th>courant</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>933020027</td>\n",
+       "      <td>561600.0</td>\n",
+       "      <td>15.181515</td>\n",
+       "      <td>4.458032</td>\n",
+       "      <td>1.180036</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>58.131255</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.227305</td>\n",
+       "      <td>25.363832</td>\n",
+       "      <td>0.40232</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               time    flowval  velval_list  depthval  qlatval  storageval  \\\n",
+       "key_index                                                                    \n",
+       "933020027  561600.0  15.181515     4.458032  1.180036      0.0   58.131255   \n",
+       "\n",
+       "           qlatCumval  kinCelerity    courant        X  \n",
+       "key_index                                               \n",
+       "933020027         0.0     4.227305  25.363832  0.40232  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"courant\"] == 25.363832473754883]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Automated plotter based on filtered dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 14400\n",
+      "14400 28800\n",
+      "28800 43200\n",
+      "43200 57600\n",
+      "57600 72000\n",
+      "72000 86400\n",
+      "86400 100800\n",
+      "100800 115200\n",
+      "115200 129600\n",
+      "129600 144000\n",
+      "144000 158400\n",
+      "158400 172800\n",
+      "172800 187200\n",
+      "187200 201600\n",
+      "201600 216000\n",
+      "216000 230400\n",
+      "230400 244800\n",
+      "244800 259200\n",
+      "259200 273600\n",
+      "273600 288000\n",
+      "288000 302400\n",
+      "302400 316800\n",
+      "316800 331200\n",
+      "331200 345600\n",
+      "345600 360000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "from plotly.offline import plot\n",
+    "import plotly.io as pio\n",
+    "\n",
+    "df_indexed = df_filtered.reset_index()\n",
+    "test_chart = go.FigureWidget()\n",
+    "for x in range(0, 25):\n",
+    "    temp_df_range_1 = single_seg_length * (x)\n",
+    "    temp_df_range_2 = single_seg_length * (x + 1)\n",
+    "    print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed[temp_df_range_1:temp_df_range_2][\"flowval\"],\n",
+    "        name=\"segment \" + str(df_indexed[\"key_index\"][temp_df_range_1]),\n",
+    "    )\n",
+    "test_chart.layout.title = \"Timestep Chart \" + str(dt)\n",
+    "plot(test_chart)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jacob.hreha/github/t-route/src/python_framework_v02/nhd_io.py:53: YAMLLoadWarning:\n",
+      "\n",
+      "calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "begin program t-route ...\n",
+      "creating supernetwork connections set\n",
+      "supernetwork connections set complete\n",
+      "... in 0.04615497589111328 seconds.\n",
+      "organizing connections into reaches ...\n",
+      "reach organization complete\n",
+      "... in 0.007277727127075195 seconds.\n",
+      "reading waterbody parameter file ...\n",
+      "waterbodies complete\n",
+      "... in 0.022621631622314453 seconds.\n",
+      "ordering waterbody subnetworks ...\n",
+      "ordering waterbody subnetworks complete\n",
+      "... in 4.4345855712890625e-05 seconds.\n",
+      "setting waterbody initial states ...\n",
+      "waterbody initial states complete\n",
+      "... in 0.0272982120513916 seconds.\n",
+      "setting channel initial states ...\n",
+      "channel initial states complete\n",
+      "... in 0.029682159423828125 seconds.\n",
+      "creating qlateral array ...\n",
+      "qlateral array complete\n",
+      "... in 8.037819862365723 seconds.\n",
+      "executing routing computation ...\n",
+      "routing ordered reaches for networks of order 2 ... \n",
+      "... complete in 9.12061858177185 seconds.\n",
+      "max_courant: [27.53112221]\n",
+      "routing ordered reaches for networks of order 1 ... \n",
+      "... complete in 11.953770399093628 seconds.\n",
+      "max_courant: [0.]\n",
+      "routing ordered reaches for networks of order 0 ... \n",
+      "writing segment output to --> ../../test/output/text/933020089.csv\n",
+      "... complete in 24.70727014541626 seconds.\n",
+      "max_courant: [126.81378174]\n",
+      "ordered reach computation complete\n",
+      "... in 23.734419584274292 seconds.\n",
+      "program complete\n",
+      "... in 33.576523303985596 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_input_folder = root.joinpath(\"test\", \"input\", \"yaml\")\n",
+    "custom_input_file = \"florence_933020089_dt300.yaml\"\n",
+    "all_results = tr.route_supernetwork(\n",
+    "    tr.connections_g,\n",
+    "    tr.networks_g,\n",
+    "    tr.qlateral_g,\n",
+    "    tr.waterbodies_df_g,\n",
+    "    tr.waterbody_initial_states_df_g,\n",
+    "    tr.channel_initial_states_df_g,\n",
+    "    custom_input_file=custom_input_folder.joinpath(custom_input_file),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "florence_933020089_dt60.yaml\n",
+      "begin program t-route ...\n",
+      "creating supernetwork connections set\n",
+      "supernetwork connections set complete\n",
+      "... in 0.02855849266052246 seconds.\n",
+      "organizing connections into reaches ...\n",
+      "reach organization complete\n",
+      "... in 0.004696369171142578 seconds.\n",
+      "reading waterbody parameter file ...\n",
+      "waterbodies complete\n",
+      "... in 0.014914751052856445 seconds.\n",
+      "ordering waterbody subnetworks ...\n",
+      "ordering waterbody subnetworks complete\n",
+      "... in 4.363059997558594e-05 seconds.\n",
+      "setting waterbody initial states ...\n",
+      "waterbody initial states complete\n",
+      "... in 0.017179250717163086 seconds.\n",
+      "setting channel initial states ...\n",
+      "channel initial states complete\n",
+      "... in 0.018418550491333008 seconds.\n",
+      "creating qlateral array ...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jacob.hreha/github/t-route/src/python_framework_v02/nhd_io.py:53: YAMLLoadWarning:\n",
+      "\n",
+      "calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "qlateral array complete\n",
+      "... in 7.231583595275879 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "  0%|          | 0/1108 [00:00<?, ?it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executing routing computation ...\n",
+      "routing ordered reaches for networks of order 2 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      " 42%|████▏     | 467/1108 [00:44<01:01, 10.47it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 45.39464354515076 seconds.\n",
+      "max_courant: [5.51265717]\n",
+      "routing ordered reaches for networks of order 1 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      " 45%|████▌     | 499/1108 [01:01<02:14,  4.53it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 61.81171536445618 seconds.\n",
+      "max_courant: [0.]\n",
+      "routing ordered reaches for networks of order 0 ... \n",
+      "writing segment output to --> ../../test/output/text/933020089.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "100%|██████████| 1108/1108 [02:22<00:00,  5.13it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 143.57751870155334 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1108/1108 [02:26<00:00,  7.55it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_courant: [25.36383247]\n",
+      "ordered reach computation complete\n",
+      "... in 146.69346976280212 seconds.\n",
+      "program complete\n",
+      "... in 154.7804775238037 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: []\n",
+      "Index: []         key_index      time   flowval  velval_list  depthval   qlatval  \\\n",
+      "0         8777735       0.0  0.004196     0.185580  0.009796  0.000035   \n",
+      "1         8777735      60.0  0.004196     0.185576  0.009795  0.000035   \n",
+      "2         8777735     120.0  0.004196     0.185573  0.009795  0.000035   \n",
+      "3         8777735     180.0  0.004196     0.185570  0.009795  0.000035   \n",
+      "4         8777735     240.0  0.004196     0.185567  0.009794  0.000035   \n",
+      "...           ...       ...       ...          ...       ...       ...   \n",
+      "359995    8780751  863700.0  0.008624     0.134758  0.029789  0.000000   \n",
+      "359996    8780751  863760.0  0.008624     0.134757  0.029788  0.000000   \n",
+      "359997    8780751  863820.0  0.008623     0.134755  0.029788  0.000000   \n",
+      "359998    8780751  863880.0  0.008623     0.134754  0.029787  0.000000   \n",
+      "359999    8780751  863940.0  0.008623     0.134752  0.029786  0.000000   \n",
+      "\n",
+      "          storageval  qlatCumval  kinCelerity   courant         X  \n",
+      "0      -6.722985e-08    0.002091     0.307107  1.675127  0.498455  \n",
+      "1      -4.190952e-08    0.004183     0.307099  1.675087  0.498455  \n",
+      "2      -4.627509e-08    0.006274     0.307094  1.675061  0.498455  \n",
+      "3      -6.810296e-08    0.008365     0.307090  1.675035  0.498455  \n",
+      "4      -7.770723e-08    0.010456     0.307085  1.675010  0.498455  \n",
+      "...              ...         ...          ...       ...       ...  \n",
+      "359995 -1.558339e+01   50.799377     0.220286  0.145244  0.492948  \n",
+      "359996 -1.558343e+01   50.799377     0.220284  0.145242  0.492948  \n",
+      "359997 -1.558348e+01   50.799377     0.220281  0.145240  0.492948  \n",
+      "359998 -1.558353e+01   50.799377     0.220279  0.145239  0.492949  \n",
+      "359999 -1.558357e+01   50.799377     0.220276  0.145237  0.492949  \n",
+      "\n",
+      "[360000 rows x 11 columns]\n",
+      "florence_933020089_dt300.yaml\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jacob.hreha/github/t-route/src/python_framework_v02/nhd_io.py:53: YAMLLoadWarning:\n",
+      "\n",
+      "calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "begin program t-route ...\n",
+      "creating supernetwork connections set\n",
+      "supernetwork connections set complete\n",
+      "... in 0.032993316650390625 seconds.\n",
+      "organizing connections into reaches ...\n",
+      "reach organization complete\n",
+      "... in 1.0221493244171143 seconds.\n",
+      "reading waterbody parameter file ...\n",
+      "waterbodies complete\n",
+      "... in 0.015247106552124023 seconds.\n",
+      "ordering waterbody subnetworks ...\n",
+      "ordering waterbody subnetworks complete\n",
+      "... in 4.076957702636719e-05 seconds.\n",
+      "setting waterbody initial states ...\n",
+      "waterbody initial states complete\n",
+      "... in 0.017312049865722656 seconds.\n",
+      "setting channel initial states ...\n",
+      "channel initial states complete\n",
+      "... in 0.020360708236694336 seconds.\n",
+      "creating qlateral array ...\n",
+      "qlateral array complete\n",
+      "... in 8.384053230285645 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "  0%|          | 0/1108 [00:00<?, ?it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executing routing computation ...\n",
+      "routing ordered reaches for networks of order 2 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      " 42%|████▏     | 467/1108 [00:08<00:11, 53.99it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 10.942792415618896 seconds.\n",
+      "max_courant: [27.53112221]\n",
+      "routing ordered reaches for networks of order 1 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      " 45%|████▌     | 499/1108 [00:12<00:32, 18.70it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 15.264461040496826 seconds.\n",
+      "max_courant: [0.]\n",
+      "routing ordered reaches for networks of order 0 ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      " 45%|████▌     | 499/1108 [00:29<00:32, 18.70it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "writing segment output to --> ../../test/output/text/933020089.csv\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "100%|██████████| 1108/1108 [00:32<00:00, 21.22it/s]\u001b[A\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "... complete in 34.94488573074341 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1108/1108 [00:33<00:00, 32.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_courant: [126.81378174]\n",
+      "ordered reach computation complete\n",
+      "... in 33.65221977233887 seconds.\n",
+      "program complete\n",
+      "... in 45.40995812416077 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       key_index      time   flowval  velval_list  depthval   qlatval  \\\n",
+      "0        8777735       0.0  0.004196     0.185580  0.009796  0.000035   \n",
+      "1        8777735     300.0  0.004196     0.185566  0.009794  0.000035   \n",
+      "2        8777735     600.0  0.004195     0.185552  0.009793  0.000035   \n",
+      "3        8777735     900.0  0.004194     0.185539  0.009792  0.000035   \n",
+      "4        8777735    1200.0  0.004193     0.185527  0.009791  0.000035   \n",
+      "...          ...       ...       ...          ...       ...       ...   \n",
+      "71995    8780751  862500.0  0.008629     0.134789  0.029799  0.000000   \n",
+      "71996    8780751  862800.0  0.008628     0.134782  0.029797  0.000000   \n",
+      "71997    8780751  863100.0  0.008627     0.134775  0.029794  0.000000   \n",
+      "71998    8780751  863400.0  0.008625     0.134767  0.029792  0.000000   \n",
+      "71999    8780751  863700.0  0.008624     0.134759  0.029789  0.000000   \n",
+      "\n",
+      "         storageval  qlatCumval  kinCelerity   courant         X  \n",
+      "0     -3.361492e-07    0.010456     0.307107  8.375633  0.498455  \n",
+      "1     -6.897608e-07    0.020913     0.307083  8.375002  0.498455  \n",
+      "2     -8.949428e-07    0.031369     0.307061  8.374394  0.498455  \n",
+      "3     -1.292210e-06    0.041826     0.307039  8.373803  0.498456  \n",
+      "4     -1.654553e-06    0.052282     0.307019  8.373238  0.498456  \n",
+      "...             ...         ...          ...       ...       ...  \n",
+      "71995  1.420508e+02   50.799377     0.220335  0.726379  0.492946  \n",
+      "71996  1.420508e+02   50.799377     0.220324  0.726342  0.492947  \n",
+      "71997  1.420507e+02   50.799377     0.220312  0.726303  0.492947  \n",
+      "71998  1.420506e+02   50.799377     0.220300  0.726264  0.492948  \n",
+      "71999  1.420505e+02   50.799377     0.220288  0.726223  0.492948  \n",
+      "\n",
+      "[72000 rows x 11 columns]         key_index      time   flowval  velval_list  depthval   qlatval  \\\n",
+      "0         8777735       0.0  0.004196     0.185580  0.009796  0.000035   \n",
+      "1         8777735      60.0  0.004196     0.185576  0.009795  0.000035   \n",
+      "2         8777735     120.0  0.004196     0.185573  0.009795  0.000035   \n",
+      "3         8777735     180.0  0.004196     0.185570  0.009795  0.000035   \n",
+      "4         8777735     240.0  0.004196     0.185567  0.009794  0.000035   \n",
+      "...           ...       ...       ...          ...       ...       ...   \n",
+      "359995    8780751  863700.0  0.008624     0.134758  0.029789  0.000000   \n",
+      "359996    8780751  863760.0  0.008624     0.134757  0.029788  0.000000   \n",
+      "359997    8780751  863820.0  0.008623     0.134755  0.029788  0.000000   \n",
+      "359998    8780751  863880.0  0.008623     0.134754  0.029787  0.000000   \n",
+      "359999    8780751  863940.0  0.008623     0.134752  0.029786  0.000000   \n",
+      "\n",
+      "          storageval  qlatCumval  kinCelerity   courant         X  \n",
+      "0      -6.722985e-08    0.002091     0.307107  1.675127  0.498455  \n",
+      "1      -4.190952e-08    0.004183     0.307099  1.675087  0.498455  \n",
+      "2      -4.627509e-08    0.006274     0.307094  1.675061  0.498455  \n",
+      "3      -6.810296e-08    0.008365     0.307090  1.675035  0.498455  \n",
+      "4      -7.770723e-08    0.010456     0.307085  1.675010  0.498455  \n",
+      "...              ...         ...          ...       ...       ...  \n",
+      "359995 -1.558339e+01   50.799377     0.220286  0.145244  0.492948  \n",
+      "359996 -1.558343e+01   50.799377     0.220284  0.145242  0.492948  \n",
+      "359997 -1.558348e+01   50.799377     0.220281  0.145240  0.492948  \n",
+      "359998 -1.558353e+01   50.799377     0.220279  0.145239  0.492949  \n",
+      "359999 -1.558357e+01   50.799377     0.220276  0.145237  0.492949  \n",
+      "\n",
+      "[360000 rows x 11 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "custom_input_file_list = [\"florence_933020089_dt60.yaml\",\"florence_933020089_dt300.yaml\"]\n",
+    "from plotly.subplots import make_subplots\n",
+    "df_indexed_300 = pd.DataFrame()\n",
+    "df_indexed_60 = pd.DataFrame()\n",
+    "for c_i in (custom_input_file_list):\n",
+    "    print(c_i)\n",
+    "    import pathlib\n",
+    "    import sys\n",
+    "    import time\n",
+    "    import glob\n",
+    "    from tqdm import tqdm\n",
+    "    import multiprocessing\n",
+    "\n",
+    "    root = pathlib.Path(\"../../\").resolve()\n",
+    "    sys.path.append(str(root.joinpath(\"src\", \"python_framework_v02\")))\n",
+    "    sys.path.append(str(root.joinpath(\"src\", \"python_framework_v01\")))\n",
+    "    sys.path.append(str(root.joinpath(\"src\", \"python_routing_v01\")))\n",
+    "    sys.path.append(\".\")\n",
+    "    import nhd_io as nio\n",
+    "    import compute_nhd_routing_SingleSeg as tr\n",
+    "    import nhd_network_utilities_v01 as nnu\n",
+    "    import nhd_reach_utilities as nru\n",
+    "\n",
+    "    custom_input_folder = root.joinpath(\"test\", \"input\", \"yaml\")\n",
+    "    custom_input_file = c_i\n",
+    "    run_pocono2_test = None\n",
+    "\n",
+    "    supernetwork_parameters = None\n",
+    "    waterbody_parameters = None\n",
+    "    if custom_input_file:\n",
+    "        (\n",
+    "            supernetwork_parameters,\n",
+    "            waterbody_parameters,\n",
+    "            forcing_parameters,\n",
+    "            restart_parameters,\n",
+    "            output_parameters,\n",
+    "            run_parameters,\n",
+    "        ) = nio.read_custom_input(custom_input_folder.joinpath(custom_input_file))\n",
+    "\n",
+    "        break_network_at_waterbodies = run_parameters.get(\n",
+    "            \"break_network_at_waterbodies\", None\n",
+    "        )\n",
+    "\n",
+    "        dt = run_parameters.get(\"dt\", None)\n",
+    "        nts = run_parameters.get(\"nts\", None)\n",
+    "        qts_subdivisions = run_parameters.get(\"qts_subdivisions\", None)\n",
+    "        debuglevel = -1 * int(run_parameters.get(\"debuglevel\", 0))\n",
+    "        verbose = run_parameters.get(\"verbose\", None)\n",
+    "        showtiming = run_parameters.get(\"showtiming\", None)\n",
+    "        percentage_complete = run_parameters.get(\"percentage_complete\", None)\n",
+    "        do_network_analysis_only = run_parameters.get(\"do_network_analysis_only\", None)\n",
+    "        assume_short_ts = run_parameters.get(\"assume_short_ts\", None)\n",
+    "        parallel_compute = run_parameters.get(\"parallel_compute\", None)\n",
+    "        cpu_pool = run_parameters.get(\"cpu_pool\", None)\n",
+    "        sort_networks = run_parameters.get(\"sort_networks\", None)\n",
+    "\n",
+    "        csv_output = output_parameters.get(\"csv_output\", None)\n",
+    "        nc_output_folder = output_parameters.get(\"nc_output_folder\", None)\n",
+    "\n",
+    "        qlat_const = forcing_parameters.get(\"qlat_const\", None)\n",
+    "        qlat_input_file = forcing_parameters.get(\"qlat_input_file\", None)\n",
+    "        qlat_input_folder = forcing_parameters.get(\"qlat_input_folder\", None)\n",
+    "        qlat_file_pattern_filter = forcing_parameters.get(\"qlat_file_pattern_filter\", None)\n",
+    "        qlat_file_index_col = forcing_parameters.get(\"qlat_file_index_col\", None)\n",
+    "        qlat_file_value_col = forcing_parameters.get(\"qlat_file_value_col\", None)\n",
+    "\n",
+    "        wrf_hydro_channel_restart_file = restart_parameters.get(\n",
+    "            \"wrf_hydro_channel_restart_file\", None\n",
+    "        )\n",
+    "        wrf_hydro_channel_ID_crosswalk_file = restart_parameters.get(\n",
+    "            \"wrf_hydro_channel_ID_crosswalk_file\", None\n",
+    "        )\n",
+    "        wrf_hydro_channel_ID_crosswalk_file_field_name = restart_parameters.get(\n",
+    "            \"wrf_hydro_channel_ID_crosswalk_file_field_name\", None\n",
+    "        )\n",
+    "        wrf_hydro_channel_restart_upstream_flow_field_name = restart_parameters.get(\n",
+    "            \"wrf_hydro_channel_restart_upstream_flow_field_name\", None\n",
+    "        )\n",
+    "        wrf_hydro_channel_restart_downstream_flow_field_name = restart_parameters.get(\n",
+    "            \"wrf_hydro_channel_restart_downstream_flow_field_name\", None\n",
+    "        )\n",
+    "        wrf_hydro_channel_restart_depth_flow_field_name = restart_parameters.get(\n",
+    "            \"wrf_hydro_channel_restart_depth_flow_field_name\", None\n",
+    "        )\n",
+    "\n",
+    "        wrf_hydro_waterbody_restart_file = restart_parameters.get(\n",
+    "            \"wrf_hydro_waterbody_restart_file\", None\n",
+    "        )\n",
+    "        wrf_hydro_waterbody_ID_crosswalk_file = restart_parameters.get(\n",
+    "            \"wrf_hydro_waterbody_ID_crosswalk_file\", None\n",
+    "        )\n",
+    "        wrf_hydro_waterbody_ID_crosswalk_file_field_name = restart_parameters.get(\n",
+    "            \"wrf_hydro_waterbody_ID_crosswalk_file_field_name\", None\n",
+    "        )\n",
+    "        wrf_hydro_waterbody_crosswalk_filter_file = restart_parameters.get(\n",
+    "            \"wrf_hydro_waterbody_crosswalk_filter_file\", None\n",
+    "        )\n",
+    "        wrf_hydro_waterbody_crosswalk_filter_file_field_name = restart_parameters.get(\n",
+    "            \"wrf_hydro_waterbody_crosswalk_filter_file_field_name\", None\n",
+    "        )\n",
+    "\n",
+    "    # Any specific commandline arguments will override the file\n",
+    "    # TODO: There are probably some pathological collisions that could\n",
+    "    # arise from this ordering ... check these out.\n",
+    "\n",
+    "    if run_pocono2_test:\n",
+    "        if verbose:\n",
+    "            print(\"running test case for Pocono_TEST2 domain\")\n",
+    "        # Overwrite the following test defaults\n",
+    "        supernetwork = \"Pocono_TEST2\"\n",
+    "        break_network_at_waterbodies = False\n",
+    "        qts_subdivisions = 1  # change qts_subdivisions = 1 as  default\n",
+    "        dt = 300 / qts_subdivisions\n",
+    "        nts = 144 * qts_subdivisions\n",
+    "        csv_output = {\"csv_output_folder\": os.path.join(root, \"test\", \"output\", \"text\")}\n",
+    "        nc_output_folder = os.path.join(root, \"test\", \"output\", \"text\")\n",
+    "        # test 1. Take lateral flow from re-formatted wrf-hydro output from Pocono Basin simulation\n",
+    "        qlat_input_file = os.path.join(\n",
+    "            root, r\"test/input/geo/PoconoSampleData2/Pocono_ql_testsamp1_nwm_mc.csv\"\n",
+    "        )\n",
+    "    if showtiming:\n",
+    "        program_start_time = time.time()\n",
+    "    if verbose:\n",
+    "        print(f\"begin program t-route ...\")\n",
+    "\n",
+    "    # STEP 1: Read the supernetwork dataset and build the connections graph\n",
+    "    if verbose:\n",
+    "        print(\"creating supernetwork connections set\")\n",
+    "    if showtiming:\n",
+    "        start_time = time.time()\n",
+    "\n",
+    "    if supernetwork_parameters:\n",
+    "        supernetwork_values = nnu.get_nhd_connections(\n",
+    "            supernetwork_parameters=supernetwork_parameters,\n",
+    "            verbose=False,\n",
+    "            debuglevel=debuglevel,\n",
+    "        )\n",
+    "\n",
+    "    else:\n",
+    "        test_folder = os.path.join(root, r\"test\")\n",
+    "        geo_input_folder = os.path.join(test_folder, r\"input\", r\"geo\")\n",
+    "        supernetwork_parameters, supernetwork_values = nnu.set_networks(\n",
+    "            supernetwork=supernetwork,\n",
+    "            geo_input_folder=geo_input_folder,\n",
+    "            verbose=False,\n",
+    "            debuglevel=debuglevel,\n",
+    "        )\n",
+    "        waterbody_parameters = nnu.set_waterbody_parameters(\n",
+    "            supernetwork=supernetwork,\n",
+    "            geo_input_folder=geo_input_folder,\n",
+    "            verbose=False,\n",
+    "            debuglevel=debuglevel,\n",
+    "        )\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(\"supernetwork connections set complete\")\n",
+    "    if showtiming:\n",
+    "        print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "\n",
+    "    connections = supernetwork_values[0]\n",
+    "    # STEP 2: Separate the networks and build the sub-graph of reaches within each network\n",
+    "    if showtiming:\n",
+    "        start_time = time.time()\n",
+    "    if verbose:\n",
+    "        print(\"organizing connections into reaches ...\")\n",
+    "    networks = nru.compose_networks(\n",
+    "        supernetwork_values,\n",
+    "        break_network_at_waterbodies=break_network_at_waterbodies,\n",
+    "        verbose=False,\n",
+    "        debuglevel=debuglevel,\n",
+    "        showtiming=showtiming,\n",
+    "    )\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(\"reach organization complete\")\n",
+    "    if showtiming:\n",
+    "        print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "        start_time = time.time()\n",
+    "    # STEP 3: Organize Network for Waterbodies\n",
+    "    if break_network_at_waterbodies:\n",
+    "        if showtiming:\n",
+    "            start_time = time.time()\n",
+    "        if verbose:\n",
+    "            print(\"reading waterbody parameter file ...\")\n",
+    "\n",
+    "        ## STEP 3a: Read waterbody parameter file\n",
+    "        waterbodies_values = supernetwork_values[12]\n",
+    "        waterbodies_segments = supernetwork_values[13]\n",
+    "        connections_tailwaters = supernetwork_values[4]\n",
+    "\n",
+    "        waterbodies_df = nio.read_waterbody_df(waterbody_parameters, waterbodies_values,)\n",
+    "        waterbodies_df = waterbodies_df.sort_index(axis=\"index\").sort_index(axis=\"columns\")\n",
+    "\n",
+    "        nru.order_networks(connections, networks, connections_tailwaters)\n",
+    "\n",
+    "        if verbose:\n",
+    "            print(\"waterbodies complete\")\n",
+    "        if showtiming:\n",
+    "            print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "            start_time = time.time()\n",
+    "\n",
+    "        ## STEP 3b: Order subnetworks above and below reservoirs\n",
+    "        if showtiming:\n",
+    "            start_time = time.time()\n",
+    "        if verbose:\n",
+    "            print(\"ordering waterbody subnetworks ...\")\n",
+    "\n",
+    "        max_network_seqorder = -1\n",
+    "        for network in networks:\n",
+    "            max_network_seqorder = max(\n",
+    "                networks[network][\"network_seqorder\"], max_network_seqorder\n",
+    "            )\n",
+    "        ordered_networks = {}\n",
+    "\n",
+    "        for terminal_segment, network in networks.items():\n",
+    "            if network[\"network_seqorder\"] not in ordered_networks:\n",
+    "                ordered_networks[network[\"network_seqorder\"]] = []\n",
+    "            ordered_networks[network[\"network_seqorder\"]].append(\n",
+    "                (terminal_segment, network)\n",
+    "            )\n",
+    "\n",
+    "        if verbose:\n",
+    "            print(\"ordering waterbody subnetworks complete\")\n",
+    "        if showtiming:\n",
+    "            print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "            start_time = time.time()\n",
+    "\n",
+    "    else:\n",
+    "        # If we are not splitting the networks, we can put them all in one order\n",
+    "        max_network_seqorder = 0\n",
+    "        ordered_networks = {}\n",
+    "        ordered_networks[0] = [\n",
+    "            (terminal_segment, network) for terminal_segment, network in networks.items()\n",
+    "        ]\n",
+    "\n",
+    "    if do_network_analysis_only:\n",
+    "        sys.exit()\n",
+    "\n",
+    "    if break_network_at_waterbodies:\n",
+    "        ## STEP 3c: Handle Waterbody Initial States\n",
+    "        if showtiming:\n",
+    "            start_time = time.time()\n",
+    "        if verbose:\n",
+    "            print(\"setting waterbody initial states ...\")\n",
+    "\n",
+    "        if wrf_hydro_waterbody_restart_file:\n",
+    "\n",
+    "            waterbody_initial_states_df = nio.get_reservoir_restart_from_wrf_hydro(\n",
+    "                wrf_hydro_waterbody_restart_file,\n",
+    "                wrf_hydro_waterbody_ID_crosswalk_file,\n",
+    "                wrf_hydro_waterbody_ID_crosswalk_file_field_name,\n",
+    "                wrf_hydro_waterbody_crosswalk_filter_file,\n",
+    "                wrf_hydro_waterbody_crosswalk_filter_file_field_name,\n",
+    "            )\n",
+    "        else:\n",
+    "            # TODO: Consider adding option to read cold state from route-link file\n",
+    "            waterbody_initial_ds_flow_const = 0.0\n",
+    "            waterbody_initial_depth_const = 0.0\n",
+    "            # Set initial states from cold-state\n",
+    "            waterbody_initial_states_df = pd.DataFrame(\n",
+    "                0, index=waterbodies_df.index, columns=[\"qd0\", \"h0\",], dtype=\"float32\"\n",
+    "            )\n",
+    "            # TODO: This assignment could probably by done in the above call\n",
+    "            waterbody_initial_states_df[\"qd0\"] = waterbody_initial_ds_flow_const\n",
+    "            waterbody_initial_states_df[\"h0\"] = waterbody_initial_depth_const\n",
+    "            waterbody_initial_states_df[\"index\"] = range(len(waterbody_initial_states_df))\n",
+    "\n",
+    "        if verbose:\n",
+    "            print(\"waterbody initial states complete\")\n",
+    "        if showtiming:\n",
+    "            print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "            start_time = time.time()\n",
+    "    # STEP 4: Handle Channel Initial States\n",
+    "    if showtiming:\n",
+    "        start_time = time.time()\n",
+    "    if verbose:\n",
+    "        print(\"setting channel initial states ...\")\n",
+    "\n",
+    "    if wrf_hydro_channel_restart_file:\n",
+    "\n",
+    "        channel_initial_states_df = nio.get_stream_restart_from_wrf_hydro(\n",
+    "            wrf_hydro_channel_restart_file,\n",
+    "            wrf_hydro_channel_ID_crosswalk_file,\n",
+    "            wrf_hydro_channel_ID_crosswalk_file_field_name,\n",
+    "            wrf_hydro_channel_restart_upstream_flow_field_name,\n",
+    "            wrf_hydro_channel_restart_downstream_flow_field_name,\n",
+    "            wrf_hydro_channel_restart_depth_flow_field_name,\n",
+    "        )\n",
+    "    else:\n",
+    "        # TODO: Consider adding option to read cold state from route-link file\n",
+    "        channel_initial_us_flow_const = 0.0\n",
+    "        channel_initial_ds_flow_const = 0.0\n",
+    "        channel_initial_depth_const = 0.0\n",
+    "        # Set initial states from cold-state\n",
+    "        channel_initial_states_df = pd.DataFrame(\n",
+    "            0, index=connections.keys(), columns=[\"qu0\", \"qd0\", \"h0\",], dtype=\"float32\"\n",
+    "        )\n",
+    "        channel_initial_states_df[\"qu0\"] = channel_initial_us_flow_const\n",
+    "        channel_initial_states_df[\"qd0\"] = channel_initial_ds_flow_const\n",
+    "        channel_initial_states_df[\"h0\"] = channel_initial_depth_const\n",
+    "        channel_initial_states_df[\"index\"] = range(len(channel_initial_states_df))\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(\"channel initial states complete\")\n",
+    "    if showtiming:\n",
+    "        print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "        start_time = time.time()\n",
+    "    # STEP 5: Read (or set) QLateral Inputs\n",
+    "    if showtiming:\n",
+    "        start_time = time.time()\n",
+    "    if verbose:\n",
+    "        print(\"creating qlateral array ...\")\n",
+    "\n",
+    "    # initialize qlateral dict\n",
+    "    qlateral = {}\n",
+    "\n",
+    "    if qlat_input_folder:\n",
+    "        qlat_files = []\n",
+    "        for pattern in qlat_file_pattern_filter:\n",
+    "            qlat_files.extend(glob.glob(qlat_input_folder + pattern))\n",
+    "        qlat_df = nio.get_ql_from_wrf_hydro(\n",
+    "            qlat_files=qlat_files,\n",
+    "            index_col=qlat_file_index_col,\n",
+    "            value_col=qlat_file_value_col,\n",
+    "        )\n",
+    "\n",
+    "    elif qlat_input_file:\n",
+    "        qlat_df = nio.get_ql_from_csv(qlat_input_file)\n",
+    "\n",
+    "    else:\n",
+    "        qlat_df = pd.DataFrame(\n",
+    "            qlat_const, index=connections.keys(), columns=range(nts), dtype=\"float32\"\n",
+    "        )\n",
+    "\n",
+    "    for index, row in qlat_df.iterrows():\n",
+    "        qlateral[index] = row.tolist()\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(\"qlateral array complete\")\n",
+    "    if showtiming:\n",
+    "        print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "        start_time = time.time()\n",
+    "    # STEP 6: Sort the ordered networks\n",
+    "    if sort_networks:\n",
+    "        if showtiming:\n",
+    "            start_time = time.time()\n",
+    "        if verbose:\n",
+    "            print(\"sorting the ordered networks ...\")\n",
+    "\n",
+    "        for nsq in range(max_network_seqorder, -1, -1):\n",
+    "            sort_ordered_network(ordered_networks[nsq], True)\n",
+    "\n",
+    "        if verbose:\n",
+    "            print(\"sorting complete\")\n",
+    "        if showtiming:\n",
+    "            print(\"... in %s seconds.\" % (time.time() - start_time))\n",
+    "            start_time = time.time()\n",
+    "    # Define the pool after we create the static global objects (and collect the garbage)\n",
+    "    if parallel_compute:\n",
+    "        import gc\n",
+    "\n",
+    "        gc.collect()\n",
+    "        pool = multiprocessing.Pool(cpu_pool)\n",
+    "\n",
+    "    flowveldepth_connect = (\n",
+    "        {}\n",
+    "    )  # dict to contain values to transfer from upstream to downstream networks\n",
+    "    ################### Main Execution Loop across ordered networks\n",
+    "    if showtiming:\n",
+    "        main_start_time = time.time()\n",
+    "    if verbose:\n",
+    "        print(f\"executing routing computation ...\")\n",
+    "\n",
+    "    compute_network_func = tr.compute_network\n",
+    "\n",
+    "    tr.connections_g = connections\n",
+    "    tr.networks_g = networks\n",
+    "    tr.qlateral_g = qlateral\n",
+    "    tr.waterbodies_df_g = waterbodies_df\n",
+    "    tr.waterbody_initial_states_df_g = waterbody_initial_states_df\n",
+    "    tr.channel_initial_states_df_g = channel_initial_states_df\n",
+    "\n",
+    "    progress_count = 0\n",
+    "    percentage_complete = True\n",
+    "    if percentage_complete:\n",
+    "        for nsq in range(max_network_seqorder, -1, -1):\n",
+    "            for terminal_segment, network in ordered_networks[nsq]:\n",
+    "                progress_count += len(network[\"all_segments\"])\n",
+    "        pbar = tqdm(total=(progress_count))\n",
+    "\n",
+    "    for nsq in range(max_network_seqorder, -1, -1):\n",
+    "\n",
+    "        if parallel_compute:\n",
+    "            nslist = []\n",
+    "        results = []\n",
+    "\n",
+    "        current_index_total = 0\n",
+    "\n",
+    "        for terminal_segment, network in ordered_networks[nsq]:\n",
+    "\n",
+    "            if percentage_complete:\n",
+    "                if current_index_total == 0:\n",
+    "                    pbar.update(0)\n",
+    "\n",
+    "            if break_network_at_waterbodies:\n",
+    "                waterbody = waterbodies_segments.get(terminal_segment)\n",
+    "            else:\n",
+    "                waterbody = None\n",
+    "            if not parallel_compute:  # serial execution\n",
+    "                if showtiming:\n",
+    "                    start_time = time.time()\n",
+    "                if verbose:\n",
+    "                    print(\n",
+    "                        f\"routing ordered reaches for terminal segment {terminal_segment} ...\"\n",
+    "                    )\n",
+    "\n",
+    "                results.append(\n",
+    "                    compute_network_func(\n",
+    "                        flowveldepth_connect=flowveldepth_connect,\n",
+    "                        terminal_segment=terminal_segment,\n",
+    "                        supernetwork_parameters=supernetwork_parameters,\n",
+    "                        waterbody_parameters=waterbody_parameters,\n",
+    "                        waterbody=waterbody,\n",
+    "                        nts=nts,\n",
+    "                        dt=dt,\n",
+    "                        qts_subdivisions=qts_subdivisions,\n",
+    "                        verbose=verbose,\n",
+    "                        debuglevel=debuglevel,\n",
+    "                        csv_output=csv_output,\n",
+    "                        nc_output_folder=nc_output_folder,\n",
+    "                        assume_short_ts=assume_short_ts,\n",
+    "                    )\n",
+    "                )\n",
+    "\n",
+    "                if showtiming:\n",
+    "                    print(\"... complete in %s seconds.\" % (time.time() - start_time))\n",
+    "                if percentage_complete:\n",
+    "                    pbar.update(len(network[\"all_segments\"]))\n",
+    "\n",
+    "            else:  # parallel execution\n",
+    "                nslist.append(\n",
+    "                    [\n",
+    "                        flowveldepth_connect,\n",
+    "                        terminal_segment,\n",
+    "                        supernetwork_parameters,  # TODO: This should probably be global...\n",
+    "                        waterbody_parameters,\n",
+    "                        waterbody,\n",
+    "                        nts,\n",
+    "                        dt,\n",
+    "                        qts_subdivisions,\n",
+    "                        verbose,\n",
+    "                        debuglevel,\n",
+    "                        csv_output,\n",
+    "                        nc_output_folder,\n",
+    "                        assume_short_ts,\n",
+    "                    ]\n",
+    "                )\n",
+    "\n",
+    "        if parallel_compute:\n",
+    "            if verbose:\n",
+    "                print(f\"routing ordered reaches for networks of order {nsq} ... \")\n",
+    "            if debuglevel <= -2:\n",
+    "                print(f\"reaches to be routed include:\")\n",
+    "                print(f\"{[network[0] for network in ordered_networks[nsq]]}\")\n",
+    "            # with pool:\n",
+    "            # with multiprocessing.Pool() as pool:\n",
+    "            results = pool.starmap(compute_network_func, nslist)\n",
+    "\n",
+    "            if showtiming:\n",
+    "                print(\"... complete in %s seconds.\" % (time.time() - start_time))\n",
+    "            if percentage_complete:\n",
+    "                pbar.update(\n",
+    "                    sum(\n",
+    "                        len(network[1][\"all_segments\"]) for network in ordered_networks[nsq]\n",
+    "                    )\n",
+    "                )\n",
+    "                # print(f\"{[network[0] for network in ordered_networks[nsq]]}\")\n",
+    "\n",
+    "        max_courant = 0\n",
+    "        maxa = []\n",
+    "        for result in results:\n",
+    "            for seg in result:\n",
+    "                maxa.extend(result[seg][:, 8:9])\n",
+    "        max_courant = max(maxa)\n",
+    "        print(f\"max_courant: {max_courant}\")\n",
+    "\n",
+    "        if (\n",
+    "            nsq > 0\n",
+    "        ):  # We skip this step for zero-order networks, i.e., those that have no downstream dependents\n",
+    "            flowveldepth_connect = (\n",
+    "                {}\n",
+    "            )  # There is no need to preserve previously passed on values -- so we clear the dictionary\n",
+    "            for i, (terminal_segment, network) in enumerate(ordered_networks[nsq]):\n",
+    "                # seg = network[\"reaches\"][network[\"terminal_reach\"]][\"reach_tail\"]\n",
+    "                seg = terminal_segment\n",
+    "                flowveldepth_connect[seg] = {}\n",
+    "                flowveldepth_connect[seg] = results[i][seg]\n",
+    "                # TODO: The value passed here could be much more specific to\n",
+    "                # TODO: exactly and only the most recent time step for the passing reach\n",
+    "\n",
+    "    if parallel_compute:\n",
+    "        pool.close()\n",
+    "\n",
+    "    if percentage_complete:\n",
+    "        pbar.close()\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(\"ordered reach computation complete\")\n",
+    "    if showtiming:\n",
+    "        print(\"... in %s seconds.\" % (time.time() - main_start_time))\n",
+    "    if verbose:\n",
+    "        print(\"program complete\")\n",
+    "    if showtiming:\n",
+    "        print(\"... in %s seconds.\" % (time.time() - program_start_time))\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    all_results_new = {}\n",
+    "    seg_courant_maxes = []\n",
+    "    time = []\n",
+    "    flowval = []  # flowval\n",
+    "    velval_list = []  # velval\n",
+    "    depthval = []  # depthval\n",
+    "    qlatval = []  # qlatval\n",
+    "    storageval = []  # storageval\n",
+    "    qlatCumval = []  # qlatCumval\n",
+    "    kinCelerity = []  # ck\n",
+    "    courant = []  # cn\n",
+    "    X = []  # X\n",
+    "\n",
+    "    for result in results:\n",
+    "        all_results_new.update(result)\n",
+    "    \n",
+    "    # print(all_results[8780801][1][0])\n",
+    "\n",
+    "    df = pd.DataFrame()\n",
+    "    # print(all_results)\n",
+    "\n",
+    "    for key, data in all_results_new.items():\n",
+    "        time.extend(data[:, 0])  # time\n",
+    "        flowval.extend(data[:, 1])  # flowval\n",
+    "        velval_list.extend(data[:, 2])  # velval\n",
+    "        depthval.extend(data[:, 3])  # depthval\n",
+    "        qlatval.extend(data[:, 4])  # qlatval\n",
+    "        storageval.extend(data[:, 5])  # storageval\n",
+    "        qlatCumval.extend(data[:, 6])  # qlatCumval\n",
+    "        kinCelerity.extend(data[:, 7])  # ck\n",
+    "        courant.extend(data[:, 8])  # cn\n",
+    "        X.extend(data[:, 9])  # X\n",
+    "#     print(all_results_new)\n",
+    "    single_seg_length = len(all_results_new[933020089][:, 0])\n",
+    "\n",
+    "    if c_i == \"florence_933020089_dt300.yaml\":\n",
+    "        single_seg_length_300 = single_seg_length\n",
+    "    else:\n",
+    "        single_seg_length_60 = single_seg_length\n",
+    "#     else:\n",
+    "#         single_seg_length_10 = single_seg_length\n",
+    "#     print(single_seg_length_300,single_seg_length_60)\n",
+    "    # single_seg_length * 611\n",
+    "\n",
+    "    key_index = all_results.keys()\n",
+    "    key_index = list(\n",
+    "        itertools.chain.from_iterable(\n",
+    "            itertools.repeat(x, single_seg_length) for x in key_index\n",
+    "        )\n",
+    "    )\n",
+    "    data = {\n",
+    "        \"key_index\": list(key_index),\n",
+    "        \"time\": list(time),\n",
+    "        \"flowval\": list(flowval),\n",
+    "        \"velval_list\": list(velval_list),\n",
+    "        \"depthval\": list(depthval),\n",
+    "        \"qlatval\": list(qlatval),\n",
+    "        \"storageval\": list(storageval),\n",
+    "        \"qlatCumval\": list(qlatCumval),\n",
+    "        \"kinCelerity\": list(kinCelerity),\n",
+    "        \"courant\": list(courant),\n",
+    "        \"X\": list(X),\n",
+    "    }\n",
+    "    df = pd.DataFrame(data)\n",
+    "    df = df.set_index(\"key_index\")\n",
+    "    df = df.reset_index()\n",
+    "    df = df.set_index(\"key_index\")\n",
+    "\n",
+    "\n",
+    "    seg_list = []\n",
+    "    seg_courant_maxes = []\n",
+    "    for seg in all_results_new:\n",
+    "        seg_list.append(seg)\n",
+    "        seg_courant_maxes.append(max(all_results_new[seg][:, tr.courant_index]))\n",
+    "    zipped = zip(seg_list, seg_courant_maxes)\n",
+    "    zipped = list(zipped)\n",
+    "    res = sorted(zipped, key=lambda x: x[1], reverse=True)\n",
+    "    # A.sort(reverse=True)\n",
+    "    res = (res)[:25]\n",
+    "    # print((res)[:25])\n",
+    "\n",
+    "    major_segments = []\n",
+    "    for x, y in res[:25]:\n",
+    "        major_segments.append(x)\n",
+    "    # major_segments\n",
+    "\n",
+    "    df_filtered = df.loc[df.index.isin(major_segments), :]\n",
+    "\n",
+    "    if c_i == \"florence_933020089_dt300.yaml\":\n",
+    "        df_indexed_300 = df_filtered.reset_index()\n",
+    "    else:\n",
+    "        df_indexed_60 = df_filtered.reset_index()\n",
+    "    print(df_indexed_300,df_indexed_60)\n",
+    "#     else:\n",
+    "#         df_indexed_10 = df_filtered.reset_index()\n",
+    "\n",
+    "test_chart = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
+    "# test_chart = go.FigureWidget()\n",
+    "for x in range(0,len(major_segments)):\n",
+    "    temp_df_range_1 = single_seg_length_300 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_300 * (x + 1)\n",
+    "    #         print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_300[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_300[temp_df_range_1:temp_df_range_2][\"flowval\"],\n",
+    "        name=\"segment flowval \" + str(df_indexed_300[\"key_index\"][temp_df_range_1]) + \" \" + str(300),\n",
+    "    )\n",
+    "    temp_df_range_1 = single_seg_length_60 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_60 * (x + 1)\n",
+    "    #         print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_60[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_60[temp_df_range_1:temp_df_range_2][\"flowval\"],\n",
+    "        name=\"segment flowval \" + str(df_indexed_60[\"key_index\"][temp_df_range_1]) + \" \" + str(60),\n",
+    "    )\n",
+    "    temp_df_range_1 = single_seg_length_300 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_300 * (x + 1)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_300[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_300[temp_df_range_1:temp_df_range_2][\"courant\"],secondary_y=True,\n",
+    "        name=\"segment courant \" + str(df_indexed_300[\"key_index\"][temp_df_range_1]) + \" \" + str(300),\n",
+    "    )\n",
+    "    temp_df_range_1 = single_seg_length_60 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_60 * (x + 1)\n",
+    "    #         print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_60[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_60[temp_df_range_1:temp_df_range_2][\"courant\"],secondary_y=True,\n",
+    "        name=\"segment courant \" + str(df_indexed_60[\"key_index\"][temp_df_range_1]) + \" \" + str(60),\n",
+    "    )\n",
+    "# test_chart.update_yaxes(title_text=\"<b>primary</b> yaxis title\", secondary_y=False)\n",
+    "# test_chart.update_yaxes(title_text=\"<b>secondary</b> yaxis title\", secondary_y=True)\n",
+    "\n",
+    "test_chart.layout.title = \"Timestep Chart 300, 60\"\n",
+    "plot(test_chart)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "test_chart = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
+    "# test_chart = go.FigureWidget()\n",
+    "\n",
+    "for x in range(0,len(major_segments)):\n",
+    "    r = lambda: random.randint(0,255)\n",
+    "    temp_color1 = ('#%02X%02X%02X' % (r(),r(),r()))\n",
+    "    temp_color2 = ('#%02X%02X%02X' % (r(),r(),r()))\n",
+    "#     print(temp_color1,temp_color2)\n",
+    "    temp_df_range_1 = single_seg_length_300 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_300 * (x + 1)\n",
+    "    #         print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_300[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_300[temp_df_range_1:temp_df_range_2][\"flowval\"],line_color=temp_color1,\n",
+    "        name=\"segment flowval \" + str(df_indexed_300[\"key_index\"][temp_df_range_1]) + \" \" + str(300),\n",
+    "\n",
+    "    )\n",
+    "    temp_df_range_1 = single_seg_length_60 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_60 * (x + 1)\n",
+    "    #         print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_60[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_60[temp_df_range_1:temp_df_range_2][\"flowval\"],line_color=temp_color2,\n",
+    "        name=\"segment flowval \" + str(df_indexed_60[\"key_index\"][temp_df_range_1]) + \" \" + str(60),\n",
+    "\n",
+    "    )\n",
+    "    temp_df_range_1 = single_seg_length_300 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_300 * (x + 1)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_300[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_300[temp_df_range_1:temp_df_range_2][\"courant\"],line_color=temp_color1,\n",
+    "        secondary_y=True,\n",
+    "        name=\"segment courant \" + str(df_indexed_300[\"key_index\"][temp_df_range_1]) + \" \" + str(300),\n",
+    "\n",
+    "    )\n",
+    "    temp_df_range_1 = single_seg_length_60 * (x)\n",
+    "    temp_df_range_2 = single_seg_length_60 * (x + 1)\n",
+    "    #         print(temp_df_range_1, temp_df_range_2)\n",
+    "    test_chart.add_scatter(\n",
+    "        x=df_indexed_60[temp_df_range_1:temp_df_range_2][\"time\"],\n",
+    "        y=df_indexed_60[temp_df_range_1:temp_df_range_2][\"courant\"],line_color=temp_color2,\n",
+    "        secondary_y=True,\n",
+    "        name=\"segment courant \" + str(df_indexed_60[\"key_index\"][temp_df_range_1]) + \" \" + str(60),\n",
+    "    )\n",
+    "test_chart.update_yaxes(title_text=\"Flow Value\", secondary_y=False)\n",
+    "test_chart.update_yaxes(title_text=\"Courant Value\", secondary_y=True)\n",
+    "test_chart.update_xaxes(title_text=\"Time Step\")\n",
+    "test_chart.layout.title = \"Timestep Chart 300, 60\"\n",
+    "plot(test_chart)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'#A79870'"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "r = lambda: random.randint(0,255)\n",
+    "gen1 = ('#%02X%02X%02X' % (r(),r(),r()))\n",
+    "gen1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_lengths = df_indexed_300\n",
+    "lengths_temp = []\n",
+    "for i, j in df_lengths.iterrows(): \n",
+    "    lengths_temp.append(supernetwork_values[0][j['key_index']]['length'])\n",
+    "df_lengths['lengths'] = pd.Series(lengths_temp, index=df_lengths.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_lengths.index.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_lengths = df_lengths.reset_index(drop=True)\n",
+    "df_lengths = df_lengths.set_index('key_index')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(major_segments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "length_list = []\n",
+    "courant_list = []\n",
+    "flowval_list = []\n",
+    "for i in major_segments:\n",
+    "    length_list.append(max(df_lengths.loc[i]['lengths'][:single_seg_length_300]))\n",
+    "    courant_list.append(max(df_lengths.loc[i]['courant'][:single_seg_length_300]))\n",
+    "    flowval_list.append(max(df_lengths.loc[i]['flowval'][:single_seg_length_300]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[126.81378173828125,\n",
+       " 73.86154174804688,\n",
+       " 47.82014846801758,\n",
+       " 41.540863037109375,\n",
+       " 36.24374771118164,\n",
+       " 23.67105484008789,\n",
+       " 17.922224044799805,\n",
+       " 13.929777145385742,\n",
+       " 12.283413887023926,\n",
+       " 10.532113075256348,\n",
+       " 9.097070693969727,\n",
+       " 8.125431060791016,\n",
+       " 8.072996139526367,\n",
+       " 7.252985954284668,\n",
+       " 6.937002658843994,\n",
+       " 6.692083358764648,\n",
+       " 6.539554119110107,\n",
+       " 6.3316121101379395,\n",
+       " 5.747074604034424,\n",
+       " 5.367031574249268,\n",
+       " 5.337118625640869,\n",
+       " 5.220866680145264,\n",
+       " 5.2163591384887695,\n",
+       " 4.962591648101807,\n",
+       " 4.876999855041504]"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courant_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         8777735\n",
+       "1         8777735\n",
+       "2         8777735\n",
+       "3         8777735\n",
+       "4         8777735\n",
+       "           ...   \n",
+       "359995    8780751\n",
+       "359996    8780751\n",
+       "359997    8780751\n",
+       "359998    8780751\n",
+       "359999    8780751\n",
+       "Name: key_index, Length: 360000, dtype: int64"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_indexed_60['key_index']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# table1 = zip(length_list,major_segments)\n",
+    "# table2 = sorted(table1, key=lambda x: x[1], reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = length_list\n",
+    "a = major_segments\n",
+    "c = courant_list\n",
+    "d = flowval_list\n",
+    "res = \"\\n\".join(\"{}      {}      {}       {}\".format(w   , x  , y   , z  ) for w, x, y, z in sorted(zip(b, d, c, a),reverse=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0      56.463199615478516      12.283413887023926       933020058\n",
+      "5.0      518.8282470703125      41.540863037109375       8778581\n",
+      "10.0      15.180281639099121      126.81378173828125       933020027\n",
+      "11.0      0.13794074952602386      47.82014846801758       933020017\n",
+      "11.0      8.245078086853027      73.86154174804688       8777735\n",
+      "14.0      15.526399612426758      8.125431060791016       933020036\n",
+      "17.0      0.5986793637275696      23.67105484008789       933020061\n",
+      "17.0      20.703197479248047      36.24374771118164       933020059\n",
+      "23.0      105.91104888916016      5.2163591384887695       8779003\n",
+      "28.0      1.60250723361969      13.929777145385742       933020020\n",
+      "42.0      0.5486629605293274      9.097070693969727       8777485\n",
+      "46.0      1.887703776359558      5.220866680145264       933020050\n",
+      "54.0      0.33712542057037354      6.692083358764648       8777477\n",
+      "54.0      27.456575393676758      17.922224044799805       8780597\n",
+      "57.0      28.86312484741211      10.532113075256348       933020023\n",
+      "75.0      0.2937638759613037      6.539554119110107       8780495\n",
+      "91.0      12.485885620117188      4.962591648101807       8780751\n",
+      "114.0      113.275390625      7.252985954284668       8778021\n",
+      "126.0      1.8319164514541626      8.072996139526367       8778795\n",
+      "145.0      41.112030029296875      5.747074604034424       8777481\n",
+      "152.0      119.80558013916016      5.367031574249268       8780607\n",
+      "164.0      91.60624694824219      6.937002658843994       8777339\n",
+      "166.0      363.17681884765625      6.3316121101379395       8777867\n",
+      "172.0      0.45590826869010925      4.876999855041504       8778947\n",
+      "177.0      377.08563232421875      5.337118625640869       8777861\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(res)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'temp-plot.html'"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_chart = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
+    "test_chart.add_scatter(\n",
+    "    x=length_list,\n",
+    "    y=courant_list,\n",
+    "    name=\"courant\" + \" \" + str(300),\n",
+    "    mode='markers',  \n",
+    ")\n",
+    "test_chart.add_scatter(\n",
+    "    x=length_list,\n",
+    "    y=flowval_list,\n",
+    "    name=\"flowval\" + \" \" + str(300),\n",
+    "    mode='markers',  \n",
+    ")\n",
+    "# test_chart.update_yaxes(title_text=\"Flow Value\", secondary_y=False)\n",
+    "# test_chart.update_yaxes(title_text=\"Courant Value\", secondary_y=True)\n",
+    "test_chart.update_xaxes(title_text=\"Length\", )\n",
+    "\n",
+    "test_chart.layout.title = \"Top 25 Values by Length - 300 \"\n",
+    "plot(test_chart)"
+   ]
   },
   {
    "cell_type": "code",
@@ -645,7 +3159,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": ""
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,
diff --git a/src/python_routing_v01/compute_nhd_routing_SingleSeg.py b/src/python_routing_v01/compute_nhd_routing_SingleSeg.py
index bb497fd83..00660a949 100644
--- a/src/python_routing_v01/compute_nhd_routing_SingleSeg.py
+++ b/src/python_routing_v01/compute_nhd_routing_SingleSeg.py
@@ -1,4 +1,4 @@
-# command line args and usage with: python compute_nhd_routing_SingleSeg.py --help
+# command line cli_args and usage with: python compute_nhd_routing_SingleSeg.py --help
 # example to run test: python compute_nhd_routing_SingleSeg.py -v --test
 # example usage: python compute_nhd_routing_SingleSeg.py -v -t -w -onc -n Mainstems_CONUS
 #                python compute_nhd_routing_SingleSeg.py -n Mainstems_CONUS --nts 1440 --parallel &
@@ -310,12 +310,12 @@ def in_wsl() -> bool:
 else:
     from mc_sseg_stime import muskingcunge_module as mc
 
-connections = None
-networks = None
-qlateral = None
-waterbodies_df = None
-waterbody_initial_states_df = None
-channel_initial_states_df = None
+connections_g = None
+networks_g = None
+qlateral_g = None
+waterbodies_df_g = None
+waterbody_initial_states_df_g = None
+channel_initial_states_df_g = None
 
 time_index = 0  # time
 flowval_index = 1  # flowval
@@ -324,9 +324,9 @@ def in_wsl() -> bool:
 qlatval_index = 4  # qlatval
 storageval_index = 5  # storageval
 qlatCumval_index = 6  # qlatCumval
-kinCelerity_index = 7 # ck
-courant_index = 8 # cn
-X_index = 9 # X
+kinCelerity_index = 7  # ck
+courant_index = 8  # cn
+X_index = 9  # X
 
 ## network and reach utilities
 import nhd_network_utilities_v01 as nnu
@@ -378,11 +378,11 @@ def compute_network(
     nc_output_folder=None,
     assume_short_ts=False,
 ):
-    global connections
-    global networks
-    global qlateral
+    global connections_g
+    global networks_g
+    global qlateral_g
 
-    network = networks[terminal_segment]
+    network = networks_g[terminal_segment]
     flowveldepth = {
         connection: np.zeros(np.array([nts, 10]))
         for connection in (network["all_segments"])
@@ -430,7 +430,7 @@ def compute_network(
                 if waterbody:
                     compute_level_pool_reach_up2down(
                         flowveldepth=flowveldepth,
-                        qlateral=qlateral,
+                        qlateral=qlateral_g,
                         qup_reach=qup_reach,
                         quc_reach=quc_reach,
                         head_segment=head_segment,
@@ -450,7 +450,7 @@ def compute_network(
                 else:
                     compute_mc_reach_up2down(
                         flowveldepth=flowveldepth,
-                        qlateral=qlateral,
+                        qlateral=qlateral_g,
                         qup_reach=qup_reach,
                         quc_reach=quc_reach,
                         head_segment=head_segment,
@@ -468,7 +468,7 @@ def compute_network(
         for x in range(network["maximum_reach_seqorder"], -1, -1):
             for head_segment, reach in ordered_reaches[x]:
                 writeArraytoCSV(
-                    connections=connections,
+                    connections=connections_g,
                     flowveldepth=flowveldepth,
                     reach=reach,
                     verbose=verbose,
@@ -478,7 +478,7 @@ def compute_network(
 
     if writeToNETCDF:
         writeArraytoNC(
-            connections=connections,
+            connections=connections_g,
             flowveldepth=flowveldepth,
             network=network,
             nts=nts,
@@ -489,7 +489,7 @@ def compute_network(
             pathToOutputFile=writeToNETCDF,
         )
 
-    #return {terminal_segment: flowveldepth[terminal_segment]}
+    # return {terminal_segment: flowveldepth[terminal_segment]}
     return flowveldepth
 
 
@@ -508,8 +508,8 @@ def compute_reach_upstream_flows(
     debuglevel=0,
     assume_short_ts=False,
 ):
-    global connections
-    global channel_initial_states_df
+    global connections_g
+    global channel_initial_states_df_g
 
     # upstream flow per reach
     qup = 0.0
@@ -518,7 +518,7 @@ def compute_reach_upstream_flows(
     if waterbody:
         upstreams_list = set()
         for rr in network["receiving_reaches"]:
-            for us in connections[rr]["upstreams"]:
+            for us in connections_g[rr]["upstreams"]:
                 upstreams_list.add(us)
         # this step was critical -- there were receiving reaches that were junctions
         # with only one of their upstreams out of the network. The other was inside
@@ -528,12 +528,12 @@ def compute_reach_upstream_flows(
 
     elif head_segment in network["receiving_reaches"]:
         # TODO: confirm this logic, to make sure we don't double count the head
-        upstreams_list = connections[reach["reach_head"]]["upstreams"]
+        upstreams_list = connections_g[reach["reach_head"]]["upstreams"]
         us_flowveldepth = flowveldepth
         us_flowveldepth.update(flowveldepth_connect)
 
     else:
-        upstreams_list = connections[reach["reach_head"]]["upstreams"]
+        upstreams_list = connections_g[reach["reach_head"]]["upstreams"]
         us_flowveldepth = flowveldepth
 
     for us in upstreams_list:
@@ -542,7 +542,7 @@ def compute_reach_upstream_flows(
 
             if ts == 0:
                 # Initialize qup from warm state array
-                qup += channel_initial_states_df.loc[us, "qd0"]
+                qup += channel_initial_states_df_g.loc[us, "qd0"]
             else:
                 qup += us_flowveldepth[us][ts - 1][flowval_index]
 
@@ -568,7 +568,7 @@ def compute_mc_reach_up2down(
     debuglevel=0,
     assume_short_ts=False,
 ):
-    global connections
+    global connections_g
 
     if debuglevel <= -2:
         print(
@@ -582,7 +582,7 @@ def compute_mc_reach_up2down(
     # next_segment = connections[current_segment]["downstream"]
 
     while True:
-        data = connections[current_segment]["data"]
+        data = connections_g[current_segment]["data"]
         # for now treating as constant per reach
         bw = data[supernetwork_parameters["bottomwidth_col"]]
         tw = data[supernetwork_parameters["topwidth_col"]]
@@ -600,9 +600,9 @@ def compute_mc_reach_up2down(
 
         if ts == 0:
             # initialize from initial states
-            qdp = channel_initial_states_df.loc[current_segment, "qd0"]
+            qdp = channel_initial_states_df_g.loc[current_segment, "qd0"]
             velp = 0
-            depthp = channel_initial_states_df.loc[current_segment, "h0"]
+            depthp = channel_initial_states_df_g.loc[current_segment, "h0"]
         else:
             qdp = flowveldepth[current_segment][ts - 1][flowval_index]
             velp = 0  # flowveldepth[current_segment]["velval"][-1]
@@ -693,11 +693,11 @@ def compute_mc_reach_up2down(
             volumec,
             qlatCum,
             ck,
-            cn, 
+            cn,
             X,
         ]
 
-        next_segment = connections[current_segment]["downstream"]
+        next_segment = connections_g[current_segment]["downstream"]
         if current_segment == reach["reach_tail"]:
             if debuglevel <= -2:
                 print(f"{current_segment} (tail)")
@@ -729,9 +729,9 @@ def compute_level_pool_reach_up2down(
     debuglevel=0,
     assume_short_ts=False,
 ):
-    global connections
-    global waterbodies_df
-    global waterbody_initial_states_df
+    global connections_g
+    global waterbodies_df_g
+    global waterbody_initial_states_df_g
 
     if debuglevel <= -2:
         print(
@@ -744,7 +744,7 @@ def compute_level_pool_reach_up2down(
     current_segment = reach["reach_tail"]
     if ts == 0:
         # Initialize from warm state
-        depthp = waterbody_initial_states_df.loc[waterbody, "h0"]
+        depthp = waterbody_initial_states_df_g.loc[waterbody, "h0"]
     else:
         depthp = flowveldepth[current_segment][ts - 1][depthval_index]
 
@@ -760,20 +760,22 @@ def compute_level_pool_reach_up2down(
     qi1 = quc
     ql = qlat
     dt = dt  # current timestep length
-    ar = waterbodies_df.loc[waterbody, wb_params["level_pool_waterbody_area"]]
-    we = waterbodies_df.loc[waterbody, wb_params["level_pool_weir_elevation"]]
-    maxh = waterbodies_df.loc[
+    ar = waterbodies_df_g.loc[waterbody, wb_params["level_pool_waterbody_area"]]
+    we = waterbodies_df_g.loc[waterbody, wb_params["level_pool_weir_elevation"]]
+    maxh = waterbodies_df_g.loc[
         waterbody, wb_params["level_pool_waterbody_max_elevation"]
     ]
-    wc = waterbodies_df.loc[waterbody, wb_params["level_pool_outfall_weir_coefficient"]]
-    wl = waterbodies_df.loc[waterbody, wb_params["level_pool_outfall_weir_length"]]
+    wc = waterbodies_df_g.loc[
+        waterbody, wb_params["level_pool_outfall_weir_coefficient"]
+    ]
+    wl = waterbodies_df_g.loc[waterbody, wb_params["level_pool_outfall_weir_length"]]
     # TODO: find the right value for this variable -- it should be in the parameter file!
     dl = (
         10 * wl
     )  # waterbodies_df.loc[waterbody, wb_params["level_pool_overall_dam_length"]]
-    oe = waterbodies_df.loc[waterbody, wb_params["level_pool_orifice_elevation"]]
-    oc = waterbodies_df.loc[waterbody, wb_params["level_pool_orifice_coefficient"]]
-    oa = waterbodies_df.loc[waterbody, wb_params["level_pool_orifice_area"]]
+    oe = waterbodies_df_g.loc[waterbody, wb_params["level_pool_orifice_elevation"]]
+    oc = waterbodies_df_g.loc[waterbody, wb_params["level_pool_orifice_coefficient"]]
+    oa = waterbodies_df_g.loc[waterbody, wb_params["level_pool_orifice_area"]]
 
     qdc, depthc = rc.levelpool_physics(
         dt, qi0, qi1, ql, ar, we, maxh, wc, wl, dl, oe, oc, oa, depthp
@@ -787,8 +789,8 @@ def compute_level_pool_reach_up2down(
         volumec = volumec + flowveldepth[current_segment][ts - 1][storageval_index]
         qlatCum = qlatCum + flowveldepth[current_segment][ts - 1][qlatCumval_index]
 
-    #TODO: There may be a more useful output value to provide here.
-    #celerity values are nullified for reservoirs.
+    # TODO: There may be a more useful output value to provide here.
+    # celerity values are nullified for reservoirs.
     ck = 0
     cn = 0
     X = 0
@@ -937,7 +939,7 @@ def writeArraytoNC(
                 flowveldepth_data["xval"].append(
                     flowveldepth[current_segment][:, X_index]
                 )
-            
+
                 # write segment flowveldepth_data['segment']
                 flowveldepth_data["segment"].append(current_segment)
                 if not TIME_WRITTEN:
@@ -1056,32 +1058,31 @@ def writeNC(
     lateralCumflow.standard_name = (
         "Cummulativelateralflow"  # this is a CF standard name
     )
-    
+
     # write  kinematic celerity
     celerity = ncfile.createVariable(
         "celerity", np.float64, ("time", "stations")
     )  # note: unlimited dimension is leftmost
     celerity.units = "ft/s"  #
     celerity[:, :] = np.transpose(np.array(flowveldepth_data["ckval"], dtype=float))
-    celerity.standard_name = "celerity" 
-    
+    celerity.standard_name = "celerity"
+
     # write  courant number
     courant = ncfile.createVariable(
         "courant", np.float64, ("time", "stations")
     )  # note: unlimited dimension is leftmost
     courant.units = "-"  #
     courant[:, :] = np.transpose(np.array(flowveldepth_data["cnval"], dtype=float))
-    courant.standard_name = "courant number" 
-    
+    courant.standard_name = "courant number"
+
     # write  X parameter
     X_param = ncfile.createVariable(
         "X", np.float64, ("time", "stations")
     )  # note: unlimited dimension is leftmost
     X_param.units = "-"  #
     X_param[:, :] = np.transpose(np.array(flowveldepth_data["xval"], dtype=float))
-    X_param.standard_name = "MC X parameter" 
-    
-    
+    X_param.standard_name = "MC X parameter"
+
     # write time in seconds since  TODO get time from lateral flow from NWM
     time = ncfile.createVariable("time", np.float64, "time")
     time.units = "seconds since 2011-08-27 00:00:00"  ## TODO get time fron NWM as argument to this function
@@ -1150,18 +1151,26 @@ def sort_ordered_network(l, reverse=False):
 
 
 # Main Routine
-def main():
-    args = _handle_args()
-
-    global connections
-    global networks
-    global qlateral
-    global waterbodies_df
-    global waterbody_initial_states_df
-    global channel_initial_states_df
-
-    supernetwork = args.supernetwork
-    custom_input_file = args.custom_input_file
+def route_supernetwork(
+    connections,
+    networks,
+    qlateral,
+    waterbodies_df,
+    waterbody_initial_states_df,
+    channel_initial_states_df,
+    custom_input_file=None,
+):
+    cli_args = _handle_args()
+
+    global connections_g
+    global networks_g
+    global qlateral_g
+    global waterbodies_df_g
+    global waterbody_initial_states_df_g
+    global channel_initial_states_df_g
+
+    if not custom_input_file:
+        custom_input_file = cli_args.custom_input_file
     supernetwork_parameters = None
     waterbody_parameters = None
     if custom_input_file:
@@ -1173,7 +1182,7 @@ def main():
             output_parameters,
             run_parameters,
         ) = nio.read_custom_input(custom_input_file)
-        
+
         break_network_at_waterbodies = run_parameters.get(
             "break_network_at_waterbodies", None
         )
@@ -1243,47 +1252,50 @@ def main():
     # arise from this ordering ... check these out.
 
     else:
-        break_network_at_waterbodies = args.break_network_at_waterbodies
-
-        dt = int(args.dt)
-        nts = int(args.nts)
-        qts_subdivisions = args.qts_subdivisions
-        qlat_const = float(args.qlat_const)
-        qlat_input_folder = args.qlat_input_folder
-        qlat_input_file = args.qlat_input_file
-        qlat_file_pattern_filter = args.qlat_file_pattern_filter
-
-        wrf_hydro_channel_restart_file = args.wrf_hydro_channel_restart_file
-        wrf_hydro_channel_ID_crosswalk_file = args.wrf_hydro_channel_ID_crosswalk_file
+        supernetwork = cli_args.supernetwork
+        break_network_at_waterbodies = cli_args.break_network_at_waterbodies
+
+        dt = int(cli_args.dt)
+        nts = int(cli_args.nts)
+        qts_subdivisions = cli_args.qts_subdivisions
+        qlat_const = float(cli_args.qlat_const)
+        qlat_input_folder = cli_args.qlat_input_folder
+        qlat_input_file = cli_args.qlat_input_file
+        qlat_file_pattern_filter = cli_args.qlat_file_pattern_filter
+
+        wrf_hydro_channel_restart_file = cli_args.wrf_hydro_channel_restart_file
+        wrf_hydro_channel_ID_crosswalk_file = (
+            cli_args.wrf_hydro_channel_ID_crosswalk_file
+        )
         wrf_hydro_channel_ID_crosswalk_file_field_name = (
-            args.wrf_hydro_channel_ID_crosswalk_file_field_name
+            cli_args.wrf_hydro_channel_ID_crosswalk_file_field_name
         )
 
-        wrf_hydro_waterbody_restart_file = args.wrf_hydro_waterbody_restart_file
+        wrf_hydro_waterbody_restart_file = cli_args.wrf_hydro_waterbody_restart_file
         wrf_hydro_waterbody_ID_crosswalk_file = (
-            args.wrf_hydro_waterbody_ID_crosswalk_file
+            cli_args.wrf_hydro_waterbody_ID_crosswalk_file
         )
         wrf_hydro_waterbody_ID_crosswalk_file_field_name = (
-            args.wrf_hydro_waterbody_ID_crosswalk_file_field_name
+            cli_args.wrf_hydro_waterbody_ID_crosswalk_file_field_name
         )
 
-        debuglevel = -1 * int(args.debuglevel)
-        verbose = args.verbose
-        showtiming = args.showtiming
-        percentage_complete = args.percentage_complete
-        do_network_analysis_only = args.do_network_analysis_only
-        if args.csv_output_folder:
-            csv_output = {"csv_output_folder": args.csv_output_folder}
+        debuglevel = -1 * int(cli_args.debuglevel)
+        verbose = cli_args.verbose
+        showtiming = cli_args.showtiming
+        percentage_complete = cli_args.percentage_complete
+        do_network_analysis_only = cli_args.do_network_analysis_only
+        if cli_args.csv_output_folder:
+            csv_output = {"csv_output_folder": cli_args.csv_output_folder}
         else:
             csv_output = None
-        nc_output_folder = args.nc_output_folder
-        assume_short_ts = args.assume_short_ts
-        parallel_compute = args.parallel_compute
-        sort_networks = args.sort_networks
-        cpu_pool = args.cpu_pool
+        nc_output_folder = cli_args.nc_output_folder
+        assume_short_ts = cli_args.assume_short_ts
+        parallel_compute = cli_args.parallel_compute
+        sort_networks = cli_args.sort_networks
+        cpu_pool = cli_args.cpu_pool
 
-    run_pocono2_test = args.run_pocono2_test
-    run_pocono1_test = args.run_pocono1_test
+    run_pocono2_test = cli_args.run_pocono2_test
+    run_pocono1_test = cli_args.run_pocono1_test
 
     if run_pocono2_test:
         if verbose:
@@ -1639,7 +1651,16 @@ def main():
             print("... in %s seconds." % (time.time() - start_time))
             start_time = time.time()
 
-    # Define them pool after we create the static global objects (and collect the garbage)
+    compute_network_func = compute_network
+
+    connections_g = connections
+    networks_g = networks
+    qlateral_g = qlateral
+    waterbodies_df_g = waterbodies_df
+    waterbody_initial_states_df_g = waterbody_initial_states_df
+    channel_initial_states_df_g = channel_initial_states_df
+
+    # Define the pool after we create the static global objects (and collect the garbage)
     if parallel_compute:
         import gc
 
@@ -1690,7 +1711,7 @@ def main():
                     )
 
                 results.append(
-                    compute_network(
+                    compute_network_func(
                         flowveldepth_connect=flowveldepth_connect,
                         terminal_segment=terminal_segment,
                         supernetwork_parameters=supernetwork_parameters,
@@ -1739,7 +1760,7 @@ def main():
                 print(f"{[network[0] for network in ordered_networks[nsq]]}")
             # with pool:
             # with multiprocessing.Pool() as pool:
-            results = pool.starmap(compute_network, nslist)
+            results = pool.starmap(compute_network_func, nslist)
 
             if showtiming:
                 print("... complete in %s seconds." % (time.time() - start_time))
@@ -1756,7 +1777,7 @@ def main():
         maxa = []
         for result in results:
             for seg in result:
-                maxa.extend(result[seg][:,8:9])
+                maxa.extend(result[seg][:, 8:9])
         max_courant = max(maxa)
         print(f"max_courant: {max_courant}")
 
@@ -1789,6 +1810,18 @@ def main():
     if showtiming:
         print("... in %s seconds." % (time.time() - program_start_time))
 
+    all_results = {}
+    for result in results:
+        all_results.update(result)
+    return all_results
+
 
 if __name__ == "__main__":
-    main()
+    route_supernetwork(
+        connections=connections_g,
+        networks=networks_g,
+        qlateral=qlateral_g,
+        waterbodies_df=waterbodies_df_g,
+        waterbody_initial_states_df=waterbody_initial_states_df_g,
+        channel_initial_states_df=channel_initial_states_df_g,
+    )
diff --git a/test/input/json/florence_933020089.json b/test/input/json/florence_933020089.json
index a77ab00c3..f74fd474e 100644
--- a/test/input/json/florence_933020089.json
+++ b/test/input/json/florence_933020089.json
@@ -7,9 +7,9 @@
         "debuglevel": -2,
         "break_network_at_waterbodies": true,
         "assume_short_ts": true,
-        "qts_subdivisions": 12,
-        "dt": 300,
-        "nts": 2016,
+        "qts_subdivisions": 60,
+        "dt": 60,
+        "nts": 10080,
         "cpu-pool": 4
     },
     "output_parameters":
diff --git a/test/input/json/florence_933020089_dt10.json b/test/input/json/florence_933020089_dt10.json
new file mode 100644
index 000000000..bb416186b
--- /dev/null
+++ b/test/input/json/florence_933020089_dt10.json
@@ -0,0 +1,87 @@
+{   
+    "run_parameters":
+    {
+        "parallel_compute": true,
+        "verbose": true,
+        "showtiming": true,
+        "debuglevel": -2,
+        "break_network_at_waterbodies": true,
+        "assume_short_ts": true,
+        "qts_subdivisions": 360,
+        "dt": 10,
+        "nts": 108000,
+        "cpu-pool": 4
+    },
+    "output_parameters":
+    {
+        "csv_output":
+        {
+            "csv_output_folder": "../../test/output/text",
+            "csv_output_segments": [933020089]
+        },
+        "nc_output_folder": "../../test/output/text"
+    },
+    "supernetwork_parameters":
+    {
+        "title_string": "Hurricante Florence cutout",
+        "geo_file_path": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "mask_file_path": "./933020089_mask.csv",
+        "mask_layer_string": "",
+        "mask_driver_string": "csv",
+        "mask_key_col": 0,
+        "key_col": 16,
+        "downstream_col": 22,
+        "length_col": 3,
+        "manningn_col": 18,
+        "manningncc_col": 19,
+        "slope_col": 8,
+        "bottomwidth_col": 0,
+        "waterbody_col": 6,
+        "waterbody_null_code": -9999,
+        "topwidth_col": 9,
+        "topwidthcc_col": 10,
+        "MusK_col": 4,
+        "MusX_col": 5,
+        "ChSlp_col": 1,
+        "terminal_code": 0,
+        "driver_string": "NetCDF",
+        "layer_string": 0
+    },
+    "waterbody_parameters": {
+        "level_pool":
+        {
+            "level_pool_waterbody_parameter_file_path": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc",
+            "level_pool_waterbody_id": "lake_id",
+            "level_pool_waterbody_area": "LkArea", 
+            "level_pool_weir_elevation": "WeirE",
+            "level_pool_waterbody_max_elevation": "LkMxE",
+            "level_pool_outfall_weir_coefficient": "WeirC",
+            "level_pool_outfall_weir_length": "WeirL",
+            "level_pool_overall_dam_length": "DamL",
+            "level_pool_orifice_elevation": "OrificeE",
+            "level_pool_orifice_coefficient": "OrificeC",
+            "level_pool_orifice_area": "OrificeA"
+        }
+    },
+    "forcing_parameters":
+    {
+        "qlat_input_folder": "../../test/input/private/florence_cutout_v21/FORCING_AnA_channel-only/",
+        "qlat_file_pattern_filter": ["/2018091[456789]*.CHRTOUT_DOMAIN1", "/2018092*.CHRTOUT_DOMAIN1"],
+        "qlat_file_index_col": "feature_id",
+        "qlat_file_value_col": "q_lateral"
+    },
+    "restart_parameters":
+    {
+        "wrf_hydro_channel_restart_file": "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1",
+        "wrf_hydro_channel_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "wrf_hydro_channel_ID_crosswalk_file_field_name": "link",
+        "wrf_hydro_channel_restart_upstream_flow_field_name": "qlink1",
+        "wrf_hydro_channel_restart_downstream_flow_field_name": "qlink2",
+        "wrf_hydro_channel_restart_depth_flow_field_name": "hlink",
+        "wrf_hydro_waterbody_restart_file": "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1",
+        "wrf_hydro_waterbody_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc",
+        "wrf_hydro_waterbody_ID_crosswalk_file_field_name": "lake_id",
+        "wrf_hydro_channel_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "wrf_hydro_waterbody_crosswalk_filter_file_field_name": "NHDWaterbodyComID"
+    }
+}
diff --git a/test/input/json/florence_933020089_dt300.json b/test/input/json/florence_933020089_dt300.json
new file mode 100644
index 000000000..452f95c82
--- /dev/null
+++ b/test/input/json/florence_933020089_dt300.json
@@ -0,0 +1,87 @@
+{   
+    "run_parameters":
+    {
+        "parallel_compute": true,
+        "verbose": true,
+        "showtiming": true,
+        "debuglevel": -2,
+        "break_network_at_waterbodies": true,
+        "assume_short_ts": true,
+        "qts_subdivisions": 12,
+        "dt": 300,
+        "nts": 3600,
+        "cpu-pool": 4
+    },
+    "output_parameters":
+    {
+        "csv_output":
+        {
+            "csv_output_folder": "../../test/output/text",
+            "csv_output_segments": [933020089]
+        },
+        "nc_output_folder": "../../test/output/text"
+    },
+    "supernetwork_parameters":
+    {
+        "title_string": "Hurricante Florence cutout",
+        "geo_file_path": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "mask_file_path": "./933020089_mask.csv",
+        "mask_layer_string": "",
+        "mask_driver_string": "csv",
+        "mask_key_col": 0,
+        "key_col": 16,
+        "downstream_col": 22,
+        "length_col": 3,
+        "manningn_col": 18,
+        "manningncc_col": 19,
+        "slope_col": 8,
+        "bottomwidth_col": 0,
+        "waterbody_col": 6,
+        "waterbody_null_code": -9999,
+        "topwidth_col": 9,
+        "topwidthcc_col": 10,
+        "MusK_col": 4,
+        "MusX_col": 5,
+        "ChSlp_col": 1,
+        "terminal_code": 0,
+        "driver_string": "NetCDF",
+        "layer_string": 0
+    },
+    "waterbody_parameters": {
+        "level_pool":
+        {
+            "level_pool_waterbody_parameter_file_path": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc",
+            "level_pool_waterbody_id": "lake_id",
+            "level_pool_waterbody_area": "LkArea", 
+            "level_pool_weir_elevation": "WeirE",
+            "level_pool_waterbody_max_elevation": "LkMxE",
+            "level_pool_outfall_weir_coefficient": "WeirC",
+            "level_pool_outfall_weir_length": "WeirL",
+            "level_pool_overall_dam_length": "DamL",
+            "level_pool_orifice_elevation": "OrificeE",
+            "level_pool_orifice_coefficient": "OrificeC",
+            "level_pool_orifice_area": "OrificeA"
+        }
+    },
+    "forcing_parameters":
+    {
+        "qlat_input_folder": "../../test/input/private/florence_cutout_v21/FORCING_AnA_channel-only/",
+        "qlat_file_pattern_filter": ["/2018091[456789]*.CHRTOUT_DOMAIN1", "/2018092*.CHRTOUT_DOMAIN1"],
+        "qlat_file_index_col": "feature_id",
+        "qlat_file_value_col": "q_lateral"
+    },
+    "restart_parameters":
+    {
+        "wrf_hydro_channel_restart_file": "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1",
+        "wrf_hydro_channel_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "wrf_hydro_channel_ID_crosswalk_file_field_name": "link",
+        "wrf_hydro_channel_restart_upstream_flow_field_name": "qlink1",
+        "wrf_hydro_channel_restart_downstream_flow_field_name": "qlink2",
+        "wrf_hydro_channel_restart_depth_flow_field_name": "hlink",
+        "wrf_hydro_waterbody_restart_file": "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1",
+        "wrf_hydro_waterbody_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc",
+        "wrf_hydro_waterbody_ID_crosswalk_file_field_name": "lake_id",
+        "wrf_hydro_channel_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "wrf_hydro_waterbody_crosswalk_filter_file_field_name": "NHDWaterbodyComID"
+    }
+}
diff --git a/test/input/json/florence_933020089_dt60.json b/test/input/json/florence_933020089_dt60.json
new file mode 100644
index 000000000..f74fd474e
--- /dev/null
+++ b/test/input/json/florence_933020089_dt60.json
@@ -0,0 +1,87 @@
+{   
+    "run_parameters":
+    {
+        "parallel_compute": true,
+        "verbose": true,
+        "showtiming": true,
+        "debuglevel": -2,
+        "break_network_at_waterbodies": true,
+        "assume_short_ts": true,
+        "qts_subdivisions": 60,
+        "dt": 60,
+        "nts": 10080,
+        "cpu-pool": 4
+    },
+    "output_parameters":
+    {
+        "csv_output":
+        {
+            "csv_output_folder": "../../test/output/text",
+            "csv_output_segments": [933020089]
+        },
+        "nc_output_folder": "../../test/output/text"
+    },
+    "supernetwork_parameters":
+    {
+        "title_string": "Hurricante Florence cutout",
+        "geo_file_path": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "mask_file_path": "./933020089_mask.csv",
+        "mask_layer_string": "",
+        "mask_driver_string": "csv",
+        "mask_key_col": 0,
+        "key_col": 16,
+        "downstream_col": 22,
+        "length_col": 3,
+        "manningn_col": 18,
+        "manningncc_col": 19,
+        "slope_col": 8,
+        "bottomwidth_col": 0,
+        "waterbody_col": 6,
+        "waterbody_null_code": -9999,
+        "topwidth_col": 9,
+        "topwidthcc_col": 10,
+        "MusK_col": 4,
+        "MusX_col": 5,
+        "ChSlp_col": 1,
+        "terminal_code": 0,
+        "driver_string": "NetCDF",
+        "layer_string": 0
+    },
+    "waterbody_parameters": {
+        "level_pool":
+        {
+            "level_pool_waterbody_parameter_file_path": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc",
+            "level_pool_waterbody_id": "lake_id",
+            "level_pool_waterbody_area": "LkArea", 
+            "level_pool_weir_elevation": "WeirE",
+            "level_pool_waterbody_max_elevation": "LkMxE",
+            "level_pool_outfall_weir_coefficient": "WeirC",
+            "level_pool_outfall_weir_length": "WeirL",
+            "level_pool_overall_dam_length": "DamL",
+            "level_pool_orifice_elevation": "OrificeE",
+            "level_pool_orifice_coefficient": "OrificeC",
+            "level_pool_orifice_area": "OrificeA"
+        }
+    },
+    "forcing_parameters":
+    {
+        "qlat_input_folder": "../../test/input/private/florence_cutout_v21/FORCING_AnA_channel-only/",
+        "qlat_file_pattern_filter": ["/2018091[456789]*.CHRTOUT_DOMAIN1", "/2018092*.CHRTOUT_DOMAIN1"],
+        "qlat_file_index_col": "feature_id",
+        "qlat_file_value_col": "q_lateral"
+    },
+    "restart_parameters":
+    {
+        "wrf_hydro_channel_restart_file": "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1",
+        "wrf_hydro_channel_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "wrf_hydro_channel_ID_crosswalk_file_field_name": "link",
+        "wrf_hydro_channel_restart_upstream_flow_field_name": "qlink1",
+        "wrf_hydro_channel_restart_downstream_flow_field_name": "qlink2",
+        "wrf_hydro_channel_restart_depth_flow_field_name": "hlink",
+        "wrf_hydro_waterbody_restart_file": "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1",
+        "wrf_hydro_waterbody_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc",
+        "wrf_hydro_waterbody_ID_crosswalk_file_field_name": "lake_id",
+        "wrf_hydro_channel_ID_crosswalk_file": "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc",
+        "wrf_hydro_waterbody_crosswalk_filter_file_field_name": "NHDWaterbodyComID"
+    }
+}
diff --git a/test/input/yaml/CustomInput.yaml b/test/input/yaml/CustomInput.yaml
index 3da863d76..c9dde9511 100644
--- a/test/input/yaml/CustomInput.yaml
+++ b/test/input/yaml/CustomInput.yaml
@@ -82,7 +82,8 @@ waterbody_parameters:
 #WRF-Hydro output file 
 forcing_parameters:
     qlat_input_folder: "../../test/input/geo/NWM_2.1_Sample_Datasets/Pocono_TEST1/example_CHRTOUT/"
-    qlat_file_pattern_filter: "/*.CHRTOUT_DOMAIN1"
+    qlat_file_pattern_filter: 
+    - "/*.CHRTOUT_DOMAIN1"
     qlat_file_index_col: feature_id
     qlat_file_value_col: q_lateral
 #WRF-Hydro restart files
diff --git a/test/input/yaml/florence_933020089_dt10.yaml b/test/input/yaml/florence_933020089_dt10.yaml
new file mode 100644
index 000000000..eb04bd067
--- /dev/null
+++ b/test/input/yaml/florence_933020089_dt10.yaml
@@ -0,0 +1,98 @@
+---
+run_parameters:
+  parallel_compute: true
+  verbose: true
+  showtiming: true
+  debuglevel: -2
+  break_network_at_waterbodies: true
+  assume_short_ts: true
+  qts_subdivisions: 360
+  dt: 10
+  nts: 60480
+  #qts, dt, and nts relationship.
+  #qts 12*n
+  #dt 300/n
+  #nts 3600*n
+  cpu-pool: 4
+output_parameters:
+  csv_output:
+    csv_output_folder: "../../test/output/text"
+    csv_output_segments:
+    - 933020089
+supernetwork_parameters:
+  title_string: Hurricante Florence cutout
+  geo_file_path: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc"
+  mask_file_path: "./933020089_mask.csv"
+  mask_layer_string: ''
+  mask_driver_string: csv
+  mask_key_col: 0
+  key_col: 16
+  downstream_col: 22
+  length_col: 3
+  manningn_col: 18
+  manningncc_col: 19
+  slope_col: 8
+  bottomwidth_col: 0
+  waterbody_col: 6
+  waterbody_null_code: -9999
+  topwidth_col: 9
+  topwidthcc_col: 10
+  MusK_col: 4
+  MusX_col: 5
+  ChSlp_col: 1
+  terminal_code: 0
+  driver_string: NetCDF
+  layer_string: 0
+    # "link",
+    # "to",
+    # "Length",
+    # "n",
+    # "nCC",
+    # "So",
+    # "BtmWdth",
+    # "TopWdth",
+    # "TopWdthCC",
+    # "NHDWaterbodyComID",
+    # "MusK",
+    # "MusX",
+    # "ChSlp",
+#waterboy parameters and assignments from lake parm file
+waterbody_parameters:
+  level_pool:
+  #WRF-Hydro lake parm file
+    level_pool_waterbody_parameter_file_path: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc"
+    level_pool_waterbody_id: lake_id
+    level_pool_waterbody_area: LkArea
+    level_pool_weir_elevation: WeirE
+    level_pool_waterbody_max_elevation: LkMxE
+    level_pool_outfall_weir_coefficient: WeirC
+    level_pool_outfall_weir_length: WeirL
+    level_pool_overall_dam_length: DamL
+    level_pool_orifice_elevation: OrificeE
+    level_pool_orifice_coefficient: OrificeC
+    level_pool_orifice_area: OrificeA
+#WRF-Hydro output file 
+forcing_parameters:
+  qlat_input_folder: "../../test/input/private/florence_cutout_v21/FORCING_AnA_channel-only/"
+  qlat_file_pattern_filter:
+  - "/2018091[456789]*.CHRTOUT_DOMAIN1"
+  - "/2018092*.CHRTOUT_DOMAIN1"
+  qlat_file_index_col: feature_id
+  qlat_file_value_col: q_lateral
+#WRF-Hydro restart files
+restart_parameters:
+    #WRF-Hydro channels restart file
+  wrf_hydro_channel_restart_file: "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1"
+  #WRF-Hydro channels ID crosswalk file
+  wrf_hydro_channel_ID_crosswalk_file: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc"
+  wrf_hydro_channel_ID_crosswalk_file_field_name: link
+  wrf_hydro_channel_restart_upstream_flow_field_name: qlink1
+  wrf_hydro_channel_restart_downstream_flow_field_name: qlink2
+  wrf_hydro_channel_restart_depth_flow_field_name: hlink
+  #WRF-Hydro waterbodies restart file
+  wrf_hydro_waterbody_restart_file: "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-14_00:00_DOMAIN1"
+  #WRF-Hydro waterbody ID crosswalk file
+  wrf_hydro_waterbody_ID_crosswalk_file: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc"
+  wrf_hydro_waterbody_ID_crosswalk_file_field_name: lake_id
+  #WRF-Hydro waterbody crosswalk filter file
+  wrf_hydro_waterbody_crosswalk_filter_file_field_name: NHDWaterbodyComID
diff --git a/test/input/yaml/florence_933020089_dt300.yaml b/test/input/yaml/florence_933020089_dt300.yaml
new file mode 100644
index 000000000..90ad26c8f
--- /dev/null
+++ b/test/input/yaml/florence_933020089_dt300.yaml
@@ -0,0 +1,98 @@
+---
+run_parameters:
+  parallel_compute: true
+  verbose: true
+  showtiming: true
+  debuglevel: -2
+  break_network_at_waterbodies: true
+  assume_short_ts: true
+  qts_subdivisions: 12
+  dt: 300
+  nts: 2880
+  #qts, dt, and nts relationship.
+  #qts 12*n
+  #dt 300/n
+  #nts 3600*n
+  cpu-pool: 4
+output_parameters:
+  csv_output:
+    csv_output_folder: "../../test/output/text"
+    csv_output_segments:
+    - 933020089
+supernetwork_parameters:
+  title_string: Hurricante Florence cutout
+  geo_file_path: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc"
+  mask_file_path: "./933020089_mask.csv"
+  mask_layer_string: ''
+  mask_driver_string: csv
+  mask_key_col: 0
+  key_col: 16
+  downstream_col: 22
+  length_col: 3
+  manningn_col: 18
+  manningncc_col: 19
+  slope_col: 8
+  bottomwidth_col: 0
+  waterbody_col: 6
+  waterbody_null_code: -9999
+  topwidth_col: 9
+  topwidthcc_col: 10
+  MusK_col: 4
+  MusX_col: 5
+  ChSlp_col: 1
+  terminal_code: 0
+  driver_string: NetCDF
+  layer_string: 0
+    # "link",
+    # "to",
+    # "Length",
+    # "n",
+    # "nCC",
+    # "So",
+    # "BtmWdth",
+    # "TopWdth",
+    # "TopWdthCC",
+    # "NHDWaterbodyComID",
+    # "MusK",
+    # "MusX",
+    # "ChSlp",
+#waterboy parameters and assignments from lake parm file
+waterbody_parameters:
+  level_pool:
+  #WRF-Hydro lake parm file
+    level_pool_waterbody_parameter_file_path: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc"
+    level_pool_waterbody_id: lake_id
+    level_pool_waterbody_area: LkArea
+    level_pool_weir_elevation: WeirE
+    level_pool_waterbody_max_elevation: LkMxE
+    level_pool_outfall_weir_coefficient: WeirC
+    level_pool_outfall_weir_length: WeirL
+    level_pool_overall_dam_length: DamL
+    level_pool_orifice_elevation: OrificeE
+    level_pool_orifice_coefficient: OrificeC
+    level_pool_orifice_area: OrificeA
+#WRF-Hydro output file 
+forcing_parameters:
+  qlat_input_folder: "../../test/input/private/florence_cutout_v21/FORCING_AnA_channel-only/"
+  qlat_file_pattern_filter:
+  - "/2018091[123456789]*.CHRTOUT_DOMAIN1"
+  - "/2018092*.CHRTOUT_DOMAIN1"
+  qlat_file_index_col: feature_id
+  qlat_file_value_col: q_lateral
+#WRF-Hydro restart files
+restart_parameters:
+    #WRF-Hydro channels restart file
+  wrf_hydro_channel_restart_file: "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-11_00:00_DOMAIN1"
+  #WRF-Hydro channels ID crosswalk file
+  wrf_hydro_channel_ID_crosswalk_file: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc"
+  wrf_hydro_channel_ID_crosswalk_file_field_name: link
+  wrf_hydro_channel_restart_upstream_flow_field_name: qlink1
+  wrf_hydro_channel_restart_downstream_flow_field_name: qlink2
+  wrf_hydro_channel_restart_depth_flow_field_name: hlink
+  #WRF-Hydro waterbodies restart file
+  wrf_hydro_waterbody_restart_file: "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-11_00:00_DOMAIN1"
+  #WRF-Hydro waterbody ID crosswalk file
+  wrf_hydro_waterbody_ID_crosswalk_file: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc"
+  wrf_hydro_waterbody_ID_crosswalk_file_field_name: lake_id
+  #WRF-Hydro waterbody crosswalk filter file
+  wrf_hydro_waterbody_crosswalk_filter_file_field_name: NHDWaterbodyComID
diff --git a/test/input/yaml/florence_933020089_dt60.yaml b/test/input/yaml/florence_933020089_dt60.yaml
new file mode 100644
index 000000000..0192415a5
--- /dev/null
+++ b/test/input/yaml/florence_933020089_dt60.yaml
@@ -0,0 +1,98 @@
+---
+run_parameters:
+  parallel_compute: true
+  verbose: true
+  showtiming: true
+  debuglevel: -2
+  break_network_at_waterbodies: true
+  assume_short_ts: true
+  qts_subdivisions: 60
+  dt: 60
+  nts: 14400
+  #qts, dt, and nts relationship.
+  #qts 12*n
+  #dt 300/n
+  #nts 3600*n
+  cpu-pool: 4
+output_parameters:
+  csv_output:
+    csv_output_folder: "../../test/output/text"
+    csv_output_segments:
+    - 933020089
+supernetwork_parameters:
+  title_string: Hurricante Florence cutout
+  geo_file_path: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc"
+  mask_file_path: "./933020089_mask.csv"
+  mask_layer_string: ''
+  mask_driver_string: csv
+  mask_key_col: 0
+  key_col: 16
+  downstream_col: 22
+  length_col: 3
+  manningn_col: 18
+  manningncc_col: 19
+  slope_col: 8
+  bottomwidth_col: 0
+  waterbody_col: 6
+  waterbody_null_code: -9999
+  topwidth_col: 9
+  topwidthcc_col: 10
+  MusK_col: 4
+  MusX_col: 5
+  ChSlp_col: 1
+  terminal_code: 0
+  driver_string: NetCDF
+  layer_string: 0
+    # "link",
+    # "to",
+    # "Length",
+    # "n",
+    # "nCC",
+    # "So",
+    # "BtmWdth",
+    # "TopWdth",
+    # "TopWdthCC",
+    # "NHDWaterbodyComID",
+    # "MusK",
+    # "MusX",
+    # "ChSlp",
+#waterboy parameters and assignments from lake parm file
+waterbody_parameters:
+  level_pool:
+  #WRF-Hydro lake parm file
+    level_pool_waterbody_parameter_file_path: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc"
+    level_pool_waterbody_id: lake_id
+    level_pool_waterbody_area: LkArea
+    level_pool_weir_elevation: WeirE
+    level_pool_waterbody_max_elevation: LkMxE
+    level_pool_outfall_weir_coefficient: WeirC
+    level_pool_outfall_weir_length: WeirL
+    level_pool_overall_dam_length: DamL
+    level_pool_orifice_elevation: OrificeE
+    level_pool_orifice_coefficient: OrificeC
+    level_pool_orifice_area: OrificeA
+#WRF-Hydro output file 
+forcing_parameters:
+  qlat_input_folder: "../../test/input/private/florence_cutout_v21/FORCING_AnA_channel-only/"
+  qlat_file_pattern_filter:
+  - "/2018091[123456789]*.CHRTOUT_DOMAIN1"
+  - "/2018092*.CHRTOUT_DOMAIN1"
+  qlat_file_index_col: feature_id
+  qlat_file_value_col: q_lateral
+#WRF-Hydro restart files
+restart_parameters:
+    #WRF-Hydro channels restart file
+  wrf_hydro_channel_restart_file: "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-11_00:00_DOMAIN1"
+  #WRF-Hydro channels ID crosswalk file
+  wrf_hydro_channel_ID_crosswalk_file: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/Route_Link.nc"
+  wrf_hydro_channel_ID_crosswalk_file_field_name: link
+  wrf_hydro_channel_restart_upstream_flow_field_name: qlink1
+  wrf_hydro_channel_restart_downstream_flow_field_name: qlink2
+  wrf_hydro_channel_restart_depth_flow_field_name: hlink
+  #WRF-Hydro waterbodies restart file
+  wrf_hydro_waterbody_restart_file: "../../test/input/private/florence_cutout_v21/NWM/RESTART_open-loop/HYDRO_RST.2018-09-11_00:00_DOMAIN1"
+  #WRF-Hydro waterbody ID crosswalk file
+  wrf_hydro_waterbody_ID_crosswalk_file: "../../test/input/private/florence_cutout_v21/NWM/DOMAIN/LAKEPARM.nc"
+  wrf_hydro_waterbody_ID_crosswalk_file_field_name: lake_id
+  #WRF-Hydro waterbody crosswalk filter file
+  wrf_hydro_waterbody_crosswalk_filter_file_field_name: NHDWaterbodyComID