diff --git a/btrdbextras/__init__.py b/btrdbextras/__init__.py
index 5e701f5..6d18cfe 100644
--- a/btrdbextras/__init__.py
+++ b/btrdbextras/__init__.py
@@ -1,5 +1,5 @@
 from .conn import Connection
 
-__version__ = "v5.11.9"
+__version__ = "v5.31.0"
 
 __all__ = ["__version__", "Connection"]
diff --git a/btrdbextras/opendss_ingest/IEEE_13_-_Create_Streams_Add_Data.ipynb b/btrdbextras/opendss_ingest/IEEE_13_-_Create_Streams_Add_Data.ipynb
new file mode 100644
index 0000000..f0ec190
--- /dev/null
+++ b/btrdbextras/opendss_ingest/IEEE_13_-_Create_Streams_Add_Data.ipynb
@@ -0,0 +1,5042 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import opendssdirect as dss\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm.notebook import tqdm_notebook, tqdm\n",
+    "import btrdb as db\n",
+    "import uuid\n",
+    "from btrdb.utils.timez import datetime_to_ns\n",
+    "import simulation_utils as sims\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import importlib\n",
+    "\n",
+    "importlib.reload(sims);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to the database\n",
+    "conn = db.connect(profile=\"collab\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_loc = \"./Models/13Bus/IEEE13Nodeckt.dss\"\n",
+    "dss.run_command(\"Redirect \" + model_loc);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create output streams\n",
+    "The following cells create the output streams or retrieve them if they have already been created. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix = \"simulated/ieee13\"\n",
+    "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nstreams = len(collections)\n",
+    "print(\"Creating\", nstreams, \"streams\")\n",
+    "for i in range(nstreams):\n",
+    "    print(collections[i] + \"/\" + names[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 234 streams under simulated/ieee13\n",
+      "Found 234 streams. Created 0 streams.\n"
+     ]
+    }
+   ],
+   "source": [
+    "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate simulated measurements\n",
+    "The following cell generates data that will be converted into streams. \n",
+    "For convenience, we back-calculate the number of samples from a user specified sample rate (`fs`) and simulation duration (`start_time` to `end_time`). However, keep in mind that the simulation has no inherent sense of time - we are abitrarily assigning timestamps to each simulation result. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We will generate 1800 samples.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The number of samples to generate\n",
+    "start_time = datetime(2022, 1, 1, 0, 0, 0)\n",
+    "end_time = datetime(2022, 1, 1, 0, 1, 0)\n",
+    "fs = 30  # Hz\n",
+    "T = int(((end_time - start_time).total_seconds()) * fs)\n",
+    "print(\"We will generate\", T, \"samples.\")\n",
+    "\n",
+    "# Generate the nanosecond timestamps for the data\n",
+    "start_ns = datetime_to_ns(start_time)\n",
+    "end_ns = datetime_to_ns(end_time)\n",
+    "timestamps = np.arange(start_ns, end_ns, 1e9 / 30, dtype=\"int\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the original loads\n",
+    "load, load_names = sims.get_loads()\n",
+    "nloads = len(load_names)\n",
+    "\n",
+    "# Generate the randomized scaling factors\n",
+    "mu = 1.1\n",
+    "sig = 0.1\n",
+    "s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n",
+    "\n",
+    "# Generate the new load values\n",
+    "new_load = s * load[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/1799 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "V, I = sims.simulate_network(new_load, load_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x12c968850>]"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(timestamps, V[\"646/VCM\"])\n",
+    "plt.plot(timestamps, V[\"646/VBM\"])\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(timestamps, V[\"646/VCA\"])\n",
+    "plt.plot(timestamps, V[\"646/VBA\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of streams simulated:\n",
+      "234\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of streams simulated:\")\n",
+    "print(len(V.keys()) + len(I.keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Push data to streams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Put voltage data into the corresponding stream\n",
+    "sims.add_all_data(timestamps, V, streams_dict, prefix)\n",
+    "# Put current data into the corresponding stream\n",
+    "sims.add_all_data(timestamps, I, streams_dict, prefix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Add long period of data\n",
+    "The following cell runs a loop which will generate and push data over a much longer period. \n",
+    "The purpose of the loop is to avoid generating all the data at once - instead, the full time period is divided into smaller time chunks, with data generated and inserted for each chunk sequentially. \n",
+    "\n",
+    "The code is divided into two cells below - the first is the initialization step. The second runs the loop. If there is an error during the loop, the second cell can be re-run and will pick up where it left off. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Collection prefix in which data will be added\n",
+    "prefix = \"simulated/ieee13\"\n",
+    "# Get the desired output streams to which data will be pushed\n",
+    "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)\n",
+    "# If the desired streams exist, retrieve them. Otherwise create them.\n",
+    "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)\n",
+    "\n",
+    "\n",
+    "# Get the original loads\n",
+    "load, load_names = sims.get_loads()\n",
+    "nloads = len(load_names)\n",
+    "\n",
+    "# Simulation time window - the FULL time range over which we want to generate data\n",
+    "start_time = datetime(2022, 1, 2, 0, 0, 0)\n",
+    "end_time = datetime(2022, 1, 3, 0, 0, 0)\n",
+    "fs = 30  # Hz\n",
+    "Ttotal = int(((end_time - start_time).total_seconds()) * fs)\n",
+    "print(\"We will generate\", Ttotal, \"samples.\")\n",
+    "\n",
+    "# The simulation time step - this is the amount of data we insert at once.\n",
+    "step = timedelta(minutes=5)\n",
+    "nsteps = int((end_time - start_time) / step)\n",
+    "\n",
+    "# Create progress bar\n",
+    "pbar = tqdm(total=nsteps, desc=\"Adding simulated data\")\n",
+    "\n",
+    "t0 = start_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1f55605fad2421f944ee0fc466c5752",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Adding simulated data:   0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
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+     "metadata": {},
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+    {
+     "data": {
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+     "metadata": {},
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+    {
+     "data": {
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+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+     "metadata": {},
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+    },
+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+     "metadata": {},
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+    {
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+     },
+     "metadata": {},
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+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+     "metadata": {},
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+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+     },
+     "metadata": {},
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+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
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+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/8999 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/82 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing data to streams:   0%|          | 0/152 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "remaining_steps = int((end_time - t0) / step)\n",
+    "pbar = tqdm(total=remaining_steps, desc=\"Adding simulated data\")\n",
+    "while t0 < end_time:\n",
+    "    # Generate the nanosecond timestamps for the data\n",
+    "    t0_ns = datetime_to_ns(t0)\n",
+    "    t1_ns = datetime_to_ns(t0 + step)\n",
+    "    timestamps = np.arange(t0_ns, t1_ns, 1e9 / fs, dtype=\"int\")\n",
+    "\n",
+    "    # The number of samples to be generated in this iteration\n",
+    "    T = len(timestamps)\n",
+    "    # Generate the randomized scaling factors\n",
+    "    mu = 1.1\n",
+    "    sig = 0.1\n",
+    "    s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n",
+    "    # Generate the new load values\n",
+    "    new_load = s * load[:, np.newaxis]\n",
+    "    # Simulate\n",
+    "    V, I = sims.simulate_network(new_load, load_names)\n",
+    "\n",
+    "    # Push data to database\n",
+    "    # Put voltage data into the corresponding stream\n",
+    "    sims.add_all_data(timestamps, V, streams_dict, prefix)\n",
+    "    # Put current data into the corresponding stream\n",
+    "    sims.add_all_data(timestamps, I, streams_dict, prefix)\n",
+    "\n",
+    "    # Increment time\n",
+    "    t0 = t0 + step\n",
+    "    pbar.update(1)\n",
+    "pbar.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DELETING streams\n",
+    "**DO NOT RUN** unless you are certain you want to delete. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 234 streams under simulated/ieee13\n",
+      "Found 234 streams. Created 0 streams.\n"
+     ]
+    }
+   ],
+   "source": [
+    "prefix = \"simulated/ieee13\"\n",
+    "# Get the desired output streams to which data will be pushed\n",
+    "collections, names, tags, annotations = sims.get_stream_info(base_col=prefix)\n",
+    "# If the desired streams exist, retrieve them. Otherwise create them.\n",
+    "streams_dict = sims.create_streams(prefix, collections, names, tags, annotations, conn)\n",
+    "\n",
+    "for key, val in streams_dict.items():\n",
+    "    print(\"Deleting\", key)\n",
+    "    ### val.obliterate() ## UNCOMMENT IF YOU WANT TO DELETE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/btrdbextras/opendss_ingest/Intro_to_Simulation_with_OpenDSS.ipynb b/btrdbextras/opendss_ingest/Intro_to_Simulation_with_OpenDSS.ipynb
new file mode 100644
index 0000000..84daa3a
--- /dev/null
+++ b/btrdbextras/opendss_ingest/Intro_to_Simulation_with_OpenDSS.ipynb
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Demo of Feeder Simulations with OpenDSS\n",
+    "This notebook demonstrates how to run powerflow simulations with OpenDSS and retrieve the simulated voltage and current measurement data.  \n",
+    "The documentation for opendssdirect can be found [here](http://dss-extensions.org/OpenDSSDirect.py/index.html). \n",
+    "\n",
+    "This notebook primarily uses functions contained in the `simulations_utils` which are convenient wrappers for working with `OpenDSSDirect`. To get more insight into the `OpenDSSDirect` API itself, you will likely need to dig into the functions from `simulations_utils`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import opendssdirect as dss\n",
+    "import matplotlib.pyplot as plt\n",
+    "import simulation_utils as sims\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import importlib\n",
+    "\n",
+    "importlib.reload(sims);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Load a feeder model. \n",
+    "OpenDSS comes packaged with some IEEE network models. By loading a model, all the model parameters get instantiated. \n",
+    "Some of the models included with OpenDSS (which have been copied into this repo) are:\n",
+    "1. IEEE 13 bus\n",
+    "2. IEEE 34 bus\n",
+    "3. IEEE 37 bus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The location of the .dss file specifying the model.\n",
+    "model_loc = \"./Models/13Bus/IEEE13Nodeckt.dss\"\n",
+    "# Activate the model in OpenDSS\n",
+    "dss.run_command(\"Redirect \" + model_loc);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model contains 16 buses\n",
+      "Model contains 41 nodes\n",
+      "Model contains 12 lines\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the names of all buses in the model\n",
+    "bus_names = dss.Circuit.AllBusNames()\n",
+    "nbuses = len(bus_names)\n",
+    "print(\"Model contains\", nbuses, \"buses\")\n",
+    "\n",
+    "# Get the names of all nodes in the model\n",
+    "# Nodes are the individual phases at every bus\n",
+    "node_names = dss.Circuit.AllNodeNames()\n",
+    "nnodes = len(node_names)\n",
+    "print(\"Model contains\", nnodes, \"nodes\")\n",
+    "\n",
+    "# Get the names of all lines in the model\n",
+    "line_names = dss.Lines.AllNames()\n",
+    "nlines = len(line_names)\n",
+    "print(\"Model contains\", nlines, \"lines\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['sourcebus', '650', 'rg60', '633', '634', '671', '645', '646', '692', '675', '611', '652', '670', '632', '680', '684']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(bus_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network connectors\n",
+    "There are several types of connectors (circuit elements that carry current between buses) in the network. Lines are only one, we can also have transformers. The following convenience function allows us to return certain types of connectors. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Transformer.sub', 'Transformer.reg1', 'Transformer.reg2', 'Transformer.reg3', 'Transformer.xfm1', 'Line.650632', 'Line.632670', 'Line.670671', 'Line.671680', 'Line.632633', 'Line.632645', 'Line.645646', 'Line.692675', 'Line.671684', 'Line.684611', 'Line.684652', 'Line.671692']\n"
+     ]
+    }
+   ],
+   "source": [
+    "lines_and_transformers = sims.get_connectors(qualified=[\"Line\", \"Transformer\"])\n",
+    "print(lines_and_transformers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Run powerflow\n",
+    "The powerflow problem is a fundamental one in electrical networks. It consists of determining the voltages and currents that will be observed throughout at the network for a given set of power demands. Practically, we tend to know the power demanded or produced by network entities, and we want to work from this to determine the currents and voltages that will result. These currents and voltages are also the measurements that we obtain from PMU sensors. \n",
+    "\n",
+    "Therefore, to generate \"synthetic\" PMU data for a network, we essentially need to run powerflow. \n",
+    "\n",
+    "We can start by running powerflow given the default load values in the model. Once powerflow is run, we retrieve the resulting voltages and currents. \n",
+    "\n",
+    "OpenDSS has a strange structure which is not fully, flexibly object oriented. Therefore, to access the properties of a certain object, such as the voltage of a bus, we have to set it as the active object in the circuit. This is demonstrated below as we retrieve the voltages of each phase after solving the PF. Unfortunately to make an element active is not very consistent. For example, loads can be activated through ```dss.Loads.Name()``` while buses must be activated through ```dss.Circuit.SetActiveBus()``` _not_ ```dss.Bus.Name()```. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run / solve powerflow\n",
+    "dss.Solution.Solve();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting the voltages and currents generated by the simulation is not totally straightforward. For example, the following line retrieves the voltages at a single bus by first setting the bus to be _active_. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Voltages at 633 [2426.425943474452, -109.95862245792392, -1300.0214661176276, -2096.2770897808787, -1120.4367179288215, 2128.6149747573713]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bus = bus_names[3]\n",
+    "dss.Circuit.SetActiveBus(bus)\n",
+    "busvolt = dss.Bus.Voltages()\n",
+    "print(\"Voltages at\", bus, busvolt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The voltages are returned as an array of real and imaginary parts for the volatge on each phase at the bus. Therefore, `len(busvolt)` will be $2 \\times$ the number of phases on the bus. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make things more convenient, the functions `v2dict` and `i2dict` retrieve all the voltage and current measurements from the model. These are returned in dictionaries, where the keys are formatted to convey what the specific data corresponds to and can be used to instantiate streams for the data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = sims.v2dict(bus_names)\n",
+    "i = sims.i2dict(lines_and_transformers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sourcebus/VAM 66393.52580623927\n",
+      "sourcebus/VAA 29.99273887927827\n"
+     ]
+    }
+   ],
+   "source": [
+    "key_list = [key for key in v.keys()]\n",
+    "print(key_list[0], v[key_list[0]])\n",
+    "print(key_list[1], v[key_list[1]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Change loads and run power flow\n",
+    "Now we want to generate a _time series_ of voltages and currents, where they vary as loads in the system change. We will do this by iteratively changing the load at each bus, running powerflow, and saving the solved voltages and currents. \n",
+    "\n",
+    "We could define load curves, but for simplicity, we will vary the loads randomly. For convenience, we do so by applying a randomized scaling to the original load in the model. That is, if the original load of bus $b$ is $l_b$, the load at time $t$ will be set to $l_b \\times s(b, t)$ where $s$ is a matrix we create. \n",
+    "\n",
+    "In this case, we choose $s$ to be random uniform and uncorrelated across nodes. The following cell gets the existing load settings in the model and uses them to generate a matrix of randomized loads. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "T = 100  # Number of time points to simulate\n",
+    "\n",
+    "# Get the original loads\n",
+    "load, load_names = sims.get_loads()\n",
+    "nloads = len(load_names)\n",
+    "\n",
+    "# Generate the randomized scaling factors\n",
+    "mu = 1.1\n",
+    "sig = 0.1\n",
+    "s = np.random.normal(loc=mu, scale=sig, size=[nloads, T])\n",
+    "\n",
+    "# Generate the new load values by scaling the original loads with randomized s\n",
+    "new_load = s * load[:, np.newaxis]\n",
+    "\n",
+    "# Visualize some of the load curves\n",
+    "plt.plot(new_load[0:5, :].T);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now to iteratively run the simulation for each load setting and retrieve the resulting voltages and currents...\n",
+    "\n",
+    "The function `simulate_network` does this all for you. It has the signature `simulate_network(loads, load_names, contypes=['Line', 'Transformer'])`. The first argument is the desired load values in an $n\\times T$ matrix (as created above), where $n$ is the number of loads we want to set. The second argument is the names of the loads to set (as returned by `get_loads()` above). Finally, the optional argument `contypes` specifies which types of connections we want to return current data for.  \n",
+    "\n",
+    "The function returns dictionaries of voltage and current, with the sames keys as `v2dict` and `i2dict` respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running simulation:   0%|          | 0/99 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "V, I = sims.simulate_network(new_load, load_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at one of the resulting voltage magnitudes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "key_list = [key for key in V.keys()]\n",
+    "\n",
+    "plt.plot(V[key_list[0]])\n",
+    "plt.title(key_list[0]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And one of the resulting current magnitudes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "key_list = [key for key in I.keys()]\n",
+    "\n",
+    "plt.plot(I[key_list[0]])\n",
+    "plt.title(key_list[0]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d44492a..0d72cc9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,8 +1,10 @@
+# -*- coding: utf-8 -*-
+#
 # Configuration file for the Sphinx documentation builder.
 #
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
+# This file does only contain a selection of the most common options. For a
+# full list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
 
 # -- Path setup --------------------------------------------------------------
 
@@ -17,17 +19,21 @@
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath("../.."))
+import btrdbextras
 
-import sphinx_glpi_theme
+sys.path.insert(0, os.path.abspath("_themes"))
+sys.path.insert(0, os.path.abspath("../.."))
 
 # -- Project information -----------------------------------------------------
 
 project = "btrdbextras"
-copyright = "2020, PingThings, Inc."
+copyright = "2023, PingThings, Inc."
 author = "PingThings, Inc."
 
-
+# The short X.Y version
+version = btrdbextras.__version__
+# The full version, including alpha/beta/rc tags
+release = version
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
@@ -36,18 +42,39 @@
 extensions = [
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
+    "sphinx.ext.todo",
     "sphinx.ext.githubpages",
     "sphinx.ext.intersphinx",
+    "numpydoc",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = ".rst"
+
+# The master toctree document.
+master_doc = "index"
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = "en"
+
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = ["build", "Thumbs.db", ".DS_Store"]
 
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = None
+
 
 # -- Options for HTML output -------------------------------------------------
 
@@ -56,6 +83,78 @@
 #
 
 
-html_theme = "glpi"
+html_theme = "alabaster"
+# html_theme = "sphinx_rtd_theme"
+html_theme = "pydata_sphinx_theme"
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+html_theme_options = {
+    "show_powered_by": False,
+    "github_user": "PingThingsIO",
+    "github_repo": "btrdbextras",
+    "travis_button": False,
+    "github_banner": False,
+    "show_related": False,
+    "note_bg": "#FFF59C",
+    "description": "An additional library of enhancements and features to "
+    "interact with the BTrDB database and Pingthings platform.",
+    "extra_nav_links": {"btrdb": "http://btrdb-python.readthedocs.io"},
+    "show_relbars": True,
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself.  Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {
+#     '**': [
+#         'sidebar.html',
+#         'localtoc.html',
+#         'relations.html',
+#         'searchbox.html',
+#     ],
+# }
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "btrdbextras"
+
+# -- Options for Epub output -------------------------------------------------
+
+# Bibliographic Dublin Core info.
+epub_title = project
+
+# The unique identifier of the text. This can be a ISBN number
+# or the project homepage.
+#
+# epub_identifier = ''
+
+# A unique identification for the text.
+#
+# epub_uid = ''
+
+# A list of files that should not be packed into the epub file.
+epub_exclude_files = ["search.html"]
+
+
+# -- Extension configuration -------------------------------------------------
+
+# -- Options for todo extension ----------------------------------------------
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = True
 
-html_theme_path = sphinx_glpi_theme.get_html_themes_path()
+numfig = True