diff --git a/.github/workflows/github-actions.yml b/.github/workflows/github-actions.yml
index a1495baea..81f36d681 100644
--- a/.github/workflows/github-actions.yml
+++ b/.github/workflows/github-actions.yml
@@ -24,10 +24,10 @@ jobs:
         run: sed 's/>/=/g' requirements.txt | sed 's/$/\.*/g' > requirements.min.txt
         shell: bash
       - name: Install Minimum Requirements
-        run: pip install --upgrade -r requirements.min.txt
+        run: python -m pip install --upgrade -r requirements.min.txt
         shell: bash
       - name: Install STUMPY And Other Dependencies
-        run: pip install --editable .[ci]
+        run: python -m pip install --editable .[ci]
         shell: bash
       - name: Run Black
         run: black --check --diff ./
@@ -35,6 +35,15 @@ jobs:
       - name: Run Flake8
         run: flake8 ./
         shell: bash
+      - name: Link OpenMP
+        run: |
+             if [ "$RUNNER_OS" == "macOS" ]; then
+                  brew link --force libomp
+             fi
+        shell: bash
+      - name: Show Full Numba Environment
+        run: python -m numba -s
+        shell: bash
       - name: Run Unit Tests
         run: ./test.sh unit
         shell: bash
@@ -54,7 +63,7 @@ jobs:
         run: python -c "import sys; print(sys.version)"
         shell: bash
       - name: Install STUMPY And Other Dependencies
-        run: pip install --editable .[ci]
+        run: python -m pip install --editable .[ci]
         shell: bash
       - name: Run Black
         run: black --check --diff ./
@@ -62,6 +71,15 @@ jobs:
       - name: Run Flake8
         run: flake8 ./
         shell: bash
+      - name: Link OpenMP
+        run: |
+             if [ "$RUNNER_OS" == "macOS" ]; then
+                  brew link --force libomp
+             fi
+        shell: bash
+      - name: Show Full Numba Environment
+        run: python -m numba -s
+        shell: bash
       - name: Run Unit Tests
         run: ./test.sh unit
         shell: bash
@@ -81,7 +99,7 @@ jobs:
         run: python -c "import sys; print(sys.version)"
         shell: bash
       - name: Install STUMPY And Other Dependencies
-        run: pip install --editable .[ci]
+        run: python -m pip install --editable .[ci]
         shell: bash
       - name: Run Black
         run: black --check --diff ./
@@ -89,6 +107,15 @@ jobs:
       - name: Run Flake8
         run: flake8 ./
         shell: bash
+      - name: Link OpenMP
+        run: |
+             if [ "$RUNNER_OS" == "macOS" ]; then
+                  brew link --force libomp
+             fi
+        shell: bash
+      - name: Show Full Numba Environment
+        run: python -m numba -s
+        shell: bash
       - name: Run Coverage Tests
         run: ./test.sh coverage
         shell: bash
diff --git a/docs/Tutorial_Top-K_Multidimensional_Motif_and_Matches_Discovery.ipynb b/docs/Tutorial_Top-K_Multidimensional_Motif_and_Matches_Discovery.ipynb
new file mode 100644
index 000000000..a75ba2d5f
--- /dev/null
+++ b/docs/Tutorial_Top-K_Multidimensional_Motif_and_Matches_Discovery.ipynb
@@ -0,0 +1,1124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background: lightpink; color:darkmagenta; font-size: 20px; padding: 10px; border: 1px solid lightgray; margin: 10px;\">\n",
+    "  &#9432;\n",
+    "<font color='red'>\n",
+    "Work-in-progress!</font>\n",
+    "<br><p style=\"font-size: 15px;\">\n",
+    "This tutorial gives an overview of how to use the so called `mmotifs` function in order to find many repeating structures in multi-dimensional time series data. Unfortunately a suboptimal data set was chosen for the demonstration of this function. Since the data set needed a lot of preprocessing, we weren't able to find meaningful motifs all the time. This problem may be tackled in a future pull request while discovering another data set.\n",
+    "</p></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Top-K Multi-Dimensional Motif and Matches Discovery\n",
+    "\n",
+    "This tutorial builds on the [Multidimensional Motif Discovery](https://stumpy.readthedocs.io/en/latest/Tutorial_Multidimensional_Motif_Discovery.html) tutorial as it generalizes the search for a single multidimensional motif to multiple multidimensional motifs and their nearest neighbors (i.e. matches). We will show how the function `mmotifs`, which extends this advanced motif and match search, works by setting and explaining the different input parameters. The `mmotifs` function is the multidimensional analogue of the one-dimensional [motifs](https://github.com/TDAmeritrade/stumpy/blob/main/stumpy/motifs.py) function that can be found in the STUMPY API. In doing so, the function uses the MDL approach inside it to calculate the number of relevant dimensions unsupervised and return those dimensions while computing the subspace.\n",
+    "\n",
+    "Note that the following function is only used to visualize all the motifs and matches that we will find."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls):\n",
+    "    num_motifs = motifs_distances.shape[0]\n",
+    "\n",
+    "    for motif_num in range(num_motifs):\n",
+    "        motif_indices = motifs_indices[motif_num]\n",
+    "        S = motifs_subspaces[motif_num]\n",
+    "\n",
+    "        k = len(S)\n",
+    "        f, axs = plt.subplots(k, 1, figsize=(20, 10), sharex=True)\n",
+    "        plt.suptitle(f\"The {motif_num + 1}. {k}-dimensional motif\")\n",
+    "\n",
+    "        for j, s in enumerate(S):\n",
+    "            time_series = df[df.columns[s]]\n",
+    "            if k > 1:\n",
+    "                ax = axs[j]\n",
+    "            else:\n",
+    "                ax = axs\n",
+    "            ax.plot(time_series, c=\"0.75\")\n",
+    "            ax.set_title(df.columns.values[s])\n",
+    "\n",
+    "            motif_idx = motif_indices[0]\n",
+    "            nn_idx = motif_indices[1]\n",
+    "            ax.plot(time_series[motif_idx : motif_idx + m], c=\"r\", linewidth=4)\n",
+    "            ax.plot(time_series[nn_idx : nn_idx + m], c=\"r\", linewidth=4)\n",
+    "\n",
+    "            # Only relevant if you want to find further matches\n",
+    "            # Set motif_indices[2:] to avoid double counting the motif pair\n",
+    "            [ax.plot(time_series[match : match + m], linewidth=3) for match in motif_indices[2:]]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Getting started\n",
+    "\n",
+    "Let's import the packages that we will need to load, analyze, and plot the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import stumpy\n",
+    "\n",
+    "plt.style.use(\n",
+    "    \"https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Loading, Preprocessing and Visualizing the Dataset\n",
+    "\n",
+    "In the following we will take a look at the [Electrical Load Measurement](https://pureportal.strath.ac.uk/en/datasets/refit-electrical-load-measurements-cleaned) dataset, that consists of electrical load measurements for different appliances from households in the UK. To reduce the amount of data somewhat, we consider only one household in the period from april 19 to may 15 of the year 2014. Also we want to take a closer look at only the five appliances `Fridge-Freezer`, `Freezer`, `Tumble Dryer`, `Diswasher` and `Washing Machine` and see if we are able to find meaningful motifs and matches on this multidimensional time series data! We simplify our life a bit more by converting the timestamps, which are sampled to 8 second intervals, to minutes in order to reduce the overall data size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fridge-Freezer</th>\n",
+       "      <th>Freezer</th>\n",
+       "      <th>Tumble Dryer</th>\n",
+       "      <th>Dishwasher</th>\n",
+       "      <th>Washing Machine</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Time</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",
+       "      <th>2014-04-19 00:00:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-19 00:01:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-19 00:02:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-19 00:03:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-19 00:04:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Fridge-Freezer  Freezer  Tumble Dryer  Dishwasher  \\\n",
+       "Time                                                                     \n",
+       "2014-04-19 00:00:00             0.0      0.0           0.0         0.0   \n",
+       "2014-04-19 00:01:00             0.0      0.0           0.0         0.0   \n",
+       "2014-04-19 00:02:00             0.0      0.0           0.0         0.0   \n",
+       "2014-04-19 00:03:00             0.0      0.0           0.0         0.0   \n",
+       "2014-04-19 00:04:00             0.0      0.0           0.0         0.0   \n",
+       "\n",
+       "                     Washing Machine  \n",
+       "Time                                  \n",
+       "2014-04-19 00:00:00              0.0  \n",
+       "2014-04-19 00:01:00              0.0  \n",
+       "2014-04-19 00:02:00              0.0  \n",
+       "2014-04-19 00:03:00              0.0  \n",
+       "2014-04-19 00:04:00              0.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start_date, end_date = \"2014-04-19\", \"2014-05-15\"\n",
+    "raw_df = pd.read_csv(\"../CLEAN_House3.csv\")\n",
+    "raw_df[\"Time\"] = pd.to_datetime(raw_df[\"Time\"])\n",
+    "colnames = {\n",
+    "    \"Appliance1\": \"Toaster\",\n",
+    "    \"Appliance2\": \"Fridge-Freezer\",\n",
+    "    \"Appliance3\": \"Freezer\",\n",
+    "    \"Appliance4\": \"Tumble Dryer\",\n",
+    "    \"Appliance5\": \"Dishwasher\",\n",
+    "    \"Appliance6\": \"Washing Machine\",\n",
+    "    \"Appliance7\": \"Television\",\n",
+    "    \"Appliance8\": \"Microwave\",\n",
+    "    \"Appliance9\": \"Kettle\",\n",
+    "}\n",
+    "raw_df = (\n",
+    "    raw_df.rename(colnames, axis=\"columns\")\n",
+    "    .loc[\n",
+    "        :,\n",
+    "        [\n",
+    "            \"Time\",\n",
+    "            \"Fridge-Freezer\",\n",
+    "            \"Freezer\",\n",
+    "            \"Tumble Dryer\",\n",
+    "            \"Dishwasher\",\n",
+    "            \"Washing Machine\",\n",
+    "        ],\n",
+    "    ]\n",
+    "    .query(\"Time >= @start_date and Time <= @end_date\")\n",
+    "    .groupby(pd.Grouper(key=\"Time\", freq=\"T\"))\n",
+    "    .sum().astype(float)\n",
+    ")\n",
+    "\n",
+    "raw_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Now our dataset contains the time series of the five appliances with timestamps now sampled in minutes. Let's visualize it!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "![fridge freezer](https://upload.wikimedia.org/wikipedia/commons/thumb/2/25/Bosch_Electronic_no_frost_fridge_freezer_%282019%29_05.jpg/135px-Bosch_Electronic_no_frost_fridge_freezer_%282019%29_05.jpg)\n",
+    "![freezer](https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Helio_House_%288800481437%29.jpg/320px-Helio_House_%288800481437%29.jpg)\n",
+    "![dishwasher](https://upload.wikimedia.org/wikipedia/commons/thumb/8/82/Dishwasher_open_for_loading.jpg/180px-Dishwasher_open_for_loading.jpg)\n",
+    "![Washing Machine and Tumble Dryer](https://upload.wikimedia.org/wikipedia/commons/thumb/8/84/Samsung%2C_IFA_2018%2C_Berlin_%28P1070290%29.jpg/320px-Samsung%2C_IFA_2018%2C_Berlin_%28P1070290%29.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Ok, now that we've marveled at those beautifully visualized household appliances we're ready to visualize those timeseries as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_list = ['b', 'c', 'g', 'm', 'y']\n",
+    "for i, colname in enumerate(raw_df.columns):\n",
+    "    plt.plot(raw_df[colname], label=colname, color=color_list[i])\n",
+    "plt.legend()\n",
+    "plt.axvspan('2014-05-02 13:00:00','2014-05-03 18:00:00',color='black',alpha=0.15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Let us zoom in on a typical afternoon, to get a better feel for this large dataset!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "zoom = raw_df.query(\"Time >= '2014-05-02 13:00:00' and Time <= '2014-05-03 18:00:00'\")\n",
+    "\n",
+    "for i, colname in enumerate(zoom.columns):\n",
+    "    plt.plot(zoom[colname], label=colname, color=color_list[i])\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "On this day you can clearly see two spikes resulting from the dishwasher usage of one household.\n",
+    "Now let us try to find the most similar patterns across our large dataset spanning four hours within the extracted month."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# m was set to 4 hours\n",
+    "m = 4 * 60\n",
+    "df = raw_df.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Before we are able to find motifs and matches with the `mmotifs` function, we first have to compute the mutli-dimensional matrix profile.\n",
+    "\n",
+    "But notice: Each row of the resultung array corresponds to each matrix profile for a given dimension, as shown below.\n",
+    "\n",
+    "|Array|\n",
+    "|:-----:|\n",
+    "|_1D Matrix Profile_ $...$ |\n",
+    "|_2D Matrix Profile_ $...$ |\n",
+    "| $\\vdots$ |\n",
+    "|_5D Matrix Profile_ $...$ |\n",
+    "\n",
+    "Therefore the dimensionality of our last row contains the nearest neighbor information of all 5 appliances. In contrast the 3D matrix profile contains the same information but only for three out of the five appliances. But the matrix profile doesn't tell us which of those dimensions are relevant. This information can only be found within the matrix profile subspace, which is one of the return values of the `mmotifs` function.\n",
+    "\n",
+    "We compute the multi-dimensional matrix profile and its indices just like in the [multidimensional motif discovery tutorial](https://stumpy.readthedocs.io/en/latest/Tutorial_Multidimensional_Motif_Discovery.html) as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "mps, indices = stumpy.mstump(df, m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Annotation Vectors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Unfortunately we first have to eliminate constant regions in order to find meaningful motifs. Otherwise we would only find the areas where no appliances are used and therefore the electrical power demand is constantly 0. This would be a very strong but meaningless motif. To avoid this problem we use the so called `Annotation Vector` approach which was already introduced in the [Guided Motif Search With Annotation Vectors](https://stumpy.readthedocs.io/en/latest/Tutorial_Annotation_Vectors.html) tutorial in the one-dimensional case. According to this approach it will be possible to re-rank candidate motifs by producing a vector that is \"parallel\" to the original time series and annotates it with the user's constraints to allow a more meaningful motif discovery.\n",
+    "\n",
+    "In order to apply the `Annotation Vector` approach to the multi-dimensional case we have to first iterate over all possible dimensions.<br>\n",
+    "<u>Remember:</u><br>\n",
+    "The Annotation Vector is a time series consisting of real-valued numbers between 0 and 1. A low value indicates that the corresponding subsequence is not a desirable motif. Likewise, a high value means that the subsequence should get more attention in the motif discovery process.<br>\n",
+    "Afterwards we have to check which subsequences should get a higher and which a lower rank:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "annotation_vector = np.ones(mps.shape, dtype=np.float64)\n",
+    "\n",
+    "for k in range(mps.shape[0]):\n",
+    "    for subseq_idx in range(mps.shape[1]):\n",
+    "        nn_idx = indices[k, subseq_idx]\n",
+    "        S = stumpy.subspace(df, m, subseq_idx, nn_idx, k)\n",
+    "        if np.count_nonzero(df.iloc[subseq_idx:subseq_idx+m, S]) <= (len(S) * m)//2 or np.count_nonzero(df.iloc[nn_idx:nn_idx+m, S]) <= (len(S) * m)//2:\n",
+    "            annotation_vector[k, subseq_idx] = 0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The code above creates the Annotation Vector that is initialized with `ones` spanning the length of the first line in the matrix profile.\n",
+    "Then, for each dimension, we find the motif/nearest neighbor subsequence pair and determine the optimal subspace as described in the [Multidimensional Motif Discovery](https://stumpy.readthedocs.io/en/latest/Tutorial_Multidimensional_Motif_Discovery.html) tutorial. Using the `if` condition we count the number of non-zero elements within the multidimensional subsequences that span the motif pair. If more than half of the values that make up these subsequences (the motif or nearest neighbor) are zero, then we set the Annotation Vector value to zero. This means that such areas are considered uninteresting and can therefore no longer be found as a motif. All in all we are trying to focus on motif pairs that have enough real, non-zero values to allow for a meaningful motif discovery using this dataset.\n",
+    "\n",
+    "Then, we can use the newly created Annotation Vector to generate a corrected multidimensional matrix profile:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "corrected_mps = mps + ((1.0 - annotation_vector) * np.max(mps))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Discovering one motif and its matches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Once we have computed the corrected multidimensional matrix profile, we are able to search for recurring structures in our multivariate time series. So let us find out how `mmotifs` works by passing specific parameters to this function and discover different motifs and matches along the way. For this we should first understand the outputs that we get from the function:\n",
+    "\n",
+    "`motifs_distances`: This array holds the distances corresponding to a set of subsequence matches for each motif. The first column always corresponds to the distance of the self-match for each motif. <br>\n",
+    "`motifs_indices`: This array holds the indices corresponding to a set of subsequence matches for each motif. Just as with the distances, the first column of the indices is for the self-match for each motif. <br>\n",
+    "`motifs_subspaces`: This list contains the `k`-dimensional subspace for each motif which tells us in which time series dimensions the motifs are located.<br>\n",
+    "`motifs_mdls`: This list contains the MDL-results for finding the dimension of each motif. Given this list, we are able to understand why the motifs are found in exactly that many dimensions when doing an unconstrained search. \n",
+    "\n",
+    "This outputs consist of as many rows as motifs are found. The `motifs_distances` and `motifs_indices` contain the multi-dimensional motif pair for each found motif and all other columns contain the subsequences most similar to the motif, ordered by size (smaller distance meaning more similarity).\n",
+    "\n",
+    "So let us do a completely unconstrained search at first and call `mmotifs` with default parameters! In this case you only have to pass the already computed multi-dimensional distance profile together with its indices to the function. Of course, the multidimensional time series or sequence in which the motifs are to be found must not be missing either! Accordingly, the simplest function call is as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls = stumpy.mmotifs(\n",
+    "    df, corrected_mps, indices\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "And of course, we can visualize the results we found with the already defined `show_motifs_matches` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Congratulations, you have found your first motif on the `Dishwasher` time series! We were able to find all 10 default matches with the motif pair (i. e. the two most similar subsequences) shown in red! \n",
+    "\n",
+    "But this doesn't always have to be the case since the `max_distance` input parameter is computed automatically depending on the distance profile if not explicitly set otherwise. Therefore in this case the `max_distance` parameter was bigger than all of the distances from the motif to its nine nearest neighbors and that's the reason why we were able to find all ten `max_matches`. The `max_matches` input parameter (which is set to 10 by default) is the maximum amount of similar matches (i.e. nearest neighbors) of a representative motif to be returned. But again, notice that the first match is always the self-match for each motif!\n",
+    "\n",
+    "Let's plot our discovered matches on top of each other to see how similar they are!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_motifs = motifs_distances.shape[0]\n",
+    "\n",
+    "for motif_num in range(num_motifs):\n",
+    "    motif_indices = motifs_indices[motif_num]\n",
+    "    subspace_num = motifs_subspaces[motif_num]\n",
+    "    subspace_names = [df.columns.values[s] for s in subspace_num]\n",
+    "\n",
+    "    figure, ax = plt.subplots(len(subspace_num), 1)\n",
+    "    plt.suptitle('Comparing the motif with its nearest neighbors', fontsize='30')\n",
+    "    plt.xlabel('Time', fontsize ='20')\n",
+    "    plt.ylabel('Electrical Power Demand', fontsize='20') \n",
+    "    \n",
+    "    for i, match_index in reversed(list(enumerate(motif_indices))):\n",
+    "        match_z_norm = stumpy.core.z_norm(df[subspace_names].values[match_index: match_index + m])\n",
+    "        if i < 2:\n",
+    "            # Motif pair\n",
+    "            ax.plot(match_z_norm, lw=4, color='red')\n",
+    "        else:\n",
+    "            # Other neighbors\n",
+    "            ax.plot(match_z_norm, lw=2)\n",
+    "\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "As you can see, the found matches are very similar to each other. Only when you zoom in very close into the plot, you can see small differences between the individual subsequences.\n",
+    "\n",
+    "What if we only want to get the motif pair without any further matches? That's easy! Simply specifiy that you want to find two matches by setting `max_matches=2` and plot everything again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls = stumpy.mmotifs(\n",
+    "    df, corrected_mps, indices, max_matches=2\n",
+    ")\n",
+    "show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Analogously, if you want to find the motif pair with its three nearest neighbors you will need to set `max_matches=5`!\n",
+    "Tip: If you also set the maximal distance that is allowed between a motif and all other subsequences in the time series to _infinity_ (i. e. numpy.inf), you can make sure that you always get _exactly_ 5 matches (even though this is not necessary here since we were already able to find 10 matches)! So let's see how this works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls = stumpy.mmotifs(\n",
+    "    df, corrected_mps, indices, max_distance=np.inf, max_matches=5\n",
+    ")\n",
+    "show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "But why did we find the motif and its neighbors only in one dimension - the `Dishwasher` dimension? Since we were doing an `unconstrained search` (we didn't tell the `mmotifs` function in how many dimensions to search for the motif and what dimensions to search in) the `mmotifs` function is using the `Minmum Description Length (MDL)` approach (as explained in [multidimensional motif discovery tutorial](https://stumpy.readthedocs.io/en/latest/Tutorial_Multidimensional_Motif_Discovery.html)) in order to find the number `k` of dimensions in which the motif is present. To understand why the motif was found in only one dimension we will take a look at the corresponding MDL plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_motifs = motifs_distances.shape[0]\n",
+    "\n",
+    "for motif_num in range(num_motifs):\n",
+    "    mdl = motifs_mdls[motif_num]\n",
+    "\n",
+    "    # Plot MDL results\n",
+    "    plt.figure()\n",
+    "    plt.plot(np.arange(len(mdl)), mdl, c='red', linewidth=\"4\")\n",
+    "    plt.title(f\"MDL results for finding the {motif_num + 1}. motif\", fontsize=\"30\")\n",
+    "    plt.xlabel(\"k (zero-based)\", fontsize=\"20\")\n",
+    "    plt.ylabel(\"Bit Size\", fontsize=\"20\")\n",
+    "    plt.xticks(range(corrected_mps.shape[0]))\n",
+    "    plt.plot(0, 18000, marker=\"v\", markersize=10, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "As we can see the `mmotifs` function selected the optimal number of dimensions automatically for us! Here the data gets compressed the most for `k=0` - i.e. in one dimension. As before we don't want to blindly trust the `mmotifs` results without a reality check. So let's see if it is really the `Dishwasher` dimension that gives the minimum compression size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Dishwasher']\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_motifs = motifs_distances.shape[0]\n",
+    "\n",
+    "for motif_num in range(num_motifs):\n",
+    "    S = motifs_subspaces[motif_num]\n",
+    "    subspace = [df.columns.values[s] for s in S]\n",
+    "    print(subspace)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "And it really seems to be the `Dishwasher` dimenison in which our one-dimensional motif is present! Thus the `mmotifs` function seems to do a good job! So from now on we will trust the judgement of `mmotifs` so we don't have to check the MDL plot and the found subspace manually every time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Discovering motifs in multiple dimensions by specifying the number of relevant dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    },
+    "tags": []
+   },
+   "source": [
+    "Because we are interested in finding truly multidimensional motifs, we have to specify in how many dimensions the `mmotifs` function should look for the most similar subsequences. Otherwise we would be limited to finding one-dimensional motifs only. For this purpose the input parameter `k` of the `mmotifs` function is used. The parameter gives the function the number of dimensions (`k+1`, since zero-based) required for discovering the motifs with the desired dimensionality. So let's try to find a motif in two dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls = stumpy.mmotifs(\n",
+    "    df, corrected_mps, indices, k=1\n",
+    ")\n",
+    "show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Congratulations! You have found your first two-dimensional motif with its nearest neighbors! The relevant dimensions are the electrical load measurements of the `Dishwasher` and the `Fridge-Freezer`. Since both appliances are commonly used in succession within a relativly short time period (before cooking the food is taken out of the fridge and after cooking the dishes are washed), the found two-dimensional motif makes sense."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Finding multiple multi-dimensional motifs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "So far, we have only ever found a single motif. This is due to the `max_motifs` parameter of the `mmotifs` function which defines the maximum number of motifs to return. If not set otherwise the parameter defaults to `max_motifs=1`. This means that we have to set this parameter explicitly if we want to find multiple multidimensional motifs. Let us, for example, try to find two two-dimensional motifs!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls = stumpy.mmotifs(\n",
+    "    df, corrected_mps, indices, max_motifs=2, k=1\n",
+    ")\n",
+    "show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "And just as expected, we get the top-2 multidimensional motifs with their `max_matches` nearest neighbors. The first two-dimensional motif is, of course, the same one we found in the last section. Our second motif, however, has been unknown. As you can see it extends over the dimensions of the `Dishwasher` and the `Freezer`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Constrained Search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    },
+    "tags": []
+   },
+   "source": [
+    "The only dimensions in which we have not yet been able to find a motif are that of the `Tumble Dryer` and `Washing Machine`. Let's see if we can also find significant motifs spanning this time series dimensions! To do so we can use the `include` parameter that is also known from the [multidimensional motif discovery tutorial](https://stumpy.readthedocs.io/en/latest/Tutorial_Multidimensional_Motif_Discovery.html). When leveraging the `include` parameter we are able to find the best `k`-dimensional motif while enforcing the utilization of the `Tumble Dryer` and `Washing Machine` dimensions.\n",
+    "\n",
+    "Remember: If you use the `include` (or `exclude`) parameter to ensure that specific time series dimensions are included (or excluded) when searching for the best multi-dimensional motif, you also have to set the `include` parameter when computing the multi-dimensional matrix profile. This is because the matrix profile may have to prioritize less important dimensions here so that motifs can also be found in dimensions which wouldn't be discovered when computing the matrix profile normally.\n",
+    "\n",
+    "Accordingly, we first calculate the \"include-specific\" multi-dimensional matrix profile to search for a motif in the - often used directly after each other - `Tumble Dryer` and `Washing Machine` dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mps, indices = stumpy.mstump(df, m, include=[2, 4])\n",
+    "corrected_mps = mps + ((1.0 - annotation_vector) * np.max(mps))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Since we have recomputed the multi-dimensional matrix profile in order to prioritize the `Tumble Dryer` and `Washing Machine`, we can now call `mmotifs` again and plot the results. Here, the `include` parameter needs to be set again!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls = stumpy.mmotifs(\n",
+    "    df, corrected_mps, indices, include=[2, 4]\n",
+    ")\n",
+    "show_motifs_matches(df, motifs_distances, motifs_indices, motifs_subspaces, motifs_mdls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "When searching for the best motif on `k` dimensions while explicitly including the `Tumble Dryer` and `Washing Machine`, we get a 3-dimensional motif spanning the dimensions of the `Washing Machine`, `Tumble Dryer` and `Dishwasher`. We only set the `include` parameter explicitly and used default values for the rest of the multi-dimensional motif search. The significance of the motifs found is questionable since many regions without an electircal power demand are found."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "source": [
+    "### <font color='red'>These results are neither sufficiently obvious nor conclusive enough for a tutorial and should be replaced with something more eye-catching and easily comprehensible. Nonetheless the usage of the `mmotifs` function was demonstrated.</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "88827a94fea606536c77cf655f78c9244bd989b098dfa2ec3c20af0b44367ad5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/stumpy/aamp.py b/stumpy/aamp.py
index 2506db277..ca253c9af 100644
--- a/stumpy/aamp.py
+++ b/stumpy/aamp.py
@@ -88,6 +88,8 @@ def _compute_diagonal(
     """
     n_A = T_A.shape[0]
     n_B = T_B.shape[0]
+    uint64_m = np.uint64(m)
+    uint64_1 = np.uint64(1)
 
     for diag_idx in range(diags_start_idx, diags_stop_idx):
         k = diags[diag_idx]
@@ -98,41 +100,54 @@ def _compute_diagonal(
             iter_range = range(-k, min(n_A - m + 1, n_B - m + 1 - k))
 
         for i in iter_range:
-            if i == 0 or (k < 0 and i == -k):
+            uint64_i = np.uint64(i)
+            uint64_j = np.uint64(i + k)
+
+            if uint64_i == 0 or uint64_j == 0:
                 p_norm = (
-                    np.linalg.norm(T_B[i + k : i + k + m] - T_A[i : i + m], ord=p) ** p
+                    np.linalg.norm(
+                        T_B[uint64_j : uint64_j + uint64_m]
+                        - T_A[uint64_i : uint64_i + uint64_m],
+                        ord=p,
+                    )
+                    ** p
                 )
             else:
                 p_norm = np.abs(
                     p_norm
-                    - np.absolute(T_B[i + k - 1] - T_A[i - 1]) ** p
-                    + np.absolute(T_B[i + k + m - 1] - T_A[i + m - 1]) ** p
+                    - np.absolute(T_B[uint64_j - uint64_1] - T_A[uint64_i - uint64_1])
+                    ** p
+                    + np.absolute(
+                        T_B[uint64_j + uint64_m - uint64_1]
+                        - T_A[uint64_i + uint64_m - uint64_1]
+                    )
+                    ** p
                 )
 
             if p_norm < config.STUMPY_P_NORM_THRESHOLD:
                 p_norm = 0.0
 
-            if T_A_subseq_isfinite[i] and T_B_subseq_isfinite[i + k]:
+            if T_A_subseq_isfinite[uint64_i] and T_B_subseq_isfinite[uint64_j]:
                 # Neither subsequence contains NaNs
-                if p_norm < P[thread_idx, i, 0]:
-                    P[thread_idx, i, 0] = p_norm
-                    I[thread_idx, i, 0] = i + k
+                if p_norm < P[thread_idx, uint64_i, 0]:
+                    P[thread_idx, uint64_i, 0] = p_norm
+                    I[thread_idx, uint64_i, 0] = uint64_j
 
                 if ignore_trivial:
-                    if p_norm < P[thread_idx, i + k, 0]:
-                        P[thread_idx, i + k, 0] = p_norm
-                        I[thread_idx, i + k, 0] = i
+                    if p_norm < P[thread_idx, uint64_j, 0]:
+                        P[thread_idx, uint64_j, 0] = p_norm
+                        I[thread_idx, uint64_j, 0] = uint64_i
 
-                    if i < i + k:
+                    if uint64_i < uint64_j:
                         # left matrix profile and left matrix profile index
-                        if p_norm < P[thread_idx, i + k, 1]:
-                            P[thread_idx, i + k, 1] = p_norm
-                            I[thread_idx, i + k, 1] = i
+                        if p_norm < P[thread_idx, uint64_j, 1]:
+                            P[thread_idx, uint64_j, 1] = p_norm
+                            I[thread_idx, uint64_j, 1] = uint64_i
 
                         # right matrix profile and right matrix profile index
-                        if p_norm < P[thread_idx, i, 2]:
-                            P[thread_idx, i, 2] = p_norm
-                            I[thread_idx, i, 2] = i + k
+                        if p_norm < P[thread_idx, uint64_i, 2]:
+                            P[thread_idx, uint64_i, 2] = p_norm
+                            I[thread_idx, uint64_i, 2] = uint64_j
 
     return
 
diff --git a/stumpy/stump.py b/stumpy/stump.py
index 282cea0f9..aeac32f3f 100644
--- a/stumpy/stump.py
+++ b/stumpy/stump.py
@@ -174,6 +174,7 @@ def _compute_diagonal(
     n_B = T_B.shape[0]
     m_inverse = 1.0 / m
     constant = (m - 1) * m_inverse * m_inverse  # (m - 1)/(m * m)
+    uint64_m = np.uint64(m)
 
     for diag_idx in range(diags_start_idx, diags_stop_idx):
         g = diags[diag_idx]
@@ -184,10 +185,14 @@ def _compute_diagonal(
             iter_range = range(-g, min(n_A - m + 1, n_B - m + 1 - g))
 
         for i in iter_range:
-            if i == 0 or (g < 0 and i == -g):
+            uint64_i = np.uint64(i)
+            uint64_j = np.uint64(i + g)
+
+            if uint64_i == 0 or uint64_j == 0:
                 cov = (
                     np.dot(
-                        (T_B[i + g : i + g + m] - M_T[i + g]), (T_A[i : i + m] - μ_Q[i])
+                        (T_B[uint64_j : uint64_j + uint64_m] - M_T[uint64_j]),
+                        (T_A[uint64_i : uint64_i + uint64_m] - μ_Q[uint64_i]),
                     )
                     * m_inverse
                 )
@@ -199,17 +204,18 @@ def _compute_diagonal(
                 #     - (T_B[i + k - 1] - M_T_m_1[i + k]) * (T_A[i - 1] - μ_Q_m_1[i])
                 # )
                 cov = cov + constant * (
-                    cov_a[i + g] * cov_b[i] - cov_c[i + g] * cov_d[i]
+                    cov_a[uint64_j] * cov_b[uint64_i]
+                    - cov_c[uint64_j] * cov_d[uint64_i]
                 )
 
-            if T_B_subseq_isfinite[i + g] and T_A_subseq_isfinite[i]:
+            if T_B_subseq_isfinite[uint64_j] and T_A_subseq_isfinite[uint64_i]:
                 # Neither subsequence contains NaNs
-                if T_B_subseq_isconstant[i + g] or T_A_subseq_isconstant[i]:
+                if T_B_subseq_isconstant[uint64_j] or T_A_subseq_isconstant[uint64_i]:
                     pearson = 0.5
                 else:
-                    pearson = cov * Σ_T_inverse[i + g] * σ_Q_inverse[i]
+                    pearson = cov * Σ_T_inverse[uint64_j] * σ_Q_inverse[uint64_i]
 
-                if T_B_subseq_isconstant[i + g] and T_A_subseq_isconstant[i]:
+                if T_B_subseq_isconstant[uint64_j] and T_A_subseq_isconstant[uint64_i]:
                     pearson = 1.0
 
                 # `ρ[thread_idx, i, :]` is sorted ascendingly and MUST be updated
@@ -217,34 +223,34 @@ def _compute_diagonal(
                 # first (i.e. smallest) element in this array. Note that a higher
                 # pearson value corresponds to a lower distance.
                 if pearson > ρ[thread_idx, i, 0]:
-                    idx = np.searchsorted(ρ[thread_idx, i], pearson)
+                    idx = np.searchsorted(ρ[thread_idx, uint64_i], pearson)
                     core._shift_insert_at_index(
-                        ρ[thread_idx, i], idx, pearson, shift="left"
+                        ρ[thread_idx, uint64_i], idx, pearson, shift="left"
                     )
                     core._shift_insert_at_index(
-                        I[thread_idx, i], idx, i + g, shift="left"
+                        I[thread_idx, uint64_i], idx, uint64_j, shift="left"
                     )
 
                 if ignore_trivial:  # self-joins only
-                    if pearson > ρ[thread_idx, i + g, 0]:
-                        idx = np.searchsorted(ρ[thread_idx, i + g], pearson)
+                    if pearson > ρ[thread_idx, uint64_j, 0]:
+                        idx = np.searchsorted(ρ[thread_idx, uint64_j], pearson)
                         core._shift_insert_at_index(
-                            ρ[thread_idx, i + g], idx, pearson, shift="left"
+                            ρ[thread_idx, uint64_j], idx, pearson, shift="left"
                         )
                         core._shift_insert_at_index(
-                            I[thread_idx, i + g], idx, i, shift="left"
+                            I[thread_idx, uint64_j], idx, uint64_i, shift="left"
                         )
 
-                    if i < i + g:
+                    if uint64_i < uint64_j:
                         # left pearson correlation and left matrix profile index
-                        if pearson > ρL[thread_idx, i + g]:
-                            ρL[thread_idx, i + g] = pearson
-                            IL[thread_idx, i + g] = i
+                        if pearson > ρL[thread_idx, uint64_j]:
+                            ρL[thread_idx, uint64_j] = pearson
+                            IL[thread_idx, uint64_j] = uint64_i
 
                         # right pearson correlation and right matrix profile index
-                        if pearson > ρR[thread_idx, i]:
-                            ρR[thread_idx, i] = pearson
-                            IR[thread_idx, i] = i + g
+                        if pearson > ρR[thread_idx, uint64_i]:
+                            ρR[thread_idx, uint64_i] = pearson
+                            IR[thread_idx, uint64_i] = uint64_j
 
     return