diff --git a/dtw_minwarplag.ipynb b/dtw_minwarplag.ipynb
deleted file mode 100644
index 3a4aea7ea28eb88df3f48462d4f9da3998be06dd..0000000000000000000000000000000000000000
--- a/dtw_minwarplag.ipynb
+++ /dev/null
@@ -1,479 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<center>\n",
-    "\n",
-    "*******************************************************************************************\n",
-    "    \n",
-    "### DYNAMIC TIME WARPING,  \n",
-    "\n",
-    "### MINIMUM-WARP OPTIMAL PATH, AND POINTWISE LAG \n",
-    "  \n",
-    "<br>\n",
-    "  \n",
-    "##### 30 JULY 2023  \n",
-    "\n",
-    "##### Juan Ignacio Mendoza Garay  \n",
-    "##### doctoral student  \n",
-    "##### Department of Music, Art and Culture Studies  \n",
-    "##### University of Jyväskylä  \n",
-    "\n",
-    "*******************************************************************************************\n",
-    "\n",
-    "</center>\n",
-    "\n",
-    "#### INFORMATION:\n",
-    "\n",
-    "\n",
-    "* Description:\n",
-    "\n",
-    "    Demonstrates the most simple (as in \"easy to understand\", not necessarily faster)\n",
-    "    algorithm for Dynamic Time Warping (also called \"classical\" version), an algorithm \n",
-    "    to traceback the optimal path (also \"classical\"), and novel algorithms (as far as I am aware)\n",
-    "    to traceback the optimal path with minimum time-warping and its pointwise lag.\n",
-    "\n",
-    "* Instructions:\n",
-    "\n",
-    "    Edit the values indicated with an arrow like this: <---  \n",
-    "    Comment/uncomment or change values as suggested by the comments.  \n",
-    "    Run the program, close your eyes and hope for the best.  \n",
-    "\n",
-    "*******************************************************************************************\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from tslearn.metrics import dtw_path\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.spatial.distance import cdist\n",
-    "from time import time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*******************************************************************************************\n",
-    "#### TEST SIGNALS:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# EXAMPLE SET 1:\n",
-    "if True: # <---\n",
-    "    \n",
-    "    x_length = 40 # <---\n",
-    "\n",
-    "    x = np.arange(0,x_length)\n",
-    "    y = np.zeros(x.size)\n",
-    "    y_1 = y.copy()\n",
-    "    y_2 = y.copy()\n",
-    "    \n",
-    "    y_1[10:20] = np.arange(0,10,1) # <---\n",
-    "    #y_2 = y_1 # <--- same shape, no lag\n",
-    "    y_2[15:25] = np.arange(0,10,1) # <--- same shape, lag forwards (delay)\n",
-    "    #y_2[5:15] = np.arange(0,10,1) # <--- same shape, lag backwards (anticipation)\n",
-    "    #y_2[12:27] = np.arange(0,15,1) # <--- different shape, lag forwards (delay)\n",
-    "    #y_2[3:18] = np.arange(0,15,1) # <--- different shape, lag backwards (anticipation)\n",
-    "\n",
-    "# EXAMPLE SET 2:\n",
-    "#   Uses x, y_1, and y_2 of example set 1.\n",
-    "if False: # <--- \n",
-    "\n",
-    "    y_1[10:20] = [1,2,3,4,5,5,4,3,2,1] # <--- \n",
-    "    y_2[15:25] = [1,2,3,4,5,5,4,3,2,1] # <--- \n",
-    "    \n",
-    "# EXAMPLE SET 3:\n",
-    "#   Uses x of example set 1.\n",
-    "if False: # <--- \n",
-    "    \n",
-    "    y_1_period = 3        # <--- \n",
-    "    y_2_period = 2        # <--- \n",
-    "    y_2_phase_shift = 0.5 # <--- \n",
-    "    \n",
-    "    y_1 = np.sin( x / y_1_period )\n",
-    "    y_2 = np.sin( ( (x / y_2_period ) + (np.pi * y_2_phase_shift ) ))\n",
-    "    \n",
-    "    \n",
-    "# EXAMPLE SET 4:\n",
-    "if False: # <--- \n",
-    "\n",
-    "    y_1 = np.array([8,7,6,5,5,6,7,8]) # <--- \n",
-    "    y_2 = np.array([5,4,4,5,6,7,8,9]) # <--- \n",
-    "    \n",
-    "    x = np.arange(0,y_1.size)\n",
-    "\n",
-    "    \n",
-    "y_1_rs = y_1.reshape(-1,1)\n",
-    "y_2_rs = y_2.reshape(-1,1)\n",
-    "    \n",
-    "plt.plot(y_1) \n",
-    "plt.plot(y_2); # the semicolon prevents printing the output of the last command"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*******************************************************************************************\n",
-    "#### METHOD 1:\n",
-    "Using the 'tslearn' library. Simple, fast, easy. Gets the job done with no hassle.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computation time of DTW 1 = 5.056027173995972\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# REFERENCE: \n",
-    "# https://tslearn.readthedocs.io/en/stable/user_guide/dtw.html\n",
-    "\n",
-    "dm_1 = cdist( y_1_rs, y_2_rs , 'cityblock' )\n",
-    "\n",
-    "tic = time()\n",
-    "optimal_path_1, dtw_score_1 = dtw_path(y_1, y_2)\n",
-    "print('computation time of DTW 1 = '+str(time()-tic))\n",
-    "\n",
-    "op_1_x = [col[0] for col in optimal_path_1]\n",
-    "op_1_y = [col[1] for col in optimal_path_1]\n",
-    "\n",
-    "plt.imshow(dm_1.T, origin='lower')\n",
-    "plt.plot(op_1_x, op_1_y, ':w',linewidth=2)\n",
-    "plt.title('DTW 1 = '+str(round(dtw_score_1,3)));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The figure above shows a heat-map of the distance matrix and the classical DTW optimal path."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*******************************************************************************************\n",
-    "#### METHOD 2:\n",
-    "Using hand-made, home-brewed, simple, raw and no-BS code written with love by your humble servant.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computation time of DTW 2 = 0.023046016693115234\n",
-      "computation time of classical optimal path = 0.013571977615356445\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# REFERENCES: \n",
-    "# Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. \n",
-    "#    IEEE Transactions on Acoustics, Speech, and Signal Processing. 26 (1): 43–49. \n",
-    "# Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion, 69-84.\n",
-    "\n",
-    "tic = time()\n",
-    "dm_2 = cdist( y_1_rs, y_2_rs , 'cityblock' )\n",
-    "\n",
-    "# initialisation:\n",
-    "C = np.empty((dm_2.shape[0]+1,dm_2.shape[1]+1))\n",
-    "C[:] = np.inf\n",
-    "C[0,0] = 0\n",
-    "\n",
-    "# main loop:\n",
-    "for i in range(1,C.shape[0]):\n",
-    "    for j in range(1,C.shape[1]):\n",
-    "        C[i,j] = dm_2[i-1,j-1] + min(C[i-1, j], C[i, j-1], C[i-1, j-1]) # goes barebones\n",
-    "        # C[i,j] = dm_2[i-1,j-1]**2 + min(C[i-1, j], C[i, j-1], C[i-1, j-1]) # gets cocky\n",
-    "\n",
-    "dtw_score_2 = C[i,j] # barebones\n",
-    "#dtw_score_2 = np.sqrt(C[i,j]) # cocky\n",
-    "\n",
-    "\n",
-    "# traceback optimal path:\n",
-    "tic_1 = time()\n",
-    "i, j = np.array(C.shape)-2\n",
-    "optimal_path_2 = [[i,j]]\n",
-    "while (i > 0) or (j > 0):\n",
-    "    \n",
-    "    tb = np.argmin((C[i, j], C[i, j + 1], C[i + 1, j]))\n",
-    "    \n",
-    "    if tb == 0:\n",
-    "        i -= 1\n",
-    "        j -= 1\n",
-    "    elif tb == 1:\n",
-    "        i -= 1\n",
-    "    else:  # (tb == 2):\n",
-    "        j -= 1\n",
-    "          \n",
-    "    optimal_path_2.append([i,j])\n",
-    "\n",
-    "print('computation time of DTW 2 = '+str(time()-tic))\n",
-    "print('computation time of classical optimal path = '+str(time()-tic_1))\n",
-    "\n",
-    "op_2_x = [col[0] for col in optimal_path_2]\n",
-    "op_2_y = [col[1] for col in optimal_path_2]\n",
-    "\n",
-    "plt.imshow(dm_2.T, origin='lower')\n",
-    "plt.plot(op_2_x, op_2_y, ':w',linewidth=2)\n",
-    "plt.title('DTW 2 = '+str(round(dtw_score_2,3)));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that the 'barebones' (default) version of method 2 returns a DTW based on absolute differences, not on the Euclidean distance as in the 'cocky' version used by the tslearn library."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computation time of minimum-warp optimal path = 0.0059490203857421875\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# traceback minimum-warp optimal path:\n",
-    "\n",
-    "tic = time()\n",
-    "i, j = np.array(C.shape)-2\n",
-    "optimal_path_3 = [[i,j]]\n",
-    "while (i > 0) or (j > 0):\n",
-    "        \n",
-    "    this_query = (C[i, j], C[i, j + 1], C[i + 1, j])\n",
-    "    this_min = np.min(this_query)\n",
-    "    these_argmins = np.where(this_query==this_min)\n",
-    "\n",
-    "    if these_argmins[0][0] == 0:\n",
-    "        \n",
-    "        if (np.any(these_argmins[0][:] == 1)) and (i > j):\n",
-    "            \n",
-    "            i -= 1\n",
-    "            \n",
-    "        elif (np.any(these_argmins[0][:] == 2)) and (j > i):\n",
-    "            \n",
-    "            j -= 1\n",
-    "            \n",
-    "        else:\n",
-    "            \n",
-    "            i -= 1\n",
-    "            j -= 1\n",
-    "            \n",
-    "    elif these_argmins[0][0] == 1:\n",
-    "        \n",
-    "        i -= 1\n",
-    "        \n",
-    "    elif these_argmins[0][0] == 2:\n",
-    "        \n",
-    "        j -= 1\n",
-    "          \n",
-    "    optimal_path_3.append([i,j])\n",
-    "\n",
-    "print('computation time of minimum-warp optimal path = '+str(time()-tic))\n",
-    "\n",
-    "op_3_x = [col[0] for col in optimal_path_3]\n",
-    "op_3_y = [col[1] for col in optimal_path_3]\n",
-    "\n",
-    "plt.imshow(dm_2.T, origin='lower')\n",
-    "plt.plot(op_2_x, op_2_y, ':w',linewidth=2)\n",
-    "plt.plot(op_3_x, op_3_y, ':r', linewidth=2)\n",
-    "plt.title('DTW 2 = '+str(round(dtw_score_2,3)));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The algorithm for the minimum-warp optimal path (in red) tries to get closer to the diagonal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pm = np.zeros( dm_2.shape )\n",
-    "\n",
-    "for i_op in range(0,len(optimal_path_3)):\n",
-    "    pm[op_3_x[i_op],op_3_y[i_op]] = 1     \n",
-    "\n",
-    "ax = plt.gca()    \n",
-    "ax.imshow(pm.T, origin='lower')\n",
-    "ax.plot( range(0,dm_2.shape[0]) , ':w',linewidth=1);\n",
-    "xy_grid = np.arange(-0.5,dm_2.shape[0]+0.5)\n",
-    "ax.grid(1,linestyle=':')\n",
-    "ax.set_xticks(xy_grid)\n",
-    "ax.set_yticks(xy_grid)\n",
-    "ax.set_xticklabels('')\n",
-    "ax.set_yticklabels('')\n",
-    "ax.set_title('minimum-warp optimal path');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# pointwise lag of minimum-warp optimal path:\n",
-    "\n",
-    "pm_r = np.fliplr(pm).T # rotate to get anti-diagonals (lag domain)\n",
-    "lags = np.zeros(pm_r.shape[0])\n",
-    "i_lags = 0\n",
-    "i_adiag = -pm_r.shape[0] + 1\n",
-    "step = 2\n",
-    "\n",
-    "while i_adiag <= pm_r.shape[0]: # iterate through anti-adiagonals\n",
-    "            \n",
-    "    this_adiag = pm_r.diagonal(i_adiag)\n",
-    "    \n",
-    "    if any(this_adiag):\n",
-    "\n",
-    "        i_center = int( np.floor(this_adiag.size / 2) )\n",
-    "\n",
-    "        if not this_adiag[int(i_center)] : # value where the anti-adiagonal intersects the main adiagonal\n",
-    "\n",
-    "            i_match = int(np.where(this_adiag)[0]) # where the path intersects this anti-diagonal\n",
-    "\n",
-    "            lags[i_lags] = i_center - i_match \n",
-    "            \n",
-    "        i_adiag += step\n",
-    "        step = 2\n",
-    "        i_lags += 1\n",
-    "        \n",
-    "    else: # see if there's anything in the next anti-diagonal\n",
-    "    \n",
-    "        i_adiag += 1\n",
-    "        step = 1\n",
-    "    \n",
-    "plt.plot(lags)\n",
-    "plt.title('pointwise lag');"
-   ]
-  }
- ],
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