Repository: UCL-Chimera/medical-waveforms (language: python)

Issue:
Nadir detection with `pyampd`
https://github.com/ig248/pyampd implementing the algorithm from https://www.mdpi.com/1999-4893/5/4/588

We would need to invert the signal, run `find_peaks()` then invert again.

From @mtc8608 experiments this looks like it will work better than the peak detector mentioned in #4 


Discussion / hints:

Fix patch:
diff --git a/examples/load_waveforms.ipynb b/examples/load_waveforms.ipynb
deleted file mode 100644
index 03f14aa..0000000
--- a/examples/load_waveforms.ipynb
+++ /dev/null
@@ -1,187 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example data loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os, sys\n",
-    "sys.path.append(os.pardir)  # Needed for sidewinder import before we install it\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sidewinder.synthetic import synthetic_arterial_pressure_data\n",
-    "from sidewinder.waveforms import Waveforms"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load some arterial pressure data\n",
-    "\n",
-    "We generate some synthetic data for demonstration purposes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "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>time</th>\n",
-       "      <th>pressure</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000</td>\n",
-       "      <td>80.036898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.005</td>\n",
-       "      <td>80.171417</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.010</td>\n",
-       "      <td>80.491293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.015</td>\n",
-       "      <td>81.096162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.020</td>\n",
-       "      <td>82.081690</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    time   pressure\n",
-       "0  0.000  80.036898\n",
-       "1  0.005  80.171417\n",
-       "2  0.010  80.491293\n",
-       "3  0.015  81.096162\n",
-       "4  0.020  82.081690"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "art = synthetic_arterial_pressure_data(\n",
-    "    systolic_pressure=120,\n",
-    "    diastolic_pressure=80,\n",
-    "    heart_rate=70,\n",
-    "    n_beats_target=5,\n",
-    "    hertz=200\n",
-    ")\n",
-    "\n",
-    "art.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
-    "ax.plot(art.time, art.pressure)\n",
-    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the data into `sidewinder`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wf = Waveforms(art)"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "f2c1554fc2f673ff3983ee6916670176b140c542ca5d14124814c0e65b9d8c04"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.9.9 64-bit ('sidewinder-lxfscp8q-py3.9': poetry)",
-   "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": "3.9.9"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
new file mode 100644
index 0000000..63b055b
--- /dev/null
+++ b/examples/tutorial.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting started with `sidewinder`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "sys.path.append(os.pardir)  # Needed for sidewinder import before we install it\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sidewinder.synthetic import synthetic_arterial_pressure_data\n",
+    "from sidewinder.waveforms import Waveforms\n",
+    "from sidewinder import features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load some waveform data\n",
+    "\n",
+    "We generate some synthetic arterial blood pressure data for demonstration purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "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>time</th>\n",
+       "      <th>pressure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>80.036898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.005</td>\n",
+       "      <td>80.171417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.010</td>\n",
+       "      <td>80.491293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.015</td>\n",
+       "      <td>81.096162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.020</td>\n",
+       "      <td>82.081690</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    time   pressure\n",
+       "0  0.000  80.036898\n",
+       "1  0.005  80.171417\n",
+       "2  0.010  80.491293\n",
+       "3  0.015  81.096162\n",
+       "4  0.020  82.081690"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = synthetic_arterial_pressure_data(\n",
+    "    systolic_pressure=120,\n",
+    "    diastolic_pressure=80,\n",
+    "    heart_rate=70,\n",
+    "    n_beats_target=5.5,\n",
+    "    hertz=200\n",
+    ")\n",
+    "\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k97F3xWDsP5LZv9Dh6+xRWkIsEwrSZA9EH3wZKn0F2AXcB/y51030o76t71JFvc0a6Tm7RxqK0FHX5tmg2tcmOcOcsixWV9ukEkyIaPRuksv2oYNd4Z1hWipT1SGjrq2DzKRYn2bzZpdmsWJPE13d7iBEJvpT3+bErSGvnyUiABUjs7AoWCbtXsiobe0kPTHWp9m8WSM9uefsktw7El862B1a63u11h9prT8xbgGPLAIYmwV8GcHOTolndF6KNPIhxGZ3kenDEgOAuaXZ3rWgMhITCoyL2+yU/l+/zOQ4zyio1HQNGZ69K/1/boKng213drN+b0uAoxK+ONDPKY69pcTHMLkwncXSwQ4ZtS2+554x875ur8z+HYkvHey/KaXuVEodo5SaYdwCHlkEqPWhFmhvs0uzqNzVRLdbyo6HApvdSUbi0Wso91Yx0rMeTRqK0GCUecvxoYMNntxbvqtRRkFDxIA62DL7F1IGMrAEnk7aqiqpBBMq6tr6rwBjqPDmnsxAHJkvHewpwLXA7zi4PORPgQwqUhz8oOn/Sh48HzStnV1s3CcjMaHA5nD1eUhJb6kJsZ6RGDmZLCTU+7iG1zC7NIt2ZzcbJPdCQl1bJ7k+LO8Bz1KEkdlJUgkmRDR5j8/O8nH2T/ZAhJba1g6fL25zU+Mpy0mWddhH4UsH+yKgTGs9X2t9ovd2UqADiwR1rZ0o5fsomqzDDi1N7c4+T+A8lIzEhI6Gdt+XiICngw2Se6FAaz2gEWzwfHYu29WIW2b/TNdsdwGeDai+mDXSswdCjk03n5F7vo5gg5F7TZJ7R+BLB3sdkBHgOCKSze4kLSGWGKtvZfeGZyRSlJkojXwI6HZrWjq6SPdxiQh4TiZzdrlZVWULXGDCJw1tTuJiLP1WgDHkpyUwIjtJci8EtHV20eFyD6yDXZqFze5iW11bACMTvrA5nFgtilQfcy8jKY7xw1JlBiIEGLnnS/Ulw+zSLJodLrbUtgYwsvDkS88vA9iklHpXyvQNzECWGBhml2axdFcjWsvVoJlaHJ5RmMwBvH6zSj01eaVcn/nq25zkJMf5VAHGMFtGQUOCr2XCepsjMxAhw2Z3kZEYO7DcK81i+e4mXLIHwlQ27+yDr8t74ODsn6zDPpwvHew7gfOA3zCAMn1KqceVUrVKqXW97rtIKbVeKeVWSlUc8vg7lFLblFKblVKnDew/IzQ1eT9oBmJOaRaN7U62y0iMqYx1hBkDWCKSnhjLhGFpcjJZCGho7/8Ux0PNLs2iSUZBTWfkni8lFg0lWUnkpcZLBzsE2Owun5eHGOaUZkslmBBgdLAH0u4VZSYyLC2BJZJ7h+m3g92rLN9KYG2vW3/+CSw85L51wPnAp73vVEpNBC4FJnl/5+9KqbA/Uq3Z7iR9AG9U8Gz4AOTNajKbY2DrCA1zy7JZsaeJzi5Zh22mhjYn2QMYhYGDIzGSe+bqaeQHMDihlGJ2qWcGQmb/zGVz+F59yTCrVNZhhwKbwxhYktzzB19OcrxOKbUfWANUAsu9X/uktf4UaDzkvo1a681HePi5wLNa606t9U5gGzDbh/hDms0x8BHskdlJ5KbGy3SLyWzeUbSBbHIEz4EzHS7ZEW+2hrbOAY2AgmcUND9NRkHNdnAUbeDL6/Y1d1Dd5AhEWMJHNrtrwJ+beakJlOUkS+6ZbDAXt+BZHnmgpZOqRsm93nxZIvJjYLLWeqTWukxrXaq1LvNzHIVAVa/vq733hTXPB83A3qjG1eCSnXI1aKbBftAYNXllJMY8Wmvq25w+VxAxeHIvm2WSe6YyZo8yEgf2+kkVptAwmCUicHD/kZwDYR5jYGmgr19PuyfLI7/Clw72dsAe6EB85R1Rr1RKVdbV1ZkdzlF5qlC4BrxEBDzrsGUkxlxNdmOT48Bev8xkz454WWZgnrbOLpzd7gEvEQFPI7+/pUNGYkzUbHeiFKQm+FaFwjAuP5W0hBipyWsyzwFdg2j3yrJo7ehi035Zh20WY2BpINWzAMbkpZCRFCu5dwhfOth3AF8qpR5SSt1r3PwcRw1Q3Ov7Iu99h9FaP6y1rtBaV+Tm5vo5DP9p7XCh9cBHQEFq8oaCZrsTyyAaefBcIMmOePMMZqOOQUZizGdzuEhPjMVi8b0KBYDFopg1Mks+N03k7HLT7uwe8MwtHNx/JK+feWwOF0lxVuJjBrYFzmJRVIzw1MMWB/nSwX4I+BBYjGf9tXHzp9eAS5VS8UqpUmAMsNTPzxFUg11HCDA2L5X0xFj5oDHRYBt5gDllnh3x62pkHbYZmh2DW94DB0diJPfMYxtE9SXDrNIsdtS395T6E8E1mE1yhsKMRAoz5BwIMw0l92aXZrKzvp3a1g4/RxW+fBmei9Va/3Cgf1gp9QywAMhRSlXjKffXCNwH5AJvKqVWaa1P01qvV0o9D2wAuoCbtdZhXYbhYJm3gb9Ze0ZiZLrFNE1216BGQAFmjvDsiF+xx8b0kkx/hiV8MJQRbCP3ZKrTPDbH4JbWwcHZv8pdjZw+pcCfYQkfHDzFcXCv35zSLD7ZUofWekB1tIV/NDsGXvnMYOyBWLaziTPLJffAtxHst73rnguUUlnGrb9f0lpfprUu0FrHaq2LtNaPaa1f9v47Xmudr7U+rdfj79Faj9Jaj9Navz2k/6oQ0FPmbRBr0cDzQbOzvp3aFrkaNIPN7hzwOjRDfloChRmJrNgt02VmMEbRBvv6zSnNYleDnQOSe6Zotg+8zJth8vB0EmItsgfCJLZBHNDV25yyLBrkHAjTDKYwg2FyYTqJsVYZnOjFlw72ZXjXYXNweUi/ZfqiXfMQloiAZ6oTkFFskwzlgwY8o9gr9kgH2ww9S0QGm3tSjcJUgzkB1xAXY2FGSaY08iY5WH1psDMQcg6EmYaSe7FWCzNGZMjnZi++HDRTeoSbv8v0RZzB1lE2TBqeRlKcVd6sJrE5nINeIgIwoySDfc0d7LVJNYpgG+xOeIPknrmGsg4UPBdIG/a10NLh8mNUwhdDWRoJB8+BkNwzh83uGvSsO3hyb+P+lp5Bjmh31A62Uuq4vn5RKZWmlJrs/5AigzFVljaIKhTguRqcOSJTPmhMYmsf/JU8wAzvOuzlskwk6JodLhJiLSTEDu4w2Bhv7skoaPANpbypYXZpFlpL7pnh4BrswX12KqWYPTKLSqlGEXRaa5odziG1e0buyfJIj75GsC9QSn2plPp/SqkzlVKzlVInKKW+pZT6P+ANIDFIcYYdm91FakIMMVZfVuEc2eyRWWza39ozGi6Cw9XtprWza9DTnAATCtJIiLXIMhETDGX9vGFOqeSeGYZS3tQwvSSDGIuS03BNYHM4sVoUqfGDG1gCmFacQY3NIdUogszu7MbVrYeWe8WZxFqVLG31OmrvT2v9A+AsYB9wEfAr4Id4Sug9pLU+QWu9LChRhqHmIaxlMhjrsKWTFlzG9FZm8uBfv1irhfKiDLmSN0GzwzWkiyPotSNeRtKCaijlTQ1JcTFMLkyX2T8TNHmX9wylAsi0kgwA1lRJmdNgsg1x7wpAYpyVyYXpcnHr1efwqta6UWv9iNb6Gm85va9rre/QWn8erADD1WBPs+ptSmE6FgWr5YMmqIa6htcwc0Qm6/e24HCGdcXJsDPYo5p7Ky/KwGpRrK6y+Sco4RN/NPLgmYFYU91Mh0tyL5ia/ZB7k4enY7UoVknuBVXPMelD7LfMLs1idbVNcg/fqoiIQfDUUR7aB01yfAyj81JYU23zT1DCJ809hyUM7YNmenEGXW7Nhn1ygRRMnhHsoeVeYpyVsfmprJbcCyp/NfKzRmbh7HazplpyL5hsDuegN/YbEuOsjJPcC7qhVj4zzB6ZhatbywUS0sEOmGbvSYBDNaUwg7U1zWit/RCV8EVT+9BquRrKizIApJEPMs9O+KHnXnlhuuRekA21xKKhvDgdQAYngqypfegXtwBTizNYXWXD7ZbcCxZ/zR4ZB62t3GMbakhhTzrYAWKzD/1KHmBqcTr1bU72NsuGj2Dp+aAZ4ijasPQE8lLjWSsd7KDyx/4H8HTSbHYXVY1SajFYDtZRHtrrl5eaQEF6AmtrJPeCqdkx9CUiANOK02np6GJnQ7sfohK+GGoNc0NGUhwjs5NYVSX7V/rtYCulkpRSv1BKPeL9foxS6qzAhxa+3G7tv0beGAWV6ZagMaapM4awydFQXpTOGmnkg6bD1Y3D1T3k5T0AU43cq7EN+W8J3/hr/wN49rDIxW1w+WtgaVqxZxRU9kAEj80xtBrmvXlmICT3fBnBfgLoBI7xfl8D/DpgEUWA1s4u3No/jcSEglRirUo6aUFks7uGXGrKMKUwg+11bbR1dvkhMtGfFof/Omhj81OJs1pkiU8Q2RxOUuOHVt7UUF6Uzo76djlwJkicXW7and1+WSIyOi+F5DirrOMNIpvdRXzM4M8P6G1acQb7WzrYH+Uz7758io3SWv8BcAFore3A4GvwRIGDmwWGfiUfH2Nl3LBUWUsYRE3eOspDKTVlKC9KR2tYLxdIQWHzYwc7LsbChOFpkntB5I8qFIYp3hmIdZJ7QeHPEVCrRTGlKF1GsIPIZh/aITO9TS3OAIj6jaq+dLCdSqlEQAMopUbhGdEWR9HzQeOHRh48y0TWVDfLho8gsflpeQ/A5ELPZitZCxoc/tokZygvTGddTYvkXpD4M/emGLknMxBBcfAUx6EPLIGn3du4rxVnl9svf0/0zWYf+vkBhokFacRIqUWfOth3Au8AxUqpp4APgNsCGlWY88dhCb1NLUqntaOL3Y12v/w90bdmu392wgPkpsYzPD1BlhkEib826hjKi9Jp6+xiR71stgoGf5wfYMhKjqMoM1GW1wWJMXs01OpLhvKidJzdbjbvb/XL3xN98+fFbUKslQkFaVE/A9FnB1spZQEygfOBa4BngAqt9ccBjyyMNdn9N1UGnnW8ICWngqXJTxt1DFOK0uW1CxKbn3PvYKlFm1/+nuibzU9VKAzlRemyRCRImtqNmVv/fHYam4yjfZlBsDT74eyO3qYVy8x7fyc5uoHbtNYNWus3tdZvaK3rgxRb2GruWQfqnw+asfkpJMRaZFdukPjjJMDeyosy2NVg75lCFYHTk3t+ev1G56WQGGuVGYgg8efsEXiWaO2W3AsKf9VRNhRlJpKZFCtLfILE5vDf7BF41mF7Zv/a/PY3w40vS0TeV0rdqpQqVkplGbeARxbG/L1EJMZqYdJwGQUNFn+VmjKUF3nWgq7bKw1FoNnsLiwKUuKGXgEGPJutJhfKRsdg0Fr7dZoaoNw7+yd7IALPXycBGpRSTCnKkBHsILH5fQTb0+5F84EzvnSwLwFuBj4FlntvlYEMKtzZ7C5S4mOI9UOpKUN5UTrr97bQ1S0bPgLJn6WmDMZmKxkFDTzjBFWLxX+FjsqLMli/twWX5F5AtXV20e3Wfh1F68k9qWUecDaHE6tFkeKH8qaGqUXpbK1tw+Hs9tvfFIfrcHXT2eX268xtWU4KKfExUX2B1G8PUGtdeoRbWTCCC1c2h9MvZcJ6Ky9Kx+HqZltd9E63BIM/S00ZMpLiKMlKklHQIPCMgPqvgwae3OvscrPlgGy2CqSeQ2b8mHvpSbGMyE6SZQZB0ORd3uOP8qaGKYXpdLs162X2L6D8vTkcwGJRlBelR/XSVl9OcrzqSLdgBBeu/L1ZAHqf6Bi9b9Zg8GcN8948Gx3ltQs0m93/F7c9JzrK6xdQPSUW/fz6TSmU3AuGQLR7Rj1lef0CKxADS+DZ6LhxXwsdruicgfBlDcOsXrfjgbuAcwIYU9jz9zpCgNLsZFKjfLolGPy9UccwtSidGpuDhjYpIR9IxhIRfxqRnUR6YqzMQASYLUAXt+WSe0Fhczj9/trlpyWQnxYvuRdgB0ew/X+B1OXWrN/b4te/Gy58WSLyP71u1wIzgJTAhxa+mvxYy9Vg8Z5sJVfygWWUmvLnJkfoNQMhm60CqjkAF7dKyVRnMARqFG2KbHQMiqZ2/1aAMUwpzJB2L8ACsTwLPCPYQNTWwx7MLrx2oNTfgUQSfx7321t5UQab9rfQ2RWd0y3B4M+jtnubXJiOUrLEJ9Bsfi7zZigvSmfzgdaoneoMhkCNok0uTPPknnTSAqrZzzXMDVOL0tlR305Lh5RaDJTmnotb/89ADEtLiNqZd1/WYL+ulHrNe3sD2Ay8HPjQwpNRaspfp1n1NrUoHVe3ZuM+2WwVKMZBJZnJ/v2gSYmPYVRuikx1BlC3W9PS4fLbUc29lRdleDdbRedUZzAYa7DT/NzBTk2IZVRuStQf2xxo/i5vaij3joKukwukgAnUxS14RrFlBPvo/gT82Xv7LXCC1vr2/n5JKfW4UqpWKbWu131ZSqn3lFJbvV8zvfcrpdS9SqltSqk1SqkZg/zvMV0gSk0Zyns2fNj8/reFh83uIsaiSI6z+v1vlxels6amGa2j92SrQGrtcKG1/2cfoNepclHaUASDze4kMdZKQqz/c29acQarqmySewESiPKmBqPU4mrpYAeMzeEi1qpICkC7N7XYc9CasfwymvjSwa4EPtNafwLUATOUUr5k0T+BhYfcdzvwgdZ6DPCB93uA04Ex3tt1wIM+/P2QFKi1TADD0xPITo6Tqc4AavLuhPdnqSlDeWE6da2d7G/p8PvfFoGrQgEwLD2BvFTZbBVI/j7oorfpJRk0tjupanQE5O9Hu0CtnwfISo6jOCuRtVLLPGBsdhfpiXEBafemFhsXSDa//+1Q50sH+1MgQSlVCPwXuBJP57lPWutPgcZD7j4XeNL77yeBr/e6/1/aYzGQoZQq8CG2kBPIRt7YbCWNfOA0B2AnvKG8Z8OHXCAFgr9PUD1UeZFstgokWwAqwBiMzVYrq5oC8vejXaDKmxrKizLkczOAbHZnwD43p3j3H0Xj6+dLB1tpre3A+cDftdYXAZMG+Xz5Wut93n/vB/K9/y4Eqno9rtp73+HBKHWdUqpSKVVZV1c3yDACJ1ClpgzlRRlsq22jvbMrIH8/2gVqJzzAxII0YixKRmICJFAlFg3Tij2brYyLaOFfgaijbBiXn0pirDWqj20OpEDnXnmhlFoMpEBtDgfPHogxeSkygn0USil1DHA58Kb3viEv1NGexXADXhCntX5Ya12hta7Izc0dahh+F8ipMvBMt7g1rJOSUwERiJMADQmxVsYNS5VGPkCMDaqBGgU1Si1K7gWGzeH/8qaGGKuFKYXpstExQIz1tYF6/aTMaWAFst0Dzx6WaNwD4UsH+/vAHcDLWuv1Sqky4KNBPt8BY+mH92ut9/4aoLjX44q894WdpgDuxoWDHzTSUARGcwCnygBmjcxiVZUNV7c7YM8RrVp6SiwGqpH3rCWU3AuMQK7BBs867A17pcxpIAR6BLun1GIULjMIhkC3e1OLPXsgqpuiaw+ELwfNfKK1Pkdr/XullAWo11p/d5DP9xpwtfffVwOv9rr/Km81kblAc6+lJGGl2RhFC9CbNSclntKcZJbtkrWEgdBkD0yJRUPFyEzszm42SLk3v+vZYBygi9uMpDjKcpJZvltyz9+M8qaB+twEzzpsZ7dbci8AmgO8/8EotShr6APD5gjcEhE4uAci2gYnfKmD/bRSKk0plQysAzYopX7sw+89AywCximlqpVS3wZ+B5yqlNoKnOL9HuAtYAewDXgEuGlQ/zUhwGZ3kRRnJT7G/+VuDLNHZrFsVyNud3RNtwRah6sbh6s7oFNls0ZmAbBs16H7f8VQ2RwukuOsxMUM5vws38wpy2LZzka6Jff8qsPlxtnlDtgSA4DpJZkArJAlWn7XZHcSY1GkxMcE7Dlml2ZRuauJLpn986vOrm7szu6AjmCPG5ZKfIwl6sqc+tISTdRat+Cp+PE2nlMcr+zvl7TWl2mtC7TWsVrrIq31Y1rrBq31yVrrMVrrU7TWjd7Haq31zVrrUVrrKVrryqH8R5kp0FeC4PmgaXa42HxADpzxp+YAneLYW35aAiVZSdLBDgBPqanA5t6c0mxaO7vYuE9GQf0p0HtXwFNqsSQricU7GgL2HNHKs4Y3MOVNDXPLsmnr7GKD5J5f9bR7ARxYirVamByFeyB86WDHeutefx14TWvtYhCbE6OFzR6Yk+R6m1PmGQVdulM6af5kLDEIxGlkvVWMzKRyV1PUbfgINM9RzcHJPemk+VcgT5Lrbd7obBbvaJBRUD9rDsLF7dxSyb1AaA5S7k0tymDd3uao2n/kSwf7IWAXkAx8qpQaAcgl5FE0O5wBf6MWZSZRmJHIkp3yQeNPTfbAj6IBHFOWTUO7U0Zi/CwYuVeQnkhJVhJL5OLWrwJ5QFdvx4zKobWjS46897Mme+DODzDkpSVQlpvM4h2Se/4U6A2qhqnF6XS43FE1++fLJsd7tdaFWuszvEs5dgMnBiG2sBTonfCGY0dl8/nW+qi6Ggy0QB9UYpg/1lNe8pMtoVfHPZwFK/eOKfOMgkru+U+zI7Bl3gzHlGUD8OV2GZzwp0DWUe5tblk2y3Y2Su750cHZo8Dm3rGjclAKPt4cPe2eL5sc85VSjyml3vZ+P5GDlUDEIZqC1MifPCGPlo4uKqWaiN/0NPJBGImZWJAWVR80wWCsAw20kybk0drRxTIZxfabYF3c5qbGMy4/lc+2Su75U3OA6ygbFozNpbWziyUyiu03tiDN3OamxlNelMGHm2r7f3CE8GWJyD+Bd4Hh3u+34KmNLQ6htQ7oUdu9HTcmlzirhQ83HQj4c0WLQNcw723BuFyW726ipUNOBfQHT+65SAvCa3f8mBziYiy8t1Fyz1+CNU0NcMrEPJbsbKTReziKGLpAHrXd2/FjckmItfDehv0Bf65ocXCTYxAGJ8blsbraFjUncvrSwc7RWj8PuAG01l2AVOo/AruzG1e3DkoHLSU+hjllWXwQRVeDgWazu4izWkiKC1yJRcOCcXl0uzWfyCi2XwSjzJshKS6G40bn8P7GA7JR1U+M3EuMDXzunTGlgG635t310knzB2eXm3Znd1DavcQ4K8ePyeW9DZJ7/mKzu7BaFKkBLLFoOGl8HlrDR1HS7vnSwW5XSmXjrRxiHAQT0KjCVDBHYQBOnZjPjrr2fjcNOLs8nQ/RN5vdSXqAS00ZZo7IJC81ntdW7+3zcVpr2ju7pDHpRzDKvPV2yoR8qhod/W6Wc3a55eRAHzQ7gpd7EwvSGJmdxFtr+z7LTGuN3Sm515+e3EsO/MUteNq9vc0drO3n2HTJPd/YHE7SE4OTe5OGpzE8PYHXfWj3IiH3fLlk+SGekxZHKaW+AHKBCwMaVZgy1jIF6qjmQ51dPpxfv7GR5yuruPPsSYf9fGd9Oz99aS2LdzaggHmjc7j+hFEcNyYnKPGFG1uAT3HszWpRnDN1OE8u2kVtSwd5aQlf+Xlrh4tHPtvJ00v2UN/WSXZyHJfNLuHa48uCMpUXboJV5s1wxpRh3PX6ep6vrGJyYfphP6/c1cgf3tlM5W7PWtFjR+Vw/fwyjh+TG5T4wk2wNskBKKU4Y0oBD326g/3NHQxL/2rutXV28fCnO3hm6R7qWjvJT4vn/BlF3DB/VMBL0YWjYJV5M5w2cRi/iFnHc8uqKC/KOOzny3Y18tf3t7DIu5F1Tmk2Ny4YxQljJfeOJJi5Z7EozptRyIMfb2dfs4OC9MSv/Nzu7OKhT3bw1JLd1Lc5yUmJ58KZRdwwvywoS2/9rc8RbKWUFZjvvR0LXA9M0lqvCUJsYSfQx8UeKjM5jtOnDOP5ZVU09VpPqLXmuWV7OONvn7FhXws3LRjFtSeUsfVAG1c8toQfPb8au7MrKDGGkya7MyhLDAxXzB1Bl1vz5KJdX7l/0/4Wzrn/C+79YCvTitO5beE4ZozI5P6PtnHq/34i1UeOIBiHBPWWkRTHWVMK+M/y6q+sJ3S7NQ9+vJ1LHl5Mjc3BjQtGccP8UWyrbePKx5byg+dW0d4puXeoYFWAMVw6qwStNU98ufMr92/e38o593/OfR9uZUqhJ/emFGbw0CfbOeUvn/DxZlmSd6imILd76UmxnD11OC+tqKGu9WDudbs1f3p3Mxf9YxFbDrRxw/xR3LhgFLsb2rnq8aV8/9mVtMqel8N4zu4Ibu4ppXjss6/m3sZ9LSz862f87YOtTC/J5LaF45heksEjn+3glL98Epb7zfocwdZadyulLtNa/y+wPkgxha1gLxEBuGnBaF5fvZffvLWRP1xYjs3u4vaX1vDu+gPMG53Nny+a1jNC84NTxnL/h9v4+8fb2LCvhSeumXXY6E00a3a4KMlKCtrzjcxJ5qzy4Tz62U7Om17EqNxk/rO8ml+8uo60hFiev/4YZnsPVwBYV9PMD59fxTVPLOWusydx9bEjgxZrqOuZPQpm7p04mldW1fCbtzbxp4s8uffD51fx0eY6zpxSwO8umEJqgiee758ylvs/2sYDH21j474WHr9mFsMzEvt5huhhc7goDOL/j5LsJM6ZOpwnvtjFBTOKGJufyksrqvnZy+tIjo/hqe/M4dhRB2f61tU0c+sLq/nmP5fx09MncO0JZUGLNdQZuRfoA7p6u2nBKF5eWcNv3trIXy6eSl1bJ7e+sIZPt9RxSUUxd50ziUTvXprvnTyWBz/ezr0fbmXT/lYeu2ZWUN9roc7mcJKXGrx+QHFWEl+fVsi/Fu3mwooixg9L49VVNdz+4lrSEmN49rq5zPWW0wRYv7eZW19Yw7efrOT2heO5fv6ooMU6VL4sEflCKXU/8BzQbtyptV4RsKjCVM9BJUEcBR03LJUbF4zigY+2s7O+nR317bR2uPjZGRP49nGlWCwH11UlxFq59bRxzByZyf88vZIrHlvCc9fNJTslPmjxhjKb3UV5UXCngH96xngWba/non98yajcFCp3NzG3LIt7L5t+2Ife5MJ0Xr35OL737ErufG09zi63NPReTUE6hbO30Xkp3HLiaO79cBu7GtrZWd9OW0cXvzx3ElfOHfGVNY1xMRZ+eOpYKkZkcvNTK7ji0SU8d/0x5KZK7gE0251MGp4W1Of86RkT+HxbAxc/tIgxeSks29XE7NIs7r9s+mFLtiYXpvPSTcdy6wurueetjbjcbm5aMDqo8YYqW5BnjwDKclP4n5NG89f3t7K7wdPuOZzd/Oa8KXxjTslXHhsXY+F7p4xh5ohMbnxqOd94ZDEv3HBMUDuVocxmdzE2LzWoz3nHGeP5ZEsdlzy0mHH5qSzd1cjskVncf/nh7d6k4em8dOOx3Pqf1fz27U10uTU3nxgeuefLJsdpwCTgl8Cfvbc/BTCmsBWsWq6H+tGp47ht4TjaOruYOSKTV28+jmtPKPtK57q3E8fl8djVFVQ12rnq8aVSKs4rGKeRHaogPZFnrp3LtOIM2p3d3HH6eJ76ztyjfvgnxll58IqZnDmlgHve2siLy6uDGm+oajJhFA08I9O3nz6e9s4uZpRk8tJNx3LVMSOPumHohLG5PPHNWexr7uCqx5f2LG2JdjZH8NaBGvLSEnj2ujlML86grbOb2xaO4+nvzDmsc21Iiovh3kunc87U4fzhnc08v6wqqPGGqmDVUT7Ud08awx2nj8fu7KZiRCZvfve4wzrXvR03JocnvzWbutZOrnpsac+SzmjXHOQlIgA5KfE8f/1cKkZk0tLh4senjeOpa+f02e7de+l0zpteyB/f3cwzS/cENd7B6ncEW2stpzb6qKndSWKslYQglJrqzWJR3LRg9IBGVOaUZfPQlTP5zpOV3PzUCp64ZhYxVl+utyJTh6ubzi530BsJgDH5qTzxzdk+P95qUfzlkqnYHE5ue3ENOanxPadDRiub3UV8jKVnWjhYLBbFDfM966x9VTEyi4eunMm3n1zGTU8t55/fnE1sFOdeZ1c3dme3Kbk3Om9guRdjtfDni6fSZHfy05fXMjwjMeo3jdvsLmIsipQglHnrzWJRXD9/1ICWDMwoyeThKyv41j+Xcf2/K/nXt+YQFxO9uefqdtPa2RXUWXdDWW4Kj10zy+fHWy2KP15YTmO7k5+/so6izMSQ3zTuy0mO2Uqpe5VSK5RSy5VSf/OW7ROHaApiFQp/WDAuj3vOm8xnW+u56/X1YV8SZyjMWN4zFPExVh66soKx+anc/NQKNu3vu1xcpGtqdwZ99HooThiby2/PL+eLbQ3c+Vp0554xkpgeJq9frNXCA5fPYFRuCjf+ezmb97eaHZKpjBNUg1HmzR+OG5PD7y6YwuIdjdz52rqozj1j1j0zOTz6LTFWC/d/Yzpj8lK46d8rQj73fLl0exaoAy7AU56vDs96bHEIm91JZpBqgfrLJbNKuH5+Gf9evIcnvthldjimMWt5z1CkxMfw+DUVJMVZ+fY/K6P6ZLqmIFeh8IcLZxZx44JRPL0kunOv0XtxmxUmHWyAtIRYHv/mLBLjrHzrn8uoj5KT6Y6k2e4Ku/KF588o4qYFo3hmaRWPR3HumbW0bihSE2J5/JqDude7kkyo8aWDXaC1/pXWeqf39msgP9CBhaNGe3iNohl+ctp4vjYxn3ve2si6for3RyqjzGG4vX4F6Yk8dvUsals7+OlLa6N2NMYWprn346+N47RJ+fz6zQ2sqrKZHY4pmtqNDarh1UkrzEjk8WtmUdfWye0vRm/umbF3xR9u9ebePW9uYPnuJrPDMUW4tnvDvblX39bJT15cE7K550sH+79KqUuVUhbv7WLg3UAHFo6CXcvVXywWxR8vnEp2chw/en51VJ5+1TOKFmYzEABTitK59WvjeGf9fl5ZVWN2OKZosjvDZpqzN4tF8ceLppKflsCPX4jO3OsZRQvD3JtcmM5tp43j/Y0H+E+UbjgO5gFd/mSxKP588TQK0hO57T+r6XBFY+6F1xKR3iYXpvOTheP5cFMtL1SGZu750sG+Fnga6PTengWuV0q1KqWie+HnIZrCdBQNPPWDf3v+FDYfaI3K6eqeK/kw/KAB+M7xZUwrzuCeNzdFZVUYz8VteOZeWkIsvzl/Cltr23jk0x1mhxN0TWF8cQvwrXmlVIzI5Ldvb4rKqjA2uzNopxf7W0p8DL85fwrb69r5+8fbzQ4n6MJxiUhv1xw7kjmlWdzz1savHPgVKvrtYGutU7XWFq11rPdm8d6XqrUObuHSENbt1jQ7wvNK3nDyhHxOmZDH/R9uo7a1w+xwgsqMOsr+ZLUofnnuJBraO7n3/a1mhxNUWmtsYZ57J47LY+GkYfz94+0caImy3Gs3p8ybv1gsirvOmUST3clf399idjhB12h3kp0Snp+bAPPH5nLutOH845Pt7G5o7/8XIki4d7AtFsWvvz4ZV7ebZbtCb5lP9Nan8bNmhwutw3Oas7efnTmRzq5u/vTuZrNDCarGdiepCTFhXS6tvCiDS2cV888vd7H1QGjvrvanlo4uut06bBsJw0/PmEBXt+b372wyO5Sgamx3kRxnJT4muCUW/WlyYTrfmF3CvxbtDvnKBv5kd3bR4XJHRO7FWhR3v77B7FCCqqndSUJs8Mub+tOY/FQW3X4yCycPMzuUw4RvbyLEhPuVoKE0J5lvzSvlheXVrK2Ong2PTXZn2E5R9/bj08aTFGfll29sCNmNH/528KCL8H79SrKT+Pbxpby0oiaqNjzawnST3KF+9LVxpMTH8Ms3oqfsolG5KCtMl9YZ8tMS+P4pY/lwUy3vbzhgdjhB4yktHP65F+yDcnwlHWw/Mes0q0C45aTRZCfHRVVt7Mb2yGjks5Lj+MGpY/lsaz3vb6w1O5ygOLi8J/xz7+YTR5ObGs/dr6/H7Y6S3IuQi9us5Dh+cMoYvtjWwLvro6OTZlSAyUqONzmSobtm3kjG5KVw1+vro2bDY7idHxBujtrBVkpl9XULZpDh4GCpqfB/s6YmxHLbaeNZvrspaqpSNNmdZEVABw3girkjGJ2Xwq/f3BAVVSmaImQEGzybrn6ycDwr99h4eWW05J4r7JfWGa6YO4Kx+Snc89aGqOikNbR7NpaF+wg2eA4QuvvcSVQ3OXgwSjY8hmv1pXDR1wj2cqDS+/XQW+VQnlQp9T2l1Dql1Hql1Pe992Uppd5TSm31fs0cynMEW6QsETFcOLOIqcUZ/PatTbR1dpkdTsA1tUdOIx9rtfCLsyayu8HO45/vMjucgLP15F5kNBTnTy9kekkGv307OirCeEbRIuO1i7FauPPsSVQ1Onjs851mhxNwkdbuHTsqh7OnDufBT7azp8FudjgBF87Vl8LBUTvYWutSrXWZ9+uht7LBPqFSajKe0n+zganAWUqp0cDtwAda6zHAB97vw0bPKFqEXA1aLIq7z5lEbWsn930Q+VUpPCPYkfNBM39srrcizFZqI7wqRSTNHoEn9355zuSoqQgTadPU80bncNqkfB74aBv7myM79xq9uZcdAUtEDD/r2fC43uxQAq4xwtq9UOPTGmylVKZSarZS6gTjNoTnnAAs0VrbtdZdwCfA+cC5wJPexzwJfH0IzxF0TXYXMRZFanyM2aH4zbTiDC6uKOLxL3ayva7N7HACpsPVjd3ZHTEj2IafnzkRZ7eb374d2VUpbHYnSkFamB3X3JcpRelcOquYJ77cxZYIrgjj6nbT2tkVEWuwe/v5mRPpcmt+89ZGs0MJqMb2TqwWRWpC5LR7w9IT+O7JY/ggwjc8RkJp4VDXbwdbKfUd4FM8pzfe7f161xCecx1wvFIqWymVBJwBFAP5Wut93sfsJ8yOYzd2wiulzA7Fr25bOJ7EWCs/ezlyjwKOtGlOw8icZK4/YRQvr6zh8631ZocTME12F+mJsVgtkZV7Pz5tPCnxMfz85XURu+GxKcKW9xiKs5K4cf4oXlu9l0+31JkdTsA0tns6aJYIy71vzitldF4Kd78RuRseI6W0cCjzZQT7e8AsYLfW+kRgOmAb7BNqrTcCvwf+C7wDrAK6D3mMBo7YoiilrlNKVSqlKuvqQueDq6k9Mq8Ec1LiueOMCSze0cgLEXoUcKSUmjqSW04azcjsJH72ytqIbSga2jsjbgQUPFUpfnbGBJbuauS5yiqzwwmInuU9Efj63bhgFGU5yfzslbU4nJGZe5G2vMcQF2Phl+d41tL/45PI3PAYqQNLocSXDnaH1roDQCkVr7XeBIwbypNqrR/TWs/UWp8ANAFbgANKqQLv8xQAR6wxprV+WGtdobWuyM3NHUoYfhWpjTzAJRXFzB6ZxT1vbqSuNfSOIx2qSFvD21tCrJV7zpvC7gY7930Ymet561ud5KREzhrQ3i6qKGJuWRa/eWtjRK6lj+RGPiHWym/On0JVo4O/Reg+lsb2yCixeCTHjs7hzPICHvw4Mjc8RlJp4VDlSwe7WimVAbwCvKeUehXYPZQnVUrleb+W4Fl//TTwGnC19yFXA68O5TmCrb7NSU5qZDbyFoviN+dPweHs5ldvRN5JV412YwQ7MhuKeaNzuGBGEQ99soNN+1vMDsfv6ts6yY3QDrZSit+cN4XOLndEnjJnHJMeiR1sgLll2VxcUcQjn+1gw97Iy71IqWF+ND8/cwJWi+KXb0Tehsf6Nk/uRdIG1VDTbwdba32e1tqmtb4L+AXwGEPfgPiiUmoD8Dpws9baBvwOOFUptRU4xft92KhvjdxGHmB0Xgo3nzia11bv5aPNkXWASU+ZtwhuKH525gRSE2L46UtrI249b31bJzkpkfvaleWm8N2TRvPm2n0Rt+mqoT2yL27Bcwx3RmIsd7y8lu4Iy72mdmdEf24WpCfy3ZPH8P7GWj7cFFm5V9/mmY3OjdCBwVDQ10Ezad6vvQ+XWQt8DqQM5Um11sdrrSdqradqrT/w3tegtT5Zaz1Ga32K1rpxKM8RTB2ublo7uyL+jXrDgjJG56Xw85fX0R5BtbEb2jxVKDIiqArFobKS4/j5mRNZscfGU0v3mB2O33R2ddPS0RWxS0QM150winH5qfzi1XW0RlBt7LrWTpSC7Ai+QMpIiuP/nT2R1VU2/m/RLrPD8Ru3W9Nkd5IdwR1sgG/NK2VUbjJ3vrY+oto9Y7lnJOee2foawX7a+7X3gTO9vwov440ayaNoAPExVn53/hRqbA7+8t4Ws8Pxm9rWTrKS4oix+lS1MmydP6OQeaOz+cPbm9hrc5gdjl80eKc5I3V5liEuxsJvL5jC/pYO/vTuZrPD8Zu6Nk/uxUZ47p0zdTgnjM3lj+9upiZCcq/Z4cKtI3d5jyEuxsJvzy+nusnBPRFUdrGutZPMpNiIzz0z9XXQzFnKU3Nufq8DZ3q+BjHGkGdMtUT6KBpAxcgsLp9TwuNf7OTLbZFR+q2utTPiZx/g4Hrebq354fOrImK6Oppyb0ZJJlfNHcG/Fu/ms62hU0FpKKIp9+75+mQAfvBsZOReNCzvMcwuzeLa48t4esmeiFkiWd8WHblnpj4vXbzl8t4MUixhy9gsEC1v1p+eMYGynGS+99wqDkRAZYO61g7y0hLMDiMoRmQnc/c5k1i8o5G/vh/+sxBGBztapjl/cvp4Ruem8P1nV7GvOfxHQqOlgw2e2ti/+vpklu5q5C/vhf8sRG2r57M/L0pevx+eOpax+Snc+vxqqhrDv6pINOWeWXyZG1ihlJoV8EjCWDSNogEkx8fwwOUzaO/s4urHl9LsCO81obURvkH1UBfOLOKimUXc9+E2nl8W3vWV61u9F7dR8volxcXw4BUz6HB1c/XjS3s26IaruijLvfNnFHHprGIe+Gg7Ty8J770QtS2edi9aBicSYq38/fKZOLvdfOfJyp4KOOGqrq0zavosZvGlgz0HWKSU2q6UWqOUWquUWhPowMJJNG4WGD8sjYeunMn2ujYueWhR2K4rdLs19W2d5KVFzweNUop7zpvC8WNy+MlLa3jii51mhzRodVF2cQswOi+VR66qYFe9nUsfXhy26+m11tRF4TT1r74+mfljc/npy2t58OPtYXtCrjGCnR9Fn52j81L4xxUz2dnQzsUPLQrbWSStNfWtzqi6uDWDLx3s04BRwEnA2cBZ3q/Cq76tk/TEWOJjrGaHElTHj8nl0atnUdPk4Nz7v2DlniazQxowm8OFq1tH3QdNXIyFR66q4GsT87n79Q3c8dIaOrvC77S5hjYnyXFWEuOiK/eOHZ3Do1dXeHLvgS9YVWUzO6QBa+nowtnljroOdqzVwkNXzuSs8gJ+/84mbn9xbVjm3oGWThJjraTEx5gdSlDNG53DP785i33NHZx93+cs3tFgdkgD1u7sxuHqjrrcCzZf6mDvBoqBk7z/tvvye9Ek0uvw9mX+2FxeuulYEuMsXPLQYp4PsyOdjdmHaBrBNhhTnjefOIpnllZx8T8Whd1oaH1bZ8RXEDmaE8bm8uJNxxIfY+GShxbxxpq9Zoc0IEbuRWMjnxBr5d5Lp/M/J43mucoqLn14Mfubw2s/y4GWDvLT4vHUQogux47K4ZWbjyUtMZbLH13C45/vDKuZiGjOvWDqt6OslLoT+Alwh/euWODfgQwq3ETyUc2+GJOfyms3H8es0kxu+88a7nx1Ha5ut9lh+eTgRp3oWEd4KKtF8ePTxvOPK2ayva6ds+/7nC+3h091mPooX0c4Nj+VV2+ex5TCdG55eiW/f2dT2FSoiPZG3mJR/Ohr4/jHFTPYsr+Vs+77nKU7w+b4B2pbO6Nm/fWRjM7z5N5J4/P45Rsb+P5zq3A4w2MmItr2jZnFl5Ho84BzgHYArfVeIDWQQYWbuigeRTNkJsfx5Ddnc+3xpTy5aDeXP7qkpwENZdHeyBsWTh7GKzfPIyMpliseXcLDn4bH2tBonj0yZKfE89S1c7hsdgkPfryda55Y2tOAhjJj/Xy0VKE4moWTC3jl5nmkJcTwjUcW88QX4TEaWtvSEfWvXWpCLA9dMZMfnzaO11bv5by/f8GehtCvMCLtXnD40sF2esv1aQClVHJgQwo/kX5Muq9irBZ+duZE/nbpNFZX2Tjj3s9CvlZ2bas08obReSm8estxnDZpGL95axO3PL2SthA/uayuNbpHsA3xMVZ+e/4Ufnv+FJbsbOT0v4V+7vU08inROwpqGJOfyiu3zGPBuFzufn0D33t2VUjnntaa2tZO8qN4BNtgsShuPnE0//zmbM+67Ps/D/lj1aWDHRy+dLCfV0o9BGQopa4F3gceDWxY4cPhjI5j0gfi3GmFvHqLZ0Tm8seW8Jf/bqYrRJeM1LZ0khxnJTnKNuocTUp8DH+/fAa3nz6et9ft45z7P2fz/lazwzqiDlc3TXYXBenSyBsum13CqzcfzL0/h3Du1bV2Eme1kJYouQeQlhDLw1dWcOvXxvLGmr2cc9/nbNzXYnZYR9TW2YXd2R1VFUT6M39sLq/fchyFGYl865+V/OGdTSGbe/VtnVhU5J/CaTZfNjn+CfgP8CIwDvh/Wut7Ax1YuNjrLdMzPEMa+d7GD0vj9f85jgtmFHHvh9u46vGlIVk3NBrLhPVHKcUN80fx1Hfm0uLo4twHPuelFdVmh3UYY1NYQXqiyZGElgkFB3Pvvg+3cfmjS2gIwSUjxkEX0bhJ7mgsFsUtJ43h6Wvn0tbZxdcf+CIka9Uf8NbAlhHsryrJTuKlm47lstnF/P3j7Vz+6JKefT6h5EBLBzkp8VgtknuB5Msmx99rrd/TWv9Ya32r1vo9pdTvgxFcONhnk0b+aJLiYvjTRVP5w4XlVO5q4twHvmDLgdAaDfWsI5RG4kiOGZXNW989jqlFGfzw+dX84pV1OLtCZ0TGuLiVEezDGbn354umsrLKxrkPfBFyo6H7WxwyAnoUc8uyefO7x1MxMpPbXlzDHS+FVik/o9MogxOHS4i18tvzy/nzRVNZXW3jrHs/Z/nu0Cphu9fWQWGm9FkCzZclIqce4b7T/R1IuDLKmg2XDvZRXVxRzLPXz8Xh6ua8B77gvQ2hsz5tX3MHBTL7cFR5aQk89Z05XHdCGf+3eDeXPryIAy2hMSLTc3GbIbl3NBfMLOKF64/B2eXmgge/5N31+80OqUd1k4PCzCSzwwhZuanx/Otbc7hpwSieWbqHix8KnUOFamUEu18XzCzilZvnkRBr5dKHF/HUkt0hs3l1r83BcPncDLijdrCVUjcqpdYC470nOBq3nYCc5Oi1t9mBUpCfLlfyfZlRkslrt8yjLDeFa/9Vyd/e34rb5HJi3W7NXpuDIrmS71OM1cJPz5jAA9+Ywab9rZx5b2iUE9vv7egPk0a+T1OLM3j9f45jTF4K1//fcv7y3hbTc8/t1uyzdVAojXyfrBbFbQvH848rZrDtQGvIlNE0ck862H0bPyyN1285jnmjc/jZy+u4/cW1dLjMnYnQWlNjc0juBUFfI9hP4zmx8VXvV+M2U2t9RRBiCwv7bJ61TNF2iuNgFKQn8sINx3D+9EL+9/0tXPd/y2npcJkWz4GWDrrcmiIZRfPJmeWecmKp3nJi/zS5nFh1k4Os5LioO8VxMPLTEnju+mO4cGYR936wle/8q5Jmh3m5V9fWibPbLdPUPlo4uYBXbzmOjKRYrnxsKY98usPU3KtqtJOZFBt1pzgORnpSLI9dPYtbTvQcKnTJw+bORDS2O+nscjNcltYF3FE72FrrZqAKmK613t3rZv7QVQjZ02iXEdABSIi18ueLp3Ln2RP5aHMtp/7lE95Zt8+UxqK6yfMhJ6+f78bmp/LqLfNYMC6Pu7zlxMyqd76nsZ2SLLk48lVCrJU/XljOr86dxKdb6vja/37CG2v2mpt7MormM6OM5tcm5nPPWxu55emVJuaeXXJvAKwWxa2njeMfV8zsmYl4a6057V6NsaxVci/g+lyDrbXuBjYrpUqCFE/Y2dXQTmm2lAYfCKUU35xXyos3HktWcjw3/HsFlzy8mE+31AX1A6e6yXMggIxgD4ynnNhMbv3aWN5cu48Ff/yI/31vC41BrhKzq97OiGx57QZCKcWVx4zkxRuPJSclnlueXsnFDy3i4821puSejGAPTO8ymu+s38/8P37EX0zIvT2Ndoqlgz1gCycP49Vb5pGbGs9NT63gon8sCnq7t7O+HYCROdJvCTRfNjlmAuuVUh8opV4zboEOLBw4nN3sa+6QN+ogTSvO4PVb5nH3OZOoarRz1eNLOef+L3i+sgq7M/CHLOyqb8eipMTiYBjlxP77gxM4bkwOf/tgK8f89gPueGltUKpVdHZ1s6/ZwQi5uB2UqcUZvHrzPH517iSqmxxc88Qyzrrvc55fFpzc217nyT25QBo4o4zmez84gRPH5XGvN/duf3ENG/YGPve6ut3UNDnktRuk0XmpvPnd4/nd+VPY4233Fv71M55btof2IBwutKOuHaWQGYgg8GUB1S8CHkWY2t0oV4JDFWO1cPWxI7l0djEvr6jh0c93ctt/1vCr1zfw9emFfGNOCRMK0gLy3FsOtDEyO1nWzw/BqNwUHrqygi0HWnnii528tKKaZ5buobwonYtmFnHO1ELSk2L9/rzVTQ7cGkZIIzFoMVYLVx4zkktmlfDKyhoe+WwHt724hl++sYGvTx/OZbNLmDQ8PSDPvb2ujZKsJMm9ISjLTeGBy2fwvQOtPPHFLl5eWc2zy6qYXJjGxRXFnBvA3Otya0ZkSbs3WFaL4tLZJZw3o5DXV+/j0c928JMX13L36xs4q7yAS2YVM6MkMyA14nfWt1OYkUhCrOReoKmBTk0opY4DLtNa3xyYkHxXUVGhKysrTXv+t9bu46anVvDG/xzH5MLANETRRmtN5e4mnl6yhzfX7sPZ5WZacQbnThvOwsnD/Fpv/OQ/f8yo3BQevqrCb38z2jW1O3llVQ3PLati0/5W4mIsLJw0jIsrijl2VDYWPx1s8Pbafdz41ApevXkeU4sz/PI3o52Re88s2cMb3tybWpTOhRXFnF1eQIYfT31b+NdPKcxI5LFrZvntb0Y7m93Jq6v28nxlFev3thAXY+G0ScO4uKKIeaNy/JZ776zbxw3/ltzzJ601K/Y08dyyKt5Ysw+7s5uy3GQurijm/OmF5PmxWstZ931GVnI8//rWbL/9zWimlFqutT5iJ8KnDrZSajrwDeAiYCfwotb6fr9GOQhmd7D/8M4mHvlsB+vuPk1GYgLAZnfy4ooaXqj0dNYAppdkcMbkAhZOHjakNYDOLjcT/t873DC/jB+fNt5fIQsvrTXr97bwQmUVr6zaS7PDRWFGIhfMLOKimUVDXr/5p3c38+An21l/92kyEhMANruTl1fW8OzSKjYfaCXOauHkCXmcP6OIE8bmDOnzztXtZtKd73LNsSP56RkT/Bi1MKyraeY/y6t5eWXNwdybUciFM4spGeLSjr+8t4X7P9zK+rsXSgWfAGjv7OLNtft4obKKZbuasFoUJ47L5aKKYhaMyx1S7jm73Ey+612umjuCn5810Y9RR6++OthHXSKilBoLXOa91QPP4emQnxiQKMPQ2ppmxg1Llc51gGQkxfHt40r59nGlbK9r4511+3lr7T7ueWsj97y1kfKidBZOHsYZkwsGvExn/d5mut2aiQUy8xAISikmF6YzuTCdO86YwHsbDvB8ZRX3fbiVez/YyoSCNE4en8cJY3OZXpJBrNWX7SAHrdvbzJi8FOlcB0hGUhzfnFfKNceOZP3eFl5cUc1rq/by9rr9pMTHsGBcLid5X7+clIGdAbB+b4t3dDwjMMGLnty7/fTxvL/xAM9XVnPfR9u498NtTChI46Txucwfmzeo3Nu4r4WROcnSuQ6Q5PgYLq4o5uKKYnbUtfHC8mpeXF7N+xtrSYmPYf7YXOaPy+W40TkDrgSyab8n96aVZAQmePEVRx3BVkq5gc+Ab2utt3nv26G1LgtifH0ycwS7q9vNjF+9x5nlBfz2/HJTYohWuxvaeXvdft5et5/VVTYAphal8/XphZw/vcindYf/+GQ7v3t7E8t+dooc9xtENTYHb6zeywcba6nc3YhbeyojnDQ+j4sqijh2VA7Wfqayu7rdVNzzPqdOyOePF00NUuTC1e3m8631vLt+P+9vPEB9mxOlYEphOmeVF3DBjCKyfehsP/rZDn795kaW/PRkOagkiPbaHLy+ei8fbqqlcncT3W5NanwMJ47P45JZxRxT1v8Srm63Zsav3uOUCfn8+WLJvWDp6nbz2bZ6/rt+P+9vrO0pzzguP5XLZhdz/swi0hL6b/f+tWgX/+/V9Xz+kxOlepafDGqJiFLq68ClwDzgHeBZ4FGtdakfAvoB8B1AA2uBbwIF3ufIBpYDV2qt+6w9ZGYH+8tt9Xzj0SU8ePkMTp9SYEoMwlPu6621+3h11V7W720hPsbC2VOHc8XcEUwtSj/qJpErHl3CvmYHH/xoQXADFj2aHS4Wba/nky11vLV2P80OF8PTE7hgZhEXzCg66qzEkh0NXPLwYv5++QzOkNwzhdvtWQL08eZa3t9Uy+oqG7FWxdcmDuOy2SV9rre/8rEl7Gm088mPZTLULEbufby5jrfW7qOlo4virEQunlnMhRVFR93rsnx3Exc8+CX3XTads6cOD3LUAjzL7zYfaOXzrfW8vmYfq6tsJMVZOau8gIsqiqkYcfTNkVc+toSd9e18dtuJAdlAGY2GtAZbKZUMnItnqchJwL+Al7XW/x1kMIXA58BErbVDKfU88BZwBvCS1vpZpdQ/gNVa6wf7+ltmdrC/9+xK/rv+ACt+capMlYWI9XubeWrJHl5ZWYPd2c2EgjTOKi/gaxPzGZ2X0vOBsqOujZP+/AnfP2UM3z9lrMlRC4AOVzfvbzzAC5XVfLa1DreG2SOzuLCiiIWTh31ldOYHz63izbX7WP7zU0j1YdRGBN6WA608u7SKF1dU0+xwUZyVyKWzSjh5Qh5j81J7Otu7G9pZ8KeP+e5JY/jBqZJ7oaDD1c276/fz3LIqvtzegEXB/LG5XFxRzLwxOV/JvR89v5o31uxlyU9P9uumVzF4a6pt/Hvx7p7NkSOzk7hwZhGnThzGmLyUntyrarQz/48fcdOC0dx62jiTo44cQ97k2OsPZeLZ6HiJ1vrkQQZTCCwGpgItwCvAfcBTwDCtdZdS6hjgLq31aX39LTM62K5uN/d/uI2/fbCVmxaM4raFskEu1LR2uHhl1V5eWlHNyj02ADKTYpk5IpOJw9N5c81e9jd38NGPF5CXKlPUoWZ/cwcvrazmP5XV7Kj31Gwty0lmalEG8bFWnlm6hxvmj+L20yX3Qo3RWXtm6R4W7/Ac+puZFMuc0mzGF6Tyxpp97G/u4L0fnuDXikDCP3Y3tPNCZTUvLK/iQEsnFgXjh6UxuzQLt9b83+LdfHteqWyQC0HtnV28vW4/L1RWsWTnwdybXZrFhII03lyzj33NHbz7gxMolFMc/cZvHWx/UUp9D7gHcAD/Bb4HLNZaj/b+vBh4W2s9+Qi/ex1wHUBJScnM3bt3By1u8BxwcfZ9nzOxII3fXVAum6xCXI3Nwedb66jc1cTy3U3s8NYA/f0F5Rw3Jsfs8EQfPKWrbHyxrZ411TZWVzfT1O7knKnDuee8KTJzFOKqm+ws2t7A4h2NLN7RQI3NQWFGIr85fwrzx+aaHZ7oQ1e3m6U7G1mys5FluxpZsaeJrm7NWd49R5J7oa3G5vDmXgNLdjZQ1eigID2B35w/hRPH5ZkdXkQJqQ62dxT8ReASwAa8APwHz4h1vx3s3sxaItLa4ZKp6TDl7HITa1Wy/ixMdXW7iRlg1QMRGiT3wpfbrenWesAVR0RokNwLnEGV6QugU4CdWus6AKXUS3g2UmYopWK01l1AEVBjQmw+kc51+IqLkQYinEnnOnxJ7oUvi0VhQTpn4Upyzxxm/F/fA8xVSiUpz+XUycAG4CPgQu9jrgZeNSE2IYQQQgghhiToHWyt9RI8S0JW4CnRZwEeBn4C/FAptQ1Pqb7Hgh2bEEIIIYQQQ2XGEhG01ncCdx5y9w5gtgnhCCGEEEII4TeyMEcIIYQQQgg/MqVMn78opeqA4NbpOygHqDfpuUXokPeBAHkfCA95HwiQ90E0GaG1PmLd0bDuYJtJKVV5tNIsInrI+0CAvA+Eh7wPBMj7QHjIEhEhhBBCCCH8SDrYQgghhBBC+JF0sAfvYbMDECFB3gcC5H0gPOR9IEDeBwJZgy2EEEIIIYRfyQi2EEIIIYQQfiQd7AFSSi1USm1WSm1TSt1udjzCHEqpx5VStUqpdWbHIsyjlCpWSn2klNqglFqvlPqe2TGJ4FNKJSilliqlVnvfB3ebHZMwj1LKqpRaqZR6w+xYhHmkgz0ASikr8ABwOjARuEwpNdHcqIRJ/gksNDsIYbou4Eda64nAXOBm+UyISp3ASVrrqcA0YKFSaq65IQkTfQ/YaHYQwlzSwR6Y2cA2rfUOrbUTeBY41+SYhAm01p8CjWbHIcyltd6ntV7h/Xcrnka10NyoRLBpjzbvt7Hem2xwikJKqSLgTOBRs2MR5pIO9sAUAlW9vq9GGlMhBKCUGglMB5aYHIowgXdZwCqgFnhPay3vg+j0V+A2wG1yHMJk0sEWQoghUkqlAC8C39dat5gdjwg+rXW31noaUATMVkpNNjkkEWRKqbOAWq31crNjEeaTDvbA1ADFvb4v8t4nhIhSSqlYPJ3rp7TWL5kdjzCX1toGfITs0YhG84BzlFK78CwhPUkp9W9zQxJmkQ72wCwDxiilSpVSccClwGsmxySEMIlSSgGPARu11n8xOx5hDqVUrlIqw/vvROBUYJOpQYmg01rfobUu0lqPxNM/+FBrfYXJYQmTSAd7ALTWXcAtwLt4NjM9r7Veb25UwgxKqWeARcA4pVS1UurbZsckTDEPuBLPSNUq7+0Ms4MSQVcAfKSUWoNnIOY9rbWUaBMiislJjkIIIYQQQviRjGALIYQQQgjhR9LBFkIIIYQQwo+kgy2EEEIIIYQfSQdbCCGEEEIIP5IOthBCCCGEEH4kHWwhhBBCCCH8SDrYQggRJpRS2b3qbe9XStV4/92mlPp7gJ7z+0qpq/r4+VlKqV8G4rmFECJcSR1sIYQIQ0qpu4A2rfWfAvgcMcAKYIb3oK0jPUZ5HzNPa20PVCxCCBFOZARbCCHCnFJqgVLqDe+/71JKPamU+kwptVspdb5S6g9KqbVKqXeUUrHex81USn2ilFqulHpXKVVwhD99ErDC6Fwrpb6rlNqglFqjlHoWQHtGaT4GzgrKf6wQQoQB6WALIUTkGYWnc3wO8G/gI631FMABnOntZN8HXKi1ngk8DtxzhL8zD1je6/vbgela63Lghl73VwLH+/2/QgghwlSM2QEIIYTwu7e11i6l1FrACrzjvX8tMBIYB0wG3vOs8MAK7DvC3ykANvb6fg3wlFLqFeCVXvfXAsP9F74QQoQ36WALIUTk6QTQWruVUi59cLONG8/nvgLWa62P6efvOICEXt+fCZwAnA38TCk1xbt8JMH7WCGEEMgSESGEiEabgVyl1DEASqlYpdSkIzxuIzDa+xgLUKy1/gj4CZAOpHgfNxZYF/CohRAiTEgHWwghoozW2glcCPxeKbUaWAUce4SHvo1nxBo8y0j+7V12shK4V2tt8/7sRODNQMYshBDhRMr0CSGEOCql1MvAbVrrrUf5eT7wtNb65OBGJoQQoUs62EIIIY5KKTUOyNdaf3qUn88CXFrrVUENTAghQph0sIUQQgghhPAjWYMthBBCCCGEH0kHWwghhBBCCD+SDrYQQgghhBB+JB1sIYQQQggh/Eg62EIIIYQQQvjR/wfZ4sT4NnH08gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "ax.plot(data.time, data.pressure)\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data into `sidewinder`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = Waveforms(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find troughs in a waveform\n",
+    "\n",
+    "For example, we could use the troughs to split our arterial blood pressure data into separate heartbeats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = features.find_troughs(wf, name='pressure')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x180 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 2.5), constrained_layout=True)\n",
+    "ax.plot(data.time, data.pressure)\n",
+    "ax.scatter(\n",
+    "    data.time[wf.features['pressure']['troughs']],\n",
+    "    data.pressure[wf.features['pressure']['troughs']],\n",
+    "    color='red',\n",
+    ")\n",
+    "ax.set(xlabel='Time (s)', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "f2c1554fc2f673ff3983ee6916670176b140c542ca5d14124814c0e65b9d8c04"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.9 64-bit ('sidewinder-lxfscp8q-py3.9': poetry)",
+   "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": "3.9.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index e541462..623ffd9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -431,6 +431,18 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "pyampd"
+version = "0.0.1"
+description = "Peak detection using AMPD and ASS-AMPD algorithms"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numpy = "*"
+scipy = "*"
+
 [[package]]
 name = "pycparser"
 version = "2.21"
@@ -638,7 +650,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.7.1,<3.11"
-content-hash = "c5bfacbf7890960310c3259422a61d8a0b7880bffc034e6814065058120cfad0"
+content-hash = "e13cc28f1bb7c0508cd25ae14f1101c8a3c2e32e358dc1c28f7defecdb58c9f8"
 
 [metadata.files]
 appnope = [
@@ -1000,6 +1012,10 @@ py = [
     {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
     {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
 ]
+pyampd = [
+    {file = "pyampd-0.0.1-py2.py3-none-any.whl", hash = "sha256:d923a4372308d447b98cd80f935a059e29323777dd26c0ac131945ad84f5f89c"},
+    {file = "pyampd-0.0.1.tar.gz", hash = "sha256:e333d00b61d2844b0ed0383b010394e0a0cb384a036dd83960cdc647cf71a755"},
+]
 pycparser = [
     {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
     {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
diff --git a/pyproject.toml b/pyproject.toml
index 068678c..c2134cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,7 @@ pandas = "^1.3.4"
 numpy = "^1.21.4"
 scipy = "^1.7.3"
 matplotlib = "^3.5.1"
+pyampd = "^0.0.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
diff --git a/sidewinder/features.py b/sidewinder/features.py
new file mode 100644
index 0000000..f2f2ca5
--- /dev/null
+++ b/sidewinder/features.py
@@ -0,0 +1,21 @@
+from pyampd import ampd
+
+from .waveforms import Waveforms
+
+
+def find_troughs(waveforms: Waveforms, name: str):
+    """Finds indices of troughs in a waveform.
+
+    Args:
+        waveforms: `sidewinder.waveforms.Waveforms` instance holding you data
+        name: Name of column in `waveforms` to find troughs in
+
+    Returns:
+        `waveforms` with trough indices added to `waveforms.features[`name`]`
+    """
+    waveforms.waveforms[name] *= -1  # invert signal, so troughs become peaks
+    waveforms.features[name]['troughs'] = ampd.find_peaks(
+        waveforms.waveforms[name]
+    )
+    waveforms.waveforms[name] *= -1  # un-invert signal
+    return waveforms
diff --git a/sidewinder/waveforms.py b/sidewinder/waveforms.py
index 66cdcdc..33982c2 100644
--- a/sidewinder/waveforms.py
+++ b/sidewinder/waveforms.py
@@ -1,9 +1,43 @@
-from dataclasses import dataclass
+from typing import Tuple, Dict
 
+import numpy as np
 import pandas as pd
 
 
-@dataclass
 class Waveforms:
     """Holds waveforms for downstream processing."""
-    waveforms: pd.DataFrame
+
+    def __init__(self, waveforms: pd.DataFrame, time_column_name: str = 'time'):
+        """
+        Args:
+            waveforms: Contains a timestamps column (in seconds) and one or more
+                waveform columns (arbitrary units)
+            time_column_name: The name of the timestamps column in `waveforms`
+        """
+        self.waveforms = waveforms
+        self.time_column_name = time_column_name
+        self._validate_arguments()
+        self.names = self._init_names()
+        self.features = self._init_features()
+
+    def _validate_arguments(self):
+        assert isinstance(self.waveforms, pd.DataFrame), (
+            '`waveforms` must be a pandas DataFrame'
+        )
+        assert self.time_column_name in self.waveforms.columns, (
+            "`waveforms` must contain a column called "
+            f"'{self.time_column_name}'"
+        )
+
+    def _init_names(self) -> Tuple[str, ...]:
+        """Makes a tuple of names of the waveform-containing columns in
+        self.waveforms"""
+        return tuple(
+            name for name in self.waveforms.columns
+            if name is not self.time_column_name
+        )
+
+    def _init_features(self) -> Dict[str, Dict[str, np.ndarray]]:
+        """Makes the holder for waveform features (the features themselves
+        haven't been derived yet)"""
+        return {name: {} for name in self.names}


Test patch:
diff --git a/tests/test_features.py b/tests/test_features.py
new file mode 100644
index 0000000..3e55623
--- /dev/null
+++ b/tests/test_features.py
@@ -0,0 +1,19 @@
+import numpy as np
+
+from sidewinder import synthetic, waveforms, features
+
+
+def test_find_troughs():
+    data = synthetic.synthetic_arterial_pressure_data(
+        systolic_pressure=120.,
+        diastolic_pressure=80.,
+        heart_rate=60.,
+        n_beats_target=2.5,
+        hertz=10.
+    )
+    w = waveforms.Waveforms(data)
+    w = features.find_troughs(w, name='pressure')
+    np.testing.assert_array_equal(
+        w.features['pressure']['troughs'],
+        np.array([0, 10, 20])
+    )
diff --git a/tests/test_waveforms.py b/tests/test_waveforms.py
new file mode 100644
index 0000000..4a82c3e
--- /dev/null
+++ b/tests/test_waveforms.py
@@ -0,0 +1,31 @@
+import pytest
+import pandas as pd
+
+from sidewinder import waveforms
+
+
+class TestWaveforms:
+    @pytest.fixture(scope='class')
+    def example_data(self):
+        return pd.DataFrame({'time': [1, 2, 3], 'signal': [0.1, 0.4, 0.8]})
+
+    @pytest.fixture(scope='class')
+    def example_waveforms(self, example_data):
+        return waveforms.Waveforms(waveforms=example_data)
+
+    def test_validate_waveforms_type(self):
+        with pytest.raises(Exception):
+            waveforms.Waveforms(waveforms=[1, 2, 3])
+
+    def test_validate_time_column_name(self, example_data):
+        with pytest.raises(Exception):
+            waveforms.Waveforms(
+                waveforms=example_data,
+                time_column_name='a_different_time_column_name'
+            )
+
+    def test_names(self, example_waveforms):
+        assert example_waveforms.names == ('signal',)
+
+    def test_features(self, example_waveforms):
+        assert example_waveforms.features == {'signal': {}}