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

Issue:
Generate simple synthetic arterial waveform
For use in testing. Could use the approach from https://pubmed.ncbi.nlm.nih.gov/28176674/


Discussion / hints:

Fix patch:
diff --git a/examples/example_arterial_pressure_waveform.ipynb b/examples/example_arterial_pressure_waveform.ipynb
new file mode 100644
index 0000000..cc8a38d
--- /dev/null
+++ b/examples/example_arterial_pressure_waveform.ipynb
@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Produce example arterial pressure waveform\n",
+    "\n",
+    "This is later used by `sidewinder.synthetic.synthetic_arterial_pressure_data()` to generate synthetic arterial pressure data.\n",
+    "\n",
+    "Data are used under the terms of the [VitalDB Data Use Agreement](https://vitaldb.net/dataset/?query=overview&documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak&sectionId=h.usmoena3l4rb). If you do not agree with this agreement, please close this file."
+   ]
+  },
+  {
+   "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 numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.signal import savgol_filter\n",
+    "import statsmodels.formula.api as smf\n",
+    "\n",
+    "from sidewinder.utils import get_root_directory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load short segment of arterial pressure waveform:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "waveforms = pd.read_csv(\n",
+    "    os.path.join(\n",
+    "        get_root_directory(),\n",
+    "        'local_data',\n",
+    "        'abp_waveform_extract.csv'\n",
+    "    ),\n",
+    "    index_col=0\n",
+    ")\n",
+    "\n",
+    "art = waveforms['SNUADC/ART'].values[100205:101980]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(14, 3), constrained_layout=True)\n",
+    "ax.plot(art)\n",
+    "ax.set(xlabel='Sample index', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform some initial smoothing so that we don't end up fitting the cubic splines to noise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "art_smooth = savgol_filter(art, window_length=39, polyorder=3)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(14, 3), constrained_layout=True)\n",
+    "ax.plot(art_smooth)\n",
+    "ax.set(xlabel='Sample index', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Isolate a nice-looking individual cardiac cycle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "smooth_beat = art_smooth[1516:]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(smooth_beat)\n",
+    "ax.set(xlabel='Sample index', ylabel='Arterial pressure (mmHg)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit a cyclic cubic spline model, to guarantee that starting and ending pressures are the same (this will eliminate discontinuities when joining multiple copies of the beat together):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time = np.linspace(0, 1, smooth_beat.size)\n",
+    "\n",
+    "mod = smf.quantreg('beat ~ cc(time, df=50)', dict(time=time, beat=smooth_beat))\n",
+    "result = mod.fit()\n",
+    "modelled_beat = result.predict()\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(time, smooth_beat, '.', label='Original')\n",
+    "ax.plot(time, modelled_beat, 'r', label='Modelled')\n",
+    "ax.set(xlabel='Sample index', ylabel='Arterial pressure (mmHg)')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the modelled beat as a binary file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.save(\n",
+    "    file=os.path.join(\n",
+    "        get_root_directory(),\n",
+    "        'sidewinder',\n",
+    "        'data',\n",
+    "        'example_arterial_pressure_waveform'\n",
+    "    ),\n",
+    "    arr=modelled_beat\n",
+    ")"
+   ]
+  }
+ ],
+ "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/load_waveforms.ipynb b/examples/load_waveforms.ipynb
index 680ef4f..03f14aa 100644
--- a/examples/load_waveforms.ipynb
+++ b/examples/load_waveforms.ipynb
@@ -4,45 +4,158 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Example data loading\n",
-    "\n",
-    "Please read and only use the data under the terms of the [VitalDB Data Use Agreement](https://vitaldb.net/dataset/?query=overview&documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak&sectionId=h.usmoena3l4rb)"
+    "# Example data loading"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os, sys\n",
-    "sys.path.append(os.pardir)\n",
+    "sys.path.append(os.pardir)  # Needed for sidewinder import before we install it\n",
     "\n",
-    "import pandas as pd\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": 14,
+   "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": {},
-   "outputs": [],
    "source": [
-    "waveforms = pd.read_csv(\n",
-    "    os.path.join(\n",
-    "        os.pardir, 'local_data', 'abp_waveform_extract.csv'),\n",
-    "        index_col=0\n",
-    ")"
+    "## Load the data into `sidewinder`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "wf = Waveforms(waveforms)"
+    "wf = Waveforms(art)"
    ]
   }
  ],
diff --git a/poetry.lock b/poetry.lock
index 9c656d3..e541462 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -69,6 +69,14 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "cycler"
+version = "0.11.0"
+description = "Composable style cycles"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "debugpy"
 version = "1.5.1"
@@ -93,6 +101,27 @@ category = "dev"
 optional = false
 python-versions = ">=2.7"
 
+[[package]]
+name = "fonttools"
+version = "4.28.5"
+description = "Tools to manipulate font files"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=13.0.0)", "xattr"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["scipy", "munkres"]
+lxml = ["lxml (>=4.0,<5)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=13.0.0)"]
+woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
+
 [[package]]
 name = "importlib-metadata"
 version = "4.8.2"
@@ -220,6 +249,33 @@ python-versions = ">=3.6"
 pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
 traitlets = "*"
 
+[[package]]
+name = "kiwisolver"
+version = "1.3.2"
+description = "A fast implementation of the Cassowary constraint solver"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "matplotlib"
+version = "3.5.1"
+description = "Python plotting package"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+kiwisolver = ">=1.0.1"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pillow = ">=6.2.0"
+pyparsing = ">=2.2.1"
+python-dateutil = ">=2.7"
+setuptools_scm = ">=4"
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.3"
@@ -251,7 +307,7 @@ python-versions = ">=3.7,<3.11"
 name = "packaging"
 version = "21.3"
 description = "Core utilities for Python packages"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6"
 
@@ -291,6 +347,21 @@ python-versions = ">=3.6"
 qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 testing = ["docopt", "pytest (<6.0.0)"]
 
+[[package]]
+name = "patsy"
+version = "0.5.2"
+description = "A Python package for describing statistical models and for building design matrices."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numpy = ">=1.4"
+six = "*"
+
+[package.extras]
+test = ["pytest", "pytest-cov", "scipy"]
+
 [[package]]
 name = "pexpect"
 version = "4.8.0"
@@ -310,6 +381,14 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "pillow"
+version = "9.0.0"
+description = "Python Imaging Library (Fork)"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "pluggy"
 version = "1.0.0"
@@ -372,7 +451,7 @@ python-versions = ">=3.5"
 name = "pyparsing"
 version = "3.0.6"
 description = "Python parsing module"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6"
 
@@ -440,6 +519,32 @@ python-versions = ">=3.6"
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 py = {version = "*", markers = "implementation_name == \"pypy\""}
 
+[[package]]
+name = "scipy"
+version = "1.7.3"
+description = "SciPy: Scientific Library for Python"
+category = "main"
+optional = false
+python-versions = ">=3.7,<3.11"
+
+[package.dependencies]
+numpy = ">=1.16.5,<1.23.0"
+
+[[package]]
+name = "setuptools-scm"
+version = "6.3.2"
+description = "the blessed package to manage your versions by scm tags"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+packaging = ">=20.0"
+tomli = ">=1.0.0"
+
+[package.extras]
+toml = ["setuptools (>=42)", "tomli (>=1.0.0)"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -448,6 +553,25 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
+[[package]]
+name = "statsmodels"
+version = "0.13.1"
+description = "Statistical computations and models for Python"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = ">=1.17"
+pandas = ">=0.25"
+patsy = ">=0.5.2"
+scipy = ">=1.3"
+
+[package.extras]
+build = ["cython (>=0.29.22)"]
+develop = ["cython (>=0.29.22)"]
+docs = ["sphinx", "nbconvert", "jupyter-client", "ipykernel", "matplotlib", "nbformat", "numpydoc", "pandas-datareader"]
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -456,6 +580,14 @@ category = "dev"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
+[[package]]
+name = "tomli"
+version = "2.0.0"
+description = "A lil' TOML parser"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "tornado"
 version = "6.1"
@@ -506,7 +638,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.7.1,<3.11"
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+content-hash = "c5bfacbf7890960310c3259422a61d8a0b7880bffc034e6814065058120cfad0"
 
 [metadata.files]
 appnope = [
@@ -585,6 +717,10 @@ colorama = [
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 ]
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+]
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@@ -616,6 +752,10 @@ entrypoints = [
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 ]
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 ]
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+    {file = "statsmodels-0.13.1.tar.gz", hash = "sha256:006ec8d896d238873af8178d5475203844f2c391194ed8d42ddac37f5ff77a69"},
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 ]
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+    {file = "tomli-2.0.0.tar.gz", hash = "sha256:c292c34f58502a1eb2bbb9f5bbc9a5ebc37bee10ffb8c2d6bbdfa8eb13cc14e1"},
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 tornado = [
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diff --git a/pyproject.toml b/pyproject.toml
index 5a39971..068678c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,10 +8,13 @@ authors = ["Your Name <you@example.com>"]
 python = ">=3.7.1,<3.11"
 pandas = "^1.3.4"
 numpy = "^1.21.4"
+scipy = "^1.7.3"
+matplotlib = "^3.5.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"
 ipykernel = "^6.6.0"
+statsmodels = "^0.13.1"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/sidewinder/data/example_arterial_pressure_waveform.npy b/sidewinder/data/example_arterial_pressure_waveform.npy
new file mode 100644
index 0000000..dd54ac5
Binary files /dev/null and b/sidewinder/data/example_arterial_pressure_waveform.npy differ
diff --git a/sidewinder/synthetic.py b/sidewinder/synthetic.py
new file mode 100644
index 0000000..2439cb1
--- /dev/null
+++ b/sidewinder/synthetic.py
@@ -0,0 +1,132 @@
+import os
+import math
+
+import numpy as np
+import pandas as pd
+from scipy import interpolate
+
+from .utils import get_root_directory
+
+
+def make_waveform_generator_from_file(filepath: str) -> interpolate.interp1d:
+    """Make scipy interpolation function from saved example waveform file. This
+    function can be used to generate resampled synthetic waveforms.
+
+    Args:
+        filepath: Path to .npy file containing a 1D numpy array of the example
+            waveform
+
+    Returns:
+        Interpolation function with domain [0, 1]
+    """
+    waveform = np.load(filepath)
+    time = np.linspace(start=0, stop=1, num=waveform.size)
+    return interpolate.interp1d(time, waveform, kind='cubic')
+
+
+def make_generator_timestamps_and_inputs(
+    cycles_per_minute: float,
+    n_cycles_target: float,
+    hertz: float
+) -> (np.ndarray, np.ndarray):
+    """Make timestamps and corresponding inputs for a synthetic waveform
+        generator function.
+
+    Args:
+        cycles_per_minute: Rate (cycles per minute)
+        n_cycles_target: Target number of cycles. Doesn't have to be an
+            integer. The number of cycles actually generated will be slightly
+            different from the target unless each cycle fits exactly within a
+            whole number of samples.
+        hertz: Sampling rate (hertz)
+
+    Returns:
+        Timestamps (seconds)
+        Inputs. The units are standardised such that 0 is the start of the
+            generated waveform and 1 is the end.
+    """
+    seconds_per_cycle = 60. / cycles_per_minute
+    dt = 1 / hertz
+    fractional_samples_per_cycle = seconds_per_cycle / dt
+    n_samples = round(fractional_samples_per_cycle * n_cycles_target)
+    n_seconds = n_samples * dt
+    n_cycles_actual = n_samples / fractional_samples_per_cycle
+    n_cycle_starts = math.ceil(n_cycles_actual)
+
+    start_times = np.zeros(n_cycle_starts)
+    start_times[1:] = np.cumsum(
+        np.repeat(seconds_per_cycle, n_cycle_starts - 1)
+    )
+
+    timestamps = np.linspace(0, n_seconds, n_samples, endpoint=False)
+
+    """`insertion_i` is the indices where `start_times` would be inserted into
+    `timestamps` to maintain order. Each element in `offsets` is the number
+    of seconds that elapse in a cycle before the first sample in that cycle."""
+    insertion_i = np.searchsorted(timestamps, start_times)
+    offsets = timestamps[insertion_i] - start_times
+
+    samples_per_cycle = np.zeros(n_cycle_starts)
+    samples_per_cycle[:-1] = insertion_i[1:] - insertion_i[:-1]
+    samples_per_cycle[-1] = n_samples - samples_per_cycle.sum()
+    samples_per_cycle = samples_per_cycle.astype(int)
+
+    def cycle_timestamps(cycle_i: int) -> np.ndarray:
+        return np.linspace(
+            start=0,
+            stop=(samples_per_cycle[cycle_i] - 1) / hertz,
+            num=samples_per_cycle[cycle_i]
+        ) + offsets[cycle_i]
+
+    inputs = np.concatenate([
+        cycle_timestamps(cycle_i) for cycle_i in range(n_cycle_starts)
+    ]) / seconds_per_cycle
+
+    return timestamps, inputs
+
+
+def synthetic_arterial_pressure_data(
+    systolic_pressure: float,
+    diastolic_pressure: float,
+    heart_rate: float,
+    n_beats_target: float,
+    hertz: float
+) -> pd.DataFrame:
+    """Make synthetic arterial pressure data.
+
+    Args:
+        systolic_pressure: Maximum systolic arterial pressure (mmHg)
+        diastolic_pressure: Minimum diastolic arterial pressure (mmHg)
+        heart_rate: Heart rate (beats per minute)
+        n_beats_target: Target number of heartbeats. Doesn't have to be an
+            integer. The number of beats actually generated will be slightly
+            different from the target unless each beat fits exactly within a
+            whole number of samples.
+        hertz: Sampling rate (hertz)
+
+    Returns:
+        Synthetic arterial pressure data with corresponding timestamps.
+    """
+    assert systolic_pressure > diastolic_pressure
+
+    timestamps, inputs = make_generator_timestamps_and_inputs(
+        cycles_per_minute=heart_rate,
+        n_cycles_target=n_beats_target,
+        hertz=hertz
+    )
+    waveform_filepath = os.path.join(
+        get_root_directory(),
+        'sidewinder',
+        'data',
+        'example_arterial_pressure_waveform.npy'
+    )
+    generator = make_waveform_generator_from_file(waveform_filepath)
+    waveform = generator(inputs)
+
+    # Scale
+    waveform -= waveform.min()
+    waveform /= waveform.max()
+    waveform *= (systolic_pressure - diastolic_pressure)
+    waveform += diastolic_pressure
+
+    return pd.DataFrame({'time': timestamps, 'pressure': waveform})
diff --git a/sidewinder/utils.py b/sidewinder/utils.py
new file mode 100644
index 0000000..2d5d697
--- /dev/null
+++ b/sidewinder/utils.py
@@ -0,0 +1,5 @@
+from pathlib import Path
+
+
+def get_root_directory() -> Path:
+    return Path(__file__).parent.parent


Test patch:
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_synthetic.py b/tests/test_synthetic.py
new file mode 100644
index 0000000..8322bdf
--- /dev/null
+++ b/tests/test_synthetic.py
@@ -0,0 +1,44 @@
+import os
+
+import numpy as np
+import pandas as pd
+from scipy import interpolate
+
+from sidewinder import utils
+from sidewinder import synthetic
+
+
+def test_make_waveform_generator_from_file():
+    waveform_filepath = os.path.join(
+        utils.get_root_directory(),
+        'sidewinder',
+        'data',
+        'example_arterial_pressure_waveform.npy'
+    )
+    generator = synthetic.make_waveform_generator_from_file(waveform_filepath)
+    assert isinstance(generator, interpolate.interp1d)
+
+
+def test_make_generator_timestamps_and_inputs():
+    timestamps, inputs = synthetic.make_generator_timestamps_and_inputs(
+        cycles_per_minute=60.,
+        n_cycles_target=2.,
+        hertz=10.
+    )
+    assert (timestamps == np.linspace(0, 2, 20, endpoint=False)).all()
+    assert (inputs == np.tile(np.linspace(0, 1, 10, endpoint=False), 2)).all()
+
+
+def test_synthetic_arterial_pressure_data():
+    art = synthetic.synthetic_arterial_pressure_data(
+        systolic_pressure=120.,
+        diastolic_pressure=80.,
+        heart_rate=60.,
+        n_beats_target=2.,
+        hertz=10.
+    )
+    assert (art.time.values == np.linspace(0, 2, 20, endpoint=False)).all()
+    assert art.pressure.max() == 120.
+    assert art.pressure.min() == 80.
+    assert art.shape == (20, 2)
+    assert isinstance(art, pd.DataFrame)
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..e135ff0
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,9 @@
+import os
+
+from sidewinder import utils
+
+
+def test_get_root_directory():
+    root_dir_files = os.listdir(utils.get_root_directory())
+    assert 'README.md' in root_dir_files
+    assert 'sidewinder' in root_dir_files