initial commit

README.md

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+# sri

example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Read data from test file\n",
+    "# Note that this file uses 30 second epochs (i.e. 2880 epochs per day)\n",
+    "epochs_per_day = 2880\n",
+    "sleep = pd.read_csv('test.csv')['sleep'].values\n",
+    "assert len(sleep)%epochs_per_day == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2135b22c160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize sleep data\n",
+    "plt.imshow(~np.reshape(sleep,(-1,epochs_per_day)).astype('uint8'),cmap='Greys',aspect=500)\n",
+    "plt.xlabel('Epoch (of %i)' % (epochs_per_day))\n",
+    "plt.ylabel('Day')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55.902777777777771"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate SRI (with 2880 epochs per day)\n",
+    "from sri_from_csv import sri\n",
+    "sri(sleep,epochs_per_day=epochs_per_day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

sri_from_csv.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+
+"""sri_from_csv.py: Calculate the Sleep Regularity Index from sleep/wake data
+
+usage: sri_from_csv.py [-h] [-e [EPOCHS]] [-c [COLUMN]] fn
+example: sri_from_csv.py -e 2880 test.csv
+
+Calculate the Sleep Regularity Index from sleep/wake data
+
+positional arguments:
+  fn                    csv file where sleep data is located (in column 'sleepcol')
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -e [EPOCHS], --epochs [EPOCHS]
+                        number of epochs per day (default: 1440)
+  -c [COLUMN], --column [COLUMN]
+                        column name in line 1 of 'fn' (csv file) to be used as sleep values (default: sleep)"""
+
+from __future__ import print_function
+
+import argparse
+import numpy as np
+import pandas as pd
+
+def sri(sleep,epochs_per_day=1440):
+    '''Returns the approximate sleep regularity index, a measure of sleep consistency, for this activity'''
+    sleep_mat = np.reshape(sleep,(epochs_per_day,-1),order='F')
+    assert len(sleep_mat[0]) == len(sleep)/epochs_per_day
+    sri = [match24(x) for x in sleep_mat if len(x)>1]
+    return 200*np.mean(sri)-100
+
+def match24(x):
+    assert np.ndim(x)==1
+    m = np.diff(x)
+    m = (m+1)%2
+    return sum(m)/len(m)
+
+def main():
+
+    parser = argparse.ArgumentParser(description='Calculate the Sleep Regularity Index from sleep/wake data')
+    parser.add_argument('fn', type=str,
+                        help='csv file where sleep data is located (in column \'sleepcol\')')
+    parser.add_argument('-e', '--epochs', type=int, nargs='?', default=1440,
+                        help='number of epochs per day (default: 1440)')
+    parser.add_argument('-c', '--column', type=str, nargs='?', default='sleep',
+                        help='column name in line 1 of \'fn\' (csv file) to be used as sleep values (default: sleep)')
+    args = parser.parse_args()
+
+    sleep = pd.read_csv(args.fn)[args.column].values
+    assert len(sleep)%args.epochs == 0
+    print('Processed %i days of data' % (len(sleep)//args.epochs))
+    print('Calculated SRI (with %i epochs per day) is %.2f' % (args.epochs,sri(sleep,epochs_per_day=args.epochs)))
+
+if __name__ == "__main__":
+    main()
+
+__author__      = "Matthew Engelhard"
+__copyright__   = "Copyright 2018, Matthew Engelhard"
+__license__ = "MIT"
+__email__ = "[email protected]"