{ "cells": [ { "cell_type": "markdown", "id": "gorgeous-ferry", "metadata": {}, "source": [ "# The hiccups\n", "\n", "As a researcher I am interested in detecting hiccup, but need more date so I want to generate some time series.\n", "The scenario I want to model is\n", "* Hiccups should be observed for a week, except during sleeping (23:00 - 8:00).\n", "* A hiccup happens once an hour at a random time\n", "* Time series should have a time point every minute\n", "\n", "Other scenarios that would nice\n", "1. Multiple hiccups occur in a bout (see https://en.wikipedia.org/wiki/Hiccup for histogram)\n", "1. Person has infrequent multiple hiccups once a day\n", "1. Person has higher chance of hiccup after eating (from 8:00 - 9:00, 12:00 - 13:00 and 18:00 - 19:00)" ] }, { "cell_type": "code", "execution_count": 1, "id": "appreciated-arkansas", "metadata": {}, "outputs": [], "source": [ "from math import floor\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sequgen.deterministic.triangular_peak import triangular_peak\n", "from sequgen.dimension import Dimension\n", "from sequgen.parameter_space import ParameterSpace\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "julian-bhutan", "metadata": {}, "outputs": [], "source": [ "timepoints = (np.concatenate((\n", " np.arange('2020-03-01T08:00', '2020-03-01T23:00', dtype='datetime64[m]'),\n", " np.arange('2020-03-02T08:00', '2020-03-02T23:00', dtype='datetime64[m]'),\n", " np.arange('2020-03-03T08:00', '2020-03-03T23:00', dtype='datetime64[m]'),\n", " np.arange('2020-03-04T08:00', '2020-03-04T23:00', dtype='datetime64[m]'),\n", " np.arange('2020-03-05T08:00', '2020-03-05T23:00', dtype='datetime64[m]'),\n", ")) - np.datetime64('2020-03-01T08:00')).astype(int) # Convert datatimes into minutes since 2020-03-01T08:00" ] }, { "cell_type": "code", "execution_count": 3, "id": "early-reduction", "metadata": {}, "outputs": [], "source": [ "space = ParameterSpace((\n", " Dimension('placement', 0, 59), # minute in an hour\n", " Dimension('height', 5, 10), # severity\n", "))\n", "\n", "hiccups = np.zeros(len(timepoints), dtype=float)\n", "for day in range(5):\n", " daily_offset = day * (24 * 60)\n", " for hour in range(15):\n", " parameters = space.sample()\n", " # add offset, watchout placement should pick a value from timepoints, not an index\n", " parameters['placement'] = floor(parameters['placement']) + daily_offset + hour * 60\n", " hiccup = triangular_peak(timepoints, width_base_left=0, width_base_right=2, **parameters) # TODO unable to make peak of 1 time point\n", " hiccups += hiccup\n", " " ] }, { "cell_type": "code", "execution_count": 6, "id": "comparative-amplifier", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot(title, x, y):\n", " plt.figure(figsize=(20, 4))\n", " plt.plot(x, y, \".b-\")\n", " plt.title(title)\n", " plt.grid(True)\n", " plt.show()\n", " \n", "plot('Hiccups', timepoints.astype('timedelta64[m]') + np.datetime64('2020-03-01T08:00'), hiccups)" ] }, { "cell_type": "code", "execution_count": null, "id": "viral-certificate", "metadata": {}, "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 5 }