{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyCircStat2 Utility Functions\n",
    "\n",
    "`pycircstat2` includes a handful of useful utility functions:\n",
    "\n",
    "- [`load_data`](#load-data)\n",
    "- [`time2float`](#converting-time-string-into-float)\n",
    "- [`data2rad`](#converting-data-onto-a-circular-scale-in-radian-and-vice-versa)\n",
    "- [`angrange`](#range)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "\n",
    "All data sets from Fisher (1993) and Zar (2010; Ch26 and 27), some from Pewsey, et al. (2014) and Mardia (1972) are included in `pycircstat2`, which can be loaded by `load_data` in `pycircstat2.utils`. Meta data of the data set can be printed if `print_meta = True`, or return along with the data set if `return_meta = True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"Title\": \"B.1 Arrival times at an intensive care unit\",\n",
      "    \"Description\": \"Arrival times on a 24-hour clock of 254 patients at an intnsive care unit, over a period of about 12 months\",\n",
      "    \"Type\": \"Vectors\",\n",
      "    \"Source\": \"Cox & Lewis (1966, pp. 254-5)\",\n",
      "    \"Examples\": \"2.1, 2.3, 2.4, 2.5, 2.6, 2.8, 2.10\",\n",
      "    \"Columns\": {\n",
      "        \"time\": {\n",
      "            \"name\": \"time\",\n",
      "            \"type\": \"vectors\"\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from pycircstat2.utils import load_data\n",
    "\n",
    "data, meta = load_data(name='B1', source='fisher', print_meta=True, return_meta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Title': 'B.1 Arrival times at an intensive care unit',\n",
       " 'Description': 'Arrival times on a 24-hour clock of 254 patients at an intnsive care unit, over a period of about 12 months',\n",
       " 'Type': 'Vectors',\n",
       " 'Source': 'Cox & Lewis (1966, pp. 254-5)',\n",
       " 'Examples': '2.1, 2.3, 2.4, 2.5, 2.6, 2.8, 2.10',\n",
       " 'Columns': {'time': {'name': 'time', 'type': 'vectors'}}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time\n",
       "1  11:00\n",
       "2  17:00\n",
       "3  23:15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:3]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting time string into float\n",
    "\n",
    "Some of the typical circular data are encoded in string in time units (e.g. `12:45 h`), we need to convert them into float for further computation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.  , 17.  , 23.25, 10.  , 12.  ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycircstat2.utils import time2float\n",
    "data_float = time2float(data['time'].values)\n",
    "data_float[:5]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting data onto a circular scale (in radian) and vice versa\n",
    "\n",
    "`data2rad(data, k)`  convert data into radian and `rad2data(rad, k)` convert radian back to the original unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycircstat2.utils import data2rad, rad2data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.87979327, 4.45058959, 6.08683577, 2.61799388, 3.14159265])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rad = data2rad(data=data_float, k=24) # k is the time units in the full cycle.\n",
    "data_rad[:5]                               # e.g. for hours, k=24; for years, k=365."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.  , 17.  , 23.25, 10.  , 12.  ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_hour = rad2data(rad=data_rad, k=24)\n",
    "data_hour[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(data_float, data_hour)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Range\n",
    "\n",
    "`pycircstat2` assumed all data fall onto a unit circle, meaning that all data points are within the range of [0, 2π). In the `Circular` class, all input data will be converted into this range by calling `angrange(rad)` in `pycircstat2.utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 3.        , 6.        , 0.        , 0.71681469])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycircstat2.utils import angrange\n",
    "angrange(np.array([0., 3., 6., np.pi * 2, 7.]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2024-11-21 16:44:20CET\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.13\n",
      "IPython version      : 8.29.0\n",
      "\n",
      "pycircstat2: 0.1.0\n",
      "\n",
      "numpy      : 2.1.3\n",
      "pycircstat2: 0.1.0\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark --time --date --timezone --updated --python --iversions --watermark -p pycircstat2"
   ]
  }
 ],
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   "display_name": ".circstat",
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