{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datetime handling\n",
    "\n",
    "This notebook illustrates the behavior of the datetime tick formatter,\n",
    "depending on duration of the displayed time range.\n",
    "\n",
    "The formatter attempts to automatically adjust the precision of the tick labels for optimal readbility,\n",
    "while retaining all the information necessary.\n",
    "This involves attaching a time offset to the axis label (`time` in the examples below),\n",
    "as well as inserting additional date or time indicators below ticks,\n",
    "when more than one date or time are present on the same axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipp as sc\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More than 100 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('1907-11-20T23:58:17'),\n",
    "                                                np.datetime64('2017-03-01T00:05:17'), 1000000))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 100 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2010-11-20T23:58:17'),\n",
    "                                                np.datetime64('2017-03-01T00:05:17'), 100000))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-01-20T23:58:17'),\n",
    "                                                np.datetime64('2017-12-01T00:05:17'), 10000))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 6 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-01-20T23:58:17'),\n",
    "                                                np.datetime64('2017-05-01T00:05:17'), 10000))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard case\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-06-17T00:58:17'),\n",
    "                                                np.datetime64('2017-06-17T18:05:17'), 100))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-06-17T03:58:17'),\n",
    "                                                np.datetime64('2017-06-18T10:05:17'), 1000))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard case\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:58:17'),\n",
    "                                                np.datetime64('2017-03-16T21:15:17'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-17T23:58:17'),\n",
    "                                                np.datetime64('2017-03-18T00:05:17'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard case\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.500'),\n",
    "                                                np.datetime64('2017-03-16T20:53:59.010'), 100))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T23:58:17.666'),\n",
    "                                                np.datetime64('2017-03-17T00:02:10.532'), 100))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard case\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.200'),\n",
    "                                                np.datetime64('2017-03-16T20:50:17.810'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.200'),\n",
    "                                                np.datetime64('2017-03-16T20:50:18.810'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T23:59:59.500'),\n",
    "                                                np.datetime64('2017-03-17T00:00:01.010'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 milliseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard case\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.123456'),\n",
    "                                                np.datetime64('2017-03-16T20:50:17.127000'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.998756'),\n",
    "                                                np.datetime64('2017-03-16T20:50:18.002122'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T23:59:59.998756'),\n",
    "                                                np.datetime64('2017-03-17T00:00:00.002122'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Less than 4 microseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard case\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.123456789'),\n",
    "                                                np.datetime64('2017-03-16T20:50:17.123458000'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T20:50:17.999998789'),\n",
    "                                                np.datetime64('2017-03-16T20:50:18.000002000'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date change\n",
    "time = sc.array(dims=['time'], values=np.arange(np.datetime64('2017-03-16T23:59:59.999998789'),\n",
    "                                                np.datetime64('2017-03-17T00:00:00.000002000'), 10))\n",
    "data = sc.DataArray(data=sc.array(dims=['time'], values=np.random.rand(time.sizes['time'])),\n",
    "                    coords={'time':time})\n",
    "data.plot()"
   ]
  }
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