{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Fake Data\n",
    "**Warning**: For the purposes this example we create (and possibly delete) a data directory at base_dir/fake_data. If that directory already exists, this notebook may overwrite the contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from kbmod.fake_data_creator import *\n",
    "from kbmod.search import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a fake stack of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the characteristics of the fake data.\n",
    "img_width = 256\n",
    "img_height = 256\n",
    "num_times = 20\n",
    "\n",
    "# Create the fake images\n",
    "ds = FakeDataSet(img_width, img_height, num_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insert a fake moving object\n",
    "\n",
    "This function creates a random moving object with a given flux that stays within the image for the entire time.  The trajectory is defined by starting pixels (x, y) and velocities (x_v, y_v) of pixels per day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=145, y=133, xv=-12.479201316833496, yv=7.321131229400635\n"
     ]
    }
   ],
   "source": [
    "trj = ds.insert_random_object(500)\n",
    "print(f\"x={trj.x}, y={trj.y}, xv={trj.x_v}, yv={trj.y_v}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print the object's location at each time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: t=0.000 at (145, 133)\n",
      "1: t=0.010 at (145, 133)\n",
      "2: t=0.020 at (145, 133)\n",
      "3: t=1.000 at (133, 140)\n",
      "4: t=1.010 at (132, 140)\n",
      "5: t=1.020 at (132, 140)\n",
      "6: t=2.000 at (120, 148)\n",
      "7: t=2.010 at (120, 148)\n",
      "8: t=2.020 at (120, 148)\n",
      "9: t=3.000 at (108, 155)\n",
      "10: t=3.010 at (107, 155)\n",
      "11: t=3.020 at (107, 155)\n",
      "12: t=4.000 at (95, 162)\n",
      "13: t=4.010 at (95, 162)\n",
      "14: t=4.020 at (95, 162)\n",
      "15: t=5.000 at (83, 170)\n",
      "16: t=5.010 at (82, 170)\n",
      "17: t=5.020 at (82, 170)\n",
      "18: t=6.000 at (70, 177)\n",
      "19: t=6.010 at (70, 177)\n"
     ]
    }
   ],
   "source": [
    "# Check the object was inserted correctly.\n",
    "t0 = ds.stack.get_single_image(0).get_time()\n",
    "for i in range(ds.stack.img_count()):\n",
    "    ti = ds.stack.get_single_image(i).get_time()\n",
    "    dt = ti - t0\n",
    "    px = int(trj.x + dt * trj.x_v + 0.5)\n",
    "    py = int(trj.y + dt * trj.y_v + 0.5)\n",
    "\n",
    "    print(f\"{i}: t={ti:.3f} at ({px}, {py})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the fake image files\n",
    "\n",
    "We save the fake images to a given base directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000000.fits', '000001.fits', '000002.fits', '000003.fits', '000004.fits', '000005.fits', '000006.fits', '000007.fits', '000008.fits', '000009.fits', '000010.fits', '000011.fits', '000012.fits', '000013.fits', '000014.fits', '000015.fits', '000016.fits', '000017.fits', '000018.fits', '000019.fits']\n"
     ]
    }
   ],
   "source": [
    "dir_path = \"./fake_data\"\n",
    "ds.save_fake_data(dir_path)\n",
    "print(os.listdir(dir_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete the fake data files.\n",
    "\n",
    "We can (optionally) delete the fake data files using the delete_fake_data() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "ds.delete_fake_data(dir_path)\n",
    "print(os.listdir(dir_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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