{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard python tools\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "findspark could not be initialized\n",
      "[<class 'pyqae.images.lazy.LazyImagePillowBackend'>]\n"
     ]
    }
   ],
   "source": [
    "# make sure pyqae is available\n",
    "import pyqae as pq\n",
    "import pyspark\n",
    "from pyqae.images import viz\n",
    "# lazy image \n",
    "from pyqae.images.lazy import paths_to_tiled_image, DiskMappedLazyImage, backends\n",
    "print(backends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = os.path.join(\"..\",\"test\",\"resources\")\n",
    "little_image_list = glob(os.path.join(base_path,\"multilayer_tif\",\"*.tif\"))\n",
    "big_image_list = [little_image_list[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 120)\n"
     ]
    }
   ],
   "source": [
    "start_img = DiskMappedLazyImage(big_image_list[0], backends[0])\n",
    "print(start_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_line_break(x_tile_size, x_dim):\n",
    "    return [(x, min(x+x_tile_size, x_dim)) for x in range(0, x_dim, x_tile_size)]\n",
    "\n",
    "def create_tiles(x_tile_size, x_dim, y_tile_size, y_dim):\n",
    "    return [(x_tile, y_tile) for x_tile in create_line_break(x_tile_size, x_dim) \n",
    "            for y_tile in create_line_break(y_tile_size, y_dim)]\n",
    "def tile_dict(tile_list):\n",
    "    return [{'x_start': int(xs), 'y_start': int(ys), \n",
    "             'x_end': int(xe), 'y_end': int(ye)} for ((xs, xe), (ys, ye)) in tile_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 256), (256, 512), (512, 768), (768, 1024), (1024, 1025)]\n"
     ]
    }
   ],
   "source": [
    "print(create_line_break(256, 1025))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[2990 3196]\n",
      "  [1134 1296]]\n",
      "\n",
      " [[1495 1794]\n",
      "  [1620 1782]]]\n",
      "[[[3131 3196]\n",
      "  [1815 1980]]\n",
      "\n",
      " [[2626 2727]\n",
      "  [1485 1650]]]\n",
      "[[[2700 2925]\n",
      "  [1480 1665]]\n",
      "\n",
      " [[3150 3196]\n",
      "  [1295 1480]]]\n"
     ]
    }
   ],
   "source": [
    "im_size = np.random.randint(1000, 4000, size = 2)\n",
    "for tile_size in np.random.randint(100, 300, size = (3,2)):\n",
    "    c_tiles = create_tiles(tile_size[0], im_size[0],tile_size[1], im_size[1])\n",
    "    tile_sum = np.sum([(xe-xs)*(ye-ys) for ((xs, xe), (ys, ye)) in c_tiles])\n",
    "    print(np.random.permutation(c_tiles)[0:2])\n",
    "    assert tile_sum == im_size[0]*im_size[1], \"Total pixel count cannot change, {} != {}\".format(im_size[0]*im_size[1], tile_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.6/site-packages/pyspark/sql/session.py:340: UserWarning: inferring schema from dict is deprecated,please use pyspark.sql.Row instead\n",
      "  warnings.warn(\"inferring schema from dict is deprecated,\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame[x_end: bigint, x_start: bigint, y_end: bigint, y_start: bigint]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark import SparkContext\n",
    "sc = SparkContext()\n",
    "sqlContext = SQLContext(sc)\n",
    "sqlContext.createDataFrame(tile_dict(c_tiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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