gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation CalcDiffMean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Video #1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import sys\n",
    "import datetime, time\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.array( [40] * 100 + [80] * 100 + [160] * 100 )\n",
    "X = [ datetime.datetime.fromtimestamp(946681200+x) for x in range(len(data)) ] \n",
    "dates = matplotlib.dates.date2num(X)\n",
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "#plt.gca().xaxis.set_major_formatter( matplotlib.dates.DateFormatter('%H:%M:%S'))\n",
    "ax.plot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# threshold freezing 60, burst = 120"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|location|start|end|burdur|buraucnorm|burmin|burmax|\n",
    "|--------|-----|---|------|----------|------|------|\n",
    "|Loc01|19.900|29.700|9.800|392.000|40.000|40.000|\n",
    "|Loc02|19.900|29.700|9.800|392.000|40.000|40.000|\n",
    "|Loc03|19.900|29.700|9.800|392.000|40.000|40.000|\n",
    "|Loc04|19.900|29.700|9.800|392.000|40.000|40.000|\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bur_auc_norm 400.0\n",
      "bur_max 40\n"
     ]
    }
   ],
   "source": [
    "print(\"bur_auc_norm\", (160-120)*10.0)\n",
    "print(\"bur_max\", (160-120))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# threshold freezing 60, burst = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|location|start|end|burdur|buraucnorm|burmin|burmax|\n",
    "|--------|-----|---|------|----------|------|------|\n",
    "|Loc01|19.800|29.700|9.900|590.000|20.000|60.000|\n",
    "|Loc02|19.800|29.700|9.900|590.000|20.000|60.000|\n",
    "|Loc03|19.800|29.700|9.900|590.000|20.000|60.000|\n",
    "|Loc04|19.800|29.700|9.900|590.000|20.000|60.000|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bur_auc_norm 600.0\n",
      "bur_max 60\n"
     ]
    }
   ],
   "source": [
    "print(\"bur_auc_norm\", (160-100)*10.0)\n",
    "print(\"bur_max\", (160-100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# threshold freezing 60, burst = 70"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|location|start|end|burdur|buraucnorm|burmin|burmax|\n",
    "|--------|-----|---|------|----------|------|------|\n",
    "|Loc01|9.900|29.700|19.800|986.000|10.000|90.000|\n",
    "|Loc02|9.900|29.700|19.800|986.000|10.000|90.000|\n",
    "|Loc03|9.900|29.700|19.800|986.000|10.000|90.000|\n",
    "|Loc04|9.900|29.700|19.800|986.000|10.000|90.000|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bur_auc_norm 1000.0\n",
      "bur_max 90\n"
     ]
    }
   ],
   "source": [
    "print(\"bur_auc_norm\", (160-70 + 80 - 70) / 2. * 20.)\n",
    "print(\"bur_max\", (160-70))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.014098690835850957"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(986-1000)/(986+7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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