STA_LTA_pam_check.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function with transient (no noise) (F3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.linalg as la\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of data points is: 500\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 5., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 4., 0., 1., 0., 3., 0., 5., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 5., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 3., 5., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 4., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 2.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 4., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 4., 1., 0., 1., 0., 1., 0., 1., 0., 3., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 3., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 3., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 2., 1., 3., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       5., 0., 1., 0., 1., 0., 1., 0., 3., 0., 1., 0., 1., 2., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       5., 1., 0., 1., 0., 1., 2., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0.,\n",
       "       1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 1., 0., 1., 0., 1., 0.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining sampling frequency, time step and time vector\n",
    "fs = 100\n",
    "dt = 1/fs\n",
    "t = np.arange(0,5,dt)#0:1/fs:5-1/fs;\n",
    "nt = len(t)\n",
    "print('the number of data points is:',nt)\n",
    "\n",
    "# Defining a simple function to analyse\n",
    "fun1 = np.zeros(nt)\n",
    "\n",
    "# changing to equaly spaced delta function\n",
    "fun1[::2]=1    # taking every second value of the vector and replace it with 1\n",
    "\n",
    "\n",
    "# introducing the transient function\n",
    "\n",
    "\n",
    "\n",
    "fun2 = fun1\n",
    "fun2[nt//2] += 3\n",
    "\n",
    "######## introducing more complex transient\n",
    "# random indeces with transient\n",
    "xt = [421,128,408,122,465,175,99,126,309,237,176,416,293,275,459,143,379,377,191,284]\n",
    "# random transient values\n",
    "tr = [2,4,4,3,2,3,5,2,3,2,4,2,3,4,5,5,3,2,4,2]\n",
    "\n",
    "tr2 = np.zeros(nt)\n",
    "\n",
    "tr2[xt]=tr\n",
    "\n",
    "fun3 = fun1 + tr2 #+ 1.5*(np.random.rand(fun2.size)) \n",
    "t2 = np.full(nt, np.nan)\n",
    "t2[xt] = tr\n",
    "s2 = np.zeros(nt)\n",
    "s2[:] = fun3\n",
    "s2[xt] = np.nan\n",
    "fun3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PEM: [1 2 3 4 5 6 7 8] length: 8\n",
      "LTA: [10 15 20 25 30 35 40 45 50] length: 9\n",
      "STA: [ 2  3  4  5  6  7  8  9 10] length: 9\n"
     ]
    }
   ],
   "source": [
    "# Setting STA/LTA parameters to be checked in the loop\n",
    "\n",
    "\n",
    "nL = 9\n",
    "nS = 9\n",
    "nP = 8\n",
    "\n",
    "trig = 1.5\n",
    "LTA_d =  np.linspace(10,50,nL,dtype=int)\n",
    "STA_d = np.linspace(2,10,nS,dtype=int)\n",
    "PEM_d = np.linspace(1,8,nP,dtype=int)\n",
    "\n",
    "# Creating empty vectors for STA and LTA\n",
    "\n",
    "STA3 = np.linspace(0,0,nt)\n",
    "\n",
    "LTA3 = np.linspace(0,0,nt)\n",
    "\n",
    "ratio3 = np.linspace(0,0,nt)\n",
    "\n",
    "print('PEM:',PEM_d, 'length:',len(PEM_d))\n",
    "print('LTA:',LTA_d, 'length:',len(LTA_d))\n",
    "print('STA:',STA_d, 'length:',len(STA_d))\n",
    "\n",
    "mat_SL = []\n",
    "mat_missSL = []\n",
    "mat_T = []\n",
    "PEMlist = []\n",
    "PEMmiss = []\n",
    "PEMtrue = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true true percentage: 0.946 STA: 2 LTA: 10 PEM: 1\n",
      "true true percentage: 0.936 STA: 3 LTA: 10 PEM: 1\n",
      "true true percentage: 0.95 STA: 4 LTA: 10 PEM: 1\n",
      "true true percentage: 0.898 STA: 5 LTA: 10 PEM: 1\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 1\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 1\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 1\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 1\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 15 PEM: 1\n",
      "true true percentage: 0.95 STA: 3 LTA: 15 PEM: 1\n",
      "true true percentage: 0.944 STA: 4 LTA: 15 PEM: 1\n",
      "true true percentage: 0.92 STA: 5 LTA: 15 PEM: 1\n",
      "true true percentage: 0.908 STA: 6 LTA: 15 PEM: 1\n",
      "true true percentage: 0.956 STA: 7 LTA: 15 PEM: 1\n",
      "true true percentage: 0.956 STA: 8 LTA: 15 PEM: 1\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 1\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 20 PEM: 1\n",
      "true true percentage: 0.93 STA: 3 LTA: 20 PEM: 1\n",
      "true true percentage: 0.948 STA: 4 LTA: 20 PEM: 1\n",
      "true true percentage: 0.92 STA: 5 LTA: 20 PEM: 1\n",
      "true true percentage: 0.946 STA: 6 LTA: 20 PEM: 1\n",
      "true true percentage: 0.904 STA: 7 LTA: 20 PEM: 1\n",
      "true true percentage: 0.914 STA: 8 LTA: 20 PEM: 1\n",
      "true true percentage: 0.938 STA: 9 LTA: 20 PEM: 1\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 25 PEM: 1\n",
      "true true percentage: 0.93 STA: 3 LTA: 25 PEM: 1\n",
      "true true percentage: 0.95 STA: 4 LTA: 25 PEM: 1\n",
      "true true percentage: 0.918 STA: 5 LTA: 25 PEM: 1\n",
      "true true percentage: 0.956 STA: 6 LTA: 25 PEM: 1\n",
      "true true percentage: 0.95 STA: 7 LTA: 25 PEM: 1\n",
      "true true percentage: 0.93 STA: 8 LTA: 25 PEM: 1\n",
      "true true percentage: 0.916 STA: 9 LTA: 25 PEM: 1\n",
      "true true percentage: 0.934 STA: 10 LTA: 25 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 30 PEM: 1\n",
      "true true percentage: 0.93 STA: 3 LTA: 30 PEM: 1\n",
      "true true percentage: 0.952 STA: 4 LTA: 30 PEM: 1\n",
      "true true percentage: 0.902 STA: 5 LTA: 30 PEM: 1\n",
      "true true percentage: 0.94 STA: 6 LTA: 30 PEM: 1\n",
      "true true percentage: 0.912 STA: 7 LTA: 30 PEM: 1\n",
      "true true percentage: 0.948 STA: 8 LTA: 30 PEM: 1\n",
      "true true percentage: 0.896 STA: 9 LTA: 30 PEM: 1\n",
      "true true percentage: 0.91 STA: 10 LTA: 30 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 35 PEM: 1\n",
      "true true percentage: 0.938 STA: 3 LTA: 35 PEM: 1\n",
      "true true percentage: 0.936 STA: 4 LTA: 35 PEM: 1\n",
      "true true percentage: 0.906 STA: 5 LTA: 35 PEM: 1\n",
      "true true percentage: 0.934 STA: 6 LTA: 35 PEM: 1\n",
      "true true percentage: 0.892 STA: 7 LTA: 35 PEM: 1\n",
      "true true percentage: 0.952 STA: 8 LTA: 35 PEM: 1\n",
      "true true percentage: 0.942 STA: 9 LTA: 35 PEM: 1\n",
      "true true percentage: 0.95 STA: 10 LTA: 35 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 40 PEM: 1\n",
      "true true percentage: 0.93 STA: 3 LTA: 40 PEM: 1\n",
      "true true percentage: 0.948 STA: 4 LTA: 40 PEM: 1\n",
      "true true percentage: 0.922 STA: 5 LTA: 40 PEM: 1\n",
      "true true percentage: 0.95 STA: 6 LTA: 40 PEM: 1\n",
      "true true percentage: 0.886 STA: 7 LTA: 40 PEM: 1\n",
      "true true percentage: 0.954 STA: 8 LTA: 40 PEM: 1\n",
      "true true percentage: 0.95 STA: 9 LTA: 40 PEM: 1\n",
      "true true percentage: 0.944 STA: 10 LTA: 40 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 45 PEM: 1\n",
      "true true percentage: 0.93 STA: 3 LTA: 45 PEM: 1\n",
      "true true percentage: 0.946 STA: 4 LTA: 45 PEM: 1\n",
      "true true percentage: 0.912 STA: 5 LTA: 45 PEM: 1\n",
      "true true percentage: 0.934 STA: 6 LTA: 45 PEM: 1\n",
      "true true percentage: 0.874 STA: 7 LTA: 45 PEM: 1\n",
      "true true percentage: 0.952 STA: 8 LTA: 45 PEM: 1\n",
      "true true percentage: 0.944 STA: 9 LTA: 45 PEM: 1\n",
      "true true percentage: 0.942 STA: 10 LTA: 45 PEM: 1\n",
      "true true percentage: 0.948 STA: 2 LTA: 50 PEM: 1\n",
      "true true percentage: 0.93 STA: 3 LTA: 50 PEM: 1\n",
      "true true percentage: 0.952 STA: 4 LTA: 50 PEM: 1\n",
      "true true percentage: 0.92 STA: 5 LTA: 50 PEM: 1\n",
      "true true percentage: 0.942 STA: 6 LTA: 50 PEM: 1\n",
      "true true percentage: 0.892 STA: 7 LTA: 50 PEM: 1\n",
      "true true percentage: 0.956 STA: 8 LTA: 50 PEM: 1\n",
      "true true percentage: 0.932 STA: 9 LTA: 50 PEM: 1\n",
      "true true percentage: 0.95 STA: 10 LTA: 50 PEM: 1\n",
      "true true percentage: 0.888 STA: 2 LTA: 10 PEM: 2\n",
      "true true percentage: 0.884 STA: 3 LTA: 10 PEM: 2\n",
      "true true percentage: 0.906 STA: 4 LTA: 10 PEM: 2\n",
      "true true percentage: 0.892 STA: 5 LTA: 10 PEM: 2\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 2\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 2\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 2\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 2\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 15 PEM: 2\n",
      "true true percentage: 0.898 STA: 3 LTA: 15 PEM: 2\n",
      "true true percentage: 0.888 STA: 4 LTA: 15 PEM: 2\n",
      "true true percentage: 0.88 STA: 5 LTA: 15 PEM: 2\n",
      "true true percentage: 0.87 STA: 6 LTA: 15 PEM: 2\n",
      "true true percentage: 0.948 STA: 7 LTA: 15 PEM: 2\n",
      "true true percentage: 0.954 STA: 8 LTA: 15 PEM: 2\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 2\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 20 PEM: 2\n",
      "true true percentage: 0.874 STA: 3 LTA: 20 PEM: 2\n",
      "true true percentage: 0.9 STA: 4 LTA: 20 PEM: 2\n",
      "true true percentage: 0.88 STA: 5 LTA: 20 PEM: 2\n",
      "true true percentage: 0.906 STA: 6 LTA: 20 PEM: 2\n",
      "true true percentage: 0.872 STA: 7 LTA: 20 PEM: 2\n",
      "true true percentage: 0.894 STA: 8 LTA: 20 PEM: 2\n",
      "true true percentage: 0.93 STA: 9 LTA: 20 PEM: 2\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 25 PEM: 2\n",
      "true true percentage: 0.87 STA: 3 LTA: 25 PEM: 2\n",
      "true true percentage: 0.904 STA: 4 LTA: 25 PEM: 2\n",
      "true true percentage: 0.88 STA: 5 LTA: 25 PEM: 2\n",
      "true true percentage: 0.93 STA: 6 LTA: 25 PEM: 2\n",
      "true true percentage: 0.928 STA: 7 LTA: 25 PEM: 2\n",
      "true true percentage: 0.91 STA: 8 LTA: 25 PEM: 2\n",
      "true true percentage: 0.908 STA: 9 LTA: 25 PEM: 2\n",
      "true true percentage: 0.922 STA: 10 LTA: 25 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 30 PEM: 2\n",
      "true true percentage: 0.87 STA: 3 LTA: 30 PEM: 2\n",
      "true true percentage: 0.9 STA: 4 LTA: 30 PEM: 2\n",
      "true true percentage: 0.87 STA: 5 LTA: 30 PEM: 2\n",
      "true true percentage: 0.902 STA: 6 LTA: 30 PEM: 2\n",
      "true true percentage: 0.892 STA: 7 LTA: 30 PEM: 2\n",
      "true true percentage: 0.926 STA: 8 LTA: 30 PEM: 2\n",
      "true true percentage: 0.87 STA: 9 LTA: 30 PEM: 2\n",
      "true true percentage: 0.888 STA: 10 LTA: 30 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 35 PEM: 2\n",
      "true true percentage: 0.878 STA: 3 LTA: 35 PEM: 2\n",
      "true true percentage: 0.884 STA: 4 LTA: 35 PEM: 2\n",
      "true true percentage: 0.874 STA: 5 LTA: 35 PEM: 2\n",
      "true true percentage: 0.896 STA: 6 LTA: 35 PEM: 2\n",
      "true true percentage: 0.874 STA: 7 LTA: 35 PEM: 2\n",
      "true true percentage: 0.934 STA: 8 LTA: 35 PEM: 2\n",
      "true true percentage: 0.918 STA: 9 LTA: 35 PEM: 2\n",
      "true true percentage: 0.926 STA: 10 LTA: 35 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 40 PEM: 2\n",
      "true true percentage: 0.87 STA: 3 LTA: 40 PEM: 2\n",
      "true true percentage: 0.896 STA: 4 LTA: 40 PEM: 2\n",
      "true true percentage: 0.882 STA: 5 LTA: 40 PEM: 2\n",
      "true true percentage: 0.912 STA: 6 LTA: 40 PEM: 2\n",
      "true true percentage: 0.87 STA: 7 LTA: 40 PEM: 2\n",
      "true true percentage: 0.938 STA: 8 LTA: 40 PEM: 2\n",
      "true true percentage: 0.934 STA: 9 LTA: 40 PEM: 2\n",
      "true true percentage: 0.918 STA: 10 LTA: 40 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 45 PEM: 2\n",
      "true true percentage: 0.87 STA: 3 LTA: 45 PEM: 2\n",
      "true true percentage: 0.894 STA: 4 LTA: 45 PEM: 2\n",
      "true true percentage: 0.864 STA: 5 LTA: 45 PEM: 2\n",
      "true true percentage: 0.894 STA: 6 LTA: 45 PEM: 2\n",
      "true true percentage: 0.858 STA: 7 LTA: 45 PEM: 2\n",
      "true true percentage: 0.936 STA: 8 LTA: 45 PEM: 2\n",
      "true true percentage: 0.926 STA: 9 LTA: 45 PEM: 2\n",
      "true true percentage: 0.92 STA: 10 LTA: 45 PEM: 2\n",
      "true true percentage: 0.882 STA: 2 LTA: 50 PEM: 2\n",
      "true true percentage: 0.87 STA: 3 LTA: 50 PEM: 2\n",
      "true true percentage: 0.9 STA: 4 LTA: 50 PEM: 2\n",
      "true true percentage: 0.888 STA: 5 LTA: 50 PEM: 2\n",
      "true true percentage: 0.902 STA: 6 LTA: 50 PEM: 2\n",
      "true true percentage: 0.868 STA: 7 LTA: 50 PEM: 2\n",
      "true true percentage: 0.942 STA: 8 LTA: 50 PEM: 2\n",
      "true true percentage: 0.912 STA: 9 LTA: 50 PEM: 2\n",
      "true true percentage: 0.922 STA: 10 LTA: 50 PEM: 2\n",
      "true true percentage: 0.828 STA: 2 LTA: 10 PEM: 3\n",
      "true true percentage: 0.83 STA: 3 LTA: 10 PEM: 3\n",
      "true true percentage: 0.862 STA: 4 LTA: 10 PEM: 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true true percentage: 0.86 STA: 5 LTA: 10 PEM: 3\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 3\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 3\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 3\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 3\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 15 PEM: 3\n",
      "true true percentage: 0.846 STA: 3 LTA: 15 PEM: 3\n",
      "true true percentage: 0.834 STA: 4 LTA: 15 PEM: 3\n",
      "true true percentage: 0.832 STA: 5 LTA: 15 PEM: 3\n",
      "true true percentage: 0.832 STA: 6 LTA: 15 PEM: 3\n",
      "true true percentage: 0.938 STA: 7 LTA: 15 PEM: 3\n",
      "true true percentage: 0.95 STA: 8 LTA: 15 PEM: 3\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 3\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 20 PEM: 3\n",
      "true true percentage: 0.814 STA: 3 LTA: 20 PEM: 3\n",
      "true true percentage: 0.854 STA: 4 LTA: 20 PEM: 3\n",
      "true true percentage: 0.83 STA: 5 LTA: 20 PEM: 3\n",
      "true true percentage: 0.87 STA: 6 LTA: 20 PEM: 3\n",
      "true true percentage: 0.836 STA: 7 LTA: 20 PEM: 3\n",
      "true true percentage: 0.874 STA: 8 LTA: 20 PEM: 3\n",
      "true true percentage: 0.916 STA: 9 LTA: 20 PEM: 3\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 25 PEM: 3\n",
      "true true percentage: 0.806 STA: 3 LTA: 25 PEM: 3\n",
      "true true percentage: 0.856 STA: 4 LTA: 25 PEM: 3\n",
      "true true percentage: 0.838 STA: 5 LTA: 25 PEM: 3\n",
      "true true percentage: 0.908 STA: 6 LTA: 25 PEM: 3\n",
      "true true percentage: 0.906 STA: 7 LTA: 25 PEM: 3\n",
      "true true percentage: 0.888 STA: 8 LTA: 25 PEM: 3\n",
      "true true percentage: 0.884 STA: 9 LTA: 25 PEM: 3\n",
      "true true percentage: 0.912 STA: 10 LTA: 25 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 30 PEM: 3\n",
      "true true percentage: 0.806 STA: 3 LTA: 30 PEM: 3\n",
      "true true percentage: 0.846 STA: 4 LTA: 30 PEM: 3\n",
      "true true percentage: 0.822 STA: 5 LTA: 30 PEM: 3\n",
      "true true percentage: 0.868 STA: 6 LTA: 30 PEM: 3\n",
      "true true percentage: 0.856 STA: 7 LTA: 30 PEM: 3\n",
      "true true percentage: 0.904 STA: 8 LTA: 30 PEM: 3\n",
      "true true percentage: 0.85 STA: 9 LTA: 30 PEM: 3\n",
      "true true percentage: 0.864 STA: 10 LTA: 30 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 35 PEM: 3\n",
      "true true percentage: 0.81 STA: 3 LTA: 35 PEM: 3\n",
      "true true percentage: 0.822 STA: 4 LTA: 35 PEM: 3\n",
      "true true percentage: 0.822 STA: 5 LTA: 35 PEM: 3\n",
      "true true percentage: 0.852 STA: 6 LTA: 35 PEM: 3\n",
      "true true percentage: 0.834 STA: 7 LTA: 35 PEM: 3\n",
      "true true percentage: 0.916 STA: 8 LTA: 35 PEM: 3\n",
      "true true percentage: 0.896 STA: 9 LTA: 35 PEM: 3\n",
      "true true percentage: 0.91 STA: 10 LTA: 35 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 40 PEM: 3\n",
      "true true percentage: 0.806 STA: 3 LTA: 40 PEM: 3\n",
      "true true percentage: 0.838 STA: 4 LTA: 40 PEM: 3\n",
      "true true percentage: 0.83 STA: 5 LTA: 40 PEM: 3\n",
      "true true percentage: 0.864 STA: 6 LTA: 40 PEM: 3\n",
      "true true percentage: 0.826 STA: 7 LTA: 40 PEM: 3\n",
      "true true percentage: 0.922 STA: 8 LTA: 40 PEM: 3\n",
      "true true percentage: 0.916 STA: 9 LTA: 40 PEM: 3\n",
      "true true percentage: 0.9 STA: 10 LTA: 40 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 45 PEM: 3\n",
      "true true percentage: 0.806 STA: 3 LTA: 45 PEM: 3\n",
      "true true percentage: 0.836 STA: 4 LTA: 45 PEM: 3\n",
      "true true percentage: 0.808 STA: 5 LTA: 45 PEM: 3\n",
      "true true percentage: 0.846 STA: 6 LTA: 45 PEM: 3\n",
      "true true percentage: 0.81 STA: 7 LTA: 45 PEM: 3\n",
      "true true percentage: 0.916 STA: 8 LTA: 45 PEM: 3\n",
      "true true percentage: 0.902 STA: 9 LTA: 45 PEM: 3\n",
      "true true percentage: 0.902 STA: 10 LTA: 45 PEM: 3\n",
      "true true percentage: 0.816 STA: 2 LTA: 50 PEM: 3\n",
      "true true percentage: 0.806 STA: 3 LTA: 50 PEM: 3\n",
      "true true percentage: 0.846 STA: 4 LTA: 50 PEM: 3\n",
      "true true percentage: 0.834 STA: 5 LTA: 50 PEM: 3\n",
      "true true percentage: 0.858 STA: 6 LTA: 50 PEM: 3\n",
      "true true percentage: 0.824 STA: 7 LTA: 50 PEM: 3\n",
      "true true percentage: 0.926 STA: 8 LTA: 50 PEM: 3\n",
      "true true percentage: 0.892 STA: 9 LTA: 50 PEM: 3\n",
      "true true percentage: 0.902 STA: 10 LTA: 50 PEM: 3\n",
      "true true percentage: 0.768 STA: 2 LTA: 10 PEM: 4\n",
      "true true percentage: 0.77 STA: 3 LTA: 10 PEM: 4\n",
      "true true percentage: 0.816 STA: 4 LTA: 10 PEM: 4\n",
      "true true percentage: 0.83 STA: 5 LTA: 10 PEM: 4\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 4\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 4\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 4\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 4\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 15 PEM: 4\n",
      "true true percentage: 0.794 STA: 3 LTA: 15 PEM: 4\n",
      "true true percentage: 0.782 STA: 4 LTA: 15 PEM: 4\n",
      "true true percentage: 0.784 STA: 5 LTA: 15 PEM: 4\n",
      "true true percentage: 0.788 STA: 6 LTA: 15 PEM: 4\n",
      "true true percentage: 0.926 STA: 7 LTA: 15 PEM: 4\n",
      "true true percentage: 0.948 STA: 8 LTA: 15 PEM: 4\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 4\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 20 PEM: 4\n",
      "true true percentage: 0.752 STA: 3 LTA: 20 PEM: 4\n",
      "true true percentage: 0.806 STA: 4 LTA: 20 PEM: 4\n",
      "true true percentage: 0.786 STA: 5 LTA: 20 PEM: 4\n",
      "true true percentage: 0.832 STA: 6 LTA: 20 PEM: 4\n",
      "true true percentage: 0.798 STA: 7 LTA: 20 PEM: 4\n",
      "true true percentage: 0.85 STA: 8 LTA: 20 PEM: 4\n",
      "true true percentage: 0.9 STA: 9 LTA: 20 PEM: 4\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 25 PEM: 4\n",
      "true true percentage: 0.746 STA: 3 LTA: 25 PEM: 4\n",
      "true true percentage: 0.808 STA: 4 LTA: 25 PEM: 4\n",
      "true true percentage: 0.794 STA: 5 LTA: 25 PEM: 4\n",
      "true true percentage: 0.882 STA: 6 LTA: 25 PEM: 4\n",
      "true true percentage: 0.878 STA: 7 LTA: 25 PEM: 4\n",
      "true true percentage: 0.86 STA: 8 LTA: 25 PEM: 4\n",
      "true true percentage: 0.864 STA: 9 LTA: 25 PEM: 4\n",
      "true true percentage: 0.9 STA: 10 LTA: 25 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 30 PEM: 4\n",
      "true true percentage: 0.746 STA: 3 LTA: 30 PEM: 4\n",
      "true true percentage: 0.794 STA: 4 LTA: 30 PEM: 4\n",
      "true true percentage: 0.774 STA: 5 LTA: 30 PEM: 4\n",
      "true true percentage: 0.832 STA: 6 LTA: 30 PEM: 4\n",
      "true true percentage: 0.822 STA: 7 LTA: 30 PEM: 4\n",
      "true true percentage: 0.878 STA: 8 LTA: 30 PEM: 4\n",
      "true true percentage: 0.822 STA: 9 LTA: 30 PEM: 4\n",
      "true true percentage: 0.838 STA: 10 LTA: 30 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 35 PEM: 4\n",
      "true true percentage: 0.748 STA: 3 LTA: 35 PEM: 4\n",
      "true true percentage: 0.766 STA: 4 LTA: 35 PEM: 4\n",
      "true true percentage: 0.77 STA: 5 LTA: 35 PEM: 4\n",
      "true true percentage: 0.808 STA: 6 LTA: 35 PEM: 4\n",
      "true true percentage: 0.796 STA: 7 LTA: 35 PEM: 4\n",
      "true true percentage: 0.892 STA: 8 LTA: 35 PEM: 4\n",
      "true true percentage: 0.872 STA: 9 LTA: 35 PEM: 4\n",
      "true true percentage: 0.89 STA: 10 LTA: 35 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 40 PEM: 4\n",
      "true true percentage: 0.746 STA: 3 LTA: 40 PEM: 4\n",
      "true true percentage: 0.784 STA: 4 LTA: 40 PEM: 4\n",
      "true true percentage: 0.778 STA: 5 LTA: 40 PEM: 4\n",
      "true true percentage: 0.816 STA: 6 LTA: 40 PEM: 4\n",
      "true true percentage: 0.784 STA: 7 LTA: 40 PEM: 4\n",
      "true true percentage: 0.902 STA: 8 LTA: 40 PEM: 4\n",
      "true true percentage: 0.892 STA: 9 LTA: 40 PEM: 4\n",
      "true true percentage: 0.88 STA: 10 LTA: 40 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 45 PEM: 4\n",
      "true true percentage: 0.746 STA: 3 LTA: 45 PEM: 4\n",
      "true true percentage: 0.782 STA: 4 LTA: 45 PEM: 4\n",
      "true true percentage: 0.754 STA: 5 LTA: 45 PEM: 4\n",
      "true true percentage: 0.798 STA: 6 LTA: 45 PEM: 4\n",
      "true true percentage: 0.764 STA: 7 LTA: 45 PEM: 4\n",
      "true true percentage: 0.892 STA: 8 LTA: 45 PEM: 4\n",
      "true true percentage: 0.874 STA: 9 LTA: 45 PEM: 4\n",
      "true true percentage: 0.878 STA: 10 LTA: 45 PEM: 4\n",
      "true true percentage: 0.756 STA: 2 LTA: 50 PEM: 4\n",
      "true true percentage: 0.746 STA: 3 LTA: 50 PEM: 4\n",
      "true true percentage: 0.794 STA: 4 LTA: 50 PEM: 4\n",
      "true true percentage: 0.782 STA: 5 LTA: 50 PEM: 4\n",
      "true true percentage: 0.812 STA: 6 LTA: 50 PEM: 4\n",
      "true true percentage: 0.786 STA: 7 LTA: 50 PEM: 4\n",
      "true true percentage: 0.906 STA: 8 LTA: 50 PEM: 4\n",
      "true true percentage: 0.868 STA: 9 LTA: 50 PEM: 4\n",
      "true true percentage: 0.878 STA: 10 LTA: 50 PEM: 4\n",
      "true true percentage: 0.712 STA: 2 LTA: 10 PEM: 5\n",
      "true true percentage: 0.714 STA: 3 LTA: 10 PEM: 5\n",
      "true true percentage: 0.768 STA: 4 LTA: 10 PEM: 5\n",
      "true true percentage: 0.796 STA: 5 LTA: 10 PEM: 5\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 5\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 5\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 5\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 15 PEM: 5\n",
      "true true percentage: 0.742 STA: 3 LTA: 15 PEM: 5\n",
      "true true percentage: 0.73 STA: 4 LTA: 15 PEM: 5\n",
      "true true percentage: 0.736 STA: 5 LTA: 15 PEM: 5\n",
      "true true percentage: 0.746 STA: 6 LTA: 15 PEM: 5\n",
      "true true percentage: 0.914 STA: 7 LTA: 15 PEM: 5\n",
      "true true percentage: 0.944 STA: 8 LTA: 15 PEM: 5\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 5\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 20 PEM: 5\n",
      "true true percentage: 0.702 STA: 3 LTA: 20 PEM: 5\n",
      "true true percentage: 0.758 STA: 4 LTA: 20 PEM: 5\n",
      "true true percentage: 0.742 STA: 5 LTA: 20 PEM: 5\n",
      "true true percentage: 0.796 STA: 6 LTA: 20 PEM: 5\n",
      "true true percentage: 0.758 STA: 7 LTA: 20 PEM: 5\n",
      "true true percentage: 0.828 STA: 8 LTA: 20 PEM: 5\n",
      "true true percentage: 0.888 STA: 9 LTA: 20 PEM: 5\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 25 PEM: 5\n",
      "true true percentage: 0.696 STA: 3 LTA: 25 PEM: 5\n",
      "true true percentage: 0.76 STA: 4 LTA: 25 PEM: 5\n",
      "true true percentage: 0.754 STA: 5 LTA: 25 PEM: 5\n",
      "true true percentage: 0.854 STA: 6 LTA: 25 PEM: 5\n",
      "true true percentage: 0.85 STA: 7 LTA: 25 PEM: 5\n",
      "true true percentage: 0.834 STA: 8 LTA: 25 PEM: 5\n",
      "true true percentage: 0.846 STA: 9 LTA: 25 PEM: 5\n",
      "true true percentage: 0.884 STA: 10 LTA: 25 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 30 PEM: 5\n",
      "true true percentage: 0.696 STA: 3 LTA: 30 PEM: 5\n",
      "true true percentage: 0.742 STA: 4 LTA: 30 PEM: 5\n",
      "true true percentage: 0.726 STA: 5 LTA: 30 PEM: 5\n",
      "true true percentage: 0.798 STA: 6 LTA: 30 PEM: 5\n",
      "true true percentage: 0.792 STA: 7 LTA: 30 PEM: 5\n",
      "true true percentage: 0.85 STA: 8 LTA: 30 PEM: 5\n",
      "true true percentage: 0.798 STA: 9 LTA: 30 PEM: 5\n",
      "true true percentage: 0.814 STA: 10 LTA: 30 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 35 PEM: 5\n",
      "true true percentage: 0.696 STA: 3 LTA: 35 PEM: 5\n",
      "true true percentage: 0.712 STA: 4 LTA: 35 PEM: 5\n",
      "true true percentage: 0.718 STA: 5 LTA: 35 PEM: 5\n",
      "true true percentage: 0.768 STA: 6 LTA: 35 PEM: 5\n",
      "true true percentage: 0.768 STA: 7 LTA: 35 PEM: 5\n",
      "true true percentage: 0.868 STA: 8 LTA: 35 PEM: 5\n",
      "true true percentage: 0.852 STA: 9 LTA: 35 PEM: 5\n",
      "true true percentage: 0.87 STA: 10 LTA: 35 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 40 PEM: 5\n",
      "true true percentage: 0.696 STA: 3 LTA: 40 PEM: 5\n",
      "true true percentage: 0.728 STA: 4 LTA: 40 PEM: 5\n",
      "true true percentage: 0.728 STA: 5 LTA: 40 PEM: 5\n",
      "true true percentage: 0.77 STA: 6 LTA: 40 PEM: 5\n",
      "true true percentage: 0.75 STA: 7 LTA: 40 PEM: 5\n",
      "true true percentage: 0.884 STA: 8 LTA: 40 PEM: 5\n",
      "true true percentage: 0.876 STA: 9 LTA: 40 PEM: 5\n",
      "true true percentage: 0.852 STA: 10 LTA: 40 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 45 PEM: 5\n",
      "true true percentage: 0.696 STA: 3 LTA: 45 PEM: 5\n",
      "true true percentage: 0.73 STA: 4 LTA: 45 PEM: 5\n",
      "true true percentage: 0.702 STA: 5 LTA: 45 PEM: 5\n",
      "true true percentage: 0.752 STA: 6 LTA: 45 PEM: 5\n",
      "true true percentage: 0.726 STA: 7 LTA: 45 PEM: 5\n",
      "true true percentage: 0.87 STA: 8 LTA: 45 PEM: 5\n",
      "true true percentage: 0.856 STA: 9 LTA: 45 PEM: 5\n",
      "true true percentage: 0.85 STA: 10 LTA: 45 PEM: 5\n",
      "true true percentage: 0.704 STA: 2 LTA: 50 PEM: 5\n",
      "true true percentage: 0.696 STA: 3 LTA: 50 PEM: 5\n",
      "true true percentage: 0.742 STA: 4 LTA: 50 PEM: 5\n",
      "true true percentage: 0.73 STA: 5 LTA: 50 PEM: 5\n",
      "true true percentage: 0.77 STA: 6 LTA: 50 PEM: 5\n",
      "true true percentage: 0.75 STA: 7 LTA: 50 PEM: 5\n",
      "true true percentage: 0.888 STA: 8 LTA: 50 PEM: 5\n",
      "true true percentage: 0.85 STA: 9 LTA: 50 PEM: 5\n",
      "true true percentage: 0.854 STA: 10 LTA: 50 PEM: 5\n",
      "true true percentage: 0.666 STA: 2 LTA: 10 PEM: 6\n",
      "true true percentage: 0.668 STA: 3 LTA: 10 PEM: 6\n",
      "true true percentage: 0.726 STA: 4 LTA: 10 PEM: 6\n",
      "true true percentage: 0.762 STA: 5 LTA: 10 PEM: 6\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 6\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 6\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 6\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 6\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 15 PEM: 6\n",
      "true true percentage: 0.694 STA: 3 LTA: 15 PEM: 6\n",
      "true true percentage: 0.682 STA: 4 LTA: 15 PEM: 6\n",
      "true true percentage: 0.692 STA: 5 LTA: 15 PEM: 6\n",
      "true true percentage: 0.706 STA: 6 LTA: 15 PEM: 6\n",
      "true true percentage: 0.902 STA: 7 LTA: 15 PEM: 6\n",
      "true true percentage: 0.94 STA: 8 LTA: 15 PEM: 6\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 6\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 20 PEM: 6\n",
      "true true percentage: 0.654 STA: 3 LTA: 20 PEM: 6\n",
      "true true percentage: 0.714 STA: 4 LTA: 20 PEM: 6\n",
      "true true percentage: 0.702 STA: 5 LTA: 20 PEM: 6\n",
      "true true percentage: 0.77 STA: 6 LTA: 20 PEM: 6\n",
      "true true percentage: 0.734 STA: 7 LTA: 20 PEM: 6\n",
      "true true percentage: 0.808 STA: 8 LTA: 20 PEM: 6\n",
      "true true percentage: 0.872 STA: 9 LTA: 20 PEM: 6\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 25 PEM: 6\n",
      "true true percentage: 0.648 STA: 3 LTA: 25 PEM: 6\n",
      "true true percentage: 0.716 STA: 4 LTA: 25 PEM: 6\n",
      "true true percentage: 0.718 STA: 5 LTA: 25 PEM: 6\n",
      "true true percentage: 0.83 STA: 6 LTA: 25 PEM: 6\n",
      "true true percentage: 0.826 STA: 7 LTA: 25 PEM: 6\n",
      "true true percentage: 0.814 STA: 8 LTA: 25 PEM: 6\n",
      "true true percentage: 0.822 STA: 9 LTA: 25 PEM: 6\n",
      "true true percentage: 0.868 STA: 10 LTA: 25 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 30 PEM: 6\n",
      "true true percentage: 0.648 STA: 3 LTA: 30 PEM: 6\n",
      "true true percentage: 0.694 STA: 4 LTA: 30 PEM: 6\n",
      "true true percentage: 0.686 STA: 5 LTA: 30 PEM: 6\n",
      "true true percentage: 0.768 STA: 6 LTA: 30 PEM: 6\n",
      "true true percentage: 0.764 STA: 7 LTA: 30 PEM: 6\n",
      "true true percentage: 0.83 STA: 8 LTA: 30 PEM: 6\n",
      "true true percentage: 0.774 STA: 9 LTA: 30 PEM: 6\n",
      "true true percentage: 0.786 STA: 10 LTA: 30 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 35 PEM: 6\n",
      "true true percentage: 0.648 STA: 3 LTA: 35 PEM: 6\n",
      "true true percentage: 0.668 STA: 4 LTA: 35 PEM: 6\n",
      "true true percentage: 0.676 STA: 5 LTA: 35 PEM: 6\n",
      "true true percentage: 0.734 STA: 6 LTA: 35 PEM: 6\n",
      "true true percentage: 0.74 STA: 7 LTA: 35 PEM: 6\n",
      "true true percentage: 0.848 STA: 8 LTA: 35 PEM: 6\n",
      "true true percentage: 0.832 STA: 9 LTA: 35 PEM: 6\n",
      "true true percentage: 0.85 STA: 10 LTA: 35 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 40 PEM: 6\n",
      "true true percentage: 0.648 STA: 3 LTA: 40 PEM: 6\n",
      "true true percentage: 0.678 STA: 4 LTA: 40 PEM: 6\n",
      "true true percentage: 0.686 STA: 5 LTA: 40 PEM: 6\n",
      "true true percentage: 0.728 STA: 6 LTA: 40 PEM: 6\n",
      "true true percentage: 0.718 STA: 7 LTA: 40 PEM: 6\n",
      "true true percentage: 0.868 STA: 8 LTA: 40 PEM: 6\n",
      "true true percentage: 0.852 STA: 9 LTA: 40 PEM: 6\n",
      "true true percentage: 0.824 STA: 10 LTA: 40 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 45 PEM: 6\n",
      "true true percentage: 0.648 STA: 3 LTA: 45 PEM: 6\n",
      "true true percentage: 0.678 STA: 4 LTA: 45 PEM: 6\n",
      "true true percentage: 0.656 STA: 5 LTA: 45 PEM: 6\n",
      "true true percentage: 0.714 STA: 6 LTA: 45 PEM: 6\n",
      "true true percentage: 0.69 STA: 7 LTA: 45 PEM: 6\n",
      "true true percentage: 0.85 STA: 8 LTA: 45 PEM: 6\n",
      "true true percentage: 0.828 STA: 9 LTA: 45 PEM: 6\n",
      "true true percentage: 0.822 STA: 10 LTA: 45 PEM: 6\n",
      "true true percentage: 0.656 STA: 2 LTA: 50 PEM: 6\n",
      "true true percentage: 0.648 STA: 3 LTA: 50 PEM: 6\n",
      "true true percentage: 0.694 STA: 4 LTA: 50 PEM: 6\n",
      "true true percentage: 0.686 STA: 5 LTA: 50 PEM: 6\n",
      "true true percentage: 0.734 STA: 6 LTA: 50 PEM: 6\n",
      "true true percentage: 0.722 STA: 7 LTA: 50 PEM: 6\n",
      "true true percentage: 0.872 STA: 8 LTA: 50 PEM: 6\n",
      "true true percentage: 0.826 STA: 9 LTA: 50 PEM: 6\n",
      "true true percentage: 0.826 STA: 10 LTA: 50 PEM: 6\n",
      "true true percentage: 0.618 STA: 2 LTA: 10 PEM: 7\n",
      "true true percentage: 0.624 STA: 3 LTA: 10 PEM: 7\n",
      "true true percentage: 0.678 STA: 4 LTA: 10 PEM: 7\n",
      "true true percentage: 0.726 STA: 5 LTA: 10 PEM: 7\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 7\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 7\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 7\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 7\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 15 PEM: 7\n",
      "true true percentage: 0.644 STA: 3 LTA: 15 PEM: 7\n",
      "true true percentage: 0.634 STA: 4 LTA: 15 PEM: 7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true true percentage: 0.65 STA: 5 LTA: 15 PEM: 7\n",
      "true true percentage: 0.662 STA: 6 LTA: 15 PEM: 7\n",
      "true true percentage: 0.896 STA: 7 LTA: 15 PEM: 7\n",
      "true true percentage: 0.936 STA: 8 LTA: 15 PEM: 7\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 7\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 20 PEM: 7\n",
      "true true percentage: 0.608 STA: 3 LTA: 20 PEM: 7\n",
      "true true percentage: 0.668 STA: 4 LTA: 20 PEM: 7\n",
      "true true percentage: 0.658 STA: 5 LTA: 20 PEM: 7\n",
      "true true percentage: 0.73 STA: 6 LTA: 20 PEM: 7\n",
      "true true percentage: 0.698 STA: 7 LTA: 20 PEM: 7\n",
      "true true percentage: 0.784 STA: 8 LTA: 20 PEM: 7\n",
      "true true percentage: 0.858 STA: 9 LTA: 20 PEM: 7\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 25 PEM: 7\n",
      "true true percentage: 0.602 STA: 3 LTA: 25 PEM: 7\n",
      "true true percentage: 0.67 STA: 4 LTA: 25 PEM: 7\n",
      "true true percentage: 0.678 STA: 5 LTA: 25 PEM: 7\n",
      "true true percentage: 0.802 STA: 6 LTA: 25 PEM: 7\n",
      "true true percentage: 0.798 STA: 7 LTA: 25 PEM: 7\n",
      "true true percentage: 0.786 STA: 8 LTA: 25 PEM: 7\n",
      "true true percentage: 0.798 STA: 9 LTA: 25 PEM: 7\n",
      "true true percentage: 0.858 STA: 10 LTA: 25 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 30 PEM: 7\n",
      "true true percentage: 0.602 STA: 3 LTA: 30 PEM: 7\n",
      "true true percentage: 0.644 STA: 4 LTA: 30 PEM: 7\n",
      "true true percentage: 0.646 STA: 5 LTA: 30 PEM: 7\n",
      "true true percentage: 0.734 STA: 6 LTA: 30 PEM: 7\n",
      "true true percentage: 0.732 STA: 7 LTA: 30 PEM: 7\n",
      "true true percentage: 0.806 STA: 8 LTA: 30 PEM: 7\n",
      "true true percentage: 0.748 STA: 9 LTA: 30 PEM: 7\n",
      "true true percentage: 0.758 STA: 10 LTA: 30 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 35 PEM: 7\n",
      "true true percentage: 0.602 STA: 3 LTA: 35 PEM: 7\n",
      "true true percentage: 0.622 STA: 4 LTA: 35 PEM: 7\n",
      "true true percentage: 0.632 STA: 5 LTA: 35 PEM: 7\n",
      "true true percentage: 0.696 STA: 6 LTA: 35 PEM: 7\n",
      "true true percentage: 0.708 STA: 7 LTA: 35 PEM: 7\n",
      "true true percentage: 0.832 STA: 8 LTA: 35 PEM: 7\n",
      "true true percentage: 0.812 STA: 9 LTA: 35 PEM: 7\n",
      "true true percentage: 0.828 STA: 10 LTA: 35 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 40 PEM: 7\n",
      "true true percentage: 0.602 STA: 3 LTA: 40 PEM: 7\n",
      "true true percentage: 0.628 STA: 4 LTA: 40 PEM: 7\n",
      "true true percentage: 0.646 STA: 5 LTA: 40 PEM: 7\n",
      "true true percentage: 0.684 STA: 6 LTA: 40 PEM: 7\n",
      "true true percentage: 0.682 STA: 7 LTA: 40 PEM: 7\n",
      "true true percentage: 0.848 STA: 8 LTA: 40 PEM: 7\n",
      "true true percentage: 0.83 STA: 9 LTA: 40 PEM: 7\n",
      "true true percentage: 0.798 STA: 10 LTA: 40 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 45 PEM: 7\n",
      "true true percentage: 0.602 STA: 3 LTA: 45 PEM: 7\n",
      "true true percentage: 0.628 STA: 4 LTA: 45 PEM: 7\n",
      "true true percentage: 0.612 STA: 5 LTA: 45 PEM: 7\n",
      "true true percentage: 0.672 STA: 6 LTA: 45 PEM: 7\n",
      "true true percentage: 0.65 STA: 7 LTA: 45 PEM: 7\n",
      "true true percentage: 0.826 STA: 8 LTA: 45 PEM: 7\n",
      "true true percentage: 0.8 STA: 9 LTA: 45 PEM: 7\n",
      "true true percentage: 0.796 STA: 10 LTA: 45 PEM: 7\n",
      "true true percentage: 0.61 STA: 2 LTA: 50 PEM: 7\n",
      "true true percentage: 0.602 STA: 3 LTA: 50 PEM: 7\n",
      "true true percentage: 0.644 STA: 4 LTA: 50 PEM: 7\n",
      "true true percentage: 0.638 STA: 5 LTA: 50 PEM: 7\n",
      "true true percentage: 0.692 STA: 6 LTA: 50 PEM: 7\n",
      "true true percentage: 0.686 STA: 7 LTA: 50 PEM: 7\n",
      "true true percentage: 0.852 STA: 8 LTA: 50 PEM: 7\n",
      "true true percentage: 0.802 STA: 9 LTA: 50 PEM: 7\n",
      "true true percentage: 0.804 STA: 10 LTA: 50 PEM: 7\n",
      "true true percentage: 0.576 STA: 2 LTA: 10 PEM: 8\n",
      "true true percentage: 0.584 STA: 3 LTA: 10 PEM: 8\n",
      "true true percentage: 0.634 STA: 4 LTA: 10 PEM: 8\n",
      "true true percentage: 0.694 STA: 5 LTA: 10 PEM: 8\n",
      "true true percentage: 0.96 STA: 6 LTA: 10 PEM: 8\n",
      "true true percentage: 0.96 STA: 7 LTA: 10 PEM: 8\n",
      "true true percentage: 0.96 STA: 8 LTA: 10 PEM: 8\n",
      "true true percentage: 0.96 STA: 9 LTA: 10 PEM: 8\n",
      "true true percentage: 0.96 STA: 10 LTA: 10 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 15 PEM: 8\n",
      "true true percentage: 0.6 STA: 3 LTA: 15 PEM: 8\n",
      "true true percentage: 0.594 STA: 4 LTA: 15 PEM: 8\n",
      "true true percentage: 0.614 STA: 5 LTA: 15 PEM: 8\n",
      "true true percentage: 0.624 STA: 6 LTA: 15 PEM: 8\n",
      "true true percentage: 0.884 STA: 7 LTA: 15 PEM: 8\n",
      "true true percentage: 0.934 STA: 8 LTA: 15 PEM: 8\n",
      "true true percentage: 0.96 STA: 9 LTA: 15 PEM: 8\n",
      "true true percentage: 0.96 STA: 10 LTA: 15 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 20 PEM: 8\n",
      "true true percentage: 0.568 STA: 3 LTA: 20 PEM: 8\n",
      "true true percentage: 0.632 STA: 4 LTA: 20 PEM: 8\n",
      "true true percentage: 0.616 STA: 5 LTA: 20 PEM: 8\n",
      "true true percentage: 0.692 STA: 6 LTA: 20 PEM: 8\n",
      "true true percentage: 0.658 STA: 7 LTA: 20 PEM: 8\n",
      "true true percentage: 0.76 STA: 8 LTA: 20 PEM: 8\n",
      "true true percentage: 0.846 STA: 9 LTA: 20 PEM: 8\n",
      "true true percentage: 0.96 STA: 10 LTA: 20 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 25 PEM: 8\n",
      "true true percentage: 0.56 STA: 3 LTA: 25 PEM: 8\n",
      "true true percentage: 0.63 STA: 4 LTA: 25 PEM: 8\n",
      "true true percentage: 0.642 STA: 5 LTA: 25 PEM: 8\n",
      "true true percentage: 0.776 STA: 6 LTA: 25 PEM: 8\n",
      "true true percentage: 0.772 STA: 7 LTA: 25 PEM: 8\n",
      "true true percentage: 0.758 STA: 8 LTA: 25 PEM: 8\n",
      "true true percentage: 0.774 STA: 9 LTA: 25 PEM: 8\n",
      "true true percentage: 0.846 STA: 10 LTA: 25 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 30 PEM: 8\n",
      "true true percentage: 0.56 STA: 3 LTA: 30 PEM: 8\n",
      "true true percentage: 0.6 STA: 4 LTA: 30 PEM: 8\n",
      "true true percentage: 0.608 STA: 5 LTA: 30 PEM: 8\n",
      "true true percentage: 0.702 STA: 6 LTA: 30 PEM: 8\n",
      "true true percentage: 0.702 STA: 7 LTA: 30 PEM: 8\n",
      "true true percentage: 0.778 STA: 8 LTA: 30 PEM: 8\n",
      "true true percentage: 0.72 STA: 9 LTA: 30 PEM: 8\n",
      "true true percentage: 0.732 STA: 10 LTA: 30 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 35 PEM: 8\n",
      "true true percentage: 0.56 STA: 3 LTA: 35 PEM: 8\n",
      "true true percentage: 0.58 STA: 4 LTA: 35 PEM: 8\n",
      "true true percentage: 0.594 STA: 5 LTA: 35 PEM: 8\n",
      "true true percentage: 0.66 STA: 6 LTA: 35 PEM: 8\n",
      "true true percentage: 0.676 STA: 7 LTA: 35 PEM: 8\n",
      "true true percentage: 0.808 STA: 8 LTA: 35 PEM: 8\n",
      "true true percentage: 0.788 STA: 9 LTA: 35 PEM: 8\n",
      "true true percentage: 0.806 STA: 10 LTA: 35 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 40 PEM: 8\n",
      "true true percentage: 0.56 STA: 3 LTA: 40 PEM: 8\n",
      "true true percentage: 0.582 STA: 4 LTA: 40 PEM: 8\n",
      "true true percentage: 0.608 STA: 5 LTA: 40 PEM: 8\n",
      "true true percentage: 0.646 STA: 6 LTA: 40 PEM: 8\n",
      "true true percentage: 0.646 STA: 7 LTA: 40 PEM: 8\n",
      "true true percentage: 0.828 STA: 8 LTA: 40 PEM: 8\n",
      "true true percentage: 0.806 STA: 9 LTA: 40 PEM: 8\n",
      "true true percentage: 0.776 STA: 10 LTA: 40 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 45 PEM: 8\n",
      "true true percentage: 0.56 STA: 3 LTA: 45 PEM: 8\n",
      "true true percentage: 0.584 STA: 4 LTA: 45 PEM: 8\n",
      "true true percentage: 0.576 STA: 5 LTA: 45 PEM: 8\n",
      "true true percentage: 0.634 STA: 6 LTA: 45 PEM: 8\n",
      "true true percentage: 0.61 STA: 7 LTA: 45 PEM: 8\n",
      "true true percentage: 0.806 STA: 8 LTA: 45 PEM: 8\n",
      "true true percentage: 0.776 STA: 9 LTA: 45 PEM: 8\n",
      "true true percentage: 0.768 STA: 10 LTA: 45 PEM: 8\n",
      "true true percentage: 0.566 STA: 2 LTA: 50 PEM: 8\n",
      "true true percentage: 0.56 STA: 3 LTA: 50 PEM: 8\n",
      "true true percentage: 0.6 STA: 4 LTA: 50 PEM: 8\n",
      "true true percentage: 0.598 STA: 5 LTA: 50 PEM: 8\n",
      "true true percentage: 0.652 STA: 6 LTA: 50 PEM: 8\n",
      "true true percentage: 0.65 STA: 7 LTA: 50 PEM: 8\n",
      "true true percentage: 0.832 STA: 8 LTA: 50 PEM: 8\n",
      "true true percentage: 0.778 STA: 9 LTA: 50 PEM: 8\n",
      "true true percentage: 0.776 STA: 10 LTA: 50 PEM: 8\n"
     ]
    }
   ],
   "source": [
    "for pp in range(len(PEM_d)):\n",
    "    PEM = int(PEM_d[pp])\n",
    "    mat_SL = np.zeros((len(STA_d),len(LTA_d)))\n",
    "    mat_missSL = np.zeros((len(STA_d),len(LTA_d)))\n",
    "    \n",
    "                # loop for LTA\n",
    "    for ll in range(len(LTA_d)):\n",
    "#        print('this is ll ',ll)\n",
    "        LTA = int(LTA_d[ll])\n",
    "        # calculation of LTA for this LTA loop\n",
    "        for k in range(len(fun1)-LTA):\n",
    "            LTA3[k] = np.mean(abs(fun3[k:(LTA+k)]))\n",
    "\n",
    "            # loop for STA            \n",
    "        for ss in range(len(STA_d)):\n",
    "#            print('this is ss ',ss)\n",
    "            STA = int(STA_d[ss])\n",
    "\n",
    "            for k in range(len(fun1)-STA):\n",
    "                STA3[k] = np.mean(abs(fun3[k:(STA+k)]))\n",
    "\n",
    "            \n",
    "            # loop for ratio\n",
    "            for k in range(len(fun1)-LTA):\n",
    "                ratio3[k+LTA] = STA3[k+LTA-STA] / LTA3[k]\n",
    "                \n",
    "                \n",
    "            # Stationary and transient separation\n",
    "            numbers3 =[]\n",
    "            cc = 0\n",
    "            for kk in range(len(ratio3)):\n",
    "                if ratio3[kk] >= trig and ratio3[kk-1] <= trig:\n",
    "                    numb3 = kk\n",
    "                    numbers3.append(numb3)\n",
    "                    cc = cc+1\n",
    "                    \n",
    "            trans3 = np.linspace(0,0,nt)\n",
    "            stat3 = np.linspace(0,0,nt)\n",
    "        \n",
    "            # FUN3 -  a loop for extracting the whole vecotr data for stationary and transient parts of the signal \n",
    "            i = 0\n",
    "            kk = 0\n",
    "            while kk <= nt-1:\n",
    "                if kk + PEM <= nt-1:\n",
    "                    # case when there is only one peak\n",
    "                    if len(numbers3) == 1:\n",
    "                        if ratio3[kk+PEM] >= trig and ratio3[kk+PEM-1] <= trig:\n",
    "                            while kk < numbers3[i]+PEM:\n",
    "                                trans3[kk] = fun3[kk]\n",
    "                                stat3[kk] =  np.nan\n",
    "                                kk = kk + 1\n",
    "                        else:\n",
    "                            stat3[kk] = fun3[kk]\n",
    "                            trans3[kk] =  np.nan\n",
    "                            kk = kk + 1\n",
    "                    elif i > len(numbers3):\n",
    "                        while kk <= nt:\n",
    "                            stat3[kk] = fun3[kk]\n",
    "                            trans3[kk] =  np.nan\n",
    "                            kk = kk + 1\n",
    "                    else:\n",
    "                        if ratio3[kk+PEM] >= trig and ratio3[kk+PEM-1] <= trig:\n",
    "                            while kk < numbers3[i]+PEM:\n",
    "                                trans3[kk] = fun3[kk]\n",
    "                                stat3[kk] =  np.nan\n",
    "                                kk = kk + 1 \n",
    "                            # dopoki numbers(i+1)+pem < kk \n",
    "                            if i <= len(numbers3):\n",
    "                                if numbers3[i]-PEM <= kk:\n",
    "                                    while numbers3[i]-PEM <= kk:\n",
    "                                        # dopoki\n",
    "                                        while kk < numbers3[i]+PEM:\n",
    "                                            trans3[kk] = fun3[kk]\n",
    "                                            stat3[kk] =  np.nan\n",
    "                                            kk = kk + 1\n",
    "                                        i = i + 1\n",
    "                                        #jesli tak, to do konca numbers(i+1)+Pem - transient\n",
    "                                        if i+1 > len(numbers3):\n",
    "                                            break\n",
    "                                else:\n",
    "                                    i = i + 1\n",
    "                                    # jesli nie, idziemy dalej normlanie\n",
    "                            else:\n",
    "                                stat3[kk] = fun3[kk]\n",
    "                                trans3[kk] =  np.nan\n",
    "                                kk = kk + 1\n",
    "                        else:\n",
    "                            stat3[kk] = fun3[kk]\n",
    "                            trans3[kk] =  np.nan\n",
    "                            kk = kk + 1\n",
    "                else:\n",
    "                    stat3[kk] = fun3[kk]\n",
    "                    trans3[kk] =  np.nan\n",
    "                    kk = kk + 1 \n",
    "            \n",
    "            #end of stationary/transient separation loop level\n",
    "            \n",
    "            # loop for calculating the percentage of found transient\n",
    "            count = 0\n",
    "            \n",
    "            for kk in range(len(xt)):\n",
    "                i_tr = xt[kk]  # index of transient\n",
    "                v_tr = fun3[i_tr] # value of function in a given transient \n",
    "                \n",
    "                if v_tr == trans3[i_tr]:\n",
    "                    \n",
    "                    count += 1     # counting how much calculated transient values fit the function\n",
    "                #print('this is transient value',trans3[i_tr],'and this is in function',v_tr,'. DOdajemy: count=',count )\n",
    "            \n",
    "            cnot = 0\n",
    "            for bb in range(len(trans3)):\n",
    "                if trans3[bb] == 0 or trans3[bb] == 1:\n",
    "                    cnot += 1    # number of data which in principle is stationary \n",
    "             \n",
    "            ctrue = 0 \n",
    "            mi = 0\n",
    "            for tt in range(len(trans3)):\n",
    "                if trans3[tt] == t2[tt]:\n",
    "                    ctrue += 1\n",
    "                    #print('tis is transient',trans3[tt],'tis is input',t2[tt],'so we count',ctrue)\n",
    "                elif stat3[tt] == s2[tt]:\n",
    "                    ctrue += 1\n",
    "                    #print('tis is stationary',stat3[tt],'tis is input',s2[tt],'so we count',ctrue)\n",
    "                else:\n",
    "                    mi += 1\n",
    "                    #print('this is missed one', mi,'value = ',fun3[tt],'stat = ',stat3[tt],'trans = ',trans3[tt])\n",
    "                        # calculating the percentage of true true detection\n",
    "            \n",
    "            true_perc = ctrue/len(trans3)\n",
    "            print('true true percentage:',true_perc,'STA:',STA,'LTA:',LTA,'PEM:',PEM) \n",
    "            mat_missSL[ss,ll] = true_perc\n",
    "            \n",
    "            \n",
    "            # calculating the percentage of fit\n",
    "            fit_perc = count/len(xt)\n",
    "            mat_SL[ss,ll] = fit_perc\n",
    "            \n",
    "#            print('this is the fit percentage',fit_perc,'so ',count,'out of ',len(xt),'are detected')\n",
    "#            print('this is STA ',STA, 'LTA',LTA ,'and PEM = ',PEM)\n",
    "            #print(trans3)\n",
    "            \n",
    "            # calculating the percentage of transient data within the chosen transient vector\n",
    "#            print('how much not transient',cnot,'. length of the transient',len(trans3),'number of nan inside',sum(~np.isnan(trans3)))\n",
    "#            print('percentage on non-transient in the vector', cnot/ (len(trans3)-sum(np.isnan(trans3))))\n",
    "            #misfit_perc = 1 - cnot/sum(~np.isnan(trans3))\n",
    "\n",
    "            #print(misfit_perc)\n",
    "            \n",
    "\n",
    "            \n",
    "            \n",
    "#            fig = plt.figure()\n",
    " #           ax1 = fig.add_subplot(111)\n",
    "  #          fig.set_size_inches(18.5, 5, forward=True)\n",
    "   #         ax1.stem(t,trans3,'b', markerfmt='bo', label='transient')\n",
    "    #        ax1.stem(t,stat3,'r', markerfmt='go', label='stationary')\n",
    "     #       ax1.plot(t,ratio3, label='ratio')\n",
    "      #      ax1.plot([0,5],[trig,trig],linestyle='dotted',label='treshold')\n",
    "       #     ax1.set_ylabel('Amplitude', fontsize=16)\n",
    "        #    ax1.set_title('Function with the transient', fontsize=20)\n",
    "         #   ax1.set_xlabel('Time', fontsize=16)\n",
    "          #  plt.ion()\n",
    "           # plt.draw()\n",
    "            #plt.pause(0.01)\n",
    "\n",
    "\n",
    "            #mat_T[ss,ll] = true_perc   \n",
    "            \n",
    "        # end STA loop level\n",
    "    PEMlist.append(mat_SL)\n",
    "    PEMmiss.append(mat_missSL)\n",
    "    #PEMtrue.append(mat_true)\n",
    "    # end LTA loop level\n",
    "    \n",
    "# end PEM loop level    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan  1.  0.  1.  0.  1.  0.  1.  5.  1.  0.  1.  0.  1.  0.  1.  0.\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan  0.  4.  0.  1.  0.\n",
      "  3.  0.  5.  0.  1.  0.  1.  0.  1.  0.  1. nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan  0.  1.  0.  1.  3.  5.  0.  1.  0.\n",
      "  1.  0.  1.  0.  1.  0.  1. nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan  0.  1.  0.  1.  0.  1.  0.  3.  0.  1.  0.\n",
      "  1.  0.  1.  0.  1. nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan  1.  0.  1.  0.  1.  2.\n",
      "  1.  3.  1.  0.  1.  0.  1.  0.  1.  0. nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan  0.  1.  0.  1.  0.\n",
      "  1.  0.  3.  0.  1.  0.  1.  2.  1.  0.  1. nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan  1.  5.  1.  0.  1.  0.  1.  2.  1.  0.\n",
      "  1.  0.  1.  0.  1.  0. nan nan nan nan nan nan nan nan nan nan nan nan\n",
      " nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True False False False False\n",
      " False False False False False False False False False False False False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True False False False False False False False False False\n",
      " False False False False False False False  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      " False False False False False False False False False False False False\n",
      " False False False False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True False False False False False False False False False False\n",
      " False False False False False False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True]\n",
      "388\n",
      "112\n",
      "112\n"
     ]
    }
   ],
   "source": [
    "print(trans3)\n",
    "print(np.isnan(trans3))\n",
    "print(sum(np.isnan(trans3)))\n",
    "print((len(trans3)-sum(np.isnan(trans3))))\n",
    "print(sum(~np.isnan(trans3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  3  4  5  6  7  8  9 10]\n",
      "9\n",
      "(8,)\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "print(STA_d)\n",
    "print(np.size(LTA_d))\n",
    "print(np.shape(PEM_d))\n",
    "print(len(PEMmiss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len(PEM_d)//2,ncols=2,constrained_layout=True)#)\n",
    "\n",
    "fig.set_size_inches(15, 15, forward=True)\n",
    "fig.suptitle(\"Function F3 - Percentage of detected transient by STA/LTA ratio with threshold = {}\".format(trig), fontsize=20)\n",
    "\n",
    "iii = 0\n",
    "for ax in axs.flat:\n",
    "    pcm = ax.pcolormesh(LTA_d,STA_d,PEMlist[iii]*100,shading='nearest') \n",
    "    ax.set_xlabel('LTA length [data points]', fontsize=16)\n",
    "    ax.set_ylabel('STA length [data points]', fontsize=16)\n",
    "    ax.set_title('PEM = {} data points'.format(PEM_d[iii]), fontsize=20)\n",
    "    iii += 1\n",
    "\n",
    "cbar = fig.colorbar(pcm, ax = axs[:,:], location='left', shrink=0.6)\n",
    "cbar.set_label('% of detected transient', fontsize = 14)\n",
    "\n",
    "fig.savefig(\"/Users/mathis/Desktop/ANIA MESS/PhD/work/Testing/report/images/SLperc_thres15.eps\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len(PEM_d)//2,ncols=2,constrained_layout=True)#)\n",
    "\n",
    "fig.set_size_inches(15, 15, forward=True)\n",
    "fig.suptitle(\"Function F3 - Percentage of true positive picks in transient vector. STA/LTA ratio with threshold = {}\".format(trig), fontsize=20)\n",
    "\n",
    "iii = 0\n",
    "for ax in axs.flat:\n",
    "    pcm = ax.pcolormesh(LTA_d,STA_d,PEMmiss[iii]*100,shading='nearest')#, vmin=0, vmax = 100) \n",
    "    ax.set_xlabel('LTA length [data points]', fontsize=16)\n",
    "    ax.set_ylabel('STA length [data points]', fontsize=16)\n",
    "    ax.set_title('PEM = {} data points'.format(PEM_d[iii]), fontsize=20)\n",
    "    iii += 1\n",
    "\n",
    "cbar = fig.colorbar(pcm, ax = axs[:,:], location='left', shrink=0.6)\n",
    "cbar.set_label('% of true positive picks in the transient vector', fontsize = 14)\n",
    "\n",
    "fig.savefig(\"/Users/mathis/Desktop/ANIA MESS/PhD/work/Testing/report/images/turetrue_thres15.eps\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 9, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking out the best STA/LTA/PEM combination\n",
    "param_check = []\n",
    "\n",
    "iii = 0\n",
    "for kkk in range(len(PEMlist)):\n",
    "    temp = PEMlist[kkk]*0.7 + PEMmiss[kkk]*0.3\n",
    "    temp = temp/2\n",
    "    param_check.append(temp)\n",
    "    #print(param_check)\n",
    "np.shape(param_check)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len(PEM_d)//2,ncols=2,constrained_layout=True)#)\n",
    "\n",
    "fig.set_size_inches(15, 15, forward=True)\n",
    "fig.suptitle(\"Function F3 - Evaluation of STA/LTA parameters settings. Threshold = {}\".format(trig), fontsize=20)\n",
    "\n",
    "iii = 0\n",
    "for ax in axs.flat:\n",
    "    pcm = ax.pcolormesh(LTA_d,STA_d,param_check[iii]*100,shading='nearest')#, vmin=0, vmax = 100) \n",
    "    ax.set_xlabel('LTA length [data points]', fontsize=16)\n",
    "    ax.set_ylabel('STA length [data points]', fontsize=16)\n",
    "    ax.set_title('PEM = {} data points'.format(PEM_d[iii]), fontsize=20)\n",
    "    iii += 1\n",
    "\n",
    "cbar = fig.colorbar(pcm, ax = axs[:,:], location='left', shrink=0.6)\n",
    "cbar.set_label('Evaluation', fontsize = 14)\n",
    "\n",
    "fig.savefig(\"/Users/mathis/Desktop/ANIA MESS/PhD/work/Testing/report/images/param_thres15.eps\")\n",
    "\n",
    "\n"
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