perlin_py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bcfcf9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Perlin Noise Implementation and its graph\n",
    "\n",
    "import random\n",
    "\n",
    "class Perlin:\n",
    "    \n",
    "    def __init__( self ):\n",
    "        self.gradients= []\n",
    "        self.lowerBound = 0  \n",
    "\n",
    "        \n",
    "        \n",
    "        #keeping 0 as lower_bound for the objects so if someone tries on a value of -negatives the out of bound error shows \n",
    "        \n",
    "    def valueAt ( self , t ):\n",
    "       \n",
    "        if( t < self.lowerBound ):\n",
    "            print(\" Error !!! Out of Bounds \")\n",
    "            return\n",
    "        \n",
    " \n",
    "        \n",
    "        # Uniformly Assign gradients g1 g2 g3 g4 ..... gn to all the points of the coordinates\n",
    "        \n",
    "        while t >= len(self.gradients) - 1 + self.lowerBound:\n",
    "            self.gradients.append(random.uniform(-1,1))\n",
    "            \n",
    "        discarded = int(self.lowerBound)\n",
    "        \n",
    "        \n",
    "        d1 = ( t - t // 1)\n",
    "        \n",
    "        d2 = d1 - 1\n",
    "        \n",
    "        a1 = self.gradients[(int)(t//1)-discarded]*d1\n",
    "        a2 = self.gradients[(int)(t//1+1)-discarded]*d2\n",
    "        \n",
    "        amt = self.__ease(d1)\n",
    "        # smoothening curve used here --->\n",
    "        \n",
    "        \n",
    "        return self.__lerp(a1,a2,amt)\n",
    "        \n",
    "        \n",
    "    def discard(self, amount):\n",
    "        gradientsToDiscard = int( amount+ self.lowerBound%1 )\n",
    "        self.gradients = self.gradients[gradientsToDiscard:]\n",
    "        self.lowerBound += amount\n",
    "        \n",
    "        \n",
    "    def __ease(self,x):\n",
    "        return 6*x**5-15*x**4+10*x**3\n",
    "    \n",
    "    def __lerp( self , start , stop , amt ):\n",
    "        return amt*(stop-start)+start\n",
    "        \n",
    "      \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef80932e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b4de27a",
   "metadata": {},
   "outputs": [],
   "source": [
    " noise = Perlin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81e59a7f",
   "metadata": {},
   "outputs": [
    {
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       " 0.10927140194948892,\n",
       " 0.10917280123458284,\n",
       " 0.10863641337093322,\n",
       " 0.10765976284723133,\n",
       " 0.1062419249671358,\n",
       " 0.1043835833515705,\n",
       " 0.10208708752769513,\n",
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       " 0.0961977070353427,\n",
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       " 0.0886280916732134,\n",
       " 0.08423841658189646,\n",
       " 0.07946290437907133,\n",
       " 0.07431718570709733,\n",
       " 0.0688190209464914,\n",
       " 0.06298835858494822,\n",
       " 0.056847393673017055,\n",
       " 0.05042062636644795,\n",
       " 0.04373492055524075,\n",
       " 0.03681956257931396,\n",
       " 0.029706320030889244,\n",
       " 0.022429500643523226,\n",
       " 0.01502601126781622,\n",
       " 0.007535416933805994]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = [ i*0.01 for i in range(600) ]\n",
    "values = [ noise.valueAt(i) for i in time]\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec0a61bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LFCZiBaqQ7VNemAFgK7Snwfcpf01hgK8ovInoiCVvv7JEYSLWgYsdLM5JJis5PiDnT0+KY1FOsg11TMPh2k4W5yaTPi8uoM/jS0RHrE7hV5YoTERSVQ5cag/Y1YTPuqJ0K55Ow5HajoAPOwGkz4tjcU6yXVH4maOJQkTuFJHTIlIlIg9P8Pgfi8gJETkiIi+LSIkTcZrwc7G1j7beITb5YbvNqawrzqCxa5CGzoGAPk84a+gcoKl7kPIALbQbb21RuiUKP3MsUYhIDPAN4C5gFfBBEVk17rCDQIWqlgM/Ar4a3ChNuPLVJwI148nHV9C2q4rJHQ7wQrvxyosyaOgaoKnLkre/OHlFsQWoUtXzqjoEPAXcO/YAVX1FVfu83+4BioIcowlTBy61k5IQS1mefxfajbeqII1Yl1idYgpHajuIdQmrF6QF5fl8LULsqsJ/nEwUhUDNmO9rvfdN5uPA8xM9ICKfEJFKEalsbm72Y4gmXO2/2MH64gxiXP7tUjpeYlwMKwpS7YpiCkdqO1mWn0piXExQnm/VgjRcYgVtfwqLYraI/D5QAXxtosdV9VFVrVDVitzc3OAGZ0JOz+AIpxu62Bjg+oTPuqIMjtR0WifZCagqR+s6WRfghXZjJcXHsiw/1VqO+5GTiaIOKB7zfZH3viuIyK3AnwP3qOpgkGIzYexITQduxe8dYyezrjiD7sERzrf0BuX5wklNWz8dfcOs9a45CZbyonSO1nWiasnbH5xMFPuAMhFZJCLxwAeAZ8YeICIbgP/AkySaHIjRhKH93g2FNhQH74oCbKhjIkfqOgCCNuPJZ21RBm29Q9S29wf1eSOVY4lCVUeAzwAvAieBp1X1uIh8SUTu8R72NSAF+KGIHBKRZyY5nTFvO3CpnaV5KaQnBXZxl8/SvBTmxcVY8XQCR2o7iY91sSw/sJMKxrOCtn/FOvnkqvoc8Ny4+7445vatQQ/KhDW3WzlY08Edq+YH7TljXMKawjS7opjAkdoOVhakER8b3M+ky+enEh/j4khdB+8uLwjqc0eisChmGzNd51t66egbDvj6ifHKizI4Xt/F8Kg7qM8bytxu5VhdF+UB7u80kYRYz2y0IzV2ReEPlihMRHl7oV2AW3eMV16UzuCImzON3UF93lB2obWXnsGRoNcnfMqL0jlWZ7PR/MEShYkoBy+1k5YYy5LclKA+r3UtfadAb316NeVFntloF1ptNtpcWaIwEWX/RU8jQFeAF9qNV5KdRFpirCWKMY7UdjIvLoYlucmOPH95kXWS9RdLFCZidPYPc6axJ+CNACciIpQXZdib0hhHaztZU5jm921op2tprmc22mGrU8yZJQoTMXz1iQoHEgV4PsGebuhmYHjUkecPJSOjbo7VdwZ9od1YsTEu1hSmcbTOEsVcWaIwEePAxXZiXPJ2R9dgKy9KZ8StnLzc5cjzh5Kq5h4Ght2OFbJ91hZmcLy+kxGbjTYnlihCUN/QCK+eaWbniUZae6xryXRVVrezsiCV5ARnlgeVW0H7bb6/g7UOJ4p1xekMDLs509jjaBzhztEFd+ZKqsr33rrEV54/RffgCACxLuGj15XyhTuWkxAbnO6b4Whk1M2hmg7ev7n46gcHSEF6IjkpCdZJFk99IjUhlkXZzhSyfXzJ+2hdB6uC1OY8ElmiCBGqyt88e5LH37jA9qU5/LcbFpMUH8vT+2r45usXqGrq4T8+XBH0Fa7h4uTlbvqHR4PWMXYiIsI6210N8Mw0WlOYHvTZZ+OVZCWRmhjL4dpO3r/Z0VDCmr3rhIhvvn6ex9+4wEevK+U7H9vCjrJcNpVk8pX3lfN3v7OWV04389e/OO50mCFr/8U2wLlCts/aonTONffQ470ijEZDI25OXu52vD4B4HIJ5UXpNhttjixRhIBDNR088vwp3rV2Pl98z6p3fAr7va0L+W/XL+Z7b11i54lGh6IMbfsvdVCQnsiCjHmOxrGuKANVOBbFM21ON3QzNOp2vD7hU16UYbPR5sgShcOGR908/OMj5KUm8sjvliMy8aX6529fzor5qfzVM8ftP/wE9le3ObJ+Yry1tsiLgzXeNu9BbqMymfLCdIZHlVMN1l5ltixROOyxXRc41dDNl+5dTVri5G2x42Nd/OXdq6nr6OexXReCGGHoq+/op75zICQSRU5KAoUZ86K6TnHwUgd5qQksSE90OhQAyr3TpaM5ec+VJQoHdfYN841XqrhlRR63r756W+xtS7K5aXkuj+26QP+QXVX4VF70LbTLcjgSj/IoL2gfuNTOxoWZk14dB9uC9ERyUuKj+t9krhxNFCJyp4icFpEqEXl4gsevF5EDIjIiIu9zIsZA+ubr5+keGOHzty+f9s986qaltPUO8YN9lwIYWXh581wrqQmxITP9sbwog0ttfbT3DjkdStC19gxysbWPDUHahnY6RIS1heFV0B4ZdfPK6Sa+9fp5fnO6yfEOuI5NjxWRGOAbwG1ALbBPRJ5R1RNjDrsE/AHwheBHGFjtvUM8/sYF3l1eMKM3uM2lWWwuzeSbr1/gQ9eUEOdQH51Qsud8K1sWZRHj8FRMn7d3V6vr5IZluQ5HE1wHL3UAODpNeSLlRRm8eqaZ3sERxxZkTtfF1l4+/f0DHKv77Qr/DQsz+I8PbyIv1ZnhPCffZbYAVap6XlWHgKeAe8ceoKrVqnoEiLj199/fe4m+oVH+x81lM/7ZP9yxmLqOfn59yrYRb+gc4EJLL9uWZDsdyttWezfqORpGn2D95WBNO7EuYc2C0Jjx5LOuOB23wvH60G6vcrmzn9/75lvUtvfzzx9Yz/6/uJWvva+c0w3dfPDRPXT2DzsSl5OJohCoGfN9rfe+iDc04uaJ3dXsKMth+fyZ7yV884o88tMS+MG+mqsfHOHePN8CwDWLQydRpM+LY3FOMoejcEz8wEXP1qfz4kOri4BvhfZBb+PIUDTqVj77X4fo7B/myY9v5d71hWSnJHB/RTGPPbiZ6tY+vvjzY47EFhHjFiLyCRGpFJHK5uZmp8O5queOXqape5CPbV80q5+PjXFx/6ZifnO6icud/X6OLrzsOddG+rw4VhWERn3CJxoXeY26lcO1HWwMofqET05KAqXZSW9PfAhFT+yuZm91G399z2rWjNs+dtuSbD57Sxk/P1TPb04HfyTByURRB4xtzFPkvW/GVPVRVa1Q1Yrc3NAeE1ZVHtt1gSW5ydxQNvtYH6goxq3wo8paP0YXft701iecbhUxXnlRBo1dgzR2DTgdStCcbuimb2g0ZNZPjLepJIsDF9tRDb2tUbsGhvmXX59lR1kO79048cDKJ29YwsKsJB55/hSjQS5uO5ko9gFlIrJIROKBDwDPOBhPUBys6eBoXScfvW7RnN7cFmYncc3iLH52qC4k/+MHQ11HP5fa+tgWQsNOPr/dXS16hp98C+2CvV/5dFWUZtLaO8SFltDbGvXff3OOjr5h/uTOFZNOK46PdfGFO5ZzqqGbF483BDU+xxKFqo4AnwFeBE4CT6vqcRH5kojcAyAim0WkFrgf+A8RCftmRz/YW0NSfAz3bZh7OeY95Qs419wbtStOd1d56hOhVMj2Wb0gnRiXRNXw04GLHWQnx1Oc5Wwblcn4+oCF2vCTbwbkvesXvGPIabx3ry2gJDuJ/3jtfFA/IDpao1DV51R1maouUdUve+/7oqo+4729T1WLVDVZVbNVdbWT8c5V7+AIzx6p5z3lBaT4YYreXWvmE+MSfnG43g/RhZ9XzzSTm5rAillMCAi0efExlOWlRFVBe//FNjaE0EK78ZbkppA+L4791aGVKL6/9xIDw24+fdPSqx4b4xIe2r6IwzUdHKzpCHxwXhFRzA4Xvzxymd6hUb/tmZCdksC1S7J59sjlqBt+Ghl18/rZFm5Ylhuyb0zlRekcre2Iin+bpq4Bqlv7uGZxaKyOn4jLJWwqyaTS22k4FAyPuvnumxfZUZbDsvzpfeC5b0MhiXEufhjE+qQliiB6at8lluQm+3UM9+7yBVxq6wv5+eH+dri2k87+4ZBe0FZelEF73zC17ZE/M21vtefNd8ui0E0UAJtKMjnX3Bsyq+afP9ZAQ9cAH72udNo/k5oYx7vWFPDs4fqgNQi1RBEkVU3dHLjk2YHNn5+Ab1mZhwhR13781dNNuAR2lOU4Hcqk1nnn7kfDjnd7L7SRHB8TctOUx/PVKfaHSJ3i6X01FGfN48ZleTP6ufdVFNE9OBK0orYliiD5wb4aYl3CezcW+fW82SkJbFqYya9ORlei+M2ZZtYXZ5CRFO90KJNaPj+V+BhXVMx82nuhjY0lmcSGeEuZdcUZxMVISBS06zv6eeNcC+/dUDTjGZDXLMqmKHMePz4wqxUFMxba/6oRYmjEzU8O1HHrynxyUhL8fv5bV+VzvL6L+o7IH+IAT5uDI7Wd3LIy3+lQphQf62JlQWrEz3zq6BviVEM3W0N82AkgMS6G1QvS394R0Umeqe3wu7P48OhyCe8uL2B3VQudfYFv62GJIgh+faqJ1t4hHtjs36sJn1u9b5gvR8lVxUvHPa/zjmm0ZndaeVEGx+q6HO/+GUj7vLOItiwKvWnKE9myKIvDNZ2ObgCmqvx4fy2bSzNZmJ00q3PctaaAEbcGZTTBEkUQ/Gh/LXmpCVw/h5XYU1mSm8yinGR2noyOJoEvHm9gaV4KS/NSnA7lqsqL0ukZHOF8S4/ToQTM3gutxMe6QmKP7Om4ZnEWQ6NuDjg4/HSktpNzzb2zuprwKS9MpyA9keePBb5OYYkiwJq7B3nldBO/s7EwYOO3IsKtK/N481wL3QPOdJcMlrbeId660MadYXA1Ab9tRhfJdYo959tYX5xBYlxoNQKczOZST0v6N8+3OhbDTw/WER/r4l3lBbM+h8sl3LF6Pq+d9bRPDyRLFAH280N1jLqV9/m5iD3eLSvzGR5V3qhy7j9/MOw80cCoW8Ni2AlgaV4KSfExEZso2nuHOFbfyXVLQnf22XipiXGsLUxn9zlnflfcbuWFYw3csCx3yu2Pp+PONfMZGnHz2pnANkO1RBFAqsoPK2tZV5xB2TQX08zWxoWZpCTE8trZ0O+eOxc/O1jPopxk1hSG9jRMnxjv3gyROkX2zfOtqML2svCoT/hsW5LN4ZqOgH8Sn8ih2g4auga4a83cP+xUlGSSmhjLb05boghbx+u7ON3Yzf2bAns1AZ4ZNtuWZPPq6eaIXQlc39HPngut3Le+MGRXY09kXXE6x+u7GBqJuP232FXVQkpC7NtrRsLFtUuyGXGrI9NkXzzWQFyM+GXWXmyMi+vLcnnldFNAf+8tUQTQj/bXEh/r4u7yBUF5vhuW5VLX0c/5EOyO6Q++6YS/44eGisG0vjiToRE3Jy9H3ur5XWdbuGZxdsivnxivoiSLuBhh97mWoD6vqvL8sQauXZJD+ry5DTv53Lg8l6buQU4E8P9XeP3rhpHBkVF+dqiO21flk57kn/8QV+NrZ/FqgC9DnaCq/PRAHRUls59O6JT13o18DgWxiVswXGrt41JbH9uXhtewE3iaNq4vzmBPkOsUJy53camtzy/DTj43LPf83gdy+GnaiUJEwuu302G/PtlER98w7wvCsJNPcVYSi3OSI7JOsa+6nbNNPdxfEby/T39ZkJ5IbmpCxCWKN7yfxrcHaNp3oG1bksPRuk66gjhT8IVjDbgEblvlv8WieamJrC1M55VTgZsef9VEISLXisgJ4JT3+3Ui8n8DFlGE+OH+WvLTEtgR5F+i65flsud8q6OLiQLhu3sukpoYyz3rwmvYCTzTlzcUZ0Rcoth1toX5aYksyU12OpRZ2bY4G7fC3vPBW6X9/LEGti7KJtvPHRpuWp7LgUvtAWt2OJ0rin8E7gBaAVT1MHB9QKKJELXtfbxyuon7NxUTE+QtOm9YlsvAsJt91c63KPCX5u5BXjh2mfs3FTMvPjzm6o+3fmEGF1pCp2vpXLndyhvnWthelhNWEwvG2rAwg4RYF7uqglOnqGrqpqqph7vW+n9q940r8nArvB6g1zKtoSdVrRl3l18+rorInSJyWkSqROThCR5PEJEfeB9/S0RK/fG8gfZfey8hwAe3Lgz6c29dnEV8rCui6hRPV9YwPKp86Jrg/336y/riDMAzNTISHK/voqNvmO1Lw2f9xHiJcTFsXZwdtETxgncF9e2r/J8o1hVlkD4vjtcDtJ5iOomiRkSuBVRE4kTkC3i2Lp0TEYkBvgHcBawCPigiq8Yd9nGgXVWX4rmy+cpcnzfQhkbc/GBfDTevyKMwI/hbQibFx7KlNCti6hSjbuV7ey5y3dJsluSGfsuOyZQXZSAChy51OB2KX/jeXK8Nw0L2WDuW5lDV1MPlzsA31HzxeCMbFmYwPz3R7+eOcQnbl+ZQH6DXMZ1E8Ung00AhUAes934/V1uAKlU9r6pDwFPAveOOuRd4wnv7R8AtEqDr3Mud/Xz4sbfYe2FuQzYvHm+gpWeID11T4qfIZu6GZbmcaeyJiG6yvz7VRH3nAB928O/TH1ISYlmWlxoxdYo3qlpYMT+VvFT/v+kF045lniui188G9qqirqOfo3WdAe0o8I/vX8/3HromIOe+aqJQ1RZV/ZCq5qtqnqr+vqr6Y05ZITB2SKvWe9+Ex6jqCNAJvOMjjIh8QkQqRaSyuXl2n6Qz5sVzqqGbf9x5ZlY/7/PdPRcpzprHDQ7OBLneO002WJfUgfTdPRfJT0sI+Zbi07G+OIPDEbA16sDwKHur27gujIedfJbnp5KbmsCuACeKnd4NhgKZKOJjA7faYTqznv5TRB4f/xWwiGZBVR9V1QpVrcjNnd0b9Lz4GD514xLePN8660U4By+1s/dCGw9uK53xRiT+tCw/hZyUBN4I80RR3dLLa2ea+b0tJcSF2YKuiaxfmEFH3zDVrX1OhzInldXtDI24w7o+4SMi7Fiaw66qloC2gn/xeCNleSksygnPGWLT+e17Fvil9+tlIA3wR8/kOqB4zPdF3vsmPEZEYoF0vLOvAuGDWxaSn5bAP+08O6tPff/+6jnS58XxwS3OFl1FhO1Ls3mjqiWsP70+uecisS7hg1uKr35wGNjw9sI753dXm4tdVS3EuiTk98eeru1lObT1DgVsZXN77xB7q9vCppHlRKYz9PTjMV/fAx4AKvzw3PuAMhFZJCLxwAeAZ8Yd8wzwoPf2+4BfawDf+RLjYvj0TUvZW9024y6sVU3dvHSikY9sKyE5ITZAEU7fdUtzaOnx7DwWjvqHRvnh/lruWDOfvLTwHgf3KctLJTk+JuwL2rvPtbBxYWZI/D/3B9+VUaDqFC+famLUrdy+OnyHT2dzPV8GzGwn8Al4aw6fAV7EM4vqaVU9LiJfEpF7vIc9BmSLSBXwx8A7ptD62/s3F1OQnsjXd56e0afxr714mqS4GP7g2tLABTcD28s8//nDdfjpF0fq6ewfDvsi9lgxLmFtUToHw7ig3dk3zNG6zrCf7TRWXloiK+an8nqAZgq+dLyBgnTP6ulwNZ0aRbeIdPn+BH4B/Ik/nlxVn1PVZaq6RFW/7L3vi6r6jPf2gKrer6pLVXWLqp73x/NOJSE2hs/dWsbBSx387ND0Ni6vrG7jxeON/Lcblvh9xeVsFaTPY0luctgWtJ/cc5Fl+SlhsQ/zTKwvzuTk5a6wXTnvayseCYXssXaU5VBZ3U7/kH//XfqHRnntbDO3r8oP24WJML2hp1RVTRvz5zJV/XEwgnPK/ZuKWVecwZd/eYrO/qn7wIy6lb/55Uny0xJ4aMeiIEU4PduX5vDW+TYGR8LrTelEfRdHajv50NaSsP7lmsj64gyGR5Xj9eHZSfaNqhaS4mPCrq341ewoy2Vo1M1bF/xbAn3tbDMDw25uD+P6BEyRKERk41RfwQwy2Fwu4W/vXUNr7yB/++yJKY99bNd5Dtd08GfvWklSfGiN2V63NIf+4VEOhtmY+E8O1BIXI9yzLjjt2YNpQ5h3kn3jXAtbF2UFdCqmE7Z4X5O/6xQvHW8kfV5c2Bf+p3pn+/oUjylws59jCSlri9L59I1L+bdXqqgozeT9m985k2nvhTa+9uJpbl+VH5JvatcsySbGJbxR5dkzIByMjLr52aF6bl6RR2ZyvNPh+F1+WiIL0hPDMlE0dA5wvrmX33N4Vl8gJMbFsKU0y6/rKUZG3bx8qpFbVuSF/fTuSROFqt4UzEBC0eduLeNwbQd/9tNjxLpc/O6YluF7L7Tx0BP7KM5M4mv3rwvJIZK0xDjWFaWzq6qFz9++3OlwpuX1qhZaegZ5b4D3GHfS+oUZYTlF1jcx4tow2h97Jq5flsPfPXeKuo5+v7Tf2XO+jY6+4bAfdoJpznoSkTUi8oCIfMT3FejAQkFsjIt///1NbC7N5PM/PMyDj+/l3189x+eeOsgHHn2TnNQEvvPxLX7bqSoQti/N4XBNx1VrLaHiJwfqyEiK46blc55YF7LWF2dQ09ZPS8+g06HMyBtVLWQlx7NifmD3f3eKb/X/yycb/XK+XxyuJyUhlhuXh+d+HWNNZ9bTXwL/6v26CfgqcM+UPxRBkhNiefLjW/lfdyznxOUuHnn+FL8+1cRHtpXy0/9+HUWZob2f03VLc3Ar7Dkf3J28ZqNncISXjjdwd/mCiBsDH2t9cSYQXg0CVZU3z7eybUm2o10HAmlJbgqLc5LZeWLuiWJoxM0Lxxu4bVU+iXHh2Rp/rOlUX98HrAMOqupHRSQfeDKwYYWW2BgXn75pKZ+6cQndgyMkx8cGfZ+J2dqwMJOk+BjeqGoJ+ZWhr5xqYnDEzd0hWO/xp7WF6cS4hEM1Hdzqx53OAqm2vZ/LnQNcE+ZF2au5bVU+j79xga6BYdISZz9SsKuqmc7+Yd5TXuDH6JwznY9tA6rqBkZEJA1o4srWG1FDREhLjAubJAGeRmFbF2WFxXqKF443kJMSz6aSTKdDCah58TEszw+vTrK+jbAqSiM/UQyPKq/NcV+HZw9fJi0xNug7XAbKVNNjvyEi24G9IpIBfBPYDxwA3gxOeMYfrluaw/nm3pBuOz4wPMorp5q4bdX8sErEs7VhYQaHazoC2ojOn/ZVt5OaGMuy/MisT/hsWJhJdnL8nIafBoZHeelEI3eumR8xQ6hTvYozwNeA9wB/BrwF3AY8qKofDUJsxk98q2h3nwvdOsWusy30DY1y55rQHh7zl/XFGXQPjnC+xR/9NQOvsrqNipLMiE/iMS7h5hV5vHKqiaER96zOsfNEIz2DI2G5v/tkJk0UqvrPqroNz/7YrcDjwAvA74hIWZDiM36wPD+VjKQ43grhgvYLxxtITYxlW5is95gr38K7cFgM2d47xNmmnogfdvK5a+18ugZGZj389HRlDYUZ87h2SeT8X55OC4+LqvoVVd0AfBC4DzgV6MCM/7hcwubSLN6a4+59gTIy6uZXJxu5dWV+xFyqX83inBRSE2PDok5RedGz5mNzlCSKHWW5ZCfH89OD0+v1NlZtex+7qlp436aiiJodNp3psbEicreIfA94HjgNvDfgkRm/2rooi0ttfUHZG3imDlzqoKNvmNvCZAaQP7hcwrqijLC4oqisbiM+xkV5Ufh2P52JuBgXd69bwM6TjTNef/TDyloA7q+IrAWjUxWzb/PuZFcL/CGejYuWqOoHVPXnwQrQ+Ievhcdc9wQPhN+cbiLGJRHXkfRq1hWnc6axO+Q7ye6tbqO8KD0i1gNM1+9sKGRoxM3zRy9P+2cGR0b5/t5L7CjLDfn1VTM11RXFnwK7gZWqeo+qfl9Ve4MUl/GzlQVppCbGsud8KCaKZjYtzAzpFe6BsGZBOiNu5Uxj6G4uNTgyyrG6zoifsjxeeVE6ZXkpfHfPxWnvS/PzQ/U0dw/yhyHWRdofpipm36yq31JVvzelEZEsEdkpIme9f074v1BEXhCRDhF51t8xRJuYt+sUoVXQbuoa4MTlLm6IgDYHM7V6gWcoJ5Rbjp+83M3wqLK+OMPpUIJKRPj49kUcr+/izWlMAlFVvvnaeVbMT42IvcTHc6py+DDwsqqW4dmHe7Kd674GfDhoUUW4rYuyON/cS1P3gNOhvO033pklkdAPZ6aKs+aRmhjLsbpOp0OZ1JHaDgDKoyxRANy3oZDs5Hgee/3CVY996UQjZ5t6+MMdi0OyQehcOZUo7gWe8N5+As9MqndQ1ZeB0L0uDzO+nvihVKd49XQzeakJrCpIczqUoBMRVi9IC+krikM1HeSkJLAgPTL2LZ+JxLgYPrythJdPNU2ZzEdG3Xz1hVMszk3m3vWR2X7GqUSRr6q+KlEDED3TXRy0pjCdpPgY3gqROsXIqJvXzzZz4/LciPwUNh2rF6Rz8nIXI6OzW9wVaIdrOlhfnB61/z4f276I7OR4vvSLE5PWKh7bdYFzzb08fOcKYsN834nJBOxVicivROTYBF/3jj1OPX/7c+pjICKfEJFKEalsbg7MBumRIC7GxaaSzJC5ojhS10nXwAjXL4u+YSefNYVpDI64Od8SevNEugaGOd/SS3mEbXs6E2mJcXzhjuXsrW7j27ur3/H4sbpOvr7zDHeszo/o6d0BSxSqequqrpng6+dAo4gUAHj/bJrjcz2qqhWqWpGbG71vOtNxzeJsTjd209Y75HQovOltKRItq7En4itoh2Kd4lhtJ6qwLgrrE2N9YHMxt67M4++eO8kLxxrevv9MYzcf+/Y+spPj+dv71kb0VZdT10nPAA96bz8I2LqMINkaQnWK3edaWDE/leyUBKdDcczinGQS41whWac45C1kr4uShXaTERG+fv961hSm88kn9/Oxb+/jc08d5J5/24VblW9/dAu5qZH9f9ipRPEIcJuInAVu9X6PiFSIyLd8B4nI68APgVtEpFZE7nAk2ghSXpRBYpzL8WmyA8OjVFa3R+y2mtMVG+Nixfy0kLyiOFLTSUl2EhlJkbd3+UylJ8XxvYe28kc3L+VMYze7qlq4u3wBz312B8sjdMe/saazcZHfqWorcMsE91cCD435fkcw44oG8bEuNi7MdLygffBSB4Mj7ohqnDZbawrT+PnBetxuDan+QIdrO6Kmv9N0JMXH8vnbl4fN/vP+FJklejOlLYuyONnQRdeAc/tov3muBZfAlsX2RrR6QTrdgyPUtPc5HcrbmroGuNw5EPX1CeNhiSIKbVmUhSrsr/b7ovtp232ulbVFGXPabjJSrAnBFdqHaz1DYdFenzAeliii0IbiTOJixLG2472DIxyq6bBhJ69l81OIcQnH60OnTnG0tgOX/HZWloluliii0Lz4GMqLMtjrUEG78mI7I261ROGVEBvDktxkTl0OnSYEJy53sSQ3hXnx0dMx1kzOEkWU2rIoiyO1nfQPBb/F9e5zLcTFCBUlVp/wWVmQxqmGEEoU9V2sWhB9bVXMxCxRRKkti7IYcSsHLwW/TvHmuVY2FGfap9UxVsxPo66jf8Yb5QRCe+8Q9Z0DUdl/y0zMEkWU2lSSiUsIep2is2+YY3WdbLNhpyusKPDMxT8dAlcVJy97iup2RWF8LFFEqbTEOFYtSAv6Cu23LrTiVqw+Mc7K+Z43Zd+btJNOeGNYaVcUxssSRRTbUprNgUvtDI0Er3Pp7nOtJMa5WL8wI2jPGQ7y0xLITIrjVEMIJIr6LvLTEsiJ4tYq5kqWKKLYlkVZDI64OVrXEbTnfPNcK5tLs0iItfrEWCLCivlpnAyBmU8nLndZfcJcwRJFFNtc6tmBNlh1ipaeQU43dlt9YhIrClI53dDNqHtOXffnZGB4lKqmHqtPmCtYoohi2SkJlOWlBK1OYW3Fp7Zyfhr9w6NcanOulUdVUw8jbmVVgS20M79liSLKbVmURWV1e1A+xe4+10pqQixrC+1NaCK+4vEpBwvaJ7xtRFbbFYUZwxJFlNuyKIuewZGgzLbZc76VrYuzIna7yLkqy0/BJXDSwSmyJy53kRwfw8KsJMdiMKHHfmOj3BbvRkaBrlPUd/RzoaWXa2zYaVKJcTEsykl2dIrsifouVhakhVS7c+M8SxRRriB9HguzkgLe98lXn4j2jYquZkVBmmNTZN1u9cx4smEnM44jiUJEskRkp4ic9f6ZOcEx60XkTRE5LiJHROT9TsQaDbYsymLvhTZUA1en2H2ulcykOFZEwW5gc7GqII2atn66HdgrpKa9j57BEZsaa97BqSuKh4GXVbUMeNn7/Xh9wEdUdTVwJ/BPIpIRvBCjx5ZFWbT3DVPV1BOQ86sqe863sm1Jtg1pXIUvkTrRysNXyLYrCjOeU4niXuAJ7+0ngPvGH6CqZ1T1rPd2PdAE5AYrwGiyNcB1ikttfdR19Nu02GlY4f0070RB+8TlLmJcwrJ8u+ozV3IqUeSr6mXv7QYgf6qDRWQLEA+cm+TxT4hIpYhUNjc3+zfSKLAwK4n8tISArafY7Vs/YfWJq1qQnkhaYqwjU2RP1HexJDeZxDhbNW+uFBuoE4vIr4D5Ezz052O/UVUVkUkHx0WkAPgu8KCqTtiUSFUfBR4FqKiocG5Za5gSEbYsyuatC62oKiL+HR7afa6VvNQEluQm+/W8kUhEvAVtZ64ofFeXxowVsEShqrdO9piINIpIgape9iaCpkmOSwN+Cfy5qu4JUKgGTzfXXxyu51xzD0vz/Df0oKq8ea6V7Uuz/Z6AItXK+an8aH8tbrcGrabT1jvE5c4Bq0+YCTk19PQM8KD39oPAz8cfICLxwE+B76jqj4IYW1TavtQzLPT62Ra/nvdsUw8tPYPW32kGVhSk0Ts0Sm17f9Ce8+09KKx1h5mAU4niEeA2ETkL3Or9HhGpEJFveY95ALge+AMROeT9Wu9ItFGgOCuJkuwkdvk5Ubx2xlMz2l5m8xCmyzfz6WQQ11P4ZjytLLBCtnmngA09TUVVW4FbJri/EnjIe/tJ4MkghxbVti/N4WcH6xgedRPnpzYbr55ppiwvhcKMeX45XzRYPj8VEc+n/DtWT1Tm878Tl7uYn5ZItu1BYSZgK7PN23aU5dA7NMqhmg6/nK9vaIS3zrdxwzK7mpiJpPhYSrOD28rjRH2XNQI0k7JEYd62bUkOLvFfneKt820Mjbq5YbkliplaVRC8TYwGhkepau6xrU/NpCxRmLelz4ujvCiDXWf9sxbl1TPNJMa52FxqUy5namVBKpfa+oLSyuNMo2ezJLuiMJOxRGGusKMsh0M1HXT0Dc35XK+eaWbb4mxbwDULb+9NEYT1FNa6w1yNJQpzhVtW5uNW+M3puV1VXGrt40JLr9UnZsmXKIJRpzhxuYuUhFiKM20PCjMxSxTmCuWF6eSmJrDzZOOczvPSiQYAblqR54+wok5BeiIZSXFBSRTH67tYZXtQmClYojBXcLmEm5fn8drpZoZGJuyYMi0vHm9gxfxUSrKtbcdsiAgr56dxIsAFbbdbOWl7UJirsERh3uHWVfl0D46wr3p2TQKbuwepvNjOnWuCswYgUq0sSON0Q1dA9zO/2NZH39CoJQozJUsU5h22L80hMc7F88cuX/3gCew80YgqQVssFqlWFqQyMOzmQktvwJ7jeH0ngG1WZKZkicK8w7z4GG5Zmc9zRxsYHp358NPzxy5Tkp1ku9nNUTAK2ifqu4h1CWX5KQF7DhP+LFGYCd27bgFtvUO8UTWzxXeNXQO8UdXC3eULrFvsHJXlpxDrksAmistdlOWnkhBrU5jN5CxRmAndsDyXtMRYnjlUP6Of+9nBOtwK791YGKDIokdCbAxL81ICmih8M56MmYolCjOhhNgY7lpTwIvHG+gdHJnWz6gqPz5Qy6aSTBbn2lCGP6wMYCuPpu4BmrsHrZBtrsoShZnU+7cU0zs0yk8O1E7r+CO1nZxp7OF3NxYFOLLosbIglYauAdp6575SfjxfArLWHeZqLFGYSW0ozqC8KJ1v765G9epTNP/zjQukJMTynnUFQYguOvg2EgrE8JNvxpM1AzRX40iiEJEsEdkpIme9f2ZOcEyJiBzwblh0XEQ+6USs0UxE+INrSznX3HvVjrKXO/t59shlHqgoJi0xLkgRRj7fRkKBSBQn6rsoypxH+jz79zJTc+qK4mHgZVUtA172fj/eZWCbqq4HtgIPi8iC4IVoAN5dXkBuagL/+uuzU15V/OuvqwD46HWlQYosOmSnJJCflsCxuk6/n/vEZduDwkyPU4niXuAJ7+0ngPvGH6CqQ6o66P02ARsmc0RCbAyfvaWMfdXtvHRi4v5PVU09/GBfDR/aupDiLGss529rCzM46udE0Ts4woWWXtsj20yLU2+++arqW/bbAORPdJCIFIvIEaAG+IqqzmyupvGL928uZll+Cl/8+bF3tB8fGXXz+R8eJiUhlj+6pcyhCCNbeVE651t6/bo3xfH6LlRhbZFdUZirC1iiEJFficixCb7uHXucesYzJhzTUNUaVS0HlgIPishkCeUTIlIpIpXNzf7ZdMf8VlyMi394YD2tPUP89ycP0D80Cngayv35T49xuKaDv71vDTm233JArC1KR9Xz5u4vh73b3ZYXZfjtnCZyxQbqxKp662SPiUijiBSo6mURKQCarnKuehE5BuwAfjTB448CjwJUVFQEroNaFFtTmM7f37+OP376EHf982vcs76QV880c7img/9x81LuXmflo0BZW+gZHjpS28E1i7P9cs7DtR0UZsyz5G6mxamhp2eAB723HwR+Pv4AESkSkXne25nAduB00CI073DfhkK+87GtJMXH8i8vn6Wle5Cv37+O/3nbMqdDi2g5KQkUZszjSK3/6hSHaztYV2z1CTM9AbuiuIpHgKdF5OPAReABABGpAD6pqg8BK4Gvi4gCAvy9qh51KF7jtb0sh+c+u4PBkVHiY1zWzylI1ham+62g3dY7RE1bP7+/tcQv5zORz5FEoaqtwC0T3F8JPOS9vRMoD3JoZpqsiVxwlRen88LxBjr7hklPmtu6h8O1HZ5zWn3CTJNNOTUmDJQXZgD45aricE0HIp4iuTHTYYnCmDDwdkG7rmPO5zpS28nS3BRSEpwaeTbhxhKFMWEgPSmO0uwkDl7qmNN5VJXDNR2sK87wS1wmOliiMCZMbCzJ5MDF9mk1aJxMbXs/rb1DrLNhJzMDliiMCRMVJVm09g7NaQ/tfdVtAGwqyfJXWCYKWKIwJkxUlHqaLFdebJ/1OfZVt5OaGMty28/czIAlCmPCxNLcFNLnxbG/ei6Joo2KkkxiXLb+xUyfJQpjwoTLJWwqyaTyYtusfr6td4iqph4qSm3YycyMJQpjwsimkkzONffOamtUX31iyyJLFGZmLFEYE0Z8TQF3n5t6x8GJ7LvQRnysi3Kb8WRmyBKFMWFkXVE6qQmx7LrK1rQT2VXVwsaFGdZ+xcyYJQpjwkhsjItrlmTz+tmWGa2naOwa4FRDNzcuzwtgdCZSWaIwJszsKMuhrqOf6ta+af/Ma2c8G3pdX5YbqLBMBLNEYUyY2b40B4BdZ6e/m+OrZ5rJTU1gZYGtnzAzZ4nCmDCzKCeZwox5vHpmeoli1K28fraFG5bl2v4hZlYsURgTZkSE21bl89rZFnoHR656/P6L7XT2D3PDMht2MrPjSKIQkSwR2SkiZ71/Zk5xbJqI1IrIvwUzRmNC2V1r5jM04uY3p69+VfHLI/UkxLq4aYUVss3sOHVF8TDwsqqWAS97v5/M3wCvBSUqY8JERWkWOSnx/PJo/ZTHjbqV5441cNPyPNt/wsyaU4niXuAJ7+0ngPsmOkhENgH5wEvBCcuY8BDjEu5et4CdJxqnXKW9q6qF5u5B3rOuIIjRmUjjVKLIV9XL3tsNeJLBFUTEBXwd+MLVTiYinxCRShGpbG6e/kwQY8LZ+zcXMzyq/PRg3aTHPLnnItnJ8dy26h2/YsZMW8AShYj8SkSOTfB179jj1LNqaKKVQ58CnlPV2qs9l6o+qqoVqlqRm2sFOxMdVsxPY8PCDJ7YXc3IqPsdj9e29/HyyUburyi21dhmTgI2aKmqt072mIg0ikiBql4WkQKgaYLDtgE7RORTQAoQLyI9qjpVPcOYqPKpG5fyh9+p5GeH6nnfpqIrHvunX50lNsbFR7aVOBSdiRRODT09Azzovf0g8PPxB6jqh1R1oaqW4hl++o4lCWOudOvKPNYUpvHVF07RPqZWcaimg58cqOXBbSUsyJjnYIQmEjiVKB4BbhORs8Ct3u8RkQoR+ZZDMRkTdkSER95bTnvfEH/0XwfpGRyhrqOfT3/vAAXp8/jMTWVOh2gigMxlo/ZQVFFRoZWVlU6HYUxQPV1Zw5/+5CjJ8TEMjriJj3Hx/T+8hrXWUtxMk4jsV9WKiR6zidXGRIAHKopZnJPMDytrSUqI4Q+uLaUkO9npsEyEsERhTISoKM2ybU5NQFivJ2OMMVOyRGGMMWZKliiMMcZMyRKFMcaYKVmiMMYYMyVLFMYYY6ZkicIYY8yULFEYY4yZUsS18BCRZuDiHE6RA7T4KRwnRcrrAHstocpeS2ia7WspUdUJ92mIuEQxVyJSOVm/k3ASKa8D7LWEKnstoSkQr8WGnowxxkzJEoUxxpgpWaJ4p0edDsBPIuV1gL2WUGWvJTT5/bVYjcIYY8yU7IrCGGPMlCxRGGOMmZIlCi8RuVNETotIlYg87HQ8syUij4tIk4gcczqWuRKRYhF5RUROiMhxEfms0zHNlogkisheETnsfS1/7XRMcyEiMSJyUESedTqWuRCRahE5KiKHRCSs91AWkQwR+ZGInBKRkyKyzW/nthqF5z89cAa4DagF9gEfVNUTjgY2CyJyPdADfEdV1zgdz1yISAFQoKoHRCQV2A/cF6b/LgIkq2qPiMQBu4DPquoeh0ObFRH5Y6ACSFPV9zgdz2yJSDVQoaphv9hORJ4AXlfVb4lIPJCkqh3+OLddUXhsAapU9byqDgFPAfc6HNOsqOprQJvTcfiDql5W1QPe293ASaDQ2ahmRz16vN/Geb/C8lOaiBQB7wa+5XQsxkNE0oHrgccAVHXIX0kCLFH4FAI1Y76vJUzfkCKViJQCG4C3HA5l1rzDNYeAJmCnqobra/kn4H8Dbofj8AcFXhKR/SLyCaeDmYNFQDPwn94hwW+JSLK/Tm6JwoQ8EUkBfgx8TlW7nI5ntlR1VFXXA0XAFhEJu6FBEXkP0KSq+52OxU+2q+pG4C7g096h23AUC2wE/n9V3QD0An6rtVqi8KgDisd8X+S9zzjMO57/Y+B7qvoTp+PxB++QwCvAnQ6HMhvXAfd4x/afAm4WkSedDWn2VLXO+2cT8FM8w9DhqBaoHXOV+iM8icMvLFF47APKRGSRtwj0AeAZh2OKet4C8GPASVX9B6fjmQsRyRWRDO/teXgmTpxyNKhZUNU/VdUiVS3F83vya1X9fYfDmhURSfZOksA7THM7EJazBVW1AagRkeXeu24B/DbpI9ZfJwpnqjoiIp8BXgRigMdV9bjDYc2KiPwXcCOQIyK1wF+q6mPORjVr1wEfBo56x/YB/kxVn3MupFkrAJ7wzrBzAU+ralhPLY0A+cBPPZ9HiAW+r6ovOBvSnPwR8D3vh93zwEf9dWKbHmuMMWZKNvRkjDFmSpYojDHGTMkShTHGmClZojDGGDMlSxTGGGOmZInCmDkQkWxv59FDItIgInXe2z0i8n+djs8Yf7Dpscb4iYj8FdCjqn/vdCzG+JNdURgTACJyo2+vBhH5KxF5QkReF5GLIvJeEfmqdx+EF7xtShCRTSLyqrdB3YveNuvGOM4ShTHBsQS4GbgHeBJ4RVXXAv3Au73J4l+B96nqJuBx4MtOBWvMWNbCw5jgeF5Vh0XkKJ42Mb5WEUeBUmA5sAbY6W0pEQNcdiBOY97BEoUxwTEIoKpuERnW3xYH3Xh+DwU4rqp+277SGH+xoSdjQsNpINe3z7GIxInIaodjMgawRGFMSPBuwfs+4Csichg4BFzraFDGeNn0WGOMMVOyKwpjjDFTskRhjDFmSpYojDHGTMkShTHGmClZojDGGDMlSxTGGGOmZInCGGPMlP4fCoiJ1WGlZC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Perlin Noise\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "\n",
    "plt.plot(time, values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e320a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "noise.discard(2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45cb23cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
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      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n",
      " Error !!! Out of Bounds \n"
     ]
    }
   ],
   "source": [
    "values = [ noise.valueAt(i) if noise.valueAt(i)!=0 else 0 for i in time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9cfd376",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 0.007535416933805994]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e92ab23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Perlin Noise\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Value\")\n",
    "\n",
    "plt.plot(time, values)\n",
    "plt.show()"
   ]
  }
 ],
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