SeismicTransforms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zw9q0J3wPec9"
   },
   "source": [
    "# Pylops - seismic transforms on GPU with cupy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8SdStKmEWCSY",
    "outputId": "324ae3a3-6855-45af-d839-2111ec645f44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYTHONPATH=\n"
     ]
    }
   ],
   "source": [
    "# install conda (https://towardsdatascience.com/conda-google-colab-75f7c867a522)\n",
    "%env PYTHONPATH="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gn_pSjdpWXPR",
    "outputId": "dfea381c-e1fb-4c41-a7cd-6a9addef1ee9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREFIX=/usr/local\n",
      "installing: python-3.6.5-hc3d631a_2 ...\n",
      "installing: ca-certificates-2018.03.07-0 ...\n",
      "installing: conda-env-2.6.0-h36134e3_1 ...\n",
      "installing: libgcc-ng-7.2.0-hdf63c60_3 ...\n",
      "installing: libstdcxx-ng-7.2.0-hdf63c60_3 ...\n",
      "installing: libffi-3.2.1-hd88cf55_4 ...\n",
      "installing: ncurses-6.1-hf484d3e_0 ...\n",
      "installing: openssl-1.0.2o-h20670df_0 ...\n",
      "installing: tk-8.6.7-hc745277_3 ...\n",
      "installing: xz-5.2.4-h14c3975_4 ...\n",
      "installing: yaml-0.1.7-had09818_2 ...\n",
      "installing: zlib-1.2.11-ha838bed_2 ...\n",
      "installing: libedit-3.1.20170329-h6b74fdf_2 ...\n",
      "installing: readline-7.0-ha6073c6_4 ...\n",
      "installing: sqlite-3.23.1-he433501_0 ...\n",
      "installing: asn1crypto-0.24.0-py36_0 ...\n",
      "installing: certifi-2018.4.16-py36_0 ...\n",
      "installing: chardet-3.0.4-py36h0f667ec_1 ...\n",
      "installing: idna-2.6-py36h82fb2a8_1 ...\n",
      "installing: pycosat-0.6.3-py36h0a5515d_0 ...\n",
      "installing: pycparser-2.18-py36hf9f622e_1 ...\n",
      "installing: pysocks-1.6.8-py36_0 ...\n",
      "installing: ruamel_yaml-0.15.37-py36h14c3975_2 ...\n",
      "installing: six-1.11.0-py36h372c433_1 ...\n",
      "installing: cffi-1.11.5-py36h9745a5d_0 ...\n",
      "installing: setuptools-39.2.0-py36_0 ...\n",
      "installing: cryptography-2.2.2-py36h14c3975_0 ...\n",
      "installing: wheel-0.31.1-py36_0 ...\n",
      "installing: pip-10.0.1-py36_0 ...\n",
      "installing: pyopenssl-18.0.0-py36_0 ...\n",
      "installing: urllib3-1.22-py36hbe7ace6_0 ...\n",
      "installing: requests-2.18.4-py36he2e5f8d_1 ...\n",
      "installing: conda-4.5.4-py36_0 ...\n",
      "installation finished.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "Location: https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh [following]\n",
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      "  9400K .......... .......... .......... .......... .......... 16%  326M 0s\n",
      "  9450K .......... .......... .......... .......... .......... 16% 68.2M 0s\n",
      "  9500K .......... .......... .......... .......... .......... 16% 82.9M 0s\n",
      "  9550K .......... .......... .......... .......... .......... 16%  184M 0s\n",
      "  9600K .......... .......... .......... .......... .......... 16%  192M 0s\n",
      "  9650K .......... .......... .......... .......... .......... 16%  115M 0s\n",
      "  9700K .......... .......... .......... .......... .......... 17%  252M 0s\n",
      "  9750K .......... .......... .......... .......... .......... 17% 81.9M 0s\n",
      "  9800K .......... .......... .......... .......... .......... 17% 55.1M 0s\n",
      "  9850K .......... .......... .......... .......... .......... 17%  116M 0s\n",
      "  9900K .......... .......... .......... .......... .......... 17%  224M 0s\n",
      "  9950K .......... .......... .......... .......... .......... 17%  196M 0s\n",
      " 10000K .......... .......... .......... .......... .......... 17%  131M 0s\n",
      " 10050K .......... .......... .......... .......... .......... 17%  132M 0s\n",
      " 10100K .......... .......... .......... .......... .......... 17%  224M 0s\n",
      " 10150K .......... .......... .......... .......... .......... 17% 81.0M 0s\n",
      " 10200K .......... .......... .......... .......... .......... 17%  206M 0s\n",
      " 10250K .......... .......... .......... .......... .......... 18%  150M 0s\n",
      " 10300K .......... .......... .......... .......... .......... 18%  203M 0s\n",
      " 10350K .......... .......... .......... .......... .......... 18%  171M 0s\n",
      " 10400K .......... .......... .......... .......... .......... 18%  211M 0s\n",
      " 10450K .......... .......... .......... .......... .......... 18%  209M 0s\n",
      " 10500K .......... .......... .......... .......... .......... 18%  174M 0s\n",
      " 10550K .......... .......... .......... .......... .......... 18%  177M 0s\n",
      " 10600K .......... .......... .......... .......... .......... 18%  194M 0s\n",
      " 10650K .......... .......... .......... .......... .......... 18%  227M 0s\n",
      " 10700K .......... .......... .......... .......... .......... 18%  176M 0s\n",
      " 10750K .......... .......... .......... .......... .......... 18% 67.2M 0s\n",
      " 10800K .......... .......... .......... .......... .......... 19%  146M 0s\n",
      " 10850K .......... .......... .......... .......... .......... 19%  232M 0s\n",
      " 10900K .......... .......... .......... .......... .......... 19%  222M 0s\n",
      " 10950K .......... .......... .......... .......... .......... 19%  191M 0s\n",
      " 11000K .......... .......... .......... .......... .......... 19% 80.9M 0s\n",
      " 11050K .......... .......... .......... .......... .......... 19%  146M 0s\n",
      " 11100K .......... .......... .......... .......... .......... 19%  187M 0s\n",
      " 11150K .......... .......... .......... .......... .......... 19%  168M 0s\n",
      " 11200K .......... .......... .......... .......... .......... 19%  198M 0s\n",
      " 11250K .......... .......... .......... .......... .......... 19% 50.7M 0s\n",
      " 11300K .......... .......... .......... .......... .......... 19% 98.6M 0s\n",
      " 11350K .......... .......... .......... .......... .......... 19% 69.1M 0s\n",
      " 11400K .......... .......... .......... .......... .......... 20%  109M 0s\n",
      " 11450K .......... .......... .......... .......... .......... 20% 94.6M 0s\n",
      " 11500K .......... .......... .......... .......... .......... 20%  262M 0s\n",
      " 11550K .......... .......... .......... .......... .......... 20%  109M 0s\n",
      " 11600K .......... .......... .......... .......... .......... 20%  179M 0s\n",
      " 11650K .......... .......... .......... .......... .......... 20% 82.0M 0s\n",
      " 11700K .......... .......... .......... .......... .......... 20%  217M 0s\n",
      " 11750K .......... .......... .......... .......... .......... 20%  129M 0s\n",
      " 11800K .......... .......... .......... .......... .......... 20%  116M 0s\n",
      " 11850K .......... .......... .......... .......... .......... 20%  169M 0s\n",
      " 11900K .......... .......... .......... .......... .......... 20% 80.9M 0s\n",
      " 11950K .......... .......... .......... .......... .......... 21%  174M 0s\n",
      " 12000K .......... .......... .......... .......... .......... 21%  140M 0s\n",
      " 12050K .......... .......... .......... .......... .......... 21%  197M 0s\n",
      " 12100K .......... .......... .......... .......... .......... 21%  186M 0s\n",
      " 12150K .......... .......... .......... .......... .......... 21%  178M 0s\n",
      " 12200K .......... .......... .......... .......... .......... 21% 79.1M 0s\n",
      " 12250K .......... .......... .......... .......... .......... 21%  202M 0s\n",
      " 12300K .......... .......... .......... .......... .......... 21%  204M 0s\n",
      " 12350K .......... .......... .......... .......... .......... 21%  166M 0s\n",
      " 12400K .......... .......... .......... .......... .......... 21% 86.5M 0s\n",
      " 12450K .......... .......... .......... .......... .......... 21% 94.4M 0s\n",
      " 12500K .......... .......... .......... .......... .......... 21% 72.2M 0s\n",
      " 12550K .......... .......... .......... .......... .......... 22%  179M 0s\n",
      " 12600K .......... .......... .......... .......... .......... 22%  189M 0s\n",
      " 12650K .......... .......... .......... .......... .......... 22% 84.0M 0s\n",
      " 12700K .......... .......... .......... .......... .......... 22%  188M 0s\n",
      " 12750K .......... .......... .......... .......... .......... 22%  109M 0s\n",
      " 12800K .......... .......... .......... .......... .......... 22%  113M 0s\n",
      " 12850K .......... .......... .......... .......... .......... 22%  187M 0s\n",
      " 12900K .......... .......... .......... .......... .......... 22% 94.7M 0s\n",
      " 12950K .......... .......... .......... .......... .......... 22%  199M 0s\n",
      " 13000K .......... .......... .......... .......... .......... 22%  201M 0s\n",
      " 13050K .......... .......... .......... .......... .......... 22%  130M 0s\n",
      " 13100K .......... .......... .......... .......... .......... 23%  108M 0s\n",
      " 13150K .......... .......... .......... .......... .......... 23%  112M 0s\n",
      " 13200K .......... .......... .......... .......... .......... 23%  100M 0s\n",
      " 13250K .......... .......... .......... .......... .......... 23%  172M 0s\n",
      " 13300K .......... .......... .......... .......... .......... 23% 99.1M 0s\n",
      " 13350K .......... .......... .......... .......... .......... 23%  122M 0s\n",
      " 13400K .......... .......... .......... .......... .......... 23%  146M 0s\n",
      " 13450K .......... .......... .......... .......... .......... 23%  112M 0s\n",
      " 13500K .......... .......... .......... .......... .......... 23%  196M 0s\n",
      " 13550K .......... .......... .......... .......... .......... 23% 97.7M 0s\n",
      " 13600K .......... .......... .......... .......... .......... 23%  101M 0s\n",
      " 13650K .......... .......... .......... .......... .......... 23%  129M 0s\n",
      " 13700K .......... .......... .......... .......... .......... 24%  131M 0s\n",
      " 13750K .......... .......... .......... .......... .......... 24% 99.0M 0s\n",
      " 13800K .......... .......... .......... .......... .......... 24%  213M 0s\n",
      " 13850K .......... .......... .......... .......... .......... 24%  207M 0s\n",
      " 13900K .......... .......... .......... .......... .......... 24% 81.5M 0s\n",
      " 13950K .......... .......... .......... .......... .......... 24%  168M 0s\n",
      " 14000K .......... .......... .......... .......... .......... 24% 98.5M 0s\n",
      " 14050K .......... .......... .......... .......... .......... 24%  132M 0s\n",
      " 14100K .......... .......... .......... .......... .......... 24%  158M 0s\n",
      " 14150K .......... .......... .......... .......... .......... 24%  111M 0s\n",
      " 14200K .......... .......... .......... .......... .......... 24%  134M 0s\n",
      " 14250K .......... .......... .......... .......... .......... 25%  109M 0s\n",
      " 14300K .......... .......... .......... .......... .......... 25%  118M 0s\n",
      " 14350K .......... .......... .......... .......... .......... 25%  152M 0s\n",
      " 14400K .......... .......... .......... .......... .......... 25% 81.9M 0s\n",
      " 14450K .......... .......... .......... .......... .......... 25%  205M 0s\n",
      " 14500K .......... .......... .......... .......... .......... 25%  113M 0s\n",
      " 14550K .......... .......... .......... .......... .......... 25%  120M 0s\n",
      " 14600K .......... .......... .......... .......... .......... 25%  171M 0s\n",
      " 14650K .......... .......... .......... .......... .......... 25%  113M 0s\n",
      " 14700K .......... .......... .......... .......... .......... 25%  124M 0s\n",
      " 14750K .......... .......... .......... .......... .......... 25%  105M 0s\n",
      " 14800K .......... .......... .......... .......... .......... 26%  202M 0s\n",
      " 14850K .......... .......... .......... .......... .......... 26%  121M 0s\n",
      " 14900K .......... .......... .......... .......... .......... 26%  116M 0s\n",
      " 14950K .......... .......... .......... .......... .......... 26%  149M 0s\n",
      " 15000K .......... .......... .......... .......... .......... 26% 83.4M 0s\n",
      " 15050K .......... .......... .......... .......... .......... 26%  206M 0s\n",
      " 15100K .......... .......... .......... .......... .......... 26%  122M 0s\n",
      " 15150K .......... .......... .......... .......... .......... 26%  108M 0s\n",
      " 15200K .......... .......... .......... .......... .......... 26%  124M 0s\n",
      " 15250K .......... .......... .......... .......... .......... 26%  198M 0s\n",
      " 15300K .......... .......... .......... .......... .......... 26%  113M 0s\n",
      " 15350K .......... .......... .......... .......... .......... 26% 66.4M 0s\n",
      " 15400K .......... .......... .......... .......... .......... 27%  174M 0s\n",
      " 15450K .......... .......... .......... .......... .......... 27%  127M 0s\n",
      " 15500K .......... .......... .......... .......... .......... 27%  208M 0s\n",
      " 15550K .......... .......... .......... .......... .......... 27%  108M 0s\n",
      " 15600K .......... .......... .......... .......... .......... 27%  119M 0s\n",
      " 15650K .......... .......... .......... .......... .......... 27%  104M 0s\n",
      " 15700K .......... .......... .......... .......... .......... 27%  178M 0s\n",
      " 15750K .......... .......... .......... .......... .......... 27% 86.7M 0s\n",
      " 15800K .......... .......... .......... .......... .......... 27%  164M 0s\n",
      " 15850K .......... .......... .......... .......... .......... 27%  214M 0s\n",
      " 15900K .......... .......... .......... .......... .......... 27%  125M 0s\n",
      " 15950K .......... .......... .......... .......... .......... 28% 90.5M 0s\n",
      " 16000K .......... .......... .......... .......... .......... 28%  119M 0s\n",
      " 16050K .......... .......... .......... .......... .......... 28%  121M 0s\n",
      " 16100K .......... .......... .......... .......... .......... 28%  210M 0s\n",
      " 16150K .......... .......... .......... .......... .......... 28%  131M 0s\n",
      " 16200K .......... .......... .......... .......... .......... 28% 84.3M 0s\n",
      " 16250K .......... .......... .......... .......... .......... 28% 85.0M 0s\n",
      " 16300K .......... .......... .......... .......... .......... 28%  197M 0s\n",
      " 16350K .......... .......... .......... .......... .......... 28%  165M 0s\n",
      " 16400K .......... .......... .......... .......... .......... 28%  121M 0s\n",
      " 16450K .......... .......... .......... .......... .......... 28%  125M 0s\n",
      " 16500K .......... .......... .......... .......... .......... 28%  114M 0s\n",
      " 16550K .......... .......... .......... .......... .......... 29%  127M 0s\n",
      " 16600K .......... .......... .......... .......... .......... 29%  121M 0s\n",
      " 16650K .......... .......... .......... .......... .......... 29% 70.5M 0s\n",
      " 16700K .......... .......... .......... .......... .......... 29%  156M 0s\n",
      " 16750K .......... .......... .......... .......... .......... 29% 99.1M 0s\n",
      " 16800K .......... .......... .......... .......... .......... 29%  328M 0s\n",
      " 16850K .......... .......... .......... .......... .......... 29%  384M 0s\n",
      " 16900K .......... .......... .......... .......... .......... 29%  199M 0s\n",
      " 16950K .......... .......... .......... .......... .......... 29%  189M 0s\n",
      " 17000K .......... .......... .......... .......... .......... 29%  100M 0s\n",
      " 17050K .......... .......... .......... .......... .......... 29% 96.8M 0s\n",
      " 17100K .......... .......... .......... .......... .......... 30%  126M 0s\n",
      " 17150K .......... .......... .......... .......... .......... 30%  217M 0s\n",
      " 17200K .......... .......... .......... .......... .......... 30%  143M 0s\n",
      " 17250K .......... .......... .......... .......... .......... 30%  104M 0s\n",
      " 17300K .......... .......... .......... .......... .......... 30%  123M 0s\n",
      " 17350K .......... .......... .......... .......... .......... 30%  199M 0s\n",
      " 17400K .......... .......... .......... .......... .......... 30%  218M 0s\n",
      " 17450K .......... .......... .......... .......... .......... 30%  118M 0s\n",
      " 17500K .......... .......... .......... .......... .......... 30%  257M 0s\n",
      " 17550K .......... .......... .......... .......... .......... 30%  207M 0s\n",
      " 17600K .......... .......... .......... .......... .......... 30%  242M 0s\n",
      " 17650K .......... .......... .......... .......... .......... 30%  259M 0s\n",
      " 17700K .......... .......... .......... .......... .......... 31%  327M 0s\n",
      " 17750K .......... .......... .......... .......... .......... 31%  335M 0s\n",
      " 17800K .......... .......... .......... .......... .......... 31%  400M 0s\n",
      " 17850K .......... .......... .......... .......... .......... 31%  149M 0s\n",
      " 17900K .......... .......... .......... .......... .......... 31% 92.1M 0s\n",
      " 17950K .......... .......... .......... .......... .......... 31% 38.8M 0s\n",
      " 18000K .......... .......... .......... .......... .......... 31% 63.5M 0s\n",
      " 18050K .......... .......... .......... .......... .......... 31%  126M 0s\n",
      " 18100K .......... .......... .......... .......... .......... 31%  274M 0s\n",
      " 18150K .......... .......... .......... .......... .......... 31%  111M 0s\n",
      " 18200K .......... .......... .......... .......... .......... 31%  109M 0s\n",
      " 18250K .......... .......... .......... .......... .......... 32%  122M 0s\n",
      " 18300K .......... .......... .......... .......... .......... 32%  110M 0s\n",
      " 18350K .......... .......... .......... .......... .......... 32%  213M 0s\n",
      " 18400K .......... .......... .......... .......... .......... 32%  125M 0s\n",
      " 18450K .......... .......... .......... .......... .......... 32% 31.0M 0s\n",
      " 18500K .......... .......... .......... .......... .......... 32% 39.4M 0s\n",
      " 18550K .......... .......... .......... .......... .......... 32% 35.2M 0s\n",
      " 18600K .......... .......... .......... .......... .......... 32% 44.0M 0s\n",
      " 18650K .......... .......... .......... .......... .......... 32% 38.0M 0s\n",
      " 18700K .......... .......... .......... .......... .......... 32%  134M 0s\n",
      " 18750K .......... .......... .......... .......... .......... 32%  102M 0s\n",
      " 18800K .......... .......... .......... .......... .......... 33% 90.8M 0s\n",
      " 18850K .......... .......... .......... .......... .......... 33%  190M 0s\n",
      " 18900K .......... .......... .......... .......... .......... 33%  185M 0s\n",
      " 18950K .......... .......... .......... .......... .......... 33%  152M 0s\n",
      " 19000K .......... .......... .......... .......... .......... 33% 76.2M 0s\n",
      " 19050K .......... .......... .......... .......... .......... 33% 88.3M 0s\n",
      " 19100K .......... .......... .......... .......... .......... 33%  186M 0s\n",
      " 19150K .......... .......... .......... .......... .......... 33%  116M 0s\n",
      " 19200K .......... .......... .......... .......... .......... 33%  110M 0s\n",
      " 19250K .......... .......... .......... .......... .......... 33%  165M 0s\n",
      " 19300K .......... .......... .......... .......... .......... 33%  139M 0s\n",
      " 19350K .......... .......... .......... .......... .......... 33%  107M 0s\n",
      " 19400K .......... .......... .......... .......... .......... 34%  235M 0s\n",
      " 19450K .......... .......... .......... .......... .......... 34%  353M 0s\n",
      " 19500K .......... .......... .......... .......... .......... 34%  134M 0s\n",
      " 19550K .......... .......... .......... .......... .......... 34%  185M 0s\n",
      " 19600K .......... .......... .......... .......... .......... 34%  235M 0s\n",
      " 19650K .......... .......... .......... .......... .......... 34%  159M 0s\n",
      " 19700K .......... .......... .......... .......... .......... 34%  194M 0s\n",
      " 19750K .......... .......... .......... .......... .......... 34% 90.7M 0s\n",
      " 19800K .......... .......... .......... .......... .......... 34%  106M 0s\n",
      " 19850K .......... .......... .......... .......... .......... 34% 91.0M 0s\n",
      " 19900K .......... .......... .......... .......... .......... 34%  222M 0s\n",
      " 19950K .......... .......... .......... .......... .......... 35%  115M 0s\n",
      " 20000K .......... .......... .......... .......... .......... 35%  121M 0s\n",
      " 20050K .......... .......... .......... .......... .......... 35%  206M 0s\n",
      " 20100K .......... .......... .......... .......... .......... 35%  113M 0s\n",
      " 20150K .......... .......... .......... .......... .......... 35%  111M 0s\n",
      " 20200K .......... .......... .......... .......... .......... 35%  139M 0s\n",
      " 20250K .......... .......... .......... .......... .......... 35%  196M 0s\n",
      " 20300K .......... .......... .......... .......... .......... 35% 71.1M 0s\n",
      " 20350K .......... .......... .......... .......... .......... 35%  119M 0s\n",
      " 20400K .......... .......... .......... .......... .......... 35%  123M 0s\n",
      " 20450K .......... .......... .......... .......... .......... 35%  199M 0s\n",
      " 20500K .......... .......... .......... .......... .......... 35%  128M 0s\n",
      " 20550K .......... .......... .......... .......... .......... 36%  176M 0s\n",
      " 20600K .......... .......... .......... .......... .......... 36%  229M 0s\n",
      " 20650K .......... .......... .......... .......... .......... 36%  226M 0s\n",
      " 20700K .......... .......... .......... .......... .......... 36%  217M 0s\n",
      " 20750K .......... .......... .......... .......... .......... 36%  173M 0s\n",
      " 20800K .......... .......... .......... .......... .......... 36%  233M 0s\n",
      " 20850K .......... .......... .......... .......... .......... 36%  211M 0s\n",
      " 20900K .......... .......... .......... .......... .......... 36%  198M 0s\n",
      " 20950K .......... .......... .......... .......... .......... 36% 46.2M 0s\n",
      " 21000K .......... .......... .......... .......... .......... 36% 55.1M 0s\n",
      " 21050K .......... .......... .......... .......... .......... 36%  226M 0s\n",
      " 21100K .......... .......... .......... .......... .......... 37%  384M 0s\n",
      " 21150K .......... .......... .......... .......... .......... 37%  326M 0s\n",
      " 21200K .......... .......... .......... .......... .......... 37%  188M 0s\n",
      " 21250K .......... .......... .......... .......... .......... 37%  185M 0s\n",
      " 21300K .......... .......... .......... .......... .......... 37%  275M 0s\n",
      " 21350K .......... .......... .......... .......... .......... 37%  260M 0s\n",
      " 21400K .......... .......... .......... .......... .......... 37%  126M 0s\n",
      " 21450K .......... .......... .......... .......... .......... 37%  197M 0s\n",
      " 21500K .......... .......... .......... .......... .......... 37% 99.7M 0s\n",
      " 21550K .......... .......... .......... .......... .......... 37%  102M 0s\n",
      " 21600K .......... .......... .......... .......... .......... 37%  123M 0s\n",
      " 21650K .......... .......... .......... .......... .......... 38%  209M 0s\n",
      " 21700K .......... .......... .......... .......... .......... 38%  122M 0s\n",
      " 21750K .......... .......... .......... .......... .......... 38%  114M 0s\n",
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      " 21850K .......... .......... .......... .......... .......... 38%  216M 0s\n",
      " 21900K .......... .......... .......... .......... .......... 38%  124M 0s\n",
      " 21950K .......... .......... .......... .......... .......... 38%  166M 0s\n",
      " 22000K .......... .......... .......... .......... .......... 38%  387M 0s\n",
      " 22050K .......... .......... .......... .......... .......... 38%  406M 0s\n",
      " 22100K .......... .......... .......... .......... .......... 38%  136M 0s\n",
      " 22150K .......... .......... .......... .......... .......... 38%  221M 0s\n",
      " 22200K .......... .......... .......... .......... .......... 38%  199M 0s\n",
      " 22250K .......... .......... .......... .......... .......... 39%  247M 0s\n",
      " 22300K .......... .......... .......... .......... .......... 39%  113M 0s\n",
      " 22350K .......... .......... .......... .......... .......... 39% 78.6M 0s\n",
      " 22400K .......... .......... .......... .......... .......... 39%  203M 0s\n",
      " 22450K .......... .......... .......... .......... .......... 39%  205M 0s\n",
      " 22500K .......... .......... .......... .......... .......... 39% 93.3M 0s\n",
      " 22550K .......... .......... .......... .......... .......... 39%  187M 0s\n",
      " 22600K .......... .......... .......... .......... .......... 39%  230M 0s\n",
      " 22650K .......... .......... .......... .......... .......... 39%  110M 0s\n",
      " 22700K .......... .......... .......... .......... .......... 39%  218M 0s\n",
      " 22750K .......... .......... .......... .......... .......... 39%  195M 0s\n",
      " 22800K .......... .......... .......... .......... .......... 40%  246M 0s\n",
      " 22850K .......... .......... .......... .......... .......... 40%  254M 0s\n",
      " 22900K .......... .......... .......... .......... .......... 40%  224M 0s\n",
      " 22950K .......... .......... .......... .......... .......... 40%  193M 0s\n",
      " 23000K .......... .......... .......... .......... .......... 40%  247M 0s\n",
      " 23050K .......... .......... .......... .......... .......... 40%  243M 0s\n",
      " 23100K .......... .......... .......... .......... .......... 40%  242M 0s\n",
      " 23150K .......... .......... .......... .......... .......... 40% 93.9M 0s\n",
      " 23200K .......... .......... .......... .......... .......... 40% 36.7M 0s\n",
      " 23250K .......... .......... .......... .......... .......... 40%  145M 0s\n",
      " 23300K .......... .......... .......... .......... .......... 40%  135M 0s\n",
      " 23350K .......... .......... .......... .......... .......... 40%  186M 0s\n",
      " 23400K .......... .......... .......... .......... .......... 41%  123M 0s\n",
      " 23450K .......... .......... .......... .......... .......... 41%  111M 0s\n",
      " 23500K .......... .......... .......... .......... .......... 41%  225M 0s\n",
      " 23550K .......... .......... .......... .......... .......... 41%  123M 0s\n",
      " 23600K .......... .......... .......... .......... .......... 41%  127M 0s\n",
      " 23650K .......... .......... .......... .......... .......... 41%  146M 0s\n",
      " 23700K .......... .......... .......... .......... .......... 41%  169M 0s\n",
      " 23750K .......... .......... .......... .......... .......... 41%  118M 0s\n",
      " 23800K .......... .......... .......... .......... .......... 41%  191M 0s\n",
      " 23850K .......... .......... .......... .......... .......... 41% 74.3M 0s\n",
      " 23900K .......... .......... .......... .......... .......... 41%  130M 0s\n",
      " 23950K .......... .......... .......... .......... .......... 42%  154M 0s\n",
      " 24000K .......... .......... .......... .......... .......... 42%  117M 0s\n",
      " 24050K .......... .......... .......... .......... .......... 42%  131M 0s\n",
      " 24100K .......... .......... .......... .......... .......... 42%  191M 0s\n",
      " 24150K .......... .......... .......... .......... .......... 42% 87.0M 0s\n",
      " 24200K .......... .......... .......... .......... .......... 42%  146M 0s\n",
      " 24250K .......... .......... .......... .......... .......... 42%  191M 0s\n",
      " 24300K .......... .......... .......... .......... .......... 42%  116M 0s\n",
      " 24350K .......... .......... .......... .......... .......... 42%  108M 0s\n",
      " 24400K .......... .......... .......... .......... .......... 42% 85.4M 0s\n",
      " 24450K .......... .......... .......... .......... .......... 42%  229M 0s\n",
      " 24500K .......... .......... .......... .......... .......... 42%  178M 0s\n",
      " 24550K .......... .......... .......... .......... .......... 43%  254M 0s\n",
      " 24600K .......... .......... .......... .......... .......... 43%  286M 0s\n",
      " 24650K .......... .......... .......... .......... .......... 43%  252M 0s\n",
      " 24700K .......... .......... .......... .......... .......... 43%  220M 0s\n",
      " 24750K .......... .......... .......... .......... .......... 43%  102M 0s\n",
      " 24800K .......... .......... .......... .......... .......... 43%  149M 0s\n",
      " 24850K .......... .......... .......... .......... .......... 43% 98.4M 0s\n",
      " 24900K .......... .......... .......... .......... .......... 43%  306M 0s\n",
      " 24950K .......... .......... .......... .......... .......... 43%  303M 0s\n",
      " 25000K .......... .......... .......... .......... .......... 43%  177M 0s\n",
      " 25050K .......... .......... .......... .......... .......... 43%  281M 0s\n",
      " 25100K .......... .......... .......... .......... .......... 44%  144M 0s\n",
      " 25150K .......... .......... .......... .......... .......... 44%  314M 0s\n",
      " 25200K .......... .......... .......... .......... .......... 44%  379M 0s\n",
      " 25250K .......... .......... .......... .......... .......... 44%  112M 0s\n",
      " 25300K .......... .......... .......... .......... .......... 44%  133M 0s\n",
      " 25350K .......... .......... .......... .......... .......... 44% 80.7M 0s\n",
      " 25400K .......... .......... .......... .......... .......... 44%  145M 0s\n",
      " 25450K .......... .......... .......... .......... .......... 44%  147M 0s\n",
      " 25500K .......... .......... .......... .......... .......... 44%  403M 0s\n",
      " 25550K .......... .......... .......... .......... .......... 44%  231M 0s\n",
      " 25600K .......... .......... .......... .......... .......... 44% 92.5M 0s\n",
      " 25650K .......... .......... .......... .......... .......... 45%  193M 0s\n",
      " 25700K .......... .......... .......... .......... .......... 45%  161M 0s\n",
      " 25750K .......... .......... .......... .......... .......... 45%  109M 0s\n",
      " 25800K .......... .......... .......... .......... .......... 45%  202M 0s\n",
      " 25850K .......... .......... .......... .......... .......... 45%  127M 0s\n",
      " 25900K .......... .......... .......... .......... .......... 45%  194M 0s\n",
      " 25950K .......... .......... .......... .......... .......... 45% 72.8M 0s\n",
      " 26000K .......... .......... .......... .......... .......... 45%  197M 0s\n",
      " 26050K .......... .......... .......... .......... .......... 45%  116M 0s\n",
      " 26100K .......... .......... .......... .......... .......... 45%  107M 0s\n",
      " 26150K .......... .......... .......... .......... .......... 45%  100M 0s\n",
      " 26200K .......... .......... .......... .......... .......... 45%  194M 0s\n",
      " 26250K .......... .......... .......... .......... .......... 46%  113M 0s\n",
      " 26300K .......... .......... .......... .......... .......... 46% 92.0M 0s\n",
      " 26350K .......... .......... .......... .......... .......... 46% 76.7M 0s\n",
      " 26400K .......... .......... .......... .......... .......... 46%  109M 0s\n",
      " 26450K .......... .......... .......... .......... .......... 46%  153M 0s\n",
      " 26500K .......... .......... .......... .......... .......... 46%  158M 0s\n",
      " 26550K .......... .......... .......... .......... .......... 46%  212M 0s\n",
      " 26600K .......... .......... .......... .......... .......... 46%  114M 0s\n",
      " 26650K .......... .......... .......... .......... .......... 46%  191M 0s\n",
      " 26700K .......... .......... .......... .......... .......... 46%  282M 0s\n",
      " 26750K .......... .......... .......... .......... .......... 46% 99.3M 0s\n",
      " 26800K .......... .......... .......... .......... .......... 47%  113M 0s\n",
      " 26850K .......... .......... .......... .......... .......... 47%  199M 0s\n",
      " 26900K .......... .......... .......... .......... .......... 47% 61.3M 0s\n",
      " 26950K .......... .......... .......... .......... .......... 47%  127M 0s\n",
      " 27000K .......... .......... .......... .......... .......... 47%  167M 0s\n",
      " 27050K .......... .......... .......... .......... .......... 47%  162M 0s\n",
      " 27100K .......... .......... .......... .......... .......... 47%  223M 0s\n",
      " 27150K .......... .......... .......... .......... .......... 47%  117M 0s\n",
      " 27200K .......... .......... .......... .......... .......... 47%  332M 0s\n",
      " 27250K .......... .......... .......... .......... .......... 47%  170M 0s\n",
      " 27300K .......... .......... .......... .......... .......... 47%  343M 0s\n",
      " 27350K .......... .......... .......... .......... .......... 47%  117M 0s\n",
      " 27400K .......... .......... .......... .......... .......... 48%  122M 0s\n",
      " 27450K .......... .......... .......... .......... .......... 48%  183M 0s\n",
      " 27500K .......... .......... .......... .......... .......... 48% 89.6M 0s\n",
      " 27550K .......... .......... .......... .......... .......... 48%  280M 0s\n",
      " 27600K .......... .......... .......... .......... .......... 48%  347M 0s\n",
      " 27650K .......... .......... .......... .......... .......... 48% 61.0M 0s\n",
      " 27700K .......... .......... .......... .......... .......... 48%  130M 0s\n",
      " 27750K .......... .......... .......... .......... .......... 48% 83.4M 0s\n",
      " 27800K .......... .......... .......... .......... .......... 48%  250M 0s\n",
      " 27850K .......... .......... .......... .......... .......... 48%  109M 0s\n",
      " 27900K .......... .......... .......... .......... .......... 48%  120M 0s\n",
      " 27950K .......... .......... .......... .......... .......... 49%  185M 0s\n",
      " 28000K .......... .......... .......... .......... .......... 49% 74.0M 0s\n",
      " 28050K .......... .......... .......... .......... .......... 49%  243M 0s\n",
      " 28100K .......... .......... .......... .......... .......... 49%  150M 0s\n",
      " 28150K .......... .......... .......... .......... .......... 49%  119M 0s\n",
      " 28200K .......... .......... .......... .......... .......... 49%  247M 0s\n",
      " 28250K .......... .......... .......... .......... .......... 49%  335M 0s\n",
      " 28300K .......... .......... .......... .......... .......... 49% 73.4M 0s\n",
      " 28350K .......... .......... .......... .......... .......... 49%  280M 0s\n",
      " 28400K .......... .......... .......... .......... .......... 49%  347M 0s\n",
      " 28450K .......... .......... .......... .......... .......... 49%  115M 0s\n",
      " 28500K .......... .......... .......... .......... .......... 50%  190M 0s\n",
      " 28550K .......... .......... .......... .......... .......... 50%  280M 0s\n",
      " 28600K .......... .......... .......... .......... .......... 50%  364M 0s\n",
      " 28650K .......... .......... .......... .......... .......... 50% 65.2M 0s\n",
      " 28700K .......... .......... .......... .......... .......... 50%  101M 0s\n",
      " 28750K .......... .......... .......... .......... .......... 50%  108M 0s\n",
      " 28800K .......... .......... .......... .......... .......... 50%  211M 0s\n",
      " 28850K .......... .......... .......... .......... .......... 50%  269M 0s\n",
      " 28900K .......... .......... .......... .......... .......... 50%  113M 0s\n",
      " 28950K .......... .......... .......... .......... .......... 50%  121M 0s\n",
      " 29000K .......... .......... .......... .......... .......... 50%  134M 0s\n",
      " 29050K .......... .......... .......... .......... .......... 50%  338M 0s\n",
      " 29100K .......... .......... .......... .......... .......... 51%  346M 0s\n",
      " 29150K .......... .......... .......... .......... .......... 51%  286M 0s\n",
      " 29200K .......... .......... .......... .......... .......... 51%  351M 0s\n",
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      " 29300K .......... .......... .......... .......... .......... 51%  318M 0s\n",
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      " 29400K .......... .......... .......... .......... .......... 51%  344M 0s\n",
      " 29450K .......... .......... .......... .......... .......... 51%  186M 0s\n",
      " 29500K .......... .......... .......... .......... .......... 51% 90.7M 0s\n",
      " 29550K .......... .......... .......... .......... .......... 51% 56.0M 0s\n",
      " 29600K .......... .......... .......... .......... .......... 51% 76.9M 0s\n",
      " 29650K .......... .......... .......... .......... .......... 52%  117M 0s\n",
      " 29700K .......... .......... .......... .......... .......... 52%  105M 0s\n",
      " 29750K .......... .......... .......... .......... .......... 52% 98.0M 0s\n",
      " 29800K .......... .......... .......... .......... .......... 52%  215M 0s\n",
      " 29850K .......... .......... .......... .......... .......... 52%  178M 0s\n",
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      " 29950K .......... .......... .......... .......... .......... 52%  122M 0s\n",
      " 30000K .......... .......... .......... .......... .......... 52%  108M 0s\n",
      " 30050K .......... .......... .......... .......... .......... 52%  113M 0s\n",
      " 30100K .......... .......... .......... .......... .......... 52%  191M 0s\n",
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      " 30300K .......... .......... .......... .......... .......... 53% 92.0M 0s\n",
      " 30350K .......... .......... .......... .......... .......... 53%  120M 0s\n",
      " 30400K .......... .......... .......... .......... .......... 53% 94.9M 0s\n",
      " 30450K .......... .......... .......... .......... .......... 53%  117M 0s\n",
      " 30500K .......... .......... .......... .......... .......... 53%  144M 0s\n",
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      " 30700K .......... .......... .......... .......... .......... 53%  126M 0s\n",
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      " 30850K .......... .......... .......... .......... .......... 54%  116M 0s\n",
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      " 34450K .......... .......... .......... .......... .......... 60%  151M 0s\n",
      " 34500K .......... .......... .......... .......... .......... 60%  222M 0s\n",
      " 34550K .......... .......... .......... .......... .......... 60%  202M 0s\n",
      " 34600K .......... .......... .......... .......... .......... 60%  109M 0s\n",
      " 34650K .......... .......... .......... .......... .......... 60%  143M 0s\n",
      " 34700K .......... .......... .......... .......... .......... 60%  212M 0s\n",
      " 34750K .......... .......... .......... .......... .......... 60%  120M 0s\n",
      " 34800K .......... .......... .......... .......... .......... 61%  130M 0s\n",
      " 34850K .......... .......... .......... .......... .......... 61%  134M 0s\n",
      " 34900K .......... .......... .......... .......... .......... 61%  144M 0s\n",
      " 34950K .......... .......... .......... .......... .......... 61%  123M 0s\n",
      " 35000K .......... .......... .......... .......... .......... 61%  145M 0s\n",
      " 35050K .......... .......... .......... .......... .......... 61%  229M 0s\n",
      " 35100K .......... .......... .......... .......... .......... 61%  115M 0s\n",
      " 35150K .......... .......... .......... .......... .......... 61% 85.0M 0s\n",
      " 35200K .......... .......... .......... .......... .......... 61%  120M 0s\n",
      " 35250K .......... .......... .......... .......... .......... 61%  124M 0s\n",
      " 35300K .......... .......... .......... .......... .......... 61%  150M 0s\n",
      " 35350K .......... .......... .......... .......... .......... 61%  200M 0s\n",
      " 35400K .......... .......... .......... .......... .......... 62%  214M 0s\n",
      " 35450K .......... .......... .......... .......... .......... 62% 85.6M 0s\n",
      " 35500K .......... .......... .......... .......... .......... 62%  169M 0s\n",
      " 35550K .......... .......... .......... .......... .......... 62%  122M 0s\n",
      " 35600K .......... .......... .......... .......... .......... 62%  110M 0s\n",
      " 35650K .......... .......... .......... .......... .......... 62%  225M 0s\n",
      " 35700K .......... .......... .......... .......... .......... 62%  242M 0s\n",
      " 35750K .......... .......... .......... .......... .......... 62% 76.5M 0s\n",
      " 35800K .......... .......... .......... .......... .......... 62%  222M 0s\n",
      " 35850K .......... .......... .......... .......... .......... 62% 99.2M 0s\n",
      " 35900K .......... .......... .......... .......... .......... 62%  136M 0s\n",
      " 35950K .......... .......... .......... .......... .......... 63%  117M 0s\n",
      " 36000K .......... .......... .......... .......... .......... 63%  108M 0s\n",
      " 36050K .......... .......... .......... .......... .......... 63%  209M 0s\n",
      " 36100K .......... .......... .......... .......... .......... 63%  218M 0s\n",
      " 36150K .......... .......... .......... .......... .......... 63% 91.2M 0s\n",
      " 36200K .......... .......... .......... .......... .......... 63%  229M 0s\n",
      " 36250K .......... .......... .......... .......... .......... 63%  148M 0s\n",
      " 36300K .......... .......... .......... .......... .......... 63%  108M 0s\n",
      " 36350K .......... .......... .......... .......... .......... 63%  128M 0s\n",
      " 36400K .......... .......... .......... .......... .......... 63%  127M 0s\n",
      " 36450K .......... .......... .......... .......... .......... 63%  126M 0s\n",
      " 36500K .......... .......... .......... .......... .......... 64%  211M 0s\n",
      " 36550K .......... .......... .......... .......... .......... 64%  119M 0s\n",
      " 36600K .......... .......... .......... .......... .......... 64%  206M 0s\n",
      " 36650K .......... .......... .......... .......... .......... 64%  136M 0s\n",
      " 36700K .......... .......... .......... .......... .......... 64%  115M 0s\n",
      " 36750K .......... .......... .......... .......... .......... 64%  144M 0s\n",
      " 36800K .......... .......... .......... .......... .......... 64%  218M 0s\n",
      " 36850K .......... .......... .......... .......... .......... 64%  111M 0s\n",
      " 36900K .......... .......... .......... .......... .......... 64%  185M 0s\n",
      " 36950K .......... .......... .......... .......... .......... 64% 84.1M 0s\n",
      " 37000K .......... .......... .......... .......... .......... 64%  236M 0s\n",
      " 37050K .......... .......... .......... .......... .......... 64%  152M 0s\n",
      " 37100K .......... .......... .......... .......... .......... 65%  145M 0s\n",
      " 37150K .......... .......... .......... .......... .......... 65%  193M 0s\n",
      " 37200K .......... .......... .......... .......... .......... 65%  143M 0s\n",
      " 37250K .......... .......... .......... .......... .......... 65%  120M 0s\n",
      " 37300K .......... .......... .......... .......... .......... 65%  235M 0s\n",
      " 37350K .......... .......... .......... .......... .......... 65%  132M 0s\n",
      " 37400K .......... .......... .......... .......... .......... 65%  110M 0s\n",
      " 37450K .......... .......... .......... .......... .......... 65%  149M 0s\n",
      " 37500K .......... .......... .......... .......... .......... 65%  212M 0s\n",
      " 37550K .......... .......... .......... .......... .......... 65%  113M 0s\n",
      " 37600K .......... .......... .......... .......... .......... 65%  114M 0s\n",
      " 37650K .......... .......... .......... .......... .......... 66%  221M 0s\n",
      " 37700K .......... .......... .......... .......... .......... 66%  135M 0s\n",
      " 37750K .......... .......... .......... .......... .......... 66%  214M 0s\n",
      " 37800K .......... .......... .......... .......... .......... 66%  138M 0s\n",
      " 37850K .......... .......... .......... .......... .......... 66%  127M 0s\n",
      " 37900K .......... .......... .......... .......... .......... 66%  229M 0s\n",
      " 37950K .......... .......... .......... .......... .......... 66% 91.9M 0s\n",
      " 38000K .......... .......... .......... .......... .......... 66%  164M 0s\n",
      " 38050K .......... .......... .......... .......... .......... 66%  232M 0s\n",
      " 38100K .......... .......... .......... .......... .......... 66%  110M 0s\n",
      " 38150K .......... .......... .......... .......... .......... 66%  146M 0s\n",
      " 38200K .......... .......... .......... .......... .......... 66%  244M 0s\n",
      " 38250K .......... .......... .......... .......... .......... 67%  135M 0s\n",
      " 38300K .......... .......... .......... .......... .......... 67%  128M 0s\n",
      " 38350K .......... .......... .......... .......... .......... 67%  110M 0s\n",
      " 38400K .......... .......... .......... .......... .......... 67%  194M 0s\n",
      " 38450K .......... .......... .......... .......... .......... 67%  217M 0s\n",
      " 38500K .......... .......... .......... .......... .......... 67%  239M 0s\n",
      " 38550K .......... .......... .......... .......... .......... 67%  103M 0s\n",
      " 38600K .......... .......... .......... .......... .......... 67%  183M 0s\n",
      " 38650K .......... .......... .......... .......... .......... 67%  241M 0s\n",
      " 38700K .......... .......... .......... .......... .......... 67%  163M 0s\n",
      " 38750K .......... .......... .......... .......... .......... 67% 82.1M 0s\n",
      " 38800K .......... .......... .......... .......... .......... 68%  238M 0s\n",
      " 38850K .......... .......... .......... .......... .......... 68%  112M 0s\n",
      " 38900K .......... .......... .......... .......... .......... 68%  150M 0s\n",
      " 38950K .......... .......... .......... .......... .......... 68%  139M 0s\n",
      " 39000K .......... .......... .......... .......... .......... 68%  220M 0s\n",
      " 39050K .......... .......... .......... .......... .......... 68%  147M 0s\n",
      " 39100K .......... .......... .......... .......... .......... 68%  138M 0s\n",
      " 39150K .......... .......... .......... .......... .......... 68%  201M 0s\n",
      " 39200K .......... .......... .......... .......... .......... 68%  147M 0s\n",
      " 39250K .......... .......... .......... .......... .......... 68%  106M 0s\n",
      " 39300K .......... .......... .......... .......... .......... 68%  258M 0s\n",
      " 39350K .......... .......... .......... .......... .......... 69%  122M 0s\n",
      " 39400K .......... .......... .......... .......... .......... 69%  129M 0s\n",
      " 39450K .......... .......... .......... .......... .......... 69%  239M 0s\n",
      " 39500K .......... .......... .......... .......... .......... 69%  138M 0s\n",
      " 39550K .......... .......... .......... .......... .......... 69%  115M 0s\n",
      " 39600K .......... .......... .......... .......... .......... 69%  240M 0s\n",
      " 39650K .......... .......... .......... .......... .......... 69%  143M 0s\n",
      " 39700K .......... .......... .......... .......... .......... 69%  135M 0s\n",
      " 39750K .......... .......... .......... .......... .......... 69%  215M 0s\n",
      " 39800K .......... .......... .......... .......... .......... 69%  227M 0s\n",
      " 39850K .......... .......... .......... .......... .......... 69% 97.6M 0s\n",
      " 39900K .......... .......... .......... .......... .......... 69%  216M 0s\n",
      " 39950K .......... .......... .......... .......... .......... 70%  181M 0s\n",
      " 40000K .......... .......... .......... .......... .......... 70% 77.6M 0s\n",
      " 40050K .......... .......... .......... .......... .......... 70%  199M 0s\n",
      " 40100K .......... .......... .......... .......... .......... 70%  200M 0s\n",
      " 40150K .......... .......... .......... .......... .......... 70% 75.5M 0s\n",
      " 40200K .......... .......... .......... .......... .......... 70%  232M 0s\n",
      " 40250K .......... .......... .......... .......... .......... 70%  234M 0s\n",
      " 40300K .......... .......... .......... .......... .......... 70%  130M 0s\n",
      " 40350K .......... .......... .......... .......... .......... 70%  124M 0s\n",
      " 40400K .......... .......... .......... .......... .......... 70%  116M 0s\n",
      " 40450K .......... .......... .......... .......... .......... 70%  120M 0s\n",
      " 40500K .......... .......... .......... .......... .......... 71%  187M 0s\n",
      " 40550K .......... .......... .......... .......... .......... 71%  120M 0s\n",
      " 40600K .......... .......... .......... .......... .......... 71%  120M 0s\n",
      " 40650K .......... .......... .......... .......... .......... 71%  126M 0s\n",
      " 40700K .......... .......... .......... .......... .......... 71%  220M 0s\n",
      " 40750K .......... .......... .......... .......... .......... 71%  111M 0s\n",
      " 40800K .......... .......... .......... .......... .......... 71%  135M 0s\n",
      " 40850K .......... .......... .......... .......... .......... 71%  104M 0s\n",
      " 40900K .......... .......... .......... .......... .......... 71%  186M 0s\n",
      " 40950K .......... .......... .......... .......... .......... 71%  203M 0s\n",
      " 41000K .......... .......... .......... .......... .......... 71%  131M 0s\n",
      " 41050K .......... .......... .......... .......... .......... 71%  124M 0s\n",
      " 41100K .......... .......... .......... .......... .......... 72%  230M 0s\n",
      " 41150K .......... .......... .......... .......... .......... 72%  109M 0s\n",
      " 41200K .......... .......... .......... .......... .......... 72%  138M 0s\n",
      " 41250K .......... .......... .......... .......... .......... 72%  222M 0s\n",
      " 41300K .......... .......... .......... .......... .......... 72%  124M 0s\n",
      " 41350K .......... .......... .......... .......... .......... 72%  109M 0s\n",
      " 41400K .......... .......... .......... .......... .......... 72%  136M 0s\n",
      " 41450K .......... .......... .......... .......... .......... 72%  211M 0s\n",
      " 41500K .......... .......... .......... .......... .......... 72%  127M 0s\n",
      " 41550K .......... .......... .......... .......... .......... 72%  174M 0s\n",
      " 41600K .......... .......... .......... .......... .......... 72%  211M 0s\n",
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      " 41700K .......... .......... .......... .......... .......... 73%  184M 0s\n",
      " 41750K .......... .......... .......... .......... .......... 73% 71.6M 0s\n",
      " 41800K .......... .......... .......... .......... .......... 73% 86.9M 0s\n",
      " 41850K .......... .......... .......... .......... .......... 73%  210M 0s\n",
      " 41900K .......... .......... .......... .......... .......... 73%  212M 0s\n",
      " 41950K .......... .......... .......... .......... .......... 73%  164M 0s\n",
      " 42000K .......... .......... .......... .......... .......... 73%  206M 0s\n",
      " 42050K .......... .......... .......... .......... .......... 73%  191M 0s\n",
      " 42100K .......... .......... .......... .......... .......... 73%  202M 0s\n",
      " 42150K .......... .......... .......... .......... .......... 73% 71.6M 0s\n",
      " 42200K .......... .......... .......... .......... .......... 73% 64.9M 0s\n",
      " 42250K .......... .......... .......... .......... .......... 74% 87.2M 0s\n",
      " 42300K .......... .......... .......... .......... .......... 74%  200M 0s\n",
      " 42350K .......... .......... .......... .......... .......... 74%  169M 0s\n",
      " 42400K .......... .......... .......... .......... .......... 74% 95.6M 0s\n",
      " 42450K .......... .......... .......... .......... .......... 74%  191M 0s\n",
      " 42500K .......... .......... .......... .......... .......... 74%  244M 0s\n",
      " 42550K .......... .......... .......... .......... .......... 74% 60.0M 0s\n",
      " 42600K .......... .......... .......... .......... .......... 74% 96.6M 0s\n",
      " 42650K .......... .......... .......... .......... .......... 74%  115M 0s\n",
      " 42700K .......... .......... .......... .......... .......... 74%  215M 0s\n",
      " 42750K .......... .......... .......... .......... .......... 74% 74.5M 0s\n",
      " 42800K .......... .......... .......... .......... .......... 75%  169M 0s\n",
      " 42850K .......... .......... .......... .......... .......... 75% 30.8M 0s\n",
      " 42900K .......... .......... .......... .......... .......... 75% 30.2M 0s\n",
      " 42950K .......... .......... .......... .......... .......... 75% 92.3M 0s\n",
      " 43000K .......... .......... .......... .......... .......... 75%  187M 0s\n",
      " 43050K .......... .......... .......... .......... .......... 75%  162M 0s\n",
      " 43100K .......... .......... .......... .......... .......... 75%  228M 0s\n",
      " 43150K .......... .......... .......... .......... .......... 75% 40.7M 0s\n",
      " 43200K .......... .......... .......... .......... .......... 75% 22.0M 0s\n",
      " 43250K .......... .......... .......... .......... .......... 75% 51.9M 0s\n",
      " 43300K .......... .......... .......... .......... .......... 75%  192M 0s\n",
      " 43350K .......... .......... .......... .......... .......... 76%  158M 0s\n",
      " 43400K .......... .......... .......... .......... .......... 76% 63.2M 0s\n",
      " 43450K .......... .......... .......... .......... .......... 76% 36.2M 0s\n",
      " 43500K .......... .......... .......... .......... .......... 76%  124M 0s\n",
      " 43550K .......... .......... .......... .......... .......... 76% 96.1M 0s\n",
      " 43600K .......... .......... .......... .......... .......... 76%  202M 0s\n",
      " 43650K .......... .......... .......... .......... .......... 76%  180M 0s\n",
      " 43700K .......... .......... .......... .......... .......... 76% 97.6M 0s\n",
      " 43750K .......... .......... .......... .......... .......... 76%  189M 0s\n",
      " 43800K .......... .......... .......... .......... .......... 76%  120M 0s\n",
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      " 43900K .......... .......... .......... .......... .......... 76%  198M 0s\n",
      " 43950K .......... .......... .......... .......... .......... 77% 71.8M 0s\n",
      " 44000K .......... .......... .......... .......... .......... 77%  186M 0s\n",
      " 44050K .......... .......... .......... .......... .......... 77%  198M 0s\n",
      " 44100K .......... .......... .......... .......... .......... 77% 77.9M 0s\n",
      " 44150K .......... .......... .......... .......... .......... 77%  176M 0s\n",
      " 44200K .......... .......... .......... .......... .......... 77% 93.4M 0s\n",
      " 44250K .......... .......... .......... .......... .......... 77%  149M 0s\n",
      " 44300K .......... .......... .......... .......... .......... 77%  142M 0s\n",
      " 44350K .......... .......... .......... .......... .......... 77%  112M 0s\n",
      " 44400K .......... .......... .......... .......... .......... 77%  194M 0s\n",
      " 44450K .......... .......... .......... .......... .......... 77%  120M 0s\n",
      " 44500K .......... .......... .......... .......... .......... 78% 98.9M 0s\n",
      " 44550K .......... .......... .......... .......... .......... 78%  125M 0s\n",
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      " 44950K .......... .......... .......... .......... .......... 78% 94.0M 0s\n",
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      " 46050K .......... .......... .......... .......... .......... 80%  113M 0s\n",
      " 46100K .......... .......... .......... .......... .......... 80% 86.3M 0s\n",
      " 46150K .......... .......... .......... .......... .......... 80% 85.0M 0s\n",
      " 46200K .......... .......... .......... .......... .......... 81%  211M 0s\n",
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      " 46700K .......... .......... .......... .......... .......... 81% 99.3M 0s\n",
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      " 47050K .......... .......... .......... .......... .......... 82%  137M 0s\n",
      " 47100K .......... .......... .......... .......... .......... 82%  228M 0s\n",
      " 47150K .......... .......... .......... .......... .......... 82%  136M 0s\n",
      " 47200K .......... .......... .......... .......... .......... 82%  240M 0s\n",
      " 47250K .......... .......... .......... .......... .......... 82%  120M 0s\n",
      " 47300K .......... .......... .......... .......... .......... 82%  150M 0s\n",
      " 47350K .......... .......... .......... .......... .......... 83%  216M 0s\n",
      " 47400K .......... .......... .......... .......... .......... 83%  238M 0s\n",
      " 47450K .......... .......... .......... .......... .......... 83% 79.1M 0s\n",
      " 47500K .......... .......... .......... .......... .......... 83%  226M 0s\n",
      " 47550K .......... .......... .......... .......... .......... 83%  114M 0s\n",
      " 47600K .......... .......... .......... .......... .......... 83%  160M 0s\n",
      " 47650K .......... .......... .......... .......... .......... 83%  152M 0s\n",
      " 47700K .......... .......... .......... .......... .......... 83%  140M 0s\n",
      " 47750K .......... .......... .......... .......... .......... 83%  133M 0s\n",
      " 47800K .......... .......... .......... .......... .......... 83%  228M 0s\n",
      " 47850K .......... .......... .......... .......... .......... 83%  248M 0s\n",
      " 47900K .......... .......... .......... .......... .......... 83%  122M 0s\n",
      " 47950K .......... .......... .......... .......... .......... 84%  156M 0s\n",
      " 48000K .......... .......... .......... .......... .......... 84%  142M 0s\n",
      " 48050K .......... .......... .......... .......... .......... 84%  237M 0s\n",
      " 48100K .......... .......... .......... .......... .......... 84% 93.7M 0s\n",
      " 48150K .......... .......... .......... .......... .......... 84%  140M 0s\n",
      " 48200K .......... .......... .......... .......... .......... 84%  245M 0s\n",
      " 48250K .......... .......... .......... .......... .......... 84%  116M 0s\n",
      " 48300K .......... .......... .......... .......... .......... 84%  148M 0s\n",
      " 48350K .......... .......... .......... .......... .......... 84%  200M 0s\n",
      " 48400K .......... .......... .......... .......... .......... 84% 85.9M 0s\n",
      " 48450K .......... .......... .......... .......... .......... 84%  236M 0s\n",
      " 48500K .......... .......... .......... .......... .......... 85%  242M 0s\n",
      " 48550K .......... .......... .......... .......... .......... 85%  133M 0s\n",
      " 48600K .......... .......... .......... .......... .......... 85%  136M 0s\n",
      " 48650K .......... .......... .......... .......... .......... 85%  245M 0s\n",
      " 48700K .......... .......... .......... .......... .......... 85%  114M 0s\n",
      " 48750K .......... .......... .......... .......... .......... 85%  126M 0s\n",
      " 48800K .......... .......... .......... .......... .......... 85%  196M 0s\n",
      " 48850K .......... .......... .......... .......... .......... 85% 90.3M 0s\n",
      " 48900K .......... .......... .......... .......... .......... 85%  209M 0s\n",
      " 48950K .......... .......... .......... .......... .......... 85%  193M 0s\n",
      " 49000K .......... .......... .......... .......... .......... 85%  117M 0s\n",
      " 49050K .......... .......... .......... .......... .......... 85%  135M 0s\n",
      " 49100K .......... .......... .......... .......... .......... 86%  226M 0s\n",
      " 49150K .......... .......... .......... .......... .......... 86% 75.3M 0s\n",
      " 49200K .......... .......... .......... .......... .......... 86%  236M 0s\n",
      " 49250K .......... .......... .......... .......... .......... 86% 77.9M 0s\n",
      " 49300K .......... .......... .......... .......... .......... 86% 66.4M 0s\n",
      " 49350K .......... .......... .......... .......... .......... 86%  207M 0s\n",
      " 49400K .......... .......... .......... .......... .......... 86%  215M 0s\n",
      " 49450K .......... .......... .......... .......... .......... 86%  104M 0s\n",
      " 49500K .......... .......... .......... .......... .......... 86%  158M 0s\n",
      " 49550K .......... .......... .......... .......... .......... 86%  199M 0s\n",
      " 49600K .......... .......... .......... .......... .......... 86% 63.4M 0s\n",
      " 49650K .......... .......... .......... .......... .......... 87%  209M 0s\n",
      " 49700K .......... .......... .......... .......... .......... 87% 73.7M 0s\n",
      " 49750K .......... .......... .......... .......... .......... 87%  160M 0s\n",
      " 49800K .......... .......... .......... .......... .......... 87%  209M 0s\n",
      " 49850K .......... .......... .......... .......... .......... 87% 81.2M 0s\n",
      " 49900K .......... .......... .......... .......... .......... 87%  235M 0s\n",
      " 49950K .......... .......... .......... .......... .......... 87% 97.1M 0s\n",
      " 50000K .......... .......... .......... .......... .......... 87% 57.6M 0s\n",
      " 50050K .......... .......... .......... .......... .......... 87% 36.5M 0s\n",
      " 50100K .......... .......... .......... .......... .......... 87%  224M 0s\n",
      " 50150K .......... .......... .......... .......... .......... 87%  217M 0s\n",
      " 50200K .......... .......... .......... .......... .......... 88%  162M 0s\n",
      " 50250K .......... .......... .......... .......... .......... 88%  138M 0s\n",
      " 50300K .......... .......... .......... .......... .......... 88%  241M 0s\n",
      " 50350K .......... .......... .......... .......... .......... 88% 80.2M 0s\n",
      " 50400K .......... .......... .......... .......... .......... 88%  125M 0s\n",
      " 50450K .......... .......... .......... .......... .......... 88%  105M 0s\n",
      " 50500K .......... .......... .......... .......... .......... 88%  219M 0s\n",
      " 50550K .......... .......... .......... .......... .......... 88%  124M 0s\n",
      " 50600K .......... .......... .......... .......... .......... 88%  238M 0s\n",
      " 50650K .......... .......... .......... .......... .......... 88%  207M 0s\n",
      " 50700K .......... .......... .......... .......... .......... 88%  216M 0s\n",
      " 50750K .......... .......... .......... .......... .......... 88% 84.8M 0s\n",
      " 50800K .......... .......... .......... .......... .......... 89% 65.6M 0s\n",
      " 50850K .......... .......... .......... .......... .......... 89%  117M 0s\n",
      " 50900K .......... .......... .......... .......... .......... 89%  215M 0s\n",
      " 50950K .......... .......... .......... .......... .......... 89%  112M 0s\n",
      " 51000K .......... .......... .......... .......... .......... 89%  153M 0s\n",
      " 51050K .......... .......... .......... .......... .......... 89%  239M 0s\n",
      " 51100K .......... .......... .......... .......... .......... 89%  117M 0s\n",
      " 51150K .......... .......... .......... .......... .......... 89%  115M 0s\n",
      " 51200K .......... .......... .......... .......... .......... 89%  130M 0s\n",
      " 51250K .......... .......... .......... .......... .......... 89%  239M 0s\n",
      " 51300K .......... .......... .......... .......... .......... 89%  241M 0s\n",
      " 51350K .......... .......... .......... .......... .......... 90% 81.5M 0s\n",
      " 51400K .......... .......... .......... .......... .......... 90%  222M 0s\n",
      " 51450K .......... .......... .......... .......... .......... 90%  240M 0s\n",
      " 51500K .......... .......... .......... .......... .......... 90%  126M 0s\n",
      " 51550K .......... .......... .......... .......... .......... 90%  130M 0s\n",
      " 51600K .......... .......... .......... .......... .......... 90%  239M 0s\n",
      " 51650K .......... .......... .......... .......... .......... 90%  137M 0s\n",
      " 51700K .......... .......... .......... .......... .......... 90%  133M 0s\n",
      " 51750K .......... .......... .......... .......... .......... 90% 97.5M 0s\n",
      " 51800K .......... .......... .......... .......... .......... 90%  233M 0s\n",
      " 51850K .......... .......... .......... .......... .......... 90%  148M 0s\n",
      " 51900K .......... .......... .......... .......... .......... 90%  135M 0s\n",
      " 51950K .......... .......... .......... .......... .......... 91%  191M 0s\n",
      " 52000K .......... .......... .......... .......... .......... 91%  128M 0s\n",
      " 52050K .......... .......... .......... .......... .......... 91%  100M 0s\n",
      " 52100K .......... .......... .......... .......... .......... 91%  113M 0s\n",
      " 52150K .......... .......... .......... .......... .......... 91%  195M 0s\n",
      " 52200K .......... .......... .......... .......... .......... 91%  125M 0s\n",
      " 52250K .......... .......... .......... .......... .......... 91%  228M 0s\n",
      " 52300K .......... .......... .......... .......... .......... 91%  120M 0s\n",
      " 52350K .......... .......... .......... .......... .......... 91%  127M 0s\n",
      " 52400K .......... .......... .......... .......... .......... 91%  152M 0s\n",
      " 52450K .......... .......... .......... .......... .......... 91%  232M 0s\n",
      " 52500K .......... .......... .......... .......... .......... 92%  143M 0s\n",
      " 52550K .......... .......... .......... .......... .......... 92%  100M 0s\n",
      " 52600K .......... .......... .......... .......... .......... 92%  342M 0s\n",
      " 52650K .......... .......... .......... .......... .......... 92%  410M 0s\n",
      " 52700K .......... .......... .......... .......... .......... 92%  133M 0s\n",
      " 52750K .......... .......... .......... .......... .......... 92%  126M 0s\n",
      " 52800K .......... .......... .......... .......... .......... 92%  247M 0s\n",
      " 52850K .......... .......... .......... .......... .......... 92%  128M 0s\n",
      " 52900K .......... .......... .......... .......... .......... 92%  105M 0s\n",
      " 52950K .......... .......... .......... .......... .......... 92%  125M 0s\n",
      " 53000K .......... .......... .......... .......... .......... 92%  120M 0s\n",
      " 53050K .......... .......... .......... .......... .......... 92%  227M 0s\n",
      " 53100K .......... .......... .......... .......... .......... 93%  138M 0s\n",
      " 53150K .......... .......... .......... .......... .......... 93%  101M 0s\n",
      " 53200K .......... .......... .......... .......... .......... 93%  246M 0s\n",
      " 53250K .......... .......... .......... .......... .......... 93%  153M 0s\n",
      " 53300K .......... .......... .......... .......... .......... 93%  132M 0s\n",
      " 53350K .......... .......... .......... .......... .......... 93%  218M 0s\n",
      " 53400K .......... .......... .......... .......... .......... 93%  253M 0s\n",
      " 53450K .......... .......... .......... .......... .......... 93% 90.1M 0s\n",
      " 53500K .......... .......... .......... .......... .......... 93%  241M 0s\n",
      " 53550K .......... .......... .......... .......... .......... 93%  206M 0s\n",
      " 53600K .......... .......... .......... .......... .......... 93% 75.2M 0s\n",
      " 53650K .......... .......... .......... .......... .......... 94%  240M 0s\n",
      " 53700K .......... .......... .......... .......... .......... 94%  150M 0s\n",
      " 53750K .......... .......... .......... .......... .......... 94%  132M 0s\n",
      " 53800K .......... .......... .......... .......... .......... 94%  209M 0s\n",
      " 53850K .......... .......... .......... .......... .......... 94%  100M 0s\n",
      " 53900K .......... .......... .......... .......... .......... 94%  120M 0s\n",
      " 53950K .......... .......... .......... .......... .......... 94%  105M 0s\n",
      " 54000K .......... .......... .......... .......... .......... 94%  201M 0s\n",
      " 54050K .......... .......... .......... .......... .......... 94%  133M 0s\n",
      " 54100K .......... .......... .......... .......... .......... 94%  123M 0s\n",
      " 54150K .......... .......... .......... .......... .......... 94%  176M 0s\n",
      " 54200K .......... .......... .......... .......... .......... 95%  132M 0s\n",
      " 54250K .......... .......... .......... .......... .......... 95% 98.8M 0s\n",
      " 54300K .......... .......... .......... .......... .......... 95%  206M 0s\n",
      " 54350K .......... .......... .......... .......... .......... 95% 82.8M 0s\n",
      " 54400K .......... .......... .......... .......... .......... 95%  164M 0s\n",
      " 54450K .......... .......... .......... .......... .......... 95%  129M 0s\n",
      " 54500K .......... .......... .......... .......... .......... 95%  114M 0s\n",
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      " 54600K .......... .......... .......... .......... .......... 95%  212M 0s\n",
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      " 54750K .......... .......... .......... .......... .......... 95%  156M 0s\n",
      " 54800K .......... .......... .......... .......... .......... 96% 63.6M 0s\n",
      " 54850K .......... .......... .......... .......... .......... 96%  214M 0s\n",
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      " 54950K .......... .......... .......... .......... .......... 96%  124M 0s\n",
      " 55000K .......... .......... .......... .......... .......... 96%  125M 0s\n",
      " 55050K .......... .......... .......... .......... .......... 96%  130M 0s\n",
      " 55100K .......... .......... .......... .......... .......... 96%  140M 0s\n",
      " 55150K .......... .......... .......... .......... .......... 96%  184M 0s\n",
      " 55200K .......... .......... .......... .......... .......... 96%  211M 0s\n",
      " 55250K .......... .......... .......... .......... .......... 96%  123M 0s\n",
      " 55300K .......... .......... .......... .......... .......... 96% 93.6M 0s\n",
      " 55350K .......... .......... .......... .......... .......... 97% 85.5M 0s\n",
      " 55400K .......... .......... .......... .......... .......... 97%  181M 0s\n",
      " 55450K .......... .......... .......... .......... .......... 97%  209M 0s\n",
      " 55500K .......... .......... .......... .......... .......... 97% 86.5M 0s\n",
      " 55550K .......... .......... .......... .......... .......... 97%  166M 0s\n",
      " 55600K .......... .......... .......... .......... .......... 97%  185M 0s\n",
      " 55650K .......... .......... .......... .......... .......... 97%  190M 0s\n",
      " 55700K .......... .......... .......... .......... .......... 97%  202M 0s\n",
      " 55750K .......... .......... .......... .......... .......... 97%  180M 0s\n",
      " 55800K .......... .......... .......... .......... .......... 97%  191M 0s\n",
      " 55850K .......... .......... .......... .......... .......... 97%  200M 0s\n",
      " 55900K .......... .......... .......... .......... .......... 97%  200M 0s\n",
      " 55950K .......... .......... .......... .......... .......... 98%  171M 0s\n",
      " 56000K .......... .......... .......... .......... .......... 98%  177M 0s\n",
      " 56050K .......... .......... .......... .......... .......... 98%  204M 0s\n",
      " 56100K .......... .......... .......... .......... .......... 98%  206M 0s\n",
      " 56150K .......... .......... .......... .......... .......... 98%  179M 0s\n",
      " 56200K .......... .......... .......... .......... .......... 98%  188M 0s\n",
      " 56250K .......... .......... .......... .......... .......... 98%  204M 0s\n",
      " 56300K .......... .......... .......... .......... .......... 98%  205M 0s\n",
      " 56350K .......... .......... .......... .......... .......... 98%  172M 0s\n",
      " 56400K .......... .......... .......... .......... .......... 98%  186M 0s\n",
      " 56450K .......... .......... .......... .......... .......... 98%  202M 0s\n",
      " 56500K .......... .......... .......... .......... .......... 99%  204M 0s\n",
      " 56550K .......... .......... .......... .......... .......... 99%  184M 0s\n",
      " 56600K .......... .......... .......... .......... .......... 99%  189M 0s\n",
      " 56650K .......... .......... .......... .......... .......... 99%  203M 0s\n",
      " 56700K .......... .......... .......... .......... .......... 99%  187M 0s\n",
      " 56750K .......... .......... .......... .......... .......... 99%  163M 0s\n",
      " 56800K .......... .......... .......... .......... .......... 99%  198M 0s\n",
      " 56850K .......... .......... .......... .......... .......... 99%  203M 0s\n",
      " 56900K .......... .......... .......... .......... .......... 99%  200M 0s\n",
      " 56950K .......... .......... .......... .......... .......... 99%  178M 0s\n",
      " 57000K .......... .......... .......... .......... .......... 99%  192M 0s\n",
      " 57050K .......... .......... .......... .......... ........  100%  201M=0.4s\n",
      "\n",
      "2021-07-24 11:08:21 (134 MB/s) - ‘Miniconda3-4.5.4-Linux-x86_64.sh’ saved [58468498/58468498]\n",
      "\n",
      "Python 3.6.5 :: Anaconda, Inc.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "MINICONDA_INSTALLER_SCRIPT=Miniconda3-4.5.4-Linux-x86_64.sh\n",
    "MINICONDA_PREFIX=/usr/local\n",
    "wget https://repo.continuum.io/miniconda/$MINICONDA_INSTALLER_SCRIPT\n",
    "chmod +x $MINICONDA_INSTALLER_SCRIPT\n",
    "./$MINICONDA_INSTALLER_SCRIPT -b -f -p $MINICONDA_PREFIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nuzzjnuKWmuv",
    "outputId": "6e04ef3a-aca3-4790-abcd-ed94f9843a1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: ...working... done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /usr/local\n",
      "\n",
      "  added / updated specs: \n",
      "    - conda\n",
      "    - python=3.6\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    cryptography-3.4.7         |   py36hd23ed53_0         1.0 MB\n",
      "    ld_impl_linux-64-2.35.1    |       h7274673_9         637 KB\n",
      "    libstdcxx-ng-9.1.0         |       hdf63c60_0         4.0 MB\n",
      "    xz-5.2.5                   |       h7b6447c_0         438 KB\n",
      "    zlib-1.2.11                |       h7b6447c_3         120 KB\n",
      "    idna-2.10                  |     pyhd3eb1b0_0          52 KB\n",
      "    libgcc-ng-9.1.0            |       hdf63c60_0         8.1 MB\n",
      "    requests-2.25.1            |     pyhd3eb1b0_0          51 KB\n",
      "    wheel-0.36.2               |     pyhd3eb1b0_0          31 KB\n",
      "    pyopenssl-20.0.1           |     pyhd3eb1b0_1          48 KB\n",
      "    certifi-2021.5.30          |   py36h06a4308_0         141 KB\n",
      "    yaml-0.2.5                 |       h7b6447c_0          87 KB\n",
      "    brotlipy-0.7.0             |py36h27cfd23_1003         349 KB\n",
      "    sqlite-3.36.0              |       hc218d9a_0         1.4 MB\n",
      "    pycosat-0.6.3              |   py36h27cfd23_0         107 KB\n",
      "    readline-8.1               |       h27cfd23_0         464 KB\n",
      "    ruamel_yaml-0.15.100       |   py36h27cfd23_0         268 KB\n",
      "    cffi-1.14.6                |   py36h400218f_0         224 KB\n",
      "    openssl-1.1.1k             |       h27cfd23_0         3.8 MB\n",
      "    tk-8.6.10                  |       hbc83047_0         3.2 MB\n",
      "    setuptools-52.0.0          |   py36h06a4308_0         933 KB\n",
      "    pysocks-1.7.1              |   py36h06a4308_0          30 KB\n",
      "    libffi-3.3                 |       he6710b0_2          54 KB\n",
      "    six-1.16.0                 |     pyhd3eb1b0_0          18 KB\n",
      "    urllib3-1.26.6             |     pyhd3eb1b0_1         106 KB\n",
      "    tqdm-4.61.2                |     pyhd3eb1b0_1          80 KB\n",
      "    conda-package-handling-1.7.3|   py36h27cfd23_1         946 KB\n",
      "    conda-4.10.3               |   py36h06a4308_0         3.1 MB\n",
      "    pip-21.1.3                 |   py36h06a4308_0         2.1 MB\n",
      "    ncurses-6.2                |       he6710b0_1         1.1 MB\n",
      "    chardet-4.0.0              |py36h06a4308_1003         213 KB\n",
      "    ca-certificates-2021.7.5   |       h06a4308_1         119 KB\n",
      "    python-3.6.13              |       h12debd9_1        32.5 MB\n",
      "    pycparser-2.20             |             py_2          94 KB\n",
      "    _libgcc_mutex-0.1          |             main           3 KB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        65.8 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    _libgcc_mutex:          0.1-main               \n",
      "    brotlipy:               0.7.0-py36h27cfd23_1003\n",
      "    conda-package-handling: 1.7.3-py36h27cfd23_1   \n",
      "    ld_impl_linux-64:       2.35.1-h7274673_9      \n",
      "    tqdm:                   4.61.2-pyhd3eb1b0_1    \n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    ca-certificates:        2018.03.07-0            --> 2021.7.5-h06a4308_1     \n",
      "    certifi:                2018.4.16-py36_0        --> 2021.5.30-py36h06a4308_0\n",
      "    cffi:                   1.11.5-py36h9745a5d_0   --> 1.14.6-py36h400218f_0   \n",
      "    chardet:                3.0.4-py36h0f667ec_1    --> 4.0.0-py36h06a4308_1003 \n",
      "    conda:                  4.5.4-py36_0            --> 4.10.3-py36h06a4308_0   \n",
      "    cryptography:           2.2.2-py36h14c3975_0    --> 3.4.7-py36hd23ed53_0    \n",
      "    idna:                   2.6-py36h82fb2a8_1      --> 2.10-pyhd3eb1b0_0       \n",
      "    libffi:                 3.2.1-hd88cf55_4        --> 3.3-he6710b0_2          \n",
      "    libgcc-ng:              7.2.0-hdf63c60_3        --> 9.1.0-hdf63c60_0        \n",
      "    libstdcxx-ng:           7.2.0-hdf63c60_3        --> 9.1.0-hdf63c60_0        \n",
      "    ncurses:                6.1-hf484d3e_0          --> 6.2-he6710b0_1          \n",
      "    openssl:                1.0.2o-h20670df_0       --> 1.1.1k-h27cfd23_0       \n",
      "    pip:                    10.0.1-py36_0           --> 21.1.3-py36h06a4308_0   \n",
      "    pycosat:                0.6.3-py36h0a5515d_0    --> 0.6.3-py36h27cfd23_0    \n",
      "    pycparser:              2.18-py36hf9f622e_1     --> 2.20-py_2               \n",
      "    pyopenssl:              18.0.0-py36_0           --> 20.0.1-pyhd3eb1b0_1     \n",
      "    pysocks:                1.6.8-py36_0            --> 1.7.1-py36h06a4308_0    \n",
      "    python:                 3.6.5-hc3d631a_2        --> 3.6.13-h12debd9_1       \n",
      "    readline:               7.0-ha6073c6_4          --> 8.1-h27cfd23_0          \n",
      "    requests:               2.18.4-py36he2e5f8d_1   --> 2.25.1-pyhd3eb1b0_0     \n",
      "    ruamel_yaml:            0.15.37-py36h14c3975_2  --> 0.15.100-py36h27cfd23_0 \n",
      "    setuptools:             39.2.0-py36_0           --> 52.0.0-py36h06a4308_0   \n",
      "    six:                    1.11.0-py36h372c433_1   --> 1.16.0-pyhd3eb1b0_0     \n",
      "    sqlite:                 3.23.1-he433501_0       --> 3.36.0-hc218d9a_0       \n",
      "    tk:                     8.6.7-hc745277_3        --> 8.6.10-hbc83047_0       \n",
      "    urllib3:                1.22-py36hbe7ace6_0     --> 1.26.6-pyhd3eb1b0_1     \n",
      "    wheel:                  0.31.1-py36_0           --> 0.36.2-pyhd3eb1b0_0     \n",
      "    xz:                     5.2.4-h14c3975_4        --> 5.2.5-h7b6447c_0        \n",
      "    yaml:                   0.1.7-had09818_2        --> 0.2.5-h7b6447c_0        \n",
      "    zlib:                   1.2.11-ha838bed_2       --> 1.2.11-h7b6447c_3       \n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n",
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /usr/local\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    _openmp_mutex-4.5          |            1_gnu          22 KB\n",
      "    libgcc-ng-9.3.0            |      h5101ec6_17         4.8 MB\n",
      "    libgomp-9.3.0              |      h5101ec6_17         311 KB\n",
      "    libstdcxx-ng-9.3.0         |      hd4cf53a_17         3.1 MB\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         8.2 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  _openmp_mutex      pkgs/main/linux-64::_openmp_mutex-4.5-1_gnu\n",
      "  libgomp            pkgs/main/linux-64::libgomp-9.3.0-h5101ec6_17\n",
      "\n",
      "The following packages will be REMOVED:\n",
      "\n",
      "  asn1crypto-0.24.0-py36_0\n",
      "  conda-env-2.6.0-h36134e3_1\n",
      "  libedit-3.1.20170329-h6b74fdf_2\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  libgcc-ng                                9.1.0-hdf63c60_0 --> 9.3.0-h5101ec6_17\n",
      "  libstdcxx-ng                             9.1.0-hdf63c60_0 --> 9.3.0-hd4cf53a_17\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\r",
      "_openmp_mutex-4.5    | 22 KB     |            |   0% \r",
      "_openmp_mutex-4.5    | 22 KB     | ########## | 100% \n",
      "\r",
      "libgcc-ng-9.3.0      | 4.8 MB    |            |   0% \r",
      "libgcc-ng-9.3.0      | 4.8 MB    | ########## | 100% \r",
      "libgcc-ng-9.3.0      | 4.8 MB    | ########## | 100% \n",
      "\r",
      "libgomp-9.3.0        | 311 KB    |            |   0% \r",
      "libgomp-9.3.0        | 311 KB    | ########## | 100% \n",
      "\r",
      "libstdcxx-ng-9.3.0   | 3.1 MB    |            |   0% \r",
      "libstdcxx-ng-9.3.0   | 3.1 MB    | ########## | 100% \r",
      "libstdcxx-ng-9.3.0   | 3.1 MB    | ########## | 100% \n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "cryptography-3.4.7   |  1.0 MB |            |   0% \r",
      "cryptography-3.4.7   |  1.0 MB | #######9   |  80% \r",
      "cryptography-3.4.7   |  1.0 MB | ########## | 100% \n",
      "\r",
      "ld_impl_linux-64-2.3 |  637 KB |            |   0% \r",
      "ld_impl_linux-64-2.3 |  637 KB | #########5 |  95% \r",
      "ld_impl_linux-64-2.3 |  637 KB | ########## | 100% \n",
      "\r",
      "libstdcxx-ng-9.1.0   |  4.0 MB |            |   0% \r",
      "libstdcxx-ng-9.1.0   |  4.0 MB | #######7   |  78% \r",
      "libstdcxx-ng-9.1.0   |  4.0 MB | ########## | 100% \n",
      "\r",
      "xz-5.2.5             |  438 KB |            |   0% \r",
      "xz-5.2.5             |  438 KB | ########## | 100% \n",
      "\r",
      "zlib-1.2.11          |  120 KB |            |   0% \r",
      "zlib-1.2.11          |  120 KB | ########## | 100% \n",
      "\r",
      "idna-2.10            |   52 KB |            |   0% \r",
      "idna-2.10            |   52 KB | ########## | 100% \n",
      "\r",
      "libgcc-ng-9.1.0      |  8.1 MB |            |   0% \r",
      "libgcc-ng-9.1.0      |  8.1 MB | #######5   |  76% \r",
      "libgcc-ng-9.1.0      |  8.1 MB | #########5 |  95% \r",
      "libgcc-ng-9.1.0      |  8.1 MB | ########## | 100% \n",
      "\r",
      "requests-2.25.1      |   51 KB |            |   0% \r",
      "requests-2.25.1      |   51 KB | ########## | 100% \n",
      "\r",
      "wheel-0.36.2         |   31 KB |            |   0% \r",
      "wheel-0.36.2         |   31 KB | ########## | 100% \n",
      "\r",
      "pyopenssl-20.0.1     |   48 KB |            |   0% \r",
      "pyopenssl-20.0.1     |   48 KB | ########## | 100% \n",
      "\r",
      "certifi-2021.5.30    |  141 KB |            |   0% \r",
      "certifi-2021.5.30    |  141 KB | ########## | 100% \n",
      "\r",
      "yaml-0.2.5           |   87 KB |            |   0% \r",
      "yaml-0.2.5           |   87 KB | ########## | 100% \n",
      "\r",
      "brotlipy-0.7.0       |  349 KB |            |   0% \r",
      "brotlipy-0.7.0       |  349 KB | ########## | 100% \n",
      "\r",
      "sqlite-3.36.0        |  1.4 MB |            |   0% \r",
      "sqlite-3.36.0        |  1.4 MB | ########7  |  87% \r",
      "sqlite-3.36.0        |  1.4 MB | ########## | 100% \n",
      "\r",
      "pycosat-0.6.3        |  107 KB |            |   0% \r",
      "pycosat-0.6.3        |  107 KB | ########## | 100% \n",
      "\r",
      "readline-8.1         |  464 KB |            |   0% \r",
      "readline-8.1         |  464 KB | ########## | 100% \n",
      "\r",
      "ruamel_yaml-0.15.100 |  268 KB |            |   0% \r",
      "ruamel_yaml-0.15.100 |  268 KB | ########## | 100% \n",
      "\r",
      "cffi-1.14.6          |  224 KB |            |   0% \r",
      "cffi-1.14.6          |  224 KB | ########## | 100% \n",
      "\r",
      "openssl-1.1.1k       |  3.8 MB |            |   0% \r",
      "openssl-1.1.1k       |  3.8 MB | #######8   |  78% \r",
      "openssl-1.1.1k       |  3.8 MB | #########8 |  99% \r",
      "openssl-1.1.1k       |  3.8 MB | ########## | 100% \n",
      "\r",
      "tk-8.6.10            |  3.2 MB |            |   0% \r",
      "tk-8.6.10            |  3.2 MB | #######8   |  79% \r",
      "tk-8.6.10            |  3.2 MB | ########## | 100% \n",
      "\r",
      "setuptools-52.0.0    |  933 KB |            |   0% \r",
      "setuptools-52.0.0    |  933 KB | ########5  |  86% \r",
      "setuptools-52.0.0    |  933 KB | ########## | 100% \n",
      "\r",
      "pysocks-1.7.1        |   30 KB |            |   0% \r",
      "pysocks-1.7.1        |   30 KB | ########## | 100% \n",
      "\r",
      "libffi-3.3           |   54 KB |            |   0% \r",
      "libffi-3.3           |   54 KB | ########## | 100% \n",
      "\r",
      "six-1.16.0           |   18 KB |            |   0% \r",
      "six-1.16.0           |   18 KB | ########## | 100% \n",
      "\r",
      "urllib3-1.26.6       |  106 KB |            |   0% \r",
      "urllib3-1.26.6       |  106 KB | ########## | 100% \n",
      "\r",
      "tqdm-4.61.2          |   80 KB |            |   0% \r",
      "tqdm-4.61.2          |   80 KB | ########## | 100% \n",
      "\r",
      "conda-package-handli |  946 KB |            |   0% \r",
      "conda-package-handli |  946 KB | #########4 |  94% \r",
      "conda-package-handli |  946 KB | ########## | 100% \n",
      "\r",
      "conda-4.10.3         |  3.1 MB |            |   0% \r",
      "conda-4.10.3         |  3.1 MB | ########1  |  82% \r",
      "conda-4.10.3         |  3.1 MB | ########## | 100% \n",
      "\r",
      "pip-21.1.3           |  2.1 MB |            |   0% \r",
      "pip-21.1.3           |  2.1 MB | #######7   |  77% \r",
      "pip-21.1.3           |  2.1 MB | #########7 |  98% \r",
      "pip-21.1.3           |  2.1 MB | ########## | 100% \n",
      "\r",
      "ncurses-6.2          |  1.1 MB |            |   0% \r",
      "ncurses-6.2          |  1.1 MB | #######8   |  78% \r",
      "ncurses-6.2          |  1.1 MB | ########## | 100% \n",
      "\r",
      "chardet-4.0.0        |  213 KB |            |   0% \r",
      "chardet-4.0.0        |  213 KB | ########## | 100% \n",
      "\r",
      "ca-certificates-2021 |  119 KB |            |   0% \r",
      "ca-certificates-2021 |  119 KB | ########## | 100% \n",
      "\r",
      "python-3.6.13        | 32.5 MB |            |   0% \r",
      "python-3.6.13        | 32.5 MB | ###1       |  31% \r",
      "python-3.6.13        | 32.5 MB | ######6    |  67% \r",
      "python-3.6.13        | 32.5 MB | ########4  |  84% \r",
      "python-3.6.13        | 32.5 MB | #########6 |  96% \r",
      "python-3.6.13        | 32.5 MB | ########## | 100% \n",
      "\r",
      "pycparser-2.20       |   94 KB |            |   0% \r",
      "pycparser-2.20       |   94 KB | ########## | 100% \n",
      "\r",
      "_libgcc_mutex-0.1    |    3 KB |            |   0% \r",
      "_libgcc_mutex-0.1    |    3 KB | ########## | 100% \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "conda install --channel defaults conda python=3.6 --yes\n",
    "conda update --channel defaults --all --yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TGf7uoS2cHqP",
    "outputId": "07fbb293-f53e-4daa-960f-d5bf49659456"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.6.13 :: Anaconda, Inc.\n"
     ]
    }
   ],
   "source": [
    "!python --version # now returns Python 3.6.10 :: Anaconda, Inc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "G0BNf0PBW1yp"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "_ = (sys.path.append(\"/usr/local/lib/python3.6/site-packages\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ODgLUKyIYufI",
    "outputId": "dc632d00-482f-478a-aef2-cc8fd555ebe5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
      "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /usr/local\n",
      "\n",
      "  added / updated specs:\n",
      "    - cudatoolkit=10.1\n",
      "    - cusignal\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    boost-1.76.0               |   py36h706143c_0         338 KB  conda-forge\n",
      "    boost-cpp-1.76.0           |       h312852a_1        17.1 MB  conda-forge\n",
      "    bzip2-1.0.8                |       h7f98852_4         484 KB  conda-forge\n",
      "    ca-certificates-2021.5.30  |       ha878542_0         136 KB  conda-forge\n",
      "    certifi-2021.5.30          |   py36h5fab9bb_0         141 KB  conda-forge\n",
      "    conda-4.10.3               |   py36h5fab9bb_0         3.1 MB  conda-forge\n",
      "    cudatoolkit-10.1.243       |       h036e899_8       427.5 MB  conda-forge\n",
      "    cudnn-7.6.5.32             |       hc0a50b0_1       250.7 MB  conda-forge\n",
      "    cupy-8.3.0                 |   py36hcaf9a05_0        22.5 MB\n",
      "    cusignal-0.18.0            |  py38_g42899d2_0         883 KB  rapidsai\n",
      "    fastrlock-0.6              |   py36hc4f0c31_1          31 KB  conda-forge\n",
      "    icu-68.1                   |       h58526e2_0        13.0 MB  conda-forge\n",
      "    libblas-3.9.0              |       9_openblas          11 KB  conda-forge\n",
      "    libcblas-3.9.0             |       9_openblas          11 KB  conda-forge\n",
      "    libgfortran-ng-11.1.0      |       h69a702a_0          18 KB  conda-forge\n",
      "    libgfortran5-11.1.0        |       h6c583b3_0         1.7 MB  conda-forge\n",
      "    liblapack-3.9.0            |       9_openblas          11 KB  conda-forge\n",
      "    libllvm10-10.0.1           |       he513fc3_3        26.4 MB  conda-forge\n",
      "    libopenblas-0.3.15         |pthreads_h8fe5266_1         9.2 MB  conda-forge\n",
      "    llvmlite-0.36.0            |   py36h05121d2_0         2.8 MB  conda-forge\n",
      "    lz4-c-1.9.3                |       h9c3ff4c_0         179 KB  conda-forge\n",
      "    nccl-2.10.3.1              |       h8b44402_0        74.6 MB  conda-forge\n",
      "    numba-0.53.1               |   py36h1328434_1         3.7 MB  conda-forge\n",
      "    numpy-1.19.5               |   py36hfc0c790_2         5.3 MB  conda-forge\n",
      "    openssl-1.1.1k             |       h7f98852_0         2.1 MB  conda-forge\n",
      "    python_abi-3.6             |          2_cp36m           4 KB  conda-forge\n",
      "    scipy-1.5.3                |   py36h9e8f40b_0        19.1 MB  conda-forge\n",
      "    zstd-1.5.0                 |       ha95c52a_0         490 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:       881.5 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  boost              conda-forge/linux-64::boost-1.76.0-py36h706143c_0\n",
      "  boost-cpp          conda-forge/linux-64::boost-cpp-1.76.0-h312852a_1\n",
      "  bzip2              conda-forge/linux-64::bzip2-1.0.8-h7f98852_4\n",
      "  cudatoolkit        conda-forge/linux-64::cudatoolkit-10.1.243-h036e899_8\n",
      "  cudnn              conda-forge/linux-64::cudnn-7.6.5.32-hc0a50b0_1\n",
      "  cupy               pkgs/main/linux-64::cupy-8.3.0-py36hcaf9a05_0\n",
      "  cusignal           rapidsai/noarch::cusignal-0.18.0-py38_g42899d2_0\n",
      "  fastrlock          conda-forge/linux-64::fastrlock-0.6-py36hc4f0c31_1\n",
      "  icu                conda-forge/linux-64::icu-68.1-h58526e2_0\n",
      "  libblas            conda-forge/linux-64::libblas-3.9.0-9_openblas\n",
      "  libcblas           conda-forge/linux-64::libcblas-3.9.0-9_openblas\n",
      "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-11.1.0-h69a702a_0\n",
      "  libgfortran5       conda-forge/linux-64::libgfortran5-11.1.0-h6c583b3_0\n",
      "  liblapack          conda-forge/linux-64::liblapack-3.9.0-9_openblas\n",
      "  libllvm10          conda-forge/linux-64::libllvm10-10.0.1-he513fc3_3\n",
      "  libopenblas        conda-forge/linux-64::libopenblas-0.3.15-pthreads_h8fe5266_1\n",
      "  llvmlite           conda-forge/linux-64::llvmlite-0.36.0-py36h05121d2_0\n",
      "  lz4-c              conda-forge/linux-64::lz4-c-1.9.3-h9c3ff4c_0\n",
      "  nccl               conda-forge/linux-64::nccl-2.10.3.1-h8b44402_0\n",
      "  numba              conda-forge/linux-64::numba-0.53.1-py36h1328434_1\n",
      "  numpy              conda-forge/linux-64::numpy-1.19.5-py36hfc0c790_2\n",
      "  python_abi         conda-forge/linux-64::python_abi-3.6-2_cp36m\n",
      "  scipy              conda-forge/linux-64::scipy-1.5.3-py36h9e8f40b_0\n",
      "  zstd               conda-forge/linux-64::zstd-1.5.0-ha95c52a_0\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  ca-certificates    pkgs/main::ca-certificates-2021.7.5-h~ --> conda-forge::ca-certificates-2021.5.30-ha878542_0\n",
      "  certifi            pkgs/main::certifi-2021.5.30-py36h06a~ --> conda-forge::certifi-2021.5.30-py36h5fab9bb_0\n",
      "  conda              pkgs/main::conda-4.10.3-py36h06a4308_0 --> conda-forge::conda-4.10.3-py36h5fab9bb_0\n",
      "  openssl              pkgs/main::openssl-1.1.1k-h27cfd23_0 --> conda-forge::openssl-1.1.1k-h7f98852_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "bzip2-1.0.8          | 484 KB    | : 100% 1.0/1 [00:00<00:00,  6.23it/s]\n",
      "libopenblas-0.3.15   | 9.2 MB    | : 100% 1.0/1 [00:01<00:00,  1.42s/it]\n",
      "scipy-1.5.3          | 19.1 MB   | : 100% 1.0/1 [00:02<00:00,  2.94s/it]\n",
      "llvmlite-0.36.0      | 2.8 MB    | : 100% 1.0/1 [00:00<00:00,  2.24it/s]\n",
      "python_abi-3.6       | 4 KB      | : 100% 1.0/1 [00:00<00:00, 38.53it/s]\n",
      "cudnn-7.6.5.32       | 250.7 MB  | : 100% 1.0/1 [00:29<00:00, 29.62s/it]              \n",
      "numpy-1.19.5         | 5.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.00s/it]               \n",
      "boost-1.76.0         | 338 KB    | : 100% 1.0/1 [00:00<00:00,  9.41it/s]\n",
      "numba-0.53.1         | 3.7 MB    | : 100% 1.0/1 [00:00<00:00,  1.23it/s]\n",
      "libcblas-3.9.0       | 11 KB     | : 100% 1.0/1 [00:00<00:00, 23.77it/s]\n",
      "libblas-3.9.0        | 11 KB     | : 100% 1.0/1 [00:00<00:00, 22.97it/s]\n",
      "libgfortran-ng-11.1. | 18 KB     | : 100% 1.0/1 [00:00<00:00, 22.35it/s]\n",
      "ca-certificates-2021 | 136 KB    | : 100% 1.0/1 [00:00<00:00, 17.86it/s]\n",
      "cupy-8.3.0           | 22.5 MB   | : 100% 1.0/1 [00:00<00:00,  1.17it/s]               \n",
      "openssl-1.1.1k       | 2.1 MB    | : 100% 1.0/1 [00:00<00:00,  2.98it/s]\n",
      "conda-4.10.3         | 3.1 MB    | : 100% 1.0/1 [00:00<00:00,  1.71it/s]\n",
      "fastrlock-0.6        | 31 KB     | : 100% 1.0/1 [00:00<00:00, 32.94it/s]\n",
      "boost-cpp-1.76.0     | 17.1 MB   | : 100% 1.0/1 [00:05<00:00,  5.71s/it]\n",
      "cusignal-0.18.0      | 883 KB    | : 100% 1.0/1 [00:00<00:00,  2.30it/s]\n",
      "liblapack-3.9.0      | 11 KB     | : 100% 1.0/1 [00:00<00:00, 22.22it/s]\n",
      "libllvm10-10.0.1     | 26.4 MB   | : 100% 1.0/1 [00:04<00:00,  4.31s/it]\n",
      "icu-68.1             | 13.0 MB   | : 100% 1.0/1 [00:01<00:00,  1.96s/it]               \n",
      "nccl-2.10.3.1        | 74.6 MB   | : 100% 1.0/1 [00:09<00:00,  9.31s/it]               \n",
      "lz4-c-1.9.3          | 179 KB    | : 100% 1.0/1 [00:00<00:00, 17.40it/s]\n",
      "libgfortran5-11.1.0  | 1.7 MB    | : 100% 1.0/1 [00:00<00:00,  3.27it/s]\n",
      "certifi-2021.5.30    | 141 KB    | : 100% 1.0/1 [00:00<00:00, 22.94it/s]\n",
      "zstd-1.5.0           | 490 KB    | : 100% 1.0/1 [00:00<00:00,  9.92it/s]\n",
      "cudatoolkit-10.1.243 | 427.5 MB  | : 100% 1.0/1 [00:52<00:00, 52.21s/it]               \n",
      "Preparing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
      "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
      "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html\n",
      "\n",
      "\b\b\\ \b\b| \b\b/ By downloading and using the cuDNN conda packages, you accept the terms and conditions of the NVIDIA cuDNN EULA -\n",
      "  https://docs.nvidia.com/deeplearning/cudnn/sla/index.html\n",
      "\n",
      "\b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n"
     ]
    }
   ],
   "source": [
    "!conda install -c rapidsai -c conda-forge cusignal cudatoolkit=10.1 --yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5wvIeMgG_90y",
    "outputId": "8e1fddd3-0eec-4111-eeec-76853d53ec91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyfftw\n",
      "  Downloading pyFFTW-0.12.0-cp36-cp36m-manylinux1_x86_64.whl (2.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 2.6 MB 42.1 MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.10 in /usr/local/lib/python3.6/site-packages (from pyfftw) (1.19.5)\n",
      "Installing collected packages: pyfftw\n",
      "Successfully installed pyfftw-0.12.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pyfftw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iZPuNh_nREcx",
    "outputId": "6ecc162b-4dce-4f54-a114-f79b1e3da160"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting cupy-cuda101==8.0.0rc1\n",
      "  Downloading cupy_cuda101-8.0.0rc1-cp36-cp36m-manylinux1_x86_64.whl (114.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 114.9 MB 14 kB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/site-packages (from cupy-cuda101==8.0.0rc1) (1.19.5)\n",
      "Requirement already satisfied: fastrlock>=0.3 in /usr/local/lib/python3.6/site-packages (from cupy-cuda101==8.0.0rc1) (0.6)\n",
      "Installing collected packages: cupy-cuda101\n",
      "Successfully installed cupy-cuda101-8.0.0rc1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Collecting pylops[advanced]\n",
      "  Downloading pylops-1.14.0-py3-none-any.whl (180 kB)\n",
      "\u001b[K     |████████████████████████████████| 180 kB 24.6 MB/s \n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.4.0 in /usr/local/lib/python3.6/site-packages (from pylops[advanced]) (1.5.3)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/site-packages (from pylops[advanced]) (1.19.5)\n",
      "Collecting spgl1\n",
      "  Downloading spgl1-0.0.2-py3-none-any.whl (28 kB)\n",
      "Collecting PyWavelets\n",
      "  Downloading PyWavelets-1.1.1-cp36-cp36m-manylinux1_x86_64.whl (4.4 MB)\n",
      "\u001b[K     |████████████████████████████████| 4.4 MB 39.4 MB/s \n",
      "\u001b[?25hRequirement already satisfied: llvmlite in /usr/local/lib/python3.6/site-packages (from pylops[advanced]) (0.36.0)\n",
      "Requirement already satisfied: numba in /usr/local/lib/python3.6/site-packages (from pylops[advanced]) (0.53.1)\n",
      "Requirement already satisfied: pyfftw in /usr/local/lib/python3.6/site-packages (from pylops[advanced]) (0.12.0)\n",
      "Collecting scikit-fmm\n",
      "  Downloading scikit-fmm-2021.7.8.tar.gz (421 kB)\n",
      "\u001b[K     |████████████████████████████████| 421 kB 71.6 MB/s \n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/site-packages (from numba->pylops[advanced]) (52.0.0.post20210125)\n",
      "Building wheels for collected packages: scikit-fmm\n",
      "  Building wheel for scikit-fmm (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for scikit-fmm: filename=scikit_fmm-2021.7.8-cp36-cp36m-linux_x86_64.whl size=208299 sha256=ce7369a3026205ea99e88d440b0fa429a8e00e0df7abde05b630600383adda6a\n",
      "  Stored in directory: /root/.cache/pip/wheels/fa/a4/6c/768c49c4fd55ea56d2b921849a05c835272c5748a8a5eb879e\n",
      "Successfully built scikit-fmm\n",
      "Installing collected packages: spgl1, scikit-fmm, PyWavelets, pylops\n",
      "Successfully installed PyWavelets-1.1.1 pylops-1.14.0 scikit-fmm-2021.7.8 spgl1-0.0.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Collecting pylops-gpu\n",
      "  Downloading pylops_gpu-0.0.1-py3-none-any.whl (40 kB)\n",
      "\u001b[K     |████████████████████████████████| 40 kB 5.9 MB/s \n",
      "\u001b[?25hCollecting pytorch-complex-tensor\n",
      "  Downloading pytorch-complex-tensor-0.0.134.tar.gz (6.7 kB)\n",
      "Requirement already satisfied: pylops in /usr/local/lib/python3.6/site-packages (from pylops-gpu) (1.14.0)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/site-packages (from pylops-gpu) (1.19.5)\n",
      "Collecting torch>=1.2.0\n",
      "  Downloading torch-1.9.0-cp36-cp36m-manylinux1_x86_64.whl (831.4 MB)\n",
      "\u001b[K     |████████████████████████████████| 831.4 MB 6.9 kB/s \n",
      "\u001b[?25hCollecting typing-extensions\n",
      "  Downloading typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)\n",
      "Collecting dataclasses\n",
      "  Downloading dataclasses-0.8-py3-none-any.whl (19 kB)\n",
      "Requirement already satisfied: scipy>=1.4.0 in /usr/local/lib/python3.6/site-packages (from pylops->pylops-gpu) (1.5.3)\n",
      "Collecting torchvision>=0.2.1\n",
      "  Downloading torchvision-0.10.0-cp36-cp36m-manylinux1_x86_64.whl (22.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 22.1 MB 1.3 MB/s \n",
      "\u001b[?25hCollecting pillow>=5.3.0\n",
      "  Downloading Pillow-8.3.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (3.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 3.0 MB 48.4 MB/s \n",
      "\u001b[?25hBuilding wheels for collected packages: pytorch-complex-tensor\n",
      "  Building wheel for pytorch-complex-tensor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pytorch-complex-tensor: filename=pytorch_complex_tensor-0.0.134-py3-none-any.whl size=8208 sha256=432fd71d3a68c3c265a5a95f0ddf13140da2b63452ecf0e21d8d36a389878f74\n",
      "  Stored in directory: /root/.cache/pip/wheels/7e/d9/ae/13d6f1cbc2ad95aa76e77fecdee9b3f0c2d9dbeeaec19b5d21\n",
      "Successfully built pytorch-complex-tensor\n",
      "Installing collected packages: typing-extensions, dataclasses, torch, pillow, torchvision, pytorch-complex-tensor, pylops-gpu\n",
      "Successfully installed dataclasses-0.8 pillow-8.3.1 pylops-gpu-0.0.1 pytorch-complex-tensor-0.0.134 torch-1.9.0 torchvision-0.10.0 typing-extensions-3.10.0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#!pip install cupy-cuda101\n",
    "!pip3 install cupy-cuda101==8.0.0rc1 --upgrade\n",
    "!pip install pylops[advanced]\n",
    "!pip install pylops-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "MnIaVrR8ZOsc"
   },
   "outputs": [],
   "source": [
    "# all RAPIDS env... : https://medium.com/dropout-analytics/installing-rapids-ai-in-google-colab-87c247f2c468\n",
    "# https://colab.research.google.com/drive/1rY7Ln6rEE1pOlfSHCYOVaqt8OvDO35J0#forceEdit=true&sandboxMode=true&scrollTo=CtNdk7PSafKP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "78Plf51qdDPs"
   },
   "outputs": [],
   "source": [
    "!rm -rf /usr/local/lib/python3.6/dist-packages/cupy*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "WJ3vTlJ7gHnQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import cupy as cp\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from scipy.sparse.linalg import inv, lsqr\n",
    "from scipy.sparse import rand\n",
    "\n",
    "from pylops.utils import dottest\n",
    "from pylops.utils.seismicevents import *\n",
    "from pylops.utils.wavelets import *\n",
    "from pylops.utils.tapers import *\n",
    "from pylops import LinearOperator\n",
    "from pylops.basicoperators import *\n",
    "from pylops.signalprocessing import *\n",
    "from pylops.waveeqprocessing import *\n",
    "from pylops_gpu import TorchOperator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PZRkV61tfCH1"
   },
   "source": [
    "# ChirpRadon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "zgT42PWyt5VD"
   },
   "outputs": [],
   "source": [
    "par = {'ot': 0,    'dt': 0.004, 'nt': 201,\n",
    "       'ox': -625, 'dx': 12.5, 'nx': 101,\n",
    "       'oy': -625, 'dy': 12.5, 'ny': 101,\n",
    "       'f0': 20}\n",
    "theta = [30, ]\n",
    "phi = [0, ]\n",
    "t0 = [0.5, ]\n",
    "amp = [1., ]\n",
    "\n",
    "# Create axis\n",
    "t, t2, x, y = makeaxis(par)\n",
    "dt, dx, dy = par['dt'], par['dx'], par['dy']\n",
    "\n",
    "# Create wavelet\n",
    "wav = ricker(t[:41], f0=par['f0'])[0]\n",
    "\n",
    "# Generate model\n",
    "_, d = linear3d(x, y, t, 1500., t0, theta, phi, amp, wav)\n",
    "\n",
    "d = cp.array(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 763
    },
    "id": "-gQo1435uVXc",
    "outputId": "7057c9a9-daf0-4b69-eb23-9a76c8556dfb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "npy, pymax = par['ny'], 5e-4\n",
    "npx, pxmax = par['nx'], 5e-4\n",
    "\n",
    "py = np.linspace(-pymax, pymax, npy)\n",
    "px = np.linspace(-pxmax, pxmax, npx)\n",
    "\n",
    "R3Op = ChirpRadon3D(t, y, x, (pymax*dy/dt, pxmax*dx/dt), dtype='float64')\n",
    "\n",
    "dL_chirp = R3Op * d.ravel()\n",
    "dadj_chirp = R3Op.H * dL_chirp\n",
    "dinv_chirp = R3Op.inverse(dL_chirp)\n",
    "\n",
    "dL_chirp = dL_chirp.reshape(par['ny'], par['nx'], par['nt'])\n",
    "dadj_chirp = dadj_chirp.reshape(par['ny'], par['nx'], par['nt'])\n",
    "dinv_chirp = dinv_chirp.reshape(par['ny'], par['nx'], par['nt'])\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(15, 6))\n",
    "axs[0].imshow(cp.asnumpy(d[par['ny']//2]).T, vmin=-1, vmax=1, cmap='seismic_r',\n",
    "              extent=(x[0], x[-1], t[-1], t[0]))\n",
    "axs[0].set_title('Input model')\n",
    "axs[0].axis('tight')\n",
    "axs[1].imshow(cp.asnumpy(dL_chirp[npy//2]).T, cmap='seismic_r', vmin=-dL_chirp.max(), vmax=dL_chirp.max(),\n",
    "              extent=(px[0], px[-1], t[-1], t[0]))\n",
    "axs[1].set_title('Radon Chirp')\n",
    "axs[1].axis('tight')\n",
    "axs[2].imshow(cp.asnumpy(dadj_chirp[par['ny']//2]).T, cmap='seismic_r', vmin=-dadj_chirp.max(), vmax=dadj_chirp.max(),\n",
    "              extent=(x[0], x[-1], t[-1], t[0]))\n",
    "axs[2].set_title('Adj Radon Chirp')\n",
    "axs[2].axis('tight')\n",
    "axs[3].imshow(cp.asnumpy(dinv_chirp[par['ny']//2]).T, cmap='seismic_r', vmin=-d.max(), vmax=d.max(),\n",
    "              extent=(x[0], x[-1], t[-1], t[0]))\n",
    "axs[3].set_title('Inv Radon Chirp')\n",
    "axs[3].axis('tight');\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(15, 6))\n",
    "axs[0].imshow(cp.asnumpy(d[:, par['nx']//2]).T, vmin=-1, vmax=1, cmap='seismic_r',\n",
    "              extent=(y[0], y[-1], t[-1], t[0]))\n",
    "axs[0].set_title('Input model')\n",
    "axs[0].axis('tight')\n",
    "axs[1].imshow(cp.asnumpy(dL_chirp[:, 85]).T, cmap='seismic_r', vmin=-dL_chirp.max(), vmax=dL_chirp.max(),\n",
    "              extent=(py[0], py[-1], t[-1], t[0]))\n",
    "axs[1].set_title('Radon Chirp')\n",
    "axs[1].axis('tight')\n",
    "axs[2].imshow(cp.asnumpy(dadj_chirp[:, par['nx']//2]).T, cmap='seismic_r', vmin=-dadj_chirp.max(), vmax=dadj_chirp.max(),\n",
    "              extent=(y[0], y[-1], t[-1], t[0]))\n",
    "axs[2].set_title('Adj Radon Chirp')\n",
    "axs[2].axis('tight')\n",
    "axs[3].imshow(cp.asnumpy(dinv_chirp[:, par['nx']//2]).T, cmap='seismic_r', vmin=-d.max(), vmax=d.max(),\n",
    "              extent=(y[0], y[-1], t[-1], t[0]))\n",
    "axs[3].set_title('Inv Radon Chirp')\n",
    "axs[3].axis('tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6aJx5v7juaK6",
    "outputId": "43750bcc-f700-426f-aa8d-800ea60289c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1 ms ± 76.3 µs per loop (mean ± std. dev. of 2 runs, 2 loops each)\n",
      "1.47 s ± 3.52 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "d_np = cp.asnumpy(d) \n",
    "\n",
    "%timeit -n2 -r2 R3Op * d.ravel()\n",
    "%timeit -n2 -r2 R3Op * d_np.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tiilLztBMkxw",
    "outputId": "5ccb3236-4666-4dd3-b2ff-68fea1463d87"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3 ms ± 176 µs per loop (mean ± std. dev. of 2 runs, 2 loops each)\n",
      "1.49 s ± 17.8 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "dL_chirp_np = cp.asnumpy(dL_chirp) \n",
    "\n",
    "%timeit -n2 -r2 R3Op.H * dL_chirp.ravel()\n",
    "%timeit -n2 -r2 R3Op.H * dL_chirp_np.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "avxL1qBsfJGR"
   },
   "source": [
    "# PhaseShift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "WketriiUfGQW"
   },
   "outputs": [],
   "source": [
    "par = {'ox':-1000, 'dx':20, 'nx':101,\n",
    "       'oy':-1000, 'dy':20, 'ny':101,\n",
    "       'ot':0, 'dt':0.004, 'nt':151,\n",
    "       'f0': 20, 'nfmax': 210}\n",
    "\n",
    "# Create axis\n",
    "t, t2, x, y = makeaxis(par)\n",
    "\n",
    "# Create wavelet\n",
    "wav = ricker(np.arange(41) * par['dt'], f0=par['f0'])[0]\n",
    "\n",
    "vrms = [2000, 2200, 3000]\n",
    "t0 = [0.1, 0.2, 0.25]\n",
    "amp = [1., 0.6, -2.]\n",
    "\n",
    "_, m = hyperbolic3d(x, y, t, t0, vrms, vrms, amp, wav)\n",
    "\n",
    "pad = 11\n",
    "taper = taper3d(par['nt'], (par['ny'], par['nx']), (3, 3))\n",
    "mpad = np.pad(m*taper, ((pad, pad), (pad, pad), (0, 0)), mode='constant')\n",
    "mpad = mpad.transpose(2, 1, 0)\n",
    "\n",
    "vel = 1500.\n",
    "zprop = 200\n",
    "freq = np.fft.rfftfreq(par['nt'], par['dt'])\n",
    "kx = np.fft.fftshift(np.fft.fftfreq(par['nx'] + 2*pad, par['dx']))\n",
    "ky = np.fft.fftshift(np.fft.fftfreq(par['ny'] + 2*pad, par['dy']))\n",
    "Pop = PhaseShift(vel, zprop, par['nt'], freq, kx, ky)\n",
    "Pop_cp = PhaseShift(vel, zprop, par['nt'], freq, kx, ky)\n",
    "\n",
    "mdown = Pop * mpad.ravel()\n",
    "mup = Pop.H * mdown.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wLaxMcyNgWux",
    "outputId": "df1903a4-484c-404c-d935-80ca42c8acbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "607 ms ± 5.25 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n",
      "The slowest run took 5.20 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "3.3 ms ± 2.24 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "mpad_cp = cp.asarray(mpad) \n",
    "\n",
    "%timeit -n2 -r2 Pop * mpad.ravel()\n",
    "%timeit -n2 -r2 Pop_cp * mpad_cp.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "afWQEuOCg8z6",
    "outputId": "6fe64c88-977a-4b41-c153-ef1943d1df63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "592 ms ± 24.9 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)\n",
      "1.33 ms ± 289 µs per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
     ]
    }
   ],
   "source": [
    "mdown_cp = cp.asarray(mdown) \n",
    "\n",
    "%timeit -n2 -r2 Pop.H * mdown.ravel()\n",
    "%timeit -n2 -r2 Pop_cp.H * mdown_cp.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zGNMUcub7mzc"
   },
   "source": [
    "# Pytorch / Cupy integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6r4FPmKa7tM4",
    "outputId": "cdedc76e-5d31-4b22-a4c8-f4ca0d928df9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "drwMFd-g75-k"
   },
   "outputs": [],
   "source": [
    "class Network(nn.Module):\n",
    "    def __init__(self, input_channels):\n",
    "        super(Network, self).__init__()\n",
    "        self.conv1 = nn.Conv3d(input_channels, input_channels // 2, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv3d(input_channels // 2, input_channels // 4, kernel_size=3, padding=1)\n",
    "        self.conv3 = nn.Conv3d(input_channels // 4, input_channels // 8, kernel_size=3, padding=1)\n",
    "        self.conv4 = nn.Conv3d(input_channels // 8, input_channels // 32, kernel_size=3, padding=1)\n",
    "        self.activation = nn.LeakyReLU(0.2)\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.activation(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.activation(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.activation(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.activation(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3WKfHRPR8fig",
    "outputId": "2efe3e61-bc39-46b8-be77-434a512a51dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Network(\n",
       "  (conv1): Conv3d(32, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  (conv2): Conv3d(16, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  (conv3): Conv3d(8, 4, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  (conv4): Conv3d(4, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "  (activation): LeakyReLU(negative_slope=0.2)\n",
       "  (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       ")"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_cpu = Network(32)\n",
    "net_gpu = Network(32)\n",
    "net_gpu.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JGdNtsnZ-njp",
    "outputId": "a795e903-5943-434b-9f4c-a204acd89608"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "937 ms ± 664 µs per loop (mean ± std. dev. of 2 runs, 2 loops each)\n",
      "1.37 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# CPU\n",
    "Pop = PhaseShift(vel, zprop, par['nt'], freq, kx, ky)\n",
    "Pop_torch_cpu = TorchOperator(Pop, pylops=True, device='cpu')\n",
    "\n",
    "# forward\n",
    "%timeit -n2 -r2 Pop_torch_cpu.apply(net_cpu(torch.ones((1, 32, 151, 123, 123))).view(-1))\n",
    "\n",
    "# backward\n",
    "y = Pop_torch_cpu.apply(net_cpu(torch.ones((1, 32, 151, 123, 123))).view(-1))\n",
    "loss = y.sum()\n",
    "%timeit -n1 -r1 loss.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "borLAPl48kDE",
    "outputId": "f86e4209-fd10-40f6-91c1-5effced1a190"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.8 ms ± 111 µs per loop (mean ± std. dev. of 2 runs, 2 loops each)\n",
      "6.83 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# GPU\n",
    "Pop_cp = PhaseShift(vel, zprop, par['nt'], freq, kx, ky)\n",
    "Pop_torch_gpu = TorchOperator(Pop, pylops=True, device=device)\n",
    "Pop_torch_gpu.apply(net_gpu(torch.ones((1, 32, 151, 123, 123)).to(device)).view(-1)) # dry run\n",
    "%timeit -n2 -r2 Pop_torch_gpu.apply(net_gpu(torch.ones((1, 32, 151, 123, 123)).to(device)).view(-1))\n",
    "\n",
    "# backward\n",
    "y = Pop_torch_gpu.apply(net_gpu(torch.ones((1, 32, 151, 123, 123)).to(device)).view(-1))\n",
    "loss = y.sum()\n",
    "%timeit -n1 -r1 loss.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "754 ms ± 275 µs per loop (mean ± std. dev. of 2 runs, 2 loops each)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Function _TorchOperatorBackward returned an invalid gradient at index 0 - expected type TensorOptions(dtype=float, device=cuda:0, layout=Strided, requires_grad=false (default), pinned_memory=false (default), memory_format=(nullopt)) but got TensorOptions(dtype=float, device=cpu, layout=Strided, requires_grad=false (default), pinned_memory=false (default), memory_format=(nullopt))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-3e2091087c8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPop_torch_cpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet_gpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m151\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m123\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m123\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n1 -r1 loss.backward()'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2346\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_local_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2347\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2348\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2349\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1174\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    219\u001b[0m                 \u001b[0mretain_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m                 create_graph=create_graph)\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pylops_gpu_3090/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m     Variable._execution_engine.run_backward(\n\u001b[0m\u001b[1;32m    131\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m         allow_unreachable=True)  # allow_unreachable flag\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Function _TorchOperatorBackward returned an invalid gradient at index 0 - expected type TensorOptions(dtype=float, device=cuda:0, layout=Strided, requires_grad=false (default), pinned_memory=false (default), memory_format=(nullopt)) but got TensorOptions(dtype=float, device=cpu, layout=Strided, requires_grad=false (default), pinned_memory=false (default), memory_format=(nullopt))"
     ]
    }
   ],
   "source": [
    "# Mixed (currently not allowed!)\n",
    "Pop = PhaseShift(vel, zprop, par['nt'], freq, kx, ky)\n",
    "Pop_torch_cpu = TorchOperator(Pop, pylops=True, device='cpu')\n",
    "\n",
    "# forward\n",
    "Pop_torch_cpu.apply(net_gpu(torch.ones((1, 32, 151, 123, 123)).to(device)).view(-1)) # dry run\n",
    "%timeit -n2 -r2 Pop_torch_cpu.apply(net_gpu(torch.ones((1, 32, 151, 123, 123)).to(device)).view(-1))\n",
    "\n",
    "# backward\n",
    "y = Pop_torch_cpu.apply(net_gpu(torch.ones((1, 32, 151, 123, 123)).to(device)).view(-1))\n",
    "loss = y.sum()\n",
    "%timeit -n1 -r1 loss.backward()"
   ]
  }
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