GPU_Clean_ML_z_SDSS_July_2021.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    },
    "colab": {
      "name": "GPU_Clean_ML_z_SDSS_July_2021.ipynb",
      "provenance": []
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "435d210b829a4db1ab755e693b2c7762": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_f70e073b723c4c2ba6221726d2162c41",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 3,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 3,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_94edb70524624c149838d18aa4344a66"
          }
        },
        "f70e073b723c4c2ba6221726d2162c41": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "94edb70524624c149838d18aa4344a66": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "0640dae32f834327a746078af70c1ef1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_3927a5c6562145b58068146b8e4d7812",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 44,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 44,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0321d1110b02431ca58bd42669a06fa6"
          }
        },
        "3927a5c6562145b58068146b8e4d7812": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "0321d1110b02431ca58bd42669a06fa6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7e91a70b503344abbbc57afdf225454d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_011c6c19abad4449a037a0458bf9b5e5",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 6,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 6,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_dc98407583894db7b83d13931bd78432"
          }
        },
        "011c6c19abad4449a037a0458bf9b5e5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "dc98407583894db7b83d13931bd78432": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "ff210ae5196a48b5a79e48c048fa3933": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_5d93d6d643ab48319c8045d47dcc824a",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 7,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 7,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d6ca2966102e4f639d26bc607b578439"
          }
        },
        "5d93d6d643ab48319c8045d47dcc824a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d6ca2966102e4f639d26bc607b578439": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "e465978852ca4ec4a5db94ed4629ea45": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_a73ea53fe4bb4268af0cf21ae38a4ccd",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 5,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 5,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_baec8c294feb4cc6bf4125a8a416d60d"
          }
        },
        "a73ea53fe4bb4268af0cf21ae38a4ccd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "baec8c294feb4cc6bf4125a8a416d60d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "982a779ad44a490c985ef5a9d1c1662e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_990c3633e8ab424c944a334c895e13be",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 5,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 3,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c6a4cf37f5a3406988abfe6d1560f8af"
          }
        },
        "990c3633e8ab424c944a334c895e13be": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "c6a4cf37f5a3406988abfe6d1560f8af": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39zMhvXMDy1p"
      },
      "source": [
        "# ML with clean SDSS sample using GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdXhs7W0Dy1q"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwOhxOwTDy1r"
      },
      "source": [
        "## Python setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kaQB6zAjDy1r"
      },
      "source": [
        "First, we install and import the packages to be used"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrvYpLWXr0La"
      },
      "source": [
        "We will start with RAPIDS.ai to have access to GPU models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cAgrcnSrtaps"
      },
      "source": [
        "Click the Runtime dropdown at the top of the page, then Change Runtime Type and confirm the instance type is GPU.\n",
        "Check the output of !nvidia-smi to make sure you've been allocated a Tesla T4, P4, or P100."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyOeX9pTtduU",
        "outputId": "c10a51b4-0537-4979-dec3-b6213906269c"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Jul 21 20:16:07 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.42.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   52C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VGep3AWr7Aj",
        "outputId": "c2aefebd-e2a8-4003-e137-79dd811b1b00"
      },
      "source": [
        "# This get the RAPIDS-Colab install files and test check your GPU.  Run this and the next cell only.\n",
        "# Please read the output of this cell.  If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps.\n",
        "!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n",
        "!python rapidsai-csp-utils/colab/env-check.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'rapidsai-csp-utils'...\n",
            "remote: Enumerating objects: 277, done.\u001b[K\n",
            "remote: Counting objects: 100% (106/106), done.\u001b[K\n",
            "remote: Compressing objects: 100% (86/86), done.\u001b[K\n",
            "remote: Total 277 (delta 59), reused 40 (delta 20), pack-reused 171\u001b[K\n",
            "Receiving objects: 100% (277/277), 80.66 KiB | 7.33 MiB/s, done.\n",
            "Resolving deltas: 100% (121/121), done.\n",
            "***********************************************************************\n",
            "Woo! Your instance has the right kind of GPU, a Tesla T4!\n",
            "***********************************************************************\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_6CSl77r_p8",
        "outputId": "c021a43f-4821-4e01-e889-b173434100ec"
      },
      "source": [
        "# This will update the Colab environment and restart the kernel.  Don't run the next cell until you see the session crash.\n",
        "!bash rapidsai-csp-utils/colab/update_gcc.sh\n",
        "import os\n",
        "os._exit(00)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updating your Colab environment.  This will restart your kernel.  Don't Panic!\n",
            "Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [697 B]\n",
            "Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n",
            "Get:8 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Get:10 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n",
            "Ign:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n",
            "Get:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [679 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Get:13 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [31.5 kB]\n",
            "Hit:14 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Get:15 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,257 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Hit:17 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,190 kB]\n",
            "Get:19 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,420 kB]\n",
            "Get:20 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [500 kB]\n",
            "Get:21 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:22 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic InRelease [20.8 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,693 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [533 kB]\n",
            "Get:25 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [38.7 kB]\n",
            "Get:26 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,785 kB]\n",
            "Get:27 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [913 kB]\n",
            "Get:28 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [44.1 kB]\n",
            "Get:29 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 Packages [50.4 kB]\n",
            "Fetched 13.4 MB in 5s (2,527 kB/s)\n",
            "Reading package lists... Done\n",
            "Added repo\n",
            "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:6 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Hit:8 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Hit:10 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:11 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:12 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Hit:13 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Hit:14 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Hit:15 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Hit:16 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic InRelease\n",
            "Reading package lists... Done\n",
            "Installing libstdc++\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Selected version '11.1.0-1ubuntu1~18.04.1' (Toolchain test builds:18.04/bionic [amd64]) for 'libstdc++6'\n",
            "The following additional packages will be installed:\n",
            "  gcc-11-base libgcc-s1\n",
            "The following NEW packages will be installed:\n",
            "  gcc-11-base libgcc-s1\n",
            "The following packages will be upgraded:\n",
            "  libstdc++6\n",
            "1 upgraded, 2 newly installed, 0 to remove and 87 not upgraded.\n",
            "Need to get 641 kB of archives.\n",
            "After this operation, 981 kB of additional disk space will be used.\n",
            "Get:1 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 gcc-11-base amd64 11.1.0-1ubuntu1~18.04.1 [19.0 kB]\n",
            "Get:2 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 libgcc-s1 amd64 11.1.0-1ubuntu1~18.04.1 [41.8 kB]\n",
            "Get:3 http://ppa.launchpad.net/ubuntu-toolchain-r/test/ubuntu bionic/main amd64 libstdc++6 amd64 11.1.0-1ubuntu1~18.04.1 [580 kB]\n",
            "Fetched 641 kB in 2s (329 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package gcc-11-base:amd64.\n",
            "(Reading database ... 160837 files and directories currently installed.)\n",
            "Preparing to unpack .../gcc-11-base_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking gcc-11-base:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Setting up gcc-11-base:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Selecting previously unselected package libgcc-s1:amd64.\n",
            "(Reading database ... 160842 files and directories currently installed.)\n",
            "Preparing to unpack .../libgcc-s1_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking libgcc-s1:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Replacing files in old package libgcc1:amd64 (1:8.4.0-1ubuntu1~18.04) ...\n",
            "Setting up libgcc-s1:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "(Reading database ... 160844 files and directories currently installed.)\n",
            "Preparing to unpack .../libstdc++6_11.1.0-1ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking libstdc++6:amd64 (11.1.0-1ubuntu1~18.04.1) over (8.4.0-1ubuntu1~18.04) ...\n",
            "Setting up libstdc++6:amd64 (11.1.0-1ubuntu1~18.04.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WF2_bg9sChr",
        "outputId": "6d2dc034-f45c-4ea2-ca7c-d141cd506fc6"
      },
      "source": [
        "# This will install CondaColab.  This will restart your kernel one last time.  Run this cell by itself and only run the next cell once you see the session crash.\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:34\n",
            "🔁 Restarting kernel...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93jcTxxpsFvf",
        "outputId": "43a2e294-d12f-4280-cac0-b407f5a64557"
      },
      "source": [
        "# you can now run the rest of the cells as normal\n",
        "import condacolab\n",
        "condacolab.check()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2upX4035sLC9",
        "outputId": "ec54ed0e-984f-443e-e823-88b7c54f8d91"
      },
      "source": [
        "# Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py <release> <packages>'\n",
        "# The <release> options are 'stable' and 'nightly'.  Leaving it blank or adding any other words will default to stable.\n",
        "# The <packages> option are default blank or 'core'.  By default, we install RAPIDSAI and BlazingSQL.  The 'core' option will install only RAPIDSAI and not include BlazingSQL, \n",
        "!python rapidsai-csp-utils/colab/install_rapids.py stable core\n",
        "import os\n",
        "os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'\n",
        "os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'\n",
        "os.environ['CONDA_PREFIX'] = '/usr/local'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing RAPIDS Stable 21.06\n",
            "Starting the RAPIDS install on Colab.  This will take about 15 minutes.\n",
            "Collecting package metadata (current_repodata.json): ...working... done\n",
            "Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): ...working... done\n",
            "Solving environment: ...working... done\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cudatoolkit=11.0\n",
            "    - gcsfs\n",
            "    - llvmlite\n",
            "    - openssl\n",
            "    - python=3.7\n",
            "    - rapids=21.06\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    abseil-cpp-20210324.0      |       h9c3ff4c_0        1011 KB  conda-forge\n",
            "    aiohttp-3.7.4.post0        |   py37h5e8e339_0         625 KB  conda-forge\n",
            "    anyio-3.2.1                |   py37h89c1867_0         138 KB  conda-forge\n",
            "    appdirs-1.4.4              |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    argon2-cffi-20.1.0         |   py37h5e8e339_2          47 KB  conda-forge\n",
            "    arrow-cpp-1.0.1            |py37h363ccdf_36_cuda        21.0 MB  conda-forge\n",
            "    arrow-cpp-proc-3.0.0       |             cuda          24 KB  conda-forge\n",
            "    async-timeout-3.0.1        |          py_1000          11 KB  conda-forge\n",
            "    async_generator-1.10       |             py_0          18 KB  conda-forge\n",
            "    attrs-21.2.0               |     pyhd8ed1ab_0          44 KB  conda-forge\n",
            "    aws-c-cal-0.4.5            |       h76129ab_8          35 KB  conda-forge\n",
            "    aws-c-common-0.5.2         |       h7f98852_0         165 KB  conda-forge\n",
            "    aws-c-event-stream-0.2.7   |       h6bac3ce_1          47 KB  conda-forge\n",
            "    aws-c-io-0.9.1             |       ha5b09cb_1         119 KB  conda-forge\n",
            "    aws-checksums-0.1.11       |       h99e32c3_3          50 KB  conda-forge\n",
            "    aws-sdk-cpp-1.8.151        |       hceb1b1e_1         4.5 MB  conda-forge\n",
            "    backcall-0.2.0             |     pyh9f0ad1d_0          13 KB  conda-forge\n",
            "    backports-1.0              |             py_2           4 KB  conda-forge\n",
            "    backports.functools_lru_cache-1.6.4|     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    bleach-3.3.1               |     pyhd8ed1ab_0         111 KB  conda-forge\n",
            "    blinker-1.4                |             py_1          13 KB  conda-forge\n",
            "    bokeh-2.2.3                |   py37h89c1867_0         7.0 MB  conda-forge\n",
            "    boost-1.74.0               |   py37h6dcda5c_3         342 KB  conda-forge\n",
            "    boost-cpp-1.74.0           |       hc6e9bd1_3        16.3 MB  conda-forge\n",
            "    brotli-1.0.9               |       h7f98852_5          17 KB  conda-forge\n",
            "    brotli-bin-1.0.9           |       h7f98852_5          19 KB  conda-forge\n",
            "    ca-certificates-2021.5.30  |       ha878542_0         136 KB  conda-forge\n",
            "    cachetools-4.2.2           |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    cairo-1.16.0               |    h6cf1ce9_1008         1.5 MB  conda-forge\n",
            "    certifi-2021.5.30          |   py37h89c1867_0         141 KB  conda-forge\n",
            "    cfitsio-3.470              |       hb418390_7         1.3 MB  conda-forge\n",
            "    click-7.1.2                |     pyh9f0ad1d_0          64 KB  conda-forge\n",
            "    click-plugins-1.1.1        |             py_0           9 KB  conda-forge\n",
            "    cligj-0.7.2                |     pyhd8ed1ab_0          10 KB  conda-forge\n",
            "    cloudpickle-1.6.0          |             py_0          22 KB  conda-forge\n",
            "    colorcet-2.0.6             |     pyhd8ed1ab_0         1.5 MB  conda-forge\n",
            "    conda-4.10.3               |   py37h89c1867_0         3.1 MB  conda-forge\n",
            "    cudatoolkit-11.0.221       |       h6bb024c_0       953.0 MB  nvidia\n",
            "    cudf-21.06.01              |cuda_11.0_py37_g101fc0fda4_2       108.4 MB  rapidsai\n",
            "    cudf_kafka-21.06.01        |py37_g101fc0fda4_2         1.7 MB  rapidsai\n",
            "    cugraph-21.06.00           | py37_gf9ffd2de_0        65.0 MB  rapidsai\n",
            "    cuml-21.06.02              |cuda11.0_py37_g7dfbf8d9e_0        78.9 MB  rapidsai\n",
            "    cupy-9.0.0                 |   py37h4fdb0f7_0        50.3 MB  conda-forge\n",
            "    curl-7.75.0                |       h979ede3_0         147 KB  conda-forge\n",
            "    cusignal-21.06.00          |  py38_ga78207b_0         1.0 MB  rapidsai\n",
            "    cuspatial-21.06.00         |  py37_g37798cd_0        15.2 MB  rapidsai\n",
            "    custreamz-21.06.01         |py37_g101fc0fda4_2          32 KB  rapidsai\n",
            "    cuxfilter-21.06.00         |  py37_g9459467_0         136 KB  rapidsai\n",
            "    cycler-0.10.0              |             py_2           9 KB  conda-forge\n",
            "    cyrus-sasl-2.1.27          |       h3274739_1         224 KB  conda-forge\n",
            "    cytoolz-0.11.0             |   py37h5e8e339_3         403 KB  conda-forge\n",
            "    dask-2021.5.0              |     pyhd8ed1ab_0           4 KB  conda-forge\n",
            "    dask-core-2021.5.0         |     pyhd8ed1ab_0         735 KB  conda-forge\n",
            "    dask-cuda-21.06.00         |           py37_0         110 KB  rapidsai\n",
            "    dask-cudf-21.06.01         |py37_g101fc0fda4_2         103 KB  rapidsai\n",
            "    datashader-0.11.1          |     pyh9f0ad1d_0        14.0 MB  conda-forge\n",
            "    datashape-0.5.4            |             py_1          49 KB  conda-forge\n",
            "    decorator-4.4.2            |             py_0          11 KB  conda-forge\n",
            "    defusedxml-0.7.1           |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    distributed-2021.5.0       |   py37h89c1867_0         1.1 MB  conda-forge\n",
            "    dlpack-0.5                 |       h9c3ff4c_0          12 KB  conda-forge\n",
            "    entrypoints-0.3            |  pyhd8ed1ab_1003           8 KB  conda-forge\n",
            "    expat-2.4.1                |       h9c3ff4c_0         182 KB  conda-forge\n",
            "    faiss-proc-1.0.0           |             cuda          24 KB  rapidsai\n",
            "    fastavro-1.4.3             |   py37h5e8e339_0         501 KB  conda-forge\n",
            "    fastrlock-0.6              |   py37hcd2ae1e_1          31 KB  conda-forge\n",
            "    fiona-1.8.20               |   py37ha0cc35a_0         1.1 MB  conda-forge\n",
            "    fontconfig-2.13.1          |    hba837de_1005         357 KB  conda-forge\n",
            "    freetype-2.10.4            |       h0708190_1         890 KB  conda-forge\n",
            "    freexl-1.0.6               |       h7f98852_0          48 KB  conda-forge\n",
            "    fsspec-2021.7.0            |     pyhd8ed1ab_0          81 KB  conda-forge\n",
            "    gcsfs-2021.7.0             |     pyhd8ed1ab_0          23 KB  conda-forge\n",
            "    gdal-3.2.2                 |   py37hb0e9ad2_0         1.5 MB  conda-forge\n",
            "    geopandas-0.9.0            |     pyhd8ed1ab_1           5 KB  conda-forge\n",
            "    geopandas-base-0.9.0       |     pyhd8ed1ab_1         950 KB  conda-forge\n",
            "    geos-3.9.1                 |       h9c3ff4c_2         1.1 MB  conda-forge\n",
            "    geotiff-1.6.0              |       hcf90da6_5         296 KB  conda-forge\n",
            "    gettext-0.19.8.1           |    h0b5b191_1005         3.6 MB  conda-forge\n",
            "    gflags-2.2.2               |    he1b5a44_1004         114 KB  conda-forge\n",
            "    giflib-5.2.1               |       h36c2ea0_2          77 KB  conda-forge\n",
            "    glog-0.4.0                 |       h49b9bf7_3         104 KB  conda-forge\n",
            "    google-auth-1.33.1         |     pyh6c4a22f_0          81 KB  conda-forge\n",
            "    google-auth-oauthlib-0.4.4 |     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    grpc-cpp-1.37.1            |       h36de60a_0         3.5 MB  conda-forge\n",
            "    hdf4-4.2.15                |       h10796ff_3         950 KB  conda-forge\n",
            "    hdf5-1.10.6                |nompi_h6a2412b_1114         3.1 MB  conda-forge\n",
            "    heapdict-1.0.1             |             py_0           7 KB  conda-forge\n",
            "    importlib-metadata-4.6.1   |   py37h89c1867_0          31 KB  conda-forge\n",
            "    ipykernel-5.5.5            |   py37h085eea5_0         167 KB  conda-forge\n",
            "    ipython-7.25.0             |   py37h085eea5_1         1.1 MB  conda-forge\n",
            "    ipython_genutils-0.2.0     |             py_1          21 KB  conda-forge\n",
            "    ipywidgets-7.6.3           |     pyhd3deb0d_0         101 KB  conda-forge\n",
            "    jedi-0.18.0                |   py37h89c1867_2         923 KB  conda-forge\n",
            "    jinja2-3.0.1               |     pyhd8ed1ab_0          99 KB  conda-forge\n",
            "    joblib-1.0.1               |     pyhd8ed1ab_0         206 KB  conda-forge\n",
            "    jpeg-9d                    |       h36c2ea0_0         264 KB  conda-forge\n",
            "    json-c-0.15                |       h98cffda_0         274 KB  conda-forge\n",
            "    jsonschema-3.2.0           |     pyhd8ed1ab_3          45 KB  conda-forge\n",
            "    jupyter-server-proxy-3.1.0 |     pyhd8ed1ab_0          27 KB  conda-forge\n",
            "    jupyter_client-6.1.12      |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    jupyter_core-4.7.1         |   py37h89c1867_0          72 KB  conda-forge\n",
            "    jupyter_server-1.9.0       |     pyhd8ed1ab_0         262 KB  conda-forge\n",
            "    jupyterlab_pygments-0.1.2  |     pyh9f0ad1d_0           8 KB  conda-forge\n",
            "    jupyterlab_widgets-1.0.0   |     pyhd8ed1ab_1         130 KB  conda-forge\n",
            "    kealib-1.4.14              |       hcc255d8_2         186 KB  conda-forge\n",
            "    kiwisolver-1.3.1           |   py37h2527ec5_1          78 KB  conda-forge\n",
            "    lcms2-2.12                 |       hddcbb42_0         443 KB  conda-forge\n",
            "    libblas-3.9.0              |       9_openblas          11 KB  conda-forge\n",
            "    libbrotlicommon-1.0.9      |       h7f98852_5          65 KB  conda-forge\n",
            "    libbrotlidec-1.0.9         |       h7f98852_5          33 KB  conda-forge\n",
            "    libbrotlienc-1.0.9         |       h7f98852_5         286 KB  conda-forge\n",
            "    libcblas-3.9.0             |       9_openblas          11 KB  conda-forge\n",
            "    libcudf-21.06.01           |cuda11.0_g101fc0fda4_2       187.7 MB  rapidsai\n",
            "    libcudf_kafka-21.06.01     |    g101fc0fda4_2         125 KB  rapidsai\n",
            "    libcugraph-21.06.00        |cuda11.0_gf9ffd2de_0       213.6 MB  rapidsai\n",
            "    libcuml-21.06.02           |cuda11.0_g7dfbf8d9e_0        95.2 MB  rapidsai\n",
            "    libcumlprims-21.06.00      |cuda11.0_gfda2e6c_0         1.1 MB  nvidia\n",
            "    libcuspatial-21.06.00      |cuda11.0_g37798cd_0         7.6 MB  rapidsai\n",
            "    libdap4-3.20.6             |       hd7c4107_2        11.3 MB  conda-forge\n",
            "    libevent-2.1.10            |       hcdb4288_3         1.1 MB  conda-forge\n",
            "    libfaiss-1.7.0             |cuda110h8045045_8_cuda        67.0 MB  conda-forge\n",
            "    libgcrypt-1.9.3            |       h7f98852_1         677 KB  conda-forge\n",
            "    libgdal-3.2.2              |       h804b7da_0        13.2 MB  conda-forge\n",
            "    libgfortran-ng-9.3.0       |      hff62375_19          22 KB  conda-forge\n",
            "    libgfortran5-9.3.0         |      hff62375_19         2.0 MB  conda-forge\n",
            "    libglib-2.68.3             |       h3e27bee_0         3.1 MB  conda-forge\n",
            "    libgpg-error-1.42          |       h9c3ff4c_0         278 KB  conda-forge\n",
            "    libgsasl-1.8.1             |       hfabc8ba_0         162 KB  conda-forge\n",
            "    libhwloc-2.3.0             |       h5e5b7d1_1         2.7 MB  conda-forge\n",
            "    libkml-1.3.0               |    h238a007_1014         591 KB  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",
            "    libnetcdf-4.7.4            |nompi_h56d31a8_107         1.3 MB  conda-forge\n",
            "    libntlm-1.4                |    h7f98852_1002          32 KB  conda-forge\n",
            "    libopenblas-0.3.15         |pthreads_h8fe5266_1         9.2 MB  conda-forge\n",
            "    libpng-1.6.37              |       h21135ba_2         306 KB  conda-forge\n",
            "    libpq-13.2                 |       hfd2b0eb_2         2.7 MB  conda-forge\n",
            "    libprotobuf-3.15.8         |       h780b84a_0         2.5 MB  conda-forge\n",
            "    librdkafka-1.5.3           |       hc49e61c_1        11.2 MB  conda-forge\n",
            "    librmm-21.06.00            |cuda11.0_gee432a0_0          57 KB  rapidsai\n",
            "    librttopo-1.1.0            |       h1185371_6         235 KB  conda-forge\n",
            "    libsodium-1.0.18           |       h36c2ea0_1         366 KB  conda-forge\n",
            "    libspatialindex-1.9.3      |       h9c3ff4c_3         4.6 MB  conda-forge\n",
            "    libspatialite-5.0.1        |       h20cb978_4         4.4 MB  conda-forge\n",
            "    libthrift-0.14.1           |       he6d91bd_2         4.5 MB  conda-forge\n",
            "    libtiff-4.2.0              |       hbd63e13_2         639 KB  conda-forge\n",
            "    libutf8proc-2.6.1          |       h7f98852_0          95 KB  conda-forge\n",
            "    libuuid-2.32.1             |    h7f98852_1000          28 KB  conda-forge\n",
            "    libuv-1.41.1               |       h7f98852_0         1.0 MB  conda-forge\n",
            "    libwebp-1.2.0              |       h3452ae3_0          85 KB  conda-forge\n",
            "    libwebp-base-1.2.0         |       h7f98852_2         815 KB  conda-forge\n",
            "    libxcb-1.13                |    h7f98852_1003         395 KB  conda-forge\n",
            "    libxgboost-1.4.2dev.rapidsai21.06|       cuda11.0_0       115.3 MB  rapidsai\n",
            "    llvmlite-0.36.0            |   py37h9d7f4d0_0         2.7 MB  conda-forge\n",
            "    locket-0.2.0               |             py_2           6 KB  conda-forge\n",
            "    mapclassify-2.4.2          |     pyhd8ed1ab_0          36 KB  conda-forge\n",
            "    markdown-3.3.4             |     pyhd8ed1ab_0          67 KB  conda-forge\n",
            "    markupsafe-2.0.1           |   py37h5e8e339_0          22 KB  conda-forge\n",
            "    matplotlib-base-3.4.2      |   py37hdd32ed1_0         7.2 MB  conda-forge\n",
            "    matplotlib-inline-0.1.2    |     pyhd8ed1ab_2          11 KB  conda-forge\n",
            "    mistune-0.8.4              |py37h5e8e339_1004          54 KB  conda-forge\n",
            "    msgpack-python-1.0.2       |   py37h2527ec5_1          91 KB  conda-forge\n",
            "    multidict-5.1.0            |   py37h5e8e339_1          67 KB  conda-forge\n",
            "    multipledispatch-0.6.0     |             py_0          12 KB  conda-forge\n",
            "    munch-2.5.0                |             py_0          12 KB  conda-forge\n",
            "    nbclient-0.5.3             |     pyhd8ed1ab_0          67 KB  conda-forge\n",
            "    nbconvert-6.1.0            |   py37h89c1867_0         548 KB  conda-forge\n",
            "    nbformat-5.1.3             |     pyhd8ed1ab_0          47 KB  conda-forge\n",
            "    nccl-2.10.3.1              |       h96e36e3_0       121.2 MB  conda-forge\n",
            "    nest-asyncio-1.5.1         |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    networkx-2.6.1             |     pyhd8ed1ab_1         1.5 MB  conda-forge\n",
            "    nodejs-14.17.1             |       h92b4a50_1        15.8 MB  conda-forge\n",
            "    notebook-6.4.0             |     pyha770c72_0         6.1 MB  conda-forge\n",
            "    numba-0.53.1               |   py37hb11d6e1_1         3.7 MB  conda-forge\n",
            "    numpy-1.21.1               |   py37h038b26d_0         6.1 MB  conda-forge\n",
            "    nvtx-0.2.3                 |   py37h5e8e339_0          55 KB  conda-forge\n",
            "    oauthlib-3.1.1             |     pyhd8ed1ab_0          87 KB  conda-forge\n",
            "    olefile-0.46               |     pyh9f0ad1d_1          32 KB  conda-forge\n",
            "    openjpeg-2.4.0             |       hb52868f_1         444 KB  conda-forge\n",
            "    openssl-1.1.1k             |       h7f98852_0         2.1 MB  conda-forge\n",
            "    orc-1.6.7                  |       heec2584_1         750 KB  conda-forge\n",
            "    packaging-21.0             |     pyhd8ed1ab_0          35 KB  conda-forge\n",
            "    pandas-1.2.5               |   py37h219a48f_0        11.8 MB  conda-forge\n",
            "    pandoc-2.14.1              |       h7f98852_0        12.0 MB  conda-forge\n",
            "    pandocfilters-1.4.2        |             py_1           9 KB  conda-forge\n",
            "    panel-0.10.3               |     pyhd8ed1ab_0         6.1 MB  conda-forge\n",
            "    param-1.11.1               |     pyh6c4a22f_0          67 KB  conda-forge\n",
            "    parquet-cpp-1.5.1          |                2           3 KB  conda-forge\n",
            "    parso-0.8.2                |     pyhd8ed1ab_0          68 KB  conda-forge\n",
            "    partd-1.2.0                |     pyhd8ed1ab_0          18 KB  conda-forge\n",
            "    pcre-8.45                  |       h9c3ff4c_0         253 KB  conda-forge\n",
            "    pexpect-4.8.0              |     pyh9f0ad1d_2          47 KB  conda-forge\n",
            "    pickle5-0.0.11             |   py37h5e8e339_0         173 KB  conda-forge\n",
            "    pickleshare-0.7.5          |          py_1003           9 KB  conda-forge\n",
            "    pillow-8.2.0               |   py37h4600e1f_1         684 KB  conda-forge\n",
            "    pixman-0.40.0              |       h36c2ea0_0         627 KB  conda-forge\n",
            "    poppler-21.03.0            |       h93df280_0        15.9 MB  conda-forge\n",
            "    poppler-data-0.4.10        |                0         3.8 MB  conda-forge\n",
            "    postgresql-13.2            |       h6303168_2         5.3 MB  conda-forge\n",
            "    proj-8.0.0                 |       h277dcde_0         3.1 MB  conda-forge\n",
            "    prometheus_client-0.11.0   |     pyhd8ed1ab_0          46 KB  conda-forge\n",
            "    prompt-toolkit-3.0.19      |     pyha770c72_0         244 KB  conda-forge\n",
            "    protobuf-3.15.8            |   py37hcd2ae1e_0         334 KB  conda-forge\n",
            "    psutil-5.8.0               |   py37h5e8e339_1         342 KB  conda-forge\n",
            "    pthread-stubs-0.4          |    h36c2ea0_1001           5 KB  conda-forge\n",
            "    ptyprocess-0.7.0           |     pyhd3deb0d_0          16 KB  conda-forge\n",
            "    py-xgboost-1.4.2dev.rapidsai21.06|   cuda11.0py37_0         151 KB  rapidsai\n",
            "    pyarrow-1.0.1              |py37hb63ea2f_36_cuda         2.4 MB  conda-forge\n",
            "    pyasn1-0.4.8               |             py_0          53 KB  conda-forge\n",
            "    pyasn1-modules-0.2.7       |             py_0          60 KB  conda-forge\n",
            "    pyct-0.4.6                 |             py_0           3 KB  conda-forge\n",
            "    pyct-core-0.4.6            |             py_0          13 KB  conda-forge\n",
            "    pydeck-0.5.0               |     pyh9f0ad1d_0         3.6 MB  conda-forge\n",
            "    pyee-7.0.4                 |     pyh9f0ad1d_0          14 KB  conda-forge\n",
            "    pygments-2.9.0             |     pyhd8ed1ab_0         754 KB  conda-forge\n",
            "    pyjwt-2.1.0                |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    pynvml-11.0.0              |     pyhd8ed1ab_0          39 KB  conda-forge\n",
            "    pyparsing-2.4.7            |     pyh9f0ad1d_0          60 KB  conda-forge\n",
            "    pyppeteer-0.2.2            |             py_1         104 KB  conda-forge\n",
            "    pyproj-3.0.1               |   py37h2bb2a07_1         484 KB  conda-forge\n",
            "    pyrsistent-0.17.3          |   py37h5e8e339_2          89 KB  conda-forge\n",
            "    python-confluent-kafka-1.5.0|   py37h8f50634_0         122 KB  conda-forge\n",
            "    python-dateutil-2.8.2      |     pyhd8ed1ab_0         240 KB  conda-forge\n",
            "    python_abi-3.7             |          2_cp37m           4 KB  conda-forge\n",
            "    pytz-2021.1                |     pyhd8ed1ab_0         239 KB  conda-forge\n",
            "    pyu2f-0.1.5                |     pyhd8ed1ab_0          31 KB  conda-forge\n",
            "    pyviz_comms-2.1.0          |     pyhd8ed1ab_0          29 KB  conda-forge\n",
            "    pyyaml-5.4.1               |   py37h5e8e339_0         189 KB  conda-forge\n",
            "    pyzmq-22.1.0               |   py37h336d617_0         500 KB  conda-forge\n",
            "    rapids-21.06.00            |cuda11.0_py37_ge3c8282_427           5 KB  rapidsai\n",
            "    rapids-xgboost-21.06.00    |cuda11.0_py37_ge3c8282_427           4 KB  rapidsai\n",
            "    re2-2021.04.01             |       h9c3ff4c_0         218 KB  conda-forge\n",
            "    requests-oauthlib-1.3.0    |     pyh9f0ad1d_0          21 KB  conda-forge\n",
            "    requests-unixsocket-0.2.0  |             py_0          14 KB  conda-forge\n",
            "    rmm-21.06.00               |cuda_11.0_py37_gee432a0_0         7.0 MB  rapidsai\n",
            "    rsa-4.7.2                  |     pyh44b312d_0          28 KB  conda-forge\n",
            "    rtree-0.9.7                |   py37h0b55af0_1          45 KB  conda-forge\n",
            "    s2n-1.0.0                  |       h9b69904_0         333 KB  conda-forge\n",
            "    scikit-learn-0.24.2        |   py37h18a542f_0         7.5 MB  conda-forge\n",
            "    scipy-1.7.0                |   py37h29e03ee_1        21.7 MB  conda-forge\n",
            "    send2trash-1.7.1           |     pyhd8ed1ab_0          17 KB  conda-forge\n",
            "    shapely-1.7.1              |   py37h2d1e849_5         438 KB  conda-forge\n",
            "    simpervisor-0.4            |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    snappy-1.1.8               |       he1b5a44_3          32 KB  conda-forge\n",
            "    sniffio-1.2.0              |   py37h89c1867_1          15 KB  conda-forge\n",
            "    sortedcontainers-2.4.0     |     pyhd8ed1ab_0          26 KB  conda-forge\n",
            "    spdlog-1.9.0               |       h4bd325d_0         340 KB  conda-forge\n",
            "    streamz-0.6.2              |     pyh44b312d_0          59 KB  conda-forge\n",
            "    tblib-1.7.0                |     pyhd8ed1ab_0          15 KB  conda-forge\n",
            "    terminado-0.10.1           |   py37h89c1867_0          26 KB  conda-forge\n",
            "    testpath-0.5.0             |     pyhd8ed1ab_0          86 KB  conda-forge\n",
            "    threadpoolctl-2.2.0        |     pyh8a188c0_0          16 KB  conda-forge\n",
            "    tiledb-2.2.9               |       h91fcb0e_0         4.0 MB  conda-forge\n",
            "    toolz-0.11.1               |             py_0          46 KB  conda-forge\n",
            "    tornado-6.1                |   py37h5e8e339_1         646 KB  conda-forge\n",
            "    traitlets-5.0.5            |             py_0          81 KB  conda-forge\n",
            "    treelite-1.3.0             |   py37hfdac9b6_0         2.7 MB  conda-forge\n",
            "    typing-extensions-3.10.0.0 |       hd8ed1ab_0           8 KB  conda-forge\n",
            "    typing_extensions-3.10.0.0 |     pyha770c72_0          28 KB  conda-forge\n",
            "    tzcode-2021a               |       h7f98852_2          68 KB  conda-forge\n",
            "    tzdata-2021a               |       he74cb21_1         121 KB  conda-forge\n",
            "    ucx-1.9.0+gcd9efd3         |       cuda11.0_0         8.2 MB  rapidsai\n",
            "    ucx-proc-1.0.0             |              gpu           9 KB  rapidsai\n",
            "    ucx-py-0.20.0              |  py37_gcd9efd3_0         294 KB  rapidsai\n",
            "    wcwidth-0.2.5              |     pyh9f0ad1d_2          33 KB  conda-forge\n",
            "    webencodings-0.5.1         |             py_1          12 KB  conda-forge\n",
            "    websocket-client-0.57.0    |   py37h89c1867_4          59 KB  conda-forge\n",
            "    websockets-8.1             |   py37h5e8e339_3          90 KB  conda-forge\n",
            "    widgetsnbextension-3.5.1   |   py37h89c1867_4         1.8 MB  conda-forge\n",
            "    xarray-0.18.2              |     pyhd8ed1ab_0         599 KB  conda-forge\n",
            "    xerces-c-3.2.3             |       h9d8b166_2         1.8 MB  conda-forge\n",
            "    xgboost-1.4.2dev.rapidsai21.06|   cuda11.0py37_0          17 KB  rapidsai\n",
            "    xorg-kbproto-1.0.7         |    h7f98852_1002          27 KB  conda-forge\n",
            "    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n",
            "    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n",
            "    xorg-libx11-1.7.2          |       h7f98852_0         941 KB  conda-forge\n",
            "    xorg-libxau-1.0.9          |       h7f98852_0          13 KB  conda-forge\n",
            "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
            "    xorg-libxext-1.3.4         |       h7f98852_1          54 KB  conda-forge\n",
            "    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n",
            "    xorg-renderproto-0.11.1    |    h7f98852_1002           9 KB  conda-forge\n",
            "    xorg-xextproto-7.3.0       |    h7f98852_1002          28 KB  conda-forge\n",
            "    xorg-xproto-7.0.31         |    h7f98852_1007          73 KB  conda-forge\n",
            "    yarl-1.6.3                 |   py37h5e8e339_2         142 KB  conda-forge\n",
            "    zeromq-4.3.4               |       h9c3ff4c_0         352 KB  conda-forge\n",
            "    zict-2.0.0                 |             py_0          10 KB  conda-forge\n",
            "    zipp-3.5.0                 |     pyhd8ed1ab_0          12 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        2.41 GB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  abseil-cpp         conda-forge/linux-64::abseil-cpp-20210324.0-h9c3ff4c_0\n",
            "  aiohttp            conda-forge/linux-64::aiohttp-3.7.4.post0-py37h5e8e339_0\n",
            "  anyio              conda-forge/linux-64::anyio-3.2.1-py37h89c1867_0\n",
            "  appdirs            conda-forge/noarch::appdirs-1.4.4-pyh9f0ad1d_0\n",
            "  argon2-cffi        conda-forge/linux-64::argon2-cffi-20.1.0-py37h5e8e339_2\n",
            "  arrow-cpp          conda-forge/linux-64::arrow-cpp-1.0.1-py37h363ccdf_36_cuda\n",
            "  arrow-cpp-proc     conda-forge/linux-64::arrow-cpp-proc-3.0.0-cuda\n",
            "  async-timeout      conda-forge/noarch::async-timeout-3.0.1-py_1000\n",
            "  async_generator    conda-forge/noarch::async_generator-1.10-py_0\n",
            "  attrs              conda-forge/noarch::attrs-21.2.0-pyhd8ed1ab_0\n",
            "  aws-c-cal          conda-forge/linux-64::aws-c-cal-0.4.5-h76129ab_8\n",
            "  aws-c-common       conda-forge/linux-64::aws-c-common-0.5.2-h7f98852_0\n",
            "  aws-c-event-stream conda-forge/linux-64::aws-c-event-stream-0.2.7-h6bac3ce_1\n",
            "  aws-c-io           conda-forge/linux-64::aws-c-io-0.9.1-ha5b09cb_1\n",
            "  aws-checksums      conda-forge/linux-64::aws-checksums-0.1.11-h99e32c3_3\n",
            "  aws-sdk-cpp        conda-forge/linux-64::aws-sdk-cpp-1.8.151-hceb1b1e_1\n",
            "  backcall           conda-forge/noarch::backcall-0.2.0-pyh9f0ad1d_0\n",
            "  backports          conda-forge/noarch::backports-1.0-py_2\n",
            "  backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n",
            "  bleach             conda-forge/noarch::bleach-3.3.1-pyhd8ed1ab_0\n",
            "  blinker            conda-forge/noarch::blinker-1.4-py_1\n",
            "  bokeh              conda-forge/linux-64::bokeh-2.2.3-py37h89c1867_0\n",
            "  boost              conda-forge/linux-64::boost-1.74.0-py37h6dcda5c_3\n",
            "  boost-cpp          conda-forge/linux-64::boost-cpp-1.74.0-hc6e9bd1_3\n",
            "  brotli             conda-forge/linux-64::brotli-1.0.9-h7f98852_5\n",
            "  brotli-bin         conda-forge/linux-64::brotli-bin-1.0.9-h7f98852_5\n",
            "  cachetools         conda-forge/noarch::cachetools-4.2.2-pyhd8ed1ab_0\n",
            "  cairo              conda-forge/linux-64::cairo-1.16.0-h6cf1ce9_1008\n",
            "  cfitsio            conda-forge/linux-64::cfitsio-3.470-hb418390_7\n",
            "  click              conda-forge/noarch::click-7.1.2-pyh9f0ad1d_0\n",
            "  click-plugins      conda-forge/noarch::click-plugins-1.1.1-py_0\n",
            "  cligj              conda-forge/noarch::cligj-0.7.2-pyhd8ed1ab_0\n",
            "  cloudpickle        conda-forge/noarch::cloudpickle-1.6.0-py_0\n",
            "  colorcet           conda-forge/noarch::colorcet-2.0.6-pyhd8ed1ab_0\n",
            "  cudatoolkit        nvidia/linux-64::cudatoolkit-11.0.221-h6bb024c_0\n",
            "  cudf               rapidsai/linux-64::cudf-21.06.01-cuda_11.0_py37_g101fc0fda4_2\n",
            "  cudf_kafka         rapidsai/linux-64::cudf_kafka-21.06.01-py37_g101fc0fda4_2\n",
            "  cugraph            rapidsai/linux-64::cugraph-21.06.00-py37_gf9ffd2de_0\n",
            "  cuml               rapidsai/linux-64::cuml-21.06.02-cuda11.0_py37_g7dfbf8d9e_0\n",
            "  cupy               conda-forge/linux-64::cupy-9.0.0-py37h4fdb0f7_0\n",
            "  curl               conda-forge/linux-64::curl-7.75.0-h979ede3_0\n",
            "  cusignal           rapidsai/noarch::cusignal-21.06.00-py38_ga78207b_0\n",
            "  cuspatial          rapidsai/linux-64::cuspatial-21.06.00-py37_g37798cd_0\n",
            "  custreamz          rapidsai/linux-64::custreamz-21.06.01-py37_g101fc0fda4_2\n",
            "  cuxfilter          rapidsai/linux-64::cuxfilter-21.06.00-py37_g9459467_0\n",
            "  cycler             conda-forge/noarch::cycler-0.10.0-py_2\n",
            "  cyrus-sasl         conda-forge/linux-64::cyrus-sasl-2.1.27-h3274739_1\n",
            "  cytoolz            conda-forge/linux-64::cytoolz-0.11.0-py37h5e8e339_3\n",
            "  dask               conda-forge/noarch::dask-2021.5.0-pyhd8ed1ab_0\n",
            "  dask-core          conda-forge/noarch::dask-core-2021.5.0-pyhd8ed1ab_0\n",
            "  dask-cuda          rapidsai/linux-64::dask-cuda-21.06.00-py37_0\n",
            "  dask-cudf          rapidsai/linux-64::dask-cudf-21.06.01-py37_g101fc0fda4_2\n",
            "  datashader         conda-forge/noarch::datashader-0.11.1-pyh9f0ad1d_0\n",
            "  datashape          conda-forge/noarch::datashape-0.5.4-py_1\n",
            "  decorator          conda-forge/noarch::decorator-4.4.2-py_0\n",
            "  defusedxml         conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0\n",
            "  distributed        conda-forge/linux-64::distributed-2021.5.0-py37h89c1867_0\n",
            "  dlpack             conda-forge/linux-64::dlpack-0.5-h9c3ff4c_0\n",
            "  entrypoints        conda-forge/noarch::entrypoints-0.3-pyhd8ed1ab_1003\n",
            "  expat              conda-forge/linux-64::expat-2.4.1-h9c3ff4c_0\n",
            "  faiss-proc         rapidsai/linux-64::faiss-proc-1.0.0-cuda\n",
            "  fastavro           conda-forge/linux-64::fastavro-1.4.3-py37h5e8e339_0\n",
            "  fastrlock          conda-forge/linux-64::fastrlock-0.6-py37hcd2ae1e_1\n",
            "  fiona              conda-forge/linux-64::fiona-1.8.20-py37ha0cc35a_0\n",
            "  fontconfig         conda-forge/linux-64::fontconfig-2.13.1-hba837de_1005\n",
            "  freetype           conda-forge/linux-64::freetype-2.10.4-h0708190_1\n",
            "  freexl             conda-forge/linux-64::freexl-1.0.6-h7f98852_0\n",
            "  fsspec             conda-forge/noarch::fsspec-2021.7.0-pyhd8ed1ab_0\n",
            "  gcsfs              conda-forge/noarch::gcsfs-2021.7.0-pyhd8ed1ab_0\n",
            "  gdal               conda-forge/linux-64::gdal-3.2.2-py37hb0e9ad2_0\n",
            "  geopandas          conda-forge/noarch::geopandas-0.9.0-pyhd8ed1ab_1\n",
            "  geopandas-base     conda-forge/noarch::geopandas-base-0.9.0-pyhd8ed1ab_1\n",
            "  geos               conda-forge/linux-64::geos-3.9.1-h9c3ff4c_2\n",
            "  geotiff            conda-forge/linux-64::geotiff-1.6.0-hcf90da6_5\n",
            "  gettext            conda-forge/linux-64::gettext-0.19.8.1-h0b5b191_1005\n",
            "  gflags             conda-forge/linux-64::gflags-2.2.2-he1b5a44_1004\n",
            "  giflib             conda-forge/linux-64::giflib-5.2.1-h36c2ea0_2\n",
            "  glog               conda-forge/linux-64::glog-0.4.0-h49b9bf7_3\n",
            "  google-auth        conda-forge/noarch::google-auth-1.33.1-pyh6c4a22f_0\n",
            "  google-auth-oauth~ conda-forge/noarch::google-auth-oauthlib-0.4.4-pyhd8ed1ab_0\n",
            "  grpc-cpp           conda-forge/linux-64::grpc-cpp-1.37.1-h36de60a_0\n",
            "  hdf4               conda-forge/linux-64::hdf4-4.2.15-h10796ff_3\n",
            "  hdf5               conda-forge/linux-64::hdf5-1.10.6-nompi_h6a2412b_1114\n",
            "  heapdict           conda-forge/noarch::heapdict-1.0.1-py_0\n",
            "  importlib-metadata conda-forge/linux-64::importlib-metadata-4.6.1-py37h89c1867_0\n",
            "  ipykernel          conda-forge/linux-64::ipykernel-5.5.5-py37h085eea5_0\n",
            "  ipython            conda-forge/linux-64::ipython-7.25.0-py37h085eea5_1\n",
            "  ipython_genutils   conda-forge/noarch::ipython_genutils-0.2.0-py_1\n",
            "  ipywidgets         conda-forge/noarch::ipywidgets-7.6.3-pyhd3deb0d_0\n",
            "  jedi               conda-forge/linux-64::jedi-0.18.0-py37h89c1867_2\n",
            "  jinja2             conda-forge/noarch::jinja2-3.0.1-pyhd8ed1ab_0\n",
            "  joblib             conda-forge/noarch::joblib-1.0.1-pyhd8ed1ab_0\n",
            "  jpeg               conda-forge/linux-64::jpeg-9d-h36c2ea0_0\n",
            "  json-c             conda-forge/linux-64::json-c-0.15-h98cffda_0\n",
            "  jsonschema         conda-forge/noarch::jsonschema-3.2.0-pyhd8ed1ab_3\n",
            "  jupyter-server-pr~ conda-forge/noarch::jupyter-server-proxy-3.1.0-pyhd8ed1ab_0\n",
            "  jupyter_client     conda-forge/noarch::jupyter_client-6.1.12-pyhd8ed1ab_0\n",
            "  jupyter_core       conda-forge/linux-64::jupyter_core-4.7.1-py37h89c1867_0\n",
            "  jupyter_server     conda-forge/noarch::jupyter_server-1.9.0-pyhd8ed1ab_0\n",
            "  jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.1.2-pyh9f0ad1d_0\n",
            "  jupyterlab_widgets conda-forge/noarch::jupyterlab_widgets-1.0.0-pyhd8ed1ab_1\n",
            "  kealib             conda-forge/linux-64::kealib-1.4.14-hcc255d8_2\n",
            "  kiwisolver         conda-forge/linux-64::kiwisolver-1.3.1-py37h2527ec5_1\n",
            "  lcms2              conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n",
            "  libblas            conda-forge/linux-64::libblas-3.9.0-9_openblas\n",
            "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.0.9-h7f98852_5\n",
            "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.0.9-h7f98852_5\n",
            "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.0.9-h7f98852_5\n",
            "  libcblas           conda-forge/linux-64::libcblas-3.9.0-9_openblas\n",
            "  libcudf            rapidsai/linux-64::libcudf-21.06.01-cuda11.0_g101fc0fda4_2\n",
            "  libcudf_kafka      rapidsai/linux-64::libcudf_kafka-21.06.01-g101fc0fda4_2\n",
            "  libcugraph         rapidsai/linux-64::libcugraph-21.06.00-cuda11.0_gf9ffd2de_0\n",
            "  libcuml            rapidsai/linux-64::libcuml-21.06.02-cuda11.0_g7dfbf8d9e_0\n",
            "  libcumlprims       nvidia/linux-64::libcumlprims-21.06.00-cuda11.0_gfda2e6c_0\n",
            "  libcuspatial       rapidsai/linux-64::libcuspatial-21.06.00-cuda11.0_g37798cd_0\n",
            "  libdap4            conda-forge/linux-64::libdap4-3.20.6-hd7c4107_2\n",
            "  libevent           conda-forge/linux-64::libevent-2.1.10-hcdb4288_3\n",
            "  libfaiss           conda-forge/linux-64::libfaiss-1.7.0-cuda110h8045045_8_cuda\n",
            "  libgcrypt          conda-forge/linux-64::libgcrypt-1.9.3-h7f98852_1\n",
            "  libgdal            conda-forge/linux-64::libgdal-3.2.2-h804b7da_0\n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-9.3.0-hff62375_19\n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-9.3.0-hff62375_19\n",
            "  libglib            conda-forge/linux-64::libglib-2.68.3-h3e27bee_0\n",
            "  libgpg-error       conda-forge/linux-64::libgpg-error-1.42-h9c3ff4c_0\n",
            "  libgsasl           conda-forge/linux-64::libgsasl-1.8.1-hfabc8ba_0\n",
            "  libhwloc           conda-forge/linux-64::libhwloc-2.3.0-h5e5b7d1_1\n",
            "  libkml             conda-forge/linux-64::libkml-1.3.0-h238a007_1014\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",
            "  libnetcdf          conda-forge/linux-64::libnetcdf-4.7.4-nompi_h56d31a8_107\n",
            "  libntlm            conda-forge/linux-64::libntlm-1.4-h7f98852_1002\n",
            "  libopenblas        conda-forge/linux-64::libopenblas-0.3.15-pthreads_h8fe5266_1\n",
            "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n",
            "  libpq              conda-forge/linux-64::libpq-13.2-hfd2b0eb_2\n",
            "  libprotobuf        conda-forge/linux-64::libprotobuf-3.15.8-h780b84a_0\n",
            "  librdkafka         conda-forge/linux-64::librdkafka-1.5.3-hc49e61c_1\n",
            "  librmm             rapidsai/linux-64::librmm-21.06.00-cuda11.0_gee432a0_0\n",
            "  librttopo          conda-forge/linux-64::librttopo-1.1.0-h1185371_6\n",
            "  libsodium          conda-forge/linux-64::libsodium-1.0.18-h36c2ea0_1\n",
            "  libspatialindex    conda-forge/linux-64::libspatialindex-1.9.3-h9c3ff4c_3\n",
            "  libspatialite      conda-forge/linux-64::libspatialite-5.0.1-h20cb978_4\n",
            "  libthrift          conda-forge/linux-64::libthrift-0.14.1-he6d91bd_2\n",
            "  libtiff            conda-forge/linux-64::libtiff-4.2.0-hbd63e13_2\n",
            "  libutf8proc        conda-forge/linux-64::libutf8proc-2.6.1-h7f98852_0\n",
            "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h7f98852_1000\n",
            "  libuv              conda-forge/linux-64::libuv-1.41.1-h7f98852_0\n",
            "  libwebp            conda-forge/linux-64::libwebp-1.2.0-h3452ae3_0\n",
            "  libwebp-base       conda-forge/linux-64::libwebp-base-1.2.0-h7f98852_2\n",
            "  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1003\n",
            "  libxgboost         rapidsai/linux-64::libxgboost-1.4.2dev.rapidsai21.06-cuda11.0_0\n",
            "  llvmlite           conda-forge/linux-64::llvmlite-0.36.0-py37h9d7f4d0_0\n",
            "  locket             conda-forge/noarch::locket-0.2.0-py_2\n",
            "  mapclassify        conda-forge/noarch::mapclassify-2.4.2-pyhd8ed1ab_0\n",
            "  markdown           conda-forge/noarch::markdown-3.3.4-pyhd8ed1ab_0\n",
            "  markupsafe         conda-forge/linux-64::markupsafe-2.0.1-py37h5e8e339_0\n",
            "  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.4.2-py37hdd32ed1_0\n",
            "  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.2-pyhd8ed1ab_2\n",
            "  mistune            conda-forge/linux-64::mistune-0.8.4-py37h5e8e339_1004\n",
            "  msgpack-python     conda-forge/linux-64::msgpack-python-1.0.2-py37h2527ec5_1\n",
            "  multidict          conda-forge/linux-64::multidict-5.1.0-py37h5e8e339_1\n",
            "  multipledispatch   conda-forge/noarch::multipledispatch-0.6.0-py_0\n",
            "  munch              conda-forge/noarch::munch-2.5.0-py_0\n",
            "  nbclient           conda-forge/noarch::nbclient-0.5.3-pyhd8ed1ab_0\n",
            "  nbconvert          conda-forge/linux-64::nbconvert-6.1.0-py37h89c1867_0\n",
            "  nbformat           conda-forge/noarch::nbformat-5.1.3-pyhd8ed1ab_0\n",
            "  nccl               conda-forge/linux-64::nccl-2.10.3.1-h96e36e3_0\n",
            "  nest-asyncio       conda-forge/noarch::nest-asyncio-1.5.1-pyhd8ed1ab_0\n",
            "  networkx           conda-forge/noarch::networkx-2.6.1-pyhd8ed1ab_1\n",
            "  nodejs             conda-forge/linux-64::nodejs-14.17.1-h92b4a50_1\n",
            "  notebook           conda-forge/noarch::notebook-6.4.0-pyha770c72_0\n",
            "  numba              conda-forge/linux-64::numba-0.53.1-py37hb11d6e1_1\n",
            "  numpy              conda-forge/linux-64::numpy-1.21.1-py37h038b26d_0\n",
            "  nvtx               conda-forge/linux-64::nvtx-0.2.3-py37h5e8e339_0\n",
            "  oauthlib           conda-forge/noarch::oauthlib-3.1.1-pyhd8ed1ab_0\n",
            "  olefile            conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n",
            "  openjpeg           conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n",
            "  orc                conda-forge/linux-64::orc-1.6.7-heec2584_1\n",
            "  packaging          conda-forge/noarch::packaging-21.0-pyhd8ed1ab_0\n",
            "  pandas             conda-forge/linux-64::pandas-1.2.5-py37h219a48f_0\n",
            "  pandoc             conda-forge/linux-64::pandoc-2.14.1-h7f98852_0\n",
            "  pandocfilters      conda-forge/noarch::pandocfilters-1.4.2-py_1\n",
            "  panel              conda-forge/noarch::panel-0.10.3-pyhd8ed1ab_0\n",
            "  param              conda-forge/noarch::param-1.11.1-pyh6c4a22f_0\n",
            "  parquet-cpp        conda-forge/noarch::parquet-cpp-1.5.1-2\n",
            "  parso              conda-forge/noarch::parso-0.8.2-pyhd8ed1ab_0\n",
            "  partd              conda-forge/noarch::partd-1.2.0-pyhd8ed1ab_0\n",
            "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0\n",
            "  pexpect            conda-forge/noarch::pexpect-4.8.0-pyh9f0ad1d_2\n",
            "  pickle5            conda-forge/linux-64::pickle5-0.0.11-py37h5e8e339_0\n",
            "  pickleshare        conda-forge/noarch::pickleshare-0.7.5-py_1003\n",
            "  pillow             conda-forge/linux-64::pillow-8.2.0-py37h4600e1f_1\n",
            "  pixman             conda-forge/linux-64::pixman-0.40.0-h36c2ea0_0\n",
            "  poppler            conda-forge/linux-64::poppler-21.03.0-h93df280_0\n",
            "  poppler-data       conda-forge/noarch::poppler-data-0.4.10-0\n",
            "  postgresql         conda-forge/linux-64::postgresql-13.2-h6303168_2\n",
            "  proj               conda-forge/linux-64::proj-8.0.0-h277dcde_0\n",
            "  prometheus_client  conda-forge/noarch::prometheus_client-0.11.0-pyhd8ed1ab_0\n",
            "  prompt-toolkit     conda-forge/noarch::prompt-toolkit-3.0.19-pyha770c72_0\n",
            "  protobuf           conda-forge/linux-64::protobuf-3.15.8-py37hcd2ae1e_0\n",
            "  psutil             conda-forge/linux-64::psutil-5.8.0-py37h5e8e339_1\n",
            "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001\n",
            "  ptyprocess         conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0\n",
            "  py-xgboost         rapidsai/linux-64::py-xgboost-1.4.2dev.rapidsai21.06-cuda11.0py37_0\n",
            "  pyarrow            conda-forge/linux-64::pyarrow-1.0.1-py37hb63ea2f_36_cuda\n",
            "  pyasn1             conda-forge/noarch::pyasn1-0.4.8-py_0\n",
            "  pyasn1-modules     conda-forge/noarch::pyasn1-modules-0.2.7-py_0\n",
            "  pyct               conda-forge/noarch::pyct-0.4.6-py_0\n",
            "  pyct-core          conda-forge/noarch::pyct-core-0.4.6-py_0\n",
            "  pydeck             conda-forge/noarch::pydeck-0.5.0-pyh9f0ad1d_0\n",
            "  pyee               conda-forge/noarch::pyee-7.0.4-pyh9f0ad1d_0\n",
            "  pygments           conda-forge/noarch::pygments-2.9.0-pyhd8ed1ab_0\n",
            "  pyjwt              conda-forge/noarch::pyjwt-2.1.0-pyhd8ed1ab_0\n",
            "  pynvml             conda-forge/noarch::pynvml-11.0.0-pyhd8ed1ab_0\n",
            "  pyparsing          conda-forge/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\n",
            "  pyppeteer          conda-forge/noarch::pyppeteer-0.2.2-py_1\n",
            "  pyproj             conda-forge/linux-64::pyproj-3.0.1-py37h2bb2a07_1\n",
            "  pyrsistent         conda-forge/linux-64::pyrsistent-0.17.3-py37h5e8e339_2\n",
            "  python-confluent-~ conda-forge/linux-64::python-confluent-kafka-1.5.0-py37h8f50634_0\n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\n",
            "  pytz               conda-forge/noarch::pytz-2021.1-pyhd8ed1ab_0\n",
            "  pyu2f              conda-forge/noarch::pyu2f-0.1.5-pyhd8ed1ab_0\n",
            "  pyviz_comms        conda-forge/noarch::pyviz_comms-2.1.0-pyhd8ed1ab_0\n",
            "  pyyaml             conda-forge/linux-64::pyyaml-5.4.1-py37h5e8e339_0\n",
            "  pyzmq              conda-forge/linux-64::pyzmq-22.1.0-py37h336d617_0\n",
            "  rapids             rapidsai/linux-64::rapids-21.06.00-cuda11.0_py37_ge3c8282_427\n",
            "  rapids-xgboost     rapidsai/linux-64::rapids-xgboost-21.06.00-cuda11.0_py37_ge3c8282_427\n",
            "  re2                conda-forge/linux-64::re2-2021.04.01-h9c3ff4c_0\n",
            "  requests-oauthlib  conda-forge/noarch::requests-oauthlib-1.3.0-pyh9f0ad1d_0\n",
            "  requests-unixsock~ conda-forge/noarch::requests-unixsocket-0.2.0-py_0\n",
            "  rmm                rapidsai/linux-64::rmm-21.06.00-cuda_11.0_py37_gee432a0_0\n",
            "  rsa                conda-forge/noarch::rsa-4.7.2-pyh44b312d_0\n",
            "  rtree              conda-forge/linux-64::rtree-0.9.7-py37h0b55af0_1\n",
            "  s2n                conda-forge/linux-64::s2n-1.0.0-h9b69904_0\n",
            "  scikit-learn       conda-forge/linux-64::scikit-learn-0.24.2-py37h18a542f_0\n",
            "  scipy              conda-forge/linux-64::scipy-1.7.0-py37h29e03ee_1\n",
            "  send2trash         conda-forge/noarch::send2trash-1.7.1-pyhd8ed1ab_0\n",
            "  shapely            conda-forge/linux-64::shapely-1.7.1-py37h2d1e849_5\n",
            "  simpervisor        conda-forge/noarch::simpervisor-0.4-pyhd8ed1ab_0\n",
            "  snappy             conda-forge/linux-64::snappy-1.1.8-he1b5a44_3\n",
            "  sniffio            conda-forge/linux-64::sniffio-1.2.0-py37h89c1867_1\n",
            "  sortedcontainers   conda-forge/noarch::sortedcontainers-2.4.0-pyhd8ed1ab_0\n",
            "  spdlog             conda-forge/linux-64::spdlog-1.9.0-h4bd325d_0\n",
            "  streamz            conda-forge/noarch::streamz-0.6.2-pyh44b312d_0\n",
            "  tblib              conda-forge/noarch::tblib-1.7.0-pyhd8ed1ab_0\n",
            "  terminado          conda-forge/linux-64::terminado-0.10.1-py37h89c1867_0\n",
            "  testpath           conda-forge/noarch::testpath-0.5.0-pyhd8ed1ab_0\n",
            "  threadpoolctl      conda-forge/noarch::threadpoolctl-2.2.0-pyh8a188c0_0\n",
            "  tiledb             conda-forge/linux-64::tiledb-2.2.9-h91fcb0e_0\n",
            "  toolz              conda-forge/noarch::toolz-0.11.1-py_0\n",
            "  tornado            conda-forge/linux-64::tornado-6.1-py37h5e8e339_1\n",
            "  traitlets          conda-forge/noarch::traitlets-5.0.5-py_0\n",
            "  treelite           conda-forge/linux-64::treelite-1.3.0-py37hfdac9b6_0\n",
            "  typing-extensions  conda-forge/noarch::typing-extensions-3.10.0.0-hd8ed1ab_0\n",
            "  typing_extensions  conda-forge/noarch::typing_extensions-3.10.0.0-pyha770c72_0\n",
            "  tzcode             conda-forge/linux-64::tzcode-2021a-h7f98852_2\n",
            "  tzdata             conda-forge/noarch::tzdata-2021a-he74cb21_1\n",
            "  ucx                rapidsai/linux-64::ucx-1.9.0+gcd9efd3-cuda11.0_0\n",
            "  ucx-proc           rapidsai/linux-64::ucx-proc-1.0.0-gpu\n",
            "  ucx-py             rapidsai/linux-64::ucx-py-0.20.0-py37_gcd9efd3_0\n",
            "  wcwidth            conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n",
            "  webencodings       conda-forge/noarch::webencodings-0.5.1-py_1\n",
            "  websocket-client   conda-forge/linux-64::websocket-client-0.57.0-py37h89c1867_4\n",
            "  websockets         conda-forge/linux-64::websockets-8.1-py37h5e8e339_3\n",
            "  widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.5.1-py37h89c1867_4\n",
            "  xarray             conda-forge/noarch::xarray-0.18.2-pyhd8ed1ab_0\n",
            "  xerces-c           conda-forge/linux-64::xerces-c-3.2.3-h9d8b166_2\n",
            "  xgboost            rapidsai/linux-64::xgboost-1.4.2dev.rapidsai21.06-cuda11.0py37_0\n",
            "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h7f98852_1002\n",
            "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0\n",
            "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000\n",
            "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n",
            "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h7f98852_0\n",
            "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0\n",
            "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n",
            "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n",
            "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h7f98852_1002\n",
            "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h7f98852_1002\n",
            "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h7f98852_1007\n",
            "  yarl               conda-forge/linux-64::yarl-1.6.3-py37h5e8e339_2\n",
            "  zeromq             conda-forge/linux-64::zeromq-4.3.4-h9c3ff4c_0\n",
            "  zict               conda-forge/noarch::zict-2.0.0-py_0\n",
            "  zipp               conda-forge/noarch::zipp-3.5.0-pyhd8ed1ab_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2020.12.5-ha878542_0 --> 2021.5.30-ha878542_0\n",
            "  certifi                          2020.12.5-py37h89c1867_1 --> 2021.5.30-py37h89c1867_0\n",
            "  conda                                4.9.2-py37h89c1867_0 --> 4.10.3-py37h89c1867_0\n",
            "  openssl                                 1.1.1j-h7f98852_0 --> 1.1.1k-h7f98852_0\n",
            "  python_abi                                    3.7-1_cp37m --> 3.7-2_cp37m\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "\n",
            "tiledb-2.2.9         | 4.0 MB    |            |   0%\n",
            "tiledb-2.2.9         | 4.0 MB    |            |   0%\n",
            "tiledb-2.2.9         | 4.0 MB    | ########## | 100%\n",
            "tiledb-2.2.9         | 4.0 MB    | ########## | 100%\n",
            "\n",
            "websocket-client-0.5 | 59 KB     |            |   0%\n",
            "websocket-client-0.5 | 59 KB     | ########## | 100%\n",
            "\n",
            "py-xgboost-1.4.2dev. | 151 KB    |            |   0%\n",
            "py-xgboost-1.4.2dev. | 151 KB    | #          |  11%\n",
            "py-xgboost-1.4.2dev. | 151 KB    | ######3    |  64%\n",
            "py-xgboost-1.4.2dev. | 151 KB    | ########## | 100%\n",
            "\n",
            "ipython_genutils-0.2 | 21 KB     |            |   0%\n",
            "ipython_genutils-0.2 | 21 KB     | ########## | 100%\n",
            "\n",
            "re2-2021.04.01       | 218 KB    |            |   0%\n",
            "re2-2021.04.01       | 218 KB    | ########## | 100%\n",
            "\n",
            "joblib-1.0.1         | 206 KB    |            |   0%\n",
            "joblib-1.0.1         | 206 KB    | ########## | 100%\n",
            "\n",
            "cudatoolkit-11.0.221 | 953.0 MB  |            |   0%\n",
            "cudatoolkit-11.0.221 | 953.0 MB  | ########## | 100%\n",
            "cudatoolkit-11.0.221 | 953.0 MB  | ########## | 100%\n",
            "\n",
            "geotiff-1.6.0        | 296 KB    |            |   0%\n",
            "geotiff-1.6.0        | 296 KB    | ########## | 100%\n",
            "\n",
            "oauthlib-3.1.1       | 87 KB     |            |   0%\n",
            "oauthlib-3.1.1       | 87 KB     | ########## | 100%\n",
            "\n",
            "prometheus_client-0. | 46 KB     |            |   0%\n",
            "prometheus_client-0. | 46 KB     | ########## | 100%\n",
            "\n",
            "jsonschema-3.2.0     | 45 KB     |            |   0%\n",
            "jsonschema-3.2.0     | 45 KB     | ########## | 100%\n",
            "\n",
            "nbformat-5.1.3       | 47 KB     |            |   0%\n",
            "nbformat-5.1.3       | 47 KB     | ########## | 100%\n",
            "\n",
            "packaging-21.0       | 35 KB     |            |   0%\n",
            "packaging-21.0       | 35 KB     | ########## | 100%\n",
            "\n",
            "libllvm10-10.0.1     | 26.4 MB   |            |   0%\n",
            "libllvm10-10.0.1     | 26.4 MB   | ###1       |  31%\n",
            "libllvm10-10.0.1     | 26.4 MB   | #######8   |  78%\n",
            "libllvm10-10.0.1     | 26.4 MB   | ########## | 100%\n",
            "libllvm10-10.0.1     | 26.4 MB   | ########## | 100%\n",
            "\n",
            "librttopo-1.1.0      | 235 KB    |            |   0%\n",
            "librttopo-1.1.0      | 235 KB    | ########## | 100%\n",
            "\n",
            "backcall-0.2.0       | 13 KB     |            |   0%\n",
            "backcall-0.2.0       | 13 KB     | ########## | 100%\n",
            "\n",
            "libgsasl-1.8.1       | 162 KB    |            |   0%\n",
            "libgsasl-1.8.1       | 162 KB    | ########## | 100%\n",
            "\n",
            "click-plugins-1.1.1  | 9 KB      |            |   0%\n",
            "click-plugins-1.1.1  | 9 KB      | ########## | 100%\n",
            "click-plugins-1.1.1  | 9 KB      | ########## | 100%\n",
            "\n",
            "sortedcontainers-2.4 | 26 KB     |            |   0%\n",
            "sortedcontainers-2.4 | 26 KB     | ########## | 100%\n",
            "\n",
            "librmm-21.06.00      | 57 KB     |            |   0%\n",
            "librmm-21.06.00      | 57 KB     | ##8        |  28%\n",
            "librmm-21.06.00      | 57 KB     | ########## | 100%\n",
            "\n",
            "async-timeout-3.0.1  | 11 KB     |            |   0%\n",
            "async-timeout-3.0.1  | 11 KB     | ########## | 100%\n",
            "\n",
            "geopandas-base-0.9.0 | 950 KB    |            |   0%\n",
            "geopandas-base-0.9.0 | 950 KB    | ########## | 100%\n",
            "geopandas-base-0.9.0 | 950 KB    | ########## | 100%\n",
            "\n",
            "lcms2-2.12           | 443 KB    |            |   0%\n",
            "lcms2-2.12           | 443 KB    | ########## | 100%\n",
            "\n",
            "blinker-1.4          | 13 KB     |            |   0%\n",
            "blinker-1.4          | 13 KB     | ########## | 100%\n",
            "\n",
            "treelite-1.3.0       | 2.7 MB    |            |   0%\n",
            "treelite-1.3.0       | 2.7 MB    | ########## | 100%\n",
            "treelite-1.3.0       | 2.7 MB    | ########## | 100%\n",
            "\n",
            "dask-core-2021.5.0   | 735 KB    |            |   0%\n",
            "dask-core-2021.5.0   | 735 KB    | ########## | 100%\n",
            "dask-core-2021.5.0   | 735 KB    | ########## | 100%\n",
            "\n",
            "parso-0.8.2          | 68 KB     |            |   0%\n",
            "parso-0.8.2          | 68 KB     | ########## | 100%\n",
            "\n",
            "rmm-21.06.00         | 7.0 MB    |            |   0%\n",
            "rmm-21.06.00         | 7.0 MB    |            |   0%\n",
            "rmm-21.06.00         | 7.0 MB    | 2          |   2%\n",
            "rmm-21.06.00         | 7.0 MB    | #          |  11%\n",
            "rmm-21.06.00         | 7.0 MB    | ####5      |  46%\n",
            "rmm-21.06.00         | 7.0 MB    | ########## | 100%\n",
            "rmm-21.06.00         | 7.0 MB    | ########## | 100%\n",
            "\n",
            "libkml-1.3.0         | 591 KB    |            |   0%\n",
            "libkml-1.3.0         | 591 KB    | ########## | 100%\n",
            "libkml-1.3.0         | 591 KB    | ########## | 100%\n",
            "\n",
            "aws-c-common-0.5.2   | 165 KB    |            |   0%\n",
            "aws-c-common-0.5.2   | 165 KB    | ########## | 100%\n",
            "\n",
            "param-1.11.1         | 67 KB     |            |   0%\n",
            "param-1.11.1         | 67 KB     | ########## | 100%\n",
            "\n",
            "webencodings-0.5.1   | 12 KB     |            |   0%\n",
            "webencodings-0.5.1   | 12 KB     | ########## | 100%\n",
            "\n",
            "bleach-3.3.1         | 111 KB    |            |   0%\n",
            "bleach-3.3.1         | 111 KB    | ########## | 100%\n",
            "\n",
            "numba-0.53.1         | 3.7 MB    |            |   0%\n",
            "numba-0.53.1         | 3.7 MB    | ########## | 100%\n",
            "numba-0.53.1         | 3.7 MB    | ########## | 100%\n",
            "\n",
            "protobuf-3.15.8      | 334 KB    |            |   0%\n",
            "protobuf-3.15.8      | 334 KB    | ########## | 100%\n",
            "protobuf-3.15.8      | 334 KB    | ########## | 100%\n",
            "\n",
            "jpeg-9d              | 264 KB    |            |   0%\n",
            "jpeg-9d              | 264 KB    | ########## | 100%\n",
            "jpeg-9d              | 264 KB    | ########## | 100%\n",
            "\n",
            "aiohttp-3.7.4.post0  | 625 KB    |            |   0%\n",
            "aiohttp-3.7.4.post0  | 625 KB    | ########## | 100%\n",
            "aiohttp-3.7.4.post0  | 625 KB    | ########## | 100%\n",
            "\n",
            "libgfortran-ng-9.3.0 | 22 KB     |            |   0%\n",
            "libgfortran-ng-9.3.0 | 22 KB     | ########## | 100%\n",
            "\n",
            "rapids-21.06.00      | 5 KB      |            |   0%\n",
            "rapids-21.06.00      | 5 KB      | ########## | 100%\n",
            "rapids-21.06.00      | 5 KB      | ########## | 100%\n",
            "\n",
            "tzdata-2021a         | 121 KB    |            |   0%\n",
            "tzdata-2021a         | 121 KB    | ########## | 100%\n",
            "\n",
            "brotli-bin-1.0.9     | 19 KB     |            |   0%\n",
            "brotli-bin-1.0.9     | 19 KB     | ########## | 100%\n",
            "\n",
            "nodejs-14.17.1       | 15.8 MB   |            |   0%\n",
            "nodejs-14.17.1       | 15.8 MB   | ####8      |  49%\n",
            "nodejs-14.17.1       | 15.8 MB   | ########## | 100%\n",
            "nodejs-14.17.1       | 15.8 MB   | ########## | 100%\n",
            "\n",
            "freexl-1.0.6         | 48 KB     |            |   0%\n",
            "freexl-1.0.6         | 48 KB     | ########## | 100%\n",
            "\n",
            "shapely-1.7.1        | 438 KB    |            |   0%\n",
            "shapely-1.7.1        | 438 KB    | ########## | 100%\n",
            "shapely-1.7.1        | 438 KB    | ########## | 100%\n",
            "\n",
            "libuuid-2.32.1       | 28 KB     |            |   0%\n",
            "libuuid-2.32.1       | 28 KB     | ########## | 100%\n",
            "\n",
            "jupyterlab_widgets-1 | 130 KB    |            |   0%\n",
            "jupyterlab_widgets-1 | 130 KB    | ########## | 100%\n",
            "\n",
            "libutf8proc-2.6.1    | 95 KB     |            |   0%\n",
            "libutf8proc-2.6.1    | 95 KB     | ########## | 100%\n",
            "\n",
            "libspatialite-5.0.1  | 4.4 MB    |            |   0%\n",
            "libspatialite-5.0.1  | 4.4 MB    | ########## | 100%\n",
            "libspatialite-5.0.1  | 4.4 MB    | ########## | 100%\n",
            "\n",
            "libfaiss-1.7.0       | 67.0 MB   |            |   0%\n",
            "libfaiss-1.7.0       | 67.0 MB   | #1         |  11%\n",
            "libfaiss-1.7.0       | 67.0 MB   | ##8        |  29%\n",
            "libfaiss-1.7.0       | 67.0 MB   | ####4      |  45%\n",
            "libfaiss-1.7.0       | 67.0 MB   | ######3    |  64%\n",
            "libfaiss-1.7.0       | 67.0 MB   | ########3  |  83%\n",
            "libfaiss-1.7.0       | 67.0 MB   | ########## | 100%\n",
            "libfaiss-1.7.0       | 67.0 MB   | ########## | 100%\n",
            "\n",
            "libblas-3.9.0        | 11 KB     |            |   0%\n",
            "libblas-3.9.0        | 11 KB     | ########## | 100%\n",
            "\n",
            "pyyaml-5.4.1         | 189 KB    |            |   0%\n",
            "pyyaml-5.4.1         | 189 KB    | ########## | 100%\n",
            "\n",
            "jupyter-server-proxy | 27 KB     |            |   0%\n",
            "jupyter-server-proxy | 27 KB     | ########## | 100%\n",
            "\n",
            "faiss-proc-1.0.0     | 24 KB     |            |   0%\n",
            "faiss-proc-1.0.0     | 24 KB     | ######7    |  68%\n",
            "faiss-proc-1.0.0     | 24 KB     | ########## | 100%\n",
            "\n",
            "cligj-0.7.2          | 10 KB     |            |   0%\n",
            "cligj-0.7.2          | 10 KB     | ########## | 100%\n",
            "\n",
            "python-confluent-kaf | 122 KB    |            |   0%\n",
            "python-confluent-kaf | 122 KB    | ########## | 100%\n",
            "\n",
            "libbrotlienc-1.0.9   | 286 KB    |            |   0%\n",
            "libbrotlienc-1.0.9   | 286 KB    | ########## | 100%\n",
            "\n",
            "xarray-0.18.2        | 599 KB    |            |   0%\n",
            "xarray-0.18.2        | 599 KB    | ########## | 100%\n",
            "xarray-0.18.2        | 599 KB    | ########## | 100%\n",
            "\n",
            "cyrus-sasl-2.1.27    | 224 KB    |            |   0%\n",
            "cyrus-sasl-2.1.27    | 224 KB    | ########## | 100%\n",
            "\n",
            "matplotlib-base-3.4. | 7.2 MB    |            |   0%\n",
            "matplotlib-base-3.4. | 7.2 MB    | ########## | 100%\n",
            "matplotlib-base-3.4. | 7.2 MB    | ########## | 100%\n",
            "\n",
            "nvtx-0.2.3           | 55 KB     |            |   0%\n",
            "nvtx-0.2.3           | 55 KB     | ########## | 100%\n",
            "\n",
            "freetype-2.10.4      | 890 KB    |            |   0%\n",
            "freetype-2.10.4      | 890 KB    | ########## | 100%\n",
            "freetype-2.10.4      | 890 KB    | ########## | 100%\n",
            "\n",
            "pthread-stubs-0.4    | 5 KB      |            |   0%\n",
            "pthread-stubs-0.4    | 5 KB      | ########## | 100%\n",
            "\n",
            "libtiff-4.2.0        | 639 KB    |            |   0%\n",
            "libtiff-4.2.0        | 639 KB    | ########## | 100%\n",
            "libtiff-4.2.0        | 639 KB    | ########## | 100%\n",
            "\n",
            "curl-7.75.0          | 147 KB    |            |   0%\n",
            "curl-7.75.0          | 147 KB    | ########## | 100%\n",
            "\n",
            "pyrsistent-0.17.3    | 89 KB     |            |   0%\n",
            "pyrsistent-0.17.3    | 89 KB     | ########## | 100%\n",
            "\n",
            "streamz-0.6.2        | 59 KB     |            |   0%\n",
            "streamz-0.6.2        | 59 KB     | ########## | 100%\n",
            "\n",
            "pyct-core-0.4.6      | 13 KB     |            |   0%\n",
            "pyct-core-0.4.6      | 13 KB     | ########## | 100%\n",
            "\n",
            "rtree-0.9.7          | 45 KB     |            |   0%\n",
            "rtree-0.9.7          | 45 KB     | ########## | 100%\n",
            "\n",
            "dask-cuda-21.06.00   | 110 KB    |            |   0%\n",
            "dask-cuda-21.06.00   | 110 KB    | #4         |  15%\n",
            "dask-cuda-21.06.00   | 110 KB    | ########7  |  87%\n",
            "dask-cuda-21.06.00   | 110 KB    | ########## | 100%\n",
            "\n",
            "openjpeg-2.4.0       | 444 KB    |            |   0%\n",
            "openjpeg-2.4.0       | 444 KB    | ########## | 100%\n",
            "openjpeg-2.4.0       | 444 KB    | ########## | 100%\n",
            "\n",
            "gcsfs-2021.7.0       | 23 KB     |            |   0%\n",
            "gcsfs-2021.7.0       | 23 KB     | ########## | 100%\n",
            "\n",
            "typing_extensions-3. | 28 KB     |            |   0%\n",
            "typing_extensions-3. | 28 KB     | ########## | 100%\n",
            "\n",
            "geopandas-0.9.0      | 5 KB      |            |   0%\n",
            "geopandas-0.9.0      | 5 KB      | ########## | 100%\n",
            "\n",
            "poppler-21.03.0      | 15.9 MB   |            |   0%\n",
            "poppler-21.03.0      | 15.9 MB   | ####3      |  44%\n",
            "poppler-21.03.0      | 15.9 MB   | ########## | 100%\n",
            "poppler-21.03.0      | 15.9 MB   | ########## | 100%\n",
            "\n",
            "zeromq-4.3.4         | 352 KB    |            |   0%\n",
            "zeromq-4.3.4         | 352 KB    | ########## | 100%\n",
            "\n",
            "click-7.1.2          | 64 KB     |            |   0%\n",
            "click-7.1.2          | 64 KB     | ########## | 100%\n",
            "\n",
            "decorator-4.4.2      | 11 KB     |            |   0%\n",
            "decorator-4.4.2      | 11 KB     | ########## | 100%\n",
            "\n",
            "conda-4.10.3         | 3.1 MB    |            |   0%\n",
            "conda-4.10.3         | 3.1 MB    | ########## | 100%\n",
            "conda-4.10.3         | 3.1 MB    | ########## | 100%\n",
            "\n",
            "multipledispatch-0.6 | 12 KB     |            |   0%\n",
            "multipledispatch-0.6 | 12 KB     | ########## | 100%\n",
            "\n",
            "multidict-5.1.0      | 67 KB     |            |   0%\n",
            "multidict-5.1.0      | 67 KB     | ########## | 100%\n",
            "\n",
            "threadpoolctl-2.2.0  | 16 KB     |            |   0%\n",
            "threadpoolctl-2.2.0  | 16 KB     | ########## | 100%\n",
            "\n",
            "libwebp-base-1.2.0   | 815 KB    |            |   0%\n",
            "libwebp-base-1.2.0   | 815 KB    | ########## | 100%\n",
            "libwebp-base-1.2.0   | 815 KB    | ########## | 100%\n",
            "\n",
            "dask-2021.5.0        | 4 KB      |            |   0%\n",
            "dask-2021.5.0        | 4 KB      | ########## | 100%\n",
            "\n",
            "libpq-13.2           | 2.7 MB    |            |   0%\n",
            "libpq-13.2           | 2.7 MB    | ########## | 100%\n",
            "libpq-13.2           | 2.7 MB    | ########## | 100%\n",
            "\n",
            "kiwisolver-1.3.1     | 78 KB     |            |   0%\n",
            "kiwisolver-1.3.1     | 78 KB     | ########## | 100%\n",
            "\n",
            "dlpack-0.5           | 12 KB     |            |   0%\n",
            "dlpack-0.5           | 12 KB     | ########## | 100%\n",
            "\n",
            "libntlm-1.4          | 32 KB     |            |   0%\n",
            "libntlm-1.4          | 32 KB     | ########## | 100%\n",
            "\n",
            "glog-0.4.0           | 104 KB    |            |   0%\n",
            "glog-0.4.0           | 104 KB    | ########## | 100%\n",
            "\n",
            "parquet-cpp-1.5.1    | 3 KB      |            |   0%\n",
            "parquet-cpp-1.5.1    | 3 KB      | ########## | 100%\n",
            "\n",
            "xorg-xproto-7.0.31   | 73 KB     |            |   0%\n",
            "xorg-xproto-7.0.31   | 73 KB     | ########## | 100%\n",
            "\n",
            "llvmlite-0.36.0      | 2.7 MB    |            |   0%\n",
            "llvmlite-0.36.0      | 2.7 MB    | ######9    |  70%\n",
            "llvmlite-0.36.0      | 2.7 MB    | ########## | 100%\n",
            "llvmlite-0.36.0      | 2.7 MB    | ########## | 100%\n",
            "\n",
            "cairo-1.16.0         | 1.5 MB    |            |   0%\n",
            "cairo-1.16.0         | 1.5 MB    | ########## | 100%\n",
            "cairo-1.16.0         | 1.5 MB    | ########## | 100%\n",
            "\n",
            "pyparsing-2.4.7      | 60 KB     |            |   0%\n",
            "pyparsing-2.4.7      | 60 KB     | ########## | 100%\n",
            "\n",
            "pygments-2.9.0       | 754 KB    |            |   0%\n",
            "pygments-2.9.0       | 754 KB    | ########## | 100%\n",
            "pygments-2.9.0       | 754 KB    | ########## | 100%\n",
            "\n",
            "boost-1.74.0         | 342 KB    |            |   0%\n",
            "boost-1.74.0         | 342 KB    | ########## | 100%\n",
            "boost-1.74.0         | 342 KB    | ########## | 100%\n",
            "\n",
            "fiona-1.8.20         | 1.1 MB    |            |   0%\n",
            "fiona-1.8.20         | 1.1 MB    | ########## | 100%\n",
            "fiona-1.8.20         | 1.1 MB    | ########## | 100%\n",
            "\n",
            "pixman-0.40.0        | 627 KB    |            |   0%\n",
            "pixman-0.40.0        | 627 KB    | ########## | 100%\n",
            "pixman-0.40.0        | 627 KB    | ########## | 100%\n",
            "\n",
            "cfitsio-3.470        | 1.3 MB    |            |   0%\n",
            "cfitsio-3.470        | 1.3 MB    | ########## | 100%\n",
            "cfitsio-3.470        | 1.3 MB    | ########## | 100%\n",
            "\n",
            "mapclassify-2.4.2    | 36 KB     |            |   0%\n",
            "mapclassify-2.4.2    | 36 KB     | ########## | 100%\n",
            "\n",
            "send2trash-1.7.1     | 17 KB     |            |   0%\n",
            "send2trash-1.7.1     | 17 KB     | ########## | 100%\n",
            "\n",
            "psutil-5.8.0         | 342 KB    |            |   0%\n",
            "psutil-5.8.0         | 342 KB    | ########## | 100%\n",
            "psutil-5.8.0         | 342 KB    | ########## | 100%\n",
            "\n",
            "s2n-1.0.0            | 333 KB    |            |   0%\n",
            "s2n-1.0.0            | 333 KB    | ########## | 100%\n",
            "s2n-1.0.0            | 333 KB    | ########## | 100%\n",
            "\n",
            "cudf-21.06.01        | 108.4 MB  |            |   0%\n",
            "cudf-21.06.01        | 108.4 MB  |            |   0%\n",
            "cudf-21.06.01        | 108.4 MB  |            |   0%\n",
            "cudf-21.06.01        | 108.4 MB  |            |   0%\n",
            "cudf-21.06.01        | 108.4 MB  | 1          |   1%\n",
            "cudf-21.06.01        | 108.4 MB  | 4          |   4%\n",
            "cudf-21.06.01        | 108.4 MB  | 8          |   9%\n",
            "cudf-21.06.01        | 108.4 MB  | #3         |  13%\n",
            "cudf-21.06.01        | 108.4 MB  | #7         |  17%\n",
            "cudf-21.06.01        | 108.4 MB  | ##1        |  22%\n",
            "cudf-21.06.01        | 108.4 MB  | ##6        |  27%\n",
            "cudf-21.06.01        | 108.4 MB  | ###1       |  31%\n",
            "cudf-21.06.01        | 108.4 MB  | ###5       |  35%\n",
            "cudf-21.06.01        | 108.4 MB  | ####       |  40%\n",
            "cudf-21.06.01        | 108.4 MB  | ####5      |  45%\n",
            "cudf-21.06.01        | 108.4 MB  | #####      |  50%\n",
            "cudf-21.06.01        | 108.4 MB  | #####4     |  54%\n",
            "cudf-21.06.01        | 108.4 MB  | #####9     |  59%\n",
            "cudf-21.06.01        | 108.4 MB  | ######4    |  64%\n",
            "cudf-21.06.01        | 108.4 MB  | ######9    |  69%\n",
            "cudf-21.06.01        | 108.4 MB  | #######3   |  73%\n",
            "cudf-21.06.01        | 108.4 MB  | #######7   |  77%\n",
            "cudf-21.06.01        | 108.4 MB  | ########1  |  82%\n",
            "cudf-21.06.01        | 108.4 MB  | ########6  |  86%\n",
            "cudf-21.06.01        | 108.4 MB  | #########  |  91%\n",
            "cudf-21.06.01        | 108.4 MB  | #########4 |  95%\n",
            "cudf-21.06.01        | 108.4 MB  | #########9 |  99%\n",
            "cudf-21.06.01        | 108.4 MB  | ########## | 100%\n",
            "\n",
            "fsspec-2021.7.0      | 81 KB     |            |   0%\n",
            "fsspec-2021.7.0      | 81 KB     | ########## | 100%\n",
            "\n",
            "libopenblas-0.3.15   | 9.2 MB    |            |   0%\n",
            "libopenblas-0.3.15   | 9.2 MB    | ####9      |  50%\n",
            "libopenblas-0.3.15   | 9.2 MB    | ########## | 100%\n",
            "libopenblas-0.3.15   | 9.2 MB    | ########## | 100%\n",
            "\n",
            "msgpack-python-1.0.2 | 91 KB     |            |   0%\n",
            "msgpack-python-1.0.2 | 91 KB     | ########## | 100%\n",
            "\n",
            "libcudf-21.06.01     | 187.7 MB  |            |   0%\n",
            "libcudf-21.06.01     | 187.7 MB  |            |   0%\n",
            "libcudf-21.06.01     | 187.7 MB  |            |   0%\n",
            "libcudf-21.06.01     | 187.7 MB  |            |   0%\n",
            "libcudf-21.06.01     | 187.7 MB  |            |   1%\n",
            "libcudf-21.06.01     | 187.7 MB  | 2          |   3%\n",
            "libcudf-21.06.01     | 187.7 MB  | 4          |   5%\n",
            "libcudf-21.06.01     | 187.7 MB  | 6          |   7%\n",
            "libcudf-21.06.01     | 187.7 MB  | 9          |   9%\n",
            "libcudf-21.06.01     | 187.7 MB  | #1         |  12%\n",
            "libcudf-21.06.01     | 187.7 MB  | #4         |  14%\n",
            "libcudf-21.06.01     | 187.7 MB  | #7         |  17%\n",
            "libcudf-21.06.01     | 187.7 MB  | #9         |  20%\n",
            "libcudf-21.06.01     | 187.7 MB  | ##2        |  22%\n",
            "libcudf-21.06.01     | 187.7 MB  | ##5        |  25%\n",
            "libcudf-21.06.01     | 187.7 MB  | ##8        |  28%\n",
            "libcudf-21.06.01     | 187.7 MB  | ###1       |  31%\n",
            "libcudf-21.06.01     | 187.7 MB  | ###3       |  34%\n",
            "libcudf-21.06.01     | 187.7 MB  | ###6       |  37%\n",
            "libcudf-21.06.01     | 187.7 MB  | ###9       |  40%\n",
            "libcudf-21.06.01     | 187.7 MB  | ####3      |  43%\n",
            "libcudf-21.06.01     | 187.7 MB  | ####6      |  46%\n",
            "libcudf-21.06.01     | 187.7 MB  | ####9      |  49%\n",
            "libcudf-21.06.01     | 187.7 MB  | #####1     |  51%\n",
            "libcudf-21.06.01     | 187.7 MB  | #####3     |  54%\n",
            "libcudf-21.06.01     | 187.7 MB  | #####6     |  56%\n",
            "libcudf-21.06.01     | 187.7 MB  | #####8     |  59%\n",
            "libcudf-21.06.01     | 187.7 MB  | ######1    |  62%\n",
            "libcudf-21.06.01     | 187.7 MB  | ######4    |  65%\n",
            "libcudf-21.06.01     | 187.7 MB  | ######7    |  68%\n",
            "libcudf-21.06.01     | 187.7 MB  | #######    |  71%\n",
            "libcudf-21.06.01     | 187.7 MB  | #######3   |  74%\n",
            "libcudf-21.06.01     | 187.7 MB  | #######6   |  76%\n",
            "libcudf-21.06.01     | 187.7 MB  | #######8   |  79%\n",
            "libcudf-21.06.01     | 187.7 MB  | ########1  |  81%\n",
            "libcudf-21.06.01     | 187.7 MB  | ########4  |  84%\n",
            "libcudf-21.06.01     | 187.7 MB  | ########6  |  87%\n",
            "libcudf-21.06.01     | 187.7 MB  | ########9  |  89%\n",
            "libcudf-21.06.01     | 187.7 MB  | #########2 |  92%\n",
            "libcudf-21.06.01     | 187.7 MB  | #########5 |  95%\n",
            "libcudf-21.06.01     | 187.7 MB  | #########7 |  98%\n",
            "libcudf-21.06.01     | 187.7 MB  | ########## | 100%\n",
            "\n",
            "cachetools-4.2.2     | 12 KB     |            |   0%\n",
            "cachetools-4.2.2     | 12 KB     | ########## | 100%\n",
            "\n",
            "requests-oauthlib-1. | 21 KB     |            |   0%\n",
            "requests-oauthlib-1. | 21 KB     | ########## | 100%\n",
            "\n",
            "orc-1.6.7            | 750 KB    |            |   0%\n",
            "orc-1.6.7            | 750 KB    | ########## | 100%\n",
            "orc-1.6.7            | 750 KB    | ########## | 100%\n",
            "\n",
            "pydeck-0.5.0         | 3.6 MB    |            |   0%\n",
            "pydeck-0.5.0         | 3.6 MB    | ########## | 100%\n",
            "pydeck-0.5.0         | 3.6 MB    | ########## | 100%\n",
            "\n",
            "matplotlib-inline-0. | 11 KB     |            |   0%\n",
            "matplotlib-inline-0. | 11 KB     | ########## | 100%\n",
            "\n",
            "xorg-kbproto-1.0.7   | 27 KB     |            |   0%\n",
            "xorg-kbproto-1.0.7   | 27 KB     | ########## | 100%\n",
            "\n",
            "nccl-2.10.3.1        | 121.2 MB  |            |   0%\n",
            "nccl-2.10.3.1        | 121.2 MB  | 6          |   6%\n",
            "nccl-2.10.3.1        | 121.2 MB  | #5         |  16%\n",
            "nccl-2.10.3.1        | 121.2 MB  | ##4        |  25%\n",
            "nccl-2.10.3.1        | 121.2 MB  | ###4       |  34%\n",
            "nccl-2.10.3.1        | 121.2 MB  | ####3      |  44%\n",
            "nccl-2.10.3.1        | 121.2 MB  | #####4     |  54%\n",
            "nccl-2.10.3.1        | 121.2 MB  | ######3    |  64%\n",
            "nccl-2.10.3.1        | 121.2 MB  | #######2   |  73%\n",
            "nccl-2.10.3.1        | 121.2 MB  | ########1  |  81%\n",
            "nccl-2.10.3.1        | 121.2 MB  | #########  |  90%\n",
            "nccl-2.10.3.1        | 121.2 MB  | #########8 |  98%\n",
            "nccl-2.10.3.1        | 121.2 MB  | ########## | 100%\n",
            "\n",
            "backports.functools_ | 9 KB      |            |   0%\n",
            "backports.functools_ | 9 KB      | ########## | 100%\n",
            "\n",
            "grpc-cpp-1.37.1      | 3.5 MB    |            |   0%\n",
            "grpc-cpp-1.37.1      | 3.5 MB    | ########8  |  88%\n",
            "grpc-cpp-1.37.1      | 3.5 MB    | ########## | 100%\n",
            "\n",
            "pillow-8.2.0         | 684 KB    |            |   0%\n",
            "pillow-8.2.0         | 684 KB    | ########## | 100%\n",
            "pillow-8.2.0         | 684 KB    | ########## | 100%\n",
            "\n",
            "dask-cudf-21.06.01   | 103 KB    |            |   0%\n",
            "dask-cudf-21.06.01   | 103 KB    | #5         |  15%\n",
            "dask-cudf-21.06.01   | 103 KB    | #########2 |  93%\n",
            "dask-cudf-21.06.01   | 103 KB    | ########## | 100%\n",
            "\n",
            "librdkafka-1.5.3     | 11.2 MB   |            |   0%\n",
            "librdkafka-1.5.3     | 11.2 MB   | #####3     |  53%\n",
            "librdkafka-1.5.3     | 11.2 MB   | ########## | 100%\n",
            "librdkafka-1.5.3     | 11.2 MB   | ########## | 100%\n",
            "\n",
            "xorg-libx11-1.7.2    | 941 KB    |            |   0%\n",
            "xorg-libx11-1.7.2    | 941 KB    | ########## | 100%\n",
            "xorg-libx11-1.7.2    | 941 KB    | ########## | 100%\n",
            "\n",
            "brotli-1.0.9         | 17 KB     |            |   0%\n",
            "brotli-1.0.9         | 17 KB     | ########## | 100%\n",
            "\n",
            "xorg-libxext-1.3.4   | 54 KB     |            |   0%\n",
            "xorg-libxext-1.3.4   | 54 KB     | ########## | 100%\n",
            "\n",
            "testpath-0.5.0       | 86 KB     |            |   0%\n",
            "testpath-0.5.0       | 86 KB     | ########## | 100%\n",
            "\n",
            "pyasn1-modules-0.2.7 | 60 KB     |            |   0%\n",
            "pyasn1-modules-0.2.7 | 60 KB     | ########## | 100%\n",
            "\n",
            "libcuspatial-21.06.0 | 7.6 MB    |            |   0%\n",
            "libcuspatial-21.06.0 | 7.6 MB    |            |   0%\n",
            "libcuspatial-21.06.0 | 7.6 MB    | 4          |   5%\n",
            "libcuspatial-21.06.0 | 7.6 MB    | ##3        |  23%\n",
            "libcuspatial-21.06.0 | 7.6 MB    | ######7    |  67%\n",
            "libcuspatial-21.06.0 | 7.6 MB    | ########## | 100%\n",
            "libcuspatial-21.06.0 | 7.6 MB    | ########## | 100%\n",
            "\n",
            "pickleshare-0.7.5    | 9 KB      |            |   0%\n",
            "pickleshare-0.7.5    | 9 KB      | ########## | 100%\n",
            "\n",
            "tzcode-2021a         | 68 KB     |            |   0%\n",
            "tzcode-2021a         | 68 KB     | ########## | 100%\n",
            "\n",
            "libevent-2.1.10      | 1.1 MB    |            |   0%\n",
            "libevent-2.1.10      | 1.1 MB    | ########## | 100%\n",
            "libevent-2.1.10      | 1.1 MB    | ########## | 100%\n",
            "\n",
            "aws-sdk-cpp-1.8.151  | 4.5 MB    |            |   0%\n",
            "aws-sdk-cpp-1.8.151  | 4.5 MB    | ########## | 100%\n",
            "aws-sdk-cpp-1.8.151  | 4.5 MB    | ########## | 100%\n",
            "\n",
            "json-c-0.15          | 274 KB    |            |   0%\n",
            "json-c-0.15          | 274 KB    | ########## | 100%\n",
            "\n",
            "xerces-c-3.2.3       | 1.8 MB    |            |   0%\n",
            "xerces-c-3.2.3       | 1.8 MB    | ########## | 100%\n",
            "xerces-c-3.2.3       | 1.8 MB    | ########## | 100%\n",
            "\n",
            "libcuml-21.06.02     | 95.2 MB   |            |   0%\n",
            "libcuml-21.06.02     | 95.2 MB   |            |   0%\n",
            "libcuml-21.06.02     | 95.2 MB   | 2          |   3%\n",
            "libcuml-21.06.02     | 95.2 MB   | 6          |   7%\n",
            "libcuml-21.06.02     | 95.2 MB   | #1         |  12%\n",
            "libcuml-21.06.02     | 95.2 MB   | #7         |  17%\n",
            "libcuml-21.06.02     | 95.2 MB   | ##2        |  23%\n",
            "libcuml-21.06.02     | 95.2 MB   | ##7        |  28%\n",
            "libcuml-21.06.02     | 95.2 MB   | ###1       |  32%\n",
            "libcuml-21.06.02     | 95.2 MB   | ###5       |  36%\n",
            "libcuml-21.06.02     | 95.2 MB   | ####1      |  41%\n",
            "libcuml-21.06.02     | 95.2 MB   | ####6      |  47%\n",
            "libcuml-21.06.02     | 95.2 MB   | #####2     |  52%\n",
            "libcuml-21.06.02     | 95.2 MB   | #####7     |  58%\n",
            "libcuml-21.06.02     | 95.2 MB   | ######3    |  64%\n",
            "libcuml-21.06.02     | 95.2 MB   | ######9    |  69%\n",
            "libcuml-21.06.02     | 95.2 MB   | #######5   |  75%\n",
            "libcuml-21.06.02     | 95.2 MB   | ########   |  81%\n",
            "libcuml-21.06.02     | 95.2 MB   | ########6  |  87%\n",
            "libcuml-21.06.02     | 95.2 MB   | #########2 |  93%\n",
            "libcuml-21.06.02     | 95.2 MB   | #########8 |  99%\n",
            "libcuml-21.06.02     | 95.2 MB   | ########## | 100%\n",
            "\n",
            "libuv-1.41.1         | 1.0 MB    |            |   0%\n",
            "libuv-1.41.1         | 1.0 MB    | ########## | 100%\n",
            "libuv-1.41.1         | 1.0 MB    | ########## | 100%\n",
            "\n",
            "pickle5-0.0.11       | 173 KB    |            |   0%\n",
            "pickle5-0.0.11       | 173 KB    | ########## | 100%\n",
            "\n",
            "aws-checksums-0.1.11 | 50 KB     |            |   0%\n",
            "aws-checksums-0.1.11 | 50 KB     | ########## | 100%\n",
            "\n",
            "cloudpickle-1.6.0    | 22 KB     |            |   0%\n",
            "cloudpickle-1.6.0    | 22 KB     | ########## | 100%\n",
            "\n",
            "jupyter_server-1.9.0 | 262 KB    |            |   0%\n",
            "jupyter_server-1.9.0 | 262 KB    | ########## | 100%\n",
            "jupyter_server-1.9.0 | 262 KB    | ########## | 100%\n",
            "\n",
            "libgcrypt-1.9.3      | 677 KB    |            |   0%\n",
            "libgcrypt-1.9.3      | 677 KB    | ########## | 100%\n",
            "libgcrypt-1.9.3      | 677 KB    | ########## | 100%\n",
            "\n",
            "entrypoints-0.3      | 8 KB      |            |   0%\n",
            "entrypoints-0.3      | 8 KB      | ########## | 100%\n",
            "\n",
            "distributed-2021.5.0 | 1.1 MB    |            |   0%\n",
            "distributed-2021.5.0 | 1.1 MB    | ########## | 100%\n",
            "distributed-2021.5.0 | 1.1 MB    | ########## | 100%\n",
            "\n",
            "libglib-2.68.3       | 3.1 MB    |            |   0%\n",
            "libglib-2.68.3       | 3.1 MB    | ########## | 100%\n",
            "libglib-2.68.3       | 3.1 MB    | ########## | 100%\n",
            "\n",
            "nbclient-0.5.3       | 67 KB     |            |   0%\n",
            "nbclient-0.5.3       | 67 KB     | ########## | 100%\n",
            "\n",
            "libprotobuf-3.15.8   | 2.5 MB    |            |   0%\n",
            "libprotobuf-3.15.8   | 2.5 MB    | ########## | 100%\n",
            "libprotobuf-3.15.8   | 2.5 MB    | ########## | 100%\n",
            "\n",
            "typing-extensions-3. | 8 KB      |            |   0%\n",
            "typing-extensions-3. | 8 KB      | ########## | 100%\n",
            "\n",
            "argon2-cffi-20.1.0   | 47 KB     |            |   0%\n",
            "argon2-cffi-20.1.0   | 47 KB     | ########## | 100%\n",
            "\n",
            "xorg-libxdmcp-1.1.3  | 19 KB     |            |   0%\n",
            "xorg-libxdmcp-1.1.3  | 19 KB     | ########## | 100%\n",
            "\n",
            "ucx-proc-1.0.0       | 9 KB      |            |   0%\n",
            "ucx-proc-1.0.0       | 9 KB      | ########## | 100%\n",
            "ucx-proc-1.0.0       | 9 KB      | ########## | 100%\n",
            "\n",
            "certifi-2021.5.30    | 141 KB    |            |   0%\n",
            "certifi-2021.5.30    | 141 KB    | ########## | 100%\n",
            "\n",
            "fontconfig-2.13.1    | 357 KB    |            |   0%\n",
            "fontconfig-2.13.1    | 357 KB    | ########## | 100%\n",
            "fontconfig-2.13.1    | 357 KB    | ########## | 100%\n",
            "\n",
            "scipy-1.7.0          | 21.7 MB   |            |   0%\n",
            "scipy-1.7.0          | 21.7 MB   | ##8        |  28%\n",
            "scipy-1.7.0          | 21.7 MB   | #######5   |  76%\n",
            "scipy-1.7.0          | 21.7 MB   | ########## | 100%\n",
            "scipy-1.7.0          | 21.7 MB   | ########## | 100%\n",
            "\n",
            "jupyterlab_pygments- | 8 KB      |            |   0%\n",
            "jupyterlab_pygments- | 8 KB      | ########## | 100%\n",
            "\n",
            "proj-8.0.0           | 3.1 MB    |            |   0%\n",
            "proj-8.0.0           | 3.1 MB    | ########## | 100%\n",
            "proj-8.0.0           | 3.1 MB    | ########## | 100%\n",
            "\n",
            "cytoolz-0.11.0       | 403 KB    |            |   0%\n",
            "cytoolz-0.11.0       | 403 KB    | ########## | 100%\n",
            "cytoolz-0.11.0       | 403 KB    | ########## | 100%\n",
            "\n",
            "jedi-0.18.0          | 923 KB    |            |   0%\n",
            "jedi-0.18.0          | 923 KB    | ########## | 100%\n",
            "jedi-0.18.0          | 923 KB    | ########## | 100%\n",
            "\n",
            "libdap4-3.20.6       | 11.3 MB   |            |   0%\n",
            "libdap4-3.20.6       | 11.3 MB   | #####6     |  56%\n",
            "libdap4-3.20.6       | 11.3 MB   | ########## | 100%\n",
            "libdap4-3.20.6       | 11.3 MB   | ########## | 100%\n",
            "\n",
            "scikit-learn-0.24.2  | 7.5 MB    |            |   0%\n",
            "scikit-learn-0.24.2  | 7.5 MB    | #          |  11%\n",
            "scikit-learn-0.24.2  | 7.5 MB    | ########## | 100%\n",
            "scikit-learn-0.24.2  | 7.5 MB    | ########## | 100%\n",
            "\n",
            "notebook-6.4.0       | 6.1 MB    |            |   0%\n",
            "notebook-6.4.0       | 6.1 MB    | ########## | 100%\n",
            "notebook-6.4.0       | 6.1 MB    | ########## | 100%\n",
            "\n",
            "pandocfilters-1.4.2  | 9 KB      |            |   0%\n",
            "pandocfilters-1.4.2  | 9 KB      | ########## | 100%\n",
            "\n",
            "boost-cpp-1.74.0     | 16.3 MB   |            |   0%\n",
            "boost-cpp-1.74.0     | 16.3 MB   | ####       |  41%\n",
            "boost-cpp-1.74.0     | 16.3 MB   | ########## | 100%\n",
            "boost-cpp-1.74.0     | 16.3 MB   | ########## | 100%\n",
            "\n",
            "ucx-py-0.20.0        | 294 KB    |            |   0%\n",
            "ucx-py-0.20.0        | 294 KB    | 5          |   5%\n",
            "ucx-py-0.20.0        | 294 KB    | ###2       |  33%\n",
            "ucx-py-0.20.0        | 294 KB    | ########## | 100%\n",
            "ucx-py-0.20.0        | 294 KB    | ########## | 100%\n",
            "\n",
            "pyct-0.4.6           | 3 KB      |            |   0%\n",
            "pyct-0.4.6           | 3 KB      | ########## | 100%\n",
            "\n",
            "xorg-libxau-1.0.9    | 13 KB     |            |   0%\n",
            "xorg-libxau-1.0.9    | 13 KB     | ########## | 100%\n",
            "\n",
            "terminado-0.10.1     | 26 KB     |            |   0%\n",
            "terminado-0.10.1     | 26 KB     | ########## | 100%\n",
            "\n",
            "nbconvert-6.1.0      | 548 KB    |            |   0%\n",
            "nbconvert-6.1.0      | 548 KB    | ########## | 100%\n",
            "nbconvert-6.1.0      | 548 KB    | ########## | 100%\n",
            "\n",
            "cupy-9.0.0           | 50.3 MB   |            |   0%\n",
            "cupy-9.0.0           | 50.3 MB   | #2         |  12%\n",
            "cupy-9.0.0           | 50.3 MB   | ###5       |  35%\n",
            "cupy-9.0.0           | 50.3 MB   | #####5     |  56%\n",
            "cupy-9.0.0           | 50.3 MB   | #######5   |  75%\n",
            "cupy-9.0.0           | 50.3 MB   | #########3 |  93%\n",
            "cupy-9.0.0           | 50.3 MB   | ########## | 100%\n",
            "\n",
            "spdlog-1.9.0         | 340 KB    |            |   0%\n",
            "spdlog-1.9.0         | 340 KB    | ########## | 100%\n",
            "\n",
            "anyio-3.2.1          | 138 KB    |            |   0%\n",
            "anyio-3.2.1          | 138 KB    | ########## | 100%\n",
            "\n",
            "pyasn1-0.4.8         | 53 KB     |            |   0%\n",
            "pyasn1-0.4.8         | 53 KB     | ########## | 100%\n",
            "\n",
            "traitlets-5.0.5      | 81 KB     |            |   0%\n",
            "traitlets-5.0.5      | 81 KB     | ########## | 100%\n",
            "\n",
            "pyzmq-22.1.0         | 500 KB    |            |   0%\n",
            "pyzmq-22.1.0         | 500 KB    | ########## | 100%\n",
            "pyzmq-22.1.0         | 500 KB    | ########## | 100%\n",
            "\n",
            "cuspatial-21.06.00   | 15.2 MB   |            |   0%\n",
            "cuspatial-21.06.00   | 15.2 MB   |            |   0%\n",
            "cuspatial-21.06.00   | 15.2 MB   |            |   1%\n",
            "cuspatial-21.06.00   | 15.2 MB   | 2          |   3%\n",
            "cuspatial-21.06.00   | 15.2 MB   | #          |  11%\n",
            "cuspatial-21.06.00   | 15.2 MB   | ###2       |  33%\n",
            "cuspatial-21.06.00   | 15.2 MB   | #####8     |  58%\n",
            "cuspatial-21.06.00   | 15.2 MB   | ########6  |  87%\n",
            "cuspatial-21.06.00   | 15.2 MB   | ########## | 100%\n",
            "\n",
            "cuxfilter-21.06.00   | 136 KB    |            |   0%\n",
            "cuxfilter-21.06.00   | 136 KB    | #1         |  12%\n",
            "cuxfilter-21.06.00   | 136 KB    | ########## | 100%\n",
            "\n",
            "panel-0.10.3         | 6.1 MB    |            |   0%\n",
            "panel-0.10.3         | 6.1 MB    | ########## | 100%\n",
            "panel-0.10.3         | 6.1 MB    | ########## | 100%\n",
            "\n",
            "snappy-1.1.8         | 32 KB     |            |   0%\n",
            "snappy-1.1.8         | 32 KB     | ########## | 100%\n",
            "\n",
            "libwebp-1.2.0        | 85 KB     |            |   0%\n",
            "libwebp-1.2.0        | 85 KB     | ########## | 100%\n",
            "\n",
            "poppler-data-0.4.10  | 3.8 MB    |            |   0%\n",
            "poppler-data-0.4.10  | 3.8 MB    | ########## | 100%\n",
            "poppler-data-0.4.10  | 3.8 MB    | ########## | 100%\n",
            "\n",
            "xorg-renderproto-0.1 | 9 KB      |            |   0%\n",
            "xorg-renderproto-0.1 | 9 KB      | ########## | 100%\n",
            "\n",
            "libhwloc-2.3.0       | 2.7 MB    |            |   0%\n",
            "libhwloc-2.3.0       | 2.7 MB    | ########## | 100%\n",
            "libhwloc-2.3.0       | 2.7 MB    | ########## | 100%\n",
            "\n",
            "hdf5-1.10.6          | 3.1 MB    |            |   0%\n",
            "hdf5-1.10.6          | 3.1 MB    | ########## | 100%\n",
            "hdf5-1.10.6          | 3.1 MB    | ########## | 100%\n",
            "\n",
            "geos-3.9.1           | 1.1 MB    |            |   0%\n",
            "geos-3.9.1           | 1.1 MB    | ########## | 100%\n",
            "geos-3.9.1           | 1.1 MB    | ########## | 100%\n",
            "\n",
            "libspatialindex-1.9. | 4.6 MB    |            |   0%\n",
            "libspatialindex-1.9. | 4.6 MB    | ########## | 100%\n",
            "libspatialindex-1.9. | 4.6 MB    | ########## | 100%\n",
            "\n",
            "pyproj-3.0.1         | 484 KB    |            |   0%\n",
            "pyproj-3.0.1         | 484 KB    | ########## | 100%\n",
            "pyproj-3.0.1         | 484 KB    | ########## | 100%\n",
            "\n",
            "abseil-cpp-20210324. | 1011 KB   |            |   0%\n",
            "abseil-cpp-20210324. | 1011 KB   | ########## | 100%\n",
            "abseil-cpp-20210324. | 1011 KB   | ########## | 100%\n",
            "\n",
            "jupyter_client-6.1.1 | 79 KB     |            |   0%\n",
            "jupyter_client-6.1.1 | 79 KB     | ########## | 100%\n",
            "\n",
            "pyee-7.0.4           | 14 KB     |            |   0%\n",
            "pyee-7.0.4           | 14 KB     | ########## | 100%\n",
            "\n",
            "libsodium-1.0.18     | 366 KB    |            |   0%\n",
            "libsodium-1.0.18     | 366 KB    | ########## | 100%\n",
            "\n",
            "jupyter_core-4.7.1   | 72 KB     |            |   0%\n",
            "jupyter_core-4.7.1   | 72 KB     | ########## | 100%\n",
            "\n",
            "xorg-xextproto-7.3.0 | 28 KB     |            |   0%\n",
            "xorg-xextproto-7.3.0 | 28 KB     | ########## | 100%\n",
            "\n",
            "aws-c-cal-0.4.5      | 35 KB     |            |   0%\n",
            "aws-c-cal-0.4.5      | 35 KB     | ########## | 100%\n",
            "\n",
            "arrow-cpp-1.0.1      | 21.0 MB   |            |   0%\n",
            "arrow-cpp-1.0.1      | 21.0 MB   | #5         |  15%\n",
            "arrow-cpp-1.0.1      | 21.0 MB   | #######    |  70%\n",
            "arrow-cpp-1.0.1      | 21.0 MB   | ########## | 100%\n",
            "arrow-cpp-1.0.1      | 21.0 MB   | ########## | 100%\n",
            "\n",
            "libxgboost-1.4.2dev. | 115.3 MB  |            |   0%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  |            |   0%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  |            |   0%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  |            |   0%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | 1          |   1%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | 4          |   5%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | 8          |   9%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #2         |  12%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #5         |  16%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #8         |  19%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ##2        |  22%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ##6        |  27%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ###        |  30%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ###5       |  35%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ###9       |  40%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ####4      |  44%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ####9      |  49%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #####4     |  54%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #####9     |  59%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ######4    |  64%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ######9    |  69%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #######4   |  74%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #######9   |  79%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ########4  |  84%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ########9  |  89%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #########4 |  94%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | #########9 |  99%\n",
            "libxgboost-1.4.2dev. | 115.3 MB  | ########## | 100%\n",
            "\n",
            "async_generator-1.10 | 18 KB     |            |   0%\n",
            "async_generator-1.10 | 18 KB     | ########## | 100%\n",
            "\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   |            |   0%\n",
            "cuml-21.06.02        | 78.9 MB   | 1          |   2%\n",
            "cuml-21.06.02        | 78.9 MB   | 6          |   6%\n",
            "cuml-21.06.02        | 78.9 MB   | #          |  11%\n",
            "cuml-21.06.02        | 78.9 MB   | #6         |  17%\n",
            "cuml-21.06.02        | 78.9 MB   | ##1        |  21%\n",
            "cuml-21.06.02        | 78.9 MB   | ##7        |  28%\n",
            "cuml-21.06.02        | 78.9 MB   | ###3       |  34%\n",
            "cuml-21.06.02        | 78.9 MB   | ###8       |  39%\n",
            "cuml-21.06.02        | 78.9 MB   | ####5      |  46%\n",
            "cuml-21.06.02        | 78.9 MB   | #####2     |  52%\n",
            "cuml-21.06.02        | 78.9 MB   | #####8     |  59%\n",
            "cuml-21.06.02        | 78.9 MB   | ######5    |  65%\n",
            "cuml-21.06.02        | 78.9 MB   | #######1   |  71%\n",
            "cuml-21.06.02        | 78.9 MB   | #######8   |  78%\n",
            "cuml-21.06.02        | 78.9 MB   | ########4  |  85%\n",
            "cuml-21.06.02        | 78.9 MB   | #########1 |  92%\n",
            "cuml-21.06.02        | 78.9 MB   | #########8 |  99%\n",
            "cuml-21.06.02        | 78.9 MB   | ########## | 100%\n",
            "\n",
            "kealib-1.4.14        | 186 KB    |            |   0%\n",
            "kealib-1.4.14        | 186 KB    | ########## | 100%\n",
            "\n",
            "cusignal-21.06.00    | 1.0 MB    |            |   0%\n",
            "cusignal-21.06.00    | 1.0 MB    | 1          |   2%\n",
            "cusignal-21.06.00    | 1.0 MB    | 9          |   9%\n",
            "cusignal-21.06.00    | 1.0 MB    | ####       |  41%\n",
            "cusignal-21.06.00    | 1.0 MB    | ########## | 100%\n",
            "cusignal-21.06.00    | 1.0 MB    | ########## | 100%\n",
            "\n",
            "simpervisor-0.4      | 9 KB      |            |   0%\n",
            "simpervisor-0.4      | 9 KB      | ########## | 100%\n",
            "\n",
            "libcudf_kafka-21.06. | 125 KB    |            |   0%\n",
            "libcudf_kafka-21.06. | 125 KB    | #2         |  13%\n",
            "libcudf_kafka-21.06. | 125 KB    | ########## | 100%\n",
            "\n",
            "libcblas-3.9.0       | 11 KB     |            |   0%\n",
            "libcblas-3.9.0       | 11 KB     | ########## | 100%\n",
            "\n",
            "ptyprocess-0.7.0     | 16 KB     |            |   0%\n",
            "ptyprocess-0.7.0     | 16 KB     | ########## | 100%\n",
            "\n",
            "ucx-1.9.0+gcd9efd3   | 8.2 MB    |            |   0%\n",
            "ucx-1.9.0+gcd9efd3   | 8.2 MB    |            |   0%\n",
            "ucx-1.9.0+gcd9efd3   | 8.2 MB    | ###5       |  35%\n",
            "ucx-1.9.0+gcd9efd3   | 8.2 MB    | ########8  |  89%\n",
            "ucx-1.9.0+gcd9efd3   | 8.2 MB    | ########## | 100%\n",
            "\n",
            "libnetcdf-4.7.4      | 1.3 MB    |            |   0%\n",
            "libnetcdf-4.7.4      | 1.3 MB    | ########## | 100%\n",
            "libnetcdf-4.7.4      | 1.3 MB    | ########## | 100%\n",
            "\n",
            "libgpg-error-1.42    | 278 KB    |            |   0%\n",
            "libgpg-error-1.42    | 278 KB    | ########## | 100%\n",
            "\n",
            "wcwidth-0.2.5        | 33 KB     |            |   0%\n",
            "wcwidth-0.2.5        | 33 KB     | ########## | 100%\n",
            "\n",
            "olefile-0.46         | 32 KB     |            |   0%\n",
            "olefile-0.46         | 32 KB     | ########## | 100%\n",
            "\n",
            "ipywidgets-7.6.3     | 101 KB    |            |   0%\n",
            "ipywidgets-7.6.3     | 101 KB    | ########## | 100%\n",
            "\n",
            "sniffio-1.2.0        | 15 KB     |            |   0%\n",
            "sniffio-1.2.0        | 15 KB     | ########## | 100%\n",
            "\n",
            "pyppeteer-0.2.2      | 104 KB    |            |   0%\n",
            "pyppeteer-0.2.2      | 104 KB    | ########## | 100%\n",
            "\n",
            "rapids-xgboost-21.06 | 4 KB      |            |   0%\n",
            "rapids-xgboost-21.06 | 4 KB      | ########## | 100%\n",
            "rapids-xgboost-21.06 | 4 KB      | ########## | 100%\n",
            "\n",
            "requests-unixsocket- | 14 KB     |            |   0%\n",
            "requests-unixsocket- | 14 KB     | ########## | 100%\n",
            "\n",
            "rsa-4.7.2            | 28 KB     |            |   0%\n",
            "rsa-4.7.2            | 28 KB     | ########## | 100%\n",
            "\n",
            "widgetsnbextension-3 | 1.8 MB    |            |   0%\n",
            "widgetsnbextension-3 | 1.8 MB    | ########## | 100%\n",
            "widgetsnbextension-3 | 1.8 MB    | ########## | 100%\n",
            "\n",
            "jinja2-3.0.1         | 99 KB     |            |   0%\n",
            "jinja2-3.0.1         | 99 KB     | ########## | 100%\n",
            "\n",
            "ipython-7.25.0       | 1.1 MB    |            |   0%\n",
            "ipython-7.25.0       | 1.1 MB    | ########## | 100%\n",
            "ipython-7.25.0       | 1.1 MB    | ########## | 100%\n",
            "\n",
            "xorg-libice-1.0.10   | 58 KB     |            |   0%\n",
            "xorg-libice-1.0.10   | 58 KB     | ########## | 100%\n",
            "\n",
            "nest-asyncio-1.5.1   | 9 KB      |            |   0%\n",
            "nest-asyncio-1.5.1   | 9 KB      | ########## | 100%\n",
            "\n",
            "numpy-1.21.1         | 6.1 MB    |            |   0%\n",
            "numpy-1.21.1         | 6.1 MB    | ########## | 100%\n",
            "numpy-1.21.1         | 6.1 MB    | ########## | 100%\n",
            "\n",
            "arrow-cpp-proc-3.0.0 | 24 KB     |            |   0%\n",
            "arrow-cpp-proc-3.0.0 | 24 KB     | ########## | 100%\n",
            "\n",
            "appdirs-1.4.4        | 13 KB     |            |   0%\n",
            "appdirs-1.4.4        | 13 KB     | ########## | 100%\n",
            "\n",
            "defusedxml-0.7.1     | 23 KB     |            |   0%\n",
            "defusedxml-0.7.1     | 23 KB     | ########## | 100%\n",
            "\n",
            "pytz-2021.1          | 239 KB    |            |   0%\n",
            "pytz-2021.1          | 239 KB    | ########## | 100%\n",
            "pytz-2021.1          | 239 KB    | ########## | 100%\n",
            "\n",
            "libthrift-0.14.1     | 4.5 MB    |            |   0%\n",
            "libthrift-0.14.1     | 4.5 MB    | ########## | 100%\n",
            "libthrift-0.14.1     | 4.5 MB    | ########## | 100%\n",
            "\n",
            "attrs-21.2.0         | 44 KB     |            |   0%\n",
            "attrs-21.2.0         | 44 KB     | ########## | 100%\n",
            "\n",
            "zict-2.0.0           | 10 KB     |            |   0%\n",
            "zict-2.0.0           | 10 KB     | ########## | 100%\n",
            "\n",
            "yarl-1.6.3           | 142 KB    |            |   0%\n",
            "yarl-1.6.3           | 142 KB    | ########## | 100%\n",
            "\n",
            "cugraph-21.06.00     | 65.0 MB   |            |   0%\n",
            "cugraph-21.06.00     | 65.0 MB   |            |   0%\n",
            "cugraph-21.06.00     | 65.0 MB   | 8          |   9%\n",
            "cugraph-21.06.00     | 65.0 MB   | #7         |  17%\n",
            "cugraph-21.06.00     | 65.0 MB   | ##5        |  26%\n",
            "cugraph-21.06.00     | 65.0 MB   | ###3       |  34%\n",
            "cugraph-21.06.00     | 65.0 MB   | ###9       |  40%\n",
            "cugraph-21.06.00     | 65.0 MB   | ####7      |  47%\n",
            "cugraph-21.06.00     | 65.0 MB   | #####2     |  53%\n",
            "cugraph-21.06.00     | 65.0 MB   | #####9     |  59%\n",
            "cugraph-21.06.00     | 65.0 MB   | ######6    |  66%\n",
            "cugraph-21.06.00     | 65.0 MB   | #######3   |  73%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########   |  80%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########7  |  88%\n",
            "cugraph-21.06.00     | 65.0 MB   | #########3 |  94%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########## | 100%\n",
            "cugraph-21.06.00     | 65.0 MB   | ########## | 100%\n",
            "\n",
            "ca-certificates-2021 | 136 KB    |            |   0%\n",
            "ca-certificates-2021 | 136 KB    | ########## | 100%\n",
            "\n",
            "expat-2.4.1          | 182 KB    |            |   0%\n",
            "expat-2.4.1          | 182 KB    | ########## | 100%\n",
            "\n",
            "munch-2.5.0          | 12 KB     |            |   0%\n",
            "munch-2.5.0          | 12 KB     | ########## | 100%\n",
            "\n",
            "locket-0.2.0         | 6 KB      |            |   0%\n",
            "locket-0.2.0         | 6 KB      | ########## | 100%\n",
            "\n",
            "aws-c-io-0.9.1       | 119 KB    |            |   0%\n",
            "aws-c-io-0.9.1       | 119 KB    | ########## | 100%\n",
            "\n",
            "prompt-toolkit-3.0.1 | 244 KB    |            |   0%\n",
            "prompt-toolkit-3.0.1 | 244 KB    | ########## | 100%\n",
            "\n",
            "libgfortran5-9.3.0   | 2.0 MB    |            |   0%\n",
            "libgfortran5-9.3.0   | 2.0 MB    | ########## | 100%\n",
            "libgfortran5-9.3.0   | 2.0 MB    | ########## | 100%\n",
            "\n",
            "mistune-0.8.4        | 54 KB     |            |   0%\n",
            "mistune-0.8.4        | 54 KB     | ########## | 100%\n",
            "\n",
            "pyarrow-1.0.1        | 2.4 MB    |            |   0%\n",
            "pyarrow-1.0.1        | 2.4 MB    | ########## | 100%\n",
            "pyarrow-1.0.1        | 2.4 MB    | ########## | 100%\n",
            "\n",
            "hdf4-4.2.15          | 950 KB    |            |   0%\n",
            "hdf4-4.2.15          | 950 KB    | ########## | 100%\n",
            "hdf4-4.2.15          | 950 KB    | ########## | 100%\n",
            "\n",
            "libcumlprims-21.06.0 | 1.1 MB    |            |   0%\n",
            "libcumlprims-21.06.0 | 1.1 MB    | ########## | 100%\n",
            "libcumlprims-21.06.0 | 1.1 MB    | ########## | 100%\n",
            "\n",
            "websockets-8.1       | 90 KB     |            |   0%\n",
            "websockets-8.1       | 90 KB     | ########## | 100%\n",
            "\n",
            "gettext-0.19.8.1     | 3.6 MB    |            |   0%\n",
            "gettext-0.19.8.1     | 3.6 MB    | ########## | 100%\n",
            "gettext-0.19.8.1     | 3.6 MB    | ########## | 100%\n",
            "\n",
            "gflags-2.2.2         | 114 KB    |            |   0%\n",
            "gflags-2.2.2         | 114 KB    | ########## | 100%\n",
            "\n",
            "custreamz-21.06.01   | 32 KB     |            |   0%\n",
            "custreamz-21.06.01   | 32 KB     | #####      |  50%\n",
            "custreamz-21.06.01   | 32 KB     | ########## | 100%\n",
            "\n",
            "markupsafe-2.0.1     | 22 KB     |            |   0%\n",
            "markupsafe-2.0.1     | 22 KB     | ########## | 100%\n",
            "\n",
            "pyjwt-2.1.0          | 17 KB     |            |   0%\n",
            "pyjwt-2.1.0          | 17 KB     | ########## | 100%\n",
            "\n",
            "fastavro-1.4.3       | 501 KB    |            |   0%\n",
            "fastavro-1.4.3       | 501 KB    | ########## | 100%\n",
            "fastavro-1.4.3       | 501 KB    | ########## | 100%\n",
            "\n",
            "postgresql-13.2      | 5.3 MB    |            |   0%\n",
            "postgresql-13.2      | 5.3 MB    | ########## | 100%\n",
            "postgresql-13.2      | 5.3 MB    | ########## | 100%\n",
            "\n",
            "ipykernel-5.5.5      | 167 KB    |            |   0%\n",
            "ipykernel-5.5.5      | 167 KB    | ########## | 100%\n",
            "\n",
            "libbrotlidec-1.0.9   | 33 KB     |            |   0%\n",
            "libbrotlidec-1.0.9   | 33 KB     | ########## | 100%\n",
            "\n",
            "libxcb-1.13          | 395 KB    |            |   0%\n",
            "libxcb-1.13          | 395 KB    | ########## | 100%\n",
            "libxcb-1.13          | 395 KB    | ########## | 100%\n",
            "\n",
            "aws-c-event-stream-0 | 47 KB     |            |   0%\n",
            "aws-c-event-stream-0 | 47 KB     | ########## | 100%\n",
            "\n",
            "pyu2f-0.1.5          | 31 KB     |            |   0%\n",
            "pyu2f-0.1.5          | 31 KB     | ########## | 100%\n",
            "\n",
            "gdal-3.2.2           | 1.5 MB    |            |   0%\n",
            "gdal-3.2.2           | 1.5 MB    | ########## | 100%\n",
            "gdal-3.2.2           | 1.5 MB    | ########## | 100%\n",
            "\n",
            "datashader-0.11.1    | 14.0 MB   |            |   0%\n",
            "datashader-0.11.1    | 14.0 MB   | ####9      |  49%\n",
            "datashader-0.11.1    | 14.0 MB   | ########## | 100%\n",
            "datashader-0.11.1    | 14.0 MB   | ########## | 100%\n",
            "\n",
            "networkx-2.6.1       | 1.5 MB    |            |   0%\n",
            "networkx-2.6.1       | 1.5 MB    | ########## | 100%\n",
            "networkx-2.6.1       | 1.5 MB    | ########## | 100%\n",
            "\n",
            "pcre-8.45            | 253 KB    |            |   0%\n",
            "pcre-8.45            | 253 KB    | ########## | 100%\n",
            "\n",
            "colorcet-2.0.6       | 1.5 MB    |            |   0%\n",
            "colorcet-2.0.6       | 1.5 MB    | ########## | 100%\n",
            "colorcet-2.0.6       | 1.5 MB    | ########## | 100%\n",
            "\n",
            "xorg-libsm-1.2.3     | 26 KB     |            |   0%\n",
            "xorg-libsm-1.2.3     | 26 KB     | ########## | 100%\n",
            "\n",
            "bokeh-2.2.3          | 7.0 MB    |            |   0%\n",
            "bokeh-2.2.3          | 7.0 MB    | ########## | 100%\n",
            "bokeh-2.2.3          | 7.0 MB    | ########## | 100%\n",
            "\n",
            "cudf_kafka-21.06.01  | 1.7 MB    |            |   0%\n",
            "cudf_kafka-21.06.01  | 1.7 MB    |            |   1%\n",
            "cudf_kafka-21.06.01  | 1.7 MB    | 6          |   6%\n",
            "cudf_kafka-21.06.01  | 1.7 MB    | ###2       |  33%\n",
            "cudf_kafka-21.06.01  | 1.7 MB    | ########## | 100%\n",
            "cudf_kafka-21.06.01  | 1.7 MB    | ########## | 100%\n",
            "\n",
            "heapdict-1.0.1       | 7 KB      |            |   0%\n",
            "heapdict-1.0.1       | 7 KB      | ########## | 100%\n",
            "\n",
            "backports-1.0        | 4 KB      |            |   0%\n",
            "backports-1.0        | 4 KB      | ########## | 100%\n",
            "\n",
            "pyviz_comms-2.1.0    | 29 KB     |            |   0%\n",
            "pyviz_comms-2.1.0    | 29 KB     | ########## | 100%\n",
            "\n",
            "pandas-1.2.5         | 11.8 MB   |            |   0%\n",
            "pandas-1.2.5         | 11.8 MB   | #####9     |  60%\n",
            "pandas-1.2.5         | 11.8 MB   | ########## | 100%\n",
            "pandas-1.2.5         | 11.8 MB   | ########## | 100%\n",
            "\n",
            "libcugraph-21.06.00  | 213.6 MB  |            |   0%\n",
            "libcugraph-21.06.00  | 213.6 MB  |            |   0%\n",
            "libcugraph-21.06.00  | 213.6 MB  | 1          |   2%\n",
            "libcugraph-21.06.00  | 213.6 MB  | 3          |   4%\n",
            "libcugraph-21.06.00  | 213.6 MB  | 5          |   5%\n",
            "libcugraph-21.06.00  | 213.6 MB  | 7          |   8%\n",
            "libcugraph-21.06.00  | 213.6 MB  | 9          |   9%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #1         |  12%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #4         |  14%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #6         |  16%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #8         |  18%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ##         |  21%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ##3        |  23%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ##4        |  25%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ##7        |  27%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ###        |  30%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ###2       |  33%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ###4       |  35%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ###6       |  37%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ###9       |  39%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ####1      |  42%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ####3      |  44%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ####6      |  46%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ####8      |  48%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #####      |  51%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #####2     |  53%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #####4     |  55%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #####7     |  57%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #####9     |  59%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ######1    |  62%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ######3    |  64%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ######5    |  66%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ######7    |  68%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #######    |  70%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #######2   |  72%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #######4   |  75%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #######6   |  77%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #######9   |  79%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########1  |  81%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########3  |  84%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########5  |  86%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########7  |  88%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########9  |  90%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #########1 |  92%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #########4 |  94%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #########6 |  96%\n",
            "libcugraph-21.06.00  | 213.6 MB  | #########8 |  98%\n",
            "libcugraph-21.06.00  | 213.6 MB  | ########## | 100%\n",
            "\n",
            "toolz-0.11.1         | 46 KB     |            |   0%\n",
            "toolz-0.11.1         | 46 KB     | ###4       |  35%\n",
            "toolz-0.11.1         | 46 KB     | ########## | 100%\n",
            "\n",
            "markdown-3.3.4       | 67 KB     |            |   0%\n",
            "markdown-3.3.4       | 67 KB     | ########## | 100%\n",
            "\n",
            "datashape-0.5.4      | 49 KB     |            |   0%\n",
            "datashape-0.5.4      | 49 KB     | ########## | 100%\n",
            "\n",
            "fastrlock-0.6        | 31 KB     |            |   0%\n",
            "fastrlock-0.6        | 31 KB     | ########## | 100%\n",
            "\n",
            "openssl-1.1.1k       | 2.1 MB    |            |   0%\n",
            "openssl-1.1.1k       | 2.1 MB    | ########## | 100%\n",
            "openssl-1.1.1k       | 2.1 MB    | ########## | 100%\n",
            "\n",
            "zipp-3.5.0           | 12 KB     |            |   0%\n",
            "zipp-3.5.0           | 12 KB     | ########## | 100%\n",
            "\n",
            "libgdal-3.2.2        | 13.2 MB   |            |   0%\n",
            "libgdal-3.2.2        | 13.2 MB   | ####8      |  48%\n",
            "libgdal-3.2.2        | 13.2 MB   | ########## | 100%\n",
            "libgdal-3.2.2        | 13.2 MB   | ########## | 100%\n",
            "\n",
            "cycler-0.10.0        | 9 KB      |            |   0%\n",
            "cycler-0.10.0        | 9 KB      | ########## | 100%\n",
            "\n",
            "python_abi-3.7       | 4 KB      |            |   0%\n",
            "python_abi-3.7       | 4 KB      | ########## | 100%\n",
            "\n",
            "tblib-1.7.0          | 15 KB     |            |   0%\n",
            "tblib-1.7.0          | 15 KB     | ########## | 100%\n",
            "\n",
            "pexpect-4.8.0        | 47 KB     |            |   0%\n",
            "pexpect-4.8.0        | 47 KB     | ########## | 100%\n",
            "\n",
            "xorg-libxrender-0.9. | 32 KB     |            |   0%\n",
            "xorg-libxrender-0.9. | 32 KB     | ########## | 100%\n",
            "\n",
            "google-auth-1.33.1   | 81 KB     |            |   0%\n",
            "google-auth-1.33.1   | 81 KB     | ########## | 100%\n",
            "\n",
            "tornado-6.1          | 646 KB    |            |   0%\n",
            "tornado-6.1          | 646 KB    | ########## | 100%\n",
            "tornado-6.1          | 646 KB    | ########## | 100%\n",
            "\n",
            "xgboost-1.4.2dev.rap | 17 KB     |            |   0%\n",
            "xgboost-1.4.2dev.rap | 17 KB     | #########5 |  96%\n",
            "xgboost-1.4.2dev.rap | 17 KB     | ########## | 100%\n",
            "\n",
            "pandoc-2.14.1        | 12.0 MB   |            |   0%\n",
            "pandoc-2.14.1        | 12.0 MB   | ######6    |  66%\n",
            "pandoc-2.14.1        | 12.0 MB   | ########## | 100%\n",
            "pandoc-2.14.1        | 12.0 MB   | ########## | 100%\n",
            "\n",
            "google-auth-oauthlib | 19 KB     |            |   0%\n",
            "google-auth-oauthlib | 19 KB     | ########## | 100%\n",
            "\n",
            "libpng-1.6.37        | 306 KB    |            |   0%\n",
            "libpng-1.6.37        | 306 KB    | ########## | 100%\n",
            "libpng-1.6.37        | 306 KB    | ########## | 100%\n",
            "\n",
            "partd-1.2.0          | 18 KB     |            |   0%\n",
            "partd-1.2.0          | 18 KB     | ########8  |  89%\n",
            "partd-1.2.0          | 18 KB     | ########## | 100%\n",
            "\n",
            "importlib-metadata-4 | 31 KB     |            |   0%\n",
            "importlib-metadata-4 | 31 KB     | ########## | 100%\n",
            "\n",
            "pynvml-11.0.0        | 39 KB     |            |   0%\n",
            "pynvml-11.0.0        | 39 KB     | ########## | 100%\n",
            "\n",
            "libbrotlicommon-1.0. | 65 KB     |            |   0%\n",
            "libbrotlicommon-1.0. | 65 KB     | ########## | 100%\n",
            "\n",
            "python-dateutil-2.8. | 240 KB    |            |   0%\n",
            "python-dateutil-2.8. | 240 KB    | ########## | 100%\n",
            "\n",
            "liblapack-3.9.0      | 11 KB     |            |   0%\n",
            "liblapack-3.9.0      | 11 KB     | ########## | 100%\n",
            "\n",
            "giflib-5.2.1         | 77 KB     |            |   0%\n",
            "giflib-5.2.1         | 77 KB     | ########## | 100%\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... 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",
            "Enabling notebook extension jupyter-js-widgets/extension...\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook:\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/pydeck.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n",
            "    \t/usr/local/etc/jupyter/nbconfig/notebook.json\n",
            "\n",
            "done\n",
            "RAPIDS conda installation complete.  Updating Colab's libraries...\n",
            "Copying /usr/local/lib/libcudf.so to /usr/lib/libcudf.so\n",
            "Copying /usr/local/lib/libnccl.so to /usr/lib/libnccl.so\n",
            "Copying /usr/local/lib/libcuml.so to /usr/lib/libcuml.so\n",
            "Copying /usr/local/lib/libcugraph.so to /usr/lib/libcugraph.so\n",
            "Copying /usr/local/lib/libxgboost.so to /usr/lib/libxgboost.so\n",
            "Copying /usr/local/lib/libcuspatial.so to /usr/lib/libcuspatial.so\n",
            "Copying /usr/local/lib/libgeos.so to /usr/lib/libgeos.so\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gFRqYVmcFlIF",
        "outputId": "5cc12414-2030-489c-8608-818437d6403b"
      },
      "source": [
        "# !pip install BorutaShap\n",
        "\n",
        "!pip install scikit-learn==0.23.2\n",
        "\n",
        "!pip install \"tune-sklearn>=0.2.1\"\n",
        "!pip install \"scikit-optimize>=0.8.1\"\n",
        "\n",
        "!pip install ppscore\n",
        "\n",
        "!pip install sklearn-som\n",
        "!pip install umap\n",
        "!pip install trimap\n",
        "!pip install \"pacmap==0.4\"\n",
        "\n",
        "!pip install cmasher\n",
        "!pip install colorcet\n",
        "!pip install mpl-scatter-density\n",
        "\n",
        "# Only for GPU usage\n",
        "!pip uninstall lightgbm -y\n",
        "# install lightgbm GPU\n",
        "!pip install lightgbm --install-option=--gpu --install-option=\"--opencl-include-dir=/usr/local/cuda/include/\" --install-option=\"--opencl-library=/usr/local/cuda/lib64/libOpenCL.so\"\n",
        "\n",
        "# !pip install --upgrade \"xgboost>=1.1.0\"\n",
        "!pip install --upgrade \"catboost>=0.23.2\"\n",
        "!pip install --upgrade shap\n",
        "!pip install pycaret"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-learn==0.23.2\n",
            "  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.8 MB 8.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.7.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.23.2) (2.2.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.21.1)\n",
            "Installing collected packages: scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 0.24.2\n",
            "    Uninstalling scikit-learn-0.24.2:\n",
            "      Successfully uninstalled scikit-learn-0.24.2\n",
            "Successfully installed scikit-learn-0.23.2\n",
            "Collecting tune-sklearn>=0.2.1\n",
            "  Downloading tune_sklearn-0.4.0-py3-none-any.whl (40 kB)\n",
            "\u001b[K     |████████████████████████████████| 40 kB 5.0 MB/s \n",
            "\u001b[?25hCollecting ray[tune]\n",
            "  Downloading ray-1.4.1-cp37-cp37m-manylinux2014_x86_64.whl (51.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 51.6 MB 34 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.7/site-packages (from tune-sklearn>=0.2.1) (1.21.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from tune-sklearn>=0.2.1) (1.7.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/site-packages (from tune-sklearn>=0.2.1) (0.23.2)\n",
            "Collecting redis>=3.5.0\n",
            "  Downloading redis-3.5.3-py2.py3-none-any.whl (72 kB)\n",
            "\u001b[K     |████████████████████████████████| 72 kB 637 kB/s \n",
            "\u001b[?25hCollecting filelock\n",
            "  Downloading filelock-3.0.12-py3-none-any.whl (7.6 kB)\n",
            "Collecting py-spy>=0.2.0\n",
            "  Downloading py_spy-0.3.7-py2.py3-none-manylinux1_x86_64.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 44.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (3.7.4.post0)\n",
            "Collecting opencensus\n",
            "  Downloading opencensus-0.7.13-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 68.5 MB/s \n",
            "\u001b[?25hCollecting aioredis\n",
            "  Downloading aioredis-1.3.1-py3-none-any.whl (65 kB)\n",
            "\u001b[K     |████████████████████████████████| 65 kB 4.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: jsonschema in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (3.2.0)\n",
            "Requirement already satisfied: protobuf>=3.15.3 in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (3.15.8)\n",
            "Collecting gpustat\n",
            "  Downloading gpustat-0.6.0.tar.gz (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 9.0 MB/s \n",
            "\u001b[?25hCollecting colorama\n",
            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (7.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (2.25.1)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (0.11.0)\n",
            "Collecting grpcio>=1.28.1\n",
            "  Downloading grpcio-1.38.1-cp37-cp37m-manylinux2014_x86_64.whl (4.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.2 MB 45.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (5.4.1)\n",
            "Collecting pydantic>=1.8\n",
            "  Downloading pydantic-1.8.2-cp37-cp37m-manylinux2014_x86_64.whl (10.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.1 MB 47.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (1.0.2)\n",
            "Collecting aiohttp-cors\n",
            "  Downloading aiohttp_cors-0.7.0-py3-none-any.whl (27 kB)\n",
            "Collecting tensorboardX\n",
            "  Downloading tensorboardX-2.4-py2.py3-none-any.whl (124 kB)\n",
            "\u001b[K     |████████████████████████████████| 124 kB 78.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/site-packages (from ray[tune]->tune-sklearn>=0.2.1) (1.2.5)\n",
            "Collecting tabulate\n",
            "  Downloading tabulate-0.8.9-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: six>=1.5.2 in /usr/local/lib/python3.7/site-packages (from grpcio>=1.28.1->ray[tune]->tune-sklearn>=0.2.1) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/site-packages (from pydantic>=1.8->ray[tune]->tune-sklearn>=0.2.1) (3.10.0.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/site-packages (from aiohttp->ray[tune]->tune-sklearn>=0.2.1) (21.2.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/site-packages (from aiohttp->ray[tune]->tune-sklearn>=0.2.1) (1.6.3)\n",
            "Requirement already satisfied: async-timeout<4.0,>=3.0 in /usr/local/lib/python3.7/site-packages (from aiohttp->ray[tune]->tune-sklearn>=0.2.1) (3.0.1)\n",
            "Requirement already satisfied: chardet<5.0,>=2.0 in /usr/local/lib/python3.7/site-packages (from aiohttp->ray[tune]->tune-sklearn>=0.2.1) (4.0.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/site-packages (from aiohttp->ray[tune]->tune-sklearn>=0.2.1) (5.1.0)\n",
            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/site-packages (from yarl<2.0,>=1.0->aiohttp->ray[tune]->tune-sklearn>=0.2.1) (2.10)\n",
            "Collecting hiredis\n",
            "  Downloading hiredis-2.0.0-cp37-cp37m-manylinux2010_x86_64.whl (85 kB)\n",
            "\u001b[K     |████████████████████████████████| 85 kB 4.9 MB/s \n",
            "\u001b[?25hCollecting nvidia-ml-py3>=7.352.0\n",
            "  Downloading nvidia-ml-py3-7.352.0.tar.gz (19 kB)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/site-packages (from gpustat->ray[tune]->tune-sklearn>=0.2.1) (5.8.0)\n",
            "Collecting blessings>=1.6\n",
            "  Downloading blessings-1.7-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/site-packages (from jsonschema->ray[tune]->tune-sklearn>=0.2.1) (4.6.1)\n",
            "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.7/site-packages (from jsonschema->ray[tune]->tune-sklearn>=0.2.1) (0.17.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from jsonschema->ray[tune]->tune-sklearn>=0.2.1) (49.6.0.post20210108)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/site-packages (from importlib-metadata->jsonschema->ray[tune]->tune-sklearn>=0.2.1) (3.5.0)\n",
            "Collecting opencensus-context==0.1.2\n",
            "  Downloading opencensus_context-0.1.2-py2.py3-none-any.whl (4.4 kB)\n",
            "Collecting google-api-core<2.0.0,>=1.0.0\n",
            "  Downloading google_api_core-1.31.0-py2.py3-none-any.whl (93 kB)\n",
            "\u001b[K     |████████████████████████████████| 93 kB 1.7 MB/s \n",
            "\u001b[?25hCollecting googleapis-common-protos<2.0dev,>=1.6.0\n",
            "  Downloading googleapis_common_protos-1.53.0-py2.py3-none-any.whl (198 kB)\n",
            "\u001b[K     |████████████████████████████████| 198 kB 67.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/site-packages (from google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (21.0)\n",
            "Requirement already satisfied: google-auth<2.0dev,>=1.25.0 in /usr/local/lib/python3.7/site-packages (from google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (1.33.1)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/site-packages (from google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (2021.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/site-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (0.2.7)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/site-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (4.2.2)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/site-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (4.7.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/site-packages (from packaging>=14.3->google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (2.4.7)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2.0dev,>=1.25.0->google-api-core<2.0.0,>=1.0.0->opencensus->ray[tune]->tune-sklearn>=0.2.1) (0.4.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests->ray[tune]->tune-sklearn>=0.2.1) (1.26.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests->ray[tune]->tune-sklearn>=0.2.1) (2021.5.30)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/site-packages (from pandas->ray[tune]->tune-sklearn>=0.2.1) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn->tune-sklearn>=0.2.1) (2.2.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn->tune-sklearn>=0.2.1) (1.0.1)\n",
            "Building wheels for collected packages: gpustat, nvidia-ml-py3\n",
            "  Building wheel for gpustat (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gpustat: filename=gpustat-0.6.0-py3-none-any.whl size=12617 sha256=e8c585988bd71c60c82c03f7bf23004144564d8b31c7239ef8489937c313b3d3\n",
            "  Stored in directory: /root/.cache/pip/wheels/e6/67/af/f1ad15974b8fd95f59a63dbf854483ebe5c7a46a93930798b8\n",
            "  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nvidia-ml-py3: filename=nvidia_ml_py3-7.352.0-py3-none-any.whl size=19191 sha256=22cbe135b3369c38295bfe507dd3b5f25743adece799e3cd48ebed9a530dc3c6\n",
            "  Stored in directory: /root/.cache/pip/wheels/df/99/da/c34f202dc8fd1dffd35e0ecf1a7d7f8374ca05fbcbaf974b83\n",
            "Successfully built gpustat nvidia-ml-py3\n",
            "Installing collected packages: googleapis-common-protos, opencensus-context, nvidia-ml-py3, hiredis, google-api-core, blessings, redis, pydantic, py-spy, opencensus, grpcio, gpustat, filelock, colorama, aioredis, aiohttp-cors, tensorboardX, tabulate, ray, tune-sklearn\n",
            "Successfully installed aiohttp-cors-0.7.0 aioredis-1.3.1 blessings-1.7 colorama-0.4.4 filelock-3.0.12 google-api-core-1.31.0 googleapis-common-protos-1.53.0 gpustat-0.6.0 grpcio-1.38.1 hiredis-2.0.0 nvidia-ml-py3-7.352.0 opencensus-0.7.13 opencensus-context-0.1.2 py-spy-0.3.7 pydantic-1.8.2 ray-1.4.1 redis-3.5.3 tabulate-0.8.9 tensorboardX-2.4 tune-sklearn-0.4.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-optimize>=0.8.1\n",
            "  Downloading scikit_optimize-0.8.1-py2.py3-none-any.whl (101 kB)\n",
            "\u001b[?25l\r\u001b[K     |███▎                            | 10 kB 24.8 MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 20 kB 29.7 MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 30 kB 22.9 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 40 kB 17.8 MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 51 kB 9.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 61 kB 9.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 71 kB 9.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 81 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 92 kB 10.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 101 kB 5.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/site-packages (from scikit-optimize>=0.8.1) (1.21.1)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/site-packages (from scikit-optimize>=0.8.1) (1.7.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-optimize>=0.8.1) (1.0.1)\n",
            "Collecting pyaml>=16.9\n",
            "  Downloading pyaml-20.4.0-py2.py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/site-packages (from scikit-optimize>=0.8.1) (0.23.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/site-packages (from pyaml>=16.9->scikit-optimize>=0.8.1) (5.4.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.20.0->scikit-optimize>=0.8.1) (2.2.0)\n",
            "Installing collected packages: pyaml, scikit-optimize\n",
            "Successfully installed pyaml-20.4.0 scikit-optimize-0.8.1\n",
            "Collecting ppscore\n",
            "  Downloading ppscore-1.2.0.tar.gz (47 kB)\n",
            "\u001b[K     |████████████████████████████████| 47 kB 3.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/site-packages (from ppscore) (1.2.5)\n",
            "Requirement already satisfied: scikit-learn<1.0.0,>=0.20.2 in /usr/local/lib/python3.7/site-packages (from ppscore) (0.23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/site-packages (from pandas<2.0.0,>=1.0.0->ppscore) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas<2.0.0,>=1.0.0->ppscore) (2021.1)\n",
            "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.7/site-packages (from pandas<2.0.0,>=1.0.0->ppscore) (1.21.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas<2.0.0,>=1.0.0->ppscore) (1.15.0)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/site-packages (from scikit-learn<1.0.0,>=0.20.2->ppscore) (1.7.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn<1.0.0,>=0.20.2->ppscore) (2.2.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn<1.0.0,>=0.20.2->ppscore) (1.0.1)\n",
            "Building wheels for collected packages: ppscore\n",
            "  Building wheel for ppscore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ppscore: filename=ppscore-1.2.0-py2.py3-none-any.whl size=13050 sha256=c3a9d04ec94bb34924b0ffef81b25e7d8f6df64fd24448b5e2bed810eff4ea35\n",
            "  Stored in directory: /root/.cache/pip/wheels/d2/3c/58/2ff786414b21713edc6f4fdb54fdee89ac37bca5edd1f60634\n",
            "Successfully built ppscore\n",
            "Installing collected packages: ppscore\n",
            "Successfully installed ppscore-1.2.0\n",
            "Collecting sklearn-som\n",
            "  Downloading sklearn_som-1.1.0-py3-none-any.whl (6.7 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from sklearn-som) (1.21.1)\n",
            "Installing collected packages: sklearn-som\n",
            "Successfully installed sklearn-som-1.1.0\n",
            "Collecting umap\n",
            "  Downloading umap-0.1.1.tar.gz (3.2 kB)\n",
            "Building wheels for collected packages: umap\n",
            "  Building wheel for umap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap: filename=umap-0.1.1-py3-none-any.whl size=3564 sha256=e1592926d5782ff365b662fce1130fb72bd43c5ef0b38621d41e1f4cc112d7d0\n",
            "  Stored in directory: /root/.cache/pip/wheels/65/55/85/945cfb3d67373767e4dc3e9629300a926edde52633df4f0efe\n",
            "Successfully built umap\n",
            "Installing collected packages: umap\n",
            "Successfully installed umap-0.1.1\n",
            "Collecting trimap\n",
            "  Downloading trimap-1.0.15.tar.gz (5.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.6 MB 6.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.16 in /usr/local/lib/python3.7/site-packages (from trimap) (0.23.2)\n",
            "Requirement already satisfied: numba>=0.34 in /usr/local/lib/python3.7/site-packages (from trimap) (0.53.1)\n",
            "Collecting annoy>=1.11\n",
            "  Downloading annoy-1.17.0.tar.gz (646 kB)\n",
            "\u001b[K     |████████████████████████████████| 646 kB 60.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.7/site-packages (from numba>=0.34->trimap) (0.36.0)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/site-packages (from numba>=0.34->trimap) (1.21.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from numba>=0.34->trimap) (49.6.0.post20210108)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.16->trimap) (1.7.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.16->trimap) (1.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.16->trimap) (2.2.0)\n",
            "Building wheels for collected packages: trimap, annoy\n",
            "  Building wheel for trimap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for trimap: filename=trimap-1.0.15-py3-none-any.whl size=14637 sha256=e833c7168bc32b12c064bbdad3c5f233805e8ff8073b72d3885b51864121b29a\n",
            "  Stored in directory: /root/.cache/pip/wheels/d2/bc/e3/827cd24969a7d2e66a66a48cda0b05b044ee816626a6113480\n",
            "  Building wheel for annoy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for annoy: filename=annoy-1.17.0-cp37-cp37m-linux_x86_64.whl size=391832 sha256=581eb498795725e7ec7e0f54f11343b2c56f004fe3c53c6f8299e180b1a57816\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/e8/1e/7cc9ebbfa87a3b9f8ba79408d4d31831d67eea918b679a4c07\n",
            "Successfully built trimap annoy\n",
            "Installing collected packages: annoy, trimap\n",
            "Successfully installed annoy-1.17.0 trimap-1.0.15\n",
            "Collecting pacmap==0.4\n",
            "  Downloading pacmap-0.4-py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: numba>=0.34 in /usr/local/lib/python3.7/site-packages (from pacmap==0.4) (0.53.1)\n",
            "Requirement already satisfied: numpy>=1.18 in /usr/local/lib/python3.7/site-packages (from pacmap==0.4) (1.21.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/site-packages (from pacmap==0.4) (0.23.2)\n",
            "Requirement already satisfied: annoy>=1.11 in /usr/local/lib/python3.7/site-packages (from pacmap==0.4) (1.17.0)\n",
            "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.7/site-packages (from numba>=0.34->pacmap==0.4) (0.36.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from numba>=0.34->pacmap==0.4) (49.6.0.post20210108)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.20->pacmap==0.4) (1.7.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.20->pacmap==0.4) (2.2.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.20->pacmap==0.4) (1.0.1)\n",
            "Installing collected packages: pacmap\n",
            "Successfully installed pacmap-0.4\n",
            "Collecting cmasher\n",
            "  Downloading cmasher-1.6.2-py3-none-any.whl (366 kB)\n",
            "\u001b[K     |████████████████████████████████| 366 kB 7.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/site-packages (from cmasher) (1.21.1)\n",
            "Requirement already satisfied: matplotlib>=2.2.4 in /usr/local/lib/python3.7/site-packages (from cmasher) (3.4.2)\n",
            "Collecting colorspacious>=1.1.0\n",
            "  Downloading colorspacious-1.1.2-py2.py3-none-any.whl (37 kB)\n",
            "Collecting e13tools>=0.9.4\n",
            "  Downloading e13tools-0.9.6-py3-none-any.whl (40 kB)\n",
            "\u001b[K     |████████████████████████████████| 40 kB 6.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.2.4->cmasher) (8.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.2.4->cmasher) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.2.4->cmasher) (0.10.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.2.4->cmasher) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=2.2.4->cmasher) (1.3.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=2.2.4->cmasher) (1.15.0)\n",
            "Installing collected packages: e13tools, colorspacious, cmasher\n",
            "Successfully installed cmasher-1.6.2 colorspacious-1.1.2 e13tools-0.9.6\n",
            "Requirement already satisfied: colorcet in /usr/local/lib/python3.7/site-packages (2.0.6)\n",
            "Requirement already satisfied: param>=1.7.0 in /usr/local/lib/python3.7/site-packages (from colorcet) (1.11.1)\n",
            "Requirement already satisfied: pyct>=0.4.4 in /usr/local/lib/python3.7/site-packages (from colorcet) (0.4.6)\n",
            "Collecting mpl-scatter-density\n",
            "  Downloading mpl_scatter_density-0.7-py3-none-any.whl (655 kB)\n",
            "\u001b[K     |████████████████████████████████| 655 kB 9.3 MB/s \n",
            "\u001b[?25hCollecting fast-histogram>=0.3\n",
            "  Downloading fast_histogram-0.9-cp37-cp37m-manylinux2010_x86_64.whl (39 kB)\n",
            "Requirement already satisfied: matplotlib>=3.0 in /usr/local/lib/python3.7/site-packages (from mpl-scatter-density) (3.4.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from mpl-scatter-density) (1.21.1)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.0->mpl-scatter-density) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.0->mpl-scatter-density) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.0->mpl-scatter-density) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.0->mpl-scatter-density) (1.3.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib>=3.0->mpl-scatter-density) (8.2.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=3.0->mpl-scatter-density) (1.15.0)\n",
            "Installing collected packages: fast-histogram, mpl-scatter-density\n",
            "Successfully installed fast-histogram-0.9 mpl-scatter-density-0.7\n",
            "\u001b[33mWARNING: Skipping lightgbm as it is not installed.\u001b[0m\n",
            "/usr/local/lib/python3.7/site-packages/pip/_internal/commands/install.py:230: UserWarning: Disabling all use of wheels due to the use of --build-option / --global-option / --install-option.\n",
            "  cmdoptions.check_install_build_global(options)\n",
            "Collecting lightgbm\n",
            "  Downloading lightgbm-3.2.1.tar.gz (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 7.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel in /usr/local/lib/python3.7/site-packages (from lightgbm) (0.36.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from lightgbm) (1.21.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from lightgbm) (1.7.0)\n",
            "Requirement already satisfied: scikit-learn!=0.22.0 in /usr/local/lib/python3.7/site-packages (from lightgbm) (0.23.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn!=0.22.0->lightgbm) (2.2.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn!=0.22.0->lightgbm) (1.0.1)\n",
            "Skipping wheel build for lightgbm, due to binaries being disabled for it.\n",
            "Installing collected packages: lightgbm\n",
            "    Running setup.py install for lightgbm ... \u001b[?25l\u001b[?25hdone\n",
            "Successfully installed lightgbm-3.2.1\n",
            "Collecting catboost>=0.23.2\n",
            "  Downloading catboost-0.26-cp37-none-manylinux1_x86_64.whl (69.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 69.2 MB 4.8 kB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/site-packages (from catboost>=0.23.2) (3.4.2)\n",
            "Collecting plotly\n",
            "  Downloading plotly-5.1.0-py2.py3-none-any.whl (20.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 20.6 MB 1.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from catboost>=0.23.2) (1.15.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/site-packages (from catboost>=0.23.2) (1.2.5)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/site-packages (from catboost>=0.23.2) (1.21.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from catboost>=0.23.2) (1.7.0)\n",
            "Collecting graphviz\n",
            "  Downloading graphviz-0.17-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/site-packages (from pandas>=0.24.0->catboost>=0.23.2) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas>=0.24.0->catboost>=0.23.2) (2021.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib->catboost>=0.23.2) (0.10.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib->catboost>=0.23.2) (8.2.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib->catboost>=0.23.2) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib->catboost>=0.23.2) (1.3.1)\n",
            "Collecting tenacity>=6.2.0\n",
            "  Downloading tenacity-8.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: tenacity, plotly, graphviz, catboost\n",
            "Successfully installed catboost-0.26 graphviz-0.17 plotly-5.1.0 tenacity-8.0.1\n",
            "Collecting shap\n",
            "  Downloading shap-0.39.0.tar.gz (356 kB)\n",
            "\u001b[K     |████████████████████████████████| 356 kB 7.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from shap) (1.21.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from shap) (1.7.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/site-packages (from shap) (0.23.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/site-packages (from shap) (1.2.5)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/site-packages (from shap) (4.59.0)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/site-packages (from shap) (0.53.1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/site-packages (from shap) (1.6.0)\n",
            "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.7/site-packages (from numba->shap) (0.36.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from numba->shap) (49.6.0.post20210108)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/site-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas->shap) (2021.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn->shap) (2.2.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn->shap) (1.0.1)\n",
            "Building wheels for collected packages: shap\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.39.0-cp37-cp37m-linux_x86_64.whl size=494327 sha256=8d2b2af742efd17e741cd30cc991fa7b243c9e6747eb775b0729f9ae0b32b53f\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/25/8f/6ae5df62c32651cd719e972e738a8aaa4a87414c4d2b14c9c0\n",
            "Successfully built shap\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.39.0 slicer-0.0.7\n",
            "Collecting pycaret\n",
            "  Downloading pycaret-2.3.2-py3-none-any.whl (263 kB)\n",
            "\u001b[K     |████████████████████████████████| 263 kB 7.6 MB/s \n",
            "\u001b[?25hCollecting cufflinks>=0.17.0\n",
            "  Downloading cufflinks-0.17.3.tar.gz (81 kB)\n",
            "\u001b[K     |████████████████████████████████| 81 kB 9.4 MB/s \n",
            "\u001b[?25hCollecting yellowbrick>=1.0.1\n",
            "  Downloading yellowbrick-1.3.post1-py3-none-any.whl (271 kB)\n",
            "\u001b[K     |████████████████████████████████| 271 kB 13.1 MB/s \n",
            "\u001b[?25hCollecting umap-learn\n",
            "  Downloading umap-learn-0.5.1.tar.gz (80 kB)\n",
            "\u001b[K     |████████████████████████████████| 80 kB 9.3 MB/s \n",
            "\u001b[?25hCollecting mlxtend>=0.17.0\n",
            "  Downloading mlxtend-0.18.0-py2.py3-none-any.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 14.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: lightgbm>=2.3.1 in /usr/local/lib/python3.7/site-packages (from pycaret) (3.2.1)\n",
            "Collecting numpy==1.19.5\n",
            "  Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 14.8 MB 175 kB/s \n",
            "\u001b[?25hCollecting wordcloud\n",
            "  Downloading wordcloud-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (366 kB)\n",
            "\u001b[K     |████████████████████████████████| 366 kB 67.5 MB/s \n",
            "\u001b[?25hCollecting textblob\n",
            "  Downloading textblob-0.15.3-py2.py3-none-any.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 47.4 MB/s \n",
            "\u001b[?25hCollecting gensim<4.0.0\n",
            "  Downloading gensim-3.8.3-cp37-cp37m-manylinux1_x86_64.whl (24.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 24.2 MB 89 kB/s \n",
            "\u001b[?25hRequirement already satisfied: ipywidgets in /usr/local/lib/python3.7/site-packages (from pycaret) (7.6.3)\n",
            "Collecting imbalanced-learn==0.7.0\n",
            "  Downloading imbalanced_learn-0.7.0-py3-none-any.whl (167 kB)\n",
            "\u001b[K     |████████████████████████████████| 167 kB 70.8 MB/s \n",
            "\u001b[?25hCollecting pyLDAvis\n",
            "  Downloading pyLDAvis-3.3.1.tar.gz (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 53.3 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn==0.23.2 in /usr/local/lib/python3.7/site-packages (from pycaret) (0.23.2)\n",
            "Collecting pyod\n",
            "  Downloading pyod-0.9.0.tar.gz (105 kB)\n",
            "\u001b[K     |████████████████████████████████| 105 kB 65.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly>=4.4.1 in /usr/local/lib/python3.7/site-packages (from pycaret) (5.1.0)\n",
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: IPython in /usr/local/lib/python3.7/site-packages (from pycaret) (7.25.0)\n",
            "Collecting kmodes>=0.10.1\n",
            "  Downloading kmodes-0.11.0-py2.py3-none-any.whl (18 kB)\n",
            "Collecting spacy<2.4.0\n",
            "  Downloading spacy-2.3.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.4 MB 21.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/site-packages (from pycaret) (3.4.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/site-packages (from pycaret) (1.0.1)\n",
            "Collecting scipy<=1.5.4\n",
            "  Downloading scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 25.9 MB 931 bytes/s \n",
            "\u001b[?25hCollecting pandas-profiling>=2.8.0\n",
            "  Downloading pandas_profiling-3.0.0-py2.py3-none-any.whl (248 kB)\n",
            "\u001b[K     |████████████████████████████████| 248 kB 51.4 MB/s \n",
            "\u001b[?25hCollecting Boruta\n",
            "  Downloading Boruta-0.3-py3-none-any.whl (56 kB)\n",
            "\u001b[K     |████████████████████████████████| 56 kB 5.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/site-packages (from pycaret) (1.2.5)\n",
            "Collecting seaborn\n",
            "  Downloading seaborn-0.11.1-py3-none-any.whl (285 kB)\n",
            "\u001b[K     |████████████████████████████████| 285 kB 61.3 MB/s \n",
            "\u001b[?25hCollecting mlflow\n",
            "  Downloading mlflow-1.19.0-py3-none-any.whl (14.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 14.4 MB 66 kB/s \n",
            "\u001b[?25hCollecting nltk\n",
            "  Downloading nltk-3.6.2-py3-none-any.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 39.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.23.2->pycaret) (2.2.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/site-packages (from cufflinks>=0.17.0->pycaret) (1.15.0)\n",
            "Collecting colorlover>=0.2.1\n",
            "  Downloading colorlover-0.3.0-py3-none-any.whl (8.9 kB)\n",
            "Requirement already satisfied: setuptools>=34.4.1 in /usr/local/lib/python3.7/site-packages (from cufflinks>=0.17.0->pycaret) (49.6.0.post20210108)\n",
            "Collecting smart-open>=1.8.1\n",
            "  Downloading smart_open-5.1.0-py3-none-any.whl (57 kB)\n",
            "\u001b[K     |████████████████████████████████| 57 kB 6.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (3.0.19)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (2.9.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (0.1.2)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (0.18.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (5.0.5)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/site-packages (from IPython->pycaret) (0.7.5)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/site-packages (from ipywidgets->pycaret) (5.1.3)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/site-packages (from ipywidgets->pycaret) (5.5.5)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/site-packages (from ipywidgets->pycaret) (1.0.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/site-packages (from ipywidgets->pycaret) (3.5.1)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.7/site-packages (from ipykernel>=4.5.1->ipywidgets->pycaret) (6.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/site-packages (from ipykernel>=4.5.1->ipywidgets->pycaret) (6.1.12)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.7/site-packages (from jedi>=0.16->IPython->pycaret) (0.8.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/site-packages (from lightgbm>=2.3.1->pycaret) (0.36.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib->pycaret) (8.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib->pycaret) (2.8.2)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib->pycaret) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib->pycaret) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib->pycaret) (0.10.0)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets->pycaret) (3.2.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets->pycaret) (4.7.1)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/site-packages (from nbformat>=4.2.0->ipywidgets->pycaret) (0.2.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->pycaret) (21.2.0)\n",
            "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->pycaret) (0.17.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->pycaret) (4.6.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/site-packages (from pandas->pycaret) (2021.1)\n",
            "Collecting visions[type_image_path]==0.7.1\n",
            "  Downloading visions-0.7.1-py3-none-any.whl (102 kB)\n",
            "\u001b[K     |████████████████████████████████| 102 kB 73.1 MB/s \n",
            "\u001b[?25hCollecting htmlmin>=0.1.12\n",
            "  Downloading htmlmin-0.1.12.tar.gz (19 kB)\n",
            "Collecting missingno>=0.4.2\n",
            "  Downloading missingno-0.5.0-py3-none-any.whl (8.8 kB)\n",
            "Collecting phik>=0.11.1\n",
            "  Downloading phik-0.11.2.tar.gz (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 33.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.24.0 in /usr/local/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret) (2.25.1)\n",
            "Collecting tangled-up-in-unicode==0.1.0\n",
            "  Downloading tangled_up_in_unicode-0.1.0-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 34.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.0.0 in /usr/local/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret) (5.4.1)\n",
            "Requirement already satisfied: tqdm>=4.48.2 in /usr/local/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret) (4.59.0)\n",
            "Requirement already satisfied: pydantic>=1.8.1 in /usr/local/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret) (1.8.2)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret) (3.0.1)\n",
            "Collecting multimethod==1.4\n",
            "  Downloading multimethod-1.4-py2.py3-none-any.whl (7.3 kB)\n",
            "Collecting bottleneck\n",
            "  Downloading Bottleneck-1.3.2.tar.gz (88 kB)\n",
            "\u001b[K     |████████████████████████████████| 88 kB 5.9 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: networkx>=2.4 in /usr/local/lib/python3.7/site-packages (from visions[type_image_path]==0.7.1->pandas-profiling>=2.8.0->pycaret) (2.6.1)\n",
            "Collecting imagehash\n",
            "  Downloading ImageHash-4.2.1.tar.gz (812 kB)\n",
            "\u001b[K     |████████████████████████████████| 812 kB 26.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.7/site-packages (from jinja2>=2.11.1->pandas-profiling>=2.8.0->pycaret) (2.0.1)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/site-packages (from pexpect>4.3->IPython->pycaret) (0.7.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/site-packages (from plotly>=4.4.1->pycaret) (8.0.1)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->IPython->pycaret) (0.2.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/site-packages (from pydantic>=1.8.1->pandas-profiling>=2.8.0->pycaret) (3.10.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests>=2.24.0->pandas-profiling>=2.8.0->pycaret) (2021.5.30)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/site-packages (from requests>=2.24.0->pandas-profiling>=2.8.0->pycaret) (4.0.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests>=2.24.0->pandas-profiling>=2.8.0->pycaret) (1.26.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests>=2.24.0->pandas-profiling>=2.8.0->pycaret) (2.10)\n",
            "Collecting plac<1.2.0,>=0.9.6\n",
            "  Downloading plac-1.1.3-py2.py3-none-any.whl (20 kB)\n",
            "Collecting blis<0.8.0,>=0.4.0\n",
            "  Downloading blis-0.7.4-cp37-cp37m-manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 46.7 MB/s \n",
            "\u001b[?25hCollecting wasabi<1.1.0,>=0.4.0\n",
            "  Downloading wasabi-0.8.2-py3-none-any.whl (23 kB)\n",
            "Collecting srsly<1.1.0,>=1.0.2\n",
            "  Downloading srsly-1.0.5-cp37-cp37m-manylinux2014_x86_64.whl (184 kB)\n",
            "\u001b[K     |████████████████████████████████| 184 kB 56.4 MB/s \n",
            "\u001b[?25hCollecting thinc<7.5.0,>=7.4.1\n",
            "  Downloading thinc-7.4.5-cp37-cp37m-manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 54.5 MB/s \n",
            "\u001b[?25hCollecting preshed<3.1.0,>=3.0.2\n",
            "  Downloading preshed-3.0.5-cp37-cp37m-manylinux2014_x86_64.whl (126 kB)\n",
            "\u001b[K     |████████████████████████████████| 126 kB 75.7 MB/s \n",
            "\u001b[?25hCollecting murmurhash<1.1.0,>=0.28.0\n",
            "  Downloading murmurhash-1.0.5-cp37-cp37m-manylinux2014_x86_64.whl (20 kB)\n",
            "Collecting catalogue<1.1.0,>=0.0.7\n",
            "  Downloading catalogue-1.0.0-py2.py3-none-any.whl (7.7 kB)\n",
            "Collecting cymem<2.1.0,>=2.0.2\n",
            "  Downloading cymem-2.0.5-cp37-cp37m-manylinux2014_x86_64.whl (35 kB)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/site-packages (from importlib-metadata->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->pycaret) (3.5.0)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/site-packages (from widgetsnbextension~=3.5.0->ipywidgets->pycaret) (6.4.0)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (20.1.0)\n",
            "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (22.1.0)\n",
            "Requirement already satisfied: Send2Trash>=1.5.0 in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.7.1)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (6.1.0)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.11.0)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.7/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.10.1)\n",
            "Requirement already satisfied: cffi>=1.0.0 in /usr/local/lib/python3.7/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.14.5)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/site-packages (from cffi>=1.0.0->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (2.20)\n",
            "Collecting PyWavelets\n",
            "  Downloading PyWavelets-1.1.1-cp37-cp37m-manylinux1_x86_64.whl (4.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.4 MB 52.5 MB/s \n",
            "\u001b[?25hCollecting alembic<=1.4.1\n",
            "  Downloading alembic-1.4.1.tar.gz (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 30.8 MB/s \n",
            "\u001b[?25hCollecting gunicorn\n",
            "  Downloading gunicorn-20.1.0-py3-none-any.whl (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 9.8 MB/s \n",
            "\u001b[?25hCollecting sqlalchemy\n",
            "  Downloading SQLAlchemy-1.4.21-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 31.9 MB/s \n",
            "\u001b[?25hCollecting docker>=4.0.0\n",
            "  Downloading docker-5.0.0-py2.py3-none-any.whl (146 kB)\n",
            "\u001b[K     |████████████████████████████████| 146 kB 81.1 MB/s \n",
            "\u001b[?25hCollecting Flask\n",
            "  Downloading Flask-2.0.1-py3-none-any.whl (94 kB)\n",
            "\u001b[K     |████████████████████████████████| 94 kB 4.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: entrypoints in /usr/local/lib/python3.7/site-packages (from mlflow->pycaret) (0.3)\n",
            "Collecting gitpython>=2.1.0\n",
            "  Downloading GitPython-3.1.18-py3-none-any.whl (170 kB)\n",
            "\u001b[K     |████████████████████████████████| 170 kB 76.7 MB/s \n",
            "\u001b[?25hCollecting querystring-parser\n",
            "  Downloading querystring_parser-1.2.4-py2.py3-none-any.whl (7.9 kB)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/site-packages (from mlflow->pycaret) (7.1.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/site-packages (from mlflow->pycaret) (21.0)\n",
            "Collecting databricks-cli>=0.8.7\n",
            "  Downloading databricks-cli-0.14.3.tar.gz (54 kB)\n",
            "\u001b[K     |████████████████████████████████| 54 kB 4.0 MB/s \n",
            "\u001b[?25hCollecting sqlparse>=0.3.1\n",
            "  Downloading sqlparse-0.4.1-py3-none-any.whl (42 kB)\n",
            "\u001b[K     |████████████████████████████████| 42 kB 1.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.7/site-packages (from mlflow->pycaret) (1.6.0)\n",
            "Collecting prometheus-flask-exporter\n",
            "  Downloading prometheus_flask_exporter-0.18.2.tar.gz (22 kB)\n",
            "Requirement already satisfied: protobuf>=3.7.0 in /usr/local/lib/python3.7/site-packages (from mlflow->pycaret) (3.15.8)\n",
            "Collecting Mako\n",
            "  Downloading Mako-1.1.4-py2.py3-none-any.whl (75 kB)\n",
            "\u001b[K     |████████████████████████████████| 75 kB 5.5 MB/s \n",
            "\u001b[?25hCollecting python-editor>=0.3\n",
            "  Downloading python_editor-1.0.4-py3-none-any.whl (4.9 kB)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/site-packages (from databricks-cli>=0.8.7->mlflow->pycaret) (0.8.9)\n",
            "Requirement already satisfied: websocket-client>=0.32.0 in /usr/local/lib/python3.7/site-packages (from docker>=4.0.0->mlflow->pycaret) (0.57.0)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "  Downloading gitdb-4.0.7-py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.9 MB/s \n",
            "\u001b[?25hCollecting smmap<5,>=3.0.1\n",
            "  Downloading smmap-4.0.0-py2.py3-none-any.whl (24 kB)\n",
            "Collecting greenlet!=0.4.17\n",
            "  Downloading greenlet-1.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (160 kB)\n",
            "\u001b[K     |████████████████████████████████| 160 kB 73.0 MB/s \n",
            "\u001b[?25hCollecting Werkzeug>=2.0\n",
            "  Downloading Werkzeug-2.0.1-py3-none-any.whl (288 kB)\n",
            "\u001b[K     |████████████████████████████████| 288 kB 68.7 MB/s \n",
            "\u001b[?25hCollecting itsdangerous>=2.0\n",
            "  Downloading itsdangerous-2.0.1-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.1.2)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.7.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.4.2)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.8.4)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (3.3.1)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.0)\n",
            "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /usr/local/lib/python3.7/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.3)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.7/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.5.1)\n",
            "Requirement already satisfied: async-generator in /usr/local/lib/python3.7/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.10)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.1)\n",
            "Collecting regex\n",
            "  Downloading regex-2021.7.6-cp37-cp37m-manylinux2014_x86_64.whl (721 kB)\n",
            "\u001b[K     |████████████████████████████████| 721 kB 55.2 MB/s \n",
            "\u001b[?25hCollecting funcy\n",
            "  Downloading funcy-1.16-py2.py3-none-any.whl (32 kB)\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
            "Collecting numexpr\n",
            "  Downloading numexpr-2.7.3-cp37-cp37m-manylinux2010_x86_64.whl (471 kB)\n",
            "\u001b[K     |████████████████████████████████| 471 kB 57.6 MB/s \n",
            "\u001b[?25hCollecting pyLDAvis\n",
            "  Downloading pyLDAvis-3.3.0.tar.gz (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 59.3 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "  Downloading pyLDAvis-3.2.2.tar.gz (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 26.0 MB/s \n",
            "\u001b[?25hCollecting future\n",
            "  Downloading future-0.18.2.tar.gz (829 kB)\n",
            "\u001b[K     |████████████████████████████████| 829 kB 48.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numba>=0.35 in /usr/local/lib/python3.7/site-packages (from pyod->pycaret) (0.53.1)\n",
            "Collecting statsmodels\n",
            "  Downloading statsmodels-0.12.2-cp37-cp37m-manylinux1_x86_64.whl (9.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.5 MB 111 kB/s \n",
            "\u001b[?25hRequirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.7/site-packages (from numba>=0.35->pyod->pycaret) (0.36.0)\n",
            "Collecting patsy>=0.5\n",
            "  Downloading patsy-0.5.1-py2.py3-none-any.whl (231 kB)\n",
            "\u001b[K     |████████████████████████████████| 231 kB 66.2 MB/s \n",
            "\u001b[?25hCollecting pynndescent>=0.5\n",
            "  Downloading pynndescent-0.5.4.tar.gz (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 40.3 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: cufflinks, htmlmin, phik, bottleneck, imagehash, alembic, databricks-cli, prometheus-flask-exporter, pyLDAvis, future, pyod, umap-learn, pynndescent\n",
            "  Building wheel for cufflinks (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=67922 sha256=072ccab6bd5c3bc85b2960d55e333cc09298754f51df4795d0b7d78e5809a59d\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/27/13/3fe67fa7ea7be444b831d117220b3b586b872c9acd4df480d0\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27085 sha256=e405160ff112976c4e3a69043099966b9c07753d20c114c645c8faa1c94c7ae6\n",
            "  Stored in directory: /root/.cache/pip/wheels/70/e1/52/5b14d250ba868768823940c3229e9950d201a26d0bd3ee8655\n",
            "  Building wheel for phik (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for phik: filename=phik-0.11.2-py3-none-any.whl size=1107412 sha256=d2039d2b64797738170bae515edee7df6076506db3758dc57bda5a1c7bd9973b\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/98/a3/b654f24edcdcdb87d1f70d65a506fcfdf15289db129c594bcd\n",
            "  Building wheel for bottleneck (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for bottleneck: filename=Bottleneck-1.3.2-cp37-cp37m-linux_x86_64.whl size=334941 sha256=1508db422781199b3fec59b355a1de8340ca7cdedaa72a82025d29fd839eb5f8\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/85/9c/a325c89ff0498660ef8a335fb4b3912939c273ea4f094af29f\n",
            "  Building wheel for imagehash (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for imagehash: filename=ImageHash-4.2.1-py2.py3-none-any.whl size=295198 sha256=ec2e067bf035ac420a00c43b55904166206c5ddb8f095ef63fa3a9bda6da76a3\n",
            "  Stored in directory: /root/.cache/pip/wheels/4c/d5/59/5e3e297533ddb09407769762985d134135064c6831e29a914e\n",
            "  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158155 sha256=df8973f9a7194e291e79cd5900d4a071908feef2156634adcced8e0a9ce59be0\n",
            "  Stored in directory: /root/.cache/pip/wheels/be/5d/0a/9e13f53f4f5dfb67cd8d245bb7cdffe12f135846f491a283e3\n",
            "  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for databricks-cli: filename=databricks_cli-0.14.3-py3-none-any.whl size=100555 sha256=f69d6dc04531f8fa0831e1feef06b4a80a4cf9ca900d0517554e8b5c7d5225e5\n",
            "  Stored in directory: /root/.cache/pip/wheels/3b/60/14/6930445b08959fbdf4e3029bac7e1f2cccb2e94df8afa00b29\n",
            "  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.18.2-py3-none-any.whl size=17399 sha256=bbc86a1e976f7323022ed512e5e821d27975234518e2550d63b16ce6818d2397\n",
            "  Stored in directory: /root/.cache/pip/wheels/6a/1e/1c/c765920cb92b2f0343d2dd8b481a407cee2823f9b4bbd2e52a\n",
            "  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyLDAvis: filename=pyLDAvis-3.2.2-py2.py3-none-any.whl size=135593 sha256=2ff77bdb7bdf71f50b9ccb3ec714fa80ded694d9658c572cf6c24ffde3f6e83d\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/b1/9b/560ac1931796b7303f7b517b949d2d31a4fbc512aad3b9f284\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491059 sha256=0837deefc1f6f8e0fe3e7a57a61f90ec1c89fdefc045f005f05bb285b4c4ba44\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-0.9.0-py3-none-any.whl size=122560 sha256=397f06c2607edb780d6c9898666c7d868214f5e2c55dd502f16fef0913c4f302\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/5f/59/5984a6116a4d19aee28d8ebeffd431364ce1cf21eb73a6ad34\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.1-py3-none-any.whl size=76566 sha256=f042e8f9eb868022e5f8a926b96a6e3de14b594f4de066f13c439057ae54b89f\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/e7/bb/347dc0e510803d7116a13d592b10cc68262da56a8eec4dd72f\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.4-py3-none-any.whl size=52359 sha256=3c9968a59d1f28a96730ffb5798d0c38625fa050206e16e9119a7a5173e28c2f\n",
            "  Stored in directory: /root/.cache/pip/wheels/d0/5b/62/3401692ddad12324249c774c4b15ccb046946021e2b581c043\n",
            "Successfully built cufflinks htmlmin phik bottleneck imagehash alembic databricks-cli prometheus-flask-exporter pyLDAvis future pyod umap-learn pynndescent\n",
            "Installing collected packages: numpy, scipy, Werkzeug, tangled-up-in-unicode, smmap, PyWavelets, murmurhash, multimethod, itsdangerous, greenlet, cymem, bottleneck, wasabi, visions, srsly, sqlalchemy, seaborn, regex, python-editor, preshed, plac, patsy, Mako, imagehash, gitdb, Flask, catalogue, blis, thinc, statsmodels, sqlparse, smart-open, querystring-parser, pynndescent, prometheus-flask-exporter, phik, numexpr, nltk, missingno, htmlmin, gunicorn, gitpython, future, funcy, docker, databricks-cli, colorlover, alembic, yellowbrick, wordcloud, umap-learn, textblob, spacy, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, kmodes, imbalanced-learn, gensim, cufflinks, Boruta, pycaret\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.1\n",
            "    Uninstalling numpy-1.21.1:\n",
            "      Successfully uninstalled numpy-1.21.1\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.0\n",
            "    Uninstalling scipy-1.7.0:\n",
            "      Successfully uninstalled scipy-1.7.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "dask-cudf 21.6.1+2.g101fc0fda4 requires cupy-cuda112, which is not installed.\n",
            "cuspatial 21.6.0 requires cython, which is not installed.\n",
            "cuml 21.6.2 requires cython, which is not installed.\n",
            "cugraph 21.6.0+0.gf9ffd2de.dirty requires cython, which is not installed.\n",
            "cudf 21.6.1+2.g101fc0fda4 requires cupy-cuda110, which is not installed.\n",
            "cudf 21.6.1+2.g101fc0fda4 requires Cython<0.30,>=0.29, which is not installed.\n",
            "cudf-kafka 21.6.1+2.g101fc0fda4 requires cython, which is not installed.\u001b[0m\n",
            "Successfully installed Boruta-0.3 Flask-2.0.1 Mako-1.1.4 PyWavelets-1.1.1 Werkzeug-2.0.1 alembic-1.4.1 blis-0.7.4 bottleneck-1.3.2 catalogue-1.0.0 colorlover-0.3.0 cufflinks-0.17.3 cymem-2.0.5 databricks-cli-0.14.3 docker-5.0.0 funcy-1.16 future-0.18.2 gensim-3.8.3 gitdb-4.0.7 gitpython-3.1.18 greenlet-1.1.0 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 itsdangerous-2.0.1 kmodes-0.11.0 missingno-0.5.0 mlflow-1.19.0 mlxtend-0.18.0 multimethod-1.4 murmurhash-1.0.5 nltk-3.6.2 numexpr-2.7.3 numpy-1.19.5 pandas-profiling-3.0.0 patsy-0.5.1 phik-0.11.2 plac-1.1.3 preshed-3.0.5 prometheus-flask-exporter-0.18.2 pyLDAvis-3.2.2 pycaret-2.3.2 pynndescent-0.5.4 pyod-0.9.0 python-editor-1.0.4 querystring-parser-1.2.4 regex-2021.7.6 scikit-plot-0.3.7 scipy-1.5.4 seaborn-0.11.1 smart-open-5.1.0 smmap-4.0.0 spacy-2.3.7 sqlalchemy-1.4.21 sqlparse-0.4.1 srsly-1.0.5 statsmodels-0.12.2 tangled-up-in-unicode-0.1.0 textblob-0.15.3 thinc-7.4.5 umap-learn-0.5.1 visions-0.7.1 wasabi-0.8.2 wordcloud-1.8.1 yellowbrick-1.3.post1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "bottleneck",
                  "numpy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BkiJPFlTCVV"
      },
      "source": [
        "**Important**: We need to restart Google's runtime for the new packages to be properly linked after installation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHXt9D2dymcN"
      },
      "source": [
        "import os\n",
        "os._exit(00)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hwt0zRfC8F7X"
      },
      "source": [
        "Now, we import the packages. Note that we are not importing some of them because:  \n",
        "1) We will not use them.  \n",
        "2) They give conflicts with the GPU configuration (I don't know why)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "OnOTISokDy1s"
      },
      "source": [
        "%matplotlib inline\n",
        "# Static plots\n",
        "#%matplotlib ipympl\n",
        "# Interactive plots\n",
        "import copy\n",
        "import numpy as np\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as mcolors\n",
        "import matplotlib.patheffects as mpe\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "# from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
        "import mpl_scatter_density\n",
        "from astropy.table import Table, join\n",
        "from astropy import units as u\n",
        "from astropy.coordinates import SkyCoord\n",
        "from astropy.visualization import LogStretch\n",
        "from astropy.visualization.mpl_normalize import ImageNormalize\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import plot_confusion_matrix, classification_report\n",
        "import ppscore as pps\n",
        "import seaborn as sns\n",
        "import cmasher as cmr\n",
        "import colorcet as cc\n",
        "import getpass\n",
        "# import pacmap\n",
        "# import umap\n",
        "# import trimap\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn_som import som\n",
        "import pandas as pd\n",
        "import re\n",
        "import shap\n",
        "# from BorutaShap import BorutaShap\n",
        "import pycaret.regression as pyr\n",
        "# import missingno as msno\n",
        "import os\n",
        "import subprocess"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhYCFSzVDy1y"
      },
      "source": [
        "Create class to normalize asymmetric colorscales  \n",
        "(from [http://chris35wills.github.io/matplotlib_diverging_colorbar/](http://chris35wills.github.io/matplotlib_diverging_colorbar/))."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "id": "5MwPV3WlDy1z"
      },
      "source": [
        "class MidpointNormalize(mcolors.Normalize):\n",
        "    \"\"\"\n",
        "    Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value)\n",
        "\n",
        "    e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100))\n",
        "    \"\"\"\n",
        "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
        "        self.midpoint = midpoint\n",
        "        mcolors.Normalize.__init__(self, vmin, vmax, clip)\n",
        "\n",
        "    def __call__(self, value, clip=None):\n",
        "        # I'm ignoring masked values and all kinds of edge cases to make a\n",
        "        # simple example...\n",
        "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
        "        return np.ma.masked_array(np.interp(value, x, y), np.isnan(value))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIJ3TuFQDy1z"
      },
      "source": [
        "For future plots, we can configure their aspect too."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "id": "kTSOZn0jDy10"
      },
      "source": [
        "cmap           = cm.get_cmap('inferno')\n",
        "cmap_alt       = cm.get_cmap('Dark2')\n",
        "cmap_alt_2     = cm.get_cmap('Greys_r')  # Shades of grey\n",
        "cmap_alt_3     = cm.get_cmap('coolwarm')  # blue to red\n",
        "dark_color     = cmap(0.4)  # RGBA color code\n",
        "light_color    = cmap(0.6)  # RGBA color code\n",
        "colors         = [cmap(0.15), cmap(0.30), cmap(0.45), cmap(0.60), cmap(0.75), cmap(0.90)]\n",
        "colors_3       = [cmap(0.25), cmap(0.50), cmap(0.75)]\n",
        "colors_3_alt   = [cmap_alt(0.25), cmap_alt(0.50), cmap_alt(0.75)]\n",
        "colors_3_alt_2 = [cmap_alt_2(0.25), cmap_alt_2(0.50), cmap_alt_2(0.75)]\n",
        "colors_8       = [cmap(0.12), cmap(0.24), cmap(0.35), cmap(0.48), cmap(0.6), cmap(0.72), cmap(0.84), cmap(0.96)]\n",
        "colors_8_alt   = [cmap_alt(0.12), cmap_alt(0.24), cmap_alt(0.35), cmap_alt(0.48), cmap_alt(0.6), cmap_alt(0.72),\\\n",
        "                  cmap_alt(0.84), cmap_alt(0.96)]\n",
        "colors_8_alt_2 = [cmap_alt_2(0.06), cmap_alt_2(0.18), cmap_alt_2(0.29), cmap_alt_2(0.42), cmap_alt_2(0.54),\\\n",
        "                  cmap_alt_2(0.66), cmap_alt_2(0.78), cmap_alt_2(0.90)]\n",
        "colors_8_alt_3 = [cmap_alt_3(0.06), cmap_alt_3(0.18), cmap_alt_3(0.29), cmap_alt_3(0.42), cmap_alt_3(0.54),\\\n",
        "                  cmap_alt_3(0.66), cmap_alt_3(0.78), cmap_alt_3(0.90)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "id": "aHhZxEwfDy10"
      },
      "source": [
        "pe1            = [mpe.Stroke(linewidth=5.0, foreground='black'),\n",
        "                  mpe.Stroke(foreground='white', alpha=1),\n",
        "                  mpe.Normal()]\n",
        "pe2            = [mpe.Stroke(linewidth=3.0, foreground='white'),\n",
        "                  mpe.Stroke(foreground='white', alpha=1),\n",
        "                  mpe.Normal()]\n",
        "\n",
        "alp_str        = r'$\\alpha$'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLa9NUQSkone"
      },
      "source": [
        "Methods to deal with loaded Pycaret models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSiHtjQRktPP"
      },
      "source": [
        "def get_final_column_names(pycaret_pipeline, sample_df):\n",
        "    for (name, method) in pycaret_pipeline.named_steps.items():\n",
        "        if method != 'passthrough' and name != 'trained_model':\n",
        "            print(f'Running {name}')\n",
        "            sample_df = method.transform(sample_df)\n",
        "    return sample_df.columns.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1TK7wtekvlx"
      },
      "source": [
        "def get_feature_importances_df(pycaret_pipeline, sample_df, n = 10):\n",
        "    \n",
        "    final_cols = get_final_column_names(pycaret_pipeline, sample_df)\n",
        "    \n",
        "    try:\n",
        "        variables = pycaret_pipeline[\"trained_model\"].feature_importances_\n",
        "        \n",
        "    except:\n",
        "        variables = np.mean([\n",
        "                        tree.feature_importances_ for tree in pycaret_pipeline[\"trained_model\"].estimators_ \n",
        "            if hasattr(tree, 'feature_importances_')\n",
        "                        ], axis=0)\n",
        "    \n",
        "    coef_df = pd.DataFrame({'Feature': final_cols, 'Importance': variables})\n",
        "    sorted_df = (\n",
        "        coef_df.sort_values(by='Importance', ascending=False)\n",
        "        .head(n)\n",
        "        .sort_values(by='Importance', ascending=True).reset_index(drop=True)\n",
        "    )\n",
        "    return sorted_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "da1mxIOzlLiY"
      },
      "source": [
        "Define specific metric for redshift values:  \n",
        "\n",
        "$$\n",
        "\\Delta z = \\mathrm{median}\\left(\\frac{|(z_{True} - z_{Predict})|}{(1 + z_{True})}\\right)\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3wpCz7wlDOQ"
      },
      "source": [
        "def delta_z(y, y_pred, **kwargs):\n",
        "    num = np.abs(y - y_pred)\n",
        "    den = 1 + y\n",
        "    return np.nanmedian(num / den)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bueyzh6Dy14"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIGZE8r3Dy14"
      },
      "source": [
        "## Reading data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unq7ykZVMIF2"
      },
      "source": [
        "First, let's download the fits file with the data.\n",
        "\n",
        "If we want to use data you have on your GDrive foldes, you can follow the steps of https://medium.com/@acpanjan/download-google-drive-files-using-wget-3c2c025a8b99"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n46l1l-RMLrQ"
      },
      "source": [
        "# !wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Pr_slP0EWMSbx0nTc4ttohnKPJGzmiqU' -O SDSSQ_DR16_only_pos_mags.fits"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "P2Q5EbdNDy15"
      },
      "source": [
        "file_name = 'SDSSQ_DR16_only_pos_mags.fits'  # 4743 objects"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmp1AymrPyqm"
      },
      "source": [
        "if not os.path.exists(file_name):\n",
        "    subprocess.run(['wget', '--no-check-certificate',\\\n",
        "                    'https://docs.google.com/uc?export=download&id=1Pr_slP0EWMSbx0nTc4ttohnKPJGzmiqU',\\\n",
        "                    '-O', file_name], check=True, text=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "Dem1vjWDDy14"
      },
      "source": [
        "machine  = getpass.getuser()\n",
        "cat_path = ''  # relative path to the same directory"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWibBIgdDy15"
      },
      "source": [
        "First, open the file as astropy Table to modify (and standardise)  \n",
        "the units of fluxes and/or magnitudes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "sJAXIlCHDy15"
      },
      "source": [
        "large_catalog_full_tab = Table.read(cat_path + file_name, format='fits')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRuBq1ElDy16",
        "outputId": "23dd0ddd-06fb-4367-a000-cec8cb1d80aa"
      },
      "source": [
        "large_catalog_full_tab.info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Table length=4743>\n",
              "   name    dtype \n",
              "--------- -------\n",
              "SDSS_NAME bytes18\n",
              "       RA float64\n",
              "      DEC float64\n",
              "        Z float64\n",
              " PSFMAG_u float32\n",
              " PSFMAG_g float32\n",
              " PSFMAG_r float32\n",
              " PSFMAG_i float32\n",
              " PSFMAG_z float32\n",
              "      FUV float64\n",
              "      NUV float64\n",
              "   W1_MAG float32\n",
              "   W2_MAG float32\n",
              "     JMAG float64\n",
              "     HMAG float64\n",
              "     KMAG float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "NBXAhuSPDy16"
      },
      "source": [
        "zero_point_star_equiv  = u.zero_point_flux(3631.1 * u.Jy)  # zero point (AB) to Jansky"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "nos3rVCBDy17"
      },
      "source": [
        "nmgy_cols = ['FUV', 'NUV']\n",
        "\n",
        "for col in nmgy_cols:\n",
        "    large_catalog_full_tab[col].unit        = u.nanomaggy\n",
        "    large_catalog_full_tab[col + '_MAG'] = u.Magnitude(large_catalog_full_tab[col].to(u.AB, zero_point_star_equiv))\n",
        "    # large_catalog_full_tab[col] = large_catalog_full_tab[col].to(u.mJy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "EzP08y3IDy17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "e84167e2-519e-4331-c49b-8c844b0f2a61"
      },
      "source": [
        "fig             = plt.figure(figsize=(7,5))\n",
        "ax1             = fig.add_subplot(111, xscale='linear', yscale='log')\n",
        "\n",
        "binss = np.logspace(np.nanmin(np.log10(large_catalog_full_tab['NUV_MAG'])),\\\n",
        "                    np.nanmax(np.log10(large_catalog_full_tab['NUV_MAG'])), 30)\n",
        "ax1.hist(large_catalog_full_tab['NUV_MAG'], bins=binss, fc='Gray', ec='k', histtype='stepfilled', alpha=0.95, lw=3.5);\n",
        "\n",
        "ax1.tick_params(which='both', top=True, right=True, direction='in')\n",
        "ax1.tick_params(axis='both', which='major', labelsize=16)\n",
        "ax1.tick_params(which='major', length=8, width=1.5)\n",
        "ax1.tick_params(which='minor', length=4, width=1.5)\n",
        "ax1.set_xlabel('$m_{\\mathrm{NUV}}$', size=20)\n",
        "ax1.set_ylabel('Frequency', size=20)\n",
        "plt.setp(ax1.spines.values(), linewidth=3.5)\n",
        "plt.setp(ax1.spines.values(), linewidth=3.5)\n",
        "ax1.invert_xaxis()\n",
        "#ax1.set_xlim(left=0.2, right=2.4)\n",
        "#ax1.set_aspect('equal', 'box')\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFgCAYAAABEyiulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5gkdXno8e+LuouILhgvo9wRNUJWV8hJTExkFIPrRpFj1GgQWW8hN55oomclagTUx2xMDEcTo0HNxAueBKNCDBJF2NVF8RbHGfGKwqLiKAbY5TrL5T1/VA00vd3T3TXd013T38/zzDMzXb+qfn9T0/32r+p3icxEkiTVyx7DDkCSJPXOBC5JUg2ZwCVJqqF7DzuA5RYRXwMOKX+9Ebh8iOFIktTOYcDe5c9XZObjGzfGuHVii4jrgTXDjkOSpB7syMx9Gh/wErokSTU0dpfQKS6brwFYs2YN69atW9LBpqenAZZ8nLqy/tYfrL/1H7/6L0fdp6en2bFjx8KvNzZvH8cEfjmwHxR/+C1btizpYJOTkwBLPk5dWf9JwPpb/y1DjWNYxrn+y1H3yclJtm7duvDrbv21vIQuSVINmcAlSaqhcbyEfpfp6emxvgQkSRo9C3lp4T57O7bAJUmqobFugfejE5skSf20kJeaOrHtZhwnctkCHA1w9NFHm8AlSSOpKYFvzczJxu1eQpckqYZM4JIk1ZAJXJKkGhrrTmwOI5NUJ5s2bWJ2drbSvmvXrmXz5s19jkiD0O0wsrFO4JJUJ7Ozs2zbtm3YYWhEjHUCdxiZpGHqtUW9kLx37drV0/OsWrWqp/Iarm6HkY11ApekYVpqi/qggw5adPvc3Bzz8/OVj6/RZgKXpCHrtUUNRfLeuHHjomWmpqbYvn17xag06kzgkjQCVq9ezcTERNfleymrlckELkkjYGJiomOLWmo01gncYWSSpFHjamSSJK1gY90CdxiZJGnUOIxMkpZZr+O6Z2ZmBhiNVjoTuCT1iTOlaTmZwCWpz6qM65Z6ZQKXpAFwXLcGzQQuSQPguG4NmsPIJEmqobFugTuRiyRp1DiRiyRJK9hYt8CdyEWSNGqcyEWSdJeZmRk2bNjQVdm1a9eyefPmAUekpTKBS9IY2Llzp5PMrDAmcEla4XqZWGbVqlUDjET9ZAKXpBWql8lh5ubmmJ+fH2A06jcTuCStUOvXr++67NTUFNu3bx9gNOo3h5FJklRDtsAlqYVelwYFlwfV8jKBS1ILLg2qUWcCl6RFuDSoRtVYJ3DnQpfUjV6XBgWXB1V13c6FPtYJXJK64dKgGkVjncCdC12SNGq6nQvdYWSSJNWQCVySpBoygUuSVEMmcEmSasgELklSDY11L3RJ46PXqVGdFlWjzgQuaSw4NapWGhO4pLHi1KhaKUzgksZOr1OjOi2qRpEJXNLYcWpUrQT2QpckqYbGugXuamSSpFHT7WpktsAlSaqhsW6BuxqZJGnUuBqZJEkrmAlckqQaGutL6JKk3c3MzLBhw4auyq5du5bNmzcPOCK1YgKXJN3Dzp07nXa2BkzgkqS79DLV7KpVqwYYiToxgUuSepoudm5ujvn5+QFGo26YwCXVSq/Lgi5wedDFrV+/vuuyU1NTbN++fYDRqBsmcEm14rKgUsEELqmWXBZU484ELqm2el0WdIHLg2olMIFLqi2XBdU4cyY2SZJqyAQuSVINmcAlSaohE7gkSTVkApckqYZM4JIk1dBYDyObnp5mcnISgC1btgw1FkmSgLvy0vT09KLlbIFLklRDY90CX7dunS1vSdJIWchLk5OTbN26tW05W+CSJNXQWLfAJQ1XlaVBXRZUKpjAJQ2NS4NK1ZnAJQ2dS4NKvTOBSxoJVZYGdVlQjTMTuKSR4NKgUm/shS5JUg2ZwCVJqiETuCRJNWQClySphkzgkiTVkL3QJS1ZlRnVwFnVpKUwgUtaMmdUk5afCVxS3zijmrR8TOCS+qrKjGrgrGp1NTMzw4YNG7oqu3btWjZv3jzgiMaHCVxSXzmj2njZuXOnt0+GxAQuSaqkl1smq1atGmAk48kELknqSS+3O+bm5pifnx9gNOPLBC5J6sn69eu7Ljs1NcX27dsHGM34MoFLuovjuaX6MIFLuovjuaX6MIFL2o3juaXRZwKXVqB+XAp3PLc02kzg0grUj0vhjueWRlvPCTwi7pOZtw0iGEn95aVwaeWq0gL/cUT8M3BWZl7e74Ak9ZeXwqWVqUoC3wN4NfCqiLgIeBfw8cy8o6+RVRAR+wIfAB4F3AL8FPgjP2honHkpXFqZ9qiwz8OBFwKfA44B/g34UUS8OSIO7l9olSRwZmY+KjMfB3wCeM+QY5Ikqe96TuCZuSszz87MSeAXgTMpWvKnApdHxPkR8ayI6OrYEbF/RLwjIr4QETdHRLb7IBARB0TERyJiR0TsjIiPRsSBDbFdn5kXNuzyeaDlsSRJqrMqLfC7ZOZ3M/PPgf24u1W+HvgocFVEnBYRD+9wmMOA5wHXlfu3FBF7ARdRfGg4CTgReCRwcUTcr81urwDO7b5GkiTVw5IS+ILM3AX8J/Ax4GogKC61/yVwRUScGRGr2+z+2cx8aGZuAM5Z5GleDhwKHJ+ZH8/Mc4HjgIOAk5sLR8QbyvKnVqyWJEkja8njwCPiCRQJ9HnAnsBO4O3A+4AjgT8DTgFWA3/YvH9m3tnlUx0HXNrYIS0zr4iIS4BnAW9riOl1wAbg2My8ud0Bp6enmZycbLlty5YtXYYlSVJ17fLQ9PT0ovtVSuARcX+KS9gnA79E0eL+GvBO4OzMvKUsOhMRHwAuAJ5DiwTegyNofTn8MuC5DbG9gbuT944lPJ80VFVnUwMXF5HGQZWJXN5L0dreC5inGLb1zsz8UqvymXlHRGwBnrKEOAEeSHGfvNm1wL5lbEcApwHfB7ZGBMDtmfnLrQ64bt06W9oaqKUkYRcVkcZDuzw0OTnJ1q1b2+5XpQX+YooE+S7gnzPz2m7iA86o8Fw9yczLKK4GSCOhH1OaOpuapFaqJPD1mfmpXnbIzEuASyo8V6PrKFvaTdq1zKWR0Y8kfNBBB1XazxnVpJWp5wTea/Luo8so7oM3Oxz45jLHIvWs6pSmUCTh9evX9zkiSXVW5R74McAJwOsy8+oW2x8OvAl4f2ZuWXKEdzsP+JuIODQzf1A+18HAE4HXVDlgYy9074Vr0JzSVFI3FvLSIHqhnwL8YqvkDZCZV0fErwFrKO59dxQRzyl/PKr8/vSIuAa4JjMX7uCfBfwJcG45TCyBNwI/BN5doR6SJNVWlQR+JHBhhzLbgGN7OGbzBC7vLL9vBSYBMvOmiHgK8HcUPd8D+Azwisy8sYfnuou90CVJo2YhLw2iF/pDKGZbW8xPy3Jdycyueo5n5lXA73R7XEmSVqoqU6nuAA7oUOYA4KYKx5YkSV2oksC/BBwfES2705ad2I4vy0mSpAGoksDfAdwf+FxEHLewSElErI6IZwGfBfammA99pC30Qm83D60kScttIS/1vRd6Zn4qIt4IvJ5i9bGMiIVJVqL8emNmXtB72JIkqRuVFjPJzDeUq4CdAvwqsA/FnOSXAu/IzE/3L8TBsRe6JGnUDLIXOnDXjGzDmpVNkqSxVuUeuCRJGrLKLXCAiNiL4t73vVptL8dtS5KkPquUwCPiRGAT8JhFimXV4y8X50KXJI2agc2FHhEbgfcBdwCfo5iL/PZejyNJkqqr0kJ+FcX627+Rmd/qczzLyl7okqRR020v9Cqd2A4Dzql78pYkqc6qJPBrgfl+ByJJkrpXJYF/ApiMiK5WEJMkSf1XJYGfCqwG3hURe/c5HkmS1IUqndjOAW4GXgb8XkR8D7i+RbnMzGOWEpw0bJs2bWJ2drby/jMzM32MRpLuViWBTzb8fD9gXZtyWeHYy8px4OpkdnaWbdu2DTsMSWNkYOPAM9PpVzV2du3aNewQJOkeRnqmtEFzHLh6sXr1aiYmJirvv5R9JY2Pga9GJo2biYkJNm7cOOwwJAmouBpZROwREadExKURsSMibm/Y9viIeGdEPKp/YUqSpEY9J/CIWAV8GjgTeARwA9A4JvwK4CXACf0IUJIk7a5KC/zVwJOB04GHAu9p3JiZ1wOfBZ625OgkSVJLVRL4CcAlmXlGZt5J6+FiVwAHLikySZLUVpUEfghwaYcy1wIPrHBsSZLUhSq90G8F9ulQ5kBaz842UpzIRZI0arqdyKVKC3waOLbszLabiFhDcf/7SxWOLUmSulAlgf8TcADwoYh4QOOGiNgHmAL2Bd615OgGbGEiF1vfkqRRsZCX1q1rN1N5ocpUqh+OiN8CNgLHAdcBRMRXgCMoVir7h8w8v9djS5Kk7lSayCUzX0Ix1vubwIMpxoEfCVwOvDQzT+lbhJIkaTeVp1LNzClgKiLuS3HJfEdm3tSvwCRJUntLngs9M28BbulDLFLfuZ63pJXKxUy0ormet6SVqucEHhE/6LJoZuYjej2+NAiu5y1ppanSAt+D1tOn7gOsKX++GritalDSILiet6SVpMowsoPbbYuIw4C3A/fDxUw0YlzPW9JK0td74Jl5eUQ8G/gG8Abg1H4ev9+cSlWSNGoGOZXqojLzVor1wl/Q72NLkqTCoHqh3w6M/A3DhalUNbocBiZp3CzkpcnJSbZu3dq2XN8TeEQ8CPjfwA/7fWyNH4eBSVJrVYaR/eUixzoAeBZFb/SRvv+tenEYmCTdU5UW+Gkdtu8E3pSZf13h2FJbDgOTpLtVSeBPbvP4nRQrk307M2+vHpLUmsPAJOluVcaBt7+jLkmSlkXfh5FJkqTBq9KJ7cCqT5aZV1XdV5Ik3a3KPfAraT0XeidZ8fkkSVKTKgn1/cDBwJOAHcA0MEcxccs6iiFkWykSvSRJGoAqCfwtwBeAvwNOz8ydCxsi4gHA6cCLgJMz87t9iVKSVHszMzNs2LChY7m1a9eyefPmZYio3qok8L8CZjPzz5s3lMn8lRFxVFnu2UuMT5K0QuzcudOZFfuoSgJ/EvCuDmW2ASdXOPaycjUySVoe3c6muGrVqgFHMvq6XY2sSgJfTeeFSh5WlpMkjbFuZ0Ccm5tjfn5+wNGsLFUS+NeA50fE32fm15o3lpfPfxf4ylKDGzRXI5OkwVq/fn1X5aampti+ffuAo6mHQa5GdjpwAXBpRHwI+CzwU+ChwNHA71FMEHN6hWNLkqQuVJlK9cKIeD7wbmAjcFLD5qCYD/33M/MzfYlQkiTtptLEKpn5kYj4JMXSoUdSjP3eAfw3cG5m3tS/ECVJUrPKM6OVSfrs8kuSJC2jJS9mEhH7RsQB/QhGkiR1p1ICj4i9I+JvI2IO+DlwRcO2X42I8yPiyH4FKUmS7qnnBB4RayimUn0lcDXwLYrOawtmgd8EXtCPACVJ0u6qtMBfCxwBbMzMI4FzGjdm5s0Ui5kcs/TwJElSK1US+LOB/8rM9y9SZjuwX7WQJElSJ1US+P7ATIcyN1IMLZMkSQNQJYHfADykQ5lDKDq3SZKkAaiSwL8MPCMi7t9qY0Q8DNhAsSKZJEkagCoJ/P8CvwCcHxGPadxQ/n4OsCfw9qWHJ0mSWqkyF/p/RcTpwBuAbwC3AUTEz4F9KYaUbcrMz/czUEmSdLdKE7lk5ukUw8TOo1i85A4ggfOBp2bmW/sWoSRJ2k3PLfCIeBKwMzMvBi7uf0iSJKmTKi3wi4Hf73cgkiSpe1VWI/s5cEu/AxmG6elpJicnAdiyZctQY5EkCbgrL01PTy9arkoLfAvw6xX2kyRJfVKlBf464IsR8UbgjMy8rc8xLZt169bZ8pYkjZSFvDQ5OcnWrVvblquSwE+lGD72F8BLI+LrwBxFL/RGmZkvrXB8SZLUQZUEvrHh54nyq5UETOCSJA1AlQR+SN+jkCRJPekqgUfEi4DpzJzJzO0DjkmSJHXQbS/0KeD4xgci4qSIuKjvEUmSpI4qTaVaOhg4uk9xSJKkHiwlgUuSpCExgUuSVEMmcEmSaqiXBN48UYskSRqSXsaBnxYRpzU/GBF3tCmfmVllnLkkSeqglwQbPR671/KSJDEzM8OGDRs6llu7di2bN29ehohGU1cJPDO9Vy5JWhY7d+5k27Ztww5j5HmJW5I0Mnbt2tVVuVWrVg04ktFnApckDd3ERLt1se5pbm6O+fn5AUdTDyZwSdLQrV+/vqtyU1NTbN/ukhzgOHBJkmrJBC5JUg2ZwCVJqiETuCRJNWQClySphkzgkiTVkAlckqQaMoFLklRDJnBJkmrIBC5JUg05laoGZtOmTczOzi7pGDMzM32KRpJWlhWXwCPi9cCJwGHAszPz40MOaWzNzs66JKAkDciKS+DAp4EPAe8bdiAqdLs8oCSpe0NP4BGxP7AJ+GXgccB9gUMy88oWZQ8A/g74LSCAC4FXZOZVC2Uy89Ky7MBjV/dWr17d9XKB7Sx1f0laSYaewCkudT8P+CrwOeDYVoUiYi/gImAeOAlI4E3AxRHx2My8aXnCVRUTExNs3Lhx2GFI0ooxCgn8s5n5UICIeBltEjjwcuBQ4NGZeXlZfgb4HnAy8LZliFWSpJEw9ASemXd2WfQ44NKF5F3ue0VEXAI8iwoJfHp6msnJyZbbtmzZ0uvhJEnqWbs8ND09veh+dRoHfgTwjRaPXwYcvsyxSJI0VENvgffggcB1LR6/Fth34ZeIOA14GfBg4Jci4u+BJ2Tmj5p3XLdunS1tSdJQtctDk5OTbN26te1+dWqBdyUzT8vM/TNzdWY+qPx5t+QtSVKd1SmBX0dDS7tBu5a5JEkrVp0uoV9GcR+82eHAN6scsLETm5fSJUmjYCEvraRObOcBT4iIQxceiIiDgSeW2yRJGhsj0QKPiOeUPx5Vfn96RFwDXJOZC3fwzwL+BDg3Il5HMZHLG4EfAu+u8rx2YpMkjZqFvNSpE9tIJHDgnKbf31l+3wpMAmTmTRHxFIqpVD9AMZXqZyimUr1xmeKUJGkkjEQCz8yuJi4v5zz/nQGHI0nSyKvTPXBJklQaiRb4sNgLXZI0alZiL3RJklQa6xa4vdAlSaOm217otsAlSaohE7gkSTVkApckqYbG+h642tu0aROzs7NLOsbMzEyfopGk3c3MzLBhw4auyq5du5bNmzcPOKLlNdYJ3GFk7c3OzrJt27ZhhyFJbe3cuXNFvk91O4xsrBO4Otu1a9ewQ5Ck3fTy3rRq1aoBRjI8Y53AHUbWndWrVzMxMbGkYyx1f0mC3t5L5ubmmJ+fH2A0g1G3xUw0wiYmJti4ceOww5Ak1q9f33XZqakptm/fPsBohste6JIk1ZAJXJKkGvIS+grTj+Ff4BAwSRp1Y53AV+IwMod/SVK9OYxszDn8S5JWtrFO4Ct9GFk/hn+BQ8AkaTk5jEwO/5KkFcxe6JIk1ZAJXJKkGjKBS5JUQyZwSZJqyAQuSVINjXUv9JU4kYskqd66ncjFFrgkSTU01i3wlT6RiySpfrqdyMUWuCRJNWQClySphkzgkiTVkAlckqQaMoFLklRDJnBJkmrIBC5JUg2ZwCVJqqGxnsjFqVQlSaPGqVQlSVrBxroF7lSqkqRR41SqkiStYCZwSZJqyAQuSVINmcAlSaohE7gkSTVkApckqYZM4JIk1ZAJXJKkGjKBS5JUQyZwSZJqyAQuSVINjfVc6KNk06ZNzM7OLvk4MzMzfYhGkjTqxjqBj9JyorOzs2zbtm2oMUiShq/b5UTHOoGPol27dg07BElSDYx1Ah/V5URXr17NxMTEko/Tj2NIkpZXt8uJjnUCH1UTExNs3Lhx2GFIkkaYvdAlSaohE7gkSTVkAl+iycnJu3oMjqOzzjqLs846a9hhDI31t/7WfzzrPwrv/SZwSZJqyAQuSVINmcAlSaohE7gkSTVkApckqYYiM4cdw7KKiB8B+wGsWbOGdevWLel4C3PVLvU4s7Oz7NixgzvvvJM999yzNrOo/eQnPwHgYQ972JAjGQ7rb/3B+o9q/efm5rj11lvZY489WLNmDWvXru3bsfv13t/pOXbs2LHw648zc//G7eOYwK8H1gw7DkmSerAjM/dpfMBL6JIk1dA4zoV+BXBI+fONwOVDjEWSpHYOA/Yuf76ieePYXUKXJGkl8BK6JEk1ZAKXJKmGTOBARDwtIi6KiLmImI+IH0XEv0XE4U3lDoiIj0TEjojYGREfjYgDu3yOPSPirRHxk4i4JSK+EBFPGkyNehMRz4mIf4+I7WVs34mIt0TE/RvKTEVEtvn6dhfPcWWbfY8fbO06xtVN3Q9epO77LHb8cv89IuLU8m9wa0R8PSJ+Z7A1606X9T8mIj4YEd8vy3w/Iv4xIh7S5XOM5LkvY+tY/7LcvhHxnoj4eUTcFBEXRkRXY5JG/PzvHxHvKN+Pbi7Py8FNZU5b5P//1i6eY0ubfV8xqHp1q5v6l+Xa1b+rMWQR8fKI+HYU+eU7EfEHfYnfe+AQES8AjgS+CFwDHAi8BjgAWJuZ2yNiL+DrwDzwOiCBNwF7AY/NzJs6PMeHgN8GXg38APhj4OnAr2Xm9CDq1a2IuBS4CjgX+BHweOA04NvAr2fmnRHxCODBTbseDHwYeGtm/p8Oz3FlebzTmjZ9JzOvW1oNquuy7gdTdCB5C3Be0yG+nJl3dHiONwOvAl4LfBV4PvBy4BmZeX6/6lJFl/U/h6Ijzb9R/O8+Ejid4rXw2My8scNzXMkInnvouv4BfI7i//3VwHXAqcARwLrM/FGH5xjl8z8J/GsZ172AY4FDMvPKhjL7A/s37Xo/4ALgY5n5vA7PsQXYFzi5adOVmTm3hPCXrJv6l+USmALe3XSImcy8ucNzvLzc7y3AhcAxwF8Af5yZ/7ikCmSmXy2+gEdTJOk/L3//U+AO4LCGMocAtwN/1uFYjyuP9eKGx+4NfAc4bwTq+uAWj72ojPkpi+z3+rLMEV08x5XAB4dd1yp1p3jjTuBlFY7/EIpEd3rT45+hePHXof6tyjypLPOSup77Hur/rPL3JzeUWQNcC7y95ud/j4afX1bW8+Au9juxLPvbXZTdAmwbdl2XUv/y8TdVOP69gZ8B/9L0+PuAnwP3WUr8XkJv73/K77eX348DLs3Mu4adZeYVwCUUL/DFHAfcRvFJb2Hf24H/BzwtIlb3K+gqMvOaFg9/ufy+3yK7vgj4amZe1v+olscS6t6tpwGrgA82Pf5BYG1EHLL7Lsunm/ovw99oaLqs23HA1Zl5ccN+O4D/oPNrf9TP/50Vdz0J+CnwX30MZ9ktof7d+jWKK5fN5/8DwC8Av7GUg5vAG0TEvSJiVUQ8kuKSxxzFJWIoLpd9o8VulwGHt3i80RHAFbn7pZbLKF7ch1WPemCOLr9/q9XGiHgiRdz/0sMxn1neZ5qPiEtH4R5oG+3q/paIuD2KPhDndXkP9AiKFljzfAMLH3o6/e8Mw6Lnvocyjepy7mH3ui322j8wIvZusW1BHc//oiLiAODJwIfKhkg3Hl++bm6LiJmIeOkAQxyUPyz/f2+Oos/Ub3axzxHl9+b/n76cfxP4PX2R4sX2XeCxFJfQflZueyDFva9m11Lc31nMYvsubB8ZEbEfcAZwYWZ+pU2xF1FcVfhwm+3N/gM4haJFcgJwK/CxiHjhEsPtqzZ1n6f4QHcyxRvXq4C1wOcj4jEdDvlA4Posr5s1qO25Lzt4nUmR4D7exWFrce6hbf07vX4Xe/3X6vx36YUUuaPbD++fBV5BcSXjOcD3gPdExOsGE95AfBD4I+CpwO9TtJ4vKu+hL2bh/Db///Tl/I/jTGyLORF4AHAoxZv0pyPiN7KpQ8NKVrYmzqW4dfDiNmX2BJ4HfCIzf97NcTPzlKZjfAy4lKJjR/PlpaFoV/fM/AnQ2Gv0cxFxAcWn6NdSvKHVXpfn/t4UH9r2A57YTQusDuceuqu/gOLD+9cyc6abwpn5l00PnVv+D7w2Is7MDp0gR0Fmntjw6+ci4lyKVvWbWOJl8KWwBd4gM7+VmV/MzA9T9BTcm6I3OhSfoFp90m736bzRYvvC3Z/Ghioi7kvRWjoUeFq27117HLAPvV0+v4csem6fA+wfEUNfyqiHugOQmT8EtgH/q8OhrwP2KXsyN6rduY+IhVbXU4Hju30DbzZq5x461r/T63ex138tzn+3IuJXgF9kCa/90oeBPSmuZNVOZt4A/Cfdvf5h9/+fvpx/E3gbmXk9xX2rhfvTl3H3/YxGhwPf7HC4y4BDyqFozfvuYgTmY4+I+wAfAX4Z2JCZs4sUP4miB2W/hsAMdSxjj3Vv1in2y4DVwCOaHl+499Xpf2fgeqj/u4DfBZ6fmZ/p09MPfRxrF/Vf7LV/VYcW5Mif/x6dRHHr7Ow+HW/o53+Junn9w+7/P305/ybwNiLioRSfNL9fPnQe8ISIOLShzMHAE9l9bHCz/wDuAzy3Yd97U7wZfioz5/sWeAVly+pDwFMoWlaXLlL2oRT3Ms/OzNuW8JwL9b8qhzgWtJe6N+13IMWlsy91KHoBxRveCU2PvxD4RjmSYWi6rX9E/C3FMJsXZ2Y3970Xe86ROPdlLN3U/zxgv4g4umG/BwDPpPNrf6TPfy8iYhXFGPZPtum934sTgFuAXj4sj4zy/D+Dzq//L1A0dlqd/2spRjFV5j1w7ron99/ADLATeBTwSop7YX9bFjsL+BOK+zcLE7m8EfghDYP7I+IgiqR/RmaeAZCZX4uIfwXOLD/tXwH8IcU48uYTOwz/QPHh4s3ATRHxhIZtP2q6nHgCxYQHbS+hRcTtFOMeX1r+/gKK4TbnU/y9Hkoxkc2RwAv6WI8qOta9TF57ULwYr6GYI+BU4M5yv7s01z0zfxYRbwNOjYgbKP7PfpciYRw30Jp1p5v6bwL+jGLs6veaylyTmQsfcut27qG7//3zKM79ByOicSKXAP668WA1PP9ExHPKH48qvz89Iq6hOLdbG4o+g+LS72Kv/cuB7Zl5TPn7b1LchvwoxXwAayha8ccBr8kOE2Ath071j4hXUbzmLwauBg6i6CM1QdP7d3P9M/O2iHg98M6I+DHFRC5PAV4CnJKZu5YU/FIGka+UL2ATxUw81wM3U0yw8lve7LoAAAQlSURBVG6aBvRTzND27xRJ/gaKHrjNZQ6mSO6nNT1+X+BtFEPTbqXo8T457LqXsV1Zxtzqq7keXwdmOxwvgamG358AXEQxbvS28u98IcW9xpGve/li+zLFG/dt5Tk8G3h0p7qXj92LYva+7RQ92meA5wy77j3Uf8siZZrrWptz38v/PkXieh9Fq+lmiolYHlf3898Qc6uvLU3lzqWYH2NVh7/nlobfDwM+Cfy4rPuNwOeBFwy73t3Wn+JKyyUULenbyr/BecCvdKp/w+MnU4xumqfohf9H/YjdqVQlSaoh74FLklRDJnBJkmrIBC5JUg2ZwCVJqiETuCRJNWQClySphkzgkiTVkAlckqQaMoFLYyYinhwRGRF/ExFHRsTHI+LaiNgRER+LiImy3OERcXZE/Kzc9olyDnhJI8AELo2fI8vvj6JYEvUO4L3AVcDxwPsi4pkUCzXsTTH39XeB3wbev+zRSmrJqVSlMRMRHwJ+j2J+8mOzXNc7Iu5PkcTvT7Foy7Mz8wvltlUUy97uD+yVmbcOI3ZJd7MFLo2fhRb4xoXkDZCZN1AsxnAv4NULybvctotikZ8A7rd8oUpqxwQujZGIuB/FpfMfZOYFLYocRLHi1r+22XZDZv5Peayp8l7665ueY7J8/EHl71si4u9bxLIxIm5s+P28iPhMm7gfUx7z2C6rKq14JnBpvDyO4nV/YfOGiDgY2JdiOcTbmrbtTbE05HTTbrcCr46IB/chtvcCTy7jaPZSiuU4d4tbGlcmcGm8LFw+/2qLbUctsu3xFJfP/7vp8YspLru/vnmHCv6T4r78ixsfjIj7ACcC78vMO/vwPNKKYAKXxstCAv9Ki21HLbLt8eX35gR+J/Aa4A8i4hFLCSwzb6fo8b4xIhrfm54JPAj456UcX1ppTODSeDkS2AV8o8W2xVrgC4m/OYGTmecDlwBv7kN87wUOBJ7a8NhLgU9l5g/7cHxpxTCBS2MiIlYDhwOzZa/yZkcB2xc6qTU5ErgF+Fabw28CnhsRR7XZ3pXM/B6wFXhJGfPDgadRJHZJDUzg0vj4JeA+tGhhR8RBwC+02bYaeAwwk5l3tDpwZn4J+Hfgr1ts3gmsafH4PsCOFo+/Fzg+Ih4IbKToFX9uq+eVxtm9hx2ApOWRmV+l6IjWatv2RbbNUyT+Tv4C+Cawvunx7wAbIiLynjNHHVlua/YR4B3ACyla4u9v7hUvyRa4pD7JzMuBfwL+tGnTPwKHAu+IiMdFxKMj4pXAC4C3tjjOLcDZwGnAI/DyudSSCVxSP50B3N74QGb+AHgS8EjgUxRzrD8feG5mfrLNcd5DMSb985nZ7r67NNacC12SpBqyBS5JUg2ZwCVJqiETuCRJNWQClySphkzgkiTVkAlckqQaMoFLklRDJnBJkmro/wOwxsg3kdNaMQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "Q0nS0FAuI0l-",
        "outputId": "37174acc-68cb-4422-a3f2-5a2d23fc2197"
      },
      "source": [
        "fig             = plt.figure(figsize=(7,5))\n",
        "ax1             = fig.add_subplot(111, xscale='linear', yscale='log')\n",
        "\n",
        "used_col        = 'Z'\n",
        "\n",
        "bins_z = np.linspace(np.nanmin(large_catalog_full_tab[used_col]),\\\n",
        "                    np.nanmax(large_catalog_full_tab[used_col]), 30)\n",
        "ax1.hist(large_catalog_full_tab[used_col], bins=bins_z, fc='Gray', ec='k', histtype='stepfilled', alpha=0.95, lw=3.5);\n",
        "\n",
        "ax1.tick_params(which='both', top=True, right=True, direction='in')\n",
        "ax1.tick_params(axis='both', which='major', labelsize=16)\n",
        "ax1.tick_params(which='major', length=8, width=1.5)\n",
        "ax1.tick_params(which='minor', length=4, width=1.5)\n",
        "ax1.set_xlabel('$z$', size=20)\n",
        "ax1.set_ylabel('Frequency', size=20)\n",
        "plt.setp(ax1.spines.values(), linewidth=3.5)\n",
        "plt.setp(ax1.spines.values(), linewidth=3.5)\n",
        "#ax1.set_xlim(left=0.2, right=2.4)\n",
        "#ax1.set_aspect('equal', 'box')\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrXcOtm4Dy17"
      },
      "source": [
        "Mask invalid entries in X-ray fluxes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPfqUvRODy18"
      },
      "source": [
        "Many magnitudes in this catalogue are in the Vega system.  \n",
        "We will transform them into the AB system."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QM6c3gunDy18"
      },
      "source": [
        "For Vega WISE magnitudes, we have the following conversion to AB values.\n",
        "\n",
        "Conversion to the AB system (from [WISE documentation](https://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec4_4h.html),  \n",
        "and [Jarrett et al., 2017](https://ui.adsabs.harvard.edu/abs/2017ApJ...836..182J/abstract))\n",
        "\n",
        "($m_{\\mathrm{AB}} = m_{\\mathrm{Vega}} + \\Delta (m)$)\n",
        "\n",
        "|Band | magnitude offset (Δm)|\n",
        "|-----|---------------------:|\n",
        "|W1   |                 2.699|\n",
        "|W2   |                 3.339|\n",
        "|W3   |                 5.174|\n",
        "|W4   |                 6.620|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfRuC4WPDy18"
      },
      "source": [
        "With UKIDSS, we can use the information from [Hewett et al., 2006](https://ui.adsabs.harvard.edu/abs/2006MNRAS.367..454H/abstract)\n",
        "\n",
        "|Band | magnitude offset (Δm)|\n",
        "|-----|---------------------:|\n",
        "|J    |                 0.938|\n",
        "|H    |                 1.379|\n",
        "|K    |                 1.900|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwKcOu_VDy19"
      },
      "source": [
        "Convert `Vega` magnitudes to `AB`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "nuaQhSzjDy19"
      },
      "source": [
        "\n",
        "vega_cols     = ['W1_MAG', 'W2_MAG', 'JMAG', 'HMAG', 'KMAG']\n",
        "vega_shift    = {'W1_MAG': 2.699, 'W2_MAG': 3.339, 'JMAG': 0.938, 'HMAG': 1.379, 'KMAG': 1.900}\n",
        "\n",
        "\n",
        "for col in vega_cols:\n",
        "    large_catalog_full_tab[col] += vega_shift[col]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ljowmiv0Dy1-"
      },
      "source": [
        "Create `pandas` DataFrame from `astropy` table."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "3pmnj2kGDy1-"
      },
      "source": [
        "\n",
        "# large_catalog_full_df = large_catalog_full_tab.to_pandas(use_nullable_int=False)\n",
        "large_catalog_full_df = large_catalog_full_tab.to_pandas()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NugdiTGKDy1_"
      },
      "source": [
        "Create `pandas`DataFrames for redhisft and coordinates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "CrK9GGH5Dy1_"
      },
      "source": [
        "redshift_df           = large_catalog_full_df['Z']\n",
        "coord_ra_deg          = large_catalog_full_df['RA']\n",
        "coord_dec_deg         = large_catalog_full_df['DEC']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuWnUMiTDy1_"
      },
      "source": [
        "Create `pandas` Series with coordinates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "yuMbcM9PDy1_"
      },
      "source": [
        "# coords_arr            = pd.Series(SkyCoord(coord_ra_deg, coord_dec_deg, unit=u.degree), index=coord_ra_deg.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6Cig1-uDy2A"
      },
      "source": [
        "Calculate colours for several pairs of magnitudes in the sample."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "I_PkUsqpDy2A"
      },
      "source": [
        "# colours_df                   = pd.DataFrame()\n",
        "large_catalog_full_df['u_g']            = large_catalog_full_df['PSFMAG_u'] - large_catalog_full_df['PSFMAG_g']\n",
        "large_catalog_full_df['g_r']            = large_catalog_full_df['PSFMAG_g'] - large_catalog_full_df['PSFMAG_r']\n",
        "large_catalog_full_df['r_i']            = large_catalog_full_df['PSFMAG_r'] - large_catalog_full_df['PSFMAG_i']\n",
        "large_catalog_full_df['i_z']            = large_catalog_full_df['PSFMAG_i'] - large_catalog_full_df['PSFMAG_z']\n",
        "large_catalog_full_df['u_r']            = large_catalog_full_df['PSFMAG_i'] - large_catalog_full_df['PSFMAG_r']  # ~U-V\n",
        "large_catalog_full_df['g_i']            = large_catalog_full_df['PSFMAG_g'] - large_catalog_full_df['PSFMAG_i']\n",
        "large_catalog_full_df['w1_w2']          = large_catalog_full_df['W1_MAG']   - large_catalog_full_df['W2_MAG']\n",
        "large_catalog_full_df['J_H']            = large_catalog_full_df['JMAG']     - large_catalog_full_df['HMAG']\n",
        "large_catalog_full_df['H_K']            = large_catalog_full_df['HMAG']     - large_catalog_full_df['KMAG']\n",
        "large_catalog_full_df['FUV_NUV']        = large_catalog_full_df['FUV_MAG']  - large_catalog_full_df['NUV_MAG']\n",
        "\n",
        "# colours_df.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "DJH0YVgdDy2B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5760c460-7ee8-4087-c85e-ddcec5479593"
      },
      "source": [
        "np.shape(large_catalog_full_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4743, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OD-imMvFDy2B"
      },
      "source": [
        "## Possible correlations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhgvic0_Dy2C"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "1DhTjAcODy2C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bed4540e-86b0-490d-fdd7-3fe8ece23cbc"
      },
      "source": [
        "print(large_catalog_full_df.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['SDSS_NAME', 'RA', 'DEC', 'Z', 'PSFMAG_u', 'PSFMAG_g', 'PSFMAG_r',\n",
            "       'PSFMAG_i', 'PSFMAG_z', 'FUV', 'NUV', 'W1_MAG', 'W2_MAG', 'JMAG',\n",
            "       'HMAG', 'KMAG', 'FUV_MAG', 'NUV_MAG', 'u_g', 'g_r', 'r_i', 'i_z', 'u_r',\n",
            "       'g_i', 'w1_w2', 'J_H', 'H_K', 'FUV_NUV'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "4uW01_NxDy2C"
      },
      "source": [
        "cols2corr = ['Z', 'PSFMAG_u', 'PSFMAG_g', 'PSFMAG_r', 'PSFMAG_i', 'PSFMAG_z', 'FUV_MAG',\\\n",
        "             'NUV_MAG', 'W1_MAG', 'W2_MAG', 'JMAG', 'HMAG', 'KMAG', 'u_g', 'g_r', 'r_i',\\\n",
        "             'i_z', 'u_r', 'g_i', 'w1_w2', 'J_H', 'H_K', 'FUV_NUV']  # From main dataframe (large_catalog_full_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "1BUDyA_QDy2D"
      },
      "source": [
        "corr_data_df                   = large_catalog_full_df.loc[:, cols2corr]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "_Vzrtt2yDy2D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76d59ef8-88d8-4fcd-c2aa-11c82f340629"
      },
      "source": [
        "corr_data_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Z', 'PSFMAG_u', 'PSFMAG_g', 'PSFMAG_r', 'PSFMAG_i', 'PSFMAG_z',\n",
              "       'FUV_MAG', 'NUV_MAG', 'W1_MAG', 'W2_MAG', 'JMAG', 'HMAG', 'KMAG', 'u_g',\n",
              "       'g_r', 'r_i', 'i_z', 'u_r', 'g_i', 'w1_w2', 'J_H', 'H_K', 'FUV_NUV'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jo1ZzvTxDy2F"
      },
      "source": [
        "Scale features for plotting correlations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "jZcSfoTXDy2F"
      },
      "source": [
        "# max_abs_scaler = MaxAbsScaler()\n",
        "min_max_scaler = MinMaxScaler(feature_range=(0., 1.))\n",
        "# x_scaled = max_abs_scaler.fit_transform(large_catalog_df.values)\n",
        "x_scaled = min_max_scaler.fit_transform(corr_data_df.values)\n",
        "norm_corr_data_df = pd.DataFrame(data=x_scaled, columns=corr_data_df.columns, index=corr_data_df.index) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63-OoPgdDy2F"
      },
      "source": [
        "Restore redshift values (since they are the target feature)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "tags": [],
        "id": "_hQb97KeDy2F"
      },
      "source": [
        "norm_corr_data_df['Z'] = corr_data_df['Z']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4U9Oy8Rr5VFl"
      },
      "source": [
        "Let's set a random seed for future aplications."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taftOP0aRU2A"
      },
      "source": [
        "seed = 42"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItGxK3zCl3rb"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XO2txRr0lkJ2"
      },
      "source": [
        "# Using Pycaret"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV0QHiBIlqf3"
      },
      "source": [
        "We will start with the non-normalised data. We will let PyCaret (https://pycaret.org) do it for us."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_haF1f21l-ne"
      },
      "source": [
        "In order to test more capabilities, we will divide our data into three sub-samples: train, test, and validation sets.  \n",
        "\n",
        "PyCaret will use tran and test sets and we will let a $10\\%$ of the data for final validation (not touched by the models).\n",
        "\n",
        "PyCaret can take, automatically, a $70\\%$ of the data for training and a $30\\%$ for testing.\n",
        "\n",
        "It will also perform most of its training routines using 10-fold CV."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMtOzZX1zRW5"
      },
      "source": [
        "seed = 42"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZi4qCa4lnOh",
        "outputId": "d343a594-f5fe-47e5-84f4-9bb34f06133d"
      },
      "source": [
        "data_train_test_df = corr_data_df.sample(frac=0.90, random_state=seed)  # Train + test sets\n",
        "data_validation_df = corr_data_df.drop(data_train_test_df.index)  # Validation data\n",
        "\n",
        "print('Shape of used data')\n",
        "print('-' * 40)\n",
        "print(f'Full Dataset size: {corr_data_df.shape}')\n",
        "print(f'Data for Modeling (Train and Test): {data_train_test_df.shape}')\n",
        "print(f'Unseen Data For Validation: {data_validation_df.shape}')\n",
        "print('-' * 40)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of used data\n",
            "----------------------------------------\n",
            "Full Dataset size: (4743, 23)\n",
            "Data for Modeling (Train and Test): (4269, 23)\n",
            "Unseen Data For Validation: (474, 23)\n",
            "----------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqPAh4-elPJT"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_K3HXzgkJFo"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8_IUiPsm8WN"
      },
      "source": [
        "Now, we set PyCaret up. There are several options, and some of them might be different depending on whether we are running regression, classification, or other tasks (https://pycaret.readthedocs.io/en/latest/api/regression.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYnIW6uJnslb"
      },
      "source": [
        "We will let PyCaret select the most relevant features by using the Boruta method (https://danielhomola.com/feature%20selection/phd/borutapy-an-all-relevant-feature-selection-method/).  \n",
        "\n",
        "Also, we want to normalise our training features (not the target) with zscores (i.e., each feature will have a mean value, mu=0, and a standard deviation, sigma=1).\n",
        "\n",
        "There are many more options (https://pycaret.readthedocs.io/en/latest/api/regression.html#pycaret.regression.setup).\n",
        "\n",
        "**Important**: Note that now we have the parameter `use_gpu='force'`. That makes PyCaret use only the GPU versions of models that have both options (CPU and GPU)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "435d210b829a4db1ab755e693b2c7762",
            "f70e073b723c4c2ba6221726d2162c41",
            "94edb70524624c149838d18aa4344a66"
          ]
        },
        "id": "3RLG2eUqnO9M",
        "outputId": "b0576dfa-fdcb-4131-a946-4bfacb3875e7"
      },
      "source": [
        "exp_mclf = pyr.setup(data=data_train_test_df, target='Z', session_id=seed, silent=True,\\\n",
        "                     feature_selection=True, feature_selection_method='boruta',\\\n",
        "                     n_jobs=-1, normalize=True, normalize_method='zscore', use_gpu='force')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(4269, 23)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(2988, 20)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(1281, 20)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>force</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>baa1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>zscore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>boruta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Transform Target</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Transform Target Method</td>\n",
              "      <td>box-cox</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id                42\n",
              "1                                   Target                 Z\n",
              "2                            Original Data        (4269, 23)\n",
              "3                           Missing Values             False\n",
              "4                         Numeric Features                22\n",
              "5                     Categorical Features                 0\n",
              "6                         Ordinal Features             False\n",
              "7                High Cardinality Features             False\n",
              "8                  High Cardinality Method              None\n",
              "9                    Transformed Train Set        (2988, 20)\n",
              "10                    Transformed Test Set        (1281, 20)\n",
              "11                      Shuffle Train-Test              True\n",
              "12                     Stratify Train-Test             False\n",
              "13                          Fold Generator             KFold\n",
              "14                             Fold Number                10\n",
              "15                                CPU Jobs                -1\n",
              "16                                 Use GPU             force\n",
              "17                          Log Experiment             False\n",
              "18                         Experiment Name  reg-default-name\n",
              "19                                     USI              baa1\n",
              "20                         Imputation Type            simple\n",
              "21          Iterative Imputation Iteration              None\n",
              "22                         Numeric Imputer              mean\n",
              "23      Iterative Imputation Numeric Model              None\n",
              "24                     Categorical Imputer          constant\n",
              "25  Iterative Imputation Categorical Model              None\n",
              "26           Unknown Categoricals Handling    least_frequent\n",
              "27                               Normalize              True\n",
              "28                        Normalize Method            zscore\n",
              "29                          Transformation             False\n",
              "30                   Transformation Method              None\n",
              "31                                     PCA             False\n",
              "32                              PCA Method              None\n",
              "33                          PCA Components              None\n",
              "34                     Ignore Low Variance             False\n",
              "35                     Combine Rare Levels             False\n",
              "36                    Rare Level Threshold              None\n",
              "37                         Numeric Binning             False\n",
              "38                         Remove Outliers             False\n",
              "39                      Outliers Threshold              None\n",
              "40                Remove Multicollinearity             False\n",
              "41             Multicollinearity Threshold              None\n",
              "42             Remove Perfect Collinearity              True\n",
              "43                              Clustering             False\n",
              "44                    Clustering Iteration              None\n",
              "45                     Polynomial Features             False\n",
              "46                       Polynomial Degree              None\n",
              "47                    Trignometry Features             False\n",
              "48                    Polynomial Threshold              None\n",
              "49                          Group Features             False\n",
              "50                       Feature Selection              True\n",
              "51                Feature Selection Method            boruta\n",
              "52            Features Selection Threshold               0.8\n",
              "53                     Feature Interaction             False\n",
              "54                           Feature Ratio             False\n",
              "55                   Interaction Threshold              None\n",
              "56                        Transform Target             False\n",
              "57                 Transform Target Method           box-cox"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWCnX9IfobkW"
      },
      "source": [
        "We can see, from the output, that some features have been discarded. This might help our models to run faster."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-q8hacjongd"
      },
      "source": [
        "At this point, we add our custom scoring function for redshifts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC45PLyuohNB",
        "outputId": "565f0388-3bdd-412c-db21-7b3dc99fe9c0"
      },
      "source": [
        "pyr.add_metric('z_met', 'delta z', delta_z, greater_is_better=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name                                                       delta z\n",
              "Display Name                                               delta z\n",
              "Score Function                <function delta_z at 0x7fa12b85a5f0>\n",
              "Scorer               make_scorer(delta_z, greater_is_better=False)\n",
              "Target                                                        pred\n",
              "Args                                                            {}\n",
              "Greater is Better                                            False\n",
              "Custom                                                        True\n",
              "Name: z_met, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xr7f13Kpo51Z"
      },
      "source": [
        "Just to have more information, we see the models that are available for use in the system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "1uO5Ke4bo-38",
        "outputId": "64010bcd-6924-405f-9621-5c49e650768e"
      },
      "source": [
        "pyr.models()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Reference</th>\n",
              "      <th>Turbo</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>cuml.linear_model.linear_regression.LinearRegr...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>cuml.linear_model.lasso.Lasso</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>cuml.linear_model.ridge.Ridge</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>Elastic Net</td>\n",
              "      <td>cuml.linear_model.elastic_net.ElasticNet</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lar</th>\n",
              "      <td>Least Angle Regression</td>\n",
              "      <td>sklearn.linear_model._least_angle.Lars</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar</th>\n",
              "      <td>Lasso Least Angle Regression</td>\n",
              "      <td>sklearn.linear_model._least_angle.LassoLars</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>omp</th>\n",
              "      <td>Orthogonal Matching Pursuit</td>\n",
              "      <td>sklearn.linear_model._omp.OrthogonalMatchingPu...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>br</th>\n",
              "      <td>Bayesian Ridge</td>\n",
              "      <td>sklearn.linear_model._bayes.BayesianRidge</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ard</th>\n",
              "      <td>Automatic Relevance Determination</td>\n",
              "      <td>sklearn.linear_model._bayes.ARDRegression</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par</th>\n",
              "      <td>Passive Aggressive Regressor</td>\n",
              "      <td>sklearn.linear_model._passive_aggressive.Passi...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ransac</th>\n",
              "      <td>Random Sample Consensus</td>\n",
              "      <td>sklearn.linear_model._ransac.RANSACRegressor</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tr</th>\n",
              "      <td>TheilSen Regressor</td>\n",
              "      <td>sklearn.linear_model._theil_sen.TheilSenRegressor</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber</th>\n",
              "      <td>Huber Regressor</td>\n",
              "      <td>sklearn.linear_model._huber.HuberRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kr</th>\n",
              "      <td>Kernel Ridge</td>\n",
              "      <td>sklearn.kernel_ridge.KernelRidge</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>Support Vector Regression</td>\n",
              "      <td>cuml.svm.svr.SVR</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>cuml.neighbors.kneighbors_regressor.KNeighbors...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>sklearn.tree._classes.DecisionTreeRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>cuml.ensemble.randomforestregressor.RandomFore...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>sklearn.ensemble._forest.ExtraTreesRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>sklearn.ensemble._weight_boosting.AdaBoostRegr...</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>sklearn.ensemble._gb.GradientBoostingRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mlp</th>\n",
              "      <td>MLP Regressor</td>\n",
              "      <td>sklearn.neural_network._multilayer_perceptron....</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xgboost</th>\n",
              "      <td>Extreme Gradient Boosting</td>\n",
              "      <td>xgboost.sklearn.XGBRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>lightgbm.sklearn.LGBMRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>catboost</th>\n",
              "      <td>CatBoost Regressor</td>\n",
              "      <td>catboost.core.CatBoostRegressor</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       Name  ...  Turbo\n",
              "ID                                           ...       \n",
              "lr                        Linear Regression  ...   True\n",
              "lasso                      Lasso Regression  ...   True\n",
              "ridge                      Ridge Regression  ...   True\n",
              "en                              Elastic Net  ...   True\n",
              "lar                  Least Angle Regression  ...   True\n",
              "llar           Lasso Least Angle Regression  ...   True\n",
              "omp             Orthogonal Matching Pursuit  ...   True\n",
              "br                           Bayesian Ridge  ...   True\n",
              "ard       Automatic Relevance Determination  ...  False\n",
              "par            Passive Aggressive Regressor  ...   True\n",
              "ransac              Random Sample Consensus  ...  False\n",
              "tr                       TheilSen Regressor  ...  False\n",
              "huber                       Huber Regressor  ...   True\n",
              "kr                             Kernel Ridge  ...  False\n",
              "svm               Support Vector Regression  ...  False\n",
              "knn                   K Neighbors Regressor  ...   True\n",
              "dt                  Decision Tree Regressor  ...   True\n",
              "rf                  Random Forest Regressor  ...   True\n",
              "et                    Extra Trees Regressor  ...   True\n",
              "ada                      AdaBoost Regressor  ...   True\n",
              "gbr             Gradient Boosting Regressor  ...   True\n",
              "mlp                           MLP Regressor  ...  False\n",
              "xgboost           Extreme Gradient Boosting  ...   True\n",
              "lightgbm    Light Gradient Boosting Machine  ...   True\n",
              "catboost                 CatBoost Regressor  ...   True\n",
              "\n",
              "[25 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLZeraop9ueY"
      },
      "source": [
        "Since we want to take advantage of the GPU, we want to only use models that are installed through RAPIDS.ai. That means, we want those which start with `cuml...` and not those with `sklearn..`. Also we will prefer those which have `Turbo=True`, they are intrinsically faster.\n",
        "\n",
        "If we want, we can also use `xgboost`, `lightgbm`, and `catboost`. They have the capabilities to use GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtfoWlHXpAV2"
      },
      "source": [
        "Now, instead of looking at some text to understand what models are better for our regression problem and the structure of our data, we can ask PyCaret to, quickly, test many models and select the ones that behave the best for us."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-hxUf-MpVwx"
      },
      "source": [
        "We want to stack (https://en.wikipedia.org/wiki/Ensemble_learning#Stacking) the five best models, so we are selecting them directly from the comparison. Also, some models are excluded from this task since, from previous runs, they behave really bad."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297,
          "referenced_widgets": [
            "0640dae32f834327a746078af70c1ef1",
            "3927a5c6562145b58068146b8e4d7812",
            "0321d1110b02431ca58bd42669a06fa6"
          ]
        },
        "id": "hZ5JGbTQpSw_",
        "outputId": "6c530636-2b37-459c-8c7e-aeeb2635a1da"
      },
      "source": [
        "# Catboost uses GPU only if rows > 50000\n",
        "gpu_ready_models = ['lr', 'ridge', 'svm', 'knn', 'rf', 'xgboost', 'lightgbm', 'catboost']\n",
        "\n",
        "# top5 = pyr.compare_models(n_select=5, exclude=['huber', 'lar', 'llar', 'lasso', 'en'], sort='z_met')\n",
        "top5 = pyr.compare_models(n_select=5, include=gpu_ready_models, sort='z_met')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>delta z</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>0.1076</td>\n",
              "      <td>0.3202</td>\n",
              "      <td>0.8312</td>\n",
              "      <td>0.1350</td>\n",
              "      <td>0.3397</td>\n",
              "      <td>0.0365</td>\n",
              "      <td>0.171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>0.1735</td>\n",
              "      <td>0.1143</td>\n",
              "      <td>0.3315</td>\n",
              "      <td>0.8201</td>\n",
              "      <td>0.1411</td>\n",
              "      <td>0.3719</td>\n",
              "      <td>0.0378</td>\n",
              "      <td>0.579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.1780</td>\n",
              "      <td>0.1048</td>\n",
              "      <td>0.3156</td>\n",
              "      <td>0.8353</td>\n",
              "      <td>0.1358</td>\n",
              "      <td>0.3792</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>1.131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>Support Vector Regression</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.1009</td>\n",
              "      <td>0.3101</td>\n",
              "      <td>0.8418</td>\n",
              "      <td>0.1309</td>\n",
              "      <td>0.3769</td>\n",
              "      <td>0.0510</td>\n",
              "      <td>0.218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xgboost</th>\n",
              "      <td>Extreme Gradient Boosting</td>\n",
              "      <td>0.1916</td>\n",
              "      <td>0.1188</td>\n",
              "      <td>0.3395</td>\n",
              "      <td>0.8132</td>\n",
              "      <td>0.1438</td>\n",
              "      <td>0.3991</td>\n",
              "      <td>0.0522</td>\n",
              "      <td>1.088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>catboost</th>\n",
              "      <td>CatBoost Regressor</td>\n",
              "      <td>0.1922</td>\n",
              "      <td>0.1073</td>\n",
              "      <td>0.3203</td>\n",
              "      <td>0.8317</td>\n",
              "      <td>0.1392</td>\n",
              "      <td>0.3986</td>\n",
              "      <td>0.0594</td>\n",
              "      <td>8.366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>0.2936</td>\n",
              "      <td>0.1770</td>\n",
              "      <td>0.4167</td>\n",
              "      <td>0.7211</td>\n",
              "      <td>0.1883</td>\n",
              "      <td>0.5640</td>\n",
              "      <td>0.1019</td>\n",
              "      <td>0.808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.2936</td>\n",
              "      <td>0.1770</td>\n",
              "      <td>0.4167</td>\n",
              "      <td>0.7211</td>\n",
              "      <td>0.1883</td>\n",
              "      <td>0.5640</td>\n",
              "      <td>0.1019</td>\n",
              "      <td>0.013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model     MAE     MSE    RMSE      R2  \\\n",
              "knn                 K Neighbors Regressor  0.1667  0.1076  0.3202  0.8312   \n",
              "rf                Random Forest Regressor  0.1735  0.1143  0.3315  0.8201   \n",
              "lightgbm  Light Gradient Boosting Machine  0.1780  0.1048  0.3156  0.8353   \n",
              "svm             Support Vector Regression  0.1799  0.1009  0.3101  0.8418   \n",
              "xgboost         Extreme Gradient Boosting  0.1916  0.1188  0.3395  0.8132   \n",
              "catboost               CatBoost Regressor  0.1922  0.1073  0.3203  0.8317   \n",
              "lr                      Linear Regression  0.2936  0.1770  0.4167  0.7211   \n",
              "ridge                    Ridge Regression  0.2936  0.1770  0.4167  0.7211   \n",
              "\n",
              "           RMSLE    MAPE  delta z  TT (Sec)  \n",
              "knn       0.1350  0.3397   0.0365     0.171  \n",
              "rf        0.1411  0.3719   0.0378     0.579  \n",
              "lightgbm  0.1358  0.3792   0.0495     1.131  \n",
              "svm       0.1309  0.3769   0.0510     0.218  \n",
              "xgboost   0.1438  0.3991   0.0522     1.088  \n",
              "catboost  0.1392  0.3986   0.0594     8.366  \n",
              "lr        0.1883  0.5640   0.1019     0.808  \n",
              "ridge     0.1883  0.5640   0.1019     0.013  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZuaOCXZp7wj"
      },
      "source": [
        "At this step, we let PyCaret stack the models we have just selected. The best model will be used as meta model, using the outputs of the other four models as new features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "7e91a70b503344abbbc57afdf225454d",
            "011c6c19abad4449a037a0458bf9b5e5",
            "dc98407583894db7b83d13931bd78432"
          ]
        },
        "id": "cZsVqMJkp8Xe",
        "outputId": "2c11fc3a-6a73-412d-a2ab-8353b382e84b"
      },
      "source": [
        "pyr_stacked = pyr.stack_models(estimator_list=top5[1:], meta_model=top5[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>delta z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.1332</td>\n",
              "      <td>0.0697</td>\n",
              "      <td>0.2639</td>\n",
              "      <td>0.8867</td>\n",
              "      <td>0.1149</td>\n",
              "      <td>0.1528</td>\n",
              "      <td>0.0287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.1457</td>\n",
              "      <td>0.1511</td>\n",
              "      <td>0.3887</td>\n",
              "      <td>0.7687</td>\n",
              "      <td>0.1355</td>\n",
              "      <td>0.1505</td>\n",
              "      <td>0.0339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.1285</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>0.2457</td>\n",
              "      <td>0.9096</td>\n",
              "      <td>0.1001</td>\n",
              "      <td>0.1251</td>\n",
              "      <td>0.0320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1483</td>\n",
              "      <td>0.0715</td>\n",
              "      <td>0.2674</td>\n",
              "      <td>0.8885</td>\n",
              "      <td>0.1308</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.0328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.1639</td>\n",
              "      <td>0.2096</td>\n",
              "      <td>0.4578</td>\n",
              "      <td>0.6876</td>\n",
              "      <td>0.1597</td>\n",
              "      <td>0.1623</td>\n",
              "      <td>0.0351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.1510</td>\n",
              "      <td>0.0812</td>\n",
              "      <td>0.2849</td>\n",
              "      <td>0.8757</td>\n",
              "      <td>0.1358</td>\n",
              "      <td>1.5791</td>\n",
              "      <td>0.0338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.1411</td>\n",
              "      <td>0.0705</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>0.8676</td>\n",
              "      <td>0.1245</td>\n",
              "      <td>0.2012</td>\n",
              "      <td>0.0325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.1434</td>\n",
              "      <td>0.0790</td>\n",
              "      <td>0.2811</td>\n",
              "      <td>0.8700</td>\n",
              "      <td>0.1311</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.0319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.1198</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.2152</td>\n",
              "      <td>0.9250</td>\n",
              "      <td>0.0999</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>0.0337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.1263</td>\n",
              "      <td>0.0507</td>\n",
              "      <td>0.2252</td>\n",
              "      <td>0.9232</td>\n",
              "      <td>0.0984</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.0288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.1401</td>\n",
              "      <td>0.0890</td>\n",
              "      <td>0.2896</td>\n",
              "      <td>0.8602</td>\n",
              "      <td>0.1231</td>\n",
              "      <td>0.3130</td>\n",
              "      <td>0.0323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0126</td>\n",
              "      <td>0.0487</td>\n",
              "      <td>0.0718</td>\n",
              "      <td>0.0712</td>\n",
              "      <td>0.0188</td>\n",
              "      <td>0.4235</td>\n",
              "      <td>0.0020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2   RMSLE    MAPE  delta z\n",
              "0     0.1332  0.0697  0.2639  0.8867  0.1149  0.1528   0.0287\n",
              "1     0.1457  0.1511  0.3887  0.7687  0.1355  0.1505   0.0339\n",
              "2     0.1285  0.0604  0.2457  0.9096  0.1001  0.1251   0.0320\n",
              "3     0.1483  0.0715  0.2674  0.8885  0.1308  0.2500   0.0328\n",
              "4     0.1639  0.2096  0.4578  0.6876  0.1597  0.1623   0.0351\n",
              "5     0.1510  0.0812  0.2849  0.8757  0.1358  1.5791   0.0338\n",
              "6     0.1411  0.0705  0.2655  0.8676  0.1245  0.2012   0.0325\n",
              "7     0.1434  0.0790  0.2811  0.8700  0.1311  0.2098   0.0319\n",
              "8     0.1198  0.0463  0.2152  0.9250  0.0999  0.1467   0.0337\n",
              "9     0.1263  0.0507  0.2252  0.9232  0.0984  0.1530   0.0288\n",
              "Mean  0.1401  0.0890  0.2896  0.8602  0.1231  0.3130   0.0323\n",
              "SD    0.0126  0.0487  0.0718  0.0712  0.0188  0.4235   0.0020"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2PfI6iyrCIq"
      },
      "source": [
        "If we didn't want to stack models, we could have just used the best-performing model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75ZF7iDZrPkY"
      },
      "source": [
        "# pyr_model  = top5[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9epmuuWrc0L"
      },
      "source": [
        "Now, we can tune the hyperparameters of the selected (stacked) model. This step should take quite a long time.  \n",
        "\n",
        "In the case of classification, I recommend using `optimize='MCC'`. That metric works better with heavily unbalanced data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "ff210ae5196a48b5a79e48c048fa3933",
            "5d93d6d643ab48319c8045d47dcc824a",
            "d6ca2966102e4f639d26bc607b578439"
          ]
        },
        "id": "VAuKfIs9rPEn",
        "outputId": "acdf10ab-7543-4811-a608-bf28f75d6876"
      },
      "source": [
        "tuned_stacked = pyr.tune_model(pyr_stacked, early_stopping='asha', optimize='z_met')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>delta z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.1211</td>\n",
              "      <td>0.0520</td>\n",
              "      <td>0.2280</td>\n",
              "      <td>0.9154</td>\n",
              "      <td>0.1026</td>\n",
              "      <td>0.1405</td>\n",
              "      <td>0.0283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.1357</td>\n",
              "      <td>0.1463</td>\n",
              "      <td>0.3825</td>\n",
              "      <td>0.7761</td>\n",
              "      <td>0.1300</td>\n",
              "      <td>0.1396</td>\n",
              "      <td>0.0322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.1260</td>\n",
              "      <td>0.0663</td>\n",
              "      <td>0.2574</td>\n",
              "      <td>0.9008</td>\n",
              "      <td>0.1054</td>\n",
              "      <td>0.1242</td>\n",
              "      <td>0.0278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1423</td>\n",
              "      <td>0.0612</td>\n",
              "      <td>0.2473</td>\n",
              "      <td>0.9046</td>\n",
              "      <td>0.1180</td>\n",
              "      <td>0.2172</td>\n",
              "      <td>0.0336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.1691</td>\n",
              "      <td>0.2132</td>\n",
              "      <td>0.4617</td>\n",
              "      <td>0.6822</td>\n",
              "      <td>0.1619</td>\n",
              "      <td>0.1757</td>\n",
              "      <td>0.0335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.1482</td>\n",
              "      <td>0.0763</td>\n",
              "      <td>0.2762</td>\n",
              "      <td>0.8832</td>\n",
              "      <td>0.1349</td>\n",
              "      <td>1.5619</td>\n",
              "      <td>0.0351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.1332</td>\n",
              "      <td>0.0703</td>\n",
              "      <td>0.2651</td>\n",
              "      <td>0.8680</td>\n",
              "      <td>0.1199</td>\n",
              "      <td>0.1828</td>\n",
              "      <td>0.0298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.1360</td>\n",
              "      <td>0.0785</td>\n",
              "      <td>0.2801</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>0.1322</td>\n",
              "      <td>0.2144</td>\n",
              "      <td>0.0277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.1141</td>\n",
              "      <td>0.0451</td>\n",
              "      <td>0.2125</td>\n",
              "      <td>0.9269</td>\n",
              "      <td>0.1009</td>\n",
              "      <td>0.1541</td>\n",
              "      <td>0.0291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.1188</td>\n",
              "      <td>0.0440</td>\n",
              "      <td>0.2097</td>\n",
              "      <td>0.9333</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>0.1385</td>\n",
              "      <td>0.0290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.1344</td>\n",
              "      <td>0.0853</td>\n",
              "      <td>0.2820</td>\n",
              "      <td>0.8662</td>\n",
              "      <td>0.1194</td>\n",
              "      <td>0.3049</td>\n",
              "      <td>0.0306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0154</td>\n",
              "      <td>0.0508</td>\n",
              "      <td>0.0758</td>\n",
              "      <td>0.0745</td>\n",
              "      <td>0.0203</td>\n",
              "      <td>0.4201</td>\n",
              "      <td>0.0026</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2   RMSLE    MAPE  delta z\n",
              "0     0.1211  0.0520  0.2280  0.9154  0.1026  0.1405   0.0283\n",
              "1     0.1357  0.1463  0.3825  0.7761  0.1300  0.1396   0.0322\n",
              "2     0.1260  0.0663  0.2574  0.9008  0.1054  0.1242   0.0278\n",
              "3     0.1423  0.0612  0.2473  0.9046  0.1180  0.2172   0.0336\n",
              "4     0.1691  0.2132  0.4617  0.6822  0.1619  0.1757   0.0335\n",
              "5     0.1482  0.0763  0.2762  0.8832  0.1349  1.5619   0.0351\n",
              "6     0.1332  0.0703  0.2651  0.8680  0.1199  0.1828   0.0298\n",
              "7     0.1360  0.0785  0.2801  0.8710  0.1322  0.2144   0.0277\n",
              "8     0.1141  0.0451  0.2125  0.9269  0.1009  0.1541   0.0291\n",
              "9     0.1188  0.0440  0.2097  0.9333  0.0884  0.1385   0.0290\n",
              "Mean  0.1344  0.0853  0.2820  0.8662  0.1194  0.3049   0.0306\n",
              "SD    0.0154  0.0508  0.0758  0.0745  0.0203  0.4201   0.0026"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGaM5TX_rtDS"
      },
      "source": [
        "After being tuned, we can see the model's properties. We can see PyCaret configurations, the meta-model parameters and the individual estimators' parameters as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bLrOsIcryZv",
        "outputId": "d26710bb-964f-40eb-b11d-24a80e6f2684"
      },
      "source": [
        "print(tuned_stacked)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "StackingRegressor(cv=KFold(n_splits=10, random_state=RandomState(MT19937) at 0x7FA10212A160,\n",
            "   shuffle=False),\n",
            "                  estimators=[('rf', RandomForestRegressor()),\n",
            "                              ('lightgbm',\n",
            "                               LGBMRegressor(boosting_type='gbdt',\n",
            "                                             class_weight=None,\n",
            "                                             colsample_bytree=1.0, device='gpu',\n",
            "                                             importance_type='split',\n",
            "                                             learning_rate=0.1, max_depth=-1,\n",
            "                                             min_child_samples=20,\n",
            "                                             min_child_weight=0.001,\n",
            "                                             min_s...\n",
            "                                            max_delta_step=0, max_depth=6,\n",
            "                                            min_child_weight=1, missing=nan,\n",
            "                                            monotone_constraints='()',\n",
            "                                            n_estimators=100, n_jobs=-1,\n",
            "                                            num_parallel_tree=1,\n",
            "                                            objective='reg:squarederror',\n",
            "                                            random_state=42, reg_alpha=0,\n",
            "                                            reg_lambda=1, scale_pos_weight=1,\n",
            "                                            subsample=1, tree_method='gpu_hist',\n",
            "                                            validate_parameters=1,\n",
            "                                            verbosity=0))],\n",
            "                  final_estimator=KNeighborsRegressor(), n_jobs=1,\n",
            "                  passthrough=True, verbose=0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8R-jHiRYsFPa"
      },
      "source": [
        "We can check how our model is doing, letting PyCaret prepare some plots (https://pycaret.org/plot-model/). Of course, we can also do it by ourselves."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378,
          "referenced_widgets": [
            "e465978852ca4ec4a5db94ed4629ea45",
            "a73ea53fe4bb4268af0cf21ae38a4ccd",
            "baec8c294feb4cc6bf4125a8a416d60d"
          ]
        },
        "id": "oaeVu9YksN7l",
        "outputId": "e21ceade-11b0-4964-b895-a8a7954fd34f"
      },
      "source": [
        "pyr.plot_model(tuned_stacked, plot='error')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gh38cObm95ZT",
        "outputId": "6b8b1427-0de5-456c-daca-135b180fd3ac"
      },
      "source": [
        "# !pip install --upgrade yellowbrick"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yellowbrick in /usr/local/lib/python3.7/site-packages (1.3.post1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/site-packages (from yellowbrick) (1.5.4)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /usr/local/lib/python3.7/site-packages (from yellowbrick) (3.4.2)\n",
            "Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.7/site-packages (from yellowbrick) (0.10.0)\n",
            "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/site-packages (from yellowbrick) (0.23.2)\n",
            "Requirement already satisfied: numpy<1.20,>=1.16.0 in /usr/local/lib/python3.7/site-packages (from yellowbrick) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from cycler>=0.10.0->yellowbrick) (1.15.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (8.2.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.4.7)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.20->yellowbrick) (1.0.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn>=0.20->yellowbrick) (2.2.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373,
          "referenced_widgets": [
            "982a779ad44a490c985ef5a9d1c1662e",
            "990c3633e8ab424c944a334c895e13be",
            "c6a4cf37f5a3406988abfe6d1560f8af"
          ]
        },
        "id": "zfgnjQDyscmR",
        "outputId": "1ba66818-31b7-4c70-99bd-211d1d488384"
      },
      "source": [
        "pyr.plot_model(tuned_stacked, plot='residuals')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "982a779ad44a490c985ef5a9d1c1662e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "IntProgress(value=0, description='Processing: ', max=5)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Finished loading model, total used 100 iterations\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "YellowbrickValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mYellowbrickValueError\u001b[0m                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-711b758e7d52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpyr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuned_stacked\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'residuals'\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/usr/local/lib/python3.7/site-packages/pycaret/regression.py\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(estimator, plot, scale, save, fold, fit_kwargs, groups, use_train_data, verbose, display_format)\u001b[0m\n\u001b[1;32m   1502\u001b[0m         \u001b[0muse_train_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_train_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0msystem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1504\u001b[0;31m         \u001b[0mdisplay_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisplay_format\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   1505\u001b[0m     )\n\u001b[1;32m   1506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pycaret/internal/tabular.py\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(estimator, plot, scale, save, fold, fit_kwargs, groups, feature_name, label, use_train_data, verbose, system, display, display_format, is_in_evaluate)\u001b[0m\n\u001b[1;32m   7238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7239\u001b[0m         \u001b[0;31m# execute the plot method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7240\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplot\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[0m\u001b[1;32m   7241\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7242\u001b[0m             \u001b[0mplot_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pycaret/internal/tabular.py\u001b[0m in \u001b[0;36mresiduals\u001b[0;34m()\u001b[0m\n\u001b[1;32m   6443\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0myellowbrick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregressor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mResidualsPlot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6445\u001b[0;31m             \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResidualsPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline_with_model\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   6446\u001b[0m             show_yellowbrick_plot(\n\u001b[1;32m   6447\u001b[0m                 \u001b[0mvisualizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvisualizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/yellowbrick/regressor/residuals.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, estimator, ax, hist, qqplot, train_color, test_color, line_color, train_alpha, test_alpha, is_fitted, **kwargs)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"density\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"frequency\"\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--> 192\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhax\u001b[0m  \u001b[0;31m# If hist is True, test the version availability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;32mTrue\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/usr/local/lib/python3.7/site-packages/yellowbrick/utils/decorators.py\u001b[0m in \u001b[0;36mfget_memoized\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfget_memoized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr_name\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---> 49\u001b[0;31m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/yellowbrick/regressor/residuals.py\u001b[0m in \u001b[0;36mhax\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    208\u001b[0m             raise YellowbrickValueError(\n\u001b[1;32m    209\u001b[0m                 (\n\u001b[0;32m--> 210\u001b[0;31m                     \u001b[0;34m\"residuals histogram requires matplotlib 2.0.2 or greater \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m                     \u001b[0;34m\"please upgrade matplotlib or set hist=False on the visualizer\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m                 )\n",
            "\u001b[0;31mYellowbrickValueError\u001b[0m: residuals histogram requires matplotlib 2.0.2 or greater please upgrade matplotlib or set hist=False on the visualizer"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kOUBhS4sj4w"
      },
      "source": [
        "If we are satisfied with the results, we can finalise the model. That means, to include the test data into the training set and re-run the models and optimise their parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpvD2crrsuX-"
      },
      "source": [
        "final_stacked = pyr.finalize_model(tuned_stacked)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNvlGgYtsyKM"
      },
      "source": [
        "Let's check its parameters:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3b-KQSQs0y6",
        "outputId": "ebbd35b3-9d20-47b2-c93d-b6e4a748453a"
      },
      "source": [
        "print(final_stacked)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "StackingRegressor(cv=KFold(n_splits=10, random_state=RandomState(MT19937) at 0x7FA1003AA9E0,\n",
            "   shuffle=False),\n",
            "                  estimators=[('rf', RandomForestRegressor()),\n",
            "                              ('lightgbm',\n",
            "                               LGBMRegressor(boosting_type='gbdt',\n",
            "                                             class_weight=None,\n",
            "                                             colsample_bytree=1.0, device='gpu',\n",
            "                                             importance_type='split',\n",
            "                                             learning_rate=0.1, max_depth=-1,\n",
            "                                             min_child_samples=20,\n",
            "                                             min_child_weight=0.001,\n",
            "                                             min_s...\n",
            "                                            max_delta_step=0, max_depth=6,\n",
            "                                            min_child_weight=1, missing=nan,\n",
            "                                            monotone_constraints='()',\n",
            "                                            n_estimators=100, n_jobs=-1,\n",
            "                                            num_parallel_tree=1,\n",
            "                                            objective='reg:squarederror',\n",
            "                                            random_state=42, reg_alpha=0,\n",
            "                                            reg_lambda=1, scale_pos_weight=1,\n",
            "                                            subsample=1, tree_method='gpu_hist',\n",
            "                                            validate_parameters=1,\n",
            "                                            verbosity=0))],\n",
            "                  final_estimator=KNeighborsRegressor(), n_jobs=1,\n",
            "                  passthrough=True, verbose=0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcGymHLotJps"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dq-xoJp7tLUn"
      },
      "source": [
        "The model and PyCaret configuration files can be saved and/or downloaded for future use (to avoid extra running time.)  \n",
        "\n",
        "\n",
        "**Important** Now that models take a lot of time to run and train, it is recommended to save the files (following lines), download them, and put them in some place you can obtain them easily (e.g., Google Drive). That way, you might save some time if you need to stop working for some time, avoiding running everything again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-MoQDhu6tKKc",
        "outputId": "398bc041-f728-48b0-ab3d-d0933c476945"
      },
      "source": [
        "pyr.save_model(final_stacked, 'model_final_SDSS_z')  # Model (it will be saved with .pkl extension)\n",
        "pyr.save_config('config_final_SDSS_z')  # Configuration file"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kDqckzHtmzk"
      },
      "source": [
        "Now, if we want to load the saved files, we can do it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9sfWg-ytrLR"
      },
      "source": [
        "# if not os.path.exists('config_final_SDSS_z'):\n",
        "#     # Run your code to get the file"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4XVGXr8twCT"
      },
      "source": [
        "# if not os.path.exists('model_final_SDSS_z.pkl'):\n",
        "#     # Run your code to get the file"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJBlTf4XuD1v"
      },
      "source": [
        "pyr.load_config('config_final_SDSS_z')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHMU9wgfuEay",
        "outputId": "80d5655f-be64-4496-ef68-7d0c3f5fd0b5"
      },
      "source": [
        "final_stacked = pyr.load_model('model_final_SDSS_z')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHC93oUvuTKL"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tO7hvMFouUhD"
      },
      "source": [
        "Now that our model is complete, we can apply it to the validation set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OT0t0U3ubJ0"
      },
      "source": [
        "First, we can just apply it, without saving the results. That way, we can obtain some statistics on the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "EV3FdKctuTx_",
        "outputId": "0b9d90b3-b531-4370-a4e7-fc27a182f56c"
      },
      "source": [
        "pyr.predict_model(final_stacked, data=data_validation_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Z</th>\n",
              "      <th>PSFMAG_u</th>\n",
              "      <th>PSFMAG_g</th>\n",
              "      <th>PSFMAG_r</th>\n",
              "      <th>PSFMAG_i</th>\n",
              "      <th>PSFMAG_z</th>\n",
              "      <th>FUV_MAG</th>\n",
              "      <th>NUV_MAG</th>\n",
              "      <th>W1_MAG</th>\n",
              "      <th>W2_MAG</th>\n",
              "      <th>JMAG</th>\n",
              "      <th>HMAG</th>\n",
              "      <th>KMAG</th>\n",
              "      <th>u_g</th>\n",
              "      <th>g_r</th>\n",
              "      <th>r_i</th>\n",
              "      <th>i_z</th>\n",
              "      <th>u_r</th>\n",
              "      <th>g_i</th>\n",
              "      <th>w1_w2</th>\n",
              "      <th>J_H</th>\n",
              "      <th>H_K</th>\n",
              "      <th>FUV_NUV</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.307457</td>\n",
              "      <td>19.738922</td>\n",
              "      <td>19.174831</td>\n",
              "      <td>18.684092</td>\n",
              "      <td>18.569454</td>\n",
              "      <td>18.129286</td>\n",
              "      <td>22.346468</td>\n",
              "      <td>21.036074</td>\n",
              "      <td>16.927895</td>\n",
              "      <td>16.493908</td>\n",
              "      <td>18.320000</td>\n",
              "      <td>17.448000</td>\n",
              "      <td>17.036000</td>\n",
              "      <td>0.564091</td>\n",
              "      <td>0.490740</td>\n",
              "      <td>0.114637</td>\n",
              "      <td>0.440168</td>\n",
              "      <td>-0.114637</td>\n",
              "      <td>0.605377</td>\n",
              "      <td>0.433987</td>\n",
              "      <td>0.872000</td>\n",
              "      <td>0.412001</td>\n",
              "      <td>1.310394</td>\n",
              "      <td>0.610793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.921844</td>\n",
              "      <td>18.032793</td>\n",
              "      <td>18.182171</td>\n",
              "      <td>17.774267</td>\n",
              "      <td>17.897682</td>\n",
              "      <td>17.834732</td>\n",
              "      <td>19.172755</td>\n",
              "      <td>18.337662</td>\n",
              "      <td>16.536222</td>\n",
              "      <td>15.785954</td>\n",
              "      <td>17.376999</td>\n",
              "      <td>17.326000</td>\n",
              "      <td>17.066000</td>\n",
              "      <td>-0.149378</td>\n",
              "      <td>0.407904</td>\n",
              "      <td>-0.123415</td>\n",
              "      <td>0.062950</td>\n",
              "      <td>0.123415</td>\n",
              "      <td>0.284489</td>\n",
              "      <td>0.750268</td>\n",
              "      <td>0.051000</td>\n",
              "      <td>0.259999</td>\n",
              "      <td>0.835093</td>\n",
              "      <td>1.093227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.259557</td>\n",
              "      <td>18.562349</td>\n",
              "      <td>18.528749</td>\n",
              "      <td>18.393641</td>\n",
              "      <td>18.335747</td>\n",
              "      <td>17.887615</td>\n",
              "      <td>19.323493</td>\n",
              "      <td>19.477445</td>\n",
              "      <td>16.581633</td>\n",
              "      <td>16.210674</td>\n",
              "      <td>17.629000</td>\n",
              "      <td>17.290000</td>\n",
              "      <td>17.021000</td>\n",
              "      <td>0.033600</td>\n",
              "      <td>0.135109</td>\n",
              "      <td>0.057894</td>\n",
              "      <td>0.448132</td>\n",
              "      <td>-0.057894</td>\n",
              "      <td>0.193003</td>\n",
              "      <td>0.370958</td>\n",
              "      <td>0.339000</td>\n",
              "      <td>0.269000</td>\n",
              "      <td>-0.153953</td>\n",
              "      <td>0.304411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1.868676</td>\n",
              "      <td>18.909988</td>\n",
              "      <td>18.805210</td>\n",
              "      <td>18.451227</td>\n",
              "      <td>18.031166</td>\n",
              "      <td>17.871866</td>\n",
              "      <td>20.886377</td>\n",
              "      <td>20.940173</td>\n",
              "      <td>17.406992</td>\n",
              "      <td>16.891535</td>\n",
              "      <td>17.643000</td>\n",
              "      <td>17.297000</td>\n",
              "      <td>17.597000</td>\n",
              "      <td>0.104778</td>\n",
              "      <td>0.353983</td>\n",
              "      <td>0.420061</td>\n",
              "      <td>0.159300</td>\n",
              "      <td>-0.420061</td>\n",
              "      <td>0.774044</td>\n",
              "      <td>0.515457</td>\n",
              "      <td>0.346000</td>\n",
              "      <td>-0.299999</td>\n",
              "      <td>-0.053796</td>\n",
              "      <td>1.872218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1.877000</td>\n",
              "      <td>20.168509</td>\n",
              "      <td>19.650352</td>\n",
              "      <td>19.128874</td>\n",
              "      <td>18.555935</td>\n",
              "      <td>18.318344</td>\n",
              "      <td>23.931509</td>\n",
              "      <td>23.081542</td>\n",
              "      <td>17.307024</td>\n",
              "      <td>16.652020</td>\n",
              "      <td>17.900999</td>\n",
              "      <td>18.379000</td>\n",
              "      <td>17.560000</td>\n",
              "      <td>0.518156</td>\n",
              "      <td>0.521479</td>\n",
              "      <td>0.572939</td>\n",
              "      <td>0.237591</td>\n",
              "      <td>-0.572939</td>\n",
              "      <td>1.094418</td>\n",
              "      <td>0.655005</td>\n",
              "      <td>-0.478001</td>\n",
              "      <td>0.819000</td>\n",
              "      <td>0.849967</td>\n",
              "      <td>1.407639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4694</th>\n",
              "      <td>0.279411</td>\n",
              "      <td>18.025873</td>\n",
              "      <td>17.880335</td>\n",
              "      <td>17.511271</td>\n",
              "      <td>17.485350</td>\n",
              "      <td>16.847416</td>\n",
              "      <td>24.200142</td>\n",
              "      <td>24.687045</td>\n",
              "      <td>15.291117</td>\n",
              "      <td>14.848494</td>\n",
              "      <td>17.250000</td>\n",
              "      <td>16.683000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>0.145538</td>\n",
              "      <td>0.369064</td>\n",
              "      <td>0.025921</td>\n",
              "      <td>0.637934</td>\n",
              "      <td>-0.025921</td>\n",
              "      <td>0.394985</td>\n",
              "      <td>0.442623</td>\n",
              "      <td>0.567000</td>\n",
              "      <td>0.183000</td>\n",
              "      <td>-0.486903</td>\n",
              "      <td>0.427834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4697</th>\n",
              "      <td>0.890832</td>\n",
              "      <td>18.610174</td>\n",
              "      <td>18.375771</td>\n",
              "      <td>18.210154</td>\n",
              "      <td>18.295279</td>\n",
              "      <td>18.234514</td>\n",
              "      <td>20.709547</td>\n",
              "      <td>18.924801</td>\n",
              "      <td>17.540905</td>\n",
              "      <td>17.100338</td>\n",
              "      <td>17.626999</td>\n",
              "      <td>17.734000</td>\n",
              "      <td>17.937001</td>\n",
              "      <td>0.234404</td>\n",
              "      <td>0.165617</td>\n",
              "      <td>-0.085125</td>\n",
              "      <td>0.060764</td>\n",
              "      <td>0.085125</td>\n",
              "      <td>0.080492</td>\n",
              "      <td>0.440567</td>\n",
              "      <td>-0.107000</td>\n",
              "      <td>-0.203001</td>\n",
              "      <td>1.784746</td>\n",
              "      <td>0.936276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4729</th>\n",
              "      <td>0.439592</td>\n",
              "      <td>19.005405</td>\n",
              "      <td>18.895096</td>\n",
              "      <td>18.898268</td>\n",
              "      <td>18.760256</td>\n",
              "      <td>18.478401</td>\n",
              "      <td>19.519502</td>\n",
              "      <td>19.303298</td>\n",
              "      <td>16.919250</td>\n",
              "      <td>16.601950</td>\n",
              "      <td>18.261999</td>\n",
              "      <td>17.806000</td>\n",
              "      <td>17.375000</td>\n",
              "      <td>0.110310</td>\n",
              "      <td>-0.003172</td>\n",
              "      <td>0.138012</td>\n",
              "      <td>0.281855</td>\n",
              "      <td>-0.138012</td>\n",
              "      <td>0.134840</td>\n",
              "      <td>0.317301</td>\n",
              "      <td>0.455999</td>\n",
              "      <td>0.431000</td>\n",
              "      <td>0.216204</td>\n",
              "      <td>0.447841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4735</th>\n",
              "      <td>1.415235</td>\n",
              "      <td>18.609880</td>\n",
              "      <td>18.312334</td>\n",
              "      <td>18.016172</td>\n",
              "      <td>17.885984</td>\n",
              "      <td>17.873287</td>\n",
              "      <td>22.005297</td>\n",
              "      <td>20.078618</td>\n",
              "      <td>17.217295</td>\n",
              "      <td>16.541204</td>\n",
              "      <td>17.860001</td>\n",
              "      <td>17.558001</td>\n",
              "      <td>17.480000</td>\n",
              "      <td>0.297546</td>\n",
              "      <td>0.296162</td>\n",
              "      <td>0.130188</td>\n",
              "      <td>0.012697</td>\n",
              "      <td>-0.130188</td>\n",
              "      <td>0.426350</td>\n",
              "      <td>0.676090</td>\n",
              "      <td>0.302000</td>\n",
              "      <td>0.078001</td>\n",
              "      <td>1.926679</td>\n",
              "      <td>1.420913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4737</th>\n",
              "      <td>1.058000</td>\n",
              "      <td>18.934023</td>\n",
              "      <td>18.419756</td>\n",
              "      <td>18.130104</td>\n",
              "      <td>18.178469</td>\n",
              "      <td>18.110374</td>\n",
              "      <td>21.399074</td>\n",
              "      <td>20.194507</td>\n",
              "      <td>17.013786</td>\n",
              "      <td>16.277304</td>\n",
              "      <td>17.841999</td>\n",
              "      <td>17.306000</td>\n",
              "      <td>17.728000</td>\n",
              "      <td>0.514267</td>\n",
              "      <td>0.289652</td>\n",
              "      <td>-0.048365</td>\n",
              "      <td>0.068094</td>\n",
              "      <td>0.048365</td>\n",
              "      <td>0.241287</td>\n",
              "      <td>0.736483</td>\n",
              "      <td>0.535999</td>\n",
              "      <td>-0.422000</td>\n",
              "      <td>1.204568</td>\n",
              "      <td>1.083823</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>474 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Z   PSFMAG_u   PSFMAG_g  ...       H_K   FUV_NUV     Label\n",
              "4     0.307457  19.738922  19.174831  ...  0.412001  1.310394  0.610793\n",
              "16    0.921844  18.032793  18.182171  ...  0.259999  0.835093  1.093227\n",
              "34    0.259557  18.562349  18.528749  ...  0.269000 -0.153953  0.304411\n",
              "64    1.868676  18.909988  18.805210  ... -0.299999 -0.053796  1.872218\n",
              "98    1.877000  20.168509  19.650352  ...  0.819000  0.849967  1.407639\n",
              "...        ...        ...        ...  ...       ...       ...       ...\n",
              "4694  0.279411  18.025873  17.880335  ...  0.183000 -0.486903  0.427834\n",
              "4697  0.890832  18.610174  18.375771  ... -0.203001  1.784746  0.936276\n",
              "4729  0.439592  19.005405  18.895096  ...  0.431000  0.216204  0.447841\n",
              "4735  1.415235  18.609880  18.312334  ...  0.078001  1.926679  1.420913\n",
              "4737  1.058000  18.934023  18.419756  ... -0.422000  1.204568  1.083823\n",
              "\n",
              "[474 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJ4S-3qkumLb"
      },
      "source": [
        "This time, let's save the values. The column **Label** has the predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37LHLqBWusUF"
      },
      "source": [
        "unseen_predictions = pyr.predict_model(final_stacked, data=data_validation_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQpKmMVIu0eZ"
      },
      "source": [
        "We can get some basic statistics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "DHFFIBkIu3px",
        "outputId": "e811e7c1-ffdb-41d8-9e8f-cfd9da3c6962"
      },
      "source": [
        "unseen_predictions[['Z', 'Label']].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Z</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>474.000000</td>\n",
              "      <td>474.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.224870</td>\n",
              "      <td>1.194677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.827325</td>\n",
              "      <td>0.751356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.073000</td>\n",
              "      <td>0.196903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.499638</td>\n",
              "      <td>0.549526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.039792</td>\n",
              "      <td>1.044979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.746000</td>\n",
              "      <td>1.657799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.852848</td>\n",
              "      <td>3.562950</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Z       Label\n",
              "count  474.000000  474.000000\n",
              "mean     1.224870    1.194677\n",
              "std      0.827325    0.751356\n",
              "min      0.073000    0.196903\n",
              "25%      0.499638    0.549526\n",
              "50%      1.039792    1.044979\n",
              "75%      1.746000    1.657799\n",
              "max      4.852848    3.562950"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ona0AYoau8fG"
      },
      "source": [
        "And, of course, we can also get a plot comparing our results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "i__6ktOvvBE-",
        "outputId": "4b7b29b3-1f0f-478c-b7cb-d4ae058a1606"
      },
      "source": [
        "fig             = plt.figure(figsize=(7,6))\n",
        "ax1             = fig.add_subplot(111, projection='scatter_density')\n",
        "\n",
        "\n",
        "norm = ImageNormalize(vmin=0., vmax=39, stretch=LogStretch())\n",
        "\n",
        "dens_1 = ax1.scatter_density(unseen_predictions.loc[:, 'Z'].sample(frac=1, random_state=seed),\\\n",
        "            unseen_predictions.loc[:, 'Label'].sample(frac=1, random_state=seed),\\\n",
        "            cmap=plt.get_cmap('cet_CET_L17'), zorder=0, dpi=15, norm=norm, alpha=1)\n",
        "\n",
        "clb = plt.colorbar(dens_1)\n",
        "clb.ax.tick_params(labelsize=14)\n",
        "clb.outline.set_linewidth(2.5)\n",
        "clb.ax.set_ylabel('Points per pixel', size=20, path_effects=pe2)\n",
        "\n",
        "ax1.set_xlabel('True z', fontsize=20)\n",
        "ax1.set_ylabel('Predicted z ', fontsize=20)\n",
        "ax1.tick_params(which='both', top=True, right=True, direction='in')\n",
        "ax1.tick_params(axis='both', which='major', labelsize=14)\n",
        "ax1.tick_params(which='major', length=8, width=1.5)\n",
        "ax1.tick_params(which='minor', length=4, width=1.5)\n",
        "plt.setp(ax1.spines.values(), linewidth=2.5)\n",
        "plt.setp(ax1.spines.values(), linewidth=2.5)\n",
        "ax1.set_xlim(left=-0.3, right=7.5)\n",
        "ax1.set_ylim(bottom=-0.3, top=7.5)\n",
        "ax1.grid(zorder=0)\n",
        "ax1.set_aspect('equal', 'datalim')\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "DwfVqeCSvXw_",
        "outputId": "fa1efa10-2f14-4175-e6be-e4e3d5c4a599"
      },
      "source": [
        "fig             = plt.figure(figsize=(8,6))\n",
        "ax1             = fig.add_subplot(111)\n",
        "\n",
        "norm_z = mcolors.Normalize(vmin=0, vmax=7)\n",
        "\n",
        "res_z_z     = unseen_predictions.loc[:, 'Label'] - unseen_predictions.loc[:, 'Z']\n",
        "delt_z      = res_z_z / (1 + unseen_predictions.loc[:, 'Z'])\n",
        "\n",
        "sc_p = ax1.scatter(unseen_predictions.loc[:, 'Label'].sample(frac=1, random_state=seed),\\\n",
        "                   delt_z.sample(frac=1, random_state=seed),\\\n",
        "                   c=unseen_predictions.loc[:, 'Z'].sample(frac=1, random_state=seed),\\\n",
        "                   s=45, ec='Gray', alpha=0.75, lw=1.2,\\\n",
        "                   cmap=plt.get_cmap('cmr.redshift', 7), zorder=10, norm=norm_z)\n",
        "ax1.axhline(y=0, ls='--', marker=None, c='Gray', alpha=0.7, lw=3.0)\n",
        "\n",
        "clb             = plt.colorbar(sc_p)\n",
        "clb.ax.tick_params(labelsize=14)\n",
        "clb.outline.set_linewidth(2.5)\n",
        "clb.ax.set_ylabel('True z', size=20, path_effects=pe2)\n",
        "\n",
        "ax1.set_xlabel('Predicted z', fontsize=20)\n",
        "ax1.set_ylabel('$\\Delta z$', fontsize=20)\n",
        "ax1.tick_params(which='both', top=True, right=True, direction='in')\n",
        "ax1.tick_params(axis='both', which='major', labelsize=14)\n",
        "ax1.tick_params(which='major', length=8, width=1.5)\n",
        "ax1.tick_params(which='minor', length=4, width=1.5)\n",
        "plt.setp(ax1.spines.values(), linewidth=2.5)\n",
        "plt.setp(ax1.spines.values(), linewidth=2.5)\n",
        "ax1.grid(zorder=0)\n",
        "ax1.set_aspect('equal', 'datalim')\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGkCAYAAAAMtU5yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXRc9X3//+edfZFmtO+y5U3yIu8bBmwM2DEhTggJoUn6Iy1tORTShhNKyHqacsIpbdOmISkJIUnbfEmDTRJig7ENxgsGb9jypsWSbVnWvkuj2efOnXt/f8hRcLxElpeRrPfjHJ9j3+Uz748kz7z0uZ/7uYphGAZCCCGEEElgSnYBQgghhBi/JIgIIYQQImkkiAghhBAiaSSICCGEECJpJIgIIYQQImkkiAghhBAiaSSICCGEECJpLMkuQAghhBA3xsGDB/mrv/qrC7arqsqOHTsoLCy84TUpsqCZEEIIMX6tW7eODRs28Morr6Aoyg1/fRkREUIIIcapvr4+nn/+ef77v/87KSEEJIhQU1NDLBbDbDZjt9uTXY4QQohRJhaLkUgksNvtzJw585q339zcTDgcvmbtuVwuiouLh3XsCy+8wJ133smMGTOu2etfqXEfRGKxGLquo+s68Xg82eUIIYQYpWKx2HVpNxwOEwwGr0vbl9PZ2clrr73Gxo0bb/hrf9i4DyJmsxld1zGZTLhcrmSXM2K1tbVomkZ5eXmyS7lmpE9jx83YL+nT2HAj+hQOh9F1HbPZfN1eA8CECafZOeLzI4kIOvqwj3/55ZdZvnw5EyZMGPFrXgvjPojY7Xbi8Tgul4uysrJklzNizz77LIFAgA0bNiS7lGtG+jR23Iz9kj6NDTeiT3V1dQSDwet++d5pdjLNNW3E558KnyKUCA37+C1btvDEE0+M+PWuFVlHRAghhBhnamtraWlpYcWKFckuRYKIEEIIMd5UV1eTmppKWlpaskuRICKEEEKMNz09PWRlZSW7DECCiBBCCDHuPProo2zdujXZZQASRIQQQgiRROP+rpmbxcsvv0xFRUWyy7impE9jx83YL+nT2HAz9mm8kRERIYQQQiSNBBEhhBBCJI0EESGEEEIkjQQRIYQQQiSNBBEhhBBCJI0EESGEEEIkjQQRIYQQQiSNBBEhhBBCJI0EESGEEEIkjQQRIYQQQiSNBBEhhBBCJI0EESGEEEIkjQQRIYQQQiSNBBEhhBBCJM2oDyJ1dXWsXbuWu+6665LHvPbaa5SVlTF79uzz/hw+fPgGViqEEEKIK2VJdgGXs3nzZp577jnmzJnDiRMnLntsYWEhO3bsuEGVCSGEEOJaGNUjIuFwmPXr17Ns2bJklyKEEEKI62BUj4g88MADwz42FArx2GOPcfjwYVwuF4899hgPPvjgsM+vra3l2Wefvei+l19+edjtCCGEGHseeuihS+574IEHKCoquoHVjC+jOogMV0ZGBmVlZTzyyCOUl5ezc+dOnnzySXJzc7njjjuG1YamaQQCgYvuq6iouJblXldjqdbhkj6NHTdjv6RPY8PV9ulS7/8w+Pkgrp+bIoisXLmSlStXDv17zZo1rF69mo0bNw47iJSXl7Nhw4brVOGNUVFRwcKFC5NdxjUlfRo7bsZ+SZ/GhmvRp8u9/9fV1REMBq+qfXFpo3qOyNUoLCykq6sr2WUIIYQQ4jJuiiDyyiuvsHnz5vO21dfXU1xcnKSKhBBCCDEcYzaI3HPPPRw4cAAAVVX5zne+Q2VlJfF4nE2bNrF7924+97nPJblKIYQQQlzOqJ4jsmbNGtra2tB1HU3TmD17NgBbt26loaGBcDgMwBe+8AVCoRBPPPEE3d3dFBUV8cILLzBnzpxkli+EEEKIP2FUB5G33nrrkvvq6uqG/q4oCo8//jiPP/74jShLCCGEENfImL00I4QQQoixT4KIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIMc78/Oc/Z8WKFcybN4/Pf/7znD59Omm1SBARQgghxpF169axfv16fvazn7Fnzx4WLVrEiy++mLR6LEl7ZSGEEELccD/96U958sknKS0tBeDJJ59Maj0yIiKEEEKME52dnbS0tBAOh/n4xz/O4sWLefTRR+no6EhaTTIiIoQQQowCelQl2j3yQKCnqmC9/DG/DxybNm3ipZdewmq18vTTT/Pkk0/yq1/9asSvfTUkiAghhBCjQNzfT9cHh0d+/pJJkJly2WMMwwDgr//6r8nPzwcGL818+tOfpqOjg7y8vBG//kjJpRkhhBBinMjKygIgLS1taFthYSEAXV1dSalJgogQQggxTuTl5ZGamkpNTc3QtpaWFgAKCgqSUpMEESGEEGKcsFgsfO5zn+PFF1+kvr6egYEBvv/977Ny5cqh0ZIbXlNSXlUIIYQQSfGlL32JSCTC5z//eWKxGCtXruSf/umfklaPBBEhhBBiHLFarXzrW9/iW9/6VrJLAeTSjBBCCCGSSIKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkmbUB5G6ujrWrl3LXXfdddnjtm7dyn333cf8+fP5xCc+wdtvv32DKhRCCCHESI3qILJ582b+5m/+hokTJ172uNraWr7yla/w93//9+zfv58nnniCp556ipMnT96gSoUQQggxEqM6iITDYdavX8+yZcsue9yrr77KbbfdxqpVq7Db7dx9990sW7aMX//61zeoUiGEEEKMhCXZBVzOAw88MKzjqquruf3228/bNnPmTPbt2zfs16qtreXZZ5+96L6XX3552O0IIYQYex566KFL7nvggQcoKiq6gdWML6M6iAyXz+fD4/Gct83r9dLf3z/sNjRNIxAIXHRfRUXFVdV3I42lWodL+jR23Iz9kj6NDVfbp0u9/8Pg54O4fm6KIAJgGMZVnV9eXs6GDRuuUTXJUVFRwcKFC5NdxjUlfRo7bsZ+SZ/GhmvRp8u9/9fV1REMBq+qfXFpo3qOyHClp6fj8/nO2+bz+cjMzExSRUIIIYQYjpsiiJSXl1NVVXXetsrKSubOnZukioQQQggxHGM2iNxzzz0cOHAAgM9+9rMcOHCAbdu2oaoqW7Zs4dChQ3z2s59NcpVCCCGEuJxRPUdkzZo1tLW1oes6mqYxe/ZsYHDxsoaGBsLhMABTp07lP//zP/mP//gPvvzlL1NSUsIPf/jDP7n+iBBCCCGSa1QHkbfeeuuS++rq6s7796pVq1i1atX1LkkIIYQQ19CYvTQjhBBCiLFPgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkkaCiBBCCCGSRoKIEEIIIZJGgogQQgghkmZUP2tGCCGEENdWWVkZVqsVRVGGtn3qU5/imWeeSUo9EkSEEEKIcebnP/85S5cuTXYZgFyaEUIIIUQSSRARQgghxplf/OIX3H333SxcuJCnn34av9+ftFrk0owQQggxCsTCYZprT4z4fEd5LubMlD953Ny5c1m0aBHf+9736Ozs5Etf+hL/+I//yPe///0Rv/bVkCAihBBCjAIBk8pxW/uIz59jUkkbxnGvvvrq0N8nTpzIk08+yaOPPko0GsXhcIz49UdKLs0IIYQQ41hRURGGYdDd3Z2U15cgIoQQQowTNTU1/Mu//Mt52+rr67FareTl5SWlJgkiQgghxDiRmZnJ+vXreemll1BVlYaGBp5//nkefPBBrFZrUmqSICKEEEKME7m5ubz00kvs2LGDpUuX8tnPfpbly5fz1a9+NWk1yWRVIYQQYhxZvHgx69atS3YZQ2RERAghhBBJI0FECCGEEEkjl2aEEFdF0zS6u7vx+/3ouo7JJL/fCCGGT4KIEGLETpw4wf7DR4gkNKLRKM2dnSxfupRJkyYluzQhxBghQUQIMSINDQ3sOniQlIWLyJ8wkYDfj97VyVu7d/NJpzNpaxIIIcYWGUMVQgBgGAaGYQz7+COVldjLppNaMgnFZEIxm0krLcM8sYTj1dXXsVIhxM1ERkSEGOf8fj+HDh+mvqkJXdeZWFjIovnzycrKuuQ5hmHQ3deHd+78C/Y58/Lpqjx2PUsWQtxEZEREiHEsGAyyYfNmTkVjuG9bjmflXTSZrfxuyxZ6enoueZ6iKDidTuLBwAX74gE/riQ8OEsIMTZJEBFiHKusqiLsTiHn9uW48vJwZmeTs3QpRmERFUeOXPbcWVOnEqypQYtGh7bFgwEip04yc9q06126EOImIZdmhBjHzra24iqbjqIo521PnTyZs7t2YhjGBft+b+7cuXR0d9O4eROW/AKikTCWvj6mT5xIaWnpjShfCHETkCAixDhmUkwYicQF241E4k+uB2KxWLh3zRpaWlpobW2lxdfPbR/5CHl5eZcML0II8cfk0owQ49i0komE60+ja9rQNsMw8J88ydSJE/9koFAUheLiYm655RYmTZpEfn6+hBAhxBWREREhxrFZs2ZxurGRzu3bcE2ZimIyEz57BlcoxMLbbk12eUKIcUCCiBDjmN1u575776W6uprTZ+pJ6DrzioqYXV6O2+1OdnlCiHFAgogQ45zdbmfBggUsWLAg2aUIIcYhmSMihBBCiKSRICKEEEKIpJEgIoQQQoikkSAihBBCiKSRyapCiGtG0zSam5uJxWJkZWWRmZkp64oIIS5LgogQ4pro7+/nZ79cT1fEIGZ2kKr5mTGxgI+uvgubzZbs8oQQo9SoDyLt7e0888wzHDlyBIfDwd13383Xvva1C97YXnvtNb7+9a9fsP0Xv/iF3JYoxHUWCoU4VH2K055yBvKmgGLCrEXwNR3C+d4ePnL3nckuUQgxSo36OSJ/93d/R3p6Otu2beNXv/oVR44c4Qc/+MFFjy0sLKSysvK8PxJChLj+Tp48SQceBtKngTL4tpKwOGnOmEPlqQbC4XCSKxRCjFajOohUVlZSU1PDV77yFTweD4WFhTz66KO8+uqr6Lqe7PKEEOf0D/jpN6ddsF21e4klFAKBQBKqEkKMBaP60kx1dTX5+flkZGQMbZs1axYDAwM0NTVRUlJy3vGhUIjHHnuMw4cP43K5eOyxx3jwwQeH9Vq1tbU8++yzF9338ssvj7gPQowH3tQU0hOt/PG4hy02gN1skJKSkpS6hBiuhx566JL7HnjgAYqKim5gNePLqA4iPp8Pj8dz3jav1wsMToz7cBDJyMigrKyMRx55hPLycnbu3MmTTz5Jbm4ud9xxx598LU3TLvlbW0VFxcg7cYONpVqHS/o0+kWjUXLx0dVeTZ+7GBQFc0Ilf+AYmRkOamtrk13iiN1s3yuQPl3M5UbttA89nVpce6M6iMDgI8mHY+XKlaxcuXLo32vWrGH16tVs3LhxWEGkvLycDRs2jLTMUaGiooKFCxcmu4xrSvo0doRCITzNrXSHOomanXg1H6Ul2XxszSocDkeyyxuRm/F7JX26uMu9/9fV1REMBq+qfXFpozqIZGRk4PP5ztv2+39nZmb+yfMLCws5duzYdalNCHG+zMxMVq5cSWNj49A6Irm5ubKOiBDiskZ1ECkvL6ezs5Pu7m6ys7MBOH78OJmZmRQXF5937CuvvILX6+Xee+8d2lZfX3/BcUKI68dmszFt2rRklyGEGENG9V0zM2fOZN68eXz3u98lEAjQ3NzMj3/8Y/78z/8cRVG45557OHDgAACqqvKd73yHyspK4vE4mzZtYvfu3Xzuc59Lci+EEEIIcSmjekQE4Pnnn+fb3/42y5cvx+FwcP/99/O3f/u3ADQ0/GF9gi984QuEQiGeeOIJuru7KSoq4oUXXmDOnDnJLF8IIYQQlzHqg0hubi4vvvjiRffV1dUN/V1RFB5//HEef/zxG1WaEEIIIa7SqL40I4QQQoibmwQRIYQQQiSNBBEhhBBCJI0EESGEEEIkjQQRIYQQQiSNBBEhhBBCJI0EESGEEEIkjQQRIYQQQiSNBBEhhBBinPrnf/5nysrKklqDBBEhhBBiHDpx4gQbN25MdhkSRIQQQojxRtd1vv3tb/Pwww8P+5ydO3fy3HPP4ff7L3nMli1buPXWW2lsbBx2uxJExpBAIMCZM2doaWkhkUgku5xRKxwOc+zYMd7ZsYN9+/bR09OT7JKEEGJUWbduHQ6Hg7Vr1w77nF/+8pf87//+LytWrKC6uvqix7S1tdHb20soFBp2u6P+oXcCEokE7+/dS019PYrLja7GSLFYuOv22ykqKkp2eaNKd3c3b7z1NqrbjTU3l4Q/wNFNm1i+aBHl5eXJLu+yDMMABh/gKIQYf4LBIEePHh3x+ZNXrSFtGMf19PTwwgsv8PLLL4/odSKRCA888ADPPvssn/70p0fUxodJEBkDDh46RHV7O5mrPoI9PR09kWCgrpYtO3bwZ5/8JB6PZ0TtxuNxAKxW67UsN2kMw2D77t3oEyeSO3/B0Ad6qL2N999/j+LiYrxeb5KrvFA8HufIkSMcrjlFJBYjPzuTWxbMpaSkJNmlCSFuoJjFSpM386rOH47nnnuOz3zmM0yePJmWlpYreg2TycR3v/tdvvKVr/CNb3yD48eP88wzz4yk3D+0eVVni+suHo9TdfIk3gULsaenA2Aym0mfOQs9I4O6urorbrOzs5PXN2/m57/8P37+y//jza1b6e3tvdal33Dd3d30BYOkl5efN6rgzi9Aycqivr4+idVdnK7rvLHlbTZUNLDfMp1D3mVs96WxbsvOEX1vhRDicvbt20dlZSWPPfbYiNtYu3YtGzduxO12s27dOj7zmc+gquqI25MgMsqFQiFimoYzO+eCfdbsXHp9vitqr7u7m41vvUVnSirpH1lD+urVtNodbNi8Gd8VtjXaqKoKFgsmq+2CfYrLfVX/Ua6XpqYmalp7qc9ZRiilENWRji+jjDr3LHbur5C5QEKIa+r111+ns7OTFStWsHTpUj71qU8BsHTpUt58881ht1NaWsru3bspKSnh+PHj3HHHHXR0dIyoJgkio5zD4cCimIj5+olEIgSDQSLRCIZhoA30k+p2X1F7h48dQymaQPbCRdjT0rCnZ5C9eAlaTi7Hjh+/Tr24MTIyMjBrGtE/mpyqaxpaRzvZ2dlJquzSmltaaDPnoJvPD0/B1EL6grGbYqRKCDF6fO1rX+Ott95i48aNbNy4kZdeegmAjRs3ctddd11RWykpKWzZsoU1a9bQ19fHqlWrqK2tveKaJIiMcg6Hg4LsLFref5+evl78sRh9AwO0VFcRb2mhrLT0ito709REIiODzu4uunt7CIfDoIB74kQa29uvUy9uDJfLRfm0afTv30e4vR3DMFAHBuje+z6ZdvuonHNhMpkwo1+wXTF0FAxMJvkvKoS4drxeL3l5eUN/srKyAMjLy8PpdF5xeyaTiR/84Ac89dRTaJrG66+/fsVtyGTVUU7TNHr7+0FViezbiyU7Bz0SId7RTrbLRWbm8Cc2nT17Fp/fjzMex+F2o2kJ+gMBYqqKSVWxms3XsSc3xrJbbsFisVC5bw99moYZKCksZMU992Aehf0rmTiR/KMn6YmH0Kx/GN3yDJwlNy3lir6/QghxpYqKioY9H23y5MmcPXv2ovseeeQRZs6cyaOPPjp0I8RwSRC5gXRdp6Ojg3g8TnZ2Ni6X60+e09jYSMRsYcoDnyTS2Um0pxtTbi6uW5bRu+Md2tvbKSgoGNZr796/H1dhEfHWFkxTp6LYHRgOB6HuLqirZdnkydeim0llMplYumQJ8+fNIxAI4HA4cF/h5asbqaCggEVlE4if3EezYyKa1UVKpIvJeier7r5LbuUVQowa3/zmN/nmN795yf233XYbe/fupbm5malTpw67XQkiN0hLSws79+whoKooFismNcacsjKWLlly2eH3YDCI2evFZDbjLijA/aHQYXanEAwGh/X6XV1dBFWV/DV30bZrFwPvbMNaUIgWi6Keqcfk96OXlKDr+k1xOcBms42J0QRFUVh150omFJ7k2IlTBMPtFE/OYv6cj43KOS1CCHE5Ho+HWbNmXdE5EkRuAJ/Px+YdO7DOmEleaRmK2Uy0p5sj+/bhsNuZP3/+Jc/1er1oVVXoiQSmD11aSMRiaAH/sNfFMAwDFAWry03x6o/QunM7gYpDmL1ebIXFuGZ5OHjmDL5AgFV33im/id9AJpOJ6dOnM3369GSXMiy6rnP69GlOnG4gpsaZUpzPjBkzkl2WEGKMkiByA5yorcXIyiZ95h9SojM7B23ePI4eOcycOXMuOX+huLiYNJuN3kMHyZi/ALPNRiIapWv/XtwmE83Nzfj9fiZNmnTZGrKzs3EoJoLNzbhyc4n5A6Td81FsOTmovb1kpKdhnjyVU1s2M7e7m5yc828X7u3tJRAIkJIyOG9Bgsr4pOs6m99+h8MNnbTYikiYU8jqaGBKdR0zJhUnuzwhxBgkQeQG6Onvx5abe8F2V24enbEY4XCY1NTUi55rNpu5d/Vq3tqxg843NmJOSSHS1UU0EiGSkcH+jg5Mp06RUlHBxPz8S9ZgsViYP2smO3ftQHc6UYNBLP19JNQYLq8Xu92OYndgycmmtbV1KIiEw2He2bWLlq4uTC4XeiRCQWYmq1auJCUl5dp8gRgcsdE0DYvFIiFnFKuvr+dwQyc12ctJWBwA+I3JRLoqsDQ0snz58iRXKIQYaySI3AApLhcdAwMXbFcHfFhMZhwOx2XPT0tL48H776e9vZ36+nreP3sW2+w52KfPgIQGmka4sYmj1dV09ffT5/OR4nZTXlrK7NmzMZ0bOamoqsKUlk7cbod4nOD+fZjtdswTSzCW3YpitWIk/jBHxDAM3tq+nU6TmZyPfRyLy4UWidBZcZAt27bxwCc/edWhwTAMqqqqOFpTQygcwWG3M7uslPnz5583VyUejxONRnE6nVgs8mObLLWnz9BqLxoKIQAoCr1p02hv246qqthsFy4oJ4QQlyLv6DfA9NJSat9+m3DJJFznRkYSagzfsWPMnDxpWM96URSFrKwsfrdpE0pGJqmLl6Cc+6COB4NohQUET9TQ43LjKCrG39PNu4eP0NnTw4rbbuPtXbswzZiJLT0dm9OJbrUSPV6J1t5GOBik8+AHpE+fjt7bw4TbbwMGl4Jv7+sj9+P3YbbbAbA4nWQvXUbHGxtpbW29qofuhUIhDhw8SG1bGynlc8jIyiLm6+dAZSX9AwOsuusuEokEe8498C+uaTisVsrLyli4YMGovB33ZhdVNRLKhXchJUxWdGPwdnMJIkKIKyFB5AbIz8/n1nnz2Lt7F4HMLBS7Da2zk4L0dG5ZsmTY7TQ2NhIDrFlZQyEEwOJ2Ezl3d02wuYlAeztmrwdNgcOVlVhNJlSHk5TiYiKRKLaUVHRdh5kz8TeexTFtGr6KQyTaWlk0YwZpaWl0dXVRU1OD4U7BZD//g8VktWLJyMDn840oiPT09LDnwAGa29vxB4J477wTa0EBNqcTm9eLIzOTk+fmqtTU1RFxu/Heehv29AyiPT0cOnKYcCTCyhUrrvi1xdWZUpxPdns9A8YU+NBoWEqwlTS3fUQLIgkhxpYf//jHrFu3ju7ubhKJBM8//zz33HMPr732GqdPn+bpp5++ovaGHUQeeughbrnlFr74xS9ecdEC5s4dfJpqQ0MD8XicvBkzKCoquqJLG5FIBJvHS7ivD0PTUM5dojAMA12Lo/X3YZ4wEe/yFZisVvR4HP+e9zlaWYm7bPrgIjMWy9AtujanE7PTiUnTsJhMLJ05k/Lyct7YvJmW7h4SFjPBQABTeweZWZnYzj3DxdB1Ev7AsNZB+WOBQIDXt75FoqgI19y5RM40YMrLp2/AR4YCTocTa0oq5qwsamtr6QuHmfDRj2E59wHnLizE4nZzYttbLJg3b8RPHhYjM2PGDKZU1xHuOkSPdyq62YY72EpptJ7SyVf28yyEGHsefPBBjh07dt42wzAAWL9+PUePHqWnp4d/+7d/G3abw14w4uDBg/zXf/0X3/rWty55zJWupjbeeL1e5s2bx+LFiykuLr7iN+2MjAwUNYbVZiN48AP0aBQAPRQieuQIRkLHs3w5pnOXekxWK67y2WhmMwONZwkGAmiqipZIoKoq8WAQJRImMysbt91OWVkZO3fvpk03yFn7cYruux+7x0v4TD09vX3oho5hGPRVVeI0dCZMmHDFX4Pq6mrUtDSyFi3G6k4FPTEYhlxuAsEgBoM/0EZcIxAIYMnKGgohv2dPS0NJTaWzs/OKX19cHafTyYP3fYw1k5ws9O1nYc+7rHT38OBH7xxaKloIcXN69dVXOXbsGDk5OfziF7/ghRdeOG//j370I7xeL6+//jqhUGjY7V7RpZn8/Hx++9vf4nA4LhpIfvKTn7Bu3Tref//9K2n2pqPrOoqiXLPfDgOBAGfPnkVVVTxWKwGHA9XnY+DNTSgOB/HeHvRQCNuECec9edbQNDB0rJlZBFta4NRJzJOnDN79osaIfHAAe3oGgfrTTMzPR9d1zra3k/2hEYiC5StofXcXA81N6Hn5mEMhHKrKPavuHtGk0ZbOLpwlJSiKgis/Dw6qqM1N2AqLiIdCGIZBpKsTBnzkzZnDqdpaDMM472tp6DpGLDasuTXi2vN4PNz7kVWsjsfRNA2Hw4GiKPKAPiFucq+++iomk4ktW7aQkpLCjh07ztufmZnJ1772Nb7+9a/z9ttvc//99w+r3Sv6JPnUpz5FKBTif/7nf3C5XDz55JMXHDNe34wMw+D06dMcqayib2AAp8POrGnTmDdv3lXd5VFZWcmeigpIT8dkd6BGo+D3Y7FaMSsKuq+fqRMmkO7xsLe6GnXAh9nhxNA09EgYeyKBr60Vk91G9MwZjIgnzasAACAASURBVFOnUM59gCtWG4m4Sm5+PivvvZfe3l4UuwPrh27LdWRmMunjn6B562Yc7W14vF5yJ1zZaE4gEODEiRO09/TQ090NGRl4MDDb7OTMm0/nwYOobe2YnA5629rQzp5h2bx5g4+ZPnCAUGsLKUV/WKPCX38au25c1UTZP6brOq2trfT19eFwOCgpKcF+boKuuDir1SphUIhxpK+vj6ysrMsu3XDrrbcC0NHRMex2r/gT8qtf/SqRSISf/vSnOJ1OHnvssStt4qZUWVnJ+8eO4Zw5i/TcXFS/n4PV1XT39nLPRz4yotGRjo4O3j98GO/tK3CdWyMkEYvR9e4upqS4mTljBh6PB4/Hg67rHD9xgvjx45jKpmOxWbGZLXS8/RbmvHwcc+eC04XW0kysphpTRhaKAnpbG1np6YRCIRRFwYhF0cJhLB+a/xEPh4mcCxAUFOILh6nZupVFs2axaOHCy/atu7ubN956G9XrxVFYSDymEqypxlpQQEZeHp6pUzE7XbS/uws7BsUlJcy6806KiweDx9TiYpr37yNS2IwlPR21pxs6Ollzx4prdhtvKBRi45a3aejy47Ok4UyEybEd4BOrV47o8pMQQtyMHA4HAxdZiuLDampqAC5YFPNyRvRO/k//9E9EIhF+8IMf4HQ6+cu//MuRNHPTUFWVD44dw7Nk6dBv7vb0DJw5uTRs3jT0YLqOjg6OV1XR2duHy+lg5rRplJWVXfLZLrUnT2IpKh4KIQBmux3v3Lk0vv8ed65cOfQbqclkYt6sWbR0duI7sA/Dm0ZvcxOKy41r4UKMlFTMbvfgHTc2G/GmRhRXKmBQUVXFqZYWFMNAj8fp2reHvBUrMVmtGIZByztvYyksoujuVZjPffhHe3s5tHM7xUVF5OXlXbR+wzB4d+9e9AkTyF0wGFi800pp2fEOXW+/RXDyFDQM1OZmzKEgi5YtY/Hixed9PfLz81myZAm1dXUMdHWS4fUyY/FiMjIy0HWdxsZGGpuaAJhQXExJSckVPyvn7R3vcqjXREveXRgmKxgGrQNn0N/ayd/8+Wew2Wz09/djtVrxer3DCpWGYRCNRlEU5U+uE3Mp4XCYmpoa6s62YjGbmDVtEmVlZTIKMUKJc3Oj7Hb7TfE8JSFutCVLlvDKK6/w7//+7zz11FMX7Pf7/Xzzm9/EZDLx8Y9/fNjtjvhXyueee45IJMK//uu/4nQ6+bM/+7ORNjXmdXV1oZpMZBaef6nA4nRiycujtbWVWCzGW7t3Y55YgnPePPyBADuPHqWto4O7Vq686IdbIBTCknPhiqz2tHR8iQSxP5on4Xa7+eynP01zczOBQIDjkTDRkklETCa0SATDYUcx2bBNmEj02FH0zk4UqxXP4iXkz5hBIhii53AFkTP1tL+xEWtGJqqvH7W/n+KVdw2FEBi8ZGMuLOJ0ff1QEAkGg9TV1dE3MECq201+Xh5dfX3k3LZ8qH+KyUTRXas4tXEDvsMVmN0pWFxOEnY7uysqCIbDpLrdnDhzBlVVsSgK+fn5rPijFTs1TWPrtndo6uvFei781ezbx4S6Ou5ZvXrYoyU+n49TzR20Z98xGEIAFAW/dzLtnW1s37GDpq5+BiIqZgVK8rJYdcftl32gXmtrK7v3H6K1qxcFmFSUx4plSy46mVNVVY4fP86x2jNEVZXJxXksmjcXh8PB+o1vcipoodtZiElPUPVeJbNP1XP/2nsljAxTc3MzFYePcLahkVA4gsVsIS3dy+JF85k3b54EEiGuwFNPPcVvf/tbfvrTn/Kb3/xm6H3wJz/5CT/60Y84ffo0uq5z5513XtF6QiMOIiaTie9973t88Ytf5Jlnnhm36wfouk5zczPBQICu7i7sdjspbjcWy+AHhZHQAXjvwAEcc+aSVlo2dG5K8QTqtm5mZkcH+RdZnj0zLY22ri6YPvhAsYQaw3/mDL7TpzCCQdra2pg6dep5b6Ymk4ni4mJaW1tRozEG+vsweTwYho7W04Nis5EIBNHDYUDBuXARSjhE7+HD2LxespfeQpfPx/xJJbjdbuJ5uew7dgx3evoF9ZldLiLRCABtbW1s3r4dzZuGNTsbra+fiqoqotEohtmMgYHCYBiJxmLg9eIwTyL93o+hKAqa34//3V3sP3SItEmT8MyZR4rLSe/p07y+bRurb7998I6dvj5cLhfhcJimQIDce+7F7HCgJxIE0jOo27uHgfXrmTZtGoWFhRQUFFx2BCMUCqEqFlSzk6DfTzAcxTAMnA4bHXEbR2obOJN3G6H0PMwJlabeOvpe38IXHrwft/vChb3a29tZv2kbtZaJBLLKUQydMx0NtG3czP/36U+QlpY2dKymafxu0xaOdsbocE8iYbdx4mw7dQ1vMikvk+qQk5bcJUPrdfj1iRgde5h+4gRz5sy5aH8Mw6C2tpaKqloGAkGyM9JYNGcWkydPvuTX4GZVVVXFlq3b6e0LEgqrtHfF0TSD7KwI3T3v4g8EWHnHHckuU4gxIyUlhd/+9rc8/PDD9PT00N/fD8CJEyeAwc+flStX8qMf/eiK2h12EFmwYMEFb7wWi4Uf/vCHPPLII3zjG9+44kf/jnWGYbB9505OdXZiKAqRri4SBYWEe3rJysjACIdJdHaQOqmEoKqSN3nKeeeb3S70rGx27NzJ5EmTKCwsJCMjA7fbjaIozJg+ncrX36C/9gQphUW0vLuLuK5jzs7GlpbOmzt2YN21C09KCm6HA0PXycnJ4f0DB+gNhxno7YVIhNTpMzC7UzBUFc3vJ95wBmtWNtqAj2hVJUZmFprXi3aiBlNVJc60dBRFYc6cOWiaxrHa2sEJo8UTzut7vL2NvNJSdF3nnd27MZWWkVc+Gxj8kO2sPUH4vd00Ha7APXkKqW43LreLgH8ArauTlAV/mF9i8XiwFRSg+f14lt1G6rkPbK/DSdRu57VNm7ClpWPNzcHU2spAUxP2vDw6D36AHosR6e1BMwwMl5OzvgFajxzBVV3NpPx81qxadckREo/Hg82I4+9ophcXqtkFJoVwOEJpoJ12TzGhlAIAEhYHXVlzaOzcQ11dHQsWLLigvQMVRzllLqI/8w9Po+3Kmo2tK8rR45WsXPGHkZ3a2lpqGjsYsOdij3QTTi2iM2su8T4bA7XH6Cm8+7xFwwzFjE+3semdXex4/wAmE8R1cDoclJdOZuH8eVQcOcquyjM0OiahOqbi7OulfutuPnZ78Lzwous6DQ0NnD5zFk3XmTyhiKlTpw6NtBiGQSKRwGw2j8m1QaLRKDt3vUdbl4ZZSXCqMU5cM1Aw0DoDGIoVZ8VxFsyfL2vRCHEFSktL2bNnD8ePH2fbtm10dQ3+Ar5o0SLWrl07olHGYQeRX/3qVxfdbrPZePHFF3n44Yc5evTomHzTGqmmpiZOtbeTeedd2Fta6Kk8jhEMong8tDWcwdbZwZxp00hJSRm8BfXcN0g3dPr7+wmGQkRiMQKRCO2tbagffIDdZKK4qIhbFy+mqKiIj965kp179nB23170rGzcixbhNJvp379vMDxkZhFOJNA72jHCYU7/+jekTJ+Oo3gCobMNKA4noXffxVoyCZPTgdp4lnhjI5bsHIxolJSlt+AqLRucqGoYhCoOEag9gb1gcITGYrEwf+ZM3juwnwG/H9IzIB5HbziDR1WZOnUq1dXV9A4MkJORiWEYGIZBT18f5Bdgyy9Ara3F7E4hnppKrM/EwNGjGJqGY+LE876eiXAYa3ExisUytJ6Iruv4mptJFBZinr8AQ1HQDQOzN43g8WM4M7KIttZjnViCc+o0LJmZGKpK7HAFWjRKYyDIkSNHWLx48UW/h6mpqeR6nExsPUFP2jJ0sw2TkWCC2oIn4adVyyMl0EzIXYBhMg8+V8WWQ3NHN38cQwzD4GxrBwHP0vN3KAr9rgLqm8+w8tymYDDIm9t306E6aDO5sMVUCgY+wOUtZCC9lIzOoyh6HGeoA7MeJ2ZNJa9tL9ZQDz4DBoJRGu3F9NhzybS7qK9qo/b07+gPRqjNvoO4ffDDNerKJhpKw7X/MKWlpTgcDhKJBJu2buNYYzdttnx0xUL+mSOUVdVy/9p7aGlpYf+RSrr7fDhsVhbMLGXhwgUjuhz0+xFDn8+Hy+WipKTkul9WMgyD6upquroHiEQ0jEScSEQjoScGF14yTLS19+J1u9i/fz+rV68eV+9bQlwLc+bMueTI7JW6JrcdOJ1Ofvazn/EP//APHDp06Fo0OSbUnzlDTFFo2rYNTVXR4ypaXS0Wlxt8/dxyyy0EQiHefOcd/P0+1E1vkLNsGX1qfPBNUdPQOtoxpaWhFBXjLi0leuggfSmpbNq+nfs+8hEmTJjA5/Ly+On/+3+45szFnZ5B8573STicuJevwFBV9FAI05QphPbuRQ2003/y5ODD8Kw2XLfdilnLRz1Tj3FuATRUFfeAj0hGJtb8P1y6UBQFR9l0ItXV5/2WmJqaihYIED5cAWYzJHSURIKc3Bw2bNpEc1cXmtNJ03u7sbucpC9aQsJiwZaWhjU9HavbjXqyjkggQFBVyUlPo8fjRQsEBhczcwxe1kuEQyh2B6qq4u8IAhD39aEGA7iW3Ypis6Ocm+9inTSJRG8vRjSMOSMD+8xZ5wIMYLVinTuP0NbNOGfOourUKRYtWkQwGKSyqoq6hhbMZjPl00qYNWsWZpuDFKvKioF3CZpScCbCuDQ/McVGXI2R0lGDx1xNb8FSYo50bIkIKc6L39prtVow6eoF280JFZvdSjwep6mpie17PqArqOK3ZtCvpBK0umkzZ7Oo/yhOexpWE+S07iNichEz2ciP9aAk4hxzziRX68FvTqXBXoKCQShmIpG3FL15Jw4NVFsqH/5Yjbhy8fmttLW1MXnyZE6cOEFFUy91uSvQzYP98OnTiHbux75lK3UdA5xxTiWcXo41HqL5yEnaunr45NqPXtFvO4FAgA2b36axN8iAJQ13Ikiu4wD3rbmLgoKCi54TDofp6+vDbreTlZV1yYDw+5Uc/3i/YRjs2bOX9/fsJxCIMOCPk+pWiGsahj4YbwdHe3T0hM6hw8cpKSmhtLR02P0SYrx65513/uQ6Yb+fqP/0009fdi7dh12TINLa2sqvf/1rampqCIfD16LJMaGxuZmYYWArL8fq8aKHw8Rqqon39YKuc+zECSgoxLPyTmyxGD11dTRsegPb9BkoFgvxpiZMHg+WrGyiVcexTZmGKTcPA7BOmsyBQxWkez2cbGhgYGCASNVxOnwDaMEArttvh0QCxWLBZLNiaBqWwkIS0QiOhYtIdHYSb24itH079vLZmDMyiDeexZ6Xj5GWRry3B1QVgkFiqooejWBoCbBZsTidNDY2UlRUhNVqZW9FBZm3Lye1ZBKq308irhJub+fk3j0oLjf2iRNxFhdjyckhevw4nXvew7NqNbqqEmtuQne6SMTjmGw2DDVGQW4unSdO0P/mJhSrFUtGJvYpk9G6ujAMCE6Zii0rC8wm4h3tGIkEJocDXVFAT6D/vt+pqWjdXZhzcgEDLBYwDHTFguFKxXC5CYYjuKJROjo6+M0bWzkTT8WXWoIST1B74DS19Y0oikIwbRK6yULqwFkc0X4arBOpd0xGVazoTi9TQ3WUtB2gN38JxVo700tXX/DzoCgKc0onc7bqDI3O7MERFMCUiJMfaWTWnDI2vLmV/WcHOMkk+lK9ZMa6KA8c46irHJ8ljXYlnWlNu1HsNs5ai6l3TEEz2Ug1dzI1egYzOg4jxiHHfFTFhkuPENcS9Pb7UBPpTIn00NbZhcftIjU19aI/t8fr6ml1lpAw21FjMXRdx2q10p46mWMn36WpYDkhz+DE67jdwxlnJu6WnSxsbmbiH41iXYphGGzetoNDA3ZashcRUTUMXSc/0oSxdTt//ecPnrdOSyKR4P3393DkaCXRqIbZrFBYkMOaNavPezPr6+vjgw8Ocer0GRQFykqnsmTJYrxeLwANDQ3s2XuI1m4zTpsFNR7D6bDicZvo9ycwmUAxKaS4TNhtCl19CgcOHGLatGlDoUbXdZqamujv76ejo4NYLCZryggBvPLKK8NesPQv/uIvrn8QSSQSbN++nfXr17Nv376hIfnxMsQZDAbxRyLYFizEnJ2DOTUVTCYseXn4f/caiZCPQGERk269FbPJjC2RIGwyk9A0IhWHsGRkYCstwz59BorZhDk9nejRo1jy8umpqSajbDq19adJKy3DvWQJxttvk7BYcay4g/B772Jyp6KfWxI93tpGvLkRIxLBiMeJVVWiWKzYJk5E6+omWlWJOTWVlFWrsdgdOACn1UrLju1oVZWo0QjxcGRwQqnVihEKUtPRQdemTdy2ZAmBSIT8SZPRwmG6DlcQONuAYbVimzkLS04uie4uIhUVOObNxzFvHoGtW4jUnSTR2YGuqljnziN18hQMTSNw7ChHamvxlM9GdThQ29tQu7qI1NViAQy7Hf87b6PYHdjycol1dWHEYmA2Y/Z4wDDAZiMx4Efr7MCamkq8uxtraRnE42Cxohg6RiSMEQqhhsMYwSA//+V6zuhptNs8KIEopvRC2lxegg17yNJ6yEyYSSgmMEBDQVMUyiK1xBUbnRRx1lbEhIEGZna+y4pbFpz3G71hGDQ1NVF7qh5/MEyJ0ou9fRcdrmIUQ6cg1srsAi8Oh4Oa9gGq0xbRH4njM5x0OdOJKjZmRU4QNrnI0npRDI0OPZNg9ky8qko47GfA7KXKOYPbg/uwGBpzQ5X0WdNxJiKYFR2fnoWq2LGgk0jo9ATCJHSdNK8XZ7iTNHN8qOZITCVqmGnv6CKuG+gomNHJVKIU6jqhlPMnTutmGx2WHNra2i4bRHRdJxaLYbPZ6O3tpaGjlzMpt9Dd3U8cM4ai0KtnkOY/y8mTJ5k9e/bQufv27efd9yro6jeR0C0o6HT1thIMbeQLD30eh8NBX18f69b/hsZmP2F18PJOa/sxGs428bnPfobU1FSqqmro6oujJRzoZjuTizX6fCplkx30+jRU1cDlNJHutaCYdKx6lNP1DbzxxpvMnz+XtLQ0Nm7cRHNrF6GwDkaMxqZWPr72o0Nr21wNXdepq6vj+PEqgsEwhYX5zJ8/l9zcC++OG21+f7t8a2srZrOZkpIS8vLyxs17voD7779/KPR/2O9vJDh69CiGYfDwww9f0RpMVxxEmpubefXVV/nd735Hb28vhmEwceJE7rvvPo4fP8677757pU2OSb29vSguFyaPF5PLNTSp0GSzYS0uJuHrx8jJob2jg9SUwfU6DJsV2+TJxBsbccxfgO73Ez12FHNaOpbCApSqKuKtzdinTGWgvY1ELIbJ7ab32DEsmVk4ymejOJ1YsrJJdHdhmjaN6P796MEAtqnTUCwWtI4O4s1N2ObOw2R3YEpLRzd0rOnpKCYTRjRKWm4OZrMFZ2kZA7t3kXLLMpxTpwEQPVGDWlVF+qIl+Opqqa6pAWNwUbOG1zdiuN3gdOJacgum1BQsmVkopaVEK4+j1p3AlJKCYrURPLAPs82Gc/YcXDMHJzHH/QM4p05F0XViHe3EtQTmoiKsxROI1Z8m0dWNfeZMbJOnkPD1E62uRvf7sRQUEDl4ENNtt2P2eDB0HfXUSRL9/djMZiLN7cQ2b8E1vRTbtFIMk4XIkSNoFgfRsy3E/VFMGDjNYbLVTpyRMJq/jhPOmQQ1iJJOnWsafZZ0MhL9TIvWk6t2c8JRSk6il/mBCjrSpoPLy4qFxSxbumTozdcwDHbufo991Q202AqImz1kaX7SYz0UWiOkpKay8Jb5lJaW8vaOXbRaCzDMNoxEFLcewEDBaqikJfw024o4bZ/EjOhJBixeevp8uFwOUt0u1FCMGdE6VMVOvW0S2YlupkdP0WXJJqw4mRSpJ0WPEHdnsiB8jAZbMbrPjDvSxKRYA4sWzRpaz6QoOx3r0TMEUhei2QYviylGgsKBs5iNBMrQDJ0/MJO45GWZaDTK/g8Osv9IFbsPVZKV7qE4J4OAptAVVAma3KiYMQwDk8lBh+5iz9695Ofnk5mZSSwW48iR43T7BkMIgIGJYNROR+cAp0+fpry8nEOHKmhsCeCP2ImrGtFolD4MdL2bo0ePsXz57fgDQXQdUuwDmBQNBcjJtKIoChaLgq4PXl3U9cEBQYjR0a2ze28dtXX1ZKSnUn+2H1/IDlgIBKKEokFef2Mzf/XwF67q7kDDMHhn+w4OVVTR3ZcgoZupq+/hRO0p7v/kxygpKQEGJ3o3NjbS39+PYRiUlJRc9jLVjaCqKhs2bKC6upqzZ89is9koKSnhtttu44477pAwMk6sXbuWtWvXXnK/pmmsWbOGN954gy9/+cvDbndYQUTTNLZt28arr77KgQMH0HWd1NRUPvOZz3D//fczf/58gMs+EO9mY7PZSESjmBQFFDASicEdioIRi6FYrIBBQlEInrvsYU5NRQ+HMbQ40YoKzDk5mD0e1PrTxE7WoasxEv39GNNnoISCYHcQiESIhcNYiorB0NGDQSy5eUSrK0kEgyR6ekhZtRqsVoxYFHNmFqaUFKIHD2IpKYFIBMPvx0j1gKbh8XiGbi12pHkJ2OyQmkoiEABdx1ZUhBEO07zjHTyTJtHQ3IwaiXLyly+D2QSxGNbCQkwez+A8FMNA13Usk6cQrakhEQxixGI4ZpUTb2oEj4dQaysmsxnruTswzGlphE6dwrViBWaPd/AyS0YmWnMTWk/34MiaYWDOzEDv78M+q5z4yZP4N28afE5ONErCHyAYinNKNUiYC8ns7ya0rwLXseOYzCYS8QQhw85pIx/dnsfU2Fl2pd5O0OLBpkUojZ5mVrgKm65yIGURQXMqViPOgNnDfvdibg3tJyfRTXrCTxwTxf2VuGwmsrMXnPem29TUxN4jJ2i3ZGEJ92HXVeJRP6eVVIyQmwlaFON4NTk5Oei6AQoM+ANoukFMsRHFzvx4N7WOUnotmego+CxeHLpKXLEQjOk4LQmmxBoAhQPuhTiNKAVaB++kDn4AGMBZYwKLwkfBlomekcmU/gZcwXbsYUi403nvaC0Nza1MmViM3+cjN95FSfQszc4SdMVEQbSFCYkOFBSy2w8ScedgUwPoZgdRRzp58S6czglUVlYSDofxeDxkZ2eTlpbGa29s4Uivzhn3AhSnB6P9DJNOHcFiqMQ9KhGLG8MY/P+RMHScWpDGjl7+53//j4UL5jBjxnRC4SgJ3QwYMDTDRWEgkKC7uwfg/2fvzWPkvM5zz98531Jfrd1d1fvCbpLdJJv7IpGiJEq2RcmKHccSookTJ3PnBp44NiA4cIAEcRYHhoEAF0bG2RAnDnJxZzDIxJY9lpdEcmQttmSJFEmJZHNnL+y9uqt6qa7lq287Z/4osiOKkiw58r13Ej1/sb+9il+d8573fZ7n5eroODXPYHV1DbdWIZ0UxGOCWk3yo+dfYP/+fbS1ZXGsy7huRBBpCksBoOnIWcRigkhprk56OJYkk5YkEwZNaUm5WmHKi7Gyusp03sCyNI7jELM0UgTk80X++Z+f4L77PnCTBPudYH5+ntOnzzO7aKB04zcYuBbBnMczz/yQ//yfN1AsFvn2t7/H1MwCK6U6iZggHjfZvGkjH/7wg2871f1vRRAERFFELBZDCMHJkyd56aWXGBkZIbo+1k1PTxMEAX19fWzevPknXPE9/EeAaZr84R/+IZ/61Kc4efIkt91229s77612Xrt2ja9//es8/vjjrKysIKXk7rvv5qGHHuLo0aPvyLDkp8X8/Dxf+MIXePXVV3Ech/vuu4/f+73fe8N7P/nkk3zlK19hamqKvr4+Hn30UR544IGfyXN1dHSQsixWRq/iHDzUmJy0IpidJVpaQtgWwbVrWN29yFQav7QKnod39QoIQfzwnVg9PaA1OopwXz5OODeL0dpK/cUfI5uaSd57L1ZzC0Gt2lDcmCYojdnbi2Ma1I4dw+zoaARBWjXKKmGE1bcBf2ICZ/v2RlO8iQnqZ89g9W0g1tq2/hlqCwtYySSGZaHCEGEaKAQym8UbH6Oyuoq3tITV00t8YCNGU4bK0z9AJBIYzU1EhQJReQ2ZSiMdB4QgnJ9DK0Vs5y6iYhEMEwQoFVEPQ4SA+vgYRnc3VncPmAa67iGEwBocwhsbo/rsMxiZDCKdBiFQxSLWrr1ENZ+wUkGuLLC0UOOl9GGW7Fa01iRjFe6onkBVy8yabczZXSzE2okwyYVLbAhmsSOPDKuYKuSiM0RnsAACakaSjmCBDd40As2C1U5dxMiGq7yQPIQWkp5wnv5ogVNnzzM0NISUEqUU3/v+U5QqZSwafiq28pixu7kY24YpYdyy2TA1wthX/ytJJ0bSt1DOXkLh4AkLW/kklMuM3UWEiYGiYLaytzZCS7DMSqydIIpoD4pctgcIhUmvN8uk3UfVSGFrn6SqUZFJRpzt7C+dJd++hys1h33uGou9t1NOdBDNXaZnYpSz08tYKObMDnp0kU2lSQB8GeNqbJDhcITU0hVSS1dwjQRKmORUBSMV5/EfnmDeM4kFFeIiJJOI0ZNrYnw1ZLLr/ZRKZdaKZbwwy4oc4vbwAsPuZU44ezFExIZglm5vjkRUpRYIynMK3z/N0lKRWrVMxtFIIYi0RdVLECkTx5Ekk412A1JKQs+lKeHS22ahAaU01VrEwmKFr/7df2Xvnp2EocIPFOeu1ghDEGjyxYAdg3EsU5BOStpaLOYLAfOLAU0Zg54Om7WKhyUNCsUqplGho82mOS1YWlbM+RG1+iXm5hZ46KGff8dlGqUUp0+fZi5foVw2ceIOhnHdpTiwKS6tks/n+ad/epJXz81xbbqCHzQWOX2dEX4wTt17nP/0v/7qT+3U+3ZQqVR44YUXOH/+PFEU0dnZyV133cXJkycZHx9fD0IAPM/jypUrnD179r1A5D2s40aZ8cSJ8Y7ffAAAIABJREFUE+9OIPLggw8ihGBoaIjf+I3f4CMf+ch/91bfjz76KFu2bOGpp56iXC7z6KOP8hd/8Re32MteunSJ3/md3+HLX/4yR44c4YUXXuCzn/0s3/jGN34mjHgpJe+76y7+n8cfJ1pdxezpQZVKhPl5RDyBWl5CByG1l34M27YTujVq01OE+TxWby8yEUf7PmiFrnuN4OHKFUCAbWN1dyGuG3WZ3T34Fy9gbx4EFSLjcayBjYjTpxGGBEM2zMIMAzwfkUo1Jv9SCTOewN64iTCfpz5yhrlYjEy2Bcfz8M6fQ7ZkUVKCYaA1CMtAIxqde2MOMpsjcfcRVL2OlgJMk6hQaBBbDQPt+ahwhSC/gKqUCRYWiO/aCUph9vTgj48R33+gEUiFIVqFhMUi9rZhsCyEYSCSjdcwcl104BMb3o6zZy9CCOqJBN7lyxh2Eq97EO00oU6/ypjdz4rdkAsrYbBmZxkNN7HdvUQkTa45A2gEtm4oWIRW7K+dxtYhoTDWt8XDGntqI5g6ZNbuJhIGXX6e9rDIhLWB3fULpKMKCEFMwtWpOaamphgYGODpp59mcmGFGauby84QvrBoD4ts8ibpCeaZs7tYrXqUrc1kvUWqlQBHV7k7ONYgwQqJ1IqEcvFEjFBYpFSFRbOd8dgAe9xzuEEKz3DIRCUEAktEJFWNOasLjSAUje8uFBZrZgahFUuFRVrqC0g7htQRnaNPEkWKUEi0t0Y12Q6R5FV7G70dCepujcWawiACpSgbKSbsfpKqSntQRCnNNdfCNSwiLZlxBjEJ6ffzrM2tMB3rRQmDcrlCGCmEgBU7RxgatAeL/Fz4AyyvRmXNo16PECmDRFxS1rBW0bx88gJRpPGDkFI5IhH3aM54RDpOa0uSoaEh6vU6rbkWMskZCsuK81frLJci0kmDzf0xXE8xNb2IaVwAYbC4XCcKNVqBAiJfs1AM2LLRoUPaXJ5wWV5tTKprlQjXVWza4GCbAtOUaASZpObSeB0vMIhZGtuxUNT4l395ml//9f90S6lKKYXruti2fZNEuV6v853vfI8rV8coV1wKxRDTlGRbmklc92bSukH6XyyUmJmv4QcCjQANMwsR2WbFfH5lvUz1bmNpaYlz585x7Ngx8vk8ExMTuK5Lc3Mz09PTGIaB53m3nOd5XoNI77qMjIxw9uxZlFIMDw+zd+/eNyVMv4d/v/jqV78KQMsbmGC+GX5iaSadTvPAAw9w9OjR/+5ByMjICBcuXODv/u7v1pu7/eZv/iaf//zn+e3f/u2bBoKvf/3r3HXXXRw9ehSA++67j8OHD/PYY4/xB3/wBz+T56vX6wit8SevERYKGOk0IhZDe3XMzi7ChQUol9FTk4Tzc0TVKiKRRKbTDdltsAJSIuwYsrkJYduotRJEEdoPEFI2VDXXuR/VHz6H0d6G0ZIjzM8TlVbBq6N3ReuZkcjziAoF0GDkcmBIdM3FyOYIZqapPvMDKlrj2DbNsRiF0iqxRBKZSoHWhCsr+BfOYbRk8SbGsXp7CRcXEE4cTAMRc8AwqD3/I6y+PozWNoJ8nvorp0AI0vc/gLAsouVllFsjXFyk/L3vYHZ1YbR3EIyOgmE0pLdho7QDNBxfr02AUjg7djb63AQBZl8fYbWG9/KLRM45ojBCVSpUnCSR0mgkQiuQsGZmGlwGrRqDOKC0ZLM3gakj8lYno9YAG4IZ+oIZWqIyMXwSkcuzmbvRNCagObMTqxYy6E1wPjHMy8nbUEg6ZZn9pZd5/Hv/TG93N2cvXqYsM5xI7seTMRLKZdbuZtVo4mD1FFN2D1JoQmFRlw6rRhO9/ixFM8tYbGMj6xKtMly/wt7aCCeT+wGBEpKCmWMz4/hWkq7aNIby6QwWmI9voGxnaQ2KTMY24Kg6gbBIqwrtwSIa2FQaoVWXKHXsIZM/zRl7iEl7AxLFxvo1dnijdOiQomxmeilHoCXoiB3VEaQO8YTFDvciobTwhYkSAjusMWb14RopuoN5UqqKJwVYKbTv4rouSjVchD1fo4MA348wdR1DGoxNupRLPl6gQSk29lp0tzfKMG49IgxhbKqOUppIQbYpZHhQcMeheygUCvzLU8+wsrxGGClmF3zceuNeUaSQEpJxk7VKyHx+scFpCkBp1rkuUkAQNrgvYaQplSOEaLx+UgoqNUXVVSSzFhu6bCq1iHI1olQOcWIa07SIOw71QFBcKpHP59fJv1prRkZGOH78FGvlCrZlsmPHNu688zCO4/DDHz3PyIVJ1ioWTRmLmYUIt65YXl7Bsm2ScU1zcwbDMKjUQqLoX99faPBZPF9Tq4UsFgrv+jh26dIlvvOd73Dx4kWmp6dpbm6mu7ub6elpFhcXOXPmDDt37iSTyVB43f1zuRy9vb187Wtf4/Tp00xNTaG15vTp05w7d46Pf/zjN5Eb5+fnOXXqFFNTU6RSKQ4cOMDw8PB7Vvv/P8F/+S//he9+97tvuE8pRaVSwfMaGe4HH3zwbV/3LQOR3/qt3+Ib3/gGf/VXf8Vf//Vfc+DAAR5++GE++MEPvqG99buN8+fP09XVRTabXd+2Y8cOSqXS+qr0tcfefffdN52/fft2Xnrppbd1r/Hxcf7+7//+Dffd4MAAbN68mYMHDwI0Bl+tkek0Vlc3RjaLTKWw+wfQUUT5n76H4cSguRlzfg5hGNS9OmE+j715EGFZGLlWhGEQFgqIeBwjEScsFAiXiiAlaI0QEN+/n/rICP7YGGZbGaunF29qCuW61E+eQO/YiUyl8MfGCKYmsbdsQwiJrlQbJRuvjrNnL1Z3D/Uzp6nPzTYkv7lWKs/8YL1fSzA707B/jznoKGpIkkdHG4TYoS0Y6TQyk0HVqlSf/xFGKo2Ixxu8jmQS7fvoapXa8WOYbW3EDxwgKhYJpqbQbh0SCazubvTaGu7xY8S2DSMzacL5eeqnTyOSKbSKCBcWUJ6HbGkhNryd6vQcF8UG1oRNr3GNjKpg6QBXJtCAoSKywTK2DugMFhh2L1OTDt1Bnl5/hgWrg7zRyv2V57AICYRF3UiAEiyaOVJRDU/aGFph6YAxe4D2oMCk1UcoTAwiKpHknLMFq3Sek35IX6TJO+1E17MSERJH+xTMViJhkFAudemgEERIFq02TB1y3tmGZzisGhkW6aAuHQ5WTyErJyharfT4cwx4kzjKxalMo4WiYiRpCZf5UPG7eNLG0R6dq4tcs/u4EhukPVpiqD6KozyaVJnIcAhW5pmQnSyYbaTDNQCWzBbGdS873EvsCs5QNHO40qE1LFIXMQoyR1pVmYxt4JKzhUgYZKI1NnmTJJTLpeR2JmMb2Fs7Q28wT9lM0FGfZ25pHqUNwlDj+4ot5KlX6mjPx7Yk1TWPel2hVENdNz7lk2u2iNmCZFwwcsXF9RRSgGUJqm7E0mrIiy++yFq5zrUZj2xLgtJaxPV4ByFgaCDOaini6nIdP9AUV0K2bXLINRvkC/96XMyWpFON55OiIbWWshGp2LZARWAZgrqvicUELU0mi8WAhCMRQuP7AcvLywgpaGuyuXDhAiMj5xgdvUoYRuQXV1kshtRckAaMX1vk5KlX6OpsZ3ximvlFn5aMiSk1Wzea5BdD/FAR+UWacg5SpHj11VdBu+hIEQbqRoyOaYBlWli2Qeo14+7o6CgnTpx4W+Nbd3c3977Ozn5kZITTp09z5coVnn/++XXjuXq9zs6dO9mxYwcTExOsrq4yNDTEjh07OH36NKVSCSEEqVSKjo4OLl68yNmzZ9fVkwBXrlxhdXUVz/Po6Ohg586dJJPJdZuH1dVVwjDkxIkTbNy4kc7OTpRSlEollpaWUErR3NxMS0sLtm1z++23Mzg4eNPzP/nkk+sW46urq1y5cuVNP/8999xDT0/PTdsef/xxXNdd//vVV1990/PvvvvuW87/j4grV67cEoy+HvF4nN///d9/R1yqtwxEPv3pT/PpT3+a559/nscee4xnnnmGkydP8sUvfpH777+fhx9+mMOHD7/tm71TrK6u3mK/fCO6XllZuSkQebNjb7yoPwlaa8IwfNPnuIGpqSkMo5HaLxYbJDohJYnDh5GJ1wRnUYTV24d/6SLRwgIpy6IUhoimJqLlZfxLF3H27m9kIRYXcE+ewB4YQKbSVJ//IapUwh8fx9m5C60hnJokzM8T37MXs6MTEXcQZ0+DsjBaW/HOjaB8D4IQTBOrsxOtIjSaaHWFqFgked9RjEwG2dRE+duPU69UyBy4DV2vExUW0UIQ338b9YsXiAqLJO95H0ZzMzKXI5qbo3bsJewtW3BPnsTq7QHDwOztQyQTGNVqg4T6wvPge8S2bsMeHGqQa1tbsbcN477wPFZHJ8H0JIkj9+BfukTl2acBAVFEtFZCGJKVp57FKzXKIU42Q7y7A+WHLMbSuNpAWd3X5a5xpqw+fGHRFeTZXr+MrXy00Ox0zxNiMmN3s2B1UJVJDtdOMm33cs7ZRiBtuoM8d1aO48sYlvaRKkIjcaWDo+qEwsTGJ6UqSK1QQjJp9THsXmGaHCmjQFw1BjIB+CJGXNXJqLV19YmlArZ7l1gyW8iFS8zZnbhGnLiq08QanoixaLaiEGzwZ2hRJVzhUDFSmDpACng5sYtlo5mt9aukVZVZuwtXOKSjClu8cbZ6o9SFw7LRQo4VIhmjKVyjpb7AVHwnlgqoGEkiDAwUlvIZRjNtdiKkpC0sUpEpNIKUbnzWM/Gd1A2HTFRm1Wjm1USGe8ovkgjLVI00Y/YAg/VRirqDTl1nf/kUE0YX5dCiRS3R6s5zbbpCrtnC8yNUpFBKoVQjC6GVxg80jaqgYLUUcr0tE0GgSSUNqrWQ5ZUyhaWAUjmiPWfg2AIhwDQEcUdimoKJGY9kQmJIQd3TLBRDOlpNwsghX/ARQpBtMmltsVirROSaTVqbTcrVCMtqZB5sR5BOSeYWQ3LNBjPzAS1NBmGoCCKBFAqty8Rsg8CP+P5Tz1OughSKlozEq2syKcikwK1rpuc9YjGwTEkYBKTjismZKiuliI5Wk96uRhBm2wbZljRhGF7vcWTQ3WkxOedRr2tMA/p7YmiliccaRlGnTp0CGs02l5eX8X0fpRRSSmzbftPswo3zbvx7dnaWYrHI/Pw8c3Nz60GE1poLFy6wfft20un0ehfpbdu2IaWkWq1iGAbxeJxMJkOxWGR0dBTfv9nIb3x8nE2bNmHbNuPj41y9epUXXniB+fl5oLGQm5qaWn/mUqnEtWvXGBsbIwxDent72bJlCx0dHVy9evWW9vOLi4tUq9U3HKdfj4sXL5LP52/atrS0dNMzv9n4D9zEjfmPjC996UtvOqcKIWhvbyeVSr3j674t1cyRI0c4cuQIS0tLfPOb3+Sxxx7jO9/5Dt/97nfp6Ojgox/9KA899NA7vvnbwY0fx7t97OuRTqff1Ab8tdiwYQMHDhwAGi/uydOn0UGADqN1BQNaN/gfUYgdhXS1t2OaJmu+j7N7D7Ufv4A/Okq4uIhwHAhDzK5uYrv3EBUWGzcyTLyLFwgmr6HqHoQBItNE5LoEZ06jqhX0WhmZTCAzGeyNG+G6c6R38TzlJ59oKFsCH2HbOLfdjtHcjA5DpONgtLUSrZUgCDCaMsR6e1FSon2fKD9PbNs2jObmBq9jZRkjl8Ps78c9dRLte4TFJTQQTIwjU0mMtnaszZtRtRr+5cuNUpHnNZajhoQgQGuNf+kCWikqTz6BvWUbxvBO3GKJYPIa0owTrdYYCzew0NZo9ta+do3Bl18ljDSB0Si/xLSPRLO/eoZh4wpoiKuGD8qLqdtZk01oAZ3BIpu9a2gU8chl2cxyKb4FXzbIfnNWJwUzS08wR8HIooQkEgZxVac7mEfTIHGGmFgEmDoiLWogJSWzifFYP7vci7QHBRatNkJh4MkY291LpFSF/dXTGGgiJCEGSe0SYjJvNboV29onpn1ikYtBxNnETsacTcRVHY1gm3uJSJhM2X2kVZlstMoP03ejhCSu6iyZOcpGiv21M2g0vcE8s4mNDNWvEpkxRKhIqzIzshuNQKLRQmKgCDDJ6Bqhtvhx6jCLZisCzWZvnD3ueZKqimvEAdHooySS+NImHVVwzTQmIQJBljLNqQQt6QSJuSuUXc1qscZ4wScINLW6orvdYm4RtGok+dCQiEtsSxBFjaxEJm2wUmoM9ppGGSWVkNTrilIloqfDpFoLcGKN8wAyKQO3rrAtgUDc8LNjoRiRSUp62i3acxZRpKm6iuk5n75uG9eTDA44zC/6VGoKy4Tudhu3rmjPGQSBZmo+IBGXDA3EmZr3qLmadMKgryeG58O5qx62JejrNKnVFVev1SlXFTFb0ttp0dNhsVZW9PXYIATzBZ+lkgRM5oswXwwZ6pcMDqRpa2tb92BKJpNY5gK5lmrjs5kCw5QkEzEe+uiHb+rn9eKLL7KwsEA+n6dYLNLZ2UlHRwf9/f23GLB1d3evj1unTp3iwIED2LbN7OwsrutiWRZSShKJxHpg43kejuPQ29tLR0cHs7OzzM/Pk8/nSafT9Pf3Y9s2pmkipbzFul9KiZSS5uZmcrkcZ8+epVqtrvNGoijC932mp6fp7u5mfn6ekydPrpf4SqXSemZkaGjoloxIoVC4KSPyVivw4eHhWzIa09PTN2VE3mr837JlC/UbztT/gXHq1CkuX77Mpz71qbfd4fzt4B1dKZfL8clPfpJPfvKTvPTSS3zta1/j6aef5m//9m/56le/+q6zubPZ7C1R7o2/Xy9ja2lpecNj367crb29nSOvazX/k2CaJnt27ODMpUt4I2eRdxxGSIEOQtTKMmJujo9+5CPrZNk/+dP/4zpB00QkElj9A8Q2b0ZmmhrBTLVCMDMDCITQaMNEpFJIO0bijjuoPvMM4fQUVn8/ZjYLYUhYKOCdOwc7d2EPDACNPiyBnCGcm0UmkqTv/yDm9SDkhkpH12qIRAJ/9Cqp3XsaXBat8AoFtOdjdvVgxONQr2MAfj5PODmJEBIj1+AKGc1NROUK2DbB5DXszZsx2toQ01PIlmyD67K2hvLquCdOYGTSWLt2N0pIo1fxzp6hikXB6WYqc5DdhWPk7XYmzW70WkMpNBm0IEUPg+EYh72zhLUIH5NRZzNKw57aCCtGE75McT6xnRUz28huaMWi1U5HWKDTnyMUNuPmRtR1HghomlSFsdhG7qq+zEZ/ikm7D0uH5MIlButjBNdVLWUzjUcMS/kcqJ5mzWymZjezoKHHn+fO6nFmrG5qMk4uXKY9KCDQZNQaVZliwhngirOZlnCFA7UzzEZdhNImkDHMyGdDMIehFYtmG3FVRxJRlmli2uea3YlJREu4ypKZpS6ddQ5MzUjiyRhbvHFW7FbsIM/2aAo3kWOy+wjxyhwb8ucYdQYJsBBCY0V1Br1xlowW2qNljicPsmxlr38jknmri9ZwmQF/miUrR4iBpUPQdRxVx9QhLcESG7wZHEOzb0OKhz70iySTSZ5++mleOnaK/GztutGtxHUjnFiMjb0xrk7U8Xx9nWDqIA0H2/TJF3w29TmMqjrlaoRpQFe7RTppokjSnitT9xSXx+t0t1ts2uCwVg6xTEEmbZBwJEpDGAmCUDY4HZaBkAaeG7C0EiCEoLfLplpTFFcUw5tN+nti1D1FEGqqdYPVNU0qEdLVZpFJ2SyXTNLJkK0b4yAgZglsW3J+NCBSEqUVyaTBxJTLSinCMA0ibTGVhx2Dgmw2we23384Pnv4hnu9e92e5/k0LTbUmSSTTREqwtLRC3HHYu3cXH//4x5mfn2dsbAzf92ltbWVwcPAmroXneZw8eZKLFy+uZ2ZXVlaoVCr09fXxsY997CfyLnbt2kVzczOLi4vk83mUUiwtLWEYBpZlkcvlaG9vZ2hoCK01x48fZ3Fxcf1ey8vLpFIpjhw5QqFQWA8gbmDTpk0cPnyYn/u5n2NhYYFnn332pv03AodkMkksFmN5efkWHqLrugghbsp+38CDDz5IsVikWCwyOTnJBz7wgXfUx+idLJ4vX778to/994w/+ZM/YW5ujkcfffRdve5PHdIcPnyYw4cPs7y8zLe+9S2+/vWvMzk5+a4a2+zcuZOFhQUKhQJtbQ3Z6dmzZ8nlcrfI53bu3Mm5c+du2jYyMsKePXveted5I3zoQx9isVhk/vx5olIJq6cXVXdRU5Ns7+6+SdZmS0H93AgylcTZvx9v5BxWVxcy14owTfyLF/EuXEDYNkZzE7K5mXB2Fqu3j2B8HCObJXnPvQjLapRFenpwT51E5fO4L72Id+E8KIWIxUgcOkzt1VNQqxGMjSI2DyJsG1SEPz1NuLSEvXUrwdVR6mtrOH191AqLUKlgNzdjBgFOUxNRLIZaK6GuXMLu7MTZvJnIMJGpVCM74vlEtRpCSNzjx7H6+yEMCaanMTJphGUSzc9jZDLEdu5qUPCEwB7agh/CwtgqI2Iz2jepKZN5kUOHIUoYoCMiLVg0WxkwplnYeB/1apnpVY+S1YytA4brVxiPb2awPkZFphBoVo3mRllF+8xZXeTCJWzlk4xqOKqOL2zs68GKaySoiASW9jlQO41FiKV86iLGotXKoeop8tflvO1hkWy4woX4XqQ0GtknGaMg24kR0hzkqQmHotmKa8RJRWU0ktHYRiJhsmzmKNpt7KuNULBaqdrNtIVLtOklIsNiqzeKo+p40mE0tom6dEgqF9AoIbmeb2uoW4TAjtmgTJAGnp0BWSFhC66ltuPHmgiNGNn8ae4p/5gFqw2EpCNYpGQ0EZkW0i1Slim0kEilQAgsQtaMNAPeFIZqcGlikcewd4kAE1NHbNHz7LTmePB993Dbbbet/+abm5t5+KEPk3zqWaZnl6hUQzIpiRTQnDE5uDeF7ysMQyCkTS6bIPA0XhASKcXw5jiRanA4YnbDkj2WiJGK15jN+ygFS6uaWt0jnWyUZWK2pKvdZnrOx/MUQkS0dcaI2VD3BbYlcWImSsHcYogfNAiuQljkiyFT8wrQOFZIW1sOaYA0avR0ZZieLVFYbpBHE45k22YHL4hoTgnWKhrbgijSlKsayxINnxgaWZmVtZCOVs3GjRvJpF+huckjjKqoqOElY1sWHa0Oc/NFqhNVgsjEkGvkF18gn1/gF37h59/SmXJiYoK5ubn1IOQGZmdnmZmZuYlM+1bo6elh69atFItFxsbGyOVydHZ2smXLFgYHBzl8+DAtLS38wz/8wy3cgEqlwtzcHIlEgl27dhGGIbOzs2itaW9vZ/fu3Rw61GgA2draSmtrK9lslqWlpfVrmKbJwMDAO17EBkHAk08+ycjIyHpWZHR0lI985CPvyNHzPbwzDA8PMzc3x1NPPcX999/a5uKnxb85t5LNZvnEJz7BJz7xCY4fP85jjz32bjwX0CCb7t27ly996Uv80R/9Eaurq3zlK1/hV3/1V9dZuV/4whc4dOgQv/zLv8zDDz/MU089xb333svTTz/NyZMn+fznP/+uPc8bwbIsPvHrv86FCxc4fvxlKhfO0dLUzIE772Tr1q3rfBKAXTt2cOzUKey+DZitbbBjB+4rryDOnAYE0coyyveJ79lLMDtDbMtWRMxBra1BEGBv3dYIQgARBAghSQxvx/d8iMWI3XYbVszBD3xkKo2zuoo3MYY/OkpUXsNoyRKtrhJMTCCdOHq1BIbkwKaNLBYKBEFA98//AtW5WYoXz2O2tmI2NeGOXsV3XZL7D2CZJpHvgyGJHzxE5Z+/R/LwnYTFIvVTpwiXiuggwD32IrHtO4ht2UowM93gv8RiiJjdINCaJnT30TI+R0mkEApqMo7QjWZkWjTa3mshsUQEwiC0ktRNTWRKlDBoDgqAwFEeIEirCtNmL6EwEGgUElOHgMCVcTrCRfKqAyUaQUQoJMPuKEWrlTOJXQQYGDriaPmHSCLWjCY8GSelKgipyVsdRAiE1iRUlcH6VTp1iasdd+GKGKuVGqsixT2VF1mKd2N6cyzKFpLaRSoX0zBYyGxBhs3sKJ8Bb4aa3YJnZTHLeUpmE9P2JjLBCnvdsxSMHIP1cWatLhbMNra7l2mKSmgkynKQQtAeLpA0NMmujXQVplFK4jkN2ZwyHcrNG3FrVSIjRpyAYvteChWf3e4FIitBW1hEiQYp2tQhQkrS1MlGKxwt/5BVo4lcuExTVAItOLR2gkzc4Fd+8RfwPI//9t/+T1ZLa6RTKVpamvjQhz7Eb/zv/Zw4cYKXT5xiaSUiVGAYdWwT4o5JKpWku6ub227bx6lTr1KrX2E2H3BtVpFOajIpSehq1qoWWcdEYWLbgkRcYBiCSAnWqgLXi2jLWmSbLZIJg5qrSMQlSoFSgnhMEUXgBRHF5RDbbgx33e02CINI3eAECPxQU63VaG3N4cQicjJieUUQRpKYLenpsFgpayqVkP4em6l8hERgSIGQkpit0brx1gkBScdgcHAjbW1t7Ni5jXzhFFYsTRCESCGIxQxisRKFFRtpNsookTJYWFFcujzOgdlZent733TccV2XtbW1W7bfkBC/tuTwVpBS8tBDD9Hc3MzIyAie55FMJrn99ts5fPgwhmFw7dq1dQ7LGz2HlJJf+ZVfYcuWLevy3W3btnHw4MF1CadhGBw9epTV1VXOnTtHqVQiFosxODjIvn376O3tXc/uvPY+bW1tDA4O3lQGiKKI73//+zz33HOMjIwQBAHlcplNmzbh+z6f+MQnbuELvod3B3/2Z3/GZz7zGX73d3+Xe++9lw9+8IN0d3e/YZlmaGjobXuNvXtFHuDQoUPrEfC7hT//8z/nj//4jzly5AiO4/Dwww/zqU99CmisCm402RscHOTLX/4yf/qnf8pnP/tZBgYG+Mu//Mu33aTr3wIpJTt37vyJ+v6Dt9/O+UuXqJXXGuWPbA7zyBGipeWGq2qlvO606uzegxSCzIZ+lp//EVorhG1hSIkhJWGLGQ0MAAAgAElEQVS5jG2ZBFJe9+gIEJFCNjfD4iJCSuyNG6mfOwtOQ4kTzMygXRcRjyNsi2h5iYxtc+TIEc6cOcPqwiJ2UxNWOo1fKlF66vsY2Rze7CzGdeVSZNmISKHWyshMBpFMoVZWoFZDXjeeUvU6wrTwLl3EHx9HVcqwcaDRKwZQno9AE1MeNiFSR0TCwBM2g944ryT2ElpJIsPC8qsMBlOkbUnMXWJNWSjDJCNCtvljVNO99HmLVGScTd41pu0eBCC1IhOt0RPMsWy2kIoqrBhN7K6dY8nMAZrWcAlf2Lyc3I8vbEJhonWARJEJ1xhgipLRhEbSGi7hWTbtwRLteo1dtRGabUHgJMnZiloijaitsLV6GYlm2Wyh1cs3+CxC0J5tIuY4WKaJv+TTZGT52Ece4OzIeV48c5FTzQcokMYzExRjHayKFAcrJwmkxd2VlygZTYDi3vKPmbR7WYtaSHs+/cE0ldatdCxfYKA1Q6gUsfryejCy0rGH9pkXSdTnqTit5MIFhv1JTEtQkmk2eZO4ZgoRS5BJZ3HqK+yMimwdGuDchTFyLKM0eL6FF9gINC2xkCe//xSTk3kWl3wiBa0tFom4ydpalY997BHe//73MzQ0xEvHXmZ2dh7TbGJ4eAt3HDp0k+Iuk8kwN5cHao1JHI0gYnw6IJ1pfIYwitHa4qO1TWvWwbIMtLBRUQUhJZcnKpiGwDJhccnDkALZZRFGmmQiRl9XnLZshO8rUgkJ0sb1HXLNivmCt67C0VoTMwO6utpRKiII80RRRKQExWWP+YLCthqqnraswA9MhLTobA1YLjX4HWEYknAk/X0p3v/+9wNw6OBBZmbmiE0usLIGQiiyTZq6ayKNm9WHWkuWSz6zPyEQyWaztLe3c/ny5ZtKIrZtk8lkblIa/iQ4jsODDz7Ivffei+u6pFKpmyaQ9vZ2mpqaSCaTN5FDDcOgp6enYe6YSnHvvffeosx5LYaGhvi1X/s1jh07xtzcHMlkkgMHDrBr1y6iKGL37t2EYcjU1BRRFNHe3s6uXbu466671v9/Lly4wDPPPMO1a9eo1+v09/czNzdHuVymUCgwMTHBlStX3raR1nt4Z/jMZz6zXmJ74okneOKJJ9702G9961ts3779bV33XQ1Efhbo6Ojgb/7mb95w3+vrdkePHl33EfmfES0tLTzy0EP847e+RXBtAmvzZoTSyKYMwZUraM9rSFZNs2FGNTdLdXYOqRXBWpn68WPE7v8gYRRhmQbN2SxT584htCK9cTPLL72I19+PSCQI5mYJZ6aJ79yFzGaJVkt446OoahVRryMAR0o+/MADGIZBb28vx0ZGCMplrHSajjsO07xtG8vnzkHgE1YryGQKO53GSCXxVleJlopEC3nC2RmMliyZu47gnj2N5XmgNUFTE5mj91O7fJlgbh41tBXDNNe5KuHMNCKKeLD6PEprtDQwTYM7qyfIh91oIekL59m7IceWTbuIv3Kc+dChxw1JUSdMdVLo3E/L4gi5wiUsHXDf2g9ZtNqIKY9cuMJErI9Of5GEqlM2m8hbnaRUhVRUwdY+Z51hDNS68dmgN05C1ajINK8k9lI0c4SGTWtQ4M7yMRztkrQle3du5+gH3s/IufMcP32etdKrEIX4wmU2s4kBs0zcNumqThClO5FSsryySuj7bKteJNcdo7Ozk9XVVV6YLJHs2IJ0XdYqVfzAJbBS+IaNpQKUNEngUzObSEdFNrFEvD6BZZroeBPd9TEGOrN8+P77mZmZYerZE1x2WvCcLJHhsNo6zNbicXYPWnR2dDAwcBjXdRkbn+DK+AS95RFKogOzHJFTq7zv7v2EQcCZ83OUqq9PmSvC0GV0bJWLYzXK1cbqdXrOY/tQgrGJWV555VXuvvsuent7+V8e6SUMw3Xi4uvR3d3NI488xLPPPU+hsNwIBmIGmUxAqaIJQpeWjIHQcdIpn1K5TrmsaW4yaGqxcZwYTeka+YLCECGOAz2dNsWVCK01QegTqDTNTTbxREg84VGuWXhhjFTMZ6hfkS8EaCVoz1r0dBjce89dzM/nuTZdIl8ISKdSKCok4hVSCY1tGezemkQaFghNJmXg+QY1T2KZ0Ja1uPeew+vl5EQiwcd+6RGuXr3KxMQkpmWSbWnm2edepFi+tZRtGOKmTOob4Yatej6fZ3x8nCAIiMVibNu2jR07drwjM6kbiMfjb9hLJ5FIcMcdd7CyssKFCxeoVCo4jsPmzZsZHh5+y4DpjZ77jVxpTdPkl37pl9iyZQtnzpwhDEO2bNnCbbfdth5UXb58mW9+85ucPHmS8+fPU61WGR4eZtu2bev8wBuckffws0F/fz9tbW0Y19t1vBXeiZnd//SByL83bNiwgf/tl3+Zb//zE6zOz0MmQ7S2hq6UsbQmGY8TjydYfvEF/KZmnJ07iSUTiEqV8sgI1aefovMD9xGzLErnzpFcKtKRzbI4MYFZrRCePQPxBCrwsdo7cDY0OBvVpVHUygpNrW3YcQdbKe7Yt2+dw9LW1sa2/n4uPfsMyeFh7KZm3IUF9EKeu+64g2deeqlhWb9zZ4OwmkoRjI8jTJNkdzeZQ3egtEaFEfcfPUppbY0fHjtG7fx57J5e/Mlr1J57FrOnBxEpzKUiZn4OP9XFavMgynCIpEnv0hl2pnyyrUlAYOh+HnjgAUzTZPvwMGNjYzz/0suM0c5S+26QJtXmjWSL5wm0IBQGUit8aXMuvo2kcumKFolJxXb3EtN2N8tGM1WZxFaz3OaeZSrWiyviZMMVuoI8nohxPr4NT9o0qTWUkigkM3YvAxTwkq3kl1YJgoAD+/exZ/cuKpUKsViM1dVVTp4+y3R+gkTaJmH10DR9iqtrLVhGkvZwibiqMboS44mnnqa/pwtFY9K5MRHc6LYiLtlYBNTMJJZQJIRPofsuEvUVjm4Q3LZvD+VymXS6oby4IbG8f61M4tVjlEg2Mhiyzgc+cBe7d+++5V183733sLi4yNzcHIZh0N/fT1NTEwsLC7S2nKDiRtd7wDQQj9Xx/ZD5go9b/9cUehAJ5hZ9kkmL8+cvcffdd63v+0ns+o0bN9Lf38/y8jLQCNgXFxcZHRvDq3s0NWX40fMvMTEbsbQcoNHU/IZ76caBTqrVGr0dIVFkXVeCa1xPkWs2qXuailsmkejEFzZNKUFnq0G+6FHzEyQSkq2bPGK2wdDgJu688w42btxILpfj9JlzrJQayq9MU5qWZoeOnOLBB+5h375968TOeDzO5cuXmZvPk0om2bp1C11dN3cwtm2bHTt2rKteoiji7NnzzBdX8IJ/VbgYMiLbZP7ETK6Ukocffph4PM7Vq1ep1+vrXI377rvvLc/9aXDnnXeSSCTo7u5mbW0Nx3HYt28fhw8fftd4gfF4fJ17+HporXnhhRc4e/bsuo9JEAScPn2alpYWOjs7WV5eJpvNksvlCIKA0dFRpqemsCyLzYOD9PX1vdec76fAD37wA1ZXV3nkkUf43Oc+x+c+97l3/R7vBSL/A9DR0cGv/9qvMj4+zvz8PEFLM11dXWzevJl4PM7q6ir/9ze/SfLIPZiZNDE7hmVb1Lq7mf32tyk99S+YlkV3ezuHPvQhOjs7CcOQV155BdfzuOy6NO3YxfL5c1RfPoYQEts06d+0iTsPHUJrTVdX103pVyEE77vnHtovXODc5StUalVyzc0M7ttHNpslcfo04dUrlKanMJqb8RYXGj1zhoaIhRG5bI7VK5fJGJItW7ZgWRbtbW18/7nniObnaW7JUpueQi8ukMvlGBoYYNPhO/jxy6cYm30VT9g42mfbQA8PfODedfLaqVOn1ieyTCbDvn37GBgY4Imnn2Nq8RkCYZEQAcmuHMdKaRxvhZxaYcnMsjGcoU2tkXIsKqFgJL6LTFRmsz+NE7mYOqRJhBzojZhbnKDulfGFxNQRWpokYyZhEBEqTWTGWDa66fMrzHQehoVjnDl7ljsPH8Y0zXUFQDwe5yOvmYTq9TrTf/9/kTUlTTLAFhLlCco1n5dePYeJoilcwfLLBHZjBSGAWH2ZrrgmFGlW4z1U4znceI5kNc9ANMu+3Q/S3t5Oe3v7Te+WEII7Dh1k547tzM3NNdQivb1v2jVWCEFHR8ctbejb29s5fMdtRC+epLjsEkSSVBxasw7FQo0wvJUv4Pu6kemK3tyP4c0gpbxJMdHV1bU+mR8/fpzlUojttND1Gv5l2Y3ILywipcnkbJX5QkAUQVvOZFOfDQgujAWYRmPiN00Dx7G4/+g9XL4yyvz8AoaRZuuWfdxxx6GbVCmtra089NEPo/7fb+MHEUpDMhHj0MED6wTd135n+/fvZ/87+LwNzsT7qVT/ifxiHdeXmFLRnjM5dPDA23KxTqVSPPLII6ysrFCtVmlqavqZWapLKdm/fz979uzBdV1isdg7Uqj8W+G6LsVikbW1NaSUpFIpPM+jWq0yMzPD1q1bEUKwceNG+vr6eOwf/5H8xDhBsYAwTM50dLD7ttt5/333vReMvEN88YtfpFgs8sgjj/zM7vFeIPI/CJZlsXXrVrZu3XrLvnw+j5XN0fI61nsym6Opr487BvrZtm3bTV4BN7T8XR0dnH7qB3iVKm0HD9J++0HKExO4I2c4fOf73pJRfoPr0tfXx+joKBdGR3n+5EkAVBSR7Ooi0dlJWKtBRwduzKHy0otY2SwLzzyNWF7mg++7d32AGry+CrnB5Wka3kZ/f/9NK+RHPvrzLC8vU6lUyGQyb8uNr6WlhV/5xYdYWlrCdV1aWlqYnZ0l/8xJzvV8AKc8T3tYATtLRYd0Vy8xldhASWQp0M6VxFZMHdIaLnO/cYWPP/IQlmWt15m//9wLFDCpZDuZyy9QFUmUYZOpzhPZSRCSYnID50ZHufMnGPotLi5Sk3EWOg/TMfM8ReVwJbUTz07TUZuEi+Ns60jjLr3MVHIIP9ZErL7CQPUq7zu4l1jM5vmTZ1kr5xFripwjuP++u29Zcb8eqVTq39RjSQjBnXcepre3hwsXL1EpV+nsamdudo7C4hKppGSl1LBLv34GzRkDKQWDgxt/6vu+Ecrlyrql+2uhtKRWrbBSNqjWr/uSGFCuRKyUIjIpkzAEyxQIKUg6Hl2dXezdu5e9e/cSBAFSyjfM2ERR9P+1d+fhUdX3/sDfZ5ZkMklmJjuQkAUICVkMCUvYFMOOhE0QlEXUasU+cLUo7S3eW7pgKdXbXrXWXx99sK23Fa2AggIVFBdQFoNg2MISsu/bZDL7cn5/xEyJScieM0Per+fJHzlzzpnPl2Ey7/me7/l+odPpkJk5HtHR0e4xC2q1us/aFRsbi1UP3Idvv81FaVkFNJoA3JGaghEjRnTrwzIoKKhHl2J6Qi6X92jCqt5SKpXuH7vdDq1WC6fTCaVSieDgYAwdOhRRUVFYuHAhLpw/j9IL5yGvKkfLVy1nkx7fukTExMVxgT4PxCDigeRyOVx2m3uSoxbidxOlqVSqNhMW2e12nL90CUaXC8rhUTDWNyD/n+9A5eODiPBw3DltWqfdvUajEUc//wLXCwtgtFqhSkmBdsQoBAb4o+7iRdSdOQNBEKBLSITDbILl4gWEyATEaTUI0mox+u7pbb6RtVy3vpXg4OBuDa4Dmj8ob/7WOHLkSCTkXoS5+htUaEbBpAiDv7EcI60FcIkiqm0CXD6A0mWDWulEYGAAAn2GQ6xpnqtBqVQiMDAQgYGBuNtqRc3R08izhLgnqAu2VSPWWoTGYc3fe0VBBtHV+QR6su+m6fc3lsJhs+KsbhLsLkAuk6PGJwJXfNSIMF7Bookp+ObiVRjqTdBpAjBxegaSkpIgCAKSk5JQUVEBmUzW7gj1uro6XLx4CdXVNQgODsKYMYltekp6QhAExMTEuP/ffPXVV7h8pQQllSKCtArYbCKqau1wOkWEhigQOcQHEWE6jP9u4qy+EhYWCm2AHOa6lotWzRRyJ5wuFxwuP4SGhqCyshqivXmfBoOIiDAZRscpIIpKDA13Ykh4EObPn+N+T7U3or9l3Zhjx465pyEfO3YssrKy+jSEtAgPD8esWX1/KUUKLpcLxcXF7suFw4cP77M1ZJRKJVJTU5Gfn49Lly65e9DCw8PdA16XLl0KuVyOg/v3Qaivxc1RTuZ0wlpZhssXLzKIeCAGEQ8UHR0NxYkTaCoqQuBN4cFYUgKZydRur8aJkyfRoFAiavYcKPz84BJdMBQVounECcy66642Xe/f53K58OG//oV6PzUQGgb1kCFQJyXD0tgIwWrDkPET4DKZIBQXQV9WCh8fH4wbMQLpCxa0CUVSUCgUWJo9H9FnvsHZy7mwGewYFhEKm1mNi+V2qGFGk28IBJcTvo4mKKxWDBFN0Pir4Ofnh6amJly6dAlFFdXwV/li3IhwqG+cQIRFgMnqhK9cRFPoaJgChgGiiBBjEcYkdb4U/JAhQxDiJ0NNww0UKcKa50eBE4LL0bzmjTYKhuo8DBs2DBkZGRBFsc0fb5VK1e6ETgBQUFCA9/cdQGWNGWaLAF8fETlnvsX8eTMxZsyYPviXbeZwOPDNN7mo1cvgowpCdV0twoKViBziA/l307SHBGvx4IOrujyJYFfFx8cjIuw0DEY9jFZfAALkMifCdE4E+GugNznh6+sLnVYDfaMBdrsIX6UApUKO5ISI78ZsDMHIkSM7na/i3Llz2Lt3L3Jzc6HX62Gz2dxToa9Zs6bLtyMONvX19di7dy8KCwuh1+uh0+kQHR2NpUuX9llvzbRp01BeXg6tVouioiL4+voiLi4Od955p3tSNIvFAqfdAcHVdkp2weGE+bu7LMmzMIh4EIfDAdt3PR53TZyIoydOwFJeBp+QUNjqauEoLsLdEye2ud5vt9txOT8fARnjoPjuMZkggzYmDtbSMly5ehXh4eGoqqpCY2MjAgMDERERgaamJuTn58Nms8HpdKLaZMKQGbOQv2c3/MeNh6BQQKnVwlxXC4fDAU1sLNBkwOrlyz3yOqtKpcLUKZMxZfIkiKKI6upq7Nx9AMVDpyG89CvEGq+iWB0HqyIAPk0FiJYVYPL0caitrcU/9x9Cgd0fdT7h8HGaEGWvQGbiCGRpNfj8xGkUCuGwqYLgZ6xEcFMhYlCL4gonXtn5fwjSBCAjJdF9nfpmCoUCc+6chIZ9B1DrFCB32iBz2qCGHVp/NXxlYvP6L0olBEHo1r9r86ROR3CjxAKrvfkD1mIHTBY7Pjp8FDExMX32Ld5ms8FsscLhkkOhlCFAE44GoxEugw2B/sCkcSOQkjLGfadIX1Kr1bj33kU4eOgjVFbWwukEVCo50u5Ih0YTiAOHvkBFnQiNVgOVnx+sVhOGDxEwdUoGZs6c2eWxDA6Hwz0gsuUuDLPZjKtXryIoKAjXrl3r8u2Ig4nL5cLevXvxxRdfoLCw0N2TGxMTA0EQsG7duj7pGVGr1Vi1ahWuX7+O4uJi9zwkQ4cOxZkzZwA098AGh4ai1FcFmenftxqLAGRaLYYPwHQOtyOHw4H777+/W8e8+OKLnX4BbsEg4gHsdjtOf/01Ll67BpvDAbWvCmljErF4zhxcvnIF9SVF0AUEIGnuXAwZMqTN8WazGXanE/7atuMrlEFBqCkvw559+1DZ0AB5oAbOJgN8XC7YXC7IQsMgU6nQVFQEh9MB0emEoFTCZTYBCIEglwMyOex2OxxmMwJ8fDwyhNys5QO9pqYGBoUWVnUI6iInYlTlWYysvwGnIEegoxEZySOQkpKCXXveR65rKGrCk5vXxgGgt8ZAdulLPHTvfDy0aiVyvjmLS/lnIUBAkNYXN2pEXDCEwKwKQWBNES7v/wjBnx7HmFFxSEtJavU6jRo1Ckvnz4blg49R5ayCUaZGkFYLP5UKQXWXMCxE26NehNLSUtQ3NMFqb/0t3e5UQG+woaCgoM8+OH19feHv7wel3ACbo3mRMv+A5stwugALYmP7dzbL8PBwPLh2NSoqKmC1WhEaGoqAgIDvZvMsh8/lfNTU2xGgAkKCApCcNAqzZ8/u9DbYmzU0NKChoaHN4mqiKKK4uBilpaUMIu0oKSlBUVGRO4QAzf9mLdtKS0vbvWW3J5RKJRITEzu83CsIAjKnTsWHFRWwFt2AwmKGKMjgDNQgaHg0km5aq2cwO3v2LF544QVcvHgRfn5+yMzMxM9+9rNbfpG41erE7amtrWUQ8RaiKOKjjz9GkckEzeQp0Gl1MFdX46tzZ5FmNiPrFhMEtVCr1fBVKmGrrQW+N9bCVlONyooKCNExiFg4HXJfXxgrylF06BACJ07E0KQkCBDgl9yAsi8+Q9XpU9DGxEB/+TKUEUMgUygAlwuizQbzlSsYnzKwb2RRFFFTUwOLxdJ89043vuGrVCr4Ok2A6IJZHQ5z7Cz4Wuohc9kR0XAOqSnJ0Ov1KK6qQ11EhjuEAIDNV4syRTiu5+dj6pQpmJl1N2ZmNd8F8//+tgt5QRNg9o+Apu4qFI3lyFWOgMOswJXrFnx79SAWzZjSaiDymDFjsKCpCeqvL+CaPQiwhCC4sQrRiibMvXtuj8Kd3W6H0yUA+P6xAhwOEXa7vdvn7IhcLseE8emoqf0cFbUtt/Q2TwAWEapCcnISCgsL++z52iMIQptBugqFAgsXLkBaWhEKCgohCM2DQHsyPsHHxwdyuRwKhaLNv52fn1+fr6V1uzAYDGhsbGwz86rL5YLBYIDBYBjQekaNGoV5S5bgqy8+R0NdHWQyOWKjozF9xox+u6vIm+j1ejzyyCN48skn8cYbb6CxsRFPPfUUtm7dij/96U/tHiMIAtauXdut5+nKEgMtGEQkVlFRgYLKSkQsWOi+rBIYEwOlvz9yj36MsWlprWaibI9CoUDK6NH4MvccNGFh8NXpILpc0F+7BkdJKVwyGYZNzIT8u+vbxuJi+I4cCSEkpHmFTV8V1JpA+I1OROOpExixeClM1dVo/NchyMLDIdhsaKivQ2xoaJ+OO+hMfX09Tn7zLY6c+hYOQQG1zImMpHjcOWVyl77pRkdHI1z1FbQN+dAHjQIEGax+IdDWX0O4Wo7hw4ejvr4eTlGAS9a2+94m+MBqa/2BVFpaCr3LB2Z1OOR2MwJrLuHrgHGo8wmDn60RzgA/mFzh8P38BGJjY1uNn5k4fjyihg3DJ59+Dn+tD6LCozBmzJge/3GMiIiA2k8Guaz1XB+C4EKgv6zd3rPeSE9Ph8HQhDPf5MJkdkAuA3S6AMyZPQOhoaH9HkQ6IpPJEBsb2+E4mq7SaDQYMWIErly5goKCAvd2Pz8/xMXFIT4+vneF3qY0Gg20Wi1kMlmrWV5lMhk0Go0k060nJCQgPj4eer0eCoWCAeQmNpsNzz77LJYtWwageQHZ2bNn4y9/+UuHx8jlcjz77LP9VhODiMQqKiqgDI9wh5AWqtBQQO2PyspKjBgxotPzjB83Dtfz81F3+F+QBQbCZbVCJYpIiR+FvAa9O4QAgN1kgjwoCIJCAYfDAfg238URGhmJAqsVVce+gDIwENayUjjKyxAdFYWJk5oneuqrUfCdsVqteHf/IXxjCYFh2Fi4ZD7wtTagLrf58sj0O6d2eg6lUomFs++G49DHKK2oQL1ci2CXHsPkRiycN9N965/WTwl/YwWMAf9O8ILLiSGOKkQN63hpcH9jORplAajzaenOFOFwONDoPwT1xjyUlJS0GqEvCAIiIyORlDjavSR7b2g0GoxNS4HRdBbV9S44nHLI5U6EaFxIGD26T+6cuZlMJsPdd09HRkY6KisroVAoEBUVNaDzSfS32bNnuyfGKisrw9ChQ5GUlISsrKwudzMPNpGRkYiJiUFsbCwKCgrgcrlahcPIyEhJ6pLJZAN2W3NfMZmtuHKttFfHdyYsLMwdQkRRRH5+Pvbu3YsFCxb0+Hl7i0FEYgqFAqKt7X8e0eWCy27rdFbKFnK5HAnx8Rg1ahSqqqqgVCoRFRWFsrIyXPj8c7gcjubLLAB8dTpYqqqgiBjSqmdBbGxEqE6HMIUCuReuos4nDK4Af9hr6yD/9gKioqIG7A6Z69evo9gIlGjHIFDe/JxWVRAKgtJw5uJJTByf0eEkXTeLiorCIw80T6+tbzRAqwlDfHy8u5dJoVBg2vg01B/7BtddDpj8I6C0mxChz8PIYFWbEBgZGYkAlxFDiz6F0lwH0WlHQuO3KFVGwOV0Qe+woNHQhFir1b0OUn+aPv0u+Af448yZczAazVCpVEi7IwWTJmX221geqb7lDoTg4GCsW7cOV65cQVFREfR6PWbMmIFhw4Z5/NgoqbQsnCcIAqKjo92378bFxWHx4sX8d+uG0LAoLFr+ZK+O76rLly9j2bJlcLlcuO+++/DUU0/1+Hl7i0FEYrGxsTiekwNTRTnUQ/59/Vt/7RrUgtCt62wAoNVqW80QGRUVBZ2vL2rPfoPQjHEQZDJo4kag5uxZyPz84DNhIgDAUlsLfc7XiI+MxJmrxbg69G5Y1M3zdFQ67TBUnkToiZOYMf2uPmh152pqa1GlCGk1bgNoDiOmegENDQ1dCiIA4O/vj7Fjx3b4+B2pqVDI5Tie8y0aKnOhlMuQNDIGd06Z1CYIGo1GOBxO1NoElPilQeG0IMxejTRzKc4okmCRqxHoMkBhqcXZc7lITk7udS+SKIpoaGiAxWKBTqdr1W65XI7MiRMxLiMDZnNzELmdeiikoFar3ZOe5eTkSPaN3pvodDo8+OCDKC0tdQeRyMhIhhAPlpiYiPPnzyM/Px+/+MUvsGnTJrz44ouS1MIgIrHAwEBMycjA8WNfwBgdA6UuCLbqSojlFZg7/a4u94h0RCaTYe6MGThw5AgqPtgHuS4IzoYGhKj9oKiuQuX+9yEofSBYLbgjPh4Wqw3FiqHuEAIALrkSZdox+DbvNO6cMnlAPuj81WoEumrbbJc7LFDC1eUQ0hWCICA5ORljxoyByb6KT5kAACAASURBVGSCj49Ph/NFnPj6DIpUMWgMHwM0GWEwm1Er0yHFdREjnKVoELWIt+XDYRJQWlaNkpKSW85m25mGhgZ8dPhjFBWVwuEU4adSIj39Dkz93jgZXgcnqbUsJ0DeQxAEjBw5Eps2bcL999+P6urqNnfO7N27t08HvreHQcQD3HHHHQgLC8PFvDwYykoQotUiKXtBn00MFRISggeWLUNhYWHzt5XR8YiJiYFMJkNlZSVsNhtCQ0Ph7++P3fsPwKJo2+1u8wmAzeGExWIZkCAycuRIDDl9DgXmKqDlA1Z0Ibz+IuKGhXVpKvjualnDoiMulwtXC0qg12RCpVJBpVLBbDajsqoatfJgpJovwuWUwWkRYLarYGiyoaampsdBxGazYc+e95F3vQYGsw9EUQaFzI5Gw2kAwF13TuvReYlo8Dp48CBee+017Nmzx72tpde2vS++3Z31uicYRDzEzYt89QeFQtHu1Mbff85hYcHQllVCj1GttvuZaxDop+qXaa7bo9PpsODuKbAc/AT6yko0CWqEO2sRo1Fi7ox5A1LD9zXPTwII+PdtijK5HDJBBtElwGmXwWRUofl2WhG+PrJejam5fv06yirq0WjyRcstug6XAlV1Dpw9m4sJ48f1ac8QEd3+MjIyUFhYiFdeeQU/+MEPYDQa8fLLLyMjI0Oywb0MIoOIKIruaauDgoLa7dlIGjMG0bl5aKy/Ar12JERBBl9LHWIbLyFzcnK3JojqrcTEREyvqYG/vz8sFgtCQkYgLi5OsjEQgiAgMS4a12/cQLkqGBAE+Pr4wFcpx3BHKVx2oCUw+CjsCNKqEBfX8wXgamtrUd/oANC6vQ6XHCazrVvjZIiIgObb/nfu3Int27fjz3/+MwICAjBp0iQ899xzktXEIDJI1NbW4vCnx1BYWQOnKECjUmJyegoy0se2GlCm1WqxbP5M+H96DFUV+XAICgTK7MjMSMLYtLQBr9vPzw/p6ekD/rwdyZwwDjdKP4Si6iTq1MMhiE7EOq8jUGGASy2DADPUfjKEBvli/vzZvepB8vPzg1olg+l7N1UJggiFXOAEW0TUI2lpadi1a5fUZbgxiAwCJpMJ/9x/CLn2UNSGz4RT7gu1sQJ1J3IhkwlI/94dJVFRUXhk1QpUVFTAbrcjLCxswC7J9BdRFFFYWIi8K1dgMVsREzMcCQkJ3e5R0Ol0WHXvQpz9Nhd5N65BrpAhNXMURo6ci+LiYtTU1EKjCcTo0aNb3b3UEyNHjkSw7ivom+ywO1t6RUQE+tkQEx3bL+NkiIgGGoPIIJCXl4dCiw+qIu6A1WaDw2qCRREEBCbjxDcXkJqS0maQUsuS87cDURTx8cef4Osz51FT54DTBei+vYaoM2exfNnSbgcGjUaDu6ZNxV3fGyva14O6dDod5s2diUP/+hh1DVaYLC4EaRQYNjQMs2fP5K2RRHRbYBAZBEora1ClCEVlTQ0sdiecggJy0Qm9TIbRNiOamppu62/XBQUFyDlzHsUVMrjE5ssZFXUizNZafPHFcWRn3yNxhR0bM2YMhg4diuvXr8NsNiMkJAQjR47kcvREdNtgEBkE/P184TBUwaAKh9VHg5YBlXJLJcwm44BN2y6VvLwrqK5zuENIMwFNZiWuXbsBi8Xi0eMtdDpdn0wJT0TkiW7vTyACAISFBCPCXAIf0YGWECKILsTYSiDI5KiqqpK2wH5msVjhdLXd7hIFOF3O5vV2iIhIEuwRGQR8fX2hUvkhs+kUKhXhsMh8EWGvgkohwBIYOeDLdPcnURRx48YN5Jw5i9raOgTpdNBo/KENEGCpF9ESxIDmW2xDgsM6Xd2YiIj6D4PIIKDRaOCn8kGBNh06YxVkLjscgdEwAQjRX4TDEQ1RFG+LwY9nz57F4SNfoKLGDrtTDoVMj/BgGTQBcpitVhgtSrhEAT4KOyLDFZg6ddJt0W4iIm/FIDIIhIeHIy4iCBU1JSgPTUOgvgCBNZdggC9KZBocPpWLsqpa3DNnplcvmGY2m/H5F1+hpNIFu7N5zIfDqURptQOxw4AJ6XEoLimDy+VCcHAYpk7JRHx8vMRVExENbgwig4AgCFgwZyZch47gRskB1JusOKe+A2ZVMEJDgmGAA01FJxF6+mtMnTJ5wOszGo0oLCyEw+HAkCFDEBYW1qNeitLSUjQ12WB3tv5v7XQpYDTZkZg4GtnZ98ButyMgIIA9IUREHoBBZJAIDAzEA8uXYO++/ThUAqhC4qBTNa+D4oQCJZpEnLl4DpkTJ/R6xd/uuHDhAj46dgq1oj/sMiWCHF8jZUQU5s2a0e06RFGE2GG2aF4fpmWxOiIi8gwMIoOIIAiwizI4/YOhUrVejM3mq4Wl0QGbzTZgQaSyshIffn4KFzTpMKkjAEGA3G6G4cYpBJ063e3emcjISAT6+0Aht8NxU6+IXOZEYIACw4cP7+smEBFRL/H23UFmSIgOgba6NttV5hoEqHwHtLfg/MXLKFIMgcl/CPDdZRKn0g/FmkR8c+lqt2+rVavVmDolE1ERgEppgULugEppRWS4iMyJGb2ecp2IiPoee0QGmZSkMci5uB+N+hswaGIAQQYfSz1iDZcxaXJyv05u5nQ6UVRUBKPRCJ1Oh1q9AWZl22nRbb5amPUOWK3Wds5ya+PGZUCn0yIn5yxq6+qg02mRkZ6GhISEvmgCERH1MQaRQSYkJARL50yH36fHUVNxDQ5BAQ3MmJCWiLR+XF23uroa+/YfQFV1A6w2EX6+AvzUSvgrRDRiRKt9fS118Pfz6VHvjCAIGDVqFEaNGtVXpRMRUT9iEBmE4uLi8Njw4SgtLYXD4UBERAQCAgL67fnsdjvee/8D5F2rg9Hqi+ZJxUQE+Ruh0+SjyS/su94ZAT5WPWIaL2HCxDGQy+X9VhMREXkGBpFBSqFQICYmZkCeq6CgANXV+ptCCAAIaDCpEB1oxR2Wi9CbrsEpU0LjMmJc8iiMy0gfkNqIiEhaDCLU7wwGA4yW1tOrA4AoyuB0KTH/7qnw8fFx985oNBppCiUiogHHIEL9LjAwEGqVgLrG1mFEEFzwUQoICgpCeHi4dAUSEZFkePsu9bu4uDgMCdchQGVFy8RiAlzQ+dsQExOJsLAwaQskIiLJsEeE+p1CocDixdmQfXAAlZX1sNqb75qJjo7GPfPn3pZTrYuiiKqqKhiNRmi1WoSEhEhdEhGRR2IQoQERGhqKdQ+uQUlJifvDeejQobdlCGloaMD+/ftRUFAAs9mMgIAAjBkzBvPnz+f08kRE38MgQgNGJpMhOjpa6jL6lcPhwO7du3H8+HEUFRXB6XRCqVSivLwcgiBgyZIlUpdIRORRGERoUBJFEVevXsXJkydRU1MDrVaLiRMnIikpqVezyxYWFqKgoAAFBQUQxebxMHa7HXl5eRg6dCjuuuuuvmoCEdFtgUGEBqWcnBx8+OGHuHjxIpqamqBWq5Gfn4+ZM2ciKyurx+dtaGhAdXW1O4S0sNvtaGpqQkNDQ29LJyK6rTCI0KBjMpnw2Wef4euvv0ZTU5N7m8FggL+/P9LS0hAc3HYNnK7w9/eHTqdrs10ul8PPzw/+/v6or6/vVf1ERLcTj759t7GxEU8//TSmTZuGKVOm4Omnn4bBYGh335MnTyIhIQGpqamtfj744IMBrpo8XXl5OWpra90hpIXVakVlZSVKSkp6fO64uDhERUW1mhdFEJrvEIqNjeV8KURE3+PRPSL/9V//BaPRiPfeew+CIOAnP/kJfv7zn+MPf/hDh8fk5uYOYIXkjQRB6PBuHZlM1qs7eXx9fbF06VIAQElJCRoaGhAWFobY2FgsXLjQ4+4SstlsuH79OvR6PdRqNUaNGgW1Wi11WUQ0iHhsEKmpqcHhw4fx7rvvIjQ0FADw1FNPYeXKlairq+tx1znRsGHDEBoaCq1WC71e796uVqsRERHR6zt7IiMj8dhjj+HGjRswmUzQarWIiYmBQuFZb7fa2lrs27MHDeVlsNXXQRGowfGwcNyzaNGArUNERORZfxlvcunSJQiCgMTERPe2xMREiKKIS5cuYerUqe0e98wzz+DLL7+EIAhYs2YNHn/88S7dBXH58mVs27at3cfefPPNnjWCPJJKpcLs2bNhMplw6dIlGAwGqNVqJCYm4q677oJWq+31c/j6+rb6v+tpRFHEoQ8/RPXF81Do6+EDQNTXw1Bbg0OCgHWPPso5T2hQWbt2bYePLV++HFFRUQNYzeDisUGkoaEB/v7+rZaCVyqVHQ72CwgIQHp6OrKzs7F9+3bk5ORgw4YN0Gq1WLVqVafP53A4Ohx/kpOT0/OGDDBvqrWr+qtNEydORFhYGPR6Pfz9/REXFwelUjkg/4ZSv056vR4lN/JhLS2BILrc20WDAQ6NFkeOHMHQoUO7fV6p29Uf2Cbv0Ns2dfT3H2j+fKD+I2kQOXr0KNavX9/uY7GxsW1ugbyV5ORk7Nq1y/37pEmTsHLlSrz//vtdCiIpKSl47733uvx8nignJwfjxo2Tuow+xTb1j/z8fHx7/Dh8AvzbPOZ0OTF06NBu1+gJ7eprbJN36Is23ervf15eXpvB7dR3JA0iWVlZyMvLa/ex48eP49FHH4XdbodSqQTQPBeD0Wjs8rodkZGROHDgQJ/VS3S7CA4OhsxPBbtcDpnT6d4uApBrdByDRUQDxmNv3x0zZgwEQcDFixfd286fPw+5XI6kpKQ2+x88eBD/+Mc/Wm3Lz8/ndT2iduh0OsSPSYIYMQwuefP3EZdMBkdIOMKjh9/2U/ETkefw2CASHByM+fPn43//939RW1uL6upq/P73v8eiRYvcgwnXrVuHffv2AWgeP7Jjxw4cO3YMdrsdx48fx+7du7F69Wopm0HksWbOno07pt0Jn8RkCKMSoEhIwqjMTCxasrTV2Cwiov7ksYNVAeCXv/wlfvnLX2LOnDkQBAGzZ8/Gf//3f7sfLy4uRmNjIwBg1qxZ2LJlC37961+jvLwcoaGh2LJlC+bNmydV+UQezcfHB3PmzcPkqVPd84jwkgwRDTSPDiIBAQF4/vnnO3z8k08+afX7ypUrsXLlyv4ui+i2EhgYiMDAQKnLIKJBymMvzRAREdHtj0GEiIiIJMMgQkRERJJhECEiIiLJMIgQERENIqWlpdi4cSMmTZqESZMm4cknn0RlZaVk9TCIEBERDSLr16+Hr68vjhw5gg8//BANDQ34+c9/Llk9DCJERESDRGNjI1JSUrB582YEBAQgJCQEK1aswOnTpyWryaPnESEiIhosjHYXLtTYenz83XZXp/toNBps37691bby8nJERET0+Hl7i0GEiIjIA/iEhiNi2QO9OL4aQPeCTH5+Pl599VX84he/6PHz9hYvzRAREQ1Cubm5WLNmDR5++GEsXLhQsjoYRIiIiAaZL774Ag899BA2bNiADRs2SFoLL80QERENIufOncOPf/xj7NixA7NmzZK6HPaIEBERDRYOhwNbtmzBxo0bPSKEAAwiREREg8bZs2dx7do1vPDCC0hNTW31U1paKklNvDRDREQ0SIwfPx55eXlSl9EKe0SIiIhIMgwiREREJBkGESIiIpIMgwgRERFJhkGEiIiIJMMgQkRERJJhECEiIiLJMIgQERGRZBhEiIiISDIMIkRERCQZBhEiIiKSDIMIERERSYZBhIiIiCTDIEJERESSYRAhIiIiyTCIEBERkWQYRIiIiEgyDCJEREQkGQYRIiIikgyDCBEREUmGQYSIiIgkwyBCREREkmEQISIiIskwiBAREZFkGESIiIhIMgwiREREJBkGESIiIpKMxweRsrIyrFq1CgkJCZ3ue+rUKaxYsQIZGRmYN28e3nrrrQGokIiIiHrKo4NIS7AYOnRop/tWV1dj/fr1WLJkCb788kv85je/wQsvvIDPP/98AColIiKinvDoIFJfX4/XX38d2dnZne67b98+REZGYtWqVVCpVMjIyMDixYuxa9euAaiUiIiIekIhdQG3MnfuXABAeXl5p/teuHABycnJrbYlJSXh8OHDXXquy5cvY9u2be0+9uabb3bpHERE5J3Wrl3b4WPLly9HVFTUAFYzuHh0EOmOhoYGjBo1qtU2nU6H+vr6Lh3vcDhgMBjafSwnJ6fX9Q0Ub6q1q9gm73E7tott8g69bVNHf/+B5s8H6j+SBpGjR49i/fr17T62YcMGbNy4sVvnE0Wxx7WkpKTgvffe6/HxniAnJwfjxo2Tuow+xTZ5j9uxXWyTd+iLNt3q739eXh6ampp6dX7qmKRBJCsrC3l5eX1yrqCgIDQ0NLTaVl9fj5CQkD45PxEREfU9jx6s2h2pqak4f/58q225ublIS0uTqCIiIiLqjFcHkXXr1mHfvn0AgEWLFqG6uhp///vfYbVacfLkSezfv/+WA5CIiIhIWh49WPWRRx7B6dOn3WM/UlNTAQA7d+7EhAkTUFxcjMbGRgBAcHAw/vznP+O5557Db3/7W0RERGDr1q2YMGGCZPUTERHRrXl0ENm5c+ctH//kk09a/T5u3Djs2bOnP0siIiKiPuTVl2aIiIjIuzGIEBERkWQYRIiIiAaRvLw8ZGdnY8aMGVKXAoBBhIiIaNA4cOAAHn30UcTExEhdihuDCBER0SBhMpnw9ttvY/LkyVKX4ubRd80QERENFnYANS5Xr47vzPLly3t8/v7CIEJEROQBIoMUeHJsUI+PDyvUA2ZbH1Y0MHhphoiIiCTDIEJERESSYRAhIiIiyTCIEBERkWQ4WJWIiGiQmDt3LsrKyuByueBwONyLyR46dAiRkZGS1MQgQkRENEj861//krqENnhphoiIiCTDIEJERESSYRAhIiIiyTCIEBERkWQYRIiIiEgyDCJEREQkGQYRIiIikgyDCBEREUmGQYSIiIgkwyBCREREkmEQISIiIskwiBAREZFkGESIiIhIMgwiREREJBkGESIiIpIMgwgRERFJhkGEiIiIJMMgQkRERJJhECEiIiLJMIgQERGRZBhEiIiISDIMIkRERCQZBhEiIiKSDIMIERERSYZBhIiIiCTDIEJERESSYRAhIiIiyTCIEBERkWQYRIiIiEgyHh9EysrKsGrVKiQkJNxyv5MnTyIhIQGpqamtfj744IMBqpSIiIi6SyF1Abdy6tQpbNq0CZmZmV0+Jjc3tx8rIiIior7k0T0i9fX1eP3115GdnS11KURERNQPPLpHZO7cuQCA8vLyLh/zzDPP4Msvv4QgCFizZg0ef/xxyGSd563Lly9j27Zt7T725ptvdvn5iYjI+6xdu7bDx5YvX46oqKgBrGZw8egg0h0BAQFIT09HdnY2tm/fjpycHGzYsAFarRarVq3q9HiHwwGDwdDuYzk5OX1dbr/xplq7im3yHrdju9gm79DbNnX09x9o/nyg/iNpEDl69CjWr1/f7mMbNmzAxo0bu3yu5ORk7Nq1y/37pEmTsHLlSrz//vtdCiIpKSl47733uvx8nignJwfjxo2Tuow+xTZ5j9uxXWyTd+iLNt3q739eXh6ampp6dX7qmKRBJCsrC3l5ef12/sjISBw4cKDfzk9ERES9c9tcmjl48CDq6+tb9X7k5+cPmut6a9euhcFg8PpenZuxTd7jdmwX2+Qdbsc2DTYefddMZ9atW4d9+/YBAJRKJXbs2IFjx47Bbrfj+PHj2L17N1avXi1xlURERNQRj+4ReeSRR3D69GmIoggASE1NBQDs3LkTEyZMQHFxMRobGwEAs2bNwpYtW/DrX/8a5eXlCA0NxZYtWzBv3jzJ6iciIqJb8+ggsnPnzls+/sknn7T6feXKlVi5cmV/lkRERER9yKsvzRAREVH3lJeXY/369cjMzMT06dPxq1/9CjabTbJ6GESIiIgGkQ0bNiAoKAiHDx/GP/7xD3zzzTd46aWXJKuHQYSIiGiQyM3NxcWLF7F582ZoNBpERkbi8ccfxzvvvAOXyyVJTR49RmQgWK1WAIDJZOrXOU362/Lly+FwOLy6Dd/HNnmP27FdbJN3GIg2mUwmAP/+vOgvPlYLwgoLenV8Zy5cuIChQ4ciODjYvS05ORl6vR5FRUWIjY3t8fP31KAPIk6nEwDgcrm8eua8lvlSvLkN38c2eY/bsV1sk3cYyDa1fF70F5nLBZXZ1K/P0dDQAI1G02qbVqsF0LzQLIOIBHx9fWG1WiGXy+Hr6yt1OURE5GGsViucTme/fUao1eoBPV/LlBieYtAHkaSkJKlLICKiQWz48OED9lzBwcFoaGhota3l95CQkAGr42YcrEpERDRIpKSkoLKyEtXV1e5t3377LUJCQgY0EN2MQYSIiGiQSEpKwtixY/H888/DYDCguLgYr776KlavXg1BECSpSRA97WIRERER9ZvKykps3boVJ06cgEqlwtKlS/HMM89ALpdLUg+DCBEREUmGl2aIiIhIMgwiXqSr6wPs2bMHCQkJSE1NbfVz5swZCaruXF5eHrKzszFjxoxb7nfo0CEsXrwY6enpWLRoET766KMBqrD7utImb3udSktLsXHjRkyaNAmTJk3Ck08+icrKynb3PXXqFFasWIGMjAzMmzcPb7311gBX2zVdbdPJkyfbfa0++OADCaq+tbNnz2LNmjXIyMjA1KlTsWnTplYDE2/mLe+prrbJ295T9B2RvMa9994r/ud//qeo1+vFkpISccmSJeLzzz/fZr/du3eLWVlZElTYfR9++KE4bdo08Uc/+tEta7506ZKYkpIiHj58WLRYLOKRI0fE1NRUMS8vbwCr7ZqutsmbXidRFMXs7Gzx6aefFg0Gg1hTUyM++OCD4g9/+MM2+1VVVYnp6eni3//+d9FsNos5OTliRkaG+Nlnn0lQ9a11tU0nTpwQR48eLUGF3dPQ0CCmp6eLf/nLX0SbzSbW1NSIa9asEZ944ok2+3rLe6o7bfK29xQ1Y4+Il/DE9QH6gslkwttvv43Jkyffcr933nkHU6dOxaxZs+Dr64uZM2di8uTJ+Oc//zlAlXZdV9vkTRobG5GSkoLNmzcjICAAISEhWLFiBU6fPt1m33379iEyMhKrVq2CSqVCRkYGFi9ejF27dklQece60yZvYbPZ8Oyzz2LdunVQKpUICQnB7Nmzcfny5Tb7est7qjttIu/EIOIlOlsf4PuMRiOeeOIJZGZmIisrC++8885Alttly5cvx7Bhwzrd78KFC0hOTm61LSkpCbm5uf1VWo91tU2A97xOGo0G27dvR0REhHtbeXl5q99beMtr1Z02tXjmmWcwZcoUTJ06Fa+++qrHfQkICwvDsmXLADTPnnn9+nXs3bsXCxYsaLOvt7xO3WkT4D3vKfq3QT+zqrfozvoAwcHBSEhIwGOPPYaUlBQcPXoUmzZtQkREBKZPnz6QZfeZjtpfX18vUUW9582vU35+Pl599VX84he/aPNYQ0MDRo0a1WqbTqfz+NfqVm0KCAhAeno6srOzsX37duTk5GDDhg3QarVYtWrVwBfbicuXL2PZsmVwuVy477778NRTT7XZx9veU11pkze/pwYz9oh4EbGLd1rffffd+Nvf/oaMjAz4+Phg7ty5mD17Nt5///1+rrB/dbX93sJbX6fc3FysWbMGDz/8MBYuXNjuPt72WnXWpuTkZOzatQt33303lEolJk2ahJUrV3rsa5WYmIjz58/jgw8+wI0bN7Bp06Z29/Om16krbfLW99RgxyDiJXq7PkBkZCSqqqr6pbaBEBQU1G77pVobob94+uv0xRdf4KGHHsKGDRuwYcOGdvdp77Wqr6/32NeqK21qj6e/VoIgYOTIkdi0aRMOHTrU5i4Tb3xPddam9nj660QMIl6jO+sDvPXWWzhw4ECrbdevX5dsHYG+kJKSgvPnz7falpubi7S0NIkq6j1ve53OnTuHH//4x9ixY8ctL0ekpqZ6zWvV1TYdPHgQ//jHP1pty8/Pdy9B7ykOHjyIe++9t9U2maz5z7xC0fpKvLe8p7rTJm97T1EzBhEv0dn6APPmzcPJkycBNI8y//Wvf43c3FzY7XZ88MEH+Pzzz/HAAw9I3IruublN999/P06ePInDhw/DZrPh4MGD+Prrr3H//fdLXGX3eOvr5HA4sGXLFmzcuBGzZs1q8/i6deuwb98+AMCiRYtQXV2Nv//977BarTh58iT279+PtWvXDnTZt9SdNimVSuzYsQPHjh2D3W7H8ePHsXv3bqxevXqgy76ljIwMFBYW4pVXXoHFYkFtbS1efvllZGRkICgoyCvfU91pkze9p+gmUt47TN1TUVEhPv7442JaWpqYmZkp/va3vxUdDocoiqI4evRo8ZNPPhFFURRdLpf4yiuviFlZWWJKSoo4b94892OeZs6cOWJKSoqYlJQkjh49WkxJSRFTUlLEkpKSVm0SRVE8fPiwOG/ePDE5OVlcsGCBR85LIYpdb5M3vU6nT59u1Zabf0pKSsSsrCzxzTffdO//9ddfi0uXLhVTUlLEmTNninv37pWw+vZ1t027du0S58yZI6ampopZWVniO++8I2H1HTt79qy4cuVKMTU1VZw8ebL44x//WKyoqBBFUfTa91RX2+RN7yn6N641Q0RERJLhpRkiIiKSDIMIERERSYZBhIiIiCTDIEJERESSYRAhIiIiyTCIEBERkWQYRIgGibVr1yIhIcH9+8mTJ5GQkICXX35Zwqra2rNnDxISErBnzx6pSyGiAcDVd4n62J49e/Czn/2szXaZTIagoCCkp6fj4Ycfxvjx4yWo7t/i4+Px4osvtlkptyuMRiN27tyJdevWtVnBlYioOxhEiPrJwoULW00dbrFYkJ+fj7fffhuffPIJduzYgUWLFklWX3BwMObNm9ejY3Nzc/HHP/4RS5cuZRAhol5hECHqJ6NHj273g/6+++7D4sWL8dxzz2H+/PlQKpUSVNc7ubm5UpdARLcJjhEhDcZV2wAAB8FJREFUGmDDhw/HxIkT0dDQgKtXrwL497iI999/H7/5zW8wceJE7Nixw31MXV0dtm3bhhkzZiAlJQWZmZl44okncO7cuTbnv3jxItatW4exY8di/Pjx+OEPf4j8/Pw2+3U0RqSoqAhPP/00pkyZgpSUFNxzzz14++234XK5AAAzZszACy+8AACYOXNmq3EnAPDuu+9i+fLlSEtLQ3p6OpYuXYo333zTfXwLk8mEbdu2Ydq0aUhNTUV2dna3xoXMmDEDCQkJHf6UlJR0+VxEJB32iBBJQKVSAWheAfZmBw8ehF6vx7PPPovY2FgAgF6vx/3334+6ujqsXLkS8fHxqKqqwltvvYXVq1fjtddew+TJkwEAZWVlePDBB+F0OrF27VrExcUhLy8Pjz76KPz9/Tutq6ioCEuXLoVWq8UPf/hDaLVaHDlyBD//+c9RUFCAn/70p9i6dStef/11nDp1Clu3bkVwcLD7+N/+9rd44403MHPmTKxcuRIOhwNHjx7Ftm3bcPnyZTz33HPufX/yk5/g8OHDmDlzJrKysqDX6/Haa68hIiKiS/+GW7duhdlsbrWtrq4Ozz33HEJCQhAUFNSl8xCRtBhEiAaY2WzGmTNnoFKpEB8f3+qxs2fP4siRIwgICHBv+9Of/oTi4mLs2rULaWlp7u2LFy/GggULsH37dvdy9X/9619hMBjw3HPPYfny5e59x4wZg5/+9Ked1va73/0OFosFu3fvdgehJUuW4IEHHsBf/vIXPPTQQ5g+fToOHjwIALjrrrsQFRUFALh8+TLeeOMNrFq1Clu3bnWf84EHHsB//Md/4N1338Xq1auRlJSEy5cv4/Dhw5gwYQJeeeUVCIIAAFi2bBnuueeeLv07Tp8+vdXvLpcLP/jBDwAAf/jDH7oUvIhIerw0Q9RPrFYrGhsb3T/V1dU4ffo01q9fj8rKSjz22GPw8/NrdczUqVNbhRAAOHDgAEaOHIm4uLhW5/Pz88P48eORl5cHvV4PAPjqq68gk8nafJhnZ2e3Oe/3GY1GfPrpp0hLS3OHEAAQBAG/+93v8Pbbb9/yHC3h5J577mlVZ2NjI+bOnQsAOHXqFADgxIkT7n1bQggABAUF9XgA7csvv4wvv/wSmzdvxrhx43p0DiIaeOwRIeonf/zjH/HHP/6xzXadToef/vSnePjhh9s81tK70MJgMKCqqgpVVVWYMGFCh89VXl4OrVaL4uJihIWFQa1Wt3pcoVAgJiYGFy5c6PAcBQUFsNvtGD58eJvHoqOjER0d3eGxAHDt2jUAwJo1azrcp6ysDABQXFwMAK0CT4uRI0fe8nna8/nnn+PVV1/F3Llz8dBDD3X7eCKSDoMIUT9ZsWIFsrOz3b/LZDLodDqMGDECcrm83WO+fznBaDQCABITE7Fly5YOnysyMhJA8y3C4eHh7e7TMi6lIxaLBQB6fBdPS62///3vERoa2u4+YWFhAOAe29FeTb6+vt163rKyMmzevBkxMTH4zW9+061jiUh6DCJE/WT48OHIzMzs1Tlagondbu/SuVQqFaxWa7uPmUymWx4bEhICAGhsbOxmlc1aah0+fDjuuOOOW+7bEkDaq7WzOm9ms9nw1FNPwWKx4KWXXur08hMReR6OESHyYIGBgYiIiEBhYSFqa2vbPF5XV9fq92HDhqG6urrNB7zNZkNhYeEtn2vIkCGQy+XuSyw3u3btGvbs2YPS0tIOj2+ZofXMmTNtHjMaja1qGjZsGAC0e4ttyy3NXbFjxw6cO3cOv/zlL9vcRkxE3oFBhMjDzZ8/Hw6HA3/7299abdfr9ViyZAkeffRR97YJEybA4XDg8OHDrfbdv39/pz0NKpUKU6dOxfXr19uEieeff77VpSGZrPlPx83hYv78+QCAt956y32Z5+bjJ02ahKKiIgDAxIkTAQCHDh1qtV9dXV2b2jty4MAB/N///R9WrFiBJUuWdOkYIvI8vDRD5OGeeOIJfPzxx/jzn/+M2tpaTJgwAbW1tdi1axdqa2vx4IMPuvddt24d9u7di61bt+LatWuIjo523yqbkpKC8+fP3/K5fvKTn+DMmTP40Y9+hB/84AcICwvD0aNH8emnn+KRRx5xj0VpGVT7/PPPY8KECVi8eDESExOxbt06/PWvf8UDDzyAlStXQqFQ4LPPPsNHH32ERYsWuQe83nHHHZg8eTKOHTuGJ598EtOmTYNer8e7776LsWPH4tNPP71lnWVlZXj22WcREBCACRMmtAk0QHMPTU/W0SGigcUgQuThdDod3nnnHbzyyis4evQo3nvvPfj5+SEtLQ3btm1z9y4AQFxcHHbu3In/+Z//wRtvvAG5XI709HS89tpreOmllzoNIvHx8XjnnXfw4osv4vXXX4fRaMTw4cPxq1/9CitWrHDvd//99+PYsWM4duwYzp8/jzlz5gAAtmzZgvj4eLz99tvYvn07XC4XYmNjsXnz5jZ3s7z00kt4/vnnceTIEXz88ceIiYlxh5/OgkhxcbG7h2fz5s3t7rNhwwZs3LjxluchIukJoiiKUhdBREREgxPHiBAREZFkGESIiIhIMgwiREREJBkGESIiIpIMgwgRERFJhkGEiIiIJMMgQkRERJJhECEiIiLJMIgQERGRZBhEiIiISDL/H/eZr4s+pX6wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}