Assignment1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Group5-CMLS-HW1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "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": "3.6.8"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "db3990776c83468782bc33a906303fcc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_53e4b970cfeb48499d2effe737349291",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_90e056387a7a4867888e4125d5b186ad",
              "IPY_MODEL_4194727a743f4e058910d68a2a9664f3"
            ]
          }
        },
        "53e4b970cfeb48499d2effe737349291": {
          "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
          }
        },
        "90e056387a7a4867888e4125d5b186ad": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_13fc05813a9a4531a9bfacdf943423df",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 4,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_4145d51401604272bf93691d20f3f826"
          }
        },
        "4194727a743f4e058910d68a2a9664f3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_0456520a579b4647b814cd92d4ca51ff",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 4/4 [00:00&lt;00:00, 24.38it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d142a450f6e64f8d87b3e1fe5777aa91"
          }
        },
        "13fc05813a9a4531a9bfacdf943423df": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "4145d51401604272bf93691d20f3f826": {
          "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
          }
        },
        "0456520a579b4647b814cd92d4ca51ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d142a450f6e64f8d87b3e1fe5777aa91": {
          "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
          }
        },
        "b17df778f1f84bf6ac25b9476ab723be": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_1a9a67242ed0489e9f9fcfc854266593",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_fd7c7bba83364894bb84bf7abf198d8a",
              "IPY_MODEL_f2addadba8f44ff4a6ee9efbbb05f9e2"
            ]
          }
        },
        "1a9a67242ed0489e9f9fcfc854266593": {
          "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
          }
        },
        "fd7c7bba83364894bb84bf7abf198d8a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_0409789247bc48dc968a35659961c1c2",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 80,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 80,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c33bc434861b4283a8fd1275621bf3c2"
          }
        },
        "f2addadba8f44ff4a6ee9efbbb05f9e2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_4daefc48668f441ebd1cae3636b044cd",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 80/80 [00:50&lt;00:00,  1.59it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e817bf67484a44de955932e8283ef314"
          }
        },
        "0409789247bc48dc968a35659961c1c2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "c33bc434861b4283a8fd1275621bf3c2": {
          "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
          }
        },
        "4daefc48668f441ebd1cae3636b044cd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "e817bf67484a44de955932e8283ef314": {
          "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
          }
        },
        "a35be4782cd24362bd1240c78204d6db": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_f70e376bbffc488ab8502e45c816f344",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_392c92c4b5354667b642ab217a67d3a1",
              "IPY_MODEL_16b91a6ee13c4b96902a7bc7af2ce9d3"
            ]
          }
        },
        "f70e376bbffc488ab8502e45c816f344": {
          "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
          }
        },
        "392c92c4b5354667b642ab217a67d3a1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_b010424806c6481785722d2e4d1f2da3",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 400,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 399,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_57b895e6cfc340b7bec86f1e8cc4cf47"
          }
        },
        "16b91a6ee13c4b96902a7bc7af2ce9d3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_eb8b656a44d44ea7ac91e9a76eb24bd4",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 399/400 [01:17&lt;00:00,  5.30it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e593320d9bca41eaad6fc0b197f6e749"
          }
        },
        "b010424806c6481785722d2e4d1f2da3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "57b895e6cfc340b7bec86f1e8cc4cf47": {
          "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
          }
        },
        "eb8b656a44d44ea7ac91e9a76eb24bd4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "e593320d9bca41eaad6fc0b197f6e749": {
          "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
          }
        },
        "f099c4f3a79b4fcc993240fedffee375": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_ad3a8fc2f792450fa0a9f22f7ddac50a",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_74d682192a6b4e88bfd4cd0d4711360f",
              "IPY_MODEL_e2bdc01e28a6413bb03f89a35ebcf71c"
            ]
          }
        },
        "ad3a8fc2f792450fa0a9f22f7ddac50a": {
          "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
          }
        },
        "74d682192a6b4e88bfd4cd0d4711360f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_7cd46b7e7ea24a8f9a856c5442c686db",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 400,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 399,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7747455a06a6460384e34320f1be7e26"
          }
        },
        "e2bdc01e28a6413bb03f89a35ebcf71c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_aa700acecfb24da692de73959cb5e09d",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 399/400 [01:15&lt;00:00,  5.46it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_b1e0c8d61607418a9a4e466d6049358a"
          }
        },
        "7cd46b7e7ea24a8f9a856c5442c686db": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "7747455a06a6460384e34320f1be7e26": {
          "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
          }
        },
        "aa700acecfb24da692de73959cb5e09d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "b1e0c8d61607418a9a4e466d6049358a": {
          "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
          }
        },
        "4eef8941ba82428dad8e84ca759b34dc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_3ad1daeee4434dc1bc6ce60f97249694",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_39b49848b790437db4529eba982e98f0",
              "IPY_MODEL_35b9cb57e9e34948843db9323a74038d"
            ]
          }
        },
        "3ad1daeee4434dc1bc6ce60f97249694": {
          "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
          }
        },
        "39b49848b790437db4529eba982e98f0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_bd646e6500ca4372a17be06864148b96",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "danger",
            "max": 400,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 399,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_616697b797ea4106b0c7bb9bf7fe9a8e"
          }
        },
        "35b9cb57e9e34948843db9323a74038d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_1a9c57a2247b4daa8693a108530a800a",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 399/400 [07:25&lt;00:01,  1.11s/it]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_78b9e939353149d49686ae1032071447"
          }
        },
        "bd646e6500ca4372a17be06864148b96": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "616697b797ea4106b0c7bb9bf7fe9a8e": {
          "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
          }
        },
        "1a9c57a2247b4daa8693a108530a800a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "78b9e939353149d49686ae1032071447": {
          "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
          }
        },
        "e57d4e7a4d4747a6b807b87481ee44a4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_6b7d29c893b24cacb9133cf9e6c583e1",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_bde6e687861946c8a8f4f05c554e7ff9",
              "IPY_MODEL_dac7c813d6714ea6bbc8b04924cccf27"
            ]
          }
        },
        "6b7d29c893b24cacb9133cf9e6c583e1": {
          "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
          }
        },
        "bde6e687861946c8a8f4f05c554e7ff9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_4eb0f5738287421eb906f5a7246d0158",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 4,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7c5d15933a4f42a1b5db788b07291898"
          }
        },
        "dac7c813d6714ea6bbc8b04924cccf27": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_104083df8d5845bb9d4055416553b416",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 4/4 [00:04&lt;00:00,  1.11s/it]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0a5a0e097cb645c283922982bae0834a"
          }
        },
        "4eb0f5738287421eb906f5a7246d0158": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "7c5d15933a4f42a1b5db788b07291898": {
          "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
          }
        },
        "104083df8d5845bb9d4055416553b416": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "0a5a0e097cb645c283922982bae0834a": {
          "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
          }
        },
        "2bc727d810ec42ea9a5d40f6add6a65e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_a1c5141f47ed45f98b24b392cf783f99",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_363a68e1473340809a034b4ce2136a82",
              "IPY_MODEL_1630af3611014debbdab87d394ae4b2a"
            ]
          }
        },
        "a1c5141f47ed45f98b24b392cf783f99": {
          "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
          }
        },
        "363a68e1473340809a034b4ce2136a82": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_77cc6ff6125040f8aef1a19467ba3a2f",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 2,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 2,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_16a685521ff444729a9247fad38c4c89"
          }
        },
        "1630af3611014debbdab87d394ae4b2a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_b9f37e9e7abf4d56b622b8222772197a",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 2/2 [00:28&lt;00:00, 14.32s/it]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_f15b8ea9443c4ac4ad5ed708981bbd3f"
          }
        },
        "77cc6ff6125040f8aef1a19467ba3a2f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_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"
          }
        },
        "16a685521ff444729a9247fad38c4c89": {
          "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
          }
        },
        "b9f37e9e7abf4d56b622b8222772197a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "f15b8ea9443c4ac4ad5ed708981bbd3f": {
          "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
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yilin10/MusicalGenreClassification/blob/master/Assignment1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jLVe0JKuO3bf"
      },
      "source": [
        "# CMLS - Assignment 1: Music Genre Classification\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqa0sub0LAkQ",
        "colab_type": "text"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Qx1zsQfLC0q",
        "colab_type": "text"
      },
      "source": [
        "This is a brief report for Computer Music: Language and Systesms in Politecnico di Milano, Group 5 Assignment 1. \n",
        "\n",
        "It is written as IPython Notebook using Google Colaboratory.\n",
        "\n",
        "The full report version is available on OverLeaf [link](https://www.overleaf.com/1494782774kwgkjvfgddzq).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oAMtiv3fi_zD"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AoIKVCIltRqu"
      },
      "source": [
        "Import of **Libraries** we needed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PM1VJfgyOXPH",
        "colab": {}
      },
      "source": [
        "# import python libraries\n",
        "import librosa\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import IPython.display as ipd\n",
        "import scipy as sp\n",
        "from tqdm.notebook import tqdm\n",
        "from time import sleep\n",
        "\n",
        "# import classification and evaluation libraries\n",
        "import sklearn.svm\n",
        "from sklearn.model_selection import train_test_split,cross_val_score\n",
        "from sklearn import datasets\n",
        "import sklearn.neighbors as neighbors\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report\n",
        "import sklearn.preprocessing as preprocessing"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hxm6tnwutXJu"
      },
      "source": [
        "Use the personal **Kaggle Api token** to enable download from Kaggle."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "X8wsW27Qk5d2",
        "outputId": "5e043147-c07a-48c4-c643-22903eb51211",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload() # Choose kaggle.json (your personal Kaggle API token)\n",
        "\n",
        "! rm -r ~/.kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! mv kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json # Change the permissions of the file."
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-75f78b8a-34ac-4e90-821b-6d0eb1eeec4a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-75f78b8a-34ac-4e90-821b-6d0eb1eeec4a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "rm: cannot remove '/root/.kaggle': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ut0yfrF6tvqA"
      },
      "source": [
        "Download of the dataset from Kaggle, and unzip the dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O-jkkBM5RLBm",
        "outputId": "28a55321-32f3-4c30-ab6c-1f7ec7d1f100",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "! pip install -q kaggle\n",
        "! mkdir ~/dataset\n",
        "! kaggle datasets download -d carlthome/gtzan-genre-collection\n",
        "! unzip -q gtzan-genre-collection.zip -d ~/dataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading gtzan-genre-collection.zip to /content\n",
            " 99% 1.13G/1.14G [00:26<00:00, 38.6MB/s]\n",
            "100% 1.14G/1.14G [00:26<00:00, 46.8MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMvXCboCo0oa",
        "colab_type": "text"
      },
      "source": [
        "## Observation and Retrieving Annotations\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muksP7rsd25t",
        "colab_type": "text"
      },
      "source": [
        "The original Dataset we are using is from [Kaggle](https://www.kaggle.com/carlthome/gtzan-genre-collection) and provided by [GTZAN](http://marsyas.info/downloads/datasets.html), which is the most widely used free database in music genre recognition field.\n",
        "\n",
        "We need to classify the four Music Genre categories **Blues, Metal, Hip-Hop, Reggae**.\n",
        "\n",
        "\n",
        "In the description part, we can see the brief infomation on this data set.\n",
        "\n",
        "```\n",
        "The dataset consists of 1000 audio tracks each 30 seconds long. \n",
        "\n",
        "It contains 10 genres, each represented by 100 tracks. \n",
        "\n",
        "The tracks are all 22050 Hz monophonic 16-bit audio files in .au format.\n",
        "```\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvcEkZ9IKqPi",
        "colab_type": "code",
        "outputId": "eb4d039f-9fae-4f6a-f1d3-ba08fb5fd9cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "db3990776c83468782bc33a906303fcc",
            "53e4b970cfeb48499d2effe737349291",
            "90e056387a7a4867888e4125d5b186ad",
            "4194727a743f4e058910d68a2a9664f3",
            "13fc05813a9a4531a9bfacdf943423df",
            "4145d51401604272bf93691d20f3f826",
            "0456520a579b4647b814cd92d4ca51ff",
            "d142a450f6e64f8d87b3e1fe5777aa91"
          ]
        }
      },
      "source": [
        "# save data in genre.csv\n",
        "genre_analysis_list = [\"blues\",\"metal\",\"hiphop\",\"reggae\"]  \n",
        "genre_list = []  \n",
        "path_list = []\n",
        "train_path = \"../root/dataset/genres/\"\n",
        "\n",
        "for each in tqdm(genre_analysis_list):\n",
        "    train_test_folder = train_path + format(each)\n",
        "    train_catg_files = [f for f in os.listdir(train_test_folder) if f.endswith('.au')]\n",
        "    for index, each_file in enumerate(train_catg_files):\n",
        "        each_file_path = os.path.join(train_test_folder, each_file)\n",
        "        genre_list.append(each)\n",
        "        path_list.append(each_file_path)\n",
        "genre_data = pd.DataFrame(list(zip(genre_list,path_list)),columns = ['Genre','Path'])\n",
        "genre_data.to_csv('./genres.csv',sep = '\\t', index = False)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db3990776c83468782bc33a906303fcc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HHhz4KHKhlJ",
        "colab_type": "text"
      },
      "source": [
        "Check the annotation retrived."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeiCrN1Xb1Gt",
        "colab_type": "code",
        "outputId": "4df42470-f4ca-4b7b-e452-1970424d33df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "genre_data.describe # get pd.DataFrame"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of       Genre                                           Path\n",
              "0     blues    ../root/dataset/genres/blues/blues.00021.au\n",
              "1     blues    ../root/dataset/genres/blues/blues.00076.au\n",
              "2     blues    ../root/dataset/genres/blues/blues.00053.au\n",
              "3     blues    ../root/dataset/genres/blues/blues.00087.au\n",
              "4     blues    ../root/dataset/genres/blues/blues.00085.au\n",
              "..      ...                                            ...\n",
              "395  reggae  ../root/dataset/genres/reggae/reggae.00049.au\n",
              "396  reggae  ../root/dataset/genres/reggae/reggae.00088.au\n",
              "397  reggae  ../root/dataset/genres/reggae/reggae.00021.au\n",
              "398  reggae  ../root/dataset/genres/reggae/reggae.00083.au\n",
              "399  reggae  ../root/dataset/genres/reggae/reggae.00098.au\n",
              "\n",
              "[400 rows x 2 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfjjS6PJ_Moo",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byNZHT1qP57Z",
        "colab_type": "text"
      },
      "source": [
        "Go through the files and check .au basic info, we can pick random samples to check according to statistic principle.\n",
        "- sample rate\n",
        "- duration in seconds\n",
        "- static tempo\n",
        "\n",
        "By executing the following code we can get the same information on this dataset which confirms with the provided info."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0PiPkNoP6Tx",
        "colab_type": "code",
        "outputId": "eff07e3c-a691-4e38-c5b8-02f9969cc57f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "b17df778f1f84bf6ac25b9476ab723be",
            "1a9a67242ed0489e9f9fcfc854266593",
            "fd7c7bba83364894bb84bf7abf198d8a",
            "f2addadba8f44ff4a6ee9efbbb05f9e2",
            "0409789247bc48dc968a35659961c1c2",
            "c33bc434861b4283a8fd1275621bf3c2",
            "4daefc48668f441ebd1cae3636b044cd",
            "e817bf67484a44de955932e8283ef314"
          ]
        }
      },
      "source": [
        "import warnings \n",
        "# ignore dw warning from librosa.beat\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
        "# get 20% of check_sample from original dataset\n",
        "check_sample = genre_data['Path'].sample(frac = 0.2, replace = False,\n",
        "                                         random_state = None, axis = 0 )\n",
        "sr_list = []\n",
        "dur_list = []\n",
        "tempo_list = []\n",
        "for p in tqdm(check_sample):\n",
        "    #read audio file \n",
        "    y, sr = librosa.load(p) \n",
        "    #1. get sample rate\n",
        "    sr_list.append(sr)\n",
        "    #2. get duration\n",
        "    dur = round(librosa.get_duration(y=y, sr=sr))\n",
        "    dur_list.append(dur)\n",
        "    #3. static tempo\n",
        "    onset_env = librosa.onset.onset_strength(y, sr=sr)\n",
        "    tempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sr)\n",
        "    tempo_list.append(round(tempo[0]))\n",
        "print(\"sample rate: \", sr_list) # sample rate = 22050\n",
        "print(\"duration in seconds: \", dur_list)\n",
        "print(\"static tempo: \", tempo_list)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b17df778f1f84bf6ac25b9476ab723be",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=80.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "sample rate:  [22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050, 22050]\n",
            "duration in seconds:  [30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]\n",
            "static tempo:  [112.0, 152.0, 144.0, 103.0, 89.0, 108.0, 89.0, 92.0, 96.0, 108.0, 92.0, 152.0, 96.0, 152.0, 92.0, 123.0, 112.0, 96.0, 103.0, 123.0, 152.0, 129.0, 144.0, 96.0, 161.0, 99.0, 112.0, 136.0, 123.0, 123.0, 103.0, 103.0, 123.0, 103.0, 144.0, 108.0, 161.0, 161.0, 103.0, 123.0, 144.0, 136.0, 152.0, 86.0, 136.0, 112.0, 136.0, 152.0, 112.0, 117.0, 117.0, 99.0, 136.0, 89.0, 129.0, 92.0, 161.0, 103.0, 108.0, 152.0, 123.0, 144.0, 112.0, 123.0, 172.0, 96.0, 129.0, 92.0, 144.0, 152.0, 108.0, 136.0, 161.0, 161.0, 117.0, 144.0, 117.0, 112.0, 129.0, 185.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzWlyjSEkCuR",
        "colab_type": "code",
        "outputId": "a2df71b0-f76d-48b1-ffa3-6ab63484e82b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print(\"--- Statistics for sample data in static tempo ---\")\n",
        "print(\"Mean\", np.mean(tempo_list))\n",
        "print(\"Min\",np.min(tempo_list))\n",
        "print(\"Max\",np.max(tempo_list))\n",
        "print(\"Median\", np.median(tempo_list))\n",
        "print(\"Std\", np.std(tempo_list))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- Statistics for sample data in static tempo ---\n",
            "Mean 123.3375\n",
            "Min 86.0\n",
            "Max 185.0\n",
            "Median 123.0\n",
            "Std 23.77706024196431\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1--vsk7VfXpB",
        "colab_type": "text"
      },
      "source": [
        "Here we can print to see the statistic info in tempo as a test. We can see in rhythmic part the standard diviation is quiet obvious, which makes it possible to distinguish different different genres."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJek8GfLI9q9",
        "colab_type": "text"
      },
      "source": [
        "**Check the quality of the Original dataset**\n",
        "\n",
        "- if it is balanced\n",
        "- if it has repetition\n",
        "- if it is able to describe differnet genre\n",
        "\n",
        "According to a research result we found [[1]](https://arxiv.org/abs/1306.1461), the audio pieces from GTZAN is not perfect, it does have repetitions, and dislabelling error.\n",
        "\n",
        "In the research result we find from the paper, they checked each audio piece both using fingerprinting of the audio and also human ears.\n",
        "\n",
        "There are different kinds of repetition and mislabelling in the GTZAN dataset(as recording repetition, artist repetiton and version repetition and others).\n",
        "\n",
        "1. **Recording repetition**: 0 error in blues, 6 in hiphop, 0 in metal, 3 in reggae\n",
        "2. **Artist unbalanced**: all blues audio come from only 9 artists; 34 % of the Reggae pieces come from artist Bob Marley(no.00-27 and no.54-60)\n",
        "3. **Version repetition**: 0 in blues, 2 in hiphop, 3 in Metal, 2 in Reggae; eg. Metal 33 is “Enter Sandman” by Metallica, and Metal 74 is a parody\n",
        "4. **Mislabelling**: there are some contentious or conscpicious labelled piece; eg. In Metal Genre, Queen's “Tie Your Mother Down” (no.58), “Tear it up” (no.59), “We Will Rock You” (no.60) might be labelled as Rock instead; In all, there are 0 error in blues, 4 in Hiphop, 13 in Metal and 1 in reggae.\n",
        "\n",
        "Thus, we know that GTZAN provides convenient origin dataset for further analysis, but is not perfect. \n",
        "\n",
        "We should take into consideration the error inside each 'Genre', especially in the categories of 'Metal' and 'Reggae' for **the evaluation stage**.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oH7PRBQyBzwJ",
        "colab_type": "text"
      },
      "source": [
        "## Choose the Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZipMFYxDvGrq",
        "colab_type": "text"
      },
      "source": [
        "We are able to analysis the genral information and store the data result we need.\n",
        "\n",
        "1. go through the genre list we are going to analysis **'blues', 'metal', 'hiphop', 'reggae'**\n",
        "2. store the file path and the genre tag\n",
        "3. extract and calculate other features we need, as **MFCC**, **Chroma_stft**, **centroid, zero crossing rate, tempo, rms**.\n",
        "4. store result in a pandas.DataFrame structure \n",
        "5. also create a .csv file named **source_data.csv** under **'./source_data.csv'**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOCemXLfRsV9",
        "colab_type": "text"
      },
      "source": [
        "Different genre has different **mfcc** pattern\n",
        "we can check the mfcc value on time series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_jAvDi4cCJA",
        "colab_type": "text"
      },
      "source": [
        "### MFCC\n",
        "pick different audio draw mfcc pattern and compare the different"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvQmn2rXciH_",
        "colab_type": "text"
      },
      "source": [
        "**Define functions** to use"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATEzlXw9-7LH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_mfcc(audio_path):\n",
        "    y, sr = librosa.load(str(audio_path), offset=0, duration=1)\n",
        "    x = librosa.feature.mfcc(y=y,sr=sr, n_mfcc=13)\n",
        "    return x\n",
        "\n",
        "def plot_mfcc(mfcc):\n",
        "    figure = plt.figure(figsize=(16, 6))\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.imshow(mfcc, origin='lower', aspect='auto')\n",
        "    plt.xlabel('Frame')\n",
        "    plt.ylabel('MFCC coefficients 0 to 13')\n",
        "    plt.colorbar()\n",
        "    plt.show()\n",
        "\n",
        "def plot_mfcc_genre_compare(gn1,gn2):#0b1m2h3r\n",
        "    ''' genre_name1, genre_name2 = string \n",
        "    from genre ana list {'blues','metal','hiphop','reggae'}\n",
        "    '''\n",
        "    g1 = genre_analysis_list.index(gn1)\n",
        "    g2 = genre_analysis_list.index(gn2)\n",
        "    path_1 = genre_data['Path'][np.random.randint(0,100)+100*g1]\n",
        "    path_2 = genre_data['Path'][np.random.randint(0,100)+100*g2]\n",
        "    # subplot\n",
        "    # g1\n",
        "    figure = plt.figure(figsize=(16, 6))\n",
        "    plt.subplot(1,2,1)\n",
        "    mfcc1 = get_mfcc(path_1)\n",
        "    plt.imshow(mfcc1, aspect='auto')\n",
        "    plt.xlabel('Frame in time series')\n",
        "    plt.ylabel('MFCC cooeffients')\n",
        "    plt.title('MFCC coefficients for: {}'.format(gn1))\n",
        "    plt.colorbar()\n",
        "    plt.tight_layout()\n",
        "\n",
        "    # g2\n",
        "    plt.subplot(1,2,2)\n",
        "    mfcc2 = get_mfcc(path_2)\n",
        "    plt.imshow(mfcc2, aspect='auto')\n",
        "    plt.xlabel('Frame in time series')\n",
        "    plt.ylabel('MFCC coefficients')\n",
        "    plt.title('MFCC coefficientsfor: {}'.format(gn2))\n",
        "    plt.colorbar()\n",
        "    plt.show()\n",
        "    plt.tight_layout()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VcYGz6MO1EF",
        "colab_type": "text"
      },
      "source": [
        "Plot MFCC test with several random samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gv30_XhDO1bD",
        "colab_type": "code",
        "outputId": "c5b23f3a-377b-4978-c687-64a1ec616288",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        }
      },
      "source": [
        "plot_mfcc_genre_compare('blues','metal')\n",
        "plot_mfcc_genre_compare('hiphop','reggae')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CnP91dbLSQm9",
        "colab_type": "text"
      },
      "source": [
        "Also check the distribution of chroma **on time axis** in  each genre\n",
        "\n",
        "### **Chroma_stft**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGlzMti1mYTQ",
        "colab_type": "code",
        "outputId": "68f71d7e-b4f5-4d1f-b65d-8a1f1637ee2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "a35be4782cd24362bd1240c78204d6db",
            "f70e376bbffc488ab8502e45c816f344",
            "392c92c4b5354667b642ab217a67d3a1",
            "16b91a6ee13c4b96902a7bc7af2ce9d3",
            "b010424806c6481785722d2e4d1f2da3",
            "57b895e6cfc340b7bec86f1e8cc4cf47",
            "eb8b656a44d44ea7ac91e9a76eb24bd4",
            "e593320d9bca41eaad6fc0b197f6e749"
          ]
        }
      },
      "source": [
        "# new plot\n",
        "# get chroma in time series\n",
        "show_chroma_dict = {'blues':[],'metal':[],'hiphop':[],'reggae':[]}\n",
        "for each in genre_analysis_list:\n",
        "    show_chroma_dict[each] = pd.DataFrame(np.zeros((12,108)))\n",
        "for each, each_class in zip(genre_data['Path'],tqdm(genre_data['Genre'])):\n",
        "    hop_length = 1024\n",
        "    y, sr = librosa.load(each, offset=0, duration=5)\n",
        "    each_sample = librosa.feature.chroma_stft(y=y, sr=sr,hop_length= hop_length)\n",
        "    # change 4/24\n",
        "    show_chroma_dict[each_class] += each_sample\n",
        "for each in genre_analysis_list:\n",
        "    show_chroma_dict[each] /= 100.00"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a35be4782cd24362bd1240c78204d6db",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=400.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvYro-aOnc4P",
        "colab_type": "code",
        "outputId": "a55c0d6f-7058-4fcc-9ba5-499facf4adcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "show_chroma_dict"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'blues':          0         1         2    ...       105       106       107\n",
              " 0   0.371506  0.367299  0.356962  ...  0.312887  0.280662  0.299450\n",
              " 1   0.356839  0.348583  0.338328  ...  0.295931  0.290436  0.285675\n",
              " 2   0.361248  0.353822  0.308794  ...  0.281588  0.325756  0.322046\n",
              " 3   0.377833  0.304207  0.271913  ...  0.277642  0.304493  0.294053\n",
              " 4   0.403950  0.333285  0.333238  ...  0.329485  0.370983  0.359286\n",
              " 5   0.393489  0.374635  0.368296  ...  0.354875  0.365418  0.355903\n",
              " 6   0.320618  0.323134  0.294987  ...  0.280005  0.296363  0.297931\n",
              " 7   0.351919  0.332332  0.306453  ...  0.276867  0.319871  0.325418\n",
              " 8   0.322796  0.279952  0.289132  ...  0.255199  0.294669  0.314025\n",
              " 9   0.380551  0.335520  0.314634  ...  0.320189  0.350425  0.391934\n",
              " 10  0.447128  0.367975  0.331460  ...  0.335378  0.346622  0.369194\n",
              " 11  0.419405  0.338581  0.330038  ...  0.284425  0.296787  0.348662\n",
              " \n",
              " [12 rows x 108 columns],\n",
              " 'hiphop':          0         1         2    ...       105       106       107\n",
              " 0   0.393432  0.476192  0.435359  ...  0.469141  0.432779  0.468480\n",
              " 1   0.373628  0.442626  0.454667  ...  0.467203  0.464929  0.482979\n",
              " 2   0.450040  0.454175  0.468732  ...  0.465151  0.487617  0.472598\n",
              " 3   0.457086  0.475149  0.491978  ...  0.455315  0.463357  0.472233\n",
              " 4   0.473612  0.474074  0.491854  ...  0.461233  0.440653  0.494031\n",
              " 5   0.448682  0.453397  0.458031  ...  0.471927  0.427280  0.479286\n",
              " 6   0.400340  0.410674  0.434195  ...  0.450269  0.431530  0.474247\n",
              " 7   0.402182  0.394415  0.432314  ...  0.445299  0.459083  0.488625\n",
              " 8   0.421265  0.421819  0.442723  ...  0.426952  0.484119  0.487799\n",
              " 9   0.441517  0.460569  0.460476  ...  0.416530  0.476700  0.491550\n",
              " 10  0.423434  0.456822  0.445622  ...  0.428105  0.415311  0.464637\n",
              " 11  0.419393  0.447392  0.436221  ...  0.457391  0.407577  0.447886\n",
              " \n",
              " [12 rows x 108 columns],\n",
              " 'metal':          0         1         2    ...       105       106       107\n",
              " 0   0.485516  0.435047  0.432665  ...  0.453492  0.433487  0.417367\n",
              " 1   0.468728  0.457930  0.467410  ...  0.491738  0.465753  0.445420\n",
              " 2   0.473154  0.480525  0.504771  ...  0.510798  0.468189  0.457271\n",
              " 3   0.511887  0.455851  0.465781  ...  0.534894  0.518156  0.493848\n",
              " 4   0.564553  0.512094  0.517745  ...  0.556863  0.559937  0.521251\n",
              " 5   0.471117  0.456213  0.455764  ...  0.488734  0.493928  0.481932\n",
              " 6   0.445021  0.416434  0.433809  ...  0.439448  0.451391  0.441198\n",
              " 7   0.449366  0.439097  0.438371  ...  0.447443  0.476046  0.483675\n",
              " 8   0.457824  0.458298  0.434934  ...  0.502692  0.510731  0.501242\n",
              " 9   0.475996  0.469299  0.419436  ...  0.487604  0.482442  0.440553\n",
              " 10  0.443434  0.454114  0.411984  ...  0.448441  0.443041  0.419557\n",
              " 11  0.469037  0.464943  0.485677  ...  0.491094  0.474520  0.452332\n",
              " \n",
              " [12 rows x 108 columns],\n",
              " 'reggae':          0         1         2    ...       105       106       107\n",
              " 0   0.465153  0.420011  0.410929  ...  0.401551  0.406247  0.412002\n",
              " 1   0.452810  0.412496  0.394966  ...  0.430403  0.419404  0.416577\n",
              " 2   0.416432  0.419646  0.391000  ...  0.471703  0.460456  0.455046\n",
              " 3   0.426484  0.428506  0.369485  ...  0.441510  0.426970  0.447343\n",
              " 4   0.457533  0.434439  0.374367  ...  0.406437  0.413250  0.437399\n",
              " 5   0.455236  0.407906  0.379778  ...  0.404923  0.411297  0.387310\n",
              " 6   0.438380  0.396606  0.385252  ...  0.400359  0.396849  0.367978\n",
              " 7   0.431395  0.421894  0.411906  ...  0.431160  0.368184  0.364482\n",
              " 8   0.407857  0.401904  0.370086  ...  0.385052  0.352111  0.362173\n",
              " 9   0.417051  0.389587  0.358435  ...  0.416109  0.369241  0.379980\n",
              " 10  0.456730  0.371824  0.386701  ...  0.374026  0.350017  0.376345\n",
              " 11  0.471538  0.406081  0.432967  ...  0.389042  0.386916  0.429313\n",
              " \n",
              " [12 rows x 108 columns]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0q2BY2JuSP_N",
        "colab_type": "code",
        "outputId": "f7c8658b-39a4-4a21-b0b7-b7bac8ada356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        }
      },
      "source": [
        "# new plot\n",
        "for each in genre_analysis_list:\n",
        "    figure = plt.figure(figsize=(6, 3))\n",
        "    plt.imshow(show_chroma_dict[each],cmap='Blues', aspect='auto' )\n",
        "    plt.xlabel('Frame(Time series)')\n",
        "    plt.ylabel('Chroma Distribution')\n",
        "    plt.title('Chroma stft for: Genre {}'.format(each))\n",
        "    plt.colorbar()\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1NyEsAqOjiG",
        "colab_type": "text"
      },
      "source": [
        "### Spectral Flatness"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVyrbmMlbniC",
        "colab_type": "text"
      },
      "source": [
        "get time series of spectral flatness"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tid0bDoy5hzb",
        "colab_type": "code",
        "outputId": "1b77e659-0322-4bcd-818b-b411a7797ff5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285,
          "referenced_widgets": [
            "f099c4f3a79b4fcc993240fedffee375",
            "ad3a8fc2f792450fa0a9f22f7ddac50a",
            "74d682192a6b4e88bfd4cd0d4711360f",
            "e2bdc01e28a6413bb03f89a35ebcf71c",
            "7cd46b7e7ea24a8f9a856c5442c686db",
            "7747455a06a6460384e34320f1be7e26",
            "aa700acecfb24da692de73959cb5e09d",
            "b1e0c8d61607418a9a4e466d6049358a"
          ]
        }
      },
      "source": [
        "sample = np.zeros((400,216))\n",
        "for each,i in zip(genre_data['Path'],tqdm(range(0,400))):\n",
        "    # read each file\n",
        "    y, sr = librosa.load(each, offset=0, duration=5)\n",
        "    each_sample = librosa.feature.spectral_flatness(y=y)[0] \n",
        "    # first line: flat\n",
        "    sample[i] = each_sample\n",
        "pd_flat = pd.DataFrame(sample)\n",
        "pd_flat['Genre'] = genre_data['Genre']\n",
        "show_flat = pd_flat.groupby(['Genre']).mean()\n",
        "# show data frame\n",
        "show_flat"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f099c4f3a79b4fcc993240fedffee375",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=400.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>176</th>\n",
              "      <th>177</th>\n",
              "      <th>178</th>\n",
              "      <th>179</th>\n",
              "      <th>180</th>\n",
              "      <th>181</th>\n",
              "      <th>182</th>\n",
              "      <th>183</th>\n",
              "      <th>184</th>\n",
              "      <th>185</th>\n",
              "      <th>186</th>\n",
              "      <th>187</th>\n",
              "      <th>188</th>\n",
              "      <th>189</th>\n",
              "      <th>190</th>\n",
              "      <th>191</th>\n",
              "      <th>192</th>\n",
              "      <th>193</th>\n",
              "      <th>194</th>\n",
              "      <th>195</th>\n",
              "      <th>196</th>\n",
              "      <th>197</th>\n",
              "      <th>198</th>\n",
              "      <th>199</th>\n",
              "      <th>200</th>\n",
              "      <th>201</th>\n",
              "      <th>202</th>\n",
              "      <th>203</th>\n",
              "      <th>204</th>\n",
              "      <th>205</th>\n",
              "      <th>206</th>\n",
              "      <th>207</th>\n",
              "      <th>208</th>\n",
              "      <th>209</th>\n",
              "      <th>210</th>\n",
              "      <th>211</th>\n",
              "      <th>212</th>\n",
              "      <th>213</th>\n",
              "      <th>214</th>\n",
              "      <th>215</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genre</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>blues</th>\n",
              "      <td>0.005861</td>\n",
              "      <td>0.007009</td>\n",
              "      <td>0.002559</td>\n",
              "      <td>0.002272</td>\n",
              "      <td>0.002763</td>\n",
              "      <td>0.002980</td>\n",
              "      <td>0.002946</td>\n",
              "      <td>0.003073</td>\n",
              "      <td>0.003155</td>\n",
              "      <td>0.003164</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>0.003586</td>\n",
              "      <td>0.003540</td>\n",
              "      <td>0.003317</td>\n",
              "      <td>0.003422</td>\n",
              "      <td>0.003816</td>\n",
              "      <td>0.002973</td>\n",
              "      <td>0.003121</td>\n",
              "      <td>0.003734</td>\n",
              "      <td>0.004268</td>\n",
              "      <td>0.003999</td>\n",
              "      <td>0.004368</td>\n",
              "      <td>0.005189</td>\n",
              "      <td>0.004626</td>\n",
              "      <td>0.004213</td>\n",
              "      <td>0.003375</td>\n",
              "      <td>0.003069</td>\n",
              "      <td>0.003515</td>\n",
              "      <td>0.003583</td>\n",
              "      <td>0.003544</td>\n",
              "      <td>0.003372</td>\n",
              "      <td>0.003750</td>\n",
              "      <td>0.004443</td>\n",
              "      <td>0.003869</td>\n",
              "      <td>0.003427</td>\n",
              "      <td>0.003296</td>\n",
              "      <td>0.003250</td>\n",
              "      <td>0.003201</td>\n",
              "      <td>0.003598</td>\n",
              "      <td>0.003874</td>\n",
              "      <td>...</td>\n",
              "      <td>0.003129</td>\n",
              "      <td>0.003445</td>\n",
              "      <td>0.003961</td>\n",
              "      <td>0.003816</td>\n",
              "      <td>0.003684</td>\n",
              "      <td>0.003760</td>\n",
              "      <td>0.003664</td>\n",
              "      <td>0.003056</td>\n",
              "      <td>0.002762</td>\n",
              "      <td>0.003249</td>\n",
              "      <td>0.003983</td>\n",
              "      <td>0.004490</td>\n",
              "      <td>0.003800</td>\n",
              "      <td>0.003285</td>\n",
              "      <td>0.003182</td>\n",
              "      <td>0.003065</td>\n",
              "      <td>0.002982</td>\n",
              "      <td>0.003117</td>\n",
              "      <td>0.003143</td>\n",
              "      <td>0.003038</td>\n",
              "      <td>0.003121</td>\n",
              "      <td>0.003875</td>\n",
              "      <td>0.003482</td>\n",
              "      <td>0.003406</td>\n",
              "      <td>0.003600</td>\n",
              "      <td>0.003786</td>\n",
              "      <td>0.003946</td>\n",
              "      <td>0.003907</td>\n",
              "      <td>0.003674</td>\n",
              "      <td>0.003634</td>\n",
              "      <td>0.003607</td>\n",
              "      <td>0.003863</td>\n",
              "      <td>0.003829</td>\n",
              "      <td>0.003356</td>\n",
              "      <td>0.002859</td>\n",
              "      <td>0.002448</td>\n",
              "      <td>0.002314</td>\n",
              "      <td>0.002704</td>\n",
              "      <td>0.004677</td>\n",
              "      <td>0.005888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hiphop</th>\n",
              "      <td>0.117201</td>\n",
              "      <td>0.120168</td>\n",
              "      <td>0.069183</td>\n",
              "      <td>0.038065</td>\n",
              "      <td>0.035945</td>\n",
              "      <td>0.029767</td>\n",
              "      <td>0.029598</td>\n",
              "      <td>0.034797</td>\n",
              "      <td>0.040853</td>\n",
              "      <td>0.045784</td>\n",
              "      <td>0.049302</td>\n",
              "      <td>0.047756</td>\n",
              "      <td>0.042556</td>\n",
              "      <td>0.044221</td>\n",
              "      <td>0.045426</td>\n",
              "      <td>0.043709</td>\n",
              "      <td>0.043664</td>\n",
              "      <td>0.044287</td>\n",
              "      <td>0.044255</td>\n",
              "      <td>0.043918</td>\n",
              "      <td>0.035335</td>\n",
              "      <td>0.037618</td>\n",
              "      <td>0.036396</td>\n",
              "      <td>0.032231</td>\n",
              "      <td>0.029268</td>\n",
              "      <td>0.031739</td>\n",
              "      <td>0.037515</td>\n",
              "      <td>0.044859</td>\n",
              "      <td>0.048819</td>\n",
              "      <td>0.053382</td>\n",
              "      <td>0.055816</td>\n",
              "      <td>0.049556</td>\n",
              "      <td>0.047654</td>\n",
              "      <td>0.048040</td>\n",
              "      <td>0.048482</td>\n",
              "      <td>0.048739</td>\n",
              "      <td>0.047684</td>\n",
              "      <td>0.046614</td>\n",
              "      <td>0.044835</td>\n",
              "      <td>0.045542</td>\n",
              "      <td>...</td>\n",
              "      <td>0.026289</td>\n",
              "      <td>0.030294</td>\n",
              "      <td>0.035505</td>\n",
              "      <td>0.037066</td>\n",
              "      <td>0.033822</td>\n",
              "      <td>0.031885</td>\n",
              "      <td>0.029592</td>\n",
              "      <td>0.028207</td>\n",
              "      <td>0.031740</td>\n",
              "      <td>0.032298</td>\n",
              "      <td>0.031764</td>\n",
              "      <td>0.031765</td>\n",
              "      <td>0.031290</td>\n",
              "      <td>0.025581</td>\n",
              "      <td>0.022650</td>\n",
              "      <td>0.029745</td>\n",
              "      <td>0.029879</td>\n",
              "      <td>0.029780</td>\n",
              "      <td>0.030299</td>\n",
              "      <td>0.030693</td>\n",
              "      <td>0.029638</td>\n",
              "      <td>0.027102</td>\n",
              "      <td>0.029555</td>\n",
              "      <td>0.034631</td>\n",
              "      <td>0.035832</td>\n",
              "      <td>0.032621</td>\n",
              "      <td>0.030896</td>\n",
              "      <td>0.028711</td>\n",
              "      <td>0.028512</td>\n",
              "      <td>0.022941</td>\n",
              "      <td>0.019098</td>\n",
              "      <td>0.020828</td>\n",
              "      <td>0.022326</td>\n",
              "      <td>0.023844</td>\n",
              "      <td>0.028291</td>\n",
              "      <td>0.034899</td>\n",
              "      <td>0.039081</td>\n",
              "      <td>0.039879</td>\n",
              "      <td>0.039261</td>\n",
              "      <td>0.036309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>metal</th>\n",
              "      <td>0.019628</td>\n",
              "      <td>0.028162</td>\n",
              "      <td>0.018124</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.017826</td>\n",
              "      <td>0.019309</td>\n",
              "      <td>0.018591</td>\n",
              "      <td>0.017554</td>\n",
              "      <td>0.017123</td>\n",
              "      <td>0.017377</td>\n",
              "      <td>0.018126</td>\n",
              "      <td>0.018138</td>\n",
              "      <td>0.016521</td>\n",
              "      <td>0.016856</td>\n",
              "      <td>0.019471</td>\n",
              "      <td>0.018924</td>\n",
              "      <td>0.018657</td>\n",
              "      <td>0.018658</td>\n",
              "      <td>0.017787</td>\n",
              "      <td>0.017310</td>\n",
              "      <td>0.017420</td>\n",
              "      <td>0.016001</td>\n",
              "      <td>0.014741</td>\n",
              "      <td>0.015159</td>\n",
              "      <td>0.015379</td>\n",
              "      <td>0.015606</td>\n",
              "      <td>0.017149</td>\n",
              "      <td>0.016930</td>\n",
              "      <td>0.018522</td>\n",
              "      <td>0.017894</td>\n",
              "      <td>0.018135</td>\n",
              "      <td>0.016827</td>\n",
              "      <td>0.018032</td>\n",
              "      <td>0.018938</td>\n",
              "      <td>0.019401</td>\n",
              "      <td>0.018490</td>\n",
              "      <td>0.017028</td>\n",
              "      <td>0.016254</td>\n",
              "      <td>0.016780</td>\n",
              "      <td>0.018452</td>\n",
              "      <td>...</td>\n",
              "      <td>0.015563</td>\n",
              "      <td>0.016222</td>\n",
              "      <td>0.016780</td>\n",
              "      <td>0.019870</td>\n",
              "      <td>0.021468</td>\n",
              "      <td>0.022320</td>\n",
              "      <td>0.019676</td>\n",
              "      <td>0.015887</td>\n",
              "      <td>0.014144</td>\n",
              "      <td>0.013170</td>\n",
              "      <td>0.013340</td>\n",
              "      <td>0.013575</td>\n",
              "      <td>0.013867</td>\n",
              "      <td>0.013914</td>\n",
              "      <td>0.014070</td>\n",
              "      <td>0.013776</td>\n",
              "      <td>0.014464</td>\n",
              "      <td>0.014803</td>\n",
              "      <td>0.015209</td>\n",
              "      <td>0.015033</td>\n",
              "      <td>0.015008</td>\n",
              "      <td>0.018282</td>\n",
              "      <td>0.021714</td>\n",
              "      <td>0.021366</td>\n",
              "      <td>0.017254</td>\n",
              "      <td>0.015596</td>\n",
              "      <td>0.015771</td>\n",
              "      <td>0.017297</td>\n",
              "      <td>0.019104</td>\n",
              "      <td>0.016472</td>\n",
              "      <td>0.014386</td>\n",
              "      <td>0.015406</td>\n",
              "      <td>0.016445</td>\n",
              "      <td>0.018099</td>\n",
              "      <td>0.018433</td>\n",
              "      <td>0.017757</td>\n",
              "      <td>0.016253</td>\n",
              "      <td>0.014507</td>\n",
              "      <td>0.022024</td>\n",
              "      <td>0.025279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reggae</th>\n",
              "      <td>0.011975</td>\n",
              "      <td>0.019704</td>\n",
              "      <td>0.016165</td>\n",
              "      <td>0.016494</td>\n",
              "      <td>0.015377</td>\n",
              "      <td>0.013849</td>\n",
              "      <td>0.013418</td>\n",
              "      <td>0.017718</td>\n",
              "      <td>0.018320</td>\n",
              "      <td>0.021303</td>\n",
              "      <td>0.025650</td>\n",
              "      <td>0.026756</td>\n",
              "      <td>0.028626</td>\n",
              "      <td>0.026918</td>\n",
              "      <td>0.024275</td>\n",
              "      <td>0.022605</td>\n",
              "      <td>0.022704</td>\n",
              "      <td>0.018960</td>\n",
              "      <td>0.018169</td>\n",
              "      <td>0.016867</td>\n",
              "      <td>0.013577</td>\n",
              "      <td>0.014703</td>\n",
              "      <td>0.020090</td>\n",
              "      <td>0.020725</td>\n",
              "      <td>0.025462</td>\n",
              "      <td>0.024599</td>\n",
              "      <td>0.022647</td>\n",
              "      <td>0.021059</td>\n",
              "      <td>0.019954</td>\n",
              "      <td>0.021522</td>\n",
              "      <td>0.019660</td>\n",
              "      <td>0.018624</td>\n",
              "      <td>0.016743</td>\n",
              "      <td>0.016725</td>\n",
              "      <td>0.021075</td>\n",
              "      <td>0.021618</td>\n",
              "      <td>0.022798</td>\n",
              "      <td>0.019860</td>\n",
              "      <td>0.019397</td>\n",
              "      <td>0.018830</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018032</td>\n",
              "      <td>0.020278</td>\n",
              "      <td>0.022948</td>\n",
              "      <td>0.021153</td>\n",
              "      <td>0.024034</td>\n",
              "      <td>0.024389</td>\n",
              "      <td>0.024232</td>\n",
              "      <td>0.025955</td>\n",
              "      <td>0.026487</td>\n",
              "      <td>0.027078</td>\n",
              "      <td>0.029061</td>\n",
              "      <td>0.030325</td>\n",
              "      <td>0.023086</td>\n",
              "      <td>0.018083</td>\n",
              "      <td>0.016925</td>\n",
              "      <td>0.017552</td>\n",
              "      <td>0.021330</td>\n",
              "      <td>0.021080</td>\n",
              "      <td>0.019372</td>\n",
              "      <td>0.020858</td>\n",
              "      <td>0.020256</td>\n",
              "      <td>0.020744</td>\n",
              "      <td>0.022557</td>\n",
              "      <td>0.018499</td>\n",
              "      <td>0.017492</td>\n",
              "      <td>0.013418</td>\n",
              "      <td>0.013328</td>\n",
              "      <td>0.017969</td>\n",
              "      <td>0.023448</td>\n",
              "      <td>0.023643</td>\n",
              "      <td>0.023549</td>\n",
              "      <td>0.021163</td>\n",
              "      <td>0.017722</td>\n",
              "      <td>0.018038</td>\n",
              "      <td>0.018892</td>\n",
              "      <td>0.019739</td>\n",
              "      <td>0.019407</td>\n",
              "      <td>0.019506</td>\n",
              "      <td>0.018257</td>\n",
              "      <td>0.014916</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 216 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             0         1         2    ...       213       214       215\n",
              "Genre                                 ...                              \n",
              "blues   0.005861  0.007009  0.002559  ...  0.002704  0.004677  0.005888\n",
              "hiphop  0.117201  0.120168  0.069183  ...  0.039879  0.039261  0.036309\n",
              "metal   0.019628  0.028162  0.018124  ...  0.014507  0.022024  0.025279\n",
              "reggae  0.011975  0.019704  0.016165  ...  0.019506  0.018257  0.014916\n",
              "\n",
              "[4 rows x 216 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO8xU4TTRfLS",
        "colab_type": "code",
        "outputId": "a30b88f6-a40e-4cf1-93d6-dd695e6fd0fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "for each in show_flat.index:\n",
        "    plt.plot(show_flat.loc[each,:])\n",
        "plt.xlabel('Frame number in Time Seies')\n",
        "plt.ylabel('Spectral Flatness')\n",
        "plt.title('Spectral Flatness for different genre')\n",
        "plt.legend(show_flat.index)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f84999dd2b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTFpOKnIOt5x",
        "colab_type": "text"
      },
      "source": [
        "### Tempo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qoneKBBR5CRR",
        "colab_type": "text"
      },
      "source": [
        "define all sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtZ5VMNrOubD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_tempo_list(audio_path_list,genre_list):\n",
        "    '''\n",
        "    function to get mean tempo for passed list\n",
        "    return pd dataframe\n",
        "    '''\n",
        "    for each,i in zip(tqdm(audio_path_list),range(400)):\n",
        "        y, sr = librosa.load(str(each))\n",
        "        onset_env = librosa.onset.onset_strength(y, sr=sr)\n",
        "        features_tempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sr)\n",
        "        x[i] = round(features_tempo[0])\n",
        "    data = list(zip(list(genre_list),x))\n",
        "    pd_tempo = pd.DataFrame(data, columns= ['Genre','Tempo'])\n",
        "    return pd_tempo"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZfnxz9WHf6w",
        "colab_type": "text"
      },
      "source": [
        "## Feature Extraction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8CTKj1vo2QN",
        "colab_type": "text"
      },
      "source": [
        "We decided to use the features of MFCC, chroma_stft, centroid, zcr, tempo, rms, spectral flatness."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_B8d6Bds_QAt",
        "colab_type": "code",
        "outputId": "e2a7dd56-ebb6-4da7-b006-c9c4cb43131d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# test tempogram\n",
        "# This is just a test to get tempogram features\n",
        "hop_length = 512\n",
        "y, sr = librosa.load(genre_data['Path'][1])\n",
        "oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)\n",
        "features_tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr,hop_length=hop_length)\n",
        "# show \n",
        "avg = np.mean(features_tempogram,axis=1)\n",
        "print(np.mean(avg,axis= 0))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.18802592051835867\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7J7U2UR2LLt",
        "colab_type": "code",
        "outputId": "5f45af99-733b-4380-9fc0-9218da047896",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# This is a test to add zero crossing rate\n",
        "y, sr = librosa.load(genre_data['Path'][1])\n",
        "feature_zcr = librosa.feature.zero_crossing_rate(y,frame_length = 2048, hop_length=512)\n",
        "# mean + std\n",
        "print(np.mean(feature_zcr),np.std(feature_zcr))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.048048310010634185 0.016315251808262494\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DvgvEfxgLJV",
        "colab_type": "code",
        "outputId": "a9c1a738-72b0-429f-e214-93a3a5358e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# test rms\n",
        "y, sr = librosa.load(genre_data['Path'][1])\n",
        "features_rms = librosa.feature.rms(y=y)\n",
        "print(np.mean(features_rms),np.std(features_rms))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.14835843 0.02661097\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaH0dQxSPSO9",
        "colab_type": "text"
      },
      "source": [
        "Now we can **extract all the features we need**.\n",
        "\n",
        "Save it both in pandas structure and .csv file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xD6qqGjHvnW",
        "colab_type": "code",
        "outputId": "06378d1b-ced4-4ced-91bc-c7c0a83d40fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "4eef8941ba82428dad8e84ca759b34dc",
            "3ad1daeee4434dc1bc6ce60f97249694",
            "39b49848b790437db4529eba982e98f0",
            "35b9cb57e9e34948843db9323a74038d",
            "bd646e6500ca4372a17be06864148b96",
            "616697b797ea4106b0c7bb9bf7fe9a8e",
            "1a9c57a2247b4daa8693a108530a800a",
            "78b9e939353149d49686ae1032071447"
          ]
        }
      },
      "source": [
        "# get and save all feature values in features.csv\n",
        "mfcc_list = []\n",
        "mfcc_value = np.zeros((400, 13))\n",
        "chroma_value = np.zeros((400, 12))\n",
        "cent_value = np.zeros((400, 2))\n",
        "zcr_value = np.zeros((400,2))\n",
        "stmp_value = np.zeros((400,1))\n",
        "rms_value = np.zeros((400,2))\n",
        "flat_value = np.zeros((400,2))\n",
        "\n",
        "columns =[]\n",
        "for each_file, index in zip(genre_data['Path'],tqdm(range(0,400))):\n",
        "    hop_length = 1024 # 512 # 2058\n",
        "    y, sr = librosa.load(each_file)\n",
        "    #print(\"Now processing:\",each_file)\n",
        "    # get feature mfcc\n",
        "    features_mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13) \n",
        "    mfcc_value[index,:] = np.mean(features_mfcc,axis=1)\n",
        "    # get feature chroma\n",
        "    features_chroma = librosa.feature.chroma_stft(y,sr)\n",
        "    chroma_value[index, :] = np.mean(features_chroma, axis=1)\n",
        "    #---------get feature tempo\n",
        "    #oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)\n",
        "    #features_tempo = librosa.feature.tempogram(onset_envelope=oenv, sr=sr,hop_length=hop_length)\n",
        "    #---------tempo_value[index,:] =  (np.mean(np.mean(features_tempo, axis =1),axis =0),np.std(np.mean(features_tempo, axis =1),axis =0))\n",
        "    \n",
        "    # spectral_centroid\n",
        "    features_cent = librosa.feature.spectral_centroid(y=y, sr=sr)\n",
        "    cent_value[index,:] =(np.mean(features_cent),np.std(features_cent))\n",
        "    \n",
        "    #get zero crossing rate\n",
        "    features_zcr = librosa.feature.zero_crossing_rate(y,frame_length=2048,hop_length=512)\n",
        "    zcr_value[index,:] =(np.mean(features_zcr),np.std(features_zcr))\n",
        "\n",
        "    # static_tempo\n",
        "    onset_env = librosa.onset.onset_strength(y, sr=sr)\n",
        "    features_tempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sr)\n",
        "    stmp_value[index,:] = round(features_tempo[0])\n",
        "\n",
        "    # rms\n",
        "    features_rms = librosa.feature.rms(y=y)\n",
        "    rms_value[index,:] = (np.mean(features_rms),np.std(features_rms))\n",
        "\n",
        "    # spectral flaness\n",
        "    features_flat = librosa.feature.spectral_flatness(y)\n",
        "    flat_value[index,:] =(np.mean(features_flat),np.std(features_flat))\n",
        "\n",
        "\n",
        "\n",
        "# feature_table is the df of mfcc_value\n",
        "features_value = pd.DataFrame(mfcc_value)\n",
        "# add other feature, chroma also inside this table\n",
        "\n",
        "# update features_value and save to .csv\n",
        "features_value = pd.concat([features_value, pd.DataFrame(chroma_value)], axis=1)\n",
        "features_value = pd.concat([features_value, pd.DataFrame(cent_value)], axis=1)\n",
        "features_value = pd.concat([features_value, pd.DataFrame(zcr_value)], axis=1)\n",
        "features_value = pd.concat([features_value, pd.DataFrame(stmp_value)], axis=1)\n",
        "features_value = pd.concat([features_value, pd.DataFrame(rms_value)], axis=1)\n",
        "features_value = pd.concat([features_value, pd.DataFrame(flat_value)], axis=1)\n",
        "\n",
        "# update the features_value dataframe column name to mfcc1 to mfcc13, chroma 1 to 12\n",
        "col_name = []\n",
        "for index in range(0,13):\n",
        "    col_name.append('mfcc'+str(index+1))\n",
        "for index in range(0,12):\n",
        "    col_name.append('chroma'+str(index+1))\n",
        "for index in range(0,2):\n",
        "    col_name.append('cent'+str(index+1))\n",
        "for index in range(0,2):\n",
        "    col_name.append('zcr'+str(index+1))\n",
        "for index in range(0,1):\n",
        "    col_name.append('tempo'+str(index+1))\n",
        "for index in range(0,2):\n",
        "    col_name.append('rms'+str(index+1))\n",
        "for index in range(0,2):\n",
        "    col_name.append('flat'+str(index+1))\n",
        "\n",
        "features_value.columns = col_name\n",
        "# save to .csv\n",
        "features_value.to_csv('./features.csv',sep = '\\t', index = False)\n",
        "print(\"Finished processing, saved all features.\")\n",
        "# need to wait some time to see the result.\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4eef8941ba82428dad8e84ca759b34dc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=400.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Finished processing, saved all features.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFpBdIqIU1gc",
        "colab_type": "code",
        "outputId": "476dd49e-c625-477c-f651-d03873728f2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "features_value"
      ],
      "execution_count": 0,
      "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>mfcc1</th>\n",
              "      <th>mfcc2</th>\n",
              "      <th>mfcc3</th>\n",
              "      <th>mfcc4</th>\n",
              "      <th>mfcc5</th>\n",
              "      <th>mfcc6</th>\n",
              "      <th>mfcc7</th>\n",
              "      <th>mfcc8</th>\n",
              "      <th>mfcc9</th>\n",
              "      <th>mfcc10</th>\n",
              "      <th>mfcc11</th>\n",
              "      <th>mfcc12</th>\n",
              "      <th>mfcc13</th>\n",
              "      <th>chroma1</th>\n",
              "      <th>chroma2</th>\n",
              "      <th>chroma3</th>\n",
              "      <th>chroma4</th>\n",
              "      <th>chroma5</th>\n",
              "      <th>chroma6</th>\n",
              "      <th>chroma7</th>\n",
              "      <th>chroma8</th>\n",
              "      <th>chroma9</th>\n",
              "      <th>chroma10</th>\n",
              "      <th>chroma11</th>\n",
              "      <th>chroma12</th>\n",
              "      <th>cent1</th>\n",
              "      <th>cent2</th>\n",
              "      <th>zcr1</th>\n",
              "      <th>zcr2</th>\n",
              "      <th>tempo1</th>\n",
              "      <th>rms1</th>\n",
              "      <th>rms2</th>\n",
              "      <th>flat1</th>\n",
              "      <th>flat2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-209.826150</td>\n",
              "      <td>124.463215</td>\n",
              "      <td>10.342456</td>\n",
              "      <td>47.010909</td>\n",
              "      <td>6.508127</td>\n",
              "      <td>15.410516</td>\n",
              "      <td>-2.005106</td>\n",
              "      <td>6.706564</td>\n",
              "      <td>-21.557632</td>\n",
              "      <td>10.718406</td>\n",
              "      <td>-5.523687</td>\n",
              "      <td>-2.199806</td>\n",
              "      <td>-3.034864</td>\n",
              "      <td>0.127189</td>\n",
              "      <td>0.141863</td>\n",
              "      <td>0.170973</td>\n",
              "      <td>0.146901</td>\n",
              "      <td>0.189883</td>\n",
              "      <td>0.232139</td>\n",
              "      <td>0.156369</td>\n",
              "      <td>0.216600</td>\n",
              "      <td>0.345021</td>\n",
              "      <td>0.384547</td>\n",
              "      <td>0.691413</td>\n",
              "      <td>0.373657</td>\n",
              "      <td>1324.293218</td>\n",
              "      <td>414.095391</td>\n",
              "      <td>0.051402</td>\n",
              "      <td>0.035431</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.113170</td>\n",
              "      <td>0.064722</td>\n",
              "      <td>0.001191</td>\n",
              "      <td>0.001261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-107.170265</td>\n",
              "      <td>88.488187</td>\n",
              "      <td>-4.169413</td>\n",
              "      <td>55.477715</td>\n",
              "      <td>-8.013872</td>\n",
              "      <td>38.415162</td>\n",
              "      <td>-6.410026</td>\n",
              "      <td>28.572667</td>\n",
              "      <td>-19.821608</td>\n",
              "      <td>13.238089</td>\n",
              "      <td>-19.253099</td>\n",
              "      <td>12.072362</td>\n",
              "      <td>-4.578394</td>\n",
              "      <td>0.324107</td>\n",
              "      <td>0.358768</td>\n",
              "      <td>0.419697</td>\n",
              "      <td>0.567364</td>\n",
              "      <td>0.303946</td>\n",
              "      <td>0.251674</td>\n",
              "      <td>0.324632</td>\n",
              "      <td>0.393840</td>\n",
              "      <td>0.453285</td>\n",
              "      <td>0.426857</td>\n",
              "      <td>0.491156</td>\n",
              "      <td>0.327088</td>\n",
              "      <td>2390.390100</td>\n",
              "      <td>553.734076</td>\n",
              "      <td>0.102261</td>\n",
              "      <td>0.039947</td>\n",
              "      <td>129.0</td>\n",
              "      <td>0.129166</td>\n",
              "      <td>0.029503</td>\n",
              "      <td>0.007607</td>\n",
              "      <td>0.006534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-89.354890</td>\n",
              "      <td>93.800390</td>\n",
              "      <td>-14.560049</td>\n",
              "      <td>54.388645</td>\n",
              "      <td>-33.502993</td>\n",
              "      <td>19.423905</td>\n",
              "      <td>-19.761012</td>\n",
              "      <td>13.031584</td>\n",
              "      <td>-13.861511</td>\n",
              "      <td>14.615304</td>\n",
              "      <td>-14.968037</td>\n",
              "      <td>10.597504</td>\n",
              "      <td>-18.022168</td>\n",
              "      <td>0.483075</td>\n",
              "      <td>0.242483</td>\n",
              "      <td>0.250919</td>\n",
              "      <td>0.235217</td>\n",
              "      <td>0.315011</td>\n",
              "      <td>0.613605</td>\n",
              "      <td>0.256483</td>\n",
              "      <td>0.262924</td>\n",
              "      <td>0.242337</td>\n",
              "      <td>0.346093</td>\n",
              "      <td>0.191305</td>\n",
              "      <td>0.222650</td>\n",
              "      <td>2428.928767</td>\n",
              "      <td>312.600973</td>\n",
              "      <td>0.134641</td>\n",
              "      <td>0.037805</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.120560</td>\n",
              "      <td>0.018036</td>\n",
              "      <td>0.008374</td>\n",
              "      <td>0.006086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-238.951198</td>\n",
              "      <td>108.705427</td>\n",
              "      <td>34.145849</td>\n",
              "      <td>30.211149</td>\n",
              "      <td>-0.502583</td>\n",
              "      <td>27.292958</td>\n",
              "      <td>-21.399484</td>\n",
              "      <td>14.171864</td>\n",
              "      <td>-24.512312</td>\n",
              "      <td>-1.884265</td>\n",
              "      <td>-28.057500</td>\n",
              "      <td>-11.255747</td>\n",
              "      <td>-16.464363</td>\n",
              "      <td>0.312232</td>\n",
              "      <td>0.216736</td>\n",
              "      <td>0.176883</td>\n",
              "      <td>0.155128</td>\n",
              "      <td>0.243879</td>\n",
              "      <td>0.456762</td>\n",
              "      <td>0.316402</td>\n",
              "      <td>0.235207</td>\n",
              "      <td>0.210369</td>\n",
              "      <td>0.269277</td>\n",
              "      <td>0.281575</td>\n",
              "      <td>0.253173</td>\n",
              "      <td>1430.492433</td>\n",
              "      <td>837.162935</td>\n",
              "      <td>0.041974</td>\n",
              "      <td>0.037936</td>\n",
              "      <td>152.0</td>\n",
              "      <td>0.133214</td>\n",
              "      <td>0.047458</td>\n",
              "      <td>0.000805</td>\n",
              "      <td>0.002144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-346.969336</td>\n",
              "      <td>139.273043</td>\n",
              "      <td>24.147860</td>\n",
              "      <td>22.209932</td>\n",
              "      <td>18.703974</td>\n",
              "      <td>11.669042</td>\n",
              "      <td>9.033813</td>\n",
              "      <td>6.945858</td>\n",
              "      <td>-2.019918</td>\n",
              "      <td>5.418928</td>\n",
              "      <td>1.674157</td>\n",
              "      <td>-2.352553</td>\n",
              "      <td>3.198617</td>\n",
              "      <td>0.390058</td>\n",
              "      <td>0.281551</td>\n",
              "      <td>0.221052</td>\n",
              "      <td>0.222528</td>\n",
              "      <td>0.248625</td>\n",
              "      <td>0.253850</td>\n",
              "      <td>0.270304</td>\n",
              "      <td>0.389678</td>\n",
              "      <td>0.448867</td>\n",
              "      <td>0.535472</td>\n",
              "      <td>0.468288</td>\n",
              "      <td>0.451422</td>\n",
              "      <td>798.451172</td>\n",
              "      <td>445.463115</td>\n",
              "      <td>0.025648</td>\n",
              "      <td>0.020114</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.052853</td>\n",
              "      <td>0.042476</td>\n",
              "      <td>0.000764</td>\n",
              "      <td>0.006836</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",
              "      <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>395</th>\n",
              "      <td>-103.136673</td>\n",
              "      <td>67.205032</td>\n",
              "      <td>2.212647</td>\n",
              "      <td>28.504494</td>\n",
              "      <td>4.172197</td>\n",
              "      <td>11.874952</td>\n",
              "      <td>14.244765</td>\n",
              "      <td>14.292828</td>\n",
              "      <td>3.261752</td>\n",
              "      <td>15.854268</td>\n",
              "      <td>8.042967</td>\n",
              "      <td>4.469097</td>\n",
              "      <td>8.679431</td>\n",
              "      <td>0.527038</td>\n",
              "      <td>0.473009</td>\n",
              "      <td>0.393383</td>\n",
              "      <td>0.333580</td>\n",
              "      <td>0.327089</td>\n",
              "      <td>0.369955</td>\n",
              "      <td>0.427632</td>\n",
              "      <td>0.414551</td>\n",
              "      <td>0.516528</td>\n",
              "      <td>0.569701</td>\n",
              "      <td>0.674864</td>\n",
              "      <td>0.695776</td>\n",
              "      <td>2539.121009</td>\n",
              "      <td>936.008816</td>\n",
              "      <td>0.094298</td>\n",
              "      <td>0.070644</td>\n",
              "      <td>99.0</td>\n",
              "      <td>0.195715</td>\n",
              "      <td>0.102957</td>\n",
              "      <td>0.043690</td>\n",
              "      <td>0.072337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>-132.977565</td>\n",
              "      <td>117.143566</td>\n",
              "      <td>-2.825532</td>\n",
              "      <td>22.537389</td>\n",
              "      <td>25.921838</td>\n",
              "      <td>0.278448</td>\n",
              "      <td>13.535487</td>\n",
              "      <td>3.408554</td>\n",
              "      <td>4.533075</td>\n",
              "      <td>0.545919</td>\n",
              "      <td>-0.066564</td>\n",
              "      <td>5.103181</td>\n",
              "      <td>-0.161246</td>\n",
              "      <td>0.337143</td>\n",
              "      <td>0.344633</td>\n",
              "      <td>0.465506</td>\n",
              "      <td>0.385335</td>\n",
              "      <td>0.419384</td>\n",
              "      <td>0.412514</td>\n",
              "      <td>0.390618</td>\n",
              "      <td>0.431549</td>\n",
              "      <td>0.443899</td>\n",
              "      <td>0.496101</td>\n",
              "      <td>0.317166</td>\n",
              "      <td>0.309023</td>\n",
              "      <td>1728.254305</td>\n",
              "      <td>521.916601</td>\n",
              "      <td>0.060468</td>\n",
              "      <td>0.032532</td>\n",
              "      <td>161.0</td>\n",
              "      <td>0.140145</td>\n",
              "      <td>0.044343</td>\n",
              "      <td>0.009812</td>\n",
              "      <td>0.014022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>-164.594570</td>\n",
              "      <td>142.707450</td>\n",
              "      <td>-41.489226</td>\n",
              "      <td>41.226193</td>\n",
              "      <td>16.171186</td>\n",
              "      <td>-5.588592</td>\n",
              "      <td>7.633069</td>\n",
              "      <td>5.620200</td>\n",
              "      <td>0.810726</td>\n",
              "      <td>-10.478095</td>\n",
              "      <td>-2.344436</td>\n",
              "      <td>-0.496347</td>\n",
              "      <td>-3.065764</td>\n",
              "      <td>0.616168</td>\n",
              "      <td>0.247922</td>\n",
              "      <td>0.224886</td>\n",
              "      <td>0.266965</td>\n",
              "      <td>0.492462</td>\n",
              "      <td>0.430814</td>\n",
              "      <td>0.326586</td>\n",
              "      <td>0.485215</td>\n",
              "      <td>0.227387</td>\n",
              "      <td>0.222506</td>\n",
              "      <td>0.141551</td>\n",
              "      <td>0.334345</td>\n",
              "      <td>1484.715976</td>\n",
              "      <td>338.400244</td>\n",
              "      <td>0.076013</td>\n",
              "      <td>0.033668</td>\n",
              "      <td>152.0</td>\n",
              "      <td>0.110707</td>\n",
              "      <td>0.042192</td>\n",
              "      <td>0.003385</td>\n",
              "      <td>0.003977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>-169.175089</td>\n",
              "      <td>110.450371</td>\n",
              "      <td>-8.545039</td>\n",
              "      <td>43.888601</td>\n",
              "      <td>0.316973</td>\n",
              "      <td>26.636772</td>\n",
              "      <td>-14.361243</td>\n",
              "      <td>13.035166</td>\n",
              "      <td>-11.153996</td>\n",
              "      <td>8.111966</td>\n",
              "      <td>-7.677317</td>\n",
              "      <td>10.930494</td>\n",
              "      <td>-8.167287</td>\n",
              "      <td>0.411790</td>\n",
              "      <td>0.332711</td>\n",
              "      <td>0.389301</td>\n",
              "      <td>0.370354</td>\n",
              "      <td>0.308819</td>\n",
              "      <td>0.386656</td>\n",
              "      <td>0.288723</td>\n",
              "      <td>0.330068</td>\n",
              "      <td>0.243243</td>\n",
              "      <td>0.297590</td>\n",
              "      <td>0.423093</td>\n",
              "      <td>0.402035</td>\n",
              "      <td>1811.234934</td>\n",
              "      <td>809.902274</td>\n",
              "      <td>0.072150</td>\n",
              "      <td>0.051697</td>\n",
              "      <td>136.0</td>\n",
              "      <td>0.123739</td>\n",
              "      <td>0.053039</td>\n",
              "      <td>0.004307</td>\n",
              "      <td>0.008161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>-138.122043</td>\n",
              "      <td>101.482168</td>\n",
              "      <td>-35.343779</td>\n",
              "      <td>36.139295</td>\n",
              "      <td>-9.510810</td>\n",
              "      <td>14.766630</td>\n",
              "      <td>-0.240599</td>\n",
              "      <td>11.524783</td>\n",
              "      <td>-2.105261</td>\n",
              "      <td>3.410188</td>\n",
              "      <td>4.206286</td>\n",
              "      <td>0.142541</td>\n",
              "      <td>9.014046</td>\n",
              "      <td>0.423793</td>\n",
              "      <td>0.391529</td>\n",
              "      <td>0.484589</td>\n",
              "      <td>0.359650</td>\n",
              "      <td>0.349037</td>\n",
              "      <td>0.375829</td>\n",
              "      <td>0.354992</td>\n",
              "      <td>0.399343</td>\n",
              "      <td>0.330324</td>\n",
              "      <td>0.478228</td>\n",
              "      <td>0.406650</td>\n",
              "      <td>0.431169</td>\n",
              "      <td>2170.544505</td>\n",
              "      <td>497.036146</td>\n",
              "      <td>0.119993</td>\n",
              "      <td>0.046936</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.092221</td>\n",
              "      <td>0.041424</td>\n",
              "      <td>0.015136</td>\n",
              "      <td>0.018855</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 34 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          mfcc1       mfcc2      mfcc3  ...      rms2     flat1     flat2\n",
              "0   -209.826150  124.463215  10.342456  ...  0.064722  0.001191  0.001261\n",
              "1   -107.170265   88.488187  -4.169413  ...  0.029503  0.007607  0.006534\n",
              "2    -89.354890   93.800390 -14.560049  ...  0.018036  0.008374  0.006086\n",
              "3   -238.951198  108.705427  34.145849  ...  0.047458  0.000805  0.002144\n",
              "4   -346.969336  139.273043  24.147860  ...  0.042476  0.000764  0.006836\n",
              "..          ...         ...        ...  ...       ...       ...       ...\n",
              "395 -103.136673   67.205032   2.212647  ...  0.102957  0.043690  0.072337\n",
              "396 -132.977565  117.143566  -2.825532  ...  0.044343  0.009812  0.014022\n",
              "397 -164.594570  142.707450 -41.489226  ...  0.042192  0.003385  0.003977\n",
              "398 -169.175089  110.450371  -8.545039  ...  0.053039  0.004307  0.008161\n",
              "399 -138.122043  101.482168 -35.343779  ...  0.041424  0.015136  0.018855\n",
              "\n",
              "[400 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuuAdpjvFTid",
        "colab_type": "text"
      },
      "source": [
        "## Feature Rescaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lisFVAGGJH-Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import sklearn.preprocessing as preprocessing\n",
        "features_rescaled = pd.DataFrame(preprocessing.scale(features_value), columns=features_value.columns, index=features_value.index)\n",
        "features_rescaled.to_csv('./re_features.csv',sep = '\\t', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVoRCoCl5oSB",
        "colab_type": "code",
        "outputId": "6f82fbdb-6c05-4418-e961-fa6aac48eadd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        }
      },
      "source": [
        "features_rescaled.describe()"
      ],
      "execution_count": 0,
      "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>mfcc1</th>\n",
              "      <th>mfcc2</th>\n",
              "      <th>mfcc3</th>\n",
              "      <th>mfcc4</th>\n",
              "      <th>mfcc5</th>\n",
              "      <th>mfcc6</th>\n",
              "      <th>mfcc7</th>\n",
              "      <th>mfcc8</th>\n",
              "      <th>mfcc9</th>\n",
              "      <th>mfcc10</th>\n",
              "      <th>mfcc11</th>\n",
              "      <th>mfcc12</th>\n",
              "      <th>mfcc13</th>\n",
              "      <th>chroma1</th>\n",
              "      <th>chroma2</th>\n",
              "      <th>chroma3</th>\n",
              "      <th>chroma4</th>\n",
              "      <th>chroma5</th>\n",
              "      <th>chroma6</th>\n",
              "      <th>chroma7</th>\n",
              "      <th>chroma8</th>\n",
              "      <th>chroma9</th>\n",
              "      <th>chroma10</th>\n",
              "      <th>chroma11</th>\n",
              "      <th>chroma12</th>\n",
              "      <th>cent1</th>\n",
              "      <th>cent2</th>\n",
              "      <th>zcr1</th>\n",
              "      <th>zcr2</th>\n",
              "      <th>tempo1</th>\n",
              "      <th>rms1</th>\n",
              "      <th>rms2</th>\n",
              "      <th>flat1</th>\n",
              "      <th>flat2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.163547e-16</td>\n",
              "      <td>1.034728e-15</td>\n",
              "      <td>-4.440892e-17</td>\n",
              "      <td>7.049916e-17</td>\n",
              "      <td>8.743006e-17</td>\n",
              "      <td>-2.664535e-17</td>\n",
              "      <td>-3.663736e-16</td>\n",
              "      <td>3.348710e-16</td>\n",
              "      <td>9.908740e-17</td>\n",
              "      <td>-1.962319e-16</td>\n",
              "      <td>3.341771e-16</td>\n",
              "      <td>2.209344e-16</td>\n",
              "      <td>1.809664e-16</td>\n",
              "      <td>-4.835195e-16</td>\n",
              "      <td>-2.332856e-16</td>\n",
              "      <td>-1.465494e-16</td>\n",
              "      <td>-4.504730e-16</td>\n",
              "      <td>-1.337819e-16</td>\n",
              "      <td>-3.629042e-16</td>\n",
              "      <td>-3.694267e-16</td>\n",
              "      <td>2.227385e-17</td>\n",
              "      <td>4.671263e-16</td>\n",
              "      <td>1.941503e-16</td>\n",
              "      <td>3.078787e-16</td>\n",
              "      <td>-4.182418e-17</td>\n",
              "      <td>3.094747e-17</td>\n",
              "      <td>-3.597123e-16</td>\n",
              "      <td>5.220824e-16</td>\n",
              "      <td>-2.405021e-16</td>\n",
              "      <td>8.021361e-17</td>\n",
              "      <td>1.146305e-16</td>\n",
              "      <td>-8.409939e-17</td>\n",
              "      <td>1.066508e-16</td>\n",
              "      <td>-1.963707e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "      <td>1.001252e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-3.021479e+00</td>\n",
              "      <td>-3.797117e+00</td>\n",
              "      <td>-3.560271e+00</td>\n",
              "      <td>-2.771183e+00</td>\n",
              "      <td>-2.682491e+00</td>\n",
              "      <td>-3.364409e+00</td>\n",
              "      <td>-2.574820e+00</td>\n",
              "      <td>-2.715545e+00</td>\n",
              "      <td>-2.438470e+00</td>\n",
              "      <td>-2.813262e+00</td>\n",
              "      <td>-2.874989e+00</td>\n",
              "      <td>-2.982377e+00</td>\n",
              "      <td>-3.073133e+00</td>\n",
              "      <td>-2.857675e+00</td>\n",
              "      <td>-2.599036e+00</td>\n",
              "      <td>-2.957918e+00</td>\n",
              "      <td>-2.367860e+00</td>\n",
              "      <td>-2.638915e+00</td>\n",
              "      <td>-3.117863e+00</td>\n",
              "      <td>-2.704511e+00</td>\n",
              "      <td>-3.053457e+00</td>\n",
              "      <td>-2.538488e+00</td>\n",
              "      <td>-2.582758e+00</td>\n",
              "      <td>-2.597180e+00</td>\n",
              "      <td>-2.685416e+00</td>\n",
              "      <td>-2.768787e+00</td>\n",
              "      <td>-1.682206e+00</td>\n",
              "      <td>-1.937006e+00</td>\n",
              "      <td>-1.899235e+00</td>\n",
              "      <td>-1.662266e+00</td>\n",
              "      <td>-2.016481e+00</td>\n",
              "      <td>-1.605535e+00</td>\n",
              "      <td>-6.656150e-01</td>\n",
              "      <td>-7.980346e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-6.864699e-01</td>\n",
              "      <td>-6.554488e-01</td>\n",
              "      <td>-7.286502e-01</td>\n",
              "      <td>-7.246703e-01</td>\n",
              "      <td>-7.034648e-01</td>\n",
              "      <td>-6.857107e-01</td>\n",
              "      <td>-7.394485e-01</td>\n",
              "      <td>-7.445617e-01</td>\n",
              "      <td>-7.542615e-01</td>\n",
              "      <td>-8.046345e-01</td>\n",
              "      <td>-7.260222e-01</td>\n",
              "      <td>-6.862412e-01</td>\n",
              "      <td>-7.149468e-01</td>\n",
              "      <td>-6.481857e-01</td>\n",
              "      <td>-6.048665e-01</td>\n",
              "      <td>-6.036656e-01</td>\n",
              "      <td>-6.621476e-01</td>\n",
              "      <td>-7.066316e-01</td>\n",
              "      <td>-6.593021e-01</td>\n",
              "      <td>-6.527770e-01</td>\n",
              "      <td>-5.835041e-01</td>\n",
              "      <td>-7.357565e-01</td>\n",
              "      <td>-6.987357e-01</td>\n",
              "      <td>-6.641790e-01</td>\n",
              "      <td>-6.350650e-01</td>\n",
              "      <td>-7.343236e-01</td>\n",
              "      <td>-7.845489e-01</td>\n",
              "      <td>-7.791097e-01</td>\n",
              "      <td>-6.844083e-01</td>\n",
              "      <td>-9.900069e-01</td>\n",
              "      <td>-7.695575e-01</td>\n",
              "      <td>-6.632327e-01</td>\n",
              "      <td>-5.098907e-01</td>\n",
              "      <td>-5.964683e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.696501e-01</td>\n",
              "      <td>6.043220e-03</td>\n",
              "      <td>-5.928621e-02</td>\n",
              "      <td>4.604820e-02</td>\n",
              "      <td>9.816163e-02</td>\n",
              "      <td>1.672599e-01</td>\n",
              "      <td>-1.276976e-01</td>\n",
              "      <td>1.494434e-01</td>\n",
              "      <td>-1.615289e-01</td>\n",
              "      <td>1.545667e-01</td>\n",
              "      <td>-5.574161e-02</td>\n",
              "      <td>8.173526e-02</td>\n",
              "      <td>-9.738192e-03</td>\n",
              "      <td>2.504438e-02</td>\n",
              "      <td>-1.111577e-02</td>\n",
              "      <td>-2.943889e-03</td>\n",
              "      <td>4.194957e-02</td>\n",
              "      <td>-4.583892e-02</td>\n",
              "      <td>3.758396e-04</td>\n",
              "      <td>-1.745682e-02</td>\n",
              "      <td>2.383689e-02</td>\n",
              "      <td>4.360864e-02</td>\n",
              "      <td>-2.988635e-02</td>\n",
              "      <td>-9.974408e-03</td>\n",
              "      <td>-4.612995e-02</td>\n",
              "      <td>7.582951e-02</td>\n",
              "      <td>-2.635457e-01</td>\n",
              "      <td>-9.344256e-02</td>\n",
              "      <td>-2.219361e-01</td>\n",
              "      <td>1.838209e-02</td>\n",
              "      <td>-2.321299e-01</td>\n",
              "      <td>-2.307338e-01</td>\n",
              "      <td>-2.832415e-01</td>\n",
              "      <td>-3.969840e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.448121e-01</td>\n",
              "      <td>6.327305e-01</td>\n",
              "      <td>6.674040e-01</td>\n",
              "      <td>7.010302e-01</td>\n",
              "      <td>6.937359e-01</td>\n",
              "      <td>7.083727e-01</td>\n",
              "      <td>8.289737e-01</td>\n",
              "      <td>7.519821e-01</td>\n",
              "      <td>8.143617e-01</td>\n",
              "      <td>8.172617e-01</td>\n",
              "      <td>6.975311e-01</td>\n",
              "      <td>7.661312e-01</td>\n",
              "      <td>7.909608e-01</td>\n",
              "      <td>6.265854e-01</td>\n",
              "      <td>6.265700e-01</td>\n",
              "      <td>6.093171e-01</td>\n",
              "      <td>6.472386e-01</td>\n",
              "      <td>6.954438e-01</td>\n",
              "      <td>6.687853e-01</td>\n",
              "      <td>6.061960e-01</td>\n",
              "      <td>6.655350e-01</td>\n",
              "      <td>6.408436e-01</td>\n",
              "      <td>6.719140e-01</td>\n",
              "      <td>6.499924e-01</td>\n",
              "      <td>6.371547e-01</td>\n",
              "      <td>6.850945e-01</td>\n",
              "      <td>6.455860e-01</td>\n",
              "      <td>6.595280e-01</td>\n",
              "      <td>4.212409e-01</td>\n",
              "      <td>9.007225e-01</td>\n",
              "      <td>7.037100e-01</td>\n",
              "      <td>3.825213e-01</td>\n",
              "      <td>1.738726e-01</td>\n",
              "      <td>3.312616e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.199507e+00</td>\n",
              "      <td>3.069193e+00</td>\n",
              "      <td>2.890171e+00</td>\n",
              "      <td>2.304428e+00</td>\n",
              "      <td>2.509991e+00</td>\n",
              "      <td>2.375337e+00</td>\n",
              "      <td>2.308378e+00</td>\n",
              "      <td>2.466289e+00</td>\n",
              "      <td>2.105561e+00</td>\n",
              "      <td>2.215130e+00</td>\n",
              "      <td>3.125507e+00</td>\n",
              "      <td>2.322753e+00</td>\n",
              "      <td>2.334336e+00</td>\n",
              "      <td>2.788885e+00</td>\n",
              "      <td>3.436844e+00</td>\n",
              "      <td>2.696761e+00</td>\n",
              "      <td>3.428770e+00</td>\n",
              "      <td>3.154630e+00</td>\n",
              "      <td>2.841294e+00</td>\n",
              "      <td>3.428881e+00</td>\n",
              "      <td>2.312064e+00</td>\n",
              "      <td>3.056003e+00</td>\n",
              "      <td>2.869476e+00</td>\n",
              "      <td>3.173975e+00</td>\n",
              "      <td>3.349387e+00</td>\n",
              "      <td>3.564519e+00</td>\n",
              "      <td>4.006308e+00</td>\n",
              "      <td>3.840810e+00</td>\n",
              "      <td>5.375217e+00</td>\n",
              "      <td>2.623387e+00</td>\n",
              "      <td>3.369157e+00</td>\n",
              "      <td>4.016856e+00</td>\n",
              "      <td>1.377135e+01</td>\n",
              "      <td>8.476856e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              mfcc1         mfcc2  ...         flat1         flat2\n",
              "count  4.000000e+02  4.000000e+02  ...  4.000000e+02  4.000000e+02\n",
              "mean   2.163547e-16  1.034728e-15  ...  1.066508e-16 -1.963707e-17\n",
              "std    1.001252e+00  1.001252e+00  ...  1.001252e+00  1.001252e+00\n",
              "min   -3.021479e+00 -3.797117e+00  ... -6.656150e-01 -7.980346e-01\n",
              "25%   -6.864699e-01 -6.554488e-01  ... -5.098907e-01 -5.964683e-01\n",
              "50%    1.696501e-01  6.043220e-03  ... -2.832415e-01 -3.969840e-01\n",
              "75%    6.448121e-01  6.327305e-01  ...  1.738726e-01  3.312616e-01\n",
              "max    2.199507e+00  3.069193e+00  ...  1.377135e+01  8.476856e+00\n",
              "\n",
              "[8 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3t1yJUPAPpp",
        "colab_type": "text"
      },
      "source": [
        " ## Visualize the feature"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmGlRWFC4RlD",
        "colab_type": "text"
      },
      "source": [
        "Check all feature values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTn6ck4J4R5d",
        "colab_type": "code",
        "outputId": "5aac6e76-8688-471f-e54b-90be737f850c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "features_value.describe()"
      ],
      "execution_count": 0,
      "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>mfcc1</th>\n",
              "      <th>mfcc2</th>\n",
              "      <th>mfcc3</th>\n",
              "      <th>mfcc4</th>\n",
              "      <th>mfcc5</th>\n",
              "      <th>mfcc6</th>\n",
              "      <th>mfcc7</th>\n",
              "      <th>mfcc8</th>\n",
              "      <th>mfcc9</th>\n",
              "      <th>mfcc10</th>\n",
              "      <th>mfcc11</th>\n",
              "      <th>mfcc12</th>\n",
              "      <th>mfcc13</th>\n",
              "      <th>chroma1</th>\n",
              "      <th>chroma2</th>\n",
              "      <th>chroma3</th>\n",
              "      <th>chroma4</th>\n",
              "      <th>chroma5</th>\n",
              "      <th>chroma6</th>\n",
              "      <th>chroma7</th>\n",
              "      <th>chroma8</th>\n",
              "      <th>chroma9</th>\n",
              "      <th>chroma10</th>\n",
              "      <th>chroma11</th>\n",
              "      <th>chroma12</th>\n",
              "      <th>cent1</th>\n",
              "      <th>cent2</th>\n",
              "      <th>zcr1</th>\n",
              "      <th>zcr2</th>\n",
              "      <th>tempo1</th>\n",
              "      <th>rms1</th>\n",
              "      <th>rms2</th>\n",
              "      <th>flat1</th>\n",
              "      <th>flat2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-123.302809</td>\n",
              "      <td>93.103490</td>\n",
              "      <td>-10.708109</td>\n",
              "      <td>44.586473</td>\n",
              "      <td>-2.647285</td>\n",
              "      <td>20.647792</td>\n",
              "      <td>-6.601616</td>\n",
              "      <td>15.119746</td>\n",
              "      <td>-7.784932</td>\n",
              "      <td>10.608834</td>\n",
              "      <td>-6.267335</td>\n",
              "      <td>7.243566</td>\n",
              "      <td>-5.888498</td>\n",
              "      <td>0.418017</td>\n",
              "      <td>0.404597</td>\n",
              "      <td>0.423493</td>\n",
              "      <td>0.415418</td>\n",
              "      <td>0.435819</td>\n",
              "      <td>0.417033</td>\n",
              "      <td>0.399524</td>\n",
              "      <td>0.421189</td>\n",
              "      <td>0.422162</td>\n",
              "      <td>0.441138</td>\n",
              "      <td>0.427015</td>\n",
              "      <td>0.424282</td>\n",
              "      <td>2259.825286</td>\n",
              "      <td>662.643585</td>\n",
              "      <td>0.106493</td>\n",
              "      <td>0.052253</td>\n",
              "      <td>122.562500</td>\n",
              "      <td>0.147598</td>\n",
              "      <td>0.053840</td>\n",
              "      <td>0.019570</td>\n",
              "      <td>0.023964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>75.266544</td>\n",
              "      <td>24.935616</td>\n",
              "      <td>22.262692</td>\n",
              "      <td>15.691987</td>\n",
              "      <td>13.527896</td>\n",
              "      <td>10.337941</td>\n",
              "      <td>10.238306</td>\n",
              "      <td>9.188245</td>\n",
              "      <td>7.979676</td>\n",
              "      <td>7.504931</td>\n",
              "      <td>7.588709</td>\n",
              "      <td>6.807039</td>\n",
              "      <td>7.048743</td>\n",
              "      <td>0.104106</td>\n",
              "      <td>0.117047</td>\n",
              "      <td>0.113602</td>\n",
              "      <td>0.113543</td>\n",
              "      <td>0.118321</td>\n",
              "      <td>0.100998</td>\n",
              "      <td>0.104287</td>\n",
              "      <td>0.111097</td>\n",
              "      <td>0.116205</td>\n",
              "      <td>0.116427</td>\n",
              "      <td>0.110050</td>\n",
              "      <td>0.111363</td>\n",
              "      <td>611.062168</td>\n",
              "      <td>269.915092</td>\n",
              "      <td>0.043832</td>\n",
              "      <td>0.021833</td>\n",
              "      <td>23.830144</td>\n",
              "      <td>0.060464</td>\n",
              "      <td>0.028050</td>\n",
              "      <td>0.028954</td>\n",
              "      <td>0.029677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-350.434656</td>\n",
              "      <td>-1.461542</td>\n",
              "      <td>-89.870197</td>\n",
              "      <td>1.155494</td>\n",
              "      <td>-38.890351</td>\n",
              "      <td>-14.089770</td>\n",
              "      <td>-32.930434</td>\n",
              "      <td>-9.800140</td>\n",
              "      <td>-27.218797</td>\n",
              "      <td>-10.478095</td>\n",
              "      <td>-28.057500</td>\n",
              "      <td>-13.032196</td>\n",
              "      <td>-27.523131</td>\n",
              "      <td>0.120889</td>\n",
              "      <td>0.100767</td>\n",
              "      <td>0.087887</td>\n",
              "      <td>0.146901</td>\n",
              "      <td>0.123971</td>\n",
              "      <td>0.102530</td>\n",
              "      <td>0.117831</td>\n",
              "      <td>0.082383</td>\n",
              "      <td>0.127545</td>\n",
              "      <td>0.140811</td>\n",
              "      <td>0.141551</td>\n",
              "      <td>0.125599</td>\n",
              "      <td>570.040355</td>\n",
              "      <td>209.158637</td>\n",
              "      <td>0.021697</td>\n",
              "      <td>0.010838</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>0.025826</td>\n",
              "      <td>0.008861</td>\n",
              "      <td>0.000322</td>\n",
              "      <td>0.000310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-174.906400</td>\n",
              "      <td>76.779914</td>\n",
              "      <td>-26.909533</td>\n",
              "      <td>33.229180</td>\n",
              "      <td>-12.151781</td>\n",
              "      <td>13.567823</td>\n",
              "      <td>-14.162847</td>\n",
              "      <td>8.287087</td>\n",
              "      <td>-13.796166</td>\n",
              "      <td>4.577661</td>\n",
              "      <td>-11.770015</td>\n",
              "      <td>2.578138</td>\n",
              "      <td>-10.921671</td>\n",
              "      <td>0.350622</td>\n",
              "      <td>0.333888</td>\n",
              "      <td>0.355001</td>\n",
              "      <td>0.340330</td>\n",
              "      <td>0.352315</td>\n",
              "      <td>0.350528</td>\n",
              "      <td>0.331533</td>\n",
              "      <td>0.356445</td>\n",
              "      <td>0.336770</td>\n",
              "      <td>0.359888</td>\n",
              "      <td>0.354013</td>\n",
              "      <td>0.353648</td>\n",
              "      <td>1811.669152</td>\n",
              "      <td>451.146869</td>\n",
              "      <td>0.072386</td>\n",
              "      <td>0.037329</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>0.101126</td>\n",
              "      <td>0.035260</td>\n",
              "      <td>0.004826</td>\n",
              "      <td>0.006284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-110.549807</td>\n",
              "      <td>93.253993</td>\n",
              "      <td>-12.026329</td>\n",
              "      <td>45.308157</td>\n",
              "      <td>-1.321026</td>\n",
              "      <td>22.374753</td>\n",
              "      <td>-7.907388</td>\n",
              "      <td>16.491150</td>\n",
              "      <td>-9.072268</td>\n",
              "      <td>11.767396</td>\n",
              "      <td>-6.689813</td>\n",
              "      <td>7.799245</td>\n",
              "      <td>-5.957054</td>\n",
              "      <td>0.420621</td>\n",
              "      <td>0.403298</td>\n",
              "      <td>0.423159</td>\n",
              "      <td>0.420175</td>\n",
              "      <td>0.430402</td>\n",
              "      <td>0.417071</td>\n",
              "      <td>0.397705</td>\n",
              "      <td>0.423834</td>\n",
              "      <td>0.427223</td>\n",
              "      <td>0.437663</td>\n",
              "      <td>0.425918</td>\n",
              "      <td>0.419151</td>\n",
              "      <td>2306.103872</td>\n",
              "      <td>591.597601</td>\n",
              "      <td>0.102402</td>\n",
              "      <td>0.047413</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>0.133580</td>\n",
              "      <td>0.047376</td>\n",
              "      <td>0.011380</td>\n",
              "      <td>0.012197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-74.830733</td>\n",
              "      <td>108.861281</td>\n",
              "      <td>4.131517</td>\n",
              "      <td>55.573270</td>\n",
              "      <td>6.725764</td>\n",
              "      <td>27.961748</td>\n",
              "      <td>1.875055</td>\n",
              "      <td>22.020500</td>\n",
              "      <td>-1.294718</td>\n",
              "      <td>16.734655</td>\n",
              "      <td>-0.980595</td>\n",
              "      <td>12.452128</td>\n",
              "      <td>-0.320192</td>\n",
              "      <td>0.483167</td>\n",
              "      <td>0.477844</td>\n",
              "      <td>0.492626</td>\n",
              "      <td>0.488816</td>\n",
              "      <td>0.518002</td>\n",
              "      <td>0.484495</td>\n",
              "      <td>0.462663</td>\n",
              "      <td>0.495036</td>\n",
              "      <td>0.496538</td>\n",
              "      <td>0.519270</td>\n",
              "      <td>0.498457</td>\n",
              "      <td>0.495149</td>\n",
              "      <td>2677.937021</td>\n",
              "      <td>836.679042</td>\n",
              "      <td>0.135365</td>\n",
              "      <td>0.061439</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>0.190093</td>\n",
              "      <td>0.064556</td>\n",
              "      <td>0.024598</td>\n",
              "      <td>0.033782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42.039432</td>\n",
              "      <td>169.539988</td>\n",
              "      <td>53.554402</td>\n",
              "      <td>80.702302</td>\n",
              "      <td>31.265146</td>\n",
              "      <td>45.173174</td>\n",
              "      <td>17.002701</td>\n",
              "      <td>37.752269</td>\n",
              "      <td>8.995744</td>\n",
              "      <td>27.212436</td>\n",
              "      <td>17.421561</td>\n",
              "      <td>23.034858</td>\n",
              "      <td>10.545053</td>\n",
              "      <td>0.707993</td>\n",
              "      <td>0.806368</td>\n",
              "      <td>0.729468</td>\n",
              "      <td>0.804244</td>\n",
              "      <td>0.808611</td>\n",
              "      <td>0.703639</td>\n",
              "      <td>0.756665</td>\n",
              "      <td>0.677732</td>\n",
              "      <td>0.776841</td>\n",
              "      <td>0.774805</td>\n",
              "      <td>0.775875</td>\n",
              "      <td>0.796815</td>\n",
              "      <td>4435.243901</td>\n",
              "      <td>1742.654028</td>\n",
              "      <td>0.274631</td>\n",
              "      <td>0.169465</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>0.351055</td>\n",
              "      <td>0.166372</td>\n",
              "      <td>0.417805</td>\n",
              "      <td>0.275216</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            mfcc1       mfcc2       mfcc3  ...        rms2       flat1       flat2\n",
              "count  400.000000  400.000000  400.000000  ...  400.000000  400.000000  400.000000\n",
              "mean  -123.302809   93.103490  -10.708109  ...    0.053840    0.019570    0.023964\n",
              "std     75.266544   24.935616   22.262692  ...    0.028050    0.028954    0.029677\n",
              "min   -350.434656   -1.461542  -89.870197  ...    0.008861    0.000322    0.000310\n",
              "25%   -174.906400   76.779914  -26.909533  ...    0.035260    0.004826    0.006284\n",
              "50%   -110.549807   93.253993  -12.026329  ...    0.047376    0.011380    0.012197\n",
              "75%    -74.830733  108.861281    4.131517  ...    0.064556    0.024598    0.033782\n",
              "max     42.039432  169.539988   53.554402  ...    0.166372    0.417805    0.275216\n",
              "\n",
              "[8 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8c3UIBKK5u8H",
        "colab_type": "text"
      },
      "source": [
        "Check **zero crossing rate**\n",
        "\n",
        "this feature might indicates the presence of how much different instruments are used"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0k1fHexG6Udz",
        "colab_type": "code",
        "outputId": "54c2f7e1-3834-473e-8739-0d35bd5ebaa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "x = features_value['zcr1']\n",
        "y = features_value['zcr2']\n",
        "# set colors\n",
        "color_to_genre = {'blues':'blue','metal':'grey','hiphop':'red','reggae':'green'}\n",
        "colors = []\n",
        "for each in genre_data['Genre']:\n",
        "    colors.append(color_to_genre[each])\n",
        "\n",
        "area = 10\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9lQZ4ri7iXQ",
        "colab_type": "text"
      },
      "source": [
        "Check **centroid**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sR0ILa67qhh",
        "colab_type": "code",
        "outputId": "fc996650-a30a-4382-8db8-1f586db0f68c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        }
      },
      "source": [
        "x = features_value['cent1']\n",
        "y = features_value['cent2']\n",
        "# set colors\n",
        "colors = []\n",
        "for each in genre_data['Genre']:\n",
        "    colors.append(color_to_genre[each])\n",
        "\n",
        "area = 10\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLMwGtgYmN5J",
        "colab_type": "text"
      },
      "source": [
        "**Check rms**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_BZcrlLmOXW",
        "colab_type": "code",
        "outputId": "55cc6173-e02c-448b-a6b0-9aa169773570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "x = features_value['rms1']\n",
        "y = features_value['rms2']\n",
        "# set colors\n",
        "colors = []\n",
        "for each in genre_data['Genre']:\n",
        "    colors.append(color_to_genre[each])\n",
        "\n",
        "area = 10\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gVvN74mvFGN",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UFSRE92vFb4",
        "colab_type": "code",
        "outputId": "6e5d864c-7098-40c7-b362-bf9ab263559d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "x = features_value['flat1']\n",
        "y = features_value['flat2']\n",
        "# set colors\n",
        "colors = []\n",
        "for each in genre_data['Genre']:\n",
        "    colors.append(color_to_genre[each])\n",
        "\n",
        "area = 10\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dQfS1TFvdip",
        "colab_type": "text"
      },
      "source": [
        "There are extra value in the flatness."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zILZ6jzrvclX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# the strange sample 4/23 didn't finish\n",
        "x1 = np.max(features_value['flat1'])\n",
        "print(\"x1=\",x1)\n",
        "features_value.loc[x1,:]\n",
        "genre_data.tail(1)\n",
        "\n",
        "# another strange sample\n",
        "x2 = np.max(features_value['flat2'])\n",
        "print(\"x2=\",x2)\n",
        "features_value.loc[x2,:]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uz3Pourp78Ys",
        "colab_type": "text"
      },
      "source": [
        "## Feature Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P35QymSI8AKk",
        "colab_type": "text"
      },
      "source": [
        "Since we have a multi dimensional feature list, although they all seems useful. But some of it might not have be helpful enough to support our classification model.\n",
        "So we can use  **sklearn.feature_selection module** to check some useless feature.\n",
        "\n",
        "**Use VarianceThreshold method**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9M2ACi4m8bUf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_selection import VarianceThreshold\n",
        "# Remove features with low variance\n",
        "X_t1 = features_rescaled\n",
        "\n",
        "sel = VarianceThreshold(threshold=(.8 * (1 - .8)))\n",
        "X_del_useless_feature = sel.fit_transform(X_t1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrjF6FZfozS5",
        "colab_type": "code",
        "outputId": "32a7dbfa-7877-48b7-d40a-a49607e895d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_del_useless_feature.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 34)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEDNFknzpMvU",
        "colab_type": "text"
      },
      "source": [
        "From the result, we can see no feature was deleted from the list.\n",
        "\n",
        "We will use those 34 columns of feature for the next process."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSSYxuLsqhv5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Data Spliting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDAYgLuqtGq4",
        "colab_type": "text"
      },
      "source": [
        "What's the benefit about data spliting,\n",
        "\n",
        "1. By dividing the data set according to a certain ratio, we can reasonably evaluate the performance of the learning algorithm in the face of unknown data.\n",
        "\n",
        "2. If the data used to evaluate the model is not independent of the sample, the evaluation result may be inaccurate. We also want this model can be applied in a wider range of situations.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_R5429yU3OEk",
        "colab_type": "text"
      },
      "source": [
        "The simple way to do the data spliting is to use *sklearn.model_selection.train_test_split* to split to **Training set** and **Testing set**.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gpR50PytP4h",
        "colab_type": "code",
        "outputId": "3101b7f2-a827-40fd-f9d0-a967ce07033f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e57d4e7a4d4747a6b807b87481ee44a4",
            "6b7d29c893b24cacb9133cf9e6c583e1",
            "bde6e687861946c8a8f4f05c554e7ff9",
            "dac7c813d6714ea6bbc8b04924cccf27",
            "4eb0f5738287421eb906f5a7246d0158",
            "7c5d15933a4f42a1b5db788b07291898",
            "104083df8d5845bb9d4055416553b416",
            "0a5a0e097cb645c283922982bae0834a"
          ]
        }
      },
      "source": [
        "# sklearn provides the function to split data\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# get the features\n",
        "features = features_rescaled # features = features_value\n",
        "# I am changing feature selection\n",
        "# get only the labeled genre result\n",
        "result = genre_data['Genre']\n",
        "\n",
        "# Use sklearn.train_test_split to split 80 % for training and 20% for testing\n",
        "# the random seed is set to 22 here in order to see a fixed output based on this.\n",
        "for each in tqdm(genre_analysis_list):\n",
        "    X_train, X_test,y_train,y_test = train_test_split(\n",
        "        features, result,\n",
        "        test_size = 0.2,\n",
        "        random_state = 72, shuffle = True) #22"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e57d4e7a4d4747a6b807b87481ee44a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_b1isGt3lYv",
        "colab_type": "text"
      },
      "source": [
        "Seconderly, another way to do the spliting, is to use **K-fold cross validating** method.\n",
        "\n",
        "The advantage of applying this method:\n",
        "\n",
        "1. we only have 400 audio piece in total, it's realatively a small amout of sample\n",
        "2. to make the future model more accurate\n",
        "\n",
        "\n",
        "**K-fold cross-validation** to do the selecting for the best parameter for the future model in order to increase the accuary dealing with unseen data.\n",
        "\n",
        "\n",
        "The workflow in this part should be like this,\n",
        "<div>\n",
        "<img src=\"https://scikit-learn.org/stable/_images/grid_search_workflow.png\" width=\"500\"/>\n",
        "</div>\n",
        "\n",
        "The general procedure for Cross-Validating is:\n",
        "\n",
        "1. Shuffle the dataset randomly.\n",
        "2. Split the dataset into k groups\n",
        "3. For each unique group:\n",
        "    - Take the group as a hold out or test data set\n",
        "    - Take the remaining groups as a training data set\n",
        "    - Fit a model on the training set and evaluate it on the test set\n",
        "    - Retain the evaluation score and discard the model\n",
        "    - Summarize the skill of the model using the sample of model evaluation scores\n",
        "\n",
        "**Reference**\n",
        "1. [Wiki](https://en.wikipedia.org/wiki/Cross-validation_(statistics))\n",
        "2. [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRGlQyghYkq9",
        "colab_type": "text"
      },
      "source": [
        "## Observing the Training Set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbnimjWFYtCE",
        "colab_type": "text"
      },
      "source": [
        "We can visusalise the feature we want to use from the splited subset **X_train** and its genre label **y_train** .\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGFkT-InkFqd",
        "colab_type": "code",
        "outputId": "2f6b477a-fe09-4b67-c8a8-e278d543d3b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# test plot 1 using\n",
        "x = X_train['cent1']\n",
        "y = X_train['zcr1']\n",
        "color_to_genre = {'blues':'blue','metal':'grey','hiphop':'red','reggae':'green'}\n",
        "\n",
        "colors = []\n",
        "for each in y_train:\n",
        "    colors.append(color_to_genre[each])\n",
        "\n",
        "area = 10\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrGaosmSrd5t",
        "colab_type": "code",
        "outputId": "8bf6d6d9-ffb6-4568-ef6e-46f626417353",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# test plot 2 using\n",
        "x = X_train['mfcc1']\n",
        "y = X_train['cent1']\n",
        "color_to_genre = {'blues':'blue','metal':'grey','hiphop':'red','reggae':'green'}\n",
        "\n",
        "colors = []\n",
        "for each in y_train:\n",
        "    colors.append(color_to_genre[each])\n",
        "\n",
        "area = 10\n",
        "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOydd3hcV5n/P/fOjGZGGs2MuqxuWbYsd1vuLbbTExITQ0hCSyBgyobAhh+hLOwGAgsJSwkLbGgJ7JKNCSWVQMzGcRzXxE1xL+q9TtFo+sz9/XFs2bK6NLIk+3yeZx6Vufecc1W+973veYuiaRoSiUQimbyo470AiUQikYwOKeQSiUQyyZFCLpFIJJMcKeQSiUQyyZFCLpFIJJMc/XhMmpqaqhUUFIz4/K6uLhISEmK3oEmAvOYrn6vtekFe83A5cOBAm6ZpaZd+f1yEvKCggP3794/4/O3bt7Nu3brYLWgSIK/5yudqu16Q1zxcFEWp7uv70rUikUgkkxwp5BKJRDLJkUIukUgkkxwp5BKJRDLJkUIukUgkkxwp5BKJRDLJGZfwQ4lkshCOhjnVdoo4XRxFyUUoijKm851pP8Ofjv+JhLgEPjj3g6TGp47pfJIrAynkEkk/RLUoP3/n5xxoOADAxpkb2VSyaUzn++GeH6JX9fhCPtwBN19f+/Uxm09y5SBdKxJJPzj9Tg41HqLAXkCONYfXzr42pvNFtSj+sJ9kczKpCak0ehrHdD7JlYO0yCWSfkgwJJBoTKS5q5lgJMi0pGljOp9e1bM0eyl76/eionL3nLvHdD7JlYMUcomkH4x6I19a+SVePPki8XHx3DHzjjGfc3PpZm6YdgNGvZEca86Yzye5MpBCLpEMQK4tlweWPXDZ5tOpOqYlj63lL7nykD5yiUQimeRIIZdIJJJJjhRyiUQimeSMWsgVRTEpivK2oihliqIcUxTlm7FYmEQikUiGRiw2OwPABk3TPIqiGICdiqL8TdO0vTEYWyKRSCSDMGoh1zRNAzznvjSce2mjHVcikUgkQ0MROjzKQRRFBxwAioCfaZr25T6O2QxsBsjIyCjdsmXLiOfzeDxYLJYRnz8Zkdd85XO1XS/Iax4u69evP6Bp2uJeb2iaFrMXYAfeAOYMdFxpaak2Gt54441RnT8Zkdd85XO1Xa+myWseLsB+rQ9NjWnUiqZpznNCflMsx5VIJBJJ/8QiaiVNURT7uc/NwPXAydGOK5FIJJKhEYuolSnA7875yVXgOU3TXonBuBKJRCIZArGIWnkXWBiDtUgkEolkBMjMTolEIpnkSCGXSCSSSY4UcolEIpnkyHrkEomkXzRN42TbSRx+ByWpJSSZk8Z7SZI+kEIukUj65c2qN3nq8FMoKKTEp/DNdd8k0Zg43suSXIJ0rUgkkn7ZW7+XZHMyU5Om4vQ7qXHVjPeSJH0ghVwikfTLjJQZtHnbaOhsQK/qybBkjPeSJH0gXSsSiaRfbi++HUuchSZPE6vzVpManzreS5L0gRRyiUTSL3pVzw3TbhjvZUgGQbpWJBKJZJIjhVwikUgmOVLIJRKJZJIjhVwikUgmOVLIJRKJZJIjhVwikUgmOVLIJRKJZJIjhVwikUgmOVLIJRKJZJIjhVwikUgmOVLIJRKJZJIjhVwikUgmOVLIJRKJZJIjhVwikUgmOVLIJRKJZJIzaiFXFCVXUZQ3FEU5rijKMUVRPh+LhUkkEolkaMSisUQY+KKmaQcVRUkEDiiK8g9N047HYGyJRCKRDMKoLXJN0xo1TTt47vNO4ASQPdpxJRKJRDI0FE3TYjeYohQAO4A5mqa5L3lvM7AZICMjo3TLli0jnsfj8WCxWEa+0EmIvOYrn6vtekFe83BZv379AU3TFvd6Q9O0mLwAC3AA2DTYsaWlpdpoeOONN0Z1/mREXvOVz0iuNxqNxn4hl5Gr7XesaaO7ZmC/1oemxqT5sqIoBuDPwDOapv0lFmNKJJL+iUQiHDhwgIaGBtLT01myZAkGg2G8lyUZJ2IRtaIAvwFOaJr2w9EvSSKRDEZ9fT11dXXYbDaampqoqqoa7yVJxpFYxJGvAj4CbFAU5fC51y0xGFcikfRDNBpF2FCgqirRaHScVyQZT0btWtE0bSegxGAtEolkiGRnZ1NTU0NHRwc2m438/PzxXpLkEjRNo7m5GafTSVpaGikpKWM2V0x85BKJ5PJiMBhYs2YNwWAQg8GAqsok7YlGU1MTe/fuRVVVTp06xdq1a0lKShqTueRvXyKZRGiaxpn2M5xsO0lUi2I0GieeiGsanD4NR45AKDTeqxk32tra0Ov12Gw2NE3D5XKN2VzSIpdIJhHPHX+OV0+/CsDK3JVsLt3c7SsfCQ2dDfzywC9x+p3cPedulucsH/0i//IXeOkl8fmCBfD5z8NEu9lcBtLS0jh79ixOpxNFUbDb7WM219X305VIJilRLcprZ18jz5ZHgb2AvXV78QQ90NEhLOCurmGP+ZuDv6HJ04RBNfCrA7/C4XOMfqH/+AdkZ0NBAbz7LjhiMOYkJDMzk9WrVzNnzhzWrl07pkIuLXKJJIZEIhECgQAmkynmLg9VUclOzKaxsxG9To/dZMdc3wyP/wcEg5CSAl//OthsQx7THXCTYEjAbDAT9UYJRoI9DwiFIBIBk2noCy0ogFOnIC4OEhPhKsvcvJi0tDTS0tLGfB4p5BJJjOjq6mLnzp34/X6SkpJYsWJFzJN0Hlz2IH8+8WdCkRB3lNyB/vnXIRqFvDyorITjx2HFiiGPd8/ce/j5Oz+n3dfOhoINpCekX3izrAz+67/ETeKuu+DGG4c26Kc/DS+8AD4fvOc9YDQO8yolw0UKuUQSIyoqKvD7/VitVtrb22lubiYnJyemc6QlpPHpxZ++6BtpQjDdbrHJOAxrHGDRlEX86MYfEYgESDGnCH/7jh3wxhuwcyfMni0s/T/8QdwgrNbBB7Xb4b77hndhklEhhVwyeaiqgtZWmD5diMUEQ6/XE41Gu5NzdDrd2E963XVCxE+fhltugZKSYQ+RaEwkkUTxRUUFPPUUJCdDXZ1wj8ybB4oiXlcjmgZvvgn798OsWeLJ5HL8boeBFHLJ5ODAAfjpT8U/VVISPPLIsK3PsWbatGk4nU7a29spLCwkIyNj7CeNi4O7747deO5zRUutVhFxUl8PbW1w773C3301cvSouLnZ7XD4sPD5r1073qvqgRRyyeRg1y5ISIDUVKiuFpbjwoXjvaoexMXFsWIY/ukJyYwZkJMjfsaJifCrX4knoKswfLCb9nbxNJKcDF4vNDaO94p6IYVcMjkoLIS33xYbe4oifMOS2BMfLyJfamuFcJ1LKw+EA/g7HVibnShpad3fvyqYPVs8odTUgMEAy5aN94p6IYVcMjm46SZhFdbWwqpVwmqUjA0mk7DCz3Gm/Qw/f+Nxrv3fvRRH7BSlz0R5+OEex1zRpKXBt74l/vamTIG0NHw+H11dXSQmJmKcAFE5UsglkwO9XmzmSYZNVItS6ahEp+rIt+UPOxP02aPPktXQSXaXyrGkTjIjPhK3bYutkAcC4uNIRTEYFH8jY+UCSkoSL8DlcvHWW28RiUQwGo2sXbuW+Pj4sZl3iEghl0hGQ1ubSEkPBuGOO0RG41jS0iIyJfPzh5Sko2kaTx16ip01OwG4vfh2NpVsGtaUJr0Jl1HcEMzeCDrFD+npg584VPbsgd/8Rnx+772wZs3Qz9U0ePZZkU2akgIPPQRZWbFbWx/U1dURiUSw2Ww4HA6am5uZOnXqmM45GFfxDoZEEgOeeAL27RMFon7wAwiHx26uI0fgq1+F730PvvtdET8+CJ6gh101u8iz5ZFjzeGV06+cb83YjaZphEKhXt8/z4emvhctwcy2pWkUTi0l/j13xPbp6OmnxSZ2Whr87nfipjhUKirgtdeEq62zE557Lnbr6oeEhAQikQh+vx9N0zCbzWM+52BIi1wiGSnRqAjPy84Wj/Q1NeD3j11K+muvic3IlBQRU19eDnPmDHiKSW8i0ZhIm7eNcCTMlMQpPVwrwWCQvXv30tHRQWpqKsuWLevORg0EAux/6y1Sn3ySL/j9JCdno/vk5uFZzENBVUUZAEURn48kXv0yxrnn5eXh9/tpbm6mqKjo8oSZDoK0yCWSkaKqcMMNQsCrqkQ0Q0LC2M03ZYqI83Y6xddDSIoy6Ax8ccUXmZo0lTkZc3hw6YM93q+pqaG9vR2bzUZLSwt1dXXd71VWVuI9eZLEri7aEhLwxhlh+/ZYXpHg058GjwdcLvjkJ0VkyFApLITrrxe/A4sFPvCB2K/vElRVZebMmaxdu5acnJwJ0Z1JWuQSST9EtSg7qndwtv0spVmlLJzSR9z6XXfBokXCopwxY2ytwjvuEK6bmhqRBDTEyJ18ez5fWvmlPt9TFKVfl4qmaYSsVjRVxehyoYbDsH79iJffLwsWwM9+Jj4f7malosCHPywEfCw3Oy8hEonw9ttv09LSgslkYtWqVVjGsTiYFHKJpB921uzk1wd/TWJcIm/VvMU3rvkGRclFPQ9SFCHgl4P4eLEZGEPy8vJobGykra2NzMxMcnNzu99LSUnhbU1j67p1LGpuJn/5crjttpjO381QBNjrhf/5HzhzBq65RhTkammB3/5WPKV84AOXJUksGo1y/PhxysvLyczMxOv1cubMGRaOY4KaFHJJ7Kmrgz/9Sfxz3nmncAlMQqqcVSQYEsiwZFDlqKLJ09RbyCcAwUiQTn8nZsWM2WweVnihwWBg1apVRKPRHrVhNE3j0KFDpKSkoCUn0zBnDnNvvHF8Mzz/9jeR4ZuZCX/8I0ybBs8/L7JQLRZh1f/gB2NeuqGsrIwTJ07Q3t5OOBzGZrNdnro6AyCFXBJbIhH44Q+F9aRpQtQfe2xSFlxaPGUxb1S+QbWzGnOcmenJEycBJhKJ4HA4aAm08Iv9v0BXpyPdmM7qmatZvnw5ev3Q/7UVReklRJqm4ff7STxXX6Wzs5NoNDq+beWcThFyGR8v/p68XtFUw2YT33M4RCTPGAt5bW0tGRkZqKpKS0sLubm5zLhcT2X9IIVcEltCIfEPlZt7QcgjEeG/nGTMSp/FI+seodHTSGFSYc9a3eNIJBJh165ddHR08ErbK8RF4rDr7TQFmzhbd5ZprdOYMoqnoI6ODnw+HwUFBVRWVgKiINhwbg5jwvXXw8GDYo+goEBUIrzzTlEPprVVZPxehgiStLQ0mpqaMJvNlJSUsG7dOmmRS64wTCaxIfb668JquummSSni58m355Nvzx/vZfTA5XLR0dGB3W7H7DLj6HSQakwFQEUdldVcW1vL/v37ATCbzaxatYq4uDisQ6lDPtbk5YmnO6dTJCTFxcHy5SLD1O8XLrxRPPlpmkZdXV33fsH5m2EgEKCmpgZVVcnLy6O0tJTKykrC4TBTp04ddxGHGAm5oihPAe8BWjRNGziwVXLl8+EPw8qV4p+qsHC8V3PFYTKZUBQFn8/HkoQl7NHtodPbSaGpkAWzFpA+iqzLmpoaTCYTZrMZl8tFNBrFNpHKBVssveP0Y1TAq6GhgXfeeYe4uDiqqqpYs2YNKSkp3XH2mqbR3NzMypUrKS4ujsmcsSJWptJvgZ8C/x2j8SSTGVWFoom3KTiR0DSNQ02H6PB1MD9jPmkJQ6/mGB8fz7Jlyzhz5gwZGRncNfuumBVuSklJobm5mUgkgqIoJIw0Ll7TJt2+iNPpRK/XY7FYcDgcuN1urFYrTqezu3FyS0vL+O8V9EFMhFzTtB2KohTEYiyJ5Grgr6f+zrPHnkGv6njZ9DKPbnh0WOdnZmaSmZkZ83VNnz4dnU6H2+0mPz9/ZLHRu3bB738vNiD/6Z8mzVNZZmYmZ8+e7Rb01NRUDAYDSUlJtLW1AZCVlTXhRBxA6S8ZYNgDCSF/pT/XiqIom4HNABkZGaVbtmwZ8Vwej2dcg+/HA3nNVw5OJzS2+kAXJjHJS0T1k5GQQdgfHv31RqMXKgGOx95ENCrKver14nNVHbCQ2ET7HUcike5QzPOCfb4WDYjmIRcTDofx+XyoqorZbB6SyI/mmtevX39A07TFl37/sv2mNU37JfBLgMWLF2vr1q0b8Vjbt29nNOdPRuQ1XxnU1MC//it0mU9xuKIO+xQvS+98m++s/g6H9x5m3bp1RKOimN+RIyLp8dprh+ilcDjg0UfFnUKvh698ZcTWcCAQoLOzE4vFgmmIVRaj0Sg6t1sk7WRniw3IcBg+9KF+z5tov+Pm5mbq6uqw2WwUFhYOKMzBYJDXXnsNvV5POBxGVdUhXctYXPPkDSeQSCYh58tyFKdOJ+xJxJLcwdfWrMZuulA35e23hRampEBZmfg4pKTBEydEW7KpU6GhAXbuHJGQe71eduzYQSAQQK/Xs2bNmgGjVlwuF3v37sXn8zF9+nRm3X47yksviZvJZz4z7PmHSldXF16vF7vd3l3oazScvw6dTsexY8eoqKhgyZIlJJ2rQ34p4XCYSCSCxWIhHA7j9XpHvYaRIoVcIrmM5OeLxvevv66Sm5TFQw9mkX2JRjY3i7pRSUmijlRLyxAHPy84TqdIlhmOD93l6m4s3Gy34/P5SEpKwuVyUVtby+zZsy8cq2lw7JjIEZg1iyM1NYTDYaxWK2fOnCFnwwZs110nhHyMSry2tLSwd+9eNE0jISGBtWvX9nJ7DJeurq7uRCiXy0UoFCIQCLBhw4Y+N33NZjN5eXnU1NQAMH/+/FHNPxpiFX74LLAOSFUUpQ74N03TfhOLsSWSK4nzNZ42bRJh0H25sUtLRTZ6TY0opjhv3hAHnzkTPv5x0ajhmmtgw4YBDw+Hw2zfvp3W2lqueeMNMgE1GsW+bh1acjKBQIBIJNJbxPbvh5/8BHQ6MBiIu/XW3hULz2WEDgeXy0UgECA5OXnQ5KOqqip0Oh0JCQk4nU7a29tHlQQFkJSUhNFopKWlBVVVSU9PJxqN0tXV1aeQK4rCwoULmTZtGjqdbvIXzdI07Z5YjCORnKexsZHOzk727NnD/Pnzx72VVqwZ6HJycuA73xGlznNyRA/kIaEoQsCvuab3e+etaJdLNBO229m7dy/Hjh0j2ePBcfo0xiVLSDGbsZ86xdzNm2loaCA3N5e8vLyeY737rojlzsiA6mpmJSSwMxLB7XYzffr0ESUP1dbW8vbbb+N0OjGZTNx8880kD3DhFouF+vp6dDodmqYNyY8/GGazmXXr1nH06FEqKioIh8OYTKYBr0dRlCHF2UejUU6cOEF9fT2RSIRIJBLTRCLpWpFMCDRN49SpU1RVVWGxWGhpaUGv19PS0sLhw4dZuXLleC/xspKSEuNG9Vu3wjPPCLFPS4NvfpOOjg50Oh1KaipBo5FoTQ1YrSjXXcf06dOZfq4np6ZpOJ1OotEodrsdddYsUZc8GASdDktJCTdmZxOJREacxl9eXo7D4ej2Ne/YsYPbbrutX7GbMWMGkUgEp9NJUVER7e3tuN1ucnNzB44c0TSx7ri4PneQzWYzixcvpqCgAJ/PR2pqakxuEo2NjZw6dYrExMTuTNFYtoeTQi6ZELS1tXHixAkSExNpaGigq6uL1NRUjEYjXV1d4728y0a4qQldfT1KScnAZvtw2btXtFOzWglXVtK4dy/p6elUVVXREQjw7i23UJSeLh4BLrLoQ6EQL7/8MqdPnwaguLiYjbffjmo0ijDDuXMhJwcFRlWLxWq14vV6MZlMGAwGIpEIoVCoXyHX6/XMnTuXcDjMtm3buv3bTqezf1+13w8//SkcPSoS1j7/+T5dQIqikJbWd4JWJBKhtraWcDhMTk7OkEU+cK659MXdl2KJFHLJhCB8rtelTqfDZDKhqmp3X8TS0tJxXt3YEwgE8DU14bvnHvSqinHGDNQ///mCX6WlhWhzM2UOB5VtbeTl5bFo0aL+rU9NE6/zsdDFxYReeRG920Wzw8Hh+npCFgvz5s0jPT2d7OxsEvtwETQ2NlJeXo6qqiiKwqlTp3j1b3+juLiYottvH1bJ3IGYO3cuTU1N1NXVYbVaycnJGVK2qtfr7d6YDYfDNDU19S/k77wjwoAKC0VN8zffFDXNh8GhQ4eoqalBURSqq6tZv379kGLHs7KyKC8vx+Vyoapqj7rvsUAKuWRCkJaWRkpKCg6HA4PBwJo1azh48CArVqy44vzjfdHwyisYW1owud2ETCYiZ8+ivvyyaCRx5gw8/jiu9naiDgftt9xCbW0tBoOhb9FqaIAf/1iEIt52G/5bb+RnOTUcWdFGYdDKgiU3YM3LIxqN4vF4esY0b98ueoMWFMCHPtQt4OFwGE3TiEQiBINBjhw5gsViGfUG43kMBgM333wzHR0dRCIRUlNTh3STiI+Px2w243A40DSNadOmDXzC+QTIEZYQaGpqEu4lVcXlcuH3+4f092kymVi/fj1dXV0cPHhw5KUP+kEKuWRCoNfrWb16NV1dXd2P1zqd7qoQcYD4gwfRMjNBVVFDIVEO+Pwm244doNPhslox19eT5XRSa7HQceYM0blzqa6upquri9zcXLHx9swzorfnlCnwwgu8kx3hcMsRCmet5IyjAl3YxaxzwtfDMqyqEt12UlNh926Ij8dy220kJibS2tqKpmmkpKSQnJyMw+HA5/MN+fo0Dd56C06eFJ3xFi8WG4CBQIA9e/aQm5tLTk4OKcPcGDgf515XV0dcXNzAlu6SJSJI/8gR0dWpr03hHmvWaG1txe/3k5aWhtlsJjMzs9sit9lsw/Kf6/V6bDZbzJ5ieowd8xElkhGiqmp3I4OxoivYxe/KfkeFo4Jrp17LTUU3jck/1nBJWrQIrb2dQEICRq8X9b3vhY0bxZtTpkBXF8kGA35NI1Bfj9VkYtbJk9RXV+MpKyNstbJnzRquuf12zOEw6HQEVZWjFgvlp5owakZIEv7fWbNmMcswC4PB0DMipbNTfLRYRIOG9naamprIyMhgxowZtLa2Eg6HcblcmEymYXWP37dPlA1PTBSlWL76VTAYKvH5fEQiEZqbmzGbzcMWchAblOc3ZgfEZIKHHhpws/NiKisrKSsrA4Tlv27dOhYuXEhqaiqRSITs7OwJU3dFCrnkquKlUy+xr34fGQkZbDm6haLkIqanjKLzTzQqBGGUN4O4jRvR//WvxH/jG6hr14pNxPMUFYHNhrWri9BHPsKcAwdITkoiVVVx//KXRIuLiWtpQef303XttZjvuQd+8ANOdHRQPXUqqcn5FNQ5qGytZEbGDK6fcT2WuD5inqdPF3OVl4u48FtuId5k6rac9Xo9S5cuJSEhAavVOqyKi9XVQkfT06GyEhobwW53oygK8fHxBINBvF7viIR8WCgKDHHddXV1mM1mTCYTLpcLt9tNWloaBQUFY7vGESCFXHJV4fQ7MevNxBviUVDoCo0wIkbT4IUX4OWXRQblgw8OL5PyUvR6sNlQN26koQF+9i/Q1gZ339DB+q0/EPOFw6SEQqR4vSIevLUVncWC12QioChYnE7xRJOaCj/6EZ633sLo8xEfH8+c9Dncu/Recqbk9P8EYjLBl78sAtjtdrDbyYlG8fl8NDc3M23aNPLz80f0BLNokYiAPC/oxcUQF5dLTU1Nt4Wfmpo68p/fGJCamsrJkycJBoPjnvAzGFLIJVcVNxXdxJGWI9S4aihKKWJm6syRDVRTAy++KML1Wlvh2Wfhn/85Jmv87W/FPmVyMvzPn3ZwxP4u8zKKWXdWE+Vhc3OF1XznncQ3NVFw5AgRgwHDJz5xwUrW6ymaNYt9+/bhcrlISkpiSvqUwUXYYBAbnV4v7NmDajRSPH/+qBspTJ8O3/ymuEcUFJzvyJZKYmIiJSUltLa2sn37dpKTk1m4cOGo0+37IhQK0dTUhE6nIzMzc1C3yMyZM7vDX/Py8jCPUbmBWCCFXHJFoGkaHb4OdKquRwGqS5maNJXHr38cp99JRkIGBt0Iiy1FIhfC+/R6sTkZIwIB8fTfYTjK81O/wna1CTV0lMfak3lf/iKYP1+YtmvWoBQWYi4vh/h4tEs2+jIyMrjuuuvw+/3D6/QeDsP3vy9uFpom2vX1V8HQ6xU1V1JTB01BzckRr4tRVRW9Xk95eXl3DoHFYulZ2yUGRKNRdu/eTXt7OwCFhYUsWLBgwHNUVR08CmaCMDE89RLJKHn+5PP8v63/j4dee4gdVTsGPNYSZyHHmjNyEQdhVq5bJ5JidDr4wAeGdFq4vhbnF/8J3/33ijC/PvjgB4WYv+t4C0NCiNyCOcTZU/jb8lSxW+hyiQMTE8FgQCsu5nnvAT7x0if4xrZv0Oxp7h4rPj6e5OTk4aWDd3SIG0VhobD+d+/u+7jOTvjWt+B73xO7l+XlQ5/jIi7OIdDr9fj9/hGNMxB+vx+n00lSUhJ2u53a2tqYzzGeSIt8shGNin8yvV6YNxMg4mK88QQ9vHTqJXKtuYSiIZ458gxr8teMbTSKqsJ994ku7kZj76JRfRCOhtn9nc8QKT9Nl8XE8t90kDpvnohKuYjiYhEGvq18Ng++rtDib6cThaj9Hs5k2phmbkC95x7IygKg2lXNiydfJMeaQ7OnmT8c+wMPLntw2JcUDoPHA9ZEG2pSknAfhcOwdGnfJ5w4IXYtp06F06fhoYeILlyI9qEPoTsXRXLmDLz6qqgKsHGjKAJ2KZfmEBSNQZtAo9GI2WzG7XYTjUZjFv8+UZBCPpnQNHjqKVFnGoQVeMst47umCYBe1ROni8Mb8hKMBPuOyBgLFKV3I+ABqHXV0t5RR2aCnYBRoaa9htR+XDImE9wyey0B5fv8sexVKvbMR/F9lm/6VO6/H9ZfpK3haBgNDVVR0ev0BCPBYV9Kays8/ZVTzN33a1KSoyz4t43EtTWKMgHXXdf3SeczQV0uOHgQX3Y2je+8A/v3oz7xBPaUafzHf4gHloMHobNT4z3vacDj8ZCZmdldbKpXDkFlpSgpUFQEMXJt6HQ6Vq1aRVVVFXq9PqZ1TiYC0rUymXC5RBBuXp6wxl58cbxXNCEw6U08sOQBABKNiTyw9IFua/x4y3Ee3fEoP9r7I9q8bWO+lpNtJ/nJvp/wh2N/wBfqmTBjibPwzkRuuqIAACAASURBVNoiosEAluYO2pfNFa6LAbhj1h38c9GvKOl8gNxsFZtNJNVcTGFSIWvy1lDrqkWv6rlz1p19jhU939WiD7b+PUrp7v/ElhCmvU2j/efPom3aBLfd1m9NcW36dDo+tAmnSeN0QoCXlXLO6tqICwU5cugQra0hgkERcpiWBmVlLvbt28fJkyfZsWMHHo+ne6zzOQSG8nL493+HLVvg298esbumLxISEpg9ezbFxcVjspk6nkiLfDJhNgsLqaNDJDVcYY+HIyYSYd6Jdn7QtghWrIAkYW15gh6e2PcERr2Rakc1vzzwS7625mtjMv87R7fyfOM2djW+TXFKMfsb9hMIB/jo/I92H5aWkMam936Fvxb+mQy9jbtXfWpIrrGCAvGrr6wUXy9adOG9Oncdu2p24Q15uXPWnazNX4vF2PMpwev18sILL9DW1kZGRgYbN27slZFojNPQh/0E9Vaac1V2ZKkkv/46y1et6jNJS9M0nj26hX90/o22uH0UTXfxgYNBOi0eDlz3HpS4OLKyhNelokKcs3RpIyaTifj4+O647F6cOCFM+Nxc4UI8cwatsLC7j+ZwiUajnD17ltbWVrKysigoKJgQCWCxRgr5ZMJoFJlpzz0nnr0/+MHxXtHE4NVXL/xM3nxTWHLp6cLVEg2SacrErDf32ASMGcEgdT98hP9q+yMRvUp9mp+U+BTybflUOip7Hb44azGLs3r1zh2QtDTR5/P0aWHdzjwXMdnubefbO77Nvrp9OANOptqmUu4o53PLPtfj/P3799PS0oLVaqWpqYmysjJKSpYRFyd+ZAA33qLjla0fIPPt36POTiNl7kx8oRBHjx5lxYoVvdbU6m1la8VW8qKJuH0BthbrSLdN5TRu8ubmcf+CBSQkGHj4YbFuiwV0ungOHPATDAa709V7MX268MvX1kI0ij8riz3bt+NyucjOzqa0tHRY2ZRVVVW89dZbRCIRzp49251mfymNjSLss6sL7r4b5vTZQn7iIoV8sjFtmogQkFzg1CnR5iw5WVhxTU2Qnk5qfCpLs5ayr34fCkoP6zhmnDyJ6+wxtOwEcr0qST4fNa4aEuMS2TRrU8ymyczsnW/U6GmkK9iFhkZ6fDqKonCw8SCapvWwOqPRaPfXmqaxe7eBJ58UWeoPPCCSSBMjTu7J2E5HqQ5Xmgldbg5qNIp2vsjUJRhUA6qiEjAo2KNxJEWCnLX4SM2ezYObPk9KgsjQNJkudDjStDyMRiMej4f09PS+C0fNmQNf+pLYJZ05k9OhEG63G5vNRl1dHTk5OcPaqDx79mx3E2mHw0FDQ0OfQv7kk+LPxmwWzY9+/OPYVhEea6SQxwhvyEuFo4JkczJZiVnjvZyri6VLRdcal0tkJObnA6AqKptLN3NT0U2Y9CamJI6BK8psZlo4kbxwAjVaC7PMebxn1edZkr2EAnsB0WiUYDBIXFxczOtyZFmyMeusoriTtxVLnIW5GXN7uQ4WLVpEZWUlnZ2dxMensmvXLPLyhPX53/8tQsbZtw+qq0mKj6egvp6qigqMhYX9xnMnmZO4f9H9bDmyhcxF1/AvVUnEF+aS9P6PYEzou1mxoih9imgv5szpNom9e/fi8XgwGAw9byqaBq+8Av/3f8L39IlP9KotHggEaGxsxOfzEQqFMBqN/dbyaW8XfzpGo/gz8vmkkF91dAW7eHTHozR7mlEUhc8v+zzzM8evEetVx5o1IiGlowNKSi5EUwA6VcfUpDGMUCgqwnTnPXz1H3+jOteK/UP3k5GYCXFxhMNhXnrppe6aHRs3biQ9PT0m03o88PMfJ+Gq/AZzCvawcF0t86bMZE3+ml7HWq1W7r33XjweD+Gwhb17VUIhsc1iP587FRcHx4+jhMMs8HiYnZuL7jOfGfDmsyp3FfOT5hMMBrtLu16M2+3G7XZjt9tHlN7ucrloaGjA7XbT3t7O3LlzLxTqOnMG/vQnsU9UViY2/j/84R7n19XVYTKZsNvt+P1+0tPTe7etO8eddwrXiqaJoohDbq83QZBCHgPKHeU0eZoosBfQ5m3j9crXpZBfThQFZs0a9TCBcACn30myOXnoyUKKArfeiunWWynWNFFC9vXXISGB6o0bqa2tJTExka6uLnbu3MmmTf24WyIRcDpFrZM5c4SjdoDY9N27he955tRMKk7fwaJVsKEIYWY31Qpn+kXFoVRV7e49ed998L//KwzYT37y3AHFxcKvEAxCXh6GpqbuphT9UVtby8GDwpWTkZHBsmXLusW8o6ODnTt3dm9SXnPNNcPu5dne3o6iKMycOROXy0VaWtqFm8X5ErpGo/DfnK/ceBF6vR6DwUBhYSEOh4PFixf3W+jrmmvEn1AwKALCJtt+qBTyGJBsTkZBocPXgTvgJseaM/hJkglFa1crj+16jA5fBznWHB5e9fDw49Fra8Wjfl4edHRgffVV8dh/jgGjJQ4dAodDmISvvQbZ2fhXbuDoUWEsz5nTU1cvHUpREDt23/ueELWMDLGX0od4rl0La1ZGULa9Dv+ogZUrRcz28uWijnkoJJ5sBuHUqVOYzWYMBgPNzc10dnZ2b2A2NjYCYLfbcTgctLW1DVvIz4/ldDq728CdpyM9HV1WFgmVleit1j7zKXJycmhvb6exsZGioqJBqxb2091tUiCFPAbkWHN4YOkDbKvcxqrcVWws3jjeS5KcIxgJ0hnoJMmchKoIJWz2NFPhqCArMYt8u/Cn76jeQYevgzxbHuUd5RxsOMjagrWDT3DmjDBvDQa44QbxvWhUVCrMzaWgoIDq6moSEhJYvXp1/+OcsygbIk6M4U6sHQ5+9CMRjQdw881wz6oakRLf2cnqT/8zh+Ys5/hxWLBAaDAv7RA+l7w8qKggfOgQ9efEKzs7u0dPTWXra6LQV0IC7Nkjxn34YfjHP4RlftNNg166xWKhubmZSCSCqqo9YrPtdjvBYJDOc9c1kjrzKSkpLFiwgJ07d6LT6Th+/DjJycl0dXWxf/9+KC3FPm8eK2+6CWNST798KASKomPRxbGaVzBSyGNEaVYppVlXfm/J8SQYCVLvrsduspNk7ntD7WIaOht4fNfjuP1uZqXP4sFlD9Lh6+CR7Y/gD/tRUHh41cOUpJUQb4gnFAlR6ahkb/1eQpEQ6QnpzEwboDpiMAg//KEQ8VAI/vhH2LRJ+GtTUlA//GE2Zmd3h9sN5G+Ozp+Ht+o0pw9vI2A2UK+0cfq0iMMOh0VU5T2//4zwpxgMmD+/mYd37CBssdOtzzYbBIO4/H664uI409jYXSSqvr6eFStWcPSowgsvQHKZmQ/aM0nKTBCRPs3NIkD9Ix/puTCnE7ZtE7Hd117bI5N1wYIFHDlyBK/Xy8yZM3tUBwyHwwSDQdxud3czhn6JREQ/TYdDtA66yDRWVRWLxUJSUhJut5u6ujo6OzsxmUyYzWZcLheuUIiLdx527oTf/U48wXz607BwYf9TXylIIZdMCvxhP4/vepxKZyV6Vc8XV3xx0BK0fz3zV7whL7m2XI40H+F463E6A534w34K7AU0dDZwoPEAJWklrJ+6ntPtp/nFgV9QnFxMhiWDJw88yY9u/FH/LpFAAPx+2hLyiapR0torUW67TTT0VRROtJ3knbLXybfnsyav9ybkxbhbamk1RmlfkoNn8TwqtAMkJ2nU1yuEQqLgIbtqICVF+FoaG6GxEX3JRZUe16+nqaqKfXV1ROfMoaG9nRkzZqDX62lpaaG9PcJPfqInPh4qA3MIljXyz+Gt4gZQWNg9jNfrpaysjC6PB8Nbb6G53eT5fEw9dgzlaxcSqkwmE0uWLOl1LZFIhLKyMtLT09E0jbq6OubPn9//z/HFF+EvfxE3i7//Hb7zne4bxvlWf16vl3A4jNVqRa/Xdz8JnG9McR6/H55+WmwRhMOiK9HPfjb5fN7DJSZCrijKTcATgA74taZp34vFuBLJec52nKXCUdG9ofzqmVcHFXKTzkQoEuquRRKniyPTkommaTR7mvGFfEy1i4gWk97E/Yvup6y5jCmWKYSiITxBz4DjY7GwNf1DPPtn0TbsPXfdzPvOdQuqddXy/d3fx6Aa2Fq+lUgkwobCDX2P4/dj+c9foFs5j2PhBiLHnMy89m7ue1hh61bh6bj5ZqDlVrqefZEWhx7FaMO6dT/J06YJYQcwGjlVUkKDpolNPZ+PlpYWTCZRAdHn0xEKiUgVQ3EaTVk3w8eniSQc+4UbwuHDh2ltbcXrdtMaiTDNbqfMZsNaWUlqJCIEdxAURekOFxw0k/LwYREkb7GIQl1NTcJnj2juUFpaSn19PampqeTl5aFpGgaDAbfbTX5+fp8RMf2Ev1+xjFrIFUXRAT8DrgfqgHcURXlJ07Tjox1bIjlPYlwimqbRGeikM9hJWvzgO1O3F99OQ2cDFY4Kbp1xKzNTZ6IqKl9Y/gUONh2kKLmIlbkru4+3xFm4a/Zd/OHYH9CpOjYv2jygCEU1hefar2XKzZ3o9Cqv1Fu42Svijxs9jWiaxpTEKbR0tXCm40z/Qu71ovf6yVQSWWcqJs7h5+Yln8VqhI9elMMU/crXeGZHMcsC/0tbykxsv9iKfWoS6u3vEe9Hoxw7dgyn04miKBgMBhITr2XLlnRsNguf/azC/PkiWk9R4P77k2DZMkBY0ceOHaOtra17Y7JLp0OJi0PvcBDU6QjMnj0kEdfphG/64MGDKIoyeDbm/PkilFCvF3F/F/UCVRSFvLy8XmGD/fXoNJngYx8TrhWdTrhWRmON+3w+KioqUFWVwsLCYbW3u5zEwiJfCpzVNK0CQFGULcBGQAq5JGbk2/P5+KKPs7V8K6tSV7GpZPCsSZvJxpdXf7nX9xdOWcjCKX07Tm8supE1+WtQFRWTfuAO6YoCVquCO2hFrwkROe+vnmqfSrwhnipnFZqmDZyWn5QES5agD4b5SF3KudK4l0R4OJ2EH/l3mmunEQlGURLi6XQGiTS1dFe+O+9+sNlshMNhAgH461+nEIk009bWzg9/mM7TT9uoqRF7nJmZwi3t84HHU8nZs2exWCwEg0GcTid6vZ747Gx8U6aQaDKR2l/oZB9kZ2eTda7M7qAWeXy82OyNRuHjH++V2DNcVq8Wm7+KMqT7Tr+cb0bR2dmJpmm0t7cPvGE9jij9peAOeQBFeT9wk6Zpnzj39UeAZZqmPXDJcZuBzQAZGRmlW7ZsGfGcHo9nQvfPGwvkNU9MgkHRW1PThPv64lpU4WiYQDiAXqfHqBvckvO43VhMpguukovp6oLWVhzhRFSPG52iYTDrMRZk9ji+o6Oju1GDXm+gvT0BvV6sLxKBoqK4bgvV6xXlazUNzGYfJpPoTRkOhzGbzej1+m4XiaqqY1JsytPZiaWjQ2wYR6NiMYNUhARxaDAobpz6Mdrp0zQNl8vVXawrEolgt/fffWqojObvev369Qc0TetlFVy2zU5N034J/BJg8eLF2rp160Y81vbt2xnN+ZMRec1XPgNe7/Hj8Ic/oCWnUHUmhH7NCnI+fDNKSs8URL/fz9GjR1FVldmzZ/PTx3YS/79vkeurhDUFXPOxb6CeU76vfU1sDiYmQl2dixtu2InJFCYhIYE1a9b0qpA4Fmzfto11L7wgdicDASHkl0bOXILLBY8+KhJ59XqRQzUWHdk0TWPXrl20traiaRp5eXksXjy8gmd9MRZ/17EQ8nrg4ltozrnvSSTjRiAcoMZVg91kJy1hcH+60+/k7fq3STAksCxnGXp1dP8a7d526tx1ZFuzSY2PQXf4khK47z6UPXuYurpYtNvpI/PTZDL1EJuPZ1fROWUnvpQUcv3HUI8dOxcCI9zRJ0+KMD1Ns7F27XUYjV2iLrjBQDQKx46J6I/Zs/t+UBg1qgr33gv/8z9igs99btBTTpwQT0EFBSJ4Z8eOsRFyRVFYvnw5TU1NKIoyobsKxULI3wGmK4oyFSHgdwOyvuoVhDfkpd3bTnpCOkb9xNzsuRh/2M9jux6jylGFTtXxhWVfYE5G/3VJA+EA333ruzR5mohoEcod5YNXStQ0ETtuMPTaTWvobODRHY8SCAeI08Xx9bVfH322r6LAhg2wYQNOJ+x8TUydPq2eCsdZVhTPIMvaW2iS4uKwTyuArCyU6mph9Z7jvvtEw6m2NvjUp2DKFCNw4ff77LPw2t+F63XefIWHHho0a39krF0rHNvnIn4Gw24XrhWnU7iHLtobjTl6vZ6cSztGT0BGLeSapoUVRXkAeA0RfviUpmnHRr0yyYSgsbOR7+38Hp3BTjItmXx19VdJNI5uM2qsKe8op9JRSYG9gHZvO387+7cBhbzd106rt5WpSVMJhANsr9rOvIx5FCUX9Z2m39UFTzwhknPmzBG1YC9yQxxuPIwv5KPAXkC1s5pDjYdiVrYhHIbHH4f6eihvauZQwzF08W6SSrby9+9/hJJLE5iuvx4OHsR19CgdSUkEDQZmRKOoqkpqqkjm7I8zfyrjszW/QFU0/ur6JK77F5E0eB7WyBjGHaK4WOyJ7toFq1aJS7yUpqYmWlpaSElJISsr64psJnExMbm/apr2qqZpMzRNm6Zp2ndiMaZkcDxBD+Ud5YPHO4+CN6vexBP0kGfLo85Vx7vN747ZXLHCZhI1Olx+F+6AmwzLwCZbijmFFHMKVc4qDjcd5nT7aZ7Y+wTf3vFtuoJdvU946y04eZJAfjbBsoOiBOxFZCZmEolG6PB1EIqGyLQMoXTrEOnsvNDvuKqlmZDaSVJGFx0nFvI/u//e+4TMTBo+9zl23XYbZ973Po5XVlJXVzf4RNEotzf8Fx3+BDqCidxU9Qssxr77i15uFAXWrROlZAoLRSanw3Hh/ba2Nvbs2UN1dTX79u3rrvtyJSMzOycpbd42vrPjO7gDbixxFv5l7b+QnhCbEqkXYzVZCUaC+EI+NDTiDZe3SPOhxkP85eRfSDGn8NH5HyXZPHh90RxrDp8q/RRby7cyN2Mu7yt534DHG/VGvrrmq+yp3cPLp19Gp+hIS0ij2llNpbOSOem9rfltpkae4Qi6NA+b3WtZzDXd7y3MXMh9C+6jrLmM24tvH3ZHoIGwWoVvuLwctIgRRVXw+wAiTLGnoGkabW1tdHZ2kpaWRmJiIr5olKDdTkJ8PL5gEJ/PN8gsgKYxpySKsUNHOALTVQeGU0fFE8gAVRkvB14vVFWJEjFvvCGM+fR0US4mPp7uFnKJiYm43W4cDkd3KOSVimy+PEk50HAAh99Bri0XV8DF2/Vvj8k81069lnUF64hoEd47871jX543EhFp2k88QbTTzc/2/RRPwMOR5iM8fejpIQ8TCAfwBD24A27C0fCgxyebk7l1xq1smLqBzkAnrV2tqIpKijml17G+ZaU8k9NBhjuMPSmL30QP9Gh6oCgK1xZey0MrHuK6wuti+liv08EXvyhcCz/6twIWlFjQY2bjB1vYvOoD1NfXs3PnTsrKynjzzTfp6upiypQpmEwmXC4XRqOR7OzsIU0U98l7mZ3exnz/O8SH3KJtzk9/KhzU56hwVPDcsefYVbOLqNZ/c+dY0dUlOvl9//uik49eL6zyjg5RLgZEsS2dTofT6SQajQ6tmcUkR1rkkxSb0UY4GsYT9BCOhkkyjY3z0qg3cv+i+8dk7D7ZuRN+/3uRJGNNJN3TgWFODqqi0uptHdIQta5aflv2W9Li0yhrKuO5Y8/xydJPDn4isLF4IzpFR527jg1TN/TZVUhJTESdv5Co0U5U0VAj/mFd4mixWIRrAYx84H039HivsbGRuLg4EhIScDqdHDrk5KWXslHVDdx5ZyeGKQ2cdJ9klnHW4E9Xq1ZBaamIJElLE5b4u+8K1UxNpcnTxHff+i4aGv6wH1/Yx3WF143q2gKBAAcPHuT4cT+KMo3Vq3OZMePCjbC8XLiW8vPFPsHRo8IiT0y8UGvLZrOxbt06HA4HVquVpDFz7E8cpJCPIUOuNTEClmQvYVPnJg42HeSOmXewPGd5zOeICR6PqNEdDouoi8FarzQ0iOIiKSmoisoiQy5/ddWgKAqbSzcPaUpvyAtAvCEeS5yFDl/HkJdr1Bt536yBXTEmvYmPl97Pbw/9Fp2q47OLPzthNtNSU1Opra0lEomgaSrPPmslLg6iUSPf+t83sK1+FgXIs+fx9TVfHzwKyWQSsX2nTokmDhZLd0Grhs4GwlqYfFs+bd42jrceH7WQnzp1isOHO3nhhdkEg2H+8Y8A//Zvpu6G03a7CBhyOIRwL18uyviuXNmjMCNWq3XY9c8nM1LIx4hDjYf41cFfAfDJRZ/sNyV8pOhUHZtmbYppg98x4b/+64LZtH+/qGw3UN700qWibGpNDUybxh13fI0lxgDxhvghxYMDFCYVMjttNsdaj2HUGdk4M/b14VfkrGBZtqhTcr7OeV9EohG2VW6jwlHB8pzlY+6aKlBV1BN1vLQ7hb/pV9Lkq+e6JXnotXiqAm9ynTmdRFMCNa4aGj2NFNgLBh/005+G558Xfo3bb++O0Mmz5WHWm6l0VBLVoj3KOLe3i8qDjY3w3vfC+vVDW38wGKSlJRFFUUlP96LXe3jzzXeoqfFRUlJCXl4u//RPognTihVi7MuQtzThkUI+BkSiEZ488CTWOGERPHngSX52y89GnWQy6dA0kb2Rny9CDWpqhIV+UU/NXkybBo8+Sri+FpxudBmZ5A9zWoPOwEMrHqLCUcHrla/z5+N/5trCa2Py1HL4MPz61+Je9KlPqYN2mNtWuY3/LvtvEo2J7KnbwyPrHhmaeI4ElwvlscfwvhtiRnULtVP2c5BNvFL/fyxRPsN11xXR6nsTT8hMVIvyfxX/R7I5mRum3TBwNyS7XVSiuoTU+FT+9Zp/5UTrCdIT0pmVduGH8cwzoudGaqpo8FxcLFqoDcb06dOZMuUwwWAIrzeJtLSTmEwOwMTBgwdJSUlhyZJ4+qiee1VzlSnL5UFDE70KVR0KCtFolNHWtJmUKIowm3bsEF/Pnj1oQaRQJMSva59nX/0+btTfiDvgxnppAakhoFN1HGg4wO7a3SSbk3ly/5NkJ2aTaxu8jkd/BIPw85+L+1AkIupc//SnA+ewVDorscRZSE9Ip8pZRVNnE9GOKC6Xi+zs7Jg1YwZEZo/fj8uSR73OzWxfKxlZFnLnN/CvdznJSvsgfzubRIunhYONB9ldu5tINEJ5RzlfWvWlEU2ZaUwhM29tr6csl0sU5jKZxM/K5RqakNtsNj72sZUsXBikutpMKHScpKQ4DAYDXq+XUGhihEBONGTUyhigV/Xcu+Be2rxttHhbuHfBvUNv5nul8bGPwYMPwmc/C1/4wqCJH2XNZeyu3U2+LZ9AOMDWs1tHPHWDpwGr0YrdJAodOf3OEY8FIlgjEhF7fkajEPY+78/RqNiVq61lRfZy/GE/Vc4qEo2J6Dv1HD58mPr6evbs2YPL5RrVmnqQlQVpacxJqCCLLrar84if+i4zSyLkZVgxG8xsKtnEXXPuIqSFyLXmkmfL41T7qZHN99prIiX0c58Tuf4X8f73iyTSsjKorITHHhOHDwWDwcCSJQm8//0q69fPIhgM4nK5yMnJuar83sNBWuRjxOq81d3xw4OVQ72i0esZznNwVIuicMHEjTLykLbrC6/nx/t+TLWrmlxbLkXJRSMeC4R1effdInVdUcQ9qtd9SdOE72X3bgDmfuADPLLuEVyH9lDQGqL6eBVGs7k7qsTj8XQ3GR41ZjP8y79gOXqUVQYDLtq42ezh2sKHexgSNpON4pRijrceR9M0ri28lkjkghbPnDmE8q8OB+5fbsFjzSIZL6annhIpp+coKYEf/AAeekiEB5rNsGWLeEAbjhZnZmZyww03EA6LYl4TZVN5oiGFfAy5qgV8BJzvkzk7fTYn205SrCvm+sI+8q+HQJu3jWpXNbdNv42i5CKKUorE78PphLffFpkjy5b1m9wSjoZp87ZhM9owGy70orz+eiFGiiJcB71wOkWmSn6+qMXy4ovk5+XB06+CwcC0zk6a1q3DmZVFXFxc7EPjrFZYuZJE4K5+DjnfXOPd5nfRq3rmpc/nqadEwqqiiNIn9w8ScfrOO+AvA2c8WPWwfM3FVVoE5wNcNE28RqrBl6MK42RHCrlkQuANefn+ru9T7aruzlSte7duSE2WQVR5ffttoZ9LV/r47lvfpd3XTjga5oZpNzAnYw5aIMDZxx6mq7melA4fvlXLyP/nR3qF4PnDfn6w+wec7TiLJc7Cl1d/uUetlAFLSZvN4tXeLnwL2dli1y8uDnJyiK+qYll6Oq7SUlJSUnr0m7ycmPQmlmYvBcQyd+0SGaMgBP0jHxm42uFbR5Mwzr6bFTV/wOEzU7n+U/TVeG/zZvjP/xSlBT760eFZ45KhI4VcMiE42nKUCkcFU5OmUu+uZ1fNLvKHGK9SWwv/8R9CeF5/HZrcnThwUGAvwBfyUdZUBvPhhQPP8Hx4Gy3JHnL0Cu9/o46/r53Cg8se7DHesZZjnG4/TYG9gEZPI6+dfW3oSVEmk0i9/OMfhaDfc48Q9RdeEN3qFQXrwoVYh9A8Ida0tIj09tzcnq4Tg0F0C2poEJZzVtbgWfhFRfDcwRs5vmQDgZDKo8v69sXMnCmEXNNG161HMjBSyCUTArPe3J0hGIgEhhWp0tgo9hczM4VYtdXbSSlKodJRSUSLcMv0WwB4rW0vmYZkdI3ttJqgZfZ8DjUeIqpFe8SCmw1iLcFIkEAkMPxqj9OmwVe+cuHr9HTRxaGiQjQ6Hovi2YOwaxf85jdCUEtLxd7zef++qgpf9ksvCffH7bcP7ga55RYh9nV1BtasgYFKdY9J6VtJD6SQSyYEs9Nn897i97KjZgfLc5ZzY9GNvN08tPoxhYXCX11VJb5etTyOD5d8jXca3iExLpEl2WKztSC5kNMLuqg2d+CLBvlHPIvtQwAAH5BJREFUsZ0F6XN6JfSUpJbw3pnv5c2qNymdUsqtedeJxpZmMyOmuFi8xonnnxet6OLj4cAB0aj+4nDA9HT4xCeGPp5eDzffHPt1SkaGFHLJhEBV1BFnqqamwiOPCFd0RoYweB2OJLJ8N5CdDHpVJGnlWHPYU7uHvDmrWVuwllxrLqvzejbTrauDM2cU5uZsYtPNm8Su3he/IkoM3HNP38WvJwEZGSIqJRQSItznRq1k0iKFXNInmqZ1F6kabXlcl0uU7DaZRG2MsWgZlpZ2oWhSY6OokOfziTm//nWoibzD38/8naLkIuo760kxp7Amf02PyKL6elEK9Xx8+Bcf0pj39NMis1GvF3GHK1f2UMFQSIi/zTZ4GZleOJ3gdgvT+NIOwvv3ixo1OTnwvveJHcnOTnHsCJzNH/+4qEXmcIjPYxXxKJkYSCGX9MnLp1/m+RPPA7CpZBO3Fd82onFCIZEMUl8vkmlOnx7eI/xIOHwYPF0apoLDHGs9yU9fN5Ff0iIybRWFsuYyOnwd7K7dzdfWfK27EUVFhRDx870g330X5ul0whpXFCGgFzl8g0GxyXrmjNDhz39elOselMpKePVVsTNrs4kdwYceuvB+XZ1IG7VaRThOQ4OYJBSCefNEgtUwW8enpIj1Sa5M5DaEpBfBSJDnTzxPtjWbbGs2z598nlBkZKnRDofwx06dKkIDDxyI8WL7IDUVGvQ72Bb6Lodt3+L3TV/mr2f+ijfk5WDjQRQU5mfMp9HTyOGmw93n5eQIva6vF9Ed02coYlcwFAKnk5oPvYfdbYeod4ve4hUVQl/z8oTl/8orQ1hcfb0oHPb006KzsaKIejQVFReOcZ7LQE1KEkK/bZtwbufnw5EjFzYDJJJzSItc0gudosMSZ8EdEJ1WEgwJ6NSRxY4lJYlokspKEVmyZs3I1nTe1WPSm3pFtDh8jv/f3pmHV13d+f917s1NbpKblSUbJAQMiyIRCKiVJYh79WfF2krFgqiMbW3Vp52KVauWmepUay39dWw7o2X66NMwnbq0MlVkbERUULZBlB2SsGSBQPZws53540Mge27ITW5u8nk9Tx64N9/v+Z4Twvt87ud8Fiz2bPeg6dMhY9ZnFBcYhrnDiI4yhIeEMzVpKgmRCbyz/x2MMTTZplbFotLT4eGHJa187FjIygLMJFi5kv0n9/P0hmdo2tJEiCOEx+c+TlRUKiB1wCor5cCwWw4flo8mSUniczp8WGLNWwanjxsn7+XnyyeAadNkN2yOCVQHt9IGFXKlHU6Hkwcve5BV21dhjGFx5uIuS7V2hcslDX4//liCPr70pZ6PYa1l1fZVrM9fL/W/Z3ybaUnTAFh7YC05O3Ow1nLbRbdxQ8YNOBzw9eyLKfkklx3FNXgbnDTZJjITMpk/dj4O42BnyU6+MuErZ8sLHzwIZScamJBQxsQFMa0DqY3hs5KdAKTFppFXlseu47u49oJU7r5bGhpdeincdpsPi0kV8aegQBo0uN3w0EPnEofgbKo9eXmyE7rdsGoVdYWFFN96K05rSTzTQFlRQIVc6YRx8eNYceUKv4wVG3suVM1ay4maUsKcYT7HZxdVFbE+fz2jY0ZTXVdNzs4cpiVNo6GpgdU7V5PoScRg+K8v/ov56fMJCwljTtocPKEethzbQnV9NZkJmcwdMxenw8niSxa3Gv+DD+Cl39TDzp0kc5THr/yI8Me+L47lM4xtjMZdWk6RI4Qm23S2iuKsWfLVHe/nv0/uoVzS49L52nVX4c7Pl1z/2lpx3Zxh3z755JKREY5j0qSz7zd+97t89MEHnDp1CrtxIxkZGVx88cU+/fyUwY8KuQLAzuKdvLb7NWLdsdxx8R0Mi2jfq7Iz6hrrOFpxlFh3bJcp9dZaXtnxCu8deq+dZd0V7hA3DuOgtr6WqroqUmPEqnUYBxGuCKrrqnEYB+4Q91kXUH55Prl5ubhD3NyZeSfDI4Z3On5uLsTVFRNDHgWOMRTkWyasXw+33CIXrFvHlFdf5YmqWnbODCH6ju+1qr3dHQdOHuD3235PfHg86w6uIzJ0IrcmJ4svJi/vrPV/8qS4zkFcUHfffS4xp7a2lvLycuLi4mhsbOTw4cMq5MpZ9LOZQvnpclZ+spLSmlJ2FO/g5W0v+3yvt8HLzzb8jBXrV/DwuofZc6Lzkqgl1SW8d+g9RseMJiYshj9+9sdux88vy+fFzS/icro4WXuSlOgUlk5dCoiQf+/S7xEeEo7L6eK7M79LiCMEb4OX5z56jgOnDrCtcBu//uTXXT5j7FgorQ6jpDYKp20gPqTiXNsZa2H1akxSEiMnTuPKL2rIipnU5XhtaT5riA6LxhPqoSjJI2b8kSOQmQnZ2TQ2SiRiaqqcaW7YIAeuANTX4969G3dlJeVlZVRUVDBihG/dkpShQa8scmPMbcCTwCRgprV2sz8mpfQv1fXV1DfWk+RJItQZSnF1sc/37ju5j/2n9jMmdgzHq4+zZt8aJgzvOIMxLCTsrGVdU19Doqf77ua/2PgL6hvrcRon7jA3j815rJW/PmNYBs9c/Uyre2rqa6iuqyY1JpWGpgaOVR7r8hlf/SqEh8ZT+EYEVzW+xYhZk1r3JouOFpUNCRGB764QSRvGDxvPqOhRFJQXEOII4eqM6+Cy8VLL+4zJ7bAybFGRnG8OG3ZmL7EWXnyRkM2bmeV0kjdtGq5rryU9Pb1Hc1AGN711rewEFgC/9cNcFD9TW19LbUMtce64Lus4J3oSmZY0jS2FWzDGnLV4QXTk00/FeJw6VSI7WuIJ9WCtpdJbSVV9FSMiOrcUY92x3Dv9XnJ25jAicgT3TOs6oNxiKT9dfrby4JGKI9TW17ZyoXT2nJkpM/n4yMeSMTqp62zRsDBYcJsTbvsS0OY01hiJ2375ZfFl/8M/9DijKTI0ksfmPMbh8sPEh8efc1u1+DcxRrIvp06VoJYFC87k/VTXwNatkJ5OpLVctHWrhERqBSqlBb0ScmvtLuibLvFK79h9fDcvbHoBb4OXK1KvYOnUpZ1GnjiMg2/P+DYF5QWEu8JbWcrr10sT3bAwyWH5yU9a1+gYEzuGpVOXsvbAWq4YfUW3onnZqMt87p1pMHw548u8te+ts8964O0HcDldfCfrO0xO6Dj7pr6pnibbRHVdNZNGTGJ++nyfntcpaWnw1FO9GsId4iZjWEaX14SEiEa3vtEtCn/kiJyCjhmjIq60w/ijl6QxJhf4QVeuFWPMMmAZQEJCwvScnJzzfl5VVRWeLotCDz56uubCqkIamhpwGid1jXUkRyUT6gylpr6GstNluJwuhoUP6zas8PhxCazAVYu3voGE+HCiPf45I/c2emloasDt7NjCbl5zfVM9jU2NFFcVE+IMwVqLxTI6uuNSsNX11ZRUlxDmDKOusY748Pjz6vvZ33T6b9zQIDHnxkiC0CAScv2/3DPmzZu3xVqb1fb9bv9HGmPWAR05Mx+11r7p6wSstb8DfgeQlZVls7Ozfb21Hbm5ufTm/mCkp2t+9sNn2XdyH/Hh8RRVFfHMpeJH/tH//IiYsBjKTpdxecLlLJu+rMtxPv4Ynnj5fQ7E/BGnwzBrZhQ/n7/C54YPnbH52GZWfbIKkEPAp7KfajdmyzVXeit54O0HSIxIxNvgpdE2cmf2nR2OvT5vPTnbchjrGUt+WT4Lxiwge0J2r+bbH+jv9dCgL9bcbdSKtfYqa+3kDr58FnGl/7kz806SPElUeCtYNGURiZ5EquqqaLJNRIdFE+OOobiq+0PNyy6DzOu3cHFGLDfOSgdXDQXlBZyqPcVzHz3Hg28/yLqD63o8v41HNhLpimRM7BgqvZUcPHWwy+ujwqJYNGURxVXF1DbUcu+0ezu9Nisli4nDJlJQUcComFHMSZvT4/kpSjChceSDlERPIk/Na+3XTYtJY+Lwiewp3YPDOFiSuaTbcYyB+VMupHDHViqoIcQRQlJUEqs/X80Xx79gRMQIXtnxChOGTTibJOML4+LGsenIJhptI8YYEjwJ3d5zZfqVzE6djcM4ujzsjHBF8MjsR6j0VuIJ9Zx3eQFFCRZ6G354C/ArYASwxhiz3Vp7rV9mpvgdl9PF9y//PocrDhMVGsWISN9ika8Zdw2RrkgKqwqZmTKTkZEjKT9dToQr4mwZ2NqG2lb35OVJV5qEBMjObl+s74rEazh+JIrK0APMy5jRqidmd2vwheMlDtasicHlghtvlEx3RRms9DZq5XXgdT/NRekHXE4XY+PGtnqv7HQZVXVVJHmSOrReHcbB7LTW1a5umXQLz3/8PAXlBWQmZLYas7QUnn5agixOn5aCUs1JkgAnTsBPf+qkvHwWHs8sbnu0+3kXFUl9qdRU2Ry6oqFBysuWlUko36FD8OMfSyhlU9OgOitUFEBdK0Oez4o/Y+WmlTQ0NZCZmMn9M+8nxNH9r8X4YeN57prnqPRWMjJyZKsNoLhYQq5TU0VMd+1qLeRbt4rYp6eL5b5pE9x0jVfCY2Ji2jWMLCiQRhH19RLC/dhj0kC4M2pqZLNITRXhzsuT561cKQUFZ8+GJUtU0JXBg6boD3Fe3/06Ea4IUmNS2V60nYLyAp/v9YR6SIpqb8WPHi2Fsg4dtJwsrufymY2tvh8TI5ZyVZVYz7E1R6WxwkMPSdB6U1Or63fsEBFPS5NmDp991vW8oqIksSYvT4Q7OxvWrDln0a9fL/0aFGWwoEI+xIkPj6eyrpLq+moMhghXRLtrysrEPVFX59uYUVHw2CON3Jv4Fx6ueYLst5eLb+QMM2ZI5mJYmHRs/9KBV8QKT02Fjz6Sh7UgOVkE//hxyZQ/elQ6DXVGYyMsXix7ww9/CIsWyXvGnDP22+wVihLUqGtliHPHxXdQ31hPUVUR90y/p139k337xN/c7Cp5+OFzzeS9Xli7VtwW8+aJxdxM/Im9zCp+DTLToOiEtHH/1rcAqSWyYIF8AfBLtwzWXM61TS2TqVPh3ntF4w8flrKzGzbAgw/K91qSnw/PPy+Cf+WVIuLGyIHn7t3y/Zkz4aKLuv/ZWCvW/6FDcv0FF/j6Ux24lJWVUVBQQEREBOnp6TjVvzQoUCEf4sSFx/HQ5Q+dfX34MPzrv8oB5cKF4s92OqWhTV6eWMKZmXJtTo60nXS7xc/9zDMtmvq29HM3NXXdY3LhQulRWVQk3RnaOMCNgTlzZIg9e2TDKCqS3pxthXz1atkPRo+Wuc2eLVntI0bAT38q+0V4eDs3fIds2wYvvCD7yptvwuOPt681E0zU1tayYcMGmpqaaGhowOv1cpEvO5oy4FEhV1rxb/8m1qzHAy+9BFdcAdXVIuzWitukmb17paS2xyMHkqWlLYR8/HiYP1/6TSYnw1e+0vlDR470qZZJSor8efSoRMOMH9/+GqdT9o1m14nDAe+/D3/7m/TkXLzYNxEH2TTcbpl+Xp6sMZiFvLq6msbGRmJiYvB6vZw4cSLQU1L8hAq50oqaGhGv0FARw6uuOhf5sWSJCNknn8Cf/iQ+6+pq6QucktK6mBYOB3zzm/CNb4i6tlFPayW6xeHwsdclYokvXy6W+Jgx4iJpy8KFYkUfPSr+d5BmDcOHw+bNMtelS9vf1xEXXigbQH6+LGHMGN/u64pNm8RFNH48XHtt1x9U/E10dDTh4eGcOnUKgAsGg69IAVTIA4a1lu1F2ymuLmZKwhSSo5K7v6kfWLRIvBylpXDddeIXv7dFNvypU/Db30qCjcslAr5wofiQm3sxtKITpfrTn+A//1M2jEWL4IYbfJvfhAny1RnJyfAv/3IuXnzXLnk/MlLcKqWlvj0HxIW0fLlY4uPHtz4DOB8OHIAXX5RPNVu3ys/vmmt6N2ZPCA0NZc6cOZSUlOB2u7U5xSBChbyfsVZ8t698mMv+qJdJT3PyZtibrLhyRZftyPqLSy4Ri7auTkII27ohTp8WkYyMlLU4ndJ4uCdUVMDPfia+bGPg3/9drFN/nbsZc26sceNg4kRxk7hccNNNPRvrwgvlyx80ezKGDZO1F/ge6ek33G43qc0NoJVBgwp5P7NnD/zhD1CQvJ3jZXGkeuLxjizgSMWRASHkICIdGdnx9xITxW/+4YcilvfdJ++XlsK6dSKWV1/d2pfekpoaiTopLxer/vTpcy6WviA0FH7wAzh2TBr9xMb2zXN8Yfx4eX5+vqzXl6bNiuILKuR+wlrLx0c+ZteJXWQmZDI9aXqHDTeKi6GkBMIiLqYxegsF5VVMTg4lJSrFr/OpqJDok/37JSHmG9/wj1gaI02Bb7xRXCmxsRKj/eyzsq7GRjkEXb68/b1VVZKheeiQxIXX1clm8OUv+34A2R07dogPPSNDKjcaI96dgWCExsXJme6hQ3IukDwwvGnKIECF3E9sLdzKi5++iCfUw/q89Tw86+F2ndatlQiKw4ehdt98IjNi+OYjRVx/ySU+F7DylTVrxPpPSZFY78xM8KXpekNTA5uObKKqrooZKTOID49vd40xYpk3U10tG1Rqqqxxzx75s604794tYYOTJom/+vRp8Y0vXtzLxZ5h7174+c9lg3n3Xdm4eur26WtiYsR9pSj+RIXcT+SX5xMWEkaCJ4G8U3kcrTjaTshra8Uau/lmqKgwlJbOYOkVfRO54PWKtdvsK27OtemOnJ05vL3/bZzGybqD61hx5YqzFQ47w+MRP/TOnfJ67tyOLezYWBH48nJxcyxZ0nVUYk8pLJQ/k5IkauXgwYEn5IrSF6iQ+4nMhEzW7F1DXlkerhAXE4dPbHeN2y0hbAcPiqBNmdJ34WfXXy9ZiQUF8pzJHbe3bMe2wm2Mih6FO8RNQXkBJ2pOdFti1uGABx6A//1fWU9zwlBL6uokKeeuu6TWSVaWzLEttbVw8qTvIYktueACSfvPy5ONRC1fZaigQu4nxsWP48nsJzlScYS02LQOwwkdDqkLtX69CM3cuX03n4QEybSsrhbr11f/eGZiJmsPrCXEEcLwiOE+H8C63Z1bv/n54vKorBS/9aOPynzeeUf82VOmSKhjcbHMubJSknd62g0rJQWefFI2ypSUvkveqayEv/9donfmzWuRBKUoAUKF3I+MjhndbZec6Gg5KOwPXK6eR2ksnLyQ9Nh0quqqmJkys1O3Sl2dHG7GxXUe4dLMn/98rlbLRx+J+FVWwiuvSKLOzp0yzqFDsvGkpooY19Z2PW5HJCf37SGitVIOd+9e2Yy3bZPNw1+HtYpyPgxJIfd6xQUQ6HpBtbWQmyuHfnPnQnz7c8V+x+V0MTttNvX1EgGSb+SQtGUdq+pqsZyPHhX/+PLlXYtnWJgIeXMFwpAQiakOCZGNprxcXkdFyQZx+rQIZl+FJPaGhgYpJNac5VlQIPNtLiSmKIFgSAm5tfDGG/CXv4gAPfQQjB3b/X19xUsvScq20wkbN0ponsu3TmZ9irXwm9/Ap5/K68svP1u4EDjne09PhyNHpJzKokWdj/e1r4nb5NgxSZtPT5dU+b/+VSJ4wsNh2jRJlCkslGzMm28emOLockmhrs2b5XVmZicZrYrSjwwpIS8ulip2o0aJFfiHP8jHYn9grVjYvlbWAxHEtDSxTAsKJPZ72DD/zKc3eL2wZcs5H/N770nyysSJImQREefW6/V27yMeMQJ+8pPWIYmJibJxHT0qUSbNn0aWLTt3X26u35fmF771Lfn5WCsbkLpVlEAzpIT8fGhoEJdAWFjn11RWwi9+IX7diRMlgsMXa3LmTBErY0Q0A5l12JKwMNnsCgrEQi4slJrkkydLs4bJkyVs8MMPpUzs1Vf7Nm5bwYuNHThr7gmhofIpRVEGCkNKyBMSpJnBG2+IP7a7RJRdu+BXvxIf6Fe/2nlhpw8+kAzK9HT4/HNxl/gScbF4sYii1yuWXUc++5ISOQT0NQ7cHxgjgv3OO/Dqq+K/j4uT9miFhSLyrRpDKIoSUIaUkBsjPtrrrxfR7O4w7fe/F+s0Lk6q9V12WccHkm3H8fWQLiSk64SVwkJJ6fZ6ZWPYt09Sz/uD+HipalhcLPHhTU1iiUZH98/zFUXxnQEYF9D3uFy+ia3DIX7QZt9uZ77Q2bOljGt+vljWHdXJPh927xY/dHP51K1b/TNuT7jrLimSlZYmVroKuaIMPIaURd5T7r5bYoaLi+H228Uy74jISOll2dDg30zN5nomJSUipIEo/BQTA/fc03/PO3wYXntNPglp3wNF8Y1eyY4x5lngJqAOOADcZa0t88fEBgIZGfDLX3bfcrIZf6fbT5oE998vSScjRohrpyXWik++pESuTUry7/P7m/p6OVT1emVTVOtfUXyjt66Vd4HJ1topwF7gkd5PaWDhcPRvO662zJghIXkeT3vXzocfSoOG//gPCe8L9haMtbUSFpqQIJ9G6utls1IUpWt6JeTW2rXW2oYzLzcCXVdXUvzK1q1itaanixUbiI4z/iQqSj515OWJi6XZIt+/X2qynE/KvqIMBYz1k8ljjPkrsNpa+0on318GLANISEiYnpOTc97PqqqqwuPxnPf9wUhHa66sFCu8+eA2JcW/nx6qq8XFERHRvxmnXq/8WV9fhbWes302w8LEUh+sCTj6ez006M2a582bt8Vam9X2/W6F3BizDkjs4FuPWmvfPHPNo0AWsMD6sDNkZWXZzc05zudBbm4u2T0tjRfkdLTmpiaJWT96FKZP92+1v7/9TWLIQ0NFQP/pn/o/6zQ3N5d33smmvl5cS/n58MQT/ulmPxDR3+uhQW/WbIzpUMi7td+stVd1M/AS4EZgvi8irvgPh6PvMgx37BDhjosTN8exY4EpHzB2LGzYIJ8OmmP6FUVpTW+jVq4DfgjMtdbW+GdKykBg2jSpBVNWJtbwqACdftxxh/jKT5yQmuVa+1tR2tNbj+r/B8KAd880Gt5orb2v17PyM7t3S02T5GQRg9DQQM9o4HPVVVIrvKREKvwFyhKOiICvfz0wz1aUYKFXQm6tHfApG8XFEpvsckm4Xl2d1E1RusYYKdeqKMrAZ9Bndh4/LoeCCQliiR84EOgZKYqi+JdBX2tlzBg5pMvLE3/vnDmBnpGiKIp/GfQWuccDjz8uPRbj4wPbEUhRFKUvGPRCDhL1kNUu8lJRFGVwELSulYMHJUFEI9cVRRnqBJ1Fbi2UlsKqVfL6ppvg1lsDOiVFUZSAEnQWeW2t1BhJTZUklbfekp6aiqIoQ5Wgs8hDQ6VN24kTIuAjR/reWk1RFGUwEnRCHhIiMeHp6fL3228fvNXwFEVRfCHohBzEKv/Hfwz0LBRFUQYG6pRQFEUJclTIFUVRghwVckVRlCBHhVxRFCXIUSFXFEUJclTIFUVRgpygCj+srT3XYV1RFEURgkbIP/8cVq6UDj833SQ1VzQRSFEUJYhcK6++Kv0bR4+GigooKgr0jBRFUQYGQSPk4eFw+rRY5MZID05FURQliIT8rrsgNlbatQ0bJh3eFUVRlCDykY8aBU8/LX/PzQ3oVBRFUQYUQWORK4qiKB3TKyE3xqwwxuwwxmw3xqw1xiT7a2KKoiiKb/TWIn/WWjvFWnsJ8BbwYz/MSVEURekBvRJya21Fi5eRgLZCVhRF6WeM7WUbemPMPwPfBMqBedba451ctwxYBpCQkDA9JyfnvJ9ZVVWFx+M57/uDEV3z4GeorRd0zT1l3rx5W6y1WW3f71bIjTHrgMQOvvWotfbNFtc9ArittU90N5msrCy7efPm7mfdCbm5uWRnZ5/3/cGIrnnwM9TWC7rmnmKM6VDIuw0/tNZe5eMzXgX+G+hWyBVFURT/0as4cmNMhrV235mXNwO7fblvy5YtJ4wx+b149HDgRC/uD0Z0zYOfobZe0DX3lLSO3uyVj9wY82dgAtAE5AP3WWuPnveAvj93c0cfLwYzuubBz1BbL+ia/UWvLHJr7a3+moiiKIpyfmhmp6IoSpATrEL+u0BPIADomgc/Q229oGv2C72OI1cURVECS7Ba5IqiKMoZVMgVRVGCnKAV8qFYedEY86wxZveZdb9ujIkN9Jz6GmPMbcaYz40xTcaYQRumZoy5zhizxxiz3xizPNDz6WuMMS8bY0qMMTsDPZf+wBgz2hjzd2PMF2d+nx/w5/hBK+QMzcqL7wKTrbVTgL3AIwGeT3+wE1gArA/0RPoKY4wT+DVwPXAhsNAYc2FgZ9XnrAKuC/Qk+pEG4PvW2guBy4Dv+PPfOGiFfChWXrTWrrXWNpx5uREYFcj59AfW2l3W2j2BnkcfMxPYb609aK2tA3KQTOlBi7V2PXAy0PPoL6y1hdbarWf+XgnsAlL8NX7QtHrriLaVFwM8nf5mKbA60JNQ/EIKcLjF6yPApQGai9LHGGPGAFOBTf4ac0ALeXeVF621jwKPnqm8eD+DoGCXL9UmjTGPIh/VXu3PufUVvlbYVJRgxxjjAf4MPNjGq9ArBrSQD8XKi92t2RizBLgRmG8HSRJAD/6dBytHgdEtXo86854yiDDGuBARf9Va+5o/xw5aH7kxJqPFS58rLwYzxpjrgB8C/89aWxPo+Sh+41MgwxiTbowJBW4H/hLgOSl+xBhjgJeAXdba5/0+frAadYGqvBhIjDH7gTCg9MxbG6219wVwSn2OMeYW4FfACKAM2G6tvTaws/I/xpgbgBcAJ/CytfafAzylPsUY80cgGynpWgw8Ya19KaCT6kOMMbOAD4DPEM0C+JG19r/9Mn6wCrmiKIoiBK1rRVEURRFUyBVFUYIcFXJFUZQgR4VcURQlyFEhVxRFCXJUyBVFUYIcFXJFUZQg5/8ALxXawJDS7VQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LlAg7ll8Lhj",
        "colab_type": "text"
      },
      "source": [
        "3d"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_RV7I6p8L-x",
        "colab_type": "code",
        "outputId": "ad3371df-f51a-4358-aad9-6583235a06d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "#plt.rcParams['figure.figsize'] = (20.0, 10.0)\n",
        "\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "m = 'o'\n",
        "xs = X_train['cent1']\n",
        "ys = X_train['zcr1']\n",
        "zs = X_train['tempo1']\n",
        "ax.scatter(xs, ys, zs, marker=m,color = colors)\n",
        "\n",
        "ax.set_xlabel('X Label')\n",
        "ax.set_ylabel('Y Label')\n",
        "ax.set_zlabel('Z Label')\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoSgWvefYfAQ",
        "colab_type": "text"
      },
      "source": [
        "## Modelling Classifier(1): KNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvVHHhDa6sxG",
        "colab_type": "text"
      },
      "source": [
        "As for classifier, we can try different approaches, \n",
        "\n",
        "1. use **K Neighbour Classfier**\n",
        "2. use **Support Vector Classifier** or others.\n",
        "\n",
        "We can test each model with the already splited dataset and try to find the best parameters to see the best result they could have."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBdy05siwszs",
        "colab_type": "text"
      },
      "source": [
        "The parameters for KNN Classifier are **searching algorithm**, **n_neighbors**, **weights** and **p** (the way to calculate distance)\n",
        "\n",
        "Here is a simple test example using the dataset we have.\n",
        "\n",
        "\n",
        "Here is a **Simple test... with random parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qH_klroVP0x1",
        "colab_type": "code",
        "outputId": "f6726bbc-1b21-4125-d78d-533ba6abcaf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "kClassifier = neighbors.KNeighborsClassifier(n_neighbors=15,weights = 'distance')\n",
        "kClassifier.fit(X_train,y_train)\n",
        "\n",
        "predictedTestLabels = kClassifier.predict(X_test)\n",
        "  \n",
        "print(kClassifier.score(X_test,y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oz41s_2h_r5F",
        "colab_type": "text"
      },
      "source": [
        "Now we can try to find a better k and weight to get higher score.\n",
        "\n",
        "\n",
        "For the **algorithm**, we can just choose **'auto'**, since this method would choose the fatest method from the other three, 'brute', 'kd_tree', 'ball_tree'.\n",
        "\n",
        "But for k and p, we will just enumerate all the combinations of different parameter set to check which has the highest score. \n",
        "\n",
        "(Is this called Grid Search to get the best parameter set?)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlolIGsJCtUG",
        "colab_type": "text"
      },
      "source": [
        "### Cross Validation: KNN \n",
        "We have training set (320 audio) and testing set(80 audio).\n",
        "\n",
        "We can use the training set also for cross validation, and save the testing set to evaluate the model for final conclusion.\n",
        "\n",
        "Using cross validation, we can improve the model,\n",
        "by searching for a better **k** with **p**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7hdcVXqA-8q",
        "colab_type": "code",
        "outputId": "e85bcd7a-2cc9-434f-8877-a54bf984be95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Test cross validation, k-fold\n",
        "# is this right? or something wrong?\n",
        "%%time\n",
        "best_p = -1\n",
        "best_score = 0.0\n",
        "best_k = -1\n",
        "for k in range(1, 11):\n",
        "    for p in range(1, 6):\n",
        "        knn_clf = neighbors.KNeighborsClassifier(n_neighbors = k, weights = \"distance\", p = p)\n",
        "        knn_clf.fit(X_train, y_train)\n",
        "        knn_score = np.mean(cross_val_score(knn_clf, X_train, y_train, cv=10, scoring='accuracy'))\n",
        "        #print(\"New cross test\",cross_val_score(knn_clf, X_train, y_train, cv=10, scoring='accuracy'))\n",
        "        if knn_score > best_score:\n",
        "            best_score = knn_score\n",
        "            best_k = k\n",
        "            best_p = p\n",
        "\n",
        "print(\"best_p = \", best_p, \", the best_k = \", best_k,\", and best_score = \", best_score)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "best_p =  1 , the best_k =  5 , and best_score =  0.8375\n",
            "CPU times: user 9.33 s, sys: 807 µs, total: 9.33 s\n",
            "Wall time: 9.33 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBvyvqqZ7kow",
        "colab_type": "text"
      },
      "source": [
        "### Testing: KNN\n",
        "So we can use the best parameter for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0Gu0VqHnW90",
        "colab_type": "code",
        "outputId": "1669347d-a903-4b56-cb74-7f6c4e34560b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "kClassifier = neighbors.KNeighborsClassifier(n_neighbors=5, weights = 'distance',p =1)\n",
        "kClassifier.fit(X_train,y_train)\n",
        "\n",
        "predictedTestLabels = kClassifier.predict(X_test) \n",
        "print(kClassifier.score(X_test,y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9o4cemlubtj",
        "colab_type": "text"
      },
      "source": [
        "**Get weighted recall F-score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjdHEYfeucDt",
        "colab_type": "code",
        "outputId": "d4b14680-1476-4d74-9fbb-f3ccdf98e30a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "y_pred = predictedTestLabels\n",
        "y_true = y_test\n",
        "f1 = f1_score(y_true, y_pred, average='weighted')\n",
        "print(\"F1 score for KNN classifier: %0.3f\"%f1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score for KNN classifier: 0.812\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYpqEc3KHEwp",
        "colab_type": "text"
      },
      "source": [
        "**Confusion Matrix**\n",
        "\n",
        "So up to now, the best parameter for current KNN model should use **k = ?, p =? **.\n",
        "\n",
        "(With different testing data, different result of parameters)\n",
        "\n",
        "We can get the percentage of the accuracy for the prediction result,then together with the genre label of the testdata we can plot the **Confusion Matrix** as followed.\n",
        "\n",
        "Each **row** in the matrix is the real genre, while each **column** is the predicted genre category classified by the model we build.\n",
        "\n",
        "From the result of this Confusion Matrix, we can clearly see that the only music genre that has been difficult for our KNN model to classify is the \"Regea\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mi9XUnPvjbdn",
        "colab_type": "code",
        "outputId": "15184405-2a02-48ce-f915-5bf8c0ad75a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        }
      },
      "source": [
        "#from sklearn.metrics import plot_confusion_matrix\n",
        "# plot confusion matrix\n",
        "title = \"Normalized confusion matrix\"\n",
        "disp = plot_confusion_matrix(kClassifier,X_test, y_test,\n",
        "                                 display_labels=genre_analysis_list, # genre names list\n",
        "                                 cmap=plt.cm.Blues,\n",
        "                                 normalize='true')\n",
        "disp.ax_.set_title(title)\n",
        "\n",
        "print()\n",
        "print(disp.confusion_matrix)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[0.94736842 0.         0.         0.05263158]\n",
            " [0.05       0.8        0.1        0.05      ]\n",
            " [0.18181818 0.09090909 0.72727273 0.        ]\n",
            " [0.10526316 0.10526316 0.         0.78947368]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7pIp54HKp1O",
        "colab_type": "text"
      },
      "source": [
        "## Modelling Classifier(2): SVC\n",
        " [SVC](https://scikit-learn.org/stable/modules/svm.html#classification)\n",
        "\n",
        "In this part, we can use the simplest svm to get get prediction of the music genre using test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3smDhj-0a8Wb",
        "colab_type": "code",
        "outputId": "4a80024a-2136-4682-dc26-387981a9536d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn import svm\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "X = X_train #feature training data\n",
        "Y = y_train # genre\n",
        "clf = svm.SVC(decision_function_shape='ovo')\n",
        "\n",
        "# the predicted result\n",
        "clf.fit(X, Y)\n",
        "#print(clf.predict(X_test))\n",
        "# check the score of the predicted result\n",
        "scores = cross_val_score(clf,features,result, cv=5)\n",
        "np.mean(scores)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8150000000000001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tbfBRthXq9L",
        "colab_type": "text"
      },
      "source": [
        "### Cross Validation: SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPi2S6euXro5",
        "colab_type": "code",
        "outputId": "a2d66317-c782-425f-d2b9-60cbeb9c0dbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2bc727d810ec42ea9a5d40f6add6a65e",
            "a1c5141f47ed45f98b24b392cf783f99",
            "363a68e1473340809a034b4ce2136a82",
            "1630af3611014debbdab87d394ae4b2a",
            "77cc6ff6125040f8aef1a19467ba3a2f",
            "16a685521ff444729a9247fad38c4c89",
            "b9f37e9e7abf4d56b622b8222772197a",
            "f15b8ea9443c4ac4ad5ed708981bbd3f"
          ]
        }
      },
      "source": [
        "\n",
        "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
        "                     'C': [1, 10, 100, 1000]},\n",
        "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n",
        "\n",
        "scores = ['precision', 'recall']\n",
        "print(\"Still searching....please wait...\")\n",
        "for score in tqdm(scores):\n",
        "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
        "    print()\n",
        "    # use Parallelism\n",
        "    # https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
        "    clf = GridSearchCV(\n",
        "        svm.SVC(), tuned_parameters, scoring='%s_macro' % score, n_jobs = -1)\n",
        "    clf.fit(X_train, y_train)\n",
        "\n",
        "    print(\"Best parameters set found on development set:\")\n",
        "    print()\n",
        "    print(clf.best_params_)\n",
        "    print()\n",
        "    print(\"Grid scores on development set:\")\n",
        "    print()\n",
        "    means = clf.cv_results_['mean_test_score']\n",
        "    stds = clf.cv_results_['std_test_score']\n",
        "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
        "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
        "              % (mean, std * 2, params))\n",
        "    print()\n",
        "\n",
        "    print(\"Detailed classification report:\")\n",
        "    print()\n",
        "    print(\"The model is trained on the full development set.\")\n",
        "    print(\"The scores are computed on the full evaluation set.\")\n",
        "    print()\n",
        "    y_true, y_pred = y_test, clf.predict(X_test)\n",
        "    print(classification_report(y_true, y_pred))\n",
        "    print()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Still searching....please wait...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2bc727d810ec42ea9a5d40f6add6a65e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "# Tuning hyper-parameters for precision\n",
            "\n",
            "Best parameters set found on development set:\n",
            "\n",
            "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "\n",
            "Grid scores on development set:\n",
            "\n",
            "0.718 (+/-0.070) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.136 (+/-0.129) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.797 (+/-0.126) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.711 (+/-0.056) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.779 (+/-0.099) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.785 (+/-0.112) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.780 (+/-0.055) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.782 (+/-0.090) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.781 (+/-0.097) for {'C': 1, 'kernel': 'linear'}\n",
            "0.772 (+/-0.059) for {'C': 10, 'kernel': 'linear'}\n",
            "0.769 (+/-0.104) for {'C': 100, 'kernel': 'linear'}\n",
            "0.760 (+/-0.067) for {'C': 1000, 'kernel': 'linear'}\n",
            "\n",
            "Detailed classification report:\n",
            "\n",
            "The model is trained on the full development set.\n",
            "The scores are computed on the full evaluation set.\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       blues       0.66      1.00      0.79        19\n",
            "      hiphop       0.85      0.85      0.85        20\n",
            "       metal       0.90      0.82      0.86        22\n",
            "      reggae       0.82      0.47      0.60        19\n",
            "\n",
            "    accuracy                           0.79        80\n",
            "   macro avg       0.81      0.79      0.77        80\n",
            "weighted avg       0.81      0.79      0.78        80\n",
            "\n",
            "\n",
            "# Tuning hyper-parameters for recall\n",
            "\n",
            "Best parameters set found on development set:\n",
            "\n",
            "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "\n",
            "Grid scores on development set:\n",
            "\n",
            "0.697 (+/-0.081) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.322 (+/-0.150) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.795 (+/-0.119) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.690 (+/-0.068) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.769 (+/-0.091) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.782 (+/-0.104) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.776 (+/-0.056) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.778 (+/-0.085) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.773 (+/-0.095) for {'C': 1, 'kernel': 'linear'}\n",
            "0.760 (+/-0.065) for {'C': 10, 'kernel': 'linear'}\n",
            "0.763 (+/-0.105) for {'C': 100, 'kernel': 'linear'}\n",
            "0.754 (+/-0.071) for {'C': 1000, 'kernel': 'linear'}\n",
            "\n",
            "Detailed classification report:\n",
            "\n",
            "The model is trained on the full development set.\n",
            "The scores are computed on the full evaluation set.\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       blues       0.66      1.00      0.79        19\n",
            "      hiphop       0.85      0.85      0.85        20\n",
            "       metal       0.90      0.82      0.86        22\n",
            "      reggae       0.82      0.47      0.60        19\n",
            "\n",
            "    accuracy                           0.79        80\n",
            "   macro avg       0.81      0.79      0.77        80\n",
            "weighted avg       0.81      0.79      0.78        80\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86oTW1U9f5nX",
        "colab_type": "text"
      },
      "source": [
        "### Testing: SVC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UQ7zfVmDEoK",
        "colab_type": "text"
      },
      "source": [
        "Then, we can see the best parameter set\n",
        "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V09EekGbz9qk",
        "colab_type": "code",
        "outputId": "5316926c-193c-4751-a3ea-66c160bce04a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        }
      },
      "source": [
        "from sklearn.metrics import plot_confusion_matrix\n",
        "# plot confusion matrix\n",
        "title = \"Normalized confusion matrix\"\n",
        "clf = svm.SVC(C=10.0,kernel ='rbf',gamma= 0.001,)\n",
        "clf.fit(X, Y)\n",
        "disp = plot_confusion_matrix(clf,X_test, y_test,\n",
        "                                 display_labels=genre_analysis_list, # genre names list\n",
        "                                 cmap=plt.cm.Blues,\n",
        "                                 normalize='true')\n",
        "disp.ax_.set_title(title)\n",
        "\n",
        "print()\n",
        "print(disp.confusion_matrix)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[1.         0.         0.         0.        ]\n",
            " [0.         0.85       0.1        0.05      ]\n",
            " [0.13636364 0.         0.81818182 0.04545455]\n",
            " [0.36842105 0.15789474 0.         0.47368421]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAEWCAYAAAA0HB+VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3dd3xV9f3H8dc7CXuEFULYoCAgKiIgLsRFRa1UseKsVK1a665atWot2p9Wa7WtqziKVoviqOLEOhAHKHuLIHuvMEWQ3M/vj3MSbmLGhXtv7g18no/HeeSM7/mez8m9+eR71vfIzHDOObfnMlIdgHPOVXWeSJ1zLk6eSJ1zLk6eSJ1zLk6eSJ1zLk6eSJ1zLk6eSPdRkkZLujQcP1/S+wmuv60kk5SVyHor2KYk/UtSvqSv4qjnGElzEhlbqkhqLWmLpMxUx7I380SaJJIWSlotqU7UvEsljU5hWKUysxfMrF+q40iAo4GTgJZm1mtPKzGzT83sgMSFlRzhd+zE8sqY2WIzq2tmBZUV177IE2lyZQLXxltJ2NLyz6pibYCFZrY11YGkg8o8GtjX+R9ncj0A3CipQWkLJR0pabykjeHPI6OWjZb0J0mfA98B7cND5SslzZW0WdLdkvaT9IWkTZJGSKoert9Q0luS1oSHum9JallGHIMlfRaO3xweChYOP0gaFi7LlvS0pBWSlkm6p/CQUVKmpL9IWitpPnBqeb8YSa0kvRbGt07SI+H8DEm3S1oUtuifk5QdLis8XXCRpMXhtn4fLrsEeAo4Ioz7j9H7FbVdk7R/OH6KpFnh73KZpBvD+X0lLY1ap3P4eWyQNFPS6VHLhkl6VNLbYT1fStqvjH0ujP+XkpaEn8sVknpKmhbW/0hU+f0kfRT+ftZKeqHwuyTp30Br4M1wf2+Oqv8SSYuBj6LmZUlqJGmppJ+GddSVNE/SL8r7rFwMzMyHJAzAQuBE4DXgnnDepcDocLwRkA9cCGQB54bTjcPlo4HFwIHh8mqAAW8A9cP524EPgfZANjALuChcvzEwEKgN1ANeBl6Pim80cGk4Phj4rJR9aAUsB/qH0/8F/gnUAZoCXwGXh8uuAL4O12kEfBzGm1VKvZnAVOChsK6awNHhsouBeeE+1Q1/f/8Ol7UN63wSqAUcEv4OOpe2H6XtV7j+/uH4CuCYcLwh0D0c7wssDcerhfHcBlQHjgc2AweEy4cB64Be4ef0AvBiGd+JwvifCPe5H/A98Hr4+2wBrAaODcvvT3CqogaQA4wBHi75HSul/ufC32utqHlZYZl+wMpwe08Cr6T6b2VvGFIewN46sCuRdgU2hn8I0Yn0QuCrEuuMBQaH46OBISWWG3BU1PRE4HdR0w9G/6GVWLcbkB81PZpyEmn4R1hUP5AbJq1aUWXOBT4Oxz8Croha1o+yE+kRwJoyln0IXBk1fQDwQ5ikCpNCy6jlXwHnlLYfZexXdCJdDFwO1C9Rpi+7EukxYeLJiFo+HLgrHB8GPBW17BTg6zI+g8L4W0TNWwcMipp+FbiujPV/Bkwu+R0rpf72pczLipr3D2A6sIzwH7cP8Q1+aJ9kZjYDeAu4pcSi5sCiEvMWEbRKCi0ppcpVUePbSpmuCyCptqR/hofImwhaMw0U+9Xbp4E5ZvbncLoNQetsRXgIuoGgddo0an+i4y25b9FaAYvMbGcpy0r+XhYRJNHcqHkro8a/I9znPTCQIPEtkvSJpCPKiGeJmUVKxBT9Oe1uPLF+hrmSXgxPO2wCngeaVFA3lP69iTaU4B/8MDNbF0N9rgKeSCvHH4BfUfyPbzlBcorWmqCVUCierrl+S9CaO9zM6gN9wvmqaEVJtwAdgUuiZi8haJE2MbMG4VDfzA4Ml68gSJCFWpeziSVAa5V+MaTk76U1sJPiySZWWwlObQAgqVn0QjMbb2YDCP4ZvA6MKCOeVip+sa/k55Qs/0fwHTgo/AwvoPjnV9b3o8zvTfiPdCjB4f+VheeLXXw8kVYCM5sHvARcEzX7HaCjpPPCCwGDgC4ErddEqEfQutkgqRFBMq+QpP5hnGeY2baofVgBvA88KKl+eFFoP0nHhkVGANdIaimpIT9ugUf7iiDx3iepjqSako4Klw0HrpfUTlJdgmTyUhmt14pMBQ6U1E1STeCuqP2sruD+2Wwz+wHYBERKqeNLglbmzZKqSeoL/BR4cQ/i2V31gC3ARkktgJtKLF9FcC55d9xGkGgvJrgY+txuHKW4MngirTxDCC4AABAeUp1G0HJcB9wMnGZmaxO0vYcJznOuBcYB78W43iCC87mztevK/RPhsl8QXHCZRXBh7BUgL1z2JDCKIHlNIrhIVCoL7mn8KcHFlMXA0nC7AM8A/yY4FbGA4GLM1THGXnI73xD83j8A5gKflShyIbAwPGy+Aji/lDp2hLH2J/hdPgb8wsy+3pOYdtMfge4E59jf5se/03uB28NTLTdWVJmkw4AbCOIvAP5MkFTL+6fnYqDw5LNzzrk95C1S55yLkydS59w+Q9Iz4YMeM8pYLkl/Dx9UmCapeyz1eiJ1zu1LhgEnl7O8P9AhHC4DHo+lUk+kzrl9hpmNAdaXU2QA8JwFxhHce51XTnkguNF5n6WsWqbq9VIdRsId2rm8Wzidq1yTJk1ca2Y5e7p+Zv02Zju3VVwQsG1rZhLc6VFoqJkN3Y3NtaD4Aw1Lw3kryltp306k1etR44CzUx1Gwn3+5SMVF3KuktSqpvKecquQ7dwW89/p91Me/d7MesSzvT2xTydS51xVIKi8XiSXUfwJvZbE8BSbnyN1zqU3ARmZsQ3xGwn8Irx63xvYGD7VVy5vkTrn0p8q7CIixmo0nKB3ryZhn7N/IOiMBzN7guDR7VMIuk78DvhlLPV6InXOpbnEHdqb2bkVLDfgN7tbrydS51z6S1CLNFk8kTrn0puozItNe8QTqXMuzclbpM45F7fEXJFPGk+kzrk0V6n3ke4RT6TOufQm/NDeOefi5i1S55yLhx/aO+dcfARk+sUm55yLj58jdc65ePihvXPOxc9bpM45FydvkTrnXBzkj4g651z8/BFR55yLh19scs65+KX5oX16p/m9wD/uOJ9vRt3LFy/elupQEu6DL2bRc+AQup9xFw8Nez/V4STU3rpvVXK/CvsjjWVIkZRtWVJbSTNKmT9aUqW/TjVZhr81jrOueTTVYSRcQUGEm+4fwct/u5JxI27n1fcn8vX8Ct8RViXsrftWdfdLnkj3dV9M/pb8Td+lOoyEmzhzIe1bNaFtyyZUr5bFmSd1551PpqU6rITYW/etSu9X5b1FdM/CS9mWA1mSXpA0W9IrkmpHL5S0JWr8LEnDwvEcSa9KGh8OR4Xzj5U0JRwmS6pXqXuzD1mxZiMtchsWTTfPbciKNRtTGFHi7K37VqX3q/AWqIqGFEl1Ij0AeMzMOgObgCtjXO9vwENm1hMYCDwVzr8R+I2ZdQOOAbYlOF7nXGVT+h/ap/qq/RIz+zwcfx64Jsb1TgS6aNd/oPqS6gKfA3+V9ALwmpktLbmipMuAywCoVjeO0PdteTnZLFuVXzS9fFU+eTnZKYwocfbWfavS++VX7ctluzFdM2o8A+htZt3CoYWZbTGz+4BLgVrA55I6/WiDZkPNrIeZ9VBWrUTswz6pe5c2fLt4DYuWrWXHDzt57X+T6N/n4FSHlRB7675V5f2SFNOQKqlukbaWdISZjQXOAz4Dfhq1fJWkzsAc4Axgczj/feBq4AEASd3MbIqk/cxsOjBdUk+gE/B1Je1LqZ66ZzBHHdaBxg3qMuOtu7lv6Ds8P3JsKkNKiKysTO6/+WwGXvMoBQXG+af3pvN+eakOKyH21n2rqvsVvGkkvVukMivZCKykDUttgfeACcBhwCzgQuAd4EYzmyDpLODPwJqwXF0zGyypCfAo0Jngn8EYM7tC0j+A44AIMBMYbGbby4oho3ZTq3HA2Unaw9TJH/9IqkNwrkitappoZnt8S2Nmo3ZW68Q/xFR268u/jGtbeyplLVIzW0jQYiypb1SZV4BXSll3LTColPlXJy5C51y6SPcWaaoP7Z1zrkKeSJ1zLk6eSJ1zLh4KhzTmidQ5l9ZEam9tioUnUudc2svISPUt7+XzROqcS3veInXOuXj4OVLnnItfurdI0/vEg3Nun1d4sSlRz9pLOlnSHEnzJN1SyvLWkj4Ou+KcJumUiur0ROqcS3vKUExDhfVImQSPl/cHugDnSupSotjtwAgzOxQ4B3isono9kTrn0psS2vtTL2Cemc03sx3Ai8CAEmUMqB+OZwPLK6rUz5E659LebpwjbSJpQtT0UDMbGjXdAlgSNb0UOLxEHXcB70u6GqhD0P9xuTyROufS3m4k0rUJ6P3pXGCYmT0o6Qjg35K6mlmkrBU8kTrn0lqCn2xaBrSKmm4Zzot2CXAygJmNlVQTaAKsLqtSP0fqnEt/inGo2Higg6R2kqoTXEwaWaLMYuAEgLBj+ZoEfSKXyVukzrn0psQ9ImpmOyVdBYwCMoFnzGympCHABDMbCfwWeFLS9QQXngZbBT3geyJ1zqW9RN6Qb2bvELyJI3renVHjs4CjdqdOT6TOufSX3g82eSJ1zqW/dH9E1BOpcy6tpfpVy7HwROqcS3ueSNPYoZ1b8/mXe9+ri3MueDbVISTN7MfPSXUISVG7emaqQ0hrsTxHn0r7dCJ1zlUN3iJ1zrl4yBOpc87FRUCa51FPpM65dOdX7Z1zLm4ZfrHJOefiID+0d865uAhvkTrnXNy8Reqcc3Hyi03OORcPP0fqnHPxEUpYx87J4onUOZf2vEXqnHNx8nOkzjkXDz9H6pxz8QmetU/vTOqJ1DmX9tI8j3oidc6lP3+yyTnn4uH9kTrnXHy8P1LnnIub90fqnHNxS/M86onUOZfm5BebnHMuLn4fqQPggy9mceuDr1AQiXDhgCO5fnC/VIcUk+MPbs6fLuxFZoZ4fvRc/v7mjGLLWzSuwyOXH0V27epkZIh7XprEB1OX0apJHT6//2d8u2ITABPmreGmf41LxS7EZMxXX3PPI69TEIlw9imHc/l5JxRb/tXUb/nTo28wZ/4KHrrjAvofe0iKIq3Yx+Nmc+ffXiMSMc49rTdXXXhiseXbd+zk2nueZ/qcpTSsX5vHh1xEq7zGLFmxjr7n30f71jkAdD+wLX++6exU7EKpPJEmkKRuQHMze6eCcn2BG83stEoJrBwFBRFuun8E/33kKprnNuD4ix6gf5+D6NQ+L9WhlStD4r6LevPz+95n+frveH/Iqbw3cQnfLN9YVOaGAQfzxpeLGPbhHDo2z2b4TSdy2PWvArBw1WaO+/2bqQo/ZgUFEe7622sMe+BymuVkM/DXD3P8kQfSoW2zojLNcxvy59+dw9MjRqcu0BgUFET4/V9fYfhDvyavaQNOufSv9Du6Kx3b7dqX4W+NI7tebT5/6Xbe+GASf3r8TZ4YMhiANi0a879hN6co+vKleR4lvfum+rFuwCmpDmJ3TJy5kPatmtC2ZROqV8vizJO6884n01IdVoW679eEhas2sWjNFn4oiPD6uAX0P6xVsTKGUa9WNQDq167OyvzvUhFqXKZ9vZg2LRrTunljqlfL4tTjD+XDL2YWK9OyWSM67dccpfl5usmzF9G2ZRPatAi+awNOPJRRn00vVub9z6bz8/49ATi17yF8NnEuZpaKcHeLpJiGVKn0RCqpraSvJQ2T9I2kFySdKOlzSXMl9ZJUR9Izkr6SNFnSAEnVgSHAIElTJA0Ky44Ny3wh6YDK3p+KrFizkRa5DYumm+c2ZMWajeWskR7yGtZm2fqtRdPL139HXsM6xco88NpUzjqqPVP/fhbDbzqBW5/7smhZ65y6fHTPabzx+5/Q+4CmlRb37lq5diN5TRsUTTdrks2qKvD5lGblmo00b7rru5aX04CVJfYlukxWVib169Qkf2PwOS9esZ5+v3yAgVf9gy+nflt5gVck7LQkliFVUnVovz/wc+BiYDxwHnA0cDpwGzAL+MjMLpbUAPgK+AC4E+hhZlcBSKoPHGNmOyWdCPwfMLC8DUu6DLgMoFXr1knYtX3HGUe048Ux83j83Vn02D+Hx359DMfc8garNmzj0OteJX/Ldg5u24jnrj+eo295gy3bfkh1yK4MTRtn89Wrf6BRdh2mfb2Ei297mo//fQv16tRMdWhhx87pfTSQqkP7BWY23cwiwEzgQwuOL6YDbYF+wC2SpgCjgZpAaVkvG3hZ0gzgIeDAijZsZkPNrIeZ9chpkpOQnSlPXk42y1blF00vX5VPXk520rcbrxX539Gi0a4WaPNGtVmRv7VYmfOP7cAbXy4EggtKNapl0rheTXbsjJC/ZTsA0xauZ+HqzezXrH6lxb47mjXJZsXqDUXTK9duJLcKfD6laZaTzfLVu75rK9ZsoFmJfYkus3NnAZu2fk/D7DrUqJ5Fo+zg8z64UyvaNm/M/CWrKy/4CmRIMQ0piy9F290eNR6Jmo4QtJIFDDSzbuHQ2sxml1LP3cDHZtYV+ClBwk0r3bu04dvFa1i0bC07ftjJa/+bRP8+B6c6rApNnr+Wds3q0zqnLtUyM/hZ73a8N2lpsTLL1m2hz4HBRbMOzbOpWS2TtZu+p3G9GkVf6jY5dWmfW59FqzdX+j7E4qBOrVi4bC1LVqxjxw87efujyZxwRIX/j9NSt06tWbBkLYuXB/vyxgeT6XdU12Jl+h3VlZffHQ/A26OnclT3DkhiXf4WCgoiACxatpYFS9fSunnjSt+HsiTy0F7SyZLmSJon6ZYyypwtaZakmZL+U1Gd6XrVfhRwtaSrzcwkHWpmk4HNQL2octnAsnB8cCXHGJOsrEzuv/lsBl7zKAUFxvmn96bzful9xR6gIGLc+uyXjLj5RDIyMhj+yVzmLNvA7wZ2Y8qCdYyatIQ7X5jAQ5ceyeUndwHg6n9+DsARnXL53cBD2VkQIWLGjf8ay4atO1K5O2XKyszkD1efycW/G0pBgXFW/150aNeMh//1Hgd1bMkJR3Vl2teLufLOYWzaso2Px87i78NG8e6/0u/qdlZWJvfcMJDzbniCSCTCoFMP54D2eTzw1Dsc0qk1/Y7uyjmn9eaau5/nqEH30KB+bR676xcAjJv6LX956l2ysjLIyMjg3ht/TsP6dSrYYuVQAjstkZQJPAqcBCwFxksaaWazosp0AG4FjjKzfEkVnuRXZV+xk9QWeCtsRSJpWDj9SuEyoCfwMHAkQat5gZmdJqkRQZKtBtwLLAaeBbYCbwMXmFnbWG9/OuywHvb5lxMSvIepl3PBs6kOIWlmP35OqkNIitrVM1MdQtI0rlttopn12NP1s9t0tiNvGRZT2feu7F3utiQdAdxlZj8Jp28FMLN7o8rcD3xjZk/FGmOZLVJJ/wDKzLJmdk2sGymx3kKga9T04DKWXV7KuusJkmy0jlHjt4flRhOcW3XO7QV242JTE0nRraOhZjY0aroFsCRqeilweIk6OgJI+hzIJEi875W30fIO7fe+pppzrsoRwZX7GK2Np/UbygI6AH2BlsAYSQeZ2YbyViiVmRU7PpRU28yq3h3XzrkqL4F3Py0Dop8sacmu6yyFlgJfmtkPwAJJ3xAk1vFlxlfRViUdIWkW8HU4fYikx3YzeOec2zMxPtUU4wWp8UAHSe3Ch3zOAUaWKPM6QWsUSU0IDvXnl1dpLLc/PQz8BFgHYGZTgT6xROycc4mQqNufzGwncBXBRevZwAgzmylpiKTTw2KjgHVhA/Jj4CYzW1devTHd/mRmS0pk+4JY1nPOuXgJEnqzfdjp0Tsl5t0ZNW7ADeEQk1gS6RJJRwImqRpwLUEmd865SrE3PCJ6BfAbgtsGlhP0wPSbZAblnHOFYj2sT+tOS8xsLXB+JcTinHOlSuVz9LGI5ap9e0lvSlojabWkNyS1r4zgnHMOCu8lrXhIlVgO7f8DjADygObAy8DwZAblnHPR9oaOnWub2b/NbGc4PE8a9rLknNs7BVftYxtSpbxn7RuFo++GXU29SPDs/SBK3DrgnHNJo/Tv2Lm8i00TCRJn4R5EdyJiBN1MOedc0lXZt4iaWbvKDMQ550pTeGifzmJ6sklSV6ALUedGzey5ZAXlnHPRqmyLtJCkPxA8wN+F4Nxof+AzwBOpc65SpHcaje2q/VnACcBKM/slcAjBKz6ccy7pJMjMUExDqsRyaL/NzCKSdoavP15N8f78nHMuqar8oT0wIXy3/JMEV/K3AGOTGpVzzkVJ8zwa07P2V4ajT0h6D6hvZtOSG5ZzzgVEat9ZH4vybsjvXt4yM5uUnJCccy5Kint2ikV5LdIHy1lmwPEJjqXS7SiIsGz9tlSHkXBrnr8o1SEkTctLX0x1CEkx97GfpzqEtFZlz5Ga2XGVGYhzzpVGQGZVTaTOOZcu9oonm5xzLpU8kTrnXByC14ikdyaNpYd8SbpA0p3hdGtJvZIfmnPOBdK9P9JYHhF9DDgCODec3gw8mrSInHOuhCr/8jvgcDPrLmkygJnlS6qe5Liccw4IrtpnpfmhfSyJ9AdJmQT3jiIpB4gkNSrnnIuS5nk0pkT6d+C/QFNJfyLoDer2pEblnHMhqQo/IlrIzF6QNJGgKz0BPzOz2UmPzDnnQmmeR2Pq2Lk18B3wZvQ8M1uczMCcc67Q3nAf6dvsegleTaAdMAc4MIlxOeccED4imuaZNJZD+4Oip8Neoa4so7hzziVWiu8RjcVuP9lkZpMkHZ6MYJxzrjRK87c2xXKO9IaoyQygO7A8aRE551yUveV1zPWixncSnDN9NTnhOOfcj1XpRBreiF/PzG6spHicc+5H0r3TkvJeNZJlZjslHVWZATnnXLTgdcypjqJ85YX3VfhziqSRki6UdGbhUBnBOeccQEb4dFNFQywknSxpjqR5km4pp9xASSapR0V1xnKOtCawjuAdTYX3kxrwWkxRO+dcHBJ5sSk8XfkocBKwFBgvaaSZzSpRrh5wLfBlLPWWl0ibhlfsZ7ArgRay3YjdOefiksBTpL2AeWY2P6hXLwIDgFklyt0N/Bm4KZZKyzu0zwTqhkO9qPHCwTnnKoHIiHEAmkiaEDVcVqKyFsCSqOml4bxdWwseOmplZm/HGmF5LdIVZjYk1or2ZZ+N/5r7nhhJQUGEgf17cemg4m+qnjB9Pn9+YiTfzF/BA7edT79jDi62fMvW7xlw2V84/ogD+f1VZ1Rm6HH54ItZ3PrgKxREIlw44EiuH9wv1SHFrO+Bzfjjud3JzBDDP53Po+8W74eneaPaPHzx4dSvXZ3MDHHvq1P5aPoKjumSy60DD6F6ZgY7CiLc8/IUvvh6dYr2IvDxuNnc8fBrRCIRzv1pb66+8KRiy7fv2Mk1dz/P9DlLaJhdhyeGXESrvMZFy5euXE/fC+7ltxf359fnBd/dXgP/SN3aNcjIyCArM4P3nkndjTtit1qka82swnOaZW5LygD+CgzenfXKS6RxN6YltQXeMrOuJeYPAcaY2QflrDssXPeVeONIpoKCCPc8+l+evPcymjXJZtDVf+e43geyX5vcojJ5OQ2457dnM+yVT0qt4x/PjeKwru0qK+SEKCiIcNP9I/jvI1fRPLcBx1/0AP37HESn9nmpDq1CGRL3nN+D8/76MSvyt/H27Sfx/pRlzF2xqajMtaceyJsTlvDv0fPokFef5649liNueZP1m7fzy7+PYdXG7zmgeTYvXH8sPW4ambJ9KSiIcNuDL/Piw1eS17QBp1z6ID85+iA6tmtWVGb4W2NpUK8WX4y4g9c/mMQ9j73JP+8eXLT8j/94neN7d/lR3S//4yoaN0iDg09BVuJuJF0GtIqabhnOK1QP6AqMDm+5agaMlHS6mU0oq9LyDu1P2PNYy2dmd5aXRKuS6XMW07p5E1rlNaZatSz69+3GR2NnFivTolkjDmjfnIxSvgwz5y5lXf5mjjysY2WFnBATZy6kfasmtG3ZhOrVsjjzpO6888m0VIcVk27tGrFw9WYWr93KDwUR3vhqMf26FTu6wzDq1QzaGfVqVWPVhm0AzFyygVUbvwdgzvKN1KyeSfWs1N2bM3n2Itq2zKFNi+BzGHBCd0Z9Or1YmVGfzuDnpwSvWTut7yF8NvEbzILLHO+OmUarvMbFEm+6KWyRJuhVI+OBDpLahW/6OAco+k9oZhvNrImZtTWztsA4oNwkCuUkUjNbH1NYFcuU9KSkmZLel1RL0jBJZwFIWijpfknTJX0laf+odftI+kLS/KjykvSApBnhOoPC+X0ljZH0dnhrwxNhMz2pVq/bRLOcBkXTuU2yWb12Y0zrRiIRHhj6Jjf+6rRkhZc0K9ZspEVuw6Lp5rkNWbEmtv1OtbyGtViR/13R9Mr8beQ1rFWszF9HzuDM3m0Zf//pPHftsdwxfOKP6jn1sJZMX5TPjp2pe2HEyjUbad501/cvr2mDH30OK9dsoHnT4LPKysqkfp2arN+4la3fbeex5z/ktxef/KN6JTj3+sf5ycUP8PwbXyR3J2KQqNufzGwncBUwCpgNjDCzmZKGSDp9T+OrjNcxdwDONbNfSRoBDCylzEYzO0jSL4CHgcLMkgccDXQi+K/xCnAm0A04BGhCcPvCmLB8L6ALsAh4Lyxb7NRAePL5MoDmLaNb+JXvxTfH0qdnp2KJ2KWHAb3aMOKLBQx9fw7d2zfmb5f05oQ/vEvYkKNj8/rcOrAb5z80OqVxxuMvz7zLrwb1pU7tGj9a9vrj15KX04C1+Zs557rH2L9NU3p327+UWipHIh9sMrN3gHdKzLuzjLJ9Y6mzMhLpAjObEo5PBNqWUmZ41M+Houa/bmYRYJakwpOORwPDzawAWCXpE6AnsAn4Kuq2huFh2WKJ1MyGAkMBDurWPe7buJo2rs/KNRuKplet3UjTJtkxrTt19iImzljAi2+N5btt2/lhZwG1a9Xg+ktOiTespMvLyWbZqvyi6eWr8snLiW2/U21F/jbyGtYumm7WsBYr8rcVK3PO0e254OHgnPak+euoUS2TRnVrsG7zdvIa1uKpK4/mumfGsWjNlkqNvaRmOdksX73r+7di9YYffQ7NchqwfHU+zYhPQD0AABb0SURBVJs2YOfOAjZt/Z5G2XWYPHMRb388lXseG8mmLdvIkKhRPYuLz+pDXvjPvUnDepzc52Amz1qcskQqYnvdcSpVRiLdHjVeANQqpYyVMR69biz/k0omxqTf79r1gFYsXraWpSvXk9u4Pu+OnsL9t5wX07p/jir3+vvjmfnN0iqRRAG6d2nDt4vXsGjZWvKaNuC1/03iybsHpzqsmExduJ52ufVo1aQOK/O3MaBXa656cmyxMsvXb+Xozrm8/MUC9s+rT41qmazbvJ36tarx7DV9uPe1aUyYtzZFe7BLt06tWbB0DYuXr6NZTjZvfDiJR//wi2Jl+h3dlZff+YoeXdvx1uipHH1YByTx+uPXFpX5y9PvUqdWDS4+qw/fbdtOJGLUrVOT77Zt55OvvuaGX/748L/SiKr/zqZKMgi4L/w5toKynwKXS3oWaAT0IbhpthPQS1I7gkP7QYQtz2TKyszktt/8jMtve5KCSIQz+vVi/7bNeOTZURzYsSXHHXEg0+cs4bohz7Jp83eMHjebR597nzeerNr9wGRlZXL/zWcz8JpHKSgwzj+9N533S/8r9gAFEeOO/0zkheuOJSMjg5c+n883yzdx44CuTF24nv9NXc6QEVO4/6Ke/OqkjpjBDc8ED7gMPr4DbZvW47rTDuS604KXRJz30GjWbd5e3iaTJisrkz9dP5DzbnicgoII55zWmwPa53H/k+9wSKdW/OSYgzj3tN5cc/fzHHn23TSoX5vH/3hRuXWuWb+ZS257GoCdOyOc0e8wjuvduTJ2p1TBk03pnUhVePUuKZWXuP1J0o0EN/MXzn9F0kLgJaA/QQv0XDObV/L2J0lbzKyugnsS7g/LG3CPmb0kqS8wBNgM7A98DFwZnhoo1UHdutvr73+e6N1OuRaNSmv07x1aXvpiqkNIirmP/TzVISRNozpZE+O5t7N9l4Pt7n+/U3FB4IIereLa1p5KaovUzBYS3JNVOP2XMoo+YGa/K7Hu4BLTdcOfRtACLe3RrU1mVvUugTvnypXmDdK0ObR3zrkyqOr2R1pZwpteE1HPaGB0IupyzqUPv2rvnHMJkO4XmzyROufSm6rwq0accy4d+KG9c84lgLdInXMuTumdRj2ROufSnIBMb5E651x80jyPeiJ1zqU7oTQ/uPdE6pxLe94idc65OAS3P6V3JvVE6pxLb7G/jyllPJE659KePyLqnHNxCDp2TnUU5fNE6pxLe37V3jnn4pTmR/aeSJ1z6c9bpM45Fwc/R+qcc/GS/Kq9c87FK73T6D6eSLds38lni9akOoyEO7ygcapDSJqlT52T6hCS4s735qQ6hLRVFd5rv08nUudc1ZDeadQTqXOuKkjzTOqJ1DmX9vzQ3jnn4pTeadQTqXOuKkjzTOqJ1DmX1oQ/2eScc/GpAv2RZqQ6AOecq4hiHGKqSzpZ0hxJ8yTdUsryGyTNkjRN0oeS2lRUpydS51yaE1JsQ4U1SZnAo0B/oAtwrqQuJYpNBnqY2cHAK8D9FdXridQ5l/ak2IYY9ALmmdl8M9sBvAgMiC5gZh+b2Xfh5DigZUWVeiJ1zqW1WA/rwzzaRNKEqOGyEtW1AJZETS8N55XlEuDdimL0i03OufQX+8WmtWbWIyGblC4AegDHVlTWE6lzLu0l8PanZUCrqOmW4bzi25NOBH4PHGtm2yuq1A/tnXNpL4HnSMcDHSS1k1QdOAcYWXxbOhT4J3C6ma2OpVJvkTrn0lsC7yM1s52SrgJGAZnAM2Y2U9IQYIKZjQQeAOoCL4d3Aiw2s9PLq9cTqXMu7SXyySYzewd4p8S8O6PGT9zdOj2ROufSmkj/J5s8kTrn0l6a51FPpM65KiDNM6knUudc2vOOnZ1zLk7pnUY9kTrnqoI0z6SeSBNg5oz5jHjpQyIR46ijD+bk/r2LLR/zyWRGfzyZjIwMatSoxvkX/oTmzZvw5Zcz+d+o8UXlli1bzW23X0SrVrmVvQul+nzCHB7450giEeNnP+nJxWcfV2z5xOnz+cvQN5m7YCX33nIuJx19cNGyFavzGfK3V1m1dgMgHhnyS5rnNqrkPdhzH3wxi1sffIWCSIQLBxzJ9YP7pTqkmCyau4gxb4/BzOhyWBd69Cn9acl5M+fx7ovvcvYVZ5PbIpc5U+cw6bNJRcvXrlrLOb8+h5y8nMoKvUzesfM+IBKJMPw/H3Dt9WfTsGE97v2/5zj4kP1p3rxJUZmevbrQ59hDAZg6ZS6vvPwx11z7cw4//EAOP/xAAJYtXcPjj/03bZJoQUGE+x57ncf/dCm5TbI5/7pHOLZ3F/ZrvSu+vKYN+OMNZ/Pcq2N+tP4dD47g0kHH0bt7R77btj2mLs7SRUFBhJvuH8F/H7mK5rkNOP6iB+jf5yA6tc9LdWjlikQijH5zND8b/DPq1q/LS0+8RPtO7WnUtPg/sB3bdzB17FRyW+76LA845AAOOOQAANauXMvb/3k7LZIo4B07F1Jgr3wcdeGCFTRt2oCcnAZkZWXSs2dnpk2dV6xMrVo1isZ37Pih1P+t48fPpkfPTkmONnYzvllCq+aNaZnXmGrVsvhJn0MYPXZWsTLNcxvRsV0eGRnF9+jbxasoKIjQu3tHAGrXqkGtmtUrLfZ4TZy5kPatmtC2ZROqV8vizJO6884n01IdVoVWLV1Fg8YNyG6UTWZWJh0P6sj82fN/VG7ch+Pofkx3srJKb0d9M/0bOh7UMdnh7pZEduycDElLbpLahr1QPwfMAO6QND7sdfqPUeXuCMt9Jmm4pBvD+T3DslMkPSBpRlS9n0qaFA5HRtV1U2nbSKb8DVto2Khe0XSDBvXIz9/8o3KjP57E7bcN5bVXP+Hsc0740fIJ47+mZ6/OSY11d6xet5HcJg2KpnObZLNm3caY1l28dC316tTkt/c8xzlX/Y2Hnn6bgoJIskJNuBVrNtIit2HRdPPchqxYE9u+p9LWTVupm123aLpudl22bN5SrMzq5avZsnEL7Q5oV2Y9c6fPpePB6ZRIE9exc7Iku5XYAXgMuJ6gz79eQDfgMEl9JPUEBgKHEPRYHX1C51/A5WbWDSiImr8aOMnMugODgL8DSOoXbq/YNpK4b7ul73Hduef/LuOMM4/l3XfGFlu2YP5yqlfPokWLNDmUitPOSAGTZy7g+ktO5fm/XcXSFesZ+cGEVIe1z7OI8dm7n3H0yUeXWWblkpVUq1aNxrmNKzGyiiWw05KkSHYiXWRm44B+4TAZmAR0Ikh6RwFvmNn3ZrYZeBNAUgOgnpkVZpz/RNVZDXhS0nTgZYLXBVDONoqRdFlhp6+bN6yPewcbNqhL/vpdLdANGzbTsGG9Msv36NmZKZPnFps3fvzstGqNAjRtnB1eKAqsWruRnMbZMa2b2ySbju2b0zKvMVmZmRx3xIF8PW95skJNuLycbJatyi+aXr4qn7yc2PY9lerUr8OWjbtaoFs2bqFuvV0t1B07drBu9Tpee+Y1hj04jJVLV/L2C2+zatmqojJzp8+lw8E/+rNJqd3s2Dklkp1It4Y/BdxrZt3CYX8ze3oP67weWEXQiu0BFJ58i2kbZjbUzHqYWY96DeK/itymbR6rV+ezdu0Gdu4sYPz42Rx8yP7FyqxatSthz5j+LU2jDhsjEWPixDn06JleifTAji1ZvHwdy1au54cfdjJqzFT69o4txgM7tGLz1u9ZH/5Rj586j/atmyYz3ITq3qUN3y5ew6Jla9nxw05e+98k+vc5uOIVUyy3RS4b1m1gY/5GCnYW8M30b2jXadchfI2aNfjVrb9i8G8HM/i3g2nWshmnnn8quS2Ci04WMebOmJt250eBtM+klXXVfhRwt6QXzGyLpBbAD8DnwD8l3RvGchow1Mw2SNos6XAz+5Kgz8BC2cBSM4tIuoigK6wytxFrf4J7KjMzg0HnnsjfH36ZSMQ48qiDaN68CSPf+JQ2bZpxSLcOjP54Ml/PXkhmZia1a9dg8C9PLVp/7twlNGpYj5ycBuVspfJlZWbyu18P4MrbnyYSiTCgX0/2a9OMx/79Pl06tKRv7y7M/GYJN9z9HJu2bGPMl7N54vn/8eoTvyUzM4MbLjmFK259EjPo3KEFZ57cK9W7FLOsrEzuv/lsBl7zKAUFxvmn96bzful9xR4gIzODY087lpHPjiQSidClexca5zZm3IfjaNq8Ke07ty93/WWLllE3uy7ZjdKv9Z3utz/JzJJTsdQWeMvMuobT1wKXhou3ABeY2beS7gLOI2hlrgbeM7MnJR0OPAlEgE8I3up3lKQOwKuAAe8BvzGzuuVto6wY23U+2O567q3E7XSaOLxlep3fSqS2OXVSHUJS3PnenFSHkDQP/LTTxHhe/3Fwt8PsrY++iKlsm8Y149rWnkpai9TMFgJdo6b/BvytlKJ/MbO7JNUGxgATw/kzw9ehEr57ekJYz1wg+jjrdzFswzlXVQky0rtBmhY35A8N3ytdE3jWzAofrzhV0q0EMS4CBqcoPudcyqV3Jk15IjWz88qY/xLwUiWH45xLM96xs3POJUCa51FPpM659OctUueci1O6d3rjidQ5l/bSO416InXOpblUP0cfC0+kzrm0l+5PNnkidc6lv/TOo55InXPpL83zqCdS51y6k7+O2Tnn4lEVnmzaK9+j5JxzlclbpM65tJfuLVJPpM65tOe3PznnXDz8hnznnItPVbjY5InUOZf2/NDeOefilO4tUr/9yTmX9hL5NmZJJ0uaI2le+D64kstrSHopXP5l+CLPcnkidc6lvwRlUkmZwKNAf6ALcG74zrholwD5ZrY/8BDw54rq9UTqnEtrAjKkmIYY9ALmmdl8M9sBvAgMKFFmAPBsOP4KcIIq6Fl6nz5HuvDr6WsH92qzqJI21wRYW0nbqmx76775fiVGm3hWnjRp4qha1dQkxuI1JU2Imh5qZkOjplsAS6KmlwKHl6ijqIyZ7ZS0EWhMOb+zfTqRmllOZW1L0gQz61FZ26tMe+u++X6lBzM7OdUxVMQP7Z1z+5JlQKuo6ZbhvFLLSMoCsoF15VXqidQ5ty8ZD3SQ1E5SdeAcYGSJMiOBi8Lxs4CPzMzKq3SfPrSvZEMrLlJl7a375vu1lwnPeV4FjAIygWfMbKakIcAEMxsJPA38W9I8YD1Bsi2XKki0zjnnKuCH9s45FydPpM45FydPpHtIUltJM0qZP1pSlbm1JFEkdZN0Sgzl+kp6q5JiKuszGiLpxArWHSbprORF5/YmfrHJJUo3oAfwTqoDqYiZ3ZnqGBIlfOJGZhZJdSz7Mm+RxidL0guSZkt6RVLt6IWStkSNnyVpWDieI+lVSePD4ahw/rGSpoTDZEn1KnNnwhbc12Fr7Jtw306U9LmkuZJ6Saoj6RlJX4UxDghvIxkCDApjHxSWHRuW+ULSAZW5L1EyJT0paaak9yXVim5tSloo6X5J08N92j9q3T5h7POjykvSA5JmhOsMCuf3lTRG0tthhxhPSErK31f4Oc2R9BwwA7gj/B5Nk/THqHJ3hOU+kzRc0o3h/J5h2SmF+xJV76eSJoXDkVF13VTaNlzIzHzYgwFoCxhwVDj9DHAjMBroEc7bElX+LGBYOP4f4OhwvDUwOxx/M6q+ukBWCvZpJ3AQwT/ZieF+ieD549eB/wMuCMs3AL4B6gCDgUei6qpfGD9wIvBqON4XeKuS96dbOD0CuAAYBpwVzlsI/D4c/0VhbGGZl8PfQxeC57MBBgL/I7h1JhdYDOSF+/U90D5c9r/CbSRpvyJAb6Afwe1MCmN9C+gD9ASmADWBesBc4MZw/RnAEeH4fcCMcLw2UDMc70BwOxBlbSPVf4PpNPihfXyWmNnn4fjzwDUxrnci0CWqH4T6kuoCnwN/lfQC8JqZLU1otLFZYGbTASTNBD40M5M0neAPuCVwemHrhuAPtXUp9WQDz0rqQPAPp1rSIy/dAjObEo5PJNiHkoZH/Xwoav7rFhwyz5KUG847GhhuZgXAKkmfECStTcBXZjYfQNLwsOwridyZKIvMbJykvxAkusnh/LoESbAe8IaZfQ98L+nNMK4GQD0zGxuW/w9wWjheDXhEUjegAOgYzu9XxjbGJGnfqhxPpPEpeRNuedM1o8YzgN7hlzzafZLeBk4BPpf0EzP7OjGhxmx71HgkajpC8H0pAAaa2ZzolSSV7PjhbuBjMztDQX+Oo5MRbAyi96cAqFVKGStjPHrdWLoWquj7kEhbw58C7jWzf0YvlHTdHtR5PbAKOITgO1r4/Sx1G24XP0can9aSjgjHzwM+K7F8laTO4bmyM6Lmvw9cXTgRtgCQtJ+ZTTezPxM8ytYpeaHvsVHA1eFFDiQdGs7fTNAKKpTNrmeYB1dadHtmUNTPseUVBD4lOBecKSmH4DD6q3BZLwWPHmaEdZX8PiTDKODi8IgGSS0kNSU4uvmppJrhstMAzGwDsDnqH1/0UzvZwIqwFX4hwSmK8rbhQp5I4zMH+I2k2UBD4PESy28hOJ/0BbAiav41QI/wxP0s4Ipw/nXhRYxpwA/Au0mNfs/cTXAIOC089L87nP8xwemKKeEFmPuBeyVNJv2PfBqGv/NrCVpl5fkvMA2YCnwE3GxmK8Nl44FHgNnAgrBsUpnZ+wSH52PD0y+vEBy6jyd4ZnwawfdoOrAxXO0S4ElJUwjObxfOfwy4SNJUgn/iW8vbRrL3rSrxR0TdPk3SQoKLg3H1zympL8HFnNMqKltZJNU1sy0K7iYZA1xmZpMK54dlbgHyzOzalAZbxaV7S8E5t+eGKniNRk3gWTObFM4/VdKtBH//i0j/Uy9pz1ukzjkXJz9H6pxzcfJE6pxzcfJE6pxzcfJE6solqSC8pWmGpJdVoj+B3awr+hn3p/Tj94lHl+0b/az3bmxjofTjN06WNb9EmS3lLS+l/F1RT3i5fZgnUleRbWbWzcy6AjvYdc8rUPRysN1mZpea2axyivQFdjuROpcKnkjd7vgU2D9sLX4qaSTBc+iZYS9Chb0DXQ5FPSU9EvZA9AFQ9DSMovptlXRy2NvQVEkfho+UXgFcH7aGj1HZPWY1VtCr00xJTxHDo5ySXpc0MVznshLLHgrnfxg+uYSk/SS9F67zqaR0fOLMpZDfR+piErY8+wPvhbO6A13NbEGYjDaaWU9JNQj6CXgfOBQ4gKD3pFxgFkFvUtH15gBPEvQmtEBSIzNbL+kJgt6z/hKW+w/wkJl9Jqk1wWOLnYE/AJ+Z2RBJpxI8tVORi8Nt1ALGS3rVzNYRPOUzwcyul3RnWPdVBD0fXWFmc8NHKx8Djt+DX6PbS3kidRWpFT5KCEGL9GmCQ+6vzGxBOL8fcLB29SifTdA7UB929ZS0XNJHpdTfGxhTWJeZrS8jjrJ6zOoDnBmu+7ak/Bj26RpJhX0ftApjXUfQMctL4fzngdfCbRwJvBy17RoxbMPtQzyRuopsM7Nu0TPChLI1ehZwtZmNKlGuwleP7IZSe8yKSm4xCR/lPJGgP87vJI2meM9c0Szc7oaSvwPnovk5UpcIo4BfS6oGIKmjpDoEz3cX9pSUBxxXyrrjCHqibxeu2yicX7I3qVJ7zAq3cV44rz9B5zHlyQbywyTaiaBFXCiDoANuwjo/M7NNwAJJPw+3IUmHVLANt4/xROoS4SmC85+TFLy24p8ERzv/JeiZfRbwHKV0UWdma4DLCA6jp7Lr0PpN4IzCi02U3WPWHwkS8UyCQ/zFFcT6HsErYmYT9A4/LmrZVoKu8GYQnAMdEs4/H7gkjG8mwdsCnCviz9o751ycvEXqnHNx8kTqnHNx8kTqnHNx8kTqnHNx8kTqnHNx8kTqnHNx8kTqnHNx+n9LDdtpDMnx6gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOAxKOIUSRHA",
        "colab_type": "text"
      },
      "source": [
        "## About Team Work\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4tuPb4xoIWX",
        "colab_type": "text"
      },
      "source": [
        "**0410 Discussion**\n",
        "1. apply preprocessing to audio files\n",
        "2. how to split our dataset to {training, testing} to make sure the difference of the two groups\n",
        "3. check if our dataset is balanced? Actually it is already balanced.\n",
        "4. which set of features can to useful for our cases\n",
        "5. apply feature selection?\n",
        "6. choose which classification method?\n",
        "7. try different parameter for the classfier\n",
        "8. evaluate the model\n",
        "9. display the result\n",
        "**0411 Discussion**\n",
        "1. do some test individually, using different classfiers\n",
        "**0422 Discussion**\n",
        "1. finish HW1: add tempo features(peakA1,A0,beat...)\n",
        "2. start to write Document, do it together\n",
        "3. still need to fix some error\n",
        "4. complete the model evaluation,optimization\n",
        "5. then, write the introduction and final conclusion\n",
        "\n",
        "**0510**\n",
        "1. write document\n",
        "\n",
        "**0514**\n",
        "1. group discussion\n",
        "2. finish final document\n"
      ]
    }
  ]
}