4_5_CM2_RESNET50_80_20_CUSTOM_LR.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "4.5 CM2-RESNET50-80:20-CUSTOM LR.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JeP6eSnypEd",
        "outputId": "67dc037a-b0de-442e-9a9e-077f7d4a8135"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting msgpack-numpy>=0.4.4.2\n",
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            "Building wheels for collected packages: filterpy\n",
            "  Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110474 sha256=cf8cd5d1ba9796986616ac6092ea57dc86cbe2578d180a4ebe236e6e23e4846f\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/e0/ee/a2b3c5caab3418c1ccd8c4de573d4cbe13315d7e8b0a55fbc2\n",
            "Successfully built filterpy\n",
            "Installing collected packages: scikit-image, ruamel.yaml.clib, msgpack-numpy, tf-slim, tensorpack, statsmodels, ruamel.yaml, opencv-python-headless, imgaug, filterpy, deeplabcut\n",
            "  Attempting uninstall: scikit-image\n",
            "    Found existing installation: scikit-image 0.18.3\n",
            "    Uninstalling scikit-image-0.18.3:\n",
            "      Successfully uninstalled scikit-image-0.18.3\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "  Attempting uninstall: imgaug\n",
            "    Found existing installation: imgaug 0.2.9\n",
            "    Uninstalling imgaug-0.2.9:\n",
            "      Successfully uninstalled imgaug-0.2.9\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
            "Successfully installed deeplabcut-2.2.0.3 filterpy-1.4.5 imgaug-0.4.0 msgpack-numpy-0.4.7.1 opencv-python-headless-4.5.4.60 ruamel.yaml-0.17.19 ruamel.yaml.clib-0.2.6 scikit-image-0.18.1 statsmodels-0.13.1 tensorpack-0.11 tf-slim-1.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install deeplabcut"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorflow_version 1.x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2ukI3ptzP85",
        "outputId": "c34f203b-3ab9-4767-89a9-1197b831a15d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow 1.x selected.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"DLClight\"]=\"True\""
      ],
      "metadata": {
        "id": "605aP-frzS_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import deeplabcut"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SagmIauLzVvb",
        "outputId": "aa13bb6b-c31d-44eb-b4d7-63c65fad5a53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDMkNANYzYJw",
        "outputId": "b17a2dfa-6c72-473c-bff7-cc390c56b6e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls \"/content/drive/MyDrive\"\n",
        "ProjectFolderName = 'largewithaug-amir-2021-12-20'\n",
        "\n",
        "VideoType = 'mp4' "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRZYeSxxzYxL",
        "outputId": "62512ea6-1bac-46bd-b11b-7ec2c845f008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " AYAMFINAL-AMIR-2021-10-07      MULTITEST1-AMIR-2021-11-03\n",
            "'belum di trained'\t        temp\n",
            " CHICKEN5-AMIR-2021-10-09      'temp 2'\n",
            "'Colab Notebooks'\t       'TRAINED BACKUP1'\n",
            " DMANIP\t\t\t       'UNTRAINED BU1 AND 2'\n",
            " largewithaug-amir-2021-12-20   VIDDATASET\n",
            "'MESSED UP'\t\t        VIDDATASET2\n",
            " MISC\t\t\t        VIDDATASET3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "project_path = '/content/drive/MyDrive/'+ ProjectFolderName\n",
        "videofile_path = ['/content/drive/MyDrive/'+ProjectFolderName+'/videos/']\n",
        "path_config_file = '/content/drive/MyDrive/'+ProjectFolderName+'/config.yaml'\n",
        "\n",
        "print('NEW project_path: ' + project_path)\n",
        "print('NEW path_config_file: ' + path_config_file)\n",
        "print('NEW videofile_path: ' + videofile_path[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCy10Yh5za6o",
        "outputId": "bda974ef-d99b-4485-c2bc-38df4c5fec1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NEW project_path: /content/drive/MyDrive/largewithaug-amir-2021-12-20\n",
            "NEW path_config_file: /content/drive/MyDrive/largewithaug-amir-2021-12-20/config.yaml\n",
            "NEW videofile_path: /content/drive/MyDrive/largewithaug-amir-2021-12-20/videos/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CHANGE EVERYTIME U START A NEW PROJECT\n",
        "!ls '/content/drive/MyDrive/largewithaug-amir-2021-12-20'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFyp-NnL3VYh",
        "outputId": "909c0a73-0e4f-4435-8a7f-f1a7c4028b52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " config.yaml   evaluation-results  'TEMP4 RESNET-50 300K'   videos\n",
            " dlc-models    labeled-data\t    training-datasets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.create_training_dataset(path_config_file, net_type='resnet_50', augmenter_type='imgaug')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jnz8tq6I3kJ0",
        "outputId": "1d8600e2-d7bd-40b2-f4c0-810401d00811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It appears that the images were labeled on a Windows system, but you are currently trying to create a training set on a Unix system. \n",
            " In this case the paths should be converted. Do you want to proceed with the conversion?\n",
            "yes/noyes\n",
            "Annotation data converted to unix format...\n",
            "Downloading a ImageNet-pretrained model from http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz....\n",
            "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.8,\n",
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              "          213,  37, 232,  31, 159,  38, 175, 195, 235, 103, 112, 214, 115])))]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AWfwfTuC3x43",
        "outputId": "fd94e850-51ee-4595-edb7-7d2f2f1541c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'alpha_r': 0.02,\n",
            " 'apply_prob': 0.5,\n",
            " 'batch_size': 1,\n",
            " 'clahe': True,\n",
            " 'claheratio': 0.1,\n",
            " 'crop_pad': 0,\n",
            " 'crop_sampling': 'hybrid',\n",
            " 'crop_size': [400, 400],\n",
            " 'cropratio': 0.4,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'decay_steps': 30000,\n",
            " 'deterministic': False,\n",
            " 'display_iters': 1000,\n",
            " 'edge': False,\n",
            " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'histeq': True,\n",
            " 'histeqratio': 0.1,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 0.05,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'lr_init': 0.0005,\n",
            " 'max_input_size': 1500,\n",
            " 'max_shift': 0.4,\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle',\n",
            " 'min_input_size': 64,\n",
            " 'mirror': False,\n",
            " 'multi_stage': False,\n",
            " 'multi_step': [[0.005, 10000],\n",
            "                [0.02, 430000],\n",
            "                [0.002, 730000],\n",
            "                [0.001, 1030000]],\n",
            " 'net_type': 'resnet_50',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': False,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'pos_dist_thresh': 17,\n",
            " 'pre_resize': [],\n",
            " 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20',\n",
            " 'regularize': False,\n",
            " 'rotation': 25,\n",
            " 'rotratio': 0.4,\n",
            " 'save_iters': 50000,\n",
            " 'scale_jitter_lo': 0.5,\n",
            " 'scale_jitter_up': 1.25,\n",
            " 'scoremap_dir': 'test',\n",
            " 'sharpen': False,\n",
            " 'sharpenratio': 0.3,\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting single-animal trainer\n",
            "Batch Size is 1\n",
            "Loading ImageNet-pretrained resnet_50\n",
            "Display_iters overwritten as 100\n",
            "Save_iters overwritten as 100\n",
            "Training parameter:\n",
            "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3], [4]], 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_50', 'num_joints': 5, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n",
            "Starting training....\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n",
            "\n",
            "Unfortunately, your original traceback can not be constructed.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-10-8ef3aacc5628>\", line 1, in <module>\n",
            "    deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 187, in train_network\n",
            "    raise e\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 183, in train_network\n",
            "    allow_growth=allow_growth,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 275, in train\n",
            "    [train_op, total_loss, merged_summaries], feed_dict=dict\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 956, in run\n",
            "    run_metadata_ptr)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1180, in _run\n",
            "    feed_dict_tensor, options, run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1359, in _do_run\n",
            "    run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1365, in _do_call\n",
            "    return fn(*args)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1350, in _run_fn\n",
            "    target_list, run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1443, in _call_tf_sessionrun\n",
            "    run_metadata)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 1823, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 1132, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 358, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
            "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 733, in getmodule\n",
            "    if ismodule(module) and hasattr(module, '__file__'):\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow/__init__.py\", line 50, in __getattr__\n",
            "    module = self._load()\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow/__init__.py\", line 44, in _load\n",
            "    module = _importlib.import_module(self.__name__)\n",
            "  File \"/usr/lib/python3.7/importlib/__init__.py\", line 127, in import_module\n",
            "    return _bootstrap._gcd_import(name[level:], package, level)\n",
            "  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\n",
            "  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\n",
            "  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\n",
            "  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\n",
            "  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\n",
            "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/__init__.py\", line 47, in <module>\n",
            "    from tensorflow.contrib import distributions\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/distributions/__init__.py\", line 29, in <module>\n",
            "    from tensorflow.contrib.distributions.python.ops import bijectors\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/distributions/__init__.py\", line 44, in <module>\n",
            "    from tensorflow.contrib.distributions.python.ops.estimator import *\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/distributions/python/ops/estimator.py\", line 21, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators.head import _compute_weighted_loss\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/__init__.py\", line 93, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn import *\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/__init__.py\", line 28, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn import *\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/__init__.py\", line 30, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn import estimators\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/__init__.py\", line 302, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/dnn.py\", line 34, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/dnn_linear_combined.py\", line 36, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.estimators import estimator\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/estimators/estimator.py\", line 52, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.learn_io import data_feeder\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/learn_io/__init__.py\", line 26, in <module>\n",
            "    from tensorflow.contrib.learn.python.learn.learn_io.dask_io import extract_dask_data\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/contrib/learn/python/learn/learn_io/dask_io.py\", line 33, in <module>\n",
            "    import dask.dataframe as dd\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/dataframe/__init__.py\", line 2, in <module>\n",
            "    from .core import (\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/dataframe/core.py\", line 25, in <module>\n",
            "    from .. import array as da\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/array/__init__.py\", line 3, in <module>\n",
            "    from .core import (\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/array/core.py\", line 63, in <module>\n",
            "    from .slicing import slice_array, replace_ellipsis, cached_cumsum\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/dask/array/slicing.py\", line 709, in <module>\n",
            "    def replace_ellipsis(n, index):\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**second training from 214400 untill 300k**"
      ],
      "metadata": {
        "id": "hMGCQLafn07j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)"
      ],
      "metadata": {
        "id": "NiuOPwEvn5_X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0bbbd410-5356-43ce-9852-e5048aba5705"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting single-animal trainer\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'alpha_r': 0.02,\n",
            " 'apply_prob': 0.5,\n",
            " 'batch_size': 1,\n",
            " 'clahe': True,\n",
            " 'claheratio': 0.1,\n",
            " 'crop_pad': 0,\n",
            " 'crop_sampling': 'hybrid',\n",
            " 'crop_size': [400, 400],\n",
            " 'cropratio': 0.4,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'decay_steps': 30000,\n",
            " 'deterministic': False,\n",
            " 'display_iters': 1000,\n",
            " 'edge': False,\n",
            " 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]},\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'histeq': True,\n",
            " 'histeqratio': 0.1,\n",
            " 'init_weights': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot-214300',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 0.05,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'lr_init': 0.0005,\n",
            " 'max_input_size': 1500,\n",
            " 'max_shift': 0.4,\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle',\n",
            " 'min_input_size': 64,\n",
            " 'mirror': False,\n",
            " 'multi_stage': False,\n",
            " 'multi_step': [[0.005, 10000],\n",
            "                [0.02, 430000],\n",
            "                [0.002, 730000],\n",
            "                [0.001, 1030000]],\n",
            " 'net_type': 'resnet_50',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': False,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'pos_dist_thresh': 17,\n",
            " 'pre_resize': [],\n",
            " 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20',\n",
            " 'regularize': False,\n",
            " 'rotation': 25,\n",
            " 'rotratio': 0.4,\n",
            " 'save_iters': 50000,\n",
            " 'scale_jitter_lo': 0.5,\n",
            " 'scale_jitter_up': 1.25,\n",
            " 'scoremap_dir': 'test',\n",
            " 'sharpen': False,\n",
            " 'sharpenratio': 0.3,\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch Size is 1\n",
            "Loading already trained DLC with backbone: resnet_50\n",
            "Display_iters overwritten as 100\n",
            "Save_iters overwritten as 100\n",
            "Training parameter:\n",
            "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3], [4]], 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'clahe': True, 'claheratio': 0.1, 'crop_sampling': 'hybrid', 'crop_size': [400, 400], 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'embossratio': 0.1, 'strength': [0.5, 1.5]}, 'histeq': True, 'histeqratio': 0.1, 'init_weights': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/train/snapshot-214300', 'lr_init': 0.0005, 'max_input_size': 1500, 'max_shift': 0.4, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/Documentation_data-largewithaug_80shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_50', 'num_joints': 5, 'pos_dist_thresh': 17, 'pre_resize': [], 'project_path': '/content/drive/MyDrive/largewithaug-amir-2021-12-20', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'sharpen': False, 'sharpenratio': 0.3, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n",
            "Starting training....\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "iteration: 299300 loss: 0.0027 lr: 0.005\n",
            "iteration: 299400 loss: 0.0025 lr: 0.005\n",
            "iteration: 299500 loss: 0.0024 lr: 0.005\n",
            "iteration: 299600 loss: 0.0027 lr: 0.005\n",
            "iteration: 299700 loss: 0.0025 lr: 0.005\n",
            "iteration: 299800 loss: 0.0027 lr: 0.005\n",
            "iteration: 299900 loss: 0.0025 lr: 0.005\n",
            "iteration: 300000 loss: 0.0023 lr: 0.005\n",
            "NumExpr defaulting to 2 threads.\n",
            "Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n",
            "\n",
            "Unfortunately, your original traceback can not be constructed.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-9-8ef3aacc5628>\", line 1, in <module>\n",
            "    deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100, saveiters=100)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 187, in train_network\n",
            "    raise e\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 183, in train_network\n",
            "    allow_growth=allow_growth,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 275, in train\n",
            "    [train_op, total_loss, merged_summaries], feed_dict=dict\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 956, in run\n",
            "    run_metadata_ptr)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1180, in _run\n",
            "    feed_dict_tensor, options, run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1359, in _do_run\n",
            "    run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1365, in _do_call\n",
            "    return fn(*args)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1350, in _run_fn\n",
            "    target_list, run_metadata)\n",
            "  File \"/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\", line 1443, in _call_tf_sessionrun\n",
            "    run_metadata)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 1823, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 1132, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 358, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 696, in getsourcefile\n",
            "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 742, in getmodule\n",
            "    os.path.realpath(f)] = module.__name__\n",
            "  File \"/usr/lib/python3.7/posixpath.py\", line 395, in realpath\n",
            "    path, ok = _joinrealpath(filename[:0], filename, {})\n",
            "  File \"/usr/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n",
            "    if not islink(newpath):\n",
            "  File \"/usr/lib/python3.7/posixpath.py\", line 171, in islink\n",
            "    st = os.lstat(path)\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EVALUATE NETWORK AT 300K+ ITERATIONS**"
      ],
      "metadata": {
        "id": "GcVQTCpGjn76"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EVAL AT 300K**"
      ],
      "metadata": {
        "id": "IEcP1DvbjqKL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "_W8Q-GDZjxwt",
        "outputId": "bac35c9f-811e-48e4-afa4-7b11d8517ce4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3], [4]],\n",
            " 'all_joints_names': ['center', 'head', 'tail', 'leftleg', 'rightleg'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_largewithaugDec20/largewithaug_amir80shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.7/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_50',\n",
            " 'num_joints': 5,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/largewithaug-amir-2021-12-20/dlc-models/iteration-0/largewithaugDec20-trainset80shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet50_largewithaugDec20shuffle1_300000  with # of training iterations: 300000\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "256it [00:42,  5.99it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-300000\n",
            "Results for 300000  training iterations: 80 1 train error: 2.77 pixels. Test error: 11.28  pixels.\n",
            "With pcutoff of 0.6  train error: 2.77 pixels. Test error: 7.69 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "Plotting...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id='74d238a0-d2f2-4fd6-aaf0-528423291999'></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 256/256 [02:59<00:00,  1.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "w5ioDBKwvou0",
        "outputId": "01b26761-16f4-4052-a0a7-6227c1e344be"
      },
      "source": [
        "#CHOSEN########### 200K\n",
        "%matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet50_largewithaugDec20shuffle1_214300  with # of training iterations: 214300\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "256it [00:23, 10.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-214300\n",
            "Results for 214300  training iterations: 80 1 train error: 3.27 pixels. Test error: 14.57  pixels.\n",
            "With pcutoff of 0.6  train error: 3.18 pixels. Test error: 7.83 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "Plotting...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div id='192f529a-6ecd-4415-8391-f2a69ee1e696'></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 256/256 [03:01<00:00,  1.41it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    }
  ]
}