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        "<a href=\"https://colab.research.google.com/github/t-willi/Simula/blob/main/testing_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.13.4)\n",
            "Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.29)\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.7/dist-packages (from wandb) (1.3.2)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (6.0)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from wandb) (57.4.0)\n",
            "Requirement already satisfied: protobuf!=4.0.*,!=4.21.0,<5,>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.17.3)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: sentry-sdk>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.9.0)\n",
            "Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.9)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n",
            "Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.9)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.1.1)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (5.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: ecg_plot in /usr/local/lib/python3.7/dist-packages (0.2.8)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact ecg_25000.zip:v0, 1383.92MB. 1 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n",
            "Done. 0:0:0.1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f0fcacd3330>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "!pip install wandb\n",
        "!pip install ecg_plot\n",
        "import wandb\n",
        "import pandas as pd\n",
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import glob\n",
        "import torch.optim as optim\n",
        "from random import shuffle\n",
        "from tqdm.auto import tqdm\n",
        "import requests\n",
        "import zipfile\n",
        "from pathlib import Path\n",
        "\n",
        "api = wandb.Api()\n",
        "artifact = api.artifact('ecg_simula/setup_weights and biases/ecg_25000.zip:v0')\n",
        "artifact.download()\n",
        "torch.manual_seed(42)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load model state\n",
        "previous_model = api.artifact(\"ecg_simula/AE_pTOP_serious_try2/Model:v19\", type='Model')\n",
        "previous_model.download()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "zwd-mbgD510m",
        "outputId": "fbe9d4be-511a-4eea-dc89-3f9bd6b90c3b"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m:   1 of 1 files downloaded.  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'./artifacts/Model:v19'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "BpY8EHOt-Syy"
      },
      "outputs": [],
      "source": [
        "def request(path=None,name=None):\n",
        "  import requests\n",
        "  from pathlib import Path\n",
        "  request = requests.get(path)\n",
        "  name=name+\".py\"\n",
        "  with open(name,\"wb\") as f:\n",
        "    f.write(request.content)\n",
        "\n",
        "unzip_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/unzip.py\"\n",
        "Dataloader_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/dataset_and_loader.py\"\n",
        "#model_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/Networks.py\"\n",
        "get_pred_no_reshape_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/get_predictions_no_reshape.py\"\n",
        "get_pred_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/get_predictions.py\"\n",
        "plt_ECG_git_dir=\"https://raw.githubusercontent.com/t-willi/NeuralNetworks/main/plot_ECG.py\"\n",
        "\n",
        "request(unzip_git_dir,\"Unzip\")\n",
        "from Unzip import unzip\n",
        "\n",
        "request(Dataloader_git_dir,\"dataset_and_loader\")\n",
        "from dataset_and_loader import Custom_dataset as CD\n",
        "from dataset_and_loader import make_loader as ml\n",
        "\n",
        "#Download and instantialize model\n",
        "# request(model_git_dir,\"Networks\")\n",
        "# from Networks import ECG_stacked_AE\n",
        "# model=ECG_AE_v1()\n",
        "\n",
        "#download prediction generator\n",
        "request(get_pred_no_reshape_git_dir,\"get_predictions\")\n",
        "from get_predictions import get_pred\n",
        "#download ECG plotter\n",
        "request(plt_ECG_git_dir,\"plot_ECG\")\n",
        "from plot_ECG import plotECG\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unzip(save_path=\"custom_data/train\",zip_path=\"/content/artifacts/ecg_25000.zip:v0/ecg_25000.zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMLrkTPF6atO",
        "outputId": "a91428b7-6d89-43f8-a083-0f38629bc538"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "custom_data/train directory exists.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "\n",
        "class Transpose1dLayer(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, kernel_size, stride, padding=11, upsample=None, output_padding=1):\n",
        "        super(Transpose1dLayer, self).__init__()\n",
        "        self.upsample = upsample\n",
        "        self.upsample_layer = torch.nn.Upsample(scale_factor=upsample)\n",
        "        reflection_pad = kernel_size // 2\n",
        "        self.reflection_pad = nn.ConstantPad1d(reflection_pad, value=0)\n",
        "        self.conv1d = torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride)\n",
        "        self.Conv1dTrans = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, padding, output_padding)\n",
        "\n",
        "    def forward(self, x):\n",
        "        if self.upsample:\n",
        "            #x = torch.cat((x, in_feature), 1)\n",
        "            return self.conv1d(self.reflection_pad(self.upsample_layer(x)))\n",
        "        else:\n",
        "            return self.Conv1dTrans(x)\n",
        "\n",
        "\n",
        "class Pulse2pulseGenerator(nn.Module):\n",
        "    def __init__(self,latent_dim=100, post_proc_filt_len=512,upsample=True):\n",
        "        super(Pulse2pulseGenerator, self).__init__()\n",
        "        # \"Dense\" is the same meaning as fully connection.\n",
        "        stride = 4\n",
        "        if upsample:\n",
        "            stride = 1\n",
        "            upsample = 5\n",
        "        # if upsample is anything but none Transpose1dLayer will do\n",
        "        # self.conv1d(self.reflection_pad(self.upsample_layer(x)))\n",
        "        # which is a 1d convolution on padded and upsampled data x\n",
        "        self.deconv_1 = Transpose1dLayer(250 , 250, 25, stride, upsample=upsample)\n",
        "        self.deconv_2 = Transpose1dLayer(250, 150, 25, stride, upsample=upsample)\n",
        "        self.deconv_3 = Transpose1dLayer(150, 50, 25, stride, upsample=upsample)\n",
        "        self.deconv_4 = Transpose1dLayer( 50, 25, 25, stride, upsample=2)\n",
        "        self.deconv_5 = Transpose1dLayer( 25, 10, 25, stride, upsample=upsample)\n",
        "        self.deconv_6 = Transpose1dLayer(  10, 7, 25, stride, upsample=2)\n",
        "\n",
        "        #new convolutional layers\n",
        "        self.conv_1 = nn.Conv1d(1, 10, 25, stride=2, padding=25 // 2)\n",
        "        self.conv_2 = nn.Conv1d(10, 25, 25, stride=5, padding= 25 // 2)\n",
        "        self.conv_3 = nn.Conv1d(25, 50 , 25, stride=2, padding= 25 // 2)\n",
        "        self.conv_4 = nn.Conv1d(50, 150 , 25, stride=5, padding= 25 // 2)\n",
        "        self.conv_5 = nn.Conv1d(150, 250 , 25, stride=5, padding= 25 // 2)\n",
        "        self.conv_6 = nn.Conv1d(250, 250 , 25, stride=5, padding= 25 // 2)\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear):\n",
        "                nn.init.kaiming_normal_(m.weight.data)\n",
        "\n",
        "    def forward(self, x):\n",
        "        if x.ndim==2:\n",
        "          x=x.unsqueeze(0)\n",
        "        x = F.leaky_relu(self.conv_1(x)) #(1,1,5000 --> 1, 10, 2500)\n",
        "        x = F.leaky_relu(self.conv_2(x)) #( --> 1, 25, 500)\n",
        "        x = F.leaky_relu(self.conv_3(x)) #(--> 1, 50, 250)\n",
        "        x = F.leaky_relu(self.conv_4(x)) # --> 1, 150, 50)\n",
        "        x = F.leaky_relu(self.conv_5(x)) #(--> 1, 250, 10)\n",
        "        x = F.leaky_relu(self.conv_6(x)) #(--> 1, 250, 2)\n",
        "        x = F.relu(self.deconv_1(x)) #(--> 1, 250, 10)\n",
        "        x = F.relu(self.deconv_2(x)) #(--> 1, 150, 50)\n",
        "        x = F.relu(self.deconv_3(x)) #( --> 1, 50, 250)\n",
        "        x = F.relu(self.deconv_4(x)) #(--> 1, 25, 500)\n",
        "        x = F.relu(self.deconv_5(x)) #(--> 1, 10, 2500)\n",
        "        x = torch.tanh(self.deconv_6(x)) #(1, 7, 5000)\n",
        "        x=x.squeeze()\n",
        "        return x\n",
        "\n",
        "model=Pulse2pulseGenerator()\n",
        "# PATH=\"/content/artifacts/Model:v19/model\"\n",
        "# model.load_state_dict(torch.load(PATH))"
      ],
      "metadata": {
        "id": "eTZy3Pqz6SkK"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir=\"custom_data/train\"\n",
        "test_dataset = CD(data_dir=data_dir,split=True,target=\"test\")\n",
        "model.to(\"cpu\")\n",
        "input,output=get_pred(test_dataset,model=model)\n",
        "combined_df=plotECG(input,output,path=\"/content/testECG\")\n",
        "\n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJGxJ88r6poX",
        "outputId": "0acb97d6-5ec6-49c4-abea-cae9e7fb24b7"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/testECG directory exists.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "\n",
        "def get_pred(dataset=None,Set=None,model=None,random=True):\n",
        "  \"\"\"\n",
        "  Function takes a Tensor Dataset as input,first a random file from the dataset is selected,\n",
        "  then the Tensor pair is recombined and shaped into a df-->df_Input. \n",
        "  X is used afterwards as input into the model. The predictions are safed as --> df_output.\n",
        "  Both dataframes are now unscaled by 5011, the max value of the whole dataset.\n",
        "  Then a tuple pair of input and output is returned.\n",
        "  \"\"\"\n",
        "  if random:\n",
        "    import random\n",
        "    limit=len(dataset)\n",
        "    rand_idx=random.randint(0,limit)\n",
        "    X,y=dataset[rand_idx]\n",
        "  if random is False:\n",
        "      X,y=Set\n",
        "  #need to combine tensors to make dataframe for plotting input and output side by side\n",
        "  full_tensor=torch.cat((X,y.squeeze()))\n",
        "  full_tensor=full_tensor.numpy()\n",
        "  df_input=pd.DataFrame(full_tensor).T\n",
        "  df_input.columns = [\"R1\",\"R2\",\"Rv1\",\"Rv2\",\"Rv3\",\"Rv4\",\"Rv5\",\"Rv6\"]\n",
        "  model.to(\"cpu\")\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    output=model(X)\n",
        "  output=output.detach().numpy()\n",
        "  output=output.squeeze().T\n",
        "  #unscale data\n",
        "  df_output = pd.DataFrame(output,columns=[\"F2\",\"Fv1\",\"Fv2\",\"Fv3\",\"Fv4\",\"Fv5\",\"Fv6\"])*5011\n",
        "  df_input=df_input*5011\n",
        "  return df_input,df_output"
      ],
      "metadata": {
        "id": "Duhh6yljNkGM"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "def plotECG(df1=None,df2=None,title=None,pad_df2=True,path=None,createECG=True):\n",
        "  \"\"\"\n",
        "  takes two dataframes with identical columns, concats them and plots them as ecg using ecg_plot\n",
        "  it also takes the first column of df1 and ads it to df1 if pad_df2 is True\n",
        "  \"\"\"\n",
        "  index=[\"real1\",\"realR2\",\"realv1\",\"realv2\",\"realv3\",\"realv4\",\"realv5\",\"realv6\",\"real_lead1\",\n",
        "         \"pred2\",\"predv1\",\"predv2\",\"predv3\",\"predv4\",\"predv5\",\"predv6\"]\n",
        "  if createECG==True:\n",
        "    ecg_path=path\n",
        "    if Path(ecg_path).is_dir():\n",
        "        print(f\"{ecg_path} directory exists.\")\n",
        "    else:\n",
        "        print(f\"Did not find {ecg_path} directory, creating one...\")\n",
        "        Path(ecg_path).mkdir(parents=True, exist_ok=True)\n",
        "    import ecg_plot\n",
        "  if pad_df2 is True:\n",
        "    if len(df1.columns)>len(df2.columns):\n",
        "      df2.insert(0, 'real_lead1', df1[\"R1\"])\n",
        "  frames=[df1/1000,df2/1000]\n",
        "  combined_df=pd.concat(frames,axis=1,join=\"outer\",)\n",
        "  if createECG is True:\n",
        "    ecg_plot.plot(combined_df.values.T, sample_rate = 500,title = title,\n",
        "                      lead_index = index )\n",
        "    ecg_plot.save_as_png('ecg',ecg_path+\"/\")\n",
        "  return combined_df"
      ],
      "metadata": {
        "id": "d-9RqL3CO7Be"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Pulse2pulseGenerator()\n",
        "PATH=\"/content/artifacts/Model:v19/model\"\n",
        "model.load_state_dict(torch.load(PATH))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvWD7P6XRPWD",
        "outputId": "485232f8-9e87-44c2-acdb-827348342ae1"
      },
      "execution_count": 395,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 395
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_loss_list=[]\n",
        "all_loss_over_time_list=[]\n",
        "all_lead_pair_list=[]\n",
        "for Set in tqdm(test_dataset):\n",
        "  df_in,df_out=get_pred(Set=Set,model=model,random=False)\n",
        "  combined_df=plotECG(df_in,df_out,createECG=False)\n",
        "  #list of averages losses betwen lead pairs\n",
        "  loss_list=[]\n",
        "  #list of losses between lead pairs over time\n",
        "  loss_over_time_list=[]\n",
        "  #lead pair list for plotting lead pairs on top of each other\n",
        "  lead_pair_list=[]\n",
        "  criterion = nn.MSELoss()\n",
        "  #going over all columns in combined dataframe\n",
        "  for i in range(8):\n",
        "    t1=(torch.Tensor(combined_df.iloc[:,i]))\n",
        "    t2=(torch.Tensor(combined_df.iloc[:,i+8]))\n",
        "    lead_pair=(t1,t2)\n",
        "    lead_pair_list.append(lead_pair)\n",
        "    loss=criterion(t1,t2)\n",
        "    loss_list.append(loss)\n",
        "    loss_over_time=(t1-t2)**2\n",
        "    loss_over_time_list.append(loss_over_time) \n",
        "  all_loss_list.append(loss_list)\n",
        "  all_loss_over_time_list.append(loss_over_time_list)\n",
        "  all_lead_pair_list.append(lead_pair_list)\n",
        "\n",
        "  \n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "61382f86208b41fcaa5c04c8a2b3c797",
            "c21d61c81f79488cb0f2c93e5342ca7a",
            "6478dc1eacad49a9b8adf7fb04932a47",
            "63e8c437e9e34c5c93f97679ce1d24e9",
            "d33678bb0e6144b28f2923d91cd1b0a0",
            "98b1f7c7a0574a3ebd500748fa4b8fad",
            "9f68752e778148109b872d38fe9ebadb",
            "eb408fcbfbfe41c7bbeac92bba0cd23d",
            "d104d0ca08c94ff98d1cb15fb15235be",
            "a761fe5ff4774b3fa5529d9847cd4241",
            "3c257dd9e23d46f39e89e8af37ecd533"
          ]
        },
        "id": "OvW153YI8xa0",
        "outputId": "560ef81c-4610-49ea-fa9a-8ea5a937ba44"
      },
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61382f86208b41fcaa5c04c8a2b3c797"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_loss_over_time_list[0][7]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqJ1dFjCMCX0",
        "outputId": "3d2f54cb-f213-4ea2-d2d3-9ac285071dba"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0013, 0.0018, 0.0079,  ..., 0.0050, 0.0075, 0.0026])"
            ]
          },
          "metadata": {},
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get average loss over time\n",
        "loss1=torch.empty(1,5000)\n",
        "loss2=torch.empty(1,5000)\n",
        "loss3=torch.empty(1,5000)\n",
        "loss4=torch.empty(1,5000)\n",
        "loss5=torch.empty(1,5000)\n",
        "loss6=torch.empty(1,5000)\n",
        "loss7=torch.empty(1,5000)\n",
        "loss8=torch.empty(1,5000)\n",
        "List_of_average_losses=[loss1,loss2,loss3,loss4,loss5,loss6,loss7,loss8]\n",
        "for k,loss_list in tqdm(enumerate(all_loss_over_time_list)):\n",
        "  for i in range(8):\n",
        "    List_of_average_losses[i]+=loss_list[i]\n",
        "for item in List_of_average_losses:\n",
        "  item/=2500\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c5cb9857d18f4c92b906d1574532967a",
            "4de40e0715994c11afb7b85c9f9ffe9d",
            "9f40985acdff4718a543447e1528fc5b",
            "05d2b443feca4c55931f4a653b51dbd8",
            "b57c543303554923b6a68d47b51137cb",
            "9042d485a055407fa7d4e82bd531c44a",
            "113dba4a8e5748f1a7882a3398e6fa97",
            "1488432413ad446daae40db5dbb2965d",
            "e9841a6eef38432c822be3762b42fe4e",
            "a1ac0d4621134c25834f3d1cddb349e6",
            "56499550891941db9ee496c9cf08bb18"
          ]
        },
        "id": "VN05SmLOCqIm",
        "outputId": "03dfc7f4-e6f0-4358-9697-46b274a1a94c"
      },
      "execution_count": 397,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5cb9857d18f4c92b906d1574532967a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_dict={\"I\":0,\n",
        "          \"II\":0,\n",
        "          \"v1\":0,\n",
        "          \"v2\":0,\n",
        "          \"v3\":0,\n",
        "          \"v4\":0,\n",
        "          \"v5\":0,\n",
        "          \"v6\":0}\n",
        "dictionaryofleads={\"0\":\"I\",\n",
        "                  \"1\":\"II\",\n",
        "                  \"2\":\"v1\",\n",
        "                  \"3\":\"v2\",\n",
        "                  \"4\":\"v3\",\n",
        "                  \"5\":\"v4\",\n",
        "                  \"6\":\"v5\",\n",
        "                  \"7\":\"v6\"}\n",
        "for k,loss_list in tqdm(enumerate(all_loss_list)):\n",
        "  for i in range(8):\n",
        "    t1=(torch.Tensor(combined_df.iloc[:,i]))\n",
        "    t2=(torch.Tensor(combined_df.iloc[:,i+8]))\n",
        "    loss=criterion(t1,t2)\n",
        "    index=dictionaryofleads[f\"{i}\"]\n",
        "    loss_dict[index]+=loss\n",
        "result = {key: value / len(all_loss_list) for key, value in loss_dict.items()}\n",
        "\n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "284c870359574b0ab440944d93e35a80",
            "3d0edd4b3fe74fc38e256d3570749d87",
            "0f9c863fddc7482597afd98435df369f",
            "cef901bf01aa4b938f2c7f36b88437fa",
            "fd23804bb8734fefb941ee936c93f4d0",
            "9d6352d248494b47bdbe1b73ed4d6e24",
            "f6475520f9ba4a92a2958f63a97bae91",
            "b8df7d932d1f48c189f5045927b067b4",
            "6470f4dfa2fe4d84bcb11b5c03f38ab6",
            "5e50cacbac964d289279f0e00c7f1c0d",
            "f8f6818b0c444690b29f1ff0ac9dd50a"
          ]
        },
        "id": "_gUxRAlp__v-",
        "outputId": "14c0a56b-698d-4212-e2d5-a3149c0be364"
      },
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "284c870359574b0ab440944d93e35a80"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f6Po_4jQGdM9",
        "outputId": "5339499e-d6ea-4b72-a0d3-ea1e510206eb"
      },
      "execution_count": 399,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'I': tensor(0.),\n",
              " 'II': tensor(0.0157),\n",
              " 'v1': tensor(0.0147),\n",
              " 'v2': tensor(0.0227),\n",
              " 'v3': tensor(0.0147),\n",
              " 'v4': tensor(0.0117),\n",
              " 'v5': tensor(0.0088),\n",
              " 'v6': tensor(0.0069)}"
            ]
          },
          "metadata": {},
          "execution_count": 399
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBm3jiaqZrDz",
        "outputId": "3b770d6e-73c3-4b5b-ab14-b4fe9eb0b74d"
      },
      "execution_count": 371,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 371
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create lists for averge loss and loss over time from random samples\n",
        "df_1,df_2=get_pred(dataset=test_dataset,Set=None,model=model,random=True)\n",
        "combined_df=plotECG(df_1,df_2,path=None,createECG=False)\n",
        "#list of averages losses betwen lead pairs\n",
        "loss_list=[]\n",
        "#list of losses between lead pairs over time\n",
        "loss_over_time_list=[]\n",
        "#lead pair list for plotting lead pairs on top of each other\n",
        "lead_pair_list=[]\n",
        "criterion = nn.MSELoss()\n",
        "#going over all columns in combined dataframe\n",
        "for i in range(8):\n",
        "  t1=(torch.Tensor(combined_df.iloc[:,i]))\n",
        "  t2=(torch.Tensor(combined_df.iloc[:,i+8]))\n",
        "  lead_pair=(t1,t2)\n",
        "  lead_pair_list.append(lead_pair)\n",
        "  loss=criterion(t1,t2)\n",
        "  loss_list.append(loss)\n",
        "  loss_over_time=(t1-t2)**2\n",
        "  loss_over_time_list.append(loss_over_time)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Y--SFx-fNJ-I"
      },
      "execution_count": 400,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axs=plt.subplots(8,figsize=(10,6))\n",
        "plt.ylabel(\"mV\")\n",
        "plt.xlabel(\"timesteps\")\n",
        "plt.title(\"Real and predicted lead pairs 1,2,v1,v2,v3,v4,v5,v6\", x=0.5,y=9.9)\n",
        "for k,(real,synth) in enumerate(lead_pair_list):\n",
        "  axs[k].plot(real.tolist(),label=\"real\",linewidth=0.5,c=\"g\",alpha=1)\n",
        "  axs[k].plot(synth.tolist(),label=\"prediction\",linewidth=0.5,c=\"r\",alpha=0.5)\n",
        "plt.legend(loc=1, bbox_to_anchor=(1,11))\n",
        "plt.savefig(f\"ECG_pairs{k}.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "7EvswlV4rjvo",
        "outputId": "719e5033-2403-4bd9-9122-d50d14f0a042"
      },
      "execution_count": 401,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_ECG_loss_over_time(input=None):\n",
        "  \"\"\"\n",
        "  enter a list of losses over time \n",
        "  \"\"\"\n",
        "  import matplotlib.pyplot as plt\n",
        "  fig, axs = plt.subplots(8,figsize=(15,6))\n",
        "\n",
        "  for k,item in enumerate(input):\n",
        "    axs[k].plot(item.squeeze())\n",
        "\n",
        "plot_ECG_loss_over_time(List_of_average_losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "LIgBjpY-YJsT",
        "outputId": "e7866c4a-46d2-41f8-8038-5455fb938b95"
      },
      "execution_count": 402,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q3osffL9b-X_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}