Figure6_rhone.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6ecc2ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-16 10:27:10.562987: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-11-16 10:27:10.668533: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2022-11-16 10:27:10.700009: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2022-11-16 10:27:11.200690: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-11-16 10:27:11.200787: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-11-16 10:27:11.200800: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import random\n",
    "import sys\n",
    "import eli5\n",
    "\n",
    "import d2d\n",
    "import importlib\n",
    "importlib.reload(d2d)\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c160257b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-16 10:27:12.727099: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
      "2022-11-16 10:27:12.727122: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "2022-11-16 10:27:12.727420: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3009816/109451254.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m                                                 \u001b[0minput_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m                                                 \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m                                                 batch_size=bs)\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lstm_whole_cable'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md2d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_and_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_step_window_shuffled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/notebooks/das-to-discharge/d2d.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, df, input_width, label_width, shift, label_columns, input_columns, shuffle, batch_size)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_window_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels_slice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/notebooks/das-to-discharge/d2d.py\u001b[0m in \u001b[0;36mmake_dataset\u001b[0;34m(self, data, shuffle, batch_size)\u001b[0m\n\u001b[1;32m    209\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             batch_size = batch_size) #default is 32\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_window\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/keras/utils/timeseries_dataset.py\u001b[0m in \u001b[0;36mtimeseries_dataset_from_array\u001b[0;34m(data, targets, sequence_length, sequence_stride, sampling_rate, batch_size, shuffle, seed, start_index, end_index)\u001b[0m\n\u001b[1;32m    243\u001b[0m     )\n\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequences_from_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtargets\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m         indices = tf.data.Dataset.zip(\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/keras/utils/timeseries_dataset.py\u001b[0m in \u001b[0;36msequences_from_indices\u001b[0;34m(array, indices_ds, start_index, end_index)\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msequences_from_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mend_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m     dataset = tf.data.Dataset.zip((dataset.repeat(), indices_ds)).map(\n\u001b[1;32m    272\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36mfrom_tensors\u001b[0;34m(tensors, name)\u001b[0m\n\u001b[1;32m    732\u001b[0m       \u001b[0mDataset\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m     \"\"\"\n\u001b[0;32m--> 734\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mTensorDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, element, name)\u001b[0m\n\u001b[1;32m   4686\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4687\u001b[0m     \u001b[0;34m\"\"\"See `Dataset.from_tensors()` for details.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4688\u001b[0;31m     \u001b[0melement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_element\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4689\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structure\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_spec_from_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4690\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tensor_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/data/util/structure.py\u001b[0m in \u001b[0;36mnormalize_element\u001b[0;34m(element, element_signature)\u001b[0m\n\u001b[1;32m    124\u001b[0m           \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m           normalized_components.append(\n\u001b[0;32m--> 126\u001b[0;31m               ops.convert_to_tensor(t, name=\"component_%d\" % i, dtype=dtype))\n\u001b[0m\u001b[1;32m    127\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpack_sequence_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpack_as\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalized_components\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/profiler/trace.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrace_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtrace_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m           \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m   1636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1637\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1638\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1640\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/framework/tensor_conversion_registry.py\u001b[0m in \u001b[0;36m_default_conversion_function\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_default_conversion_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m   \u001b[0;32mdel\u001b[0m \u001b[0mas_ref\u001b[0m  \u001b[0;31m# Unused.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mconstant_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m    266\u001b[0m   \"\"\"\n\u001b[1;32m    267\u001b[0m   return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 268\u001b[0;31m                         allow_broadcast=True)\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m    277\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tf.constant\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m   \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_eager_impl\u001b[0;34m(ctx, value, dtype, shape, verify_shape)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m   \u001b[0;34m\"\"\"Creates a constant on the current device.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m   \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/comcat/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m    100\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "SEED = 1\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)\n",
    "\n",
    "file = \"/data/fast0/datasets/Rhone_data_continuous_highpass.h5\"\n",
    "\n",
    "filt = 'Highpass'\n",
    "\n",
    "history = {}\n",
    "val_performance = {}\n",
    "performance = {}\n",
    "\n",
    "ww = 200\n",
    "bs = 32\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)\n",
    "\n",
    "input_columns = list(np.arange(0, 2308, 1))\n",
    "\n",
    "\n",
    "linear_model, lstm_model, dnn_model, df_all_chan, das_data_all, f  = d2d.import_data(filename = file, input_columns = input_columns)\n",
    "\n",
    "da = df_all_chan.to_numpy()\n",
    "\n",
    "multi_step_window_shuffled = d2d.WindowGenerator(df_all_chan,\n",
    "                                                input_width=ww,\n",
    "                                                label_width=1, \n",
    "                                                shift=0,\n",
    "                                                label_columns=['Discharge'],\n",
    "                                                input_columns=input_columns,\n",
    "                                                shuffle=True,\n",
    "                                                batch_size=bs)\n",
    "\n",
    "history['lstm_whole_cable'] = d2d.compile_and_fit(lstm_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "\n",
    "val_performance['lstm_whole_cable'] = lstm_model.evaluate(multi_step_window_shuffled.val)\n",
    "performance['lstm_whole_cable'] = lstm_model.evaluate(multi_step_window_shuffled.test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccbb0b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_set = d2d.WindowGenerator(df_all_chan,\n",
    "                                    input_width=ww,\n",
    "                                    label_width=1, \n",
    "                                    shift=0,\n",
    "                                    label_columns=['Discharge'],\n",
    "                                    input_columns=input_columns,\n",
    "                                    shuffle=False,\n",
    "                                    batch_size=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc974696",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lstm_model.predict(prediction_set.train).squeeze()\n",
    "\n",
    "predictions_real = (predictions * prediction_set.dis_std) + prediction_set.dis_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aaf173f",
   "metadata": {},
   "outputs": [],
   "source": [
    "real_dis = []\n",
    "\n",
    "for i in prediction_set.train.as_numpy_iterator():\n",
    "    real_dis.append(i[-1])\n",
    "    \n",
    "real_dis = np.asarray(real_dis)\n",
    "real_dis = real_dis.reshape(real_dis.shape[0] * real_dis.shape[1])\n",
    "\n",
    "real_dis = (real_dis * prediction_set.dis_std) + prediction_set.dis_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01a4dbd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "fig, ax = plt.subplots(figsize = [15,10])\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "ax.plot(predictions_real, label = 'Predicted', lw = 3, color = 'brown', linestyle = '--')\n",
    "ax.plot(real_dis, label = 'Observed', lw = 3, color = 'steelblue')\n",
    "\n",
    "ax.set_ylabel('Discharge (m$^3$/s)', size=20)\n",
    "ax.set_xlabel('Time Step', size=20)\n",
    "\n",
    "ax.tick_params(labelsize=16)\n",
    "\n",
    "ax.legend(prop={'size': 20})\n",
    "\n",
    "#fig.savefig('discharge_train_timeseries.eps', dpi=1200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71cd6e5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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