weather.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'distutils'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01morchard\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01moc\u001b[39;00m\n",
      "File \u001b[0;32m~/venvs/orchard/lib/python3.12/site-packages/tensorflow/__init__.py:30\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;124;03mTop-level module of TensorFlow. By convention, we refer to this module as\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03m`tf` instead of `tensorflow`, following the common practice of importing\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;124;03mthis file with a file generated from [`api_template.__init__.py`](https://www.github.com/tensorflow/tensorflow/blob/master/tensorflow/api_template.__init__.py)\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdistutils\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_distutils\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01minspect\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_inspect\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'distutils'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "\n",
    "import orchard as oc\n",
    "import importlib\n",
    "import pickle\n",
    "\n",
    "importlib.reload(oc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps, horizon, features, learning_rate = oc.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = oc.unpickle_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 21:05:46.240775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5554 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Ti, pci bus id: 0000:26:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "ws = pd.read_csv(\"weather_station_05_05_24.csv\")\n",
    "ws.set_index(\"ob_time\", inplace=True)\n",
    "ws.drop([\"id\", \"remote_id\", \"air_quality\", \"wind_gust_speed\"], axis=1, inplace=True)\n",
    "ws.index = pd.to_datetime(ws.index)\n",
    "ws.replace(\"\\\\N\", np.nan, inplace=True)\n",
    "ws[\"ground_temperature\"] = pd.to_numeric(ws[\"ground_temperature\"])\n",
    "ws.rename(\n",
    "    {\n",
    "        \"ambient_temperature\": \"air_temperature\",\n",
    "        \"ground_temperature\": \"q10cm_soil_temp\",\n",
    "        \"air_pressure\": \"msl_pressure\",\n",
    "        \"humidity\": \"rltv_hum\",\n",
    "        \"rainfall\": \"prcp_amt\",\n",
    "    },\n",
    "    axis=1,\n",
    "    inplace=True,\n",
    ")\n",
    "ws.dropna(inplace=True)\n",
    "# Using heathrow as a template\n",
    "ws = ws.reindex(df[\"greater-london\"][\"00708_heathrow\"].columns, axis=1)\n",
    "\n",
    "ws_data = oc.build_station_dataset(ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b261601b4be74a208211a9f98cecda28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  2%|1         | 2/102 [00:02<01:55,  1.16s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = oc.build_dataset(df, steps=steps, horizon=horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "m, h = oc.run_model(ds, 2, steps, horizon, features, learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"model.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"model-hist.pkl\", \"rb\") as f:\n",
    "    history = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1714939714.600808  643807 service.cc:145] XLA service 0x7efc9c005490 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1714939714.600841  643807 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3060 Ti, Compute Capability 8.6\n",
      "2024-05-05 21:08:34.619124: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-05-05 21:08:34.726878: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      5/Unknown \u001b[1m2s\u001b[0m 15ms/step"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1714939715.942536  643807 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1714939716.595101  646069 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'triton_gemm_dot_421', 12 bytes spill stores, 12 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 210ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 21:08:37.206951: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "/home/sam/miniconda3/lib/python3.11/contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(ws_data)\n",
    "preds = oc.inverse_min_max_scale(preds.reshape(-1, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pd.DataFrame(preds).rename(\n",
    "    {i: v for i, v in enumerate(df[\"greater-london\"][\"00708_heathrow\"].columns)}, axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>msl_pressure</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>rltv_hum</th>\n",
       "      <th>prcp_amt</th>\n",
       "      <th>q10cm_soil_temp</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
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       "      <td>147.598358</td>\n",
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       "      <td>9.135987</td>\n",
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       "      <td>6.056806</td>\n",
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       "      <td>9.851267</td>\n",
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       "      <td>0.453785</td>\n",
       "      <td>10.594161</td>\n",
       "      <td>14.584659</td>\n",
       "      <td>5.975589</td>\n",
       "      <td>2016.151855</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>32.119640</td>\n",
       "      <td>1009.153198</td>\n",
       "      <td>11.848878</td>\n",
       "      <td>64.655037</td>\n",
       "      <td>5.363936</td>\n",
       "      <td>16.793474</td>\n",
       "      <td>0.509486</td>\n",
       "      <td>11.705148</td>\n",
       "      <td>16.015907</td>\n",
       "      <td>6.478028</td>\n",
       "      <td>2016.459717</td>\n",
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       "      <th>...</th>\n",
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       "      <td>14.978581</td>\n",
       "      <td>20.290174</td>\n",
       "      <td>8.185390</td>\n",
       "      <td>2017.623779</td>\n",
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       "      <td>152.913849</td>\n",
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       "      <td>0.464564</td>\n",
       "      <td>10.813495</td>\n",
       "      <td>14.850080</td>\n",
       "      <td>6.068029</td>\n",
       "      <td>2016.203125</td>\n",
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      ],
      "text/plain": [
       "      wind_direction  wind_speed  msl_pressure  air_temperature   rltv_hum  \\\n",
       "0         147.598358   27.099962   1002.536743         9.358970  58.874374   \n",
       "1         150.233246   26.746599   1000.858276         9.135987  58.454990   \n",
       "2         151.389313   27.740942   1003.561279         9.851267  60.101315   \n",
       "3         159.130371   28.256359   1003.095276         9.953615  60.385761   \n",
       "4         174.841583   32.119640   1009.153198        11.848878  64.655037   \n",
       "...              ...         ...           ...              ...        ...   \n",
       "3231      235.495148   41.021214   1020.745483        15.939187  73.663719   \n",
       "3232      152.913849   28.199055   1004.270569        10.130008  60.811325   \n",
       "3233      161.067749   28.634239   1003.588928        10.124517  60.808479   \n",
       "3234      108.691780   19.461376    992.540894         5.620829  50.202049   \n",
       "3235      117.021194   20.776581    992.659485         6.376991  52.119999   \n",
       "\n",
       "      prcp_amt  q10cm_soil_temp         7          8          9        10  \\\n",
       "0     4.558279        14.322002  0.431314  10.112719  13.924548  5.718246   \n",
       "1     4.640740        14.509536  0.445848  10.371317  14.504462  6.056806   \n",
       "2     4.741766        14.875090  0.453785  10.594161  14.584659  5.975589   \n",
       "3     4.917437        15.377058  0.471841  10.984317  15.297280  6.366114   \n",
       "4     5.363936        16.793474  0.509486  11.705148  16.015907  6.478028   \n",
       "...        ...              ...       ...        ...        ...       ...   \n",
       "3231  6.877132        21.395954  0.651578  14.978581  20.290174  8.185390   \n",
       "3232  4.847184        15.180410  0.464564  10.813495  14.850080  6.068029   \n",
       "3233  4.976761        15.561032  0.477220  11.107643  15.449863  6.433063   \n",
       "3234  3.345695        10.630088  0.314158   7.715415  10.813956  4.593719   \n",
       "3235  3.708898        11.650837  0.360454   8.443911  12.095635  5.123036   \n",
       "\n",
       "               11  \n",
       "0     2015.982300  \n",
       "1     2016.247803  \n",
       "2     2016.151855  \n",
       "3     2016.447266  \n",
       "4     2016.459717  \n",
       "...           ...  \n",
       "3231  2017.623779  \n",
       "3232  2016.203125  \n",
       "3233  2016.491943  \n",
       "3234  2015.282227  \n",
       "3235  2015.637085  \n",
       "\n",
       "[3236 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>msl_pressure</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>rltv_hum</th>\n",
       "      <th>prcp_amt</th>\n",
       "      <th>q10cm_soil_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ob_time</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-30 18:43:39</th>\n",
       "      <td>29.99</td>\n",
       "      <td>8.47</td>\n",
       "      <td>1006.80</td>\n",
       "      <td>16.24</td>\n",
       "      <td>73.22</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2023-09-30 18:48:41</th>\n",
       "      <td>35.53</td>\n",
       "      <td>7.86</td>\n",
       "      <td>1006.80</td>\n",
       "      <td>16.22</td>\n",
       "      <td>74.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.13</td>\n",
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       "    <tr>\n",
       "      <th>2023-09-30 18:53:43</th>\n",
       "      <td>33.67</td>\n",
       "      <td>6.96</td>\n",
       "      <td>1006.79</td>\n",
       "      <td>16.20</td>\n",
       "      <td>75.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.13</td>\n",
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       "    <tr>\n",
       "      <th>2023-09-30 18:58:45</th>\n",
       "      <td>24.92</td>\n",
       "      <td>6.69</td>\n",
       "      <td>1006.75</td>\n",
       "      <td>16.17</td>\n",
       "      <td>75.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.13</td>\n",
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       "    <tr>\n",
       "      <th>2023-09-30 19:03:47</th>\n",
       "      <td>35.20</td>\n",
       "      <td>6.64</td>\n",
       "      <td>1006.81</td>\n",
       "      <td>16.16</td>\n",
       "      <td>75.30</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-05 18:05:04</th>\n",
       "      <td>45.14</td>\n",
       "      <td>4.41</td>\n",
       "      <td>991.44</td>\n",
       "      <td>19.16</td>\n",
       "      <td>50.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.19</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-05 18:10:05</th>\n",
       "      <td>42.01</td>\n",
       "      <td>4.01</td>\n",
       "      <td>991.43</td>\n",
       "      <td>19.15</td>\n",
       "      <td>51.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.19</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-05 18:15:07</th>\n",
       "      <td>45.53</td>\n",
       "      <td>4.18</td>\n",
       "      <td>991.42</td>\n",
       "      <td>19.13</td>\n",
       "      <td>51.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.19</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-05 18:20:09</th>\n",
       "      <td>40.21</td>\n",
       "      <td>4.06</td>\n",
       "      <td>991.43</td>\n",
       "      <td>19.07</td>\n",
       "      <td>52.22</td>\n",
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       "    <tr>\n",
       "      <th>2024-05-05 18:25:11</th>\n",
       "      <td>41.12</td>\n",
       "      <td>4.05</td>\n",
       "      <td>991.45</td>\n",
       "      <td>19.03</td>\n",
       "      <td>52.37</td>\n",
       "      <td>0.0</td>\n",
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       "<p>39569 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     wind_direction  wind_speed  msl_pressure  \\\n",
       "ob_time                                                         \n",
       "2023-09-30 18:43:39           29.99        8.47       1006.80   \n",
       "2023-09-30 18:48:41           35.53        7.86       1006.80   \n",
       "2023-09-30 18:53:43           33.67        6.96       1006.79   \n",
       "2023-09-30 18:58:45           24.92        6.69       1006.75   \n",
       "2023-09-30 19:03:47           35.20        6.64       1006.81   \n",
       "...                             ...         ...           ...   \n",
       "2024-05-05 18:05:04           45.14        4.41        991.44   \n",
       "2024-05-05 18:10:05           42.01        4.01        991.43   \n",
       "2024-05-05 18:15:07           45.53        4.18        991.42   \n",
       "2024-05-05 18:20:09           40.21        4.06        991.43   \n",
       "2024-05-05 18:25:11           41.12        4.05        991.45   \n",
       "\n",
       "                     air_temperature  rltv_hum  prcp_amt  q10cm_soil_temp  \n",
       "ob_time                                                                    \n",
       "2023-09-30 18:43:39            16.24     73.22       0.0            14.13  \n",
       "2023-09-30 18:48:41            16.22     74.63       0.0            14.13  \n",
       "2023-09-30 18:53:43            16.20     75.21       0.0            14.13  \n",
       "2023-09-30 18:58:45            16.17     75.07       0.0            14.13  \n",
       "2023-09-30 19:03:47            16.16     75.30       0.0            14.13  \n",
       "...                              ...       ...       ...              ...  \n",
       "2024-05-05 18:05:04            19.16     50.95       0.0            11.19  \n",
       "2024-05-05 18:10:05            19.15     51.35       0.0            11.19  \n",
       "2024-05-05 18:15:07            19.13     51.37       0.0            11.19  \n",
       "2024-05-05 18:20:09            19.07     52.22       0.0            11.19  \n",
       "2024-05-05 18:25:11            19.03     52.37       0.0            11.19  \n",
       "\n",
       "[39569 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 65ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-05 21:22:51.769842: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "/home/sam/miniconda3/lib/python3.11/contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 14 into shape (12)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[47], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m xbatch, ybatch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28miter\u001b[39m(test)\u001b[38;5;241m.\u001b[39mget_next()\n\u001b[1;32m     15\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(ybatch\u001b[38;5;241m.\u001b[39mnumpy()[:days]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m---> 16\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(oc\u001b[38;5;241m.\u001b[39minverse_min_max_scale(preds[:days, \u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m12\u001b[39m)))\n\u001b[1;32m     17\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(key)\n\u001b[1;32m     18\u001b[0m ax\u001b[38;5;241m.\u001b[39mvlines(\n\u001b[1;32m     19\u001b[0m     vline, ymin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, ymax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdotted\u001b[39m\u001b[38;5;124m\"\u001b[39m, transform\u001b[38;5;241m=\u001b[39max\u001b[38;5;241m.\u001b[39mget_xaxis_transform()\n\u001b[1;32m     20\u001b[0m )\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 14 into shape (12)"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(18, 10))\n",
    "days = 14\n",
    "\n",
    "model_configs = {0: {\"test_ds\": ws_data, \"model\": model}}\n",
    "\n",
    "vline = np.linspace(0, days * 24, days + 1)\n",
    "\n",
    "for (key, val), ax in zip(model_configs.items(), axs):\n",
    "\n",
    "    test = val[\"test_ds\"]\n",
    "    preds = val[\"model\"].predict(test)\n",
    "\n",
    "    xbatch, ybatch = iter(test).get_next()\n",
    "\n",
    "    ax.plot(ybatch.numpy()[:days].reshape(-1))\n",
    "    ax.plot(oc.inverse_min_max_scale(preds.reshape(-1, 12))[:, 3])\n",
    "    ax.set_title(key)\n",
    "    ax.vlines(\n",
    "        vline, ymin=0, ymax=1, linestyle=\"dotted\", transform=ax.get_xaxis_transform()\n",
    "    )\n",
    "    ax.legend([\"Actual\", \"Predicted\"])\n",
    "\n",
    "plt.xlabel(\"Hours Cumulative\")\n",
    "print(\"First Two Weeks of Predictions\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "orchard",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}