fmda/visualizations.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "code",
   5    "execution_count": null,
   6    "id": "dd71eb39-f938-440e-99a8-3dc02ee55102",
   7    "metadata": {},
   8    "outputs": [],
   9    "source": [
  10     "from utils import read_pkl\n",
  11     "from data_funcs import combine_nested\n",
  12     "import pandas as pd\n",
  13     "import matplotlib.pyplot as plt\n",
  14     "import plotly.express as px\n",
  15     "import plotly.graph_objects as go\n",
  16     "import numpy as np\n",
  17     "from mpl_toolkits.basemap import Basemap\n",
  18     "from matplotlib.patches import Polygon"
  19    ]
  20   },
  21   {
  22    "cell_type": "code",
  23    "execution_count": null,
  24    "id": "4197b2ae-d8e3-4878-b87b-795f7622c753",
  25    "metadata": {},
  26    "outputs": [],
  27    "source": [
  28     "dat = read_pkl(\"train.pkl\")"
  29    ]
  30   },
  31   {
  32    "cell_type": "code",
  33    "execution_count": null,
  34    "id": "524d7d5f-7b07-4e9f-87e6-a1853bf4ef27",
  35    "metadata": {},
  36    "outputs": [],
  37    "source": [
  38     "list([*dat.keys()]).index('CHRO3_202401')"
  39    ]
  40   },
  41   {
  42    "cell_type": "code",
  43    "execution_count": null,
  44    "id": "df0ef300-c4d8-4120-889a-7765da096d9a",
  45    "metadata": {},
  46    "outputs": [],
  47    "source": [
  48     "d = combine_nested(dat)"
  49    ]
  50   },
  51   {
  52    "cell_type": "code",
  53    "execution_count": null,
  54    "id": "14b96f79-b8bd-403d-9a4a-8a3b9ae87ee6",
  55    "metadata": {},
  56    "outputs": [],
  57    "source": [
  58     "d.keys()"
  59    ]
  60   },
  61   {
  62    "cell_type": "code",
  63    "execution_count": null,
  64    "id": "116d180c-324a-4804-b5d4-28707aac7bc9",
  65    "metadata": {},
  66    "outputs": [],
  67    "source": [
  68     "locs = d['loc']\n",
  69     "locs.keys()"
  70    ]
  71   },
  72   {
  73    "cell_type": "code",
  74    "execution_count": null,
  75    "id": "d28d3760-92ad-4e14-90fd-69020c2c4b56",
  76    "metadata": {},
  77    "outputs": [],
  78    "source": [
  79     "# NW GACC\n",
  80     "bbox = [42, -124.6, 49, -116.4]"
  81    ]
  82   },
  83   {
  84    "cell_type": "code",
  85    "execution_count": null,
  86    "id": "eb6ad122-815b-4f83-8049-c3e28fb62c12",
  87    "metadata": {},
  88    "outputs": [],
  89    "source": [
  90     "df = pd.DataFrame({key: locs[key] for key in ['STID', 'lat', 'lon']})\n",
  91     "df"
  92    ]
  93   },
  94   {
  95    "cell_type": "code",
  96    "execution_count": null,
  97    "id": "4c04a46d-7096-4fad-8c20-807e409f8f80",
  98    "metadata": {},
  99    "outputs": [],
 100    "source": [
 101     "def make_st_map_interactive(df):\n",
 102     "    fig = go.Figure(go.Scattermapbox(\n",
 103     "        lat=df['lat'],\n",
 104     "        lon=df['lon'],\n",
 105     "        mode='markers',\n",
 106     "        marker=go.scattermapbox.Marker(\n",
 107     "            size=10,\n",
 108     "            opacity=0.7,\n",
 109     "        ),\n",
 110     "        text=df['STID'],\n",
 111     "        name='',\n",
 112     "        showlegend=False  # Turn off legend\n",
 113     "    ))\n",
 114     "\n",
 115     "    # Add Points\n",
 116     "    center_lon=df['lon'].median()\n",
 117     "    center_lat=df['lat'].median()\n",
 118     "    fig.update_layout(\n",
 119     "        mapbox_style=\"open-street-map\",\n",
 120     "        mapbox_center=dict(lat=center_lat, lon=center_lon)\n",
 121     "    )\n",
 122     "    # Add Lines for Bounding Box\n",
 123     "    \n",
 124     "    fig.add_trace(go.Scattermapbox(\n",
 125     "        mode=\"lines\",\n",
 126     "        lon=[df['lon'].min(), df['lon'].min(), df['lon'].max(), df['lon'].max(), df['lon'].min()],\n",
 127     "        lat=[df['lat'].min(), df['lat'].max(), df['lat'].max(), df['lat'].min(), df['lat'].min()],\n",
 128     "        marker=dict(size=5, color=\"black\"),\n",
 129     "        line=dict(width=1.5, color=\"black\"),\n",
 130     "        showlegend=False\n",
 131     "    ))\n",
 132     "    \n",
 133     "    fig.update_layout(\n",
 134     "        margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0},\n",
 135     "        mapbox_zoom =5,\n",
 136     "        mapbox_center={\"lat\": np.median(df.lat), \"lon\": np.median(df.lon)},  # Center the map on desired location\n",
 137     "        width=1000,\n",
 138     "        height=800\n",
 139     "    )\n",
 140     "    return fig"
 141    ]
 142   },
 143   {
 144    "cell_type": "code",
 145    "execution_count": null,
 146    "id": "1fd6cfd9-8ce5-44a8-a618-fc06ba898666",
 147    "metadata": {},
 148    "outputs": [],
 149    "source": [
 150     "make_st_map_interactive(df)"
 151    ]
 152   },
 153   {
 154    "cell_type": "code",
 155    "execution_count": null,
 156    "id": "39374ba7-e4fb-4a7d-a9b0-7bca76e2f950",
 157    "metadata": {},
 158    "outputs": [],
 159    "source": [
 160     "st = \"HOOI1\"\n",
 161     "ind = d['loc']['STID'].index(st)"
 162    ]
 163   },
 164   {
 165    "cell_type": "code",
 166    "execution_count": null,
 167    "id": "62053cfb-d882-4044-90c5-38165572596e",
 168    "metadata": {},
 169    "outputs": [],
 170    "source": [
 171     "fig, ax = plt.subplots()\n",
 172     "plt.title(f\"RAWS Station {st}\")\n",
 173     "ax.plot(d['y'][ind][3000:3500], linestyle='-',c='#468a29',label='FMC Observed')\n",
 174     "plt.legend(loc='upper right')\n",
 175     "ax.set_ylabel(\"Fuel Moisture Content (%)\")\n",
 176     "ax.set_xlabel(\"Hour\")\n",
 177     "plt.savefig(\"outputs/fmc_plot2.png\")\n",
 178     "# Increase the thickness of the axis borders\n",
 179     "ax.spines['top'].set_linewidth(1.5)\n",
 180     "ax.spines['bottom'].set_linewidth(1.5)\n",
 181     "ax.spines['left'].set_linewidth(1.5)\n",
 182     "ax.spines['right'].set_linewidth(1.5)\n",
 183     "\n",
 184     "plt.savefig(\"outputs/fmc_plot2.png\")"
 185    ]
 186   },
 187   {
 188    "cell_type": "code",
 189    "execution_count": null,
 190    "id": "58961640-8836-4920-8a08-e891f5b17c34",
 191    "metadata": {},
 192    "outputs": [],
 193    "source": []
 194   },
 195   {
 196    "cell_type": "code",
 197    "execution_count": null,
 198    "id": "0dcdc7ca-da86-430f-a38b-ad5abe4abfe6",
 199    "metadata": {},
 200    "outputs": [],
 201    "source": []
 202   },
 203   {
 204    "cell_type": "code",
 205    "execution_count": null,
 206    "id": "86d94b91-fbb4-47fb-bba0-e41551f361b5",
 207    "metadata": {},
 208    "outputs": [],
 209    "source": []
 210   }
 211  ],
 212  "metadata": {
 213   "kernelspec": {
 214    "display_name": "Python 3 (ipykernel)",
 215    "language": "python",
 216    "name": "python3"
 217   },
 218   "language_info": {
 219    "codemirror_mode": {
 220     "name": "ipython",
 221     "version": 3
 222    },
 223    "file_extension": ".py",
 224    "mimetype": "text/x-python",
 225    "name": "python",
 226    "nbconvert_exporter": "python",
 227    "pygments_lexer": "ipython3",
 228    "version": "3.10.13"
 229   }
 230  },
 231  "nbformat": 4,
 232  "nbformat_minor": 5
 233 }