jh-dev/find_RAWS_station_outputs.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "markdown",
   5    "id": "b1100419-e2ec-46b6-89d3-327421288025",
   6    "metadata": {},
   7    "source": [
   8     "## Find Data Availability for Stations\n",
   9     "\n",
  10     "Find which RAWS stations in Colorado have data availability for variables of interest to fuel moisture model."
  11    ]
  12   },
  13   {
  14    "cell_type": "code",
  15    "execution_count": 1,
  16    "id": "edb47918-af48-4002-8a66-7e78c7d10c77",
  17    "metadata": {},
  18    "outputs": [
  19     {
  20      "name": "stdout",
  21      "output_type": "stream",
  22      "text": [
  23       "Requirement already satisfied: MesoPy in /Users/jmandel/miniconda3/lib/python3.9/site-packages (2.0.3)\r\n"
  24      ]
  25     }
  26    ],
  27    "source": [
  28     "!pip install MesoPy\n",
  29     "from MesoPy import Meso\n",
  30     "\n",
  31     "outpath = \".\"\n",
  32     "\n",
  33     "# bounding_box = \"-115, 38, -110, 40\"\n",
  34     "meso_token=\"4192c18707b848299783d59a9317c6e1\"\n",
  35     "m=Meso(meso_token)"
  36    ]
  37   },
  38   {
  39    "cell_type": "code",
  40    "execution_count": 2,
  41    "id": "37683794-406d-4524-99d3-2c167ff444ea",
  42    "metadata": {},
  43    "outputs": [],
  44    "source": [
  45     "time_start = \"201806010800\"  # June 1 2018 08:00 in format yyyymmddHHMM\n",
  46     "time_s2    = \"201806010900\"  # June 1 2018 09:00 in format yyyymmddHHMM \n",
  47     "time_end   = \"201907200900\"  # June 20 2018 09:00 in format yyyymmddHHMM\n",
  48     "\n",
  49     "vars='air_temp,relative_humidity,precip_accum,fuel_moisture'"
  50    ]
  51   },
  52   {
  53    "cell_type": "code",
  54    "execution_count": 3,
  55    "id": "7dfb0387-6535-4976-9e9a-62e3cf9a69cd",
  56    "metadata": {},
  57    "outputs": [],
  58    "source": [
  59     "meso_obss = m.timeseries(time_start, time_s2, state=\"CO\", \n",
  60     "                             showemptystations = '0', vars=vars)"
  61    ]
  62   },
  63   {
  64    "cell_type": "code",
  65    "execution_count": 4,
  66    "id": "2255f323-2685-47b1-b862-f86eca6a3991",
  67    "metadata": {},
  68    "outputs": [],
  69    "source": [
  70     "import pandas as pd\n",
  71     "import numpy as np\n",
  72     "\n",
  73     "station_df = pd.DataFrame(columns=['STID', 'air_temp', 'relative_humidity', 'precip_accum', 'fuel_moisture'],\n",
  74     "                  index=range(0, len(meso_obss[\"STATION\"])))"
  75    ]
  76   },
  77   {
  78    "cell_type": "code",
  79    "execution_count": 5,
  80    "id": "6f4fbe0d-dd16-4c1c-950e-97e23e5a1918",
  81    "metadata": {},
  82    "outputs": [],
  83    "source": [
  84     "for i in range(0, station_df.shape[0]):\n",
  85     "    station_df[\"STID\"][i] = meso_obss[\"STATION\"][i][\"STID\"]\n",
  86     "    station_df[\"air_temp\"][i] = int(\"air_temp\" in meso_obss[\"STATION\"][i][\"SENSOR_VARIABLES\"].keys())\n",
  87     "    station_df[\"relative_humidity\"][i] = int(\"relative_humidity\" in meso_obss[\"STATION\"][i][\"SENSOR_VARIABLES\"].keys())\n",
  88     "    station_df[\"precip_accum\"][i] = int(\"precip_accum\" in meso_obss[\"STATION\"][i][\"SENSOR_VARIABLES\"].keys())\n",
  89     "    station_df[\"fuel_moisture\"][i] = int(\"fuel_moisture\" in meso_obss[\"STATION\"][i][\"SENSOR_VARIABLES\"].keys())"
  90    ]
  91   },
  92   {
  93    "cell_type": "code",
  94    "execution_count": 6,
  95    "id": "de8ec8c9-4af7-4bdc-9dbc-1b361d011654",
  96    "metadata": {},
  97    "outputs": [
  98     {
  99      "data": {
 100       "text/html": [
 101        "<div>\n",
 102        "<style scoped>\n",
 103        "    .dataframe tbody tr th:only-of-type {\n",
 104        "        vertical-align: middle;\n",
 105        "    }\n",
 106        "\n",
 107        "    .dataframe tbody tr th {\n",
 108        "        vertical-align: top;\n",
 109        "    }\n",
 110        "\n",
 111        "    .dataframe thead th {\n",
 112        "        text-align: right;\n",
 113        "    }\n",
 114        "</style>\n",
 115        "<table border=\"1\" class=\"dataframe\">\n",
 116        "  <thead>\n",
 117        "    <tr style=\"text-align: right;\">\n",
 118        "      <th></th>\n",
 119        "      <th>STID</th>\n",
 120        "      <th>air_temp</th>\n",
 121        "      <th>relative_humidity</th>\n",
 122        "      <th>precip_accum</th>\n",
 123        "      <th>fuel_moisture</th>\n",
 124        "    </tr>\n",
 125        "  </thead>\n",
 126        "  <tbody>\n",
 127        "    <tr>\n",
 128        "      <th>123</th>\n",
 129        "      <td>CPTC2</td>\n",
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 145        "      <td>CHRC2</td>\n",
 146        "      <td>1</td>\n",
 147        "      <td>1</td>\n",
 148        "      <td>1</td>\n",
 149        "      <td>1</td>\n",
 150        "    </tr>\n",
 151        "    <tr>\n",
 152        "      <th>144</th>\n",
 153        "      <td>LKGC2</td>\n",
 154        "      <td>1</td>\n",
 155        "      <td>1</td>\n",
 156        "      <td>1</td>\n",
 157        "      <td>1</td>\n",
 158        "    </tr>\n",
 159        "    <tr>\n",
 160        "      <th>149</th>\n",
 161        "      <td>CCEC2</td>\n",
 162        "      <td>1</td>\n",
 163        "      <td>1</td>\n",
 164        "      <td>1</td>\n",
 165        "      <td>1</td>\n",
 166        "    </tr>\n",
 167        "  </tbody>\n",
 168        "</table>\n",
 169        "</div>"
 170       ],
 171       "text/plain": [
 172        "      STID air_temp relative_humidity precip_accum fuel_moisture\n",
 173        "123  CPTC2        1                 1            1             1\n",
 174        "124  CHAC2        1                 1            1             1\n",
 175        "125  CHRC2        1                 1            1             1\n",
 176        "144  LKGC2        1                 1            1             1\n",
 177        "149  CCEC2        1                 1            1             1"
 178       ]
 179      },
 180      "execution_count": 6,
 181      "metadata": {},
 182      "output_type": "execute_result"
 183     }
 184    ],
 185    "source": [
 186     "station_df[station_df[\"fuel_moisture\"]==1].head()"
 187    ]
 188   },
 189   {
 190    "cell_type": "code",
 191    "execution_count": 7,
 192    "id": "99dde77e-5f6f-4e45-babf-f7599e8fca14",
 193    "metadata": {},
 194    "outputs": [],
 195    "source": [
 196     "# write output\n",
 197     "station_df.to_csv(outpath+\"/station_df_co.csv\", index=False)"
 198    ]
 199   },
 200   {
 201    "cell_type": "markdown",
 202    "id": "8f63bc34-6deb-48bd-9aa9-c2f5a4cc293e",
 203    "metadata": {},
 204    "source": [
 205     "## Plot Timeseries for a Station\n",
 206     "\n",
 207     "Visualize the available data."
 208    ]
 209   },
 210   {
 211    "cell_type": "code",
 212    "execution_count": 8,
 213    "id": "d45d76e5-ccb1-45b6-a187-25d0d1f91b8e",
 214    "metadata": {},
 215    "outputs": [
 216     {
 217      "ename": "NameError",
 218      "evalue": "name 'lon' is not defined",
 219      "output_type": "error",
 220      "traceback": [
 221       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
 222       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
 223       "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m e \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m   \u001b[38;5;66;03m# tolerance\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m bb \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[43mlon\u001b[49m \u001b[38;5;241m-\u001b[39m e, lat \u001b[38;5;241m-\u001b[39m e, lon \u001b[38;5;241m+\u001b[39m e, lat \u001b[38;5;241m+\u001b[39m e)\n\u001b[1;32m      3\u001b[0m meso_ts \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39mtimeseries(time_start, time_end, stid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCPTC2\u001b[39m\u001b[38;5;124m\"\u001b[39m, showemptystations \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mvars\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mvars\u001b[39m)\n",
 224       "\u001b[0;31mNameError\u001b[0m: name 'lon' is not defined"
 225      ]
 226     }
 227    ],
 228    "source": [
 229     "e = 0.01   # tolerance\n",
 230     "bb = '%s, %s, %s, %s' % (lon - e, lat - e, lon + e, lat + e)\n",
 231     "meso_ts = m.timeseries(time_start, time_end, stid=\"CPTC2\", showemptystations = '0', vars=vars)   # ask the object for data"
 232    ]
 233   },
 234   {
 235    "cell_type": "code",
 236    "execution_count": null,
 237    "id": "05e37b54-e014-44e8-b0f4-e3d53b5dbf86",
 238    "metadata": {},
 239    "outputs": [],
 240    "source": [
 241     "from datetime import datetime, timedelta, time\n",
 242     "import numpy as np\n",
 243     "import matplotlib.pyplot as plt\n",
 244     "import pytz\n",
 245     "station = meso_ts['STATION'][0]\n",
 246     "time_str  = station['OBSERVATIONS']['date_time']\n",
 247     "obs_time = [datetime.strptime(t, '%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=pytz.UTC) for t in time_str]\n",
 248     "start_time = obs_time[0].replace(minute=0)     # remember obs_time and start_time for later\n",
 249     "end_time = obs_time[-1]\n",
 250     "obs_data = np.array(station['OBSERVATIONS'][\"fuel_moisture_set_1\"])\n",
 251     "\n",
 252     "\n",
 253     "%matplotlib inline\n",
 254     "plt.figure(figsize=(16,4))\n",
 255     "plt.plot(obs_data,linestyle='-',c='k',label='10-h fuel data')\n",
 256     "plt.title(station['STID'] + ' 10 h fuel moisture data')\n",
 257     "plt.xlabel('Time (hours)') \n",
 258     "plt.ylabel('Fuel moisture content (%)')\n",
 259     "plt.legend()"
 260    ]
 261   },
 262   {
 263    "cell_type": "code",
 264    "execution_count": null,
 265    "id": "d58d9cb6-f87f-47a2-8845-97c6d6c15358",
 266    "metadata": {},
 267    "outputs": [],
 268    "source": [
 269     "obs_data = np.array(station['OBSERVATIONS'][\"relative_humidity_set_1\"])\n",
 270     "\n",
 271     "\n",
 272     "%matplotlib inline\n",
 273     "plt.figure(figsize=(16,4))\n",
 274     "plt.plot(obs_data,linestyle='-',c='k')\n",
 275     "plt.title(station['STID'] + ' relative humidity data')\n",
 276     "plt.xlabel('Time (hours)') \n",
 277     "plt.ylabel('Relative Humidity (%)')"
 278    ]
 279   },
 280   {
 281    "cell_type": "code",
 282    "execution_count": null,
 283    "id": "20176db9-997d-4a46-8d73-f2e7a08ee9d4",
 284    "metadata": {},
 285    "outputs": [],
 286    "source": [
 287     "obs_data = np.array(station['OBSERVATIONS'][\"precip_accum_set_1\"])\n",
 288     "\n",
 289     "\n",
 290     "%matplotlib inline\n",
 291     "plt.figure(figsize=(16,4))\n",
 292     "plt.plot(obs_data,linestyle='-',c='k')\n",
 293     "plt.title(station['STID'] + ' precip accum data')\n",
 294     "plt.xlabel('Time (hours)') \n",
 295     "plt.ylabel('Precipitation Accumulated (mm)')"
 296    ]
 297   },
 298   {
 299    "cell_type": "code",
 300    "execution_count": null,
 301    "id": "2cf93527-578c-4ce2-b4f6-1508b9dbfa7c",
 302    "metadata": {},
 303    "outputs": [],
 304    "source": [
 305     "obs_data = np.array(station['OBSERVATIONS'][\"air_temp_set_1\"])\n",
 306     "\n",
 307     "\n",
 308     "%matplotlib inline\n",
 309     "plt.figure(figsize=(16,4))\n",
 310     "plt.plot(obs_data,linestyle='-',c='k')\n",
 311     "plt.title(station['STID'] + ' air temp data')\n",
 312     "plt.xlabel('Time (hours)') \n",
 313     "plt.ylabel('Air Temp (C)')"
 314    ]
 315   },
 316   {
 317    "cell_type": "code",
 318    "execution_count": null,
 319    "id": "45264085-d516-4803-bcc6-d0a64cc7e1c9",
 320    "metadata": {},
 321    "outputs": [],
 322    "source": []
 323   },
 324   {
 325    "cell_type": "code",
 326    "execution_count": null,
 327    "id": "0730d61b-6d71-407a-a31b-8b16494f1921",
 328    "metadata": {},
 329    "outputs": [],
 330    "source": []
 331   }
 332  ],
 333  "metadata": {
 334   "kernelspec": {
 335    "display_name": "Python 3 (ipykernel)",
 336    "language": "python",
 337    "name": "python3"
 338   },
 339   "language_info": {
 340    "codemirror_mode": {
 341     "name": "ipython",
 342     "version": 3
 343    },
 344    "file_extension": ".py",
 345    "mimetype": "text/x-python",
 346    "name": "python",
 347    "nbconvert_exporter": "python",
 348    "pygments_lexer": "ipython3",
 349    "version": "3.9.13"
 350   }
 351  },
 352  "nbformat": 4,
 353  "nbformat_minor": 5
 354 }