fmda/data/synopticpy_tutorial.ipynb

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   6    "metadata": {},
   7    "source": [
   8     "# Collect RAWS Data with SynopticPy"
   9    ]
  10   },
  11   {
  12    "cell_type": "markdown",
  13    "id": "84ed774d-7bef-48e7-b9a6-8f4ba4e17d81",
  14    "metadata": {},
  15    "source": [
  16     "## Setup"
  17    ]
  18   },
  19   {
  20    "cell_type": "code",
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  24    "outputs": [],
  25    "source": [
  26     "import matplotlib.pyplot as plt\n",
  27     "from datetime import datetime\n",
  28     "\n",
  29     "from synoptic.services import stations_timeseries"
  30    ]
  31   },
  32   {
  33    "cell_type": "markdown",
  34    "id": "4fbefa25-ac25-4164-adbc-187f9740b6b8",
  35    "metadata": {},
  36    "source": [
  37     "## Station Time Series"
  38    ]
  39   },
  40   {
  41    "cell_type": "code",
  42    "execution_count": null,
  43    "id": "8f05be2b-0120-4637-b5d2-a258211b2650",
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  47     "params = dict(\n",
  48     "    stid=[\"PLFI1\"],\n",
  49     "    vars=[\"air_temp\", \"relative_humidity\", \"precip_accum\", \"fuel_moisture\", \"wind_speed\", \"solar_radiation\", \"soil_moisture\"],\n",
  50     "    start=datetime(2024, 1, 1),\n",
  51     "    end=datetime(2024, 6, 1),\n",
  52     ")\n",
  53     "\n",
  54     "a = stations_timeseries(verbose=\"HIDE\", **params)\n",
  55     "\n",
  56     "a"
  57    ]
  58   },
  59   {
  60    "cell_type": "code",
  61    "execution_count": null,
  62    "id": "8ce7b02c-4149-443e-b29d-da8cdf12583e",
  63    "metadata": {},
  64    "outputs": [],
  65    "source": [
  66     "type(a)"
  67    ]
  68   },
  69   {
  70    "cell_type": "code",
  71    "execution_count": null,
  72    "id": "880122b3-f947-4d0f-b391-841db74c8eef",
  73    "metadata": {},
  74    "outputs": [],
  75    "source": [
  76     "import numpy as np\n",
  77     "np.diff(a.precip_accum, 1)"
  78    ]
  79   },
  80   {
  81    "cell_type": "code",
  82    "execution_count": null,
  83    "id": "7068d270-d6e8-4afc-9bfe-881e8d7d4c6f",
  84    "metadata": {},
  85    "outputs": [],
  86    "source": [
  87     "a.precip_accum"
  88    ]
  89   },
  90   {
  91    "cell_type": "code",
  92    "execution_count": null,
  93    "id": "e711774a-f8be-4f60-ac8c-8908f5c192ec",
  94    "metadata": {},
  95    "outputs": [],
  96    "source": [
  97     "print(f'Station Elevation: {a.attrs[\"ELEVATION\"]}')\n",
  98     "print(f'Lat: {a.attrs[\"latitude\"]}')\n",
  99     "print(f'Lon: {a.attrs[\"longitude\"]}')\n",
 100     "print(f'Temp Units: {a.attrs[\"UNITS\"][\"air_temp\"]}')\n",
 101     "print(f'Wind Units: {a.attrs[\"UNITS\"][\"wind_speed\"]}')\n",
 102     "# print(f'Solar Rad. Units: {a.attrs[\"UNITS\"][\"solar_radiation\"]}')"
 103    ]
 104   },
 105   {
 106    "cell_type": "code",
 107    "execution_count": null,
 108    "id": "2058d705-a2d3-48d9-9151-a4536512057e",
 109    "metadata": {},
 110    "outputs": [],
 111    "source": [
 112     "a.attrs"
 113    ]
 114   },
 115   {
 116    "cell_type": "code",
 117    "execution_count": null,
 118    "id": "ccddffba-f785-4fb8-a053-b87ad648ef9c",
 119    "metadata": {},
 120    "outputs": [],
 121    "source": [
 122     "plt.plot(a.fuel_moisture)"
 123    ]
 124   },
 125   {
 126    "cell_type": "code",
 127    "execution_count": null,
 128    "id": "6901c78a-8f6f-4af7-8fbe-e0e15dea0b6c",
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 146    "mimetype": "text/x-python",
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 148    "nbconvert_exporter": "python",
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 150    "version": "3.12.5"
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 155 }