pysumma_forcing.ipynb

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   "source": [
    "## Code for Creating SUMMA Forcing Files\n",
    "* Taken from, at some point, Bart's code for creating SUMMA forcing files / ncdfs into the correct format "
   ]
  },
  {
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   "source": [
    "# import packages \n",
    "# %matplotlib widget\n",
    "%matplotlib inline\n",
    "\n",
    "# plotting packages \n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "# interactive plotting\n",
    "\"\"\"\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots # adding for subplots\n",
    "import plotly.figure_factory as ff\n",
    "\"\"\"\n",
    "# data packages \n",
    "# pandas is the most popular Python data analysis/manipulation package, which is built upon NumPy.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "# import datetime class\n",
    "# an aware object can locate itself relative to other aware objects. An aware object represents a specific moment in time that is not open to interpretation.\n",
    "# A naive object does not contain enough information to unambiguously locate itself relative to other date/time objects.\n",
    "from datetime import datetime\n",
    "\n",
    "import csv \n",
    "import copy \n",
    "import os.path "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164d0505-02a4-4bee-a265-68086bed3917",
   "metadata": {},
   "source": [
    "## About DataFrames\n",
    "A Spark DataFrame is an immutable set of objects organized into columns and distributed across nodes in a cluster. DataFrames are a SparkSQL data abstraction and are similar to relational database tables or Python Pandas DataFrames. The first step is to import csv into Pandas DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d615c5-bfbc-4c4a-855b-dd1931b64224",
   "metadata": {},
   "source": [
    "## About Pandas\n",
    "\n",
    "pandas has two main data structures: Series and DataFrame.\n",
    "\n",
    "Series: a 1-dimensional labeled array that can hold any data type such as integers, strings, floating points, or Python objects. It has row/axis labels as the index.\n",
    "\n",
    "DataFrame: a 2-dimensional labeled data structure with columns of potentially different types.\n",
    "It also contains row labels as the index.\n"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>system:index</th>\n",
       "      <th>longwave_radiation</th>\n",
       "      <th>pressure</th>\n",
       "      <th>shortwave_radiation</th>\n",
       "      <th>specific_humidity</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time_convert</th>\n",
       "      <th>time</th>\n",
       "      <th>total_precipitation</th>\n",
       "      <th>wind_u</th>\n",
       "      <th>wind_v</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>362.76</td>\n",
       "      <td>92359.62</td>\n",
       "      <td>114.504</td>\n",
       "      <td>0.008863</td>\n",
       "      <td>17.930039</td>\n",
       "      <td>10/1/21 0:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>3.463077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>362.81</td>\n",
       "      <td>92384.62</td>\n",
       "      <td>51.908</td>\n",
       "      <td>0.009004</td>\n",
       "      <td>16.750039</td>\n",
       "      <td>10/1/21 1:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>2.984644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>362.86</td>\n",
       "      <td>92409.52</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009145</td>\n",
       "      <td>15.560000</td>\n",
       "      <td>10/1/21 2:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>2.576141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>356.85</td>\n",
       "      <td>92434.32</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009287</td>\n",
       "      <td>14.380000</td>\n",
       "      <td>10/1/21 3:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>2.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>356.87</td>\n",
       "      <td>92456.54</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009220</td>\n",
       "      <td>13.910000</td>\n",
       "      <td>10/1/21 4:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>2.220360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   system:index  longwave_radiation  pressure  shortwave_radiation  \\\n",
       "0             0              362.76  92359.62              114.504   \n",
       "1             1              362.81  92384.62               51.908   \n",
       "2             2              362.86  92409.52                0.000   \n",
       "3             3              356.85  92434.32                0.000   \n",
       "4             4              356.87  92456.54                0.000   \n",
       "\n",
       "   specific_humidity  temperature  time_convert          time  \\\n",
       "0           0.008863    17.930039  10/1/21 0:00  1.630000e+12   \n",
       "1           0.009004    16.750039  10/1/21 1:00  1.630000e+12   \n",
       "2           0.009145    15.560000  10/1/21 2:00  1.630000e+12   \n",
       "3           0.009287    14.380000  10/1/21 3:00  1.630000e+12   \n",
       "4           0.009220    13.910000  10/1/21 4:00  1.630000e+12   \n",
       "\n",
       "   total_precipitation  wind_u  wind_v  windspeed  \n",
       "0                  0.0    1.73   -3.00   3.463077  \n",
       "1                  0.0    1.16   -2.75   2.984644  \n",
       "2                  0.0    0.58   -2.51   2.576141  \n",
       "3                  0.0    0.00   -2.26   2.260000  \n",
       "4                  0.0   -0.04   -2.22   2.220360  "
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   "source": [
    "# Order doesnt matter\n",
    "# Load csv into Python as pandas DataFrame. Anything that is a number is int type and anything\n",
    "# thats NAN is object type\n",
    "ds =  pd.read_csv(\"/Users/ianwhidden/pysumma/NLDAS2_WY22.csv\")\n",
    "\n",
    "# below is the metadata from the NLDAS website\n",
    "# 61:APCP:Precipitation hourly total [kg/m^2] Hourly backward accumulated\n",
    "# 205:DLWRF:Longwave radiation flux downwards (surface) [W/m^2]\n",
    "# 204:DSWRF:Shortwave radiation flux downwards (surface) [W/m^2]\n",
    "# 1:PRES:Surface pressure [Pa]\n",
    "# 51:SPFH:2-m above ground Specific humidity [kg/kg]\n",
    "# 11:TMP:2-m above ground Temperature [K]\n",
    "# 33:UGRD:10-m above ground Zonal wind speed [m/s]\n",
    "#zonal wind is positive if it blows from the west and negative if from the east.\n",
    "# 34:VGRD:10-m above ground Meridional wind speed [m/s]\n",
    "#meridional wind is positive if from the south, and negative if from the north\n",
    "\n",
    "\n",
    "# remove extra columns in NLDAS data\n",
    "del ds[\"id\"]\n",
    "del ds[\"latitude\"]\n",
    "del ds[\"longitude\"]\n",
    "del ds[\".geo\"]\n",
    "\n",
    "ds.head()\n",
    "\n",
    "# Print row  values of second and third using a list of indexes:\n",
    "# print(ds.loc[[2,3,4]])"
   ]
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       "      <th>specific_humidity</th>\n",
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       "      <th>time_convert</th>\n",
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       "      <th>total_precipitation</th>\n",
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       "      <th>wind_v</th>\n",
       "      <th>windspeed</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2021-10-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>362.76</td>\n",
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       "      <th>2021-10-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>362.81</td>\n",
       "      <td>92384.62</td>\n",
       "      <td>51.908</td>\n",
       "      <td>0.009004</td>\n",
       "      <td>16.750039</td>\n",
       "      <td>10/1/21 1:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>2.984644</td>\n",
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       "    <tr>\n",
       "      <th>2021-10-01 02:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>362.86</td>\n",
       "      <td>92409.52</td>\n",
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       "      <td>10/1/21 2:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>2.576141</td>\n",
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       "    <tr>\n",
       "      <th>2021-10-01 03:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>356.85</td>\n",
       "      <td>92434.32</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009287</td>\n",
       "      <td>14.380000</td>\n",
       "      <td>10/1/21 3:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>2.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01 04:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>356.87</td>\n",
       "      <td>92456.54</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009220</td>\n",
       "      <td>13.910000</td>\n",
       "      <td>10/1/21 4:00</td>\n",
       "      <td>1.630000e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>2.220360</td>\n",
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      "text/plain": [
       "                     system:index  longwave_radiation  pressure  \\\n",
       "time                                                              \n",
       "2021-10-01 00:00:00             0              362.76  92359.62   \n",
       "2021-10-01 01:00:00             1              362.81  92384.62   \n",
       "2021-10-01 02:00:00             2              362.86  92409.52   \n",
       "2021-10-01 03:00:00             3              356.85  92434.32   \n",
       "2021-10-01 04:00:00             4              356.87  92456.54   \n",
       "\n",
       "                     shortwave_radiation  specific_humidity  temperature  \\\n",
       "time                                                                       \n",
       "2021-10-01 00:00:00              114.504           0.008863    17.930039   \n",
       "2021-10-01 01:00:00               51.908           0.009004    16.750039   \n",
       "2021-10-01 02:00:00                0.000           0.009145    15.560000   \n",
       "2021-10-01 03:00:00                0.000           0.009287    14.380000   \n",
       "2021-10-01 04:00:00                0.000           0.009220    13.910000   \n",
       "\n",
       "                     time_convert          time  total_precipitation  wind_u  \\\n",
       "time                                                                           \n",
       "2021-10-01 00:00:00  10/1/21 0:00  1.630000e+12                  0.0    1.73   \n",
       "2021-10-01 01:00:00  10/1/21 1:00  1.630000e+12                  0.0    1.16   \n",
       "2021-10-01 02:00:00  10/1/21 2:00  1.630000e+12                  0.0    0.58   \n",
       "2021-10-01 03:00:00  10/1/21 3:00  1.630000e+12                  0.0    0.00   \n",
       "2021-10-01 04:00:00  10/1/21 4:00  1.630000e+12                  0.0   -0.04   \n",
       "\n",
       "                     wind_v  windspeed  \n",
       "time                                    \n",
       "2021-10-01 00:00:00   -3.00   3.463077  \n",
       "2021-10-01 01:00:00   -2.75   2.984644  \n",
       "2021-10-01 02:00:00   -2.51   2.576141  \n",
       "2021-10-01 03:00:00   -2.26   2.260000  \n",
       "2021-10-01 04:00:00   -2.22   2.220360  "
      ]
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# Pandas DatetimeIndex.date attribute outputs an\n",
    "# index object containing the date values present in each of the entries of the DatetimeIndex.\n",
    "# Im fairly certain this just changes the index to the column of the datetime object.\n",
    "# Format should be 'Year-Month-Day HR:MIN:SEC'\n",
    "ds.index = pd.DatetimeIndex(ds['time_convert'], name='time')\n",
    "\n",
    "\n",
    "ds.head()\n",
    "\n",
    "# The timestamp is Unix timestamp and has 13 numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9c985c6-28e9-46a7-8741-f08aba97c211",
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   "source": [
    "# set variable to columns in dataframe\n",
    "shortwave = ds.shortwave_radiation\n",
    "longwave = ds.longwave_radiation\n",
    "precip = ds.total_precipitation\n",
    "pressure = ds.pressure\n",
    "airtemp = ds.temperature\n",
    "windspd = ds.windspeed\n",
    "# spechum = niwot_spechum\n",
    "spechum = ds.specific_humidity\n",
    "# The actual time and date gets converted into the milliseconds, and it follows the Unix time January 1st, 1970.\n",
    "# Because it is the date when the time for the Unix computer started.\n",
    "# The NLDAS data starts at 00:00 GMT on Friday, October 1, 2021 and\n",
    "# ends at Thu Sep 29 2022 23:00:00 GMT+0000\n",
    "timestamp = ds.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61effcd0-4e70-41ff-aa5c-ffdbcaf81644",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USING BARTS OLD CODE FOR CREATING \n",
    "# Attributes in the netCDF file, with their units and the full length name of the variable \n",
    "attrs = {\n",
    "   'airpres':  {'units': 'Pa', 'long_name': 'Air pressure'},\n",
    "   'airtemp':  {'units': 'K', 'long_name': 'Air temperature'},\n",
    "   'spechum':  {'units': 'g g-1', 'long_name': 'Specific humidity'},\n",
    "   'windspd':  {'units': 'Wind speed', 'long_name': 'm s-1'},\n",
    "   'SWRadAtm': {'units': 'W m-2', 'long_name': 'Downward shortwave radiation'},\n",
    "   'LWRadAtm': {'units': 'W m-2', 'long_name': 'Downward longwave radiation'},\n",
    "   'pptrate':  {'units': 'kg m-2 s-1', 'long_name': 'Precipitation rate'}}\n",
    "\n",
    "# Latitude, longitude, and elevation (m) for UPLMET in the Andrews\n",
    "lats = [44.2072180256268] \n",
    "lons = [-122.119450090239]\n",
    "elev = 1050\n",
    "\n",
    "# Need to better understand what the below lines do. \n",
    "bounds = ds.index\n",
    "shape = (len(bounds), 1, )\n",
    "# set dimensions of Xarray dataset\n",
    "dims = ('time', 'hru', )\n",
    "# Do I need to use drop_vars method to remove cordinates? Or do I need to ID time as a data variable, rather than a coordinate? Or are both needed?\n",
    "coords = {'time': bounds}\n",
    "\n",
    "# Time stepping\n",
    "# Forcing timestep units: The user can specify the time units as <units> since <reference time>,\n",
    "# where <units> is one of seconds, minutes, hours, or days and <reference time> is specified as YYYY-MM-DD hh:mm.\n",
    "met_data = xr.Dataset(coords=coords)\n",
    "met_data.time.encoding['calendar'] = 'standard'\n",
    "met_data.time.encoding['units'] = 'hours since 2021-10-01'\n",
    "\n",
    "# The variables in SUMMA that are in the forcings\n",
    "summa_vars = ['airpres', 'airtemp', 'spechum', \n",
    "              'windspd', 'SWRadAtm', 'LWRadAtm', 'pptrate']\n",
    "\n",
    "# From the docs: DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to\n",
    "# support metadata aware operations. The API is similar to that for the pandas Series or DataFrame, but DataArray objects\n",
    "# can have any number of dimensions, and their contents have fixed data types.\n",
    "for varname in summa_vars:\n",
    "    met_data[varname] = xr.DataArray(data=np.full(shape, np.nan),\n",
    "                                     coords=coords, dims=dims,\n",
    "                                     name=varname, attrs=attrs[varname])"
   ]
  },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 8736, hru: 1)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2021-10-01 ... 2022-09-29T23:00:00\n",
       "Dimensions without coordinates: hru\n",
       "Data variables:\n",
       "    airpres    (time, hru) float64 9.236e+04 9.238e+04 ... 9.241e+04 9.24e+04\n",
       "    airtemp    (time, hru) float64 17.93 16.75 15.56 14.38 ... 16.13 16.06 15.98\n",
       "    spechum    (time, hru) float64 0.008863 0.009004 ... 0.008456 0.008265\n",
       "    windspd    (time, hru) float64 3.463 2.985 2.576 2.26 ... 2.349 1.91 1.863\n",
       "    SWRadAtm   (time, hru) float64 114.5 51.91 0.0 0.0 ... 491.8 429.3 340.2\n",
       "    LWRadAtm   (time, hru) float64 362.8 362.8 362.9 356.9 ... 339.8 339.8 339.8\n",
       "    pptrate    (time, hru) float64 0.0 0.0 0.0 0.0 ... 0.0336 0.0974 0.0334\n",
       "    hruId      (hru) int32 1001\n",
       "    latitude   (hru) float64 43.2\n",
       "    longitude  (hru) float64 243.2\n",
       "    data_step  float64 3.6e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-36f84cef-af5a-450b-94f3-924807bf8406' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36f84cef-af5a-450b-94f3-924807bf8406' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8736</li><li><span>hru</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6d77e4e9-e923-4a19-8536-e29bfa5f84af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d77e4e9-e923-4a19-8536-e29bfa5f84af' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-10-01 ... 2022-09-29T23:00:00</div><input id='attrs-e53053e9-3fc1-4f98-afdd-9749decfd0b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e53053e9-3fc1-4f98-afdd-9749decfd0b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24fd33d2-7e4b-4d4a-ad3a-f7de118fc8f1' class='xr-var-data-in' type='checkbox'><label for='data-24fd33d2-7e4b-4d4a-ad3a-f7de118fc8f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-10-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2021-10-01T02:00:00.000000000&#x27;, ..., &#x27;2022-09-29T21:00:00.000000000&#x27;,\n",
       "       &#x27;2022-09-29T22:00:00.000000000&#x27;, &#x27;2022-09-29T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a5e73d8-3dc9-4f22-9625-7d6f019a2607' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0a5e73d8-3dc9-4f22-9625-7d6f019a2607' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>airpres</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.236e+04 9.238e+04 ... 9.24e+04</div><input id='attrs-d09bc30e-ec5d-411a-961c-4f569d9ec285' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d09bc30e-ec5d-411a-961c-4f569d9ec285' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-389c8ed0-6a89-49cb-96dc-0a02471145e7' class='xr-var-data-in' type='checkbox'><label for='data-389c8ed0-6a89-49cb-96dc-0a02471145e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Air pressure</dd></dl></div><div class='xr-var-data'><pre>array([[92359.62],\n",
       "       [92384.62],\n",
       "       [92409.52],\n",
       "       ...,\n",
       "       [92427.88],\n",
       "       [92411.85],\n",
       "       [92395.7 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>airtemp</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.93 16.75 15.56 ... 16.06 15.98</div><input id='attrs-fdaf3f35-6f8d-4c06-994d-810eff540a26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdaf3f35-6f8d-4c06-994d-810eff540a26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34b39359-b964-49af-affc-0cbb6cd54c12' class='xr-var-data-in' type='checkbox'><label for='data-34b39359-b964-49af-affc-0cbb6cd54c12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Air temperature</dd></dl></div><div class='xr-var-data'><pre>array([[17.93003906],\n",
       "       [16.75003906],\n",
       "       [15.56      ],\n",
       "       ...,\n",
       "       [16.13      ],\n",
       "       [16.06      ],\n",
       "       [15.98      ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spechum</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.008863 0.009004 ... 0.008265</div><input id='attrs-3ca0ac97-0c98-4b47-be51-9c089ea13dc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ca0ac97-0c98-4b47-be51-9c089ea13dc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4095932b-7a38-431c-8d85-5022ff9b9541' class='xr-var-data-in' type='checkbox'><label for='data-4095932b-7a38-431c-8d85-5022ff9b9541' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>g g-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>array([[0.0088626],\n",
       "       [0.0090038],\n",
       "       [0.0091452],\n",
       "       ...,\n",
       "       [0.0086473],\n",
       "       [0.008456 ],\n",
       "       [0.0082646]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>windspd</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.463 2.985 2.576 ... 1.91 1.863</div><input id='attrs-5669aa40-908d-48b7-9e51-89b31b43a0b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5669aa40-908d-48b7-9e51-89b31b43a0b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0564fc2-7cd9-43df-9ec5-6e564975eb7a' class='xr-var-data-in' type='checkbox'><label for='data-e0564fc2-7cd9-43df-9ec5-6e564975eb7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Wind speed</dd><dt><span>long_name :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[3.46307667],\n",
       "       [2.98464403],\n",
       "       [2.57614052],\n",
       "       ...,\n",
       "       [2.34917007],\n",
       "       [1.9104188 ],\n",
       "       [1.86300832]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWRadAtm</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>114.5 51.91 0.0 ... 429.3 340.2</div><input id='attrs-6b6b376c-211a-4072-be43-a89985099ce5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b6b376c-211a-4072-be43-a89985099ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57a89fbb-a243-4440-b9b4-c4d76d1f6e2d' class='xr-var-data-in' type='checkbox'><label for='data-57a89fbb-a243-4440-b9b4-c4d76d1f6e2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>long_name :</span></dt><dd>Downward shortwave radiation</dd></dl></div><div class='xr-var-data'><pre>array([[114.504],\n",
       "       [ 51.908],\n",
       "       [  0.   ],\n",
       "       ...,\n",
       "       [491.831],\n",
       "       [429.315],\n",
       "       [340.192]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWRadAtm</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>362.8 362.8 362.9 ... 339.8 339.8</div><input id='attrs-2ad5b430-8d9b-40c9-b2f9-7f6e59195a90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ad5b430-8d9b-40c9-b2f9-7f6e59195a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71a40805-dbc9-4ec7-a520-17d538282504' class='xr-var-data-in' type='checkbox'><label for='data-71a40805-dbc9-4ec7-a520-17d538282504' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>long_name :</span></dt><dd>Downward longwave radiation</dd></dl></div><div class='xr-var-data'><pre>array([[362.76],\n",
       "       [362.81],\n",
       "       [362.86],\n",
       "       ...,\n",
       "       [339.76],\n",
       "       [339.77],\n",
       "       [339.78]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pptrate</span></div><div class='xr-var-dims'>(time, hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.0974 0.0334</div><input id='attrs-ad082c2d-e955-4b14-88e0-2886a36b1643' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad082c2d-e955-4b14-88e0-2886a36b1643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b46ddf1-bc18-4b8a-8f58-e0f47fe07469' class='xr-var-data-in' type='checkbox'><label for='data-3b46ddf1-bc18-4b8a-8f58-e0f47fe07469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>long_name :</span></dt><dd>Precipitation rate</dd></dl></div><div class='xr-var-data'><pre>array([[0.    ],\n",
       "       [0.    ],\n",
       "       [0.    ],\n",
       "       ...,\n",
       "       [0.0336],\n",
       "       [0.0974],\n",
       "       [0.0334]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hruId</span></div><div class='xr-var-dims'>(hru)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dee0e4e8-4dce-44b9-baf4-5af823d0baf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dee0e4e8-4dce-44b9-baf4-5af823d0baf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f99669-3d4e-42e8-a428-4d40f5fbf34d' class='xr-var-data-in' type='checkbox'><label for='data-59f99669-3d4e-42e8-a428-4d40f5fbf34d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Index of hydrological response unit (HRU)</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>v_type :</span></dt><dd>scalarv</dd></dl></div><div class='xr-var-data'><pre>array([1001], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04c072bd-f12f-44f3-ab72-a56f452f50da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04c072bd-f12f-44f3-ab72-a56f452f50da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ee19efe-3915-4038-bb78-4e1860f9fd74' class='xr-var-data-in' type='checkbox'><label for='data-6ee19efe-3915-4038-bb78-4e1860f9fd74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of HRU&#x27;s centroid</dd><dt><span>units :</span></dt><dd>Decimal degree north</dd></dl></div><div class='xr-var-data'><pre>array([43.2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(hru)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a221f37e-b410-4404-bee4-9058ad1e9990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a221f37e-b410-4404-bee4-9058ad1e9990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42c3b757-d57b-4006-8966-805fbee286a9' class='xr-var-data-in' type='checkbox'><label for='data-42c3b757-d57b-4006-8966-805fbee286a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of HRU&#x27;s centroid</dd><dt><span>units :</span></dt><dd>Decimal degree east</dd></dl></div><div class='xr-var-data'><pre>array([243.2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_step</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.6e+03</div><input id='attrs-ec10a646-c331-4146-9bc4-68231355d2d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec10a646-c331-4146-9bc4-68231355d2d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b546e65d-25e1-4681-acc1-58b136a27318' class='xr-var-data-in' type='checkbox'><label for='data-b546e65d-25e1-4681-acc1-58b136a27318' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3600.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94873d06-cbd7-496b-a862-07dffcaa10b0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-94873d06-cbd7-496b-a862-07dffcaa10b0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 8736, hru: 1)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2021-10-01 ... 2022-09-29T23:00:00\n",
       "Dimensions without coordinates: hru\n",
       "Data variables:\n",
       "    airpres    (time, hru) float64 9.236e+04 9.238e+04 ... 9.241e+04 9.24e+04\n",
       "    airtemp    (time, hru) float64 17.93 16.75 15.56 14.38 ... 16.13 16.06 15.98\n",
       "    spechum    (time, hru) float64 0.008863 0.009004 ... 0.008456 0.008265\n",
       "    windspd    (time, hru) float64 3.463 2.985 2.576 2.26 ... 2.349 1.91 1.863\n",
       "    SWRadAtm   (time, hru) float64 114.5 51.91 0.0 0.0 ... 491.8 429.3 340.2\n",
       "    LWRadAtm   (time, hru) float64 362.8 362.8 362.9 356.9 ... 339.8 339.8 339.8\n",
       "    pptrate    (time, hru) float64 0.0 0.0 0.0 0.0 ... 0.0336 0.0974 0.0334\n",
       "    hruId      (hru) int32 ...\n",
       "    latitude   (hru) float64 ...\n",
       "    longitude  (hru) float64 ...\n",
       "    data_step  float64 3.6e+03"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set set met_data with variables and hru ID\n",
    "\n",
    "met_data['airpres' ].loc[{'hru': 0}] = pressure      #air pressure [Pa]\n",
    "met_data['airtemp' ].loc[{'hru': 0}] = airtemp       #temperature [K]\n",
    "met_data['windspd' ].loc[{'hru': 0}] = windspd       #wind speed [m s-1]\n",
    "met_data['SWRadAtm'].loc[{'hru': 0}] = shortwave     #shortwave [W m-2]\n",
    "met_data['LWRadAtm'].loc[{'hru': 0}] = longwave      #longwave [w m-2]\n",
    "met_data['pptrate' ].loc[{'hru': 0}] = precip        #precip [kg m-2 s-1]\n",
    "met_data['spechum' ].loc[{'hru': 0}] = spechum       #specific humidity [g/g-1]\n",
    "\n",
    "# For the model run at the Andrews I believe that I need to create the local_attributes netCDF for the forcings.\n",
    "# In the below code I use the Local Attributes from the reynolds experiment. The plan is to edit the 'snow_zLocalAttributes.nc'\n",
    "# to contain appropriate info for my site.\n",
    "\n",
    "# The below uses attributes from the snow_zlocal_attributes netCDF, instead of creating a new local attributes NetCDF for my forcing\n",
    "# So, using hruID, lat, long, and data_step from a previously constructed NetCDF. \n",
    "# I do not have a local attribute NetCDF file set up for the Andrews.\n",
    "\n",
    "\n",
    "# NetCDF groups are not supported as part of the Dataset data model. Instead, groups can be loaded individually as Dataset\n",
    "# objects. To do so, pass a group keyword argument to the open_dataset() function.\n",
    "# The group can be specified as a path-like string, e.g., to access subgroup 'bar' within group 'foo' pass '/foo/bar' as the group argument.\n",
    "# In a similar way, the group keyword argument can be given to the Dataset.to_netcdf() method to write to a group in a netCDF file. When writing multiple groups in one file, pass mode='a' to Dataset.to_netcdf() to ensure that each call does not delete the file.\n",
    "\n",
    "# Use attributes from the local_attributes netCDF instead of recreating them for the forcings. It may be best to recreate, rather than using reynolds.\n",
    "ds_local_attrs = xr.open_dataset('/Users/ianwhidden/pysumma/tutorial/Andrews_data/Andrews_test/settings/snow_zLocalAttributes.nc')\n",
    "#lsosmet = xr.open_dataset('/Users/ianwhidden/local_attributes.nc')\n",
    "\n",
    "met_data['hruId'] = ds_local_attrs['hruId']\n",
    "# Lat and long fields don't need to be included in forcing data\n",
    "met_data['latitude'] = ds_local_attrs['latitude']\n",
    "met_data['longitude'] = ds_local_attrs['longitude']\n",
    "# The data_step variable is the length of a timestep of the forcing data in seconds.\n",
    "# 3600 seconds in a hr. This variable should be a data variable, rather than a coordinate.\n",
    "#met_data['data_step'] = [3600.]\n",
    "met_data['data_step'] = xr.Variable([], 3600.0)\n",
    "\n",
    "# Write dataset contents to a netCDF file\n",
    "met_data.to_netcdf('/Users/ianwhidden/pysumma/tutorial/Andrews_data/Andrews_test/forcing/nldas2022_netCDF.nc')\n",
    "\n",
    "# About NetCDF and Xarrary: \"Xarray is based on the netCDF data model, so netCDF files on disk directly\n",
    "# correspond to Dataset objects (more accurately, a group in a netCDF file directly\n",
    "# corresponds to a Dataset object.)\"\n",
    "\n",
    "met_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddad5a66-bbe4-4b23-ad78-e90d3630e2e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# view contents of snow_zLocalAttributes.nc\n",
    "print(lsosmet)\n",
    "print(met_data)\n",
    "# same as met_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cc46c60-78a2-40fa-b2cc-c4932005f1f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code below for another way to view contents of NetCDF. \n",
    "\n",
    "\"\"\"\n",
    "import netCDF4 as nc\n",
    "fn = '/Users/ianwhidden/pysumma/tutorial/Andrews_data/Andrews_test/settings/snow_zLocalAttributes.nc'\n",
    "snow_zLocalAttributes = nc.Dataset(fn)\n",
    "snow_zLocalAttributes\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5434794d-20c3-4a8f-9d93-ceefd1da1356",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61f37f03-76b2-4f3c-a2b1-eebc4aeaa284",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6413a4-f03f-449d-b267-76eca0cea4b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbfb2aa0-ebfe-4f5c-b8b2-a9908390bf81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33b949dc-744b-4d31-9167-4ab1ce40c9fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b20ecb-52f7-47eb-8416-c3587f99aca1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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