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   "execution_count": 1,
   "id": "a7234975",
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    "collapsed": false,
    "tags": [
     "remove-cell"
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   "source": [
    "#|hide\n",
    "#|default_exp pyrnet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb103de",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# PyrNet high level data\n",
    "In the following high-level functions to read and examine PyrNet data are collected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80d7f7ee",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "import pyproj\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "from scipy.interpolate import interp1d\n",
    "from toolz import valfilter\n",
    "import pkg_resources as pkg_res\n",
    "\n",
    "# python -m pip install git+https://github.com/hdeneke/trosat-base.git#egg=trosat-base\n",
    "from trosat import sunpos as sp\n",
    "\n",
    "from pyrnet import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bbcda61",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# extra imports for demonstration\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663329e8",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Load Data from Thredds-Server\n",
    "Acquire processed data from server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "381bd621",
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   "source": [
    "#|export\n",
    "# campaign file name map for hdcp2 data\n",
    "campaign_pfx = {\n",
    "    'eifel': 'hope',\n",
    "    'hope_juelich': 'hope',\n",
    "    'hope_melpitz': 'hopm',\n",
    "    'lindenberg': 'ioprao',\n",
    "    'melcol': 'mcol',\n",
    "}\n",
    "\n",
    "# TROPOS thredds urls templates\n",
    "DATA_URL = \"https://tds.tropos.de/thredds/dodsC/scccJher/{dt:%Y}_{campaign}/old/nc/\"\n",
    "FNAME_FMT = '{campaign_pfx}_trop_pyrnet00_l1_rsds_v00_{dt:%Y%m%d}000000.nc'\n",
    "\n",
    "# configuration constants\n",
    "SOLCONST = 1359.0 # Solar constant in Wm-2\n",
    "MAX_MISSING = 1000 # Maximum allowed number of missing records\n",
    "MIN_GOOD = 85400 # Minimum allowed number of good records\n",
    "GEOD = pyproj.Geod(ellps='WGS84')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "472d8d35",
   "metadata": {
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   "source": [
    "#|export\n",
    "def read_hdcp2( dt, fill_gaps=True, resample=False, campaign='hope_juelich' ):\n",
    "    \"\"\"\n",
    "    Read HDCP2-formatted datafiles from the pyranometer network\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    dt: datetime.date\n",
    "        The date of the data to read\n",
    "    fill_gaps: bool\n",
    "        A flag indicating whether gaps should be filled by interpolation\n",
    "    resample: bool\n",
    "        not implemented, no effects\n",
    "    campaign: str\n",
    "        specify campaign ['eifel','hope_juelich','hope_melpitz','lindenberg','melcol']\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    dataset : xarray.Dataset\n",
    "        The pyranometer network observations\n",
    "    \"\"\"\n",
    "    # load dataset\n",
    "    fname = DATA_URL + FNAME_FMT\n",
    "    fname = fname.format(dt=dt,\n",
    "                         campaign=campaign,\n",
    "                         campaign_pfx=campaign_pfx[campaign])\n",
    "    ds = xr.open_dataset(fname,mask_and_scale=False)\n",
    "\n",
    "    # select good stations\n",
    "    igood = (np.sum(ds.rsds.data<-900.0,axis=0)<MAX_MISSING)&(np.sum(ds.rsds_flag==1,axis=0)>MIN_GOOD)\n",
    "    ds = ds.isel(nstations=igood)\n",
    "\n",
    "    # fill gaps if requested\n",
    "    if fill_gaps==True:\n",
    "        x = (ds.time-ds.time[0])/np.timedelta64(1,'s')\n",
    "        for i in np.arange(ds.dims['nstations']):\n",
    "            y = ds.rsds.data[:,i]\n",
    "            m = y>-990.0\n",
    "            if not np.all(m):\n",
    "                f = interp1d(x[m],y[m],'linear',bounds_error=False,fill_value='extrapolate')\n",
    "                ds.rsds[~m,i]=f(x[~m])\n",
    "    # add additional DataArrays\n",
    "    jd = (ds.time.data-np.datetime64(sp.EPOCH_JD2000_0))/np.timedelta64(1,'D')\n",
    "    ds['esd'] = sp.earth_sun_distance(jd[0]+0.5)\n",
    "    szen = sp.sun_angles(jd[:,None],ds.lat.data[None,:],ds.lon.data[None,:])[0]\n",
    "    ds['szen']    = xr.DataArray(szen,dims=('time','nstations'),coords={'time':ds.time.data})\n",
    "    ds['mu0']     = np.cos(np.deg2rad(ds.szen))\n",
    "    ds['gtrans']  = ds.rsds/ds.esd**2/SOLCONST/ds['mu0']\n",
    "    return ds.rename({'rsds_flag':'qaflag','rsds':'ghi'})"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "4db6f0f8",
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   "source": [
    "#|export\n",
    "def get_xy_coords(lon, lat, lonc=None, latc=None):\n",
    "    \"\"\"\n",
    "    Calculate Cartesian coordinates of network stations, relative to the mean\n",
    "    lon/lat of the stations\n",
    "    \"\"\"\n",
    "    n  = len(lon)\n",
    "    if lonc is None:\n",
    "        lonc = lon.mean()\n",
    "    if latc is None:\n",
    "        latc = lat.mean()\n",
    "\n",
    "    az, _, d = np.array([GEOD.inv(lonc, latc, lon[i], lat[i]) for i in range(n)]).T\n",
    "    x = d*np.sin(np.deg2rad(az))\n",
    "    y = d*np.cos(np.deg2rad(az))\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "08e5dcfc",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "# read pyrnet data and add coordinates\n",
    "def read_pyrnet(date, campaign):\n",
    "    \"\"\" Read pyrnet data and add coordinates\n",
    "    \"\"\"\n",
    "    pyr = read_hdcp2(date, campaign=campaign)\n",
    "    x,y = get_xy_coords(pyr.lon,pyr.lat)\n",
    "    pyr['x'] = xr.DataArray(x,dims=('nstations'))\n",
    "    pyr['y'] = xr.DataArray(y,dims=('nstations'))\n",
    "    return pyr"
   ]
  },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (nstations: 52, time: 86400, nv: 2)\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2013-07-15 ... 2013-07-15T23:59:59\n",
       "Dimensions without coordinates: nstations, nv\n",
       "Data variables: (12/13)\n",
       "    station_id  (nstations) int8 ...\n",
       "    lon         (nstations) float32 6.408 6.381 6.405 6.386 ... 6.449 6.44 6.437\n",
       "    lat         (nstations) float32 50.92 50.9 50.91 50.93 ... 50.89 50.89 50.88\n",
       "    zag         float32 ...\n",
       "    time_bnds   (time, nv) datetime64[ns] ...\n",
       "    ghi         (time, nstations) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ...          ...\n",
       "    esd         float64 1.017\n",
       "    szen        (time, nstations) float64 107.4 107.4 107.4 ... 107.6 107.6\n",
       "    mu0         (time, nstations) float64 -0.2994 -0.2998 ... -0.3025 -0.3027\n",
       "    gtrans      (time, nstations) float64 -0.0 -0.0 -0.0 -0.0 ... -0.0 -0.0 -0.0\n",
       "    x           (nstations) float64 -1.523e+03 -3.439e+03 ... 704.5 496.9\n",
       "    y           (nstations) float64 2.869e+03 456.1 ... -321.2 -1.071e+03\n",
       "Attributes:\n",
       "    Title:            Shortwave broadband downwelling shortwave radiation (su...\n",
       "    Institution:      Leibniz Institute for Tropospheric Research (TROPOS), L...\n",
       "    Contact_person:   Andreas Macke (Email: andreas.macke@tropos.de)\n",
       "    Source:           EKO Pyranometer (Model No: ML-020VM; EKO Instruments Co...\n",
       "    History:          Data processed with WRITEPYRFLUXNC_6.pro IDL code at TR...\n",
       "    Conventions:      CF-1.6 where applicable\n",
       "    Processing_date:  File created on 2014-06-17, 13:35:33 UTC\n",
       "    Author:           Madhavan Bomidi (Email: madhavan.bomidi@tropos.de)\n",
       "    Comments:         HOPE-JUELICH Campaign (April 2013 - July 2013)\n",
       "    License:          For non-commercial use only. This data is subject to th...</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-ef45fc17-05cc-469f-9cc1-c8e5e279ca62' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ef45fc17-05cc-469f-9cc1-c8e5e279ca62' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nstations</span>: 52</li><li><span class='xr-has-index'>time</span>: 86400</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d2c72e2a-2ab5-41d4-8a36-60021b24a7fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2c72e2a-2ab5-41d4-8a36-60021b24a7fb' 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'>2013-07-15 ... 2013-07-15T23:59:59</div><input id='attrs-126c5438-a953-46fc-91fe-03ac516f0d73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-126c5438-a953-46fc-91fe-03ac516f0d73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d7d0f2c-fcb4-405c-8b7b-732d458722f5' class='xr-var-data-in' type='checkbox'><label for='data-0d7d0f2c-fcb4-405c-8b7b-732d458722f5' 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;2013-07-15T00:00:00.000000000&#x27;, &#x27;2013-07-15T00:00:01.000000000&#x27;,\n",
       "       &#x27;2013-07-15T00:00:02.000000000&#x27;, ..., &#x27;2013-07-15T23:59:57.000000000&#x27;,\n",
       "       &#x27;2013-07-15T23:59:58.000000000&#x27;, &#x27;2013-07-15T23:59:59.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6822eaa-fb26-45bd-a4c8-e23939ea454e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6822eaa-fb26-45bd-a4c8-e23939ea454e' class='xr-section-summary' >Data variables: <span>(13)</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>station_id</span></div><div class='xr-var-dims'>(nstations)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-be390dbf-f83b-411c-8def-98c563d30a51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be390dbf-f83b-411c-8def-98c563d30a51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd9db55-3074-4a79-b240-6120f4e8ebab' class='xr-var-data-in' type='checkbox'><label for='data-cbd9db55-3074-4a79-b240-6120f4e8ebab' 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>_Unsigned :</span></dt><dd>false</dd><dt><span>long_name :</span></dt><dd>Station index</dd><dt><span>units :</span></dt><dd>1b</dd><dt><span>comment :</span></dt><dd>Pyranometer station numbers </dd><dt><span>valid_range :</span></dt><dd>[  1 100]</dd></dl></div><div class='xr-var-data'><pre>[52 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(nstations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.408 6.381 6.405 ... 6.44 6.437</div><input id='attrs-4797d1a9-c9fc-48ae-ac1a-b62adcde4296' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4797d1a9-c9fc-48ae-ac1a-b62adcde4296' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3a6502a-b392-44eb-b726-b04bcc028e4e' class='xr-var-data-in' type='checkbox'><label for='data-d3a6502a-b392-44eb-b726-b04bcc028e4e' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[6.35 6.51]</dd></dl></div><div class='xr-var-data'><pre>array([6.408407, 6.381179, 6.404592, 6.386205, 6.422904, 6.424812, 6.423558,\n",
       "       6.417454, 6.402166, 6.42344 , 6.418735, 6.397747, 6.399952, 6.410856,\n",
       "       6.406604, 6.406316, 6.429215, 6.436959, 6.428119, 6.441958, 6.410782,\n",
       "       6.450551, 6.445059, 6.457555, 6.453817, 6.470559, 6.407298, 6.47805 ,\n",
       "       6.473388, 6.479889, 6.474764, 6.474844, 6.46441 , 6.459046, 6.462047,\n",
       "       6.425693, 6.413103, 6.426064, 6.428853, 6.446495, 6.418302, 6.413043,\n",
       "       6.388179, 6.381359, 6.4365  , 6.437391, 6.442037, 6.418577, 6.428725,\n",
       "       6.448805, 6.440081, 6.43713 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(nstations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>50.92 50.9 50.91 ... 50.89 50.88</div><input id='attrs-c8e669b2-4b22-480f-8366-a956f7aec923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8e669b2-4b22-480f-8366-a956f7aec923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba86fb1-57e9-4eaa-b641-298de5e2895f' class='xr-var-data-in' type='checkbox'><label for='data-5ba86fb1-57e9-4eaa-b641-298de5e2895f' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[50.84 50.96]</dd></dl></div><div class='xr-var-data'><pre>array([50.92002 , 50.89832 , 50.908817, 50.927933, 50.92457 , 50.92295 ,\n",
       "       50.91557 , 50.939365, 50.927505, 50.91121 , 50.906338, 50.889423,\n",
       "       50.896927, 50.876648, 50.87651 , 50.88097 , 50.900646, 50.898136,\n",
       "       50.90128 , 50.902386, 50.915047, 50.883907, 50.875088, 50.88011 ,\n",
       "       50.875496, 50.860813, 50.91163 , 50.877457, 50.88048 , 50.884468,\n",
       "       50.884335, 50.890015, 50.892166, 50.899513, 50.895405, 50.884052,\n",
       "       50.908413, 50.881935, 50.87653 , 50.880875, 50.861153, 50.93308 ,\n",
       "       50.93326 , 50.9414  , 50.87082 , 50.867634, 50.856617, 50.851254,\n",
       "       50.856277, 50.88944 , 50.891342, 50.8846  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zag</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88f5656c-ac91-45b2-91c9-5690d33a9c5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88f5656c-ac91-45b2-91c9-5690d33a9c5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b703842-4f55-441a-8007-a967c938e8cf' class='xr-var-data-in' type='checkbox'><label for='data-5b703842-4f55-441a-8007-a967c938e8cf' 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>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>Height of all the pyranometer instruments above ground</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1df470e-9405-4032-918a-0f252e7a0ec1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1df470e-9405-4032-918a-0f252e7a0ec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16c9c411-a2f6-430f-baf1-cb0a948fc091' class='xr-var-data-in' type='checkbox'><label for='data-16c9c411-a2f6-430f-baf1-cb0a948fc091' 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>time interval boundaries</dd><dt><span>comment :</span></dt><dd>Start and end times of each averaging period</dd></dl></div><div class='xr-var-data'><pre>[172800 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ghi</span></div><div class='xr-var-dims'>(time, nstations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-5b69d40e-81e6-4814-8d2e-ef9780fe67bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b69d40e-81e6-4814-8d2e-ef9780fe67bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5543f3e4-84e0-4794-855f-01a1015cc286' class='xr-var-data-in' type='checkbox'><label for='data-5543f3e4-84e0-4794-855f-01a1015cc286' 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>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>long_name :</span></dt><dd>Shortwave broadband downwelling irradiance (surface)</dd><dt><span>missing_value :</span></dt><dd>-999.0</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Spectral range 0.35-1.5 micron. Calibrated against reference thermopile pyranometer by the manufacturer. For further details, refer to radiation sensor &amp; data description document</dd><dt><span>valid_range :</span></dt><dd>[   0. 1400.]</dd><dt><span>ancillary_variables :</span></dt><dd>rsds_flag</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qaflag</span></div><div class='xr-var-dims'>(time, nstations)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-31e0d8c7-ce51-44f3-a5c4-b4e3824e7c5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31e0d8c7-ce51-44f3-a5c4-b4e3824e7c5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b4f67f6-38eb-4da8-9b76-805b1976a59b' class='xr-var-data-in' type='checkbox'><label for='data-3b4f67f6-38eb-4da8-9b76-805b1976a59b' 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>_Unsigned :</span></dt><dd>false</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux_in_air status_flag</dd><dt><span>valid_range :</span></dt><dd>[1 4]</dd><dt><span>flag_values :</span></dt><dd>[1 2 3 4]</dd><dt><span>flag_meanings :</span></dt><dd>good_data okey_but_sometimes_dubious_data bad_data_ignore_completely no_observation</dd><dt><span>comment :</span></dt><dd>Observational flags based on levelling &amp; cleanliness observation during battery replacement. Applied over the entire previous week data (for details, refer to radiation sensor &amp; data description document)</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       ...,\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>esd</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.017</div><input id='attrs-d0132a6b-61bf-4cdc-bb13-af5f9aadc2d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0132a6b-61bf-4cdc-bb13-af5f9aadc2d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3b7f7a4-197a-4c55-b984-492cc00b25e4' class='xr-var-data-in' type='checkbox'><label for='data-d3b7f7a4-197a-4c55-b984-492cc00b25e4' 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(1.01670983)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>szen</span></div><div class='xr-var-dims'>(time, nstations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>107.4 107.4 107.4 ... 107.6 107.6</div><input id='attrs-ab9ea087-fa21-4e8d-bd86-84404be7e64f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab9ea087-fa21-4e8d-bd86-84404be7e64f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bebf553-ba69-496d-a359-e21d7cc84550' class='xr-var-data-in' type='checkbox'><label for='data-5bebf553-ba69-496d-a359-e21d7cc84550' 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([[107.42156355, 107.44462125, 107.43292787, ..., 107.44989916,\n",
       "        107.44846449, 107.45534094],\n",
       "       [107.42134562, 107.4444044 , 107.43271004, ..., 107.44967925,\n",
       "        107.44824497, 107.45512152],\n",
       "       [107.4211275 , 107.44418736, 107.43249202, ..., 107.44945915,\n",
       "        107.44802527, 107.45490191],\n",
       "       ...,\n",
       "       [107.58360038, 107.60665094, 107.59496401, ..., 107.61194899,\n",
       "        107.61051169, 107.61738754],\n",
       "       [107.58338373, 107.60643538, 107.59474747, ..., 107.61173036,\n",
       "        107.61029346, 107.6171694 ],\n",
       "       [107.5831669 , 107.60621963, 107.59453074, ..., 107.61151154,\n",
       "        107.61007505, 107.61695108]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu0</span></div><div class='xr-var-dims'>(time, nstations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2994 -0.2998 ... -0.3025 -0.3027</div><input id='attrs-f4987fa0-a05b-496d-8ca0-e2aa93cae64f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4987fa0-a05b-496d-8ca0-e2aa93cae64f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2581e72b-6de6-4519-8045-cf6a6ddcf869' class='xr-var-data-in' type='checkbox'><label for='data-2581e72b-6de6-4519-8045-cf6a6ddcf869' 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([[-0.2993999 , -0.29978385, -0.29958914, ..., -0.29987173,\n",
       "        -0.29984784, -0.29996234],\n",
       "       [-0.29939627, -0.29978024, -0.29958552, ..., -0.29986807,\n",
       "        -0.29984419, -0.29995868],\n",
       "       [-0.29939264, -0.29977663, -0.29958189, ..., -0.2998644 ,\n",
       "        -0.29984053, -0.29995503],\n",
       "       ...,\n",
       "       [-0.30209705, -0.30248054, -0.30228611, ..., -0.30256867,\n",
       "        -0.30254476, -0.30265914],\n",
       "       [-0.30209344, -0.30247695, -0.30228251, ..., -0.30256503,\n",
       "        -0.30254113, -0.30265551],\n",
       "       [-0.30208984, -0.30247336, -0.3022789 , ..., -0.30256139,\n",
       "        -0.3025375 , -0.30265188]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gtrans</span></div><div class='xr-var-dims'>(time, nstations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -0.0 -0.0 ... -0.0 -0.0 -0.0</div><input id='attrs-30a5c418-5e45-4bd8-a7c0-43b7db91479b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30a5c418-5e45-4bd8-a7c0-43b7db91479b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9fb1db8-774f-4d50-b735-54f58f88c688' class='xr-var-data-in' type='checkbox'><label for='data-c9fb1db8-774f-4d50-b735-54f58f88c688' 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([[-0., -0., -0., ..., -0., -0., -0.],\n",
       "       [-0., -0., -0., ..., -0., -0., -0.],\n",
       "       [-0., -0., -0., ..., -0., -0., -0.],\n",
       "       ...,\n",
       "       [-0., -0., -0., ..., -0., -0., -0.],\n",
       "       [-0., -0., -0., ..., -0., -0., -0.],\n",
       "       [-0., -0., -0., ..., -0., -0., -0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(nstations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.523e+03 -3.439e+03 ... 496.9</div><input id='attrs-651699b7-36ae-49ba-b3e8-ac74e257c0d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-651699b7-36ae-49ba-b3e8-ac74e257c0d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41b5efc5-9361-4056-a351-f66440e08afc' class='xr-var-data-in' type='checkbox'><label for='data-41b5efc5-9361-4056-a351-f66440e08afc' 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([-1523.21789156, -3439.40089217, -1791.89101714, -3083.86902155,\n",
       "        -503.77832644,  -369.58212546,  -457.86743022,  -886.67830278,\n",
       "       -1961.7151677 ,  -466.19377611,  -797.18366412, -2274.31312328,\n",
       "       -2118.75945234, -1352.22789805, -1651.50097082, -1671.61263636,\n",
       "         -60.01074819,   484.74115707,  -137.12722818,   836.39589035,\n",
       "       -1356.31414142,  1441.41183565,  1055.10928336,  1934.47780844,\n",
       "        1671.5400057 ,  2850.84360052, -1601.46398945,  3377.07103393,\n",
       "        3048.74821239,  3505.98938454,  3145.33941875,  3150.59366407,\n",
       "        2416.26544707,  2038.53637261,  2249.86724004,  -307.9454203 ,\n",
       "       -1193.27400429,  -281.78434034,   -85.54857598,  1156.05680926,\n",
       "        -828.4727058 , -1196.86700353, -2944.77389456, -3423.59175639,\n",
       "         452.7009251 ,   515.470396  ,   842.7557061 ,  -809.27260981,\n",
       "         -94.61764288,  1318.37245149,   704.50351599,   496.92820059])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(nstations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.869e+03 456.1 ... -1.071e+03</div><input id='attrs-db45d3bc-1ea6-4a8f-8e71-f963a215f5b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db45d3bc-1ea6-4a8f-8e71-f963a215f5b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e129b2d4-920c-45ad-b536-77f3fa208348' class='xr-var-data-in' type='checkbox'><label for='data-e129b2d4-920c-45ad-b536-77f3fa208348' 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([ 2869.40060535,   456.06430866,  1623.10501646,  3750.24277367,\n",
       "        3375.05398904,  3195.10857797,  2373.95463257,  5021.25021335,\n",
       "        3702.16754058,  1888.89798165,  1347.0154099 ,  -534.20916763,\n",
       "         300.46230415, -1955.74600113, -1970.93682243, -1474.84225649,\n",
       "         713.79237428,   434.57843153,   784.2394671 ,   907.3726864 ,\n",
       "        2315.97259013, -1148.14636221, -2129.38191201, -1570.23425912,\n",
       "       -2083.81269339, -3716.69568096,  1935.80493499, -1864.85838616,\n",
       "       -1528.53614986, -1084.78111063, -1099.86488646,  -467.97425474,\n",
       "        -229.0226476 ,   588.15380822,   131.19342247, -1132.2110939 ,\n",
       "        1577.94954427, -1367.73789965, -1969.07666128, -1485.59176533,\n",
       "       -3679.64244822,  4321.94438334,  4343.0117873 ,  5248.49538434,\n",
       "       -2604.33844389, -2958.6806661 , -4184.21393251, -4780.88041897,\n",
       "       -4222.05011596,  -532.8421976 ,  -321.20082545, -1071.08717099])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae85f576-d7bb-48d6-ad94-7531d3c399d7' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae85f576-d7bb-48d6-ad94-7531d3c399d7' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b73070d9-acee-4d5b-82f9-04a51abd1e36' class='xr-index-data-in' type='checkbox'/><label for='index-b73070d9-acee-4d5b-82f9-04a51abd1e36' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-07-15 00:00:00&#x27;, &#x27;2013-07-15 00:00:01&#x27;,\n",
       "               &#x27;2013-07-15 00:00:02&#x27;, &#x27;2013-07-15 00:00:03&#x27;,\n",
       "               &#x27;2013-07-15 00:00:04&#x27;, &#x27;2013-07-15 00:00:05&#x27;,\n",
       "               &#x27;2013-07-15 00:00:06&#x27;, &#x27;2013-07-15 00:00:07&#x27;,\n",
       "               &#x27;2013-07-15 00:00:08&#x27;, &#x27;2013-07-15 00:00:09&#x27;,\n",
       "               ...\n",
       "               &#x27;2013-07-15 23:59:50&#x27;, &#x27;2013-07-15 23:59:51&#x27;,\n",
       "               &#x27;2013-07-15 23:59:52&#x27;, &#x27;2013-07-15 23:59:53&#x27;,\n",
       "               &#x27;2013-07-15 23:59:54&#x27;, &#x27;2013-07-15 23:59:55&#x27;,\n",
       "               &#x27;2013-07-15 23:59:56&#x27;, &#x27;2013-07-15 23:59:57&#x27;,\n",
       "               &#x27;2013-07-15 23:59:58&#x27;, &#x27;2013-07-15 23:59:59&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=86400, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01f99457-f216-4cea-96c8-3b84d81b7d5a' class='xr-section-summary-in' type='checkbox'  ><label for='section-01f99457-f216-4cea-96c8-3b84d81b7d5a' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Title :</span></dt><dd>Shortwave broadband downwelling shortwave radiation (surface)</dd><dt><span>Institution :</span></dt><dd>Leibniz Institute for Tropospheric Research (TROPOS), Leipzig</dd><dt><span>Contact_person :</span></dt><dd>Andreas Macke (Email: andreas.macke@tropos.de)</dd><dt><span>Source :</span></dt><dd>EKO Pyranometer (Model No: ML-020VM; EKO Instruments Co. Ltd., Japan )</dd><dt><span>History :</span></dt><dd>Data processed with WRITEPYRFLUXNC_6.pro IDL code at TROPOS</dd><dt><span>Conventions :</span></dt><dd>CF-1.6 where applicable</dd><dt><span>Processing_date :</span></dt><dd>File created on 2014-06-17, 13:35:33 UTC</dd><dt><span>Author :</span></dt><dd>Madhavan Bomidi (Email: madhavan.bomidi@tropos.de)</dd><dt><span>Comments :</span></dt><dd>HOPE-JUELICH Campaign (April 2013 - July 2013)</dd><dt><span>License :</span></dt><dd>For non-commercial use only. This data is subject to the HD(CP)2 data policy to be found at hdcp2.zmaw.de and in the HD(CP)2 Observation Data Product Standard.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (nstations: 52, time: 86400, nv: 2)\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2013-07-15 ... 2013-07-15T23:59:59\n",
       "Dimensions without coordinates: nstations, nv\n",
       "Data variables: (12/13)\n",
       "    station_id  (nstations) int8 ...\n",
       "    lon         (nstations) float32 6.408 6.381 6.405 6.386 ... 6.449 6.44 6.437\n",
       "    lat         (nstations) float32 50.92 50.9 50.91 50.93 ... 50.89 50.89 50.88\n",
       "    zag         float32 ...\n",
       "    time_bnds   (time, nv) datetime64[ns] ...\n",
       "    ghi         (time, nstations) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ...          ...\n",
       "    esd         float64 1.017\n",
       "    szen        (time, nstations) float64 107.4 107.4 107.4 ... 107.6 107.6\n",
       "    mu0         (time, nstations) float64 -0.2994 -0.2998 ... -0.3025 -0.3027\n",
       "    gtrans      (time, nstations) float64 -0.0 -0.0 -0.0 -0.0 ... -0.0 -0.0 -0.0\n",
       "    x           (nstations) float64 -1.523e+03 -3.439e+03 ... 704.5 496.9\n",
       "    y           (nstations) float64 2.869e+03 456.1 ... -321.2 -1.071e+03\n",
       "Attributes:\n",
       "    Title:            Shortwave broadband downwelling shortwave radiation (su...\n",
       "    Institution:      Leibniz Institute for Tropospheric Research (TROPOS), L...\n",
       "    Contact_person:   Andreas Macke (Email: andreas.macke@tropos.de)\n",
       "    Source:           EKO Pyranometer (Model No: ML-020VM; EKO Instruments Co...\n",
       "    History:          Data processed with WRITEPYRFLUXNC_6.pro IDL code at TR...\n",
       "    Conventions:      CF-1.6 where applicable\n",
       "    Processing_date:  File created on 2014-06-17, 13:35:33 UTC\n",
       "    Author:           Madhavan Bomidi (Email: madhavan.bomidi@tropos.de)\n",
       "    Comments:         HOPE-JUELICH Campaign (April 2013 - July 2013)\n",
       "    License:          For non-commercial use only. This data is subject to th..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|dropout\n",
    "date = dt.datetime(2013,7,15)\n",
    "pyr  = read_pyrnet(date, 'hope_juelich')\n",
    "pyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ca9f035",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pyranometer locations\n",
    "tstart = int(10.5*3600)\n",
    "tstop  = tstart+2*3600\n",
    "tslice = slice(tstart,tstop)\n",
    "\n",
    "plt.figure()\n",
    "p = plt.plot(pyr.x.data,pyr.y.data,'+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e65b7cfc",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tstart = int(13.0*3600)\n",
    "tstop  = tstart+2*3600\n",
    "tslice = slice(tstart,tstop)\n",
    "\n",
    "tlim = [\n",
    "    mdates.date2num(pyr.time.data[tslice][0]),\n",
    "    mdates.date2num(pyr.time.data[tslice][-1])\n",
    "]\n",
    "\n",
    "fig,ax = plt.subplots(1,1)\n",
    "\n",
    "i_sy = np.argsort(-pyr.y.data)\n",
    "im1 = ax.imshow(pyr.gtrans[tslice,i_sy].T, extent=[tlim[0],tlim[1], 1, 1+len(i_sy)], aspect=\"auto\", cmap=\"gray_r\", vmin=0.0, vmax=0.8)\n",
    "_ = ax.set_ylabel(\"# Station\")\n",
    "\n",
    "\n",
    "date_fmt = mdates.DateFormatter('%H:%M')\n",
    "xticks = ax.get_xticks()\n",
    "ax.xaxis_date()\n",
    "ax.xaxis.set_major_formatter(date_fmt)\n",
    "_ = ax.set_xlabel(\"Time [UTC]\")\n",
    "fig.colorbar(im1, ax=ax)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b27dd74",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Read Calibration Files\n",
    "Calibration factors are collected in share/pyrnet_calibration.json. The following function looks up the nearest calibration in time and fill missing values with earlier calibrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f772e0b2",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order of calibration lookup\n",
      "2019-03-01 -> ['2019-04' '2017-04']\n",
      "2018-01-01 -> ['2017-04' '2019-04']\n"
     ]
    }
   ],
   "source": [
    "# pyrnet_calibration.json structure\n",
    "calib = {\n",
    "    \"2017-04\":{\n",
    "        \"001\":[7.1,7.2],\n",
    "        \"002\":[7.51,7.61],\n",
    "        \"003\":[6.9,6.91]\n",
    "    },\n",
    "    \"2019-04\": {\n",
    "        \"001\":[7,None],\n",
    "        \"002\":[7.5,7.6]\n",
    "    }\n",
    "}\n",
    "\n",
    "date1 = np.datetime64(\"2019-03-01\") # closer to \"2019-04\" calibration\n",
    "date2 = np.datetime64(\"2018-01-01\") # closer to \"2017-04\" calibration\n",
    "\n",
    "# parse calibration dates\n",
    "cdates = pd.to_datetime(list(calib.keys())).values\n",
    "\n",
    "# sort calib keys beginning with nearest\n",
    "skeys1 = np.array(list(calib.keys()))[np.argsort(np.abs(date1 - cdates))]\n",
    "skeys2 = np.array(list(calib.keys()))[np.argsort(np.abs(date2 - cdates))]\n",
    "print(\"Order of calibration lookup\")\n",
    "print(date1, '->' ,skeys1)\n",
    "print(date2, '->' ,skeys2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46a1049",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Lookup calibration, update with the most recent calibration but fill with earlier calibration if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31483738",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'001': [7, 7.2], '002': [7.5, 7.6], '003': [6.9, 6.91]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example for date1\n",
    "for i, key in enumerate(skeys1[::-1]):\n",
    "    if i==0:\n",
    "        c = calib[key].copy()\n",
    "        continue\n",
    "\n",
    "    isNone = lambda x: np.any([xi is None for xi in x])\n",
    "    isNotNone = lambda x: np.all([xi is not None for xi in x])\n",
    "    # update with newer calibrations which not include None values\n",
    "    c.update(valfilter(isNotNone, calib[key]))\n",
    "\n",
    "    # update only not None values\n",
    "    for k,v in valfilter(isNone, calib[key]).items():\n",
    "        newv = [c[k][i] if vi is None else vi for i,vi in enumerate(v)]\n",
    "        c.update({k:newv})\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a347827",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'001': [7.1, 7.2], '002': [7.51, 7.61], '003': [6.9, 6.91]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example for date2\n",
    "for i, key in enumerate(skeys2[::-1]):\n",
    "    if i==0:\n",
    "        c = calib[key].copy()\n",
    "        continue\n",
    "\n",
    "    isNone = lambda x: np.any([xi is None for xi in x])\n",
    "    isNotNone = lambda x: np.all([xi is not None for xi in x])\n",
    "    # update with newer calibrations which not include None values\n",
    "    c.update(valfilter(isNotNone, calib[key]))\n",
    "\n",
    "    # update only not None values\n",
    "    for k,v in valfilter(isNone, calib[key]).items():\n",
    "        newv = [c[k][i] if vi is None else vi for i,vi in enumerate(v)]\n",
    "        c.update({k:newv})\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c478027b",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2017-04': {'001': [7.1, 7.2], '002': [7.51, 7.61], '003': [6.9, 6.91]},\n",
       " '2019-04': {'001': [7, None], '002': [7.5, 7.6]}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1b2c864",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "def read_calibration(cfile:str, cdate):\n",
    "    \"\"\"\n",
    "    Parse calibration json file\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    cfile: str\n",
    "        Path of the calibration.json\n",
    "    cdate: list, ndarray, or scalar of type float, datetime or datetime64\n",
    "        A representation of time. If float, interpreted as Julian date.\n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Calibration dictionary sorted by box number.\n",
    "    \"\"\"\n",
    "    cdate = utils.to_datetime64(cdate)\n",
    "    calib = utils.read_json(cfile)\n",
    "    # parse calibration dates\n",
    "    cdates = pd.to_datetime(list(calib.keys())).values\n",
    "    # sort calib keys beginning with nearest\n",
    "    skeys = np.array(list(calib.keys()))[np.argsort(np.abs(cdate - cdates))]\n",
    "    # lookup calibration factors\n",
    "    for i, key in enumerate(skeys[::-1]):\n",
    "        if i==0:\n",
    "            c = calib[key].copy()\n",
    "            continue\n",
    "        isNone = lambda x: np.any([xi is None for xi in x])\n",
    "        isNotNone = lambda x: np.all([xi is not None for xi in x])\n",
    "        # update with newer calibrations which not include None values\n",
    "        c.update(valfilter(isNotNone, calib[key]))\n",
    "        # update only not None values\n",
    "        for k,v in valfilter(isNone, calib[key]).items():\n",
    "            newv = [c[k][i] if vi is None else vi for i,vi in enumerate(v)]\n",
    "            c.update({k:newv})\n",
    "    return c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d40c9d",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Example using the package default pyrnet_calibration.json:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a50ab1f",
   "metadata": {
    "collapsed": false,
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'001': [7.73, 6.98],\n",
       " '002': [7.41, None],\n",
       " '003': [7.51, None],\n",
       " '004': [7.62, 8.23],\n",
       " '005': [7.48, 6.37],\n",
       " '006': [6.71, None],\n",
       " '007': [7.52, 7.2],\n",
       " '008': [7.58, None],\n",
       " '009': [7.59, 6.85],\n",
       " '010': [7.6, 6.74],\n",
       " '011': [7.32, None],\n",
       " '012': [7.6, 6.87],\n",
       " '013': [7.16, None],\n",
       " '014': [7.71, 7.3],\n",
       " '015': [7.59, 6.77],\n",
       " '016': [7.62, None],\n",
       " '017': [7.28, None],\n",
       " '018': [7.57, 6.9],\n",
       " '019': [7.57, None],\n",
       " '020': [7.32, None],\n",
       " '021': [7.4, None],\n",
       " '022': [7.43, None],\n",
       " '023': [7.46, None],\n",
       " '024': [7.73, 6.59],\n",
       " '025': [7.46, 7.14],\n",
       " '026': [7.64, 7.05],\n",
       " '027': [None, None],\n",
       " '028': [7.21, None],\n",
       " '029': [7.61, None],\n",
       " '030': [7.3, None],\n",
       " '031': [7.23, None],\n",
       " '032': [7.7, 6.94],\n",
       " '033': [7.74, 6.89],\n",
       " '034': [6.8, None],\n",
       " '035': [7.62, 7.1],\n",
       " '036': [None, None],\n",
       " '037': [7.72, 6.73],\n",
       " '038': [7.62, 6.93],\n",
       " '039': [7.27, None],\n",
       " '040': [7.15, None],\n",
       " '041': [7.38, None],\n",
       " '042': [7.15, None],\n",
       " '043': [7.41, 7.2],\n",
       " '044': [7.26, 7.05],\n",
       " '045': [7.52, None],\n",
       " '046': [7.7, 7.13],\n",
       " '047': [7.47, 7.4],\n",
       " '048': [7.1, None],\n",
       " '049': [7.74, 7.32],\n",
       " '050': [7.51, None],\n",
       " '051': [7.67, None],\n",
       " '052': [7.46, None],\n",
       " '053': [7.63, 6.48],\n",
       " '054': [7.58, 7.22],\n",
       " '055': [7.41, 6.51],\n",
       " '056': [7, None],\n",
       " '057': [6.95, None],\n",
       " '058': [7.62, 6.9],\n",
       " '059': [7.07, None],\n",
       " '060': [7.64, 6.62],\n",
       " '061': [7.53, None],\n",
       " '062': [7.59, None],\n",
       " '063': [7.63, None],\n",
       " '064': [7.38, None],\n",
       " '065': [7.47, None],\n",
       " '066': [7.48, None],\n",
       " '067': [7.46, None],\n",
       " '068': [7.01, None],\n",
       " '069': [7.33, None],\n",
       " '070': [6.88, None],\n",
       " '071': [7.6, None],\n",
       " '072': [7.58, None],\n",
       " '073': [7.41, None],\n",
       " '074': [7.61, None],\n",
       " '075': [6.91, None],\n",
       " '076': [7.63, None],\n",
       " '077': [7.51, None],\n",
       " '078': [6.93, None],\n",
       " '079': [7.62, None],\n",
       " '080': [7.3, None],\n",
       " '081': [7.64, None],\n",
       " '082': [None, None],\n",
       " '083': [7.44, None],\n",
       " '084': [7.46, None],\n",
       " '085': [7.67, None],\n",
       " '086': [7.45, 6.69],\n",
       " '087': [7.42, 7.11],\n",
       " '088': [7, None],\n",
       " '089': [7.59, None],\n",
       " '090': [7.64, None],\n",
       " '091': [7.47, None],\n",
       " '092': [7.35, None],\n",
       " '093': [7.51, None],\n",
       " '094': [6.95, None],\n",
       " '095': [7.47, None],\n",
       " '096': [7.65, None],\n",
       " '097': [6.9, None],\n",
       " '098': [None, None],\n",
       " '099': [7.51, None],\n",
       " '100': [7.32, None]}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|dropout\n",
    "fn = pkg_res.resource_filename(\"pyrnet\", \"share/pyrnet_calibration.json\")\n",
    "read_calibration(fn,cdate=np.datetime64(\"2018-09-10\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a852e9f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Read Box Serial numbers\n",
    "Similar to reading the calibration, pyranometers attached to each box are stored in .json format. Reassigning of pyranometers to certain boxes might happen. Different to the calibration we will look up the most recent entry (not in the future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "557a4674",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order of calibration lookup\n",
      "2019-03-01 -> 2017-04\n",
      "2019-05-01 -> 2019-04\n"
     ]
    }
   ],
   "source": [
    "# pyrnet_calibration.json structure\n",
    "pyrnetmap = {\n",
    "    \"2019-04\": {\n",
    "        \"001\":[\"S11\",None],\n",
    "        \"002\":[\"S21\",\"S33\"]\n",
    "    },\n",
    "    \"2017-04\":{\n",
    "        \"001\":[\"S11\",\"S21\"],\n",
    "        \"002\":[\"S21\",\"S22\"],\n",
    "        \"003\":[\"S31\",None]\n",
    "    },\n",
    "}\n",
    "\n",
    "date1 = np.datetime64(\"2019-03-01\") # before \"2019-04\"\n",
    "date2 = np.datetime64(\"2019-05-01\") # after \"2019-04\"\n",
    "\n",
    "# parse key dates\n",
    "# require sort for lookup later\n",
    "cdates = pd.to_datetime(list(pyrnetmap.keys())).values\n",
    "isort = np.argsort(cdates)\n",
    "\n",
    "# lookup most recent key\n",
    "skeys1 = np.array(list(pyrnetmap.keys()))[isort][np.sum(date1>cdates)-1]\n",
    "skeys2 = np.array(list(pyrnetmap.keys()))[isort][np.sum(date2>cdates)-1]\n",
    "print(\"Order of calibration lookup\")\n",
    "print(date1, '->' ,skeys1)\n",
    "print(date2, '->' ,skeys2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704bea1f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now we only have to look it up, no merging needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3295ea0d",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "def get_pyrnet_mapping(fn:str, date):\n",
    "    \"\"\"\n",
    "    Parse box - serial number mapping  json file\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fn: str\n",
    "        Path of the mapping.json\n",
    "    date: list, ndarray, or scalar of type float, datetime or datetime64\n",
    "        A representation of time. If float, interpreted as Julian date.\n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Calibration dictionary sorted by box number.\n",
    "    \"\"\"\n",
    "    date = utils.to_datetime64(date)\n",
    "    pyrnetmap = utils.read_json(fn)\n",
    "    # parse key dates\n",
    "    # require sort for lookup later\n",
    "    cdates = pd.to_datetime(list(pyrnetmap.keys())).values\n",
    "    isort = np.argsort(cdates)\n",
    "\n",
    "    # lookup most recent key\n",
    "    skey = np.array(list(pyrnetmap.keys()))[isort][np.sum(date>cdates)-1]\n",
    "\n",
    "    return pyrnetmap[skey]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f8ffaf",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lookup Serial, Boxnumber, calibration at certain date\n",
    "Utility to lookup  box metadata for a certain date.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf2948ac",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#|export\n",
    "def meta_lookup(date,*,serial=None,box=None,cfile=None, mapfile=None):\n",
    "    if cfile is None:\n",
    "        cfile = pkg_res.resource_filename(\"pyrnet\", \"share/pyrnet_calibration.json\")\n",
    "    if mapfile is None:\n",
    "        mapfile = pkg_res.resource_filename(\"pyrnet\", \"share/pyrnet_station_map.json\")\n",
    "\n",
    "    map = get_pyrnet_mapping(mapfile,date)\n",
    "    calib = read_calibration(cfile,date)\n",
    "\n",
    "    if serial is None and box is not None:\n",
    "        box=int(box)\n",
    "        return f\"{box:03d}\", map[f\"{box:03d}\"], calib[f\"{box:03d}\"]\n",
    "    elif serial is not None and box is None:\n",
    "        res = valfilter(lambda x: serial in x, map)\n",
    "        box = list(res.keys())[0]\n",
    "        serial = res[box]\n",
    "        return box,serial,calib[box]\n",
    "    else:\n",
    "        raise ValueError(\"At least one of [station,box] have to be specified.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86dfb9bf",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('006', ['S12078.061', ''], [6.71, None])\n",
      "('010', ['S12128.010', 'S12137.019'], [7.6, 6.74])\n"
     ]
    }
   ],
   "source": [
    "date = np.datetime64(\"2018-10-01\")\n",
    "print(meta_lookup(date,serial=\"S12078.061\"))\n",
    "print(meta_lookup(date,box=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb34abc1",
   "metadata": {
    "collapsed": false,
    "tags": [
     "remove-cell",
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jonas/miniconda3/envs/pyrnet/lib/python3.11/site-packages/nbformat/__init__.py:92: MissingIDFieldWarning: Code cell is missing an id field, this will become a hard error in future nbformat versions. You may want to use `normalize()` on your notebooks before validations (available since nbformat 5.1.4). Previous versions of nbformat are fixing this issue transparently, and will stop doing so in the future.\n",
      "  validate(nb)\n"
     ]
    }
   ],
   "source": [
    "#|hide\n",
    "import nbdev.export\n",
    "import nbformat as nbf\n",
    "name = \"pyrnet\"\n",
    "\n",
    "# Export python module\n",
    "nbdev.export.nb_export( f\"{name}.ipynb\" ,f\"../../src/pyrnet\")\n",
    "\n",
    "# Export to docs\n",
    "ntbk = nbf.read(f\"{name}.ipynb\", nbf.NO_CONVERT)\n",
    "\n",
    "text_search_dict = {\n",
    "    \"#|hide\": \"remove-cell\",  # Remove the whole cell\n",
    "    \"#|dropcode\": \"hide-input\",  # Hide the input w/ a button to show\n",
    "    \"#|dropout\": \"hide-output\"  # Hide the output w/ a button to show\n",
    "}\n",
    "for cell in ntbk.cells:\n",
    "    cell_tags = cell.get('metadata', {}).get('tags', [])\n",
    "    for key, val in text_search_dict.items():\n",
    "            if key in cell['source']:\n",
    "                if val not in cell_tags:\n",
    "                    cell_tags.append(val)\n",
    "    if len(cell_tags) > 0:\n",
    "        cell['metadata']['tags'] = cell_tags\n",
    "    nbf.write(ntbk, f\"../../docs/source/nbs/{name}.ipynb\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "pyrnet",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
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 },
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