{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "858846c1",
   "metadata": {},
   "source": [
    "# Bias Correction Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f594617",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'bias_correction' from 'pypwsqc' (/home/IWS/seidel/miniconda3/envs/opensense_training/lib/python3.11/site-packages/pypwsqc/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mImportError\u001b[39m                               Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpoligrain\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplg\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxarray\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mxr\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpypwsqc\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m bias_correction \u001b[38;5;28;01mas\u001b[39;00m bc\n",
      "\u001b[31mImportError\u001b[39m: cannot import name 'bias_correction' from 'pypwsqc' (/home/IWS/seidel/miniconda3/envs/opensense_training/lib/python3.11/site-packages/pypwsqc/__init__.py)"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import poligrain as plg\n",
    "import xarray as xr\n",
    "\n",
    "from pypwsqc import bias_correction as bc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7965ba48",
   "metadata": {},
   "source": [
    "## Data download and preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5960c6ee",
   "metadata": {},
   "source": [
    "In this example, we use an open PWS dataset from Amsterdam published by de Vos et al. (2019). By running the cell below, a NetCDF-file will be downloaded to your current repository (if your machine is connected to the internet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bdf540a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "100 5687k  100 5687k    0     0  19.4M      0 --:--:-- --:--:-- --:--:-- 19.4M\n"
     ]
    }
   ],
   "source": [
    "!curl -OL https://github.com/OpenSenseAction/OS_data_format_conventions/raw/main/notebooks/data/OpenSense_PWS_example_format_data.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36fa8a22",
   "metadata": {},
   "source": [
    "### Load the PWS data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb2305bf",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 237MB\n",
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       "    longitude  (id) float64 1kB 4.671 4.675 4.677 4.678 ... 5.036 5.041 5.045\n",
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       "Attributes:\n",
       "    title:                 PWS data from Amsterdam\n",
       "    file author:           Maximilian Graf\n",
       "    institution:           Wageningen University and Research, Department of ...\n",
       "    date:                  2022-10-18 10:32:00\n",
       "    source:                Netamo PWS\n",
       "    history:               Data derived and reformated from the originally pu...\n",
       "    naming convention:     OpenSense-0.1\n",
       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
       "    reference:             https://doi.org/10.1029/2019GL083731\n",
       "    comment:               </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-713cbaed-4c52-4f4d-bcfb-fc2c0f56272d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-713cbaed-4c52-4f4d-bcfb-fc2c0f56272d' 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>: 219168</li><li><span class='xr-has-index'>id</span>: 134</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e41225f-afe8-4015-92fd-70d2473ce62d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e41225f-afe8-4015-92fd-70d2473ce62d' class='xr-section-summary' >Coordinates: <span>(5)</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'>2016-05-01T00:05:00 ... 2018-06-01</div><input id='attrs-83911f6f-d718-45b1-b627-9c7931e885cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83911f6f-d718-45b1-b627-9c7931e885cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c878c66b-483f-4666-88b2-c0f003c69179' class='xr-var-data-in' type='checkbox'><label for='data-c878c66b-483f-4666-88b2-c0f003c69179' 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>unit :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-05-01T00:05:00.000000000&#x27;, &#x27;2016-05-01T00:10:00.000000000&#x27;,\n",
       "       &#x27;2016-05-01T00:15:00.000000000&#x27;, ..., &#x27;2018-05-31T23:50:00.000000000&#x27;,\n",
       "       &#x27;2018-05-31T23:55:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27; &#x27;ams2&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;</div><input id='attrs-94d4809e-831b-40bc-92d1-8ee35551e5ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94d4809e-831b-40bc-92d1-8ee35551e5ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dfe0813-d0ae-48e5-94af-197031c9bb18' class='xr-var-data-in' type='checkbox'><label for='data-3dfe0813-d0ae-48e5-94af-197031c9bb18' 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;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;, &#x27;ams11&#x27;, &#x27;ams12&#x27;, &#x27;ams13&#x27;, &#x27;ams14&#x27;, &#x27;ams15&#x27;, &#x27;ams16&#x27;, &#x27;ams17&#x27;,\n",
       "       &#x27;ams18&#x27;, &#x27;ams19&#x27;, &#x27;ams20&#x27;, &#x27;ams21&#x27;, &#x27;ams22&#x27;, &#x27;ams23&#x27;, &#x27;ams24&#x27;, &#x27;ams25&#x27;,\n",
       "       &#x27;ams26&#x27;, &#x27;ams27&#x27;, &#x27;ams28&#x27;, &#x27;ams29&#x27;, &#x27;ams30&#x27;, &#x27;ams31&#x27;, &#x27;ams32&#x27;, &#x27;ams33&#x27;,\n",
       "       &#x27;ams34&#x27;, &#x27;ams35&#x27;, &#x27;ams36&#x27;, &#x27;ams37&#x27;, &#x27;ams38&#x27;, &#x27;ams39&#x27;, &#x27;ams40&#x27;, &#x27;ams41&#x27;,\n",
       "       &#x27;ams42&#x27;, &#x27;ams43&#x27;, &#x27;ams44&#x27;, &#x27;ams45&#x27;, &#x27;ams46&#x27;, &#x27;ams47&#x27;, &#x27;ams48&#x27;, &#x27;ams49&#x27;,\n",
       "       &#x27;ams50&#x27;, &#x27;ams51&#x27;, &#x27;ams52&#x27;, &#x27;ams53&#x27;, &#x27;ams54&#x27;, &#x27;ams55&#x27;, &#x27;ams56&#x27;, &#x27;ams57&#x27;,\n",
       "       &#x27;ams58&#x27;, &#x27;ams59&#x27;, &#x27;ams60&#x27;, &#x27;ams61&#x27;, &#x27;ams62&#x27;, &#x27;ams63&#x27;, &#x27;ams64&#x27;, &#x27;ams65&#x27;,\n",
       "       &#x27;ams66&#x27;, &#x27;ams67&#x27;, &#x27;ams68&#x27;, &#x27;ams69&#x27;, &#x27;ams70&#x27;, &#x27;ams71&#x27;, &#x27;ams72&#x27;, &#x27;ams73&#x27;,\n",
       "       &#x27;ams74&#x27;, &#x27;ams75&#x27;, &#x27;ams76&#x27;, &#x27;ams77&#x27;, &#x27;ams78&#x27;, &#x27;ams79&#x27;, &#x27;ams80&#x27;, &#x27;ams81&#x27;,\n",
       "       &#x27;ams82&#x27;, &#x27;ams83&#x27;, &#x27;ams84&#x27;, &#x27;ams85&#x27;, &#x27;ams86&#x27;, &#x27;ams87&#x27;, &#x27;ams88&#x27;, &#x27;ams89&#x27;,\n",
       "       &#x27;ams90&#x27;, &#x27;ams91&#x27;, &#x27;ams92&#x27;, &#x27;ams93&#x27;, &#x27;ams94&#x27;, &#x27;ams95&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;,\n",
       "       &#x27;ams98&#x27;, &#x27;ams99&#x27;, &#x27;ams100&#x27;, &#x27;ams101&#x27;, &#x27;ams102&#x27;, &#x27;ams103&#x27;, &#x27;ams104&#x27;,\n",
       "       &#x27;ams105&#x27;, &#x27;ams106&#x27;, &#x27;ams107&#x27;, &#x27;ams108&#x27;, &#x27;ams109&#x27;, &#x27;ams110&#x27;, &#x27;ams111&#x27;,\n",
       "       &#x27;ams112&#x27;, &#x27;ams113&#x27;, &#x27;ams114&#x27;, &#x27;ams115&#x27;, &#x27;ams116&#x27;, &#x27;ams117&#x27;, &#x27;ams118&#x27;,\n",
       "       &#x27;ams119&#x27;, &#x27;ams120&#x27;, &#x27;ams121&#x27;, &#x27;ams122&#x27;, &#x27;ams123&#x27;, &#x27;ams124&#x27;, &#x27;ams125&#x27;,\n",
       "       &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;, &#x27;ams132&#x27;,\n",
       "       &#x27;ams133&#x27;, &#x27;ams134&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;nan&#x27; &#x27;nan&#x27; &#x27;nan&#x27; ... &#x27;nan&#x27; &#x27;nan&#x27;</div><input id='attrs-6412428c-e0b6-44c3-887b-29b4a499c31c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6412428c-e0b6-44c3-887b-29b4a499c31c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3328f95d-2818-45b9-9300-5cfe5647c269' class='xr-var-data-in' type='checkbox'><label for='data-3328f95d-2818-45b9-9300-5cfe5647c269' 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>meters</dd><dt><span>longname :</span></dt><dd>meters_above_sea</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;,\n",
       "       &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;],\n",
       "      dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.31 52.3 52.31 ... 52.3 52.26</div><input id='attrs-b6939d17-c41f-4d6e-b0eb-b7f8f14c3455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6939d17-c41f-4d6e-b0eb-b7f8f14c3455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cca31a6-3199-44ad-a9ba-d11ea7fce171' class='xr-var-data-in' type='checkbox'><label for='data-7cca31a6-3199-44ad-a9ba-d11ea7fce171' 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>degrees in WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([52.305896  , 52.302044  , 52.308427  , 52.3460125 , 52.3185718 ,\n",
       "       52.29778   , 52.296925  , 52.2934974 , 52.29419571, 52.25049431,\n",
       "       52.24811396, 52.26693085, 52.260747  , 52.38318474, 52.2609028 ,\n",
       "       52.3795406 , 52.3742245 , 52.385828  , 52.351625  , 52.27627871,\n",
       "       52.25582208, 52.43208637, 52.34437   , 52.279142  , 52.36049   ,\n",
       "       52.277776  , 52.37934869, 52.3554281 , 52.43358697, 52.355287  ,\n",
       "       52.35273245, 52.3456794 , 52.355926  , 52.27835152, 52.27971491,\n",
       "       52.283123  , 52.436889  , 52.431829  , 52.28069   , 52.2926813 ,\n",
       "       52.3876584 , 52.36617   , 52.3155607 , 52.3561545 , 52.353684  ,\n",
       "       52.35675837, 52.34473808, 52.344777  , 52.324336  , 52.311689  ,\n",
       "       52.3743658 , 52.3267374 , 52.311614  , 52.368619  , 52.365304  ,\n",
       "       52.3114207 , 52.3360639 , 52.36694431, 52.345668  , 52.361872  ,\n",
       "       52.3774156 , 52.3544442 , 52.37659878, 52.30656433, 52.347122  ,\n",
       "       52.347146  , 52.364318  , 52.420592  , 52.3922401 , 52.37126378,\n",
       "       52.384832  , 52.2956804 , 52.3383171 , 52.3702157 , 52.304167  ,\n",
       "       52.3477135 , 52.3474113 , 52.35736482, 52.42248595, 52.3630175 ,\n",
       "       52.296284  , 52.430344  , 52.37586384, 52.385221  , 52.40346573,\n",
       "       52.352911  , 52.313202  , 52.4287962 , 52.291897  , 52.291065  ,\n",
       "       52.3438503 , 52.2958859 , 52.342303  , 52.370533  , 52.29106666,\n",
       "       52.38675   , 52.389641  , 52.29942216, 52.356894  , 52.42842   ,\n",
       "       52.349484  , 52.37919928, 52.360095  , 52.35515544, 52.37839   ,\n",
       "       52.36428   , 52.39224409, 52.40145   , 52.3182354 , 52.3340836 ,\n",
       "       52.33424189, 52.3416245 , 52.33705723, 52.33636389, 52.337291  ,\n",
       "       52.271513  , 52.3819577 , 52.269474  , 52.334801  , 52.353509  ,\n",
       "       52.300295  , 52.2996697 , 52.32541   , 52.26782176, 52.326555  ,\n",
       "       52.36057803, 52.247674  , 52.427009  , 52.262895  , 52.313112  ,\n",
       "       52.30725   , 52.4314243 , 52.301688  , 52.2634545 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.671 4.675 4.677 ... 5.041 5.045</div><input id='attrs-1fd3d5ef-353d-4ab3-8ae5-13c5b8573885' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fd3d5ef-353d-4ab3-8ae5-13c5b8573885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-261f6092-6b43-4931-8197-8a4e1940a3a2' class='xr-var-data-in' type='checkbox'><label for='data-261f6092-6b43-4931-8197-8a4e1940a3a2' 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>degrees in WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array([4.670664  , 4.67494   , 4.677275  , 4.6775537 , 4.6785928 ,\n",
       "       4.678602  , 4.678811  , 4.6914236 , 4.69311403, 4.69746144,\n",
       "       4.70325708, 4.71668363, 4.721024  , 4.7266384 , 4.7294838 ,\n",
       "       4.7443698 , 4.7464102 , 4.750101  , 4.756874  , 4.76169092,\n",
       "       4.76310423, 4.77926731, 4.78131   , 4.783426  , 4.789773  ,\n",
       "       4.795101  , 4.80228758, 4.8053591 , 4.80548646, 4.80853   ,\n",
       "       4.80870933, 4.8096695 , 4.815386  , 4.81591776, 4.81805215,\n",
       "       4.820963  , 4.823946  , 4.830253  , 4.831394  , 4.8382181 ,\n",
       "       4.8407331 , 4.84121   , 4.8443933 , 4.8457529 , 4.846606  ,\n",
       "       4.84722229, 4.85252214, 4.852834  , 4.857523  , 4.861786  ,\n",
       "       4.86189724, 4.8644629 , 4.866243  , 4.866419  , 4.868215  ,\n",
       "       4.870087  , 4.8724196 , 4.87465899, 4.876575  , 4.877924  ,\n",
       "       4.8779407 , 4.8789889 , 4.88051044, 4.88236411, 4.885412  ,\n",
       "       4.885653  , 4.886345  , 4.887069  , 4.8906832 , 4.89147718,\n",
       "       4.8918962 , 4.8930799 , 4.8951159 , 4.8951679 , 4.896876  ,\n",
       "       4.897039  , 4.8970442 , 4.89851619, 4.89944615, 4.8997121 ,\n",
       "       4.899845  , 4.900383  , 4.90048094, 4.900914  , 4.90278912,\n",
       "       4.903877  , 4.905173  , 4.9058957 , 4.906561  , 4.90671   ,\n",
       "       4.9072082 , 4.909413  , 4.90966   , 4.911188  , 4.91129446,\n",
       "       4.9164974 , 4.91687   , 4.91849311, 4.919527  , 4.91978   ,\n",
       "       4.923808  , 4.92543972, 4.925732  , 4.92613967, 4.928533  ,\n",
       "       4.928972  , 4.92969803, 4.94701   , 4.9542801 , 4.95708   ,\n",
       "       4.95919931, 4.9594646 , 4.96209728, 4.96230016, 4.96429   ,\n",
       "       4.964432  , 4.9651068 , 4.966738  , 4.969634  , 4.971247  ,\n",
       "       4.971332  , 4.9713795 , 4.974268  , 4.97452145, 4.978757  ,\n",
       "       4.97953569, 4.994072  , 4.996017  , 5.028072  , 5.03076   ,\n",
       "       5.032035  , 5.0356554 , 5.041156  , 5.0450199 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc1dd1bc-7215-4688-9d93-c814c056e60f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc1dd1bc-7215-4688-9d93-c814c056e60f' class='xr-section-summary' >Data variables: <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>rainfall</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... nan 0.0 0.0 0.0</div><input id='attrs-b2f6426d-12fc-412d-b353-686407528c1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2f6426d-12fc-412d-b353-686407528c1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da193c39-4b55-4943-8a49-d359d5707d93' class='xr-var-data-in' type='checkbox'><label for='data-da193c39-4b55-4943-8a49-d359d5707d93' 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>name :</span></dt><dd>rainfall</dd><dt><span>long_name :</span></dt><dd>rainfall amount per time unit</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>coverage_contant_type :</span></dt><dd>physicalMeasurement</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a83e638e-479d-4543-97ba-8d44ddce7bae' class='xr-section-summary-in' type='checkbox'  ><label for='section-a83e638e-479d-4543-97ba-8d44ddce7bae' class='xr-section-summary' >Indexes: <span>(2)</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-ba13c07d-9c2d-4150-9262-ed85b9b0980c' class='xr-index-data-in' type='checkbox'/><label for='index-ba13c07d-9c2d-4150-9262-ed85b9b0980c' 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;2016-05-01 00:05:00&#x27;, &#x27;2016-05-01 00:10:00&#x27;,\n",
       "               &#x27;2016-05-01 00:15:00&#x27;, &#x27;2016-05-01 00:20:00&#x27;,\n",
       "               &#x27;2016-05-01 00:25:00&#x27;, &#x27;2016-05-01 00:30:00&#x27;,\n",
       "               &#x27;2016-05-01 00:35:00&#x27;, &#x27;2016-05-01 00:40:00&#x27;,\n",
       "               &#x27;2016-05-01 00:45:00&#x27;, &#x27;2016-05-01 00:50:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2018-05-31 23:15:00&#x27;, &#x27;2018-05-31 23:20:00&#x27;,\n",
       "               &#x27;2018-05-31 23:25:00&#x27;, &#x27;2018-05-31 23:30:00&#x27;,\n",
       "               &#x27;2018-05-31 23:35:00&#x27;, &#x27;2018-05-31 23:40:00&#x27;,\n",
       "               &#x27;2018-05-31 23:45:00&#x27;, &#x27;2018-05-31 23:50:00&#x27;,\n",
       "               &#x27;2018-05-31 23:55:00&#x27;, &#x27;2018-06-01 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=219168, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b946c411-810a-4a71-8b28-665c03b6676b' class='xr-index-data-in' type='checkbox'/><label for='index-b946c411-810a-4a71-8b28-665c03b6676b' 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(Index([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;,\n",
       "       ...\n",
       "       &#x27;ams125&#x27;, &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;,\n",
       "       &#x27;ams132&#x27;, &#x27;ams133&#x27;, &#x27;ams134&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;id&#x27;, length=134))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb573965-73b2-4bbd-8a66-6b9d42ef7579' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb573965-73b2-4bbd-8a66-6b9d42ef7579' 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>PWS data from Amsterdam</dd><dt><span>file author :</span></dt><dd>Maximilian Graf</dd><dt><span>institution :</span></dt><dd>Wageningen University and Research, Department of Environmental Sciences</dd><dt><span>date :</span></dt><dd>2022-10-18 10:32:00</dd><dt><span>source :</span></dt><dd>Netamo PWS</dd><dt><span>history :</span></dt><dd>Data derived and reformated from the originally published dataset</dd><dt><span>naming convention :</span></dt><dd>OpenSense-0.1</dd><dt><span>license restrictions :</span></dt><dd>CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.1029/2019GL083731</dd><dt><span>comment :</span></dt><dd></dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 237MB\n",
       "Dimensions:    (time: 219168, id: 134)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2MB 2016-05-01T00:05:00 ... 2018-06-01\n",
       "  * id         (id) <U6 3kB 'ams1' 'ams2' 'ams3' ... 'ams132' 'ams133' 'ams134'\n",
       "    elevation  (id) <U3 2kB 'nan' 'nan' 'nan' 'nan' ... 'nan' 'nan' 'nan' 'nan'\n",
       "    latitude   (id) float64 1kB 52.31 52.3 52.31 52.35 ... 52.43 52.3 52.26\n",
       "    longitude  (id) float64 1kB 4.671 4.675 4.677 4.678 ... 5.036 5.041 5.045\n",
       "Data variables:\n",
       "    rainfall   (id, time) float64 235MB 0.0 0.0 0.0 0.0 0.0 ... nan 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    title:                 PWS data from Amsterdam\n",
       "    file author:           Maximilian Graf\n",
       "    institution:           Wageningen University and Research, Department of ...\n",
       "    date:                  2022-10-18 10:32:00\n",
       "    source:                Netamo PWS\n",
       "    history:               Data derived and reformated from the originally pu...\n",
       "    naming convention:     OpenSense-0.1\n",
       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
       "    reference:             https://doi.org/10.1029/2019GL083731\n",
       "    comment:               "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pws_orig = xr.open_dataset(\"OpenSense_PWS_example_format_data.nc\")\n",
    "ds_pws_orig.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ceaf31",
   "metadata": {},
   "source": [
    "This package handles rainfall data as `xarray`  Datasets. The data set must have `time` and `id` dimensions, `latitude` and `longitude` as coordinates, and `rainfall` as data variable.\n",
    "\n",
    "An example of how to convert .csv data to a `xarray` dataset is found [here](https://github.com/OpenSenseAction/OS_data_format_conventions/blob/main/notebooks/PWS_example_dataset.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52adc16f",
   "metadata": {},
   "source": [
    "### Aggregate PWS data to hourly values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aaabe07",
   "metadata": {},
   "source": [
    "For this bias correction example, we will aggregate the PWS data to hourly values \n",
    "\n",
    "For the aggreation, the new value for an hour is considered as valid if at least 10 out 12 5-min values within one hour have valid data. This can be set by the `min count` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db96cb85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.77 s, sys: 124 ms, total: 9.9 s\n",
      "Wall time: 10.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ds_pws = ds_pws_orig.resample(time=\"1h\").sum(min_count=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9048a05e",
   "metadata": {},
   "source": [
    "Add cartesian coordinates to dataset using `poligrain` tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2ed3ba1-8191-4a8e-8efc-2348d2a70520",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if lat /lon instead of latitude/longitude\n",
    "ds_pws.coords[\"x\"], ds_pws.coords[\"y\"] = plg.spatial.project_point_coordinates(\n",
    "    ds_pws.longitude,\n",
    "    ds_pws.latitude,\n",
    "    target_projection=\"EPSG:25831\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f026e64e",
   "metadata": {},
   "source": [
    "Let's look at the first PWS station in the data set, now with x and y coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80d22a62",
   "metadata": {},
   "outputs": [
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 292kB\n",
       "Dimensions:    (time: 18265)\n",
       "Coordinates:\n",
       "    id         &lt;U6 24B &#x27;ams1&#x27;\n",
       "    elevation  &lt;U3 12B &#x27;nan&#x27;\n",
       "    latitude   float64 8B 52.31\n",
       "    longitude  float64 8B 4.671\n",
       "  * time       (time) datetime64[ns] 146kB 2016-05-01 ... 2018-06-01\n",
       "    x          float64 8B 6.139e+05\n",
       "    y          float64 8B 5.796e+06\n",
       "Data variables:\n",
       "    rainfall   (time) float64 146kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
       "Attributes:\n",
       "    title:                 PWS data from Amsterdam\n",
       "    file author:           Maximilian Graf\n",
       "    institution:           Wageningen University and Research, Department of ...\n",
       "    date:                  2022-10-18 10:32:00\n",
       "    source:                Netamo PWS\n",
       "    history:               Data derived and reformated from the originally pu...\n",
       "    naming convention:     OpenSense-0.1\n",
       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
       "    reference:             https://doi.org/10.1029/2019GL083731\n",
       "    comment:               </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-590e6f9a-c96b-43b7-8ee2-df623e871de7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-590e6f9a-c96b-43b7-8ee2-df623e871de7' 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>: 18265</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5fe3265b-ed14-41b0-a540-72ca19ffa5c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5fe3265b-ed14-41b0-a540-72ca19ffa5c9' class='xr-section-summary' >Coordinates: <span>(7)</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>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27;</div><input id='attrs-cc159ab0-b962-4e75-b511-0660f17f1394' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc159ab0-b962-4e75-b511-0660f17f1394' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d593d43b-ff1a-4aab-bd59-93586f878626' class='xr-var-data-in' type='checkbox'><label for='data-d593d43b-ff1a-4aab-bd59-93586f878626' 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;ams1&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;nan&#x27;</div><input id='attrs-eb7aca79-57ba-4eba-bff5-08c1147c60b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb7aca79-57ba-4eba-bff5-08c1147c60b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4f6c71b-24cd-47bc-b6f8-d07034b8ad97' class='xr-var-data-in' type='checkbox'><label for='data-f4f6c71b-24cd-47bc-b6f8-d07034b8ad97' 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>meters</dd><dt><span>longname :</span></dt><dd>meters_above_sea</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;nan&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.31</div><input id='attrs-36e3192e-1c3f-4474-aa1a-efca15f53800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36e3192e-1c3f-4474-aa1a-efca15f53800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f19282-7c41-475e-a1cf-314abfdd99c4' class='xr-var-data-in' type='checkbox'><label for='data-97f19282-7c41-475e-a1cf-314abfdd99c4' 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>degrees in WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array(52.305896)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.671</div><input id='attrs-8bbe4496-ee82-439a-807c-66c23afa1fab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bbe4496-ee82-439a-807c-66c23afa1fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-082df28d-9b23-4f27-9bc3-6599ab29984b' class='xr-var-data-in' type='checkbox'><label for='data-082df28d-9b23-4f27-9bc3-6599ab29984b' 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>degrees in WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array(4.670664)</pre></div></li><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'>2016-05-01 ... 2018-06-01</div><input id='attrs-57219df5-c556-408d-a3c1-50fe395eaba5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57219df5-c556-408d-a3c1-50fe395eaba5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5eae54e-db92-48a2-aba6-d57ed191c3c9' class='xr-var-data-in' type='checkbox'><label for='data-d5eae54e-db92-48a2-aba6-d57ed191c3c9' 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>unit :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-05-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-01T02:00:00.000000000&#x27;, ..., &#x27;2018-05-31T22:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-31T23:00:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.139e+05</div><input id='attrs-e1ff10b2-8285-4016-945a-f37132a21fb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1ff10b2-8285-4016-945a-f37132a21fb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05979a13-2579-4602-a4f4-6758b26264c5' class='xr-var-data-in' type='checkbox'><label for='data-05979a13-2579-4602-a4f4-6758b26264c5' 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>projection :</span></dt><dd>EPSG:25831</dd></dl></div><div class='xr-var-data'><pre>array(613904.54048209)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.796e+06</div><input id='attrs-2108816c-8afb-4312-abc7-7a9a504e06ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2108816c-8afb-4312-abc7-7a9a504e06ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7095c4c9-b217-4643-85c2-2ae1ba6f74a5' class='xr-var-data-in' type='checkbox'><label for='data-7095c4c9-b217-4643-85c2-2ae1ba6f74a5' 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>projection :</span></dt><dd>EPSG:25831</dd></dl></div><div class='xr-var-data'><pre>array(5796375.93552898)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4cbf4bf8-64a8-48af-971d-93e4907f9fcf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4cbf4bf8-64a8-48af-971d-93e4907f9fcf' class='xr-section-summary' >Data variables: <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>rainfall</span></div><div class='xr-var-dims'>(time)</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 0.0 nan</div><input id='attrs-285f8691-45c9-438b-80a1-80c4b958907d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-285f8691-45c9-438b-80a1-80c4b958907d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1d788e3-fe2b-4c0f-9d5b-6872e4e4fa8f' class='xr-var-data-in' type='checkbox'><label for='data-c1d788e3-fe2b-4c0f-9d5b-6872e4e4fa8f' 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>name :</span></dt><dd>rainfall</dd><dt><span>long_name :</span></dt><dd>rainfall amount per time unit</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>coverage_contant_type :</span></dt><dd>physicalMeasurement</dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  0., ...,  0.,  0., nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06a50e22-cc27-45e7-8dbd-51a7d2b59db2' class='xr-section-summary-in' type='checkbox'  ><label for='section-06a50e22-cc27-45e7-8dbd-51a7d2b59db2' 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-726034ce-eaf5-4fec-993e-611644521506' class='xr-index-data-in' type='checkbox'/><label for='index-726034ce-eaf5-4fec-993e-611644521506' 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;2016-05-01 00:00:00&#x27;, &#x27;2016-05-01 01:00:00&#x27;,\n",
       "               &#x27;2016-05-01 02:00:00&#x27;, &#x27;2016-05-01 03:00:00&#x27;,\n",
       "               &#x27;2016-05-01 04:00:00&#x27;, &#x27;2016-05-01 05:00:00&#x27;,\n",
       "               &#x27;2016-05-01 06:00:00&#x27;, &#x27;2016-05-01 07:00:00&#x27;,\n",
       "               &#x27;2016-05-01 08:00:00&#x27;, &#x27;2016-05-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2018-05-31 15:00:00&#x27;, &#x27;2018-05-31 16:00:00&#x27;,\n",
       "               &#x27;2018-05-31 17:00:00&#x27;, &#x27;2018-05-31 18:00:00&#x27;,\n",
       "               &#x27;2018-05-31 19:00:00&#x27;, &#x27;2018-05-31 20:00:00&#x27;,\n",
       "               &#x27;2018-05-31 21:00:00&#x27;, &#x27;2018-05-31 22:00:00&#x27;,\n",
       "               &#x27;2018-05-31 23:00:00&#x27;, &#x27;2018-06-01 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=18265, freq=&#x27;h&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb59f6f4-6b12-480c-8893-828f7a40aa7d' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb59f6f4-6b12-480c-8893-828f7a40aa7d' 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>PWS data from Amsterdam</dd><dt><span>file author :</span></dt><dd>Maximilian Graf</dd><dt><span>institution :</span></dt><dd>Wageningen University and Research, Department of Environmental Sciences</dd><dt><span>date :</span></dt><dd>2022-10-18 10:32:00</dd><dt><span>source :</span></dt><dd>Netamo PWS</dd><dt><span>history :</span></dt><dd>Data derived and reformated from the originally published dataset</dd><dt><span>naming convention :</span></dt><dd>OpenSense-0.1</dd><dt><span>license restrictions :</span></dt><dd>CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.1029/2019GL083731</dd><dt><span>comment :</span></dt><dd></dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 292kB\n",
       "Dimensions:    (time: 18265)\n",
       "Coordinates:\n",
       "    id         <U6 24B 'ams1'\n",
       "    elevation  <U3 12B 'nan'\n",
       "    latitude   float64 8B 52.31\n",
       "    longitude  float64 8B 4.671\n",
       "  * time       (time) datetime64[ns] 146kB 2016-05-01 ... 2018-06-01\n",
       "    x          float64 8B 6.139e+05\n",
       "    y          float64 8B 5.796e+06\n",
       "Data variables:\n",
       "    rainfall   (time) float64 146kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 nan\n",
       "Attributes:\n",
       "    title:                 PWS data from Amsterdam\n",
       "    file author:           Maximilian Graf\n",
       "    institution:           Wageningen University and Research, Department of ...\n",
       "    date:                  2022-10-18 10:32:00\n",
       "    source:                Netamo PWS\n",
       "    history:               Data derived and reformated from the originally pu...\n",
       "    naming convention:     OpenSense-0.1\n",
       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
       "    reference:             https://doi.org/10.1029/2019GL083731\n",
       "    comment:               "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pws.isel(id=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd8c77a",
   "metadata": {},
   "source": [
    "### Load reference data set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ca54e2",
   "metadata": {},
   "source": [
    "The bias correction is done by using a quantile mapping based on gamma funtions. For this we need a referecence data. In this example 20 randomly chosen time series from pixels from the gauge-adjusteed KNMI radar product over the Amsterdam Metropolitan area are used as reference for the bias correction.\n",
    "The following cell loads this data set and adds cartesian coordinates as shown above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1607c32d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# path needs to be changed for github\n",
    "ds_ref = xr.open_dataset(\"./data/RadarRef_AMS.nc\")\n",
    "ds_ref.load()\n",
    "ds_ref.coords[\"x\"], ds_ref.coords[\"y\"] = plg.spatial.project_point_coordinates(\n",
    "    ds_ref.lon,\n",
    "    ds_ref.lat,\n",
    "    target_projection=\"EPSG:25831\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd92cb5",
   "metadata": {},
   "source": [
    "## Selecting the nearest reference station"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354b73da",
   "metadata": {},
   "source": [
    "In this example, we want to bias correct the data from the PWS 'ams65' First we look for the closest neighbor in the reference data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80d655cd",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 40B\n",
       "Dimensions:      (id: 1, n_closest: 1)\n",
       "Coordinates:\n",
       "  * id           (id) &lt;U6 24B &#x27;ams65&#x27;\n",
       "Dimensions without coordinates: n_closest\n",
       "Data variables:\n",
       "    distance     (id, n_closest) float64 8B 784.0\n",
       "    neighbor_id  (id, n_closest) object 8B &#x27;4&#x27;</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-9ae77a57-f9d0-4653-b452-49ad1fd68137' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ae77a57-f9d0-4653-b452-49ad1fd68137' 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'>id</span>: 1</li><li><span>n_closest</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cb90716a-8a25-4e62-9c98-6dcd0becea5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb90716a-8a25-4e62-9c98-6dcd0becea5c' 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'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams65&#x27;</div><input id='attrs-86080f82-d875-4667-b726-126c02960742' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86080f82-d875-4667-b726-126c02960742' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a2a21f2-70d9-4d1b-aa4a-76470fbfc7e1' class='xr-var-data-in' type='checkbox'><label for='data-9a2a21f2-70d9-4d1b-aa4a-76470fbfc7e1' 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;ams65&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e31eeeec-85a2-410c-b41d-04f19a9ab359' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e31eeeec-85a2-410c-b41d-04f19a9ab359' class='xr-section-summary' >Data variables: <span>(2)</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>distance</span></div><div class='xr-var-dims'>(id, n_closest)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>784.0</div><input id='attrs-c71ada74-cf99-4f64-963f-536dc8eb2f5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c71ada74-cf99-4f64-963f-536dc8eb2f5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de2e2e5-518d-4015-a333-d8d0db0b5637' class='xr-var-data-in' type='checkbox'><label for='data-0de2e2e5-518d-4015-a333-d8d0db0b5637' 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([[784.04504589]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neighbor_id</span></div><div class='xr-var-dims'>(id, n_closest)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;4&#x27;</div><input id='attrs-732ef6cc-6c73-4b43-9189-1a8703d0956f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-732ef6cc-6c73-4b43-9189-1a8703d0956f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cba2833-9f92-4901-87ec-13bd2e75a1c1' class='xr-var-data-in' type='checkbox'><label for='data-2cba2833-9f92-4901-87ec-13bd2e75a1c1' 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;4&#x27;]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28478393-fbe4-402e-b5c5-fee91b7e719c' class='xr-section-summary-in' type='checkbox'  ><label for='section-28478393-fbe4-402e-b5c5-fee91b7e719c' 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>id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ec1a6ed-5799-4a72-b866-371740484a93' class='xr-index-data-in' type='checkbox'/><label for='index-8ec1a6ed-5799-4a72-b866-371740484a93' 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(Index([&#x27;ams65&#x27;], dtype=&#x27;object&#x27;, name=&#x27;id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55144718-2393-44ec-a7d2-7f0b285d3a5b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55144718-2393-44ec-a7d2-7f0b285d3a5b' 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> Size: 40B\n",
       "Dimensions:      (id: 1, n_closest: 1)\n",
       "Coordinates:\n",
       "  * id           (id) <U6 24B 'ams65'\n",
       "Dimensions without coordinates: n_closest\n",
       "Data variables:\n",
       "    distance     (id, n_closest) float64 8B 784.0\n",
       "    neighbor_id  (id, n_closest) object 8B '4'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pws_id = \"ams65\"\n",
    "# use poligrain tool to find nearest neighbor.\n",
    "max_distance = 25e3\n",
    "closest_neighbor = plg.spatial.get_closest_points_to_point(\n",
    "    ds_points=ds_pws.sel(id=pws_id),\n",
    "    ds_points_neighbors=ds_ref,\n",
    "    max_distance=max_distance,\n",
    "    n_closest=1,\n",
    ")\n",
    "closest_neighbor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4354ef2b",
   "metadata": {},
   "source": [
    "Plot time series for reference data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d4d8aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f098eab6950>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref_id = str(closest_neighbor.neighbor_id.data[0][0])\n",
    "ds_ref.sel(id=ref_id).rainfall.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7d08b4",
   "metadata": {},
   "source": [
    "Plot time series for selected PWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfac73df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0980041c50>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_pws.sel(id=pws_id).rainfall.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77556fce",
   "metadata": {},
   "source": [
    "Convert data to `np.array`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ddd277df",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = ds_ref.sel(id=ref_id).rainfall.data\n",
    "pws = ds_pws.sel(id=pws_id).rainfall.data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c91889",
   "metadata": {},
   "source": [
    "## Fitting data to a theoretical gamma distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5559a6a4",
   "metadata": {},
   "source": [
    "Fit theoretical gamma distribution to the PWS and the reference data. Since rainfall data is highly skewed, we only fit the data above a certain threshold (here 1mm) and treat the values between 0mm and the threshold as censored values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7219878c",
   "metadata": {},
   "outputs": [],
   "source": [
    "shape_ref, scale_ref, p0_ref = bc.fit_gamma_params_with_threshold(ref, 1)\n",
    "print(\n",
    "    f\"Optimal parameters: shape = {shape_ref:.5f}, scale = {scale_ref:.5f}, p0 = {p0_ref:.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b4877f",
   "metadata": {},
   "source": [
    "Fit parameters for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "909da680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal parameters: shape = 0.26362, scale = 2.12160, p0 = 0.83\n"
     ]
    }
   ],
   "source": [
    "shape_ref, scale_ref, p0_ref = bc.fit_gamma_params_with_threshold(ref, 1)\n",
    "print(\n",
    "    f\"Optimal parameters: shape = {shape_ref:.5f}, scale = {scale_ref:.5f}, p0 = {p0_ref:.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1beb2d54",
   "metadata": {},
   "source": [
    "Fit parameters for PWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27db9927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal parameters: shape = 0.49763, scale = 0.98254, p0 = 0.85\n"
     ]
    }
   ],
   "source": [
    "shape_pws, scale_pws, p0_pws = bc.fit_gamma_params_with_threshold(pws, 1)\n",
    "print(\n",
    "    f\"Optimal parameters: shape = {shape_pws:.5f}, scale = {scale_pws:.5f}, p0 = {p0_pws:.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f38f63",
   "metadata": {},
   "source": [
    "Do the bias correction using quantile mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cd85c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "pws_corr = bc.qq_gamma(pws, shape_pws, scale_pws, p0_pws, shape_ref, scale_ref, p0_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b013a135",
   "metadata": {},
   "source": [
    "Create a new `xr.Dataset` for the selected PWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a82490c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_pws = ds_pws.sel(id=pws_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37e62436",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 292kB\n",
       "Dimensions:    (time: 18265)\n",
       "Coordinates:\n",
       "    id         &lt;U6 24B &#x27;ams65&#x27;\n",
       "    elevation  &lt;U3 12B &#x27;nan&#x27;\n",
       "    latitude   float64 8B 52.35\n",
       "    longitude  float64 8B 4.885\n",
       "  * time       (time) datetime64[ns] 146kB 2016-05-01 ... 2018-06-01\n",
       "    x          float64 8B 6.284e+05\n",
       "    y          float64 8B 5.801e+06\n",
       "Data variables:\n",
       "    rainfall   (time) float64 146kB nan nan nan nan nan ... 0.0 0.0 0.0 0.0 nan\n",
       "Attributes:\n",
       "    title:                 PWS data from Amsterdam\n",
       "    file author:           Maximilian Graf\n",
       "    institution:           Wageningen University and Research, Department of ...\n",
       "    date:                  2022-10-18 10:32:00\n",
       "    source:                Netamo PWS\n",
       "    history:               Data derived and reformated from the originally pu...\n",
       "    naming convention:     OpenSense-0.1\n",
       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
       "    reference:             https://doi.org/10.1029/2019GL083731\n",
       "    comment:               </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-1dd84e8b-b7bb-4ced-9bca-e77eb5f75dea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1dd84e8b-b7bb-4ced-9bca-e77eb5f75dea' 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>: 18265</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f9ee78fc-b215-4140-a873-8fd29e39b8c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9ee78fc-b215-4140-a873-8fd29e39b8c1' class='xr-section-summary' >Coordinates: <span>(7)</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>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams65&#x27;</div><input id='attrs-bb4e9d3f-0dd3-4e7f-af73-17e9eb006be9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb4e9d3f-0dd3-4e7f-af73-17e9eb006be9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e71b578-eb4a-4568-84a9-342eafe2bd14' class='xr-var-data-in' type='checkbox'><label for='data-8e71b578-eb4a-4568-84a9-342eafe2bd14' 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;ams65&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;nan&#x27;</div><input id='attrs-25eeacea-3790-441c-8a61-9c65f29090e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25eeacea-3790-441c-8a61-9c65f29090e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76c0f8fc-23ab-475d-8a7d-05c4d8a6af37' class='xr-var-data-in' type='checkbox'><label for='data-76c0f8fc-23ab-475d-8a7d-05c4d8a6af37' 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>meters</dd><dt><span>longname :</span></dt><dd>meters_above_sea</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;nan&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.35</div><input id='attrs-f5dc5d66-3a0f-43c7-9f9f-f724092cd489' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5dc5d66-3a0f-43c7-9f9f-f724092cd489' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a86a747-5182-4734-b101-0ae0428346e8' class='xr-var-data-in' type='checkbox'><label for='data-8a86a747-5182-4734-b101-0ae0428346e8' 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>degrees in WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array(52.347122)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.885</div><input id='attrs-255a827b-594f-4e21-aafe-8c7c1a6026f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-255a827b-594f-4e21-aafe-8c7c1a6026f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca44bb6f-8cea-4a41-89f4-2107ad5b006d' class='xr-var-data-in' type='checkbox'><label for='data-ca44bb6f-8cea-4a41-89f4-2107ad5b006d' 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>degrees in WGS84 projection</dd></dl></div><div class='xr-var-data'><pre>array(4.885412)</pre></div></li><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'>2016-05-01 ... 2018-06-01</div><input id='attrs-89330900-6219-4c60-a29b-614951af3d7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89330900-6219-4c60-a29b-614951af3d7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89ca41db-1090-4894-b0ef-1f22ba0bd9cb' class='xr-var-data-in' type='checkbox'><label for='data-89ca41db-1090-4894-b0ef-1f22ba0bd9cb' 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>unit :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-05-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-01T02:00:00.000000000&#x27;, ..., &#x27;2018-05-31T22:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-31T23:00:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.284e+05</div><input id='attrs-13426c3e-3e3e-4430-a590-34e424f2989a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13426c3e-3e3e-4430-a590-34e424f2989a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2419b6ad-1a28-4cca-9767-028927d11ddc' class='xr-var-data-in' type='checkbox'><label for='data-2419b6ad-1a28-4cca-9767-028927d11ddc' 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>projection :</span></dt><dd>EPSG:25831</dd></dl></div><div class='xr-var-data'><pre>array(628425.17312363)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.801e+06</div><input id='attrs-cdc5963e-2c81-4bc3-bdfd-bfb6847c9785' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdc5963e-2c81-4bc3-bdfd-bfb6847c9785' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64a1f47b-6c3c-4dcf-ab99-b6a8a76144d6' class='xr-var-data-in' type='checkbox'><label for='data-64a1f47b-6c3c-4dcf-ab99-b6a8a76144d6' 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>projection :</span></dt><dd>EPSG:25831</dd></dl></div><div class='xr-var-data'><pre>array(5801320.4511611)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af461113-cffc-447b-84ee-cab38ea0b7e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af461113-cffc-447b-84ee-cab38ea0b7e5' class='xr-section-summary' >Data variables: <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>rainfall</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.0 0.0 0.0 nan</div><input id='attrs-43aee7f0-a20d-4a00-b0d7-db85d398aae9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43aee7f0-a20d-4a00-b0d7-db85d398aae9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edc1ada2-7451-441e-80ab-4a452e9e8213' class='xr-var-data-in' type='checkbox'><label for='data-edc1ada2-7451-441e-80ab-4a452e9e8213' 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>name :</span></dt><dd>rainfall</dd><dt><span>long_name :</span></dt><dd>rainfall amount per time unit</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>coverage_contant_type :</span></dt><dd>physicalMeasurement</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ...,  0.,  0., nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6b9c211-f899-467d-8c09-d18bf2481796' class='xr-section-summary-in' type='checkbox'  ><label for='section-c6b9c211-f899-467d-8c09-d18bf2481796' 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-bbfaa420-444f-4e61-bd31-51df1d84faf0' class='xr-index-data-in' type='checkbox'/><label for='index-bbfaa420-444f-4e61-bd31-51df1d84faf0' 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;2016-05-01 00:00:00&#x27;, &#x27;2016-05-01 01:00:00&#x27;,\n",
       "               &#x27;2016-05-01 02:00:00&#x27;, &#x27;2016-05-01 03:00:00&#x27;,\n",
       "               &#x27;2016-05-01 04:00:00&#x27;, &#x27;2016-05-01 05:00:00&#x27;,\n",
       "               &#x27;2016-05-01 06:00:00&#x27;, &#x27;2016-05-01 07:00:00&#x27;,\n",
       "               &#x27;2016-05-01 08:00:00&#x27;, &#x27;2016-05-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2018-05-31 15:00:00&#x27;, &#x27;2018-05-31 16:00:00&#x27;,\n",
       "               &#x27;2018-05-31 17:00:00&#x27;, &#x27;2018-05-31 18:00:00&#x27;,\n",
       "               &#x27;2018-05-31 19:00:00&#x27;, &#x27;2018-05-31 20:00:00&#x27;,\n",
       "               &#x27;2018-05-31 21:00:00&#x27;, &#x27;2018-05-31 22:00:00&#x27;,\n",
       "               &#x27;2018-05-31 23:00:00&#x27;, &#x27;2018-06-01 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=18265, freq=&#x27;h&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a078b5b-857f-417e-92cf-5ea7f2f06c7b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9a078b5b-857f-417e-92cf-5ea7f2f06c7b' 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>PWS data from Amsterdam</dd><dt><span>file author :</span></dt><dd>Maximilian Graf</dd><dt><span>institution :</span></dt><dd>Wageningen University and Research, Department of Environmental Sciences</dd><dt><span>date :</span></dt><dd>2022-10-18 10:32:00</dd><dt><span>source :</span></dt><dd>Netamo PWS</dd><dt><span>history :</span></dt><dd>Data derived and reformated from the originally published dataset</dd><dt><span>naming convention :</span></dt><dd>OpenSense-0.1</dd><dt><span>license restrictions :</span></dt><dd>CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.1029/2019GL083731</dd><dt><span>comment :</span></dt><dd></dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 292kB\n",
       "Dimensions:    (time: 18265)\n",
       "Coordinates:\n",
       "    id         <U6 24B 'ams65'\n",
       "    elevation  <U3 12B 'nan'\n",
       "    latitude   float64 8B 52.35\n",
       "    longitude  float64 8B 4.885\n",
       "  * time       (time) datetime64[ns] 146kB 2016-05-01 ... 2018-06-01\n",
       "    x          float64 8B 6.284e+05\n",
       "    y          float64 8B 5.801e+06\n",
       "Data variables:\n",
       "    rainfall   (time) float64 146kB nan nan nan nan nan ... 0.0 0.0 0.0 0.0 nan\n",
       "Attributes:\n",
       "    title:                 PWS data from Amsterdam\n",
       "    file author:           Maximilian Graf\n",
       "    institution:           Wageningen University and Research, Department of ...\n",
       "    date:                  2022-10-18 10:32:00\n",
       "    source:                Netamo PWS\n",
       "    history:               Data derived and reformated from the originally pu...\n",
       "    naming convention:     OpenSense-0.1\n",
       "    license restrictions:  CC-BY 4.0 https://creativecommons.org/licenses/by/...\n",
       "    reference:             https://doi.org/10.1029/2019GL083731\n",
       "    comment:               "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_pws"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae351ac",
   "metadata": {},
   "source": [
    "Add the bias corrected values as new data variable to the existing `xr.Dataset`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c2a429fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_dim = ds_pws.rainfall.shape\n",
    "rainfall_bcorr = np.full(var_dim, np.nan)\n",
    "# dimensions might be swapped which causes an error. Transpose (rainfall_bcorr.T) is a first fix for this\n",
    "selected_pws[\"rainfall_biascorr\"] = (selected_pws[\"rainfall\"].dims, pws_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18ad52a",
   "metadata": {},
   "source": [
    "Check for first valid entry in PWS rainfall data and define start and end dates for comparison plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ce912559",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_valid_index = selected_pws.rainfall.notnull().argmax(dim=\"time\")\n",
    "start_date = selected_pws.time.isel(time=first_valid_index)\n",
    "end_date = str(selected_pws.time.data[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b98260c",
   "metadata": {},
   "source": [
    "Plot original and bias corrected cumulatice rainfall sums for the selected PWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b6a123b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_pws.rainfall.sel(time=slice(start_date, end_date)).cumsum().plot(\n",
    "    label=\"pws raw\"\n",
    ")\n",
    "selected_pws.rainfall_biascorr.sel(time=slice(start_date, end_date)).cumsum().plot(\n",
    "    label=\"pws bias corrected\"\n",
    ")\n",
    "ds_ref.sel(id=ref_id).rainfall.sel(time=slice(start_date, end_date)).cumsum().plot(\n",
    "    label=\"reference\"\n",
    ")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Rainfall [mm]\")\n",
    "plt.xlabel(\"Date\")\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5d1ed1",
   "metadata": {},
   "source": [
    "Plot Hexbinplot coparison with reference before and after bias correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32901e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 1\n",
    "da_pws_raw = selected_pws.rainfall.sel(time=slice(start_date, end_date)).data.flatten()[\n",
    "    :-1\n",
    "]\n",
    "da_pws_biascorr = selected_pws.rainfall_biascorr.sel(\n",
    "    time=slice(start_date, end_date)\n",
    ").data.flatten()[:-1]\n",
    "da_ref = (\n",
    "    ds_ref.sel(id=ref_id).rainfall.sel(time=slice(start_date, end_date)).data.flatten()\n",
    ")\n",
    "\n",
    "rainfall_metrics = plg.validation.calculate_rainfall_metrics(\n",
    "    reference=da_ref,\n",
    "    estimate=da_pws_raw,\n",
    "    ref_thresh=threshold,\n",
    "    est_thresh=threshold,\n",
    ")\n",
    "rainfall_metrics_1 = plg.validation.calculate_rainfall_metrics(\n",
    "    reference=da_ref,\n",
    "    estimate=da_pws_biascorr,\n",
    "    ref_thresh=threshold,\n",
    "    est_thresh=threshold,\n",
    ")\n",
    "# plotting the scatter density plots\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "fig.suptitle(f\"Rainfall Comparison: {pws_id} vs Ref_{ref_id}\", fontsize=12, y=1.05)\n",
    "hx = plg.validation.plot_hexbin(\n",
    "    da_ref,\n",
    "    da_pws_raw,\n",
    "    ref_thresh=threshold,\n",
    "    est_thresh=threshold,\n",
    "    # extent=[0,11,0,11],\n",
    "    ax=ax[0],\n",
    ")\n",
    "ax[0].set_title(\"Raw PWS Data\")\n",
    "ax[0].set_xlabel(\"Ref_\" + ref_id + \" [mm]\")\n",
    "ax[0].set_ylabel(pws_id + \" [mm]\")\n",
    "hx = plg.validation.plot_hexbin(\n",
    "    da_ref,\n",
    "    da_pws_biascorr,\n",
    "    ref_thresh=threshold,\n",
    "    est_thresh=threshold,\n",
    "    # extent=[0,11,0,11],\n",
    "    ax=ax[1],\n",
    ")\n",
    "ax[1].set_title(\"Bias corrected PWS Data\")\n",
    "ax[1].set_xlabel(\"Ref_\" + ref_id + \" [mm]\")\n",
    "ax[1].set_ylabel(pws_id + \" [mm]\")\n",
    "# adding metrics to the plot for subplot 0\n",
    "plotted_metrics = (\n",
    "    f\"pearson r: {np.round(rainfall_metrics['pearson_correlation_coefficient'], 2)}\\n\"\n",
    "    f\"rmse: {np.round(rainfall_metrics['root_mean_square_error'], 2)} mm/h\\n\"\n",
    "    f\"bias: {np.round(rainfall_metrics['percent_bias'], 2)}%\\n\"\n",
    "    f\"N points: {rainfall_metrics['N_all']}\\n\"\n",
    "    f\"N nans: {rainfall_metrics['N_nan']}\"\n",
    ")\n",
    "ax[0].text(\n",
    "    0.95,\n",
    "    0.55,\n",
    "    plotted_metrics,\n",
    "    fontsize=8,\n",
    "    transform=ax[0].transAxes,\n",
    "    verticalalignment=\"center\",\n",
    "    horizontalalignment=\"right\",\n",
    "    bbox={\"facecolor\": \"white\", \"alpha\": 0.8},\n",
    ")\n",
    "# adding metrics to the plot for subplot 1\n",
    "plotted_metrics_1 = (\n",
    "    f\"pearson r: {np.round(rainfall_metrics_1['pearson_correlation_coefficient'], 2)}\\n\"\n",
    "    f\"rmse: {np.round(rainfall_metrics_1['root_mean_square_error'], 2)} mm/h\\n\"\n",
    "    f\"bias: {np.round(rainfall_metrics_1['percent_bias'], 2)}%\\n\"\n",
    "    f\"N points: {rainfall_metrics_1['N_all']}\\n\"\n",
    "    f\"N nans: {rainfall_metrics_1['N_nan']}\"\n",
    ")\n",
    "ax[1].text(\n",
    "    0.95,\n",
    "    0.55,\n",
    "    plotted_metrics_1,\n",
    "    fontsize=8,\n",
    "    transform=ax[1].transAxes,\n",
    "    verticalalignment=\"center\",\n",
    "    horizontalalignment=\"right\",\n",
    "    bbox={\"facecolor\": \"white\", \"alpha\": 0.8},\n",
    ")\n",
    "ax[0].set_xlim(0, 12)\n",
    "ax[0].set_ylim(0, 12)\n",
    "ax[1].set_xlim(0, 12)\n",
    "ax[1].set_ylim(0, 12)\n",
    ";"
   ]
  }
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