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    "# Extreme Value Analysis using Extremes.jl\n",
    "\n",
    "This module provides an easy-to-use wrapper for the `Extremes.jl` Julia package, enabling seamless integration with `xarray` for extreme value analysis. However, do note that `juliacall` is not installed by default when installing `xHydro`. Consult the installation page for instructions.\n",
    "\n",
    "The `Extremes.jl` package is specifically designed for analyzing extreme values and offers a variety of powerful features:\n",
    "\n",
    "- Block Maxima and Threshold Exceedance methods, including popular distributions such as `genextreme`, `gumbel_r`, and `genpareto`.\n",
    "- Flexible parameter estimation techniques, supporting methods like `Probability-Weighted Moments (PWM)`, `Maximum Likelihood Estimation (MLE)`, and `Bayesian Estimation`.\n",
    "- Compatibility with both stationary and non-stationary models for flexible modeling of future extreme events.\n",
    "- Return level estimation for quantifying the risk of extreme events over different return periods.\n",
    "\n",
    "For further information on the `Extremes.jl` package, consult the following resources:\n",
    "- [Extremes.jl - JSS Article](https://doi.org/10.18637/jss.v109.i06)\n",
    "- [Extremes.jl GitHub Repository](https://github.com/jojal5/Extremes.jl)\n"
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    "import os\n",
    "\n",
    "os.environ[\"PYTHON_JULIACALL_AUTOLOAD_IPYTHON_EXTENSION\"] = (\n",
    "    \"no\"  # To prevent random crashes with GitHub's testing interface\n",
    ")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pooch\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import xhydro.extreme_value_analysis as xhe\n",
    "from xhydro.testing.helpers import deveraux\n",
    "\n",
    "clear_output(wait=False)"
   ]
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    "## Data acquisition\n",
    "\n",
    "This example will use climate data from the `GFDL-ESM4.1` model to demonstrate non-stationarity. The dataset includes annual total precipitation data from 1955 to 2100, spanning 97 virtual stations across the province of Quebec. For more information on how to access precipitation data or perform block maxima, consult the [Local frequency analyses](local_frequency_analysis.ipynb) notebook.\n"
   ]
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     "end_time": "2024-12-11T20:54:28.052959Z",
     "start_time": "2024-12-11T20:54:27.909829Z"
    }
   },
   "outputs": [],
   "source": [
    "file = deveraux().fetch(\"extreme_value_analysis/NOAA_GFDL_ESM4.zip\", pooch.Unzip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13kB\n",
       "Dimensions:       (station_num: 5, time: 146)\n",
       "Coordinates:\n",
       "  * station_num   (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * time          (time) datetime64[ns] 1kB 1955-01-01 1956-01-01 ... 2100-01-01\n",
       "    station_name  (station_num, time) object 6kB &#x27;Dozois&#x27; ... &#x27;Lac St-Francois&#x27;\n",
       "Data variables:\n",
       "    total_precip  (station_num, time) float64 6kB 983.6 1.204e+03 ... 1.065e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1543980c-ca22-46f9-b658-c9078c7d7f62' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1543980c-ca22-46f9-b658-c9078c7d7f62' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>time</span>: 146</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b61eb765-eab3-4f36-9d37-12d693cadc32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b61eb765-eab3-4f36-9d37-12d693cadc32' class='xr-section-summary' >Coordinates: <span>(3)</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'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-5b3be0ac-9252-4542-b815-7731d1c08992' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b3be0ac-9252-4542-b815-7731d1c08992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f65a945d-5d3b-4691-869b-786ea3dfa6b0' class='xr-var-data-in' type='checkbox'><label for='data-f65a945d-5d3b-4691-869b-786ea3dfa6b0' 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([1001, 1004, 1008, 1009, 1012])</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'>1955-01-01 ... 2100-01-01</div><input id='attrs-fa0370f0-a8ec-4d11-aa51-1e97ae4ccaee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa0370f0-a8ec-4d11-aa51-1e97ae4ccaee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7643a187-1e52-417a-9edc-f8d8daebe688' class='xr-var-data-in' type='checkbox'><label for='data-7643a187-1e52-417a-9edc-f8d8daebe688' 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;1955-01-01T00:00:00.000000000&#x27;, &#x27;1956-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-01-01T00:00:00.000000000&#x27;, &#x27;1958-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1960-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-01-01T00:00:00.000000000&#x27;, &#x27;1962-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1963-01-01T00:00:00.000000000&#x27;, &#x27;1964-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1965-01-01T00:00:00.000000000&#x27;, &#x27;1966-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1967-01-01T00:00:00.000000000&#x27;, &#x27;1968-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1969-01-01T00:00:00.000000000&#x27;, &#x27;1970-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1971-01-01T00:00:00.000000000&#x27;, &#x27;1972-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1973-01-01T00:00:00.000000000&#x27;, &#x27;1974-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1975-01-01T00:00:00.000000000&#x27;, &#x27;1976-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1977-01-01T00:00:00.000000000&#x27;, &#x27;1978-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1982-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-01-01T00:00:00.000000000&#x27;, &#x27;1984-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-01-01T00:00:00.000000000&#x27;, &#x27;1986-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-01-01T00:00:00.000000000&#x27;, &#x27;1988-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-01-01T00:00:00.000000000&#x27;, &#x27;1990-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1992-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-01-01T00:00:00.000000000&#x27;, &#x27;1994-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-01-01T00:00:00.000000000&#x27;, &#x27;1996-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2025-01-01T00:00:00.000000000&#x27;, &#x27;2026-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2027-01-01T00:00:00.000000000&#x27;, &#x27;2028-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2029-01-01T00:00:00.000000000&#x27;, &#x27;2030-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2031-01-01T00:00:00.000000000&#x27;, &#x27;2032-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2033-01-01T00:00:00.000000000&#x27;, &#x27;2034-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2035-01-01T00:00:00.000000000&#x27;, &#x27;2036-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2037-01-01T00:00:00.000000000&#x27;, &#x27;2038-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2039-01-01T00:00:00.000000000&#x27;, &#x27;2040-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2041-01-01T00:00:00.000000000&#x27;, &#x27;2042-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2043-01-01T00:00:00.000000000&#x27;, &#x27;2044-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2045-01-01T00:00:00.000000000&#x27;, &#x27;2046-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2047-01-01T00:00:00.000000000&#x27;, &#x27;2048-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2049-01-01T00:00:00.000000000&#x27;, &#x27;2050-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2051-01-01T00:00:00.000000000&#x27;, &#x27;2052-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2053-01-01T00:00:00.000000000&#x27;, &#x27;2054-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2055-01-01T00:00:00.000000000&#x27;, &#x27;2056-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2057-01-01T00:00:00.000000000&#x27;, &#x27;2058-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2059-01-01T00:00:00.000000000&#x27;, &#x27;2060-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2061-01-01T00:00:00.000000000&#x27;, &#x27;2062-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2063-01-01T00:00:00.000000000&#x27;, &#x27;2064-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2065-01-01T00:00:00.000000000&#x27;, &#x27;2066-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2067-01-01T00:00:00.000000000&#x27;, &#x27;2068-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2069-01-01T00:00:00.000000000&#x27;, &#x27;2070-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2071-01-01T00:00:00.000000000&#x27;, &#x27;2072-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2073-01-01T00:00:00.000000000&#x27;, &#x27;2074-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2075-01-01T00:00:00.000000000&#x27;, &#x27;2076-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2077-01-01T00:00:00.000000000&#x27;, &#x27;2078-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2079-01-01T00:00:00.000000000&#x27;, &#x27;2080-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2081-01-01T00:00:00.000000000&#x27;, &#x27;2082-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2083-01-01T00:00:00.000000000&#x27;, &#x27;2084-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2085-01-01T00:00:00.000000000&#x27;, &#x27;2086-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2087-01-01T00:00:00.000000000&#x27;, &#x27;2088-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2089-01-01T00:00:00.000000000&#x27;, &#x27;2090-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2091-01-01T00:00:00.000000000&#x27;, &#x27;2092-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2093-01-01T00:00:00.000000000&#x27;, &#x27;2094-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2095-01-01T00:00:00.000000000&#x27;, &#x27;2096-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2097-01-01T00:00:00.000000000&#x27;, &#x27;2098-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2099-01-01T00:00:00.000000000&#x27;, &#x27;2100-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_name</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Dozois&#x27; ... &#x27;Lac St-Francois&#x27;</div><input id='attrs-df5c8430-c3b4-4ebc-afcb-a2c9605f34f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df5c8430-c3b4-4ebc-afcb-a2c9605f34f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d146997-d191-41dc-9e63-ad8e4536e0b6' class='xr-var-data-in' type='checkbox'><label for='data-7d146997-d191-41dc-9e63-ad8e4536e0b6' 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;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "...\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b26171a7-b00a-4e5d-afc9-2af455892c45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b26171a7-b00a-4e5d-afc9-2af455892c45' 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>total_precip</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>983.6 1.204e+03 ... 1.065e+03</div><input id='attrs-0be3c368-ccea-4b7b-abed-26fcbf6e87fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0be3c368-ccea-4b7b-abed-26fcbf6e87fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-573e596b-2c30-40ec-9f03-94752356da13' class='xr-var-data-in' type='checkbox'><label for='data-573e596b-2c30-40ec-9f03-94752356da13' 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>mm y-1</dd></dl></div><div class='xr-var-data'><pre>array([[ 983.6, 1203.6, 1222.3,  834.3, 1133.3,  978.9, 1184. , 1290.5,\n",
       "        1089.8, 1030.5, 1070.8, 1222.9,  969.6,  977.4,  996.4, 1199.7,\n",
       "        1236.6, 1005.5, 1193.6, 1004.5, 1151.8,  968.5, 1236.9, 1015.3,\n",
       "        1155.5,  887.6, 1057.2, 1019.8, 1028.5, 1104.7, 1074.6,  969.8,\n",
       "         781.7,  875.4,  944.5,  860. ,  752.7, 1137.8,  798.2,  824.3,\n",
       "         886.1,  885. ,  968.5, 1092.6,  928.3, 1016.7,  881.3,  822.1,\n",
       "         842.5,  877.3, 1152.8, 1019.3,  913. , 1052.6, 1145. ,  989.6,\n",
       "         953.6, 1139.4,  862.7, 1001.7, 1151.5, 1066.8, 1265.4, 1149. ,\n",
       "        1179.2, 1207.3, 1264.7, 1106.4,  866.1,  993.8, 1223.4,  981.1,\n",
       "        1058.4,  784.7, 1110. , 1162.5,  963.9, 1158.5, 1230.5, 1112.7,\n",
       "        1305.9,  952.9, 1395.6, 1035. , 1243. , 1099.3,  766.7, 1057.8,\n",
       "        1228.2, 1002. , 1104.7, 1231.4, 1421.1, 1514.8, 1075.9, 1129.7,\n",
       "        1139.8, 1242. , 1354.9, 1034.8, 1071.8,  979.6, 1092.4, 1358.3,\n",
       "         954.4,  996.1, 1215.3, 1056.1, 1304. , 1358.1, 1145.6,  979. ,\n",
       "        1398.2,  945.3, 1177.5,  930.9, 1165. , 1121.4, 1054.6, 1279.5,\n",
       "        1163.5, 1367.6, 1622.6, 1418.4, 1375.1, 1096.5, 1129.6,  994.8,\n",
       "        1100.6, 1324.3, 1199.5, 1228.1, 1183.6, 1093.7, 1086.6, 1186.1,\n",
       "        1367.8, 1397. , 1389. , 1141. ,    nan,    nan,    nan,  995.8,\n",
       "        1105.8, 1065. ],\n",
       "       [ 983.6, 1203.6, 1222.3,  872.7, 1133.3,  978.9, 1184. , 1290.5,\n",
       "...\n",
       "        1105.8, 1065. ],\n",
       "       [ 983.6, 1203.6, 1222.3,  872.7, 1133.3,  978.9, 1256.2, 1290.5,\n",
       "        1089.8,  946.5, 1070.8, 1222.9, 1053.8,  977.4,  996.4, 1199.7,\n",
       "        1236.6, 1005.5, 1193.6, 1004.5, 1151.8,  968.5, 1236.9, 1015.3,\n",
       "        1155.5,  887.6, 1057.2, 1019.8, 1028.5, 1104.7, 1074.6,  969.8,\n",
       "         781.7,  875.4,  944.5,  860. ,  752.7, 1137.8,  798.2,  824.3,\n",
       "         886.1,  885. ,  968.5, 1092.6,  928.3, 1016.7,  881.3,  822.1,\n",
       "         842.5,  877.3, 1152.8, 1019.3,  913. , 1052.6, 1145. ,  989.6,\n",
       "         953.6, 1139.4,  862.7, 1001.7, 1151.5, 1066.8, 1265.4, 1149. ,\n",
       "        1179.2, 1207.3, 1264.7, 1106.4,  866.1,  993.8, 1223.4,  981.1,\n",
       "        1058.4,  784.7, 1110. , 1162.5,  963.9, 1158.5, 1230.5, 1112.7,\n",
       "        1305.9,  952.9, 1395.6, 1035. , 1243. , 1099.3,  766.7, 1057.8,\n",
       "        1228.2, 1002. , 1104.7, 1231.4, 1421.1, 1514.8, 1075.9, 1129.7,\n",
       "        1139.8, 1242. , 1354.9, 1034.8, 1071.8,  979.6, 1092.4, 1358.3,\n",
       "         954.4,  996.1, 1215.3, 1056.1, 1304. , 1358.1, 1145.6,  979. ,\n",
       "        1398.2,  945.3, 1177.5,  930.9, 1165. , 1121.4, 1054.6, 1279.5,\n",
       "        1163.5, 1367.6, 1622.6, 1418.4, 1375.1, 1096.5, 1129.6,  994.8,\n",
       "        1100.6, 1324.3, 1199.5, 1228.1, 1183.6, 1093.7, 1086.6, 1186.1,\n",
       "        1367.8, 1397. , 1389. , 1141. ,    nan,    nan,    nan,  995.8,\n",
       "        1105.8, 1065. ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-203489ef-2e9f-46c1-9ada-8d3c9dec81a5' class='xr-section-summary-in' type='checkbox'  ><label for='section-203489ef-2e9f-46c1-9ada-8d3c9dec81a5' 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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a0553aef-2e2e-4dff-b8ff-8c936a88edaa' class='xr-index-data-in' type='checkbox'/><label for='index-a0553aef-2e2e-4dff-b8ff-8c936a88edaa' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ce541b37-50d6-4f16-9c75-911363cce3d0' class='xr-index-data-in' type='checkbox'/><label for='index-ce541b37-50d6-4f16-9c75-911363cce3d0' 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;1955-01-01&#x27;, &#x27;1956-01-01&#x27;, &#x27;1957-01-01&#x27;, &#x27;1958-01-01&#x27;,\n",
       "               &#x27;1959-01-01&#x27;, &#x27;1960-01-01&#x27;, &#x27;1961-01-01&#x27;, &#x27;1962-01-01&#x27;,\n",
       "               &#x27;1963-01-01&#x27;, &#x27;1964-01-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2091-01-01&#x27;, &#x27;2092-01-01&#x27;, &#x27;2093-01-01&#x27;, &#x27;2094-01-01&#x27;,\n",
       "               &#x27;2095-01-01&#x27;, &#x27;2096-01-01&#x27;, &#x27;2097-01-01&#x27;, &#x27;2098-01-01&#x27;,\n",
       "               &#x27;2099-01-01&#x27;, &#x27;2100-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=146, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4bdf96ba-4be6-4a89-a01a-c7a3d299c533' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4bdf96ba-4be6-4a89-a01a-c7a3d299c533' 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: 13kB\n",
       "Dimensions:       (station_num: 5, time: 146)\n",
       "Coordinates:\n",
       "  * station_num   (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * time          (time) datetime64[ns] 1kB 1955-01-01 1956-01-01 ... 2100-01-01\n",
       "    station_name  (station_num, time) object 6kB 'Dozois' ... 'Lac St-Francois'\n",
       "Data variables:\n",
       "    total_precip  (station_num, time) float64 6kB 983.6 1.204e+03 ... 1.065e+03"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(file[0], parse_dates=[0])[\n",
    "    [\"time\", \"station_num\", \"station_name\", \"total_precip\"]\n",
    "]\n",
    "# That dataset is a CSV file, so we need to format it\n",
    "ds = df.to_xarray()\n",
    "ds = ds.set_coords([\"time\", \"station_num\", \"station_name\"])\n",
    "ds = ds.set_index(index=[\"station_num\", \"time\"])\n",
    "ds = ds.unstack(\"index\")\n",
    "ds[\"total_precip\"].attrs[\"units\"] = \"mm y-1\"\n",
    "\n",
    "# Take a subset for the example\n",
    "ds = ds.isel(station_num=slice(0, 5))\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T20:54:28.202587Z",
     "start_time": "2024-12-11T20:54:28.105446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa05413bb30>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[7, 3])\n",
    "ds.isel(station_num=0).total_precip.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\"><b>WARNING</b>\n",
    "    \n",
    "Currently, there is no way to provide `Extremes.jl` with a predefined set of parameters to directly calculate return levels. Until this functionality is implemented in either `xHydro` or `Extremes.jl`, the `.fit()` and `.return_level()` functions should be considered independent. Specifically, the `.return_level()` function will first estimate the distribution parameters before calculating the return levels.\n",
    "    \n",
    "</div>\n",
    "\n",
    "## Parameter estimation\n",
    "\n",
    "The `xhydro.extreme_value_analysis.fit` function serves as the interface between `xHydro` and the `Extremes.jl` package. Most of the arguments mirror those used in the `xhydro.frequency_analysis.local.fit` function. The statistical distribution names have been made to align with those in `SciPy`. Below are a few key differences:\n",
    "\n",
    "- Bayesian Method (`BAYES`): When using the `BAYES` method, you can specify two additional parameters:\n",
    "  - `niter`: Number of iterations for the Bayesian inference algorithm.\n",
    "  - `warmup`: Number of warmup iterations for the Bayesian inference.\n",
    "- Confidence Intervals:  A significant addition to this function is the `confidence_level` parameter, which simplifies the process of obtaining confidence interval compared to the other options available in `xHydro`, as detailed in the other frequency analysis notebooks.\n",
    "\n",
    "In this example, we will estimate a Generalized Extreme Value (GEV) distribution (`genextreme`) using the Probability Weighted Moments (`PWM`) method. Additionally, we will calculate and return the 95% confidence intervals for the estimated parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function fit in module xhydro.extreme_value_analysis.parameterestimation:\n",
      "\n",
      "fit(ds: 'xr.Dataset', locationcov: 'list[str] | None' = None, scalecov: 'list[str] | None' = None, shapecov: 'list[str] | None' = None, variables: 'list[str] | None' = None, dist: 'str | scipy.stats.rv_continuous' = 'genextreme', method: 'str' = 'ML', dim: 'str' = 'time', confidence_level: 'float' = 0.95, niter: 'int' = 5000, warmup: 'int' = 2000) -> 'xr.Dataset'\n",
      "    Fit an array to a univariate distribution along a given dimension.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    ds : xr.DataSet\n",
      "        Xarray Dataset containing the data to be fitted.\n",
      "    locationcov : list[str]\n",
      "        List of names of the covariates for the location parameter.\n",
      "    scalecov : list[str]\n",
      "        List of names of the covariates for the scale parameter.\n",
      "    shapecov : list[str]\n",
      "        List of names of the covariates for the shape parameter.\n",
      "    variables : list[str]\n",
      "        List of variables to be fitted.\n",
      "    dist : str or rv_continuous distribution object\n",
      "        Name of the univariate distribution or the distribution object itself.\n",
      "        Supported distributions are genextreme, gumbel_r, genpareto.\n",
      "    method : {\"ML\", \"PWM\", \"BAYES}\n",
      "        Fitting method, either maximum likelihood (ML), probability weighted moments (PWM) or bayesian (BAYES).\n",
      "    dim : str\n",
      "        Specifies the dimension along which the fit will be performed (default: \"time\").\n",
      "    confidence_level : float\n",
      "        The confidence level for the confidence interval of each parameter.\n",
      "    niter : int\n",
      "        The number of iterations of the bayesian inference algorithm for parameter estimation (default: 5000).\n",
      "    warmup : int\n",
      "        The number of warmup iterations of the bayesian inference algorithm for parameter estimation (default: 2000).\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    xr.Dataset\n",
      "        Dataset of fitted distribution parameters and confidence interval values.\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    Coordinates for which all values are NaNs will be dropped before fitting the distribution. If the array still\n",
      "    contains NaNs or has less valid values than the number of parameters for that distribution,\n",
      "    the distribution parameters will be returned as NaNs.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(xhe.fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T20:54:35.121255Z",
     "start_time": "2024-12-11T20:54:28.257188Z"
    }
   },
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 460B\n",
       "Dimensions:             (station_num: 5, dparams: 3)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * dparams             (dparams) &lt;U5 60B &#x27;shape&#x27; &#x27;loc&#x27; &#x27;scale&#x27;\n",
       "Data variables:\n",
       "    total_precip        (station_num, dparams) float64 120B 0.2012 ... 155.8\n",
       "    total_precip_lower  (station_num, dparams) float64 120B 0.08579 ... 135.3\n",
       "    total_precip_upper  (station_num, dparams) float64 120B 0.3243 ... 174.1</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-d687c2d2-4c8b-4377-ae9d-00afd4ef71f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d687c2d2-4c8b-4377-ae9d-00afd4ef71f6' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>dparams</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f6e71671-8ea3-4835-a510-8f5f411bb688' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6e71671-8ea3-4835-a510-8f5f411bb688' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-36240dcf-c5ce-4e7d-80a1-e879a295bd54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36240dcf-c5ce-4e7d-80a1-e879a295bd54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b695030c-fe45-4840-aaf8-6c43b12a278d' class='xr-var-data-in' type='checkbox'><label for='data-b695030c-fe45-4840-aaf8-6c43b12a278d' 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([1001, 1004, 1008, 1009, 1012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dparams</span></div><div class='xr-var-dims'>(dparams)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;shape&#x27; &#x27;loc&#x27; &#x27;scale&#x27;</div><input id='attrs-e75d1a8f-5199-4888-b0a0-903d2ddef4c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e75d1a8f-5199-4888-b0a0-903d2ddef4c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b2c5d0-6ecf-405c-906f-4dbcf99bb3b8' class='xr-var-data-in' type='checkbox'><label for='data-76b2c5d0-6ecf-405c-906f-4dbcf99bb3b8' 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;shape&#x27;, &#x27;loc&#x27;, &#x27;scale&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39187bd8-dad8-4d96-bef7-28e1c3f7a039' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39187bd8-dad8-4d96-bef7-28e1c3f7a039' class='xr-section-summary' >Data variables: <span>(3)</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>total_precip</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2012 1.034e+03 ... 155.8</div><input id='attrs-c34f68f7-f775-4d34-b467-1974db85dfcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c34f68f7-f775-4d34-b467-1974db85dfcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5762f298-0d51-4d0c-8494-775b1957f447' class='xr-var-data-in' type='checkbox'><label for='data-5762f298-0d51-4d0c-8494-775b1957f447' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Distribution parameters</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Probability weighted moments</dd></dl></div><div class='xr-var-data'><pre>array([[2.01246407e-01, 1.03353303e+03, 1.55768556e+02],\n",
       "       [1.97733417e-01, 1.03371204e+03, 1.55028252e+02],\n",
       "       [1.97923074e-01, 1.03402854e+03, 1.55538643e+02],\n",
       "       [1.96561566e-01, 1.03315216e+03, 1.55869422e+02],\n",
       "       [1.99595469e-01, 1.03410614e+03, 1.55809991e+02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_lower</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08579 1.007e+03 ... 135.3</div><input id='attrs-f9581ce2-aac6-4cf5-9366-f87f85e7ea45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9581ce2-aac6-4cf5-9366-f87f85e7ea45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-351457f5-7420-4acd-b95c-d3e931cb2f70' class='xr-var-data-in' type='checkbox'><label for='data-351457f5-7420-4acd-b95c-d3e931cb2f70' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Lower limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Probability weighted moments</dd></dl></div><div class='xr-var-data'><pre>array([[8.57906590e-02, 1.00708507e+03, 1.35335380e+02],\n",
       "       [8.72127716e-02, 1.00685290e+03, 1.34892661e+02],\n",
       "       [8.09720810e-02, 1.00697277e+03, 1.34653229e+02],\n",
       "       [8.30418972e-02, 1.00505742e+03, 1.35171381e+02],\n",
       "       [8.74067547e-02, 1.00670790e+03, 1.35288868e+02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_upper</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3243 1.063e+03 ... 174.1</div><input id='attrs-0fd3fade-4fbc-4fd3-b5ae-c4d374d3341e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fd3fade-4fbc-4fd3-b5ae-c4d374d3341e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27d3e209-b38b-4202-9ba9-b99a0839af3d' class='xr-var-data-in' type='checkbox'><label for='data-27d3e209-b38b-4202-9ba9-b99a0839af3d' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Upper limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Probability weighted moments</dd></dl></div><div class='xr-var-data'><pre>array([[3.24278248e-01, 1.06270596e+03, 1.74919649e+02],\n",
       "       [3.22688025e-01, 1.06194446e+03, 1.73210253e+02],\n",
       "       [3.24333849e-01, 1.06361182e+03, 1.73790245e+02],\n",
       "       [3.22316469e-01, 1.06199002e+03, 1.74375953e+02],\n",
       "       [3.23358800e-01, 1.06313813e+03, 1.74083093e+02]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0becebac-8970-4d5c-a551-4b62244e33bf' class='xr-section-summary-in' type='checkbox'  ><label for='section-0becebac-8970-4d5c-a551-4b62244e33bf' 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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4387f36e-d300-4077-88b1-0455c839f385' class='xr-index-data-in' type='checkbox'/><label for='index-4387f36e-d300-4077-88b1-0455c839f385' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dparams</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b006540a-4883-4fb3-9dc9-b3bdfed874bd' class='xr-index-data-in' type='checkbox'/><label for='index-b006540a-4883-4fb3-9dc9-b3bdfed874bd' 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;shape&#x27;, &#x27;loc&#x27;, &#x27;scale&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dparams&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fc5a007-749e-4b3a-abc8-5c7f714d0d9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2fc5a007-749e-4b3a-abc8-5c7f714d0d9b' 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: 460B\n",
       "Dimensions:             (station_num: 5, dparams: 3)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * dparams             (dparams) <U5 60B 'shape' 'loc' 'scale'\n",
       "Data variables:\n",
       "    total_precip        (station_num, dparams) float64 120B 0.2012 ... 155.8\n",
       "    total_precip_lower  (station_num, dparams) float64 120B 0.08579 ... 135.3\n",
       "    total_precip_upper  (station_num, dparams) float64 120B 0.3243 ... 174.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_stationary = xhe.fit(\n",
    "    ds,\n",
    "    dist=\"genextreme\",\n",
    "    method=\"PWM\",\n",
    "    variables=[\"total_precip\"],\n",
    "    confidence_level=0.95,\n",
    ")\n",
    "fit_stationary"
   ]
  },
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   "source": [
    "## Return levels\n",
    "\n",
    "As mentioned in the warning above, the `xhydro.extreme_value_analysis.return_level` function cannot accept pre-defined parameters and `Extremes.jl` must compute them internally. Therefore, with the inclusion of the `return_period` argument, all function parameters remain the same.\n",
    "\n",
    "In this example, we will estimate a Gumbel distribution (`gumbel_r`) using the Maximum Likelihood (`ML`) method. Additionally, we will calculate and return the 95% confidence intervals for the estimated parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function return_level in module xhydro.extreme_value_analysis.parameterestimation:\n",
      "\n",
      "return_level(ds: 'xr.Dataset', locationcov: 'list[str] | None' = None, scalecov: 'list[str] | None' = None, shapecov: 'list[str] | None' = None, variables: 'list[str] | None' = None, dist: 'str | scipy.stats.rv_continuous' = 'genextreme', method: 'str' = 'ML', dim: 'str' = 'time', confidence_level: 'float' = 0.95, return_period: 'float' = 100, niter: 'int' = 5000, warmup: 'int' = 2000, threshold_pareto: 'float | None' = None, nobs_pareto: 'int | None' = None, nobsperblock_pareto: 'int | None' = None) -> 'xr.Dataset'\n",
      "    Compute the return level associated with a return period based on a given distribution.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    ds : xr.DataSet\n",
      "        Xarray Dataset containing the data for return level calculations.\n",
      "    locationcov : list[str]\n",
      "        List of names of the covariates for the location parameter.\n",
      "    scalecov : list[str]\n",
      "        List of names of the covariates for the scale parameter.\n",
      "    shapecov : list[str]\n",
      "        List of names of the covariates for the shape parameter.\n",
      "    variables : list[str]\n",
      "        List of variables to be fitted.\n",
      "    dist : str or rv_continuous distribution object\n",
      "        Name of the univariate distribution or the distribution object itself.\n",
      "        Supported distributions are genextreme, gumbel_r, genpareto.\n",
      "    method : {\"ML\", \"PWM\", \"BAYES}\n",
      "        Fitting method, either maximum likelihood (ML), probability weighted moments (PWM) or bayesian (BAYES).\n",
      "    dim : str\n",
      "        Specifies the dimension along which the fit will be performed (default: \"time\").\n",
      "    confidence_level : float\n",
      "        The confidence level for the confidence interval of each parameter.\n",
      "    return_period : float\n",
      "        Return period used to compute the return level.\n",
      "    niter : int\n",
      "        The number of iterations of the bayesian inference algorithm for parameter estimation (default: 5000).\n",
      "    warmup : int\n",
      "        The number of warmup iterations of the bayesian inference algorithm for parameter estimation (default: 2000).\n",
      "    threshold_pareto : float\n",
      "        The value above which the Pareto distribution is applied.\n",
      "    nobs_pareto : int\n",
      "        The total number of observations used when applying the Pareto distribution.\n",
      "    nobsperblock_pareto : int\n",
      "        The number of observations per block when applying the Pareto distribution.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    xr.Dataset\n",
      "        Dataset of with the return level and the confidence interval values.\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    Coordinates for which all values are NaNs will be dropped before fitting the distribution. If the array still\n",
      "    contains NaNs or has less valid values than the number of parameters for that distribution,\n",
      "    the distribution parameters will be returned as NaNs.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(xhe.return_level)"
   ]
  },
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    "ExecuteTime": {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 168B\n",
       "Dimensions:             (station_num: 5, return_period: 1)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * return_period       (return_period) int64 8B 100\n",
       "Data variables:\n",
       "    total_precip        (station_num, return_period) float64 40B 1.704e+03 .....\n",
       "    total_precip_lower  (station_num, return_period) float64 40B 1.609e+03 .....\n",
       "    total_precip_upper  (station_num, return_period) float64 40B 1.8e+03 ... ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8b4cce72-09f0-43a1-bf88-41034024772f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b4cce72-09f0-43a1-bf88-41034024772f' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>return_period</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e3529b06-c6ce-4770-9cac-c04f601cf309' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3529b06-c6ce-4770-9cac-c04f601cf309' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-d8bbaf46-22fb-44a4-9ac0-3de2f5f78616' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8bbaf46-22fb-44a4-9ac0-3de2f5f78616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0024cec-d593-4085-be99-35896b96b151' class='xr-var-data-in' type='checkbox'><label for='data-b0024cec-d593-4085-be99-35896b96b151' 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([1001, 1004, 1008, 1009, 1012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>return_period</span></div><div class='xr-var-dims'>(return_period)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-6432caf8-13bf-45da-9035-84b828da1821' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6432caf8-13bf-45da-9035-84b828da1821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0f5099-c75c-48e4-a01f-5986db79eaac' class='xr-var-data-in' type='checkbox'><label for='data-bc0f5099-c75c-48e4-a01f-5986db79eaac' 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([100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81895c27-385b-48cd-91d7-30595c80cba6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81895c27-385b-48cd-91d7-30595c80cba6' class='xr-section-summary' >Data variables: <span>(3)</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>total_precip</span></div><div class='xr-var-dims'>(station_num, return_period)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.704e+03 1.702e+03 ... 1.704e+03</div><input id='attrs-6e1d59cc-985d-48b7-b6f0-0c4ffbd59f41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e1d59cc-985d-48b7-b6f0-0c4ffbd59f41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-628b27d1-53d5-499e-8c86-2b833bb9ef61' class='xr-var-data-in' type='checkbox'><label for='data-628b27d1-53d5-499e-8c86-2b833bb9ef61' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Distribution parameters</dd><dt><span>dist :</span></dt><dd>gumbel_r</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[1704.21907707],\n",
       "       [1702.035359  ],\n",
       "       [1703.88169726],\n",
       "       [1703.05378644],\n",
       "       [1704.28426762]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_lower</span></div><div class='xr-var-dims'>(station_num, return_period)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.609e+03 1.607e+03 ... 1.609e+03</div><input id='attrs-a8fa839b-54a3-4450-ab69-3167b9f9b796' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8fa839b-54a3-4450-ab69-3167b9f9b796' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8379877-7885-4050-b57a-430579cc2c78' class='xr-var-data-in' type='checkbox'><label for='data-c8379877-7885-4050-b57a-430579cc2c78' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Lower limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>gumbel_r</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[1608.62762521],\n",
       "       [1606.82352684],\n",
       "       [1608.41502731],\n",
       "       [1607.47815874],\n",
       "       [1608.73599027]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_upper</span></div><div class='xr-var-dims'>(station_num, return_period)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+03 1.797e+03 ... 1.8e+03</div><input id='attrs-0bc74973-3ba1-406d-b9e9-749b58be89e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bc74973-3ba1-406d-b9e9-749b58be89e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af0f610a-47eb-47c8-ac54-153a54f94ca7' class='xr-var-data-in' type='checkbox'><label for='data-af0f610a-47eb-47c8-ac54-153a54f94ca7' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Upper limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>gumbel_r</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[1799.81052893],\n",
       "       [1797.24719116],\n",
       "       [1799.34836721],\n",
       "       [1798.62941414],\n",
       "       [1799.83254497]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b581e8c1-71ac-4900-b480-c64cd0764420' class='xr-section-summary-in' type='checkbox'  ><label for='section-b581e8c1-71ac-4900-b480-c64cd0764420' 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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f52f714c-aa26-42fc-8f20-3d1e87bc0c54' class='xr-index-data-in' type='checkbox'/><label for='index-f52f714c-aa26-42fc-8f20-3d1e87bc0c54' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>return_period</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3bca821f-7037-4208-b750-ea78962d2c80' class='xr-index-data-in' type='checkbox'/><label for='index-3bca821f-7037-4208-b750-ea78962d2c80' 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([100], dtype=&#x27;int64&#x27;, name=&#x27;return_period&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb6fa0c4-1347-46bd-89ee-8870328e3e17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eb6fa0c4-1347-46bd-89ee-8870328e3e17' 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: 168B\n",
       "Dimensions:             (station_num: 5, return_period: 1)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * return_period       (return_period) int64 8B 100\n",
       "Data variables:\n",
       "    total_precip        (station_num, return_period) float64 40B 1.704e+03 .....\n",
       "    total_precip_lower  (station_num, return_period) float64 40B 1.609e+03 .....\n",
       "    total_precip_upper  (station_num, return_period) float64 40B 1.8e+03 ... ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "rtnlv_stationary = xhe.return_level(\n",
    "    ds,\n",
    "    dist=\"gumbel_r\",\n",
    "    method=\"ML\",\n",
    "    variables=[\"total_precip\"],\n",
    "    confidence_level=0.95,\n",
    "    return_period=100,\n",
    ")\n",
    "rtnlv_stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-stationary model\n",
    "\n",
    "So far, we've skipped three additional arguments—`locationcov`, `scalecov`, and `shapecov`—that accept variable names. These arguments allow you to introduce a non-linear aspect to the statistical model. In non-stationary models, explanatory variables (covariates) can be used to capture changes in model parameters over time or across different conditions. These covariates can represent factors such as time, geographic location, global temperature increases or CO2 concentrations, or any other variable that may influence the distribution parameters.\n",
    "\n",
    "Also, note that the `PWM` method cannot be used with non-stationary models.\n",
    "\n",
    "For this example, we'll keep it simple and assume that the location parameter varies as a linear function of the year. To do this, we'll need to add a new variable containing the year to our dataset and then provide this variable to the `locationcov` argument.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T20:54:37.928189Z",
     "start_time": "2024-12-11T20:54:37.909414Z"
    }
   },
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       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 19kB\n",
       "Dimensions:       (station_num: 5, time: 146)\n",
       "Coordinates:\n",
       "  * station_num   (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * time          (time) datetime64[ns] 1kB 1955-01-01 1956-01-01 ... 2100-01-01\n",
       "    station_name  (station_num, time) object 6kB &#x27;Dozois&#x27; ... &#x27;Lac St-Francois&#x27;\n",
       "Data variables:\n",
       "    total_precip  (station_num, time) float64 6kB 983.6 1.204e+03 ... 1.065e+03\n",
       "    year          (station_num, time) int64 6kB 1955 1956 1957 ... 2099 2100</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-50208493-909b-401e-87d4-908adc6528ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-50208493-909b-401e-87d4-908adc6528ab' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>time</span>: 146</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-29631063-c7ca-4edd-99fd-8bfa6a174e59' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29631063-c7ca-4edd-99fd-8bfa6a174e59' class='xr-section-summary' >Coordinates: <span>(3)</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'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-6b087e23-ce55-49fc-830c-4d051f6f6587' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b087e23-ce55-49fc-830c-4d051f6f6587' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69f01456-6fea-44e6-a715-e32097543953' class='xr-var-data-in' type='checkbox'><label for='data-69f01456-6fea-44e6-a715-e32097543953' 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([1001, 1004, 1008, 1009, 1012])</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'>1955-01-01 ... 2100-01-01</div><input id='attrs-5a0e6e92-c5cd-4160-8d30-397ed002ed1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a0e6e92-c5cd-4160-8d30-397ed002ed1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25a01afa-f05a-462e-8a58-347ec8d878ba' class='xr-var-data-in' type='checkbox'><label for='data-25a01afa-f05a-462e-8a58-347ec8d878ba' 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;1955-01-01T00:00:00.000000000&#x27;, &#x27;1956-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-01-01T00:00:00.000000000&#x27;, &#x27;1958-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1960-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-01-01T00:00:00.000000000&#x27;, &#x27;1962-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1963-01-01T00:00:00.000000000&#x27;, &#x27;1964-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1965-01-01T00:00:00.000000000&#x27;, &#x27;1966-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1967-01-01T00:00:00.000000000&#x27;, &#x27;1968-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1969-01-01T00:00:00.000000000&#x27;, &#x27;1970-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1971-01-01T00:00:00.000000000&#x27;, &#x27;1972-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1973-01-01T00:00:00.000000000&#x27;, &#x27;1974-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1975-01-01T00:00:00.000000000&#x27;, &#x27;1976-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1977-01-01T00:00:00.000000000&#x27;, &#x27;1978-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1982-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-01-01T00:00:00.000000000&#x27;, &#x27;1984-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-01-01T00:00:00.000000000&#x27;, &#x27;1986-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-01-01T00:00:00.000000000&#x27;, &#x27;1988-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-01-01T00:00:00.000000000&#x27;, &#x27;1990-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1992-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-01-01T00:00:00.000000000&#x27;, &#x27;1994-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-01-01T00:00:00.000000000&#x27;, &#x27;1996-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2025-01-01T00:00:00.000000000&#x27;, &#x27;2026-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2027-01-01T00:00:00.000000000&#x27;, &#x27;2028-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2029-01-01T00:00:00.000000000&#x27;, &#x27;2030-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2031-01-01T00:00:00.000000000&#x27;, &#x27;2032-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2033-01-01T00:00:00.000000000&#x27;, &#x27;2034-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2035-01-01T00:00:00.000000000&#x27;, &#x27;2036-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2037-01-01T00:00:00.000000000&#x27;, &#x27;2038-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2039-01-01T00:00:00.000000000&#x27;, &#x27;2040-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2041-01-01T00:00:00.000000000&#x27;, &#x27;2042-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2043-01-01T00:00:00.000000000&#x27;, &#x27;2044-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2045-01-01T00:00:00.000000000&#x27;, &#x27;2046-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2047-01-01T00:00:00.000000000&#x27;, &#x27;2048-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2049-01-01T00:00:00.000000000&#x27;, &#x27;2050-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2051-01-01T00:00:00.000000000&#x27;, &#x27;2052-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2053-01-01T00:00:00.000000000&#x27;, &#x27;2054-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2055-01-01T00:00:00.000000000&#x27;, &#x27;2056-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2057-01-01T00:00:00.000000000&#x27;, &#x27;2058-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2059-01-01T00:00:00.000000000&#x27;, &#x27;2060-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2061-01-01T00:00:00.000000000&#x27;, &#x27;2062-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2063-01-01T00:00:00.000000000&#x27;, &#x27;2064-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2065-01-01T00:00:00.000000000&#x27;, &#x27;2066-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2067-01-01T00:00:00.000000000&#x27;, &#x27;2068-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2069-01-01T00:00:00.000000000&#x27;, &#x27;2070-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2071-01-01T00:00:00.000000000&#x27;, &#x27;2072-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2073-01-01T00:00:00.000000000&#x27;, &#x27;2074-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2075-01-01T00:00:00.000000000&#x27;, &#x27;2076-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2077-01-01T00:00:00.000000000&#x27;, &#x27;2078-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2079-01-01T00:00:00.000000000&#x27;, &#x27;2080-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2081-01-01T00:00:00.000000000&#x27;, &#x27;2082-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2083-01-01T00:00:00.000000000&#x27;, &#x27;2084-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2085-01-01T00:00:00.000000000&#x27;, &#x27;2086-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2087-01-01T00:00:00.000000000&#x27;, &#x27;2088-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2089-01-01T00:00:00.000000000&#x27;, &#x27;2090-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2091-01-01T00:00:00.000000000&#x27;, &#x27;2092-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2093-01-01T00:00:00.000000000&#x27;, &#x27;2094-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2095-01-01T00:00:00.000000000&#x27;, &#x27;2096-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2097-01-01T00:00:00.000000000&#x27;, &#x27;2098-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2099-01-01T00:00:00.000000000&#x27;, &#x27;2100-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_name</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Dozois&#x27; ... &#x27;Lac St-Francois&#x27;</div><input id='attrs-b225c47c-1ee3-41d8-bfd6-b65c5d57ac62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b225c47c-1ee3-41d8-bfd6-b65c5d57ac62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbd0c271-2000-44d1-a0e3-eb036aa4c888' class='xr-var-data-in' type='checkbox'><label for='data-bbd0c271-2000-44d1-a0e3-eb036aa4c888' 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;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "...\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7b642f1-ebbc-4e76-bf94-616156ef140e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7b642f1-ebbc-4e76-bf94-616156ef140e' 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>total_precip</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>983.6 1.204e+03 ... 1.065e+03</div><input id='attrs-5b1dd34b-745b-4236-9dba-db5832dc7c42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b1dd34b-745b-4236-9dba-db5832dc7c42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-690d2b77-b511-4882-afd6-fe4052189464' class='xr-var-data-in' type='checkbox'><label for='data-690d2b77-b511-4882-afd6-fe4052189464' 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>mm y-1</dd></dl></div><div class='xr-var-data'><pre>array([[ 983.6, 1203.6, 1222.3,  834.3, 1133.3,  978.9, 1184. , 1290.5,\n",
       "        1089.8, 1030.5, 1070.8, 1222.9,  969.6,  977.4,  996.4, 1199.7,\n",
       "        1236.6, 1005.5, 1193.6, 1004.5, 1151.8,  968.5, 1236.9, 1015.3,\n",
       "        1155.5,  887.6, 1057.2, 1019.8, 1028.5, 1104.7, 1074.6,  969.8,\n",
       "         781.7,  875.4,  944.5,  860. ,  752.7, 1137.8,  798.2,  824.3,\n",
       "         886.1,  885. ,  968.5, 1092.6,  928.3, 1016.7,  881.3,  822.1,\n",
       "         842.5,  877.3, 1152.8, 1019.3,  913. , 1052.6, 1145. ,  989.6,\n",
       "         953.6, 1139.4,  862.7, 1001.7, 1151.5, 1066.8, 1265.4, 1149. ,\n",
       "        1179.2, 1207.3, 1264.7, 1106.4,  866.1,  993.8, 1223.4,  981.1,\n",
       "        1058.4,  784.7, 1110. , 1162.5,  963.9, 1158.5, 1230.5, 1112.7,\n",
       "        1305.9,  952.9, 1395.6, 1035. , 1243. , 1099.3,  766.7, 1057.8,\n",
       "        1228.2, 1002. , 1104.7, 1231.4, 1421.1, 1514.8, 1075.9, 1129.7,\n",
       "        1139.8, 1242. , 1354.9, 1034.8, 1071.8,  979.6, 1092.4, 1358.3,\n",
       "         954.4,  996.1, 1215.3, 1056.1, 1304. , 1358.1, 1145.6,  979. ,\n",
       "        1398.2,  945.3, 1177.5,  930.9, 1165. , 1121.4, 1054.6, 1279.5,\n",
       "        1163.5, 1367.6, 1622.6, 1418.4, 1375.1, 1096.5, 1129.6,  994.8,\n",
       "        1100.6, 1324.3, 1199.5, 1228.1, 1183.6, 1093.7, 1086.6, 1186.1,\n",
       "        1367.8, 1397. , 1389. , 1141. ,    nan,    nan,    nan,  995.8,\n",
       "        1105.8, 1065. ],\n",
       "       [ 983.6, 1203.6, 1222.3,  872.7, 1133.3,  978.9, 1184. , 1290.5,\n",
       "...\n",
       "        1105.8, 1065. ],\n",
       "       [ 983.6, 1203.6, 1222.3,  872.7, 1133.3,  978.9, 1256.2, 1290.5,\n",
       "        1089.8,  946.5, 1070.8, 1222.9, 1053.8,  977.4,  996.4, 1199.7,\n",
       "        1236.6, 1005.5, 1193.6, 1004.5, 1151.8,  968.5, 1236.9, 1015.3,\n",
       "        1155.5,  887.6, 1057.2, 1019.8, 1028.5, 1104.7, 1074.6,  969.8,\n",
       "         781.7,  875.4,  944.5,  860. ,  752.7, 1137.8,  798.2,  824.3,\n",
       "         886.1,  885. ,  968.5, 1092.6,  928.3, 1016.7,  881.3,  822.1,\n",
       "         842.5,  877.3, 1152.8, 1019.3,  913. , 1052.6, 1145. ,  989.6,\n",
       "         953.6, 1139.4,  862.7, 1001.7, 1151.5, 1066.8, 1265.4, 1149. ,\n",
       "        1179.2, 1207.3, 1264.7, 1106.4,  866.1,  993.8, 1223.4,  981.1,\n",
       "        1058.4,  784.7, 1110. , 1162.5,  963.9, 1158.5, 1230.5, 1112.7,\n",
       "        1305.9,  952.9, 1395.6, 1035. , 1243. , 1099.3,  766.7, 1057.8,\n",
       "        1228.2, 1002. , 1104.7, 1231.4, 1421.1, 1514.8, 1075.9, 1129.7,\n",
       "        1139.8, 1242. , 1354.9, 1034.8, 1071.8,  979.6, 1092.4, 1358.3,\n",
       "         954.4,  996.1, 1215.3, 1056.1, 1304. , 1358.1, 1145.6,  979. ,\n",
       "        1398.2,  945.3, 1177.5,  930.9, 1165. , 1121.4, 1054.6, 1279.5,\n",
       "        1163.5, 1367.6, 1622.6, 1418.4, 1375.1, 1096.5, 1129.6,  994.8,\n",
       "        1100.6, 1324.3, 1199.5, 1228.1, 1183.6, 1093.7, 1086.6, 1186.1,\n",
       "        1367.8, 1397. , 1389. , 1141. ,    nan,    nan,    nan,  995.8,\n",
       "        1105.8, 1065. ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1955 1956 1957 ... 2098 2099 2100</div><input id='attrs-a0127b24-6c43-48fd-9d68-5314524b7ae6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0127b24-6c43-48fd-9d68-5314524b7ae6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccc6516a-38bb-4263-aa5e-052bd618eb37' class='xr-var-data-in' type='checkbox'><label for='data-ccc6516a-38bb-4263-aa5e-052bd618eb37' 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([[1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
       "        1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,\n",
       "        1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "        1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "        1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "        2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,\n",
       "        2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031,\n",
       "        2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042,\n",
       "        2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053,\n",
       "        2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064,\n",
       "        2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075,\n",
       "        2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086,\n",
       "        2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097,\n",
       "        2098, 2099, 2100],\n",
       "       [1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
       "        1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,\n",
       "        1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "        1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "        1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "        2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,\n",
       "...\n",
       "        2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053,\n",
       "        2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064,\n",
       "        2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075,\n",
       "        2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086,\n",
       "        2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097,\n",
       "        2098, 2099, 2100],\n",
       "       [1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
       "        1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,\n",
       "        1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "        1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "        1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "        2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,\n",
       "        2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031,\n",
       "        2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042,\n",
       "        2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053,\n",
       "        2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064,\n",
       "        2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075,\n",
       "        2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086,\n",
       "        2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097,\n",
       "        2098, 2099, 2100]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb2cb390-fd0d-40a2-80cd-4a5f87bfd358' class='xr-section-summary-in' type='checkbox'  ><label for='section-eb2cb390-fd0d-40a2-80cd-4a5f87bfd358' 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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-323fdf4a-3d6c-4459-8123-57be19377889' class='xr-index-data-in' type='checkbox'/><label for='index-323fdf4a-3d6c-4459-8123-57be19377889' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a08efce2-64c8-4cbe-baaa-bb4c17b3d848' class='xr-index-data-in' type='checkbox'/><label for='index-a08efce2-64c8-4cbe-baaa-bb4c17b3d848' 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;1955-01-01&#x27;, &#x27;1956-01-01&#x27;, &#x27;1957-01-01&#x27;, &#x27;1958-01-01&#x27;,\n",
       "               &#x27;1959-01-01&#x27;, &#x27;1960-01-01&#x27;, &#x27;1961-01-01&#x27;, &#x27;1962-01-01&#x27;,\n",
       "               &#x27;1963-01-01&#x27;, &#x27;1964-01-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2091-01-01&#x27;, &#x27;2092-01-01&#x27;, &#x27;2093-01-01&#x27;, &#x27;2094-01-01&#x27;,\n",
       "               &#x27;2095-01-01&#x27;, &#x27;2096-01-01&#x27;, &#x27;2097-01-01&#x27;, &#x27;2098-01-01&#x27;,\n",
       "               &#x27;2099-01-01&#x27;, &#x27;2100-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=146, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-640b2b6e-94b3-4182-9c5f-020808f03872' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-640b2b6e-94b3-4182-9c5f-020808f03872' 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: 19kB\n",
       "Dimensions:       (station_num: 5, time: 146)\n",
       "Coordinates:\n",
       "  * station_num   (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * time          (time) datetime64[ns] 1kB 1955-01-01 1956-01-01 ... 2100-01-01\n",
       "    station_name  (station_num, time) object 6kB 'Dozois' ... 'Lac St-Francois'\n",
       "Data variables:\n",
       "    total_precip  (station_num, time) float64 6kB 983.6 1.204e+03 ... 1.065e+03\n",
       "    year          (station_num, time) int64 6kB 1955 1956 1957 ... 2099 2100"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[\"year\"] = ds.time.dt.year.broadcast_like(ds[\"total_precip\"])\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of the `.fit()` function, adding a covariate will introduce a new entry under the `dparams` dimension. For this example, it created a new entry called `loc_year_covariate` under the `dparams` dimension.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T20:54:40.472181Z",
     "start_time": "2024-12-11T20:54:37.945894Z"
    }
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 808B\n",
       "Dimensions:             (station_num: 5, dparams: 4)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * dparams             (dparams) &lt;U18 288B &#x27;shape&#x27; &#x27;loc&#x27; ... &#x27;scale&#x27;\n",
       "Data variables:\n",
       "    total_precip        (station_num, dparams) float64 160B 0.2051 ... 144.3\n",
       "    total_precip_lower  (station_num, dparams) float64 160B 0.1022 ... 127.0\n",
       "    total_precip_upper  (station_num, dparams) float64 160B 0.308 ... 164.0</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-2b949fdc-bb84-4319-854b-58b445c5d554' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b949fdc-bb84-4319-854b-58b445c5d554' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>dparams</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-88eaa7e8-3d15-453e-afde-f812b793e2ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88eaa7e8-3d15-453e-afde-f812b793e2ef' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-ceba440c-5d12-480a-841b-43931cb5576c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ceba440c-5d12-480a-841b-43931cb5576c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cefb705-2c6a-4db6-ae1c-7710d853c9c8' class='xr-var-data-in' type='checkbox'><label for='data-6cefb705-2c6a-4db6-ae1c-7710d853c9c8' 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([1001, 1004, 1008, 1009, 1012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dparams</span></div><div class='xr-var-dims'>(dparams)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;shape&#x27; &#x27;loc&#x27; ... &#x27;scale&#x27;</div><input id='attrs-62f36a0d-2db7-474f-8b32-8312c99bc914' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62f36a0d-2db7-474f-8b32-8312c99bc914' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95b5a89b-4d84-4826-8a4e-2a85d3b1588c' class='xr-var-data-in' type='checkbox'><label for='data-95b5a89b-4d84-4826-8a4e-2a85d3b1588c' 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;shape&#x27;, &#x27;loc&#x27;, &#x27;loc_year_covariate&#x27;, &#x27;scale&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-980d9b4d-d6a7-49ce-b7af-289a93048002' class='xr-section-summary-in' type='checkbox'  checked><label for='section-980d9b4d-d6a7-49ce-b7af-289a93048002' class='xr-section-summary' >Data variables: <span>(3)</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>total_precip</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2051 -2e+03 1.5 ... 1.472 144.3</div><input id='attrs-bccb73c1-c359-4aa2-a6a0-44382f575656' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bccb73c1-c359-4aa2-a6a0-44382f575656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4f63f42-bcef-4eff-98fe-3b263462382e' class='xr-var-data-in' type='checkbox'><label for='data-d4f63f42-bcef-4eff-98fe-3b263462382e' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Distribution parameters</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.05134645e-01, -2.00037863e+03,  1.49983238e+00,\n",
       "         1.44060967e+02],\n",
       "       [ 2.04780752e-01, -1.97017301e+03,  1.48509801e+00,\n",
       "         1.43836764e+02],\n",
       "       [ 2.03455201e-01, -1.93871053e+03,  1.46964816e+00,\n",
       "         1.44253479e+02],\n",
       "       [ 2.01116077e-01, -1.97726062e+03,  1.48830415e+00,\n",
       "         1.43974057e+02],\n",
       "       [ 2.03569840e-01, -1.94265951e+03,  1.47158639e+00,\n",
       "         1.44316309e+02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_lower</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1022 -3.228e+03 ... 0.8645 127.0</div><input id='attrs-f9b39028-58c9-4c19-999c-d51ce1444b44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9b39028-58c9-4c19-999c-d51ce1444b44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd037734-a633-4084-a2d0-1cd5798f9a70' class='xr-var-data-in' type='checkbox'><label for='data-dd037734-a633-4084-a2d0-1cd5798f9a70' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Lower limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.02228634e-01, -3.22769006e+03,  8.93751210e-01,\n",
       "         1.26796169e+02],\n",
       "       [ 1.02384927e-01, -3.19853015e+03,  8.78478783e-01,\n",
       "         1.26620782e+02],\n",
       "       [ 9.97637451e-02, -3.16813481e+03,  8.62554698e-01,\n",
       "         1.26956382e+02],\n",
       "       [ 9.61853236e-02, -3.20351448e+03,  8.82787903e-01,\n",
       "         1.26677958e+02],\n",
       "       [ 9.97237168e-02, -3.17200390e+03,  8.64535952e-01,\n",
       "         1.27004583e+02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_upper</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.308 -773.1 2.106 ... 2.079 164.0</div><input id='attrs-10cee741-04ed-47d0-a11d-bb97d659f552' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10cee741-04ed-47d0-a11d-bb97d659f552' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d8a10bf-6c06-4465-b91e-d0faa312a3a3' class='xr-var-data-in' type='checkbox'><label for='data-9d8a10bf-6c06-4465-b91e-d0faa312a3a3' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Upper limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.08040657e-01, -7.73067200e+02,  2.10591355e+00,\n",
       "         1.63676573e+02],\n",
       "       [ 3.07176577e-01, -7.41815877e+02,  2.09171723e+00,\n",
       "         1.63393515e+02],\n",
       "       [ 3.07146657e-01, -7.09286252e+02,  2.07674163e+00,\n",
       "         1.63907208e+02],\n",
       "       [ 3.06046829e-01, -7.51006746e+02,  2.09382040e+00,\n",
       "         1.63631697e+02],\n",
       "       [ 3.07415963e-01, -7.13315109e+02,  2.07863682e+00,\n",
       "         1.63987759e+02]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79dae49e-55a4-4ef0-86fa-5eca16e16917' class='xr-section-summary-in' type='checkbox'  ><label for='section-79dae49e-55a4-4ef0-86fa-5eca16e16917' 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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c1723f3d-033c-4fe0-97d5-030f5e16f7ad' class='xr-index-data-in' type='checkbox'/><label for='index-c1723f3d-033c-4fe0-97d5-030f5e16f7ad' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dparams</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0310dac6-bd2f-49e0-bb7f-46f5dec7d2f7' class='xr-index-data-in' type='checkbox'/><label for='index-0310dac6-bd2f-49e0-bb7f-46f5dec7d2f7' 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;shape&#x27;, &#x27;loc&#x27;, &#x27;loc_year_covariate&#x27;, &#x27;scale&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dparams&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15955d4b-5f42-42ae-8e9c-5b0c59b192c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15955d4b-5f42-42ae-8e9c-5b0c59b192c0' 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: 808B\n",
       "Dimensions:             (station_num: 5, dparams: 4)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * dparams             (dparams) <U18 288B 'shape' 'loc' ... 'scale'\n",
       "Data variables:\n",
       "    total_precip        (station_num, dparams) float64 160B 0.2051 ... 144.3\n",
       "    total_precip_lower  (station_num, dparams) float64 160B 0.1022 ... 127.0\n",
       "    total_precip_upper  (station_num, dparams) float64 160B 0.308 ... 164.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_non_stationary = xhe.fit(\n",
    "    ds,\n",
    "    dist=\"genextreme\",\n",
    "    method=\"ML\",\n",
    "    variables=[\"total_precip\"],\n",
    "    locationcov=[\"year\"],\n",
    "    confidence_level=0.95,\n",
    ")\n",
    "fit_non_stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of the `.return_level()` function, adding a covariate preserves the original dimensions, including the dimension along which the `return_level` is computed (e.g., time )."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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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",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25kB\n",
       "Dimensions:             (station_num: 5, time: 146, return_period: 1)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * time                (time) datetime64[ns] 1kB 1955-01-01 ... 2100-01-01\n",
       "    station_name        (station_num, time) object 6kB &#x27;Dozois&#x27; ... &#x27;Lac St-F...\n",
       "  * return_period       (return_period) int64 8B 100\n",
       "Data variables:\n",
       "    total_precip        (station_num, time) float64 6kB 1.557e+03 ... 1.771e+03\n",
       "    total_precip_lower  (station_num, time) float64 6kB 1.457e+03 ... 1.672e+03\n",
       "    total_precip_upper  (station_num, time) float64 6kB 1.656e+03 ... 1.87e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-79f02ed9-d186-460d-8e2c-b5c1c9964dd5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79f02ed9-d186-460d-8e2c-b5c1c9964dd5' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>time</span>: 146</li><li><span class='xr-has-index'>return_period</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ccb8c1f-1989-4ffb-95ba-8ad8c0ccdbcf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ccb8c1f-1989-4ffb-95ba-8ad8c0ccdbcf' class='xr-section-summary' >Coordinates: <span>(4)</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'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-e9ab827f-f624-45c6-bfee-9e88a13b6566' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9ab827f-f624-45c6-bfee-9e88a13b6566' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91eebaaf-139b-4328-aec9-bbe9a603ab93' class='xr-var-data-in' type='checkbox'><label for='data-91eebaaf-139b-4328-aec9-bbe9a603ab93' 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([1001, 1004, 1008, 1009, 1012])</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'>1955-01-01 ... 2100-01-01</div><input id='attrs-c09c5178-c021-47f7-aa90-d291af0a9d8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c09c5178-c021-47f7-aa90-d291af0a9d8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e66707f4-51a0-4894-92d3-92d44d3bfcb3' class='xr-var-data-in' type='checkbox'><label for='data-e66707f4-51a0-4894-92d3-92d44d3bfcb3' 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;1955-01-01T00:00:00.000000000&#x27;, &#x27;1956-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-01-01T00:00:00.000000000&#x27;, &#x27;1958-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1960-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1961-01-01T00:00:00.000000000&#x27;, &#x27;1962-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1963-01-01T00:00:00.000000000&#x27;, &#x27;1964-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1965-01-01T00:00:00.000000000&#x27;, &#x27;1966-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1967-01-01T00:00:00.000000000&#x27;, &#x27;1968-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1969-01-01T00:00:00.000000000&#x27;, &#x27;1970-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1971-01-01T00:00:00.000000000&#x27;, &#x27;1972-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1973-01-01T00:00:00.000000000&#x27;, &#x27;1974-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1975-01-01T00:00:00.000000000&#x27;, &#x27;1976-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1977-01-01T00:00:00.000000000&#x27;, &#x27;1978-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1982-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-01-01T00:00:00.000000000&#x27;, &#x27;1984-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-01-01T00:00:00.000000000&#x27;, &#x27;1986-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-01-01T00:00:00.000000000&#x27;, &#x27;1988-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-01-01T00:00:00.000000000&#x27;, &#x27;1990-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1992-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-01-01T00:00:00.000000000&#x27;, &#x27;1994-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-01-01T00:00:00.000000000&#x27;, &#x27;1996-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-01-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2025-01-01T00:00:00.000000000&#x27;, &#x27;2026-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2027-01-01T00:00:00.000000000&#x27;, &#x27;2028-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2029-01-01T00:00:00.000000000&#x27;, &#x27;2030-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2031-01-01T00:00:00.000000000&#x27;, &#x27;2032-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2033-01-01T00:00:00.000000000&#x27;, &#x27;2034-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2035-01-01T00:00:00.000000000&#x27;, &#x27;2036-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2037-01-01T00:00:00.000000000&#x27;, &#x27;2038-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2039-01-01T00:00:00.000000000&#x27;, &#x27;2040-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2041-01-01T00:00:00.000000000&#x27;, &#x27;2042-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2043-01-01T00:00:00.000000000&#x27;, &#x27;2044-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2045-01-01T00:00:00.000000000&#x27;, &#x27;2046-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2047-01-01T00:00:00.000000000&#x27;, &#x27;2048-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2049-01-01T00:00:00.000000000&#x27;, &#x27;2050-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2051-01-01T00:00:00.000000000&#x27;, &#x27;2052-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2053-01-01T00:00:00.000000000&#x27;, &#x27;2054-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2055-01-01T00:00:00.000000000&#x27;, &#x27;2056-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2057-01-01T00:00:00.000000000&#x27;, &#x27;2058-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2059-01-01T00:00:00.000000000&#x27;, &#x27;2060-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2061-01-01T00:00:00.000000000&#x27;, &#x27;2062-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2063-01-01T00:00:00.000000000&#x27;, &#x27;2064-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2065-01-01T00:00:00.000000000&#x27;, &#x27;2066-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2067-01-01T00:00:00.000000000&#x27;, &#x27;2068-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2069-01-01T00:00:00.000000000&#x27;, &#x27;2070-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2071-01-01T00:00:00.000000000&#x27;, &#x27;2072-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2073-01-01T00:00:00.000000000&#x27;, &#x27;2074-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2075-01-01T00:00:00.000000000&#x27;, &#x27;2076-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2077-01-01T00:00:00.000000000&#x27;, &#x27;2078-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2079-01-01T00:00:00.000000000&#x27;, &#x27;2080-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2081-01-01T00:00:00.000000000&#x27;, &#x27;2082-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2083-01-01T00:00:00.000000000&#x27;, &#x27;2084-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2085-01-01T00:00:00.000000000&#x27;, &#x27;2086-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2087-01-01T00:00:00.000000000&#x27;, &#x27;2088-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2089-01-01T00:00:00.000000000&#x27;, &#x27;2090-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2091-01-01T00:00:00.000000000&#x27;, &#x27;2092-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2093-01-01T00:00:00.000000000&#x27;, &#x27;2094-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2095-01-01T00:00:00.000000000&#x27;, &#x27;2096-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2097-01-01T00:00:00.000000000&#x27;, &#x27;2098-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2099-01-01T00:00:00.000000000&#x27;, &#x27;2100-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_name</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Dozois&#x27; ... &#x27;Lac St-Francois&#x27;</div><input id='attrs-5a9d7c65-6709-4d8e-9160-c0b47076db41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a9d7c65-6709-4d8e-9160-c0b47076db41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dae1538-5add-4ded-87e5-1102d27a71ba' class='xr-var-data-in' type='checkbox'><label for='data-4dae1538-5add-4ded-87e5-1102d27a71ba' 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;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "        &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;, &#x27;Dozois&#x27;,\n",
       "...\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;,\n",
       "        &#x27;Lac St-Francois&#x27;, &#x27;Lac St-Francois&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>return_period</span></div><div class='xr-var-dims'>(return_period)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-782a44ea-d1b1-44c2-9536-7db57734fbb8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-782a44ea-d1b1-44c2-9536-7db57734fbb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c681956-7d9a-44df-b6e9-b04a75c5a665' class='xr-var-data-in' type='checkbox'><label for='data-5c681956-7d9a-44df-b6e9-b04a75c5a665' 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([100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3522e92-f5dc-47fb-8de1-3f491ab0df31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3522e92-f5dc-47fb-8de1-3f491ab0df31' class='xr-section-summary' >Data variables: <span>(3)</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>total_precip</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.557e+03 1.558e+03 ... 1.771e+03</div><input id='attrs-85664f30-665e-41e0-8c81-7b95849943e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85664f30-665e-41e0-8c81-7b95849943e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d2a5778-fff8-4a41-9aa1-d9807957c055' class='xr-var-data-in' type='checkbox'><label for='data-4d2a5778-fff8-4a41-9aa1-d9807957c055' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Distribution parameters</dd><dt><span>dist :</span></dt><dd>gumbel_r</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[1556.83014568, 1558.30651307, 1559.78288045, 1561.25924783,\n",
       "        1562.73561522, 1564.2119826 , 1565.68834998, 1567.16471737,\n",
       "        1568.64108475, 1570.11745213, 1571.59381952, 1573.0701869 ,\n",
       "        1574.54655429, 1576.02292167, 1577.49928905, 1578.97565644,\n",
       "        1580.45202382, 1581.9283912 , 1583.40475859, 1584.88112597,\n",
       "        1586.35749335, 1587.83386074, 1589.31022812, 1590.78659551,\n",
       "        1592.26296289, 1593.73933027, 1595.21569766, 1596.69206504,\n",
       "        1598.16843242, 1599.64479981, 1601.12116719, 1602.59753457,\n",
       "        1604.07390196, 1605.55026934, 1607.02663672, 1608.50300411,\n",
       "        1609.97937149, 1611.45573888, 1612.93210626, 1614.40847364,\n",
       "        1615.88484103, 1617.36120841, 1618.83757579, 1620.31394318,\n",
       "        1621.79031056, 1623.26667794, 1624.74304533, 1626.21941271,\n",
       "        1627.6957801 , 1629.17214748, 1630.64851486, 1632.12488225,\n",
       "        1633.60124963, 1635.07761701, 1636.5539844 , 1638.03035178,\n",
       "        1639.50671916, 1640.98308655, 1642.45945393, 1643.93582132,\n",
       "        1645.4121887 , 1646.88855608, 1648.36492347, 1649.84129085,\n",
       "        1651.31765823, 1652.79402562, 1654.270393  , 1655.74676038,\n",
       "        1657.22312777, 1658.69949515, 1660.17586253, 1661.65222992,\n",
       "        1663.1285973 , 1664.60496469, 1666.08133207, 1667.55769945,\n",
       "        1669.03406684, 1670.51043422, 1671.9868016 , 1673.46316899,\n",
       "...\n",
       "        1659.09796709, 1660.55261213, 1662.00725716, 1663.4619022 ,\n",
       "        1664.91654723, 1666.37119226, 1667.8258373 , 1669.28048233,\n",
       "        1670.73512737, 1672.1897724 , 1673.64441744, 1675.09906247,\n",
       "        1676.5537075 , 1678.00835254, 1679.46299757, 1680.91764261,\n",
       "        1682.37228764, 1683.82693268, 1685.28157771, 1686.73622274,\n",
       "        1688.19086778, 1689.64551281, 1691.10015785, 1692.55480288,\n",
       "        1694.00944791, 1695.46409295, 1696.91873798, 1698.37338302,\n",
       "        1699.82802805, 1701.28267309, 1702.73731812, 1704.19196315,\n",
       "        1705.64660819, 1707.10125322, 1708.55589826, 1710.01054329,\n",
       "        1711.46518832, 1712.91983336, 1714.37447839, 1715.82912343,\n",
       "        1717.28376846, 1718.7384135 , 1720.19305853, 1721.64770356,\n",
       "        1723.1023486 , 1724.55699363, 1726.01163867, 1727.4662837 ,\n",
       "        1728.92092874, 1730.37557377, 1731.8302188 , 1733.28486384,\n",
       "        1734.73950887, 1736.19415391, 1737.64879894, 1739.10344397,\n",
       "        1740.55808901, 1742.01273404, 1743.46737908, 1744.92202411,\n",
       "        1746.37666915, 1747.83131418, 1749.28595921, 1750.74060425,\n",
       "        1752.19524928, 1753.64989432, 1755.10453935, 1756.55918439,\n",
       "        1758.01382942, 1759.46847445, 1760.92311949, 1762.37776452,\n",
       "                  nan,           nan,           nan, 1768.19634466,\n",
       "        1769.65098969, 1771.10563473]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_lower</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.457e+03 1.459e+03 ... 1.672e+03</div><input id='attrs-9611bda0-57e9-4228-8309-1fdaa0124ddf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9611bda0-57e9-4228-8309-1fdaa0124ddf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abf67b34-1d8b-41e5-bc14-e9e7bffeac1c' class='xr-var-data-in' type='checkbox'><label for='data-abf67b34-1d8b-41e5-bc14-e9e7bffeac1c' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Lower limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>gumbel_r</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[1457.33024922, 1459.07593276, 1460.81864098, 1462.55834978,\n",
       "        1464.29503511, 1466.02867291, 1467.75923916, 1469.48670988,\n",
       "        1471.21106114, 1472.93226905, 1474.65030981, 1476.36515966,\n",
       "        1478.07679495, 1479.78519209, 1481.49032761, 1483.19217814,\n",
       "        1484.89072043, 1486.58593134, 1488.27778789, 1489.96626723,\n",
       "        1491.65134666, 1493.33300367, 1495.0112159 , 1496.68596118,\n",
       "        1498.35721753, 1500.02496321, 1501.68917664, 1503.34983651,\n",
       "        1505.00692173, 1506.66041145, 1508.3102851 , 1509.95652234,\n",
       "        1511.59910315, 1513.23800777, 1514.87321676, 1516.50471096,\n",
       "        1518.13247155, 1519.75648005, 1521.37671831, 1522.99316851,\n",
       "        1524.60581322, 1526.21463538, 1527.81961828, 1529.42074564,\n",
       "        1531.01800156, 1532.61137055, 1534.20083754, 1535.7863879 ,\n",
       "        1537.36800741, 1538.94568232, 1540.51939933, 1542.08914561,\n",
       "        1543.65490878, 1545.21667696, 1546.77443875, 1548.32818324,\n",
       "        1549.87790002, 1551.42357921, 1552.96521142, 1554.50278779,\n",
       "        1556.03629998, 1557.56574021, 1559.09110119, 1560.61237623,\n",
       "        1562.12955913, 1563.64264429, 1565.15162664, 1566.65650167,\n",
       "        1568.15726543, 1569.65391456, 1571.14644623, 1572.6348582 ,\n",
       "        1574.11914881, 1575.59931695, 1577.07536208, 1578.54728427,\n",
       "        1580.01508412, 1581.47876283, 1582.93832215, 1584.39376442,\n",
       "...\n",
       "        1569.85739714, 1571.33261899, 1572.80368257, 1574.27058561,\n",
       "        1575.73332637, 1577.19190373, 1578.64631713, 1580.09656661,\n",
       "        1581.54265279, 1582.98457685, 1584.42234059, 1585.85594636,\n",
       "        1587.2853971 , 1588.71069633, 1590.13184815, 1591.5488572 ,\n",
       "        1592.96172874, 1594.37046854, 1595.77508297, 1597.17557895,\n",
       "        1598.57196393, 1599.96424593, 1601.35243351, 1602.73653574,\n",
       "        1604.11656226, 1605.49252319, 1606.8644292 , 1608.23229146,\n",
       "        1609.59612162, 1610.95593186, 1612.31173481, 1613.66354361,\n",
       "        1615.01137185, 1616.35523359, 1617.69514333, 1619.03111604,\n",
       "        1620.36316708, 1621.69131228, 1623.01556785, 1624.33595044,\n",
       "        1625.65247705, 1626.96516511, 1628.27403239, 1629.57909705,\n",
       "        1630.88037758, 1632.17789284, 1633.47166201, 1634.76170458,\n",
       "        1636.04804038, 1637.33068951, 1638.60967239, 1639.88500969,\n",
       "        1641.15672238, 1642.42483167, 1643.68935902, 1644.95032612,\n",
       "        1646.20775491, 1647.46166752, 1648.71208631, 1649.9590338 ,\n",
       "        1651.20253273, 1652.442606  , 1653.67927667, 1654.91256796,\n",
       "        1656.14250324, 1657.369106  , 1658.59239987, 1659.81240858,\n",
       "        1661.02915597, 1662.24266599, 1663.45296267, 1664.66007011,\n",
       "                  nan,           nan,           nan, 1669.45709179,\n",
       "        1670.64861674, 1671.83709823]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_upper</span></div><div class='xr-var-dims'>(station_num, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.656e+03 1.658e+03 ... 1.87e+03</div><input id='attrs-12decbfa-223e-4432-ade4-ceda72d54105' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12decbfa-223e-4432-ade4-ceda72d54105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa2d462c-bc51-421a-bd58-e26997e9d996' class='xr-var-data-in' type='checkbox'><label for='data-fa2d462c-bc51-421a-bd58-e26997e9d996' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Upper limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>gumbel_r</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><pre>array([[1656.33004214, 1657.53709337, 1658.74711992, 1659.96014588,\n",
       "        1661.17619532, 1662.39529229, 1663.61746081, 1664.84272486,\n",
       "        1666.07110837, 1667.30263522, 1668.53732923, 1669.77521414,\n",
       "        1671.01631362, 1672.26065125, 1673.5082505 , 1674.75913473,\n",
       "        1676.01332721, 1677.27085107, 1678.53172929, 1679.79598472,\n",
       "        1681.06364005, 1682.33471781, 1683.60924035, 1684.88722984,\n",
       "        1686.16870824, 1687.45369734, 1688.74221867, 1690.03429357,\n",
       "        1691.32994312, 1692.62918816, 1693.93204929, 1695.23854681,\n",
       "        1696.54870076, 1697.86253091, 1699.18005669, 1700.50129726,\n",
       "        1701.82627143, 1703.1549977 , 1704.48749421, 1705.82377878,\n",
       "        1707.16386883, 1708.50778144, 1709.8555333 , 1711.20714071,\n",
       "        1712.56261956, 1713.92198534, 1715.28525311, 1716.65243753,\n",
       "        1718.02355278, 1719.39861264, 1720.77763039, 1722.16061888,\n",
       "        1723.54759048, 1724.93855707, 1726.33353005, 1727.73252032,\n",
       "        1729.1355383 , 1730.54259388, 1731.95369644, 1733.36885484,\n",
       "        1734.78807741, 1736.21137196, 1737.63874574, 1739.07020547,\n",
       "        1740.50575733, 1741.94540694, 1743.38915936, 1744.8370191 ,\n",
       "        1746.2889901 , 1747.74507575, 1749.20527884, 1750.66960163,\n",
       "        1752.1380458 , 1753.61061243, 1755.08730205, 1756.56811463,\n",
       "        1758.05304955, 1759.54210561, 1761.03528105, 1762.53257355,\n",
       "...\n",
       "        1748.33853705, 1749.77260527, 1751.21083175, 1752.65321879,\n",
       "        1754.09976809, 1755.5504808 , 1757.00535747, 1758.46439805,\n",
       "        1759.92760195, 1761.39496795, 1762.86649428, 1764.34217858,\n",
       "        1765.82201791, 1767.30600874, 1768.794147  , 1770.28642801,\n",
       "        1771.78284655, 1773.28339681, 1774.78807245, 1776.29686654,\n",
       "        1777.80977163, 1779.32677969, 1780.84788219, 1782.37307002,\n",
       "        1783.90233357, 1785.4356627 , 1786.97304676, 1788.51447458,\n",
       "        1790.05993448, 1791.60941431, 1793.16290143, 1794.7203827 ,\n",
       "        1796.28184452, 1797.84727285, 1799.41665318, 1800.98997055,\n",
       "        1802.56720957, 1804.14835444, 1805.73338893, 1807.32229642,\n",
       "        1808.91505987, 1810.51166189, 1812.11208467, 1813.71631008,\n",
       "        1815.32431961, 1816.93609442, 1818.55161532, 1820.17086282,\n",
       "        1821.79381709, 1823.42045803, 1825.05076522, 1826.68471798,\n",
       "        1828.32229536, 1829.96347614, 1831.60823886, 1833.25656183,\n",
       "        1834.90842311, 1836.56380056, 1838.22267185, 1839.88501442,\n",
       "        1841.55080556, 1843.22002236, 1844.89264176, 1846.56864053,\n",
       "        1848.24799533, 1849.93068263, 1851.61667884, 1853.30596019,\n",
       "        1854.99850287, 1856.69428291, 1858.39327631, 1860.09545894,\n",
       "                  nan,           nan,           nan, 1866.93559752,\n",
       "        1868.65336265, 1870.37417123]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a25185a-e919-4ccf-9bfd-4dc821610b47' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a25185a-e919-4ccf-9bfd-4dc821610b47' class='xr-section-summary' >Indexes: <span>(3)</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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-87d4bebc-3430-4f7a-a33e-a33fe43e511c' class='xr-index-data-in' type='checkbox'/><label for='index-87d4bebc-3430-4f7a-a33e-a33fe43e511c' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-84661e99-70a2-4a8e-a219-bc8642ba8014' class='xr-index-data-in' type='checkbox'/><label for='index-84661e99-70a2-4a8e-a219-bc8642ba8014' 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;1955-01-01&#x27;, &#x27;1956-01-01&#x27;, &#x27;1957-01-01&#x27;, &#x27;1958-01-01&#x27;,\n",
       "               &#x27;1959-01-01&#x27;, &#x27;1960-01-01&#x27;, &#x27;1961-01-01&#x27;, &#x27;1962-01-01&#x27;,\n",
       "               &#x27;1963-01-01&#x27;, &#x27;1964-01-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2091-01-01&#x27;, &#x27;2092-01-01&#x27;, &#x27;2093-01-01&#x27;, &#x27;2094-01-01&#x27;,\n",
       "               &#x27;2095-01-01&#x27;, &#x27;2096-01-01&#x27;, &#x27;2097-01-01&#x27;, &#x27;2098-01-01&#x27;,\n",
       "               &#x27;2099-01-01&#x27;, &#x27;2100-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=146, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>return_period</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-28ca95fb-bdfc-4b89-a74b-ba7513d7894e' class='xr-index-data-in' type='checkbox'/><label for='index-28ca95fb-bdfc-4b89-a74b-ba7513d7894e' 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([100], dtype=&#x27;int64&#x27;, name=&#x27;return_period&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b025b51-917f-4763-83ad-4bc8d827c18f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b025b51-917f-4763-83ad-4bc8d827c18f' 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: 25kB\n",
       "Dimensions:             (station_num: 5, time: 146, return_period: 1)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * time                (time) datetime64[ns] 1kB 1955-01-01 ... 2100-01-01\n",
       "    station_name        (station_num, time) object 6kB 'Dozois' ... 'Lac St-F...\n",
       "  * return_period       (return_period) int64 8B 100\n",
       "Data variables:\n",
       "    total_precip        (station_num, time) float64 6kB 1.557e+03 ... 1.771e+03\n",
       "    total_precip_lower  (station_num, time) float64 6kB 1.457e+03 ... 1.672e+03\n",
       "    total_precip_upper  (station_num, time) float64 6kB 1.656e+03 ... 1.87e+03"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rtnlv_non_stationary = xhe.return_level(\n",
    "    ds,\n",
    "    dist=\"gumbel_r\",\n",
    "    method=\"ML\",\n",
    "    dim=\"time\",\n",
    "    variables=[\"total_precip\"],\n",
    "    locationcov=[\"year\"],\n",
    "    confidence_level=0.95,\n",
    "    return_period=100,\n",
    ")\n",
    "\n",
    "rtnlv_non_stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of the return level using the stationary and non-stationary model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9f63a61340>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_figheight(4)\n",
    "fig.set_figwidth(6)\n",
    "\n",
    "# Stationary fit\n",
    "plt.plot(\n",
    "    np.array([1, 1]),\n",
    "    np.array(\n",
    "        [\n",
    "            rtnlv_stationary.total_precip_lower.isel(station_num=0),\n",
    "            rtnlv_stationary.total_precip_upper.isel(station_num=0),\n",
    "        ]\n",
    "    ),\n",
    "    \"black\",\n",
    "    label=\"Stationary\",\n",
    ")\n",
    "plt.scatter(\n",
    "    np.array([1]),\n",
    "    np.array([rtnlv_stationary.total_precip.isel(station_num=0)]),\n",
    "    c=\"black\",\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    np.array([2, 2]),\n",
    "    np.array(\n",
    "        [\n",
    "            rtnlv_non_stationary.total_precip_lower.isel(station_num=0, time=0),\n",
    "            rtnlv_non_stationary.total_precip_upper.isel(station_num=0, time=0),\n",
    "        ]\n",
    "    ),\n",
    "    \"red\",\n",
    "    label=\"Non-Stationary (1955)\",\n",
    ")\n",
    "plt.scatter(\n",
    "    np.array([2]),\n",
    "    np.array([rtnlv_non_stationary.total_precip.isel(station_num=0, time=0)]),\n",
    "    c=\"red\",\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    np.array([3, 3]),\n",
    "    np.array(\n",
    "        [\n",
    "            rtnlv_non_stationary.total_precip_lower.isel(station_num=0, time=-1),\n",
    "            rtnlv_non_stationary.total_precip_upper.isel(station_num=0, time=-1),\n",
    "        ]\n",
    "    ),\n",
    "    \"green\",\n",
    "    label=\"Non-Stationary (2100)\",\n",
    ")\n",
    "plt.scatter(\n",
    "    np.array([3]),\n",
    "    np.array([rtnlv_non_stationary.total_precip.isel(station_num=0, time=-1)]),\n",
    "    c=\"green\",\n",
    ")\n",
    "\n",
    "plt.xlim([0, 4])\n",
    "plt.xticks([])\n",
    "plt.ylabel(\"Total precipitation (mm)\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with `dask.array` Chunks\n",
    "\n",
    "Currently, the Python-to-Julia interaction is not thread-safe. To mitigate potential issues, it is recommended to use the `dask.scheduler=\"processes\"` option when computing results. This ensures that tasks are executed in separate Python processes, providing better isolation and avoiding thread-related conflicts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-11T20:54:43.238969Z",
     "start_time": "2024-12-11T20:54:43.188645Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_c = ds.chunk({\"time\": -1, \"station_num\": 1})\n",
    "\n",
    "fit_stationary_c = xhe.fit(\n",
    "    ds_c,\n",
    "    dist=\"genextreme\",\n",
    "    method=\"ml\",\n",
    "    variables=[\"total_precip\"],\n",
    "    confidence_level=0.95,\n",
    ")\n",
    "fit_stationary_c = fit_stationary_c.compute(scheduler=\"processes\")\n",
    "clear_output(wait=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 460B\n",
       "Dimensions:             (station_num: 5, dparams: 3)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * dparams             (dparams) &lt;U5 60B &#x27;shape&#x27; &#x27;loc&#x27; &#x27;scale&#x27;\n",
       "Data variables:\n",
       "    total_precip        (station_num, dparams) float64 120B dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;\n",
       "    total_precip_lower  (station_num, dparams) float64 120B dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;\n",
       "    total_precip_upper  (station_num, dparams) float64 120B dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</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-6a378ca5-a896-46ae-9182-bf6c41d70504' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6a378ca5-a896-46ae-9182-bf6c41d70504' 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'>station_num</span>: 5</li><li><span class='xr-has-index'>dparams</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-863cc7e3-0f15-4b88-8b8b-54a0103baf40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-863cc7e3-0f15-4b88-8b8b-54a0103baf40' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>station_num</span></div><div class='xr-var-dims'>(station_num)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1001 1004 1008 1009 1012</div><input id='attrs-7a2b3a99-bc3f-4294-ba10-578b85d64011' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a2b3a99-bc3f-4294-ba10-578b85d64011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a36c9c97-48b0-40f4-a759-a45f4f371e9d' class='xr-var-data-in' type='checkbox'><label for='data-a36c9c97-48b0-40f4-a759-a45f4f371e9d' 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([1001, 1004, 1008, 1009, 1012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dparams</span></div><div class='xr-var-dims'>(dparams)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;shape&#x27; &#x27;loc&#x27; &#x27;scale&#x27;</div><input id='attrs-ea82216c-4422-47a7-9baa-178ebce170ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea82216c-4422-47a7-9baa-178ebce170ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90268ba7-b265-42f7-9a20-4f8dd0a0fa8c' class='xr-var-data-in' type='checkbox'><label for='data-90268ba7-b265-42f7-9a20-4f8dd0a0fa8c' 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;shape&#x27;, &#x27;loc&#x27;, &#x27;scale&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbcb0bb5-cb0b-401d-bcd0-599ab53495fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbcb0bb5-cb0b-401d-bcd0-599ab53495fd' class='xr-section-summary' >Data variables: <span>(3)</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>total_precip</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</div><input id='attrs-a5973f5c-bf92-4533-a798-804b66369a8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5973f5c-bf92-4533-a798-804b66369a8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9f85e6b-32a1-4a7c-844f-079771979d80' class='xr-var-data-in' type='checkbox'><label for='data-c9f85e6b-32a1-4a7c-844f-079771979d80' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Distribution parameters</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                        <td> 120 B </td>\n",
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       "                        <td> (5, 3) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 4 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_lower</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</div><input id='attrs-e3a34097-33cc-4f34-befc-15e39b569d59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3a34097-33cc-4f34-befc-15e39b569d59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5fee697-1722-430a-a8ce-bf24355ee0a9' class='xr-var-data-in' type='checkbox'><label for='data-a5fee697-1722-430a-a8ce-bf24355ee0a9' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Lower limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 120 B </td>\n",
       "                        <td> 24 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5, 3) </td>\n",
       "                        <td> (1, 3) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precip_upper</span></div><div class='xr-var-dims'>(station_num, dparams)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 3), meta=np.ndarray&gt;</div><input id='attrs-68d508d5-0f7d-473e-83a5-0bca8666ee4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68d508d5-0f7d-473e-83a5-0bca8666ee4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b792a26-2c1e-46a1-b1f2-e311adce4b12' class='xr-var-data-in' type='checkbox'><label for='data-3b792a26-2c1e-46a1-b1f2-e311adce4b12' 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>mm y-1</dd><dt><span>long_name :</span></dt><dd>Upper limit of confidence interval for the distribution parameters</dd><dt><span>confidence_level :</span></dt><dd>0.95</dd><dt><span>dist :</span></dt><dd>genextreme</dd><dt><span>method :</span></dt><dd>Maximum likelihood</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 120 B </td>\n",
       "                        <td> 24 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5, 3) </td>\n",
       "                        <td> (1, 3) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 5 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"122\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"72\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"48\" x2=\"72\" y2=\"48\" />\n",
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       "  <line x1=\"0\" y1=\"120\" x2=\"72\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 72.0,0.0 72.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"36.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
       "  <text x=\"92.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,92.000000,60.000000)\">5</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-989a06d1-bccb-4b55-996f-098cc70313ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-989a06d1-bccb-4b55-996f-098cc70313ac' 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>station_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7c208c6b-d49c-4421-89a0-a3a97c1d9ca0' class='xr-index-data-in' type='checkbox'/><label for='index-7c208c6b-d49c-4421-89a0-a3a97c1d9ca0' 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([1001, 1004, 1008, 1009, 1012], dtype=&#x27;int64&#x27;, name=&#x27;station_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dparams</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-07b52d5c-4168-4aea-95c6-bee7149cb488' class='xr-index-data-in' type='checkbox'/><label for='index-07b52d5c-4168-4aea-95c6-bee7149cb488' 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;shape&#x27;, &#x27;loc&#x27;, &#x27;scale&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dparams&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1cf507ea-0d1d-4403-92c3-c76f88e22f01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cf507ea-0d1d-4403-92c3-c76f88e22f01' 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: 460B\n",
       "Dimensions:             (station_num: 5, dparams: 3)\n",
       "Coordinates:\n",
       "  * station_num         (station_num) int64 40B 1001 1004 1008 1009 1012\n",
       "  * dparams             (dparams) <U5 60B 'shape' 'loc' 'scale'\n",
       "Data variables:\n",
       "    total_precip        (station_num, dparams) float64 120B dask.array<chunksize=(1, 3), meta=np.ndarray>\n",
       "    total_precip_lower  (station_num, dparams) float64 120B dask.array<chunksize=(1, 3), meta=np.ndarray>\n",
       "    total_precip_upper  (station_num, dparams) float64 120B dask.array<chunksize=(1, 3), meta=np.ndarray>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_stationary_c"
   ]
  }
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