{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40aea30b-fabd-462d-87e1-849e9eb751cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "import cftime\n",
    "import numpy as np\n",
    "\n",
    "from oggm import cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b3e8adb7-3a0c-4ca1-8975-ce9505ea328c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with xr.open_dataset('/home/users/fmaussion/www_oggm/climate/era5/monthly/v1.1/era5_monthly_prcp_1979-2019.nc') as dse:\n",
    "#    dse = dse\n",
    "# dse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1f4f1e73-0b9f-4895-8654-53e9a21c2aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with xr.open_dataset('orig/P_monthly.nc') as ds:\n",
    "    ds = ds.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0b6b52e3-1682-40ba-8206-3ce73606466c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avoid year zero and align with temp\n",
    "ds = ds.sel(year=slice(1, 2023))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a3189858-45e1-4f69-9ea4-28a209e4fd82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assume ds is your Dataset\n",
    "# Create a full time index\n",
    "months = ds.month.values\n",
    "years = ds.year.values\n",
    "\n",
    "# Broadcast year and month in correct order\n",
    "year_grid, month_grid = xr.broadcast(ds.year, ds.month)\n",
    "\n",
    "# Flatten both\n",
    "years = year_grid.values.ravel()\n",
    "months = month_grid.values.ravel()\n",
    "\n",
    "# Create cftime datetime objects (assuming 15th of each month)\n",
    "# Choose the appropriate calendar, e.g., 'standard', 'noleap', '360_day'\n",
    "calendar = 'noleap'  # or change as needed\n",
    "cftime_dates = np.array([\n",
    "    cftime.num2date(\n",
    "        cftime.date2num(cftime.DatetimeNoLeap(y, m, 1), units='days since 0001-01-01', calendar=calendar),\n",
    "        units='days since 0001-01-01',\n",
    "        calendar=calendar\n",
    "    )\n",
    "    for y, m in zip(years, months)\n",
    "])\n",
    "\n",
    "# Stack 'month' and 'year' into a single 'time' dimension\n",
    "precip = ds.P_monthly.stack(time=(\"year\", \"month\"))\n",
    "precip[\"time\"] = cftime_dates\n",
    "\n",
    "# Unstack lon, lat\n",
    "precip = precip.transpose(\"lon\", \"lat\", \"time\")\n",
    "\n",
    "# Convert to stupid unit\n",
    "precip = precip * 1000 \n",
    "dimo = np.array([cfg.DAYS_IN_MONTH[m - 1] for m in precip['time.month']])\n",
    "precip = precip / (dimo * (60 * 60 * 24))\n",
    "precip['units'] = 'kg m-2 s-1'\n",
    "\n",
    "# Wrap into new Dataset if needed\n",
    "ds_new = xr.Dataset({\"pr\": precip})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "804f8fb0-7508-4cb8-99e2-1763142a43c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_new.to_netcdf('unstacked/p_monthly.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b8f6a72-087a-45fc-8b97-54eaf34eebbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5cd32c71-7eeb-403f-86c3-f06411bc3794",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ds_new.pr.sel(lat=46.806783, lon=10.775030, method='nearest') * dimo * (60 * 60 * 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f01ee87d-efdf-4a50-8947-e87202b3ccc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.resample(time='YS').sum().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4aa4869b-aafa-43ce-be55-3f5de22f3a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "with xr.open_dataset('orig/Tp_monthly.nc') as ds:\n",
    "    ds = ds.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "9025e770-ea91-4a5c-a6b4-979c332e3dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avoid year zero and align with temp\n",
    "ds = ds.sel(year=slice(1, 2023))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "abe30541-94aa-4fb3-80aa-d0a960dd59fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assume ds is your Dataset\n",
    "# Create a full time index\n",
    "months = ds.month.values\n",
    "years = ds.year.values\n",
    "\n",
    "# Broadcast year and month in correct order\n",
    "year_grid, month_grid = xr.broadcast(ds.year, ds.month)\n",
    "\n",
    "# Flatten both\n",
    "years = year_grid.values.ravel()\n",
    "months = month_grid.values.ravel()\n",
    "\n",
    "# Create cftime datetime objects (assuming 15th of each month)\n",
    "# Choose the appropriate calendar, e.g., 'standard', 'noleap', '360_day'\n",
    "calendar = 'noleap'  # or change as needed\n",
    "cftime_dates = np.array([\n",
    "    cftime.num2date(\n",
    "        cftime.date2num(cftime.DatetimeNoLeap(y, m, 1), units='days since 0001-01-01', calendar=calendar),\n",
    "        units='days since 0001-01-01',\n",
    "        calendar=calendar\n",
    "    )\n",
    "    for y, m in zip(years, months)\n",
    "])\n",
    "\n",
    "# Stack 'month' and 'year' into a single 'time' dimension\n",
    "temp = ds.Tp_monthly.stack(time=(\"year\", \"month\"))\n",
    "temp[\"time\"] = cftime_dates\n",
    "\n",
    "# Unstack lon, lat\n",
    "temp = temp.transpose(\"lon\", \"lat\", \"time\")\n",
    "\n",
    "# Wrap into new Dataset if needed\n",
    "ds_new = xr.Dataset({\"tas\": temp + 273.15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "63713a9a-fe0c-467e-9683-9f2c79f374a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_new.to_netcdf('unstacked/tp_monthly.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a0569fe3-769b-4f87-92ff-80eb13a65883",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ds_new.tas.sel(lat=46.806783, lon=10.775030, method='nearest') - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "17abcde0-5183-40f1-8707-944311b4fefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.resample(time='YS').mean().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c90fd906-ee54-40ac-987f-0b3cc3601a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "with xr.open_dataset('orig/TpNAT_monthly.nc') as ds:\n",
    "    ds = ds.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0b011fd3-c885-45d0-af4c-c07fd784a2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avoid year zero and align with temp\n",
    "ds = ds.sel(year=slice(1, 2023))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4dbfbabc-2224-4ad8-b701-08eb884617bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
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       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
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       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
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       "\n",
       ".xr-obj-type {\n",
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       "}\n",
       "\n",
       ".xr-sections {\n",
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       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
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       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\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",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "  display: grid;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\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",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\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",
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       "\n",
       ".xr-var-dims {\n",
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       "}\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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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;\n",
       "Dimensions:        (lon: 14, lat: 8, month: 12, year: 2020)\n",
       "Coordinates:\n",
       "  * lon            (lon) float32 4.5 5.5 6.5 7.5 8.5 ... 14.5 15.5 16.5 17.5\n",
       "  * lat            (lat) float32 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5\n",
       "  * month          (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * year           (year) uint16 1 2 3 4 5 6 7 ... 2015 2016 2017 2018 2019 2020\n",
       "Data variables:\n",
       "    TpNAT_monthly  (lon, lat, month, year) float32 11.79 12.47 ... -1.474 -2.377</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-6007a768-02be-4b3f-a203-e0a46557c4b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6007a768-02be-4b3f-a203-e0a46557c4b5' 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'>lon</span>: 14</li><li><span class='xr-has-index'>lat</span>: 8</li><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>year</span>: 2020</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fd1f8926-738d-4557-a48d-88ee4e12c55e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd1f8926-738d-4557-a48d-88ee4e12c55e' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.5 5.5 6.5 7.5 ... 15.5 16.5 17.5</div><input id='attrs-29081af2-1296-42ff-8498-c7203d6b8309' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29081af2-1296-42ff-8498-c7203d6b8309' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3bdc6f-232d-4f2f-9903-6db4750e1e4b' class='xr-var-data-in' type='checkbox'><label for='data-5c3bdc6f-232d-4f2f-9903-6db4750e1e4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>description :</span></dt><dd>Longitude of Alps grid</dd></dl></div><div class='xr-var-data'><pre>array([ 4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5,\n",
       "       16.5, 17.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>42.5 43.5 44.5 ... 47.5 48.5 49.5</div><input id='attrs-232b4a07-52a3-4422-998a-566d755fa2a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-232b4a07-52a3-4422-998a-566d755fa2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96dc8d8f-7502-4fc1-9af9-6626bd1e8953' class='xr-var-data-in' type='checkbox'><label for='data-96dc8d8f-7502-4fc1-9af9-6626bd1e8953' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>description :</span></dt><dd>Latitude of Alps grid</dd></dl></div><div class='xr-var-data'><pre>array([42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-292c928b-1688-4e4c-bf4b-da1caf6759f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-292c928b-1688-4e4c-bf4b-da1caf6759f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b706e83f-7d3e-4100-ae94-ee706994dccd' class='xr-var-data-in' type='checkbox'><label for='data-b706e83f-7d3e-4100-ae94-ee706994dccd' 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>description :</span></dt><dd>Month number (1–12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2017 2018 2019 2020</div><input id='attrs-4f5eed2c-c68e-43f1-bd24-db05372a3c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f5eed2c-c68e-43f1-bd24-db05372a3c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad3c17fe-666c-4c61-8c3c-df9fe5e9ed42' class='xr-var-data-in' type='checkbox'><label for='data-ad3c17fe-666c-4c61-8c3c-df9fe5e9ed42' 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>description :</span></dt><dd>Calendar year</dd></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2018, 2019, 2020], dtype=uint16)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5a6f3aa-3981-46cf-9cc9-bc257cd598fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5a6f3aa-3981-46cf-9cc9-bc257cd598fe' 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>TpNAT_monthly</span></div><div class='xr-var-dims'>(lon, lat, month, year)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.79 12.47 12.53 ... -1.474 -2.377</div><input id='attrs-a140171f-e9e3-4861-b753-40d834770d8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a140171f-e9e3-4861-b753-40d834770d8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb5a5e5c-60bc-4c51-8827-bdc97436a25b' class='xr-var-data-in' type='checkbox'><label for='data-eb5a5e5c-60bc-4c51-8827-bdc97436a25b' 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>C</dd><dt><span>description :</span></dt><dd>Monthly T anomalies added to (1960–1990) monthly climatology</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 1.17865133e+01,  1.24653254e+01,  1.25329685e+01, ...,\n",
       "           1.14888420e+01,  1.20736609e+01,  1.21616735e+01],\n",
       "         [ 1.13580351e+01,  1.20368471e+01,  1.21044903e+01, ...,\n",
       "           1.10603638e+01,  1.16451826e+01,  1.17331953e+01],\n",
       "         [ 1.14299726e+01,  1.21087847e+01,  1.21764278e+01, ...,\n",
       "           1.11323013e+01,  1.17171202e+01,  1.18051329e+01],\n",
       "         ...,\n",
       "         [ 1.81745777e+01,  1.88533916e+01,  1.89210339e+01, ...,\n",
       "           1.78769073e+01,  1.84617252e+01,  1.85497379e+01],\n",
       "         [ 1.53187513e+01,  1.59975634e+01,  1.60652065e+01, ...,\n",
       "           1.50210800e+01,  1.56058989e+01,  1.56939116e+01],\n",
       "         [ 1.30057821e+01,  1.36845942e+01,  1.37522373e+01, ...,\n",
       "           1.27081108e+01,  1.32929296e+01,  1.33809423e+01]],\n",
       "\n",
       "        [[ 8.20881844e+00,  8.75649834e+00,  9.12220669e+00, ...,\n",
       "           8.08798981e+00,  8.67280865e+00,  8.76082134e+00],\n",
       "         [ 8.39975929e+00,  8.94743919e+00,  9.31314754e+00, ...,\n",
       "           8.27893066e+00,  8.86374950e+00,  8.95176220e+00],\n",
       "         [ 9.59655857e+00,  1.01442385e+01,  1.05099468e+01, ...,\n",
       "           9.47572994e+00,  1.00605488e+01,  1.01485615e+01],\n",
       "...\n",
       "           9.21346664e+00,  8.76675034e+00,  7.47943783e+00],\n",
       "         [ 3.57055998e+00,  2.88890839e+00,  4.29290962e+00, ...,\n",
       "           4.06391001e+00,  3.61719370e+00,  2.32988071e+00],\n",
       "         [-6.29669785e-01, -1.31132126e+00,  9.26797837e-02, ...,\n",
       "          -1.36319876e-01, -5.83036184e-01, -1.87034917e+00]],\n",
       "\n",
       "        [[-3.71585751e+00, -4.43580341e+00, -2.89050102e+00, ...,\n",
       "          -3.06419468e+00, -3.69011021e+00, -4.59237289e+00],\n",
       "         [-2.14204788e+00, -2.86199403e+00, -1.31669128e+00, ...,\n",
       "          -1.49038517e+00, -2.11630058e+00, -3.01856303e+00],\n",
       "         [ 1.62245727e+00,  9.02511120e-01,  2.44781399e+00, ...,\n",
       "           2.27412009e+00,  1.64820445e+00,  7.45942116e-01],\n",
       "         ...,\n",
       "         [ 7.58347416e+00,  6.86352777e+00,  8.40883064e+00, ...,\n",
       "           8.23513699e+00,  7.60922098e+00,  6.70695877e+00],\n",
       "         [ 2.59153771e+00,  1.87159157e+00,  3.41689444e+00, ...,\n",
       "           3.24320054e+00,  2.61728477e+00,  1.71502256e+00],\n",
       "         [-1.50005317e+00, -2.21999931e+00, -6.74696565e-01, ...,\n",
       "          -8.48390460e-01, -1.47430599e+00, -2.37656832e+00]]]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87a99d1a-766e-4707-9925-875874b09cd8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87a99d1a-766e-4707-9925-875874b09cd8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (lon: 14, lat: 8, month: 12, year: 2020)\n",
       "Coordinates:\n",
       "  * lon            (lon) float32 4.5 5.5 6.5 7.5 8.5 ... 14.5 15.5 16.5 17.5\n",
       "  * lat            (lat) float32 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5\n",
       "  * month          (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * year           (year) uint16 1 2 3 4 5 6 7 ... 2015 2016 2017 2018 2019 2020\n",
       "Data variables:\n",
       "    TpNAT_monthly  (lon, lat, month, year) float32 11.79 12.47 ... -1.474 -2.377"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6376603e-4aa8-4577-99a8-75ae358e4d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assume ds is your Dataset\n",
    "# Create a full time index\n",
    "months = ds.month.values\n",
    "years = ds.year.values\n",
    "\n",
    "# Broadcast year and month in correct order\n",
    "year_grid, month_grid = xr.broadcast(ds.year, ds.month)\n",
    "\n",
    "# Flatten both\n",
    "years = year_grid.values.ravel()\n",
    "months = month_grid.values.ravel()\n",
    "\n",
    "# Create cftime datetime objects (assuming 15th of each month)\n",
    "# Choose the appropriate calendar, e.g., 'standard', 'noleap', '360_day'\n",
    "calendar = 'noleap'  # or change as needed\n",
    "cftime_dates = np.array([\n",
    "    cftime.num2date(\n",
    "        cftime.date2num(cftime.DatetimeNoLeap(y, m, 1), units='days since 0001-01-01', calendar=calendar),\n",
    "        units='days since 0001-01-01',\n",
    "        calendar=calendar\n",
    "    )\n",
    "    for y, m in zip(years, months)\n",
    "])\n",
    "\n",
    "# Stack 'month' and 'year' into a single 'time' dimension\n",
    "temp = ds.TpNAT_monthly.stack(time=(\"year\", \"month\"))\n",
    "temp[\"time\"] = cftime_dates\n",
    "\n",
    "# Unstack lon, lat\n",
    "temp = temp.transpose(\"lon\", \"lat\", \"time\")\n",
    "\n",
    "# Wrap into new Dataset if needed\n",
    "ds_new = xr.Dataset({\"tas\": temp + 273.15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "071bfbff-1fa7-417f-82b1-cde1fddd191f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_new.to_netcdf('unstacked/tp_nat_monthly.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "9f7d4aea-85f2-49b7-88b5-d480814f94b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_nat = ds_new.tas.sel(lat=46.806783, lon=10.775030, method='nearest') - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7e8f5f93-01bf-4463-aed4-78ddca3b45a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.resample(time='YS').mean().plot();\n",
    "ts_nat.resample(time='YS').mean().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2676e43-93a8-4ac3-a5d4-e3f372870bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with xr.open_dataset('orig/elevation_alps.nc') as ds:\n",
    "    ds = ds.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c768535-98e4-49a5-8b18-ba6eec0fa15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.elevation.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5929ff9-59d4-47b2-96a9-9995ac05fc58",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot rename 'longitude' because it is not a variable or dimension in this dataset",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrename\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlon\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.miniconda3/envs/oggm_env/lib/python3.10/site-packages/xarray/core/dataset.py:3587\u001b[0m, in \u001b[0;36mDataset.rename\u001b[0;34m(self, name_dict, **names)\u001b[0m\n\u001b[1;32m   3585\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m name_dict\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m   3586\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims:\n\u001b[0;32m-> 3587\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   3588\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot rename \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m because it is not a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3589\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable or dimension in this dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3590\u001b[0m         )\n\u001b[1;32m   3592\u001b[0m variables, coord_names, dims, indexes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rename_all(\n\u001b[1;32m   3593\u001b[0m     name_dict\u001b[38;5;241m=\u001b[39mname_dict, dims_dict\u001b[38;5;241m=\u001b[39mname_dict\n\u001b[1;32m   3594\u001b[0m )\n\u001b[1;32m   3595\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_replace(variables, coord_names, dims\u001b[38;5;241m=\u001b[39mdims, indexes\u001b[38;5;241m=\u001b[39mindexes)\n",
      "\u001b[0;31mValueError\u001b[0m: cannot rename 'longitude' because it is not a variable or dimension in this dataset"
     ]
    }
   ],
   "source": [
    "ds = ds.rename({'longitude':'lon', 'latitude':'lat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38312c3b-08fd-407f-b24b-bd937d88554e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5464300-16c1-4195-b648-a91792e19579",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ce22243-3ab3-4680-af6a-3759d38c2233",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = sorted(glob.glob('orig/ensemble/TpNAT_monthly_ens*.nc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5e3927bb-be82-4c9f-a1bc-02395f2fab9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n",
      "/tmp/ipykernel_517498/1858522089.py:34: FutureWarning: updating coordinate 'time', which is a PandasMultiIndex, would leave the multi-index level coordinates ['year', 'month'] in an inconsistent state. This will raise an error in the future. Use `.drop_vars(['time', 'year', 'month'])` to drop the coordinates' values before assigning new coordinate values.\n",
      "  temp[\"time\"] = cftime_dates\n"
     ]
    }
   ],
   "source": [
    "for f in fs:\n",
    "    with xr.open_dataset(f) as ds:\n",
    "        ds = ds.load()\n",
    "\n",
    "    # Avoid year zero and align them all\n",
    "    ds = ds.sel(year=slice(1, 2012))\n",
    "\n",
    "    # Assume ds is your Dataset\n",
    "    # Create a full time index\n",
    "    months = ds.month.values\n",
    "    years = ds.year.values\n",
    "    \n",
    "    # Broadcast year and month in correct order\n",
    "    year_grid, month_grid = xr.broadcast(ds.year, ds.month)\n",
    "    \n",
    "    # Flatten both\n",
    "    years = year_grid.values.ravel()\n",
    "    months = month_grid.values.ravel()\n",
    "    \n",
    "    # Create cftime datetime objects (assuming 15th of each month)\n",
    "    # Choose the appropriate calendar, e.g., 'standard', 'noleap', '360_day'\n",
    "    calendar = 'noleap'  # or change as needed\n",
    "    cftime_dates = np.array([\n",
    "        cftime.num2date(\n",
    "            cftime.date2num(cftime.DatetimeNoLeap(y, m, 1), units='days since 0001-01-01', calendar=calendar),\n",
    "            units='days since 0001-01-01',\n",
    "            calendar=calendar\n",
    "        )\n",
    "        for y, m in zip(years, months)\n",
    "    ])\n",
    "    \n",
    "    # Stack 'month' and 'year' into a single 'time' dimension\n",
    "    temp = ds.TpNAT_monthly.stack(time=(\"year\", \"month\"))\n",
    "    temp = temp.drop_vars(['time', 'year', 'month'])\n",
    "    temp[\"time\"] = cftime_dates\n",
    "    \n",
    "    # Unstack lon, lat\n",
    "    temp = temp.transpose(\"lon\", \"lat\", \"time\")\n",
    "    \n",
    "    # Wrap into new Dataset if needed\n",
    "    ds_new = xr.Dataset({\"tas\": temp + 273.15})\n",
    "    ds_new.to_netcdf(f'unstacked/ensemble/tp_nat_monthly_{os.path.basename(f).split('_')[-1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6cd68610-8c8d-45cf-9e90-6ecd5736dab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name,\n",
       ".xr-group-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-group-name::before {\n",
       "  content: \"📁\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-name,\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 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-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 20px auto;\n",
       "  width: 100%;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 20px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\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",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\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",
       "  border-color: var(--xr-background-color-row-even);\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",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\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-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\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",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11MB\n",
       "Dimensions:  (lon: 14, lat: 8, time: 24144)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 56B 4.5 5.5 6.5 7.5 8.5 ... 13.5 14.5 15.5 16.5 17.5\n",
       "  * lat      (lat) float32 32B 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5\n",
       "  * time     (time) object 193kB 0001-01-01 00:00:00 ... 2012-12-01 00:00:00\n",
       "Data variables:\n",
       "    tas      (lon, lat, time) float32 11MB 284.6 284.2 284.3 ... 276.4 272.4</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-55f5a394-efef-467a-8d68-5ce662ed0674' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55f5a394-efef-467a-8d68-5ce662ed0674' 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'>lon</span>: 14</li><li><span class='xr-has-index'>lat</span>: 8</li><li><span class='xr-has-index'>time</span>: 24144</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e16d1a90-7d88-45f5-8e7c-741141babb86' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e16d1a90-7d88-45f5-8e7c-741141babb86' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.5 5.5 6.5 7.5 ... 15.5 16.5 17.5</div><input id='attrs-c9027b58-90c4-402c-ad28-bd45b0541aca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9027b58-90c4-402c-ad28-bd45b0541aca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47543129-51b5-4c87-b8c7-6e6d712c322f' class='xr-var-data-in' type='checkbox'><label for='data-47543129-51b5-4c87-b8c7-6e6d712c322f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>description :</span></dt><dd>Longitude of Alps grid</dd></dl></div><div class='xr-var-data'><pre>array([ 4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5,\n",
       "       16.5, 17.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>42.5 43.5 44.5 ... 47.5 48.5 49.5</div><input id='attrs-4e9416ef-9868-4296-8095-a160ed3f22f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e9416ef-9868-4296-8095-a160ed3f22f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-070e777b-2b9f-41a1-bdff-bc34f08bfb6a' class='xr-var-data-in' type='checkbox'><label for='data-070e777b-2b9f-41a1-bdff-bc34f08bfb6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>description :</span></dt><dd>Latitude of Alps grid</dd></dl></div><div class='xr-var-data'><pre>array([42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5], dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>0001-01-01 00:00:00 ... 2012-12-...</div><input id='attrs-02b1d783-e6f5-4ac5-85ff-fa1cf4877465' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02b1d783-e6f5-4ac5-85ff-fa1cf4877465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b89f86fc-ab30-43a3-bdf2-b47878fc2a4b' class='xr-var-data-in' type='checkbox'><label for='data-b89f86fc-ab30-43a3-bdf2-b47878fc2a4b' 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([cftime.DatetimeNoLeap(1, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 3, 1, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2012, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2012, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      shape=(24144,), dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5042538-169a-444e-b75a-1b6b919c5e11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b5042538-169a-444e-b75a-1b6b919c5e11' 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>tas</span></div><div class='xr-var-dims'>(lon, lat, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>284.6 284.2 284.3 ... 276.4 272.4</div><input id='attrs-7f703828-fbd6-430a-b773-479b568f50da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f703828-fbd6-430a-b773-479b568f50da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a12a1f-e13f-447a-accc-47c4db1052b9' class='xr-var-data-in' type='checkbox'><label for='data-00a12a1f-e13f-447a-accc-47c4db1052b9' 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>C</dd><dt><span>description :</span></dt><dd>Monthly T anomalies added to (1960–1990) monthly climatology</dd></dl></div><div class='xr-var-data'><pre>array([[[284.62915, 284.2409 , 284.26578, ..., 290.13348, 287.8137 ,\n",
       "         285.82037],\n",
       "        [280.8708 , 281.3075 , 282.8314 , ..., 287.88278, 284.3196 ,\n",
       "         281.8984 ],\n",
       "        [275.0254 , 276.2001 , 279.0212 , ..., 284.08557, 279.33484,\n",
       "         276.46848],\n",
       "        ...,\n",
       "        [273.54367, 274.74863, 277.66995, ..., 281.99066, 277.26544,\n",
       "         274.61594],\n",
       "        [274.41797, 275.499  , 278.39514, ..., 282.47696, 277.89978,\n",
       "         275.29327],\n",
       "        [274.1485 , 275.11246, 277.99646, ..., 281.9958 , 277.4373 ,\n",
       "         274.88922]],\n",
       "\n",
       "       [[284.5409 , 284.2451 , 284.34216, ..., 290.39334, 287.92758,\n",
       "         285.86246],\n",
       "        [279.12994, 279.8645 , 281.77747, ..., 286.74304, 282.7808 ,\n",
       "         280.21826],\n",
       "        [272.00146, 273.34714, 276.33618, ..., 281.73944, 276.83127,\n",
       "         273.74457],\n",
       "...\n",
       "        [271.43573, 273.67166, 277.4353 , ..., 282.3262 , 277.0489 ,\n",
       "         273.0216 ],\n",
       "        [271.06958, 273.1328 , 277.02878, ..., 282.38412, 277.1285 ,\n",
       "         273.166  ],\n",
       "        [269.64563, 271.2773 , 274.95108, ..., 280.75143, 275.70203,\n",
       "         271.8399 ]],\n",
       "\n",
       "       [[285.1046 , 284.95358, 285.38876, ..., 291.52893, 288.8083 ,\n",
       "         286.61795],\n",
       "        [275.3258 , 276.63104, 279.24088, ..., 285.1844 , 280.9787 ,\n",
       "         277.30078],\n",
       "        [271.59686, 273.78455, 277.3689 , ..., 282.89606, 278.17233,\n",
       "         273.65128],\n",
       "        ...,\n",
       "        [271.37985, 273.83115, 277.8642 , ..., 282.79315, 277.46753,\n",
       "         273.28094],\n",
       "        [270.56113, 272.77032, 276.80414, ..., 282.5956 , 277.35355,\n",
       "         273.17178],\n",
       "        [269.78662, 271.45074, 275.18484, ..., 281.40854, 276.40997,\n",
       "         272.3846 ]]], shape=(14, 8, 24144), dtype=float32)</pre></div></li></ul></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 11MB\n",
       "Dimensions:  (lon: 14, lat: 8, time: 24144)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 56B 4.5 5.5 6.5 7.5 8.5 ... 13.5 14.5 15.5 16.5 17.5\n",
       "  * lat      (lat) float32 32B 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5\n",
       "  * time     (time) object 193kB 0001-01-01 00:00:00 ... 2012-12-01 00:00:00\n",
       "Data variables:\n",
       "    tas      (lon, lat, time) float32 11MB 284.6 284.2 284.3 ... 276.4 272.4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 128,
   "id": "e558d66c-e0cf-45e7-a05e-7e72d29351eb",
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (lon: 14, lat: 8, month: 12, year: 2020)\n",
       "Coordinates:\n",
       "  * lon            (lon) float32 4.5 5.5 6.5 7.5 8.5 ... 14.5 15.5 16.5 17.5\n",
       "  * lat            (lat) float32 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5\n",
       "  * month          (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * year           (year) uint16 1 2 3 4 5 6 7 ... 2015 2016 2017 2018 2019 2020\n",
       "Data variables:\n",
       "    TpNAT_monthly  (lon, lat, month, year) float32 11.79 12.47 ... -1.474 -2.377</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-6007a768-02be-4b3f-a203-e0a46557c4b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6007a768-02be-4b3f-a203-e0a46557c4b5' 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'>lon</span>: 14</li><li><span class='xr-has-index'>lat</span>: 8</li><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>year</span>: 2020</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fd1f8926-738d-4557-a48d-88ee4e12c55e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd1f8926-738d-4557-a48d-88ee4e12c55e' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.5 5.5 6.5 7.5 ... 15.5 16.5 17.5</div><input id='attrs-29081af2-1296-42ff-8498-c7203d6b8309' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29081af2-1296-42ff-8498-c7203d6b8309' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3bdc6f-232d-4f2f-9903-6db4750e1e4b' class='xr-var-data-in' type='checkbox'><label for='data-5c3bdc6f-232d-4f2f-9903-6db4750e1e4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>description :</span></dt><dd>Longitude of Alps grid</dd></dl></div><div class='xr-var-data'><pre>array([ 4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5,\n",
       "       16.5, 17.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>42.5 43.5 44.5 ... 47.5 48.5 49.5</div><input id='attrs-232b4a07-52a3-4422-998a-566d755fa2a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-232b4a07-52a3-4422-998a-566d755fa2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96dc8d8f-7502-4fc1-9af9-6626bd1e8953' class='xr-var-data-in' type='checkbox'><label for='data-96dc8d8f-7502-4fc1-9af9-6626bd1e8953' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>description :</span></dt><dd>Latitude of Alps grid</dd></dl></div><div class='xr-var-data'><pre>array([42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-292c928b-1688-4e4c-bf4b-da1caf6759f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-292c928b-1688-4e4c-bf4b-da1caf6759f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b706e83f-7d3e-4100-ae94-ee706994dccd' class='xr-var-data-in' type='checkbox'><label for='data-b706e83f-7d3e-4100-ae94-ee706994dccd' 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>description :</span></dt><dd>Month number (1–12)</dd></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 ... 2017 2018 2019 2020</div><input id='attrs-4f5eed2c-c68e-43f1-bd24-db05372a3c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f5eed2c-c68e-43f1-bd24-db05372a3c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad3c17fe-666c-4c61-8c3c-df9fe5e9ed42' class='xr-var-data-in' type='checkbox'><label for='data-ad3c17fe-666c-4c61-8c3c-df9fe5e9ed42' 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>description :</span></dt><dd>Calendar year</dd></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3, ..., 2018, 2019, 2020], dtype=uint16)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5a6f3aa-3981-46cf-9cc9-bc257cd598fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5a6f3aa-3981-46cf-9cc9-bc257cd598fe' 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>TpNAT_monthly</span></div><div class='xr-var-dims'>(lon, lat, month, year)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>11.79 12.47 12.53 ... -1.474 -2.377</div><input id='attrs-a140171f-e9e3-4861-b753-40d834770d8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a140171f-e9e3-4861-b753-40d834770d8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb5a5e5c-60bc-4c51-8827-bdc97436a25b' class='xr-var-data-in' type='checkbox'><label for='data-eb5a5e5c-60bc-4c51-8827-bdc97436a25b' 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>C</dd><dt><span>description :</span></dt><dd>Monthly T anomalies added to (1960–1990) monthly climatology</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 1.17865133e+01,  1.24653254e+01,  1.25329685e+01, ...,\n",
       "           1.14888420e+01,  1.20736609e+01,  1.21616735e+01],\n",
       "         [ 1.13580351e+01,  1.20368471e+01,  1.21044903e+01, ...,\n",
       "           1.10603638e+01,  1.16451826e+01,  1.17331953e+01],\n",
       "         [ 1.14299726e+01,  1.21087847e+01,  1.21764278e+01, ...,\n",
       "           1.11323013e+01,  1.17171202e+01,  1.18051329e+01],\n",
       "         ...,\n",
       "         [ 1.81745777e+01,  1.88533916e+01,  1.89210339e+01, ...,\n",
       "           1.78769073e+01,  1.84617252e+01,  1.85497379e+01],\n",
       "         [ 1.53187513e+01,  1.59975634e+01,  1.60652065e+01, ...,\n",
       "           1.50210800e+01,  1.56058989e+01,  1.56939116e+01],\n",
       "         [ 1.30057821e+01,  1.36845942e+01,  1.37522373e+01, ...,\n",
       "           1.27081108e+01,  1.32929296e+01,  1.33809423e+01]],\n",
       "\n",
       "        [[ 8.20881844e+00,  8.75649834e+00,  9.12220669e+00, ...,\n",
       "           8.08798981e+00,  8.67280865e+00,  8.76082134e+00],\n",
       "         [ 8.39975929e+00,  8.94743919e+00,  9.31314754e+00, ...,\n",
       "           8.27893066e+00,  8.86374950e+00,  8.95176220e+00],\n",
       "         [ 9.59655857e+00,  1.01442385e+01,  1.05099468e+01, ...,\n",
       "           9.47572994e+00,  1.00605488e+01,  1.01485615e+01],\n",
       "...\n",
       "           9.21346664e+00,  8.76675034e+00,  7.47943783e+00],\n",
       "         [ 3.57055998e+00,  2.88890839e+00,  4.29290962e+00, ...,\n",
       "           4.06391001e+00,  3.61719370e+00,  2.32988071e+00],\n",
       "         [-6.29669785e-01, -1.31132126e+00,  9.26797837e-02, ...,\n",
       "          -1.36319876e-01, -5.83036184e-01, -1.87034917e+00]],\n",
       "\n",
       "        [[-3.71585751e+00, -4.43580341e+00, -2.89050102e+00, ...,\n",
       "          -3.06419468e+00, -3.69011021e+00, -4.59237289e+00],\n",
       "         [-2.14204788e+00, -2.86199403e+00, -1.31669128e+00, ...,\n",
       "          -1.49038517e+00, -2.11630058e+00, -3.01856303e+00],\n",
       "         [ 1.62245727e+00,  9.02511120e-01,  2.44781399e+00, ...,\n",
       "           2.27412009e+00,  1.64820445e+00,  7.45942116e-01],\n",
       "         ...,\n",
       "         [ 7.58347416e+00,  6.86352777e+00,  8.40883064e+00, ...,\n",
       "           8.23513699e+00,  7.60922098e+00,  6.70695877e+00],\n",
       "         [ 2.59153771e+00,  1.87159157e+00,  3.41689444e+00, ...,\n",
       "           3.24320054e+00,  2.61728477e+00,  1.71502256e+00],\n",
       "         [-1.50005317e+00, -2.21999931e+00, -6.74696565e-01, ...,\n",
       "          -8.48390460e-01, -1.47430599e+00, -2.37656832e+00]]]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87a99d1a-766e-4707-9925-875874b09cd8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87a99d1a-766e-4707-9925-875874b09cd8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (lon: 14, lat: 8, month: 12, year: 2020)\n",
       "Coordinates:\n",
       "  * lon            (lon) float32 4.5 5.5 6.5 7.5 8.5 ... 14.5 15.5 16.5 17.5\n",
       "  * lat            (lat) float32 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5\n",
       "  * month          (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * year           (year) uint16 1 2 3 4 5 6 7 ... 2015 2016 2017 2018 2019 2020\n",
       "Data variables:\n",
       "    TpNAT_monthly  (lon, lat, month, year) float32 11.79 12.47 ... -1.474 -2.377"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "2bdd59da-77c9-4155-a72d-18e0a1f202e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c835cc59-3e50-4bd1-ad3c-647d5fb451f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c25c1b26-e2f9-4e83-867d-92ce00f36b1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1583624-7217-434c-9eba-5611989e9f92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0190c936-ba26-447d-aff4-c2fc58c3c608",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb998282-f323-4f13-b348-d97270bb7082",
   "metadata": {},
   "outputs": [],
   "source": [
    "with xr.open_dataset('unstacked/p_monthly.nc') as ds:\n",
    "    ds = ds.isel(time=0).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a058c839-7fb3-408c-8f8a-52460656cd5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.pr.T.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d0049a4-3bca-4476-a104-b4b3caeccd94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lmr_mira_hist_cl_002'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens_member = 2\n",
    "f'lmr_mira_hist_cl_{ens_member:03d}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a70fbd62-b519-411f-81e4-ccfad7713a0c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "no ensemble member?",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mno ensemble member?\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[31mValueError\u001b[39m: no ensemble member?"
     ]
    }
   ],
   "source": [
    "raise ValueError('no ensemble member?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1698754d-21d2-445f-94b2-04ad3dd510d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}