{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate GlacierMIP3 netcdf files for submission "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File naming convention "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`{contributor}_{rgi-region}_{aggregation-level}_{period}_{gcm}_{ssp}.nc`\n",
    "\n",
    "All characters should be **lower case**, except the `{contributor}` entry for which you can choose the case. Underscores are forbidden in all brackets entries `{}` to avoid confusion.\n",
    "- {contributor}: model or contributor name - if more than one model configuration is submitted, make it explicit in the name (e.g. `OGGM-dyn`, `OGGM-vas`)\n",
    "- {rgi-region}: for example `rgi09`\n",
    "- {aggregation-level}: either `sum` (regional totals) or `glaciers` (individual glaciers)\n",
    "- {period}: shuffled reference period (example: `1901-1920`)\n",
    "- {gcm}: name of used gcm\n",
    "- {ssp}: name of used ssp or `hist`\n",
    "\n",
    "**Examples**:\n",
    "- `Zekollari_rgi08_sum_2061-2080_ipsl-cm6a-lr_ssp585.nc`\n",
    "- `OGGM-dyn_rgi11_glaciers_1901-1920_ipsl-cm6a-lr_hist.nc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File format "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Netcdf](https://www.unidata.ucar.edu/software/netcdf/) files are very common in the Earth Sciences, and should be writable by any scientific programming language. Here we shortly describe the format of the file followed by a code sample in python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output netcdf files should have the following format (as done for the template files visible below):\n",
    "- no compression, but numbers should be stored in `float32`\n",
    "- units should be in SI units: area in `m2`, volume in `m3`\n",
    "- time should be in `calendar years`: the volume and area should correspond to the state of that glacier at that time stamp (i.e., 1st of January)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File format: regional sums"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we simply display the format of the template file for reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xarray.Dataset {\n",
      "dimensions:\n",
      "\tsimulation_year = 1001 ;\n",
      "\n",
      "variables:\n",
      "\tint16 simulation_year(simulation_year) ;\n",
      "\tfloat32 volume_m3(simulation_year) ;\n",
      "\t\tvolume_m3:units = m3 ;\n",
      "\t\tvolume_m3:long_name = Regional glacier volume at timestamp ;\n",
      "\tfloat32 area_m2(simulation_year) ;\n",
      "\t\tarea_m2:units = m2 ;\n",
      "\t\tarea_m2:long_name = Regional glacier area at timestamp ;\n",
      "\n",
      "// global attributes:\n",
      "\t:contributor = Zekollari ;\n",
      "\t:contributor_email = bli.bla@blo.be ;\n",
      "\t:creation_date = 29/06/2021 ;\n",
      "\t:rgi-region = rgi08 ;\n",
      "\t:aggregation-level = sum ;\n",
      "\t:period = 2061-2080 ;\n",
      "\t:gcm = ipsl-cm6a-lr ;\n",
      "\t:ssp = ssp585 ;\n",
      "\t:information = Anything you find useful here ;\n",
      "}"
     ]
    }
   ],
   "source": [
    "filename = 'Zekollari_rgi08_sum_2061-2080_ipsl-cm6a-lr_ssp585.nc'\n",
    "with xr.open_dataset(filename) as ds:\n",
    "    ds = ds.load()\n",
    "ds.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File format: glacier per glacier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we simply display the format of the template file for reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xarray.Dataset {\n",
      "dimensions:\n",
      "\trgi_id = 3 ;\n",
      "\tsimulation_year = 1001 ;\n",
      "\n",
      "variables:\n",
      "\tint16 simulation_year(simulation_year) ;\n",
      "\tobject rgi_id(rgi_id) ;\n",
      "\tfloat32 volume_m3(simulation_year, rgi_id) ;\n",
      "\t\tvolume_m3:units = m3 ;\n",
      "\t\tvolume_m3:long_name = Glacier volume at timestamp ;\n",
      "\tfloat32 area_m2(simulation_year, rgi_id) ;\n",
      "\t\tarea_m2:units = m2 ;\n",
      "\t\tarea_m2:long_name = Glacier area at timestamp ;\n",
      "\n",
      "// global attributes:\n",
      "\t:contributor = OGGM-dyn ;\n",
      "\t:contributor_email = bli.bla@blo.be ;\n",
      "\t:creation_date = 29/06/2021 ;\n",
      "\t:rgi-region = rgi11 ;\n",
      "\t:aggregation-level = glaciers ;\n",
      "\t:period = 1901-1920 ;\n",
      "\t:gcm = ipsl-cm6a-lr ;\n",
      "\t:ssp = hist ;\n",
      "\t:information = Anything you find useful here ;\n",
      "}"
     ]
    }
   ],
   "source": [
    "filename = 'OGGM-dyn_rgi11_glaciers_1901-1920_ipsl-cm6a-lr_hist.nc'\n",
    "with xr.open_dataset(filename) as ds:\n",
    "    ds = ds.load()\n",
    "ds.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example code: writing GlacierMIP3 NetCDF files with python and xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prerequisite: you need to have the [netcdf4-python](http://unidata.github.io/netcdf4-python/) and [xarray](http://xarray.pydata.org) packages installed for this to work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regional sums "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the `Zekollari_rgi08_sum_2061-2080_ipsl-cm6a-lr_ssp585.nc` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run info\n",
    "contributor = 'Zekollari'\n",
    "rgi_reg = 'rgi08'\n",
    "agg_level = 'sum'\n",
    "period = '2061-2080'\n",
    "gcm = 'ipsl-cm6a-lr'\n",
    "ssp = 'ssp585'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Zekollari_rgi08_sum_2061-2080_ipsl-cm6a-lr_ssp585.nc'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = f'{contributor}_{rgi_reg}_{agg_level}_{period}_{gcm}_{ssp}.nc'\n",
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fake data and time arrays\n",
    "time = np.arange(1001)\n",
    "volume = 1 / np.linspace(0.1, 1, 1001)**2\n",
    "area = 1 / np.linspace(0.1, 1, 1001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds = xr.Dataset()\n",
    "\n",
    "ds.attrs['contributor'] = contributor\n",
    "ds.attrs['contributor_email'] = 'bli.bla@blo.be'\n",
    "ds.attrs['creation_date'] = date.today().strftime(\"%d/%m/%Y\")\n",
    "ds.attrs['rgi-region'] = rgi_reg\n",
    "ds.attrs['aggregation-level'] = agg_level\n",
    "ds.attrs['period'] = period\n",
    "ds.attrs['gcm'] = gcm\n",
    "ds.attrs['ssp'] = ssp\n",
    "ds.attrs['information'] = 'Anything you find useful here'\n",
    "\n",
    "ds['simulation_year'] = (('simulation_year'), time)\n",
    "\n",
    "varname = 'volume_m3'\n",
    "ds[varname] = (('simulation_year'), volume)\n",
    "ds[varname].attrs['units'] = 'm3'\n",
    "ds[varname].attrs['long_name'] = 'Regional glacier volume at timestamp'\n",
    "\n",
    "varname = 'area_m2'\n",
    "ds[varname] = (('simulation_year'), area)\n",
    "ds[varname].attrs['units'] = 'm2'\n",
    "ds[varname].attrs['long_name'] = 'Regional glacier area at timestamp'\n",
    "\n",
    "# This is the same for all files\n",
    "encoding = {\n",
    "    'simulation_year': {\"dtype\": \"int16\"},\n",
    "    'volume_m3': {\"dtype\": \"float32\"},\n",
    "    'area_m2': {\"dtype\": \"float32\"},\n",
    "}\n",
    "\n",
    "ds.to_netcdf(filename, encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.volume_m3.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.area_m2.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Glacier per glacier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very similar, but with one more dimension.\n",
    "\n",
    "We create the `OGGM-dyn_rgi11_glaciers_1901-1920_ipsl-cm6a-lr_hist.nc` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run info\n",
    "contributor = 'OGGM-dyn'\n",
    "rgi_reg = 'rgi11'\n",
    "agg_level = 'glaciers'\n",
    "period = '1901-1920'\n",
    "gcm = 'ipsl-cm6a-lr'\n",
    "ssp = 'hist'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OGGM-dyn_rgi11_glaciers_1901-1920_ipsl-cm6a-lr_hist.nc'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = f'{contributor}_{rgi_reg}_{agg_level}_{period}_{gcm}_{ssp}.nc'\n",
    "filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fake data and time arrays\n",
    "time = np.arange(1001)\n",
    "volume = np.array([1 / np.linspace(0.1, 1, 1001)**x for x in [2, 3, 4]]).T\n",
    "area = np.sqrt(volume) \n",
    "rgi_id = ['RGI60-11.00897', 'RGI60-11.00898', 'RGI60-11.00899']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds = xr.Dataset()\n",
    "\n",
    "ds.attrs['contributor'] = contributor\n",
    "ds.attrs['contributor_email'] = 'bli.bla@blo.be'\n",
    "ds.attrs['creation_date'] = date.today().strftime(\"%d/%m/%Y\")\n",
    "ds.attrs['rgi-region'] = rgi_reg\n",
    "ds.attrs['aggregation-level'] = agg_level\n",
    "ds.attrs['period'] = period\n",
    "ds.attrs['gcm'] = gcm\n",
    "ds.attrs['ssp'] = ssp\n",
    "ds.attrs['information'] = 'Anything you find useful here'\n",
    "\n",
    "ds['simulation_year'] = (('simulation_year'), time)\n",
    "ds['rgi_id'] = (('rgi_id'), rgi_id)\n",
    "\n",
    "varname = 'volume_m3'\n",
    "ds[varname] = (('simulation_year', 'rgi_id'), volume)\n",
    "ds[varname].attrs['units'] = 'm3'\n",
    "ds[varname].attrs['long_name'] = 'Glacier volume at timestamp'\n",
    "\n",
    "varname = 'area_m2'\n",
    "ds[varname] = (('simulation_year', 'rgi_id'), area)\n",
    "ds[varname].attrs['units'] = 'm2'\n",
    "ds[varname].attrs['long_name'] = 'Glacier area at timestamp'\n",
    "\n",
    "# This is the same for all files\n",
    "encoding = {\n",
    "    'simulation_year': {\"dtype\": \"int16\"},\n",
    "    'volume_m3': {\"dtype\": \"float32\"},\n",
    "    'area_m2': {\"dtype\": \"float32\"},\n",
    "}\n",
    "\n",
    "ds.to_netcdf(filename, encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.volume_m3.plot(hue='rgi_id');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.area_m2.plot(hue='rgi_id');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}