LMDZ_Gallery.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e21494d9-4bb6-446d-af1b-47c330bff6e5",
   "metadata": {},
   "source": [
    "<img src=\"https://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png\" width=\"600px\"> <img src=\"https://www.ipsl.fr/wp-content/themes/ipsltheme/themes/logo/logo_ipsl_2.png\" width=\"60px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b1fbbf3-700c-45b5-aedc-319e37f669b1",
   "metadata": {},
   "source": [
    "# Plotting LMDZ outputs with matplotlib and cartopy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258fcf6a-f105-402f-8213-f2006c9c6c02",
   "metadata": {},
   "source": [
    "## License\n",
    "This software is governed by the CeCILL license license under French law and abiding by the rules of distribution of free software. You can use, modify and/ or redistribute the software under the terms of the CeCILL license as circulated by CEA, CNRS and INRIA.\n",
    "\n",
    "Warning, to install, configure, run, use any of Olivier Marti's software or to read the associated documentation you'll need at least one (1) brain in a reasonably working order. Lack of this implement will void any warranties (either express or implied). O. Marti assumes no responsability for errors, omissions, data loss, or any other consequences caused directly or indirectly by the usage of his software by incorrectly or partially configured personal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824686eb-cac8-4f74-a317-7ac85bb5bc06",
   "metadata": {},
   "source": [
    "## Contact\n",
    "mailto:olivier.marti@lsce.ipsl.fr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bddfb93-1d7b-43c0-b68a-3c966fb47fa2",
   "metadata": {},
   "source": [
    "## SVN Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "304964a3-bcc2-45ab-a91b-d0282a062036",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "SVN = { 'Author' : \"$Author: $\", 'Date' : \"$Date: $\", 'Revision': \"$Revision: $\",\n",
    "        'Id' : \"$Id:  $\", 'HeadURL' : \"$HeadUrl: $\" }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c8cd60e-a24c-41af-a2c1-6ebd062a6cf7",
   "metadata": {},
   "source": [
    "# Initialize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261ccbf8-32ed-472a-b866-02153dd57cbd",
   "metadata": {},
   "source": [
    "## Classic modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5dadc7e3-adec-4554-aadb-8b85f746bc4a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np, xarray as xr\n",
    "import matplotlib.pyplot as plt, matplotlib as mpl\n",
    "import cartopy.mpl.ticker, cartopy.crs as ccrs, cartopy, cartopy.util as cutil\n",
    "import importlib, sys, os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b599544-1233-4b69-a741-9cad1e931e40",
   "metadata": {},
   "source": [
    "## Specific module to simplify access to IPSL model outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77c1487b-4bc7-4285-a41b-68a9c63e6c4d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import libIGCM_sys\n",
    "rad = np.deg2rad (1.0)\n",
    "Mach = libIGCM_sys.Mach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3188f8a4-be9f-4dc1-a425-e4f689136eeb",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine            :  Spip\n",
      "Version python     :  3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:34:54) [Clang 16.0.6 ]\n",
      "Version numpy      :  1.26.4\n",
      "Version xarray     :  2024.5.0\n",
      "Version Matplotlib :  3.8.4\n",
      "Version Cartopy    :  0.23.0\n"
     ]
    }
   ],
   "source": [
    "print ( 'Machine            : ', Mach                )\n",
    "print ( 'Version python     : ', sys.version         )\n",
    "print ( 'Version numpy      : ', np.__version__      )\n",
    "print ( 'Version xarray     : ', xr.__version__      )\n",
    "print ( 'Version Matplotlib : ', mpl.__version__     )\n",
    "print ( 'Version Cartopy    : ', cartopy.__version__ )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09bc02a-7841-4c71-a219-991ce307fc76",
   "metadata": {},
   "source": [
    "## Specific module for LMDZ\n",
    "\n",
    "Use `help(lmdz)` to get some details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "318b2e4b-1178-4dfd-9090-cc113387fc56",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import lmdz\n",
    "importlib.reload (lmdz)\n",
    "Ra = lmdz.RA\n",
    "#lmdz.set_options(Debug=True, Stack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a4d0215-e93a-4bbd-b596-6f95c529a318",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Debug': False, 'Trace': False, 'Depth': -1}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmdz.get_options ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ac0bca-524d-401b-8a62-71992878c6a0",
   "metadata": {},
   "source": [
    "# Read and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "385efb7c-afe2-4c2e-a274-384006986ebc",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "mm = libIGCM_sys.config ( JobName='TR6kCM6AS-Sr01', TagName='IPSLCM6', ExperimentName='Holocene',\n",
    "                         SpaceName='PROD',\n",
    "                          #Source='TGCC_thredds', TGCC_User='p86caub', TGCC_Group='gencmip6', \n",
    "                         )\n",
    "mm.Dir    = os.path.join ( mm.R_SAVE, 'ATM', 'Analyse', 'SE' )\n",
    "mm.Period = 'SE_5300_5399_1M'"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f2738fac-1fbf-4c27-b95c-2c80da2ef42f",
   "metadata": {},
   "source": [
    "mm = libIGCM_sys.config ( JobName='CM65v420-LR-SKL-pi-05', TagName='IPSLCM6', ExperimentName='piControl', SpaceName='DEVT',\n",
    "                          #Source='TGCC_thredds', TGCC_User='p86caub', TGCC_Group='gencmip6', \n",
    "                          Out='Dict' )\n",
    "globals().update (mm)\n",
    "\n",
    "mm.Dir    = os.path.join ( mm.R_SAVE, 'ATM', 'Output', 'MO' )\n",
    "mm.Period = '23300101_23391231_1M'\n",
    "time_sel = { 'time_counter':slice (0, 2) }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19085a11-51d3-4aa8-8a1b-94bdc7181e14",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f_h = '/Users/marti/Data/IGCM_OUT/IPSLCM6/PROD/Holocene/TR6kCM6AS-Sr01/ATM/Analyse/SE/TR6kCM6AS-Sr01_SE_5300_5399_1M_histmth.nc'\n"
     ]
    }
   ],
   "source": [
    "f_h = os.path.join ( mm.Dir, f'{mm.JobName}_{mm.Period}_histmth.nc' )\n",
    "\n",
    "print ( f'{f_h = }' )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e479ddf-9c12-4e1b-ad44-e8f1da21d570",
   "metadata": {},
   "source": [
    "## Open files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e35f8d27-a23e-4ba7-9446-49b8d7523808",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#kw = {'chunks':{\"time_counter\":8}}\n",
    "kw={}\n",
    "d_h =  xr.open_dataset (f_h, use_cftime=True, decode_times=False, **kw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab8a510-df70-4f4d-8d62-e8e7810fcd60",
   "metadata": {},
   "source": [
    "## Read variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d1e3b4e-f16a-45e3-9b48-54d338d0000c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "t2m      = d_h['t2m']  - lmdz.RT0   # Air temperature : change from °K to °C\n",
    "temp     = d_h['temp'] - lmdz.RT0\n",
    "psol     = d_h['psol']\n",
    "pres     = d_h['pres']\n",
    "presnivs = d_h['presnivs']\n",
    "u10m     = d_h['u10m']\n",
    "v10m     = d_h['v10m']\n",
    "wind10m  = d_h['wind10m']\n",
    "vitu     = d_h['vitu']\n",
    "vitv     = d_h['vitv']\n",
    "vitw     = d_h['vitw']\n",
    "sst      = (d_h['tsol_oce'] - lmdz.RT0).where ( d_h['pourc_oce']>0, np.nan) # SST : change from °K to °C\n",
    "\n",
    "lon = d_h ['lon'] ; lat = d_h ['lat']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1928817a-8757-4e25-82e1-740fb1d18e85",
   "metadata": {},
   "source": [
    "## Standard pressure levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a7cab04-788a-4443-ad05-c4b511b13c83",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "std_lev = np.array ( [1013, 1000, 975, 950, 925, 900, 875, 850, 825, \n",
    "                       800,  750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250,\n",
    "                       200,  150, 100,  70,  50,  30,  20,  10]) * 100.\n",
    "\n",
    "std_lev = xr.DataArray ( std_lev, dims=('pressure',), coords= (std_lev,) )\n",
    "std_lev.attrs.update ( {'units':'Pa', 'standard_name':'Pressure levels'} )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d176a7cb-62b6-4254-96b8-c0012a3f68cc",
   "metadata": {},
   "source": [
    "## Font size for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7836ea74-14d5-48b1-8fd1-1154371defc5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#plt.style.use (\"dark_background\")\n",
    "plt.rcParams ['font.size'] = '16'\n",
    "\n",
    "background = plt.rcParams['figure.facecolor']\n",
    "foreground = plt.rcParams['lines.color']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27be598a-0e79-4b29-838f-2f504db13fe1",
   "metadata": {},
   "source": [
    "### Get logo files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85655577-f90c-4905-baaa-0df91a8f682d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def GetLogo ( url, File='Logo.png' ) : \n",
    "    import requests, os\n",
    "    from pathlib import Path\n",
    "    File = Path (File)\n",
    "    if \"http\" in url : \n",
    "        suffix = File.suffix\n",
    "        if suffix == '' : \n",
    "            suffix = '.png'\n",
    "            File = File + suffix\n",
    "        if not File.exists () :\n",
    "            data = requests.get(url).content\n",
    "            file = open ( f'{File}', 'wb')\n",
    "            file.write (data)\n",
    "        Logo = mpl.image.imread ( f'{File}')\n",
    "    else :\n",
    "        Logo = mpl.image.imread (url)\n",
    "    return Logo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "455121a6-591b-4d2f-9cb0-7c130341f3d9",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f49ac8-5a17-4629-a9c0-f2d136bf5335",
   "metadata": {},
   "source": [
    "## Simple time plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67f0d7d5-7e9c-47e3-923b-45899fc98d2c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x155d55450>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2m.sel ( {'lat':-60, 'lon':40}, 'nearest' ).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d5eae19-01bd-4e68-b3be-2bf958d6bddb",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x16c6c4990>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2m.sel ( {'lat':slice(-30,-70)} ).mean (dim='lon').plot ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44f8a3a-8670-4dec-8481-1b7a7d968fb9",
   "metadata": {},
   "source": [
    "## Scalars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c3ab472-d14e-4456-8b30-d51671ea937a",
   "metadata": {},
   "source": [
    "### Robinson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e8d8bde9-9215-4fbd-a982-5df9389f1d2b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'PopStack' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 21\u001b[0m\n\u001b[1;32m     18\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_global ()\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Select data\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m tt, xx, yy \u001b[38;5;241m=\u001b[39m \u001b[43mlmdz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_cyclic\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msst\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# Color filing\u001b[39;00m\n\u001b[1;32m     24\u001b[0m mmap \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mcontourf (xx, yy, tt, \n\u001b[1;32m     25\u001b[0m                     cmap\u001b[38;5;241m=\u001b[39mcmap, norm\u001b[38;5;241m=\u001b[39mnorm, levels\u001b[38;5;241m=\u001b[39mlevels, transform\u001b[38;5;241m=\u001b[39mProjIn, extend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/Python/Library/lmdz.py:568\u001b[0m, in \u001b[0;36madd_cyclic\u001b[0;34m(ptab, x, y, axis, cyclic, precision)\u001b[0m\n\u001b[1;32m    564\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m                      : new_coords\u001b[38;5;241m.\u001b[39mappend (ptab\u001b[38;5;241m.\u001b[39mcoords[dim]\u001b[38;5;241m.\u001b[39mvalues)\n\u001b[1;32m    566\u001b[0m ztab \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mDataArray (ztab, dims\u001b[38;5;241m=\u001b[39mptab\u001b[38;5;241m.\u001b[39mdims, coords\u001b[38;5;241m=\u001b[39mnew_coords)\n\u001b[0;32m--> 568\u001b[0m \u001b[43mPopStack\u001b[49m ( \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madd_cyclic\u001b[39m\u001b[38;5;124m'\u001b[39m )\n\u001b[1;32m    569\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ztab, xx, yy\n",
      "\u001b[0;31mNameError\u001b[0m: name 'PopStack' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates the color palette\n",
    "#levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-2, 30)\n",
    "levels= np.linspace (-2, 30, 17) \n",
    "palet  = mpl.cm.plasma\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Describe the model projection in the file\n",
    "ProjIn = ccrs.PlateCarree (central_longitude=0) \n",
    "\n",
    "# Projection for plots\n",
    "ProjPlot = ccrs.Robinson (central_longitude=270) # Plotted projection\n",
    "\n",
    "# Creates the figure\n",
    "fig, ax = plt.subplots ( figsize=(15,6), subplot_kw={'projection':ProjPlot} )\n",
    "ax.set_global ()\n",
    "\n",
    "# Select data\n",
    "tt, xx, yy = lmdz.add_cyclic (sst[0], lon, lat)\n",
    "\n",
    "# Color filing\n",
    "mmap = ax.contourf (xx, yy, tt, \n",
    "                    cmap=cmap, norm=norm, levels=levels, transform=ProjIn, extend='both')\n",
    "    \n",
    "# Contour lines\n",
    "mlin = ax.contour (xx, yy, tt, colors='k', levels=levels, transform=ProjIn, linewidths=0.25)\n",
    "\n",
    "# Add labels to contour lines\n",
    "ax.clabel (mlin, colors=['k'], manual=False,  # Automatic placement\n",
    "        inline=True,  # Cut the line where the label will be placed.\n",
    "        fmt='{:3.1f} '.format, fontsize='xx-small' )\n",
    "\n",
    "# Beautify\n",
    "ax.gridlines (draw_labels=True, crs=ProjIn,)\n",
    "ax.coastlines ()\n",
    "ax.add_feature(cartopy.feature.LAND, color='red')\n",
    "\n",
    "# Color palette\n",
    "plt.colorbar(mmap, label=\"°C\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d3c956-e241-4d2d-8bab-839329d95183",
   "metadata": {},
   "source": [
    "### Orthographic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5773ce5-0fd6-40db-9bc2-14d83245cdc0",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-2, 30)\n",
    "palet  = mpl.cm.RdYlGn\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Describe the model projection in the file\n",
    "ProjIn = ccrs.PlateCarree (central_longitude=0) \n",
    "\n",
    "# Projection for plots\n",
    "ProjPlot = ccrs.Orthographic (central_longitude=-20, central_latitude=-60) # Plotted projection\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport, with the associated projection\n",
    "ax = fig.add_subplot (1, 1, 1, projection=ProjPlot)\n",
    "\n",
    "ax.set_global ()\n",
    "\n",
    "# Select data\n",
    "tt, xx, yy = lmdz.add_cyclic (sst[0], lon, lat)\n",
    "\n",
    "# Color filing\n",
    "mmap = ax.contourf ( xx, yy, tt, \n",
    "                    cmap=cmap, norm=norm, levels=levels, transform=ProjIn, extend='both')\n",
    "    \n",
    "# Contour lines\n",
    "mlin3 = ax.contour (xx, yy, tt, colors='k', levels=levels[::3], transform=ProjIn, linewidths=1)\n",
    "mlin  = ax.contour (xx, yy, tt, colors='k', levels=levels, transform=ProjIn, linewidths=0.25)\n",
    "\n",
    "# Add labels to contour lines\n",
    "ax.clabel (mlin3, colors=['k'], manual=False,  # Automatic placement\n",
    "        inline=True,  # Cut the line where the label will be placed.\n",
    "        fmt='{:3.1f} '.format, fontsize='xx-small' )\n",
    "\n",
    "# Beautify\n",
    "ax.gridlines (draw_labels=True, crs=ProjIn,)\n",
    "ax.coastlines ()\n",
    "\n",
    "plt.colorbar ( mmap, label=\"°C\" )\n",
    "\n",
    "plt.suptitle ( \"Sea Surface Temperature on LMDZ grid\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b799e6af-5f88-4786-8605-9a5c63f08a1d",
   "metadata": {},
   "source": [
    "# Vertical slices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d72765-ea2f-485b-8a56-4197484dd7ec",
   "metadata": {},
   "source": [
    "## Zonal mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca312ba8-7aec-441a-a7bb-333b02aa772b",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=18).tick_values (-60, 30)\n",
    "palet  = mpl.cm.gnuplot\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Extract data\n",
    "tt = temp.isel( time_counter=0 ).mean(dim=['lon',])\n",
    "\n",
    "# Color filing\n",
    "mmap = ax.contourf (lat, presnivs/100, tt,\n",
    "         cmap=cmap, norm=norm, levels=levels, extend='both')\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LatitudeFormatter (number_format='.0f'))\n",
    "ax.set_xlabel (\"Latitude\") ; ax.set_ylabel (\"Pressure\")\n",
    "ax.yaxis.set_major_formatter (cartopy.mpl.ticker.LatitudeFormatter (number_format='.0f'))\n",
    "\n",
    "plt.colorbar (mmap, label=\"°C\")\n",
    "\n",
    "# Contour lines\n",
    "mlin3 = ax.contour (lat, presnivs/100, tt, colors='k', levels=levels[::2], linewidths=1)\n",
    "mlin  = ax.contour (lat, presnivs/100, tt, colors='k', levels=levels, linewidths=0.25)\n",
    "\n",
    "# Add labels to contour lines\n",
    "ax.clabel (mlin3, colors=['k'], manual=False,  # Automatic placement\n",
    "        inline=True,  # Cut the line where the label will be placed.\n",
    "        fmt='{:3.1f} '.format, fontsize='xx-small' )\n",
    "\n",
    "ax.set_title ( \"Latitudinal slice of temperature\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb00f33f-ae5b-4a8a-847e-35ac49b41726",
   "metadata": {},
   "source": [
    "### Equator slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "276b2b28-a620-4e3e-9bd7-b3e25c6ef00a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-40, 40)\n",
    "palet  = mpl.cm.PiYG_r\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Extract data\n",
    "vu = vitu.isel( time_counter=0 ).sel( {'lat':0} )\n",
    "\n",
    "# Color filing\n",
    "mmap = ax.contourf (lon, presnivs/100, vu,\n",
    "         cmap=cmap, norm=norm, levels=levels, extend='both')\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LongitudeFormatter(number_format='.0f'))\n",
    "ax.set_xlabel (\"Latitude\") ; ax.set_ylabel (\"Pressure\")\n",
    "\n",
    "plt.colorbar (mmap, label=\"$m \\cdot s^{-1}$\")\n",
    "\n",
    "# Contour lines\n",
    "mlin3 = ax.contour (lon, presnivs/100, vu, colors='k', )#levels=levels[::3], linewidths=1)\n",
    "mlin  = ax.contour (lon, presnivs/100, vu, colors='k', )#levels=levels, linewidths=0.25)\n",
    "\n",
    "# Add labels to contour lines\n",
    "ax.clabel (mlin3, colors=['k'], manual=False,  # Automatic placement\n",
    "        inline=True,  # Cut the line where the label will be placed.\n",
    "        fmt='{:3.1f} '.format, fontsize='xx-small' )\n",
    "\n",
    "ax.set_title ( \"Temperature along equator\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad94f14-30e8-4c27-9e92-4855c053ee1e",
   "metadata": {},
   "source": [
    "### Zonal mean on standard pressure levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f395e078-d812-441d-8a4a-4a06e019b237",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-0.040, 0.040)\n",
    "palet  = mpl.cm.RdBu_r\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Extract and interpolate data\n",
    "w1  = vitw.isel(time_counter=0)\n",
    "xp1 = pres.isel(time_counter=0)\n",
    "\n",
    "w2 = lmdz.interp1d (x=std_lev/100, xp=xp1/100, yp=w1, zdim='presnivs', method='linear'  )\n",
    "ww = w2.mean(dim=['lon'])\n",
    "\n",
    "# Color filing\n",
    "mmap = ax.contourf (lat, std_lev/100, ww,\n",
    "         cmap=cmap, norm=norm, levels=levels, extend='both')\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LatitudeFormatter(number_format='.0f'))\n",
    "ax.set_xlabel (\"Latitude\") ; ax.set_ylabel (\"Pressure\")\n",
    "\n",
    "plt.colorbar (mmap, label=\"$Pa \\cdot s^{-1}$\")\n",
    "\n",
    "# Contour lines\n",
    "mlin3 = ax.contour (lat, std_lev/100, ww, colors='k', levels=levels[::3], linewidths=1)\n",
    "mlin  = ax.contour (lat, std_lev/100, ww, colors='k', levels=levels, linewidths=0.25)\n",
    "\n",
    "# Add labels to contour lines\n",
    "ax.clabel (mlin3, colors=['k'], manual=False,  # Automatic placement\n",
    "        inline=True,  # Cut the line where the label will be placed.\n",
    "        fmt='{:3.1f} '.format, fontsize='xx-small' )\n",
    "\n",
    "ax.set_title ( \"Zonal average of vertical velocity\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da1e4de-cb5e-42b0-9a5c-8bbe4cd07c46",
   "metadata": {},
   "source": [
    "# Winds vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78115523-4418-413a-b82e-c1e5ee56ca96",
   "metadata": {},
   "source": [
    "## Horizontal maps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0980d23d-51dc-4a6f-a61c-56540643112c",
   "metadata": {},
   "source": [
    "### Robinson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e34a736-03ee-4762-8cbb-193e80f7fae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (0, 10)\n",
    "palet  = mpl.cm.plasma_r\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Describe the model projection in the file\n",
    "ProjIn = ccrs.PlateCarree (central_longitude=0) \n",
    "\n",
    "# Projection for plots\n",
    "#ProjPlot = ccrs.Robinson (central_longitude=270) # Plotted projection\n",
    "ProjPlot = ccrs.PlateCarree (central_longitude=270) # Plotted projection\n",
    "\n",
    "\n",
    "# Creates the figure\n",
    "fig, ax = plt.subplots ( figsize=(15,6), subplot_kw={'projection':ProjPlot}, layout=\"constrained\")\n",
    "#ax.set_global ()\n",
    "ax.set_extent ([-80,40,-10,70], crs=ProjIn)\n",
    "\n",
    "# Select data\n",
    "ux = u10m.isel (time_counter=0)\n",
    "vy = v10m.isel (time_counter=0)\n",
    "uv = wind10m.isel (time_counter=0)\n",
    "\n",
    "ux, xx, yy = lmdz.add_cyclic ( u10m   .isel(time_counter=0), lon, lat)\n",
    "vy, xx, yy = lmdz.add_cyclic ( v10m   .isel(time_counter=0), lon, lat)\n",
    "uv, xx, yy = lmdz.add_cyclic ( wind10m.isel(time_counter=0), lon, lat)\n",
    "\n",
    "#\n",
    "mver = ax.quiver (xx.values, yy.values, ux.values, vy.values, uv.values,\n",
    "                  pivot='tip', scale=200, width=2E-3, transform=ProjIn,\n",
    "                  cmap=cmap, norm=norm)\n",
    "\n",
    "# Beautify\n",
    "ax.gridlines (draw_labels=True, crs=ProjIn,)\n",
    "ax.coastlines ()\n",
    "plt.colorbar (mver, label=\"$m \\cdot s^{-1}$\")\n",
    "\n",
    "ax.set_title ( \"Wind speed @ 10m\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05bf775-a889-4ac2-9b53-ac56d8fe58d5",
   "metadata": {},
   "source": [
    "### Orthographic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff9c393-ac48-49e4-a63a-c705d16bba95",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Describe the model projection in the file\n",
    "ProjIn = ccrs.PlateCarree ( central_longitude=0) \n",
    "\n",
    "# Projection for plots\n",
    "ProjPlot = ccrs.Orthographic (central_latitude=-70, central_longitude=0) # Plotted projection\n",
    "\n",
    "# Creates the figure\n",
    "fig, ax = plt.subplots ( figsize=(15,6), subplot_kw={'projection':ProjPlot} )\n",
    "\n",
    "ux = u10m.isel(time_counter=0)\n",
    "vy = v10m.isel(time_counter=0)\n",
    "uv = wind10m.isel(time_counter=0)\n",
    "\n",
    "# Select data\n",
    "ux, xx, yy = lmdz.add_cyclic ( u10m.isel(time_counter=0), lon, lat)\n",
    "vy, xx, yy = lmdz.add_cyclic ( v10m.isel(time_counter=0), lon, lat)\n",
    "uv, xx, yy = lmdz.add_cyclic ( wind10m.isel(time_counter=0), lon, lat)\n",
    "\n",
    "# Correct a Cartopy bug in Orthographic projection. See help(lmdz.correct_uv) for details\n",
    "ux, vy = lmdz.correct_uv (ux, vy, xx, yy)\n",
    "\n",
    "# Plot vectors\n",
    "mver = ax.quiver (xx.values, yy.values, ux.values, vy.values, uv.values,\n",
    "                  pivot='tail', scale=100, width=3E-3, transform=ProjIn )\n",
    "    \n",
    "# Set plot limits \n",
    "width = 0.6 ; ax.set_xlim (-lmdz.RA*width, lmdz.RA*width) ; ax.set_ylim (-lmdz.RA*width, lmdz.RA*width)\n",
    "\n",
    "# Beautify\n",
    "ax.coastlines ()\n",
    "ax.add_feature (cartopy.feature.LAND, alpha=0.4)\n",
    "\n",
    "# Color palette\n",
    "plt.colorbar (mver, ax=ax, label=\"$m \\cdot s^{-1}$\" )\n",
    "\n",
    "ax.set_title ( \"Wind speed @ 10m\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38de5304-244f-4c13-a36a-7a4ba8a3a854",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "ax.get_xlim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b77cb84-37cc-474e-9b18-7e8e4caedc93",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "ax.get_xlim()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bd8f364-7c1f-4484-8c90-2ad251ce34d3",
   "metadata": {},
   "source": [
    "## Zonal mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "401d90dd-f9f9-470a-880b-22690f5050f5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-0.040, 0.040)\n",
    "palet  = mpl.cm.viridis\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Select data\n",
    "vy, xx, yy = lmdz.add_cyclic ( vitv.isel(time_counter=0), lon, lat)\n",
    "wz, xx, yy = lmdz.add_cyclic ( vitw.isel(time_counter=0), lon, lat)\n",
    "tt, xx, yy = lmdz.add_cyclic ( temp.isel(time_counter=0), lon, lat)\n",
    "pp, xx, yy = lmdz.add_cyclic ( pres.isel(time_counter=0), lon, lat)\n",
    "\n",
    "# Interpolate data\n",
    "vyp = lmdz.interp1d (x=std_lev, xp=pp, yp=vy, zdim='presnivs', method='linear'  )\n",
    "wzp = lmdz.interp1d (x=std_lev, xp=pp, yp=wz, zdim='presnivs', method='linear'  )\n",
    "ttp = lmdz.interp1d (x=std_lev, xp=pp, yp=tt, zdim='presnivs', method='linear'  )\n",
    "\n",
    "# Zonal mean\n",
    "vyp_m = vyp.mean (dim=['lon'])\n",
    "wzp_m = wzp.mean (dim=['lon'])\n",
    "ttp_m = ttp.mean (dim=['lon'])\n",
    "pp_m  = pp.mean  (dim=['lon'])\n",
    "\n",
    "# Computes scale factors to get correct direction of vectors\n",
    "ax.set_ylim (1000,0) ; ax.set_xlim (-90, 90)\n",
    "xmin,xmax=ax.get_xlim() ; ymin,ymax=ax.get_ylim()\n",
    "delta_Y = ax.get_window_extent().height\n",
    "delta_X = ax.get_window_extent().width\n",
    "fact_v  = delta_X / np.abs(xmax-xmin) / ( Ra*np.pi/180. )\n",
    "fact_w  = delta_Y / np.abs(ymax-ymin)\n",
    "\n",
    "# Plot vectors\n",
    "aa, bb = np.meshgrid (yy, std_lev/100)\n",
    "mver = ax.quiver (aa, bb, vyp_m, -wzp_m*fact_w/fact_v/100, ttp_m,\n",
    "                  pivot='tip', scale=150, width=3E-3)\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LatitudeFormatter(number_format='.0f'))\n",
    "ax.set_xlabel (\"Latitude\") ; ax.set_ylabel (\"Pressure\")\n",
    "\n",
    "plt.colorbar (mver, label=\"°C\", ax=ax)\n",
    "\n",
    "ax.set_title ( \"LMDZ Zonal mean of velocity, colored by temperature\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b96eeb4-63a9-4af4-8fbb-11f740ffd4df",
   "metadata": {},
   "source": [
    "## Equatorial section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ddf6d8-69b6-44dc-8eec-efe3a50fdee7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-0.040, 0.040)\n",
    "palet  = mpl.cm.viridis\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Select data\n",
    "ux, xx, yy = lmdz.add_cyclic ( vitv.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "wz, xx, yy = lmdz.add_cyclic ( vitw.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "tt, xx, yy = lmdz.add_cyclic ( temp.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "pp, xx, yy = lmdz.add_cyclic ( pres.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "\n",
    "# Interpolate data\n",
    "uxp = lmdz.interp1d (x=std_lev, xp=pp, yp=ux, zdim='presnivs', method='linear'  )\n",
    "wzp = lmdz.interp1d (x=std_lev, xp=pp, yp=wz, zdim='presnivs', method='linear'  )\n",
    "ttp = lmdz.interp1d (x=std_lev, xp=pp, yp=tt, zdim='presnivs', method='linear'  )\n",
    "\n",
    "# Computes scale factors to get correct direction of vectors\n",
    "ax.set_ylim (1000,0) ; ax.set_xlim (-180, 180)\n",
    "xmin,xmax=ax.get_xlim() ; ymin,ymax=ax.get_ylim()\n",
    "delta_Y = ax.get_window_extent().height\n",
    "delta_X = ax.get_window_extent().width\n",
    "fact_u  = delta_X / np.abs(xmax-xmin) / ( Ra*np.pi/180. )\n",
    "fact_w  = delta_Y / np.abs(ymax-ymin)\n",
    "\n",
    "# Plot vectors\n",
    "aa, bb = np.meshgrid (xx, std_lev/100)\n",
    "mver = ax.quiver (aa, bb, uxp, -(wzp*fact_w/fact_u)/100, ttp,\n",
    "                  pivot='tip', scale=200, width=3E-3)\n",
    "\n",
    "ax.invert_yaxis()\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LongitudeFormatter(number_format='.0f'))\n",
    "ax.set_xlabel (\"Longitude\") ; ax.set_ylabel (\"Pressure\")\n",
    "\n",
    "plt.colorbar (mver, label=\"°C\")\n",
    "\n",
    "ax.set_title ( \"LMDZ wind along equator, colored by temperature\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f7013f1-c861-4137-b750-5e0e82afd6b4",
   "metadata": {},
   "source": [
    "# Streamlines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27a84a7-bf28-4775-a30e-863b19d39d4b",
   "metadata": {},
   "source": [
    "## Robinson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73fd4480-3c06-4396-9f1d-21827ec9c5bd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-50, 40)\n",
    "palet  = mpl.cm.RdYlGn_r\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "# Describe the model projection in the file\n",
    "ProjIn   = ccrs.PlateCarree (central_longitude=0)\n",
    "\n",
    "# Projection for plots\n",
    "ProjPlot = ccrs.Robinson (central_longitude=270)\n",
    "\n",
    "# Define the figure\n",
    "fig = plt.figure(figsize=(15,6))\n",
    "ax = fig.add_subplot (1, 1, 1, projection=ProjPlot)\n",
    "\n",
    "# Select data\n",
    "ux, xx, yy = lmdz.add_cyclic ( u10m.isel(time_counter=0), lon, lat)\n",
    "vy, xx, yy = lmdz.add_cyclic ( v10m.isel(time_counter=0), lon, lat)\n",
    "uv, xx, yy = lmdz.add_cyclic ( wind10m.isel(time_counter=0), lon, lat)\n",
    "tt, xx, yy = lmdz.add_cyclic ( t2m.isel(time_counter=0), lon, lat)\n",
    "\n",
    "# Plot the stream lines\n",
    "col = ax.streamplot ( xx.values, yy.values, ux.values, vy.values,\n",
    "                     transform=ProjIn, density=4, color=tt.values,\n",
    "                     linewidth=uv.values*0.3,\n",
    "                     cmap=cmap, norm=norm)\n",
    "\n",
    "# Beautify\n",
    "ax.set_global ()\n",
    "ax.coastlines ()\n",
    "ax.gridlines  (draw_labels=True, crs=ProjIn)\n",
    "ax.add_feature (cartopy.feature.LAND, zorder=0)\n",
    "\n",
    "# Colorbar\n",
    "plt.colorbar (col.lines, orientation='vertical', ax=fig.axes, label='°C', shrink=0.7)\n",
    "\n",
    "ax.set_title ( \"LMDZ wind, colored by temperature\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5ae6de-4c45-4218-9265-ae9ad70a4b98",
   "metadata": {},
   "source": [
    "## Orthographic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de73409b-a7f2-4164-8cec-bd4c7b560b6d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=33).tick_values (-20, 30)\n",
    "palet  = mpl.cm.hot_r\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Describe the model projection in the file\n",
    "ProjIn   = ccrs.PlateCarree (central_longitude=0)\n",
    "\n",
    "# Projection for plots\n",
    "ProjPlot = ccrs.Orthographic (central_latitude=-70, central_longitude=270)\n",
    "\n",
    "# Create the figure\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot (1, 1, 1, projection=ProjPlot)\n",
    "\n",
    "# Select data\n",
    "ux, xx, yy = lmdz.add_cyclic ( u10m.isel(time_counter=0)   , lon, lat)\n",
    "vy, xx, yy = lmdz.add_cyclic ( v10m.isel(time_counter=0)   , lon, lat)\n",
    "uv, xx, yy = lmdz.add_cyclic ( wind10m.isel(time_counter=0), lon, lat)\n",
    "tt, xx, yy = lmdz.add_cyclic ( t2m.isel(time_counter=0)    , lon, lat)\n",
    "\n",
    "yy, xx = xr.broadcast (yy, xx)\n",
    "\n",
    "# Correct a Cartopy bug in Orthographic projection. See help(lmdz.correct_uv) for details\n",
    "ux, vy = lmdz.correct_uv ( ux, vy, xx, yy)\n",
    "\n",
    "# Streamplot in orthographic needs to have limits on region\n",
    "jmin=60 ; jmax=lat.shape[0]\n",
    "\n",
    "col = ax.streamplot (xx[jmin:jmax].values, yy[jmin:jmax].values, \n",
    "                     ux[jmin:jmax].values, vy[jmin:jmax].values,\n",
    "                     transform=ProjIn, density=2, cmap=cmap, norm=norm,\n",
    "                     color    = tt[jmin:jmax].values,\n",
    "                     linewidth=uv[jmin:jmax].values*0.4, arrowsize=2)\n",
    "\n",
    "# Set plot limits \n",
    "width = 1; ax.set_xlim (-Ra*width, Ra*width) ; ax.set_ylim (-Ra*width, Ra*width)\n",
    "\n",
    "# Beautify\n",
    "ax.coastlines ()\n",
    "ax.gridlines  (draw_labels=True, crs=ProjIn)\n",
    "ax.add_feature (cartopy.feature.LAND, zorder=0)\n",
    "\n",
    "plt.colorbar (col.lines, orientation='vertical', ax=fig.axes, label='°C', shrink=0.7)\n",
    "\n",
    "ax.set_title ( \"LMDZ wind, colored by temperature\", fontsize=\"small\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788396a8-3a99-4b76-966b-c48c17d751c7",
   "metadata": {},
   "source": [
    "## Zonal mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1c0ea1d-689b-4871-8136-649ac421d872",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-40, 30)\n",
    "palet  = mpl.cm.hsv\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Select data\n",
    "vy, xx, yy = lmdz.add_cyclic ( vitv.isel(time_counter=0), lon, lat)\n",
    "wz, xx, yy = lmdz.add_cyclic ( vitw.isel(time_counter=0), lon, lat)\n",
    "tt, xx, yy = lmdz.add_cyclic ( temp.isel(time_counter=0), lon, lat)\n",
    "pp, xx, yy = lmdz.add_cyclic ( pres.isel(time_counter=0), lon, lat)\n",
    "\n",
    "# Interpolate data on regular pressure lev\n",
    "reg_lev = np.linspace ( 0, 1000, 100) * 100\n",
    "reg_lev = xr.DataArray ( reg_lev, dims=('pressure'), coords=(reg_lev,))\n",
    "vyp = lmdz.interp1d (x=reg_lev, xp=pp, yp=vy, zdim='presnivs', method='linear'  )\n",
    "wzp = lmdz.interp1d (x=reg_lev, xp=pp, yp=wz, zdim='presnivs', method='linear'  )\n",
    "ttp = lmdz.interp1d (x=reg_lev, xp=pp, yp=tt, zdim='presnivs', method='linear'  )\n",
    "\n",
    "# Zonal mean\n",
    "vyp_m = vyp.mean (dim=['lon'])\n",
    "wzp_m = wzp.mean (dim=['lon'])\n",
    "ttp_m = ttp.mean (dim=['lon'])\n",
    "pp_m  = pp.mean  (dim=['lon'])\n",
    "\n",
    "# Revert latitudes\n",
    "yy = yy[::-1]\n",
    "vyp_m = vyp_m [:, ::-1]\n",
    "wzp_m = wzp_m [:, ::-1]\n",
    "ttp_m = ttp_m [:, ::-1]\n",
    "\n",
    "# Computes scale factors to get correct direction of vectors\n",
    "ax.set_ylim (1000,0) ; ax.set_xlim (-90, 90)\n",
    "xmin,xmax=ax.get_xlim() ; ymin,ymax=ax.get_ylim()\n",
    "delta_Y = ax.get_window_extent().height\n",
    "delta_X = ax.get_window_extent().width\n",
    "fact_v  = delta_X / np.abs(xmax-xmin) / ( Ra*np.pi/180. )\n",
    "fact_w  = delta_Y / np.abs(ymax-ymin)\n",
    "\n",
    "# Plot vectors\n",
    "aa, bb = np.meshgrid (yy, reg_lev/100)\n",
    "\n",
    "mcol = ax.streamplot (aa, bb, vyp_m.values, (-wzp_m*fact_w/fact_v/100).values,\n",
    "                      density=3, color=ttp_m.values, cmap=cmap, norm=norm,\n",
    "                      linewidth=1, arrowsize=1)\n",
    "\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LatitudeFormatter(number_format='.0f'))\n",
    "ax.set_xlabel (\"Latitude\") ; ax.set_ylabel (\"Pressure (hPa)\")\n",
    "\n",
    "plt.colorbar (mcol.lines, label=\"°C\")\n",
    "\n",
    "ax.set_title ( \"LMDZ wind, colored by temperature\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c07525-9678-440f-9c35-5733db43fb00",
   "metadata": {},
   "source": [
    "## Equatorial section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ddcbef2-3e51-487a-844f-083937acda70",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Creates the color palette\n",
    "levels = mpl.ticker.MaxNLocator (nbins=17).tick_values (-40, 40)\n",
    "palet  = mpl.cm.spring_r\n",
    "colors = palet (np.linspace (0, 1, len(levels)+1))\n",
    "cmap   = mpl.colors.ListedColormap(colors)\n",
    "cmap.set_bad ('grey')\n",
    "norm   = mpl.colors.BoundaryNorm (levels, ncolors=cmap.N+1, clip=False, extend='both')\n",
    "\n",
    "# Creates the figure\n",
    "fig = plt.figure (figsize=(15,6))\n",
    "\n",
    "# Creates the viewport\n",
    "ax = fig.add_subplot (1, 1, 1)\n",
    "\n",
    "# Select data\n",
    "ux, xx, yy = lmdz.add_cyclic ( vitu.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "wz, xx, yy = lmdz.add_cyclic ( vitw.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "tt, xx, yy = lmdz.add_cyclic ( temp.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "pp, xx, yy = lmdz.add_cyclic ( pres.isel(time_counter=0).sel(lat=0), lon, lat)\n",
    "\n",
    "# Interpolate data on regular pressure lev\n",
    "reg_lev = np.linspace ( 0, 1000, 100) * 100\n",
    "reg_lev = xr.DataArray ( reg_lev, dims=('pressure'), coords=(reg_lev,))\n",
    "uxp = lmdz.interp1d (x=reg_lev, xp=pp, yp=ux, zdim='presnivs', method='linear'  )\n",
    "wzp = lmdz.interp1d (x=reg_lev, xp=pp, yp=wz, zdim='presnivs', method='linear'  )\n",
    "ttp = lmdz.interp1d (x=reg_lev, xp=pp, yp=tt, zdim='presnivs', method='linear'  )\n",
    "\n",
    "# Computes scale factors to get correct direction of vectors\n",
    "ax.set_ylim (1000,0) ; ax.set_xlim (-180, 180)\n",
    "xmin,xmax=ax.get_xlim() ; ymin,ymax=ax.get_ylim()\n",
    "delta_Y = ax.get_window_extent().height\n",
    "delta_X = ax.get_window_extent().width\n",
    "fact_v  = delta_X / np.abs(xmax-xmin) / ( Ra*np.pi/180. )\n",
    "fact_w  = delta_Y / np.abs(ymax-ymin)\n",
    "\n",
    "# Plot vectors\n",
    "aa, bb = np.meshgrid (xx, reg_lev/100)\n",
    "\n",
    "mcol = ax.streamplot (aa, bb, uxp.values, (-wzp*fact_w/fact_v/100).values,\n",
    "                     density=3, color = ttp.values, \n",
    "                     linewidth=1, arrowsize=1,\n",
    "                     cmap=cmap, norm=norm)\n",
    "\n",
    "ax.xaxis.set_major_formatter(cartopy.mpl.ticker.LongitudeFormatter (number_format='.0f'))\n",
    "ax.set_xlabel (\"Longitude\") ; ax.set_ylabel (\"Pressure (hPa)\")\n",
    "\n",
    "plt.colorbar (mcol.lines, label=\"°C\", ax=ax)\n",
    "\n",
    "ax.set_title ( \"LMDZ wind, colored by temperature\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f6a2a97-e975-45ce-861f-c1018e604e19",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from IPython.display import Image\n",
    "\n",
    "Bunny = GetLogo (\"https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F2f95d3fe-13e7-4220-84b1-9c12d760f233_950x535.jpeg\"\n",
    ", 'Bugs_Bunny.jpeg')\n",
    "\n",
    "Image (filename='Bugs_Bunny.jpeg', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ad627e-71c9-4f1d-82b4-a0ab9af6ea67",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11 (FULL)",
   "language": "python",
   "name": "full"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}