deploy: c642621

_sources/domains.ipynb

@@ -108,7 +108,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `dummy='topo'` argument means, we want a dummy variable in the dataset to see how the domain looks like. For the dummy topography, we use the `cdo topo` operator in the background. So maybe you have to install `python-cdo`, e.g., `conda install -c conda-forge python-cdo`. Working with xarray datasets means, that we can use all the nice functions of xarray including plotting, e.g.,"
+    "The `dummy='topo'` argument means, we want a dummy variable in the dataset to see how the domain looks like. For the dummy topography, we use the `cdo topo` operator in the background. So maybe you have to install `python-cdo`, e.g., `conda install -c conda-forge python-cdo`. Working with xarray datasets means, that we can use all the nice functions of xarray including plotting. The latest versions of `py-cordex` also come with an xarray accessor that allows you, e.g., to get a quick overview of the domain, using, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from matplotlib import pyplot as plt\n",
+    "\n",
+    "# plt.figure(figsize=(8,8))\n",
+    "eur11.cx.map()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use the dummy topography to plot an overview:"
    ]
   },
   {
@@ -148,7 +167,7 @@
     "    import cartopy.feature as cf\n",
     "    import matplotlib.pyplot as plt\n",
     "\n",
-    "    plt.figure(figsize=(20, 10))\n",
+    "    plt.figure(figsize=(10, 10))\n",
     "    projection = ccrs.PlateCarree()\n",
     "    transform = ccrs.RotatedPole(pole_latitude=pole[1], pole_longitude=pole[0])\n",
     "    # ax = plt.axes(projection=projection)\n",
@@ -210,7 +229,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The domains are actually created from csv tables. To checkout the tables you can have a look at `dm.TABLES`. This is a dictionary of dataframes created during the import of the model from a number of csv tables that define standard cordex domains. E.g., available tables are:"
+    "The domains are actually created from [a csv table](https://github.com/WCRP-CORDEX/domain-tables/blob/main/rotated-latitude-longitude.csv). The domains are created using the `create_dataset` function, in which the data from the table is fed, e.g.:"
    ]
   },
   {
@@ -219,14 +238,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cx.domains.tables[\"cordex-high-res\"]"
+    "cx.create_dataset?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The domains are created using the `create_dataset` function, e.g.:"
+    "Let's create the EUR-11 domain manually from the numbers in the table:"
    ]
   },
   {
@@ -235,14 +254,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "help(cx.create_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's create the EUR-11 domain manually from the numbers in the table:"
+    "cx.domains.table.loc[\"EUR-11\"]"
    ]
   },
   {
@@ -287,45 +299,6 @@
     "You can now use the `create_dataset` function to create any domain as an xarray dataset."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Check out the Africa domain!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "afr11 = cx.cordex_domain(\"AFR-11\", dummy=\"topo\")\n",
-    "afr11"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pole = (\n",
-    "    afr11.rotated_latitude_longitude.grid_north_pole_longitude,\n",
-    "    afr11.rotated_latitude_longitude.grid_north_pole_latitude,\n",
-    ")\n",
-    "pole"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(afr11.topo, pole)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -394,7 +367,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, let's plot all cordex core domains into one overview:"
+    "Now, let's plot all cordex core domains into one overview. We will identify them by their resolution:"
    ]
   },
   {
@@ -403,10 +376,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "table = cx.domains.table\n",
+    "\n",
     "plots(\n",
     "    [\n",
     "        cx.cordex_domain(name, dummy=\"topo\")\n",
-    "        for name in cx.domains.tables[\"cordex-core\"].index\n",
+    "        for name in table.index\n",
+    "        if table.loc[name].dlon == 0.22\n",
     "    ],\n",
     "    borders=False,\n",
     ")"
@@ -433,6 +409,15 @@
     "We will use some CMIP6 model data from the ESGF to show how we can do the regridding:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -441,9 +426,11 @@
    "source": [
     "import xarray as xr\n",
     "\n",
-    "ds = xr.open_dataset(\n",
-    "    \"https://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-HR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-HR_historical_r1i1p1f1_gn_199501-199912.nc\"\n",
-    ")\n",
+    "# https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc\n",
+    "ds = xr.open_dataset(\"orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc\")\n",
+    "# ds = xr.open_dataset(\n",
+    "#    \"https://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-HR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-HR_historical_r1i1p1f1_gn_199501-199912.nc\"\n",
+    "# )\n",
     "# ds = xr.open_dataset(\n",
     "#    \"http://esgf-data3.ceda.ac.uk/thredds/dodsC/esg_cmip6/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon/tas/gn/v20190406/tas_Amon_UKESM1-0-LL_historical_r1i1p1f2_gn_185001-194912.nc\"\n",
     "# )\n",
@@ -495,7 +482,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "remap_cdo = Cdo().remapbil(\"EUR-11_grid.nc\", input=ds.isel(time=0), returnXArray=\"tas\")\n",
+    "remap_cdo = Cdo().remapbil(\"EUR-11_grid.nc\", input=ds, returnXArray=\"orog\")\n",
     "remap_cdo"
    ]
   },
@@ -505,7 +492,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(remap_cdo - 273.5).plot()"
+    "remap_cdo.plot()"
    ]
   },
   {
@@ -547,7 +534,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "remap_xe = regridder(ds.tas.isel(time=0))"
+    "remap_xe = regridder(ds.orog)"
    ]
   },
   {
@@ -556,7 +543,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(remap_xe - 273.5).plot()"
+    "remap_xe.plot()"
    ]
   },
   {
@@ -585,7 +572,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -599,7 +586,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,