Merge pull request #221 from cecilehannay/main

add notebooks for ADF and uxarray

_toc.yml

@@ -173,6 +173,8 @@ chapters:
     - file: notebooks/diagnostics/additional/postprocessing
     - file: notebooks/diagnostics/additional/cvdp
     - file: notebooks/diagnostics/additional/analysis_tools
+    - file: notebooks/diagnostics/additional/adf
+    - file: notebooks/diagnostics/additional/uxarray
 
 - file: notebooks/resources/resources_overview
   sections:

notebooks/basics/cesm_workspaces.ipynb

@@ -19,11 +19,11 @@
     "\n",
     "\n",
     "There are 5 main directories (see figure, below) you will need to navigate:\n",
-    "1) The path to your CESM source code. This is referred to as `$SRCROOT` (or `$CESMCODE`) and contains the `$CIMEROOT`.\n",
-    "2) The path to the input data, referred to as `$CESMDATA`.\n",
-    "3) The path to your experiment case directories. This is referred to as `$CASEROOT`.\n",
-    "4) The path to your build and run directories, referred to as the `$EXEROOT` and `$RUNDIR`, respectively.\n",
-    "5) The path to your archived model output, referred to as `$DOUT_S_ROOT`.\n",
+    "1) The path to your **CESM source code**. This is referred to as `$SRCROOT` (or `$CESMCODE`) and contains the `$CIMEROOT`.\n",
+    "2) The path to the **input data**, referred to as `$CESMDATA`.\n",
+    "3) The path to your experiment **case directories**. This is referred to as `$CASEROOT`.\n",
+    "4) The path to your **build** and **run directories**, referred to as the `$EXEROOT` and `$RUNDIR`, respectively.\n",
+    "5) The path to your **archived model output**, referred to as `$DOUT_S_ROOT`.\n",
     "\n",
     "We will investigate these more detail in the remainder of the Tutorial.\n",
     "\n",

notebooks/basics/code_overview.ipynb

@@ -72,7 +72,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

notebooks/diagnostics/additional/additional.ipynb

@@ -46,7 +46,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

notebooks/diagnostics/additional/adf.ipynb

@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3",
+   "metadata": {},
+   "source": [
+    "# ADF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Learning Goals\n",
+    "\n",
+    "- Enter learning goals here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0",
+   "metadata": {},
+   "source": [
+    "_______________\n",
+    "\n",
+    "## Subsection 1\n",
+    "\n",
+    "Info here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "815e0869-0518-4cf9-9417-cd9b08965ca1",
+   "metadata": {},
+   "source": [
+    "_______________\n",
+    "\n",
+    "## Subsection 2\n",
+    "\n",
+    "Info here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/diagnostics/additional/analysis_tools.ipynb

@@ -327,7 +327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

notebooks/diagnostics/additional/cvdp.ipynb

@@ -57,7 +57,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

notebooks/diagnostics/additional/large_ensembles.ipynb

@@ -69,7 +69,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

notebooks/diagnostics/additional/uxarray.ipynb

@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3",
+   "metadata": {},
+   "source": [
+    "# Large Ensembles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Learning Goals\n",
+    "\n",
+    "- Enter learning goals here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0",
+   "metadata": {},
+   "source": [
+    "_______________\n",
+    "\n",
+    "## Subsection 1\n",
+    "\n",
+    "Info here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "815e0869-0518-4cf9-9417-cd9b08965ca1",
+   "metadata": {},
+   "source": [
+    "_______________\n",
+    "\n",
+    "## Subsection 2\n",
+    "\n",
+    "Info here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}