More templates (#10)

* add satellite

* monthly notebook

* satellite done

* add climate projection template monthly

* all templates and use stringify dates

* template update

* cleanup

.cruft.json

@@ -1,6 +1,6 @@
 {
   "template": "https://github.com/ecmwf-projects/cookiecutter-conda-package",
-  "commit": "c7cd369f71b4cfc647fc7c1972cc3c45860256d5",
+  "commit": "c4101cc46ee7312553f246d8806aebc7160edcc4",
   "checkout": null,
   "context": {
     "cookiecutter": {

.pre-commit-config.yaml

@@ -41,10 +41,10 @@ repos:
     args: [--autofix]
     additional_dependencies: [toml-sort<0.22.0]
 - repo: https://github.com/PyCQA/pydocstyle.git
-  rev: 6.1.1
+  rev: 6.2.2
   hooks:
   - id: pydocstyle
-    additional_dependencies: [toml]
+    additional_dependencies: [tomli]
     exclude: tests|docs
 - repo: https://github.com/nbQA-dev/nbQA
   rev: 1.5.3

notebooks/climate_projections/01-Application_Template_Global_Timeseries_Monthly.ipynb

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Climate Projections Monthly: Global timeseries"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import libraries\n",
+    "\n",
+    "Switch warnings off for better readability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "from c3s_eqc_automatic_quality_control import diagnostics, download, plot\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collection_id = \"projections-cmip6\"\n",
+    "\n",
+    "year_start = 2008\n",
+    "year_stop = 2009\n",
+    "\n",
+    "request = {\n",
+    "    \"format\": \"zip\",\n",
+    "    \"temporal_resolution\": \"monthly\",\n",
+    "    \"experiment\": \"historical\",\n",
+    "    \"variable\": \"near_surface_air_temperature\",\n",
+    "    \"model\": \"cmcc_cm2_sr5\",\n",
+    "    \"year\": [str(year) for year in range(year_start, year_stop + 1)],\n",
+    "    \"month\": [f\"{month:02d}\" for month in range(1, 12 + 1)],\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define transform function\n",
+    "\n",
+    "Drop bounds as they are not used and create issues with dask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_bounds_and_spatial_weighted_mean(ds):\n",
+    "    return diagnostics.spatial_weighted_mean(ds.drop_dims(\"bnds\"))\n",
+    "\n",
+    "\n",
+    "def drop_bounds_and_spatial_weighted_std(ds):\n",
+    "    return diagnostics.spatial_weighted_std(ds.drop_dims(\"bnds\"))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute spatially-weighted global mean timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_mean = download.download_and_transform(\n",
+    "    collection_id,\n",
+    "    request,\n",
+    "    transform_func=drop_bounds_and_spatial_weighted_mean,\n",
+    "    split_all=True,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute spatially-weighted global std timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_std = download.download_and_transform(\n",
+    "    collection_id,\n",
+    "    request,\n",
+    "    transform_func=drop_bounds_and_spatial_weighted_std,\n",
+    "    split_all=True,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot and save figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"_\".join(\n",
+    "    [\"global-timeseries\", collection_id, request[\"temporal_resolution\"]]\n",
+    ")\n",
+    "title = filename.replace(\"-\", \" \").replace(\"_\", \" \").title()\n",
+    "fig = plot.shaded_std(\"tas\", ds_mean, ds_std, title=title)\n",
+    "fig.show()\n",
+    "fig.write_image(filename + \".png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eqc",
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "39a16a1176456aec0710d6d8dd097fdfd8eece03838aebbaaddfca0f16ac2477"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}