Adding jupyter notebook that mirrors run_all.py

code/run_all.ipynb

@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook executes all data processing and plotting functions that are part of the publication.\n",
+    "It is structured in two blocks, the first one does the data processing, the second step does the plotting. \n",
+    "Running the first block on your machine might proove difficult if you do not have access to the DKRZ computing infrastructure. \n",
+    "Therefore, the first block can be bypassed by retrieving the intermediate outputs from zenodo (link) and only running the plotting scripts. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start a dask client.\n",
+    "from dask.distributed import Client\n",
+    "client = Client(silence_logs=50)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prevent Figures from showing up here and cluttering this notebook\n",
+    "%matplotlib agg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import compute_country_aggregates\n",
+    "import compute_signals\n",
+    "import plot_lu_maps\n",
+    "import plot_s10_maps\n",
+    "import plot_s10_scatter\n",
+    "import plot_s10_country_heatmaps\n",
+    "import plot_temperature_gradient\n",
+    "import compute_plot_pattern_correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Calculations**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute changes in 10m wind speeds\n",
+    "compute_signals.calculate_signals(time_aggregation=\"annual\", variable_id=\"sfcWind\")\n",
+    "compute_signals.calculate_signals(time_aggregation=\"monthly\", variable_id=\"sfcWind\")\n",
+    "compute_signals.calculate_signals(\"annual\", \"sfcWind\", full_ensemble=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute changes in near-surface air temperature (tas), skin temperature (ts) and sea ice cover (sic)\n",
+    "for variable in [\"tas\", \"ts\", \"sic\"]:\n",
+    "    print(variable)\n",
+    "    compute_signals.calculate_signals(time_aggregation=\"monthly\", variable_id=variable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute tas changes for full ensemble\n",
+    "compute_signals.calculate_signals(\"annual\", \"tas\", full_ensemble=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute changes in tas - ts, a proxy for stability change\n",
+    "compute_signals.compute_monthly_stability_change()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute country aggregates\n",
+    "compute_country_aggregates.compute_all(True)  # onshore\n",
+    "compute_country_aggregates.compute_all(False) # offshore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### **Plots**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Maps of 10m wind speeds\n",
+    "plot_s10_maps.make_s10_maps()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_s10_maps.make_aggregate_monthly_plots(\"tas-ts\", \"mean\", [\"historical\", \"rcp45\", \"rcp85\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scatter plots of wind change per country\n",
+    "plot_s10_scatter.make_s10_scatter(onshore=False)\n",
+    "plot_s10_scatter.make_s10_scatter(onshore=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Heatmaps of wind per country\n",
+    "plot_s10_country_heatmaps.make_s10_heatmaps(onshore=False)\n",
+    "plot_s10_country_heatmaps.make_s10_heatmaps(onshore=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Maps of land use change forcing data\n",
+    "plot_lu_maps.make_LUH_maps()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scatter plot of land use forcing vs wind speed change\n",
+    "compute_plot_pattern_correlation.make_plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Temperature gradient plots\n",
+    "plot_temperature_gradient.make_all_plots()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Shutting down the dask client again\n",
+    "client.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cmip-processing",
+   "language": "python",
+   "name": "cmip-processing"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}