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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# How to access and load TRACMIP data from the Pangeo cloud\n",
+ "\n",
+ "1. \"load\" the Tracmip collection \n",
+ "2. get some basic info on Tracmip collection\n",
+ "3. load monthly mean precip for the aquaControl simulation\n",
+ "4. plot meridional zonal-mean time-mean profile for one model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import xarray as xr\n",
+ "import zarr\n",
+ "import gcsfs\n",
+ "\n",
+ "xr.set_options(display_style='html')\n",
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_format = 'retina' "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('https://storage.googleapis.com/cmip6/tracmip.csv')\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'df' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-3-36bdea2f3883>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_pr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"frequency == 'Amon' & variable == 'pr' & experiment == 'aquaControl'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "df_pr = df.query(\"frequency == 'Amon' & variable == 'pr' & experiment == 'aquaControl'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. \"Load\" Tracmip collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from intake import open_catalog\n",
+ "\n",
+ "# get whole pangeo catalogue\n",
+ "cat = open_catalog(\"https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/climate.yaml\")\n",
+ "# select tracmip collection\n",
+ "col = cat.tracmip()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Basic info on the collection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "print collection to screen: this shows that there is 3 output frequencies (monthly-mean, daily-mean, 3-hr snapshots), \n",
+ "11 experiments (6 are due to the CALTECH model with changed atmosperic opacity), and 47 variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "col"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "print starting and end portion of the collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "col.df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "col.df.tail()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "print some further information on the collection (i.e., dataframe)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "col.df.columns.unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "col.df.model.unique()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "col.df.experiment.unique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Now actually load the monthly-mean precip data for the aquaControl experiment, use a dictionary for this"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "note: the option \"zarr_kwargs={'consolidated': True}\" for to_dataset_dicts does not seem necessary but is still included here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_dict = col.search(frequency=\"Amon\", experiment=\"aquaControl\",\n",
+ " variable=\"pr\").to_dataset_dict(zarr_kwargs={'consolidated': True})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Plot zonal-mean time-mean precip for last 20 years for CNRM-AM5 model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_dict['CNRM-AM5.aquaControl.Amon']['pr']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(ds_dict['CNRM-AM5.aquaControl.Amon'].lat, \n",
+ " ds_dict['CNRM-AM5.aquaControl.Amon']['pr'].isel(time=slice(120,360)).mean(['lon', 'time'])*86400)\n",
+ "plt.xlabel('degree latitude')\n",
+ "plt.ylabel('precipitation (mm/day)')\n",
+ "plt.title('CNRM-AM5.aquaControl.Amon')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}