Working example for how to access TRACMIP in Pangeo Cloud; for this deleted...

Working example for how to access TRACMIP in Pangeo Cloud; for this deleted previous non-working example load_data.ipynb

pangeo/load_data.ipynb

-{
- "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
-}