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* upstream/master: Add drop to api.rst under pending deprecations (pydata#3561) replace duplicate method _from_vars_and_coord_names (pydata#3565) propagate indexes in to_dataset, from_dataset (pydata#3519) Switch examples to notebooks + scipy19 docs improvements (pydata#3557) fix whats-new.rst (pydata#3554)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# GRIB Data Example " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "GRIB format is commonly used to disemminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import xarray as xr\n", | ||
| "import matplotlib.pyplot as plt" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "To read GRIB data, you can use `xarray.load_dataset`. The only extra code you need is to specify the engine as `cfgrib`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ds = xr.tutorial.load_dataset('era5-2mt-2019-03-uk.grib', engine='cfgrib')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Let's create a simple plot of 2-m air temperature in degrees Celsius:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ds = ds - 273.15\n", | ||
| "ds.t2m[0].plot(cmap=plt.cm.coolwarm)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "With CartoPy, we can create a more detailed plot, using built-in shapefiles to help provide geographic context:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import cartopy.crs as ccrs\n", | ||
| "import cartopy\n", | ||
| "fig = plt.figure(figsize=(10,10))\n", | ||
| "ax = plt.axes(projection=ccrs.Robinson())\n", | ||
| "ax.coastlines(resolution='10m')\n", | ||
| "plot = ds.t2m[0].plot(cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={'shrink':0.6})\n", | ||
| "plt.title('ERA5 - 2m temperature British Isles March 2019')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Finally, we can also pull out a time series for a given location easily:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ds.t2m.sel(longitude=0,latitude=51.5).plot()\n", | ||
| "plt.title('ERA5 - London 2m temperature March 2019')" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.7.3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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