{ "cells": [ { "cell_type": "markdown", "id": "metadata", "metadata": { "editable": false }, "source": [ "
In this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem.
\n", "We will be using data from Copernicus Atmosphere Monitoring Service\n", "and more precisely PM2.5 (Particle Matter < 2.5 ΞΌm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial.
\n", "\n", "\n", "π¬ Remark
\n", "This tutorial uses data on a regular latitude-longitude grid. More complex and irregular grids are not discussed in this tutorial. In addition,\n", "this tutorial is not meant to cover all the different possibilities offered by Xarrays but shows functionalities we find useful for day to day\n", "analysis.
\n", "
\n", "\n", "Agenda
\n", "In this tutorial, we will cover:
\n", "\n", "
\n", "- Analysis
\n", "\n", "
\n", "- Import Python packages
\n", "
Some packages may need to be installed first. For example cmcrameri is missing, so we need to install it by entering the following command in a new cell of your Jupyter Notebook:
Then we need to import all the necessary packages in our Jupyter Notebook.
\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "cell-3", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [], "source": [ "import numpy as np\n", "import xarray as xr\n", "import cartopy.crs as ccrs\n", "import matplotlib.pyplot as plt\n", "import cmcrameri.cm as cmc\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "cell-4", "metadata": { "editable": false }, "source": [ "The netCDF dataset can now be opened with Xarray:
\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "6ddadbc3-3926-48e9-812e-a60829fe9021", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2022-05-29 17:52:39-- https://zenodo.org/record/5805953/files/CAMS-PM2_5-20211222.netcdf\n", "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n", "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 108646028 (104M) [application/octet-stream]\n", "Saving to: βCAMS-PM2_5-20211222.netcdfβ\n", "\n", "CAMS-PM2_5-20211222 100%[===================>] 103.61M 23.9MB/s in 5.6s \n", "\n", "2022-05-29 17:52:45 (18.5 MB/s) - βCAMS-PM2_5-20211222.netcdfβ saved [108646028/108646028]\n", "\n" ] } ], "source": [ "!wget https://zenodo.org/record/5805953/files/CAMS-PM2_5-20211222.netcdf" ] }, { "cell_type": "code", "execution_count": 4, "id": "cell-5", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [], "source": [ "dset = xr.open_dataset(\"CAMS-PM2_5-20211222.netcdf\")" ] }, { "cell_type": "markdown", "id": "cell-6", "metadata": { "editable": false }, "source": [ "Once opened, we can get metadata using print statement.
Below is what you should get if everything goes fine.
\n", "\n", "\n", "π₯ Output
\n", "\n", "<xarray.Dataset>\n", " Dimensions: (longitude: 700, latitude: 400, level: 1, time: 97)\n", " Coordinates:\n", " * longitude (longitude) float32 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95\n", " * latitude (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05\n", " * level (level) float32 0.0\n", " * time (time) timedelta64[ns] 00:00:00 01:00:00 ... 4 days 00:00:00\n", "Data variables:\n", " pm2p5_conc (time, level, latitude, longitude) float32 0.4202 ... 7.501\n", "Attributes:\n", " title: PM25 Air Pollutant FORECAST at the Surface\n", " institution: Data produced by Meteo France\n", " source: Data from ENSEMBLE model\n", " history: Model ENSEMBLE FORECAST\n", " FORECAST: Europe, 20211222+[0H_96H]\n", " summary: ENSEMBLE model hourly FORECAST of PM25 concentration at the...\n", " project: MACC-RAQ (http://macc-raq.gmes-atmosphere.eu)\n", "
\n", "\n", "π‘ Command not found
\n", "If you get an error with the previous command, first check the location of the input file
\n", "CAMS-PM2_5-20211222.netcdf:\n", "it needs to be in the same directory as your Jupyter Notebook.
We can identify 4 different sections:
\n", "We can also get metadata information for each coordinate and data variables using β.β followed by the coordinate or data variable name.
\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "cell-9", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "β Questions CAM PM2.5 Dataset
\n", "What is the name of the variable for Particle matter < 2.5 ΞΌm and its physical units?
\n", "\n", "\n", "Hint: Select the text with your mouse to see the answerπ Solution
\n", "\n", "
\n", "- To get metadata information from
\n", "pm2p5_concData variable, we use its variable name and print it. Printing it will only print metadata, not the values.\n", "\n", "
\n", "- Variable name:
\n", "mass_concentration_of_pm2p5_ambient_aerosol_in_air- Units:
\n", "Β΅g/m3\n", "\n", "β¨οΈ Input: Python
\n", "\n", "print(dset.pm2p5_conc)\n", "\n", "\n", "π₯ Output
\n", "\n", "<xarray.DataArray 'pm2p5_conc' (time: 97, level: 1, latitude: 400, longitude: 700)>\n", "[27160000 values with dtype=float32]\n", " Coordinates:\n", "* longitude (longitude) float32 335.0 335.1 335.2 335.4 ... 44.75 44.85 44.95\n", "* latitude (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05\n", "* level (level) float32 0.0\n", "* time (time) timedelta64[ns] 00:00:00 01:00:00 ... 4 days 00:00:00\n", " Attributes:\n", " species: PM2.5 Aerosol\n", " units: Β΅g/m3\n", " value: hourly values\n", " standard_name: mass_concentration_of_pm2p5_ambient_aerosol_in_air\n", "
\n", "\n", "π¬ Different ways to access Data variables
\n", "To access a variable or coordinate, we can use β.β or specify its name as a string between squared brackets β[β β]β. For example:
\n", "\n", "print(dset['pm2p5_conc'])\n", "# or alternatively\n", "print(dset.pm2p5_conc)\n", "When we print a variable or coordinate, we do not get all the individual values but a
\n", "DataArraythat contains a lot of very useful metadata such as coordinates (if they have some), all the attributes such as the name, the physical units, etc.
We often want to select elements from the coordinates for instance to subset a geographical area or select specific times or a specific time range.
\n", "There are two different ways to make a selection:
\n", "You should see that the coordinate time βdisappearedβ from the Dimensions and now the variable pm2p5_conc is a 3D field with longitude, latitude and level.
When selecting elements by the value of the coordinate, we need to use the same datatype. For instance, to select an element from\n",
"time, we need to use timedelta64. The code below will give the same result as isel(time=0).
The output will be very similar to what we did previously when selecting from coordinates by index.
\n", "\n", "\n", "β Select a single time for PM2.5
\n", "How to select the forecast for December, 24th 2021 at 12:00 UTC?
\n", "\n", "\n", "Hint: Select the text with your mouse to see the answerπ Solution
\n", "Data starts on December, 22nd 2021 at 00:00 UTC so we need to add 2 days and 12 hours to select the correct time index.
\n", "\n", "\n", "β¨οΈ Input: Python
\n", "\n", "print(dset.sel(time=(np.timedelta64(2,'D')+ np.timedelta64(12,'h'))))\n", "\n", "\n", "π₯ Output
\n", "\n", "<xarray.Dataset>\n", "Dimensions: (longitude: 700, latitude: 400, level: 1)\n", "Coordinates:\n", "* longitude (longitude) float32 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95\n", "* latitude (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05\n", "* level (level) float32 0.0\n", "time timedelta64[ns] 2 days 12:00:00\n", "Data variables:\n", " pm2p5_conc (level, latitude, longitude) float32 0.4499 0.4421 ... 10.71\n", "Attributes:\n", " title: PM25 Air Pollutant FORECAST at the Surface\n", " institution: Data produced by Meteo France\n", "source: Data from ENSEMBLE model\n", "history: Model ENSEMBLE FORECAST\n", " FORECAST: Europe, 20211222+[0H_96H]\n", " summary: ENSEMBLE model hourly FORECAST of PM25 concentration at the...\n", " project: MACC-RAQ (http://macc-raq.gmes-atmosphere.eu)\n", "
To plot a map, you need to select a variable with data on geographical coordinates (latitude, longitude). In addition, coordinates need to be sorted, and preferably in increasing order. This is not the case for the coordinate βlongitudeβ which is given between 360 and 0.
\n", "Letβs shift the longitudes by 180 degrees so that they come in the range of -180 to 180.
\n", "We print the longitudes before and after shifting them so we can see what is happening.
\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "cell-15", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The longitude values are between 335.05 and 44.95 degrees.
Letβs now shift the longitudes to get values between -180, 180 degrees.
Indeed, the longitudes have been shifted and now the values are between -24.95 and 44.95.
We will get a figure like the one below:
\n", "\n", "\n", "π¬ What about
\n", "levelNote that in the previous plot, we did not need to select
\n", "levelbecause there is one value only. However, if we had more than one level, we would need to add a selection on the level before plotting
There are many ways to customize your plots and we will only detail what we think is important for creating publication ready figures:
\n", "\n", "\n", "
And you should get the following plot:
\n", "Now, we will plot several times on the same figure in different sub-plots; we will not plot all the times (too many) but the first 24 forecasted values.
\n", "Firstly, we need to create a list of times and convert it to pandas datetime in order to make it easier to format times when plotting:
Secondly, we need to use the same plotting method as earlier, but we pass additional parameters:
\n", "vmin = 0and vmax = 35 to set the minimum and maximum values when plotting (this is useful to highlight features in your plot)subplot_kws={\"projection\": proj_plot} to project data on a non-default projection. See cartopy projection for more information about projections.col='time' because we will plot several time;col_wrap=4 to have a maximum of 4 plots per row. If we have more times to plot, then the next figures will be on another row.robust=True and aspect=dset.dims[\"longitude\"] / dset.dims[\"latitude\"] are additional parameters to make each subplot with a βsensibleβ figsize.cmap=cmc.roma_r to select a non-default and color-blind friendly colormap (see scientific colormaps).In the second part of our plot, we are going to customize each subplot (this is why we loop for each of them and get their axes) by adding:
\n", "coastlines: we pass a parameter 10m to get coastlines with a high resolution (non-default);set_title to set a title for each subplot.\n", "\n", "β PM2.5 over Italy
\n", "Using a Multi-plot between Rome and Naples, can you tell us if the forecasted PM2.5 will increase or decrease during the first 24 hours?
\n", "\n", "\n", "Hint: Select the text with your mouse to see the answerπ Solution
\n", "We will select a sub-area: 11. East to 15.0 East and 40. N to 43. N. PM2.5 will increase and reach values close to 35 ΞΌm.m-3.\n", "We will use
\n", "sliceto select the area and we slice latitudes withlatitude=slice(47.3, 36.5)and notlatitude=slice(36.5, 47.3).\n", "The reason is that when using slice, you need to specify values using the same order as in the coordinates. Latitudes are specified in\n", "decreasing order for CAMS.\n", "\n", "β¨οΈ Input: Python
\n", "\n", "fig = plt.figure(1, figsize=[10,10])\n", "\n", "# We're using cartopy to project our data.\n", "# (see documentation on cartopy)\n", "proj_plot = ccrs.Mercator()\n", "\n", "# We need to project our data to the new projection and for this we use <code>transform</code>.\n", "# we set the original data projection in transform (here PlateCarree)\n", "p = dset.sel(time=slice(np.timedelta64(1,'h'),np.timedelta64(1,'D'))).sel(latitude=slice(43., 40.),\n", " longitude=slice(11.,15.))['pm2p5_conc'].plot(transform=ccrs.PlateCarree(),\n", " vmin = 0, vmax = 35,\n", " subplot_kws={\"projection\": proj_plot},\n", " col='time', col_wrap=4,\n", " robust=True,\n", " aspect=dset.dims[\"longitude\"] / dset.dims[\"latitude\"], # for a sensible figsize\n", " cmap=cmc.roma_r)\n", "# We have to set the map's options on all axes\n", "for ax,i in zip(p.axes.flat, (np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))).values):\n", " ax.coastlines('10m')\n", " ax.set_title(\"CAMS PM2.5 \" + pd.to_datetime(i).strftime(\"%d %b %H:%S UTC\"), fontsize=12)\n", "# Save your figure\n", "plt.savefig(\"CAMS-PM2_5-fc-multi-Italy.png\")\n", "\n", "
Sometimes we may want to make more complex selections with criteria on the values of a given variable and not only on its coordinates. For this purpose, we use the where method. For instance, we may want to only keep PM2.5 if values are greater than 25 ΞΌm.m-3 (or any threshold you would like to choose).
Where\n", "\n", "π¬ What happened?
\n", "Each element of the dataset where the criteria within the
\n", "wherestatement is not met, e.g. when PM2.5 <= 25, will be set tonan.\n", "You may not see any changes when printing the dataset but if you look carefuly atpm2p5_concvalues, you will see manynan.
Letβs plot one time to better see what happened:
\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "cell-29", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "We can then make the same multi-plot as earlier (over Italy) but with a where statement to mask values lower than 25 ΞΌm.m-3:
We often want to compute the mean of all our datasets, or along a dimension (for instance time). If you do not pass any argument to the operation then it is done over all dimensions.
\n", "When we do not specify any parameters, we get a single value.
\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "cell-33", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "π₯ Output
\n", "\n", "<xarray.Dataset>\n", "Dimensions: ()\n", " Data variables:\n", " pm2p5_conc float32 9.118\n", "
\n", "\n", "β Maximum PM2.5 over Italy
\n", "What is the maximum forecasted PM2.5 value over the Rome-Naples region?
\n", "\n", "\n", "Hint: Select the text with your mouse to see the answerπ Solution
\n", "We select the same sub-area: 11. East to 15.0 East and 40. N to 43. N and compute the maximum with
\n", "max. The maximum PM2.5 value is 59.13694382 ΞΌm.m-3 (that is rounded to 59.14).\n", "\n", "β¨οΈ Input: Python
\n", "\n", "dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).max()\n", "\n", "\n", "π₯ Output
\n", "\n", "xarray.Dataset\n", "Dimensions:\n", "Coordinates: (0)\n", "Data variables:\n", "pm2p5_conc\n", "()\n", "float64\n", "59.14\n", "array(59.13694382)\n", "Attributes: (0)\n", "
\n", "\n", "β Find when the maximum PM2.5 is forecasted
\n", "When is the maximum PM2.5 value forecasted?
\n", "\n", "\n", "π Solution
\n", "We will select a sub-area: 11. East to 15.0 East and 40. N to 43. N and average over the entire selected area and search where the maximum PM2.5 value of 59.13694382 ΞΌm.m-3 is found. The maximum PM2.5 value occurs on 2021-12-22 at 20:00 UTC.
\n", "\n", "\n", "β¨οΈ Input: Python
\n", "\n", "dset_tmean = dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).max(dim=('latitude', 'longitude'))\n", "dset_tmean_max = dset_tmean.where(dset_tmean['pm2p5_conc'] == 59.13694382, drop=True)\n", "print(dset_tmean_max)\n", "\n", "\n", "π₯ Output
\n", "\n", "<xarray.Dataset>\n", "Dimensions: (time: 1, level: 1)\n", "Coordinates:\n", "* level (level) float32 0.0\n", "* time (time) timedelta64[ns] 20:00:00\n", "Data variables:\n", " pm2p5_conc (time, level) float32 59.14\n", "
\n", "\n", "π¬ Pixel size when averaging
\n", "We average over a relatively small area so we do not make a weighted average. Use weighted averages when averaging over the entire globe or over a large area where the pixel sizes may vary (depending on the latitude).
\n", "
The resampling frequency is lower than our original data, so we would need to apply a global operation on the data we group together such as mean, min, max:
\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "cell-35", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "π₯ Output
\n", "\n", "<xarray.Dataset>\n", "Dimensions: (time: 5, longitude: 700, latitude: 400, level: 1)\n", "Coordinates:\n", "* time (time) timedelta64[ns] 0 days 1 days 2 days 3 days 4 days\n", "* longitude (longitude) float32 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95\n", "* latitude (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05\n", "* level (level) float32 0.0\n", "Data variables:\n", " pm2p5_conc (time, level, latitude, longitude) float32 0.4298 ... 7.501\n", "
When the resampling frequency is higher than the original data, we need to indicate how to fill the gaps, for instance, interpolate and indicate which interpolation method to apply or select nearest values, etc.:
\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "cell-37", "metadata": { "attributes": { "classes": [ "python" ], "id": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "π¬ Be careful when sub-sampling!
\n", "Increasing the frequency of your data e.g. artificially creating data may not be scientifically relevant. Please use it carefully! Interpolating is not always scientifically relevant and sometimes you may prefer to choose a different method, like taking the nearest value for instance:
\n", "\n", "\n", "β¨οΈ Input: Python
\n", "\n", "dset.resample(time='30min').nearest()\n", "
\n", "\n", "β PM2.5 over Italy in the next 4 days
\n", "Using a Multi-plot between Rome and Naples, and making averages per day, can you tell us if forecasted PM2.5 will increase or decrease?
\n", "\n", "\n", "Hint: Select the text with your mouse to see the answerπ Solution
\n", "PM2.5 over Italy is overall decreasing over the next 4 forecasted days.
\n", "\n", "\n", "β¨οΈ Input: Python
\n", "\n", "fig = plt.figure(1, figsize=[10,10])\n", "\n", "# We're using cartopy to project our data.\n", "# (see documentation on cartopy)\n", "proj_plot = ccrs.Mercator()\n", "\n", "sub_dset = dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).resample(time='1D').mean()\n", "# We need to project our data to the new projection and for this we use <code>transform</code>.\n", "# we set the original data projection in transform (here PlateCarree)\n", "p = sub_dset['pm2p5_conc'].plot(transform=ccrs.PlateCarree(),\n", " vmin = 0, vmax = 35,\n", " subplot_kws={\"projection\": proj_plot},\n", " col='time', col_wrap=5,\n", " robust=True,\n", " aspect=dset.dims[\"longitude\"] / dset.dims[\"latitude\"], # for a sensible figsize\n", " cmap=cmc.roma_r)\n", "# We have to set the map's options on all axes\n", "for ax,i in zip(p.axes.flat, (np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))).values):\n", " ax.coastlines('10m')\n", " ax.set_title(\"CAMS PM2.5 \" + pd.to_datetime(i).strftime(\"%d %b %H:%S UTC\"), fontsize=12)\n", "# Save your figure\n", "plt.savefig(\"CAMS-PM2_5-fc-multi-Italy-mean-per-day.png\")\n", "\n", "\n", "\n", "
\n", "\n", "π¬
\n", "GroubyversusresampleUse
\n", "groupbyinstead ofresamplewhen you wish to group over a dimension that is nottime.groupbyis very similar to resample but can be applied to any coordinates and not only to time.
Well done! Pangeo is a fantastic community with many more resources for learning and/or contributing! Please, if you use any Python packages from the Pangeo ecosystem, do not forget to cite Pangeo Abernathey et al. 2017, Abernathey et al. 2021, Gentemann et al. 2021 and Sambasivan et al. 2021!
\n", "Have a look at the Pangeo Tutorial Gallery to pick up your next Pangeo training material!
\n" ] }, { "cell_type": "markdown", "id": "final-ending-cell", "metadata": { "editable": false }, "source": [ "# Key Points\n", "\n", "- Pangeo ecosystem enables big data analysis in geosciences\n", "- Xarray is an important Python package for big data analysis in geosciences\n", "- Xarray can be used to read, select, mask and plot netCDF data\n", "- Xarray can also be used to perform global operations such as mean, max, min or resampling data\n", "\n", "# Congratulations on successfully completing this tutorial!\n", "\n", "Please [fill out the feedback on the GTN website](https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/tutorial.html#feedback) and check there for further resources!\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ad4a3790-823e-4441-bad4-e83cf4abe332", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 5 }