The practical aims at familiarzing you with the Panoply Galaxy interactive environment. Panoply is among the most popular tool to visualize geo-referenced data stored in Network Common Data Form (netCDF). It provides a graphical interface for inspecting (show metadata) and visualizing netCDF data. It supports many features to customize your plots and we will introduce some of them in this lesson.
There are many online services to get climate data, and it is often difficult to know which ones are up-to date and which resources to trust.
Different services provide different Application Programming Interfaces (API), use different terminologies, different file formats etc., which make it difficult for new users to master them all.
Therefore in this tutorial, we will be focusing on the usage of Climate data in Network Common data Form (netCDF) because it is the most common data format for storing Climate data.
We will be using a freely available dataset containing Essential Climate Variables (sea ice area fraction, surface temperature) from Copernicus Climate Data Store. We will learn to use panoply to visualize the sea ice area fraction over the poles (southern and northern poles) and surface temperatures for two different years (1979 and 2018).
NetCDF format
NetCDF data format is a binary format and to be able to read or visualize it, we would need to use dedicated software or libraries that can handle this “special” format. It is self-describing and machine-independent data format that supports the creation, access, and sharing of array-oriented scientific data. NetCDF files usually have the extension .nc or .netcdf.
For climate and forecast data stored in NetCDF format there are (non-mandatory) conventions on metadata (CF Convention).
Click galaxy-uploadUpload Data at the top of the tool panel
Select galaxy-wf-editPaste/Fetch Data
Paste the link(s) into the text field
Press Start
Close the window
As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:
Go into Data (top panel) then Data libraries
Navigate to the correct folder as indicated by your instructor.
On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
Select the desired files
Click on Add to Historygalaxy-dropdown near the top and select as Datasets from the dropdown menu
In the pop-up window, choose
“Select history”: the history you want to import the data to (or create a new one)
Click on Import
Check that the datatype is netcdf
Files you uploaded are in netcdf format. In Galaxy, Datatypes are, by default, automatically guessed. Here, as netcdf is a derivative of the h5 format, Galaxy automatically affect the h5 datatype to netcdf files. To cope with that, one can change the datatype manually, once datasets uploaded (as shown below) OR you can directly specify datatype on the upload tool form so Galaxy will not try to automatically guess it.
Click on the galaxy-pencilpencil icon for the dataset to edit its attributes
In the central panel, click galaxy-chart-select-dataDatatypes tab on the top
In the galaxy-chart-select-dataAssign Datatype, select datatypes from “New type” dropdown
Tip: you can start typing the datatype into the field to filter the dropdown menu
Click the Save button
Rename Datasets
As “https://zenodo.org/record/3697454/files/ecv_1979.nc” is not a beautiful name and can give errors for some tools, it is a good practice to change the dataset name by something more meaningfull. For example by removing https://zenodo.org/record/3697454/files/ to obtain ecv_1979.nc and ecv_2018.nc, respectively.
Click on the galaxy-pencilpencil icon for the dataset to edit its attributes
In the central panel, change the Name field
Click the Save button
Add a tag to the dataset corresponding to copernicus
Datasets can be tagged. This simplifies the tracking of datasets across the Galaxy interface. Tags can contain any combination of letters or numbers but cannot contain spaces.
To tag a dataset:
Click on the dataset to expand it
Click on Add Tagsgalaxy-tags
Add tag text. Tags starting with # will be automatically propagated to the outputs of tools using this dataset (see below).
Press Enter
Check that the tag appears below the dataset name
Tags beginning with # are special!
They are called Name tags. The unique feature of these tags is that they propagate: if a dataset is labelled with a name tag, all derivatives (children) of this dataset will automatically inherit this tag (see below). The figure below explains why this is so useful. Consider the following analysis (numbers in parenthesis correspond to dataset numbers in the figure below):
a set of forward and reverse reads (datasets 1 and 2) is mapped against a reference using Bowtie2 generating dataset 3;
dataset 3 is used to calculate read coverage using BedTools Genome Coverageseparately for + and - strands. This generates two datasets (4 and 5 for plus and minus, respectively);
datasets 4 and 5 are used as inputs to Macs2 broadCall datasets generating datasets 6 and 8;
datasets 6 and 8 are intersected with coordinates of genes (dataset 9) using BedTools Intersect generating datasets 10 and 11.
Now consider that this analysis is done without name tags. This is shown on the left side of the figure. It is hard to trace which datasets contain “plus” data versus “minus” data. For example, does dataset 10 contain “plus” data or “minus” data? Probably “minus” but are you sure? In the case of a small history like the one shown here, it is possible to trace this manually but as the size of a history grows it will become very challenging.
The right side of the figure shows exactly the same analysis, but using name tags. When the analysis was conducted datasets 4 and 5 were tagged with #plus and #minus, respectively. When they were used as inputs to Macs2 resulting datasets 6 and 8 automatically inherited them and so on… As a result it is straightforward to trace both branches (plus and minus) of this analysis.
Panoply is available as a Galaxy interactive environment and may not be available on all Galaxy servers.
Currently Panoply in Galaxy is available on useGalaxy.eu instance, on the “Interactive tools” tool panel section or, as all interactive tools, from the dedicated usGalaxy.eu subdomain: Live.useGalaxy.eu
The plot represent the surface temperature over the entire world.
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Figure 3: Plot map
The date of the default plot is 1st January 1979 at 00:00:00.
To plot another date, change either:
Initial time of forecast (give a value between 1 and 12, corresponding to each month of year 1979.
Click on the date and scroll down to select the date of your choice.
Save your plot
Click on the tab File (from your plot window) to store your plot by selecting Save Image As
Double click on the folder outputs to enter this folder and save your plot.
You need to make sure to save all your plot in the outputs folder otherwise you can loose all your plots once to close panoply.
Change colormap
Always make sure you use color blind friendly palettes.
To change the default colormap, click on tab “Scale” (bottom of your plot window) and select another “Color Table” (you can scroll down to go through all the different available colormap).
Save your plot using Save Image As and make sure to choose another name to avoid overwritting your preceding plot.
Create another plot window for sea ice area fraction (siconc) and make a new geo-referenced map
Question
What kind of colormap could you use to highlight the extent of sea-ice?
What projection would be best to use for showing the extent of sea-ice over the two poles?
Any colormap that shows low values (close to 0) in light color so we can focus on values that are close to 1. For instance, CP_PuBu_08.cpt.
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Figure 6: Sea-ice colormap
Using Orthographic projection is best for showing the northern and southern poles. One advantage is that you can choose to center the plot over 90 degrees latitude. To have both the northern and southern poles at the same time, choose Stereographic (Two hemispheres).
Figure 7: Plot sea-ice using orthographic projection
Export Animation
Hands-on: Export animation
From your previous plot window, click on File and select Export Animation. Save your plot using either MOV or AVI format.
It goes through each plot e.g. for each month and create an animation where you can see the evolution of sea-ice extent from January 1979 to December 1979.
You will be able to download the resulting movie from Galaxy once you quit Panoply.
Create timeseries
Hands-on: Create 1D plot
Double click on the variable t2m, click on Create and select Create horizontal line plot along time axis (make sure to switch to time).
We have now learnt how to analyze climate data using Panoply. We only use one of the two datasets so we strongly encourage you to do the same exercises with the second dataset ecv_2018.nc. Please note that when comparing surface temperature or sea-ice area fraction from 1979 and 2018, you would not be able to conclude anything regarding climate change. For any climate studies, long term timeseries (between 20 to 30 years) are necessary to establish climate trends.
You've Finished the Tutorial
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Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
@misc{climate-panoply,
author = "Anne Fouilloux",
title = "Visualize Climate data with Panoply netCDF viewer (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{https://training.galaxyproject.org/training-material/topics/climate/tutorials/panoply/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
doi = {10.1371/journal.pcbi.1010752},
url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
year = 2023,
month = {jan},
publisher = {Public Library of Science ({PLoS})},
volume = {19},
number = {1},
pages = {e1010752},
author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
editor = {Francis Ouellette},
title = {Galaxy Training: A powerful framework for teaching!},
journal = {PLoS Comput Biol} Computational Biology}
}
Congratulations on successfully completing this tutorial!
You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.
Anne Fouilloux
Questions:
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