add new argo tuto

topics/climate/tutorials/argo_pangeo/faqs/index.md

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+---
+layout: faq-page
+---

topics/climate/tutorials/argo_pangeo/tutorial.bib

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+
+# This is the bibliography file for your tutorial.
+#
+# To add bibliography (bibtex) entries here, follow these steps:
+#  1) Find the DOI for the article you want to cite
+#  2) Go to https://doi2bib.org and fill in the DOI
+#  3) Copy the resulting bibtex entry into this file
+#
+# To cite the example below, in your tutorial.md file
+# use {% cite Batut2018 %}
+#
+# If you want to cite an online resourse (website etc)
+# you can use the 'online' format (see below)
+#
+# You can remove the examples below
+
+@article{Batut2018,
+  doi = {10.1016/j.cels.2018.05.012},
+  url = {https://doi.org/10.1016/j.cels.2018.05.012},
+  year = {2018},
+  month = jun,
+  publisher = {Elsevier {BV}},
+  volume = {6},
+  number = {6},
+  pages = {752--758.e1},
+  author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and
+           Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton
+           and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and
+           Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and
+           Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{i}}rez and Devon Ryan and
+           Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and
+           Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Bj\"{o}rn Gr\"{u}ning},
+  title = {Community-Driven Data Analysis Training for Biology},
+  journal = {Cell Systems}
+}
+
+@online{gtn-website,
+  author = {GTN community},
+  title = {GTN Training Materials: Collection of tutorials developed and maintained by the worldwide Galaxy community},
+  url = {https://training.galaxyproject.org},
+  urldate = {2021-03-24}
+}

topics/climate/tutorials/argo_pangeo/tutorial.md

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+---
+layout: tutorial_hands_on
+
+title: Analyse Argo data
+questions:
+- How to use Pangeo ecosystem to analyse ocean data?
+- How to process Argo data to visulise ocean temperature variations?
+objectives:
+- How to fetch Argo Data ?
+- How to get metadata with xarray netcdf tools ?
+- Learn to get temperature variations.
+- Learn to use an interactive tool to visualise Argo temperature variable on a interactive map.
+time_estimation: 1H
+key_points:
+- Argo data
+- Pangeo Ecosystem
+- Galaxy Earth System
+tags:
+  - earth-system
+  - ocean
+  - pangeo
+  - argo
+contributions:
+  authorship:
+    - Marie59
+  funding:
+    - fairease
+    - eurosciencegateway
+
+---
+
+The ocean is a key component of the Earth climate system. It thus needs a continuous real-time monitoring to help scientists better understand its dynamic and predict its evolution. All around the world, oceanographers have managed to join their efforts and set up a Global Ocean Observing System among which Argo is a key component.
+Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. 
+
+The data used in this tutorial are from the Argo gliders network. We are interested in the following variables: water temperature, latitude, longitude and time. Our main objective is to plot the water temperature with respect to time. For this we will be using the netCDF xarray tools available in the Galaxy Europe (or your favourite Galaxy Instance) server.
+
+
+These tools are part of the Pangeo ecosystem in which the next generation of open-source analysis tools for ocean, atmosphere and climate science can be developed, distributed, and sustained. These tools must be scalable in order to meet the current and future challenges of big data, and these solutions should leverage the existing expertise outside of the geoscience community.
+
+This tutorial is part of set of tutorials on the [Galaxy Earth System](https://earth-system.usegalaxy.eu/) supported by the [EOSC FAIR-EASE project](https://fairease.eu/).
+
+> <agenda-title></agenda-title>
+>
+> In this tutorial, we will cover:
+>
+> 1. TOC
+> {:toc}
+>
+{: .agenda}
+
+> <details-title>Short introduction on how Galaxy works</details-title>
+>
+> You can come back to where you left off the tutorial anytime by clicking {% icon level %}.
+>
+> > <hands-on-title>Log in to Galaxy</hands-on-title>
+> > 1. Open your favorite browser (Chrome, Safari or Firefox as your browser, not Internet Explorer!)
+> > 2. Browse to your [Galaxy instance](https://earth-system.usegalaxy.eu/)
+> > 3. On the top pannel go to **Login or Register**
+> >
+> >
+> {: .hands_on}
+>
+> The Galaxy homepage is divided into three panels:
+> * Tools on the left
+> * Viewing panel in the middle
+> * History of analysis and files on the right
+>
+> ![Screenshot of the Galaxy interface, the tools panel is on the left, the main panel is in the center, and the history is on the right.]({% link shared/images/galaxy_interface.png %} "Galaxy interface explanation")
+>
+> The first time you use Galaxy, there will be no files in your history panel.
+{: .details}
+---
+
+
+
+# Argo gliders data
+Argo is a global network of nearly 4000 autonomous probes measuring pressure, temperature and salinity from the surface to 2000m depth every 10 days. The localisation of these probes is nearly random between the 60th parallels. All probes data are collected by satellite in real-time, processed by several data centers and finally merged in a single dataset (collecting more than 2 millions of vertical profiles data) made freely available to anyone through a ftp server or monthly zip snapshots.
+
+Each Argo probe is an autonomous, free drifting, profiling float, i.e. a probe that can’t control its trajectory but is able to control its buoyancy and thus to move up and down the water column as it wishes. Argo floats continuously operate the same program, or cycle, illustrated in the figure below. After 9 to 10 days of free drift at a parking depth of about 1000m, a typical Argo float dives down to 2000m and then shoals back to the surface while measuring pressure, temperature and salinity. Once it reaches the surface, the float sends by satellite its measurements to a data center where they are processed in real time and made freely available on the web in less than 24h00.
+
+![Schema of a typical 10 days program, cycle, of an Argo float](../../images/argo_pangeo/argofloats_cycle.png "10 days program, cycle, of an Argo float")
+
+Here we will focus on the Caribbean sea surrounding the Antilles during April and May. The 9th of April 2021 an eruption of the volcano La Soufriere Saint Vincent (Antilles) occured. Another tutorials on this event is available on the [Galaxy Training Network](https://training.galaxyproject.org/training-material/topics/climate/tutorials/sentinel5_data/tutorial.html).
+
+## Get Argo data
+
+> <tip-title> History set up on Galaxy </tip-title>
+> > <hands-on-title> History management </hands-on-title>
+> >
+> > Create a new history for this tutorial and give it a name (example: “Argo data with Pangeo”) for you to find it again later if needed.
+> > 
+> {% snippet faqs/galaxy/histories_create_new.md box_type="none" %}
+> {: .hands_on}
+{: .tip}
+
+> <hands-on-title> Arge data fetching </hands-on-title>
+>
+>
+> 1. {% tool [Argo data access](toolshed.g2.bx.psu.edu/repos/ecology/argo_getdata/argo_getdata/0.1.15+galaxy0) %} with the following parameters:
+>    - {% icon param-select %} *"We have preconfigured some mode of operations for you. What mode do you want to use?"*: `🏊 standard mode simplifies the dataset, remove most of its jargon and return a priori good data` 
+>    - {% icon param-select %} *"How do you want to select your data of interest ?"*: `🗺 For a space/time domain` 
+>        - *"Input longitude min (+east/-west)"*: `-75.0` 
+>        - *"Input longitude max (+east/-west)"*: `-45.0` 
+>        - *"Input latitude min (+north/-south)"*: `20.0` 
+>        - *"Input latitude max (+north/-south)"*: `30.0`
+>        - *"Input pressure min (db)"*: `0.0` 
+>        - *"Input pressure max (db)"*: `10.0` 
+>        - *"Input starting date"*: `2021-04` 
+>        - *"Input ending date"*: `2021-06` 
+>    - {% icon param-select %} *"Which kind of dataset do you want ?"*: `Physical parameters: temperature, salinity, pressure` 
+>
+> 2. **Run Tool**
+> 3. After a couple minutes an `Argo data` output will appear green in your history.
+> 4. Check that your data are in netcdf format with {% icon galaxy-pencil %}, it should be `netcdf`
+>
+{% snippet faqs/galaxy/datasets_change_datatype.md datatype="nectdf" box_type="tip" %}
+{: .hands_on}
+
+# Argo data analysed, managed, and visualised by th Pangeo tools
+
+## Xarray tools
+
+First, we'll use 2 xarray netcdf tools. xarray, formerly known as xray, is a python package which enables us to play with gridded data. This package shares most of its features from numpy, but in a more convenient manner by keeping track of labels in arrays. The gridded data is mainly available in netCDF data format. Thus xarray comes very handy while dealing with netCDF files.
+
+## Knowing more about hour data
+After fetching the required Argo data, the following stage is to obtain the meta info or meta data of the file. The very purpose of these steps are to obtain the information about dimensions, variables, global attributes, etc. The coordinate info helps to know about the actual data entries present under the various variables.
+
+## Get metadata
+
+> <hands-on-title> NetCDF dataset with Xarray metadata Galaxy Tool </hands-on-title>
+>
+> 1. {% tool [NetCDF xarray Metadata Info](toolshed.g2.bx.psu.edu/repos/ecology/xarray_metadata_info/xarray_metadata_info/2022.3.0+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Netcdf file"*: `output_argo` (output of **Argo data access** {% icon tool %})
+>
+{: .hands_on}
+
+In info file output file, we can identify 4 different sections:
+
+1. Dimensions: name of dimensions and corresponding number of elements;
+2. Coordinates: contains coordinate arrays (longitude, latitude, level and time) with their values.
+3. Data variables: contains all the variables available in the dataset. Here, we only have one variable. For each variable, we get information on its shape and values.
+4. Global Attributes: at this level, we get the global attributes of the dataset. Each attribute has a name and a value.
+
+## Coordinates information
+
+> <hands-on-title> Get Coordinate information with Xarray Coordinate </hands-on-title>
+>
+> 1. {% tool [NetCDF xarray Coordinate Info](toolshed.g2.bx.psu.edu/repos/ecology/xarray_coords_info/xarray_coords_info/2022.3.0+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Netcdf file"*: `output_argo` (output of **Argo data access** {% icon tool %})
+>
+{: .hands_on}
+
+View {% icon galaxy-eye %} the 5 generated outputs:
+
+1. latitude: a tabular file containing all the latitude values of our Xarray dataset;
+2. longitude: a tabular file containing all the longitudes values;
+3. level: this file contains information on all the different levels (here, we have surface data so level=0 meter);
+4. time: this tabular file contains all the forecast times. In our case, these are relative to December 22, 2021;
+5. version: this is a text file returning the Xarray package version. It is useful when publishing your Galaxy workflow.
+
+![History with the newly generated outputs](../../images/argo_pangeo/argo_history.png "History with the newly generated outputs")
+
+## Timeseries visualisation
+
+> <hands-on-title> NetCDF timeseries Extractor </hands-on-title>
+>
+> 1. {% tool [NetCDF timeseries Extractor](toolshed.g2.bx.psu.edu/repos/ecology/timeseries_extraction/timeseries_extraction/2022.3.0+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Input netcdf file"*: `output_argo` (output of **Argo data access** {% icon tool %})
+>    - {% icon param-file %} *"Tabular of variables"*: `output` (output of **NetCDF xarray Metadata Info** {% icon tool %})
+>    - *"Choose the variable to plot"*: ``
+>    - *"Datetime selection"*: `No`
+>    - In *"Advanced Plotting Options"*:
+>        - *"Plot title"*: `Temperature`
+>        - *"Label for x-axis"*: `Time`
+>        - *"Label for y-axis"*: `Temperature`
+>
+>
+{: .hands_on}
+
+# Visualisation mapping with a Galaxy Earth System's tool
+
+The Earth System is a complex and dynamic system that encompasses the interactions between the atmosphere, oceans, land, and biosphere. Understanding and analyzing data from the Earth System Model (ESM) are essential, for example to predict and mitigate the impacts of climate change. The ESM that the project tries to implement includes coastal water dynamics, ocean bio-geochemical in-situ data, marine omics observations, volcano activities and land degradation.
+
+To know more keep an {% icon galaxy-eye %} open for all related blogs on the [Galaxy Community Hub](https://galaxyproject.org/news/) or go try the tools directly on [Galaxy Earth System](https://earth-system.usegalaxy.eu/). 
+
+![Galaxy Earth System welcome page image](../../images/argo_pangeo/earth_system_galaxy.png "Galaxy Earth System welcome page image")
+
+## Ocean Data View (ODV)
+Ocean Data View (ODV) is a software package for the interactive exploration, analysis and visualization of oceanographic and other geo-referenced profile, time-series, trajectory or sequence data. To know more about ODV go check the [official page](https://odv.awi.de/).
+
+ODV is now integrated in Galaxy as an interactive tool. This kind of tools is working differently than classical tools as it allows the user to interact with a dedicated graphical interface. This kind of tools is used to give access to Jupyter notebooks, RStudio or R Shiny apps for example.
+
+> <hands-on-title> Launch ODV</hands-on-title>
+>
+> 1. {% tool [ODV](interactive_tool_odv) %} with the following parameters:
+>    - *"Select if you are using a ODV collection in a zip folder or if you have your own raw data"*: `The data you are using are Netcdf or tabular text files`
+>        - {% icon param-file %} *"Netcdf or tabular text file. For text file, odv format is recommanded."*: `timeseries_tabular` (output of **NetCDF timeseries Extractor** {% icon tool %})
+>
+> 2. Click on **Run Tool**
+>
+> {% snippet faqs/galaxy/interactive_tools_open.md tool="ODV" %}
+>
+> > <tip-title>ODV - Disconnected</tip-title>
+> > If at one point your ODV interface becomes grey with a red panel on the top "X ODV - Disconnected", do NOT panic ;) you just need to reload your tab (circular arrow top left)
+> {: .tip}
+>
+> > <tip-title>Copy pasting between computer and ODV</tip-title>
+> > You can expand the ODV left panel (where there are 3 dots, vertically) to access the "clipboard" menu, and paste the content you want to paste on a ODV form. From there you can copy paste everything from one side to the other. Then, click outside of this panel to collapse it.
+> >
+> > ![Image showing in transparent on the left of the ODV interface the clipboard](../../images/argo_pangeo/clipboard.png)
+> {: .tip}
+{: .hands_on}
+
+
+> <hands-on-title> Visualise with ODV </hands-on-title>
+>
+> In the new tab in ODV.
+> 1. Click on close of the pop-up screen for the check for Updates
+>
+> 2. Go the top left and click on **File**, then on **Open...**
+>
+> 3. On the pop-up screen on the left pannel select **ODV**, then the folder **galaxy**, then **data**. 
+> You should see a folder open it (double clicking)
+>
+> 4. In the bottom of the pop-up window in **Files of type** select **All Files (*)**
+>
+> 5. Click on **Open** in the bottom right
+>
+> 6. On the left smaller map right click and select **Zoom**
+>
+> 7. Then move your cursor on the map you should see a red rectangle moving along
+>
+> 8. Reduce the rectangular to have the selection you want on the map. It can be something similar to the following image (no need to be exactly the same).
+>
+> 9. Once you're happy with your selection click on **Enter** on your keyboard.
+>
+> There your data should be openning an you can now visualise them !
+>
+{: .hands_on}
+
+![Visualistation on a ODV map](../../images/argo_pangeo/argo_odv_open.png "ODV map of the Argo data")
+
+> <hands-on-title> Visualise the temperature on a map with ODV </hands-on-title>
+> 1. Go on the big **+** on the top left
+> 2. Select "1 SURFACE Window"
+> 3. On the central white rectangular next to the map click right and and select **Properties...**
+> 4. Go to the **Data** tab of the pop-up window 
+> 5. Select for **Z-axis** the variable **TEMP @ N POINTS=first** as the following image.
+> ![Going to the properties and modify the data to visualise the temperature](../../images/argo_pangeo/odv_properties.png)
+> 6. Then, click on **OK**.
+> 
+> > <tip-title>Change your visualisation properties</tip-title>
+> > 1. Go on the central map 
+> > 2. Click right and select **Properties...**
+> > 3. For example make your data dots bigger in "Display Style" increase the number below "Symbol Size" to 30, click **OK**
+> >
+> > You can now see bigger dots representing your data.
+> {: .tip}
+{: .hands_on}
+
+> <hands-on-title>Save your visualisation map</hands-on-title>
+> 1. Click right on the map select **Save Plot As...**
+> 2. In the pop-up screen go to the folder **ODV**, **galaxy**, **outputs**.
+> 3. In **File name** rename your view (for example subset_temp_argo_data)
+> 4. In **Files of type** select `PNG (*.png *.PNG)` and **Save** then **OK** and **OK**.
+> Once you're finished with ODV :
+> 5. On th top left click on **File** select **Exit**
+> 6. If you want to save the other window also click on **Yes**. Here we don't need it so click **No**.
+{: .hands_on}
+
+
+![ODV result map](../../images/argo_pangeo/odv_result.png)
+
+# Conclusion
+
+Awesome ! You now know how to get Argo data, then get meta data and other information within the Pangeo ecosystem and finally visualise these data with an Earth System tool, Ocean Data View.
+![The full workflow](../../images/argo_pangeo/workflow.png)
+
+
+# Extra information
+Coming up soon even more tutorials on and other Earth-System related trainings. Keep an {% icon galaxy-eye %} open if you are interested!

topics/climate/tutorials/argo_pangeo/workflows/index.md

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+---
+layout: workflow-list
+---