18 USEFUL PYTHON LIBRARIES FOR OCEANOGRAPHIC DATA ANALYSIS
INTRODUCTION:
Python has gained a lot of popularity in the subject of oceanography due to its adaptability, simplicity, and accessibility to a large number of libraries for scientific computing and data processing.
PYTHON:
Python is a high-level, all-purpose programming language that Guido van Rossum initially introduced in 1991. Python places a strong emphasis on code readability thanks to its simple, clear syntax, which enables programmers to express concepts in less code than they would in other languages. Programming styles supported by Python include procedural, object-oriented, and functional programming. It is appropriate for a variety of applications and domains since it has a large variety of built-in data types, including numbers, texts, lists, dictionaries, and sets. Furthermore, Python offers a sizable standard library that contains a wide range of modules and functions for activities including file I/O, networking, web development, and more.
OCEANOGRAPHY:
Oceanography is the scientific study of the ocean, which includes physical, biological, chemical, and geological aspects. Oceanographers investigate a wide range of topics related to the ocean, including its currents, waves, tides, temperature, salinity, and the circulation of water. They also study marine life, including the diversity and distribution of organisms, their adaptations to the marine environment, and the intricate food webs that exist in the ocean.
PYTHON LIBRARIES AND USES
Python libraries are a potent tool that can assist you in creating Python programs that are more effective and efficient.
Because of its adaptability, broad scientific community, and open-source nature, Python is commonly utilized in oceanography. NumPy, SciPy, Pandas, and Matplotlib are just a few of the many useful libraries and tools that enable effective data processing and visualization. Moreover, Python is a preferred option for oceanographers processing complex oceanographic data due to its interaction with other languages, robust community support, and rather easy learning curve.
Some commonly used Python libraries in the context of oceanography and scientific computing include:
Numpy: An essential library for Python’s numerical processing, NumPy offers effective array manipulation and mathematical operations. It is necessary for managing huge datasets and running multidimensional array calculations.
Pandas:
Data structures like DataFrames are offered by Pandas, a potent toolkit for data manipulation and analysis, to facilitate the processing of tabular data. It provides tools for doing calculations on datasets and for filtering, sorting, grouping, and merging them.
Scipy:
A library that supports a variety of scientific and numerical computing modules, SciPy was created on top of NumPy. It has modules for signal processing, linear algebra, interpolation, optimization, and more.
Matplotlib:
Users can build a wide range of static, animated, and interactive visualizations using the powerful Python charting toolkit Matplotlib. In order to create plots, charts, histograms, and other graphical representations of oceanographic data, it is frequently utilized.
Seaborn: is developed on top of Matplotlib, a high-level visualization library. Seaborn delivers a streamlined user interface along with attractive and educational statistical visuals. It is very helpful for building intricate statistical charts and identifying patterns in data.
Xarray:
In oceanography, Xarray is a potent Python module for working with labeled multidimensional arrays and datasets. It supports a wide range of file formats, including NetCDF, and makes it simple to do operations like loading, altering, and analyzing gridded data. It also works well with other scientific libraries like NumPy and Pandas. Data interpretation and alignment are made simpler by its capacity to handle coordinates and label data, leading to more effective calculations and analyses.
Octant:
For the analysis of climate and oceanographic data, Python has a specific library called Octant. For effective gridded dataset editing and display, it provides a high-level interface and tools. Octant allows users to build perceptive charts, streamlines, and vertical sections and supports a number of popular file formats and actions, including slicing and averaging. In addition, it simplifies data processing responsibilities for oceanographers by facilitating coordinate system handling, time series management, derived quantity calculation, and basic statistical analysis.
SeaPy:
The open-source Python library known as SEAPY, or “Python for Earth and Atmospheric Sciences,” is used to analyze and visualize scientific data in the fields of oceanography, meteorology, and atmospheric sciences. It offers functions for reading, manipulating, and displaying gridded datasets and supports a number of file types. Data interpolation, filtering, subsetting, and statistical analysis are just a few of the capabilities that SEAPY offers in an effort to streamline data management and free up researchers to concentrate on their own data analysis and interpretation.
Cartopy:
A library for geospatial data processing and mapping in Python. Cartopy facilitates the creation of maps, projections, and geospatial plots, allowing oceanographers to visualize and analyze spatial data related to the ocean.
Basemap:
This is a Python library that offers tools for using matplotlib to make maps and visualizations. You can add geographic features, draw coastlines, plot data on maps, and alter map projections.
Okean:
An oceanographic and related field-focused Python library. It provides capabilities and resources for processing data, conducting statistical analysis, and visualizing oceanographic data. Okean features handling formats for oceanographic data, interpolation, spectrum analysis, and other things.
Dateutil:
It is a Python package that offers tools for manipulating dates and times. It provides features like date string parsing, time zone management, calculating time differences, and working with repeating events, extending the functionality of the basic datetime module.
Netcdf4:
Netcdf4 is a Python library for reading and writing data in the NetCDF format, which is commonly used in atmospheric and oceanographic research. It provides a high-level interface to efficiently handle multidimensional arrays, metadata, and attributes stored in NetCDF files.
GSW: (Gibbs SeaWater Oceanographic Package)
It is used to determine the characteristics of seawater and associated values. It uses the thermodynamic equations to calculate the temperature, salinity, pressure, density, and other characteristics of saltwater. In oceanography and ocean modeling, GSW is widely employed.
PySAL: The Python Spatial Analysis Library
This is a library for modeling and doing spatial analysis. It offers a variety of tools and techniques, such as spatial autocorrelation, geographic clustering, spatial regression, and more, for the analysis and visualization of spatial data. Several disciplines, such as geography, urban design, and environmental studies, can use PySAL.
Pyroms:
Pyroms is a Python library specifically designed for handling ocean model grids and remapping data between different grid types. It provides functions for creating, manipulating, and visualizing ocean model grids, as well as tools for interpolating and regridding data onto different grid systems.
Glob:
Glob is a module in the Python standard library that provides functionality for searching and matching file patterns. It allows users to find files using wildcards and patterns, making it useful for file management and data processing tasks where batch processing of multiple files is required.
CMOcean:
CMOcean is a Python module that offers a selection of colormaps created especially for the viewing of oceanographic data. It includes a variety of colormaps that are tuned for various variables as well as aesthetically pleasing and perceptually consistent color schemes for plotting and visualizing oceanographic data.
YOUTUBE CHANNELS FOR TUTORIAL AND JOURNALS
you can find some useful YouTube videos regarding python and Oceanographic Data analysis
https://www.youtube.com/watch?v=WYmAu0GiSU4 (SciPy 2018: Scientific Computing with Python Conference. Harnessing the Power of Scientific Python | Noelle Held and Jaclyn Saunders)
2.https://www.youtube.com/watch?v=G-fz8L9xHIs (SatPy: A Python Library for Weather Satellite Processing | SciPy 2018 | David Hoese: A Python Library for Weather Satellite Processing )
3. https://www.youtube.com/watch?v=XjHzLUnHeM0 (Perceptual Color Maps in matplotlib for Oceanography | SciPy 2015 | Kristen Thyng: Perceptual Color Maps in matplotlib for Oceanography)
4. https://www.youtube.com/watch?v=kJXUUO5M4ok&t=978s
Introduction to Geospatial Data Analysis with Python | SciPy 2018 Tutorial | Serge Rey
5. https://www.youtube.com/watch?v=txhjhjWqF7c&t=1175s
Hands-on Satellite Imagery Analysis | SciPy 2018 Tutorial | Sara Safavi, Dana Bauer
6. https://www.youtube.com/watch?v=MF-WH01Qw0g
Building a Weather App using NOAA Open Data & Jupyter Notebooks | SciPy 2018 | Fernandes & Signell
7.https://www.youtube.com/watch?v=OKQlUdPY0Jc
Development of MetPy’s Declarative Plotting Interface | SciPy 2018 | May & Leeman
8.https://www.youtube.com/watch?v=Ofa0-l1pjAk
Oceanographic Data & Numerical Model Output | SciPy 2018 | Kristen Thyng
9.https://www.youtube.com/watch?v=arXiv-TM7DY
Image Analysis in Python with SciPy and scikit-image | SciPy 2018 Tutorial | Stefan van der Walt
10. https://www.youtube.com/watch?v=Hd_ydJeyr5M
A Python API for Earth
11.https://www.youtube.com/watch?v=lKcwuPnSHIQ
Introduction to Numerical Computing with NumPy | SciPy 2017 Tutorial | Dillon Niederhut
12.https://www.youtube.com/watch?v=MW9wmGsscrs
Iris & Cartopy: Python packages for Atmospheric and Oceanographic science; SciPy 2013 Presentation
13.https://www.youtube.com/watch?v=gJd-Ohf1FfM
Big Data Oceanography — James Munroe
14.https://www.youtube.com/watch?v=bGulPZh_-Mo
Global Hydrology Analysis Using Python | SciPy 2015 | Mattheus Ueckermann
15.https://www.youtube.com/watch?v=BS7a_FzYBQw
16.https://www.youtube.com/watch?v=E8wO3qMevV8
Software for understanding robot data: Spatial Temporal Oceanographic Query System (STOQS)
17.https://www.youtube.com/watch?v=EA8v770EcxE
Advances in delivery and access tools for coastal ocean model data; SciPy 2013 Presentation
18.https://www.youtube.com/watch?v=heFaYLKVZY4
Computational Statistics II | SciPy 2015 Tutorial | Chris Fonnesbeck
19.https://www.youtube.com/watch?v=BV30Sk1CrM0
Standardized Framework for Working with Met-Ocean Data | SciPy 2015 | Richard Signell
20.https://www.youtube.com/watch?v=44fsoeh9sc8
Eddy tracking with py-eddy-tracker. Dr. Evan Mason
