Data Visualization Methods

The main focus of pycmap (and the broader Simons CMAP project) is to provide the user with a unifying API to extract subsets of datasets with different dimensions and different spatio-temporal resolutions. This will drastically reduce the time-consuming data collection and preparation processes, especially considering the massive size of the archived multi-decadal global oceanic datasets generated by satellites or numerical models.

The simple and unified interface of the pycmap API enables the general public and scientists to dive into the vast, and often under underutilized, ocean datasets without being concerned about the underlying dataset file structure. Visualizing the retrieved data is often a good entry point to explore and study these datasets and pycmap offers a number of built-in methods for simple interactive data visualizations. The pycmap visualization engine can be set to either of these interactive data visualization libraries: plotly, or bokeh. Plotly has a larger variation of graphs while bokeh is faster to render the graph object.

The default visualization library is plotly; the code block below shows how to change the visualization engine (see API page for more details).

Note

Data visualization requires a valid API key which can be obtained from https://simonscmap.com/apikeymanagement. It is not necessary to set the API key every time because the API properties are stored locally after being called the first time.

Example

import pycmap
pycmap.API(vizEngine='bokeh')  # vizEngine options: 'plotly' ,'bokeh'

Data Visualization Methods

Histogram Plot	Histogram Plot
Time Series Plot	Time Series Plot
Regional Map, Contour Plot, 3D Surface Plot	Regional Map, Contour Plot, 3D Surface Plot
Section Map, Section Contour	Section Map, Section Contour
Depth Profile	Depth Profile
Cruise Track Plot	Cruise Track Plot
Correlation Matrix	Correlation Matrix
Correlation Matrix Along Cruise Track	Correlation Matrix Along Cruise Track
Climatology	Climatology