Large-scale visualisation/analysis platform for climate data: Difference between revisions
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Further it is possible to use RPC on different data hosting machines to move the computation to the data. |
Further it is possible to use RPC on different data hosting machines to move the computation to the data. |
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= Tasks = |
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* getting familiar with PyViz and the underlying technologies e.g. Bokeh |
* getting familiar with PyViz and the underlying technologies e.g. Bokeh |
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* implement scripts that enhance the current framwork with streaming technologies such as AJAX |
* implement scripts that enhance the current framwork with streaming technologies such as AJAX |
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* look into possibilties to use RPC for moving the computation to the data |
* look into possibilties to use RPC for moving the computation to the data |
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= Requirements = |
= Requirements = |
Revision as of 15:48, 19 September 2019
Description
Many analysis tasks in climate research are mean and/or variance calculations over small (but many) netcdf files. Currently these analysis tasks are done in Python using numpy and xarray. The overarching goal of this project is to build an interactive web-framework for data analysis and discovery using the Python Visualization library PyViz. The groundwork for such a framework is already laid out in a former Praktikum.
Task
The task for this semester is to integrate data streaming technology for visualization. There are several techniques already supported by the underlying technology e.g. AJAX data sources. Further it is possible to use RPC on different data hosting machines to move the computation to the data.
- getting familiar with PyViz and the underlying technologies e.g. Bokeh
- implement scripts that enhance the current framwork with streaming technologies such as AJAX
- look into possibilties to use RPC for moving the computation to the data
Requirements
- Programming experience in Python and some Javascript