Large-scale visualisation/analysis platform for climate data: Difference between revisions
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= Description = |
= Description = |
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Many analysis tasks in climate research are mean and/or variance calculations over small (but many) netcdf files. Currently these |
Many analysis tasks in climate research are mean and/or variance calculations over small (but many) netcdf files. Currently these |
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analysis tasks are done in Python using [http://www.numpy.org/ numpy] and [http://xarray.pydata.org/en/stable/ xarray]. The overarching goal of this project is to build an interactive web-framework for data analysis and discovery using the Python Visualization library [http://pyviz.org/ PyViz]. |
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= Groundwork = |
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The groundwork for a [https://github.com/ucyo/praktClimaAnalyse framework] is already laid out and can be build upon by the student. |
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= Task = |
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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. |
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Further it is possible to use RPC on different data hosting machines to move the computation to the data. |
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* 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 |
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* look into possibilties to use RPC for moving the computation to the data |
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The goal of this project is to develop an interactive web-framework/interface to these basic analysis frameworks using [https://plot.ly/python/ plotly], [https://mpld3.github.io/examples/index.html#example-gallery mpl3d] |
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= Tasks = |
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* getting familiar with the firmware update procedures of different hardware vendors like e.g. IBM, HP, Dell, SuperMicro |
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* implement scripts that use the firmware update tools provided by the vendors to check for available updates |
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* integrate the scripts into the SCC monitoring system |
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= Requirements = |
= Requirements = |
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* Programming experience in Python and |
* Programming experience in Python and some Javascript |
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= Contact = |
= Contact = |
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Latest revision as of 15:50, 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.
Groundwork
The groundwork for a framework is already laid out and can be build upon by the student.
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
