Posts tagged python

Basemap on levante

Basemap has reached end-of-life and we won’t install basemap system wide (python 3 module) but you can easily install it into a personal conda environment like this

On Levante:

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About data on curvilinear or rotated regional grids

2D Climate data can be sampled using different grid types and topologies, which might make a difference when it comes to data analysis and visualization. As the grid lines of regular or rectilinear grids are aligned with the axes of the geopgraphical lat-lon coordinate system, these model grids are relatively easy to deal with. A common, but more complex case is that of a curvilinear or a rotated (regional) grid. In this blog article we want to illuminate this case a bit; we describe how to identify a curvilinear grid, and we demonstrate how to visualize the data using the “normal” cylindric equidistant map projection.

Data can not only be stored in different file formats (e.g. netCDF, GRIB), but also in different data structures. Besides its spatial dimension (e.g. 1D, 2D, 3D), we need to have a closer look at the grid and the topology used. As the time dependency of the data is encoded as the time dimension, a variable might be called a 3D variable although the spatial grid is only 2D.

Curvilinear de-rotated grid

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Deprecated Python environments

Original post.

Since several years, we are offering Python environments on Mistral. Many of them are not updated any more and should not be used for new development. However, older scripts may rely on these environments and the versions of their installed packages.

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CF Python package added to the software tree

According to this link:

The Python cf package is an Earth Science data analysis library that is built on a complete implementation of the CF data model. The cf package implements the CF data model 1 for its internal data structures and so is able to process any CF-compliant dataset. It is not strict about CF-compliance, however, so that partially conformant datasets may be ingested from existing datasets and written to new datasets. This is so that datasets that are partially conformant may nonetheless be modified in memory.

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Enable NCL Kernel in Jupyterhub

can’t use NCL (Python) as kernel in Jupyter

This tutorial won’t work

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GLIBC and the container-based workaround

Have you ever tried to install/use a software on Mistral and seen a message like this?

This is for example one of the reasons why PyTorch is not available in our python3 module. Those software packages require a newer version of glibc. Unfortunately, most of Mistral nodes are based on CentOS 6 kernel. To check the version of glibc:

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