Wed 11 March 2015

Filed under Blog

Tags python ogr gdal

So I re-compiled the Python GDAL packages to bump the version to 1.11.2, compile in the HDF4 tweak that we required to open more than 32 HDF files simultaneously, and make sure that GDAL works with MODIS data. There's still a glitch, which means that I have compiled the library against GEOS v3.3.3 rather than v3.4.2, but since this isn't a major issue for me at the moment, I won't bother with it at the moment.

Here's how to get things going using the Miniconda Python installation (you can also install all the other packages in Continuum's repositories, but these are the ones that we mostly use):

wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
bash ./Miniconda-latest-Linux-x86_64.sh
export PATH=/opt/anaconda/bin:$PATH
conda install numpy scipy matplotlib ipython ipython-notebook conda-build
conda install scikit-learn pandas spyder numba numexpr cython binstar
conda install -c https://conda.binstar.org/jgomezdans proj4
conda install -c https://conda.binstar.org/jgomezdans hdf4
conda install -c https://conda.binstar.org/jgomezdans gdal
conda install geos

As you can see, the last line install geos again (v3.3.3), otherwise, GDAL complains. This is a minor niggle, and I can open both netcdf and HDF files, as well as GeoTIFFs and so on.

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Thu 29 January 2015

Filed under Blog

Tags python ogr gdal

Today I had the unenviable task of stitching together and reprojecting a large number of Shapefiles (yes, shapefiles are still used. Nothing has changed since 1992, I guess). The process is greatly simplified by using OGR's virtual datasets for vectors in a way that is reminiscent of how you ...

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Tue 02 September 2014

Filed under Blog

Tags python gdal linux

Installing GDAL and its Python bindings on Linux is typically quite easy, provided you are using Debian or Ubuntu, as they already have packages compiled for them. The packages are compiled so as to support a large number of different file formats. However, if you are using other Linux distributions ...

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Sun 23 March 2014

Filed under Blog

Tags python radiative transfer

I use radiative transfer (RT) models quite extensively. While most of the ones you might want to use practically are not very complicated to get your head around, they are numerically expensive (what with all these numerical integration going on). So while you could code them up in Python, it ...

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Tue 03 September 2013

Filed under Blog

Tags MODIS python remote-sensing

This post introduces simple BRDF modelling (i.e. how can we account for the variation in surface reflectance due to acquisition geometry?) using some MODIS data. You can find the whole post in wakari. If you accept to not escape the HTML & JS, you will find a link to the ...

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