The PCRaster team, in particular Koko Alberti who recently joined our team, has developed prototype software to run PCRaster models as web simulations, at a very high (~90 m) resolution, for almost any location on earth! The current facility includes prototype models for sea level change, snow cover, and water erosion. The web simulations are available here (login required). For information and to request a login, please email email@example.com.
In a previous post we mentioned Canopy as a Python distribution that can be used to develop PCRaster Python models. The PCRaster Python package is compatible with other Python distributions though. One user informed us that he liked using Anaconda, and at our institute we installed WinPython for our students.
Yesterday, Oliver Schmitz, one of our team members, received his PhD at Utrecht University. The title of his PhD thesis is “Integrating environmental component models. Development of a software framework”. You can reach him at firstname.lastname@example.org if you would like to receive his PhD thesis (or to congratulate him!). A short description of his thesis research is here.
We are glad to announce the final release of PCRaster-4.0.1! We fixed several bugs, some of them might affect model outcomes. Please read the changes document carefully. In addition, this is the first release fully supporting 64-bit Windows systems.
For more information, visit the PCRaster 4.0.1 download page:
We have updated our course material. Have a look at the Courses section for two new distance learning courses on PCRaster Python; without tutor support the courses are free of charge.
We are happy to announce the final release of PCRaster-4.0.0! For more information, visit the PCRaster 4.0.0 download page.
It is possible that your PCRaster models are not executing as fast as the could on your Linux system. That can happen because the default memory allocation and de-allocation rules that are in effect on Linux are optimized for system wide efficiency, instead of raw performance. For more information about this, see this document.
You can tune the memory allocation and de-allocation logic using environment variables (note the trailing underscore):
export MALLOC_MMAP_MAX_=0 export MALLOC_TRIM_THRESHOLD_=-1
You may want to check if these values help you to squeeze a bit of extra performance out of your models.
I have released a second test version of the upcoming PCRaster-4.0.0 release. See the PCRaster 4.0.0 download page for more information. This version has a much faster numpy2pcr operation and we fixed a bug in the resample command. Unless an important issue is found with this release, the next release will be the final 4.0.0 release.
I have released a test version of the upcoming PCRaster-4.0.0 release. See the PCRaster 4.0.0 download page for more information.