Preferences

Functions

Setting and getting global preferences is done with the following functions:

brian.set_global_preferences(**kwds)

Set global preferences for Brian

Usage:

``set_global_preferences(...)``

where … is a list of keyword assignments.

brian.get_global_preference(k)

Get the value of the named global preference

Global configuration file

If you have a module named brian_global_config anywhere on your Python path, Brian will attempt to import it to define global preferences. For example, to automatically enable weave compilation for all your Brian projects, create a file brian_global_config.py somewhere in the Python path with the following contents:

from brian.globalprefs import *
set_global_preferences(useweave=True)

Global preferences for Brian

The following global preferences have been defined:

defaultclock = Clock(dt=0.1*msecond)
The default clock to use if none is provided or defined in any enclosing scope.
useweave_linear_diffeq = False
Whether to use weave C++ acceleration for the solution of linear differential equations. Note that on some platforms, typically older ones, this is faster and on some platforms, typically new ones, this is actually slower.
useweave = False
Defines whether or not functions should use inlined compiled C code where defined. Requires a compatible C++ compiler. The gcc and g++ compilers are probably the easiest option (use Cygwin on Windows machines). See also the weavecompiler global preference.
weavecompiler = gcc
Defines the compiler to use for weave compilation. On Windows machines, installing Cygwin is the easiest way to get access to the gcc compiler.
gcc_options = ['-ffast-math']
Defines the compiler switches passed to the gcc compiler. For gcc versions 4.2+ we recommend using -march=native. By default, the -ffast-math optimisations are turned on - if you need IEEE guaranteed results, turn this switch off.
openmp = False
Whether or not to use OpenMP pragmas in generated C code. If supported on your compiler (gcc 4.2+) it will use multiple CPUs and can run substantially faster. However, if you are already running several simulations in parallel this will not improve the speed and may even slow it down. In addition, for smaller networks or for simpler neuron models the parallelisation overheads can make it take longer.
usecodegen = False
Whether or not to use experimental code generation support.
usecodegenweave = False
Whether or not to use C with experimental code generation support.
usecodegenstateupdate = True
Whether or not to use experimental code generation support on state updaters.
usecodegenreset = False
Whether or not to use experimental code generation support on resets. Typically slower due to weave overheads, so usually leave this off.
usecodegenthreshold = True
Whether or not to use experimental code generation support on thresholds.
usenewpropagate = False
Whether or not to use experimental new C propagation functions.
usecstdp = False
Whether or not to use experimental new C STDP.
brianhears_usegpu = False
Whether or not to use the GPU (if available) in Brian.hears. Support is experimental at the moment, and requires the PyCUDA package to be installed.
magic_useframes = True
Defines whether or not the magic functions should search for objects defined only in the calling frame or if they should find all objects defined in any frame. This should be set to False if you are using Brian from an interactive shell like IDLE or IPython where each command has its own frame, otherwise set it to True.