Setting and getting global preferences is done with the following functions:
Set global preferences for Brian
where ... is a list of keyword assignments.
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
brian_global_config.py somewhere in the Python path with the following
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.
g++compilers are probably the easiest option (use Cygwin on Windows machines). See also the
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-mathoptimisations 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
Falseif you are using Brian from an interactive shell like IDLE or IPython where each command has its own frame, otherwise set it to