cddm.conf

Configuration and constants

Module Contents

cddm.conf.get_home_dir()

Return user home directory

cddm.conf.HOMEDIR

Users home directory

cddm.conf.CDDM_CONFIG_DIR

Config directory where cddm.ini lives

cddm.conf.NUMBA_CACHE_DIR

Numba cache directory, where compiled functions are written

cddm.conf.CONF

Configuration file filename

cddm.conf.NUMBA_CACHE = False

Specifiess whether we use numba caching or not

cddm.conf.is_module_installed(name)

Checks whether module with name ‘name’ is istalled or not

cddm.conf.detect_number_of_cores()

detect_number_of_cores()

Detect the number of cores in this system.

Returns

out – The number of cores in this system.

Return type

int

cddm.conf.disable_mkl_threading()

Disables mkl threading.

cddm.conf.enable_mkl_threading()

Enables mkl threading.

class cddm.conf.CDDMConfig

Bases: object

DDMM settings are here. You should use the set_* functions in the conf.py module to set these values

cddm.conf.print_config()

Prints all compile-time and run-time configurtion parameters and settings.

cddm.conf.set_verbose(level)

Sets verbose level (0-2) used by compute functions.

cddm.conf.set_showlib(name)

Set library for live video view

cddm.conf.set_fftlib(name='numpy.fft')

Sets fft library. Returns previous setting.

cddm.conf.set_rfft2lib(name='numpy.fft')

Sets fft library. Returns previous setting.

cddm.conf.set_fft_threads(n)

Sets number of threads used in fft functions.

cddm.conf.set_numba_threads(n)

Sets number of threads used in numba-accelerated functions.