import lenstronomy
import os
import yaml
import numpy as np
"""
From pyautolens:
Depending on if we're using a super computer, we want two different numba decorators:
If on laptop:
@numba.jit(nopython=True, cache=True, parallel=False)
If on super computer:
@numba.jit(nopython=True, cache=False, parallel=True)
"""
# in case the xdg library is installed, the import statement with pyxdg can raise an error
# to avoid it, we draw back to the ~/.config directory in case this import fails.
# TODO come up with more permanent solution of path to configuration directory
try:
from xdg.BaseDirectory import xdg_config_home
except ImportError:
xdg_config_home = '~/.config'
user_config_file = os.path.join(xdg_config_home, "lenstronomy", "config.yaml")
module_path = os.path.dirname(lenstronomy.__file__)
default_config_file = os.path.join(module_path, 'Conf', 'conf_default.yaml')
if os.path.exists(user_config_file ):
conf_file = user_config_file
else:
conf_file = default_config_file
with open(conf_file) as file:
# The FullLoader parameter handles the conversion from YAML
# scalar values to Python the dictionary format
conf = yaml.load(file, Loader=yaml.FullLoader)
numba_conf = conf['numba']
nopython = numba_conf['nopython']
cache = numba_conf['cache']
parallel = numba_conf['parallel']
numba_enabled = numba_conf['enable']
fastmath = numba_conf['fastmath']
error_model = numba_conf['error_model']
if numba_enabled:
try:
import numba
except ImportError:
numba_enabled = False
__all__ = ['jit', 'generated_jit']
[docs]def jit(nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model):
if numba_enabled:
def wrapper(func):
return numba.jit(func, nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model)
else:
def wrapper(func):
return func
return wrapper
[docs]def generated_jit(nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model):
""" Wrapper around numba.generated_jit. Allows you to redirect a function to another based on its type - see the Numba docs for more info"""
if numba_enabled:
def wrapper(func):
return numba.generated_jit(func, nopython=nopython, cache=cache, parallel=parallel, fastmath=fastmath, error_model=error_model)
else:
def wrapper(func):
return func
return wrapper
@generated_jit()
def nan_to_num(x, posinf=1e10, neginf=-1e10, nan=0.):
"""
Implements a Numba equivalent to np.nan_to_num (with copy=False!) array or scalar in Numba.
Behaviour is the same as np.nan_to_num with copy=False, although it only supports 1-dimensional arrays and scalar inputs.
"""
# The generated_jit part is necessary because of the need to support both arrays and scalars for all input functions.
if (isinstance(x, numba.types.Array) or isinstance(x, np.ndarray)) and x.ndim > 0:
return nan_to_num_arr if numba_enabled else nan_to_num_arr(x, posinf, neginf, nan)
else:
return nan_to_num_single if numba_enabled else nan_to_num_single(x, posinf, neginf, nan)
@jit()
def nan_to_num_arr(x, posinf=1e10, neginf=-1e10, nan=0.):
"""Part of the Numba implementation of np.nan_to_num - see nan_to_num"""
for i in range(len(x)):
if np.isnan(x[i]):
x[i] = nan
if np.isinf(x[i]):
if x[i] > 0:
x[i]=posinf
else:
x[i]=neginf
return x
@jit()
def nan_to_num_single(x, posinf=1e10, neginf=-1e10, nan=0.):
"""Part of the Numba implementation of np.nan_to_num - see nan_to_num"""
if np.isnan(x):
return nan
elif np.isinf(x):
if x > 0:
return posinf
else:
return neginf
else:
return x