# We import matplotlib first, because we need control on the backend
# Indeed, if no DISPLAY variable is set, matplotlib 2.0 crashes (at the moment, 05/26/2017)
import pandas as pd
pd.set_option("max_columns", None)
import os
import warnings
if os.environ.get("DISPLAY") is None:
warnings.warn(
"No DISPLAY variable set. Using backend for graphics without display (Agg)"
)
import matplotlib as mpl
mpl.use("Agg")
# Workaround to a CFITSIO issue
try:
import ROOT
except ImportError:
pass
try:
import pyLikelihood
except ImportError:
pass
# Import version (this has to be placed before the import of serialization
# since __version__ needs to be defined at that stage)
from ._version import get_versions
__version__ = get_versions()["version"]
del get_versions
# Finally import the serialization machinery
from .io.serialization import *
from .exceptions.custom_exceptions import custom_warnings
import glob
import imp
import traceback
# Import everything from astromodels
from astromodels import *
# Now import the optimizers first (to avoid conflicting libraries problems)
from .minimizer.minimization import _minimizers, LocalMinimization, GlobalMinimization
# This must be here before the automatic import of subpackages,
# otherwise we will incur in weird issues with other packages
# using similar names (for example, the io package)
from .exceptions import custom_exceptions
from .plugin_prototype import PluginPrototype
try:
# noinspection PyUnresolvedReferences
from cthreeML.pyModelInterfaceCache import pyToCppModelInterfaceCache
except ImportError:
custom_warnings.warn(
"The cthreeML package is not installed. You will not be able to use plugins which require "
"the C/C++ interface (currently HAWC)",
custom_exceptions.CppInterfaceNotAvailable,
)
# Now look for plugins
# This verifies if a module is importable
[docs]def is_module_importable(module_full_path):
try:
_ = imp.load_source("__", module_full_path)
except:
return False, traceback.format_exc()
else:
return True, "%s imported ok" % module_full_path
plugins_dir = os.path.join(os.path.dirname(__file__), "plugins")
found_plugins = glob.glob(os.path.join(plugins_dir, "*.py"))
# Filter out __init__
found_plugins = filter(lambda x: x.find("__init__") < 0, found_plugins)
_working_plugins = {}
_not_working_plugins = {}
# Loop over each candidates plugins and check if it is importable
for i, module_full_path in enumerate(found_plugins):
plugin_name = os.path.splitext(os.path.basename(module_full_path))[0]
is_importable, failure_traceback = is_module_importable(module_full_path)
if not is_importable:
custom_warnings.warn(
"Could not import plugin %s. Do you have the relative instrument software installed "
"and configured?" % os.path.basename(module_full_path),
custom_exceptions.CannotImportPlugin,
)
_not_working_plugins[plugin_name] = failure_traceback
continue
else:
# First get the instrument name
try:
exec("from threeML.plugins.%s import __instrument_name" % plugin_name)
except ImportError:
# This module does not contain a plugin, continue
continue
# Now import the plugin itself
import_command = "from threeML.plugins.%s import %s" % (
plugin_name,
plugin_name,
)
try:
exec(import_command)
except ImportError:
pass
else:
_working_plugins[__instrument_name] = plugin_name
# Now some convenience functions
[docs]def get_available_plugins():
"""
Print a list of available plugins
:return:
"""
print("Available plugins:\n")
for instrument, class_name in _working_plugins.items():
print("%s for %s" % (class_name, instrument))
def _display_plugin_traceback(plugin):
print("#############################################################")
print("\nCouldn't import plugin %s" % plugin)
print("\nTraceback:\n")
print(_not_working_plugins[plugin])
print("#############################################################")
[docs]def is_plugin_available(plugin):
"""
Test whether the plugin for the provided instrument is available
:param plugin: the name of the plugin class
:return: True or False
"""
if plugin in _working_plugins.values():
# FIXME
if plugin == "FermipyLike":
try:
_ = FermipyLike.__new__(FermipyLike, test=True)
except:
# Do not register it
_not_working_plugins[plugin] = traceback.format_exc()
_display_plugin_traceback(plugin)
return False
return True
else:
if plugin in _not_working_plugins:
_display_plugin_traceback(plugin)
return False
else:
raise RuntimeError("Plugin %s is not known" % plugin)
# Import the classic Maximum Likelihood Estimation package
from .classicMLE.joint_likelihood import JointLikelihood
# Import the Bayesian analysis
from .bayesian.bayesian_analysis import BayesianAnalysis
# Import the DataList class
from .data_list import DataList
from threeML.io.plotting.model_plot import plot_spectra, plot_point_source_spectra
from threeML.io.plotting.light_curve_plots import plot_tte_lightcurve
from threeML.io.plotting.post_process_data_plots import (
display_spectrum_model_counts,
display_photometry_model_magnitudes,
)
# Import the joint likelihood set
from .classicMLE.joint_likelihood_set import (
JointLikelihoodSet,
JointLikelihoodSetAnalyzer,
)
from .classicMLE.likelihood_ratio_test import LikelihoodRatioTest
from .classicMLE.goodness_of_fit import GoodnessOfFit
from .io.calculate_flux import calculate_point_source_flux
# Added by JM. step generator for time-resolved fits
from .utils.step_parameter_generator import step_generator
from .parallel.parallel_client import parallel_computation
#
from threeML.io.uncertainty_formatter import interval_to_errors
# Import optical filters
# from threeML.plugins.photometry.filter_factory import threeML_filter_library
# import time series builder, soon to replace the Fermi plugins
from threeML.utils.data_builders import *
# Import catalogs
from threeML.catalogs import *
# Import GBM downloader
from threeML.utils.data_download.Fermi_GBM.download_GBM_data import (
download_GBM_trigger_data,
)
# Import LLE downloader
from threeML.utils.data_download.Fermi_LAT.download_LLE_data import (
download_LLE_trigger_data,
)
# Now read the configuration and make it available as threeML_config
from .config.config import threeML_config
import astropy.units as u
import os
# Import the LAT data downloader
from threeML.utils.data_download.Fermi_LAT.download_LAT_data import download_LAT_data
# Import the results loader
from threeML.analysis_results import load_analysis_results, load_analysis_results_hdf, convert_fits_analysis_result_to_hdf
# Import the plot_style context manager and the function to create new styles
from .io.plotting.plot_style import (
plot_style,
create_new_plotting_style,
get_available_plotting_styles,
)
# Check that the number of threads is set to 1 for all multi-thread libraries
# otherwise numpy operations will be way slower than what they could be, since
# we never perform huge numpy operations, we instead perform many of them. In this
# situation, opening threads introduces overhead with no performance gain. This solution
# allows cores to be used for multi-cpu computation with the parallel client
var_to_check = ["OMP_NUM_THREADS", "MKL_NUM_THREADS", "NUMEXPR_NUM_THREADS"]
for var in var_to_check:
num_threads = os.environ.get(var)
if num_threads is not None:
try:
num_threads = int(num_threads)
except ValueError:
custom_warnings.warn(
"Your env. variable %s is not an integer, which doesn't make sense. Set it to 1 "
"for optimum performances." % var,
RuntimeWarning,
)
else:
custom_warnings.warn(
"Env. variable %s is not set. Please set it to 1 for optimal performances in 3ML"
% var,
RuntimeWarning,
)