# 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("display.max_columns", None)
import os
import traceback
import warnings
# Workaround to avoid a segmentation fault with ROOT and a CFITSIO issue
# LEAVE THESE HERE BEFORE ANY THREEML IMPORT
try:
import ROOT
except ImportError:
pass
try:
import pyLikelihood
except ImportError:
pass
from pathlib import Path
from threeML.io.logging import setup_logger
from .config import (
threeML_config,
show_configuration,
get_current_configuration_copy,
)
log = setup_logger(__name__)
log.propagate = False
if threeML_config["logging"]["startup_warnings"]:
log.info("Starting 3ML!")
log.warning("WARNINGs here are [red]NOT[/red] errors")
log.warning(
"but are inform you about optional packages that can be installed"
)
log.warning(
"[red] to disable these messages, turn off start_warning in your config file[/red]"
)
if os.environ.get("DISPLAY") is None:
if threeML_config["logging"]["startup_warnings"]:
log.warning(
"no display variable set. using backend for graphics without display (agg)"
)
import matplotlib as mpl
mpl.use("Agg")
# 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
import traceback
from importlib.machinery import SourceFileLoader
# Import everything from astromodels
from astromodels import *
# Finally import the serialization machinery
from .io.serialization import *
# Now import the optimizers first (to avoid conflicting libraries problems)
from .minimizer.minimization import (
GlobalMinimization,
LocalMinimization,
_minimizers,
)
from .plugin_prototype import PluginPrototype
# from .exceptions.custom_exceptions import custom_warnings
# 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)
try:
# noinspection PyUnresolvedReferences
from cthreeML.pyModelInterfaceCache import pyToCppModelInterfaceCache
except ImportError:
if threeML_config.logging.startup_warnings:
log.warning(
"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:
_ = SourceFileLoader("__", str(module_full_path)).load_module()
except:
return False, traceback.format_exc()
else:
return True, "%s imported ok" % module_full_path
plugins_dir = Path(__file__).parent / "plugins"
found_plugins = plugins_dir.glob("*.py")
# Filter out __init__
# found_plugins = filter(lambda x: str(x).find("__init__") < 0, found_plugins)
# Filter out __init__
found_plugins = filter(lambda x: str(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 = module_full_path.stem
is_importable, failure_traceback = is_module_importable(module_full_path)
if not is_importable:
if threeML_config.logging.startup_warnings:
log.warning(
f"Could not import plugin {module_full_path.name}. Do you have the relative instrument software installed "
"and configured?"
# custom_exceptions.CannotImportPlugin,
)
_not_working_plugins[plugin_name] = failure_traceback
continue
else:
# First get the instrument name
try:
exec(f"from threeML.plugins.{plugin_name} import __instrument_name")
except ImportError:
# This module does not contain a plugin, continue
continue
# Now import the plugin itself
import_command = (
f"from threeML.plugins.{plugin_name} import {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(f"{class_name} for {instrument}")
def _display_plugin_traceback(plugin):
if threeML_config.logging.startup_warnings:
log.warning(
"#############################################################"
)
log.warning("\nCouldn't import plugin %s" % plugin)
log.warning("\nTraceback:\n")
log.warning(_not_working_plugins[plugin])
log.warning(
"#############################################################"
)
[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:
log.error(f"Plugin {plugin} is not known")
raise RuntimeError()
# Import the classic Maximum Likelihood Estimation package
import os
import astropy.units as u
# Import the results loader
from threeML.analysis_results import (
convert_fits_analysis_result_to_hdf,
load_analysis_results,
load_analysis_results_hdf,
)
# Import catalogs
from threeML.catalogs import (
FermiGBMBurstCatalog,
FermiGBMTriggerCatalog,
FermiLATSourceCatalog,
FermiPySourceCatalog,
FermiLLEBurstCatalog,
SwiftGRBCatalog,
)
from threeML.io import (
activate_warnings,
silence_warnings,
update_logging_level,
silence_logs,
silence_progress_bars,
activate_progress_bars,
toggle_progress_bars,
quiet_mode,
loud_mode,
debug_mode,
activate_logs,
)
from threeML.io.plotting.light_curve_plots import plot_tte_lightcurve
from threeML.io.plotting.model_plot import (
plot_point_source_spectra,
plot_spectra,
)
from threeML.io.plotting.post_process_data_plots import (
display_photometry_model_magnitudes,
display_spectrum_model_counts,
)
#
from threeML.io.uncertainty_formatter import interval_to_errors
# import time series builder, soon to replace the Fermi plugins
from threeML.utils.data_builders import *
from threeML.utils.data_download.Fermi_GBM.download_GBM_data import (
download_GBM_trigger_data,
download_GBM_daily_data,
)
# Import the LAT data downloader
from threeML.utils.data_download.Fermi_LAT.download_LAT_data import (
download_LAT_data,
)
# Import LLE downloader
from threeML.utils.data_download.Fermi_LAT.download_LLE_data import (
download_LLE_trigger_data,
)
# Import the Bayesian analysis
from .bayesian.bayesian_analysis import BayesianAnalysis
from .classicMLE.goodness_of_fit import GoodnessOfFit
from .classicMLE.joint_likelihood import JointLikelihood
# Import the joint likelihood set
from .classicMLE.joint_likelihood_set import (
JointLikelihoodSet,
JointLikelihoodSetAnalyzer,
)
from .classicMLE.likelihood_ratio_test import LikelihoodRatioTest
# Now read the configuration and make it available as threeML_config
from .data_list import DataList
from .io.calculate_flux import calculate_point_source_flux
# Import the plot_style context manager and the function to create new styles
from .parallel.parallel_client import parallel_computation
# Added by JM. step generator for time-resolved fits
from .utils.step_parameter_generator import step_generator
from .io import get_threeML_style, set_threeML_style
# Import optical filters
# from threeML.plugins.photometry.filter_factory import threeML_filter_library
# Import GBM downloader
# 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:
if threeML_config.logging.startup_warnings:
log.warning(
"Your env. variable %s is not an integer, which doesn't make sense. Set it to 1 "
"for optimum performances." % var,
# RuntimeWarning,
)
else:
if threeML_config.logging.startup_warnings:
log.warning(
"Env. variable %s is not set. Please set it to 1 for optimal performances in 3ML"
% var
# RuntimeWarning,
)
del os
del Path
del warnings
del SourceFileLoader