def loaded_response():
rsp = OGIPResponse(os.path.join(get_test_datasets_directory(),
'bn090217206',
'bn090217206_n6_weightedrsp.rsp'))
return rsp
def loaded_response():
rsp = OGIPResponse(
os.path.join(get_test_datasets_directory(), 'bn090217206',
'bn090217206_n6_weightedrsp.rsp'))
return rsp
import os
import numpy as np
import pytest
from conftest import get_test_datasets_directory
from threeML.io.file_utils import within_directory
from threeML.utils.time_interval import TimeIntervalSet
from threeML.utils.time_series.event_list import EventListWithDeadTime, EventList
__this_dir__ = os.path.join(os.path.abspath(os.path.dirname(__file__)))
datasets_dir = get_test_datasets_directory()
def is_within_tolerance(truth, value, relative_tolerance=0.01):
assert truth != 0
if abs((truth - value) / truth) <= relative_tolerance:
return True
else:
return False
def test_event_list_constructor():
dummy_times = np.linspace(0, 10, 10)
dummy_energy = np.zeros_like(dummy_times)
start = 0
stop = 10
import os
import numpy as np
import pytest
from conftest import get_test_datasets_directory
from threeML.io.file_utils import within_directory
from threeML.utils.time_interval import TimeIntervalSet
from threeML.utils.time_series.event_list import EventListWithDeadTime, EventList
__this_dir__ = os.path.join(os.path.abspath(os.path.dirname(__file__)))
datasets_dir = get_test_datasets_directory()
def is_within_tolerance(truth, value, relative_tolerance=0.01):
assert truth != 0
if abs((truth - value) / truth) <= relative_tolerance:
return True
else:
return False
def test_event_list_constructor():
dummy_times = np.linspace(0, 10, 10)
dummy_energy = np.zeros_like(dummy_times)
start = 0
stop = 10
evt_list = EventList(arrival_times=dummy_times,
import pytest
from conftest import get_test_datasets_directory
from threeML import *
from threeML.io.file_utils import within_directory
from threeML.plugins.OGIPLike import OGIPLike
from threeML.plugins.SwiftXRTLike import SwiftXRTLike
from threeML.utils.OGIP.response import OGIPResponse
from threeML.utils.spectrum.pha_spectrum import PHASpectrum
from threeML.utils.statistics.likelihood_functions import *
__this_dir__ = os.path.join(os.path.abspath(os.path.dirname(__file__)))
__example_dir = get_test_datasets_directory()
class AnalysisBuilder(object):
def __init__(self, plugin):
self._plugin = plugin
self._shapes = {}
self._shapes['normal'] = Powerlaw
self._shapes['cpl'] = Cutoff_powerlaw
@property
def keys(self):
return self._shapes.keys()
def get_jl(self, key):
assert key in self._shapes
data_list = DataList(self._plugin)