def __init__(self, **kwargs):
self.__dict__.update(**kwargs)
force_gradient(use_gradient=self.use_gradient)
np.seterr(all='ignore')
self.datadir = 'data'
self.plotdir = 'plots'
self.seddir = 'seds'
for dir in [self.seddir, self.datadir, self.plotdir]:
if not os.path.exists(dir): os.makedirs(dir)
def __init__(self, **kwargs):
self.__dict__.update(**kwargs)
force_gradient(use_gradient=self.use_gradient)
np.seterr(all='ignore')
self.datadir='data'
self.plotdir='plots'
self.seddir='seds'
for dir in [self.seddir, self.datadir, self.plotdir]:
if not os.path.exists(dir): os.makedirs(dir)
def __init__(self, **kwargs):
self.__dict__.update(**kwargs)
self.input_kwargs = kwargs
force_gradient(use_gradient=False)
np.seterr(all='ignore')
self.dirdict = dict(data='data', plots='plots', seds='seds')
for dir in self.dirdict:
if not os.path.exists(dir): os.makedirs(dir)
self.radiopsr_loader = RadioPSRLoader(self.radiopsr_data, self.bigfile)
def __init__(self, **kwargs):
self.__dict__.update(**kwargs)
self.input_kwargs = kwargs
force_gradient(use_gradient=False)
np.seterr(all='ignore')
self.dirdict = dict(data='data',plots='plots',seds='seds')
for dir in self.dirdict:
if not os.path.exists(dir): os.makedirs(dir)
self.radiopsr_loader = RadioPSRLoader(self.radiopsr_data, self.bigfile)
def run(name, snrdata, latdata):
force_gradient(use_gradient=False)
roi = build_roi(name, snrdata, latdata)
results = dict()
kwargs = dict(plotdir='plotdir')
results['pointlike'] = pointlike_analysis(roi, name, **kwargs)
pointlike_plots(roi)
results['gtlike'] = gtlike_analysis(roi, name, **kwargs)
savedict(results, 'results_%s.yaml' % name)
def run(name, snrdata, latdata):
force_gradient(use_gradient=False)
roi = build_roi(name, snrdata, latdata)
results = dict()
kwargs = dict(plotdir='plotdir')
results['pointlike']=pointlike_analysis(roi,name, **kwargs)
pointlike_plots(roi)
results['gtlike']=gtlike_analysis(roi,name, **kwargs)
savedict(results, 'results_%s.yaml' % name)
from uw.like.roi_diffuse import DiffuseSource
from uw.like.roi_extended import ExtendedSource
from uw.like.Models import PowerLaw
from uw.like.SpatialModels import Disk
from uw.like.roi_monte_carlo import MonteCarlo
from uw.like.pointspec import DataSpecification, SpectralAnalysis
from uw.like.pointspec_helpers import PointSource
from lande.utilities.tools import savedict
from lande.fermi.likelihood.diffuse import get_sreekumar
from lande.fermi.data.catalogs import dict2fgl
from lande.fermi.likelihood.tools import force_gradient
from lande.fermi.likelihood.save import sourcedict, spectrum_to_dict, pointlike_model_to_flux
force_gradient(use_gradient=False)
parser = ArgumentParser()
parser.add_argument("i",type=int)
parser.add_argument("--type", required=True)
parser.add_argument("--index",type=float, required=True)
parser.add_argument("--min-flux",type=float, required=True)
parser.add_argument("--max-flux",type=float, required=True)
parser.add_argument("--min-extension",type=float, required=True)
parser.add_argument("--max-extension",type=float, required=True)
args=parser.parse_args()
i=args.i
min_flux_mc = args.min_flux
max_flux_mc = args.max_flux
