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