diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/__init__.py | 0 | ||||
| -rw-r--r-- | utils/enums.py | 68 | ||||
| -rw-r--r-- | utils/fr.py | 353 | ||||
| -rw-r--r-- | utils/gf.py | 105 | ||||
| -rw-r--r-- | utils/mcmc.py | 167 | ||||
| -rw-r--r-- | utils/misc.py | 230 |
6 files changed, 923 insertions, 0 deletions
diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/utils/__init__.py diff --git a/utils/enums.py b/utils/enums.py new file mode 100644 index 0000000..31885de --- /dev/null +++ b/utils/enums.py @@ -0,0 +1,68 @@ +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : March 17, 2018 + +""" +Define Enums for the BSM flavour ratio analysis +""" + +from enum import Enum + + +class DataType(Enum): + REAL = 1 + FAKE = 2 + ASMIMOV = 3 + + +class FitCateg(Enum): + HESESPL = 1 + HESEDPL = 2 + ZPSPL = 3 + ZPDPL = 4 + NUNUBAR2 = 5 + + +class FitFlagCateg(Enum): + DEFAULT = 1 + XS = 2 + + +class FitPriorsCateg(Enum): + DEFAULT = 1 + XS = 2 + + +class Likelihood(Enum): + FLAT = 1 + GAUSSIAN = 2 + GOLEMFIT = 3 + + +class Priors(Enum): + UNIFORM = 1 + LOGUNIFORM = 2 + JEFFREYS = 3 + + +class SteeringCateg(Enum): + BASELINE = 1 + HOLEICE = 2 + ABSORPTION = 3 + SCATTERING = 4 + SCATTERING_ABSORPTION = 5 + STD = 6 + STD_HALF1 = 7 + STD_HALF2 = 8 + LONGLIFE = 9 + DPL = 10 + + +class XSCateg(Enum): + HALF = 1 + NOM = 2 + TWICE = 3 + TWICE01 = 4 + TWICE02 = 5 + diff --git a/utils/fr.py b/utils/fr.py new file mode 100644 index 0000000..ddcb5d2 --- /dev/null +++ b/utils/fr.py @@ -0,0 +1,353 @@ +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : March 17, 2018 + +""" +Useful functions for the BSM flavour ratio analysis +""" + +from __future__ import absolute_import, division + +import sys + +import numpy as np +from scipy import linalg + + +MASS_EIGENVALUES = [7.40E-23, 2.515E-21] +"""SM mass eigenvalues""" + + +def angles_to_fr(src_angles): + """Convert angular projection of the source flavour ratio back into the + flavour ratio. + + Parameters + ---------- + src_angles : list, length = 2 + sin(phi)^4 and cos(psi)^2 + + Returns + ---------- + flavour ratios (nue, numu, nutau) + + Examples + ---------- + >>> print angles_to_fr((0.3, 0.4)) + (0.38340579025361626, 0.16431676725154978, 0.45227744249483393) + + """ + sphi4, c2psi = src_angles + + psi = (0.5)*np.arccos(c2psi) + + sphi2 = np.sqrt(sphi4) + cphi2 = 1. - sphi2 + spsi2 = np.sin(psi)**2 + cspi2 = 1. - spsi2 + + x = sphi2*cspi2 + y = sphi2*spsi2 + z = cphi2 + return x, y, z + + +def angles_to_u(bsm_angles): + """Convert angular projection of the mixing matrix elements back into the + mixing matrix elements. + + Parameters + ---------- + bsm_angles : list, length = 4 + sin(12)^2, cos(13)^4, sin(23)^2 and deltacp + + Returns + ---------- + unitary numpy ndarray of shape (3, 3) + + Examples + ---------- + >>> from fr import angles_to_u + >>> print angles_to_u((0.2, 0.3, 0.5, 1.5)) + array([[ 0.66195018+0.j , 0.33097509+0.j , 0.04757188-0.6708311j ], + [-0.34631487-0.42427084j, 0.61741198-0.21213542j, 0.52331757+0.j ], + [ 0.28614067-0.42427084j, -0.64749908-0.21213542j, 0.52331757+0.j ]]) + + """ + s12_2, c13_4, s23_2, dcp = bsm_angles + dcp = np.complex128(dcp) + + c12_2 = 1. - s12_2 + c13_2 = np.sqrt(c13_4) + s13_2 = 1. - c13_2 + c23_2 = 1. - s23_2 + + t12 = np.arcsin(np.sqrt(s12_2)) + t13 = np.arccos(np.sqrt(c13_2)) + t23 = np.arcsin(np.sqrt(s23_2)) + + c12 = np.cos(t12) + s12 = np.sin(t12) + c13 = np.cos(t13) + s13 = np.sin(t13) + c23 = np.cos(t23) + s23 = np.sin(t23) + + p1 = np.array([[1 , 0 , 0] , [0 , c23 , s23] , [0 , -s23 , c23]] , dtype=np.complex128) + p2 = np.array([[c13 , 0 , s13*np.exp(-1j*dcp)] , [0 , 1 , 0] , [-s13*np.exp(1j*dcp) , 0 , c13]] , dtype=np.complex128) + p3 = np.array([[c12 , s12 , 0] , [-s12 , c12 , 0] , [0 , 0 , 1]] , dtype=np.complex128) + + u = np.dot(np.dot(p1, p2), p3) + return u + + +def cardano_eqn(ham): + """Diagonalise the effective Hamiltonian 3x3 matrix into the form + h_{eff} = UE_{eff}U^{dagger} using the procedure in PRD91, 052003 (2015). + + Parameters + ---------- + ham : numpy ndarray of shape (3, 3) + sin(12)^2, cos(13)^4, sin(23)^2 and deltacp + + Returns + ---------- + unitary numpy ndarray of shape (3, 3) + + Examples + ---------- + >>> import numpy as np + >>> from fr import cardano_eqn + >>> ham = np.array( + >>> [[ 0.66195018+0.j , 0.33097509+0.j , 0.04757188-0.6708311j ], + >>> [-0.34631487-0.42427084j, 0.61741198-0.21213542j, 0.52331757+0.j ], + >>> [ 0.28614067-0.42427084j, -0.64749908-0.21213542j, 0.52331757+0.j ]] + >>> ) + >>> print cardano_eqn(ham) + array([[-0.11143379-0.58863683j, -0.09067747-0.48219068j, 0.34276625-0.08686465j], + [ 0.14835519+0.47511473j, -0.18299305+0.40777481j, 0.31906300+0.82514223j], + [-0.62298966+0.07231745j, -0.61407815-0.42709603j, 0.03660313+0.30160428j]]) + + """ + if np.shape(ham) != (3, 3): + raise ValueError( + 'Input matrix should be a square and dimension 3, ' + 'got\n{0}'.format(ham) + ) + + a = -np.trace(ham) + b = (0.5) * ((np.trace(ham))**2 - np.trace(np.dot(ham, ham))) + c = -linalg.det(ham) + + Q = (1/9.) * (a**2 - 3*b) + R = (1/54.) * (2*a**3 - 9*a*b + 27*c) + theta = np.arccos(R / np.sqrt(Q**3)) + + E1 = -2 * np.sqrt(Q) * np.cos(theta/3.) - (1/3.)*a + E2 = -2 * np.sqrt(Q) * np.cos((theta - 2.*np.pi)/3.) - (1/3.)*a + E3 = -2 * np.sqrt(Q) * np.cos((theta + 2.*np.pi)/3.) - (1/3.)*a + + A1 = ham[1][2] * (ham[0][0] - E1) - ham[1][0]*ham[0][2] + A2 = ham[1][2] * (ham[0][0] - E2) - ham[1][0]*ham[0][2] + A3 = ham[1][2] * (ham[0][0] - E3) - ham[1][0]*ham[0][2] + + B1 = ham[2][0] * (ham[1][1] - E1) - ham[2][1]*ham[1][0] + B2 = ham[2][0] * (ham[1][1] - E2) - ham[2][1]*ham[1][0] + B3 = ham[2][0] * (ham[1][1] - E3) - ham[2][1]*ham[1][0] + + C1 = ham[1][0] * (ham[2][2] - E1) - ham[1][2]*ham[2][0] + C2 = ham[1][0] * (ham[2][2] - E2) - ham[1][2]*ham[2][0] + C3 = ham[1][0] * (ham[2][2] - E3) - ham[1][2]*ham[2][0] + + N1 = np.sqrt(abs(A1*B1)**2 + abs(A1*C1)**2 + abs(B1*C1)**2) + N2 = np.sqrt(abs(A2*B2)**2 + abs(A2*C2)**2 + abs(B2*C2)**2) + N3 = np.sqrt(abs(A3*B3)**2 + abs(A3*C3)**2 + abs(B3*C3)**2) + + mm = np.array([ + [np.conjugate(B1)*C1 / N1, np.conjugate(B2)*C2 / N2, np.conjugate(B3)*C3 / N3], + [A1*C1 / N1, A2*C2 / N2, A3*C3 / N3], + [A1*B1 / N1, A2*B2 / N2, A3*B3 / N3] + ]) + return mm + + +def normalise_fr(fr): + """Normalise an input flavour combination to a flavour ratio. + + Parameters + ---------- + fr : list, length = 3 + flavour combination + + Returns + ---------- + numpy ndarray flavour ratio + + Examples + ---------- + >>> from fr import normalise_fr + >>> print normalise_fr((1, 2, 3)) + array([ 0.16666667, 0.33333333, 0.5 ]) + + """ + return np.array(fr) / float(np.sum(fr)) + + +NUFIT_U = angles_to_u((0.307, (1-0.02195)**2, 0.565, 3.97935)) +"""NuFIT mixing matrix (s_12^2, c_13^4, s_23^2, dcp)""" + + +def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, + nufit_u=NUFIT_U, no_bsm=False, fix_mixing=False, + fix_scale=False, scale=None, check_uni=True): + """Construct the BSM mixing matrix from the BSM parameters. + + Parameters + ---------- + theta : list, length > 3 + BSM parameters + + dim : int + Dimension of BSM physics + + energy : float + Energy in GeV + + mass_eigenvalues : list, length = 2 + SM mass eigenvalues + + nufit_u : numpy ndarray, dimension 3 + SM NuFIT mixing matrix + + no_bsm : bool + Turn off BSM behaviour + + fix_mixing : bool + Fix the BSM mixing angles + + fix_scale : bool + Fix the BSM scale + + scale : float + Used with fix_scale - scale at which to fix + + check_uni : bool + Check the resulting BSM mixing matrix is unitary + + Returns + ---------- + unitary numpy ndarray of shape (3, 3) + + Examples + ---------- + >>> from fr import params_to_BSMu + >>> print params_to_BSMu((0.2, 0.3, 0.5, 1.5, -20), dim=3, energy=1000) + array([[ 0.18658169 -6.34190523e-01j, -0.26460391 +2.01884200e-01j, 0.67247096 -9.86808417e-07j], + [-0.50419832 +2.14420570e-01j, -0.36013768 +5.44254868e-01j, 0.03700961 +5.22039894e-01j], + [-0.32561308 -3.95946524e-01j, 0.64294909 -2.23453580e-01j, 0.03700830 +5.22032403e-01j]]) + + """ + if np.shape(nufit_u) != (3, 3): + raise ValueError( + 'Input matrix should be a square and dimension 3, ' + 'got\n{0}'.format(ham) + ) + + if fix_mixing: + s12_2, c13_4, s23_2, dcp, sc2 = 0.5, 1.0-1E-6, 0.5, 0., theta + elif fix_scale: + s12_2, c13_4, s23_2, dcp = theta + sc2 = np.log10(scale) + else: + s12_2, c13_4, s23_2, dcp, sc2 = theta + sc2 = np.power(10., sc2) + sc1 = sc2 / 100. + + mass_matrix = np.array( + [[0, 0, 0], [0, mass_eigenvalues[0], 0], [0, 0, mass_eigenvalues[1]]] + ) + sm_ham = (1./(2*energy))*np.dot(nufit_u, np.dot(mass_matrix, nufit_u.conj().T)) + if no_bsm: + eg_vector = cardano_eqn(sm_ham) + else: + new_physics_u = angles_to_u((s12_2, c13_4, s23_2, dcp)) + scale_matrix = np.array( + [[0, 0, 0], [0, sc1, 0], [0, 0, sc2]] + ) + bsm_term = (energy**(dim-3)) * np.dot(new_physics_u, np.dot(scale_matrix, new_physics_u.conj().T)) + + bsm_ham = sm_ham + bsm_term + eg_vector = cardano_eqn(bsm_ham) + + if check_uni: + tu = test_unitarity(eg_vector) + if not abs(np.trace(tu) - 3.) < 1e-5 or \ + not abs(np.sum(tu) - 3.) < 1e-5: + raise AssertionError( + 'Matrix is not unitary!\neg_vector\n{0}\ntest ' + 'u\n{1}'.format(eg_vector, tu) + ) + return eg_vector + + +def test_unitarity(x, prnt=False): + """Test the unitarity of a matrix. + + Parameters + ---------- + x : numpy ndarray + Matrix to evaluate + + prnt : bool + Print the result + + Returns + ---------- + numpy ndarray + + Examples + ---------- + >>> from fr import test_unitarity + >>> x = np.identity(3) + >>> print test_unitarity(x) + array([[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]) + + """ + f = abs(np.dot(x, x.conj().T)) + if prnt: + print 'Unitarity test:\n{0}'.format(f) + return f + + +def u_to_fr(source_fr, matrix): + """Compute the observed flavour ratio assuming decoherence. + + Parameters + ---------- + source_fr : list, length = 3 + Source flavour ratio components + + matrix : numpy ndarray, dimension 3 + Mixing matrix + + Returns + ---------- + Measured flavour ratio + + Examples + ---------- + >>> from fr import params_to_BSMu, u_to_fr + >>> print u_to_fr((1, 2, 0), params_to_BSMu((0.2, 0.3, 0.5, 1.5, -20), 3, 1000)) + array([ 0.33740075, 0.33176584, 0.33083341]) + + """ + # TODO(shivesh): energy dependence + composition = np.einsum( + 'ai, bi, a -> b', abs(matrix)**2, abs(matrix)**2, source_fr + ) + ratio = composition / np.sum(source_fr) + return ratio + diff --git a/utils/gf.py b/utils/gf.py new file mode 100644 index 0000000..766c161 --- /dev/null +++ b/utils/gf.py @@ -0,0 +1,105 @@ +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : March 17, 2018 + +""" +Useful GolemFit wrappers for the BSM flavour ratio analysis +""" + +from __future__ import absolute_import, division + +import argparse +from functools import partial + +import GolemFitPy as gf + +from utils.enums import * +from utils.misc import enum_keys, enum_parse + + +def data_distributions(fitter): + hdat = fitter.GetDataDistribution() + binedges = np.asarray([fitter.GetZenithBinsData(), fitter.GetEnergyBinsData()]) + return hdat, binedges + + +def fit_flags(fitflag_categ): + flags = gf.FitParametersFlag() + if fitflag_categ is FitFlagCateg.xs: + # False means it's not fixed in minimization + flags.NeutrinoAntineutrinoRatio = False + return flags + + +def fit_params(fit_categ): + params = gf.FitParameters() + params.astroNorm = 7.5 + params.astroDeltaGamma = 0.9 + if fit_categ is FitCateg.hesespl: + params.astroNormSec = 0 + elif fit_categ is FitCateg.hesedpl: + params.astroNormSec=7.0 + elif fit_categ is FitCateg.zpspl: + # zero prompt, single powerlaw + params.promptNorm = 0 + params.astroNormSec = 0 + elif fit_categ is FitCateg.zpdpl: + # zero prompt, double powerlaw + params.promptNorm = 0 + params.astroNormSec=7.0 + elif fit_categ is FitCateg.nunubar2: + params.NeutrinoAntineutrinoRatio = 2 + + +def fit_priors(fitpriors_categ): + priors = gf.Priors() + if fitpriors_categ == FitPriorsCateg.xs: + priors.promptNormCenter = 1 + priors.promptNormWidth = 3 + priors.astroDeltaGammaCenter = 0 + priors.astroDeltaGammaWidth = 1 + return priors + + +def gen_steering_params(steering_categ, quiet=False): + params = gf.SteeringParams() + if quiet: params.quiet = True + params.fastmode = False + params.do_HESE_reshuffle = False + params.numc_tag = steering_categ.name + params.baseline_astro_spectral_index = -2. + if steering_categ is SteeringCateg.LONGLIFE: + params.years = [999] + params.numc_tag = 'std_half1' + if steering_categ is SteeringCateg.DPL: + params.diffuse_fit_type = gf.DiffuseFitType.DoublePowerLaw + params.numc_tag = 'std_half1' + return params + + +def gf_argparse(parser): + parser.add_argument( + '--data', default='real', type=partial(enum_parse, c=DataType), + choices=enum_keys(DataType), help='select datatype' + ) + parser.add_argument( + '--ast', default='baseline', type=partial(enum_parse, c=SteeringCateg), + choices=enum_keys(SteeringCateg), + help='use asimov/fake dataset with specific steering' + ) + parser.add_argument( + '--aft', default='hesespl', type=partial(enum_parse, c=FitCateg), + choices=enum_keys(FitCateg), + help='use asimov/fake dataset with specific Fit' + ) + parser.add_argument( + '--axs', default='nom', type=partial(enum_parse, c=XSCateg), + choices=enum_keys(XSCateg), + help='use asimov/fake dataset with xs scaling' + ) + parser.add_argument( + '--priors', default='uniform', type=partial(enum_parse, c=Priors), + choices=enum_keys(Priors), help='Bayesian priors' + ) + diff --git a/utils/mcmc.py b/utils/mcmc.py new file mode 100644 index 0000000..d91764f --- /dev/null +++ b/utils/mcmc.py @@ -0,0 +1,167 @@ +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : March 17, 2018 + +""" +Useful functions to use an MCMC for the BSM flavour ratio analysis +""" + +from __future__ import absolute_import, division + +import os +import errno + +import argparse +from functools import partial + +import emcee +import tqdm + +import numpy as np +from scipy.stats import multivariate_normal + +from utils import fr as fr_utils +from utils.enums import Likelihood +from utils.misc import enum_keys, enum_parse, parse_bool + + +def lnprior(theta, paramset): + """Priors on theta.""" + ranges = paramset.ranges + for value, range in zip(theta, ranges): + if range[0] <= value <= range[1]: + pass + else: return -np.inf + return 0. + + +def mcmc(p0, triangle_llh, lnprior, ndim, nwalkers, burnin, nsteps, ntemps=1, threads=1): + """Run the MCMC.""" + sampler = emcee.PTSampler( + ntemps, nwalkers, ndim, triangle_llh, lnprior, threads=threads + ) + + print "Running burn-in" + for result in tqdm.tqdm(sampler.sample(p0, iterations=burnin), total=burnin): + pos, prob, state = result + sampler.reset() + print "Finished burn-in" + + print "Running" + for _ in tqdm.tqdm(sampler.sample(pos, iterations=nsteps), total=nsteps): + pass + print "Finished" + + samples = sampler.chain[0, :, :, :].reshape((-1, ndim)) + print 'acceptance fraction', sampler.acceptance_fraction + print 'sum of acceptance fraction', np.sum(sampler.acceptance_fraction) + print 'np.unique(samples[:,0]).shape', np.unique(samples[:,0]).shape + try: + print 'autocorrelation', sampler.acor + except: + print 'WARNING : NEED TO RUN MORE SAMPLES' + + return samples + + +def mcmc_argparse(parser): + parser.add_argument( + '--likelihood', default='gaussian', type=partial(enum_parse, c=Likelihood), + choices=enum_keys(Likelihood), help='likelihood contour' + ) + parser.add_argument( + '--run-mcmc', type=parse_bool, default='True', + help='Run the MCMC' + ) + parser.add_argument( + '--burnin', type=int, default=100, + help='Amount to burnin' + ) + parser.add_argument( + '--nwalkers', type=int, default=100, + help='Number of walkers' + ) + parser.add_argument( + '--nsteps', type=int, default=2000, + help='Number of steps to run' + ) + + +def gaussian_llh(fr, fr_bf, sigma): + """Multivariate gaussian likelihood.""" + cov_fr = np.identity(3) * sigma + return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr)) + + +def gaussian_seed(paramset, ntemps, nwalkers): + """Get gaussian seed values for the MCMC.""" + ndim = len(paramset) + p0 = np.random.normal( + paramset.values, paramset.stds, size=[ntemps, nwalkers, ndim] + ) + return p0 + + +def save_chains(chains, outfile): + """Save the chains. + + Parameters + ---------- + chains : numpy ndarray + MCMC chains to save + + outfile : str + Output file location of chains + + """ + try: + os.makedirs(outfile[:-len(os.path.basename(outfile))]) + except OSError as exc: # Python >2.5 + if exc.errno == errno.EEXIST and os.path.isdir(outfile[:-len(os.path.basename(outfile))]): + pass + else: + raise + print 'Saving chains to location {0}'.format(outfile+'.npy') + np.save(outfile+'.npy', chains) + + +def triangle_llh(theta, args): + """-Log likelihood function for a given theta.""" + if args.fix_source_ratio: + fr1, fr2, fr3 = args.source_ratio + bsm_angles = theta[-5:] + else: + fr1, fr2, fr3 = fr_utils.angles_to_fr(theta[-2:]) + bsm_angles = theta[-7:-2] + + u = fr_utils.params_to_BSMu( + theta = bsm_angles, + dim = args.dimension, + energy = args.energy, + no_bsm = args.no_bsm, + fix_mixing = args.fix_mixing, + fix_scale = args.fix_scale, + scale = args.scale + ) + + fr = fr_utils.u_to_fr((fr1, fr2, fr3), u) + fr_bf = args.measured_ratio + if args.likelihood is Likelihood.FLAT: + return 1. + elif args.likelihood is Likelihood.GAUSSIAN: + return gaussian_llh(fr, fr_bf, args.sigma_ratio) + elif args.likelihood is Likelihood.GOLEMFIT: + raise NotImplementedError('TODO') + import GolemFitPy as gf + from collections import namedtuple + datapaths = gf.DataPaths() + IceModels = namedtuple('IceModels', 'std_half2') + fitters = IceModels(*[ + gf.GolemFit(datapaths, + gf.gen_steering_params(SteeringCateg.__members__[ice]), + xs_params(XSCateg.nom)) for ice in IceModels._fields]) + for fitter in fitters: + fitter.SetFitParametersFlag(fit_flags(FitFlagCateg.xs)) + fitter.SetFitPriors(fit_priors(FitPriorsCateg.xs)) + diff --git a/utils/misc.py b/utils/misc.py new file mode 100644 index 0000000..5c3eb2e --- /dev/null +++ b/utils/misc.py @@ -0,0 +1,230 @@ +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : March 17, 2018 + +""" +Misc functions for the BSM flavour ratio analysis +""" + +from __future__ import absolute_import, division + +import os +import errno +from collections import Sequence +import multiprocessing + +import numpy as np + +from utils.enums import Likelihood + + +class Param(object): + """Parameter class to store parameters. + """ + def __init__(self, name, value, ranges, std=None, tex=None): + self._ranges = None + self._tex = None + + self.name = name + self.value = value + self.ranges = ranges + self.std = std + self.tex = tex + + @property + def ranges(self): + return tuple(self._ranges) + + @ranges.setter + def ranges(self, values): + self._ranges = [val for val in values] + + @property + def tex(self): + return r'{0}'.format(self.tex) + + @tex.setter + def tex(self, t): + self._tex = t if t is not None else r'{\rm %s}' % self.name + + +class ParamSet(Sequence): + """Container class for a set of parameters. + """ + def __init__(self, *args): + param_sequence = [] + for arg in args: + try: + param_sequence.extend(arg) + except TypeError: + param_sequence.append(arg) + + # Disallow duplicated params + all_names = [p.name for p in param_sequence] + unique_names = set(all_names) + if len(unique_names) != len(all_names): + duplicates = set([x for x in all_names if all_names.count(x) > 1]) + raise ValueError('Duplicate definitions found for param(s): ' + + ', '.join(str(e) for e in duplicates)) + + # Elements of list must be Param type + assert all([isinstance(x, Param) for x in param_sequence]), \ + 'All params must be of type "Param"' + + self._params = param_sequence + + def __len__(self): + return len(self._params) + + def __getitem__(self, i): + if isinstance(i, int): + return self._params[i] + elif isinstance(i, basestring): + return self._by_name[i] + + def __getattr__(self, attr): + try: + return super(ParamSet, self).__getattribute__(attr) + except AttributeError: + t, v, tb = sys.exc_info() + try: + return self[attr] + except KeyError: + raise t, v, tb + + def __iter__(self): + return iter(self._params) + + @property + def _by_name(self): + return {obj.name: obj for obj in self._params} + + @property + def names(self): + return tuple([obj.name for obj in self._params]) + + @property + def values(self): + return tuple([obj.value for obj in self._params]) + + @property + def ranges(self): + return tuple([obj.ranges for obj in self._params]) + + @property + def stds(self): + return tuple([obj.std for obj in self._params]) + + @property + def params(self): + return self._params + + def to_dict(self): + return {obj.name: obj.value for obj in self._params} + + +def gen_outfile_name(args): + """Generate a name for the output file based on the input args. + + Parameters + ---------- + args : argparse + argparse object to print + + """ + mr = args.measured_ratio + si = args.sigma_ratio + if args.fix_source_ratio: + sr = args.source_ratio + if args.fix_mixing: + outfile = args.outfile+'_{0:03d}_{1:03d}_{2:03d}_{3:04d}_sfr_{4:03d}_{5:03d}_{6:03d}_DIM{7}_fix_mixing'.format( + int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), int(si*1000), + int(sr[0]*100), int(sr[1]*100), int(sr[2]*100), args.dimension + ) + elif args.fix_scale: + outfile = args.outfile+'_{0:03d}_{1:03d}_{2:03d}_{3:04d}_sfr_{4:03d}_{5:03d}_{6:03d}_DIM{7}_fixed_scale_{8}'.format( + int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), int(si*1000), + int(sr[0]*100), int(sr[1]*100), int(sr[2]*100), args.dimension, + args.scale + ) + else: + outfile = args.outfile+'_{0:03d}_{1:03d}_{2:03d}_{3:04d}_sfr_{4:03d}_{5:03d}_{6:03d}_DIM{7}_single_scale'.format( + int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), int(si*1000), + int(sr[0]*100), int(sr[1]*100), int(sr[2]*100), args.dimension + ) + else: + if args.fix_mixing: + outfile = args.outfile+'_{0:03d}_{1:03d}_{2:03d}_{3:04d}_DIM{4}_fix_mixing'.format( + int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), + int(si*1000), args.dimension + ) + elif args.fix_scale: + outfile = args.outfile+'_{0:03d}_{1:03d}_{2:03d}_{3:04d}_DIM{4}_fixed_scale_{5}'.format( + int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), + int(si*1000), args.dimension, args.scale + ) + else: + outfile = args.outfile+'_{0:03d}_{1:03d}_{2:03d}_{3:04d}_DIM{4}'.format( + int(mr[0]*100), int(mr[1]*100), int(mr[2]*100), + int(si*1000), args.dimension + ) + if args.likelihood is Likelihood.FLAT: outfile += '_flat' + return outfile + + +def parse_bool(s): + """Parse a string to a boolean. + + Parameters + ---------- + s : str + String to parse + + Returns + ---------- + bool + + Examples + ---------- + >>> from misc import parse_bool + >>> print parse_bool('true') + True + + """ + if s.lower() == 'true': + return True + elif s.lower() == 'false': + return False + else: + raise ValueError + + +def print_args(args): + """Print the input arguments. + + Parameters + ---------- + args : argparse + argparse object to print + + """ + arg_vars = vars(args) + for key in arg_vars.iterkeys(): + print '== {0:<25} = {1}'.format(key, arg_vars[key]) + + +def enum_keys(e): + return e.__members__.keys() + + +def enum_parse(s, c): + return c[s.upper()] + + +def thread_type(t): + if t.lower() == 'max': + return multiprocessing.cpu_count() + else: + return int(t) + |