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| author | shivesh <s.p.mandalia@qmul.ac.uk> | 2019-04-25 17:01:48 +0100 |
|---|---|---|
| committer | shivesh <s.p.mandalia@qmul.ac.uk> | 2019-04-25 17:01:48 +0100 |
| commit | 349a48bf498c3cd342876eb9e66110fd8dbf8b9b (patch) | |
| tree | 63eec258df1b77da5a9d627f2e0865f83e0a8fb0 | |
| parent | ff83600b0ac2f2ed9f0270b905313ea42c90e3f3 (diff) | |
| download | GolemFlavor-349a48bf498c3cd342876eb9e66110fd8dbf8b9b.tar.gz GolemFlavor-349a48bf498c3cd342876eb9e66110fd8dbf8b9b.zip | |
refactor emcee scripts
| -rwxr-xr-x | fig2.py | 2 | ||||
| -rwxr-xr-x | mc_texture.py | 252 | ||||
| -rwxr-xr-x | mc_unitary.py (renamed from fr.py) | 21 | ||||
| -rw-r--r-- | plot_llh/LVCPT.py | 420 | ||||
| -rw-r--r-- | plot_llh/MinimalTools.py | 166 | ||||
| -rw-r--r-- | plot_llh/PhysConst.py | 390 | ||||
| -rw-r--r-- | plot_llh/angles_to_fr.py | 49 | ||||
| -rw-r--r-- | plot_llh/bayes_contours.csv | 7 | ||||
| -rw-r--r-- | plot_llh/make_plots.py | 118 | ||||
| -rw-r--r-- | plot_llh/sample.py | 145 | ||||
| -rw-r--r-- | plot_llh/trajectory.ipynb | 59 | ||||
| -rw-r--r-- | submitter/contour_dag.py | 3 | ||||
| -rw-r--r-- | submitter/mc_texture_dag.py | 73 | ||||
| -rw-r--r-- | submitter/mc_texture_submit.sub | 39 | ||||
| -rw-r--r-- | submitter/mcmc_dag.py | 123 | ||||
| -rw-r--r-- | submitter/mcmc_submit.sub | 41 | ||||
| -rw-r--r-- | utils/fr.py | 76 | ||||
| -rw-r--r-- | utils/llh.py | 49 |
18 files changed, 500 insertions, 1533 deletions
@@ -65,7 +65,7 @@ def parse_args(args=None): ) parser.add_argument( '--datadir', type=str, - help='Path to directory containing MultiNest runs' + help='Path to directory containing contour chains' ) if args is None: return parser.parse_args() else: return parser.parse_args(args.split()) diff --git a/mc_texture.py b/mc_texture.py new file mode 100755 index 0000000..a22b4d0 --- /dev/null +++ b/mc_texture.py @@ -0,0 +1,252 @@ +#! /usr/bin/env python +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : April 25, 2019 + +""" +Sample points for a specific scenario +""" + +from __future__ import absolute_import, division + +import argparse +from copy import deepcopy +from functools import partial + +import numpy as np + +from utils import fr as fr_utils +from utils import llh as llh_utils +from utils import mcmc as mcmc_utils +from utils import misc as misc_utils +from utils import plot as plot_utils +from utils.enums import MCMCSeedType, ParamTag, PriorsCateg, Texture +from utils.param import Param, ParamSet + + +def define_nuisance(): + """Define the nuisance parameters.""" + tag = ParamTag.SM_ANGLES + nuisance = [] + g_prior = PriorsCateg.GAUSSIAN + lg_prior = PriorsCateg.LIMITEDGAUSS + e = 1e-9 + nuisance.extend([ + Param(name='s_12_2', value=0.307, seed=[0.26, 0.35], ranges=[0., 1.], std=0.013, tex=r's_{12}^2', prior=lg_prior, tag=tag), + Param(name='c_13_4', value=(1-(0.02206))**2, seed=[0.950, 0.961], ranges=[0., 1.], std=0.00147, tex=r'c_{13}^4', prior=lg_prior, tag=tag), + Param(name='s_23_2', value=0.538, seed=[0.31, 0.75], ranges=[0., 1.], std=0.069, tex=r's_{23}^2', prior=lg_prior, tag=tag), + Param(name='dcp', value=4.08404, seed=[0+e, 2*np.pi-e], ranges=[0., 2*np.pi], std=2.0, tex=r'\delta_{CP}', tag=tag), + Param( + name='m21_2', value=7.40E-23, seed=[7.2E-23, 7.6E-23], ranges=[6.80E-23, 8.02E-23], + std=2.1E-24, tex=r'\Delta m_{21}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag + ), + Param( + name='m3x_2', value=2.494E-21, seed=[2.46E-21, 2.53E-21], ranges=[2.399E-21, 2.593E-21], + std=3.3E-23, tex=r'\Delta m_{3x}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag + ) + ]) + return ParamSet(nuisance) + + +def get_paramsets(args, nuisance_paramset): + """Make the paramsets for generating the Asmimov MC sample and also running + the MCMC. + """ + asimov_paramset = [] + llh_paramset = [] + + llh_paramset.extend( + [x for x in nuisance_paramset.from_tag(ParamTag.SM_ANGLES)] + ) + + for parm in llh_paramset: + parm.value = args.__getattribute__(parm.name) + + boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension] + tag = ParamTag.SCALE + llh_paramset.append( + Param( + name='logLam', value=np.mean(boundaries), ranges=boundaries, std=3, + tex=r'{\rm log}_{10}\left (\Lambda^{-1}' + \ + misc_utils.get_units(args.dimension)+r'\right )', + tag=tag + ) + ) + llh_paramset = ParamSet(llh_paramset) + + tag = ParamTag.BESTFIT + flavour_angles = fr_utils.fr_to_angles([1, 1, 1]) + asimov_paramset.extend([ + Param(name='astroFlavorAngle1', value=flavour_angles[0], ranges=[ 0., 1.], std=0.2, tag=tag), + Param(name='astroFlavorAngle2', value=flavour_angles[1], ranges=[-1., 1.], std=0.2, tag=tag), + ]) + asimov_paramset = ParamSet(asimov_paramset) + + return asimov_paramset, llh_paramset + + +def nuisance_argparse(parser): + nuisance = define_nuisance() + for parm in nuisance: + parser.add_argument( + '--'+parm.name, type=float, default=parm.value, + help=parm.name+' to inject' + ) + + +def process_args(args): + """Process the input args.""" + if args.texture is Texture.NONE: + raise ValueError('Must assume a BSM texture') + args.source_ratio = fr_utils.normalise_fr(args.source_ratio) + + args.binning = np.logspace( + np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1 + ) + + +def parse_args(args=None): + """Parse command line arguments""" + parser = argparse.ArgumentParser( + description="BSM flavour ratio analysis", + formatter_class=misc_utils.SortingHelpFormatter, + ) + parser.add_argument( + '--seed', type=misc_utils.seed_parse, default='25', + help='Set the random seed value' + ) + parser.add_argument( + '--threads', type=misc_utils.thread_type, default='1', + help='Set the number of threads to use (int or "max")' + ) + parser.add_argument( + '--spectral-index', type=float, default='-2', + help='Astro spectral index' + ) + parser.add_argument( + '--datadir', type=str, default='./untitled', + help='Path to store chains' + ) + fr_utils.fr_argparse(parser) + mcmc_utils.mcmc_argparse(parser) + nuisance_argparse(parser) + misc_utils.remove_option(parser, 'injected_ratio') + if args is None: return parser.parse_args() + else: return parser.parse_args(args.split()) + + +def gen_identifier(args): + f = '_DIM{0}'.format(args.dimension) + f += '_sfr_' + misc_utils.solve_ratio(args.source_ratio) + f += '_{0}'.format(misc_utils.str_enum(args.texture)) + return f + + +def gen_figtext(args): + """Generate the figure text.""" + t = r'$' + t += r'{\rm Source\:flavour\:ratio}'+r'\:=\:({0})'.format( + misc_utils.solve_ratio(args.source_ratio).replace('_', ':') + ) + t += '$\n' + r'${\rm Texture}'+r' = {0}'.format( + misc_utils.str_enum(args.texture) + ) + t += '$\n' + r'${\rm Dimension}'+r' = {0}$'.format(args.dimension) + return t + + +def triangle_llh(theta, args, llh_paramset): + """Log likelihood function for a given theta.""" + if len(theta) != len(llh_paramset): + raise AssertionError( + 'Dimensions of scan is not the same as the input ' + 'params\ntheta={0}\nparamset]{1}'.format(theta, llh_paramset) + ) + for idx, param in enumerate(llh_paramset): + param.value = theta[idx] + + return 1. # Flat LLH + + +def ln_prob(theta, args, llh_paramset): + dc_llh_paramset = deepcopy(llh_paramset) + lp = llh_utils.lnprior(theta, paramset=dc_llh_paramset) + if not np.isfinite(lp): + return -np.inf + return lp + triangle_llh( + theta, + args = args, + llh_paramset = dc_llh_paramset, + ) + + +def main(): + args = parse_args() + process_args(args) + misc_utils.print_args(args) + + if args.seed is not None: + np.random.seed(args.seed) + + asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance()) + + prefix = '' + outfile = args.datadir + '/mc_texture' + prefix + gen_identifier(args) + print '== {0:<25} = {1}'.format('outfile', outfile) + + print 'asimov_paramset', asimov_paramset + print 'llh_paramset', llh_paramset + + if args.run_mcmc: + ln_prob_eval = partial( + ln_prob, + llh_paramset = llh_paramset, + args = args, + ) + + if args.mcmc_seed_type == MCMCSeedType.UNIFORM: + p0 = mcmc_utils.flat_seed( + llh_paramset, nwalkers=args.nwalkers + ) + elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN: + p0 = mcmc_utils.gaussian_seed( + llh_paramset, nwalkers=args.nwalkers + ) + + samples = mcmc_utils.mcmc( + p0 = p0, + ln_prob = ln_prob_eval, + ndim = len(llh_paramset), + nwalkers = args.nwalkers, + burnin = args.burnin, + nsteps = args.nsteps, + args = args, + threads = args.threads + ) + + frs = np.array( + map(lambda x: fr_utils.flux_averaged_BSMu( + x, args, args.spectral_index, llh_paramset + ), samples), + dtype=float + ) + mcmc_utils.save_chains(frs, outfile) + + of = outfile[:5]+outfile[5:].replace('data', 'plots')+'_posterior' + plot_utils.chainer_plot( + infile = outfile+'.npy', + outfile = of, + outformat = ['png'], + args = args, + llh_paramset = llh_paramset, + fig_text = gen_figtext(args, llh_paramset) + ) + print "DONE!" + + +main.__doc__ = __doc__ + + +if __name__ == '__main__': + main() @@ -5,12 +5,11 @@ # date : March 17, 2018 """ -HESE BSM flavour ratio MCMC analysis script +Sample points only assuming unitarity """ from __future__ import absolute_import, division -import os import argparse from copy import deepcopy from functools import partial @@ -59,7 +58,7 @@ def get_paramsets(args, nuisance_paramset): hypo_paramset = ParamSet(hypo_paramset) tag = ParamTag.BESTFIT - flavour_angles = fr_utils.fr_to_angles(args.injected_ratio) + flavour_angles = fr_utils.fr_to_angles(args.source_ratio) asimov_paramset.extend([ Param(name='astroFlavorAngle1', value=flavour_angles[0], ranges=[ 0., 1.], std=0.2, tag=tag), @@ -81,7 +80,7 @@ def nuisance_argparse(parser): def process_args(args): """Process the input args.""" - args.injected_ratio = fr_utils.normalise_fr(args.injected_ratio) + args.source_ratio = fr_utils.normalise_fr(args.source_ratio) def parse_args(args=None): @@ -91,8 +90,8 @@ def parse_args(args=None): formatter_class=misc_utils.SortingHelpFormatter, ) parser.add_argument( - '--injected-ratio', type=float, nargs=3, default=[1, 2, 0], - help='Set the central value for the injected flavour ratio at source' + '--source-ratio', type=float, nargs=3, default=[1, 2, 0], + help='Set the source flavour ratio' ) parser.add_argument( '--seed', type=misc_utils.seed_parse, default='26', @@ -113,14 +112,14 @@ def parse_args(args=None): def gen_identifier(args): - f = '_INJ_{0}'.format(misc_utils.solve_ratio(args.injected_ratio)) + f = '_INJ_{0}'.format(misc_utils.solve_ratio(args.source_ratio)) return f def gen_figtext(args, asimov_paramset): f = '' - f += 'Injected ratio = {0}'.format( - misc_utils.solve_ratio(args.injected_ratio) + f += 'Source ratio = {0}'.format( + misc_utils.solve_ratio(args.source_ratio) ) for param in asimov_paramset: f += '\nInjected {0:20s} = {1:.3f}'.format( @@ -165,7 +164,7 @@ def main(): asimov_paramset, hypo_paramset = get_paramsets(args, define_nuisance()) prefix = '' - outfile = args.datadir + '/fr' + prefix + gen_identifier(args) + outfile = args.datadir + '/mc_unitary' + prefix + gen_identifier(args) print '== {0:<25} = {1}'.format('outfile', outfile) print 'asimov_paramset', asimov_paramset @@ -200,7 +199,7 @@ def main(): mmxs = map(fr_utils.angles_to_u, samples) frs = np.array( - [fr_utils.u_to_fr(args.injected_ratio, x) for x in mmxs] + [fr_utils.u_to_fr(args.source_ratio, x) for x in mmxs] ) mcmc_utils.save_chains(frs, outfile) diff --git a/plot_llh/LVCPT.py b/plot_llh/LVCPT.py deleted file mode 100644 index b226429..0000000 --- a/plot_llh/LVCPT.py +++ /dev/null @@ -1,420 +0,0 @@ - -# coding: utf-8 - -## The Theory - -import numpy -import MinimalTools as MT -import PhysConst as PC -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.collections as mco -import matplotlib as mpl -import scipy.interpolate as interpolate -import scipy.integrate as integrate -import scipy as sp -from numpy import linalg as LA - -use_cython = False - -if use_cython: - import cython.cLVCPT as clv - -mpl.rc('font', family='serif', size=20) - -pc = PC.PhysicsConstants() - -degree = np.pi/180.0 -pc.th12 = 33.36*degree#33.48*degree -pc.th23 = 45.*degree#42.3*degree -pc.th13 = 8.66*degree#8.5*degree -pc.delta1 = 0.0#300.0*degree#306.*degree # perhaps better just set to 0. -pc.dm21sq = 7.5e-5 -pc.dm31sq = 2.47e-3#2.457e-3 -pc.Refresh() - -MT.calcU(pc) -DELTAM2 = MT.flavorM2(pc) - -def Hamiltonian(Enu, LVATERM = np.zeros((3,3), dtype=numpy.complex), - LVCTERM = np.zeros((3,3), dtype=numpy.complex)): - return DELTAM2/(2.0*Enu) + LVATERM + Enu*LVCTERM - -def OscProbFromMixingMatrix(alpha, beta, MixMatrix): - return sum([(np.absolute(MixMatrix[i][alpha])*np.absolute(MixMatrix[i][beta]))**2 for i in range(pc.numneu)] ) - #return sum([(np.absolute(MixMatrix[i][alpha]))**2*(np.absolute(MixMatrix[i][beta]))**2 for i in range(pc.numneu)] ) - #prob = 0.0; - #for i in range(pc.numneu) : - # prob += (np.absolute(MixMatrix[i][alpha]))**2*(np.absolute(MixMatrix[i][beta]))**2 - #return prob - -def OscProb(alpha, Enu, LVATERM = np.zeros((3,3), dtype=numpy.complex), - LVCTERM = np.zeros((3,3), dtype=numpy.complex)): - eigvals, eigvec = MT.eigenvectors(Hamiltonian(Enu, LVATERM=LVATERM, LVCTERM=LVCTERM)) - #print eigvec.dtype - if use_cython: - return [ clv.OscProbFromMixingMatrix(alpha,beta,eigvec) for beta in range(pc.numneu)] - else: - return [ OscProbFromMixingMatrix(alpha,beta,eigvec) for beta in range(pc.numneu)] - -def FlavorRatio(initial_flavor_ratio, Enu, LVATERM = np.zeros((3,3), dtype=numpy.complex), - LVCTERM = np.zeros((3,3), dtype=numpy.complex)): - final_flavor_ratio = [0.0]*pc.numneu - osc_prob_array = [OscProb(beta,Enu,LVATERM=LVATERM,LVCTERM=LVCTERM) for beta in range(pc.numneu)] - - for alpha in range(pc.numneu): - for beta,phi in enumerate(initial_flavor_ratio): - final_flavor_ratio[alpha] += osc_prob_array[beta][alpha]*phi - return final_flavor_ratio - -def RRR(initial_flavor_ratio, Enu, LVATERM = np.zeros((3,3), dtype=numpy.complex), - LVCTERM = np.zeros((3,3), dtype=numpy.complex)): - ffr = FlavorRatio(initial_flavor_ratio,Enu,LVATERM=LVATERM,LVCTERM=LVCTERM) - return ffr[1]/ffr[0] -def SSS(initial_flavor_ratio, Enu, LVATERM = np.zeros((3,3), dtype=numpy.complex), - LVCTERM = np.zeros((3,3), dtype=numpy.complex)): - ffr = FlavorRatio(initial_flavor_ratio,Enu,LVATERM=LVATERM,LVCTERM=LVCTERM) - return ffr[2]/ffr[1] - -def PointToList(p1,p2): - return [[p1[0],p2[0]],[p1[1],p2[1]]] - -def PointFromFlavor(origin,scale,flavor_ratio_list): - nu_e_vec = np.array([1.,0.])*scale - nu_mu_vec = np.array([1./2.,np.sqrt(3.)/2.])*scale - nu_tau_vec = np.array([-1./2.,np.sqrt(3.)/2.])*scale - fpos = origin + flavor_ratio_list[0]*nu_e_vec + flavor_ratio_list[1]*nu_mu_vec - return [fpos[0],fpos[1]] - -def MakeFlavorTriangle(list_of_flavor_ratios, scale = 8, - p = np.array([0.,0.]), save_file = False, PlotPoints = False, PlotTrayectories = False, figure = None, alpha = 1.0, - filename = "triangle",icolor = "green", icolormap = "Greens", divisions = 5, initial_flavor_ratio = [1,0,0], - term = "a", subdivisions = False, triangle_collection = None, color_scale = "lin", return_fig = True, addtext = "", - add_default_text = True, ilw = 1., slw = 0.75, output_format = "eps", inner_line_color = "k", plot_color_bar = False, - levels=[0.68, 0.90]): - # i will be nice ... - list_of_flavor_ratios = np.array(list_of_flavor_ratios) - - if figure == None: - fig = plt.figure(figsize=(scale,scale), frameon = False) - else: - fig = figure - - ax = fig.add_axes([0, 0, 1, 1]) - ax.axis('off') - - # delete extra lines - frame = plt.gca() - frame.axes.get_xaxis().set_visible(False) - frame.axes.get_yaxis().set_visible(False) - - s0 = np.array([1.,0.])*scale - s1 = np.array([1./2.,np.sqrt(3.)/2.])*scale - s2 = np.array([1./2.,-np.sqrt(3.)/2.])*scale - - # make triangle outer frame - - plt.plot(*PointToList(p, p+s0), color = "k", lw = 4) - plt.plot(*PointToList(p, p+s1), color = "k", lw = 2) - plt.plot(*PointToList(p+s0, p+s1), color = "k", lw = 2) - - # put outer triangle labels - - # ax.text((p+s0*0.5+s0*0.025)[0], (p+s0*0.5-np.array([0,0.15*scale]))[1],r"$\alpha^{\oplus}_{e}$", - # horizontalalignment="center",fontsize = 50, zorder = 10) - # ax.text((p+s1*0.5-0.2*s0)[0], (p+s1*0.5+0.1*s0)[1]+scale*0.1,r"$\alpha^{\oplus}_{\tau}$", - # horizontalalignment="center",fontsize = 50, zorder = 10, rotation = 60.) - # ax.text((p+s1*0.5 + 0.7*s0)[0], (p+s1*0.5 + 0.6*s0)[1]+0.05*scale,r"$\alpha^{\oplus}_{\mu}$", - # horizontalalignment="center",fontsize = 50, zorder = 10, rotation = -60) - - ax.text((p+s0*0.5+s0*0.025)[0], (p+s0*0.5-np.array([0,0.15*scale]))[1],r"$f_{e}^{\oplus}$", - horizontalalignment="center",fontsize = 40, zorder = 10) - ax.text((p+s1*0.5-0.1*s0)[0], (p+s1*0.5 + 0.6*s0)[1]+0.05*scale,r"$f_{\tau}^{\oplus}$", - horizontalalignment="center",fontsize = 40, zorder = 10) - ax.text((p+s1*0.5 + 0.6*s0)[0], (p+s1*0.5 + 0.6*s0)[1]+0.05*scale,r"$f_{\mu}^{\oplus}$", - horizontalalignment="center",fontsize = 40, zorder = 10) - - # construct triangle grid - fsl = 25 - for i in range(divisions+1): - subsize = 1./float(divisions) - - ax.text((p+s0*subsize*float(i))[0], (p+s0*subsize*float(i)-np.array([0,0.05*scale]))[1],str(i*subsize), - horizontalalignment="center",fontsize = fsl, rotation=60.) - plt.plot(*PointToList(p+s0*subsize*float(i), p+s1+s2*subsize*float(i)), color = inner_line_color, lw = ilw, ls = "dashed", zorder = -1) - ax.text((p+s1-s1*subsize*float(i)-np.array([0.06*scale,0.0]))[0], (p+s1-s1*subsize*float(i))[1],str(i*subsize), - horizontalalignment="center",fontsize = fsl, rotation=-60.) - plt.plot(*PointToList(p+s0*subsize*float(divisions-i), p+s1-s1*subsize*float(i)), color = inner_line_color, lw = ilw, ls = "dashed", zorder = -1) - - ax.text((p+s1+s2*subsize*float(i)+np.array([0.05*scale,0.0]))[0], (p+s1+s2*subsize*float(i))[1],str((divisions-i)*subsize), - horizontalalignment="center",fontsize = fsl) - plt.plot(*PointToList(p+s1*subsize*float(divisions-i), p+s1+s2*subsize*float(i)), color = inner_line_color, lw = ilw, ls = "dashed", zorder = -1) - - if subdivisions and i < divisions: - plt.plot(*PointToList(p+s0*subsize*float(i+0.5), p+s1+s2*subsize*float(i+0.5)), color = inner_line_color, lw = slw, ls = "dotted", zorder = -1) - if subdivisions and i > 0: - plt.plot(*PointToList(p+s0*subsize*float(divisions-(i-0.5)), p+s1-s1*subsize*float(i-0.5)), color = inner_line_color, lw = slw, ls = "dotted", zorder = -1) - plt.plot(*PointToList(p+s1*subsize*float(divisions-(i-0.5)), p+s1+s2*subsize*float(i-0.5)), color = inner_line_color, lw = slw, ls = "dotted", zorder = -1) - - levels = np.array(sorted(levels) + [1.0001]) - - level_colors = np.linspace(1.0, 0.0, len(levels)) - - total_points = float(sum([ triangle.number_of_points for triangle in triangle_collection])) - max_points = float(max([ triangle.number_of_points for triangle in triangle_collection])) - triangle_collection = list(reversed(sorted(triangle_collection, key=lambda x: x.number_of_points))) - color_map = plt.get_cmap(icolormap) - color_i = 0 - - total_mass = 0.0 - - # plot triangle collection - if (triangle_collection != None): - # get total number of points - for i,triangle in enumerate(triangle_collection): - print float(i) / float(len(triangle_collection)), color_i, level_colors[color_i] - total_mass += float(triangle.number_of_points) / float(total_points) - if total_mass > levels[color_i]: - print 'mass:', total_mass - color_i += 1 - if triangle.number_of_points > 0: - xx,yy = zip(*triangle.coordinates) - if color_scale == "lin": - plt.fill(xx,yy,lw = 0., zorder = -0.8, color = color_map(0.75), alpha = level_colors[color_i]) - elif color_scale == "log": - plt.fill(xx,yy,lw = 0., zorder = -0.8, color = color_map(0.75), alpha = level_colors[color_i]) - else : - raise NameError('Error. Love CA.') - - # plot flavor ratio points - if PlotTrayectories : - if len(list_of_flavor_ratios.shape) == 3 : - for flavor_ratio_l in list_of_flavor_ratios: - flv_ratio_coords = map(lambda f:PointFromFlavor(p,scale,np.array(f)),flavor_ratio_l) - xc, yc = zip(*flv_ratio_coords) - plt.plot(xc,yc, color = color_map(level_colors[color_i]), - ms = 10, linewidth = 4, zorder = 0) - elif len(list_of_flavor_ratios.shape) == 2 : - flv_ratio_coords = map(lambda f:PointFromFlavor(p,scale,np.array(f)),list_of_flavor_ratios) - xc, yc = zip(*flv_ratio_coords) - - plt.plot(xc,yc, color = icolor, - ms = 10, linewidth = 4, zorder = 0) - else: - raise NameError('Check your input flavor list array and the joined flag. Love CA.') - elif PlotPoints: - if len(list_of_flavor_ratios.shape) !=2 : - print len(list_of_flavor_ratios.shape) - raise NameError('Check your input flavor list array and the joined flag. Love CA.') - for i,flavor_ratio in enumerate(list_of_flavor_ratios): - if len(icolor) != len(list_of_flavor_ratios): - icolor_ = icolor - else: - icolor_ = icolor[i] - plt.plot(*PointFromFlavor(p,scale,np.array(flavor_ratio)), color = color_map(level_colors[color_i]), - marker = 'o', ms = 10, linewidth = 0, - markeredgecolor=None,markeredgewidth=0.1, zorder = 1000) - - # put back color scale axis - if add_default_text: - ax.text((s0/5.+0.9*s1)[0],(s0/5.+0.9*s1)[1], - "LV "+term+"-term scan with\n $\ \phi_e:\ \phi_\\mu:\ \phi_\\tau = "+str(initial_flavor_ratio[0])+":\ "+str(initial_flavor_ratio[1])+":\ "+str(initial_flavor_ratio[2])+"$"+" \n "+addtext, - fontsize = 20) - - if(save_file): - # save figure - plt.savefig("./plots/"+filename+"."+output_format, dpi = 600, bbox_inches='tight') - else: - # show figure - plt.show() - if return_fig: - return fig - - -def s_bario(p,p0,p1,p2): - return (p0[1]*p2[0] - p0[0]*p2[1] + (p2[1] - p0[1])*p[0] + (p0[0] - p2[0])*p[1]) - -def t_bario(p,p0,p1,p2): - return (p0[0]*p1[1] - p0[1]*p1[0] + (p0[1] - p1[1])*p[0] + (p1[0] - p0[0])*p[1]) - -def IsInTriangle(p,p0,p1,p2,area): - s = s_bario(p,p0,p1,p2) - t = t_bario(p,p0,p1,p2) - #print s,t,2.0*area - s - t - return s >= -1.e-15 and t >= -1.0e-15 and s+t <= 2.0*area - - -class Triangle: - coordinates = [] - area = 0.0 - number_of_points = 0.0 - n_t = 0 - i = 0 - j = 0 - orientation = "" - - def IsPointIn(self,point): - p0 = self.coordinates[0] - p1 = self.coordinates[1] - p2 = self.coordinates[2] - return IsInTriangle(point,p0,p1,p2,self.area) - - -def GenerateTriangles(scale, divisions, p = np.array([0.,0.])): - s0 = np.array([1.,0.])*scale/float(divisions) - s1 = np.array([1./2.,np.sqrt(3.)/2.])*scale/float(divisions) - s2 = np.array([1./2.,-np.sqrt(3.)/2.])*scale/float(divisions) - - area = np.sqrt(3)*(LA.norm(s0)/2.0)**2 - - n_t = 0 - - triangle_collection = [] - for i in range(divisions): - for j in range(divisions-i): - lower_triangle = Triangle() - - p0_l = p + i*s0 + j*s1 - p1_l = p0_l + s0 - p2_l = p0_l + s1 - - lower_triangle.coordinates = [p0_l,p1_l,p2_l] - lower_triangle.n_t = n_t - lower_triangle.i = i - lower_triangle.j = j - lower_triangle.orientation = "L" - lower_triangle.area = area - - n_t += 1 - # append to triangle collection - triangle_collection.append(lower_triangle) - - upper_triangle = Triangle() - - p0_u = p2_l - p1_u = p1_l - p2_u = p1_l + s1 - - upper_triangle.coordinates = [p0_u,p1_u,p2_u] - upper_triangle.n_t = n_t - upper_triangle.i = i - upper_triangle.j = j - upper_triangle.orientation = "U" - upper_triangle.area = area - - n_t += 1 - # append to triangle collection - triangle_collection.append(upper_triangle) - return triangle_collection - -def AddPointToTriangleCollectionLegacy(flavor_ratio, triangle_collection, - p = np.array([0.,0.]), scale = 8, divisions = 10): - point = PointFromFlavor(p,scale,np.array(flavor_ratio)) - electron = 0; tau = 2; - # the silly way - for triangle in triangle_collection: - if(triangle.IsPointIn(point)): - triangle.number_of_points += 1. - -def AddPointToTriangleCollection(flavor_ratio, triangle_collection, - p = np.array([0.,0.]), scale = 8, divisions = 10): - point = PointFromFlavor(p,scale,np.array(flavor_ratio)) - electron = 0; muon = 1; tau = 2; - # the smart way - u_i = int(flavor_ratio[electron]*float(divisions)) - u_j = int(flavor_ratio[muon]*float(divisions)) - index = u_i*(2*divisions-u_i+1) + 2*u_j - if triangle_collection[index].IsPointIn(point): - triangle_collection[index].number_of_points += 1. - else: - triangle_collection[index+1].number_of_points += 1. -# legacy - #elif triangle_collection[index+1].IsPointIn(point): - # triangle_collection[index+1].number_of_points += 1. - #else: - # print "Math error." - # print point, "\n",u_i, u_j, "\n", triangle_collection[index].coordinates, "\n", triangle_collection[index+1].coordinates - # raise NameError("Error triangle location math") - -class AnarchySampling: - def __init__(self, n_sample, LV_scale_1, LV_scale_2, term): - self.n_sample = n_sample - self.th12_sample = np.arcsin(np.sqrt(np.random.uniform(0.,1., size=n_sample))) - self.th13_sample = np.arccos(np.sqrt(np.sqrt(np.random.uniform(0.,1., size=n_sample)))) - self.th23_sample = np.arcsin(np.sqrt(np.random.uniform(0.,1., size=n_sample))) - self.delta_sample = np.random.uniform(0.,2.*np.pi, size=n_sample) - - self.LV_scale_1 = LV_scale_1 - self.LV_scale_2 = LV_scale_2 - - self.term = term - -def GenerateFlavorRatioPoints(Initial_Flavor_Ratio, SamplingObject, gamma = 2.0, - Log10Emax = 7., Log10Emin = 4.0, Epoints = 30, - save_list = False, save_avg = True): - flavor_tray_list = [] - flavor_avg_list = [] - - # energy things - - Erange = np.logspace(Log10Emin,Log10Emax,Epoints) # in GeV - Emin = Erange[0] - Emax = Erange[-1] - - if gamma == 1 or gamma == 1.0: - spectral_normalization = np.log(Emax)-np.log(Emin) - else: - spectral_normalization = (Emax**(1.-gamma) - Emin**(1.-gamma))/(1.-gamma) - - spectral_function = lambda Enu: Enu**(-gamma)/spectral_normalization - - # loop over random parameters - for i in range(SamplingObject.n_sample): - lv_term = MT.LVP() - - lv_term.th12 = SamplingObject.th12_sample[i] - lv_term.th13 = SamplingObject.th13_sample[i] - lv_term.th23 = SamplingObject.th23_sample[i] - lv_term.delta1 = SamplingObject.delta_sample[i] - - lv_term.LVS21 = SamplingObject.LV_scale_1 - lv_term.LVS31 = SamplingObject.LV_scale_2 - - lv_term.Refresh() - - LVTERM = MT.LVTerm(lv_term); - - if SamplingObject.term == "a": - flavor_ratio_list = np.array(map(lambda Enu : FlavorRatio(Initial_Flavor_Ratio, Enu*pc.GeV, LVATERM = LVTERM), Erange)) - elif SamplingObject.term == "c": - flavor_ratio_list = np.array(map(lambda Enu : FlavorRatio(Initial_Flavor_Ratio, Enu*pc.GeV, LVCTERM = LVTERM), Erange)) - else : - raise NameError('Only a or c term.'+ str(term)) - - if save_avg: - if Epoints != 1: - flavor_avg = [0.]*lv_term.numneu - for alpha in range(lv_term.numneu): - #inter = interpolate.interp1d(Erange,flavor_ratio_list[:,alpha]) - inter = interpolate.UnivariateSpline(Erange,flavor_ratio_list[:,alpha]) - flavor_avg[alpha] = integrate.quad(lambda Enu : inter(Enu)*spectral_function(Enu), - Emin,Emax, limit = 75, epsrel = 1e-2, epsabs = 1.0e-2)[0] - #flavor_avg[alpha] = integrate.quadrature(lambda Enu : inter(Enu)*spectral_function(Enu), - # Emin,Emax, maxiter = 75, rtol = 1e-3, tol = 1.e-3)[0] - flavor_avg_list.append(flavor_avg) - else: - flavor_avg = flavor_ratio_list[0] - flavor_avg_list.append(flavor_avg) - - if save_list: - flavor_tray_list.append(flavor_ratio_list) - - if save_list and save_avg: - return flavor_tray_list, flavor_avg_list - elif save_list: - return flavor_tray_list - elif save_avg: - return flavor_avg_list - else : - print "Math is broken." - return None diff --git a/plot_llh/MinimalTools.py b/plot_llh/MinimalTools.py deleted file mode 100644 index 4ae8360..0000000 --- a/plot_llh/MinimalTools.py +++ /dev/null @@ -1,166 +0,0 @@ -import numpy as np -from PhysConst import PhysicsConstants - -def eigenvectors(M): - """ Calculates the eigenvectors and eigenvalues ordered by eigenvalue size - - @type M : matrix - @param M : matrix M - - @rtype : list - @return : [eigenvalues list, eigenvector list] - """ - D,V = np.linalg.eig(M) - DV = [] - VT = V.T - for i,eigenvalue in enumerate(D): - DV.append([eigenvalue,VT[i]]) - - DV = sorted(DV,key = lambda x : x[0].real)#np.abs(x[0].real)) - - V2 = [] - D2 = [] - for e in DV: - V2.append(e[1]) - D2.append(e[0]) - return D2,V2 - -# General Rotation Matrix -def R(i,j,cp,param): - """ Rotation Matrix - Calculates the R_ij rotations. Also incorporates CP-phases when necesary. - @type i : int - @param i : i-column. - @type j : int - @param j : j-row. - @type cp : int - @param cp : if cp = 0 : no CP-phase. else CP-phase = CP_array[cp] - - @rtype : numpy.array - @return : returns the R_ij rotation matrix. - """ - # if cp = 0 -> no complex phase - # R_ij, i<j - if(j<i): - # no funny business - l = i - i = j - j = l - - # rotation matrix generator - R = np.zeros([param.numneu,param.numneu],complex) - # diagonal terms - for k in np.arange(0,param.numneu,1): - if(k != i-1 and k != j-1): - R[k,k] = 1.0 - else : - R[k,k] = param.c[i,j] - # non-diagonal terms - if(cp != 0): - sd = np.sin(param.dcp[cp]) - cd = np.cos(param.dcp[cp]) - faseCP = complex(cd,sd) - else: - faseCP = complex(1.0,0.0) - - R[i-1,j-1] = param.s[i,j]*faseCP.conjugate() - R[j-1,i-1] = -param.s[i,j]*faseCP - return R - -def calcU(param): - """ Defining the mixing matrix parametrization. - @type param : PhysicsConstants - @param param : set of physical parameters to be used. - - @rtype : None - @return : Sets mixing matrix. - """ - if(param.numneu == 2): - return self.R(1,2,0,param) - elif(param.numneu == 3): - return np.dot(R(2,3,0,param),np.dot(R(1,3,1,param),R(1,2,0,param))) - elif(param.numneu == 4): - return np.dot(R(3,4,0,param),np.dot(R(2,4,2,param),np.dot(R(1,4,0,param),np.dot(R(2,3,0,param),np.dot(R(1,3,1,param),R(1,2,0,param)))))) - elif(param.numneu == 5): - return np.dot(R(4,5,0,param),np.dot(R(3,5,0,param),np.dot(R(2,5,0,param),np.dot(R(1,5,3,param),np.dot(R(3,4,0,param),np.dot(R(2,4,0,param),np.dot(R(1,4,2,param),np.dot(R(2,3,0,param),np.dot(R(1,3,1,param),R(1,2,0,param)))))))))) - elif(param.numneu == 6): - # 3+3 twin-sterile-neutrino model - return np.dot(R(3,6,0,param),np.dot(R(2,5,0,param),np.dot(R(1,4,0,param),np.dot(R(2,3,0,param),np.dot(R(1,3,1,param),R(1,2,0,param)))))) - else: - print "Sorry, too many neutrinos. Not yet implemented! =(." - quit() - - # antineutrino case - if param.neutype == "antineutrino" : - return self.U.conjugate() - - - -def massM2(param): - """ Mass term in the neutrino mass basis. - - @type param : PhysicsConstants - @param param : set of physical parameters to be used. - - @rtype : numpy array - @return : mass matrix in mass basis. - """ - M2 = np.zeros([param.numneu,param.numneu],complex) - for k in np.arange(1,param.numneu,1): - M2[k,k] = param.dmsq[k+1] - return M2 - -def flavorM2(param): - """ Mass term in the neutrino flavor basis. - - @type param : PhysicsConstants - @param param : set of physical parameters to be used. - - @rtype : numpy array - @return : mass matrix in flavor basis. - """ - U = calcU(param) - return np.dot(U,np.dot(massM2(param),U.conjugate().T)) - -class LVP(PhysicsConstants): - def __init__(self): - super(LVP,self).__init__() - self.th12 = 0.0 - self.th13 = 0.0 - self.th23 = 0.0 - self.delta1 = 0.0 - self.deltaCP = 0.0 - super(LVP,self).Refresh() - - # LVS - self.LVS21 = 0.0 # - self.LVS31 = 0.0 # - self.LVS41 = 0.0 # - self.LVS51 = 0.0 # - self.LVS61 = 0.0 # - # SQUARED MASS DIFFERENCE MATRIX - self.LVS = np.zeros([self.numneumax+2],float) - self.LVS[2] = self.LVS21 - self.LVS[3] = self.LVS31 - self.LVS[4] = self.LVS41 - self.LVS[5] = self.LVS51 - self.LVS[6] = self.LVS61 - - def Refresh(self): - super(LVP,self).Refresh() - LVS = self.LVS - LVS[2] = self.LVS21 - LVS[3] = self.LVS31 - LVS[4] = self.LVS41 - LVS[5] = self.LVS51 - LVS[6] = self.LVS61 - -def DiagonalMatrixLV(param): - DD = np.zeros([param.numneu,param.numneu],complex) - for k in np.arange(1,param.numneu,1): - DD[k,k] = param.LVS[k+1] - return DD - -def LVTerm(LVparam): - U = calcU(LVparam) - return np.dot(U,np.dot(DiagonalMatrixLV(LVparam),U.conjugate().T)) diff --git a/plot_llh/PhysConst.py b/plot_llh/PhysConst.py deleted file mode 100644 index 89a0be0..0000000 --- a/plot_llh/PhysConst.py +++ /dev/null @@ -1,390 +0,0 @@ -""" -Author : C.A. Arguelles -Date : 10/MAY/2011 - -Contains Physics constants and global variables. - -Log : -- Modified on 23/ABR/2012 by C.Arguelles - + Changed the definition of PhysicsConstants to - include an __init__ to separate the class global - properties from its instances. -""" - -# python standard modules -import numpy as np - -class PhysicsConstants(object): - - def __init__(self): - ## PHYSICS CONSTANTS - #=========================================================================== - # NAME - #=========================================================================== - - self.name = "STD" # Default values - self.linestyle = "solid" # Default linestyle in plots - self.markerstyle = "*" # Default marker style - self.colorstyle = "red" # Default color style - self.savefilename = "output.dat" # Default color style - - #=============================================================================== - # ## MATH - #=============================================================================== - self.PI=3.14159265 # Pi - self.PIby2=1.5707963268 # Pi/2 - self.sqr2=1.4142135624 # Sqrt[2] - self.ln2 = np.log(2.0) - - #=============================================================================== - # ## EARTH - #=============================================================================== - self.EARTHRADIUS = 6371.0 # [km] Earth radius - #=============================================================================== - # ## SUN - #=============================================================================== - self.SUNRADIUS = 109*self.EARTHRADIUS # [km] Sun radius - - #=============================================================================== - # # PHYSICAL CONSTANTS - #=============================================================================== - self.GF = 1.16639e-23 # [eV^-2] Fermi Constant - self.Na = 6.0221415e+23 # [mol cm^-3] Avogadro Number - self.sw_sq = 0.2312 # [dimensionless] sin(th_weinberg) ^2 - self.G = 6.67300e-11 # [m^3 kg^-1 s^-2] - self.alpha = 1.0/137.0 # [dimensionless] fine-structure constant - - #=============================================================================== - # ## UNIT CONVERSION FACTORS - #=============================================================================== - # Energy - self.TeV = 1.0e12 # [eV/TeV] - self.GeV = 1.0e9 # [eV/GeV] - self.MeV = 1.0e6 # [eV/MeV] - self.keV = 1.0e3 # [eV/keV] - self.Joule = 1/1.60225e-19 # [eV/J] - # Mass - self.kg = 5.62e35 # [eV/kg] - self.gr = 1e-3*self.kg # [eV/g] - # Time - self.sec = 1.523e15 # [eV^-1/s] - self.hour = 3600.0*self.sec # [eV^-1/h] - self.day = 24.0*self.hour # [eV^-1/d] - self.year = 365.0*self.day # [eV^-1/yr] - self.yearstosec = self.sec/self.year # [s/yr] - # Distance - self.meter = 5.076e6 # [eV^-1/m] - self.cm = 1.0e-2*self.meter # [eV^-1/cm] - self.km = 1.0e3*self.meter # [eV^-1/km] - self.fermi = 1.0e-15*self.meter # [eV^-1/fm] - self.angstrom = 1.0e-10*self.meter # [eV^-1/A] - self.AU = 149.60e9*self.meter # [eV^-1/AU] - self.parsec = 3.08568025e16*self.meter# [eV^-1/parsec] - # Integrated Luminocity # review - self.picobarn = 1.0e-36*self.cm**2 # [eV^-2/pb] - self.femtobarn = 1.0e-39*self.cm**2 # [eV^-2/fb] - # Presure - self.Pascal = self.Joule/self.meter**3 # [eV^4/Pa] - self.hPascal = 100.0*self.Pascal # [eV^4/hPa] - self.atm = 101325.0*self.Pascal # [eV^4/atm] - self.psi = 6893.0*self.Pascal # [eV^4/psi] - # Temperature - self.kelvin = 1/1.1604505e4 # [eV/K] - # Angle - self.degree = self.PI/180.0 # [rad/degree] - # magnetic field - self.T = 0.000692445 # [eV^2/T] - - # old notation - self.cm3toev3 = 7.68351405e-15 # cm^3-> ev^3 - self.KmtoEv =5.0677288532e+9 # km -> eV - self.yearstosec = 31536.0e3 # years -> sec - - #=============================================================================== - # ## NEUTRINO OSCILLATION PARAMETERS ## - #=============================================================================== - - self.numneu = 3 # number of neutrinos - self.numneumax = 6 # maximum neutrino number - self.neutype = 'neutrino' - #neutype = 'antineutrino' - - # values updated according to 1209.3023 Table 1 FreeFluxes + RSBL - - # MIXING ANGLES - - self.th12 = 0.579639 - self.th13 = 0.150098 - self.th23 = self.PIby2/2.0 - self.th14 = 0.0 - self.th24 = 0.0 - self.th34 = 0.0 - self.th15 = 0.0 - self.th25 = 0.0 - self.th35 = 0.0 - self.th45 = 0.0 - self.th16 = 0.0 - self.th26 = 0.0 - self.th36 = 0.0 - self.th46 = 0.0 - self.th56 = 0.0 - - # mixing angles matrix array - self.th = np.zeros([self.numneumax+1,self.numneumax+1],float) - self.th[1,2] = self.th12 - self.th[1,3] = self.th13 - self.th[2,3] = self.th23 - self.th[1,4] = self.th14 - self.th[2,4] = self.th24 - self.th[3,4] = self.th34 - self.th[1,5] = self.th15 - self.th[2,5] = self.th25 - self.th[3,5] = self.th35 - self.th[4,5] = self.th45 - self.th[1,6] = self.th16 - self.th[2,6] = self.th26 - self.th[3,6] = self.th36 - self.th[4,6] = self.th46 - self.th[5,6] = self.th56 - - self.s12 = np.sin(self.th12) - self.c12 = np.cos(self.th12) - self.s13 = np.sin(self.th13) - self.c13 = np.cos(self.th13) - self.s23 = np.sin(self.th23) - self.c23 = np.cos(self.th23) - self.s14 = np.sin(self.th14) - self.c14 = np.cos(self.th14) - self.s24 = np.sin(self.th24) - self.c24 = np.cos(self.th24) - self.s34 = np.sin(self.th34) - self.c34 = np.cos(self.th34) - self.s15 = np.sin(self.th15) - self.c15 = np.cos(self.th15) - self.s25 = np.sin(self.th25) - self.c25 = np.cos(self.th25) - self.s35 = np.sin(self.th35) - self.c35 = np.cos(self.th35) - self.s45 = np.sin(self.th45) - self.c45 = np.cos(self.th45) - self.s16 = np.sin(self.th16) - self.c16 = np.cos(self.th16) - self.s26 = np.sin(self.th26) - self.c26 = np.cos(self.th26) - self.s36 = np.sin(self.th36) - self.c36 = np.cos(self.th36) - self.s46 = np.sin(self.th46) - self.c46 = np.cos(self.th46) - self.s56 = np.sin(self.th56) - self.c56 = np.cos(self.th56) - - # cos(th_ij) matrix array - self.c = np.zeros([self.numneumax+1,self.numneumax+1],float) - self.c[1,2] = self.c12 - self.c[1,3] = self.c13 - self.c[1,4] = self.c14 - self.c[2,3] = self.c23 - self.c[2,4] = self.c24 - self.c[3,4] = self.c34 - self.c[1,5] = self.c15 - self.c[2,5] = self.c25 - self.c[3,5] = self.c35 - self.c[4,5] = self.c45 - self.c[1,6] = self.c16 - self.c[2,6] = self.c26 - self.c[3,6] = self.c36 - self.c[4,6] = self.c46 - self.c[5,6] = self.c56 - - # sin(th_ij) matrix array - self.s = np.zeros([self.numneumax+1,self.numneumax+1],float) - self.s[1,2] = self.s12 - self.s[1,3] = self.s13 - self.s[1,4] = self.s14 - self.s[2,3] = self.s23 - self.s[2,4] = self.s24 - self.s[3,4] = self.s34 - self.s[1,5] = self.s15 - self.s[2,5] = self.s25 - self.s[3,5] = self.s35 - self.s[4,5] = self.s45 - self.s[1,6] = self.s16 - self.s[2,6] = self.s26 - self.s[3,6] = self.s36 - self.s[4,6] = self.s46 - self.s[5,6] = self.s56 - - # CP PHASES - #self.delta21=3.3e-5 - #self.delta32=3.1e-3 - #self.delta31=self.delta32+self.delta21 - #self.deltaCP=self.PIby2 - - # CP Phases - self.deltaCP = 5.235987 - self.delta1 = self.deltaCP - self.delta2 = 0.0 - self.delta3 = 0.0 - - # d-CP phases - self.dcp = np.zeros([self.numneumax-2+1],complex) - self.dcp[0] = 1.0 - self.dcp[1] = self.delta1 - self.dcp[2] = self.delta2 - self.dcp[3] = self.delta3 - - # SQUARED MASS DIFFERENCE - self.dm21sq = 7.50e-5 # [eV^2] - self.dm31sq = 2.47e-3 # [eV^2] - self.dm32sq = -2.43e-3 # [eV^2] - # STERILE - self.dm41sq = 0.0 # [eV^2] - self.dm51sq = 0.0 # [eV^2] - self.dm61sq = 0.0 # [eV^2] - # SQUARED MASS DIFFERENCE MATRIX - self.dmsq = np.zeros([self.numneumax+2],float) - self.dmsq[2] = self.dm21sq - self.dmsq[3] = self.dm31sq - self.dmsq[4] = self.dm41sq - self.dmsq[5] = self.dm51sq - self.dmsq[6] = self.dm61sq - - self.dm2 = np.zeros([self.numneumax+1,self.numneumax+1],float) - self.dm2[1,2] = self.dm21sq - self.dm2[1,3] = self.dm31sq - self.dm2[2,3] = self.dm32sq - self.dm2[1,4] = self.dm41sq - self.dm2[1,5] = self.dm51sq - self.dm2[1,6] = self.dm61sq - - # MIXING MATRIX - self.U = None - - #=============================================================================== - # # PARTICLE MASSES - #=============================================================================== - self.muon_mass = 0.10565 # [GeV] - self.neutron_mass = 0.939565 # [GeV] - self.proton_mass = 0.938272 # [GeV] - self.electron_mass = 0.510998910e-3 # [GeV] - - self.atomic_mass_unit = 1.660538e-24 # [g] - - ## names - self.electron = 0 - self.muon = 1 - self.tau = 2 - self.sterile1 = 3 - self.sterile2 = 4 - self.sterile3 = 5 - - #=============================================================================== - # REFRESH - #=============================================================================== - - def Refresh(self): - # Refresh angles - self.s12 = np.sin(self.th12) - self.c12 = np.cos(self.th12) - self.s13 = np.sin(self.th13) - self.c13 = np.cos(self.th13) - self.s23 = np.sin(self.th23) - self.c23 = np.cos(self.th23) - self.s14 = np.sin(self.th14) - self.c14 = np.cos(self.th14) - self.s24 = np.sin(self.th24) - self.c24 = np.cos(self.th24) - self.s34 = np.sin(self.th34) - self.c34 = np.cos(self.th34) - self.s15 = np.sin(self.th15) - self.c15 = np.cos(self.th15) - self.s25 = np.sin(self.th25) - self.c25 = np.cos(self.th25) - self.s35 = np.sin(self.th35) - self.c35 = np.cos(self.th35) - self.s45 = np.sin(self.th45) - self.c45 = np.cos(self.th45) - self.s16 = np.sin(self.th16) - self.c16 = np.cos(self.th16) - self.s26 = np.sin(self.th26) - self.c26 = np.cos(self.th26) - self.s36 = np.sin(self.th36) - self.c36 = np.cos(self.th36) - self.s46 = np.sin(self.th46) - self.c46 = np.cos(self.th46) - self.s56 = np.sin(self.th56) - self.c56 = np.cos(self.th56) - - th = self.th - th[1,2] = self.th12 - th[1,3] = self.th13 - th[2,3] = self.th23 - th[1,4] = self.th14 - th[2,4] = self.th24 - th[3,4] = self.th34 - th[1,5] = self.th15 - th[2,5] = self.th25 - th[3,5] = self.th35 - th[4,5] = self.th45 - th[1,6] = self.th16 - th[2,6] = self.th26 - th[3,6] = self.th36 - th[4,6] = self.th46 - th[5,6] = self.th56 - # Refresh cos(th_ij) - c = self.c - c[1,2] = self.c12 - c[1,3] = self.c13 - c[1,4] = self.c14 - c[2,3] = self.c23 - c[2,4] = self.c24 - c[3,4] = self.c34 - c[1,5] = self.c15 - c[2,5] = self.c25 - c[3,5] = self.c35 - c[4,5] = self.c45 - c[1,6] = self.c16 - c[2,6] = self.c26 - c[3,6] = self.c36 - c[4,6] = self.c46 - c[5,6] = self.c56 - # Refresh sin(th_ij) - s = self.s - self.s[1,2] = self.s12 - self.s[1,3] = self.s13 - self.s[1,4] = self.s14 - self.s[2,3] = self.s23 - self.s[2,4] = self.s24 - self.s[3,4] = self.s34 - self.s[1,5] = self.s15 - self.s[2,5] = self.s25 - self.s[3,5] = self.s35 - self.s[4,5] = self.s45 - self.s[1,6] = self.s16 - self.s[2,6] = self.s26 - self.s[3,6] = self.s36 - self.s[4,6] = self.s46 - self.s[5,6] = self.s56 - # Refresh CP-Phases - dcp = self.dcp - dcp[0] = 1.0 - dcp[1] = self.delta1 - dcp[2] = self.delta2 - dcp[3] = self.delta3 - #dcp[4] = self.delta2 - # Refresh Square mass differences - dmsq = self.dmsq - dmsq[2] = self.dm21sq - dmsq[3] = self.dm31sq - dmsq[4] = self.dm41sq - dmsq[5] = self.dm51sq - dmsq[6] = self.dm61sq - - dm2 = self.dm2 - dm2[1,2] = self.dm21sq - dm2[1,3] = self.dm31sq - dm2[2,3] = self.dm32sq - dm2[1,4] = self.dm41sq - dm2[1,5] = self.dm51sq - dm2[1,6] = self.dm61sq - diff --git a/plot_llh/angles_to_fr.py b/plot_llh/angles_to_fr.py deleted file mode 100644 index ce1e4ed..0000000 --- a/plot_llh/angles_to_fr.py +++ /dev/null @@ -1,49 +0,0 @@ -#! /usr/bin/env python -from __future__ import absolute_import, division - -import sys -sys.path.extend(['.', '../']) - -import numpy as np - -from utils import fr as fr_utils -from utils.enums import MixingScenario - -SOURCE = [0, 1, 0] - -bsm = True -SCALE = 1E-45 -DIMENSION = 6 -FIX_MIXING = MixingScenario.T13 -ENERGY = 1E6 - - -if len(sys.argv)< 2: - print sys.argv - print "Usage: angles_to_fr.py input_filepath." - exit(1) - -infile = sys.argv[1] -outfile = infile[:-4] + '_proc.npy' - -d = np.load(infile) - -def m_fr(theta): - if not bsm: - s_12_2, c_13_4, s_23_2, dcp, m21_2, m3x_2 = theta - sm_u = fr_utils.angles_to_u((s_12_2, c_13_4, s_23_2, dcp)) - sm_u = np.array(sm_u, dtype=np.complex256) - return fr_utils.u_to_fr(SOURCE, sm_u) - elif bsm: - s_12_2, c_13_4, s_23_2, dcp, m21_2, m3x_2 = theta[:6] - sm_u = fr_utils.angles_to_u((s_12_2, c_13_4, s_23_2, dcp)) - bsm_u = np.array( - fr_utils.params_to_BSMu( - theta[6:], fix_scale=True, scale=SCALE, dim=DIMENSION, - energy=ENERGY, sm_u=sm_u - ), dtype=np.complex256 - ) - return fr_utils.u_to_fr(SOURCE, bsm_u) - -pd = np.array(map(m_fr, d)) -np.save(outfile, pd) diff --git a/plot_llh/bayes_contours.csv b/plot_llh/bayes_contours.csv deleted file mode 100644 index d0bc3fe..0000000 --- a/plot_llh/bayes_contours.csv +++ /dev/null @@ -1,7 +0,0 @@ -contour_68_upper,,,contour_68_lower,,,contour_90_upper,,,contour_90_lower,, -a,b,c,a,b,c,a,b,c,a,b,c -0.004241382440711872,0.3068136808118171,0.688944936747471,0.0023431606621458767,0.6426179526151782,0.355038886722676,0.003984245266406006,0.23803447470695244,0.7579812800266416,0.00185873303048395,0.747360118022478,0.25078114894703807 -0.10210039517264513,0.23867939040493313,0.6592202144224217,0.2265707968816993,0.49337780145261984,0.2800514016656809,0.10935946567301186,0.1652123613349607,0.7254281729920274,0.23593471812270522,0.6080100593018414,0.15605522257545337 -0.2522395018228548,0.14228992209114588,0.6054705760859993,0.41529102131856216,0.35987046976674486,0.22483850891469304,0.23346941572672392,0.08917978691650502,0.6773507973567711,0.45253594502145517,0.43398092848801995,0.1134831264905248 -0.3683515714362703,0.08269734534713502,0.5489510832165947,0.5921641154891925,0.19733554738306838,0.21050033712773913,0.3266627686296485,0.0383846610445428,0.6349525703258088,0.6482920818003852,0.26104295319739335,0.09066496500222145 -0.5169721920255199,0.0019131994171133482,0.48111460855736676,0.7412324298280606,0.001983635173622461,0.25678393499831703,0.3867924300270267,0.000882077323683228,0.61232549264929,0.9061045710391897,0.003593695348557302,0.09030173361225291 diff --git a/plot_llh/make_plots.py b/plot_llh/make_plots.py deleted file mode 100644 index 67d621d..0000000 --- a/plot_llh/make_plots.py +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/python - -import sys -sys.path.append('/Users/Teps/Theory') -#import header as h -#sys.path.append('/Users/Teps/Theory/HESE') -#import anarchy_header as ah -sys.path.append('/Users/Teps/Theory/HESE/Carlos') -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.pyplot as plt -from matplotlib import rc, rcParams -import MinimalTools as MT -import PhysConst as PC -import LVCPT as LVCPT -import numpy as np - -import sys,os - -rc('text', usetex=True) -rc('font', **{'family':'serif', 'serif':['Computer Modern'], 'size':18}) -cols = ['#29A2C6','#FF6D31','#FFCB18','#73B66B','#EF597B', '#333333'] - -font = {'family' : 'serif', - 'weight' : 'bold', - 'size' : 18} - -if len(sys.argv)< 2: - print sys.argv - print "Usage: make_plots.py input_filepath." - exit(1) - -#colors_schemes = ["Greens","Reds","Blues","PuRd","summer"] -colors_schemes = ["Greens","Reds","Blues","spring","summer"] -#colors_schemes = ["Greens","Reds","Blues","cool","summer"] -#colors_schemes = ["Greens","Reds","Blues","PuRd","summer"] -#colors_schemes = ["Blues","Greens","Reds","PuRd","summer"] -#colors_schemes = ["Greys","Greens","Reds","PuRd","summer"] -#colors_schemes = ["Greys","Greys","Greys","Greys","summer"] -#colors_schemes = ["PuRd","summer"] -output_format = "pdf" - -# if True then will plot all files in the same figure -use_same_canvas = True -figure = None - -for i in range(len(sys.argv)-1): - infile = sys.argv[i+1] - print "Load data: " + str(infile) - if infile[-3:] == 'txt': - flavor_list = np.genfromtxt(infile) - else: - flavor_list = np.load(infile) - if len(flavor_list[~np.isfinite(flavor_list)]) != 0: - fl = [] - for x in flavor_list: - if np.sum(~np.isfinite(x)) == 0: - fl.append(x.tolist()) - flavor_list = np.array(fl) - print flavor_list - print "Done loading data" - - if not use_same_canvas : - filename = "triangle_plot_"+os.path.basename(sys.argv[i+1])[:-4] - else : - filename = "triangle_plot"+os.path.basename(sys.argv[i+1])[:-4] - - # plots scale and diviions - scale = 8 - divisions = 80 - - print "Begin making plot ..." - triangle_collection = LVCPT.GenerateTriangles(scale,divisions*2) - map(lambda f : LVCPT.AddPointToTriangleCollection(f,triangle_collection, scale = scale, divisions = divisions*2),flavor_list) - - if use_same_canvas: - figure = LVCPT.MakeFlavorTriangle(flavor_list, divisions = 5, save_file=True, - filename = filename + "_hist", icolor = "g", icolormap = colors_schemes[i], - triangle_collection=triangle_collection, figure = figure, alpha = 0.8, - initial_flavor_ratio = [0,1,0], subdivisions = True, color_scale = "log", - output_format = output_format, inner_line_color ="silver",add_default_text = False, - plot_color_bar =True) - - else: - figure = LVCPT.MakeFlavorTriangle(flavor_list, divisions = 5, save_file=True, - filename = filename + "_hist", icolor = "g", icolormap = colors_schemes[i], - triangle_collection=triangle_collection, alpha = 0.8, - initial_flavor_ratio = [0,1,0], subdivisions = True, color_scale = "log", - output_format = output_format, inner_line_color = "silver",add_default_text = False, - plot_color_bar =True) - - print "Done making plot: " + filename + "_hist."+output_format - -ax = figure.get_axes()[0] -#ax.plot(6.5-0.35,5.5+0.3+0.35,"o", color = "grey", ms = 10, markeredgewidth = 0.1, alpha = 0.9) -#ax.text(6.7-0.35,5.44+0.3+0.35,r"$(1-x:x:0)$", fontsize = 16) -#ax.axvline(x = 7.9) -fsz = 32 -ax.plot(6.5-0.1,5.5+0.3+0.35+0.2+0.2,"o", color = "gold", ms = 25, markeredgewidth = 0.1, alpha = 0.9) -ax.text(6.7-0.1,5.44+0.3+0.35+0.2+0.2,r"$(1:2:0)$", fontsize = fsz) - -ax.plot(6.5-0.1,5.5+0.35+0.2,"o", color = "#2B653E", ms = 25, markeredgewidth = 0.1, alpha = 0.9) -ax.text(6.7-0.1,5.44+0.35+0.2,r"$(1:0:0)$", fontsize = fsz) - -ax.plot(6.5-0.1,5.5-0.3+0.35-0.2+0.2,"o", color = "#CA323D", ms = 25, markeredgewidth = 0.1, alpha = 0.9) -ax.text(6.7-0.1,5.44-0.3+0.35-0.2+0.2,r"$(0:1:0)$", fontsize = fsz) - -ax.plot(6.5-0.1,5.5-0.3+0.35-0.3-0.4+0.2,"o", color = "#2D4676", ms = 25, markeredgewidth = 0.1, alpha = 0.9) -ax.text(6.7-0.1,5.44-0.3+0.35-0.3-0.4+0.2,r"$(0:0:1)$", fontsize = fsz) - -plt.savefig("./plots/"+filename+"."+output_format, dpi = 600, bbox_inches='tight') - -exit(1) - -##os.system("cd plots") -##os.system("gs triangle_plot_hist.eps") -##os.system("cd ..") - diff --git a/plot_llh/sample.py b/plot_llh/sample.py deleted file mode 100644 index 4309627..0000000 --- a/plot_llh/sample.py +++ /dev/null @@ -1,145 +0,0 @@ -#! /usr/bin/env python -""" -Sample points for a specific scenario -""" - -from __future__ import absolute_import, division - -import sys -sys.path.extend(['.', '../']) - -import argparse -from functools import partial - -import numpy as np -from scipy.stats import multivariate_normal - -from utils import fr as fr_utils -from utils import llh as llh_utils -from utils import mcmc as mcmc_utils -from utils import misc as misc_utils -from utils.param import Param, ParamSet, get_paramsets -from utils.enums import MixingScenario, MCMCSeedType - - -def triangle_llh(theta, args): - """-Log likelihood function for a given theta.""" - fr = fr_utils.angles_to_fr(theta) - fr_bf = args.measured_ratio - cov_fr = np.identity(3) * args.sigma_ratio - return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr)) - - -def ln_prior(theta): - """Priors on theta.""" - sphi4, c2psi = theta - # Flavour ratio bounds - if 0. <= sphi4 <= 1.0 and -1.0 <= c2psi <= 1.0: - pass - else: return -np.inf - return 0. - - -def ln_prob(theta, args): - """Prob function for mcmc.""" - lp = ln_prior(theta) - if not np.isfinite(lp): - return -np.inf - return lp + triangle_llh(theta, args) - - -def process_args(args): - """Process the input args.""" - if args.fix_mixing is MixingScenario.NONE: - raise AssertionError('Must set a mixing scenario using --fix-mixing') - if not args.fix_source_ratio: - raise AssertionError('Must set source ratio using --fix-source-ratio') - - args.source_ratio = fr_utils.normalise_fr(args.source_ratio) - if args.fix_mixing is MixingScenario.T12: - s12_2, c13_4, s23_2, dcp = 0.5, 1.0, 0.0, 0. - elif args.fix_mixing is MixingScenario.T13: - s12_2, c13_4, s23_2, dcp = 0.0, 0.25, 0.0, 0. - elif args.fix_mixing is MixingScenario.T23: - s12_2, c13_4, s23_2, dcp = 0.0, 1.0, 0.5, 0. - - mm = np.array( - fr_utils.angles_to_u((s12_2, c13_4, s23_2, dcp)), dtype=np.complex256 - ) - args.measured_ratio = fr_utils.u_to_fr(args.source_ratio, mm) - - if not args.fix_scale: - args.scale, args.scale_region = fr_utils.estimate_scale(args) - - -def parse_args(args=None): - """Parse command line arguments""" - parser = argparse.ArgumentParser( - description="BSM flavour ratio analysis", - formatter_class=misc_utils.SortingHelpFormatter, - ) - parser.add_argument( - '--seed', type=misc_utils.seed_parse, default='25', - help='Set the random seed value' - ) - parser.add_argument( - '--threads', type=misc_utils.thread_type, default='1', - help='Set the number of threads to use (int or "max")' - ) - parser.add_argument( - '--outfile', type=str, default='./untitled', - help='Path to output chains' - ) - parser.add_argument( - '--plot-statistic', type=misc_utils.parse_bool, default='False', - help='Plot MultiNest evidence or LLH value' - ) - fr_utils.fr_argparse(parser) - llh_utils.likelihood_argparse(parser) - mcmc_utils.mcmc_argparse(parser) - if args is None: return parser.parse_args() - else: return parser.parse_args(args.split()) - - -def main(): - args = parse_args() - process_args(args) - misc_utils.print_args(args) - - if args.seed is not None: - np.random.seed(args.seed) - - paramset = ParamSet([ - Param(name='sphi4', value=0.5, ranges=[0.0, 1.0], std=0.2), - Param(name='c2psi', value=0.0, ranges=[-1.0, 1.0], std=0.2) - ]) - - outfile = misc_utils.gen_outfile_name(args) - print '== {0:<25} = {1}'.format('outfile', outfile) - - if args.run_mcmc: - ndim = len(paramset) - print paramset - - if args.mcmc_seed_type == MCMCSeedType.UNIFORM: - p0 = mcmc_utils.flat_seed(paramset, nwalkers=args.nwalkers) - - samples = mcmc_utils.mcmc( - p0 = p0, - ln_prob = partial(ln_prob, args=args), - ndim = ndim, - nwalkers = args.nwalkers, - burnin = args.burnin, - nsteps = args.nsteps, - threads = args.threads - ) - fr_chains = np.array(map(fr_utils.angles_to_fr, samples), dtype=float) - mcmc_utils.save_chains(fr_chains, outfile) - print "DONE!" - - -main.__doc__ = __doc__ - - -if __name__ == '__main__': - main() diff --git a/plot_llh/trajectory.ipynb b/plot_llh/trajectory.ipynb index d7f695b..7660b56 100644 --- a/plot_llh/trajectory.ipynb +++ b/plot_llh/trajectory.ipynb @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -379,6 +379,7 @@ " [0.40636667 0.35579595 0.23783738]\n", " [0.40636667 0.35579595 0.23783738]\n", " [0.40636667 0.35579595 0.23783738]]\n", + "(200000, 3)\n", "\n", "[[0.54269219 0.226958 0.23034981]\n", " [0.54269219 0.226958 0.23034981]\n", @@ -392,16 +393,34 @@ ], "source": [ "print SM_120\n", + "print SM_120.shape\n", "print\n", "print SM_100" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "metadata": {}, "outputs": [ { + "ename": "IOError", + "evalue": "[Errno 2] No such file or directory: '$HOME/fig2.png'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", + 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\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 612\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '$HOME/fig2.png'" + ] + }, + { "data": { "image/png": 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\n", "text/plain": [ @@ -541,12 +560,46 @@ " fontsize=fontsize+5, horizontalalignment='center')\n", "\n", "# save\n", - "fig.savefig('./plots/fig2.png', bbox_inches='tight', dpi=150)\n", + "fig.savefig('$HOME/fig2.png', bbox_inches='tight', dpi=150)\n", "#fig.savefig('./plots/fig2.pdf', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x432 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nbins = 25\n", + "\n", + "fig = plt.figure(figsize=(6, 6))\n", + "ax = fig.add_subplot(111)\n", + "tax = plot_utils.get_tax(ax, scale=nbins, ax_labels=ax_labels)\n", + "\n", + "KF_FR = np.load('kf.npy')\n", + "plot_utils.flavour_contour(\n", + " frs = KF_FR,\n", + " ax = ax,\n", + " nbins = nbins,\n", + " coverage = 68,\n", + " linewidth = 1,\n", + " color = 'black'\n", + ")" + ] + }, + { + "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ diff --git a/submitter/contour_dag.py b/submitter/contour_dag.py index 49c0fa9..0dcdf1e 100644 --- a/submitter/contour_dag.py +++ b/submitter/contour_dag.py @@ -3,9 +3,6 @@ import os import numpy as np -gfsource = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' -condor_script = gfsource + '/scripts/flavour_ratio/submitter/contour_submit.sub' - injected_ratios = [ (1, 1, 1), (1, 0, 0), diff --git a/submitter/mc_texture_dag.py b/submitter/mc_texture_dag.py new file mode 100644 index 0000000..f37b231 --- /dev/null +++ b/submitter/mc_texture_dag.py @@ -0,0 +1,73 @@ +#! /usr/bin/env python + +import os +import numpy as np + +source_ratios = [ + (1, 0, 0), + (0, 1, 0) +] + +textures = [ + 'OET', 'OUT' +] + +datadir = '/data/user/smandalia/flavour_ratio/data/mc_texture' + +prefix = '' +# prefix = '_noprior' + +golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' +condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/mc_texture_submit.sub' + +GLOBAL_PARAMS = {} + +GLOBAL_PARAMS.update(dict( + threads = 1, + seed = 26 +)) + +# Emcee +GLOBAL_PARAMS.update(dict( + run_mcmc = 'True', + burnin = 200, + nsteps = 1000, + nwalkers = 60, + mcmc_seed_type = 'uniform' +)) + +# FR +GLOBAL_PARAMS.update(dict( + binning = '6e4 1e7 20', + dimension = 6, + no_bsm = 'False' +)) + +# Plot +GLOBAL_PARAMS.update(dict( + plot_angles = 'False', + plot_elements = 'False', +)) + +dagfile = 'dagman_mc_texture_{0}'.format(GLOBAL_PARAMS['data']) +dagfile += prefix + '.submit' + +with open(dagfile, 'w') as f: + job_number = 1 + for src in source_ratios: + print 'src', src + for tex in textures: + print 'texture', tex + f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) + f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, src[0])) + f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, src[1])) + f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, src[2])) + f.write('VARS\tjob{0}\ttexture="{1}"\n'.format(job_number, tex)) + for key in GLOBAL_PARAMS.iterkeys(): + f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) + f.write('VARS\tjob{0}\tdatadir="{1}"\n'.format(job_number, datadir)) + job_number += 1 + +print 'total jobs = {0}'.format(job_number - 1) +print 'dag file = {0}'.format(dagfile) + diff --git a/submitter/mc_texture_submit.sub b/submitter/mc_texture_submit.sub new file mode 100644 index 0000000..7dccb81 --- /dev/null +++ b/submitter/mc_texture_submit.sub @@ -0,0 +1,39 @@ +Executable = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/mc_texture.py +Arguments = "--source-ratio $(sr0) $(sr1) $(sr2) --datadir $(datadir) --seed $(seed) --threads $(threads) --run-mcmc $(run_mcmc) --burnin $(burnin) --nsteps $(nsteps) --nwalkers $(nwalkers) --mcmc-seed-type $(mcmc_seed_type) --plot-angles $(plot_angles) --plot-elements $(plot_elements) --binning $(binning) --dimension $(dimension) --no-bsm $(no_bsm) --texture $(texture)" + +# All logs will go to a single file +log = /scratch/smandalia/flavour_ratio/submitter/logs/job_$(Cluster).log +output = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).out +error = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).err + +getenv = True +# environment = "X509_USER_PROXY=x509up_u14830" + +request_memory = 8GB +request_cpus = 1 + +initialdir = /home/smandalia/condor + +Universe = vanilla +Notification = never + +# +AccountingGroup="quicktest.$ENV(USER)" +# +AccountingGroup="sanctioned.$ENV(USER)" +# run on both SL5 and 6 +# +WantRHEL6 = True +# +WantSLC6 = False + +# # run on OSG +# +WantGlidein = True + +# +TransferOutput="" + ++NATIVE_OS = True +# Requirements = IS_GLIDEIN && HAS_CVMFS_icecube_opensciencegrid_org && (OpSysAndVer =?= "CentOS6" || OpSysAndVer =?= "RedHat6" || OpSysAndVer =?= "SL6") +# Requirements = IS_GLIDEIN +# Requirements = (OpSysMajorVer =?= 6) + +# GO! +queue + + diff --git a/submitter/mcmc_dag.py b/submitter/mcmc_dag.py deleted file mode 100644 index 1356965..0000000 --- a/submitter/mcmc_dag.py +++ /dev/null @@ -1,123 +0,0 @@ -#! /usr/bin/env python - -import os -import numpy as np - -full_scan_mfr = [ - # (1, 1, 1), (1, 0, 0) -] - -fix_sfr_mfr = [ - # (1, 1, 1, 1, 2, 0), - # (1, 1, 1, 1, 0, 0), - (1, 1, 1, 0, 1, 0), - # (1, 1, 1, 0, 0, 1), - # (1, 1, 0, 1, 2, 0), - # (1, 1, 0, 1, 0, 0), - # (1, 1, 0, 0, 1, 0), - # (1, 0, 0, 1, 0, 0), - # (0, 1, 0, 0, 1, 0), - # (1, 2, 0, 1, 2, 0), - # (1, 2, 0, 0, 1, 0), -] - -GLOBAL_PARAMS = {} - -# MCMC -GLOBAL_PARAMS.update(dict( - run_mcmc = 'True', - burnin = 250, - nsteps = 1000, - nwalkers = 60, - seed = 25, - mcmc_seed_type = 'uniform' -)) - -# FR -dimension = [6] -# dimension = [4, 5, 7, 8] -# dimension = [3, 4, 5, 6, 7, 8] -GLOBAL_PARAMS.update(dict( - threads = 1, - binning = '6e4 1e7 20', - no_bsm = 'False', - scale_region = "1E10", - energy_dependance = 'spectral', - spectral_index = -2, - fix_mixing = 'T13', - fix_mixing_almost = 'False', - fold_index = 'True' -)) - -# Likelihood -GLOBAL_PARAMS.update(dict( - likelihood = 'golemfit', - sigma_ratio = '0.01' -)) - -# GolemFit -GLOBAL_PARAMS.update(dict( - ast = 'p2_0', - data = 'real' -)) - -# Plot -GLOBAL_PARAMS.update(dict( - plot_angles = 'True', - plot_elements = 'False', -)) - -outfile = 'dagman_FR_MCMC_{0}_{1}.submit'.format(GLOBAL_PARAMS['likelihood'], - GLOBAL_PARAMS['fix_mixing']) -golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' -condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/mcmc_submit.sub' - -with open(outfile, 'w') as f: - job_number = 1 - for dim in dimension: - print 'dimension', dim - outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}/'.format( - GLOBAL_PARAMS['likelihood'], dim - ) - for frs in fix_sfr_mfr: - print 'frs', frs - outchains = outchain_head + '/fix_ifr/' + '{0}/'.format(GLOBAL_PARAMS['fix_mixing']) - if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian': - outchains += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_')) - outchains += '{0}/'.format(GLOBAL_PARAMS['data'].lower()) - outchains += 'mcmc_chain' - f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) - f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0])) - f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1])) - f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) - f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, True)) - f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, frs[3])) - f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, frs[4])) - f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, frs[5])) - for key in GLOBAL_PARAMS.iterkeys(): - f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) - f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) - job_number += 1 - - # for frs in full_scan_mfr: - # print 'frs', frs - # outchains = outchain_head + '/full/' - # if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian': - # outchains += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_')) - # outchains += 'mcmc_chain' - # f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) - # f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - # f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0])) - # f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1])) - # f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) - # f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, False)) - # f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, 0)) - # for key in GLOBAL_PARAMS.iterkeys(): - # f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) - # f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) - # job_number += 1 - - print 'dag file = {0}'.format(outfile) diff --git a/submitter/mcmc_submit.sub b/submitter/mcmc_submit.sub deleted file mode 100644 index f98049c..0000000 --- a/submitter/mcmc_submit.sub +++ /dev/null @@ -1,41 +0,0 @@ -Executable = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/fr.py -Arguments = "--ast $(ast) --burnin $(burnin) --data $(data) --dimension $(dimension) --fix-mixing $(fix_mixing) --fix-source-ratio $(fix_source_ratio) --likelihood $(likelihood) --measured-ratio $(mr0) $(mr1) $(mr2) --no-bsm $(no_bsm) --nsteps $(nsteps) --nwalkers $(nwalkers) --outfile $(outfile) --plot-angles $(plot_angles) --plot-elements $(plot_elements) --run-mcmc $(run_mcmc) --scale-region $(scale_region) --seed $(seed) --sigma-ratio $(sigma_ratio) --source-ratio $(sr0) $(sr1) $(sr2) --threads $(threads) --energy-dependance $(energy_dependance) --spectral-index $(spectral_index) --binning $(binning) --fix-mixing-almost $(fix_mixing_almost) --fold-index $(fold_index) --mcmc-seed-type $(mcmc_seed_type)" - -# All logs will go to a single file -log = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).log -output = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).out -error = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).err - -getenv = True -# environment = "X509_USER_PROXY=x509up_u14830" - -# Stage user cert to the node (Gridftp-Users is already on CVMFS) -# transfer_input_files = /tmp/x509up_u14830 - -# but do not try to copy outputs back (see: https://htcondor-wiki.cs.wisc.edu/index.cgi/tktview?tn=3081) -# +TransferOutput="" -Transfer_output_files = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/metaouts/ - -request_memory = 3GB -request_cpus = 1 - -Universe = vanilla -Notification = never - -# +AccountingGroup="sanctioned.$ENV(USER)" -# run on both SL5 and 6 -# +WantRHEL6 = True -# +WantSLC6 = False - -# # run on OSG -# +WantGlidein = True - -# +TransferOutput="" - -+NATIVE_OS = True -# Requirements = IS_GLIDEIN && HAS_CVMFS_icecube_opensciencegrid_org && (OpSysAndVer =?= "CentOS6" || OpSysAndVer =?= "RedHat6" || OpSysAndVer =?= "SL6") -# Requirements = IS_GLIDEIN -# Requirements = (OpSysMajorVer =?= 6) - -# GO! -queue diff --git a/utils/fr.py b/utils/fr.py index b8eba44..bf0fb56 100644 --- a/utils/fr.py +++ b/utils/fr.py @@ -13,7 +13,7 @@ from functools import partial import numpy as np -from utils.enums import Texture +from utils.enums import ParamTag, Texture from utils.misc import enum_parse, parse_bool import mpmath as mp @@ -309,14 +309,14 @@ 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, +def params_to_BSMu(bsm_angles, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, sm_u=NUFIT_U, no_bsm=False, texture=Texture.NONE, check_uni=True, epsilon=1e-7): """Construct the BSM mixing matrix from the BSM parameters. Parameters ---------- - theta : list, length > 3 + bsm_angles : list, length > 3 BSM parameters dim : int @@ -359,18 +359,18 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, 'got\n{0}'.format(sm_u) ) - if not isinstance(theta, (list, tuple)): - theta = [theta] + if not isinstance(bsm_angles, (list, tuple)): + bsm_angles = [bsm_angles] z = 0.+1e-9 if texture is Texture.OEU: - np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = 0.5, 1.0, z, z, theta + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = 0.5, 1.0, z, z, bsm_angles elif texture is Texture.OET: - np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = z, 0.25, z, z, theta + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = z, 0.25, z, z, bsm_angles elif texture is Texture.OUT: - np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = z, 1.0, 0.5, z, theta + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = z, 1.0, 0.5, z, bsm_angles else: - np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = theta + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = bsm_angles sc2 = np.power(10., sc2) sc1 = sc2 / 100. @@ -395,6 +395,64 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, return eg_vector +def flux_averaged_BSMu(theta, args, spectral_index, llh_paramset): + if len(theta) != len(llh_paramset): + raise AssertionError( + 'Length of MCMC scan is not the same as the input ' + 'params\ntheta={0}\nparamset]{1}'.format(theta, llh_paramset) + ) + + for idx, param in enumerate(llh_paramset): + param.value = theta[idx] + + bin_centers = np.sqrt(args.binning[:-1]*args.binning[1:]) + bin_width = np.abs(np.diff(args.binning)) + + source_flux = np.array( + [fr * np.power(bin_centers, spectral_index) + for fr in args.source_ratio] + ).T + + bsm_angles = llh_paramset.from_tag( + [ParamTag.SCALE, ParamTag.MMANGLES], values=True + ) + + m_eig_names = ['m21_2', 'm3x_2'] + ma_names = ['s_12_2', 'c_13_4', 's_23_2', 'dcp'] + + if set(m_eig_names+ma_names).issubset(set(llh_paramset.names)): + mass_eigenvalues = [x.value for x in llh_paramset if x.name in m_eig_names] + sm_u = angles_to_u( + [x.value for x in llh_paramset if x.name in ma_names] + ) + else: + mass_eigenvalues = MASS_EIGENVALUES + sm_u = NUFIT_U + + if args.no_bsm: + fr = u_to_fr(source_flux, np.array(sm_u, dtype=np.complex256)) + else: + mf_perbin = [] + for i_sf, sf_perbin in enumerate(source_flux): + u = params_to_BSMu( + bsm_angles = bsm_angles, + dim = args.dimension, + energy = bin_centers[i_sf], + mass_eigenvalues = mass_eigenvalues, + sm_u = sm_u, + no_bsm = args.no_bsm, + texture = args.texture, + ) + fr = u_to_fr(sf_perbin, u) + mf_perbin.append(fr) + measured_flux = np.array(mf_perbin).T + intergrated_measured_flux = np.sum(measured_flux * bin_width, axis=1) + averaged_measured_flux = (1./(args.binning[-1] - args.binning[0])) * \ + intergrated_measured_flux + fr = averaged_measured_flux / np.sum(averaged_measured_flux) + return fr + + def test_unitarity(x, prnt=False, rse=False, epsilon=None): """Test the unitarity of a matrix. diff --git a/utils/llh.py b/utils/llh.py index 9821695..5a0eea7 100644 --- a/utils/llh.py +++ b/utils/llh.py @@ -79,58 +79,13 @@ def triangle_llh(theta, args, asimov_paramset, llh_paramset): 'Length of MCMC scan is not the same as the input ' 'params\ntheta={0}\nparamset]{1}'.format(theta, llh_paramset) ) - for idx, param in enumerate(llh_paramset): - param.value = theta[idx] hypo_paramset = asimov_paramset for param in llh_paramset.from_tag(ParamTag.NUISANCE): hypo_paramset[param.name].value = param.value - bin_centers = np.sqrt(args.binning[:-1]*args.binning[1:]) - bin_width = np.abs(np.diff(args.binning)) spectral_index = -hypo_paramset['astroDeltaGamma'].value - - source_flux = np.array( - [fr * np.power(bin_centers, spectral_index) - for fr in args.source_ratio] - ).T - - bsm_angles = llh_paramset.from_tag( - [ParamTag.SCALE, ParamTag.MMANGLES], values=True - ) - - m_eig_names = ['m21_2', 'm3x_2'] - ma_names = ['s_12_2', 'c_13_4', 's_23_2', 'dcp'] - - if set(m_eig_names+ma_names).issubset(set(llh_paramset.names)): - mass_eigenvalues = [x.value for x in llh_paramset if x.name in m_eig_names] - sm_u = fr_utils.angles_to_u( - [x.value for x in llh_paramset if x.name in ma_names] - ) - else: - mass_eigenvalues = fr_utils.MASS_EIGENVALUES - sm_u = fr_utils.NUFIT_U - - if args.no_bsm: - fr = fr_utils.u_to_fr(source_flux, np.array(sm_u, dtype=np.complex256)) - else: - mf_perbin = [] - for i_sf, sf_perbin in enumerate(source_flux): - u = fr_utils.params_to_BSMu( - theta = bsm_angles, - dim = args.dimension, - energy = bin_centers[i_sf], - mass_eigenvalues = mass_eigenvalues, - sm_u = sm_u, - no_bsm = args.no_bsm, - texture = args.texture, - ) - fr = fr_utils.u_to_fr(sf_perbin, u) - mf_perbin.append(fr) - measured_flux = np.array(mf_perbin).T - intergrated_measured_flux = np.sum(measured_flux * bin_width, axis=1) - averaged_measured_flux = (1./(args.binning[-1] - args.binning[0])) * \ - intergrated_measured_flux - fr = averaged_measured_flux / np.sum(averaged_measured_flux) + # Assigning llh_paramset values from theta happens in this function. + fr = fr_utils.flux_averaged_BSMu(theta, args, spectral_index, llh_paramset) flavour_angles = fr_utils.fr_to_angles(fr) # print 'flavour_angles', map(float, flavour_angles) |