diff options
| -rwxr-xr-x | fr.py | 8 | ||||
| -rw-r--r-- | plot_llh/.ipynb_checkpoints/testing-checkpoint.ipynb | 164 | ||||
| -rw-r--r-- | plot_llh/mcmc_mixing.py | 2 | ||||
| -rw-r--r-- | plot_llh/paper.mplstyle | 27 | ||||
| -rw-r--r-- | plot_llh/sample.py | 65 | ||||
| -rw-r--r-- | plot_llh/testing.ipynb | 164 | ||||
| -rwxr-xr-x | plot_sens.py | 6 | ||||
| -rwxr-xr-x | sens.py | 6 | ||||
| -rw-r--r-- | submitter/mcmc_dag.py | 2 | ||||
| -rw-r--r-- | submitter/sens_dag.py | 2 | ||||
| -rw-r--r-- | utils/enums.py | 7 | ||||
| -rw-r--r-- | utils/fr.py | 21 | ||||
| -rw-r--r-- | utils/misc.py | 10 | ||||
| -rw-r--r-- | utils/param.py | 4 | ||||
| -rw-r--r-- | utils/plot.py | 4 |
15 files changed, 462 insertions, 30 deletions
@@ -21,8 +21,8 @@ from utils import likelihood 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 EnergyDependance, Likelihood, MCMCSeedType -from utils.enums import ParamTag, PriorsCateg +from utils.enums import EnergyDependance, Likelihood, MixingScenario +from utils.enums import MCMCSeedType, ParamTag, PriorsCateg from utils.param import Param, ParamSet, get_paramsets @@ -69,9 +69,9 @@ def nuisance_argparse(parser): def process_args(args): """Process the input args.""" - if args.fix_mixing and args.fix_scale: + if args.fix_mixing is not MixingScenario.NONE and args.fix_scale: raise NotImplementedError('Fixed mixing and scale not implemented') - if args.fix_mixing and args.fix_mixing_almost: + if args.fix_mixing is not MixingScenario.NONE and args.fix_mixing_almost: raise NotImplementedError( '--fix-mixing and --fix-mixing-almost cannot be used together' ) diff --git a/plot_llh/.ipynb_checkpoints/testing-checkpoint.ipynb b/plot_llh/.ipynb_checkpoints/testing-checkpoint.ipynb new file mode 100644 index 0000000..029b933 --- /dev/null +++ b/plot_llh/.ipynb_checkpoints/testing-checkpoint.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(200000, 3)\n" + ] + } + ], + "source": [ + "d = np.load('./mcmc_chain__0_1_0_1.0E-02_0.00_0.25_0.00.npy')\n", + "print d.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.07368225, 0.89173689, 0.03458086],\n", + " [0.07368225, 0.89173689, 0.03458086],\n", + " [0.06341465, 0.91041192, 0.02617342],\n", + " ...,\n", + " [0.06355231, 0.92517012, 0.01127757],\n", + " [0.06984286, 0.9162129 , 0.01394424],\n", + " [0.06738615, 0.91975897, 0.01285488]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import ternary" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./paper.mplstyle')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 576x576 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_aspect('equal')\n", + "\n", + "# Boundary and Gridlines\n", + "scale = 1\n", + "fig, tax = ternary.figure(ax=ax, scale=scale)\n", + "\n", + "# Draw Boundary and Gridlines\n", + "tax.boundary(linewidth=1.0)\n", + "tax.gridlines(color='black', multiple=scale/5.)\n", + "tax.gridlines(color='grey', multiple=scale/10., linewidth=0.5, alpha=0.6)\n", + "\n", + "# Set Axis labels and Title\n", + "fontsize = 18\n", + "tax.left_axis_label(r\"$f_{\\tau}^{\\oplus}$\", fontsize=fontsize)\n", + "tax.right_axis_label(r\"$f_{\\mu}^{\\oplus}$\", fontsize=fontsize)\n", + "tax.bottom_axis_label(r\"$f_{e}^{\\oplus}$\", fontsize=fontsize)\n", + "\n", + "# Remove default Matplotlib axis\n", + "tax.clear_matplotlib_ticks()\n", + "\n", + "# Plot\n", + "tax.scatter(d, marker='.', s=0.1, alpha=0.05, color='red', label='test')\n", + "\n", + "# Legend\n", + "tax.legend(fontsize=fontsize)\n", + "\n", + "# Set ticks\n", + "tax.ticks(axis='blr', multiple=scale/5., linewidth=1, fontsize=fontsize, tick_formats='%.1f')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/plot_llh/mcmc_mixing.py b/plot_llh/mcmc_mixing.py index 015e74f..6045ab5 100644 --- a/plot_llh/mcmc_mixing.py +++ b/plot_llh/mcmc_mixing.py @@ -31,7 +31,7 @@ def normalise_fr(fr): SOURCE_FR = normalise_fr([1, 1, 1]) SIGMA = 0.001 -ANGLES = (np.sin(np.pi/4.)**2, 0, 0, 0) +ANGLES = (np.sin(np.pi/4.)**2, 1.0, 0, 0) def angles_to_u(bsm_angles): diff --git a/plot_llh/paper.mplstyle b/plot_llh/paper.mplstyle new file mode 100644 index 0000000..fcfaf31 --- /dev/null +++ b/plot_llh/paper.mplstyle @@ -0,0 +1,27 @@ +figure.figsize : 5, 5 # figure size in inches +savefig.dpi : 600 # figure dots per inch + +font.size: 18 +font.family: serif +font.serif: Computer Modern, Latin Modern Roman, Bitstream Vera Serif +text.usetex: True + +axes.prop_cycle: cycler('color', ['29A2C6','FF6D31','73B66B','EF597B', '333333', 'FFCB18']) +axes.grid: False + +lines.linewidth: 2 +xtick.labelsize: medium +ytick.labelsize: medium +xtick.minor.visible: True # visibility of minor ticks on x-axis +ytick.minor.visible: True # visibility of minor ticks on y-axis +xtick.major.size: 6 # major tick size in points +xtick.minor.size: 3 # minor tick size in points +ytick.major.size: 6 # major tick size in points +ytick.minor.size: 3 # minor tick size in points +xtick.major.width: 1 +xtick.minor.width: 1 +ytick.major.width: 1 +ytick.minor.width: 1 + +legend.frameon: False +legend.fontsize: 12 diff --git a/plot_llh/sample.py b/plot_llh/sample.py new file mode 100644 index 0000000..469a538 --- /dev/null +++ b/plot_llh/sample.py @@ -0,0 +1,65 @@ +#! /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 utils import fr as fr_utils +from utils import misc as misc_utils +from utils.param import Param, ParamSet, get_paramsets + + +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) + 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) + + asimov_paramset, llh_paramset = get_paramsets(args, ParamSet()) + outfile = misc_utils.gen_outfile_name(args) + print '== {0:<25} = {1}'.format('outfile', outfile) + + +main.__doc__ = __doc__ + + +if __name__ == '__main__': + main() diff --git a/plot_llh/testing.ipynb b/plot_llh/testing.ipynb new file mode 100644 index 0000000..029b933 --- /dev/null +++ b/plot_llh/testing.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(200000, 3)\n" + ] + } + ], + "source": [ + "d = np.load('./mcmc_chain__0_1_0_1.0E-02_0.00_0.25_0.00.npy')\n", + "print d.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.07368225, 0.89173689, 0.03458086],\n", + " [0.07368225, 0.89173689, 0.03458086],\n", + " [0.06341465, 0.91041192, 0.02617342],\n", + " ...,\n", + " [0.06355231, 0.92517012, 0.01127757],\n", + " [0.06984286, 0.9162129 , 0.01394424],\n", + " [0.06738615, 0.91975897, 0.01285488]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import ternary" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./paper.mplstyle')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 576x576 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_aspect('equal')\n", + "\n", + "# Boundary and Gridlines\n", + "scale = 1\n", + "fig, tax = ternary.figure(ax=ax, scale=scale)\n", + "\n", + "# Draw Boundary and Gridlines\n", + "tax.boundary(linewidth=1.0)\n", + "tax.gridlines(color='black', multiple=scale/5.)\n", + "tax.gridlines(color='grey', multiple=scale/10., linewidth=0.5, alpha=0.6)\n", + "\n", + "# Set Axis labels and Title\n", + "fontsize = 18\n", + "tax.left_axis_label(r\"$f_{\\tau}^{\\oplus}$\", fontsize=fontsize)\n", + "tax.right_axis_label(r\"$f_{\\mu}^{\\oplus}$\", fontsize=fontsize)\n", + "tax.bottom_axis_label(r\"$f_{e}^{\\oplus}$\", fontsize=fontsize)\n", + "\n", + "# Remove default Matplotlib axis\n", + "tax.clear_matplotlib_ticks()\n", + "\n", + "# Plot\n", + "tax.scatter(d, marker='.', s=0.1, alpha=0.05, color='red', label='test')\n", + "\n", + "# Legend\n", + "tax.legend(fontsize=fontsize)\n", + "\n", + "# Set ticks\n", + "tax.ticks(axis='blr', multiple=scale/5., linewidth=1, fontsize=fontsize, tick_formats='%.1f')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/plot_sens.py b/plot_sens.py index 610ee95..a297e17 100755 --- a/plot_sens.py +++ b/plot_sens.py @@ -23,7 +23,7 @@ from utils import gf as gf_utils from utils import likelihood as llh_utils from utils import misc as misc_utils from utils import plot as plot_utils -from utils.enums import EnergyDependance, Likelihood, ParamTag +from utils.enums import EnergyDependance, Likelihood, MixingScenario, ParamTag from utils.enums import PriorsCateg, SensitivityCateg, StatCateg from utils.param import Param, ParamSet, get_paramsets @@ -72,9 +72,9 @@ def nuisance_argparse(parser): def process_args(args): """Process the input args.""" - if args.fix_mixing and args.fix_scale: + if args.fix_mixing not MixingScenario.NONE and args.fix_scale: raise NotImplementedError('Fixed mixing and scale not implemented') - if args.fix_mixing and args.fix_mixing_almost: + if args.fix_mixing not MixingScenario.NONE and args.fix_mixing_almost: raise NotImplementedError( '--fix-mixing and --fix-mixing-almost cannot be used together' ) @@ -23,7 +23,7 @@ from utils import gf as gf_utils from utils import likelihood as llh_utils from utils import misc as misc_utils from utils import plot as plot_utils -from utils.enums import EnergyDependance, Likelihood, ParamTag +from utils.enums import EnergyDependance, Likelihood, MixingScenario, ParamTag from utils.enums import PriorsCateg, SensitivityCateg, StatCateg from utils.param import Param, ParamSet, get_paramsets @@ -72,9 +72,9 @@ def nuisance_argparse(parser): def process_args(args): """Process the input args.""" - if args.fix_mixing and args.fix_scale: + if args.fix_mixing is not MixingScenario.NONE and args.fix_scale: raise NotImplementedError('Fixed mixing and scale not implemented') - if args.fix_mixing and args.fix_mixing_almost: + if args.fix_mixing is not MixingScenario.NONE and args.fix_mixing_almost: raise NotImplementedError( '--fix-mixing and --fix-mixing-almost cannot be used together' ) diff --git a/submitter/mcmc_dag.py b/submitter/mcmc_dag.py index 23586ac..b7e2c84 100644 --- a/submitter/mcmc_dag.py +++ b/submitter/mcmc_dag.py @@ -44,7 +44,7 @@ GLOBAL_PARAMS.update(dict( scale_region = "1E10", energy_dependance = 'spectral', spectral_index = -2, - fix_mixing = 'False', + fix_mixing = 'None', fix_mixing_almost = 'False', fold_index = 'True' )) diff --git a/submitter/sens_dag.py b/submitter/sens_dag.py index 1dc0ed8..b3dcda8 100644 --- a/submitter/sens_dag.py +++ b/submitter/sens_dag.py @@ -51,7 +51,7 @@ GLOBAL_PARAMS.update(dict( scale_region = "1E10", energy_dependance = 'spectral', spectral_index = -2, - fix_mixing = 'False', + fix_mixing = 'None', fix_mixing_almost = 'False', fold_index = 'True' )) diff --git a/utils/enums.py b/utils/enums.py index 7fcddae..2450ff7 100644 --- a/utils/enums.py +++ b/utils/enums.py @@ -28,6 +28,13 @@ class Likelihood(Enum): GF_FREQ = 4 +class MixingScenario(Enum): + T12 = 1 + T13 = 2 + T23 = 3 + NONE = 4 + + class ParamTag(Enum): NUISANCE = 1 SM_ANGLES = 2 diff --git a/utils/fr.py b/utils/fr.py index cb830ea..c6d7a52 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 EnergyDependance +from utils.enums import EnergyDependance, MixingScenario from utils.misc import enum_parse, parse_bool import mpmath as mp @@ -303,8 +303,9 @@ def fr_argparse(parser): help='Fix the source flavour ratio' ) parser.add_argument( - '--fix-mixing', type=parse_bool, default='False', - help='Fix all mixing parameters to bi-maximal mixing' + '--fix-mixing', type=partial(enum_parse, c=MixingScenario), + default='None', choices=MixingScenario, + help='Fix all mixing parameters to choice of maximal mixing' ) parser.add_argument( '--fix-mixing-almost', type=parse_bool, default='False', @@ -350,7 +351,7 @@ NUFIT_U = angles_to_u((0.307, (1-0.02195)**2, 0.565, 3.97935)) def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, - sm_u=NUFIT_U, no_bsm=False, fix_mixing=False, + sm_u=NUFIT_U, no_bsm=False, fix_mixing=MixingScenario.NONE, fix_mixing_almost=False, fix_scale=False, scale=None, check_uni=True, epsilon=1e-7): """Construct the BSM mixing matrix from the BSM parameters. @@ -375,7 +376,7 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, no_bsm : bool Turn off BSM behaviour - fix_mixing : bool + fix_mixing : MixingScenario Fix the BSM mixing angles fix_mixing_almost : bool @@ -409,13 +410,17 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, 'got\n{0}'.format(sm_u) ) - if fix_mixing and fix_mixing_almost: + if fix_mixing is not MixingScenario.NONE and fix_mixing_almost: raise NotImplementedError( '--fix-mixing and --fix-mixing-almost cannot be used together' ) - if fix_mixing: - s12_2, c13_4, s23_2, dcp, sc2 = 0.5, 1.0-1E-6, 0.5, 0., theta + if fix_mixing is MixingScenario.T12: + s12_2, c13_4, s23_2, dcp, sc2 = 0.5, 1.0, 0.0, 0., theta + elif fix_mixing is MixingScenario.T13: + s12_2, c13_4, s23_2, dcp, sc2 = 0.0, 0.25, 0.0, 0., theta + elif fix_mixing is MixingScenario.T23: + s12_2, c13_4, s23_2, dcp, sc2 = 0.0, 1.0, 0.5, 0., theta elif fix_mixing_almost: s12_2, c13_4, dcp = 0.5, 1.0-1E-6, 0. s23_2, sc2 = theta diff --git a/utils/misc.py b/utils/misc.py index 837c145..ed14941 100644 --- a/utils/misc.py +++ b/utils/misc.py @@ -19,7 +19,7 @@ from operator import attrgetter import numpy as np -from utils.enums import Likelihood +from utils.enums import Likelihood, MixingScenario class SortingHelpFormatter(argparse.HelpFormatter): @@ -42,16 +42,16 @@ def gen_identifier(args): f += '_sfr_{0:G}_{1:G}_{2:G}_mfr_{3:G}_{4:G}_{5:G}'.format( sr1, sr2, sr3, mr1, mr2, mr3 ) - if args.fix_mixing: - f += '_fix_mixing' + if args.fix_mixing is not MixingScenario.NONE: + f += '_{0}'.format(args.fix_mixing) elif args.fix_mixing_almost: f += '_fix_mixing_almost' elif args.fix_scale: f += '_fix_scale_{0}'.format(args.scale) else: f += '_mfr_{3:03d}_{4:03d}_{5:03d}'.format(mr1, mr2, mr3) - if args.fix_mixing: - f += '_fix_mixing' + if args.fix_mixing is not MixingScenario.NONE: + f += '_{0}'.format(args.fix_mixing) elif args.fix_mixing_almost: f += '_fix_mixing_almost' elif args.fix_scale: diff --git a/utils/param.py b/utils/param.py index f8d64eb..fe0a0a0 100644 --- a/utils/param.py +++ b/utils/param.py @@ -18,7 +18,7 @@ import numpy as np from utils.plot import get_units from utils.fr import fr_to_angles -from utils.enums import DataType, Likelihood, ParamTag, PriorsCateg +from utils.enums import DataType, Likelihood, MixingScenario, ParamTag, PriorsCateg class Param(object): @@ -237,7 +237,7 @@ def get_paramsets(args, nuisance_paramset): ]) asimov_paramset = ParamSet(asimov_paramset) - if not args.fix_mixing and not args.fix_mixing_almost: + if args.fix_mixing is not MixingScenario.NONE and not args.fix_mixing_almost: tag = ParamTag.MMANGLES llh_paramset.extend([ Param(name='np_s_12^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{12}^2', tag=tag), diff --git a/utils/plot.py b/utils/plot.py index ea3c852..fb58176 100644 --- a/utils/plot.py +++ b/utils/plot.py @@ -31,7 +31,7 @@ from getdist import plots, mcsamples from utils import misc as misc_utils from utils.enums import DataType, EnergyDependance -from utils.enums import Likelihood, ParamTag, StatCateg +from utils.enums import Likelihood, MixingScenario, ParamTag, StatCateg from utils.fr import angles_to_u, angles_to_fr plt.style.use(os.environ['GOLEMSOURCEPATH']+'/GolemFit/scripts/paper/paper.mplstyle') @@ -228,7 +228,7 @@ def chainer_plot(infile, outfile, outformat, args, llh_paramset): trns_ranges = np.array(ranges)[nu_index,].tolist() trns_axes_labels = np.array(axes_labels)[nu_index,].tolist() - if not args.fix_mixing: + if args.fix_mixing is not MixingScenario.NONE: trns_axes_labels += \ [r'\mid \tilde{U}_{e1} \mid' , r'\mid \tilde{U}_{e2} \mid' , r'\mid \tilde{U}_{e3} \mid' , \ r'\mid \tilde{U}_{\mu1} \mid' , r'\mid \tilde{U}_{\mu2} \mid' , r'\mid \tilde{U}_{\mu3} \mid' , \ |
