def __init__(self, app, ini=None, base_dir=None):
"""
Initialize of GUI components.
"""
super(MainWindow, self).__init__()
if base_dir is None: base_dir = batchJob.getCodeRootPath()
os.chdir(base_dir)
self.updating = False
self.app = app
self.base_dir = base_dir
# GUI setup
self.createWidgets()
self.createActions()
self.createMenus()
self.createStatusBar()
self.setAttribute(Qt.WA_DeleteOnClose)
self.setWindowTitle("GetDist GUI")
# Allow to shutdown the GUI with Ctrl+C
signal.signal(signal.SIGINT, signal.SIG_DFL)
# Path for .ini file
self.iniFile = ini or MCSamples.default_getdist_settings
# Path of root directory
self.rootdirname = None
self.plot_module = 'GetDistPlots'
self._resetGridData()
self._resetPlotData()
lastDir = self.getSettings().value('lastSearchDirectory')
if lastDir:
self.openDirectory(lastDir)
def BAOdensity(prob_file):
d = loadtxt(prob_file)
ix = 0
prob = np.zeros((alpha_npoints, alpha_npoints))
alpha_perp = np.zeros(alpha_npoints)
alpha_pl = np.zeros(alpha_npoints)
for i in range(alpha_npoints):
for j in range(alpha_npoints):
alpha_perp[i] = d[ix, 0]
alpha_pl[j] = d[ix, 1]
prob[j, i] = d[ix, 2]
ix += 1
prob = prob / np.max(prob)
return alpha_perp, alpha_pl, prob
alpha_perp, alpha_pl, prob = BAOdensity(batchJob.getCodeRootPath() + 'data/sdss_DR11CMASS_consensus.dat')
densityG = BAOdensityG()
perp = alpha_perp * DA_fid
para = H_fid / alpha_pl
density = GetDistPlots.Density2D()
density.pts = prob
density.x1 = perp
density.x2 = para
density.contours = exp(-np.array([1.509, 2.4477]) ** 2 / 2)
root = 'base_plikHM_TT_lowTEB_lensing'
FAP, f8 = np.meshgrid(FAP, f8)
like = (FAP - FAPbar) ** 2 * invcov[0, 0] + 2 * (FAP - FAPbar) * (f8 - f8bar) * invcov[0, 1] + (f8 - f8bar) ** 2 * invcov[1, 1]
density = GetDistPlots.Density2D()
density.pts = exp(-like / 2)
density.x1 = FAP
density.x2 = f8
density.contours = exp(-np.array([1.509, 2.4477]) ** 2 / 2)
return density
FAPbar = 0.6725
f8bar = 0.4412
density = RSDdensity(FAPbar, f8bar, batchJob.getCodeRootPath() + 'data/sdss_DR11CMASS_RSD_bao_invcov_Samushia.txt')
g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=True, density=density)
# CS = contourf(FAP, f8, like, origin='lower', levels=[2.279, 5.991], colors='r')
FAPbar = .683
f8bar = 0.422
density = RSDdensity(FAPbar, f8bar, batchJob.getCodeRootPath() + 'data/sdss_DR11CMASS_RSD_bao_invcov_Beutler.txt')
g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=False, density=density, ls=':', alpha=0.5)
g.add_2d_contours(roots[0], 'FAP057', 'fsigma8z057', filled=True, plotno=3)
g.add_legend(['BOSS CMASS (Samushia et al.)', 'BOSS CMASS (Beutler et al.)', s.defplanck + '+lensing'], legend_loc='upper left')
d = loadtxt(prob_file)
ix = 0
prob = np.zeros((alpha_npoints, alpha_npoints))
alpha_perp = np.zeros(alpha_npoints)
alpha_pl = np.zeros(alpha_npoints)
for i in range(alpha_npoints):
for j in range(alpha_npoints):
alpha_perp[i] = d[ix, 0]
alpha_pl[j] = d[ix, 1]
prob[j, i] = d[ix, 2]
ix += 1
prob = prob / np.max(prob)
return alpha_perp, alpha_pl, prob
alpha_perp, alpha_pl, prob = BAOdensity(batchJob.getCodeRootPath() +
'data/sdss_DR11CMASS_consensus.dat')
densityG = BAOdensityG()
perp = alpha_perp * DA_fid
para = H_fid / alpha_pl
density = GetDistPlots.Density2D()
density.pts = prob
density.x1 = perp
density.x2 = para
density.contours = exp(-np.array([1.509, 2.4477])**2 / 2)
root = 'base_plikHM_TT_lowTEB_lensing'
s = GetDistPlots.defaultSettings
s.legend_frame = False
s.figure_legend_frame = False
s.prob_label = r'$P/P_{\rm max}$'
s.norm_prob_label = 'Probability density'
s.prob_y_ticks = True
s.param_names_for_labels = 'clik_units.paramnames'
s.alpha_filled_add = 0.85
s.solid_contour_palefactor = 0.6
s.solid_colors = [('#8CD3F5', '#006FED'), ('#F7BAA6', '#E03424'), ('#D1D1D1', '#A1A1A1'), 'g', 'cadetblue', 'indianred']
s.axis_marker_lw = 0.6
s.lw_contour = 1
use_plot_data = MCSamples.use_plot_data
rootdir = MCSamples.default_grid_root or os.path.join(batchJob.getCodeRootPath(), 'main')
output_base_dir = MCSamples.output_base_dir or batchJob.getCodeRootPath()
H0_gpe = [70.6, 3.3]
# various Omegam sigma8 constraints for plots
def planck_lensing(omm, sigma):
# g60_full
return (0.591 + 0.021 * sigma) * omm ** (-0.25)
def plotBounds(omm, data, c='gray'):
pylab.fill_between(omm, data(omm, -2), data(omm, 2), facecolor=c, alpha=0.15, edgecolor=c, lw=0)
pylab.fill_between(omm, data(omm, -1), data(omm, 1), facecolor=c, alpha=0.25, edgecolor=c, lw=0)