def getChiScan():
'''
get the \chi^2 scan
1st line:
2nd line: settings
3rd line: best fit
4-end lines: scan
'''
### Constants
sizeX = 100
sizeY = 100
name = '.scan.dat'
### Create a file in /tmp with the scan minus the two first lines
idx = 0
f = open(path__+name)
tmp_f = open('/tmp/scan.txt','w')
for line in f:
if (idx>=2): tmp_f.write(line)
idx += 1
tmp_f.close()
f.close()
### Data
data = numpy.loadtxt('/tmp/scan.txt')
### Best Fit
alphaPara_bestFit = data[0][11]
alphaPerp_bestFit = data[0][12]
chi2_bestFit = data[0][-1]
print alphaPara_bestFit, alphaPerp_bestFit, chi2_bestFit
### Scan
alphaPara = data[1:,11]
alphaPerp = data[1:,12]
chi2 = data[1:,-1]
### Put data in 2D array
alphaPara_2D = numpy.zeros( shape=(sizeX,sizeY) )
alphaPerp_2D = numpy.zeros( shape=(sizeX,sizeY) )
chi2_2D = numpy.zeros( shape=(sizeX,sizeY) )
for k in numpy.arange(0,chi2.size):
i = k/sizeY
j = k%sizeY
alphaPara_2D[i][j] = alphaPara[k]
alphaPerp_2D[i][j] = alphaPerp[k]
chi2_2D[i][j] = chi2[k]
edge = [0.5, 1.5, 0.5, 1.5]
### Get best fit plus bootstrap
nbBoot = 10000
bestFit = numpy.zeros( shape=(2,nbBoot+1) )
chi2Boot = numpy.zeros( shape=(nbBoot+1) )
bestFit[0,0] = alphaPerp_bestFit
bestFit[1,0] = alphaPara_bestFit
chi2Boot[0] = chi2_bestFit
for i in range(0,nbBoot):
try:
data = numpy.loadtxt('/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/BACKUP_2015_01_15/FitsFile_DR12_Guy/BaoFit_q_f/Bootstraps/boot_'+str(i).zfill(4)+'/bao2D.save.pars')
dataChi2 = numpy.loadtxt('/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/BACKUP_2015_01_15/FitsFile_DR12_Guy/BaoFit_q_f/Bootstraps/boot_'+str(i).zfill(4)+'/bao2D.fit.chisq')
bestFit[0,i+1] = data[12,1]
bestFit[1,i+1] = data[11,1]
chi2Boot[i+1] = dataChi2[0]
#print '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy/BaoFit_q_f/Bootstraps/boot_'+str(i).zfill(4)+'/bao2D.save.pars'
#print bestFit[0,i+1], bestFit[1,i+1]
except Exception:
print i
### Get the delta of chi^2
chi2_2D[ (chi2_2D==0.) ] = float('nan')
chi2_2D[ (chi2_2D!=0.) ] = chi2_2D[ (chi2_2D!=0.) ]-chi2_bestFit
### Plot the data
plotChi2Scan(chi2_2D,edge,True, bestFit,'\\alpha_{\\perp}','\\alpha_{\\parallel}','\\Delta \\chi^{2} = \\chi^{2}-\\chi^{2}_{best \\, fit}')
###
print numpy.mean(bestFit[0,1:][ bestFit[0,1:]>0. ])
print numpy.mean(bestFit[1,1:])
functionChi2 = interpolate.interp2d(alphaPerp_2D, alphaPara_2D, chi2_2D)
chi2Realisation = numpy.asarray( [ functionChi2(bestFit[0,i], bestFit[1,i]) for i in range(0,nbBoot+1) ] )
### Get the Delta chi^2 value for fiducial model
chi2Fiducial = functionChi2(1.,1.)
print chi2Fiducial
print chi2Realisation[ chi2Realisation >= chi2Fiducial ].size
plt.hist(bestFit[0,1:][ bestFit[0,1:]>0. ], bins=20)
plt.show()
plt.hist(bestFit[1,1:][ bestFit[0,1:]>0. ], bins=20)
plt.show()
plt.hist(chi2Boot[1:][ bestFit[0,1:]>0. ]-chi2Boot[0], bins=20)
plt.show()
plt.hist(chi2Realisation[ bestFit[0,1:]>0. ], bins=20)
plt.show()
myTools.fitAGaussian(bestFit[0,1:][ bestFit[0,1:]>0.6 ], 50, [1,0.,1.], False)
myTools.fitAGaussian(bestFit[0,1:][ bestFit[0,1:]>0.6 ], 50, [1,0.,1.], True)
myTools.fitAGaussian(bestFit[1,1:][ bestFit[0,1:]>0.6 ], 50, [1,0.,1.], False)
myTools.fitAGaussian(bestFit[1,1:][ bestFit[0,1:]>0.6 ], 50, [1,0.,1.], True)
myTools.fitAGaussian(chi2Boot[1:][ bestFit[0,1:]>0.6 ]-chi2Boot[0], 50, [1,0.,50.], False)
myTools.fitAGaussian(chi2Boot[1:][ bestFit[0,1:]>0.6 ]-chi2Boot[0], 50, [1,0.,50.], True)
from scipy.stats import chisqprob
### Get number under one sigma, two sigma ...
nbParametrDiff = 2
print ' parameter number difference = ', nbParametrDiff
tmp_a = chi2Realisation[1:]
print tmp_a.size
print 1.-chisqprob(2.3, 2)
sigma = [2.30, 6.18, 11.83, 19.33, 28.74]
for i in range(0,5):
print ' Delta Chi^2 = ', sigma[i], ' sigma = ', i+1, ' nbReal = ', tmp_a[ tmp_a<sigma[i] ].size, ' expected = ', tmp_a.size*(1.-chisqprob(sigma[i], 2)), chisqprob(sigma[i], 2)
return
def getHistoResiduals():
"""
"""
###
yyy = xi_resAll__[numpy.isfinite(xi_resAll__)]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(yyy, bins=100)
plt.ticklabel_format(style="sci", axis="z", scilimits=(0, 0))
plt.grid()
plt.xlabel(r"$(\xi_{simu}-\xi_{fit})/\sigma_{simu}$")
plt.ylabel(r"$\#$")
plt.rc("font", **{"size": "30"})
plt.tick_params(axis="both", which="major", labelsize=30)
plt.grid(True, which="both")
mng = plt.get_current_fig_manager()
textstr = "$Nb=%d$ \n $\mu=%.5e +/- %.2e$ \n $\sigma=%.5e +/- %.2e$" % (
yyy.size,
numpy.mean(yyy),
numpy.std(yyy) / numpy.sqrt(yyy.size),
numpy.std(yyy),
numpy.std(yyy) / numpy.sqrt(yyy.size),
)
props = dict(boxstyle="round", facecolor="wheat", alpha=0.5)
ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=30, verticalalignment="top", bbox=props)
plt.show()
### Fit a gaussian
myTools.fitAGaussian(yyy, 100, [1, 0.0, 1.0], False)
myTools.fitAGaussian(yyy, 100, [1, 0.0, 1.0], True)
### Constants
nbBins = 100
### Plot the residuals as a function of bin index
xxx = numpy.arange(0.0, nbBin__)
yyy = xi_dat__ - xi_fit__
yyy[(xi_fit__ == 0.0)] = 0.0
yer = xi_err__
yer[(xi_fit__ == 0.0)] = 0.0
plt.errorbar(xxx, yyy, yerr=yer, linestyle="", marker="o", color="blue")
plt.xlabel(r"$index \, bin$")
plt.ylabel(r"$\xi_{data}-\xi_{fit}$")
myTools.deal_with_plot(False, False, True)
plt.show()
### Plot the residuals/err as a function of bin index
xxx = numpy.arange(0.0, nbBin__)
yyy = (xi_dat__ - xi_fit__) / xi_err__
yyy[(xi_fit__ == 0.0)] = 0.0
plt.errorbar(xxx, yyy, linestyle="", marker="o", color="blue")
plt.xlabel(r"$index \, bin$")
plt.ylabel(r"$\frac{\xi_{data}-\xi_{fit}}{\sigma}$")
myTools.deal_with_plot(False, False, True)
plt.show()
### Plot the distribution
yyy = (xi_dat__[(xi_err__ != 0.0)] - xi_fit__[(xi_err__ != 0.0)]) / xi_err__[(xi_err__ != 0.0)]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(yyy, bins=nbBins)
plt.ticklabel_format(style="sci", axis="z", scilimits=(0, 0))
plt.grid()
plt.xlabel(r"$\frac{data-fit}{\sigma_{data}}$")
plt.ylabel(r"$\#$")
plt.rc("font", **{"size": "30"})
plt.tick_params(axis="both", which="major", labelsize=30)
plt.grid(True, which="both")
mng = plt.get_current_fig_manager()
# mng.resize(*mng.window.maxsize())
textstr = "$\mu=%.5e$\n$\sigma=%.5e$" % (numpy.mean(yyy), numpy.std(yyy))
props = dict(boxstyle="round", facecolor="wheat", alpha=0.5)
ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=30, verticalalignment="top", bbox=props)
plt.show()
# fitAGaussian(yyy, 50, [1,0.,1.],'\\frac{data-fit}{\\sigma_{data}}','\\#')
return
def plotBosseBOSS():
dic_class = {
'minXi': 0.,
'maxXi': 200.,
'nbBin': 50,
'nbBinM': 25,
'nb_Sub_Sampling': 80,
'size_bin_calcul_s': 1.,
'size_bin_calcul_m': 0.02,
'nb_wedges' : 4,
'remove_residuales' : 'lambda_OBS',
'index_multipole_max' : 4,
'path_to_txt_file_folder': '',
'correlation': 'q_f',
'f1': 'LYA',
'f2': '',
'q1': 'QSO',
'q2': '',
'name' : 'Mean \, Simulation'
}
dic_simu = {
'path_to_simu' : '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1575/',
'nb_box' : 10,
'nb_simu' : 10,
'prefix' : '_withRand_XYZ_nicolasEstimator'
}
#dic_class['path_to_txt_file_folder'] = '/home/gpfs/manip/mnt0607/bao/hdumasde/Mock_JMLG/v1575/Box_000/Simu_000/Results_withRand_XYZ_nicolasEstimator/'
#corr4 = correlation_3D.Correlation3D(dic_class)
#corr4.save_list_realisation_simulation(dic_class, dic_simu)
#corr4.set_values_on_mean_simulation(dic_simu)
dic_class['q1'] = 'QSO'
dic_class['remove_residuales'] = 'lambda_OBS'
#dic_class['path_to_txt_file_folder'] = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_nicolasEstimator_coAdd_2016_05_26/'
dic_class['path_to_txt_file_folder'] = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_nicolasEstimator_2016_05_26/'
dic_class['name'] = 'LYA - LYA'
corr = correlation_3D.Correlation3D(dic_class)
#corr.save_list_realisation('subsampling', 80)
corr.set_error_on_covar_matrix('subsampling')
#print corr._meanZ
#corr4.print_null_test()
#print ''
#corr4.print_null_test('subsampling')
#print ''
#corr4.print_null_test('subsampling','_from_fit')
###
#dic_class['q1'] = 'QSO'
#dic_class['remove_residuales'] = 'lambda_OBS'
#dic_class['path_to_txt_file_folder'] = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_nicolasEstimator_coAdd_2016_05_26/'
#dic_class['path_to_txt_file_folder'] = '/home/gpfs/manip/mnt0607/bao/hdumasde/Results/Txt/FitsFile_DR12_Guy_nicolasEstimator_2016_05_26/'
#dic_class['name'] = 'residuals \, LYA - QSO \, (shuffle \, Forest)'
#dic_class['name'] = 'Sky - Sky'
#corr5 = correlation_3D.Correlation3D(dic_class)
#corr5.set_values_on_mean_simulation_or_realisation_type(None,'randomQSOXYZ')
#corr5.save_list_realisation('shuffleForest', 1000)
#corr5.save_list_realisation('subsampling', 80)
#corr5.set_error_on_covar_matrix('subsampling')
#print corr5._meanZ
#corr5.print_null_test()
#print ''
#corr5.print_null_test('randomQSOXYZ')
#print ''
#corr5.print_null_test('randomQSOXYZ','_from_fit')
#return
#corr5.plot_cov_cor_matrix('subsampling','1D')
#corr5._xi1D[:,1] /= corr5._xi1D[:,2]
#corr5._xi2D[:,:,1] /= corr5._xi2D[:,:,2]
#corr5.plot_1d(0)
#corr5.plot_2d(0)
#corr5.plot_2d(1)
#corr4.plot_2d(2)
###
corr_1 = copy.deepcopy(corr)
corr_1._name = 'randomQSO \, XYZ'
corr_1.set_values_on_mean_simulation_or_realisation_type(None,'randomQSOXYZ')
###
corr_2 = copy.deepcopy(corr)
corr_2._name = 'randomQSO'
corr_2.set_values_on_mean_simulation_or_realisation_type(None,'randomQSO')
###
corr_3 = copy.deepcopy(corr)
corr_3._name = 'shuffleForest'
corr_3.set_values_on_mean_simulation_or_realisation_type(None,'shuffleForest')
###
corr_4 = copy.deepcopy(corr)
corr_4._name = 'shuffleQSO'
corr_4.set_values_on_mean_simulation_or_realisation_type(None,'shuffleQSO')
corr_1.print_null_test()
print ''
corr_1.print_null_test('randomQSOXYZ')
print ''
corr_1.print_null_test('randomQSOXYZ','_from_fit')
color = ['blue','red','green','orange','black','blue']
plt.hist(corr_1._xi2D[:,:,1].flatten()/corr_1._xi2D[:,:,2].flatten(),histtype='step',bins=30,label=r'$'+corr_1._name+'$', color=color[0])
plt.hist(corr_2._xi2D[:,:,1].flatten()/corr_2._xi2D[:,:,2].flatten(),histtype='step',bins=30,label=r'$'+corr_2._name+'$', color=color[1])
plt.hist(corr_3._xi2D[:,:,1].flatten()/corr_3._xi2D[:,:,2].flatten(),histtype='step',bins=30,label=r'$'+corr_3._name+'$', color=color[2])
plt.hist(corr_4._xi2D[:,:,1].flatten()/corr_4._xi2D[:,:,2].flatten(),histtype='step',bins=30,label=r'$'+corr_4._name+'$', color=color[3])
#plt.hist(corr._xi2D[:,:,1].flatten()/corr._xi2D[:,:,2].flatten(),histtype='step',bins=30,label=r'$'+corr._name+'$',color=color[4])
plt.show()
myTools.fitAGaussian( (corr._xi2D[:,:,1]/corr._xi2D[:,:,2]).flatten(), 50,p0=[1,0.,1.],log=True)
myTools.fitAGaussian( (corr_1._xi2D[:,:,1]/corr_1._xi2D[:,:,2]).flatten(), 50, p0=[1,0.,1.],log=True)
myTools.fitAGaussian( (corr_2._xi2D[:,:,1]/corr_2._xi2D[:,:,2]).flatten(), 50,p0=[1,0.,1.],log=True)
myTools.fitAGaussian( (corr_3._xi2D[:,:,1]/corr_3._xi2D[:,:,2]).flatten(), 50, p0=[1,0.,1.],log=True)
myTools.fitAGaussian( (corr_4._xi2D[:,:,1]/corr_4._xi2D[:,:,2]).flatten(), 50, p0=[1,0.,1.],log=True)
corr_1.plot_1d(0,[corr_2,corr_3,corr_4],False)
corr_1.plot_1d(1,[corr_2,corr_3,corr_4],False)
corr_1.plot_1d(2,[corr_2,corr_3,corr_4],False)
corr_1.plot_we(0,[corr_2,corr_3,corr_4],False)
corr_1.plot_we(1,[corr_2,corr_3,corr_4],False)
corr_1.plot_we(2,[corr_2,corr_3,corr_4],False)
for i in numpy.arange(0,50):
corr.plot_slice_2d(i,None,[corr6])
return