def test_selfChi2():
ens = Ensemble.read(
"Data/all/Om0.295_Ol0.705_w-1.878_ns0.960_si0.100/subfield1/sigma05/power_spectrum.npy"
)
chi2 = ens.selfChi2()
assert chi2.shape[0] == ens.data.shape[0]
#Plot histogram
fig, ax = plt.subplots()
n, bins, patch = ax.hist(chi2,
bins=50,
normed=True,
histtype="stepfilled",
alpha=0.5)
#Compare to chi2 distribution
ax.plot(stats.chi2.pdf(bins, ens.data.shape[1]))
#Labels
ax.set_xlabel(r"$\chi^2$")
ax.set_ylabel(r"$P(\chi^2)$")
#Save figure
fig.savefig("self_chi2.png")
def test_save_and_load():
conv_ensemble.save("ensemble_saved.npy")
conv_ensemble.save("ensemble_saved", format="matlab", appendmat=True)
conv_ensemble_new = Ensemble.read("ensemble_saved.npy")
assert conv_ensemble_new.num_realizations == conv_ensemble.num_realizations
assert conv_ensemble_new.data.shape == conv_ensemble.data.shape
def test_save_and_load():
conv_ensemble.save("ensemble_saved.npy")
conv_ensemble.save("ensemble_saved",format="matlab",appendmat=True)
conv_ensemble_new = Ensemble.read("ensemble_saved.npy")
assert conv_ensemble_new.num_realizations == conv_ensemble.num_realizations
assert conv_ensemble_new.data.shape == conv_ensemble.data.shape
def confusionMatrix(descriptor,measurement_list,measurement_covariance):
#Instantiate a FisherAnalysis instance
analysis = FisherAnalysis()
#Populate with the models
for measurement in measurement_list:
ens = Ensemble.read(measurement.savename(descriptor))
analysis.add_model(measurement.model.squeeze(with_ns=True),ens.mean())
#Compute the covariance matrix
covariance = Ensemble.read(measurement_covariance.savename(descriptor)).covariance()
#################################################################################
#####Now we are ready to compute the confusion matrix, for each parameter########
#################################################################################
#Allocate space for confusion matrix
confusion_matrix = np.zeros((4,3,3))
#Find where are the variations for each model parameter
locations = analysis.where()
#Cycle over parameters
for n in range(4):
l0 = analysis._fiducial
l1,l2 = locations[n]
print("[+] n={0}, fiducial: {1}, variation 1: {2}, variation 2:{3}".format(n,analysis.parameter_set[l0],analysis.parameter_set[l1],analysis.parameter_set[l2]))
for i,m in enumerate([l0,l1,l2]):
#Load the ensemble
ens = Ensemble.read(measurement_list[m].savename(descriptor))
#Compute the confusion matrix
confusion_matrix[n,i] = analysis.classify(ens.data,covariance,labels=[l0,l1,l2],confusion=True)
#Return the confusion matrix for the selected descriptor
return confusion_matrix
def test_pca():
pca_ensemble = Ensemble.read("Data/ensemble_pca.npy")
pca = pca_ensemble.principalComponents()
assert len(pca.explained_variance_) == pca_ensemble.data.shape[1]
fig, ax = plt.subplots(1, 2, figsize=(16, 8))
ax[0].plot(pca.explained_variance_)
ax[1].plot(pca.explained_variance_.cumsum())
ax[0].set_xlabel(r"$n$")
ax[1].set_xlabel(r"$n$")
ax[0].set_ylabel(r"$\lambda_n$")
ax[1].set_ylabel(r"$\sum^n\lambda_n$")
fig.savefig("pca.png")
def test_pca():
pca_ensemble = Ensemble.read("Data/ensemble_pca.npy")
pca = pca_ensemble.principalComponents()
assert len(pca.explained_variance_)==pca_ensemble.data.shape[1]
fig,ax = plt.subplots(1,2,figsize=(16,8))
ax[0].plot(pca.explained_variance_)
ax[1].plot(pca.explained_variance_.cumsum())
ax[0].set_xlabel(r"$n$")
ax[1].set_xlabel(r"$n$")
ax[0].set_ylabel(r"$\lambda_n$")
ax[1].set_ylabel(r"$\sum^n\lambda_n$")
fig.savefig("pca.png")
def test_selfChi2():
ens = Ensemble.read("Data/all/Om0.295_Ol0.705_w-1.878_ns0.960_si0.100/subfield1/sigma05/power_spectrum.npy")
chi2 = ens.selfChi2()
assert chi2.shape[0]==ens.data.shape[0]
#Plot histogram
fig,ax = plt.subplots()
n,bins,patch = ax.hist(chi2,bins=50,normed=True,histtype="stepfilled",alpha=0.5)
#Compare to chi2 distribution
ax.plot(stats.chi2.pdf(bins,ens.data.shape[1]))
#Labels
ax.set_xlabel(r"$\chi^2$")
ax.set_ylabel(r"$P(\chi^2)$")
#Save figure
fig.savefig("self_chi2.png")
def pbBias(cmd_args,feature_name="convergence_power_s0_nb100",title="Power spectrum",kappa_models=("Born",),callback=None,variation_idx=(0,),bootstrap_size=1000,resample=1000,return_results=False,fontsize=22):
#Initialize plot
fig,ax = plt.subplots(len(variation_idx),3,figsize=(24,8*len(variation_idx)))
ax = np.atleast_2d(ax)
##################
#Load in the data#
##################
#Observation
bootstrap_mean = lambda e: e.values.mean(0)
feature_ray = Ensemble.read(os.path.join(fiducial["c0"].getMapSet("kappa").home,feature_name+".npy"),callback_loader=callback).bootstrap(bootstrap_mean,bootstrap_size=bootstrap_size,resample=resample,seed=0)
#Containers for cosmological model
modelFeatures = dict()
for mf in kappa_models:
modelFeatures[mf] = dict()
parameters = dict()
for model in models:
parameters[model.cosmo_id] = np.array([model.cosmology.Om0,model.cosmology.w0,model.cosmology.sigma8])
for mf in kappa_models:
try:
modelFeatures[mf][model.cosmo_id] = Ensemble.read(os.path.join(model["c0"].getMapSet("kappa"+mf).home,feature_name+".npy"),callback_loader=callback)
except IOError:
pass
#Fit each model
for mf in kappa_models:
#Select correct
features = modelFeatures[mf]
###############################
#Compute the covariance matrix#
###############################
features_covariance = features[fiducial.cosmo_id].cov()
################################################
#Load in the feature to fit, bootstrap the mean#
################################################
feature_born = features[fiducial.cosmo_id].bootstrap(bootstrap_mean,bootstrap_size=bootstrap_size,resample=resample,seed=0)
for nv,v in enumerate(variation_idx):
###############################
#Initialize the FisherAnalysis#
###############################
ftr = np.array([features[m.cosmo_id].values.mean(0) for m in [fiducial] + variations[v]])
par = np.array([parameters[m.cosmo_id] for m in [fiducial] + variations[v]])
fisher = FisherAnalysis.from_features(ftr,par,parameter_index=["Om","w0","si8"])
#############
####Fit######
#############
fitted_parameters_born = fisher.fit(feature_born,features_covariance)
fitted_parameters_ray = fisher.fit(feature_ray,features_covariance)
if return_results:
assert len(kappa_models)==1
assert len(variation_idx)==1
return fitted_parameters_born,fitted_parameters_ray
##########
#Plotting#
##########
for n,p in enumerate(fisher.parameter_names):
fitted_parameters_born[p].plot.hist(bins=50,ax=ax[nv,n],edgecolor="none",label=r"${\rm Control}$")
fitted_parameters_ray[p].plot.hist(bins=50,ax=ax[nv,n],edgecolor="none",label=r"${\rm Observation}$")
ax[nv,n].set_xlabel(plab[p],fontsize=fontsize)
ax[nv,n].set_title(title)
ax[nv,n].legend(loc="upper right",mode="expand",ncol=2,prop={"size":20})
#Labels
for a in ax.flatten():
plt.setp(a.get_xticklabels(),rotation=30)
#Save
fig.tight_layout()
fig.savefig("{0}/bornBias_{1}.{0}".format(cmd_args.type,feature_name))
