def test_where_errors(self):
from numpypy import where, array
raises(ValueError, "where([1, 2, 3], [3, 4, 5])")
raises(ValueError, "where([1, 2, 3], [3, 4, 5], [6, 7])")
assert where(True, 1, 2) == array(1)
assert where(False, 1, 2) == array(2)
assert (where(True, [1, 2, 3], 2) == [1, 2, 3]).all()
assert (where(False, 1, [1, 2, 3]) == [1, 2, 3]).all()
assert (where([1, 2, 3], True, False) == [True, True, True]).all()
def best_model_sel(prob_treshold,survey_type="random", pro_path="/home/jemejia/CosmicVarianceLAES/"):
ID_file=pro_path + "data/mock_survey/" + "ID_" + survey_type + "_surveys.dat"
p_values_file= pro_path + "data/mock_survey/" + "p_values_FOF_ID_" + survey_type + "_surveys.dat"
ks_data=np.loadtxt(p_values_file)
m_min_arr = ks_data[:,0]
m_max_arr =ks_data[:,1]
f_occ_arr =ks_data[:,2]
ID_survey_arr = ks_data[:,3]
model_prob_arr = ks_data[:,4]
print np.size(m_min_arr)
#choosing th models with ks test probabilities greater than prob_treshold
index=np.where(model_prob_arr>=prob_treshold)
print np.size(index)
best_models=np.empty( [ np.size(index) , np.size( ks_data[0,:] ) ] )
del(ks_data)
best_models[:,0]=m_min_arr[index]
best_models[:,1]=m_max_arr[index]
best_models[:,2]=f_occ_arr[index]
best_models[:,3]= ID_survey_arr[index]
best_models[:,4]=model_prob_arr[index]
print "the number of selected models are", np.size(best_models[:,0])
return best_models
def test_sub_where(self):
from numpypy import where, ones, zeros, array
a = array([1, 2, 3, 0, -3])
v = a.view(self.NoNew)
b = where(array(v) > 0, ones(5), zeros(5))
assert (b == [1, 1, 1, 0, 0]).all()
# where returns an ndarray irregardless of the subtype of v
assert not isinstance(b, self.NoNew)
def test_where(self):
from numpypy import where, ones, zeros, array
a = [1, 2, 3, 0, -3]
a = where(array(a) > 0, ones(5), zeros(5))
assert (a == [1, 1, 1, 0, 0]).all()
def test_where_invalidates(self):
from numpypy import where, ones, zeros, array
a = array([1, 2, 3, 0, -3])
b = where(a > 0, ones(5), zeros(5))
a[0] = 0
assert (b == [1, 1, 1, 0, 0]).all()
def test_where_broadcast(self):
from numpypy import array, where
a = where(array([[1, 2, 3], [4, 5, 6]]) > 3, [1, 1, 1], 2)
assert (a == [[2, 2, 2], [1, 1, 1]]).all()
a = where(True, [1, 1, 1], 2)
assert (a == [1, 1, 1]).all()
def test_where_differing_dtypes(self):
from numpypy import array, ones, zeros, where
a = [1, 2, 3, 0, -3]
a = where(array(a) > 0, ones(5, dtype=int), zeros(5, dtype=float))
assert (a == [1, 1, 1, 0, 0]).all()
def best_model_correlation(best_model_array, theta_min,theta_max,theta_bins, survey_type="random",field="full", distance=6558.3, obs_surveys=12,x_width=46.0,y_width=35.0, z_depth=41.0 ,box_length=250,random_cat_number=16, pro_path="/home/jemejia/CosmicVariance/"):
print "computing correlation functions of the selected models"
dmh_path=pro_path+"data/dark_matter/FOF/"
laes_path=pro_path+"data/laes/FOF/"
n_i= int( box_length/x_width)
n_j= int( box_length/y_width)
n_k= int( box_length/z_depth)
n_models = n_i * n_j * n_k
ID_file=pro_path + "data/mock_survey/" + "ID_" + survey_type + "_surveys.dat"
ID_data=np.loadtxt(ID_file,dtype='int')
survey_ID=ID_data[:,0]
field_ID=ID_data[:,1]
i_field = ID_data[:,2]
j_field = ID_data[:,3]
k_field = ID_data[:,4]
moc_surveys=survey_ID[-1]
ID_arr=best_models_array[:,3]
index_eq_ID=np.where(ID_arr== 1)
cat_number= index_eq_ID[0]-1
i_fields_to_measure=[]
j_fields_to_measure=[]
k_fields_to_measure=[]
m_min_to_measure=[]
m_max_to_measure=[]
f_occ_to_measure=[]
#choosing the subcatalogs of the best fields.
best_correlation=np.empty([len(ID_arr),theta_bins])
std_correlation=np.empty([len(ID_arr),theta_bins])
#for w in range( len(ID_arr) ):
for w in range( 7 ):
index=np.where( survey_ID == int(ID_arr[w]) )
S_ID=survey_ID[index]
ID_ini=S_ID[0]
ID_end=int(ID_ini+obs_surveys)
m_min=best_models[w,0]
m_max=best_models[w,1]
f_occ=best_models[w,2]
print "model:",w,"parameters:" ,m_min, m_max, f_occ
i_s=i_field[ID_ini:ID_end]
j_s=j_field[ID_ini:ID_end]
k_s=k_field[ID_ini:ID_end]
corr=np.zeros( (len(i_s),theta_bins) )
corr_peebles=np.zeros( (len(i_s),theta_bins) )
corr_standard=np.zeros( (len(i_s),theta_bins) )
corr_laes=np.zeros(theta_bins)
if(field=="large"):
i_range=7
print "large field"
else:
i_range=np.size(i_s)
print "full field"
print "number of sub-catalogs=",i_range
for i in range( i_range ):
dmh_filename=dmh_path+"halos_bolshoi_"+str(i_s[i])+"-"+str(j_s[i])+"-"+str(k_s[i])+".csv"
halos_prop=np.loadtxt(dmh_filename,delimiter=",",skiprows=12)
halo_mass=halos_prop[:,4]
x_halos=halos_prop[:,0]
y_halos=halos_prop[:,1]
z_halos=halos_prop[:,2]
numbers=np.arange(len(halo_mass))
halo_index=np.where( (halo_mass< m_max) & (halo_mass> m_min) )
halo_mass_sel=halo_mass[halo_index]
halo_index=numbers[halo_index]
np.random.shuffle(halo_index)
n_halos=np.size(halo_mass_sel)
del halo_mass_sel
n_laes=int(f_occ*n_halos)
lae_index=halo_index[0:n_laes]
x_laes=x_halos[lae_index]
y_laes=y_halos[lae_index]
del x_halos
del y_halos
del z_halos
del halo_mass
#random cat histogram generation
#P.xlabel(r'$\theta$', fontsize=16)
#P.ylabel(r"$\xi(\theta)$",fontsize=16)
if(w==0):
if(i==0):
print w,i
print "computing RR (it takes much time but it is only computed once)"
x_random= x_width*np.random.random_sample(n_laes*random_cat_number)
y_random=y_width*np.random.random_sample(n_laes*random_cat_number)
RR,bins=RR_histogram(x_laes,y_laes,x_random,y_random,distance,theta_min,theta_max,theta_bins,cat_number=random_cat_number)
print RR
print "subcat number ",i,"i j k=",i_s[i],j_s[i],k_s[i]
#random-survey histogram generation
Xmin=x_width*i_s[i]
Xmax=Xmin + x_width
Ymin=y_width*j_s[i]
Ymax=Ymin + y_width
x_random= Xmin + ( Xmax - Xmin )*np.random.random_sample(n_laes)
y_random=Ymin + ( Ymax - Ymin )*np.random.random_sample(n_laes)
DR,bins=DR_histogram(x_laes,y_laes,x_random,y_random,distance,theta_min,theta_max,theta_bins,cat_number=1)
#survey histogram generation
DD,bins=DD_histogram(x_laes,y_laes,distance,theta_min,theta_max,theta_bins)
corr[i,:]=landy_correlation(DD,RR,DR)
print "CORR_landy=",corr[i,:]
corr_laes=np.mean(corr,axis=0)
std_corr=np.std(corr,axis=0)
print "corr_landy=",corr_laes, "std_landy=",std_corr
best_correlation[w,:]=corr_laes
std_correlation[w,:]=std_corr
dtheta=(theta_max - theta_min)/theta_bins
correlation_data=np.empty(( np.size(corr_laes) , 3 ) )
model='{0}_{1}_{2}'.format(m_min, m_max, f_occ)
model_name = 'model_{0}_{1}_{2}'.format(m_min, m_max, f_occ)
filename=pro_path + "data/mock_survey/" + "correlation_best_models/" + survey_type + "_correlation_" + model_name + ".dat"
angles = np.linspace( theta_min + dtheta/2.0 , theta_max - dtheta/2.0, theta_bins )
correlation_data[:,0]=angles
correlation_data[:,1]=best_correlation[w,:]
correlation_data[:,2]=std_correlation[w,:]
np.savetxt(filename,correlation_data)
#P.errorbar(correlation_data[:,0]+2.0*w, correlation_data[:,1], correlation_data[:,2],label=model,elinewidth=2.0)
file_plot=pro_path + "data/mock_survey/" + "correlation_best_models/" + survey_type + "_" + field +"_"+ "correlation_plots" + ".png"
#P.legend(shadow=False)
obs_correlation_file=pro_path + "data/obs/hayashino_whole_SSA22_field.txt"
obs_correlation=np.loadtxt(obs_correlation_file,skiprows=4)
#P.ylim(ymax=0.6)
#P.xlim(xmax=1040)
#P.errorbar(obs_correlation[0:theta_bins,0]-3.0, obs_correlation[0:theta_bins,1], obs_correlation[0:theta_bins,2],label="Hayashino et al 2004",elinewidth=3.0,fmt="o-")
#P.legend(shadow=False)
#P.title(survey_type)
#P.savefig(file_plot)
#P.figure()
return best_correlation,std_correlation,angles