v1.1

New Path
Correct description

training/DNN_AngularRec.py

@@ -19,8 +19,8 @@ nbatch = 132
 # load and prepare data
 # ----------------------------------------------------------------------
 
-Folder = '/home/JGlombitza/DataLink/Public/Paper/PreProData'
-filenames=glob.glob(Folder + "/Data_PlanarWaveShower_PrePro_NewMC_Mixed_Composition*")
+Folder = '../Data_preprocessed'
+filenames=glob.glob(Folder + "/PlanarWaveShower_PrePro*")
 
 N = 10000*len(filenames)
 a = 10000 # packagesize
@@ -30,10 +30,10 @@ showeraxis = np.zeros(N*3).reshape(N,3)
 Energy = np.zeros(N)
 for i in range(0,len(filenames)):
     data = np.load(filenames[i])
-    Input1[a*i:a*(i+1)] = data['Input1'].reshape(a,9,9,80,1)
-    showeraxis[a*i:a*(i+1)] = data['showeraxis'].reshape(a,3)
-    Input2[a*i:a*(i+1)] = data['Input2'].reshape(a,9,9,2)
-    Energy[a*i:a*(i+1)] = data['Energy'].reshape(a)
+    Input1[a*i:a*(i+1)] = data['Input1']
+    showeraxis[a*i:a*(i+1)] = data['showeraxis']
+    Input2[a*i:a*(i+1)] = data['Input2']
+    Energy[a*i:a*(i+1)] = data['Energy']
 
 print('Loaded %i showers' %N )
 

training/DNN_AngularRec_OnlyTime.py

@@ -18,8 +18,8 @@ nbatch = 132
 # load and prepare data
 # ----------------------------------------------------------------------
 
-Folder = '/home/JGlombitza/DataLink/Public/Paper/PreProData'
-filenames=glob.glob(Folder + "/Data_PlanarWaveShower_PrePro_NewMC_Mixed_Composition*")
+Folder = '../Data_preprocessed'
+filenames=glob.glob(Folder + "/PlanarWaveShower_PrePro*")
 
 N = 10000*len(filenames)
 a = 10000 # packagesize
@@ -28,9 +28,9 @@ showeraxis = np.zeros(N*3).reshape(N,3)
 Energy = np.zeros(N)
 for i in range(0,len(filenames)):
     data = np.load(filenames[i])
-    showeraxis[a*i:a*(i+1)] = data['showeraxis'].reshape(a,3)
+    showeraxis[a*i:a*(i+1)] = data['showeraxis']
     Input2[a*i:a*(i+1)] = data['Input2'][:,:,:,0].reshape(a,9,9,1)
-    Energy[a*i:a*(i+1)] = data['Energy'].reshape(a)
+    Energy[a*i:a*(i+1)] = data['Energy']
 
 ## Reshape, Split to test and train sets
 ntest  = 40000

training/DNN_Energy.py

@@ -13,16 +13,12 @@ lr = 0.001
 log_dir="."
 nbatch = 132
 
-def DenselyConnectedSepConv(z, nfilter, **kwargs):
-    c = SeparableConv2D(nfilter, (3, 3), padding = 'same', depth_multiplier=1, **kwargs)(z)
-    return concatenate([z, c], axis=-1)
-
 # ----------------------------------------------------------------------
 # load and prepare data
 # ----------------------------------------------------------------------
 
-Folder = '/home/JGlombitza/DataLink/Public/Paper/PreProData'
-filenames=glob.glob(Folder + "/Data_PlanarWaveShower_PrePro_NewMC_Mixed_Composition*")
+Folder = '../Data_preprocessed'
+filenames=glob.glob(Folder + "/PlanarWaveShower_PrePro*")
 
 N = 10000*len(filenames)
 a = 10000 # packagesize
@@ -31,9 +27,9 @@ Input2 = np.zeros(N*9*9*2).reshape(N,9,9,2)
 Energy = np.zeros(N)
 for i in range(0,len(filenames)):
     data = np.load(filenames[i])
-    Input1[a*i:a*(i+1)] = data['Input1'].reshape(a,9,9,80,1)
+    Input1[a*i:a*(i+1)] = data['Input1']
     Energy[a*i:a*(i+1)] = data['Energy']
-    Input2[a*i:a*(i+1)] = data['Input2'].reshape(a,9,9,2)
+    Input2[a*i:a*(i+1)] = data['Input2']
 
 ## Reshape, Split to test and train sets
 ntest  = 40000
@@ -67,7 +63,7 @@ x = concatenate([TimeProcess, inputT0])
 # Densely connected convolutions
 nfilter =12
 for i in range(4):
-    x = DenselyConnectedSepConv(x, nfilter, **kwargs)
+    x = tools.DenselyConnectedSepConv(x, nfilter, **kwargs)
     nfilter = 2*nfilter
 x = SeparableConv2D(nfilter, (3, 3), padding = 'same', depth_multiplier=1, **kwargs)(x)
 x = Flatten()(x)

training/DNN_Energy_OnlySignal.py

@@ -18,8 +18,8 @@ nbatch = 132
 # load and prepare data
 # ----------------------------------------------------------------------
 
-Folder = '/home/JGlombitza/DataLink/Public/Paper/PreProData'
-filenames=glob.glob(Folder + "/Data_PlanarWaveShower_PrePro_NewMC_Mixed_Composition*")
+Folder = '../Data_preprocessed'
+filenames=glob.glob(Folder + "/PlanarWaveShower_PrePro*")
 
 N = 10000*len(filenames)
 a = 10000 # packagesize

training/DNN_Xmax.py

@@ -17,8 +17,8 @@ nbatch = 132
 # load and prepare data
 # ----------------------------------------------------------------------
 
-Folder = '/home/JGlombitza/DataLink/Public/Paper/PreProData'
-filenames=glob.glob(Folder + "/Data_PlanarWaveShower_PrePro_NewMC_Mixed_Composition*")
+Folder = '../Data_preprocessed'
+filenames=glob.glob(Folder + "/PlanarWaveShower_PrePro*")
 
 N = 10000*len(filenames)
 a = 10000 # packagesize
@@ -28,10 +28,10 @@ Xmax = np.zeros(N)
 Energy = np.zeros(N)
 for i in range(0,len(filenames)):
     data = np.load(filenames[i])
-    Input1[a*i:a*(i+1)] = data['Input1'].reshape(a,9,9,80,1)
+    Input1[a*i:a*(i+1)] = data['Input1']
     Xmax[a*i:a*(i+1)] = data['Xmax']
     Energy[a*i:a*(i+1)] = data['Energy']
-    Input2[a*i:a*(i+1)] = data['Input2'].reshape(a,9,9,2)
+    Input2[a*i:a*(i+1)] = data['Input2']
 
 ## Reshape, Split to test and train sets
 ntest  = 40000

training/tools.py

@@ -13,13 +13,17 @@ def Distance(y_true, y_pred):
     mean, var = tf.nn.moments((y_true-y_pred), axes=[0])
     return tf.sqrt(var)
 
-def MeanElong(y_true, y_pred): ##inGrad
+
+def MeanElong(y_true, y_pred):
+    ''' Metric to control the mean of the angulardistance distribution '''
     return 57.29577951308232*K.mean(tf.minimum(tf.acos((y_pred[:,0]*y_true[:,0] + y_pred[:,1]*y_true[:,1] + y_pred[:,2]*y_true[:,2])/(tf.sqrt(y_pred[:,0]*y_pred[:,0]+y_pred[:,1]*y_pred[:,1]+y_pred[:,2]*y_pred[:,2])*tf.sqrt(y_true[:,0]*y_true[:,0]+y_true[:,1]*y_true[:,1]+y_true[:,2]*y_true[:,2]))),1))
 
+
 def Percentile68(x):
     ''' Calculates 68% quantile of distribution'''
     return np.percentile(x, 68)
 
+
 def vec2ang(v):
     '''Calculates phi and theta for given shower axis'''
     x, y, z = np.asarray(v).T
@@ -27,11 +31,13 @@ def vec2ang(v):
     theta = np.pi/2 - np.arctan2(z, (x**2 + y**2)**.5)
     return np.rad2deg(phi), np.rad2deg(theta)
 
+
 def DenselyConnectedSepConv(z, nfilter, **kwargs):
     ''' Densely Connected SeparableConvolution2D Layer'''
     c = SeparableConv2D(nfilter, (3, 3), padding = 'same', depth_multiplier=1, **kwargs)(z)
     return concatenate([z, c], axis=-1)
 
+
 def PlotHistosXmaxReconstruction(y_true, y_pred, log_dir, name, Energy):
     ''' Plottingfunction to evaluate the reconstruction of the shower depths Xmax
         plots: predicted Xmax against MC Xmax and the energy dependence of the reconstruction (embedded)'''
@@ -41,7 +47,7 @@ def PlotHistosXmaxReconstruction(y_true, y_pred, log_dir, name, Energy):
     ax1.scatter(y_true, y_pred, s = 2, color='royalblue', alpha=.90)
     ax1.set_xlabel(r'$X_{max,\;true}$  $[g/cm^{2}]$')
     ax1.set_ylabel(r'$X_{max,\;DNN}$  $[g/cm^{2}]$')
-    left, bottom, width, height = [0.23, 0.6, 0.3, 0.26] # Links Oben
+    left, bottom, width, height = [0.23, 0.6, 0.3, 0.26]
     ax1.text(0.95,0.2, r"$\mu = $ %.3f " % np.mean(reco) + r"$[g/cm^{2}]$" + "\n" + "$\sigma = $ %.3f " % np.std(reco) + r"$[g/cm^{2}]$", verticalalignment = 'top', horizontalalignment = 'right', transform=ax1.transAxes, backgroundcolor='w')
     ax1.plot([np.min(y_true), np.max(y_true)], [np.min(y_true), np.max(y_true)], color='red')
     # Energy dependence plot
@@ -59,14 +65,13 @@ def PlotHistosXmaxReconstruction(y_true, y_pred, log_dir, name, Energy):
     fig.savefig(log_dir + '/Xmax_embedding_%s.png' %name)
     return reco
 
+
 def PlotHistosEnergyReconstruction(y_true, y_pred, log_dir, name):
     ''' Plottingfunction to evaluate the reconstruction of the cosmic ray energy
         plots: predicted Energy against MC Energy and the energy dependence of the relative energy resolution (embedded)'''
     y_true = y_true.reshape(y_true.shape[0],1)
     reco = y_true-y_pred
-
-
-# Embedding plot
+    # Embedding plot
     fig, ax1 = plt.subplots(1)
     ax1.scatter(y_true, y_pred, s = 2, color='royalblue', alpha=.90)
     ax1.set_xlabel(r'$Energy_{true}\;[EeV]$')
@@ -87,17 +92,18 @@ def PlotHistosEnergyReconstruction(y_true, y_pred, log_dir, name):
     fig.savefig(log_dir + '/Energy_embedding_%s.png' %name)
     return reco
 
+
 def PlotHistosAngularReconstruction(y_true, y_pred, log_dir, name, Energy, Signal=0):
     ''' Plotting function to evaluate the reconstruction of the shower axis
         plots: angular distance distribution and the energy dependence of the angular reconstruction (embedded)'''
     phi, theta = vec2ang(y_true)
     angulardistance = 180 / np.pi * np.arccos(np.clip(np.sum(y_true*y_pred, axis=-1)/np.linalg.norm(y_pred, axis=-1)/np.linalg.norm(y_true, axis=-1),-1,1))
     resolution = Percentile68(angulardistance)
-# Embedding plot
+    # Embedding plot
     fig, ax1 = plt.subplots(1)
     ax1.hist(angulardistance, bins=np.linspace(0, 4, 51))
     ax1.set(xlabel=r'$Angulardistance\;\delta\;[^\circ]$', ylabel=r'$N$')
-    left, bottom, width, height = [0.56, 0.6, 0.31, 0.27] # Rechts Oben
+    left, bottom, width, height = [0.56, 0.6, 0.31, 0.27]
     ax2 = fig.add_axes([left, bottom, width, height])
     x_bins = 12
     logE = 18 + np.log10(Energy)