<jupyter_start><jupyter_text>Loss plot<jupyter_code>GXY_losses = np.load('./Loss/GXY_losses.npy')
DY_losses = np.load('./Loss/DY_losses.npy')
C_gen_losses = np.load('./Loss/C_gen_losses.npy')
C_dis_losses = np.load('./Loss/C_dis_losses.npy')
C_gen_dis_losses = np.load('./Loss/C_gen_dis_conv_losses.npy')
C_L2_conv_losses = np.load('./Loss/C_L2_conv_losses.npy')
C_dis_gen_losses = np.load('./Loss/C_dis_conv_losses.npy')
plt.figure(figsize=(15,10))
plt.title('Loss of Annual Data to Daily Data Generator(Convolutional Version)',fontsize = 20)
plt.xlabel('Training Step',fontsize=15)
plt.ylabel('Loss',fontsize=15)
plt.plot((C_gen_losses+10*(C_L2_conv_losses)))
plt.show()
plt.figure(figsize=(15,10))
plt.title('Loss of Daily Data Discriminator (Convolutional Version)',fontsize = 20)
plt.xlabel('Training Step',fontsize=15)
plt.ylabel('Loss',fontsize=15)
plt.plot(C_dis_gen_losses[:-20])
plt.show()
plt.figure(figsize=(15,10))
plt.title('Loss of Texas Data to Pacific Data Generator (Annual)',fontsize = 20)
plt.xlabel('Training Step',fontsize=15)
plt.ylabel('Loss',fontsize=15)
plt.plot(GXY_losses)
plt.show()
plt.figure(figsize=(15,10))
plt.title('Loss of Pacific Data Discriminator (Annual)',fontsize = 20)
plt.xlabel('Training Step',fontsize=15)
plt.ylabel('Loss',fontsize=15)
plt.plot(DY_losses)
plt.show()
plt.figure(figsize=(15,10))
plt.title('Loss of Annual Data to Daily Data Generator',fontsize = 20)
plt.xlabel('Training Step',fontsize=15)
plt.ylabel('Loss',fontsize=15)
plt.plot(C_gen_losses)
plt.show()
plt.figure(figsize=(15,10))
plt.title('Loss of Daily Data Discriminator',fontsize = 20)
plt.xlabel('Training Step',fontsize=15)
plt.ylabel('Loss',fontsize=15)
plt.plot(C_dis_losses)
plt.show()
a=np.corrcoef(np.sum(daily_SynPacific_data,axis=1)[:,np.sum(
    np.sum(daily_PSCA_data.astype(float),axis=1),axis = 0)!=0].transpose().astype(float))
b=np.corrcoef(np.sum(daily_PSCA_data.astype(float),axis=1).transpose())
a=np.corrcoef(np.sum(daily_SynPacific_data,axis=1).transpose().astype(float))
b=np.corrcoef(np.sum(daily_PSCA_data.astype(float),axis=1).transpose())
b[np.isnan(b)]=0
for i in range(b.shape[0]):
    b[i,i] =1
c=np.corrcoef(np.sum(daily_PSTexas_data.astype(float),axis=1).transpose())
tf.losses.mean_squared_error(a,c)
tf.losses.mean_squared_error(a,b)<jupyter_output><empty_output>
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