def draw_phase(testh5_phases, h5key, train_target, ID_crystal, definition):
# ****** load the h5 file
print('testh5_phases[0] : ', testh5_phases[0])
trained_df_phase2 = pd.read_hdf(testh5_phases[0], h5key_phases[0])
trained_df_phase3 = pd.read_hdf(testh5_phases[1], h5key_phases[1])
Phi1, Phi2, Phi_c, Theta1, Theta2, Theta_c = getCrystalPhiAndTheta(ID_crystal, definition)
if train_target == 'phi':
trained_df_phase2['res_rec'] = trained_df_phase2.phi - trained_df_phase2.mcPhi
trained_df_phase2['res_pre'] = trained_df_phase2.prePhi - trained_df_phase2.mcPhi
trained_df_phase3['res_rec'] = trained_df_phase3.phi - trained_df_phase3.mcPhi
trained_df_phase3['res_pre'] = trained_df_phase3.prePhi - trained_df_phase3.mcPhi
mc_col = 'mcPhi'
rec_col = 'phi'
pre_col = 'prePhi'
angle1 = Phi1
angle2 = Phi2
angle_c = Phi_c
Range_Cry = [Phi1*0.99,Phi2*1.01]
Range = [-3.2, 3.2]
elif train_target == 'theta':
trained_df_phase2['res_rec'] = trained_df_phase2.theta - trained_df_phase2.mcTheta
trained_df_phase2['res_pre'] = trained_df_phase2.preTheta - trained_df_phase2.mcTheta
trained_df_phase3['res_rec'] = trained_df_phase3.theta - trained_df_phase3.mcTheta
trained_df_phase3['res_pre'] = trained_df_phase3.preTheta - trained_df_phase3.mcTheta
mc_col = 'mcTheta'
rec_col = 'theta'
pre_col = 'preTheta'
angle1 = Theta1
angle2 = Theta2
angle_c = Theta_c
Range_Cry = [Theta1*0.99,Theta2*1.01]
Range = [0.4, 2.4]
else:
print("Wrong train target!")
print(trained_df_phase2[0:10])
trained_df_sub_phase2 = trained_df_phase2[(trained_df_phase2.mcPhi>=Phi1)&(trained_df_phase2.mcPhi<=Phi2)&(trained_df_phase2.mcTheta>=Theta1)&(trained_df_phase2.mcTheta<=Theta2)]
trained_df_sub_phase3 = trained_df_phase3[(trained_df_phase3.mcPhi>=Phi1)&(trained_df_phase3.mcPhi<=Phi2)&(trained_df_phase3.mcTheta>=Theta1)&(trained_df_phase3.mcTheta<=Theta2)]
print(trained_df_sub_phase2[0:10])
plt.hist(trained_df_sub_phase2.res_pre, bins=50, range=[-0.03, 0.03], alpha=1.0, linewidth=1, fill=False, histtype='step', label = 'Phase 2', normed = 100)
plt.hist(trained_df_sub_phase3.res_pre, bins=50, range=[-0.03, 0.03], alpha=1.0, linewidth=1, fill=False, histtype='step', label = 'Phase 3', normed = 100)
legend = plt.legend(loc="best")#(loc="best")
plt.xlabel(r'$' + '\\' + train_target + '_{NN}$ - $' + '\\'+ train_target +'_{truth}$')
plt.ylabel(r'')
frame = legend.get_frame()
frame.set_facecolor('none') # 璁剧疆鍥句緥legend鑳屾櫙閫忔槑
plt.title('')
plt.grid(True, alpha=0.8)
plt.savefig(trained_dist_res_c_phases, dpi=300)
plt.figure()
plt.hist(trained_df_sub_phase2.preTheta, bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step', label = 'Phase 2', normed = 100)
plt.hist(trained_df_sub_phase3.preTheta, bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step', label = 'Phase 3', normed = 100)
legend = plt.legend(loc="best")#(loc="best")
plt.xlabel(r'$' + '\\' + train_target + '_{NN}$')
plt.ylabel(r'')
frame = legend.get_frame()
frame.set_facecolor('none') # 璁剧疆鍥句緥legend鑳屾櫙閫忔槑
plt.title('')
plt.grid(True, alpha=0.8)
plt.savefig(trained_dist_c_phases, dpi=300)
plt.show()
def draw_result(testh5, h5key, train_target, ID_crystal, definition):
# ****** load the h5 file
trained_df = pd.read_hdf(testh5, h5key)
Phi1, Phi2, Phi_c, Theta1, Theta2, Theta_c = getCrystalPhiAndTheta(ID_crystal, definition)
print("Phi1: ", Phi1, " Phi2: ", Phi2)
print("Theta1: ", Theta1, " Theta2: ", Theta2)
if train_target == 'phi':
trained_df['res_rec'] = trained_df.phi-trained_df.mcPhi
trained_df['res_pre'] = trained_df.prePhi-trained_df.mcPhi
mc_col = 'mcPhi'
rec_col = 'phi'
pre_col = 'prePhi'
angle1 = Phi1
angle2 = Phi2
angle_c = Phi_c
Range_Cry = [Phi1*0.99,Phi2*1.01]
Range = [-3.2, 3.2]
elif train_target == 'theta':
trained_df['res_rec'] = trained_df.theta-trained_df.mcTheta
trained_df['res_pre'] = trained_df.preTheta-trained_df.mcTheta
mc_col = 'mcTheta'
rec_col = 'theta'
pre_col = 'preTheta'
angle1 = Theta1
angle2 = Theta2
angle_c = Theta_c
Range_Cry = [Theta1*0.99,Theta2*1.01]
Range = [0.4, 2.4]
else:
print("Wrong train target!")
print(trained_df[0:10])
# fig1 = plt.figure()
# plt.style.use('ggplot')
col_list = [rec_col,pre_col,mc_col]
trained_df_sub = trained_df[col_list][(trained_df.mcPhi>=Phi1)&(trained_df.mcPhi<=Phi2)&(trained_df.mcTheta>=Theta1)&(trained_df.mcTheta<=Theta2)]
trained_df_sub.plot.hist(bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step')
legend = plt.legend(loc="best", labels=(r'$\theta_{rec}$', r'$\theta_{NN}$', r'$\theta_{Truth}$'))#(loc="best")
plt.plot([angle1, angle1],[0, 50], 'k--')
plt.text(angle1, 0, 'A', fontdict={'size': 10, 'color': 'blue'})
plt.plot([angle2, angle2],[0, 50], 'k--')
plt.text(angle2, 0, 'B', fontdict={'size': 10, 'color': 'blue'})
plt.plot([angle_c, angle_c],[0, 50], 'k--')
plt.text(angle_c, 0, 'C', fontdict={'size': 10, 'color': 'blue'})
plt.xlabel(r'$' + '\\'+ train_target + '$')
plt.ylabel(r'')
frame = legend.get_frame()
frame.set_facecolor('none') # 璁剧疆鍥句緥legend鑳屾櫙閫忔槑
plt.title('')
plt.grid(True, alpha=0.8)
plt.savefig(trained_dist_c, dpi=300)
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
fig1 = plt.figure()
axScatter = plt.axes(rect_scatter, xlabel=r'$'+ '\\' + train_target + '_{rec}$ [rad]', ylabel=r'$'+ '\\' + train_target + '_{Truth}$ [rad]')
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
# no labels
nullfmt = NullFormatter() # no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
# the 2d histgram plot
axScatter.hist2d(trained_df_sub[rec_col], trained_df_sub[mc_col],(50,50), range=[Range_Cry, Range_Cry],cmap = 'PuBu')
axScatter.grid(True, alpha=0.8)
axHistx.hist(trained_df_sub[rec_col], bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step')
axHisty.hist(trained_df_sub[mc_col], bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step', orientation='horizontal')
axHistx.grid(True, alpha=0.8)
axHisty.grid(True, alpha=0.8)
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
plt.savefig(trained_dist_c_RecVsMC, dpi=300)
fig2 = plt.figure()
axScatter = plt.axes(rect_scatter, xlabel=r'$'+ '\\' + train_target + '_{NN}$ [rad]', ylabel=r'$'+ '\\' + train_target + '_{Truth}$ [rad]')
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
# no labels
nullfmt = NullFormatter() # no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
# the 2d histgram plot
current_cmap = plt.cm.get_cmap('PuBu')
current_cmap.set_bad(color='blue')
current_cmap.set_under(color='white', alpha=1.0)
axScatter.hist2d(trained_df_sub[pre_col],trained_df_sub[mc_col],(50,50), range = [Range_Cry, Range_Cry], cmap = current_cmap)
axScatter.grid(True, alpha=0.8)
axHistx.hist(trained_df_sub[pre_col], bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step')
axHisty.hist(trained_df_sub[mc_col], bins=50, range=Range_Cry, alpha=1.0, linewidth=1, fill=False, histtype='step', orientation='horizontal')
axHistx.grid(True, alpha=0.8)
axHisty.grid(True, alpha=0.8)
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
plt.savefig(trained_dist_c_PreVsMC, dpi=300)
trained_df[col_list].plot.hist(bins=288, range = Range, alpha=1., linewidth=0.6, fill=False, histtype='step')
plt.xlabel(r'$'+'\\'+ train_target + '$')
plt.ylabel(r'')
legend = plt.legend(loc="best", labels=(r'$\theta_{rec}$', r'$\theta_{NN}$', r'$\theta_{Truth}$'))
frame = legend.get_frame()
frame.set_facecolor('none') # 璁剧疆鍥句緥legend鑳屾櫙閫忔槑
plt.title('')
plt.grid(True, alpha=0.8)
plt.savefig(trained_dist, dpi=300)
trained_df[['res_rec', 'res_pre']].plot.hist(bins=200, range=[-0.03, 0.03], alpha=1.0, fill=False, histtype='step')
plt.xlabel(r'$' + '\\' + train_target + '$ - $' + '\\'+ train_target +'_{truth}$')
plt.ylabel(r'')
plt.legend(loc = 'best', labels=('res_rec', 'res_NN'))
plt.grid(True, alpha=0.8)
plt.savefig(trained_dist_res, dpi=300)
trained_df[['res_rec', 'res_pre']][(trained_df.mcPhi>=Phi1)&(trained_df.mcPhi<=Phi2)&(trained_df.mcTheta>=Theta1)&(trained_df.mcTheta<=Theta2)].plot.hist(bins = 50, range=[-0.03, 0.03], alpha=1.0, fill=False, histtype='step')
plt.xlabel(r'$' + '\\'+ train_target + '$- $'+'\\'+train_target+'_{truth}$')
plt.ylabel(r'')
plt.legend(loc = 'best', labels=('res_rec', 'res_NN'))
plt.grid(True, alpha=0.8)
plt.savefig(trained_dist_res_c, dpi=300)
plt.show()
