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ca7358e95a
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ca7358e95a | ||
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afec9ea818 |
4 changed files with 226 additions and 51 deletions
112
BB-chaos.py
112
BB-chaos.py
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@ -30,13 +30,13 @@ particles = {
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'z': 1
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},
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'Helium': {
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'E0_min': 10, # MeV
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'E0_min': 100, # MeV
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'E0_max': 5000, # MeV
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'm0': 6.644657e-27,
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'z': 2
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},
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'Muon': {
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'E0_min': 10, # MeV
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'E0_min': 1 , # MeV
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'E0_max': 5000, # MeV
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'm0': 1.883531627e-28,
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'z': 1
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@ -100,6 +100,22 @@ materials = {
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}
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}
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materials = {
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'Si': {
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'ne': (2.336e3 / (28.085e-3)) * 14.0 * N_A, # Elektronendichte in Si
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'I': 173 * e, # Ionisierungsenergie in Silizium in J
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'd': 300e-6 # Dicke in m (300um)
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},
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'BGO': {
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# Elektronendichte in BGO berechnen (gewichteter Durchschnitt)
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'ne': (4 * (9.78e3 / (208.98e-3)) * 83 * N_A + 3 * (5.323e3 / (72.63e-3)) * 32 * N_A + 12 * (1.429e3 / (16.00e-3)) * 8 * N_A) / (4 + 3 + 12),
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# Ionisierungsenergie in BGO (gewichteter Durchschnitt)
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'I': (4 * 250 * e + 3 * 290 * e + 12 * 150 * e) / (4 + 3 + 12),
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# Dicke in m (2cm)
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'd': 2e-2
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}
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}
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# #Output the parameters for each material
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# print("Materials and their parameters:\n")
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# for material, params in materials.items():
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@ -162,6 +178,11 @@ def plot_Eloss_E():
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'BGO': '-' # Linienstil für BGO (nun geändert)
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}
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linestyles = {
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'Si': '-', # Linienstil für Si
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'BGO': ':' # Linienstil für BGO (nun geändert)
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}
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colors = {
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'Proton': 'red',
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'Helium': 'green',
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@ -181,7 +202,8 @@ def plot_Eloss_E():
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E = np.linspace(particle_params['E0_min'], particle_params['E0_max'], 100000) * 1e6 * e # Umrechnung in Joule
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dEdx = bethebloch(E, particle_params['m0'], particle_params['z'], params['ne'], params['I'])
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dEdx_cm = dEdx / (1e6 * e) * 1e-2 # eV/m to MeV/cm
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linewidth = 1.5 if material != 'BGO' else 3
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#linewidth = 1.5 if material != 'BGO' else 3
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linewidth = 2
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plt.plot(E / (1e6 * e), dEdx_cm, color=colors[name], linestyle=linestyles[material], linewidth=linewidth)
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if f'{name}' not in legend_particle_labels:
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@ -193,11 +215,11 @@ def plot_Eloss_E():
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plt.xscale('log')
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plt.yscale('log')
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plt.xlabel('kinetic energy E [MeV]')
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plt.ylabel('Stopping Power in MeV/cm')
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plt.xlabel('kinetic energy E [MeV]',fontsize=15)
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plt.ylabel('stopping power in MeV/cm',fontsize=15)
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plt.title('Energy loss in Si, BGO, Bi, Ge, and O')
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legend_particle = plt.legend(handles=legend_particle_handles, labels=legend_particle_labels, loc='upper center', title="Particles", frameon=True, fontsize=12*1.5)
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legend_material = plt.legend(handles=legend_material_handles, labels=legend_material_labels, loc='lower left', title="Materials", frameon=True, fontsize=12*1.5)
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legend_particle = plt.legend(handles=legend_particle_handles, labels=legend_particle_labels, loc='lower left', title="Particles", frameon=True, fontsize=15, title_fontsize=15)
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legend_material = plt.legend(handles=legend_material_handles, labels=legend_material_labels, loc='upper right', title="Materials", frameon=True, fontsize=15, title_fontsize=15)
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plt.gca().add_artist(legend_particle)
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plt.gca().add_artist(legend_particle)
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@ -217,21 +239,25 @@ def plot_Eloss_E():
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E = particle_params['m0'] * c**2 * (gam - 1) # Berechnung der kinetischen Energie aus gamma
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dEdx = bethebloch(E, particle_params['m0'], particle_params['z'], params['ne'], params['I']) # Energieverlust pro Distanz
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dEdx_cm = dEdx / (1e6 * e) * 1e-2 # Umrechnung von eV/m in MeV/cm
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linewidth = 1.5 if material != 'BGO' else 3
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plt.plot(beta_gamma, dEdx_cm, color=colors[name], linestyle=linestyles[material], linewidth=linewidth)
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#linewidth = 1.5 if material != 'BGO' else 3
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linewidth = 2
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f=1
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if name == 'Muon': f = 0.8
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plt.plot(beta_gamma, dEdx_cm, color=colors[name], linestyle=linestyles[material], linewidth=f*linewidth)
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# Plot-Einstellungen für den zweiten Plot
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plt.xscale('log')
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plt.yscale('log')
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plt.xlabel(r'$\beta \cdot \gamma$')
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plt.ylabel('Stopping Power in MeV/cm')
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plt.xlabel(r'$\beta \cdot \gamma$',fontsize=15)
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plt.ylabel('stopping power in MeV/cm',fontsize=15)
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plt.title('Energy loss in Si, BGO, Bi, Ge, and O (βγ)')
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legend_particle = plt.legend(handles=legend_particle_handles, labels=legend_particle_labels, loc='upper center', title="Particles", frameon=True, fontsize=12*1.5)
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legend_material = plt.legend(handles=legend_material_handles, labels=legend_material_labels, loc='upper right', title="Materials", frameon=True, fontsize=12*1.5)
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legend_particle = plt.legend(handles=legend_particle_handles, labels=legend_particle_labels, loc='lower left', title="Particles", frameon=True, fontsize=15, title_fontsize=15)
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legend_material = plt.legend(handles=legend_material_handles, labels=legend_material_labels, loc='upper right', title="Materials", frameon=True, fontsize=15, title_fontsize=15)
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plt.gca().add_artist(legend_particle)
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plt.gca().add_artist(legend_material)
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plt.grid(True, which="both", ls="--", lw=0.5)
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plt.tight_layout(rect=[0, 0, 0.95, 1])
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plt.show()
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@ -253,7 +279,7 @@ def plot_only_Si():
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plt.xscale('log')
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plt.yscale('log')
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plt.xlabel('kinetic energy E [MeV]')
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plt.ylabel('Stopping Power in MeV/cm')
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plt.ylabel('stopping power in MeV/cm')
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plt.title('Energy loss in silicon')
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plt.legend()
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plt.grid(True, which="both", ls="--", lw=0.5)
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@ -278,11 +304,12 @@ def plot_only_Si():
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plt.xscale('log')
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plt.yscale('log')
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plt.xlabel(r'$\beta \cdot \gamma$')
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plt.ylabel('Stopping Power in MeV/cm')
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plt.ylabel('stopping power in MeV/cm')
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plt.title('Energy loss in silicon (βγ)')
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plt.legend()
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plt.grid(True, which="both", ls="--", lw=0.5)
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plt.tight_layout(rect=[0, 0, 0.95, 1])
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plt.show()
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@ -291,7 +318,7 @@ def plot_only_Si():
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def calculate_CHAOS(params):
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#E0s = np.linspace(params['E0_min'], params['E0_max'], 1000)
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E0s = np.logspace(np.log10(params['E0_min']), np.log10(params['E0_max']), 1000)
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dx = 1e-7
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dx = 1e-9
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results = []
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Si_data = materials['Si']
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BGO_data = materials['BGO']
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@ -308,36 +335,39 @@ def calculate_CHAOS(params):
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return results
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results_p = calculate_CHAOS(particles['Proton'])
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results_h = calculate_CHAOS(particles['Helium'])
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results_mu = calculate_CHAOS(particles['Muon'])
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# results_p = calculate_CHAOS(particles['Proton'])
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# results_h = calculate_CHAOS(particles['Helium'])
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# results_mu = calculate_CHAOS(particles['Muon'])
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def format_frame(results, particle_name):
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df = pd.DataFrame(results, columns=["E0", "EA", "EC", "ED", "EE", "E_rest"])
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df = df.round(3)
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df.to_csv(f'hists_sim/BB-{particle_name}.histlog', sep=' ')
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return df
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# def format_frame(results, particle_name):
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# df = pd.DataFrame(results, columns=["E0", "EA", "EC", "ED", "EE", "E_rest"])
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# df = df.round(3)
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# df.to_csv(f'hists_sim/BB-{particle_name}.histlog', sep=' ')
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# return df
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frame_p = format_frame(results_p, 'Proton')
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frame_h = format_frame(results_h, 'Helium')
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frame_mu = format_frame(results_mu, 'Muon')
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# frame_p = format_frame(results_p, 'Proton')
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# frame_h = format_frame(results_h, 'Helium')
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# frame_mu = format_frame(results_mu, 'Muon')
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print(frame_p)
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print(frame_h)
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print(frame_mu)
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# print(frame_p)
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# print(frame_h)
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# print(frame_mu)
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plt.plot(frame_p["ED"], frame_p["EC"], 'x', label="proton")
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plt.plot(frame_h["ED"], frame_h["EC"], 'x', label="helium")
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plt.plot(frame_mu["ED"], frame_mu["EC"], 'x', label="muon")
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# plt.plot(frame_p["ED"], frame_p["EC"], 'x', label="proton")
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# plt.plot(frame_h["ED"], frame_h["EC"], 'x', label="helium")
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# plt.plot(frame_mu["ED"], frame_mu["EC"], 'x', label="muon")
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# plt.xlabel('Energieverlust im BGO (MeV)')
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# #plt.ylabel('Verhältnis der Energieverluste (E_si2 / E_si1)')
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# plt.ylabel('Energieverluste in (A)')
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# plt.title('Energieverluste von Teilchen in Detektoren')
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# plt.yscale('log')
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# plt.ylim(0.01, 100)
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# plt.legend()
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# plt.show()
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plot_Eloss_E()
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plt.xlabel('Energieverlust im BGO (MeV)')
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#plt.ylabel('Verhältnis der Energieverluste (E_si2 / E_si1)')
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plt.ylabel('Energieverluste in (A)')
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plt.title('Energieverluste von Teilchen in Detektoren')
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plt.yscale('log')
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plt.ylim(0.01, 100)
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plt.legend()
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plt.show()
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@ -146,7 +146,9 @@ if args.lim:
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if args.xray:
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nameadd = nameadd + '_xray'
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if args.res: nameadd=nameadd+"_res"+str(args.res[0])
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if args.res:
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if float(args.res[0]) > 1.0: nameadd=nameadd+"_res"+str(int(args.res[0]))
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else: nameadd=nameadd+"_res0"+str(int(float(args.res[0])*10))
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if args.equal: nameadd = nameadd + '_equal'
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global axes
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@ -431,7 +433,7 @@ def generate_hist_D_MeV_log():
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def generate_hist_xlog():
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minX = 1
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maxX = 3500
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maxX = 10000
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maxX = 100000
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resX = 1000*resV
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bin_x = int((maxX-minX)/resX)
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edges_x = np.logspace(np.log10(minX), np.log10(maxX), bin_x)
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@ -452,7 +454,7 @@ def generate_hist_xlog():
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if l[0] == 'H':
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timestamp = int(l[1])
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T = get_T(float(l[8]))
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if l[0] == 'EI' and len(l)>3*18 and conditions(l,T,args) and isMask_other(l,T,mask):
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if l[0] == 'EI' and len(l)>3*18 and conditions(l,T,args):# and isMask_other(l,T,mask):
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for i,chan in enumerate(chans):
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#b = value_HL(l,chan,u)
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x_expr, x_op, x_next_op, x_next_channel = parse_channel_expression(chan)
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141
Ecalib.py
Normal file
141
Ecalib.py
Normal file
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@ -0,0 +1,141 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Feb 25 14:02:18 2025
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Energy calibration for CHAOS
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@author: ava
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"""
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import argparse
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import sys
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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from scipy import special
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from scipy import constants
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from scipy.optimize import curve_fit
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import os.path
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from fit_functions import lan
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from fit_functions import mips
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keV = True
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factor = 1/0.00362
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plt.figure(figsize=(17,10))
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fchaos = 'histograms/chaos_sd_floata_cut-AC_ch-a.1dhistlog'
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fproton = 'histograms/protona_cut-AC_ch-a.1dhistlog'
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fmuon = 'histograms/mua_cut-AC_ch-a.1dhistlog'
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fe = 'histograms/e-a_cut-AC_ch-a.1dhistlog'
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fhe = 'histograms/hea_cut-AC_ch-a.1dhistlog'
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# fchaos = 'histograms/chaos_sd_floata_cut-AC_res05_ch-a.1dhistlog'
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# fproton = 'histograms/protona_cut-AC_res05_ch-a.1dhistlog'
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# fmuon = 'histograms/mua_cut-AC_res05_ch-a.1dhistlog'
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# fe = 'histograms/e-a_cut-AC_res05_ch-a.1dhistlog'
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#fhe = 'histograms/hea_cut-AC_res05_ch-a.1dhistlog'
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data_chaos = pd.read_csv(fchaos, sep = '\s+', skiprows=0)
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data_p = pd.read_csv(fproton, sep = '\s+', skiprows=0)
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data_m = pd.read_csv(fmuon, sep = '\s+', skiprows=0)
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data_e = pd.read_csv(fe, sep = '\s+', skiprows=0)
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data_h = pd.read_csv(fhe, sep = '\s+', skiprows=0)
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datas = [data_chaos,data_p, data_m, data_e, data_h]
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names = ["fligt","proton","muon","electron","helium"]
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ch = pd.read_csv(fchaos, sep=' ', nrows=0)
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energy = ch.columns[0]
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resV = data_chaos[energy][1]-data_chaos[energy][0]
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# channels = ['A1','A2','C1','C2']
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# for ch in channels:
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# plt.step(data_chaos[energy], np.multiply(data_chaos[ch], resV),label = ch, lw = 2)
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# plt.step(data_p[energy], np.multiply(data_p[ch], resV),label = str(ch)+'(P)', lw = 2)
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# for i in range (0,len(datas)):
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# plt.step(datas[i][energy], np.add(datas[i]['A1'],datas[i]['A2']),label = names[i]+'-A', lw = 2)
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# plt.step(datas[i][energy], np.add(datas[i]['C1'],datas[i]['C2']),label = names[i]+'-C', lw = 2)
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# i=0
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# plt.step(datas[i][energy], np.add(datas[i]['A1'],datas[i]['A2']),label = names[i]+'-A', lw = 2)
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# plt.step(datas[i][energy], np.add(datas[i]['C1'],datas[i]['C2']),label = names[i]+'-C', lw = 2)
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[80000, 85, 10, 700, 24000000]
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# plt.plot(data_chaos[energy][a:b], lan(data_chaos[energy], *[200000, 85, 10, 14000, 200])[a:b], 'r--')
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# plt.plot(data_chaos[energy][a:b], lan(data_chaos[energy], *[180000, 85, 10, 14000, 200])[a:b], 'b--')
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# plt.plot(data_chaos[energy][a:b], lan(data_chaos[energy], *[160000, 85, 10, 14000, 200])[a:b], 'k--')
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#AC chaos proton muon
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startparams=[[2000, 91, 14, 180, 1000],[60000, 83, 9, 11000, 600],[154000, 85, 9, 16000, 170],[120000, 85, 8, 5000, 200],[]]
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a = [86,88,87,87]
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b = [124,129,120,127]
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a = [2*86,2*88,2*87,2*87]
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b = [2*124,2*129,2*120,2*127]
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for i in []:
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ydata=np.add(datas[i]['A1'],datas[i]['A2'])
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plt.step(datas[i][energy], ydata,label = names[i]+'-A', lw = 2)
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sig = np.power(ydata+1, 0.5)
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par, cov = curve_fit(lan, datas[i][energy][a[i]:b[i]], ydata[a[i]:b[i]], startparams[i], sigma = sig[a[i]:b[i]], absolute_sigma=True)
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print(par)
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plt.plot(data_chaos[energy][a[i]:b[i]], lan(data_chaos[energy], *par)[a[i]:b[i]], 'k--')
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ydata=np.add(datas[i]['C1'],datas[i]['C2'])
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plt.step(datas[i][energy], ydata,label = names[i]+'-C', lw = 2)
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sig = np.power(ydata+1, 0.5)
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par, cov = curve_fit(lan, datas[i][energy][a[i]:b[i]], ydata[a[i]:b[i]], startparams[i], sigma = sig[a[i]:b[i]], absolute_sigma=True)
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print(par)
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plt.plot(data_chaos[energy][a[i]:b[i]], lan(data_chaos[energy], *par)[a[i]:b[i]], 'g--')
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# i=3
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# plt.step(datas[i][energy], np.add(datas[i]['A1'],datas[i]['A2']),label = names[i]+'-A', lw = 2)
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# plt.step(datas[i][energy], np.add(datas[i]['C1'],datas[i]['C2']),label = names[i]+'-C', lw = 2)
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################# BGO ###################
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a=150
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b=160
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i=0
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plt.vlines(datas[i][energy][a],0,100000,colors='red')
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plt.vlines(datas[i][energy][b],0,100000,colors='blue')
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#D chaos proton muon electron
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startparams=[[2000, 91, 14, 180, 1000],[60000, 83, 9, 11000, 600],[154000, 85, 9, 16000, 170],[120000, 85, 8, 5000, 200],[]]
|
||||
a = [95,103,103,0, 134]
|
||||
b = [122,140,124,0,127]
|
||||
c = 126
|
||||
d = 150
|
||||
|
||||
|
||||
for i in [0,1,2,3,4]:
|
||||
ydata=np.add(datas[i]['D1'],datas[i]['D2'])
|
||||
plt.step(datas[i][energy], ydata,label = names[i]+'-D', lw = 2)
|
||||
#sig = np.power(ydata+1, 0.5)
|
||||
#par, cov = curve_fit(lan, datas[i][energy][a[i]:b[i]], ydata[a[i]:b[i]], startparams[i], sigma = sig[a[i]:b[i]], absolute_sigma=True)
|
||||
#print(par)
|
||||
#plt.plot(data_chaos[energy][a[i]:b[i]], lan(data_chaos[energy], *par)[a[i]:b[i]], 'k--')
|
||||
|
||||
fs = 24
|
||||
ylab = 'counts'
|
||||
xlab = 'signal in ' + energy
|
||||
plt.yscale('log')
|
||||
plt.xscale('log')
|
||||
|
||||
plt.ylabel(ylab, fontsize=fs + 5)
|
||||
plt.xlabel(xlab, fontsize=fs + 5)
|
||||
|
||||
plt.xticks(fontsize=fs)
|
||||
plt.yticks(fontsize=fs)
|
||||
plt.tick_params(axis='both', size=7, width=1.5)
|
||||
|
||||
plt.xlim(10, 100000)
|
||||
plt.ylim(0.9, 200000)
|
||||
|
||||
plt.legend(fontsize=fs-1, loc="upper right",ncol=1)
|
||||
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
14
xplot.py
14
xplot.py
|
|
@ -91,7 +91,7 @@ def do_1dhist():
|
|||
if not Itime==None:
|
||||
plt.step(data[energy], np.multiply(data[ch], f*3600/resV/Itime),label=ch, lw=2)
|
||||
else:
|
||||
plt.step(data[energy], data[ch], label=ch)
|
||||
plt.step(data[energy], data[ch]/resV, label=ch)
|
||||
|
||||
#limits = args.limits if args.limits else [-100, 10000, 0.1, 100000]
|
||||
limits = args.limits if args.limits else [0, 100, 10, 10000]
|
||||
|
|
@ -114,7 +114,7 @@ def do_1dhist():
|
|||
#plt.axvline(x=43, color='black', linestyle='--', linewidth=2, label='43 mV')
|
||||
|
||||
|
||||
plot_layout(title, energy, limits,None,None,None)
|
||||
plot_layout(title, energy, limits,None,None,None,None)
|
||||
|
||||
|
||||
save_or_show_plot(channels)
|
||||
|
|
@ -148,7 +148,7 @@ def do_1dhist_morefiles(files):
|
|||
|
||||
title = args.title if args.title else f'Histplot: {", ".join([f.split("/")[-1] for f in files])}'
|
||||
|
||||
plot_layout(title, energy, limits,None,None,None)
|
||||
plot_layout(title, energy, limits,None,None,None,None)
|
||||
|
||||
save_or_show_plot(channels)
|
||||
|
||||
|
|
@ -333,7 +333,7 @@ def do_2dhist():
|
|||
xx = np.linspace(limits[0], limits[1], 10000)
|
||||
plt.plot(xx, xx, color='black', linewidth=0.5)
|
||||
|
||||
plot_layout(tit,energy, limits,xlab,ylab,cbar)
|
||||
plot_layout(tit,energy, limits,xlab,ylab,cbar,None)
|
||||
|
||||
save_or_show_plot([])
|
||||
|
||||
|
|
@ -360,7 +360,7 @@ def read_Itime(file, end):
|
|||
|
||||
###############################################################################
|
||||
|
||||
def plot_layout(title, energy, limits, x, y, cbar):
|
||||
def plot_layout(title, energy, limits, x, y, cbar, Itime):
|
||||
fs = 15
|
||||
fs = 24
|
||||
|
||||
|
|
@ -370,6 +370,8 @@ def plot_layout(title, energy, limits, x, y, cbar):
|
|||
add = '$_{Si}$'
|
||||
add = ""
|
||||
ylab = 'counts / h / ' + energy + add
|
||||
if Itime == None:
|
||||
ylab = 'counts / ' + energy + add
|
||||
xlab = 'signal in ' + energy + add
|
||||
plt.yscale('log')
|
||||
|
||||
|
|
@ -411,7 +413,7 @@ def plot_layout(title, energy, limits, x, y, cbar):
|
|||
cbar.ax.tick_params(labelsize=fs)
|
||||
|
||||
if plt.gca().get_legend_handles_labels()[0]: # Gibt eine Liste von Künstlern (Handles) und Labels zurück
|
||||
plt.legend(fontsize=fs-1, loc="upper right",ncol=4)
|
||||
plt.legend(fontsize=fs-1, loc="upper right",ncol=1)
|
||||
#plt.legend(fontsize=fs+5, loc="upper right")
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue