import pycatima as catima import numpy as np import pandas as pd from scipy.interpolate import interp1d from scipy.integrate import cumulative_trapezoid import matplotlib.pyplot as plt # GAS SETUP P_TORR = 400 TEMP_K = 293.15 R = 8.3144 # Gas density p_pa = P_TORR * 133.322 molar_density = p_pa / (R * TEMP_K) m_he = 4.0026 m_c = 12.0000 m_o = 15.9949 m_mix_avg = (0.96 * m_he) + (0.04 * (m_c + 2*m_o)) rho_g_cm3 = (molar_density * m_mix_avg) / 1e6 print(f"Gas density = {rho_g_cm3:.6e} g/cm^3") # MATERIAL material_def = [ (m_he, 2, 0.96), (m_c, 6, 0.04), (m_o, 8, 0.08) ] gas_mix = catima.Material(material_def) gas_mix.density(rho_g_cm3) # FUNCTION def make_E_vs_x( z, mass_u, emax_mev, label, npoints=500 ): projectile = catima.Projectile(mass_u, z) # Energy grid E = np.linspace(0.01, emax_mev, npoints) # Stopping power array S_mass = np.zeros_like(E) for i, energy in enumerate(E): projectile.T(energy / mass_u) # MeV / (g/cm^2) S_mass[i] = catima.dedx(projectile, gas_mix) # Convert to MeV/cm S_linear = S_mass * rho_g_cm3 # Sort descending energy sort_idx = np.argsort(E)[::-1] E = E[sort_idx] S_linear = S_linear[sort_idx] # Integrate dx/dE = 1/S(E) invS = 1.0 / S_linear x = cumulative_trapezoid( invS, E, initial=0 ) x = -x # Output table output = pd.DataFrame({ "Distance_cm": x, "Energy_MeV": E }) outfile = f"E_vs_x_{label}.dat" output.to_csv( outfile, sep='\t', index=False ) print(f"Saved: {outfile}") return x, E # RUN #proton parameters: z=1, mass_u=1.0078, emax_mev=20 #alpha parameters: z=2, mass_u=4.0026, emax_mev=40 x, E = make_E_vs_x( z=1, mass_u=1.0078, emax_mev=20, label="proton" ) x, E = make_E_vs_x( z=2, mass_u=4.0026, emax_mev=40, label="alpha" ) # PLOT plt.figure(figsize=(8,6)) plt.plot(x, E) plt.xlabel("Distance in Gas (cm)") plt.ylabel("Energy (MeV)") plt.title("Energy Loss Curve") plt.grid(True) plt.show() #gives data in units of Energy (MeV) and Distance (cm). To convert to E(x), you can use the cumulative energy