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