ANASEN_analysis/ELoss/EXtable.py
2026-05-12 16:01:10 -04:00

131 lines
2.2 KiB
Python

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