62 lines
1.2 KiB
Python
62 lines
1.2 KiB
Python
print("Experiment 6.1\n")
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measured_activity = 5.2e-6 # curies
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elapsed_time = 193.92 # s
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sigma_u1 = 35331 # counts
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background_time = 193.94 # s
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sigma_b = 558 # counts
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R = sigma_u1 / elapsed_time - sigma_b / background_time
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print(f"R: {R:.3e} counts / s")
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print(f"R: {R/3.7e10*1e6:.3e} microcurie")
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eps_ip = 0.101 # from figure 6.2
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f = 0.6617
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d = 100 # mm
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r = 38 / 2 # mm
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G = r**2 / 4 / d**2
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A = R / eps_ip / G / f
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print(f"G: {G:.3e}")
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print(f"A: {A:.3e} decays / s")
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print(f"A: {A/3.7e10*1e6:.3e} microcurie")
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A = 4.5 * 3.7e10 / 1e6
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eps_ip_est = R / A / G / f
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print(f"eps_ips: {eps_ip_est:.3e}")
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sigma_u1 = [10122, 11763, 14464, 17476, 22073, 28268, 38084]
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sigma_b = 130
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d = [100, 90, 80, 70, 60, 50, 40]
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t = 60 # s
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import numpy as np
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n = len(sigma_u1)
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R = np.empty(n)
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G = np.empty(n)
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eps_ip = np.empty(n)
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for i, value in enumerate(sigma_u1):
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print(i, value)
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R[i] = (value - sigma_b) / t
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G[i] = r**2 / 4 / d[i] ** 2
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eps_ip[i] = R[i] / A / G[i] / f
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print("R")
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print(R.transpose())
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print("G")
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print(G.transpose())
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print("eps_ip")
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print(eps_ip.transpose())
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import matplotlib.pyplot as plt
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plt.plot(d, eps_ip, ".b")
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plt.xlabel("Distance [mm]")
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plt.ylabel("$\epsilon_{ip}$")
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plt.grid("both")
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plt.savefig("exercise6_4.png")
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plt.show()
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