def read_bin_plot_npz(filename, legendlabel, linestyle, color, y_offset=1.0):
data = np.load(filename)
a, e, N_broken = data['a_fin'], data['e_fin'], data['N_broken']
a_bins, N_a, a_binwidth = calcFrequency(a, N_bins, log=True)
da = a_bins * (np.exp(a_binwidth) - 1.0)
plt.loglog(a_bins / au,
N_a / (da / au) * y_offset,
label=legendlabel,
linestyle=linestyle,
color=color)
N_b = 1000
#Monte Carlo
#Number of encounters
print('M Mean =', T * encounterRate(n_p, v_rms, b_min, b_max, v_min, v_max))
N_enc_MC = np.random.poisson(
T * encounterRate(n_p, v_rms, b_min, b_max, v_min, v_max))
#b values
#b_MC = draw_b(b_max, N_enc_MC)
#v values
#v_MC = maxwell.rvs(scale=v_rms, size=N_enc_MC)
v_MC = draw_vmaxwellian(v_rms, v_min, v_max, N_enc_MC)
#Bin b values
#b_bins_MC, N_b_MC, db_MC = calcFrequency(b_MC, N_b)
#Bin v values
v_bins_MC, N_v_MC, dv_MC = calcFrequency(v_MC, N_v)
'''
#Binning
#b bins for encounter rate
dlogb = (np.log(b_max)-np.log(b_min))/N_b
b = np.array([b_min*np.exp(i*dlogb) for i in range(N_b)])
db_B = b * (np.exp(dlogb) - 1.0)
#print('b =', b)
#print('db_B =', db_B)
#db_B = (b_max - b_min)/(N_b)
#b = np.array([b_min + i*db_B for i in range(N_b)])
#v bins for encounter rate
dlogv = (np.log(v_max)-np.log(v_min))/N_v
plt.title('v distribution')
ax1 = fig.add_subplot(111, projection='3d')
ax1.scatter(v_vectors[:,0], v_vectors[:,1], v_vectors[:,2])
plt.show()
fig = plt.figure()
plt.title('b distribution')
ax2 = fig.add_subplot(111, projection='3d')
ax2.scatter(b_vectors[:,0], b_vectors[:,1], b_vectors[:,2])
plt.show()
'''
N_bins = 50
plt.title('z test for v')
vz_bins, N_vz, d_vz = calcFrequency(v_vectors[:, 2], N_bins)
plt.plot(vz_bins, N_vz / N_vec / d_vz)
plt.show()
plt.title('z test for b')
bz_bins, N_bz, d_bz = calcFrequency(b_vectors[:, 2], N_bins)
plt.plot(bz_bins, N_bz / N_vec / d_bz)
plt.plot()
plt.show()
plt.title('phi test for v')
vphi_bins, N_vphi, d_vphi = calcFrequency(v_phis, N_bins)
plt.plot(vphi_bins, N_vphi / N_vec / d_vphi)
plt.show()
plt.title('phi test for b')
print('Saving data')
np.savez('simulation_test_{}Msol_{}e{}.npz'.format(int(M_p*mass_scale()/(2.0*10.0**30.0)), int(N_bin/10**int(np.floor(np.log10(N_bin)))), int(np.floor(np.log10(N_bin)))), a_fin=a_fin, e_fin=e_fin, N_broken=N_broken)
'''
#Load data
#print('Loading data')
#data = np.load('binary_pop.npz')
#a_fin, e_fin, N_broken = data['a_fin'], data['e_fin'], data['N_broken']
print('Number of binaries broken =', N_broken)
print('Plotting')
#Plot final and initial distributions
#Number of bins
N_bins = 50
a_bins_ini, N_a_ini, a_ini_binwidth = calcFrequency(a_ini, N_bins, log=True)
a_bins_fin, N_a_fin, a_fin_binwidth = calcFrequency(a_fin, N_bins, log=True)
#Actual bin widths
da_ini = a_bins_ini * (np.exp(a_ini_binwidth) - 1.0)
da_fin = a_bins_fin * (np.exp(a_fin_binwidth) - 1.0)
e_bins_ini, N_e_ini, e_ini_binwidth = calcFrequency(e_ini, N_bins)
e_bins_fin, N_e_fin, e_fin_binwidth = calcFrequency(e_fin, N_bins)
#Semi-major axis
plt.loglog(a_bins_ini * length_scale() / au,
N_a_ini / (da_ini * length_scale() / au))
plt.loglog(a_bins_fin * length_scale() / au,
N_a_fin / (da_fin * length_scale() / au))
#plt.xlim(10.0**3.0, 3.0*10.0**5.0)
#plt.ylim(5.0*10.0**(-1.0), 3.0*10.0**3.0)
N_enc_F[i] = np.size(b_F)
#Mean time method
N_enc_T = np.zeros(N_N_enc)
for i in range(N_N_enc):
#time passed
t = 0.0
while t <= T:
rate = encounterRate(n_p, v_rms, b_min, b_max, v_min, v_max)
t += np.random.exponential(1.0 / rate)
N_enc_T[i] += 1
#print('N_enc_T =', N_enc_T)
#Bin N_enc values
#print('N_enc_B =', N_enc_B)
N_enc_bins_MC, N_N_enc_MC, dN_enc_MC = calcFrequency(N_enc_MC, 100)
N_enc_bins_T, N_N_enc_T, dN_enc_T = calcFrequency(N_enc_T, 100)
N_enc_bins_F, N_N_enc_F, dN_enc_F = calcFrequency(N_enc_F, 100)
#Plot N_enc distribution
plt.plot(N_enc_bins_MC, N_N_enc_MC / N_N_enc / dN_enc_MC, label='Monte Carlo')
plt.plot(N_enc_bins_T, N_N_enc_T / N_N_enc / dN_enc_T, label='Mean time')
plt.plot(N_enc_bins_F, N_N_enc_F / N_N_enc / dN_enc_F, label='Filter')
plt.plot([MC_mean] * np.size(N_N_enc_MC),
N_N_enc_MC / N_N_enc / dN_enc_MC,
label='MC mean')
plt.xlabel('Number of encounters')
plt.ylabel('Probability density of number of encounters')
plt.legend()
plt.show()