Jorge Gonzalez
0c27b98e2e
Now n_e is given as the density at the boundary and n_i at the boundary is calculated once Z is known. This aims to eliminate the iterative process.
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
import readPhi
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import readF
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import readZlist
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import matplotlib.pyplot as plt
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import glob
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import numpy as np
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from scipy.constants import e, k
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m_i = 1.9712e-25
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# paths = ['../quasiNeutral_fullAblation/','../Poisson_fullAblation/']
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# paths = ['../quasiNeutral_partialAblation/','../Poisson_partialAblation/','../PoissonTi_partialAblation/','../2024-11-05_13.56.23/']
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# paths = ['../2024-11-11_13.58.54/']
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# paths = ['../quasiNeutral_fullAblation/','../Poisson_fullAblation/', '../quasiNeutral_partialAblation/', '../Poisson_partialAblation/']
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# paths = ['../2024-12-02_21.07.52/', '../Poisson_50ns_T30Z11/']
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# paths = ['../2024-12-10_18.45.17/', '../Poisson_50ns_T30Z11/']
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# paths = ['../2024-12-11_12.38.27/', '../Poisson_polytropic_fa_T30Z11/', '../Poisson_fa_T30Z11/']
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# paths = ['../Poisson_partialAblation/','../Poisson_partialAblation_lowerT/','../Poisson_partialAblation_lowT/','../Poisson_partialAblation_highT/']
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paths = ['../polytropic_80ns_T30/']
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labels = [path[3:-1] for path in paths]
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for path, label in zip(paths, labels):
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Zlist = readZlist.read(path+'ZList.csv')
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filesCum_i = sorted(glob.glob(path+'time_*_fCum_i.csv'))
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_, _, v, _ = readF.read(filesCum_i[-1])
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sumF = np.zeros(len(v))
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for Z in Zlist:
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filename='time_*_Z_{:.1f}_fCum_i.csv'.format(Z)
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filesCum_i = sorted(glob.glob(path+filename))
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time, x, v, f_i = readF.read(filesCum_i[-1])
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sumF += f_i[0]
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plt.plot(v**2*m_i*0.5/e, f_i[0]*e/m_i/v, label=Z)
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print(time)
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plt.plot(v**2*m_i*0.5/e, sumF*e/m_i/v, label='sum', color='k', linestyle='dashed')
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plt.yscale('log')
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plt.ylim([1e16,5e27])
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plt.ylabel('Sum f(e) / sqrt(e) (m^-3 eV^-1)')
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plt.xscale('log')
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plt.xlim([1e0,1e4])
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plt.xlabel('e (eV)')
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plt.legend()
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# plt.title('r = {:.1f} mm, time_max={:.1f} ns, '.format(x[0]*1e3, time*1e9) + label)
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plt.show()
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