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.
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
import readPhi
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import readMom
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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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# paths = ['../quasiNeutral_fullAblation/','../2024-09-26_11.48.04/']
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# paths = ['../2024-09-26_12.47.11/']
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# paths = ['../quasiNeutral_partialAblation/','../Poisson_partialAblation/']
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# paths = ['../2024-10-02_14.30.44/']
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# paths = ['../quasiNeutral_fullAblation/','../Poisson_fullAblation/']
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paths = ['../2025-04-09_16.45.52/']
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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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filesPhi = sorted(glob.glob(path+'time_*_phi.csv'))
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start = 80
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end = 85#len(filesPhi)
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every = 1
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fig, ax = plt.subplots(4, sharex='all')
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ax[1].set_yscale('log')
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ax[1].set_ylim(bottom=1e10, top=1e24)
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_, r, _, _, _ = readPhi.read(filesPhi[0])
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for t in range(start,end+1,every):
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sum_Zni = np.zeros(len(r))
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ave_ui = np.zeros(len(r))
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ave_Ti = np.zeros(len(r))
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for Z in Zlist:
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filename='time_*_Z_{:.1f}_mom_i.csv'.format(Z)
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filesMom_i = sorted(glob.glob(path+filename))
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fileMom_i = filesMom_i[t]
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time, r, n_i, u_i, T_i, Zave = readMom.read(fileMom_i)
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sum_Zni += Zave*n_i
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ave_ui += Zave*n_i*u_i
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ave_Ti += Zave*n_i*T_i
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ave_ui = np.divide(ave_ui, sum_Zni, out=np.zeros_like(ave_ui), where=sum_Zni!=0.0)
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ave_Ti = np.divide(ave_Ti, sum_Zni, out=np.zeros_like(ave_Ti), where=sum_Zni!=0.0)
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filePhi = filesPhi[t]
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time, r, phi, E, n_e = readPhi.read(filePhi)
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ax[0].plot(r, phi, label='t = {:.1f} ns'.format(time*1e9))
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# ax[0].plot(r, E, label='t = {:.1f} ns'.format(time*1e9))
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# ax[0].plot(r, (Zave*n_i - n_e), label='t = {:.1f} ns'.format(time*1e9))
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ax[1].plot(r, sum_Zni)
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ax[1].plot(r, n_e, color='k', linestyle='dashed')
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ax[2].plot(r, ave_ui)
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ax[3].plot(r, ave_Ti)
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ax[0].set_title(label)
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ax[0].legend()
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plt.show()
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