339 lines
12 KiB
Python
339 lines
12 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Sep 12 12:24:24 2022
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@author: anna
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"""
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# -*- coding: utf-8 -*-
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import numpy as np
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import matplotlib.pyplot as plt
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import csv
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from scipy.optimize import curve_fit
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def read_csv(file_name):
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with open(file_name) as file:
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reader = list(csv.reader(file, delimiter=';',
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quotechar=',', quoting=csv.QUOTE_MINIMAL))
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return reader
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def make_latex_table(data):
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table = []
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table.append("\\begin{table}".replace('//', '\\'))
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table.append("\label{}".replace('/', '\\'))
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table.append('\caption{}'.replace('/', '\\'))
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leng = len(data[0])
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stroka = 'c'.join(['|' for _ in range(leng+1)])
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table.append('\\begin{tabular}{'.replace('//', '\\')+stroka+'}')
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table.append('\hline')
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for i in range(len(data)):
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table.append(' & '.join(data[i]) + ' \\\\')
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table.append('\hline')
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table.append("\end{tabular}".replace('/', '\\'))
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table.append("\end{table}".replace('/', '\\'))
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return table
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def make_point_grafic(x, y, xlabel, ylabel, caption, xerr, yerr,
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subplot=None, color=None, center=None, s=15):
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if not subplot:
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subplot = plt
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if type(yerr) == float or type(yerr) == int:
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yerr = [yerr for _ in y]
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if type(xerr) == float or type(xerr) == int:
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xerr = [xerr for _ in x]
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if xerr[1] != 0 or yerr[1] != 0:
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subplot.errorbar(x, y, yerr=yerr, xerr=xerr, linewidth=4,
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linestyle='', label=caption, color=color,
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ecolor=color, elinewidth=1, capsize=3.4,
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capthick=1.4)
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else:
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subplot.scatter(x, y, linewidth=0.005, label=caption,
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color=color, edgecolor='black', s=s)
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# ax = plt.subplots()
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# ax.grid())
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if not center:
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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else:
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ax = plt.gca()
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.spines['bottom'].set_position('zero')
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ax.spines['left'].set_position('zero')
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ax.set_xlabel(ylabel, labelpad=-180, fontsize=14) # +
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ax.set_ylabel(xlabel, labelpad=-260, rotation=0, fontsize=14)
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def make_line_grafic(xmin, xmax, xerr, yerr, xlabel, ylabel, k, b, caption,
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subplot=None, color=None, linestyle='-'):
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if not subplot:
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subplot = plt
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x = np.arange(xmin, xmax, (xmax-xmin)/10000)
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subplot.plot(x, k*x+b, label=caption, color=color, linewidth=2.4,
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linestyle=linestyle)
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def make_graffic(x, y, xlabel, ylabel, caption_point, xerr, yerr, k=None,
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b=None, filename=None, color=None, koef=[0.9, 1.1]):
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if not color:
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color = ['limegreen', 'indigo']
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make_point_grafic(x, y, xlabel=xlabel,
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ylabel=ylabel, caption=caption_point,
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xerr=xerr, yerr=yerr, subplot=plt, color=color[0])
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if k and b:
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make_line_grafic(xmin=min(x)-1, xmax=max(x)+1, xerr=xerr, yerr=yerr,
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xlabel='', ylabel='', k=k, b=b,
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caption='Theoretical dependence', subplot=plt,
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color='red')
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if type(yerr) == float or type(yerr) == int:
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yerr = [yerr for _ in y]
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k, b, sigma = approx(x, y, b, yerr)
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sigma[0] = abs(k*((sigma[0]/k)**2+(np.mean(yerr)/np.mean(y))**2 +
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(np.mean(xerr)/np.mean(x))**2)**0.5)
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if (b != 0):
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sigma[1] = abs(b*((sigma[1]/b)**2+(np.mean(yerr)/np.mean(y))**2 +
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(np.mean(xerr)/np.mean(x))**2)**0.5)
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else:
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sigma[1] = 0
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make_line_grafic(xmin=min(x)*koef[0], xmax=max(x)*koef[1], xerr=xerr,
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yerr=yerr, xlabel='', ylabel='', k=k, b=b, caption=None,
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subplot=plt, color=color[1])
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plt.legend()
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return k, b, sigma
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def approx(x, y, b, sigma_y, f=None):
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if sigma_y[0] != 0:
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sigma_y = [1/i**2 for i in sigma_y]
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else:
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sigma_y = np.array([1 for _ in y])
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if f is None:
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if b == 0:
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def f(x, k):
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return k*x
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k, sigma = curve_fit(f, xdata=x, ydata=y, sigma=sigma_y)
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sigma = np.sqrt(np.diag(sigma))
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return k, b, [sigma, 0]
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else:
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def f(x, k, b):
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return x*k + b
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k, sigma = curve_fit(f, xdata=x, ydata=y, sigma=sigma_y)
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sigma_b = np.sqrt(sigma[1][1])
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b = k[1]
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k = k[0]
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sigma = np.sqrt(sigma[0][0])
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return k, b, [sigma, sigma_b]
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else:
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k, sigma = curve_fit(f, xdata=x, ydata=y, sigma=sigma_y)
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sigma = np.sqrt(np.diag(sigma))
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b = k[1]
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k = k[0]
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return k, b, sigma
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def find_delivation(data):
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data = np.array(data).astype(np.float)
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s = sum(data)/len(data)
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su = 0
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for i in data:
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su += (i-s)**2
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return (su/(len(data)-1))**0.5
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def make_dic(filename):
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data = np.array(read_csv(filename))
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data = np.transpose(data)
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dic = {}
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for i in range(len(data)):
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dic[data[i][0]] = np.array(data[i][1:]).astype(np.float)
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data = dic
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return data
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def make_fun(A0, T):
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def f(t, k, b):
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return A0/(1+A0*b*t)-k*0*A0*t/T
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return f
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def make_fun_grafic(xmin, xmax, xerr, yerr, xlabel, ylabel, f, k, b, caption,
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subplot=None, color=None):
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if not subplot:
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subplot = plt
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x = np.arange(xmin, xmax, (xmax-xmin)/10000)
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subplot.plot(x, f(x, k, b), label=caption, color=color)
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def make_all():
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plt.figure(dpi=500, figsize=(8, 5))
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vac_discharge()
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plt.savefig('U(I)_discharge')
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plt.show()
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plt.figure(dpi=500, figsize=(8, 5))
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vac_probe()
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def vac_discharge():
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data = make_dic('V(I)_discharge.csv')
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data['I_1'] *= 6/150
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data['U_1'] *= 10
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x = []
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y = []
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for i in range(len(data['U_1'])):
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if data['I_1'][i] <= 1.8:
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y.append(data['U_1'][i])
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x.append(data['I_1'][i])
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x = np.array(x)
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y = np.array(y)
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k, b, sigma = make_graffic(y=y, x=x, xlabel='I, mA',
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ylabel='U, V', caption_point='', xerr=0.003*x,
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yerr=0.002*y)
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make_point_grafic(y=data['U_1'], x=data['I_1'], ylabel='U, V',
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xlabel='I, mA', caption='', xerr=0.003*data['I_1'],
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yerr=0.002*data['U_1'])
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print('R_dif=', k*10**3, '+-', sigma[0]*10**3)
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def vac_probe():
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big_data = {'I_en': [], 'I_en_sigma': [], 'I_in': [], 'k': [],
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'I_in_sigma': [], 'k_sigma': []}
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I_p = [1.5, 3, 3.4]
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for i in I_p:
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name = 'Probe_'+str(i)+'.csv'
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num = int(i+0.6)-2
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data = make_dic(name)
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cap = '$I_p$ = ' + str(i)+' mA'
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x = data['U']
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y = data['I']
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xlabel = 'U, V'
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ylabel = 'I, $\mu$A'
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color = colors[num]
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x_lin_big = []
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x_lin_sm = []
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y_lin_big = []
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y_lin_sm = []
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x_lin_ave = []
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y_lin_ave = []
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for j in range(len(data['U'])):
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if data['U'][j] >= 12.5:
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x_lin_big.append(data['U'][j])
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y_lin_big.append(data['I'][j])
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elif data['U'][j] <= -12.5:
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x_lin_sm.append(data['U'][j])
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y_lin_sm.append(data['I'][j])
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elif data['U'][j] <= 6 and data['U'][j] >= -6:
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x_lin_ave.append(data['U'][j])
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y_lin_ave.append(data['I'][j])
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xerr = 0.003
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yerr = 0.002
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k, b, sigma = make_graffic(x_lin_big, y_lin_big, xlabel=xlabel,
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ylabel=ylabel, caption_point='', xerr=0,
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yerr=0, color=color)
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big_data['I_en'].append(b*10**(-6)*3*10**9)
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big_data['I_en_sigma'].append(sigma[1]*10**(-6)*3*10**9)
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make_line_grafic(0, xmax=max(x_lin_big), xerr=0, yerr=0, xlabel=xlabel,
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ylabel=ylabel, k=k, b=b, caption='', linestyle=':',
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color=color[1])
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plt.scatter(0, b, color=color[1], marker=0, s=15, linewidths=5)
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k, b, sigma = make_graffic(x_lin_sm, y_lin_sm, xlabel=xlabel,
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ylabel=ylabel, caption_point='', xerr=0,
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yerr=0, color=color, koef=[1.1, 0.9])
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big_data['I_in'].append(-b*10**(-6)*3*10**9)
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big_data['I_in_sigma'].append(sigma[1]*10**(-6)*3*10**9)
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make_line_grafic(xmax=0, xmin=min(x_lin_sm), xerr=0, yerr=0,
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xlabel=xlabel, ylabel=ylabel, k=k, b=b, caption='',
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linestyle=':', color=color[1])
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make_point_grafic(x, y, xlabel, ylabel, caption=cap, xerr=xerr*x,
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yerr=yerr*y, center=True, color=color[0])
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plt.scatter(0, b, color=color[1], marker=0, s=15, linewidths=5)
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k, b, sigma = approx(x_lin_ave, y_lin_ave, b=2, sigma_y=[0])
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big_data['k'].append(k*10**(-6)*3*10**9*3*10**2)
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big_data['k_sigma'].append(sigma[0]*10**(-6)*3*10**9*3*10**2)
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plt.legend()
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plt.savefig('I(U)_probe')
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plt.show()
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for i in big_data.keys():
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big_data[i] = np.array(big_data[i])
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k_b = 1.38*10**(-16)
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e = 4.8 * 10**(-10)
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T_e = 1/2*big_data['I_in']/big_data['k']*e/k_b # ЭВ
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T_e_sigma = T_e*((big_data['I_in_sigma']/big_data['I_in'])**2+
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(big_data['k_sigma']/big_data['k'])**2)**0.5
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print('T_e = ', *T_e, 'К')
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print('T_e_sigma = ', *T_e_sigma, 'К')
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print('T_e = ', *T_e/11606, 'эВ')
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print('T_e_sigma = ', *T_e_sigma/11606, 'эВ')
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S = np.pi * 0.2 * 5.2 * 10 ** (-2)
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m_i = 22 * 1.66 * 10 ** (-24)
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m_e = 9.1 * 10 ** (-28)
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n_i = 2.5*big_data['I_in']/e/S*(m_i/2/T_e/k_b)**0.5
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n_i_sigma = n_i*((big_data['I_in_sigma']/big_data['I_in'])**2+
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1/4*(T_e_sigma/T_e)**2)**0.5
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print('n_i = ', *n_i/10**(10), '10^10')
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print('n_i_sigma = ', *n_i_sigma/10**(10), '10^10')
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w_p = (4*np.pi*n_i*e**2/m_e)**0.5
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w_p_sigma = w_p * n_i_sigma/n_i/2
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print('w_p =', *w_p/10**9, '10^9 рад/с')
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print('w_p_sigma =', *w_p_sigma/10**9, '10^9 рад/с')
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r_De = (k_b*T_e/4/np.pi/n_i/e**2)**0.5
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r_De_sigma = r_De * ((T_e_sigma/T_e)**2 + (n_i_sigma/n_i)**2)**0.5
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print('r_De =', *r_De*10**3, '10^-3 см')
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print('r_De_sigma =', *r_De_sigma*10**3, '10^-3 см')
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T_i = 300
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r_D = (k_b*T_i/4/np.pi/n_i/e**2)**0.5
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r_D_sigma = r_D * n_i_sigma/n_i
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print('r_D =', *r_D*10**3, '10^-3 см')
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print('r_D_sigma =', *r_D_sigma*10**3, '10^-3 см')
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|||
|
|
N_D = 4/3*np.pi*n_i*r_D**3
|
|||
|
|
N_D_sigma = N_D * (9*(r_D_sigma/r_D)**2 + (n_i_sigma/n_i)**2)**0.5
|
|||
|
|
print('N_D = ', *N_D)
|
|||
|
|
print('N_D_sigma = ', *N_D_sigma)
|
|||
|
|
alpha = n_i * k_b * T_e / (2*133*10)
|
|||
|
|
alpha_sigma = alpha * ((T_e_sigma/T_e)**2 + (n_i_sigma/n_i)**2)**0.5
|
|||
|
|
print('alpha = ', *alpha*10**5, '10^-5')
|
|||
|
|
print('alpha_sigma = ', *alpha_sigma*10**5, '10^-5')
|
|||
|
|
|
|||
|
|
|
|||
|
|
plt.figure(dpi=500, figsize=(8, 5))
|
|||
|
|
ax = plt.gca()
|
|||
|
|
ax.set_xlabel("radius [m]", fontsize=16)
|
|||
|
|
ax.set_ylabel(r"surface area ($m^2$)", fontsize=16, color="blue")
|
|||
|
|
for label in ax.get_yticklabels():
|
|||
|
|
label.set_color("blue")
|
|||
|
|
|
|||
|
|
I_p = np.array(I_p)
|
|||
|
|
make_point_grafic(I_p, T_e/10**4, ylabel='$T_e, 10^4 \cdot K$',
|
|||
|
|
xlabel='$I_p$, mA', caption='', xerr=I_p*xerr,
|
|||
|
|
yerr=T_e_sigma/10**4, color='blue', s=60)
|
|||
|
|
ax2 = ax.twinx()
|
|||
|
|
ax2.set_ylabel(r"volume ($m^3$)", fontsize=16, color="red")
|
|||
|
|
for label in ax2.get_yticklabels():
|
|||
|
|
label.set_color("red")
|
|||
|
|
|
|||
|
|
make_point_grafic(I_p, n_i/10**10, ylabel='$n_e, 10^{10} \cdot см^{-3}$',
|
|||
|
|
xlabel='$I_p$, mA', caption='', xerr=I_p*xerr,
|
|||
|
|
yerr=n_i_sigma/10**10, color='red', s=30)
|
|||
|
|
|
|||
|
|
plt.savefig('T,n(I_p)')
|
|||
|
|
plt.show()
|
|||
|
|
|
|||
|
|
|
|||
|
|
colors = [['green', 'mediumpurple'], ['orange', 'sandybrown'],
|
|||
|
|
['maroon', 'rosybrown'], ['darkblue', 'gold'],
|
|||
|
|
['crimson', 'greenyellow'], ['indigo', 'lightgreen']]
|
|||
|
|
make_all()
|