# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import csv from scipy.optimize import curve_fit def read_csv(file_name): with open(file_name) as file: reader = list(csv.reader(file, delimiter=';', quotechar=',', quoting=csv.QUOTE_MINIMAL)) return reader def make_latex_table(data): table = [] table.append("\\begin{table}".replace('//', '\\')) table.append("\label{}".replace('/', '\\')) table.append('\caption{}'.replace('/', '\\')) leng = len(data[0]) stroka = 'c'.join(['|' for _ in range(leng+1)]) table.append('\\begin{tabular}{'.replace('//', '\\')+stroka+'}') table.append('\hline') for i in range(len(data)): table.append(' & '.join(data[i]) + ' \\\\') table.append('\hline') table.append("\end{tabular}".replace('/', '\\')) table.append("\end{table}".replace('/', '\\')) return table def make_point_grafic(x, y, xlabel, ylabel, caption, xerr, yerr, subplot=None, color=None, center=None, s=15): if not subplot: subplot = plt if type(yerr) == float or type(yerr) == int: yerr = [yerr for _ in y] if type(xerr) == float or type(xerr) == int: xerr = [xerr for _ in x] if xerr[1] != 0 or yerr[1] != 0: subplot.errorbar(x, y, yerr=yerr, xerr=xerr, linewidth=4, linestyle='', label=caption, color=color, ecolor=color, elinewidth=1, capsize=3.4, capthick=1.4) else: subplot.scatter(x, y, linewidth=0.005, label=caption, color=color, edgecolor='black', s=s) # ax = plt.subplots() # ax.grid()) if not center: plt.xlabel(xlabel) plt.ylabel(ylabel) else: ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_position('zero') ax.spines['left'].set_position('zero') ax.set_xlabel(ylabel, labelpad=-180, fontsize=14) # + ax.set_ylabel(xlabel, labelpad=-260, rotation=0, fontsize=14) def make_line_grafic(xmin, xmax, xerr, yerr, xlabel, ylabel, k, b, caption, subplot=None, color=None, linestyle='-'): if not subplot: subplot = plt x = np.arange(xmin, xmax, (xmax-xmin)/10000) subplot.plot(x, k*x+b, label=caption, color=color, linewidth=1.4, linestyle=linestyle) def make_graffic(x, y, xlabel, ylabel, caption_point, xerr, yerr, k=None, b=None, filename=None, color=None, koef=[0.9, 1.1], s=None): if not color: color = ['limegreen', 'indigo'] if not s: make_point_grafic(x, y, xlabel=xlabel, ylabel=ylabel, caption=caption_point, xerr=xerr, yerr=yerr, subplot=plt, color=color[0]) else: make_point_grafic(x, y, xlabel=xlabel, ylabel=ylabel, caption=caption_point, xerr=xerr, yerr=yerr, subplot=plt, color=color[0], s=s) if k and b: make_line_grafic(xmin=min(x)-1, xmax=max(x)+1, xerr=xerr, yerr=yerr, xlabel='', ylabel='', k=k, b=b, caption='Theoretical dependence', subplot=plt, color='red') if type(yerr) == float or type(yerr) == int: yerr = [yerr for _ in y] k, b, sigma = approx(x, y, b, yerr) sigma[0] = abs(k*((sigma[0]/k)**2+(np.mean(yerr)/np.mean(y))**2 + (np.mean(xerr)/np.mean(x))**2)**0.5) if (b != 0): sigma[1] = abs(b*((sigma[1]/b)**2+(np.mean(yerr)/np.mean(y))**2 + (np.mean(xerr)/np.mean(x))**2)**0.5) else: sigma[1] = 0 make_line_grafic(xmin=min(x)*koef[0], xmax=max(x)*koef[1], xerr=xerr, yerr=yerr, xlabel='', ylabel='', k=k, b=b, caption=None, subplot=plt, color=color[1]) plt.legend() return k, b, sigma def approx(x, y, b, sigma_y, f=None): if sigma_y[0] != 0: sigma_y = [1/i**2 for i in sigma_y] else: sigma_y = np.array([1 for _ in y]) if f is None: if b == 0: def f(x, k): return k*x k, sigma = curve_fit(f, xdata=x, ydata=y, sigma=sigma_y) sigma = np.sqrt(np.diag(sigma)) return k, b, [sigma, 0] else: def f(x, k, b): return x*k + b k, sigma = curve_fit(f, xdata=x, ydata=y, sigma=sigma_y) sigma_b = np.sqrt(sigma[1][1]) b = k[1] k = k[0] sigma = np.sqrt(sigma[0][0]) return k, b, [sigma, sigma_b] else: k, sigma = curve_fit(f, xdata=x, ydata=y, sigma=sigma_y) sigma = np.sqrt(np.diag(sigma)) b = k[1] k = k[0] return k, b, sigma def find_delivation(data): data = np.array(data).astype(np.float) s = sum(data)/len(data) su = 0 for i in data: su += (i-s)**2 return (su/(len(data)-1))**0.5 def make_dic(filename): data = np.array(read_csv(filename)) data = np.transpose(data) dic = {} for i in range(len(data)): dic[data[i][0]] = np.array(data[i][1:]).astype(np.float) data = dic return data def make_fun(A0, T): def f(t, k, b): return A0/(1+A0*b*t)-k*0*A0*t/T return f def make_fun_grafic(xmin, xmax, xerr, yerr, xlabel, ylabel, f, k, b, caption, subplot=None, color=None): if not subplot: subplot = plt x = np.arange(xmin, xmax, (xmax-xmin)/10000) subplot.plot(x, f(x, k, b), label=caption, color=color) def make_all(): part_1_1() part_1_2() part_2() part_3() def part_1_1(): plt.figure(dpi=500, figsize=(8, 5)) data = make_dic("dnu(tau).csv") x = 1 / data["tau"]*100 y = data['d_nu']/10**4 xlabel = "1/"+greek_letters[48]+'$,10^4$Гц' ylabel = greek_letters[0]+greek_letters[41]+'$,10^4$ Гц' k, b, sigma = make_graffic(x, y, xlabel, ylabel, caption_point='', xerr=0, yerr=0, s=40) print(k, sigma[0]) plt.savefig("dnu(tau)") plt.show() def part_1_2(): fig, ax = plt.subplots(figsize=(10, 6)) data = make_dic("a(n).csv") v = 1000 t = 50 * 10 **- 6 T = 1 / v N = 200 V0 = 300 vu = [n/T for n in range(2, N)] c = [abs(V0*t/T*np.sin(n*t*np.pi/T)/(n*t*np.pi/T)) for n in range(2, N)] for i in range(len(vu)): plt.scatter(vu[i], c[i], linewidth=0.005, label='', color='purple', s=15) ax.vlines(vu[i], 0, c[i], colors='purple') ax.set_xlabel(greek_letters[41]+', Hz') ax.set_ylabel("$a_n$, мВ") plt.savefig("a(n)") plt.show() def part_2(): plt.figure(dpi=500, figsize=(8, 5)) data = make_dic("T(dnu).csv") x = 1 / data["T"]*10**3 y = data['dnu'] xlabel = "1/"+greek_letters[48]+'$, $Гц' ylabel = greek_letters[0]+greek_letters[41]+'$,10^4$ Гц' k, b, sigma = make_graffic(x, y, xlabel, ylabel, caption_point='', xerr=0, yerr=0, s=40) print(k, sigma[0], -sigma[0]/k*100) plt.savefig("T(dnu)") plt.show() def part_3(): plt.figure(dpi=500, figsize=(8, 5)) data = make_dic("a(m).csv") otn = data['a_b']/data['a_c'] k, b, sigma = make_graffic(data['m']/100, otn, 'm', '$а_{бок}/a_{осн}$', caption_point='', xerr=0, yerr=0, s=40) print(k, sigma[0]) plt.savefig("a(m)") plt.show() greek_letters=[chr(code) for code in range(916,980)] print(greek_letters) make_all()