310 lines
9.3 KiB
Python
310 lines
9.3 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Oct 6 19:17:27 2022
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@author: anna
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"""
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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], cap=1):
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if not color:
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color = ['limegreen', 'indigo']
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if cap == 1:
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cap_point = caption_point
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line_point = None
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else:
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line_point = caption_point
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cap_point = None
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make_point_grafic(x, y, xlabel=xlabel,
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ylabel=ylabel, caption=cap_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,
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caption=line_point, 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[0], b, [sigma[0], 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 B(x, b, c, d):
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return x**2*b+x*c+d
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def make_grad():
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data = make_dic('grad.csv')
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f_0 = 0.1
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f = data['Ф']-f_0
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s = 75 * 10 ** -4
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b = f * 10**-3 / s
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x = data['I']
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y = b
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xlabel = '$I_M$, А'
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ylabel = '$B$, Тл'
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caption = ''
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xerr = 1/100*x
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yerr = 1/100*y
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plt.figure(dpi=500, figsize=(8, 5))
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make_point_grafic(x, y, xlabel, ylabel, caption, xerr, yerr)
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a, sigma = curve_fit(B, x, y)
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sigma = abs(np.sqrt(np.diag(sigma))/a)
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eps = (np.min(sigma)**2+0.01**2)**0.5
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x_range = np.arange(min(x), max(x), step=0.001)
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y_fit = B(x_range, a[0], a[1], a[2])
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plt.plot(x_range, y_fit)
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plt.savefig('градуировка')
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plt.show()
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print('a = ', a)
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return (a, eps)
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class Exp:
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def __init__(self, e, b, I):
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self.e = e
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self.b = b
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self.I = I
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def make_exp(a, eps_b):
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plt.figure(dpi=500, figsize=(10, 6))
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data = make_dic('exp.csv')
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eds = chr(949)
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h=1*10**-3
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e = (data['U_34']-data['U_0'])/10**3
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b = np.array(B(data['I'], a[0], a[1], a[2]))
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big_data = []
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e_big = []
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b_big = []
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I_big = []
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# x=[]
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# y=[]
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# for i in range(len(b)):
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# if True:
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# x.append(b[i] * data['I_t'][i])
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# y.append(e[i])
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# x=np.array(x)
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# y=np.array(y)
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# xlabel = 'I$_{обр} \cdot B$, мА$\cdot $ Tл'
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# ylabel = eds+'$_x$, мВ'
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# caption_point = ''
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# xerr = abs(x*(eps_b**2+0.01**2)**0.5)
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# yerr = abs(5*10**-5*y)
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# k, b1, sigma = make_graffic(x, y, xlabel, ylabel, caption_point, xerr, yerr, b=0)
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# print('all ', -k/h, '+-', sigma[0]/h)
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# plt.show()
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# plt.figure(dpi=500, figsize=(8, 5))
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I_t = data['I_t'][0]
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for i in range(len(data['I'])):
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if (I_t == data['I_t'][i]):
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e_big.append(e[i])
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b_big.append(b[i])
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I_big.append(data['I_t'][i])
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else:
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exp = Exp(e_big, b_big, I_big)
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big_data.append(exp)
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e_big = []
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b_big = []
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I_big = []
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I_t = data['I_t'][i]
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i -= 1
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colors = [['forestgreen', 'rosybrown'], ['darkgoldenrod', 'mediumpurple'],
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['maroon', 'sandybrown'], ['darkblue', 'gold'],
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['crimson', 'greenyellow'], ['indigo', 'lightgreen'], ['yellow', 'purple']]
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count = 0
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data = {'k': [], 'I': [], 'sig_k': []}
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for i in big_data:
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x = np.array(i.b)
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y = np.array(i.e)
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xlabel = 'B, Тл'
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ylabel = '$U_\perp$, мВ'
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caption_point = 'I = ' + str(i.I[0])+' мА'
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xerr = abs(x*eps_b)
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yerr = abs(5*10**-5*y)
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color = ['black', colors[count][1]]
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count += 1
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k, b, sigma = make_graffic(x, y, xlabel, ylabel, caption_point,
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xerr, yerr, color=color, cap=2, b=0)
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data['k'].append(k)
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data['I'].append(i.I[0])
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data['sig_k'].append(sigma[0])
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plt.savefig('E(B)')
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plt.figure(dpi=500, figsize=(8, 5))
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x = np.array(data['I'])
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y = np.array(data['k'])
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xlabel = 'I, мА'
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ylabel = 'K, мВ/Тл'
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caption_point = ''
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xerr = abs(x*1/100)
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yerr = data['sig_k']
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k, b, sigma = make_graffic(x, y, xlabel, ylabel, caption_point, xerr, yerr, b=0, koef=[1.1, 1.1])
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plt.savefig('K(I)')
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print('R_x ',k*h*10**6, '+-', sigma[0]*10**6*h)
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e_e =1.6*10**-19
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n = 1/(-k*h*e_e)*10**-21
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sigma_n = n*abs(sigma[0]/k)
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print('n = ', n, '+-', sigma_n)
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sig = 1/4.097*5/4/10**-3
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sig_sig = sig*((5*10**-5)**2+(1/100)**2)**0.5
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print('sigma = ', sig, '+-', sig_sig)
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b = -sig*k*h*10**4
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sig_b = b*((sig_sig/sig)**2+(sigma[0]/k)**2)**0.5
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print('b = ', b, '+-', sig_b)
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def make_all():
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(a, eps_B) = make_grad()
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make_exp(a, eps_B)
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make_all()
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