Удалить 'Analize.py'

Analize.py

@@ -1,116 +0,0 @@
-from contextlib import redirect_stderr
-import numpy as np
-from matplotlib import pyplot as plt
-import csv
-import scipy.constants as sp
-
-
-def main():
-    print("Code is running.")
-    plot_UI(open_CSV("U(I).csv"))
-    plot_UIs(open_CSV("1,5mA.csv"), '[1,5mA]')
-    plot_UIs(open_CSV("3mA.csv"), '[3mA]')
-    plot_UIs(open_CSV("5mA.csv"), '[5mA]')
-
-
-def open_CSV(filename):
-    with open('/home/dmitrii/Physics Labs/3.5.1/' + filename, newline="") as csvfile:
-        datareader = csv.reader(csvfile, delimiter=",", quotechar="|")
-        data = []
-        for row in datareader:
-            res = [float(i) for i in row]
-            data.append(res)
-        data = np.array(data)
-        return data
-
-
-def plot_UI(data):
-    plt.scatter(data[:, 1][0:23], data[:, 0][0:23], color="g")
-    plt.scatter(data[:, 1][23:len(data)], data[:, 0]
-                [23:len(data)], color="orange")
-    plt.xticks(np.arange(min(data[:, 1])-0.25, max(data[:, 1])+0.25, 0.25))
-    plt.yticks(np.arange(min(data[:, 0])-1, max(data[:, 0])+1, 1))
-    A = np.vstack([data[:, 1][5:12], np.ones(len(data[:, 1][5:12]))]).T
-    alpha = np.dot((np.dot(np.linalg.inv(np.dot(A.T, A)), A.T)),
-                   data[:, 0][5:12])
-    var = str(round(alpha[1]*10**3))
-    delta = str(abs(round(alpha[0]*10**3)))
-    print("Максимальное дифференциальное сопротивление разряда: {}".format(
-        var) + u" \u00B1 " + "{}, Ом".format(delta))
-    plt.plot(data[:, 1][5:12], alpha[0]*data[:, 1][5:12]+alpha[1], 'r')
-    plt.ylabel('U, В', fontsize=20)
-    plt.xlabel('I, мкА', fontsize=20)
-    plt.grid()
-    plt.savefig('U(I).png')
-    plt.show()
-
-
-def centre(data):
-    xdiff = [data[n]-data[n-1] for n in range(1, len(data))]
-    return sum(xdiff)/2
-
-
-def bfl(data):
-    line = np.polyfit(data[:, 0], data[:, 1], 1)
-    return line
-
-
-def plot_UIs(data, num):
-    ax = plt.gca()
-    ax.spines['top'].set_color('none')
-    ax.spines['left'].set_position('zero')
-    ax.spines['right'].set_color('none')
-    ax.spines['bottom'].set_position('zero')
-    plt.scatter(data[:, 0], data[:, 1], color="g",
-                marker="+", s=100)  # Make a scatter plot
-    data = np.array(sorted(data, key=lambda x: x[0], reverse=True))
-    line1 = bfl(data[len(data)-5:len(data)])
-    line2 = bfl(data[0:5])
-    aprox = bfl(data[4:len(data)-5])
-    x1 = (line1[1]-aprox[1])/(aprox[0]-line1[0])
-    y1 = line1[0]*x1+line1[1]
-    plt.plot(x1, y1, marker='+', color='red')
-    x2 = (line2[1]-aprox[1])/(aprox[0]-line2[0])
-    y2 = line2[0]*x2+line2[1]
-    plt.plot(x2, y2, marker='+', color='red')
-    plt.plot((data[:, 0][len(data)-5:len(data)]), (data[:, 0]
-             [len(data)-5:len(data)]*line1[0]+line1[1]), color="orange")
-    plt.plot([x1, 0], [y1, line1[1]], linestyle="dashed", color="orange",
-             label='Ток насыщения: ' + str(round(line1[1], 2)) + ' ,А')
-    plt.plot(data[:, 0][0:5], data[:, 0]
-             [0:5]*line2[0]+line2[1], color="orange")
-    plt.plot([x2, 0], [y2, line2[1]], linestyle="dashed", color="orange",
-             label='Ток насыщения: ' + str(round(line2[1], 3)) + ' ,А')
-    S = sp.pi*0.2*10**-4*5.2*10**-4
-    me = 22*1.66*10**-27
-    Te = x2/2*11400
-    ni = line2[1]/0.4/sp.e/S/np.sqrt(2*sp.k*Te/me)/10**14
-    wp = np.sqrt(4*np.pi*ni*sp.e**2/me)*10**10
-    rde = np.sqrt(sp.k*Te/4/sp.pi/ni/sp.e**2)*10**-5
-    rd = np.sqrt(sp.k*300/4/sp.pi/ni/sp.e**2)
-    Nd = 4/3*sp.pi*rd**3*ni
-    print("-----Exp{}-----".format(num))
-    print("Ток насыщения: {},{}".format(
-        round(line1[1], 3), round(line2[1], 3)))
-    print("Te = {}".format(Te   ))
-    print("Ni = {}".format(ni))
-    print("wp = {}".format(wp))
-    print("rde = {}".format(rde))
-    print("rd = {}".format(rd))
-    print("Nd = {}".format(Nd))
-    print("---------------")
-    plt.xlabel('U, В', fontsize=20)
-    plt.ylabel('I, мкА', fontsize=20)
-    plt.grid()
-    ax.legend()
-    plt.savefig(num + '.png')
-    plt.show()
-    return plt
-
-
-def plotAll(plot1, plot2, plot3):
-    return True
-
-
-if __name__ == "__main__":
-    main()