first commit

1,5mA.csv

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+24.98,27.17
+22.2,26.39
+19.3,25.41
+16.15,24.39
+13.22,23.29
+10.24,21.32
+7.97,18.88
+6.22,15.96
+4.39,12.55
+2.14,6.32
+0.69,2.6
+-25.1,-29.19
+-22.12,-28.19
+-19.07,-27.2
+-16.13,-26.18
+-13.12,-24.34
+-10.42,-22.97
+-8.17,-20.06
+-6.08,-16.45
+-4.37,-11.93
+-2.02,-6.33
+-0.5,-1.65

3mA.csv

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+24.98,56.46
+21.78,54.35
+19.03,53.45
+16.18,58.73
+13.49,26
+10.41,45.8
+7.77,39.75
+5.96,33.4
+4.39,26.79
+1.92,14.53
+0.55,6.48
+-25.1,-60.56
+-21.87,-58.7
+-19.14,-57.05
+-16.4,-55.78
+-13.2,-52.7
+-10.12,-47.06
+-8.1,-42.24
+-6.16,-35.07
+-4.26,-26.24
+-2.11,-14.42
+-0.5,-5.19

5mA.csv

@@ -0,0 +1,22 @@
+24.98,105.85
+21.94,103.84
+19.09,101.27
+16,97.68
+13.1,91.76
+9.74,80.93
+8.1,73.68
+6.12,62.93
+4.1,49.76
+2.1,32.23
+0.5,18.01
+-25,-113.54
+-22.05,-110.88
+-19.31,-108.29
+-16.39,-103.44
+-12.91,-96.33
+-10.23,-87.22
+-7.88,-75.27
+-6,-64.18
+-4.1,-49.99
+-1.9,-30.78
+-0.5,-18.87

Analize.py

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+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()

U(I).csv

@@ -0,0 +1,43 @@
+34.85,0.5
+34.3,0.8
+33.76,1
+33.33,1.16
+32.96,1.4
+32.39,1.6
+30.24,1.8
+29.02,2
+28.19,2.2
+27.59,2.4
+26.69,2.6
+26.25,2.8
+27.08,3
+27.06,3.2
+26.51,3.4
+25.82,3.6
+25.33,3.8
+24.91,4.04
+24.68,4.24
+24.48,4.4
+24.29,4.64
+24.21,4.76
+24.37,4.56
+24.61,4.32
+24.75,4.2
+24.99,4.04
+25.45,3.8
+26.07,3.56
+26.57,3.4
+27.09,2.34
+27.26,3
+26.43,2.8
+26.44,2.6
+27.49,2.4
+28.31,2.2
+29.04,1.96
+30.33,1.8
+32.62,1.56
+32.88,1.4
+33.32,1.2
+33.74,1.04
+34.22,0.8
+34.59,0.6