upd

course2/sem3/labs/lab3.02_done/scripts/lab_3_02.py

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+from matplotlib import pyplot as plt
+import numpy as np
+
+N = 16
+
+Rs = np.arange(0,1600,100)
+print(Rs)
+
+Us = np.array([
+    0.1, 0.0, 1.7, 2.6, 3.4, 4.0, 4.6, 5.0, 5.4, 5.7, 6.0, 6.3, 6.5, 6.7, 6.9, 6.9
+])
+Is = np.array([
+    .015, .015, .012, .011, .010, .009, .008, .007, .007, .006, .006, .005, .005, .005, .005, .005
+])
+len(Us), len(Is)
+
+plt.grid()
+plt.scatter(Is, Us, marker="d", color="brown", s=6)
+plt.xlabel("$I$")
+plt.ylabel("U(I)")
+
+coeffs_U = np.polyfit(Is, Us, 1)  # линейная аппроксимация
+approx_Is = np.linspace(min(Is), max(Is), 100)
+approx_Us = np.polyval(coeffs_U, approx_Is)
+plt.plot(approx_Is, approx_Us, '--', color="black", label="Аппроксимация")
+plt.legend()
+
+
+# plt.title("График зависимости U(I)")
+# plt.show()
+plt.savefig("1.png", bbox_inches="tight", dpi=300)
+plt.show()
+
+
+r, Epsilon = np.polyfit(Is, Us, 1)
+r *= -1
+print(f"r = {r}\tEpsilon = {Epsilon}")
+
+P = Epsilon*Is
+Pr = Us*Is
+Ps = Is*Is*r
+
+arr = (np.vstack((Us, Is, Pr, Ps, P))).T
+np.set_printoptions(precision=3)
+print(
+    arr
+)
+
+arr[:, 1:] *= 1000
+for i in range(arr.shape[0]):
+    for j in range(arr.shape[1]):
+        print(f"[{arr[i, j]: .3f}]", end=', ')
+    print()
+
+
+plt.scatter(Is, Pr, s=5, color="purple", marker="d", label="$P_R=P_R(I)$")
+plt.scatter(Is, Ps, s=5, color="red", marker="s", label="$P_S=P_S(I)$")
+plt.scatter(Is, P, s=5, color="blue", marker="o", label="$P=P(I)$")
+plt.legend()
+
+coeffs_Pr = np.polyfit(Is, Pr, 2)  # квадратичная аппроксимация
+approx_Pr = np.polyval(coeffs_Pr, approx_Is)
+plt.plot(approx_Is, approx_Pr, '--', color="purple", alpha=0.3, label="Аппроксимация $P_R(I)$")
+plt.legend()
+
+
+# plt.title("Графики зависимости мощностей $P, P_R, P_S$ от силы тока")
+plt.xlabel("$I$")
+
+plt.grid()
+# plt.show()
+plt.savefig("2.png", bbox_inches="tight", dpi=300)
+plt.show()
+
+I_star = Epsilon/(2*r)
+print(f"I* = {I_star:.4f}")
+
+i = np.polyfit(Is, Pr, 2)
+approx_Is = np.linspace(min(Is), max(Is))
+approx = np.polyval(i, approx_Is)
+
+
+plt.scatter(Is, Pr, s=5, color="purple", marker="d", label="$P_R=P_R(I)$")
+plt.plot(approx_Is, approx, color="purple", alpha=.2, linestyle='--', label="Аппроксимация $P_r(I)$")
+plt.grid()
+
+
+plt.axvline([I_star], color="grey", linestyle='--', linewidth=1, alpha=.5, label="$X=I^*=0.0075$")
+plt.legend()
+
+# plt.show()
+plt.savefig("3.png", bbox_inches="tight", dpi=300)
+plt.show()
+
+i = np.polyfit(Is, Pr, 2)
+P_Rmax = np.polyval(i, I_star)
+print(f"P_Rmax = {P_Rmax:.3f}")
+
+plt.scatter(Is, Pr)
+# plt.plot(np.linspace(Is[0], Is[-1]), )
+
+R =  P_Rmax / I_star**2
+print(f"R = {R:.3f}")
+
+plt.show()
+
+eta = Pr / P
+
+print(eta)
+
+plt.scatter(Is, eta, s=5, color="blue", label="$\eta=\eta(I)$")
+plt.xlabel("$I$")
+plt.ylabel("$\eta (I)$")
+# plt.title("Значения КПД")
+
+plt.axhline([0.5], label="$\eta=0.5$", color="grey", linestyle="--", alpha=.5)
+
+plt.xlim(0, 0.017)
+# plt.ylim(0, 0.7)
+
+approx_x = np.linspace(0, 0.0155)
+i = np.polyfit(Is, eta, 1)
+approx = np.polyval(i, approx_x)
+
+plt.plot(approx_x, approx, color="blue", linestyle='--', alpha=.2, label="Аппроксимация $\eta=\eta (I)$")
+
+
+plt.legend()
+plt.grid()
+
+# plt.show()
+plt.savefig("4.png", bbox_inches="tight", dpi=300)
+plt.show()
+
+eta = np.reshape(eta, (16, 1))
+eta 
+
+arr
+
+arr = np.hstack((arr, eta))
+
+for i in range(arr.shape[0]):
+    for j in range(arr.shape[1]):
+        print(f"[{arr[i, j]: .3f}]", end=', ')
+    print()