Structurization + 120 and 117 prak

1 files changed, 91 insertions, 0 deletions

diff --git a/vtek2/vtek2.py b/vtek2/vtek2.py
new file mode 100755
index 0000000..c4acfad
--- /dev/null
+++ b/vtek2/vtek2.py
@@ -0,0 +1,91 @@
+from praklib import *
+from math import pow, sqrt 
+
+
+PI = 3.141592657 # ;)
+
+def main():
+    timer = [];
+    photo = [];
+    Ns = [];
+    Tsqs = [];
+    STsqs = [];
+    H = float(input('Введите H и погрешность H: '))
+    H_err = float(input())
+    m0 = int(input('Введите m0 (количество измерений): '))
+    n = float(input('Введите n (количество колебаний на 1 измерение): '))
+    subj_err = 0.3
+
+    print('\n', '-' * 35, "\nОБРАБОТКА С РУЧНЫМИ ЧАСАМИ\n", '-' * 35);
+    for i in range(m0 + 1):
+        print("введите t_1, t_2, t_3 для N  = ", i);
+        list = [float(input()) for _ in range(3)];
+        mean = avg(list);
+        dev = stddev(list);
+        mean_err = sqrt(dev * dev + pow(1e-3 * mean, 2) + subj_err*subj_err);
+        T = mean / n;
+        T_err = mean_err / n;
+        tsq = T * T;
+        tsq_err = 2 * T * T_err;
+        timer.append((i, tsq, tsq_err));
+        print(f"avg = {mean}; err = {mean_err}; T = {T} ± {T_err}\nT^2 = {tsq} ± {tsq_err}");
+    Ns = np.array([i[0] for i in timer]);
+    Tsqs = np.array([i[1] for i in timer]);
+    STsqs = np.array([i[2] for i in timer]);
+    res = mnk(Ns, Tsqs, STsqs);
+    print(f"A = {res['a']} ± {res['sa']}\nB = {res['b']} ± {res['sb']}");
+    g = 4 * PI * PI * H / res['a'] / m0;
+    g_err = g * sqrt(pow(res['sa'] / res['a'], 2) + pow(H_err / H, 2));
+    l = res['b'] * g / 4 / PI / PI;
+    l_err = l * sqrt(pow(res['sb'] / res['b'], 2) + pow(g_err/g , 2));
+    print(f"g = {g} ± {g_err}\nl = {l} ± {l_err}");
+
+
+    print('\n', '-' * 35, "\nОБРАБОТКА С ФОТОДАТЧИКАМИ\n", '-' * 35);
+    for i in range(m0 + 1):
+        print("Введите t, St для N = ", i);
+        mean = float(input())
+        dev = float(input())
+        mean_err = sqrt(dev * dev + pow(1e-3 * mean, 2));
+        T = 2 * mean;
+        T_err = 2 * mean_err;
+        tsq = T * T;
+        tsq_err = 2 * T * T_err;
+        photo.append((i, tsq, tsq_err));
+        print(f"T = {T} ± {T_err}\nT^2 = {tsq} ± {tsq_err}");
+    Ns = np.array([i[0] for i in photo]);
+    Tsqs = np.array([i[1] for i in photo]);
+    STsqs = np.array([i[2] for i in photo]);
+    res = mnk(Ns, Tsqs, STsqs);
+    print(f"A = {res['a']} ± {res['sa']}\nB = {res['b']} ± {res['sb']}");
+    g = 4 * PI * PI * H / res['a'] / m0;
+    g_err = g * sqrt(pow(res['sa'] / res['a'], 2) + pow(H_err / H, 2));
+    l = res['b'] * g / 4 / PI / PI;
+    l_err = l * sqrt(pow(res['sb'] / res['b'], 2) + pow(g_err/g , 2));
+    print(f"g = {g} ± {g_err}\nl = {l} ± {l_err}");
+    
+    print('\n', '-' * 35, "\nОБРАБОТКА ВТОРОГО УПРАЖНЕНИЯ\n", '-' * 35);
+    omegas = [];
+    Us = [0.3 * (i+1) for i in range(6)];
+    U_errs = [0.003 * i + 0.002 for i in Us];
+    U_errs[0] -= 0.0015;
+    for U, U_err in zip(Us, U_errs):
+        tg = float(input('Введите t_д: '));
+        tg_err = float(input('Введите погрешность t_д: '));
+        omega = 2*PI / tg;
+        omega_err = omega * tg_err / tg;
+        omegas.append((U, omega, U_err, omega_err));
+        print(f"\nomega = {omega} ± {omega_err}");
+    with open("outputU.data", "w+") as f:
+        for a, b, c, d in omegas:
+            f.write(f"{a} {b} {c} {d}\n");
+    with open("outputT2_timer.data", "w+") as f:
+        for a, b, c in timer:
+            f.write(f"{a} {b} {c}\n")
+    with open("outputT2_photo.data", "w+") as f:
+        for a, b, c in photo:
+            f.write(f"{a} {b} {c}\n")
+
+
+if __name__ == "__main__": main();
+