Structurization + 120 and 117 prak

1 files changed, 0 insertions, 96 deletions

diff --git a/prak2.py b/prak2.py
deleted file mode 100755
index f812dc7..0000000
--- a/prak2.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/home/main/coding/py/venv/bin/python3
-
-import math 
-import numpy as np
-
-def getvarbyval(var, ls):
-    for name, value in ls.items():
-        if value is var:
-            return name
-
-def avg(l): 
-    return sum(l)/len(l)
-
-def stddev(l):
-    s = len(l)
-    a = avg(l)
-    return math.sqrt(sum(( (i - a)**2 for i in l )) / (s * (s-1)))
-
-def sigsum(stddev, sig_sist):
-    return math.sqrt(stddev**2 + sig_sist**2)
-
-def sigma(func, args, sigmas):
-    s = 0
-    dx = 1e-7
-    for i in range(len(args)):
-        argsdx = args.copy()
-        argsdx[i] += dx
-        s += ( sigmas[i] * (func(*argsdx) - func(*args))/dx )**2
-    return math.sqrt(s)
-
-def mnk(xs, ys, ss):
-    sss = ss*ss
-    d = np.sum(xs*xs/sss)*np.sum(1/sss) - np.sum(xs/sss)**2
-    da = np.sum(xs*ys/sss)*np.sum(1/sss) - np.sum(xs/sss)*np.sum(ys/sss)
-    db = np.sum(xs*xs/sss)*np.sum(ys/sss) - np.sum(xs/sss)*np.sum(xs*ys/sss)
-    a = da/d
-    b = db/d
-    sa = math.sqrt(np.sum(1/sss)/d)
-    sb = math.sqrt(np.sum(xs*xs/sss)/d)
-    return {"a": a, "b": b, "sa": sa, "sb": sb}
-
-def v(l, t): return l/t
-def v2(l, t): return (l/t)**2
-def a_teor(dh2, L): return 9.807 * dh2 / L
-
-def main():
-    print("Введите L1-L3 (м), l1-l3 (м), M1-M3 (г)")
-    Ls = [float(input()) for _ in range(3)]
-    ls = [float(input()) for _ in range(3)]
-    Ms = [float(input())/1000.0 for _ in range(3)]
-    
-    for i in Ls, ls, Ms:
-        sig_sist = float(input(f"сист. погрешность для {getvarbyval(i, locals())}(м/Кг): "))
-        print(f"{getvarbyval(i, locals())}: mean={avg(i)}; stddev={stddev(i)}; sigma={sigsum(sig_sist, stddev(i))}")
-        i.append(sigsum(sig_sist, stddev(i)))
-
-    print("Введите 3 массы тележек");
-    h = 6 # mm
-    rest = {float(input())/1000.0 : [[round(0.2 * (j+1), 1), 0, 0] for j in range(5)] for i in range(3)}
-    for m in sorted(rest.keys()):
-        for x in rest[m]:
-            print(f"введите t1, t2, t3 для dh=6mm, m={m*1000}г, x={x[0]}м")
-            ts = [float(input()) for _ in range(3)]
-            x[1] = avg(ts)
-            x[2] = stddev(ts)
-            print(f"t_avg = {x[1]}, t_dev = {x[2]}")
-    
-    l = avg(ls[:-1]); sl = ls[-1]
-    resv = {i : [np.zeros((5,), dtype=float) for _ in range(4)] for i in sorted(rest.keys())}
-    for m in sorted(rest.keys()):
-        for i, x in enumerate(rest[m]):
-            args = [l, x[1]]
-            sigmas = [sl, x[2]]
-            _v = v(*args)
-            _v2 = v2(*args)
-            _v2s = sigma(v2, args, sigmas)
-            resv[m][0][i] = x[0]
-            resv[m][1][i] = _v
-            resv[m][2][i] = _v2
-            resv[m][3][i] = _v2s
-            print(f"для m={m*1000}г, x={x[0]}м: v={_v}, v^2={_v2}, sigma_v={_v2s}")
-    for m in sorted(resv.keys()):
-        data = resv[m]
-        d = mnk(data[0], data[2], data[3])
-        A = d['a']; 
-        sA = d['sa']
-        B = d['b']; sB = d['sb']
-        a = A/2; sa = sA/2
-        a_t = a_teor(h / 1e3, avg(Ls[:-1]))
-        sa_t = sigma(a_teor, [h/1e3, avg(Ls[:-1])], [float(input("Введите погрешность dh_2: ")), Ls[-1]])
-        print(f"A = {A}+-{sA}\nB = {B}+-{sB}\na = {a}+-{sa}\na_teor = {a_t}, sigma_a_teor = {sa_t}")
-
-if __name__ == "__main__":
-    main()
-
-