import IPython.display
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math
from IPython import display
from docx import Document
from docx.shared import Inches

data9_1 = [[1, 0.9, 1.586], [0.818, 1, 1.442]]
# data9_1 =[[1, 0.8, 1.586], [0.553, 1, 1.07]]
x_1 = 1000 * data9_1[0][0] / data9_1[0][2]
x_2 = 1000 * data9_1[1][1] / data9_1[1][2]
L_1 = x_1 / (2 * math.pi)
L_2 = x_2 / (2 * math.pi)
x_m1 = 1000 * data9_1[0][1] / data9_1[0][2]
x_m2 = 1000 * data9_1[1][0] / data9_1[1][2]
M_1 = x_m1 / (2 * math.pi)
M_2 = x_m2 / (2 * math.pi)
#
first = pd.DataFrame({"U1,V": [data9_1[0][0], data9_1[1][0]],
                      "U2,V": [data9_1[0][1], data9_1[1][1]],
                      "I,mA": [data9_1[0][2], data9_1[1][2]],
                      "x_i,Om": [round(x_1, 2), round(x_2, 2)],
                      "L_i,mH": [round(L_1, 2), round(L_2, 2)],
                      "x_mi,Om": [round(x_m1, 2), round(x_m2, 2)],
                      "|M|,mH": [round(M_1, 2), round(M_2, 2)]}
                     )
print(first)
print(f"|M_ср|={(M_1 + M_2) / 2}", f"X_m_ср={(x_m1 + x_m2) / 2}")
print(f"k_1={M_1/pow(L_1*L_2,1/2)}\nk_2={M_2/pow(L_1*L_2,1/2)}")
data9_2 = [[1, 0.487, 0.513, 0.407], [1, 0.336, 0.665, 5.272]]
# data9_2 = [[1,    0.465,  0.535,  0.369], [1, 0.364, 0.636, 1.441]]
# data9_2=[[1, 0.223, 0.771, 1.761], [1, 0.439, 0.561, 0.387]]
x_mean = (x_m1 + x_m2) / 2
M_mean = (M_1 + M_2) / 2

L_eq1 = L_1 + L_2 - 2 * M_mean
L_eq2 = L_1 + L_2 + 2 * M_mean
x_eq1 = math.pi * 2 * L_eq1
x_eq2 = math.pi * 2 * L_eq2
I_th1 = 1000 * data9_2[0][0] / x_eq1
U_11 = I_th1 * (x_1 - x_mean) / 1000
U_21 = I_th1 * (x_2 - x_mean) / 1000

I_th2 = 1000 * data9_2[1][0] / x_eq2
U_12 = I_th2 * (x_1 + x_mean) / 1000
U_22 = I_th2 * (x_2 + x_mean) / 1000
print("\n\n")
second = pd.DataFrame({"U,V": [data9_2[0][0], data9_2[1][0]],
                       "U1,V": [data9_2[0][1], data9_2[1][1]],
                       "U2,V": [data9_2[0][2], data9_2[1][2]],
                       "I,mA": [data9_2[0][3], data9_2[1][3]],
                       "I_th,mA": [I_th1, I_th2],
                       "U1_th,V": [U_11, U_12],
                       "U2_th,V": [U_21, U_22],
                       "L_eq,mH": [L_eq1, L_eq2]})
print(second)
print(f"x_1={x_eq2}", f"x_2={x_eq1}")
data9_3 = [[1, 1.641], [1, 21]]
L_sog = (L_1 * L_2 - M_mean * M_mean) / (L_1 + L_2 - 2 * M_mean)
I_sog = data9_3[0][0] / (2 * math.pi * L_sog)
L_vst = (L_1 * L_2 - M_mean * M_mean) / (L_1 + L_2 + 2 * M_mean)
I_vst = data9_3[0][0] / (2 * math.pi * L_vst)
third = pd.DataFrame({"U": [data9_3[0][0], data9_3[1][0]],
                      "I": [data9_3[0][1], data9_3[1][1]],
                      "I_th": [I_sog * 1000, I_vst * 1000],
                      "L_экв": [L_sog, L_vst]})
print("\n", third)

R_1 = 1000
R_2 = 220

t = [90, 70, 60, 40, 30, 10]
A = 0.82
print([A * t[i] / 100 for i in range(len(t))])


def frequency_response(M, R, L_1, L_2, f):
    w = 2 * np.pi * f
    num = abs(M) * R
    first = pow((L_1 * L_2 - M * M) * pow(10, -6), 2)
    second = pow((L_1 * R) * pow(10, -3) / w, 2)
    den = w * pow(first + second, 0.5)
    return num / den / 1000

document = Document()
table1 = document.add_table(rows=2, cols=8)
table1.cell(0,0).text = "1"
table1.cell(0,1).text = str(data9_1[0][0])
table1.cell(0,2).text = str(data9_1[0][1])
table1.cell(0,3).text = str(data9_1[0][2])
table1.cell(0,4).text = str(round(x_1, 2))
table1.cell(0,5).text = str(round(L_1, 2))
table1.cell(0,6).text = str(round(x_m1,2))
table1.cell(0,7).text = str(round(M_1,2))
table1.cell(1,0).text = "2"
table1.cell(1,1).text = str(data9_1[1][0])
table1.cell(1,2).text = str(data9_1[1][1])
table1.cell(1,3).text = str(data9_1[1][2])
table1.cell(1,4).text = str(round(x_2, 2))
table1.cell(1,5).text = str(round(L_2, 2))
table1.cell(1,6).text = str(round(x_m2,2))
table1.cell(1,7).text = str(round(M_2,2))
table1.style = "Table Grid"

frequency = [1.588, 4.886, 6.8, 11.54, 15.033, 33.23, 294.374, 2786, 5765, 7509, 12743, 17734, 54566]
exp =       [0.088, 0.261, 0.350, 0.521, 0.609, 0.798, 0.882, 0.791, 0.615, 0.528, 0.355, 0.266, 0.09]
th = [frequency_response(M_mean, R_1, L_1, L_2, frequency[i]) for i in range(len(frequency))]


table = document.add_table(rows=14, cols=6)
for i in range(13):
    table.cell(i+1, 0).text = str(round(frequency[i],3))
    table.cell(i+1, 1).text = str(round(exp[i],3))
    table.cell(i+1, 2).text = str(round(th[i],3))
table.style = "Table Grid"
table.cell(0, 0).text = "frequency"
table.cell(0, 1).text = "exp"
table.cell(0, 2).text = "th"

# Строим график с двумя кривыми
data1 = pd.DataFrame({"frequency": frequency, "exp": exp, "th": th})
# IPython.display.display(data1)
# print(data1)
plt.figure(figsize=(10, 6))
plt.plot(frequency, exp, label="exp", linestyle='-', )
plt.plot(frequency, th, label="th", linestyle='-.', )
plt.xscale("log", base=2)
plt.xlabel("Частота (Гц)")
plt.ylabel("АЧХ R=1000")
plt.legend()
plt.show()
frequency = [1.487, 4.886, 6.8, 11.54, 15.033, 29.115, 142.269, 695, 1345, 1754, 2976, 4142, 12743]
exp =       [0.080, 0.252, 0.336, 0.499, 0.581, 0.735, 0.822, 0.733, 0.579, 0.496, 0.334, 0.250, 0.085]
th = [frequency_response(M_mean, R_2, L_1, L_2, frequency[i]) for i in range(len(frequency))]
data2 = pd.DataFrame({"frequency": frequency, "exp": exp, "th": th})
# print(data2)

# IPython.display.display(data2)

plt.figure(figsize=(10, 6))
plt.plot(frequency, exp, label="exp", linestyle='-', )
plt.plot(frequency, th, label="th", linestyle='-.', )
plt.xscale("log", base=2)
plt.xlabel("Частота (Гц)")
plt.ylabel("АЧХ R=220")
plt.legend()
plt.show()
for i in range(13):
    table.cell(i+1, 3).text = str(round(frequency[i],3))
    table.cell(i+1, 4).text = str(round(exp[i],3))
    table.cell(i+1, 5).text = str(round(th[i],3))

table.cell(0, 3).text = "frequency"
table.cell(0, 4).text = "exp"
table.cell(0, 5).text = "th"
document.save("document.docx")