Copy_of_Untitled1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Untitled1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/suponev/Draw.io/blob/master/Copy_of_Untitled1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JqW2DXPaVSY",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LP87fjPXjaY-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26QFyx8hZ0c5",
        "colab_type": "code",
        "outputId": "7b094ab5-6f7f-4474-e9a1-9c07a39b138e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "import random\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import cmath\n",
        "import math\n",
        "# Сигма\n",
        "sig = 2\n",
        "\n",
        "# Константа h\n",
        "h = 0.3\n",
        "\n",
        "# Массив значений x\n",
        "x = np.arange(-50, 50 , 0.001);\n",
        "\n",
        "\n",
        "# Задание сигнала\n",
        "def rnd(x):\n",
        "    return \n",
        "  \n",
        "# Задание сигнала\n",
        "def signal(x):\n",
        "    return 5*np.exp(-(x+2)**2/(2*sig**2))-4*np.exp(-(x-4)**2/(2*sig**2))-17*np.exp(-(x-20)**2/(2*sig**2))\n",
        "  \n",
        "# Задание аппроксимирующей функции\n",
        "def phi(x):\n",
        "    return np.exp(-(x)**2/(2*sig**2)) \n",
        "# Задание аппроксимирующей функции\n",
        "def model(x , A ,m):\n",
        "    return A[0]*phi(x+m[0])+A[1]*phi(x+m[1])+A[2]*phi(x+m[2])\n",
        "  \n",
        "# Расчет мемента k (для исходного сигнала) \n",
        "def mu(signal,k):\n",
        "    return signal*np.exp(-1j*k*h*x)\n",
        "  \n",
        "# Расчет alpha (для аппрокимирующей функции)\n",
        "def alpha(x,k):\n",
        "    return phi(x)*np.exp(-1j*h*k*x)\n",
        "  \n",
        "# Функция вывода массива\n",
        "def printarr(arr , name):\n",
        "    print(name)\n",
        "    for i in range(len(arr)):\n",
        "      print(arr[i])\n",
        "    print('')\n",
        "     \n",
        "def calcM(b):\n",
        "    det = b[2] * b[2] * b[2]  +  b[1] * b[1] * b[4]  +  b[3] * b[3] * b[0]  -  b[0] * b[2] * b[4]  -  2 * b[1] * b[2] * b[3];\n",
        "    det1 = b[3] * b[2] * b[2] + b[1] * b[1] * b[5] + b[4] * b[3] * b[0] - b[0] * b[2] * b[5] - b[4] * b[1] * b[2] - b[1] * b[3] * b[3];\n",
        "    det2 = b[2] * b[2] * b[4] + b[3] * b[5] * b[0] + b[3] * b[1] * b[4] - b[0] * b[4] * b[4] - b[5] * b[2] * b[1] - b[3] * b[3] * b[2];\n",
        "    det3 = b[2] * b[2] * b[5] + b[1] * b[4] * b[4] + b[3] * b[3] * b[3] - b[3] * b[2] * b[4] - b[3] * b[4] * b[2] - b[3] * b[1] * b[5];\n",
        "    coeff=[1,det1 / det,det2 / det,det3 / det]\n",
        "\n",
        "    return np.roots(coeff)\n",
        "\n",
        "# Генерируем шум  \n",
        "noise = np.random.normal(0, 1, len(x))\n",
        "\n",
        "s = signal(x) #+noise\n",
        "\n",
        "b = []\n",
        "for k in [0,1,2,3,4,5]:\n",
        "    #комплексные моменты исходного сигнала\n",
        "    muk = np.trapz(mu(s,k), x);\n",
        "    bk = muk/np.trapz(alpha(x,k), x);\n",
        "    b.append(bk);\n",
        "# print(b);\n",
        "\n",
        "M=[0,0,0] \n",
        "A=[0,0,0]\n",
        "m=[0,0,0] \n",
        "\n",
        "M = calcM(b)\n",
        "\n",
        "printarr(M,'Массив M')\n",
        "\n",
        "detA = np.linalg.det(np.array([[1, 1, 1], [M[0], M[1], M[2]], [M[0] * M[0], M[1] * M[1], M[2] * M[2]]]));\n",
        "A[0] = np.linalg.det(np.array([[b[0], 1, 1], [b[1], M[1], M[2]],[ b[2],M[1] * M[1], M[2] * M[2]]])) / detA;\n",
        "A[1] = np.linalg.det(np.array([[1, b[0], 1], [M[0], b[1], M[2]],[ M[0] * M[0], b[2], M[2] * M[2]]])) / detA;\n",
        "A[2] = np.linalg.det(np.array([[1, 1, b[0]], [M[0], M[1], b[1]],[ M[0] * M[0], M[1] * M[1], b[2]]])) / detA;\n",
        "\n",
        "printarr(A,'Массив А')\n",
        "\n",
        "m[0]=np.log(M[0])/h/1j\n",
        "m[1]=np.log(M[1])/h/1j\n",
        "m[2]=np.log(M[2])/h/1j\n",
        "\n",
        "print(\"Массив m (сдвиги)\")\n",
        "print(m[0])\n",
        "print(m[1])\n",
        "print(m[2])\n",
        "\n",
        "# plt.plot(x, sp.real)\n",
        "plt.plot(x , s )\n",
        "plt.plot(x , model(x , A,m) )\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Массив M\n",
            "(-2.967725461844498+0.2441542666759135j)\n",
            "(0.39259094106174325-0.48425089256369297j)\n",
            "(0.42727086474615916+0.3280777402611161j)\n",
            "\n",
            "Массив А\n",
            "(-0.2622110637609336-0.4091224521260374j)\n",
            "(-6.527736866215556-9.714745607749492j)\n",
            "(-9.210052070023515+10.123868059875523j)\n",
            "\n",
            "Массив m (сдвиги)\n",
            "(10.198358946525762-3.6372286096729933j)\n",
            "(-2.965180230257207+1.5752272675946606j)\n",
            "(2.182747819629407+2.06200134207879j)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py:538: ComplexWarning: Casting complex values to real discards the imaginary part\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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7i5wfD6Z/UxGEonE+ft9Bav1e7rvnJg32ZaKoAd8YEwM+BjwCvAp82xhzuJjXVA52xTut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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}