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RonSheely · Jul 28, 2023 · 93252dc · 93252dc
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diff --git a/1-sinusoid.ipynb b/1-sinusoid.ipynb
diff --git a/4-DSPL_Divide _ Conquer(1_6_23).ipynb → 10-Divide _ Conquer.ipynb b/4-DSPL_Divide _ Conquer(1_6_23).ipynb → 10-Divide _ Conquer.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "6a749398",
    "metadata": {},
    "outputs": [],
@@ -13,8 +13,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "f8f59a71",
+   "execution_count": 3,
+   "id": "7fb2cb10",
    "metadata": {},
    "outputs": [
     {
@@ -35,21 +35,13 @@
     "M = 3\n",
     "x = np.array([4,2,0,5,7,2,4,9,1,2,3,8,0,2,3])\n",
     "np.shape(x)\n",
-    "y = x.reshape(L,M, order='F')\n",
+    "y = x.reshape(L,M,order='F')\n",
     "print(y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7fb2cb10",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "222ce56e",
    "metadata": {},
    "outputs": [],
@@ -65,8 +57,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "4bf3e133",
+   "execution_count": 9,
+   "id": "fb11ddff",
    "metadata": {},
    "outputs": [
     {
@@ -83,17 +75,19 @@
     }
    ],
    "source": [
-    "F = np.zeros((L,M),dtype = 'complex_')\n",
+    "F = np.zeros((L,M), dtype= 'complex_')\n",
     "print(np.shape(F))\n",
+    "\n",
     "for l in range(L):\n",
-    "    F[l] = DFT(y[l,:],M) #row\n",
-    "    #np.shape(F)\n",
-    "    print(F[l])"
+    "    F[l] = DFT(y[l,:],M) # for Row\n",
+    "    # np.shape(F)\n",
+    "    print(F[l])\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "37f26696",
    "metadata": {},
    "outputs": [
@@ -119,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "971059df",
    "metadata": {},
    "outputs": [
@@ -144,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "id": "b563a847",
    "metadata": {},
    "outputs": [
@@ -161,14 +155,6 @@
    "source": [
     "print(abs(np.fft.fft(x,N)))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53bef6ec",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/11-MovingAverage.ipynb b/11-MovingAverage.ipynb
diff --git a/2-signal.ipynb b/2-signal.ipynb
diff --git a/3-convolution.ipynb b/3-convolution.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3693637f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  4.  8.  8.  3. -2. -1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#convulation sum EX: 2.3.2\n",
+    "#1st method\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = [1, 2, 3, 1]\n",
+    "h = [1, 2, 1, -1]\n",
+    "lx = len(x)\n",
+    "lh = len(h)\n",
+    "y = np.zeros(lx+lh-1)\n",
+    "\n",
+    "for m in range(lx):\n",
+    "    for n in range(lh):\n",
+    "        y[m+n] = y[m+n] + x[m]*h[n]\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a84b4ef8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  4  8  8  3 -2 -1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#2nd method relates with 1st\n",
+    "z = np.convolve(x, h, 'full')\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b78f4be2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  4  8  8  3 -2 -1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#3rd method indivitual\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = [1, 2, 3, 1]\n",
+    "h = [1, 2, 1, -1]\n",
+    "\n",
+    "z = np.convolve(x, h, 'full')\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58064ae7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4-Convolution-np.ipynb b/4-Convolution-np.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def conv(x, h):\n",
+    "    lx = len(x)\n",
+    "    lh = len(h)\n",
+    "    y = np.zeros(lx+lh-1)\n",
+    "    \n",
+    "    for m in range(lx):\n",
+    "        for n in range(lh):\n",
+    "            y[m+n] = y[m+n] + x[m]*h[n]\n",
+    "    return y\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  4.  8.  8.  3. -2. -1.]\n",
+      "[ 2.  8. 18. 22. 16.  4.  0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [1,2,3,1]\n",
+    "h = [1, 2, 1, -1]\n",
+    "\n",
+    "y = conv(x, h)\n",
+    "print(y)\n",
+    "\n",
+    "\n",
+    "\n",
+    "yd2 = conv(x, h) + conv(x, h2)\n",
+    "print(yd2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 2. 3. 1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = np.zeros(1)\n",
+    "d[0] = 1\n",
+    "\n",
+    "#identity law\n",
+    "yi = conv(x, d)\n",
+    "print(yi)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  4.  8.  8.  3. -2. -1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#communitative property\n",
+    "yc = conv(h, x)\n",
+    "print(yc) \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  6. 19. 37. 47. 36. 12. -5. -5. -1.]\n",
+      "[ 1.  6. 19. 37. 47. 36. 12. -5. -5. -1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#assosiative law \n",
+    "h2 = [1,2 ,3, 1]\n",
+    "ya = conv(yc, h2)\n",
+    "print(ya) \n",
+    "yb = conv(h2, yc)\n",
+    "print(yb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  4.  8.  8.  4.  2.  9. 14. 13.  6.  1.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#distributive law \n",
+    "yd1 = conv(x, h + h2)\n",
+    "print(yd1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}