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rsmarinho · Oct 23, 2020 · 95032c2 · 95032c2
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diff --git a/LDC2006S16.wav b/LDC2006S16.wav
diff --git a/README.md b/README.md
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+# pdscodes
+
+Code snippets and notebooks used in PDS classes.
+
+All (almost) codes are in Python3 language.
+
+---
+
+## Sampling and quantization
+- [quantization](https://github.com/rsmarinho/pdscodes/blob/master/quantization.py): 1d signal quantization examples
+- [quantization2](https://github.com/rsmarinho/pdscodes/blob/master/quantization2.py): 2d signal quantization examples
+- [nyquist](https://github.com/rsmarinho/pdscodes/blob/master/nyquist.py): Shannon-Nyquist Sampling Theorem examples
+
+## Signals and operations
+- [impulse](https://github.com/rsmarinho/pdscodes/blob/master/impulse.py): Impulse signal examples
+- [step](https://github.com/rsmarinho/pdscodes/blob/master/step.py): Step signal examples
+- [operations](https://github.com/rsmarinho/pdscodes/blob/master/operations.py): Signal operations examples
+- [convolution](https://github.com/rsmarinho/pdscodes/blob/master/convolution.py): Convolution function example
+
+## Discrete Fourier
+- [dft](https://github.com/rsmarinho/pdscodes/blob/master/dft.ipynb) (jupyter notebook): Discrete Fourier Transform notebook
+- [fft](https://github.com/rsmarinho/pdscodes/blob/master/fft.ipynb) (jupyter notebook): Fast Fourier Transform notebook
+
+## Homework (Trabalhos Práticos)
+All home works are jupyter notebooks. These files can be opened using google-colab.
+
+- [TP01](https://github.com/rsmarinho/pdscodes/blob/master/TP01.ipynb)
+- [TP02](https://github.com/rsmarinho/pdscodes/blob/master/TP02.ipynb)
+- [TP04](https://github.com/rsmarinho/pdscodes/blob/master/TP04.ipynb)
+- [TP05](https://github.com/rsmarinho/pdscodes/blob/master/TP05.ipynb)
+
+---
+
+## Auxiliary files
+- [arroz e feijao](https://github.com/rsmarinho/pdscodes/blob/master/LDC2006S16.wav): Audio wave file used in some examples (SPOLTECH audio file)
+- [barbara](https://github.com/rsmarinho/pdscodes/blob/master/LDC2006S16.wav): Image (png) file used in some examples
+- [paganini](https://github.com/rsmarinho/pdscodes/blob/master/LDC2006S16.wav): Audio wave file used in some examples (from wikimedia)
+- [table notes](https://github.com/rsmarinho/pdscodes/blob/master/notas_musicais.jpg): Image containing the frequencies of musical tones. Don't know the author.
diff --git a/TP01.ipynb b/TP01.ipynb
@@ -0,0 +1,193 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "TP01.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "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/rsmarinho/pdscodes/blob/master/TP01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GqOc8H84dZHV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# here goes your include modules...\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QIVQHZMBda6I",
+        "colab_type": "text"
+      },
+      "source": [
+        "**TP01 - Convolução Linear em duas dimensões.**\n",
+        "\n",
+        "---\n",
+        "\n",
+        "A convolução linear é a operação linear em que um sistema LIT quando estimulado por um sinal qualquer $x[n]$, tem sua resposta dada por $$y[n]=\\sum_{n=0}^\\infty x[k]h[n-k]$$\n",
+        "\n",
+        "No caso de o sinal de estímulo ter mais de uma dimensão (duas por exemplo), é preciso que o sistema tenha o mesmo número de dimensões. Nesse caso a convolução terá resposta dada por $$y[n,m]=\\sum_{n=m=0}^\\infty x[k,l]h[n-k,m-l]$$\n",
+        "\n",
+        "Com essas informações, responda às seguintes questões:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y2gBvT4Fhplr",
+        "colab_type": "text"
+      },
+      "source": [
+        "**1.** Crie uma função de convolução linear bidimensional na forma definida abaixo. A variável ans deve retornar o resultado da convolução."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QMmrvsK-hqJA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def conv2d(M, img):\n",
+        "  # code goes here...\n",
+        "\n",
+        "  return ans"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-DHg9Lk9gDPx",
+        "colab_type": "text"
+      },
+      "source": [
+        "**2.** Suponha que um sistema linear é definido por uma matriz $M$ de tamanho 3x3. Essa matriz deve ser criada de acordo com as nove primeiras letras de seu nome (a=1, b=2, c=3, ...). Proceda com a convolução linear entre a matriz $M$ e a imagem [barbara.jpg](https://github.com/rsmarinho/pdscodes/blob/master/barbara.png)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "j5XBGYGalpQP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kHA7wnMnlpum",
+        "colab_type": "text"
+      },
+      "source": [
+        "**3.** Utilize as seguintes matrizes como função de transferência do sistema e execute seu código para cada uma delas."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "khvLFiIbl32F",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "h1 = np.array([[1.96412803e-05, 1.76805171e-03, 1.96412803e-05], \n",
+        "               [1.76805171e-03, 1.59154943e-01, 1.76805171e-03], \n",
+        "               [1.96412803e-05, 1.76805171e-03, 1.96412803e-05]])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ucbRT8bDl-TE",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "h2 = np.array([[1.96412803e-05, 5.74002351e-04, 1.76805171e-03, 5.74002351e-04, 1.96412803e-05], \n",
+        "               [5.74002351e-04, 1.67748076e-02, 5.16700450e-02, 1.67748076e-02, 5.74002351e-04], \n",
+        "               [1.76805171e-03, 5.16700450e-02, 1.59154943e-01, 5.16700450e-02, 1.76805171e-03], \n",
+        "               [5.74002351e-04, 1.67748076e-02, 5.16700450e-02, 1.67748076e-02, 5.74002351e-04], \n",
+        "               [1.96412803e-05, 5.74002351e-04, 1.76805171e-03, 5.74002351e-04, 1.96412803e-05]])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bLvTRIkWmcWm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "h3 = np.array([[1.96412803e-05, 2.39279779e-04, 1.07237757e-03, 1.76805171e-03, 1.07237757e-03, 2.39279779e-04, 1.96412803e-05],\n",
+        "               [2.39279779e-04, 2.91502447e-03, 1.30642333e-02, 2.15392793e-02, 1.30642333e-02, 2.91502447e-03, 2.39279779e-04],\n",
+        "               [1.07237757e-03, 1.30642333e-02, 5.85498315e-02, 9.65323526e-02, 5.85498315e-02, 1.30642333e-02, 1.07237757e-03],\n",
+        "               [1.76805171e-03, 2.15392793e-02, 9.65323526e-02, 1.59154943e-01, 9.65323526e-02, 2.15392793e-02, 1.76805171e-03],\n",
+        "               [1.07237757e-03, 1.30642333e-02, 5.85498315e-02, 9.65323526e-02, 5.85498315e-02, 1.30642333e-02, 1.07237757e-03],\n",
+        "               [2.39279779e-04, 2.91502447e-03, 1.30642333e-02, 2.15392793e-02, 1.30642333e-02, 2.91502447e-03, 2.39279779e-04],\n",
+        "               [1.96412803e-05, 2.39279779e-04, 1.07237757e-03, 1.76805171e-03, 1.07237757e-03, 2.39279779e-04, 1.96412803e-05]])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KYvjj3YwnHZw",
+        "colab_type": "text"
+      },
+      "source": [
+        "Após rodar seu código explique quais suas impressões sobre as imagens (processadas e não-processada), o que você achou de diferente e qual sua interpretação do processamento que o filtro faz."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qUVJfGVkkyII",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}