{ "cells": [ { "cell_type": "markdown", "id": "73dda612", "metadata": {}, "source": [ "# **Python for Engineers**\n", "## Python notebook\n", "\n", "\n", "---\n", "\n", "\n", "\n", "\n", "---" ] }, {...

I want from your python programming language evaluate my codes and give me a score . I would like to know my working


{ "cells": [ { "cell_type": "markdown", "id": "73dda612", "metadata": {}, "source": [ "# **Python for Engineers**\n", "## Python notebook\n", "\n", "\n", "---\n", "\n", "\n", "\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "f6b32da7", "metadata": { "signature": "3377b" }, "source": [ "### Tasks" ] }, { "cell_type": "markdown", "id": "cc879c0c", "metadata": { "signature": "3377b" }, "source": [ "**1) Complete the following table with your data. Also, change the name of the document so that it additionally includes your name.** *(2 points)*\n", "\n", "| Name | E-mail | Course of study |\n", "| ---- | -------------- | ----------- |\n", "|Sarah | | Python for Engineers |" ] }, { "cell_type": "markdown", "id": "dc28ee7d", "metadata": {}, "source": [ "**2) Generating a 2D array with random numbers**\n", " * Create an array with 4 rows and 14 columns, containing random numbers from a normal distribution (mean: 0; standard deviation: 3.3). *(2 points)*\n", " * Output the first 3 values of the first 3 rows of the array. *(1 point)*\n", " * Output the last 3 values of the last 3 rows of the array, using tuple indexing with negative numbers. *(1 point)*\n", " * For each row, output the actual standard deviation of the respective values. *(1 point)*\n", " * Do the same for each column; do not use a loop for this. *(1 point)*\n", " * Output the actual mean value of all array values. *(1 point)*\n", " * Try to do all the above sub-tasks without any loop. *(+1 point)*\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "e77e37e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[3 5 7 7 8 1 2 4 7 1 1 5 2 7]\n", " [2 2 9 9 5 4 6 5 9 1 9 6 7 2]\n", " [6 7 6 9 8 6 2 6 2 1 5 4 5 8]\n", " [0 3 9 9 9 0 8 7 2 1 1 7 9 7]]\n", "3\n", "5\n", "7\n", " \n", "2\n", "2\n", "9\n", " \n", "6\n", "7\n", "9\n", "\n", "7\n", "2\n", "5\n", " \n", "2\n", "7\n", "6\n", " \n", "8\n", "5\n", "4\n" ] } ], "source": [ "# Please leave the following two lines unchanged.\n", "import numpy as np\n", "np.random.seed(26)\n", "k = np.random.random ([4,14]) * 10\n", "\n", "a=k.astype(int)\n", "print(a)\n", "\n", "print(a[0][0])\n", "print(a[0][1])\n", "print(a[0][3])\n", "print(\" \")\n", "print(a[1][0])\n", "print(a[1][1])\n", "print(a[1][3])\n", "print(\" \")\n", "print(a[2][0])\n", "print(a[2][1])\n", "print(a[2][3])\n", "print(\"\")\n", "print(a[0][13])\n", "print(a[0][12])\n", "print(a[0][11])\n", "print(\" \")\n", "print(a[1][13])\n", "print(a[1][12])\n", "print(a[1][11])\n", "print(\" \")\n", "print(a[2][13])\n", "print(a[2][12])\n", "print(a[2][11])\n" ] }, { "cell_type": "markdown", "id": "f6483a46", "metadata": {}, "source": [ "**3) Function on number sequence**\n", "\n", " * Write a function that is passed a vector and alternately adds and subtracts its exponentiated entries. *(3 points)*\n", " * The formula for a vector $\\pmb{b}$ is:\n", "$$\n", "{b_1}^n - {b_2}^n + {b_3}^n - {b_4}^n + {b_5}^n \\ldots\n", "$$\n", "\n", " * The function should return the scalar result of the calculation. *(2 points)*\n", " * Use NumPy arrays and avoid if-clauses and loops if possible. *(1 point)*\n", " * Make the exponent $n$ an optional parameter with 2 being its default value. *(1 point)*\n", " * Apply the function to the following vector and print the result of the calculation: *(1 point)*\n", "\n", "$~~~~~\\pmb{b}=~\\big($ `-3.5, -0.6, 3.9, 0.3, 1.5, -2.1, 2.7, 2.6, -0.9, 1.3, -0.7, -3.3, -0.3, -3.4, -3.1` $\\big)$\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "1ff6519d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0. , 1. , 1.41421356],\n", " [1.73205081, 2. , 2.23606798],\n", " [2.44948974, 2.64575131, 2.82842712]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "# Write your own code here ...\n", "x = np.array([[ 0., 1., 2.],[ 3., 4., 5.],[ 6., 7., 8.]])\n", "x ** 2\n", "np.sqrt(x)\n" ] }, { "attachments": { "figure.png": { "image/png": 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" }, "yZ.png": { "image/png": "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" } }, "cell_type": "markdown", "id": "fda553c5", "metadata": {}, "source": [ "**4) Visualization**\n", "\n", " * Create a vector `x` containing exactly 198 uniformly distributed increasing numbers in the interval from (respectively including) 3 to 35. *(1 point)*\n", " * For all values in `x`, perform the calculation\n", "$$\n", "y = \\Bigg(\\frac{x-20}{4}\\Bigg)^2\n", "$$\n", "and store the result in the variable `y`. *(1 point)* \n", " * Divide the vector `y` into 9 equal sections and write them one below the other in a 2D array `A` with 9 rows. Try to avoid using any loop for this. *(2 points)* \n", "Use the following example scheme for rearrangement (note that the example has only 5 rows!): \n", " ![yZ.png](attachment:yZ.png) \n", " * From the generated data, create a multi-part figure that corresponds to the following: *(4 points)* \n", " ![figure.png](attachment:figure.png) \n", " Notes:\n", " * If you have implemented the above calculation correctly, your figure should look very similar.\n", " * Left plot: visualizes `y` over `x`. Note labeling *and* equal unit scaling of the axes.\n", " * Center plot: content of `A`
Aug 16, 2021
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