Tutorial_chapter5_input_and_output.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To use the examples in this chapter, first run the code below to import the right libraries.**\n",
    "\n",
    "We use the numpy library show some examples. However, you do not need to learn how to use this library (except the `random` module, but more on that in next chapter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# =================================\n",
    "# Imports\n",
    "# =================================\n",
    "from numpy import random\n",
    "from dataclasses import dataclass\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Input and output\n",
    "\n",
    "This chapter describes how data can be read from a file as input for a model, and how the output of a model can be printed to the screen, written to a file, or plotted in a graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `print` statement is used for output of data to the screen of the computer. A simple example is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x = 5\n",
    "print(\"x =\", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above example is very useful for the most simple use cases; we've used similar print statements throughout the previous chapters. However, it is not very versatile. For example, try changing `x = 5` to `x= 5/3`. The result is that `x` is printed with many decimals.\n",
    "\n",
    "In the next example we will use an f-string, which allows us to insert variables into strings (there are more ways for doing so in Python, for more information on f-string and other options [click here](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python)). An f-string by placing a `f` in front of the string, two examples are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 5/3\n",
    "\n",
    "print(\"i =\", i) # the basic method\n",
    "\n",
    "print(f\"i = {i}\") # Using f-strings to insert variable i into the string \n",
    "\n",
    "print(f\"i = {i:.2f}\") # Using f-strings to insert variable i into the string (with 2 decimal numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above example shows multiple ways to print `i = 5/3` to the screen. Let us take the last example: `f\"i = {i:.2f}\"`. The `f` before the string indicates that the string is formatted as an f-string. The curly brackets indicate that we want to insert a variable, and the formatting we want to use in the form of `{variable:format}` (or `{variable}` if we do not wish to specify the format). In this case the variable is `i` and format is `.2f` (which denotes *fixed-point number format* with 2 decimal numbers).  There are many different format types, such as `d` (for integer numbers), `f` (for floats), and `g` (for any number). \n",
    "\n",
    "Another example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 5\n",
    "r = 3.14\n",
    "print(f\"{i:4d}/{r:8.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This fragment has the effect that the values of `i` and `r` are written to the screen as follows:\n",
    "\n",
    "       5/    3.14\n",
    "          \n",
    "The value of `i` is written in `d` format, as int value, and the value of `r` is written in `f` format, as floating value. The symbols `d` and `f` originate respectively from \"decimal\", and \"floating point\" numbers. The numbers 4 respectively 8.2 denote that the integer value is written 4 positions wide (that is, 3 spaces and a \"`5`\" character), and that the floating value is written 8 positions wide, with 2 characters after the decimal point (that is, 4 spaces and the text \"`3.14`\"). A list of format specifiers is given in the table below:\n",
    "\n",
    "| Format specifier | Result                                                                                             |\n",
    "|------------------|----------------------------------------------------------------------------------------------------|\n",
    "| `:b`             | Binary format                                                                                      |\n",
    "| `:d`             | Decimal format                                                                                     |\n",
    "| `:10d`           | Decimal format (at least 10 positions wide)                                                        |\n",
    "| `:e`             | Scientific format                                                                                  |\n",
    "| `:f`             | Fix point number format                                                                            |\n",
    "| `:9f`            | Fix point number format (at least 9 positions wide)                                                |\n",
    "| `:.4f`           | Fix point number format (with 4 characters after the decimal point)                                |\n",
    "| `:9.4f`          | Fix point number format (at least 9 positions wide, and with 4 characters after the decimal point) |\n",
    "| `:g`             | General format                                                                                     |\n",
    "| `:n`             | Number format                                                                                      |\n",
    "| `:%`             | Percentage format  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, there are also a few special character sequences called *escape sequence* which allow to print characters like horizontal tab (which means jump to next tab position in the output\"), or newline (which means \"go to the next line in the output\") in a format string. An escape sequence consists of two characters. First a backslash character `\\`, followed by a second character. The escape sequence are presented below:\n",
    "\n",
    "| Escape sequence | Meaning           |\n",
    "|-----------------|-------------------|\n",
    "| `\\n`            | New line          |\n",
    "| `\\t`            | Horizontal tab    |\n",
    "| `\\\"`            | The character `\"` |\n",
    "| `\\\\`            | The character `\\` |\n",
    "\n",
    "An example is shown below. The value of `j` is written at the tab position, the output goes to the next line again at the first tab position, and outputs the value of `r`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 5\n",
    "j = 10\n",
    "r = 3.14\n",
    "print(f\"{i:6d}\\t{j:d}\\n\\t{r:.2f}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Read and write files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within Python, it is possible to read and write files. To do this, firstly the *open* function is used. An example to write to a file is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"example.txt\",\"w\")\n",
    "\n",
    "f.write(\"This is an example on how create and write data into a .txt file \\nA new line can be created as well\\n\")\n",
    "f.write(\"We can continue writing by calling the write function\\n\")\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, the `open` function is used to open/create a file called `\"example.txt\"`. The character `w` is used to indicate that the file should be overwritten, or newly created. The function `.write` is used to write new data into the opened file, defined by a string. Finally, the file can be closed with `.close()`.\n",
    "\n",
    "Instead of overwriting a file, new data can also be appended to a file. In order to use the append mode, the character `a` should be used. Now, new data can be appended to the `example.txt` file, again using the `.write` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"example.txt\",\"a\")\n",
    "\n",
    "f.write(\"This line will be appended to the file\")\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a file can also be read, using the `.read` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"example.txt\",\"r\")\n",
    "\n",
    "if f.mode == \"r\":\n",
    "    contents = f.read()\n",
    "    print(contents)\n",
    "    \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to read the file, the character `r` should be used in the `open` function. Now, the function `.read()` can be used to read the contents of the file. For this, the file should be opened in read-mode, which is checked by `if f.mode==\"r\"`.\n",
    "\n",
    "A more elaborate explanation on reading and writing files can be found [here](https://www.guru99.com/reading-and-writing-files-in-python.html), however, this will not be used in the scope of this course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A nice way to get an overview of your data is to plot this. In the next paragraphs, different kind of plots in Python will be shortly elaborated, a more elaborate guide on how to plot can be found [here](https://realpython.com/python-matplotlib-guide/).\n",
    "\n",
    "The `matplotlib.pyplot` library is used to get these plots. This library can be imported by entering `import matplotlib.pyplot as plt`. All plotting options from the `matplotlib.pyplot` library can be found [here](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html). In this section, we will go into more depth on the line, step, bar, and histogram plot types (which are only a few of the plot types in the `matplotlib.pyplot` library), and how the style of the plot can be changed (titles, axis labels, etc.). A large gallery containing pretty much every example you would ever need can be found [here](https://matplotlib.org/stable/gallery/index.html).\n",
    "\n",
    "Below we show the most simple method of creating a simple line plot. We first create some data (using the `numpy` library), and then we plot it by first creating a figure using `plt.figure()`, then we draw a plot with two lines using `plt.plot(x, y1, x, y2)`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 5, 0.2)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "fig = plt.figure()\n",
    "line = plt.plot(x, y1, x, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3.1 Line plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `plt.plot` can be used to create a simple *line plot*. An example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 5, 0.2)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.plot(x, y1, label='sin(x)')\n",
    "plt.plot(x, y2, color='green', marker='o', linestyle='dashed',\n",
    "                linewidth=2, markersize=5, label='cos(x)')\n",
    "plt.xlabel(\"X-axis, e.g. Time [s]\")\n",
    "plt.ylabel(\"Y-axis, e.g. Distance [m]\")\n",
    "plt.title(\"Title for your plot\")\n",
    "plt.grid()\n",
    "plt.legend(title='Parameter where:');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plt.plot(x,y)` will just create a simple line. As shown above, the line can be adjusted by adding several inputs to `plt.plot`, for example:\n",
    "- `color`, which changes the color of the line\n",
    "- `marker`, which determines the shape of the markers used at every datapoint\n",
    "- `linestyle`, which determines the linestyle, in this case (e.g. dashed)\n",
    "- `linewidth`, which determines the linewidth\n",
    "- `markersize`, which changes the size of the marker at every datapoint\n",
    "- `label`, which is used to generate a legend for the plot\n",
    "\n",
    "To create a new figure as output of a section, `fig, ax1 = plt.subplots()` can be used.\n",
    "\n",
    "Furthermore, labels can be added to the axes of the graph, as well as a title and a legend. This can be done by the functions `plt.xlabel()` for the label on the x-axis, `plt.ylabel()` for the label on the y-axis, and `plt.title()` for the title of the plot. If labels are defined for lines in the plot (which is done by the input `label=`), `plt.legend()` can be used to add a legend to the plot. \n",
    "\n",
    "Finally, to add a grid to the plot, `plt.grid()` can be used. \n",
    "\n",
    "More information on the line plot can be found at https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3.2 Step plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A *step plot* is similar to a line plot, only now the `plt.step()` function is used.\n",
    "\n",
    "More information on the step plot can be found at https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.step.html#matplotlib.pyplot.step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 5, 0.2)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.step(x, y1, label='sin(x)')\n",
    "plt.step(x, y2, color='green', marker='o', linestyle='dashed',\n",
    "                linewidth=2, markersize=5, label='cos(x)')\n",
    "plt.xlabel(\"X-axis, e.g. Time [s]\")\n",
    "plt.ylabel(\"Y-axis, e.g. Distance [m]\")\n",
    "plt.title(\"Title for your plot\")\n",
    "plt.grid()\n",
    "plt.legend(title='Parameter where:');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3.3 Bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A bar plot is used to present categorical data, with bars of which the height represents the frequency of each category. To create a *bar plot*, the function `plt.bar` can be used. For the bar plot, there are different inputs compared to the line plot. An example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [\"Product 1\",\"Product 2\",\"Product 3\",\"Product 4\",\"Product 5\"]\n",
    "y = [5, 3, 5, 2, 9]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.bar(x, y, width=0.6, color='red')\n",
    "plt.xlabel(\"X-axis, e.g. Product\")\n",
    "plt.ylabel(\"Y-axis, e.g. Occurance [-]\")\n",
    "plt.title(\"Title for your plot\")\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each bar, the width (w.r.t the x-axis) can be defined by `width=`. Furthermore labeling the x- and y-axes are the same as for line plots, as well as other layout-options for the graph.\n",
    "\n",
    "More information on the bar plot can be found at https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3.4 Histogram plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a histogram, one can visualize the distribution of a dataset. To create a histogram, the function `plt.hist` is used. An example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * random.randn(10000)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.hist(x, bins=50, density=True, color='blue')\n",
    "plt.xlabel(\"X-axis, e.g. Time [hours]\")\n",
    "plt.ylabel(\"Y-axis, e.g. Probability [-]\")\n",
    "plt.title(\"Title for your plot\")\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, `x` contains data in which the numbers 1 to 6 occur in different amounts. This is visualized in the histogram using `plt.hist`, where the number of bins can be defined by `bins=` (if not defined, this will be 10 bins). The number of bins represents the number of bars that will be present in the plot. When the input `density=True` is used, the probability of each x-axis unit will be visualized, otherwise the occurence will be shown.\n",
    "\n",
    "More information on the histogram plot can be found at https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (pychEnv)",
   "language": "python",
   "name": "pychenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}