{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-24bdaef0687142bb", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# Plotting with Matplotlib\n", "\n", "```{admonition} Interactive page\n", ":class: warning, dropdown\n", "This is an interactive book page. Press launch button at the top right side.\n", "```\n", "\n", "To start making our own plots with Matplotlib, we will need the [`pyplot` module](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) of `matplotlib`, which we import like this: \n", "\n", "```\n", "import matplotlib.pyplot as plt\n", "```\n", "\n", "In the examples below, we will mainly showcase how to use Matplotlib to make scatter and line plots. To see example code for other plot types, check out Matplotlib's [plot types gallery](https://matplotlib.org/stable/plot_types/index.html).\n", "\n", "## Matplotlib with NumPy\n", "\n", "### Plotting one dataset\n", "\n", "The [routine for making line plots](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) of your data is `plt.plot()`.\n", "In its simplest form, you can just give an array and ask Python to plot it for you:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Let's again load time-voltage data and store columns in separate vectors\n", "data = np.loadtxt(\"v_vs_time.dat\")\n", "t = data[:,0]\n", "v = data[:,1]\n", "\n", "# Simple line plot\n", "plt.figure()\n", "plt.plot(v)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-2dad246fc48c5b7d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "```{admonition} plt.figure()\n", ":class: note\n", "Note that `plt.figure()` in the code above is not stricly necessary, as `plt.plot()` creates a new figure implicitly if one doesn't already exist. `plt.figure()` is needed when we want to create multiple figures in the same script, customize figure size and other properties, or create subplots. You will see examples of this below.\n", "```\n", "\n", "The *y*-axis is clearly the measured voltage. But what is the *x*-axis? If you give the `plt.plot()` command only one array, it will use that array data for the *y*-axis, and use the index number for the *x*-axis. Since our dataset has 1,000 points, the *x*-axis runs from 0 to 1000. \n", "\n", "This seems quite obvious now that we know, but it's not obvious at all just from looking at the plot. Remember: we should **ALWAYS add axes labels to our plots!**\n", "To do this, you can use the `plt.xlabel()` and `plt.ylabel()` commands:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Line plot with axes labels\n", "plt.figure()\n", "plt.plot(v)\n", "plt.xlabel(\"Point number\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-67a673593aba72e2", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Sometimes it's also useful to add a grid to your plots - this depends on the data you're showing, and whether grids are cluttering or aiding in reading the plot. To add grids, use `plt.grid()`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Line plot with axes labels and grids\n", "plt.figure()\n", "plt.plot(v)\n", "plt.xlabel(\"Point number\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-526802bd953d3531", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "What if we want to actually plot the voltage vs. time, and not point number? For this, we need to give `plt.plot()` **two arguments**. We also want to plot the data as **points** instead of a connected line. We can do this by adding `'.'` after arrays in `plt.plot()`. \n", "In the code below, try to change `plt.plot(t,v,'k.',markersize=1)` by changing `k.` into `r.` or `g.`, and changing markersize." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Time vs. voltage plot with axes labels\n", "plt.figure()\n", "plt.plot(t, v, 'k.', markersize=1)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-2f9e48b6a1d65039", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "All graphs so far have been displayed within our Python environment. But what if we wanted to use this plot in a document?\n", "Fortunately, Matplotlib has functions that easily allow you to **save your plot** in PNG or PDF formats, perfect for importing them into Word, [LaTeX](https://en.wikipedia.org/wiki/LaTeX), or another type of document you use (e.g., for reporting your lab data). To save a plot, you can use `plt.savefig('plot_name.png')` or `plt.savefig('plot_name.pdf')`.\n", "\n", "```\n", "plt.figure()\n", "plt.plot(t,v)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.savefig(\"myplot.pdf\")\n", "plt.show()\n", "```" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-6a6c5a44f576d0c0", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Do you want a different point size? Or a different symbol? You can see how to change all of this by looking at the [documentation page](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html) of the plot function, or by bringing up the built-in help with `?`.\n", "\n", "\n", "### Plotting multiple datasets in one plot\n", "\n", "You may want to plot more than one thing in your plot. To do this, you simply have to **run the plot command twice**:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [ "remove-output" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# A \"fake\" dataset to illustrate plotting\n", "v2 = np.random.normal(10, 0.5, 1000)\n", "\n", "plt.figure()\n", "plt.plot(t, v, 'k.', markersize=1)\n", "plt.plot(t, v2, 'r+')\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-1d9eda6969d17554", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Matplotlib will automatically change the color of the second dataset (you can also control this manually, see the [documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html)). In that case, as we mentioned, we must add a legend to our plot. Furthermore, if you want a really big figure, you can also adjust the figure size:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "tags": [ "remove-output" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# A large scatter plot with two datasets, axes lables, and a legend\n", "plt.figure(figsize=(12,8))\n", "plt.plot(t, v, 'k.', markersize=1, label=\"Voltage 1\")\n", "plt.plot(t, v2, 'r+', markersize=1, label=\"Voltage 2\")\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-afe34d963cea156d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "### Plotting functions \n", "\n", "Often in science, we go through a long mathematical derivation to come up with a formula describing a phenomenon. Sometimes this formula is very complicated, so it's handy to be able to plot the formula to get a feeling for what the function looks like. \n", "\n", "We've learned above how to plot data, but how do we plot a function in Python?\n", "\n", "It turns out it is actually pretty easy. Let's look at a concrete example: let's plot the height of a [projectile](https://en.wikipedia.org/wiki/Projectile) as a function of the distance it travels. Using Newton's laws, we can find that the projectile's [trajectory](https://en.wikipedia.org/wiki/Trajectory#Uniform_gravity,_neither_drag_nor_wind) is given by: \n", "\n", "$$\n", "y = -\\frac{g}{2 v_0^2 \\cos^2 \\theta} x^2 + x \\tan \\theta\n", "$$\n", "\n", "The first step is to make a NumPy array `x` that includes the points at which we want to evaluate (calculate) the function. Let's guess and say we want to look in the range of $x$ from 0 to 12 meters. We also need to pick the number of points: to get a smooth curve, let's pick 1,000 points:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "x = np.linspace(0, 12, 1000)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-67ab3373ae0fa8cc", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now we want to calculate $y$ for all of these $x$ points. Let's say we pick an angle of 45 degrees and an initial velocity $v_0$ of 10 m/s. We can then directly use NumPy \"vectorized\" calculations to calculate the values of `y` for all of our `x` points using a single line of code: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Parameters\n", "v0 = 10 # m/s\n", "theta = 45/180*np.pi # Python works with angles in Radians, and np.pi = pi (3.14596...)\n", "g = 9.8 # m/s^2\n", "\n", "# A vectorized calculation to calculate y for all values of x\n", "y = -g/(2*v0**2*np.cos(theta)**2)*x**2 + x*np.tan(theta)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-3103dddee2c9f829", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now, let's plot it!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(x, y)\n", "plt.xlabel(\"Distance (m)\")\n", "plt.ylabel(\"Height (m)\")\n", "plt.axhline(0, ls=\":\", c=\"grey\") # horizontal grey line\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-a1372d935bab78f8", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "We can now also see how easy it is to **combine plotting functions with plotting data**. For example, in our voltage data above, if we want to plot a straight line function over the data: " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [ "remove-output" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "line = 2*t\n", "plt.figure()\n", "plt.plot(t, v, '.')\n", "plt.plot(t, line, '--', lw=2)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-ecd3b09678cacd45", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "(This is actually a plot we've [already seen](../chapter10/data-visualization.ipynb), so now you know the type of code that produced it!)\n", "\n", "Here, we also used the `linewidth` parameter (which can be shorted to `lw` to save typing) to make the line a bit wider so it is easier to see, and used the `'--'` plot format specifier to make it a dashed line. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matplotlib with pandas\n", "\n", "All Matplotlib plots above were made with input data stored in NumPy arrays. However, we mentioned that Matplotlib also works with pandas. Let's look at the same time-voltage plotting example, but this time imported using pandas. In the code, notice how you can seamlessly **select columns from your pandas DataFrame** for plotting. Notice also that we can **add names to DataFrame columns** if they're missing (as is the case with the time-voltage file)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Import data as data frame voltage time (df_vt). File has no column names, so header=None.\n", "# Also specify that delimiter is not a default comma for CSVs, but a space.\n", "df_vt = pd.read_csv(\"v_vs_time.dat\", header=None, delimiter=\" \")\n", "\n", "# Add names to columns\n", "df_vt.columns =['Time (s)', 'Voltage (V)']\n", "\n", "df_vt.head(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Plotting the data\n", "plt.plot(df_vt['Time (s)'], df_vt['Voltage (V)'], '.', color='b')\n", "plt.xlabel('Time (s)')\n", "plt.ylabel('Voltage (V)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Different ways to use Matplotlib\n", "\n", "So far, we've been plotting with `plt.`. If you start looking online for plotting code using Matplotlib, you will see that there are **different ways** of drawing plots with Matplotlib (see [this Stack Overflow post](https://stackoverflow.com/questions/37970424/what-is-the-difference-between-drawing-plots-using-plot-axes-or-figure-in-matpl)). It's good to be aware of these differences, so we'll here briefly touch upon them. In practice, when more than one way can be used to produce a plot, **you can choose** whichever way of plotting you wish, as long as it produces a correct plot. \n", "\n", "These are three methods which produce the same plot:\n", "\n", "```\n", "# 1st method\n", "plt.plot(x, y)\n", "\n", "# 2nd method\n", "ax = plt.subplot()\n", "ax.plot(x, y)\n", "\n", "# 3rd method\n", "figure = plt.figure()\n", "ax = figure.add_subplot(111)\n", "ax.plot(x, y)\n", "\n", "```\n", "\n", "To understand each of them, it's useful to understand Matplotlib's vocabulary.\n", "\n", "Principal objects in Matplotlib are **figure** and **axes** (note that *axes* is a bit of a misleading term, as you may be thinking of *x*- and *y*-axes, but that's not what axes in Matplotlib are):\n", "- **A figure is like a canvas** - you specify its dimensions, background color, etc. You use it by placing other objects on it (mostly axes, but also text labels, etc.), and save its contents with `savefig`.\n", "- **Axes** offers \"tools\" such as `.plot`, `.scatter`, and `.hist`. You can place one or several axes inside a figure.\n", "\n", "\n", "```{figure} ../images/chapter10/fig-ax.png\n", "---\n", "height: 350px\n", "name: fig-ax\n", "---\n", "Visualizing Matplotlib figure and axes (not to be confused with *x*- and *y*-axes).\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First method\n", "\n", "```\n", "plt.plot(x, y)\n", "```\n", "\n", "The first method based on Pyplot is the simplest. When you call `plt.plot(x, y)`, Matplotlib implicitly creates a figure and an axes object if they don't already exist. \n", "\n", "Sometimes, this simple method is sufficient, e.g., when you are doing exploratory plotting of your data or **quickly generating uncomplicated plots**." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Voltage data plotting - 1st method\n", "plt.plot(t, v)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second method\n", "\n", "```\n", "ax = plt.subplot()\n", "ax.plot(x, y)\n", "```\n", "\n", "The second method creates a subplot (which is specific axes within a figure) using `plt.subplot()`. In the first line of code, `plt.subplot()` returns an axes object `ax`, which you can use to call plotting methods in the second line. This way, you have explicit control over the axes object, allowing more customization and clarity when managing multiple plots. \n", "\n", "This method is appropriate when you need **more control over the axes** and are creating multiple subplots." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Voltage data plotting - 2nd method\n", "ax = plt.subplot()\n", "ax.plot(t, v)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how we can plot two subplots one next to another. The three digits in `plt.subplot` denote:\n", "1. number of rows\n", "2. number of columns\n", "3. subplot number\n", "\n", "Therefore, with `plt.subplot(1, 2, 1)` and `plt.subplot(1, 2, 2)`, we're telling Matplotlib that we'll have one row and two columns (i.e., subplots side by side). First we plot the one with data `(t, v)`, then one with data `(t, v2)` (the \"fake\" voltage).\n", "\n", "Note: the range of the *y*-axis is automatically rescaled, so we use `set_ylim` to make *y* comparable between the subplots." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Voltage data plotting with subplots - 2nd method\n", "ax1 = plt.subplot(1, 2, 1)\n", "ax1.plot(t, v)\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "ax1.title.set_text('Voltage 1') # Subplot title\n", "\n", "ax2 = plt.subplot(1, 2, 2)\n", "ax2.plot(t, v2)\n", "plt.xlabel(\"Time (s)\")\n", "ax2.title.set_text('Voltage 2') # Subplot title\n", "ax.set_ylim([0,20]) # Set y range to 0-20\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{exercise}\n", ":class: dropdown\n", "In the code above with two subplots, try to make the subplots appear one below another rather than side by side. To do this, tinker with the numbers inside `plt.subplot`.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Third method\n", "\n", "```\n", "figure = plt.figure()\n", "ax = figure.add_subplot(111)\n", "ax.plot(x, y)\n", "```\n", "\n", "The third method starts by explicitly creating a figure object with `plt.figure()` (remember, we've also seen this line above, even though it wasn't always necessary). In the second line, it then adds a subplot to this figure using `figure.add_subplot(111)`, where 111 means a grid with **one** row, **one** column, and this is the **first** subplot. In the last line, we then plot the data. \n", "\n", "This method provides **the most control over both the figure and axes objects**. You can specify figure-level attributes (like size) and axes-level attributes (like position within the figure). You can use this method for complex plotting scenarios, where you need control over the figure and axes, or when dealing with multiple figures and subplots." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "remove-output" ] }, "outputs": [], "source": [ "# Voltage data plotting subplots - 3rd method\n", "figure = plt.figure()\n", "\n", "ax1 = figure.add_subplot(121)\n", "ax1.plot(t, v)\n", "ax1.title.set_text('Voltage 1')\n", "\n", "ax2 = figure.add_subplot(122)\n", "ax2.plot(t, v2)\n", "ax2.title.set_text('Voltage 2')\n", "ax2.set_ylim([0,20])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "thebe-remove-input-init" ] }, "outputs": [], "source": [ "import micropip\n", "await micropip.install(\"jupyterquiz\")\n", "from jupyterquiz import display_quiz\n", "import json\n", "\n", "with open(\"questions2.json\", \"r\") as file:\n", " questions=json.load(file)\n", " \n", "display_quiz(questions, border_radius=0)" ] } ], "metadata": { "celltoolbar": "Create Assignment", "jupytext": { "formats": "ipynb,md" }, "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": "3.11.4" }, "toc": { "base_numbering": "5", "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "249.797px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }