{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Plotting a single sequence\n", "\n", "This snippet shows how to plot a single sequence of events as an event plot.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we import what we will be using throughout the snippet:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from thebeat import Sequence, SoundStimulus, SoundSequence\n", "import numpy as np" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "From a Sequence object\n", "----------------------\n", "Plotting :py:class:`~thebeat.core.Sequence` objects is easy. The :py:class:`~thebeat.core.Sequence` class already has a method that does it (:py:meth:`~thebeat.core.Sequence.plot_sequence`).\n", "As :py:class:`~thebeat.core.Sequence` objects do not contain information about the durations of the events, we use an arbitrary ``linewidth`` for the lines. If none is supplied it uses 1/10th of the smallest IOI as the default." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "nbsphinx": "hidden", "tags": [] }, "outputs": [], "source": [ "# We suppress warnings, but let's hide that to avoid confusion\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seq = Sequence.generate_isochronous(n_events=10, ioi=0.5)\n", "seq.plot_sequence(x_axis_label=\"Time (s)\"); # uses the default linewidth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "As you might have noticed in the example above, we passed the ``plot_sequence`` method the argument ``x_axis_label``. This is one of the so-called keyword arguments (``**kwargs``) that can be supplied to most of the plotting functions in this package. For a reference, see e.g. :py:func:`thebeat.helpers.plot_single_sequence`." ] }, { "cell_type": "raw", "metadata": { "pycharm": { "name": "#%% md\n" }, "raw_mimetype": "text/restructuredtext" }, "source": [ "Now we can adjust the plot a little already by passing the :py:meth:`~thebeat.core.Sequence.plot_sequence` method a ``title``, a `matplotlib style `_,\n", "a ``linewidth``, and an output size of the figure (in inches). How to additionally adjust the plot we learn at the end of this snippet." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seq = Sequence.generate_isochronous(n_events=5, ioi=500)\n", "seq.plot_sequence(style='seaborn-paper', title='My awesome sequence', linewidth=100, figsize=(4, 2));" ] }, { "cell_type": "raw", "metadata": { "pycharm": { "name": "#%% md\n" }, "raw_mimetype": "text/restructuredtext" }, "source": [ "From a SoundSequence object\n", "---------------------------\n", ":py:class:`~thebeat.core.SoundSequence` objects differ from :py:class:`~thebeat.core.Sequence` objects in that they also contain event durations. So, we do not\n", "have to supply linewidths (though we can), but the linewidths are given by how long the events are." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We use a seed to make sure you get the same random output as we\n", "rng = np.random.default_rng(seed=123)\n", "\n", "# Let's create 10 stimuli with random durations between 20 and 150 milliseconds\n", "stims = [SoundStimulus.generate(duration_ms=x) for x in rng.integers(low=20, high=250, size=10)]\n", "\n", "# Then we create a randomly timed Sequence\n", "seq = Sequence.generate_random_normal(n_events=10, mu=500, sigma=50, rng=rng)\n", "\n", "# Create the SoundSequence object\n", "stimseq = SoundSequence(stims, seq)\n", "\n", "# And plot!\n", "stimseq.plot_sequence(style='bmh', title='Random StimSeq');" ] }, { "cell_type": "raw", "metadata": { "pycharm": { "name": "#%% md\n" }, "raw_mimetype": "text/restructuredtext" }, "source": [ "From a list of onsets\n", "---------------------\n", "If we want to plot onsets that do not start with zero, we can use the :py:meth:`thebeat.core.Sequence.from_onsets` method and then plot." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seq = Sequence.from_onsets([200, 500, 1000, 1400, 2300])\n", "seq.plot_sequence(title = \"Non-zero first onset\", figsize = (8,3), style=\"seaborn-ticks\");" ] }, { "cell_type": "raw", "metadata": { "pycharm": { "name": "#%% md\n" }, "raw_mimetype": "text/restructuredtext" }, "source": [ "Adjusting the figure\n", "--------------------\n", "Now, of course the standard settings for the plot are great, but what if we want to adjust the plot some more?\n", "\n", "The plotting functions and methods used above all return a matplotlib :class:`~matplotlib.figure.Figure` object, and a matplotlib :class:`~matplotlib.axes.Axes` object.\n", "\n", "These objects we can manipulate, and then show or save, as in the example below. We might want to suppress displaying the unadjusted plot, which we can do by passing ``suppress_display=True`` to the plotting method." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Generate isochronous sequence\n", "seq = Sequence.generate_isochronous(n_events=10, ioi=500)\n", "\n", "# Use the Sequence.plot() method\n", "fig, ax = seq.plot_sequence(title='My isochronous sequence',\n", " suppress_display=True,\n", " figsize=(8, 3))\n", "\n", "# Add in some text with a box around it\n", "box_properties = dict(boxstyle='round', facecolor='white', alpha=0.8)\n", "ax.text(3900, 0.8, s=\"nPVI = 0\", bbox=box_properties, fontsize=14)\n", "\n", "# Show\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext", "tags": [] }, "source": [ "Plotting onto an existing Axes object\n", "-------------------------------------\n", "\n", "Finally, sometimes it is useful to be able to create a *matplotlib* :class:`~matplotlib.axes.Axes` and :class:`~matplotlib.figure.Figure` object first, and then use one of the plotting functions of *thebeat* to plot onto it. This we can do by passing the existing :class:`~matplotlib.axes.Axes` object to the ``ax`` parameter. Below is an example. The ``tight_layout`` parameter makes sure all the labels are plotted nicely. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "# Create initial Figure and Axes\n", "fig, axs = plt.subplots(nrows=2, ncols=1, tight_layout=True)\n", "\n", "# Create two sequences\n", "seq1 = Sequence.generate_isochronous(n_events=5, ioi=500)\n", "seq2 = Sequence.generate_isochronous(n_events=5, ioi=300)\n", "\n", "# Plot onto existing Axes objects\n", "seq1.plot_sequence(ax=axs[0])\n", "seq2.plot_sequence(ax=axs[1])\n", "\n", "# Add a title and show\n", "fig.suptitle('Two plots in one')\n", "fig.show()" ] } ], "metadata": { "celltoolbar": "Raw-celnotatie", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 4 }