{
"cells": [
{
"cell_type": "markdown",
"id": "b9cd0898-9bc0-4f1a-88d3-e07b7727d240",
"metadata": {},
"source": [
"# Saving and manipulating plots"
]
},
{
"cell_type": "raw",
"id": "ee3d7dfd-57cc-4abf-9d8d-8a66e36717bc",
"metadata": {
"raw_mimetype": "text/restructuredtext",
"tags": []
},
"source": [
"Saving or manipulating plots is easy. All `thebeat`'s plotting functions internally use `matplotlib `_. The functions return a :class:`matplotlib.figure.Figure` object, representing the entire plot, and a :class:`matplotlib.axes.Axes` object, which represents the actual graph in the plot. These objects we can use to save or manipulate the plots."
]
},
{
"cell_type": "markdown",
"id": "697f730a-21d7-4496-aeb7-c692d64628c6",
"metadata": {},
"source": [
"---\n",
"\n",
"## Saving plots"
]
},
{
"cell_type": "raw",
"id": "e72b7277-30a3-48e9-b486-1e44611ac742",
"metadata": {
"raw_mimetype": "text/restructuredtext",
"tags": []
},
"source": [
"In the example below we create a recurrence plot, and save it to disk using :meth:`~matplotlib.figure.Figure.savefig`. We ask for a plot with a dpi of 600, so we get better quality."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bbeac28b-faed-45c2-b46b-2bae803318a8",
"metadata": {
"nbsphinx": "hidden",
"tags": []
},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore', message=\"Matplotlib \")\n",
"warnings.filterwarnings('ignore', message=\"FixedFormatter \")\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1ce85949-eeea-4b56-a5ea-fc98ee09a20f",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from thebeat import Sequence\n",
"from thebeat.visualization import recurrence_plot\n",
"\n",
"seq = Sequence(iois=[300, 400, 300, 400])\n",
"fig, ax = recurrence_plot(seq, dpi=600)\n",
"fig.savefig('recurrence_plot.png')"
]
},
{
"cell_type": "markdown",
"id": "92534175-a96f-4f17-8a46-81ffa58ccb68",
"metadata": {},
"source": [
"## Adding in title, x_axis label etc.\n",
"*thebeat*'s plotting functions contain a few arguments for commonly encountered manipulations, such as adding in a title, changing the figure size, etc. These are the so-called keyword arguments (``**kwargs``). "
]
},
{
"cell_type": "raw",
"id": "75539a84-5512-422b-ac37-d2655b0d7aa5",
"metadata": {
"raw_mimetype": "text/restructuredtext",
"tags": []
},
"source": [
"To see a list of available keyword arguments, see e.g. :py:func:`thebeat.helpers.plot_single_sequence`."
]
},
{
"cell_type": "markdown",
"id": "d55691b2-2fbf-4cc9-b155-3a53bf08efe8",
"metadata": {},
"source": [
"## Manipulating plots"
]
},
{
"cell_type": "raw",
"id": "a527cddb-badd-4022-a2b4-60a52479a6ba",
"metadata": {
"raw_mimetype": "text/restructuredtext",
"tags": []
},
"source": [
"Manipulating plots works similarly. We can use the resulting :class:`matplotlib.figure.Figure` and :class:`matplotlib.axes.Axes` objects for manipulation, and only show the figure afterwards."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7fa03aa9-55c2-4df0-a2b5-891324403b74",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = recurrence_plot(seq, suppress_display=True)\n",
"ax.set_xticklabels([0, 1, 2, 3, 4], fontsize=18)\n",
"fig.show()"
]
},
{
"cell_type": "raw",
"id": "4df58188-4101-4c08-ab04-b2bcbc9fc7dd",
"metadata": {
"raw_mimetype": "text/restructuredtext",
"tags": []
},
"source": [
"More info on how to manipulate plots can be found in the `matplotlib documentation `_."
]
}
],
"metadata": {
"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": 5
}