{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f3f5cd41",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from lets_plot.mapping import as_discrete\n",
"from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"id": "15508d12-1922-4c06-b9c4-6643387922a8",
"metadata": {},
"source": [
"# override_aes in guide_legend()ΒΆ\n",
"The `override_aes` parameter can be used to specify aesthetic parameters of legend keys"
]
},
{
"cell_type": "markdown",
"id": "a3031c6a-3ef7-4ca2-bd5c-912c9ac4dfe3",
"metadata": {},
"source": [
"## Demo"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b0516089-88bf-4a33-acfb-fab6ecefee14",
"metadata": {},
"outputs": [],
"source": [
"mpg = pd.read_csv (\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "24bcdf82-a6a8-4327-97a5-470d32b2e741",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# By default alpha is not applied to the legend.\n",
"\n",
"base_mpg_plot = ggplot(mpg, aes('displ', 'hwy', color='drv')) + \\\n",
" geom_point(size=4, alpha=0.2, stroke=0)\n",
"base_mpg_plot"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e8dd95ce-d2f5-4663-8e76-0d51f0a203dd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Applying alpha and size for better look.\n",
"\n",
"base_mpg_plot + guides(color=guide_legend(override_aes={'alpha': 0.4, 'size': 8}))"
]
},
{
"cell_type": "markdown",
"id": "f5b38a9a-5835-4030-85ce-0fcc9c8602c9",
"metadata": {},
"source": [
"## Experiments with parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f8b4c449",
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"\n",
"n = 4\n",
"x = list(range(n))\n",
"y = np.random.rand(n)\n",
"v = np.random.rand(n)\n",
"data = {'x': x, 'y': y, 'v': v}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4e04a12d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Base plot\n",
"p = ggplot(data, aes('x', 'y')) + geom_point(aes(color=as_discrete('x'))) + ggsize(400, 200)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "04c6a892",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Specifying common aesthetics for all the legend labels.\n",
"\n",
"p + guides(color=guide_legend(\n",
" override_aes=dict(shape=22, size=8, stroke=1.8, fill='light-grey', alpha=0.6)\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "98097225",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using a list of values.\n",
"\n",
"p + guides(color=guide_legend(\n",
" override_aes=dict(color=['red', 'blue', 'green', 'pink'])\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "89ef8a97-02cc-4d23-9b84-fccb493628a4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use 'None' to keep the original value.\n",
"\n",
"p + guides(color=guide_legend(\n",
" override_aes=dict(color=['red', None, 'green', None])\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6e9cb997",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using a list that is smaller than the number of legend keys.\n",
"# The last value spreads to the rest of the keys.\n",
"\n",
"p + guides(color=guide_legend(\n",
" override_aes=dict(color=['grey'], size=[10, 8])\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fa2b1a19-995c-4596-8dfc-d6831b46b646",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using a list that is smaller than the number of legend keys.\n",
"# 'None' can be used to stop value propagation; the rest keys will get their original values.\n",
"\n",
"p + guides(color=guide_legend(\n",
" override_aes=dict(color=['grey', None], size=[10, None, 8])\n",
"))"
]
},
{
"cell_type": "markdown",
"id": "ccebef12-d878-48aa-a497-627d284d2fbc",
"metadata": {},
"source": [
"## Reproduced an example from [rbind.io](https://aosmith.rbind.io/2020/07/09/ggplot2-override-aes/)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "612092f4-190b-411c-bda3-6f5e57d8ef2f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat = pd.DataFrame({\n",
" 'g1': ['High', 'Low', 'High', 'Low', 'High', 'Low', 'High', 'Low', 'High', 'Low', 'High', 'Low', 'High', 'Low', 'High', 'Low'],\n",
" 'g2': ['Control', 'Control', 'Treatment', 'Treatment', 'Control', 'Control', 'Treatment', 'Treatment', 'Control', 'Control', 'Treatment', 'Treatment', 'Control', 'Control', 'Treatment', 'Treatment'],\n",
" 'x': [0.42, 0.39, 0.56, 0.59, 0.17, 0.95, 0.85, 0.25, 0.31, 0.75, 0.58, 0.9, 0.6, 0.86, 0.61, 0.61],\n",
" 'y': [-1.4, 3.6, 1.1, -0.1, 0.5, 0, -1.8, 0.8, -1.1, -0.6, 0.2, 0.3, 1.1, 1.6, 0.9, -0.6]\n",
"})\n",
"\n",
"bp = ggplot(dat, aes('x', 'y', color='g1', shape='g2')) + \\\n",
" geom_point(size=5, alpha=0.6)\n",
"bp"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "16324c8c-df68-4159-b4d0-9eb50790bf8a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Customized scales and default legend do not correspond well.\n",
"\n",
"cp = ggplot(dat, aes(x='x', y='y', fill='g1', shape='g2')) + \\\n",
" geom_point(size=5, color='black') + \\\n",
" scale_fill_manual(values=[\"#002F70\", \"#EDB4B5\"]) + \\\n",
" scale_shape_manual(values=[21, 24])\n",
"cp"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f4f54d77-5bd3-4477-83c2-8998da6fdab2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This can be fixed via override_aes.\n",
"\n",
"cp + guides(\n",
" fill=guide_legend(override_aes={'shape': 21}),\n",
" shape=guide_legend(override_aes={'fill': 'black'})\n",
")\n"
]
},
{
"cell_type": "markdown",
"id": "dd3315a9-2a01-43e8-bf3e-749283bd94c8",
"metadata": {},
"source": [
"## Testing"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f2889194",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# `fill` and `size` are mapped to the same variable. \n",
"# The result is not defined if the same legend is set via different aesthetics.\n",
"\n",
"pie_plot = ggplot({'n': ['a', 'b', 'c']}) + geom_pie(aes(fill='n', size='n'), stat='identity')\n",
"gggrid([\n",
" pie_plot,\n",
" pie_plot + guides(\n",
" fill=guide_legend(override_aes=dict(color='blue')),\n",
" size=guide_legend(override_aes=dict(stroke=4, color='black'))\n",
" )\n",
"]) + ggsize(800, 300)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "12cd9926-b947-407d-8106-294b02c2a062",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verifying different aesthetics\n",
"\n",
"p + guides(color=guide_legend(override_aes=dict(\n",
" color=['red', 'blue', 'green', 'pink'], \n",
" shape=22, \n",
" size=8, \n",
" stroke=1.8, \n",
" fill='white', \n",
" alpha=0.4\n",
")))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "52eb6673-d236-4d27-8af2-5e0a8398d63e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# An empty dictionary\n",
"\n",
"p + guides(color=guide_legend(override_aes={}))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "7c7db11a-ad6d-44a4-aa2f-e6143c5aab1a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Invalid or 'None' values\n",
"\n",
"p + guides(color=guide_legend(override_aes=dict(color=[None], shape=-11, size=0, stroke=-2)))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7b57d837-9a59-431b-83f7-5e041032e200",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# An empty list of values\n",
"\n",
"p + guides(color=guide_legend(override_aes=dict(color=[])))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7a5ca960-6362-4317-9eb5-f0d99ea776da",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# `override_aes` in the case when some legend labels are combined into one;\n",
"# see issue https://github.com/JetBrains/lets-plot/issues/1129 for details.\n",
"\n",
"df = pd.DataFrame({\n",
" 'category': ['A', 'B', 'C', 'D'],\n",
" 'value': [10, 15, 20, 25]\n",
"})\n",
"\n",
"ggplot(df) + ggsize(400,200) + \\\n",
" geom_point(aes(x='category', y='value', color='category')) + \\\n",
" scale_color_manual(values=['red', 'blue', 'green', 'black'], labels=['Label A', 'Label B', 'Label C', 'Label C']) + \\\n",
" guides(color = guide_legend(override_aes=dict(color=['pink', 'gray', 'green', 'orange'])))\n"
]
},
{
"cell_type": "markdown",
"id": "4f4a7d72-7dd4-4ee8-965d-bff65fa2eb74",
"metadata": {},
"source": [
"## Custom legend"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a113117f-67db-48e6-9cc2-9e443c5252a3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verifying how `overrides_aes` works for custom legends.\n",
"# Note that the same effect can be achieved easier by using direct parameters.\n",
"\n",
"m_plot = ggplot(data, aes('x', 'y')) + \\\n",
" geom_point(manual_key=\"Point\") + \\\n",
" geom_line(manual_key=\"Line\") \n",
"\n",
"gggrid([\n",
" m_plot,\n",
" m_plot + guides(manual=guide_legend(override_aes=dict(size=6))),\n",
" m_plot + guides(manual=guide_legend(override_aes=dict(size=[6, 2], color=['red', 'blue'])))\n",
"]) + ggsize(900, 200)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f274f43f-231e-4d78-a382-6c6b91bfeb6c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Changing the order in the legend via `index` in `layer_key`\n",
"\n",
"m_plot2 = ggplot(data, aes('x', 'y')) + \\\n",
" geom_point(manual_key=layer_key(\"Point\", index = 1)) + \\\n",
" geom_line(manual_key=layer_key(\"Line\", index = 0))\n",
"\n",
"gggrid([\n",
" m_plot2,\n",
" m_plot2 + guides(manual=guide_legend(override_aes=dict(size=[2], color=['red', 'blue'])))\n",
"]) + ggsize(600, 200)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ab8d56a-eedc-4e80-b710-b31a9e0eddc2",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.8.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}