{
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{
"cell_type": "markdown",
"id": "eec756ff-2fe9-4267-bfd3-829d7d19a6ba",
"metadata": {},
"source": [
"# `ggtb()`: `size_zoomin` and `size_basis` Parameters for Geometry Scaling\n",
"\n",
"## `size_zoomin` parameter\n",
"\n",
"The `size_zoomin` parameter accepts an integer value:\n",
"\n",
"* `0` — zoom in disabled (default)\n",
"* `-1` — unlimited zoom in\n",
"* Any other positive number — maximum zoom-in limit. For example, `2` means the geometry can be scaled up to **2×**.\n",
"\n",
"## `size_basis` parameter\n",
"\n",
"The `size_basis` parameter accepts a string value: `x`, `y`, `min`, or `max` (default: `max`).\n",
"It defines which axis is used to calculate the scaling factor.\n",
"\n",
"* `x` and `y` specify the corresponding axis.\n",
"* `min` uses the smaller scaling factor.\n",
"* `max` uses the larger scaling factor."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1c382fab-6e5b-4fdf-b185-c36c386aee18",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from lets_plot import *\n",
"import pandas as pd\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"id": "6c0c8116-990c-403c-bf49-696247f642a3",
"metadata": {},
"source": [
"#### Enable 'Unlimited' Zoom\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c287276a-2732-4bbd-b07d-33f32737a5f9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_point(x=0, y=0, size=10) + ggtb(\n",
" size_zoomin=-1 # <-- Point size can grow without limits.\n",
")"
]
},
{
"cell_type": "markdown",
"id": "da9be487-a3b4-4a1a-b6b3-70d1fd01f2c0",
"metadata": {},
"source": [
"#### Limit Maximum Zoom to 3× Original Size"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1079f295-4c9a-415a-b230-85226422198b",
"metadata": {},
"outputs": [],
"source": [
"mpg = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6379f40f-53d3-47c1-9113-0d7493262490",
"metadata": {},
"outputs": [],
"source": [
"p = (\n",
" ggplot(mpg, \n",
" aes('engine horsepower', 'miles per gallon', \n",
" color=as_discrete('origin of car',order=-1))) \n",
" + theme_grey() + ggtitle('Efficiency vs Engine Horsepower')\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4a11b4c2-3857-476a-aebf-a23de27ac2e5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_jitter(seed=42) + ggtb(\n",
" size_zoomin=3 # <-- Point size can grow to 3x of the original size max.\n",
")"
]
}
],
"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": "3.9.23"
}
},
"nbformat": 4,
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}