{
"cells": [
{
"cell_type": "markdown",
"id": "f03a934b-ae4a-4a90-9f51-c3e31318ef0b",
"metadata": {},
"source": [
"# `geom_band()`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "833d8096-b8e9-4629-a607-36ab8dd19858",
"metadata": {},
"outputs": [],
"source": [
"from lets_plot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e384c6dc-9f36-4739-b8e7-074fa29e3759",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"id": "a4ff11ce-204c-4c6a-98aa-c3d3a036c375",
"metadata": {},
"source": [
"## Default"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "686d7988-172f-40e5-8d67-9aef835ae0ac",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_band(xmin=-1, xmax=1)"
]
},
{
"cell_type": "markdown",
"id": "8a6c2c0f-ac51-443f-9296-65c9982fc225",
"metadata": {},
"source": [
"## Both vertical and horizontal"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c9a87d76-36c8-4452-90c7-ba07cbaee0b7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + \\\n",
" geom_band(aes(\n",
" xmin=[-1.5, .5],\n",
" xmax=[-.5, 1.5],\n",
" )) + \\\n",
" geom_band(aes(\n",
" ymin=[-1.5, .5],\n",
" ymax=[-.5, 1.5],\n",
" ))"
]
},
{
"cell_type": "markdown",
"id": "30ebbe1c-af00-4178-b70b-671670e5a8bb",
"metadata": {},
"source": [
"## Additional parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b3c48799-f6d9-44c0-8122-187deddecd45",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + \\\n",
" geom_band(aes(\n",
" xmin=[-1, 0],\n",
" xmax=[0, 1],\n",
" fill=[\"a\", \"b\"],\n",
" ), size=3, alpha=.5, linetype='dashed', show_legend=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5974b2e5-ef31-4c68-b8fd-22b07916827b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# stat\n",
"\n",
"data = {\n",
" 'x': ['a', 'a', 'a', 'b', 'b', 'b'],\n",
" 'y': [1, 2, 3, 4, 5, 6],\n",
"}\n",
"\n",
"ggplot(data, aes('x', 'y')) + geom_band(aes(fill='x'), stat='boxplot')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4ce86dbe-a0ce-4570-a94e-6ae4dc4664d8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# position\n",
"\n",
"ggplot() + geom_band(aes(xmin=[-1], xmax=[1]), position=position_nudge(x=1)) + xlim(-1, 3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6cc1bbc8-5beb-42c7-b81e-1e76f2ae3b8c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sampling\n",
"\n",
"n = 20\n",
"ggplot() + geom_band(aes(xmin=list(range(n)), xmax=list(range(1, 1+n)), fill=list(range(n))), size=0, sampling=sampling_random(n-1, seed=42))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e367e11f-e931-4b97-a235-acc4d0ae91c0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + \\\n",
" geom_band(aes(xmin=[-3, 1], xmax=[-1, 3], paint_a=['a', 'b'], paint_b=['A', 'B']), \\\n",
" color_by='paint_a', fill_by='paint_b', size=1.5) + \\\n",
" scale_brewer('paint_a', palette='Set2') + \\\n",
" scale_brewer('paint_b', palette='Pastel2')"
]
},
{
"cell_type": "markdown",
"id": "6472f358-6e3a-418f-84d3-b9d0531c1602",
"metadata": {},
"source": [
"## Tooltips"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bec0ee9f-6a70-4976-bf50-d7507c689a45",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {\n",
" 'xstart': [ 0, 2, 3, 6, None],\n",
" 'xend': [ 1, 4, 5, 7, None],\n",
" 'y_from': [-1, -4, -5, None, -9],\n",
" 'y_to': [ 0, -2, -3, None, -8],\n",
" 'color': [\"black\", \"red\", \"blue\", \"green\", \"yellow\"],\n",
"}\n",
"\n",
"p1 = ggplot(data, aes(xmin='xstart', xmax='xend'))\n",
"p2 = ggplot(data, aes(ymin='y_from', ymax='y_to'))\n",
"\n",
"gggrid([\n",
" p1 + geom_band(aes(fill='color'), alpha=.2) + scale_fill_identity(),\n",
" p2 + geom_band(aes(fill='color'), alpha=.2) + scale_fill_identity(),\n",
" p1 + geom_band(tooltips='none'),\n",
" p1 + geom_band(tooltips=layer_tooltips().line(\"min|^xmin\").line(\"max|^xmax\").line(\"color|@color\")),\n",
"], ncol=2)"
]
},
{
"cell_type": "markdown",
"id": "3acb00b9-318c-4ef5-9139-35da16236602",
"metadata": {},
"source": [
"## Different coordinate systems"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e50bb8f4-6dba-406f-92a5-9a30788dc21c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"limits = [-4.25, 4.25]\n",
"\n",
"p = ggplot({'xmin': [-4, -2.5, -1, 1.5, 3], \\\n",
" 'xmax': [-3, -1.5, 1, 2.5, 4], \\\n",
" 'ymin': [-4, -1, 3, None, None], \\\n",
" 'ymax': [-3, 1, 4, None, None]}) + \\\n",
" geom_band(aes(xmin='xmin', xmax='xmax'), alpha=.2, size=0, fill=\"red\") + \\\n",
" geom_band(aes(ymin='ymin', ymax='ymax'), alpha=.2, size=0, fill=\"blue\")\n",
"\n",
"gggrid([\n",
" p + ggtitle(\"Default coord\"),\n",
" p + coord_flip(xlim=limits, ylim=limits) + ggtitle(\"coord_flip()\"),\n",
" p + coord_fixed(xlim=limits, ylim=limits) + ggtitle(\"coord_fixed()\"),\n",
" p + coord_polar(xlim=limits, ylim=limits) + ggtitle(\"coord_polar()\"),\n",
"], ncol=2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "db355aea-13f2-44ac-a242-4859d23d2807",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# See: https://gadflyjl.org/stable/gallery/geometries/#[Geom.band](@ref),-[Geom.hband](@ref),-[Geom.vband](@ref)\n",
"\n",
"import pandas as pd\n",
"\n",
"presidential_df = pd.read_csv(\"../../f-24e/data/presidential.csv\", parse_dates=[\"start\", \"end\"])\n",
"economics_df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/economics.csv\", parse_dates=[\"date\"])\n",
"\n",
"ggplot() + \\\n",
" geom_band(aes(xmin=\"start\", xmax=\"end\", fill=\"party\"), data=presidential_df, size=0, alpha=.4) + \\\n",
" geom_line(aes(\"date\", \"unemploy\"), data=economics_df) + \\\n",
" scale_y_continuous(trans='log10') + \\\n",
" ggsize(1000, 200)"
]
},
{
"cell_type": "markdown",
"id": "0cdd5a43-d7ba-47bd-bb4d-1d24f641ad0f",
"metadata": {},
"source": [
"## Tests"
]
},
{
"cell_type": "markdown",
"id": "c6ca8ed3-ea0a-4afd-bb59-301329235f65",
"metadata": {},
"source": [
"### Boundary Value Analysis"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1cd04ae3-9a65-40ad-8969-8f593c45d36e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"class BVATest:\n",
" def __init__(self, data, title, orientation='x', show=True):\n",
" self.data = data\n",
" self.title = title\n",
" self.orientation = orientation\n",
" self.show = show\n",
"\n",
" def to_plot(self):\n",
" min_aes, max_aes = '{0}min'.format(self.orientation), '{0}max'.format(self.orientation)\n",
" return ggplot(self.data) + geom_band(aes(**{min_aes: min_aes, max_aes: max_aes})) + \\\n",
" ggtitle(self.title)\n",
"\n",
"show_all = True\n",
"bva_tests = [\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[],\n",
" xmax=[],\n",
" ),\n",
" title=\"Empty dataset\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[-1],\n",
" xmax=[1],\n",
" ),\n",
" title=\"One value\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[0],\n",
" xmax=[0],\n",
" ),\n",
" title=\"min=max\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[1],\n",
" xmax=[-1],\n",
" ),\n",
" title=\"min>max\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[-1, 0],\n",
" xmax=[0, 1],\n",
" ),\n",
" title=\"Joint bands\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[-2, -1],\n",
" xmax=[1, 2],\n",
" ),\n",
" title=\"Intersected bands\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" ymin=[-1],\n",
" ymax=[1],\n",
" ),\n",
" orientation='y',\n",
" title=\"y-oriented\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[-1, float('inf'), float('-inf'), -2, -3],\n",
" xmax=[1, 3, 2, float('-inf'), float('inf')],\n",
" ),\n",
" title=\"Inf value\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[-1, None, -2],\n",
" xmax=[1, 2, None],\n",
" ),\n",
" title=\"None value\",\n",
" ),\n",
" BVATest(\n",
" data=dict(\n",
" xmin=[-1, np.nan, -2],\n",
" xmax=[1, 2, np.nan],\n",
" ),\n",
" title=\"np.nan value\",\n",
" ),\n",
"]\n",
"\n",
"gggrid([\n",
" t.to_plot()\n",
" for t in filter(lambda t: show_all or t.show, bva_tests)\n",
"], ncol=3)"
]
},
{
"cell_type": "markdown",
"id": "08118cba-1642-43f5-8f75-6bf11b8b9625",
"metadata": {},
"source": [
"### Regression Testing"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "284bbd6e-a863-499f-a743-57dad0a2e36a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Only zoom without clipping saves the band, if it's half-visible\n",
"\n",
"data = dict(xmin=[0], xmax=[2])\n",
"lims = [-1, 1]\n",
"\n",
"gggrid([\n",
" ggplot(data) + geom_band(aes(xmin='xmin', xmax='xmax')) + xlim(*lims),\n",
" ggplot(data) + geom_band(aes(xmin='xmin', xmax='xmax')) + coord_cartesian(xlim=lims),\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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}