{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "historic-minority",
"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": "code",
"execution_count": 2,
"id": "specialized-peoples",
"metadata": {},
"outputs": [],
"source": [
"df = {\n",
" 'x' : [1, 1, 2, 2, 1.5],\n",
" 'y' : [1, 2, 1, 2, 1.5],\n",
" 'text': [\"bottom-left\", \"top-left\", \"bottom-right\", \"top-right\", \"center\"]\n",
"}\n",
"\n",
"w, h = 400, 250\n",
"\n",
"p = ggplot(df, aes('x', 'y')) + geom_point(size=3) + ggsize(w,h) + theme_classic() \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "worth-eagle",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_text(aes(label = 'text'), size=8, hjust = 'inward', angle=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "optical-spokesman",
"metadata": {},
"outputs": [],
"source": [
"angles = [0,45,90,135,180,225,270,315]\n",
"\n",
"def justificationWithAngles(hjust=None, vjust=None):\n",
" bunch = GGBunch()\n",
" for i in range(len(angles)):\n",
" angle = angles[i]\n",
" \n",
" row = int(i / 2)\n",
" column = i % 2\n",
" \n",
" pp = p + geom_text(aes(label = 'text'), size=8, hjust = hjust, vjust = vjust, angle=angle) +\\\n",
" ggtitle(\"Angle: \" + str(angle))\n",
" \n",
" bunch.add_plot(pp, column * w, row * h, w, h)\n",
" \n",
" return bunch.show() \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "published-designation",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"justificationWithAngles(hjust='inward')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "filled-surrey",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"justificationWithAngles(hjust='outward')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "meaningful-wrestling",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"justificationWithAngles(vjust='inward')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "short-necessity",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"justificationWithAngles(vjust='outward')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}