{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b1f7c080-7fb0-428b-918e-04c4a65cfd36",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from lets_plot import *\n",
"from lets_plot.plot.coord import coord_polar"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "eebabc41-5de3-44ae-b0e8-ec30de4c94e1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"id": "bc520db3-bdee-4a28-9c32-d15874029531",
"metadata": {},
"source": [
"##### Util for `PlotSpecDebugger.kt`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d493053b-3a9a-4409-97fe-57be18f9dac6",
"metadata": {},
"outputs": [],
"source": [
"def dump_plot(plot, display=False):\n",
" import json\n",
"\n",
" try:\n",
" import clipboard\n",
" except:\n",
" clipboard = None\n",
" \n",
" from lets_plot._type_utils import standardize_dict\n",
" \n",
" plot_dict = standardize_dict(plot.as_dict())\n",
" plot_json = json.dumps(plot_dict, indent=2)\n",
" \n",
" if clipboard:\n",
" clipboard.copy('')\n",
" clipboard.copy(str(plot_json))\n",
" else:\n",
" if display is None:\n",
" display = True\n",
"\n",
" return plot"
]
},
{
"cell_type": "markdown",
"id": "ab190a94-9770-4c6b-9bb6-caaf120e60a9",
"metadata": {},
"source": [
"##### Data:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a258b6d0-767b-4684-bcbf-4dc6fba6126f",
"metadata": {},
"outputs": [],
"source": [
"subj_short = ['CS', 'Math', 'Phys', 'Chem', 'Bio']\n",
"subj = ['Computer Science', 'Mathematics', 'Physic', 'Chemistry', 'Biology']\n",
"john = {\n",
" 'subj_short': subj_short,\n",
" 'subj': subj,\n",
" 'subj_id': [1, 2, 3, 4, 5],\n",
" 'student': ['John'] * 5,\n",
" 'score': [19, 15, 18, 12, 9]\n",
"}\n",
"\n",
"bob = {\n",
" 'subj_short': subj_short,\n",
" 'subj': subj,\n",
" 'subj_id': [1, 2, 3, 4, 5],\n",
" 'student': ['Bob'] * 5,\n",
" 'score': [7, 18, 10, 18, 19]\n",
"}\n",
"\n",
"def merge(dest, other):\n",
" for k, v in other.items():\n",
" list = dest.get(k, [])\n",
" list.extend(v)\n",
" list.append(v[0]) # close path\n",
" dest[k] = list\n",
"\n",
"\n",
"radar_df = {}\n",
"merge(radar_df, john)\n",
"merge(radar_df, bob)"
]
},
{
"cell_type": "markdown",
"id": "1581c9c7-d23d-4b91-a530-26d521368c7b",
"metadata": {},
"source": [
"### Minimal radar plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e0fcea18-3b15-4c8c-9e64-dc879390ff7d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = ggplot(radar_df, aes(x='subj_short', y='score', color='student')) \\\n",
" + coord_polar(ylim=[0, 20]) \\\n",
" + theme(\n",
" axis_ticks_y=element_blank(), \n",
" axis_title_y=element_blank(),\n",
" axis_title_x=element_blank(),\n",
" axis_tooltip=element_blank(),\n",
" )\n",
"\n",
"def radar_plot(df):\n",
" p = ggplot(df) \\\n",
" + geom_path(aes(x='subj_short', y='score', color='student'), flat=True, tooltips='none') \\\n",
" + geom_point(aes(x='subj_short', y='score', color='student'), tooltips=layer_tooltips().title(\"@student\").line(\"Subject|@subj\").line(\"Score|@score\")) \\\n",
" + coord_polar(ylim=[0, 20]) \\\n",
" + theme(\n",
" axis_ticks_y=element_blank(), \n",
" axis_title_y=element_blank(),\n",
" axis_title_x=element_blank(),\n",
" axis_tooltip=element_blank(),\n",
" )\n",
" return p\n",
"\n",
"p + geom_path(flat=True, tooltips='none') \\\n",
" + geom_point(tooltips=layer_tooltips().title(\"@student\").line(\"Subject|@subj\").line(\"Score|@score\"))"
]
},
{
"cell_type": "markdown",
"id": "2211b40b-3ceb-4dd3-85f2-e17442f74f60",
"metadata": {},
"source": [
"### Issues:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fae09e23-a849-4311-af91-571e7bcd9901",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + aes(x='subj', y='score', color='student') + geom_path(flat=True, tooltips='none') \\\n",
" + geom_point(tooltips=layer_tooltips().title(\"@student\").line(\"Subject|@subj\").line(\"Score|@score\"))"
]
},
{
"cell_type": "markdown",
"id": "769dea86-ecf9-43c3-b200-376534f84364",
"metadata": {},
"source": [
"1. Long names get rotated\n",
"2. Double tooltip on Computer Science\n",
"3. Plot is aligned to top, leaving large empty space at bottom"
]
}
],
"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": "3.8.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}