{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ternary Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Make ternary plots in Plotly and Python!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Learn about API authentication here: https://plotly.com/python/getting-started\n",
"
Find your api_key here: https://plotly.com/settings/api"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'1.9.9'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import plotly\n",
"plotly.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Basic Tenary Plot with Markers"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from plotly.offline import init_notebook_mode, iplot, plot\n",
"import plotly.graph_objs as go\n",
"init_notebook_mode()\n",
"\n",
"rawData = [\n",
" {'journalist':75,'developer':25,'designer':0,'label':'point 1'},\n",
" {'journalist':70,'developer':10,'designer':20,'label':'point 2'},\n",
" {'journalist':75,'developer':20,'designer':5,'label':'point 3'},\n",
" {'journalist':5,'developer':60,'designer':35,'label':'point 4'},\n",
" {'journalist':10,'developer':80,'designer':10,'label':'point 5'},\n",
" {'journalist':10,'developer':90,'designer':0,'label':'point 6'},\n",
" {'journalist':20,'developer':70,'designer':10,'label':'point 7'},\n",
" {'journalist':10,'developer':20,'designer':70,'label':'point 8'},\n",
" {'journalist':15,'developer':5,'designer':80,'label':'point 9'},\n",
" {'journalist':10,'developer':10,'designer':80,'label':'point 10'},\n",
" {'journalist':20,'developer':10,'designer':70,'label':'point 11'},\n",
"];\n",
"\n",
"def makeAxis(title, tickangle): \n",
" return {\n",
" 'title': title,\n",
" 'titlefont': { 'size': 20 },\n",
" 'tickangle': tickangle,\n",
" 'tickfont': { 'size': 15 },\n",
" 'tickcolor': 'rgba(0,0,0,0)',\n",
" 'ticklen': 5,\n",
" 'showline': True,\n",
" 'showgrid': True\n",
" }\n",
"\n",
"data = [{ \n",
" 'type': 'scatterternary',\n",
" 'mode': 'markers',\n",
" 'a': [i for i in map(lambda x: x['journalist'], rawData)],\n",
" 'b': [i for i in map(lambda x: x['developer'], rawData)],\n",
" 'c': [i for i in map(lambda x: x['designer'], rawData)],\n",
" 'text': [i for i in map(lambda x: x['label'], rawData)],\n",
" 'marker': {\n",
" 'symbol': 100,\n",
" 'color': '#DB7365',\n",
" 'size': 14,\n",
" 'line': { 'width': 2 }\n",
" },\n",
" }]\n",
"\n",
"layout = {\n",
" 'ternary': {\n",
" 'sum': 100,\n",
" 'aaxis': makeAxis('Journalist', 0),\n",
" 'baxis': makeAxis('
Developer', 45),\n",
" 'caxis': makeAxis('
Designer', -45)\n",
" },\n",
" 'annotations': [{\n",
" 'showarrow': False,\n",
" 'text': 'Simple Ternary Plot with Markers',\n",
" 'x': 0.5,\n",
" 'y': 1.3,\n",
" 'font': {'size': 15}\n",
" }]\n",
"}\n",
"\n",
"fig = {'data': data, 'layout': layout}\n",
"iplot(fig, validate=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Soil Types Ternary Plot"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"from plotly.offline import init_notebook_mode, iplot\n",
"import plotly.graph_objs as go\n",
"import urllib2 # note this will only work in 2.7 now, will upload six package later for compatibility\n",
"\n",
"init_notebook_mode()\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url = 'https://gist.githubusercontent.com/davenquinn/988167471993bc2ece29/raw/f38d9cb3dd86e315e237fde5d65e185c39c931c2/data.json'\n",
"req = urllib2.Request(url)\n",
"opener = urllib2.build_opener()\n",
"f = opener.open(req)\n",
"json = json.loads(f.read())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_data(json):\n",
" \n",
" return ([dict(name=k, type='scatterternary', mode='lines', \n",
" a=map(lambda x: x['clay'],json[k]), \n",
" b=map(lambda x: x['sand'],json[k]),\n",
" c=map(lambda x: x['silt'],json[k]), \n",
" line={'color': '#c00'}) for k in json])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = create_data(json)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def makeAxis(title): \n",
" return {\n",
" 'title': title,\n",
" 'ticksuffix': '%',\n",
" 'min': 0.01,\n",
" 'linewidth': 2,\n",
" 'ticks': 'outside',\n",
" 'ticklen': 8,\n",
" 'showgrid': True,\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"layout = {\n",
" 'ternary': {\n",
" 'sum': 100,\n",
" 'aaxis': makeAxis('Clay'),\n",
" 'baxis': makeAxis('Sand'),\n",
" 'caxis': makeAxis('Silt')\n",
" },\n",
" 'showlegend': False,\n",
" 'width': 700,\n",
" 'annotations': [{\n",
" 'showarrow': False,\n",
" 'text': 'Replica of Daven Quinn\\'s block',\n",
" 'x': 0.50,\n",
" 'y': 1.3\n",
" }]\n",
" }\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig = {'data': data, 'layout': layout}"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iplot(fig, validate=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already up-to-date: publisher in /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages\r\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning:\n",
"\n",
"Did you \"Save\" this notebook before running this command? Remember to save, always save.\n",
"\n"
]
}
],
"source": [
"# from IPython.display import display, HTML\n",
"\n",
"# display(HTML(''))\n",
"# display(HTML(''))\n",
"\n",
"# ! pip install publisher --upgrade\n",
"# import publisher\n",
"# publisher.publish(\n",
"# 'ternary.ipynb', 'python/ternary-plots/', 'Python Ternary Plots | plotly',\n",
"# 'How to make Ternary plots in Python with Plotly.',\n",
"# name = 'Ternary',\n",
"# thumbnail='thumbnail/ternary.jpg', language='python',\n",
"# page_type='example_index', has_thumbnail='true', display_as='scientific', order=32) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}