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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sat Jun 15 20:56:01 PDT 2019\r\n"
]
}
],
"source": [
"import numpy as np, matplotlib.pyplot as plt, pandas as pd\n",
"pd.set_option('display.max_rows', 8)\n",
"!date\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. Pick a seed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(123456)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Pick a visualization package"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'altair'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.choice('matplotlib altair seaborn'.split())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Pick a published visualization"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Health-Related Quality of Life Scores for Patients with Pulmonary Arterial Hypertension. Shown are mean (SE) scores on each domain and summary component score of the SF36. Numerically higher scores indicate better health-related quality of life. All domain and summary scores are significantly lower than the US population normal score of 50 (p < 0.001 for each).\r\n"
]
},
{
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests, IPython.display\n",
"r = requests.get('http://viziometrics.org/api/pmc/image/search/',\n",
" params=dict(keywords='quality', number=100, qrandom=True))\n",
"results = r.json()\n",
"for r in results:\n",
" if r['class_name'] == 'visualization':\n",
" break\n",
"\n",
"caption = r['caption']\n",
"s3_key = r['img_loc']\n",
"\n",
"r = requests.get('http://s3-us-west-2.amazonaws.com/escience.washington.edu.viziometrics/{}'.format(s3_key))\n",
"print(caption)\n",
"IPython.display.Image(data=r.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Reproduce visualization with viz package"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"data = pd.DataFrame(index=['Physical Function', 'Role-Physical', 'Bodily Pain', 'General Health',\n",
" 'Vitality', 'Social Function', 'Role-Emotional', 'Mental Health',\n",
" 'Physical Component', 'Mental Component'],\n",
" columns=['score_type', 'val', 'ub'])\n",
"\n",
"data['score_type'] = 'Domain Scores'\n",
"data.loc[['Physical Component', 'Mental Component'], 'score_type'] = 'Summary Scores'\n",
"\n",
"data['val'] = [32, 36, 46, 36, 44, 40, 35, 45, 36, 44]\n",
"data['ub'] = [33, 37, 47, 37, 45, 41, 36, 46, 37, 45]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RendererRegistry.enable('notebook')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import altair as alt\n",
"alt.renderers.enable('notebook')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>score_type</th>\n",
" <th>val</th>\n",
" <th>ub</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Physical Function</td>\n",
" <td>Domain Scores</td>\n",
" <td>32</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Role-Physical</td>\n",
" <td>Domain Scores</td>\n",
" <td>36</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Bodily Pain</td>\n",
" <td>Domain Scores</td>\n",
" <td>46</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>General Health</td>\n",
" <td>Domain Scores</td>\n",
" <td>36</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Role-Emotional</td>\n",
" <td>Domain Scores</td>\n",
" <td>35</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Mental Health</td>\n",
" <td>Domain Scores</td>\n",
" <td>45</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Physical Component</td>\n",
" <td>Summary Scores</td>\n",
" <td>36</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Mental Component</td>\n",
" <td>Summary Scores</td>\n",
" <td>44</td>\n",
" <td>45</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" index score_type val ub\n",
"0 Physical Function Domain Scores 32 33\n",
"1 Role-Physical Domain Scores 36 37\n",
"2 Bodily Pain Domain Scores 46 47\n",
"3 General Health Domain Scores 36 37\n",
".. ... ... ... ..\n",
"6 Role-Emotional Domain Scores 35 36\n",
"7 Mental Health Domain Scores 45 46\n",
"8 Physical Component Summary Scores 36 37\n",
"9 Mental Component Summary Scores 44 45\n",
"\n",
"[10 rows x 4 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"data\": {\"name\": \"data-2965dadbac021e16d6a8b7d0b186dea2\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"index\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"val\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.3.0.json\", \"datasets\": {\"data-2965dadbac021e16d6a8b7d0b186dea2\": [{\"index\": \"Physical Function\", \"score_type\": \"Domain Scores\", \"val\": 32, \"ub\": 33}, {\"index\": \"Role-Physical\", \"score_type\": \"Domain Scores\", \"val\": 36, \"ub\": 37}, {\"index\": \"Bodily Pain\", \"score_type\": \"Domain Scores\", \"val\": 46, \"ub\": 47}, {\"index\": \"General Health\", \"score_type\": \"Domain Scores\", \"val\": 36, \"ub\": 37}, {\"index\": \"Vitality\", \"score_type\": \"Domain Scores\", \"val\": 44, \"ub\": 45}, {\"index\": \"Social Function\", \"score_type\": \"Domain Scores\", \"val\": 40, \"ub\": 41}, {\"index\": \"Role-Emotional\", \"score_type\": \"Domain Scores\", \"val\": 35, \"ub\": 36}, {\"index\": \"Mental Health\", \"score_type\": \"Domain Scores\", \"val\": 45, \"ub\": 46}, {\"index\": \"Physical Component\", \"score_type\": \"Summary Scores\", \"val\": 36, \"ub\": 37}, {\"index\": \"Mental Component\", \"score_type\": \"Summary Scores\", \"val\": 44, \"ub\": 45}]}};\n",
"var opt = {};\n",
"var type = \"vega-lite\";\n",
"var id = \"dbd57825-ffc4-4e12-a23d-9ad37396c2f5\";\n",
"\n",
"var output_area = this;\n",
"\n",
"require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
" var target = document.createElement(\"div\");\n",
" target.id = id;\n",
" target.className = \"vega-embed\";\n",
"\n",
" var style = document.createElement(\"style\");\n",
" style.textContent = [\n",
" \".vega-embed .error p {\",\n",
" \" color: firebrick;\",\n",
" \" font-size: 14px;\",\n",
" \"}\",\n",
" ].join(\"\\\\n\");\n",
"\n",
" // element is a jQuery wrapped DOM element inside the output area\n",
" // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
" // IPython.display.html#IPython.display.Javascript.__init__\n",
" element[0].appendChild(target);\n",
" element[0].appendChild(style);\n",
"\n",
" vega.render(\"#\" + id, spec, type, opt, output_area);\n",
"}, function (err) {\n",
" if (err.requireType !== \"scripterror\") {\n",
" throw(err);\n",
" }\n",
"});\n"
],
"text/plain": [
"<vega.vegalite.VegaLite at 0x7fd8760d9320>"
]
},
"metadata": {
"jupyter-vega": "#dbd57825-ffc4-4e12-a23d-9ad37396c2f5"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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"
},
"metadata": {
"jupyter-vega": "#dbd57825-ffc4-4e12-a23d-9ad37396c2f5"
},
"output_type": "display_data"
}
],
"source": [
"alt.Chart(data.reset_index()).mark_bar().encode(x='index', y='val')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"layer\": [{\"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"index\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"val\"}}}, {\"mark\": \"errorbar\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"index\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"ub\"}}}], \"data\": {\"name\": \"data-2965dadbac021e16d6a8b7d0b186dea2\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.3.0.json\", \"datasets\": {\"data-2965dadbac021e16d6a8b7d0b186dea2\": [{\"index\": \"Physical Function\", \"score_type\": \"Domain Scores\", \"val\": 32, \"ub\": 33}, {\"index\": \"Role-Physical\", \"score_type\": \"Domain Scores\", \"val\": 36, \"ub\": 37}, {\"index\": \"Bodily Pain\", \"score_type\": \"Domain Scores\", \"val\": 46, \"ub\": 47}, {\"index\": \"General Health\", \"score_type\": \"Domain Scores\", \"val\": 36, \"ub\": 37}, {\"index\": \"Vitality\", \"score_type\": \"Domain Scores\", \"val\": 44, \"ub\": 45}, {\"index\": \"Social Function\", \"score_type\": \"Domain Scores\", \"val\": 40, \"ub\": 41}, {\"index\": \"Role-Emotional\", \"score_type\": \"Domain Scores\", \"val\": 35, \"ub\": 36}, {\"index\": \"Mental Health\", \"score_type\": \"Domain Scores\", \"val\": 45, \"ub\": 46}, {\"index\": \"Physical Component\", \"score_type\": \"Summary Scores\", \"val\": 36, \"ub\": 37}, {\"index\": \"Mental Component\", \"score_type\": \"Summary Scores\", \"val\": 44, \"ub\": 45}]}};\n",
"var opt = {};\n",
"var type = \"vega-lite\";\n",
"var id = \"a8dd6910-2a09-4522-869e-9399c73fcc2c\";\n",
"\n",
"var output_area = this;\n",
"\n",
"require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
" var target = document.createElement(\"div\");\n",
" target.id = id;\n",
" target.className = \"vega-embed\";\n",
"\n",
" var style = document.createElement(\"style\");\n",
" style.textContent = [\n",
" \".vega-embed .error p {\",\n",
" \" color: firebrick;\",\n",
" \" font-size: 14px;\",\n",
" \"}\",\n",
" ].join(\"\\\\n\");\n",
"\n",
" // element is a jQuery wrapped DOM element inside the output area\n",
" // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
" // IPython.display.html#IPython.display.Javascript.__init__\n",
" element[0].appendChild(target);\n",
" element[0].appendChild(style);\n",
"\n",
" vega.render(\"#\" + id, spec, type, opt, output_area);\n",
"}, function (err) {\n",
" if (err.requireType !== \"scripterror\") {\n",
" throw(err);\n",
" }\n",
"});\n"
],
"text/plain": [
"<vega.vegalite.VegaLite at 0x7fd8760d2dd8>"
]
},
"metadata": {
"jupyter-vega": "#a8dd6910-2a09-4522-869e-9399c73fcc2c"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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"
},
"metadata": {
"jupyter-vega": "#a8dd6910-2a09-4522-869e-9399c73fcc2c"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the base chart\n",
"base = alt.Chart(data.reset_index())\n",
"\n",
"bars = base.mark_bar().encode(x='index', y='val')\n",
"\n",
"# generate the error bars\n",
"errorbars = base.mark_errorbar().encode(\n",
" x=\"index\",\n",
" y=\"ub\",\n",
")\n",
"\n",
"bars + errorbars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm stuck, and I didn't even start trying to split into the two panels, or adding the reference line. I also didn't try to shift the baseline..."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(9.0, 15, 'Summary\\nScores')"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# too bad I didn't get mpl for the viz package...\n",
"\n",
"xx = np.arange(len(data)) + .5*(data.score_type == 'Summary Scores')\n",
"\n",
"plt.bar(xx, data.val, yerr=[np.zeros_like(data.ub), data.ub-data.val],\n",
" color='k', capsize=5)\n",
"plt.xticks(xx, data.index, rotation=90)\n",
"plt.axis(ymin=30, ymax=54)\n",
"plt.axhline(50, color='grey', linestyle='dashed')\n",
"\n",
"plt.yticks([30, 40, 50], ['Worse QOL', 40, 'US pop norm → 50',])\n",
"plt.text(-1, 50, 'Better QOL\\n', ha='right', va='bottom')\n",
"plt.text(.5*(xx[3]+xx[4]), 15, 'Domain Scores', ha='center', va='top', fontsize='xx-large')\n",
"plt.text(.5*(xx[-1]+xx[-2]), 15, 'Summary\\nScores', ha='center', va='top', fontsize='xx-large')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (abie-dev)",
"language": "python",
"name": "python_abie_dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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