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@slochower
Created April 4, 2019 21:42
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-04T21:42:41.427547Z",
"start_time": "2019-04-04T21:42:40.604194Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dslochower/data/applications/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"/home/dslochower/data/applications/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from adjustText import adjust_text"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-04T21:42:41.438492Z",
"start_time": "2019-04-04T21:42:41.429413Z"
}
},
"outputs": [],
"source": [
"sns.set()\n",
"# Increase font size and linewidth\n",
"sns.set_context(\"talk\")\n",
"sns.set_style(\"whitegrid\")\n",
"# Use LaTeX, setup to use Helvetica. This can be safely commented to make\n",
"# the installation footprint of running this code smaller -- for example,\n",
"# in Docker.\n",
"mpl.rc(\"text\", usetex=True)\n",
"mpl.rcParams[\"text.latex.preamble\"] = [\n",
" r\"\\usepackage{amsmath}\",\n",
" r\"\\usepackage{helvet}\",\n",
" r\"\\usepackage[EULERGREEK]{sansmath}\",\n",
" r\"\\sansmath\",\n",
" r\"\\renewcommand{\\familydefault}{\\sfdefault}\",\n",
" r\"\\usepackage[T1]{fontenc}\",\n",
" r\"\\usepackage{graphicx}\",\n",
"]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-04T21:42:41.533595Z",
"start_time": "2019-04-04T21:42:41.440220Z"
}
},
"outputs": [],
"source": [
"y = [-3.23699263, -0.54456433, 0.20217487, -1.44078391, -0.78948806,\n",
" -0.81549368, -0.97963177, -2.32024519, 0.34184152, 0.46509836,\n",
" -1.76327222, -1.56498244, -3.02585238, -2.86919078, -3.47999394,\n",
" -3.77758859, -2.5877522 , -1.80491232, -2.526196 , -3.06515993,\n",
" -2.36805878, -2.70040166, -3.91359614, -2.22447965, 0.7640068 ,\n",
" -2.38088152, -0.92465422, -1.19117003, -2.93342067, -2.01116791,\n",
" -3.54750411, -3.82991756, -3.94651576, -1.78754848, -0.84244187,\n",
" -3.01377016, -3.52857263, -4.54753495, -3.43264859, -4.50096442,\n",
" -1.81978513, -2.6241278 , -0.2903559 , -2.31358002, -1.7794558 ,\n",
" -1.16060225, -1.47757417, -1.38101259, -3.02237171, -2.68588094,\n",
" -2.94559638, -3.52893628, -1.86992102, -1.94929421, -2.19472414,\n",
" -1.33931439, -3.38591933, -2.21107682, -1.68876558, -2.82784556,\n",
" -2.78404039, -2.79673503, -3.3122249 , -3.4467254 , -3.15764325,\n",
" -2.48772997, -1.90925866, -3.30145098, -2.67868524, -2.39169231,\n",
" -4.14278251, -2.02767067, -2.71383148, -2.79790091, -1.61335746,\n",
" -2.13741747, -3.30559616, -3.13831503, -2.14559937, -2.86021049,\n",
" -3.54868509, -2.91169486, -2.57786984, -1.54087007, -1.92992343,\n",
" -1.08725733]\n",
"x = [-0.0161027, -0.6169925, -0.3350268, -0.8463236, -0.2649001,\n",
" 0.263536 , -0.5799436, -1.6652319, -1.1174503, 0.3427462,\n",
" -1.8420998, -0.9363596, -2.8243131, -2.1775888, -2.258214 ,\n",
" -3.8064848, -2.8686834, -2.4651664, -2.4879757, -2.988915 ,\n",
" -1.8456243, -3.0724616, -3.1280186, -2.3726023, 0.6530084,\n",
" -1.5620065, -0.622306 , -0.8369253, -3.2377346, -2.4213402,\n",
" -2.4820995, -3.4885414, -2.6058268, -0.1664973, -0.5603354,\n",
" -2.3255117, -3.0258249, -3.9816332, -3.7844925, -4.8114521,\n",
" -0.9196491, -1.0646948, -1.1131432, -1.541888 , -1.5861297,\n",
" -1.0254831, -0.2885203, -1.5484045, -2.7793058, -2.8406313,\n",
" -2.956916 , -3.4659735, -2.155047 , -1.593361 , -1.4957267,\n",
" -0.6940514, -3.3304536, -1.8914391, -1.6630039, -2.233587 ,\n",
" -2.515146 , -2.1678027, -2.9482368, -3.1193589, -2.7309703,\n",
" -1.374944 , -1.0082914, -1.8818131, -2.6820504, -2.0042225,\n",
" -2.9587328, -1.6377112, -2.0194303, -2.7068954, -1.3345921,\n",
" -1.2353552, -2.4949591, -2.6567135, -1.8066403, -1.9879283,\n",
" -3.2668661, -2.2376836, -1.8738583, -0.9246734, -1.4970534,\n",
" -0.8857849]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-04T21:42:41.642566Z",
"start_time": "2019-04-04T21:42:41.535912Z"
}
},
"outputs": [],
"source": [
"x_label = [-1.064695, -2.258214, -3.128019, -3.981633]\n",
"y_label = [-2.624128, -3.479994, -3.913596, -4.547535]\n",
"text = [\"a-pam-p\", \"a-ham-p\", \"a-hpa-p\", \"a-oam-p\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-04T21:43:08.145274Z",
"start_time": "2019-04-04T21:43:07.595880Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb57393d7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, figsize=(6 * 1.2, 6))\n",
"\n",
"ax.errorbar(x, y, c=\"0.5\", fmt=\"o\", markersize=8, markeredgecolor=\"k\", markeredgewidth=0.2)\n",
"texts = []\n",
"\n",
"colors = sns.color_palette(\"husl\")\n",
"\n",
"for index, (i, j, k) in enumerate(zip(x_label, y_label, text)):\n",
" ax.errorbar(i, j, c = colors[index],\n",
" fmt=\"o\", markersize=8, markeredgecolor=\"k\", markeredgewidth=0.2)\n",
" texts.append(plt.text(i, j, k, size=28, color = colors[index], bbox=dict(facecolor=\"w\", alpha=0.8)))\n",
"\n",
"ax.plot([-50, 50], [-50, 50], ls=\"-\", c=\"0.3\", zorder=-1, lw=\"0.5\")\n",
"ax.set_ylim(-8, 2)\n",
"ax.set_xlim(-8, 2)\n",
"\n",
"adjust_text(texts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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