Provides a custom plotting style for matplotlib and other libraries building on it like pandas or seaborn. The plotting style is set to aproximately the styles described in our wiki: https://wiki.cpi.imtek.uni-freiburg.de/ScientificWriting
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140 lines
1.3 MiB
140 lines
1.3 MiB
4 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "aquatic-principle",
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"metadata": {},
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"source": [
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"# Prettyplots: design your own plots in the interactive mode"
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]
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},
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{
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"cell_type": "markdown",
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"id": "industrial-casino",
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"metadata": {},
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"source": [
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"### Load the libraries and mock data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "substantial-underwear",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import matplotlib as mpl\n",
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"import numpy as np\n",
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"\n",
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"with open('test_data.pkl', 'rb') as f:\n",
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" plot_data = pickle.load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw():\n",
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" for ctr, (k,v) in enumerate(plot_data.items()):\n",
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" mean = v\n",
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" sem = np.random.random(size=len(mean))*10 + 50\n",
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" if k == 'LSTM':\n",
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" sem=0\n",
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" plt.plot(mean, label=k)\n",
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" last_line_color = plt.gca().lines[-1].get_color()\n",
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" plt.fill_between(range(len(mean)), mean - sem, mean + sem, alpha=0.5, color=last_line_color)\n",
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"\n",
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" plt.suptitle(\"Example data\")\n",
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" plt.xlabel(\"Frequency [Hz]\")\n",
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" plt.ylabel(\"Concentration [µg/ml]\")"
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]
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},
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{
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"source": [
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"## default styling"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 432x288 with 1 Axes>",
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"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"290.99625pt\" version=\"1.1\" viewBox=\"0 0 388.965625 290.99625\" width=\"388.965625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n <cc:Work>\r\n <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n <dc:date>2021-05-05T15:46:49.769746</dc:date>\r\n <dc:format>image/svg+xml</dc:format>\r\n <dc:creator>\r\n <cc:Agent>\r\n <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\r\n </cc:Agent>\r\n </dc:creator>\r\n </cc:Work>\r\n </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n <g id=\"patch_1\">\r\n <path d=\"M 0 290.99625 \r\nL 388.965625 290.99625 \r\nL 388.965625 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n </g>\r\n <g id=\"axes_1\">\r\n <g id=\"patch_2\">\r\n <path d=\"M 46.965625 253.44 \r\nL 381.765625 253.44 \r\nL 381.765625 36 \r\nL 46.965625 36 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n </g>\r\n <g id=\"PolyCollection_1\">\r\n <defs>\r\n <path d=\"M 62.183807 -84.472089 \r\nL 62.183807 -48.911037 \r\nL 62.335989 -50.576402 \r\nL 62.48817 -49.040532 \r\nL 62.640352 -48.100195 \r\nL 62.792534 -49.673149 \r\nL 62.944716 -49.955466 \r\nL 63.096898 -48.970936 \r\nL 63.24908 -50.688687 \r\nL 63.401261 -50.016673 \r\nL 63.553443 -51.149769 \r\nL 63.705625 -48.249389 \r\nL 63.857807 -48.486394 \r\nL 64.009989 -51.061478 \r\nL 64.16217 -51.137763 \r\nL 64.314352 -50.472145 \r\nL 64.466534 -49.0905 \r\nL 64.618716 -51.747325 \r\nL 64.770898 -49.359771 \r\nL 64.92308 -50.618302 \r\nL 65.075261 -50.378969 \r\nL 65.227443 -50.490296 \r\nL 65.379625 -52.002649 \r\nL 65.531807 -50.523036 \r\nL 65.683989 -51.180823 \r\nL 65.83617 -51.582174 \r\nL 65.988352 -52.762721 \r\nL 66.140534 -52.45355 \r\nL 66.292716 -53.625699 \r\nL 66.444898 -53.182149 \r\nL 66.59708 -51.271557 \r\nL 66.749261 -53.345097 \r\nL 66.901443 -54.097007 \r\nL 67.053625 -52.651053 \r\nL 67.205807 -51.688474 \r\nL 67.357989 -53.261974 \r\nL 67.51017 -51.886069 \r\nL 67.662352 -54.586905 \r\nL 67.814534 -53.673924 \r\nL 67.966716 -53.722764 \r\nL 68.118898 -53.106229 \r\nL 68.27108 -52.996135 \r\nL 68.423261 -53.501633 \r\nL 68.575443 -52.58389 \r\nL 68.727625 -51.43451 \r\nL 68.879807 -52.452685 \r\nL 69.031989 -51.033178 \r\nL 69.18417 -54.303498 \r\nL 69.336352 -52.123907 \r\nL 69.488534 -52.734114 \r\nL 69.640716 -51.456559 \r\nL 69.792898 -51.252482 \r\nL 69.94508 -52.311352 \r\nL 70.097261 -51.965828 \r\nL 70.249443 -53.456863 \r\nL 70.401625 -53.316468 \r\nL 70.553807 -52.659784 \r\nL 70.705989 -52.910907 \r\nL 70.85817 -54.724414 \r\nL 71.010352 -52.460953 \r\nL 71.162534 -52.70834 \r\nL 71.314716 -52.296001 \r\nL 71.466898 -54.560385 \r\nL 71.61908 -52.691051 \r\nL 71.771261 -53.092313 \r\nL 71.923443 -55.296461 \r\nL 72.075625 -52.868177 \r\nL 72.227807 -55.211835 \r\nL 72.379989 -53.130397 \r\nL 72.53217 -52.69706 \r\nL 72.684352 -52.072744 \r\nL 72.836534 -54.422415 \r\nL 72.988716 -53.559348 \r\nL 73.140898 -54.490108 \r\nL 73.29308 -52.20819 \r\nL 73.445261 -52.708161 \r\nL 73.597443 -54.179427 \r\nL 73.749625 -53.067391 \r\nL 73.901807 -54.296435 \r\nL 74.053989 -54.942343 \r\nL 74.20617 -52.715177 \r\nL 74.358352 -53.192047 \r\nL 74.510534 -53.850153 \r\nL 74.662716 -54.155041 \r\nL 74.814898 -55.170292 \r\nL 74.96708 -52.922254 \r\nL 75.119261 -52.675002 \r\nL 75.271443 -52.773536 \r\nL 75.423625 -54.710567 \r\nL 75.575807 -54.354431 \r\nL 75.727989 -55.096675 \r\nL 75.88017 -52.290223 \r\nL 76.032352 -52.841802 \r\nL 76.184534 -53.798164 \r\nL 76.336716 -53.559
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"image/png": "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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"source": [
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"draw()"
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]
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},
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{
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"source": [
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"## CPI styling"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 1400x800 with 1 Axes>",
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}
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],
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},
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