{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Good Software - analysis notebook\n",
"## Reproducing all panels from Figure 1\n",
"\n",
"<br />\n",
"This notebook contains all Python code necessary to reproduce all panels from the Figure 1 of [our paper](./), starting from the raw dataset. If you are interested in reproducing our results, you can use Binder to get started immediately (no requirements). For more information, visit [our wiki page](./).\n",
"\n",
"**Github:** https://github.com/smangul1/good.software/wiki\n",
"\n",
"**Manuscript:** https://www.biorxiv.org/content/early/2018/10/25/452532\n",
"\n",
"**Twitter thread:** https://twitter.com/serghei_mangul/status/1055561147431043072"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pylab as pl\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scikit_posthocs as sph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Some definitions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Legend for link statuses\n",
"legendStatus = { -1 : 'Time out', 1 : 'Accessible', 3 : 'Redirected', 4 : 'Broken' }\n",
"\n",
"# Colors for each status\n",
"colors = { -1 : (0.96,0.65,0.51), 1 : (0.57,0.77,0.87), 3 : (0.02,0.44,0.69), 4 : (0.8,0.,0.13) }\n",
"\n",
"# Scheme 2:\n",
"colors2 = { 'bg_unreach' : (1.0,0.8,0.6), \n",
" 'fg_unreach' : (0.8,0.,0.), \n",
" 'bg_accessb' : (0.7,1.0,0.7), \n",
" 'fg_accessb' : (0.0,0.0,0.8)\n",
" }\n",
"\n",
"# Will only consider these years\n",
"initialYear = 2005"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Util functions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def simpleaxis(ax):\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" ax.get_xaxis().tick_bottom()\n",
" ax.get_yaxis().tick_left()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing and parsing data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>type</th>\n",
" <th>journal</th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" <th>link</th>\n",
" <th>code</th>\n",
" <th>flag.uniqueness</th>\n",
" <th>newtest</th>\n",
" <th>oldcode</th>\n",
" <th>concated</th>\n",
" <th>httpsthing</th>\n",
" <th>github.com</th>\n",
" <th>0.690657769</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>15268767.0</td>\n",
" <td>2004</td>\n",
" <td>(http://discover.nci.nih.gov)</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15268767(http://discover.nci.nih.gov)</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>15268767.0</td>\n",
" <td>2004</td>\n",
" <td>(http://dtp.nci.nih.gov)</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15268767(http://dtp.nci.nih.gov)</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>21398627.0</td>\n",
" <td>2011</td>\n",
" <td>(http://globin.cse.psu.edu/html/docs/sim4.html</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>21398627(http://globin.cse.psu.edu/html/docs/s...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>body</td>\n",
" <td>BMC_Genomics</td>\n",
" <td>11472633.0</td>\n",
" <td>2001</td>\n",
" <td>(http://rgd.mcw.edu/RHMAPSERVER/)</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>11472633(http://rgd.mcw.edu/RHMAPSERVER/)</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>19426459.0</td>\n",
" <td>2009</td>\n",
" <td>(http://www.eumodic.org/documents/EMPReSSslim%...</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>19426459(http://www.eumodic.org/documents/EMPR...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type journal id year \\\n",
"0 body BMC_Bioinformatics 15268767.0 2004 \n",
"1 body BMC_Bioinformatics 15268767.0 2004 \n",
"2 body Nucleic_Acids_Res 21398627.0 2011 \n",
"3 body BMC_Genomics 11472633.0 2001 \n",
"4 body BMC_Bioinformatics 19426459.0 2009 \n",
"\n",
" link code flag.uniqueness \\\n",
"0 (http://discover.nci.nih.gov) -1 0 \n",
"1 (http://dtp.nci.nih.gov) -1 0 \n",
"2 (http://globin.cse.psu.edu/html/docs/sim4.html -1 0 \n",
"3 (http://rgd.mcw.edu/RHMAPSERVER/) -1 0 \n",
"4 (http://www.eumodic.org/documents/EMPReSSslim%... -1 0 \n",
"\n",
" newtest oldcode concated \\\n",
"0 http -1.0 15268767(http://discover.nci.nih.gov) \n",
"1 http -1.0 15268767(http://dtp.nci.nih.gov) \n",
"2 http -1.0 21398627(http://globin.cse.psu.edu/html/docs/s... \n",
"3 http -1.0 11472633(http://rgd.mcw.edu/RHMAPSERVER/) \n",
"4 http -1.0 19426459(http://www.eumodic.org/documents/EMPR... \n",
"\n",
" httpsthing github.com 0.690657769 \n",
"0 0 False NaN \n",
"1 0 False NaN \n",
"2 0 False NaN \n",
"3 0 False NaN \n",
"4 0 False NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"links = pd.read_csv('eval.links.bulk.csv')\n",
"links.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 48392 entries, 0 to 48391\n",
"Data columns (total 13 columns):\n",
"type 48392 non-null object\n",
"journal 48392 non-null object\n",
"id 48093 non-null float64\n",
"year 48392 non-null int64\n",
"link 48392 non-null object\n",
"code 48392 non-null int64\n",
"flag.uniqueness 48392 non-null int64\n",
"newtest 30019 non-null object\n",
"oldcode 30019 non-null float64\n",
"concated 48392 non-null object\n",
"httpsthing 48392 non-null int64\n",
"github.com 48392 non-null bool\n",
"0.690657769 0 non-null float64\n",
"dtypes: bool(1), float64(3), int64(4), object(5)\n",
"memory usage: 4.5+ MB\n"
]
}
],
"source": [
"links.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parsing from code into human-readable status:\n",
"\n",
"* -1 : time out\n",
"* 0..299 : good\n",
"* 300..399 : redirected\n",
"* \\>= 400 : not found\n",
"\n",
"To make it easier, let's map these codes to -1 for time out, 1 for accessible, 3 for redirected, and 4 for not found."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"links = links[ links.year >= initialYear ]\n",
"links['status'] = ( links['code'] // 100 )\n",
"links.loc[ links['status'] > 4, 'status' ] = 4\n",
"links.loc[ (links['status'] >= 0) & (links['status'] < 3), 'status' ] = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the histogram of statuses"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"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": [
"statuses = np.array( links.status )\n",
"\n",
"\n",
"f, ax = pl.subplots(1, 1)\n",
"ax.hist( statuses, np.arange(-1.5,6,1), edgecolor=(0.2,0.2,0.2), lw=3, \n",
" color=(0.2,0.6,1.0) )\n",
"\n",
"\n",
"ax.set_ylabel('Count')\n",
"ax.set_xticks([-1, 1, 3, 4])\n",
"ax.set_xticklabels( ['Time out', 'Accessible', 'Redirected', 'Not found'], rotation=-45 )\n",
"\n",
"pl.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5259 21953 13106 6957] [-1.2 -0.8 1.8 3.8 4.8]\n",
"0.11124272871496563\n",
"0.46436805922792174\n",
"0.27722897937599156\n",
"0.1471602326811211\n"
]
}
],
"source": [
"count, bin_values = np.histogram(statuses, bins = [-1.2,-0.8,1.8,3.8,4.8])\n",
"\n",
"print(count, bin_values)\n",
"total = float(count.sum())\n",
"\n",
"for j in range(4):\n",
" print(count[j]/total)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filtering out repeated links"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To avoid using repeated links, let's create another dataframe."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(36702, 14)\n"
]
},
{
"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>type</th>\n",
" <th>journal</th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" <th>link</th>\n",
" <th>code</th>\n",
" <th>newtest</th>\n",
" <th>oldcode</th>\n",
" <th>concated</th>\n",
" <th>httpsthing</th>\n",
" <th>github.com</th>\n",
" <th>0.690657769</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>21398627.0</td>\n",
" <td>2011</td>\n",
" <td>(http://globin.cse.psu.edu/html/docs/sim4.html</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>21398627(http://globin.cse.psu.edu/html/docs/s...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>19426459.0</td>\n",
" <td>2009</td>\n",
" <td>(http://www.eumodic.org/documents/EMPReSSslim%...</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>19426459(http://www.eumodic.org/documents/EMPR...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>16026597.0</td>\n",
" <td>2005</td>\n",
" <td>(http://www.fda.gov/cder/genomics/</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>16026597(http://www.fda.gov/cder/genomics/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>16026597.0</td>\n",
" <td>2005</td>\n",
" <td>(http://www.fda.gov/oc/initiatives/criticalpath/</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>16026597(http://www.fda.gov/oc/initiatives/cri...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>body</td>\n",
" <td>PLoS_Comput_Biol</td>\n",
" <td>22022247.0</td>\n",
" <td>2011</td>\n",
" <td>(http://www.ks.uiuc.edu/Development/MDTools/io...</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>22022247(http://www.ks.uiuc.edu/Development/MD...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type journal id year \\\n",
"2 body Nucleic_Acids_Res 21398627.0 2011 \n",
"4 body BMC_Bioinformatics 19426459.0 2009 \n",
"5 body BMC_Bioinformatics 16026597.0 2005 \n",
"6 body BMC_Bioinformatics 16026597.0 2005 \n",
"7 body PLoS_Comput_Biol 22022247.0 2011 \n",
"\n",
" link code newtest oldcode \\\n",
"2 (http://globin.cse.psu.edu/html/docs/sim4.html -1 http -1.0 \n",
"4 (http://www.eumodic.org/documents/EMPReSSslim%... -1 http -1.0 \n",
"5 (http://www.fda.gov/cder/genomics/ -1 http -1.0 \n",
"6 (http://www.fda.gov/oc/initiatives/criticalpath/ -1 http -1.0 \n",
"7 (http://www.ks.uiuc.edu/Development/MDTools/io... -1 http -1.0 \n",
"\n",
" concated httpsthing github.com \\\n",
"2 21398627(http://globin.cse.psu.edu/html/docs/s... 0 False \n",
"4 19426459(http://www.eumodic.org/documents/EMPR... 0 False \n",
"5 16026597(http://www.fda.gov/cder/genomics/ 0 False \n",
"6 16026597(http://www.fda.gov/oc/initiatives/cri... 0 False \n",
"7 22022247(http://www.ks.uiuc.edu/Development/MD... 0 False \n",
"\n",
" 0.690657769 status \n",
"2 NaN -1 \n",
"4 NaN -1 \n",
"5 NaN -1 \n",
"6 NaN -1 \n",
"7 NaN -1 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniqueLinks = links[ links['flag.uniqueness'] == 0 ]\n",
"print(uniqueLinks.shape)\n",
"uniqueLinks = uniqueLinks.drop(columns=['flag.uniqueness'])\n",
"uniqueLinks.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>journal</th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" <th>link</th>\n",
" <th>code</th>\n",
" <th>newtest</th>\n",
" <th>oldcode</th>\n",
" <th>concated</th>\n",
" <th>httpsthing</th>\n",
" <th>github.com</th>\n",
" <th>0.690657769</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6387</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608157.0</td>\n",
" <td>2005</td>\n",
" <td>www.human-siRNA-database.net</td>\n",
" <td>301</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15608157www.human-siRNA-database.net</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22888</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608157.0</td>\n",
" <td>2005</td>\n",
" <td>http://www.human-siRNA-database.net</td>\n",
" <td>404</td>\n",
" <td>http</td>\n",
" <td>301.0</td>\n",
" <td>15608157http://www.human-siRNA-database.net</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1890</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608160.0</td>\n",
" <td>2005</td>\n",
" <td>http://infernal.wustl.edu/</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15608160http://infernal.wustl.edu/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2814</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608160.0</td>\n",
" <td>2005</td>\n",
" <td>http://rfam.wustl.edu/</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15608160http://rfam.wustl.edu/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17798</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608160.0</td>\n",
" <td>2005</td>\n",
" <td>http://www.sanger.ac.uk/Software/Rfam/</td>\n",
" <td>301</td>\n",
" <td>http</td>\n",
" <td>301.0</td>\n",
" <td>15608160http://www.sanger.ac.uk/Software/Rfam/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>341</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608162.0</td>\n",
" <td>2005</td>\n",
" <td>http://aosearch.cgb.ki.se</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15608162http://aosearch.cgb.ki.se</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23661</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608162.0</td>\n",
" <td>2005</td>\n",
" <td>http://siRNA.cgb.ki.se</td>\n",
" <td>503</td>\n",
" <td>http</td>\n",
" <td>301.0</td>\n",
" <td>15608162http://siRNA.cgb.ki.se</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>307</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608164.0</td>\n",
" <td>2005</td>\n",
" <td>http://alpha.protres.ru/trnadbase</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15608164http://alpha.protres.ru/trnadbase</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43622</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608164.0</td>\n",
" <td>2005</td>\n",
" <td>http://www.tRNA.uni-bayreuth.de</td>\n",
" <td>200</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15608164http://www.tRNA.uni-bayreuth.de</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12125</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608166.0</td>\n",
" <td>2005</td>\n",
" <td>http://redpoll.pharmacy.ualberta.ca/CCDB</td>\n",
" <td>301</td>\n",
" <td>http</td>\n",
" <td>301.0</td>\n",
" <td>15608166http://redpoll.pharmacy.ualberta.ca/CCDB</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14188</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608166.0</td>\n",
" <td>2005</td>\n",
" <td>http://www.cs.ualberta.ca/~bioinfo/PA/</td>\n",
" <td>301</td>\n",
" <td>http</td>\n",
" <td>301.0</td>\n",
" <td>15608166http://www.cs.ualberta.ca/~bioinfo/PA/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14189</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608166.0</td>\n",
" <td>2005</td>\n",
" <td>http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB</td>\n",
" <td>301</td>\n",
" <td>http</td>\n",
" <td>301.0</td>\n",
" <td>15608166http://www.cs.ualberta.ca/~bioinfo/PA/...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36369</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608172.0</td>\n",
" <td>2005</td>\n",
" <td>http://otl.stanford.edu/</td>\n",
" <td>200</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15608172http://otl.stanford.edu/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46290</th>\n",
" <td>abstract</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608172.0</td>\n",
" <td>2005</td>\n",
" <td>http://motif.stanford.edu/eblocks/</td>\n",
" <td>404</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15608172http://motif.stanford.edu/eblocks/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1326</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>15608174.0</td>\n",
" <td>2005</td>\n",
" <td>http://ekhidna.biocenter.helsinki.fi:8080/down...</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>15608174http://ekhidna.biocenter.helsinki.fi:8...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type journal id year \\\n",
"6387 body Nucleic_Acids_Res 15608157.0 2005 \n",
"22888 abstract Nucleic_Acids_Res 15608157.0 2005 \n",
"1890 body Nucleic_Acids_Res 15608160.0 2005 \n",
"2814 abstract Nucleic_Acids_Res 15608160.0 2005 \n",
"17798 abstract Nucleic_Acids_Res 15608160.0 2005 \n",
"341 body Nucleic_Acids_Res 15608162.0 2005 \n",
"23661 abstract Nucleic_Acids_Res 15608162.0 2005 \n",
"307 abstract Nucleic_Acids_Res 15608164.0 2005 \n",
"43622 abstract Nucleic_Acids_Res 15608164.0 2005 \n",
"12125 body Nucleic_Acids_Res 15608166.0 2005 \n",
"14188 body Nucleic_Acids_Res 15608166.0 2005 \n",
"14189 abstract Nucleic_Acids_Res 15608166.0 2005 \n",
"36369 abstract Nucleic_Acids_Res 15608172.0 2005 \n",
"46290 abstract Nucleic_Acids_Res 15608172.0 2005 \n",
"1326 body Nucleic_Acids_Res 15608174.0 2005 \n",
"\n",
" link code newtest \\\n",
"6387 www.human-siRNA-database.net 301 http \n",
"22888 http://www.human-siRNA-database.net 404 http \n",
"1890 http://infernal.wustl.edu/ -1 http \n",
"2814 http://rfam.wustl.edu/ -1 http \n",
"17798 http://www.sanger.ac.uk/Software/Rfam/ 301 http \n",
"341 http://aosearch.cgb.ki.se -1 http \n",
"23661 http://siRNA.cgb.ki.se 503 http \n",
"307 http://alpha.protres.ru/trnadbase -1 http \n",
"43622 http://www.tRNA.uni-bayreuth.de 200 NaN \n",
"12125 http://redpoll.pharmacy.ualberta.ca/CCDB 301 http \n",
"14188 http://www.cs.ualberta.ca/~bioinfo/PA/ 301 http \n",
"14189 http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB 301 http \n",
"36369 http://otl.stanford.edu/ 200 NaN \n",
"46290 http://motif.stanford.edu/eblocks/ 404 NaN \n",
"1326 http://ekhidna.biocenter.helsinki.fi:8080/down... -1 http \n",
"\n",
" oldcode concated httpsthing \\\n",
"6387 -1.0 15608157www.human-siRNA-database.net 0 \n",
"22888 301.0 15608157http://www.human-siRNA-database.net 0 \n",
"1890 -1.0 15608160http://infernal.wustl.edu/ 0 \n",
"2814 -1.0 15608160http://rfam.wustl.edu/ 0 \n",
"17798 301.0 15608160http://www.sanger.ac.uk/Software/Rfam/ 0 \n",
"341 -1.0 15608162http://aosearch.cgb.ki.se 0 \n",
"23661 301.0 15608162http://siRNA.cgb.ki.se 0 \n",
"307 -1.0 15608164http://alpha.protres.ru/trnadbase 0 \n",
"43622 NaN 15608164http://www.tRNA.uni-bayreuth.de 0 \n",
"12125 301.0 15608166http://redpoll.pharmacy.ualberta.ca/CCDB 0 \n",
"14188 301.0 15608166http://www.cs.ualberta.ca/~bioinfo/PA/ 0 \n",
"14189 301.0 15608166http://www.cs.ualberta.ca/~bioinfo/PA/... 0 \n",
"36369 NaN 15608172http://otl.stanford.edu/ 0 \n",
"46290 NaN 15608172http://motif.stanford.edu/eblocks/ 0 \n",
"1326 -1.0 15608174http://ekhidna.biocenter.helsinki.fi:8... 0 \n",
"\n",
" github.com 0.690657769 status \n",
"6387 False NaN 3 \n",
"22888 False NaN 4 \n",
"1890 False NaN -1 \n",
"2814 False NaN -1 \n",
"17798 False NaN 3 \n",
"341 False NaN -1 \n",
"23661 False NaN 4 \n",
"307 False NaN -1 \n",
"43622 False NaN 1 \n",
"12125 False NaN 3 \n",
"14188 False NaN 3 \n",
"14189 False NaN 3 \n",
"36369 False NaN 1 \n",
"46290 False NaN 4 \n",
"1326 False NaN -1 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"repeated_data = pd.concat(g for _, g in uniqueLinks.groupby(\"id\") if len(g) > 1)\n",
"repeated_data.head(15)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 36702 entries, 2 to 48391\n",
"Data columns (total 13 columns):\n",
"type 36702 non-null object\n",
"journal 36702 non-null object\n",
"id 36536 non-null float64\n",
"year 36702 non-null int64\n",
"link 36702 non-null object\n",
"code 36702 non-null int64\n",
"newtest 22872 non-null object\n",
"oldcode 22872 non-null float64\n",
"concated 36702 non-null object\n",
"httpsthing 36702 non-null int64\n",
"github.com 36702 non-null bool\n",
"0.690657769 0 non-null float64\n",
"status 36702 non-null int64\n",
"dtypes: bool(1), float64(3), int64(4), object(5)\n",
"memory usage: 3.7+ MB\n"
]
}
],
"source": [
"uniqueLinks.info()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>id</th>\n",
" <th>year</th>\n",
" <th>code</th>\n",
" <th>oldcode</th>\n",
" <th>httpsthing</th>\n",
" <th>0.690657769</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3.653600e+04</td>\n",
" <td>36702.000000</td>\n",
" <td>36702.000000</td>\n",
" <td>22872.000000</td>\n",
" <td>36702.000000</td>\n",
" <td>0.0</td>\n",
" <td>36702.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.282358e+07</td>\n",
" <td>2012.014332</td>\n",
" <td>237.005531</td>\n",
" <td>222.919902</td>\n",
" <td>0.137813</td>\n",
" <td>NaN</td>\n",
" <td>1.772628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.722272e+06</td>\n",
" <td>3.483438</td>\n",
" <td>116.124262</td>\n",
" <td>134.608017</td>\n",
" <td>0.345182</td>\n",
" <td>NaN</td>\n",
" <td>1.541269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.560816e+07</td>\n",
" <td>2005.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.978383e+07</td>\n",
" <td>2009.000000</td>\n",
" <td>200.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.318078e+07</td>\n",
" <td>2012.000000</td>\n",
" <td>200.000000</td>\n",
" <td>301.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2.595065e+07</td>\n",
" <td>2015.000000</td>\n",
" <td>301.000000</td>\n",
" <td>301.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.916934e+07</td>\n",
" <td>2017.000000</td>\n",
" <td>999.000000</td>\n",
" <td>405.000000</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id year code oldcode httpsthing \\\n",
"count 3.653600e+04 36702.000000 36702.000000 22872.000000 36702.000000 \n",
"mean 2.282358e+07 2012.014332 237.005531 222.919902 0.137813 \n",
"std 3.722272e+06 3.483438 116.124262 134.608017 0.345182 \n",
"min 1.560816e+07 2005.000000 -1.000000 -1.000000 0.000000 \n",
"25% 1.978383e+07 2009.000000 200.000000 -1.000000 0.000000 \n",
"50% 2.318078e+07 2012.000000 200.000000 301.000000 0.000000 \n",
"75% 2.595065e+07 2015.000000 301.000000 301.000000 0.000000 \n",
"max 2.916934e+07 2017.000000 999.000000 405.000000 2.000000 \n",
"\n",
" 0.690657769 status \n",
"count 0.0 36702.000000 \n",
"mean NaN 1.772628 \n",
"std NaN 1.541269 \n",
"min NaN -1.000000 \n",
"25% NaN 1.000000 \n",
"50% NaN 1.000000 \n",
"75% NaN 3.000000 \n",
"max NaN 4.000000 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniqueLinks.describe()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percent of repeated links: 22.4%\n"
]
}
],
"source": [
"print( 'Percent of repeated links: %.1f%%' % ( (1 - len(uniqueLinks)/len(links))*100. ) )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"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": [
"statuses = np.array( uniqueLinks.status )\n",
"\n",
"f, ax = pl.subplots(1, 1)\n",
"ax.hist( statuses, np.arange(-1.5,6,1), edgecolor=(0.2,0.2,0.2), lw=3, \n",
" color=(0.2,0.6,1.0) )\n",
"\n",
"\n",
"ax.set_ylabel('Count')\n",
"ax.set_xticks([-1, 1, 3, 4])\n",
"ax.set_xticklabels( ['Time out', 'Accessible', 'Redirected', 'Not found'], rotation=-45 )\n",
"\n",
"pl.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(19209,)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(uniqueLinks.id).shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fixing http -> https redirection\n",
"\n",
"Due to increased security awareness in the past few years, a number of links marked as redirections are in fact just changing the http protocol to https. We will not consider them as redirection, if the rest of the URL is the same."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# redirection_checking = pd.read_csv('http2https.redirected.csv', \n",
"# header=None, names=['pubmed_id', 'url', 'status', 'https_redirect']\n",
"# )\n",
"# redirection_checking.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# for index, row in redirection_checking.iterrows():\n",
"# if row['https_redirect'] == 'True' and uniqueLinks[ uniqueLinks.link == row['url']].shape[0] > 0:\n",
"# idx = np.where(uniqueLinks.link == row['url'])[0][0]\n",
"# uniqueLinks.iloc[idx,6] = 1"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>type</th>\n",
" <th>journal</th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" <th>link</th>\n",
" <th>code</th>\n",
" <th>newtest</th>\n",
" <th>oldcode</th>\n",
" <th>concated</th>\n",
" <th>httpsthing</th>\n",
" <th>github.com</th>\n",
" <th>0.690657769</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>body</td>\n",
" <td>Nucleic_Acids_Res</td>\n",
" <td>21398627.0</td>\n",
" <td>2011</td>\n",
" <td>(http://globin.cse.psu.edu/html/docs/sim4.html</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>21398627(http://globin.cse.psu.edu/html/docs/s...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>19426459.0</td>\n",
" <td>2009</td>\n",
" <td>(http://www.eumodic.org/documents/EMPReSSslim%...</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>19426459(http://www.eumodic.org/documents/EMPR...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>16026597.0</td>\n",
" <td>2005</td>\n",
" <td>(http://www.fda.gov/cder/genomics/</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>16026597(http://www.fda.gov/cder/genomics/</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>body</td>\n",
" <td>BMC_Bioinformatics</td>\n",
" <td>16026597.0</td>\n",
" <td>2005</td>\n",
" <td>(http://www.fda.gov/oc/initiatives/criticalpath/</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>16026597(http://www.fda.gov/oc/initiatives/cri...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>body</td>\n",
" <td>PLoS_Comput_Biol</td>\n",
" <td>22022247.0</td>\n",
" <td>2011</td>\n",
" <td>(http://www.ks.uiuc.edu/Development/MDTools/io...</td>\n",
" <td>-1</td>\n",
" <td>http</td>\n",
" <td>-1.0</td>\n",
" <td>22022247(http://www.ks.uiuc.edu/Development/MD...</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type journal id year \\\n",
"2 body Nucleic_Acids_Res 21398627.0 2011 \n",
"4 body BMC_Bioinformatics 19426459.0 2009 \n",
"5 body BMC_Bioinformatics 16026597.0 2005 \n",
"6 body BMC_Bioinformatics 16026597.0 2005 \n",
"7 body PLoS_Comput_Biol 22022247.0 2011 \n",
"\n",
" link code newtest oldcode \\\n",
"2 (http://globin.cse.psu.edu/html/docs/sim4.html -1 http -1.0 \n",
"4 (http://www.eumodic.org/documents/EMPReSSslim%... -1 http -1.0 \n",
"5 (http://www.fda.gov/cder/genomics/ -1 http -1.0 \n",
"6 (http://www.fda.gov/oc/initiatives/criticalpath/ -1 http -1.0 \n",
"7 (http://www.ks.uiuc.edu/Development/MDTools/io... -1 http -1.0 \n",
"\n",
" concated httpsthing github.com \\\n",
"2 21398627(http://globin.cse.psu.edu/html/docs/s... 0 False \n",
"4 19426459(http://www.eumodic.org/documents/EMPR... 0 False \n",
"5 16026597(http://www.fda.gov/cder/genomics/ 0 False \n",
"6 16026597(http://www.fda.gov/oc/initiatives/cri... 0 False \n",
"7 22022247(http://www.ks.uiuc.edu/Development/MD... 0 False \n",
"\n",
" 0.690657769 status \n",
"2 NaN -1 \n",
"4 NaN -1 \n",
"5 NaN -1 \n",
"6 NaN -1 \n",
"7 NaN -1 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniqueLinks.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4360 16720 9789 5833] [-1.2 -0.8 1.8 3.8 4.8]\n",
"0.11879461609721541\n",
"0.45556100484987194\n",
"0.26671571031551417\n",
"0.1589286687373985\n"
]
}
],
"source": [
"statuses = uniqueLinks.status\n",
"count, bin_values = np.histogram(statuses, bins = [-1.2,-0.8,1.8,3.8,4.8])\n",
"\n",
"print(count, bin_values)\n",
"total = float(count.sum())\n",
"\n",
"for j in range(4):\n",
" print(count[j]/total)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.2777232848346139"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(count[0]+count[-1])/total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constructing a dataframe with only abstracts"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 7183 entries, 22 to 48382\n",
"Data columns (total 13 columns):\n",
"type 7183 non-null object\n",
"journal 7183 non-null object\n",
"id 7182 non-null float64\n",
"year 7183 non-null int64\n",
"link 7183 non-null object\n",
"code 7183 non-null int64\n",
"newtest 4185 non-null object\n",
"oldcode 4185 non-null float64\n",
"concated 7183 non-null object\n",
"httpsthing 7183 non-null int64\n",
"github.com 7183 non-null bool\n",
"0.690657769 0 non-null float64\n",
"status 7183 non-null int64\n",
"dtypes: bool(1), float64(3), int64(4), object(5)\n",
"memory usage: 736.5+ KB\n"
]
}
],
"source": [
"uniqueLinks_abs = uniqueLinks[ uniqueLinks.type == 'abstract' ]\n",
"uniqueLinks_abs.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of entries per journal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's evaluate the number of entries detected per journal."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Journal name \t\t Count\n",
"Nat_Biotechnol\t\t 180\n",
"Genome_Med\t\t 352\n",
"Nat_Methods\t\t 403\n",
"Genome_Biol\t\t 904\n",
"BMC_Syst_Biol\t\t 912\n",
"Bioinformatics\t\t 3131\n",
"PLoS_Comput_Bio\t\t 3226\n",
"BMC_Bioinformat\t\t 6840\n",
"BMC_Genomics\t\t 7651\n",
"Nucleic_Acids_R\t\t 13103\n"
]
}
],
"source": [
"journals = np.array( uniqueLinks.journal )\n",
"journalTitles = np.unique(journals)\n",
"\n",
"jhash = {}\n",
"for jid,journal in enumerate(journalTitles):\n",
" jhash[journal] = jid\n",
"listxs = np.arange(0,11,1)\n",
"\n",
"journals_num = [ jhash[journal] for journal in journals ]\n",
"\n",
"counts, bins = np.histogram( journals_num, bins=listxs )\n",
"\n",
"newIDX = np.argsort(counts)\n",
"\n",
"f, ax = pl.subplots(1, 1)\n",
"ax.barh( bins[0:-1], counts[newIDX], 0.8, edgecolor=(0.2,0.2,0.2), lw=2, \n",
" color=(0.2,0.6,1.0) )\n",
"\n",
"ax.spines['bottom'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"ax.get_xaxis().tick_top()\n",
"ax.get_yaxis().tick_left()\n",
"\n",
"ax.text( 5000, 11.3, 'Entries count', fontsize=12 )\n",
"\n",
"ax.set_yticks( listxs )\n",
"ax.set_yticklabels( journalTitles[newIDX], rotation=0, fontsize=12 )\n",
"\n",
"pl.show()\n",
"\n",
"print('Journal name \\t\\t Count')\n",
"for jid,journal in enumerate(journalTitles[newIDX]):\n",
" print('%s\\t\\t %d' %(journal[:15],counts[newIDX][jid]) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also detect how many of them were usable (i.e. accessible or redirected)."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Journal name \t\t Count\n",
"Nat_Biotechnol\t\t 149\n",
"Genome_Med\t\t 312\n",
"Nat_Methods\t\t 326\n",
"BMC_Syst_Biol\t\t 638\n",
"Genome_Biol\t\t 746\n",
"Bioinformatics\t\t 2448\n",
"PLoS_Comput_Bio\t\t 2529\n",
"BMC_Bioinformat\t\t 4651\n",
"BMC_Genomics\t\t 5699\n",
"Nucleic_Acids_R\t\t 9011\n"
]
}
],
"source": [
"usable_links = uniqueLinks[(uniqueLinks.status == 1) | (uniqueLinks.status == 3)]\n",
"journals = np.array( usable_links.journal )\n",
"journalTitles = np.unique(journals)\n",
"\n",
"jhash = {}\n",
"for jid,journal in enumerate(journalTitles):\n",
" jhash[journal] = jid\n",
"listxs = np.arange(0,11,1)\n",
"\n",
"journals_num = [ jhash[journal] for journal in journals ]\n",
"\n",
"counts, bins = np.histogram( journals_num, bins=listxs )\n",
"\n",
"newIDX = np.argsort(counts)\n",
"\n",
"f, ax = pl.subplots(1, 1)\n",
"ax.barh( bins[0:-1], counts[newIDX], 0.8, edgecolor=(0.2,0.2,0.2), lw=2, \n",
" color=(0.2,0.6,1.0) )\n",
"\n",
"ax.spines['bottom'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"ax.get_xaxis().tick_top()\n",
"ax.get_yaxis().tick_left()\n",
"\n",
"ax.text( 5000, 11.3, 'Entries count', fontsize=12 )\n",
"\n",
"ax.set_yticks( listxs )\n",
"ax.set_yticklabels( journalTitles[newIDX], rotation=0, fontsize=12 )\n",
"\n",
"pl.show()\n",
"\n",
"print('Journal name \\t\\t Count')\n",
"for jid,journal in enumerate(journalTitles[newIDX]):\n",
" print('%s\\t\\t %d' %(journal[:15],counts[newIDX][jid]) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of entries per abstract"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Same for the number of abstracts"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Journal name \t\t Count\n",
"Nat_Biotechnol\t\t 1\n",
"Genome_Med\t\t 18\n",
"Nat_Methods\t\t 20\n",
"Genome_Biol\t\t 60\n",
"BMC_Syst_Biol\t\t 104\n",
"PLoS_Comput_Bio\t\t 259\n",
"BMC_Genomics\t\t 386\n",
"BMC_Bioinformat\t\t 1161\n",
"Bioinformatics\t\t 1866\n",
"Nucleic_Acids_R\t\t 3308\n"
]
}
],
"source": [
"journals = np.array( uniqueLinks_abs.journal )\n",
"journalTitles = np.unique(journals)\n",
"\n",
"jhash = {}\n",
"for jid,journal in enumerate(journalTitles):\n",
" jhash[journal] = jid\n",
"listxs = np.arange(0,11,1)\n",
"\n",
"journals_num = [ jhash[journal] for journal in journals ]\n",
"\n",
"counts, bins = np.histogram( journals_num, bins=listxs )\n",
"\n",
"newIDX = np.argsort(counts)\n",
"\n",
"f, ax = pl.subplots(1, 1)\n",
"ax.barh( bins[0:-1], counts[newIDX], 0.8, edgecolor=(0.2,0.2,0.2), lw=2, \n",
" color=(0.2,0.6,1.0) )\n",
"\n",
"ax.spines['bottom'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"ax.get_xaxis().tick_top()\n",
"ax.get_yaxis().tick_left()\n",
"\n",
"ax.text( 1200, 11.3, 'Entries count', fontsize=12 )\n",
"\n",
"ax.set_yticks( listxs )\n",
"ax.set_yticklabels( journalTitles[newIDX], rotation=0, fontsize=12 )\n",
"\n",
"pl.show()\n",
"\n",
"print('Journal name \\t\\t Count')\n",
"for jid,journal in enumerate(journalTitles[newIDX]):\n",
" print('%s\\t\\t %d' %(journal[:15],counts[newIDX][jid]) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of of links per year"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"uLinks_year = uniqueLinks.groupby(['year', 'status']).agg('count')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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></th>\n",
" <th>type</th>\n",
" <th>journal</th>\n",
" <th>id</th>\n",
" <th>link</th>\n",
" <th>code</th>\n",
" <th>newtest</th>\n",
" <th>oldcode</th>\n",
" <th>concated</th>\n",
" <th>httpsthing</th>\n",
" <th>github.com</th>\n",
" <th>0.690657769</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th>status</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">2005</th>\n",
" <th>-1</th>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>419</td>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>421</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>400</td>\n",
" <td>400</td>\n",
" <td>399</td>\n",
" <td>400</td>\n",
" <td>400</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>400</td>\n",
" <td>400</td>\n",
" <td>400</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>315</td>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>316</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>428</td>\n",
" <td>428</td>\n",
" <td>414</td>\n",
" <td>428</td>\n",
" <td>428</td>\n",
" <td>194</td>\n",
" <td>194</td>\n",
" <td>428</td>\n",
" <td>428</td>\n",
" <td>428</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">2006</th>\n",
" <th>-1</th>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>366</td>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>375</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>424</td>\n",
" <td>424</td>\n",
" <td>424</td>\n",
" <td>424</td>\n",
" <td>424</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>424</td>\n",
" <td>424</td>\n",
" <td>424</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>398</td>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>399</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>384</td>\n",
" <td>384</td>\n",
" <td>377</td>\n",
" <td>384</td>\n",
" <td>384</td>\n",
" <td>169</td>\n",
" <td>169</td>\n",
" <td>384</td>\n",
" <td>384</td>\n",
" <td>384</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">2007</th>\n",
" <th>-1</th>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>358</td>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>360</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>435</td>\n",
" <td>435</td>\n",
" <td>430</td>\n",
" <td>435</td>\n",
" <td>435</td>\n",
" <td>97</td>\n",
" <td>97</td>\n",
" <td>435</td>\n",
" <td>435</td>\n",
" <td>435</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>427</td>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>441</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>405</td>\n",
" <td>405</td>\n",
" <td>404</td>\n",
" <td>405</td>\n",
" <td>405</td>\n",
" <td>177</td>\n",
" <td>177</td>\n",
" <td>405</td>\n",
" <td>405</td>\n",
" <td>405</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type journal id link code newtest oldcode concated \\\n",
"year status \n",
"2005 -1 421 421 419 421 421 421 421 421 \n",
" 1 400 400 399 400 400 82 82 400 \n",
" 3 316 316 315 316 316 316 316 316 \n",
" 4 428 428 414 428 428 194 194 428 \n",
"2006 -1 375 375 366 375 375 375 375 375 \n",
" 1 424 424 424 424 424 94 94 424 \n",
" 3 399 399 398 399 399 399 399 399 \n",
" 4 384 384 377 384 384 169 169 384 \n",
"2007 -1 360 360 358 360 360 360 360 360 \n",
" 1 435 435 430 435 435 97 97 435 \n",
" 3 441 441 427 441 441 441 441 441 \n",
" 4 405 405 404 405 405 177 177 405 \n",
"\n",
" httpsthing github.com 0.690657769 \n",
"year status \n",
"2005 -1 421 421 0 \n",
" 1 400 400 0 \n",
" 3 316 316 0 \n",
" 4 428 428 0 \n",
"2006 -1 375 375 0 \n",
" 1 424 424 0 \n",
" 3 399 399 0 \n",
" 4 384 384 0 \n",
"2007 -1 360 360 0 \n",
" 1 435 435 0 \n",
" 3 441 441 0 \n",
" 4 405 405 0 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uLinks_year.head(12)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Grouping by status and retrieving the percent for each status per year."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"tseries = {}\n",
"for j,status in enumerate([-1, 1, 3, 4]):\n",
" tseries[status] = np.array( uLinks_year.iloc[j::4].link )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x158.4 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"barsize=0.75\n",
"\n",
"f, ax = pl.subplots(1, 1, figsize=(4,2.2) )\n",
"\n",
"b = np.zeros( tseries[status].shape[0] )\n",
"years = np.arange(2005,2018,1)\n",
"\n",
"for zlabel,status in enumerate([4,-1,1,3]):\n",
" ax.bar( years, tseries[status] + b, barsize, label=legendStatus[status], \n",
" color=colors[status], zorder=-1-zlabel )\n",
" b += tseries[status]\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"ax.set_ylabel(\"Number of links\")\n",
"ax.set_yticks( np.arange(0,4001,1000) )\n",
"ax.set_yticklabels( np.array( np.arange(0,4001,1000), dtype=int ), fontsize=8 )\n",
"\n",
"ax.set_xlabel(\"Year\")\n",
"ax.set_xticks( np.arange( 2005,2020,3 ) )\n",
"ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )\n",
"\n",
"pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4, \n",
" handletextpad=0.2, columnspacing=0.8, handlelength=1)\n",
"\n",
"pl.tight_layout()\n",
"pl.savefig('NumLinks per year - all.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Percent of links per year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normalizing the number of links for each year."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"tseries_norm = np.zeros( tseries[-1].shape )\n",
"for status in [-1, 1, 3, 4]:\n",
" tseries_norm += tseries[status]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 273.6x194.4 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"barsize=0.75\n",
"\n",
"f, ax = pl.subplots(1, 1, figsize=(3.8,2.7) )\n",
"\n",
"b = np.zeros( tseries[status].shape[0] )\n",
"years = np.arange(2005,2018,1)\n",
"\n",
"for zlabel,status in enumerate([4,-1,1,3]):\n",
" ax.bar( years, ( tseries[status]/tseries_norm + b )*100., \n",
" barsize, label=legendStatus[status], \n",
" color=colors[status], zorder=-1-zlabel )\n",
" b += tseries[status]/tseries_norm\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"ax.set_ylabel(\"Percent of links (%)\")\n",
"ax.set_yticks( np.arange(0,101,25) )\n",
"ax.set_yticklabels( np.array( np.arange(0,101,25), dtype=int ), fontsize=8 )\n",
"\n",
"ax.set_xlabel(\"Year\")\n",
"ax.set_xlim(2004.3, 2017.7)\n",
"ax.set_xticks( np.arange( 2005,2018,3 ) )\n",
"ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )\n",
"\n",
"pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4, \n",
" handletextpad=0.2, columnspacing=0.6, handlelength=1)\n",
"\n",
"pl.tight_layout()\n",
"pl.savefig('1a.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of links as function of time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counting total number per year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 244.8x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(3.4, 2.) )\n",
"\n",
"pl.plot( years, tseries[1] + tseries[3], 'o-', \n",
" markersize=7, label='Accessible + Redirected', color=colors2['fg_accessb'],\n",
" markeredgecolor=colors2['fg_accessb'], markerfacecolor=colors2['bg_accessb'] )\n",
"pl.plot( years, tseries[-1] + tseries[4], 's-', \n",
" markersize=6, label='Broken + Time out', color=colors2['fg_unreach'],\n",
" markeredgecolor=colors2['fg_unreach'], markerfacecolor=colors2['bg_unreach'] )\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.ylabel('Number of links')\n",
"pl.ylim(0,4050)\n",
"\n",
"pl.xlabel('Time (years)')\n",
"pl.xlim(2004.5,2017.5)\n",
"pl.xticks([2005,2010,2015])\n",
"\n",
"pl.legend(bbox_to_anchor=(0.9,1.5), frameon=False, handlelength=1.3)\n",
"\n",
"pl.savefig('1b.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ratio between accessible links in 2017 in reference to that in 2005"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ratio: 4.07\n",
"Average growth ratio: 25.6%\n"
]
}
],
"source": [
"numAccessible = tseries[1] + tseries[3]\n",
"\n",
"ratio = numAccessible[-1] / numAccessible[0]\n",
"print('Ratio: %.2f' % ratio)\n",
"\n",
"avgGrowthRatio = (numAccessible[-1] - numAccessible[0]) / (2017-2005) / numAccessible[0]\n",
"print( 'Average growth ratio: %.1f%%' % (avgGrowthRatio*100) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ratio between unreachable links in 2017 in reference to that in 2005"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ratio: 0.39\n",
"Average growth ratio: -5.1%\n"
]
}
],
"source": [
"numUnreachable = tseries[-1] + tseries[4]\n",
"\n",
"ratio = numUnreachable[-1] / numUnreachable[0]\n",
"print('Ratio: %.2f' % ratio)\n",
"\n",
"\n",
"avgGrowthRatio = (numUnreachable[-1] - numUnreachable[0]) / (2017-2005) / numUnreachable[0]\n",
"print( 'Average growth ratio: %.1f%%' % (avgGrowthRatio*100) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parsing Github and SourceForge"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"def count_status(statuses):\n",
" count, bin_values = np.histogram(statuses, bins = [-1.2,-0.8,1.8,3.8,4.8])\n",
"\n",
" print(count, bin_values)\n",
" total = float(count.sum())\n",
"\n",
" for j in range(4):\n",
" print(count[j]/total)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 1553 188 43] [-1.2 -0.8 1.8 3.8 4.8]\n",
"0.0\n",
"0.8705156950672646\n",
"0.10538116591928251\n",
"0.024103139013452915\n",
"[ 5 608 315 64] [-1.2 -0.8 1.8 3.8 4.8]\n",
"0.005040322580645161\n",
"0.6129032258064516\n",
"0.3175403225806452\n",
"0.06451612903225806\n",
"[ 4355 14559 9286 5726] [-1.2 -0.8 1.8 3.8 4.8]\n",
"0.12836762365147675\n",
"0.42913989270765784\n",
"0.27371337617166774\n",
"0.16877910746919766\n"
]
}
],
"source": [
"find_github = lambda x: 'github.com' in x.link\n",
"find_sourceforge = lambda x: ('sourceforge.net' in x.link) or ('.sf.net' in x.link)\n",
"find_others = lambda x: ('github.com' not in x.link) and ('sourceforge.net' not in x.link) and ('.sf.net' not in x.link)\n",
"\n",
"# uniqLabs_github = uniqueLinks_abs[ uniqueLinks_abs.apply(find_github, axis=1) ]\n",
"# uniqLabs_sourceforge = uniqueLinks_abs[ uniqueLinks_abs.apply(find_sourceforge, axis=1) ]\n",
"# uniqLabs_others = uniqueLinks_abs[ uniqueLinks_abs.apply(find_others, axis=1) ]\n",
"\n",
"uniqLabs_github = uniqueLinks[ uniqueLinks.apply(find_github, axis=1) ]\n",
"uniqLabs_sourceforge = uniqueLinks[ uniqueLinks.apply(find_sourceforge, axis=1) ]\n",
"uniqLabs_others = uniqueLinks[ uniqueLinks.apply(find_others, axis=1) ]\n",
"\n",
"count_status(uniqLabs_github.status)\n",
"count_status(uniqLabs_sourceforge.status)\n",
"count_status(uniqLabs_others.status)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5475833911663602\n"
]
}
],
"source": [
"import scipy.stats as statsrich\n",
"oddsratio, pvalue = statsrich.fisher_exact([[(3+86),(138)], [(1050+2521), (2135+897)]])\n",
"print(oddsratio)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 108x100.8 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(1.5, 1.4) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data_broken = np.array([0.16877910746919766, 0.06451612903225806, 0.024103139013452915])\n",
"data_timeout = np.array([0.12836762365147675, 0.005040322580645161, 0])\n",
"data_accessible = np.array([0.42913989270765784, 0.6129032258064516, 0.8705156950672646])\n",
"data_redirected = np.array([0.27371337617166774, 0.3175403225806452, 0.10538116591928251])\n",
"\n",
"pl.bar( [0,1,2], 100*(data_broken+data_timeout+data_accessible+data_redirected), 0.7, color=colors[3], lw=1.3, capsize=8 )\n",
"pl.bar( [0,1,2], 100*(data_broken+data_timeout+data_accessible), 0.7, color=colors[1], lw=1.3, capsize=8 )\n",
"pl.bar( [0,1,2], 100*(data_broken+data_timeout), 0.7, color=colors[-1], lw=1.3, capsize=8 )\n",
"pl.bar( [0,1,2], 100*(data_broken), 0.7, color=colors[4], lw=1.3, capsize=8 )\n",
"\n",
"ax.set_ylim(0,100)\n",
"ax.set_ylabel('Percent of Links')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xlim(-0.5,2.5)\n",
"ax.set_xticks([0,1,2])\n",
"ax.set_xticklabels( ['Others','Source\\nForge','GitHub'], \n",
" rotation=-45, fontsize=9)\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('1f.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 144x115.2 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots(1, 1, figsize=(2.,1.6))\n",
"\n",
"data_broken = np.array([0.16877910746919766, 0.06451612903225806, 0.024103139013452915])*100\n",
"data_timeout = np.array([0.12836762365147675, 0.005040322580645161, 0])*100\n",
"\n",
"data_unreachable = data_broken + data_timeout\n",
"\n",
"ax.barh( [0,1,2], data_unreachable, 0.6, edgecolor=(1.0,0.0,0.0), lw=0.1, color=(1.0,0.0,0.0) )\n",
"\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"ax.get_xaxis().tick_bottom()\n",
"ax.get_yaxis().tick_left()\n",
"\n",
"ax.text( -0.05, -1.5, 'Unreachable links (%)', fontsize=12 )\n",
"\n",
"ax.set_xticks( [0, 10, 20, 30] )\n",
"ax.set_yticks( [0, 1, 2] )\n",
"ax.set_yticklabels( ['Others', 'Source\\nForge', 'GitHub'], rotation=0, fontsize=11 )\n",
"\n",
"pl.savefig('1h.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"norm\n",
"year\n",
"2005 1565\n",
"2006 1582\n",
"2007 1641\n",
"2008 2079\n",
"2009 2835\n",
"2010 2954\n",
"2011 2783\n",
"2012 2962\n",
"2013 3232\n",
"2014 4108\n",
"2015 3844\n",
"2016 3875\n",
"2017 3242\n",
"Name: type, dtype: int64\n",
"github\n",
"year\n",
"2009 0.070547\n",
"2010 0.507786\n",
"2011 0.682716\n",
"2012 1.553005\n",
"2013 2.753713\n",
"2014 5.209348\n",
"2015 9.469303\n",
"2016 13.006452\n",
"Name: type, dtype: float64\n",
"sf\n",
"year\n",
"2005 1.022364\n",
"2006 2.085967\n",
"2007 2.071907\n",
"2008 2.308802\n",
"2009 2.574956\n",
"2010 3.588355\n",
"2011 3.377650\n",
"2012 4.287643\n",
"2013 2.629950\n",
"2014 3.042843\n",
"2015 2.523413\n",
"2016 2.606452\n",
"2017 1.634793\n",
"Name: type, dtype: float64\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 172.8x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tseries_norm = uniqueLinks.groupby(['year']).agg('count').type\n",
"tseries_GH = uniqLabs_github.groupby(['year']).agg('count').type[:-1]/tseries_norm[4:-1]*100\n",
"years_gh = range(2009,2017)\n",
"tseries_SF = uniqLabs_sourceforge.groupby(['year']).agg('count').type/tseries_norm[:]*100\n",
"years_sf = range(2005,2018)\n",
"\n",
"print(\"norm\")\n",
"print(tseries_norm)\n",
"print(\"github\")\n",
"print(tseries_GH)\n",
"print(\"sf\")\n",
"print(tseries_SF)\n",
"\n",
"f, ax = pl.subplots( 1, 1, figsize=(2.4, 2.) )\n",
"\n",
"\n",
"gh_color=(0.0,0.0,0.5)\n",
"gh_incolor=(0.3,0.8,1.0)\n",
"pl.plot( years_gh, tseries_GH, 's-', zorder=2,\n",
" markersize=5, label='GitHub', color=gh_color,\n",
" markeredgecolor=gh_color, markerfacecolor=gh_incolor )\n",
"\n",
"sf_color=(0.8,0.0,0.0)\n",
"sf_incolor=(1.0,0.8,0.8)\n",
"pl.plot( years_sf, tseries_SF, 'o-', zorder=1,\n",
" markersize=5, label='SourceForge', color=sf_color,\n",
" markeredgecolor=sf_color, markerfacecolor=sf_incolor )\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.ylabel('Percent of links')\n",
"pl.ylim(0,22)\n",
"ax.set_xticks([2005, 2010, 2015])\n",
"\n",
"pl.xlabel('Year')\n",
"pl.xlim(2004.5,2016.5)\n",
"\n",
"pl.legend(bbox_to_anchor=(0.85,1.), frameon=False, handlelength=1.3, fontsize=10)\n",
"\n",
"pl.savefig('1g.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of links per year - abstract only"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"uLinks_ya = uniqueLinks_abs.groupby(['year', 'status']).agg('count')\n",
"\n",
"tseries_abs = {}\n",
"for j,status in enumerate([-1, 1, 3, 4]):\n",
" tseries_abs[status] = np.array( uLinks_ya.iloc[j::4].link )"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x158.4 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"barsize=0.75\n",
"\n",
"f, ax = pl.subplots(1, 1, figsize=(4,2.2) )\n",
"\n",
"b = np.zeros( tseries_abs[status].shape[0] )\n",
"years = np.arange(2005,2018,1)\n",
"\n",
"for zlabel,status in enumerate([4,-1,1,3]):\n",
" ax.bar( years, tseries_abs[status] + b, barsize, label=legendStatus[status], \n",
" color=colors[status], zorder=-1-zlabel )\n",
" b += tseries_abs[status]\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"ax.set_ylabel(\"Number of links\")\n",
"#ax.set_yticks( np.arange(0,2001,500) )\n",
"#ax.set_yticklabels( np.array( np.arange(0,2001,500), dtype=int ), fontsize=8 )\n",
"\n",
"ax.set_xlabel(\"Year\")\n",
"ax.set_xticks( np.arange( 2005,2020,3 ) )\n",
"ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )\n",
"\n",
"pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4, \n",
" handletextpad=0.2, columnspacing=0.8, handlelength=1)\n",
"\n",
"pl.tight_layout()\n",
"pl.savefig('NumLinks per year - Abstract.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"tseries_norm = np.zeros( tseries_abs[-1].shape )\n",
"for status in [-1, 1, 3, 4]:\n",
" tseries_norm += tseries_abs[status]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x158.4 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"barsize=0.75\n",
"\n",
"f, ax = pl.subplots(1, 1, figsize=(4,2.2) )\n",
"\n",
"b = np.zeros( tseries_abs[status].shape[0] )\n",
"years = np.arange(2005,2018,1)\n",
"\n",
"for zlabel,status in enumerate([4,-1,1,3]):\n",
" ax.bar( years, 100*(tseries_abs[status] + b)/tseries_norm, barsize, \n",
" label=legendStatus[status], \n",
" color=colors[status], zorder=-1-zlabel )\n",
" b += tseries_abs[status]\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"ax.set_ylabel(\"Percent of links (%)\")\n",
"ax.set_yticks( np.arange(0,101,25) )\n",
"ax.set_yticklabels( np.array( np.arange(0,101,25), dtype=int ), fontsize=8 )\n",
"\n",
"ax.set_xlabel(\"Year\")\n",
"ax.set_xticks( np.arange( 2005,2020,3 ) )\n",
"ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )\n",
"\n",
"pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4, \n",
" handletextpad=0.2, columnspacing=0.8, handlelength=1)\n",
"\n",
"pl.tight_layout()\n",
"pl.savefig('Percent per year - Abstract.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Status vs IF of current journal "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List of journals considered in this study:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Nucleic_Acids_Res' 'BMC_Bioinformatics' 'PLoS_Comput_Biol'\n",
" 'BMC_Genomics' 'Bioinformatics' 'Nat_Methods' 'Genome_Biol'\n",
" 'BMC_Syst_Biol' 'Nat_Biotechnol' 'Genome_Med']\n"
]
}
],
"source": [
"journals = uniqueLinks.journal.unique()\n",
"print( journals )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Impact factors were retrieved from this address: http://admin-apps.webofknowledge.com/JCR/JCR?RQ=HOME"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>year</th>\n",
" <th>BMC_Genomics</th>\n",
" <th>Nucleic_Acids_Res</th>\n",
" <th>Bioinformatics</th>\n",
" <th>BMC_Bioinformatics</th>\n",
" <th>PLoS_Comput_Biol</th>\n",
" <th>Genome_Biol</th>\n",
" <th>BMC_Syst_Biol</th>\n",
" <th>Nat_Biotechnol</th>\n",
" <th>Nat_Methods</th>\n",
" <th>Genome_Med</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016</td>\n",
" <td>3.729</td>\n",
" <td>10.162</td>\n",
" <td>7.307</td>\n",
" <td>2.448</td>\n",
" <td>4.542</td>\n",
" <td>11.908</td>\n",
" <td>2.303</td>\n",
" <td>41.667</td>\n",
" <td>25.062</td>\n",
" <td>7.071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015</td>\n",
" <td>3.867</td>\n",
" <td>9.202</td>\n",
" <td>5.766</td>\n",
" <td>2.435</td>\n",
" <td>4.587</td>\n",
" <td>11.313</td>\n",
" <td>2.208</td>\n",
" <td>43.113</td>\n",
" <td>25.328</td>\n",
" <td>5.846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>3.986</td>\n",
" <td>9.112</td>\n",
" <td>4.981</td>\n",
" <td>2.576</td>\n",
" <td>4.620</td>\n",
" <td>10.810</td>\n",
" <td>2.435</td>\n",
" <td>41.514</td>\n",
" <td>32.072</td>\n",
" <td>5.809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>4.041</td>\n",
" <td>8.808</td>\n",
" <td>4.621</td>\n",
" <td>2.672</td>\n",
" <td>4.829</td>\n",
" <td>10.465</td>\n",
" <td>2.853</td>\n",
" <td>39.080</td>\n",
" <td>25.953</td>\n",
" <td>4.942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012</td>\n",
" <td>4.397</td>\n",
" <td>8.278</td>\n",
" <td>5.323</td>\n",
" <td>3.024</td>\n",
" <td>4.867</td>\n",
" <td>10.288</td>\n",
" <td>2.982</td>\n",
" <td>32.438</td>\n",
" <td>23.565</td>\n",
" <td>3.906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2011</td>\n",
" <td>4.073</td>\n",
" <td>8.026</td>\n",
" <td>5.468</td>\n",
" <td>2.751</td>\n",
" <td>5.215</td>\n",
" <td>9.036</td>\n",
" <td>3.148</td>\n",
" <td>23.268</td>\n",
" <td>19.276</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2010</td>\n",
" <td>4.206</td>\n",
" <td>7.836</td>\n",
" <td>4.877</td>\n",
" <td>3.029</td>\n",
" <td>5.515</td>\n",
" <td>6.885</td>\n",
" <td>3.565</td>\n",
" <td>31.090</td>\n",
" <td>20.721</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2009</td>\n",
" <td>3.759</td>\n",
" <td>7.479</td>\n",
" <td>4.926</td>\n",
" <td>3.428</td>\n",
" <td>5.759</td>\n",
" <td>6.626</td>\n",
" <td>4.064</td>\n",
" <td>29.495</td>\n",
" <td>16.874</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2008</td>\n",
" <td>3.926</td>\n",
" <td>6.878</td>\n",
" <td>4.328</td>\n",
" <td>3.781</td>\n",
" <td>5.895</td>\n",
" <td>6.153</td>\n",
" <td>3.706</td>\n",
" <td>22.297</td>\n",
" <td>13.651</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2007</td>\n",
" <td>4.180</td>\n",
" <td>6.954</td>\n",
" <td>5.039</td>\n",
" <td>3.493</td>\n",
" <td>6.236</td>\n",
" <td>6.589</td>\n",
" <td>0.000</td>\n",
" <td>22.848</td>\n",
" <td>15.478</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2006</td>\n",
" <td>4.029</td>\n",
" <td>6.317</td>\n",
" <td>4.894</td>\n",
" <td>3.617</td>\n",
" <td>4.914</td>\n",
" <td>7.172</td>\n",
" <td>0.000</td>\n",
" <td>22.672</td>\n",
" <td>14.959</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2005</td>\n",
" <td>4.092</td>\n",
" <td>7.552</td>\n",
" <td>6.019</td>\n",
" <td>4.958</td>\n",
" <td>0.000</td>\n",
" <td>9.712</td>\n",
" <td>0.000</td>\n",
" <td>22.738</td>\n",
" <td>6.741</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2004</td>\n",
" <td>3.250</td>\n",
" <td>7.260</td>\n",
" <td>5.742</td>\n",
" <td>5.423</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>22.355</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2003</td>\n",
" <td>0.000</td>\n",
" <td>6.575</td>\n",
" <td>6.701</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>17.721</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year BMC_Genomics Nucleic_Acids_Res Bioinformatics BMC_Bioinformatics \\\n",
"0 2016 3.729 10.162 7.307 2.448 \n",
"1 2015 3.867 9.202 5.766 2.435 \n",
"2 2014 3.986 9.112 4.981 2.576 \n",
"3 2013 4.041 8.808 4.621 2.672 \n",
"4 2012 4.397 8.278 5.323 3.024 \n",
"5 2011 4.073 8.026 5.468 2.751 \n",
"6 2010 4.206 7.836 4.877 3.029 \n",
"7 2009 3.759 7.479 4.926 3.428 \n",
"8 2008 3.926 6.878 4.328 3.781 \n",
"9 2007 4.180 6.954 5.039 3.493 \n",
"10 2006 4.029 6.317 4.894 3.617 \n",
"11 2005 4.092 7.552 6.019 4.958 \n",
"12 2004 3.250 7.260 5.742 5.423 \n",
"13 2003 0.000 6.575 6.701 0.000 \n",
"\n",
" PLoS_Comput_Biol Genome_Biol BMC_Syst_Biol Nat_Biotechnol Nat_Methods \\\n",
"0 4.542 11.908 2.303 41.667 25.062 \n",
"1 4.587 11.313 2.208 43.113 25.328 \n",
"2 4.620 10.810 2.435 41.514 32.072 \n",
"3 4.829 10.465 2.853 39.080 25.953 \n",
"4 4.867 10.288 2.982 32.438 23.565 \n",
"5 5.215 9.036 3.148 23.268 19.276 \n",
"6 5.515 6.885 3.565 31.090 20.721 \n",
"7 5.759 6.626 4.064 29.495 16.874 \n",
"8 5.895 6.153 3.706 22.297 13.651 \n",
"9 6.236 6.589 0.000 22.848 15.478 \n",
"10 4.914 7.172 0.000 22.672 14.959 \n",
"11 0.000 9.712 0.000 22.738 6.741 \n",
"12 0.000 0.000 0.000 22.355 0.000 \n",
"13 0.000 0.000 0.000 17.721 0.000 \n",
"\n",
" Genome_Med \n",
"0 7.071 \n",
"1 5.846 \n",
"2 5.809 \n",
"3 4.942 \n",
"4 3.906 \n",
"5 0.000 \n",
"6 0.000 \n",
"7 0.000 \n",
"8 0.000 \n",
"9 0.000 \n",
"10 0.000 \n",
"11 0.000 \n",
"12 0.000 \n",
"13 0.000 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IFdata = pd.read_csv('impact_factors_timeseries.csv')\n",
"IFdata = IFdata.fillna(0)\n",
"IFdata"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"count = {}\n",
"Z = np.zeros( (10) )\n",
"for status in [1,3,-1,4]:\n",
" count[status] = np.zeros( (10) )\n",
"\n",
"def journalID(journalName): \n",
" if journalName in journals:\n",
" return np.argwhere(journals == journalName)[0][0]\n",
" else: return -1\n",
"\n",
"for index, row in uniqueLinks.iterrows():\n",
" jid = journalID(row.journal)\n",
" if jid != -1:\n",
" count[row['status']][ jid ] += 1\n",
" Z[ jid ] += 1\n",
" \n",
"\n",
"# Normalizing\n",
"for status in [1,3,-1,4]:\n",
" count[status] /= Z"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"IFs = np.array( IFdata.iloc[0] )[1:]\n",
"newIDX = np.argsort( IFs )\n",
"IFs = IFs[newIDX]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 259.2x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"barsize=0.7\n",
"\n",
"f, ax = pl.subplots(1, 1, figsize=(3.6,2.0) )\n",
"\n",
"IFs_x = np.arange(10)\n",
"\n",
"ax.bar( IFs_x, count[4][newIDX], barsize, label='Broken', \n",
" color=colors[4], zorder=-1 )\n",
"b = np.copy( count[4][newIDX] )\n",
"\n",
"ax.bar( IFs_x, b+count[-1][newIDX], barsize, label='Time out', \n",
" color=colors[-1], zorder=-2 )\n",
"b += count[-1][newIDX]\n",
"\n",
"ax.bar( IFs_x, b+count[1][newIDX], barsize, label='Good', \n",
" color=colors[1], zorder=-3 )\n",
"b += count[1][newIDX]\n",
"\n",
"ax.bar( IFs_x, b+count[3][newIDX], barsize, label='Redirect',\n",
" color=colors[3], zorder=-4 )\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"ax.set_ylabel(\"Percent (%)\")\n",
"ax.set_yticks( np.arange(0,1.1,0.25) )\n",
"ax.set_yticklabels( np.array( np.arange(0,1.1,0.25)*100, dtype=int ), fontsize=8 )\n",
"\n",
"ax.set_xlabel(\"Current Impact Factor\")\n",
"ax.set_xticks( IFs_x )\n",
"ax.set_xticklabels( ['2.3','2.5','3.7','4.5','7.1','7.3','10','12','25','41'], \n",
" fontsize=8, )\n",
"\n",
"pl.tight_layout()\n",
"pl.savefig('Percent per IF.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Status vs IF - abstracts only"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List of journals considered in this study:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Bioinformatics' 'BMC_Bioinformatics' 'Nucleic_Acids_Res' 'BMC_Genomics'\n",
" 'BMC_Syst_Biol' 'PLoS_Comput_Biol' 'Genome_Med' 'Genome_Biol'\n",
" 'Nat_Methods' 'Nat_Biotechnol']\n"
]
}
],
"source": [
"journals = uniqueLinks_abs.journal.unique()\n",
"print( journals )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Impact factors were retrieved from this address: http://admin-apps.webofknowledge.com/JCR/JCR?RQ=HOME"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"count = {}\n",
"Z = np.zeros( (10) )\n",
"for status in [1,3,-1,4]:\n",
" count[status] = np.zeros( (10) )\n",
"\n",
"def journalID(journalName): \n",
" if journalName in journals:\n",
" return np.argwhere(journals == journalName)[0][0]\n",
" else: return -1\n",
"\n",
"for index, row in uniqueLinks_abs.iterrows():\n",
" jid = journalID(row.journal)\n",
" if jid != -1:\n",
" count[row['status']][ jid ] += 1\n",
" Z[ jid ] += 1\n",
" \n",
"\n",
"# Normalizing\n",
"for status in [1,3,-1,4]:\n",
" count[status] /= Z"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"IFs = np.array( IFdata.iloc[0] )[1:]\n",
"newIDX = np.argsort( IFs )\n",
"IFs = IFs[newIDX]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 252x158.4 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"barsize=0.7\n",
"\n",
"f, ax = pl.subplots(1, 1, figsize=(3.5,2.2) )\n",
"\n",
"IFs_x = np.arange(10)\n",
"\n",
"ax.bar( IFs_x, count[4][newIDX], barsize, label='Broken', \n",
" color=colors[4], zorder=-1 )\n",
"b = np.copy( count[4][newIDX] )\n",
"\n",
"ax.bar( IFs_x, b+count[-1][newIDX], barsize, label='Time out', \n",
" color=colors[-1], zorder=-2 )\n",
"b += count[-1][newIDX]\n",
"\n",
"ax.bar( IFs_x, b+count[1][newIDX], barsize, label='Good', \n",
" color=colors[1], zorder=-3 )\n",
"b += count[1][newIDX]\n",
"\n",
"ax.bar( IFs_x, b+count[3][newIDX], barsize, label='Redirect',\n",
" color=colors[3], zorder=-4 )\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"ax.set_ylabel(\"Percent (%)\")\n",
"ax.set_yticks( np.arange(0,1.1,0.25) )\n",
"ax.set_yticklabels( np.array( np.arange(0,1.1,0.25)*100, dtype=int ), fontsize=8 )\n",
"\n",
"ax.set_xlabel(\"Current Impact Factor\")\n",
"ax.set_xticks( IFs_x )\n",
"ax.set_xticklabels( ['2.3','2.5','3.7','4.5','7.1','7.3','10','12','25','41'], \n",
" fontsize=8, )\n",
"\n",
"pl.tight_layout()\n",
"pl.savefig('Percent per IF - abstracts.pdf')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Status vs IF at the time of publication"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"IFpub = {}\n",
"for status in [1,3,-1,4]:\n",
" IFpub[status] = []\n",
"\n",
"for index, row in uniqueLinks.iterrows():\n",
" year = row.year\n",
" if ( year >= 2003 ) and ( year <= 2017 ) :\n",
" \n",
" # there's no IF for 2017 yet\n",
" if year == 2017: year = 2016\n",
" \n",
" yearIDX = -(year - 2003) + 13\n",
" IF_at_time = IFdata[row.journal].iloc[ yearIDX ]\n",
" IFpub[row.status].append( IF_at_time )"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 201.6x144 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(2.8, 2.0) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [IFpub[4], IFpub[-1], IFpub[1], IFpub[3]]\n",
"\n",
"parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False, \n",
" showextrema=False, widths=0.7, points=200 )\n",
" \n",
"colors_list = [colors[j] for j in [4, -1, 1, 3]]\n",
"\n",
"j = 0\n",
"for pc in parts['bodies']:\n",
" pc.set_facecolor(colors_list[j])\n",
" pc.set_edgecolor( (0.2,0.2,0.2) )\n",
" pc.set_edgecolors( (0.2,0.2,0.2) )\n",
" pc.set_alpha(1)\n",
" j += 1\n",
" \n",
"\n",
"def buttpos(x): return [x-0.05,x+0.05]\n",
"for j in range(4):\n",
" pl.plot(buttpos(j), [np.mean(data[j])]*2, color=\"k\", \n",
" linewidth=6, solid_capstyle=\"butt\", zorder=4)\n",
"\n",
"for j in range(4):\n",
" ax.text( j-0.4, -2, 'n='+str(len(data[j])), fontsize=7 )\n",
"\n",
"ax.set_ylim(-3,14)\n",
"ax.set_ylabel('Impact factor')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xticks([0,1,2,3])\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'], \n",
" rotation=-25, fontsize=8 )\n",
"pl.tight_layout()\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('1c_old.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24.27790915038259 1.0928479654487711e-15\n",
" type journal id link code newtest oldcode concated httpsthing \\\n",
"year \n",
"2005 390 390 390 390 390 245 245 390 390 \n",
"2006 464 464 464 464 464 283 283 464 464 \n",
"2007 428 428 428 428 428 268 268 428 428 \n",
"2008 536 536 536 536 536 315 315 536 536 \n",
"2009 729 729 729 729 729 424 424 729 729 \n",
"2010 691 691 691 691 691 391 391 691 691 \n",
"2011 592 592 592 592 592 324 324 592 592 \n",
"2012 657 657 656 657 657 356 356 657 657 \n",
"2013 668 668 668 668 668 372 372 668 668 \n",
"2014 577 577 577 577 577 307 307 577 577 \n",
"2015 524 524 524 524 524 310 310 524 524 \n",
"2016 579 579 579 579 579 378 378 579 579 \n",
"2017 348 348 348 348 348 212 212 348 348 \n",
"\n",
" github.com 0.690657769 status \n",
"year \n",
"2005 390 0 390 \n",
"2006 464 0 464 \n",
"2007 428 0 428 \n",
"2008 536 0 536 \n",
"2009 729 0 729 \n",
"2010 691 0 691 \n",
"2011 592 0 592 \n",
"2012 657 0 657 \n",
"2013 668 0 668 \n",
"2014 577 0 577 \n",
"2015 524 0 524 \n",
"2016 579 0 579 \n",
"2017 348 0 348 \n"
]
}
],
"source": [
"from scipy import stats\n",
"f_value, p_value = stats.f_oneway(data[0], data[1], data[3], data[2])\n",
"print(f_value, p_value)\n",
"\n",
"print(uniqueLinks_abs.groupby(['year']).agg('count'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evolution of IF of each category over the years"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"IFpub = {}\n",
"Z = {}\n",
"for status in [1,3,-1,4]:\n",
" IFpub[status] = np.zeros(12)\n",
" Z[status] = np.zeros(12)\n",
"\n",
"for index, row in uniqueLinks.iterrows():\n",
" year = row.year\n",
" if ( year >= 2003 ) and ( year <= 2017 ) :\n",
" \n",
" # there's no IF for 2017 yet\n",
" if year == 2017: year = 2016\n",
" \n",
" yearIDX = year - 2005\n",
" IF_at_time = IFdata[row.journal].iloc[ yearIDX ]\n",
" IFpub[row.status][yearIDX] += IF_at_time\n",
" Z[row.status][yearIDX] += 1"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for s in [4,-1,1,3]:\n",
" pl.plot( IFpub[s]/Z[s], color=colors[s] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieving altmetric data"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"def parseAltmetricData(filename):\n",
"\n",
" parsedData = {}\n",
"\n",
" inputfile = open( filename, 'r' )\n",
"\n",
" # skipping first row\n",
" inputfile.readline()\n",
"\n",
" for line in inputfile:\n",
"\n",
" lineSplit = line.split('\\n')[0].split('[')\n",
"\n",
" ls_comma = lineSplit[0].split(',')\n",
" pmid = int( ls_comma[0] )\n",
" parsedData[pmid] = {}\n",
"\n",
" score = float( ls_comma[1] )\n",
" parsedData[pmid]['score'] = score\n",
"\n",
" numReaders = int( ls_comma[2] )\n",
" parsedData[pmid]['numReaders'] = numReaders\n",
"\n",
" cited = int( ls_comma[3] )\n",
" parsedData[pmid]['cited'] = cited\n",
"\n",
" scopus = lineSplit[1][:-1]\n",
" parsedData[pmid]['scopus'] = scopus\n",
"\n",
" return parsedData\n",
"\n",
"dictAltmetric = parseAltmetricData('./links.bulk.altmetric.csv')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Altmetric records: 13889 \n"
]
}
],
"source": [
"print( 'Number of Altmetric records: %d ' % (len(dictAltmetric.keys())) )"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"mpl.rcParams['hatch.linewidth'] = 3 # previous pdf hatch linewidth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Altmetric score"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"scores = {}\n",
"for status in [1,3,-1,4]:\n",
" scores[status] = []\n",
"\n",
"for index, row in uniqueLinks.iterrows():\n",
" if row.id in dictAltmetric.keys():\n",
" numYears = (2018 - year)\n",
" scores[ row.status ].append( dictAltmetric[row.id]['score'] )"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 252x108 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(3.5, 1.5) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [scores[4], scores[-1], scores[1], scores[3]]\n",
"\n",
"parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False, \n",
" showextrema=False, widths=0.7, points=500 )\n",
"\n",
"j = 0\n",
"for pc in parts['bodies']:\n",
" pc.set_facecolor(colors_list[j])\n",
" pc.set_edgecolor( (0.2,0.2,0.2) )\n",
" pc.set_edgecolors( (0.2,0.2,0.2) )\n",
" pc.set_alpha(1)\n",
" j += 1\n",
" \n",
"\n",
"def buttpos(x): return [x-0.05,x+0.05]\n",
"for j in range(4):\n",
" pl.plot(buttpos(j), [np.mean(data[j])]*2, color=\"k\", \n",
" linewidth=6, solid_capstyle=\"butt\", zorder=4)\n",
"\n",
"for j in range(4):\n",
" ax.text( j-0.3, -2.5, 'n='+str(len(data[j])), fontsize=7 )\n",
"\n",
"ax.set_ylim(-4,30)\n",
"ax.set_ylabel('Altmetric score')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xticks([0,1,2,3])\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'], \n",
" rotation=-25, fontsize=8)\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('Altmetric Score vs status.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 129.6x100.8 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(1.8, 1.4) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [ np.mean(scores[4]), np.mean(scores[-1]), np.mean(scores[1] + scores[3]) ]\n",
"vardata = [ np.std(scores[4])/np.sqrt(len(scores[4])), \n",
" np.std(scores[-1])/np.sqrt(len(scores[-1])), \n",
" np.std(scores[1] + scores[3])/np.sqrt(len(scores[1] + scores[3])) ]\n",
"\n",
"pl.bar( [0,1,2], data, 0.7, yerr=vardata, color=colors_list, \n",
" edgecolor=[(0.2,0.2,0.2)]*3, lw=1.3, capsize=8 )\n",
"\n",
"pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color=colors_list[2:], hatch=\"//\",\n",
" edgecolor=[(0.1,0.4,1.0)]*3, lw=1.5, capsize=8 )\n",
"pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color='none',\n",
" edgecolor=[(0.0,0.0,0.0)]*3, lw=1.5, capsize=8 )\n",
"\n",
"ax.set_ylabel('Altmetric score')\n",
"ax.set_ylim(0,18)\n",
"ax.set_yticks([0,5,10,15])\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xticks([0,1,2])\n",
"ax.set_xlim(-0.5,2.5)\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible'], \n",
" rotation=-25, fontsize=9)\n",
"\n",
"ax.text(1.3, 18, '*', fontsize=12)\n",
"ax.plot([2,2,0.5,0.5], [16,18,18,16], color='k', lw=1.5)\n",
"ax.plot([1,0], [16,16], color='k', lw=1.5)\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('1c.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KruskalResult(statistic=118.4228015588113, pvalue=1.6869222402985409e-25)\n",
"[[-1.00000000e+00 5.16142689e-02 2.47344772e-17]\n",
" [ 5.16142689e-02 -1.00000000e+00 4.15518624e-14]\n",
" [ 2.47344772e-17 4.15518624e-14 -1.00000000e+00]]\n"
]
}
],
"source": [
"from scipy.stats.mstats import kruskalwallis\n",
"print(kruskalwallis(scores[-1],scores[4],scores[1],scores[3]))\n",
"\n",
"lists = scores[-1]+scores[4]+scores[1]+scores[3]\n",
"categories = len(scores[-1])*[-1]+len(scores[4])*[4]+len(scores[1]+scores[3])*[1]\n",
"\n",
"print(sph.posthoc_dunn([scores[-1],scores[4],(scores[1]+scores[3])], p_adjust='fdr_bh'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Altmetric number of readers"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"scores = {}\n",
"for status in [1,3,-1,4]:\n",
" scores[status] = []\n",
"\n",
"for index, row in uniqueLinks.iterrows():\n",
" if row.id in dictAltmetric.keys():\n",
" numYears = (2018 - year)\n",
" scores[ row.status ].append( dictAltmetric[row.id]['numReaders'] / numYears )"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 252x129.6 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(3.5, 1.8) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [scores[4], scores[-1], scores[1], scores[3]]\n",
"\n",
"parts = ax.violinplot( data, [0,1,2,3], showmeans=True, showmedians=False, \n",
" showextrema=False, widths=0.7, points=500 )\n",
"\n",
"j = 0\n",
"for pc in parts['bodies']:\n",
" pc.set_facecolor(colors_list[j])\n",
" pc.set_edgecolor( (0.2,0.2,0.2) )\n",
" pc.set_edgecolors( (0.2,0.2,0.2) )\n",
" pc.set_alpha(1)\n",
" j += 1\n",
" \n",
"\n",
"def buttpos(x): return [x-0.05,x+0.05]\n",
"for j in range(4):\n",
" pl.plot(buttpos(j), [np.mean(data[j])]*2, color=\"k\", \n",
" linewidth=6, solid_capstyle=\"butt\", zorder=4)\n",
"\n",
"# for j in range(4):\n",
"# ax.text( j-0.4, -2, 'n='+str(len(data[j])), fontsize=7 )\n",
"\n",
"ax.set_ylim(0,20)\n",
"ax.set_ylabel('Altmetric readership')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xticks([0,1,2,3])\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'], rotation=-25 )\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('Altmetric numReaders vs status.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAACFCAYAAADRsIPvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAFEtJREFUeJztnXuYlVW9xz9fBpC4KUgpIoUZXrJ4SPFeilTmlVS8HDVPkaadsvSYlrcUTbuZHlNTQ81I5aSmoj5lR4+Kp8QbKt5SMkq8oYKDMg4qAd/zx1obNtPMnr1nv5v97pn1eZ732bf1rvXbM9+9Lr/fusg2iUTe6FVvAxKJ9kjCTOSSJMxELknCTOSSJMxELknCTOSSJMxELknCbDAkfUXS+HrbUWuUHOyNgaRjgDeAdYH5wGhgoe1b6mpYjUjCbBAk9QGOA74EvAtcBfzK9sq6GlYjUlPeWLjocUXR625HqjEbhNiULwQGE5ryzYDXbc+oq2E1IlfClNQEzAZesb2PpKHA9cAo4AXgYNuL62dh/ZH0FeAF2zPrbEpNyVtTfhzwbNHrk4G7bY8G7o6vO2WPPfYwoZnrdpftq23fW287KrwqpndXbqoFkjYG9gbOBU6Ib38RGB+fTwNmAt/rLK9FixZlb2AFTJ06lenTp9fVhqw47LDDOProo9d6uXmqMS8EvgsUjzI3sL0AID5+qKObJR0tabak2QsXLqytpZ0wffp05syZU1cbsmDOnDl1+4HlosaUtA/whu1Hu+o8tj0VmAowbty4unecx44dy8yZM+ttRlWMHz++bmXnQpjAzsBESXsB/YDBkq4FXpc03PYCScMJDuZEDyAXwrR9CnAKQKwxT7T9JUnnAV8Gfhwfb621Lccff3zVzXDh/mprnLFjx3LhhRdWlUejkgthluDHwA2SjgReBA6qsz1lMXDgwHqbkAljx46tW9m58mNmxbhx4zx79ux6m5FYjSq9IU+j8kRiFUmYiVyShJnIJUmYiVxSk1G5pA2B7Qhx0kdsv1aLchLdl8xrTElHAQ8DBwAHAg9K+mrW5VTDlClTkNTla8qUKfX+Ct2ezN1FkuYCO9l+M75eH5hle/NMCypBe+6i5uZmWlpaOr33kEMOAeD6668vu7xBgwYxdOjQyozsWVTsLqpFU/4yUKyAFuClGpRTNs3Nzew/cSKt773Xadq5c+cCMGnSpLLzH9CvH7fcdlsSZ4bUQpivAA9JupXQx/wi8LCkEwBsX1CDMkvS0tJC63vv8e3nWxj2/oqSaU9ethyAs59+q6y8F63TxEWjQxlJmNlRi1H5PGAGqyeI3gosAAbFq24Me38FG7y/suTVx9DHdJqucHUm9FKkvm7HZF5j2j4r6zyz5pcs5gpK14jj+EeHn32N9TiGIRWX27afO3nyZCZPntxu2nL7uvPnzwe6Xz+3U2FKGgC8W1gmKqkX0M/20jbpLrR9vKTbaWc6ve2JGdlcNccwpEvCqobm5mb2328/Wpcu7Twxlfd1B/Tvzy0zZnQbcZZTY94NfA54J77uD9wJ7NQm3TXx8WfZmNa9aGlpoXXpUn42aUeGD+7fafojLgxTT6+Z/NlO0y5YspQTb3qgW/VzyxFmP9sFUWL7HUn/8pe1/Wh8vC9D+7odwwf3Z+TQzqfF9evTBFBW2krYfq/jmffc4xTchItfegivWNbl/NTUlyEjt1/1epPRY5h95yVV21mOMFslbW37MQBJ2xB2gmjfUGlnYArwkZi/CKv7Plq1td2Mc393Dz+8eWbJNAMOO6PDz049YDynHTihojLnPfc4i195ggHDxwAwcOPtWb5crFi52tXY1Mv07m1a5v8JgEEf+cwaebRN/+4/Q/rWBU/yzNKlNDc3V11zlyPM44EbJb0aXw8HDimR/irgP4FHCbtFJDrgtAMnVCysarHNgOFjOOe6adhw8r678t5bpd3MBYG2R7/1RvKj2+9DgtMP/zLL3m3NpEvRqTBtPyJpC2BzQu33nO1/lrjlbdt3VGVVoubYcMesYQw9+MVV731y0xb23GkRaidOU0j/1LxBZaWvlg6FKWmC7XskHdDmo9GSsH1zm/Rbx6f3xrU6NwPvFz4vdAUS+aASka1tUULpGnNX4B5g33Y+M0F4xZzf5vW4NunXbpuV6JDly5VrUUIJYdo+M/os77B9Q2cZ2d4tU8sSNaN44JJHUUInIcnoVD+2kgwlHSdpsAJXSnpM0u5VWZmoCVmK0g41cVaUEyu/S9KJkkZKGlq4SqT/qu0lwO6ELV0mE5bhJnJE1qK8Y9awNWriainHXVSY5PvNYluAjvySBev2Aq62/YRU64o/UQlNvZy5KIvTZ0E5wtzS9hoTGSX1K5H+UUl3ApsAp0gaxJobZSXqTO/ezrUoobymfFaZ7xFrxjMI+1huGyd69CU054kck4Uom3pltxqilB9zQ2AE8AFJn2J1Ez2YMJGjHWNtSTNsb1P03pvAm5lZnMicLET5yU1bmNXbLCsVeqmAUk35F4CvABsDxbPOlwCnlrjvQUnb2n6kevMStSYrUe650yJmXZqdXaX8mNOAaZIm2b6pgjx3A74u6QWgldWTOMZUZWkic7IUZdbD23IGP/dLugrYyPaekj4O7Gj7qg7S79kVQySNBH4DbEgYLE21/fN0QEBtyLMoobzBz9XA/wAbxdd/Jcw4ahfb84GRwIT4fGmZ5SwHvmN7S2AH4JvxR9ClAwISpcmzKKE8wQyLIcmVwUgvp8R0NklnEjbwPyW+1Qe4trNCbC8oTPSw3UI4vWIEYZXltJhsGrBfGTYnStAIsfJyhNkaNy0wgKQdgLdLpN8fmEjoX2L7VSpcHSlpFPAp4CHKPCAgT4cD5J1GiJWX08c8AbgN2FTS/cAHCVu/dMSy6DYqCHlAJQZJGgjcBBxve0m5QaO8HQ7QCOQ5Vl7OROHHJO3K6onCczuZKHyDpF8C60n6GiGkeUU5xsSDPG8Criua75kOCKgBtYqVZyXNcpbvNhHi3qNi+t3jROF2d9Sw/TNJnyf4OzcHzrB9VxnliLAs49k2ed/GWj4goLvTXWLltwPvAU9RZsw7CrFTMbZhZ+AI4ClJhWMjTqVBDwjIM40QKy9HmBuX4xyX1EKJcwNtDy51v+0/0/GuYJ0vrk5URVax8pUZLT8sR5h3SNrd9p2lEtkeBCDpbOA1wgYIAg6nznsWJUqTx1h5Oe6iB4FbJL0raYmkFklLSqT/gu1LbbfYXmL7MqD8Pf0Sa5UsI0BZUo4wzwd2BPrbHmx7UCfN8gpJh0tqktRL0uGk9eW5JM9hyXKE+TzwtMvfevgw4GDg9XgdFN9L5Ig8ixLK62MuAGZKuoM114l35C56gRBGTOSYPIsSyhPmP+LVN15QYvQdl10cCWxFOEk33GDn6oCAnkwjxMrLEeZfbN9Y/IakUr7Ea4DnCBONzyaMyp/tsoWJzGmEWHk5fcxTynyvwMdsfx9ojZON9wY+2RXjErWlIWPlkvYkhCJHSLqo6KPBhLmTHVHwZL0l6RMEn+aoKu1MZEwjx8pfJWwlODE+FmghbDPYEVMlDQG+T4hzDySsnEzkhIaOldt+AnhC0rVxcnBZ2L4yPr2PjjdFSNSRho6VS3qK1ZOD1/iIEovLJG0A/JDy1wglckAjxcr36WKevyasEzotvv4rYTFZEmZOaahYue357V2EdebfLZFnRWuEEvUlr7Hysg6gkjSW1aHGf/Cvm7YWU+kaoUSdyHNYslQfczPg34BDCVu8XE84rbezDVorXSOUqAN5FiWUrjGfA/4E7Gv7bwCSSrmJVp2aRtgmu9w1Qok6kGdRQunIzySCc/xeSVdI+iydnDsddyA+3/Zy28/YfjqJMn80Qqy81ODnFtuHAFsAMwlO9Q0kXdbJ1tV3SpqUNmvNL90iVm671fZ1tvchjMjnUHqblhOAG4H3y5zxnqgTDRkrbw/bzcAv49VRmrS+pwHIe6y8nNlFiW5GI8TKkzB7II0QK0/CTAD524M9CTORmbO9d+8kzERG5DVWnoTZg8lzWDIJs4eSZ1FCgwhT0h6S5kr6m6S0B3sG5FmU0ADCjPtz/oJwGsbHgUPjrPhEF2noWHmO2A74m+2/214G/Ja000dVNEKsvKKQZJ0YAbxU9PplYPuuZLRonaZMDKomzwVLlmZuQ6V5/vPNOSy6fTxNvcys3u7wRLPly7WGiDtL/86CJ+m73qYV2dIRKn+vrPoQd/34gu2j4usjgO1sf6tNuqOBo+PLzYG5VRQ7DMjW/9E18mBHFjYssr1HJTc0Qo35MuFAqwIbE9a8r0HxqRXVImm27XFZ5NXodtTLhkboYz4CjJa0iaS+hOUet9XZpkSNyX2NaXu5pGMJxwY2Ab+y/UydzUrUmNwLE8D2H4A/rMUiM+kSZEAe7KiLDbkf/CR6Jo3Qx0z0QJIwE7mkxwhT0gclXRPPxVyb5faVdIGkXdZmubHsDdd2mVnRY4QJ7AtsDXxD0oy4qWzNiWHUPwOnSbpf0t5ro1xJWwAz2my62zD0CGHGo6b3AfaKa+XnA7vFz2rumYgnCf+JcLzMwVGgh9aqPEnbEU497k/YUYV45lLDrPVvCHdRBkwEPgd8WtICwp7wd8Kq3ehqgiTFs9uHEc7DPCwebz0GWD+m6RV3MMmqzB2BLxGWWD9I8P0WdkkpnHJMBec21YVuX2PG4132BM4FdgH+QogcLZT0C0mbtEmf5UyPQg01GXgminIb4DOE3U2KBVN1uZI2Bk4EdgKWEbYZ/7CkfpK+KGmMI9WWVWt6Qo25N7Ch7SMAJP0c+B1wBWEDsLMk/cH2bwFsr4jpVM0/MN6/UlIf4POEPekhnIH0RqxJ94jPHysqt0s1aKyVzwemE3bnO5lwuMOpwH/FZGMlzQR+YvutonubCuXnhZ4gzOHAxyWdQDh+cDFBHB+1vTmApF0k/QDYErjG9q1ROJ8iDJrOqaK5/SZBfA/F2nlz4FxJg4FLgUcl9Qe+FzchW9XkVvjDWA5cZPv++HqmpNMIk6tfs31WzHd74N1oy3Dbs/ImSugBTbntS4BPAH2Ak4D/JfQ3pwLEpvVQ4AXgcsKofYt4+1EhC69UpIJyC6J6C/hVfP4t4D7brwD7Ac/aPgiYARwh6WxJR7a5n3LKtf1WQZRFA7ptgT2AJ4qSPgKMJ/wovivpztgvXUU8oLauA6WeUGNiezHwE0nnxbd6szoGPIYwSr/N9kJJWwPbSmol7HT3g5hHl5p127+GVX3dUcDpsYbcm7BkhPj+LoRBy/mSltm+pigPV9LEFw3obidsOX6gpFdtP0w4SWQX4ArbN8fv2zfauBXwfHRx1ZUeIcwCRf/Y4hriaWBn2wvj6wOArxNqtPuBQXHbxfVsr/IJxhpFFYjlvZg3kj4PfChOToFwvOFRtudJagEGxHQ7E2br/zpuaFbp970q5tME/Ef8cbwEbG/7tJjmMUmDJJ1IEO1OkqYRugWr+tuVfNcs6PZNeRm8DgyQdI+ki4EHgCcJ+80fCJxOGOE+AMEdI2mTOLjt0j/K9l2E2pEoiPdtz5H0YcLhsFdLOpcgyt7AHyXtVZxH7FmU9f+zfaXtyQT3UQvwpqQDipLsTfB73mB7LKGvvYGk3pLWq+a7dpU0uygiaSfgXduPSzoTWCd+dJ3tZyR9ALgYmEdwzv+d0GdtrWLAQhxgOQrzKkKNdg1h0d2rwBRCc7yr7YskrUuovedX8V23J+xj+jzwU0ILMZZwusgQgjC3JhyJM5ZwNPiPbD9XlEdNR/KpxozE0enjsdlaQdh89gnCqBzCQGgCoenfB3gFGBwHRtvEPCr+ldt+nHACnQhunqsITvHpwM8JormWcNjCKMIZStdKurGtD7aCMh+KEbALbC8hCO/h6FI7A/gxMIjwdziI4F6bBCDpIzGPVaKMHoZssZ2uDi5gPeAeYFOCL/Co+HgPYXT/aWB/4A3g24SDFLIod13glpjnAIKLaRhBqN+Jac4BvhGfb9Lm/qYKyxsSv9dM4Nii9w8knFby0fj6IOBhwqTtveN7fQjHf28EDMzsb1/vf37eL2BAfNyd4MAeFq8j4/t3EZrFLeI/bb+Myl2X0L+9meA5GEFYX1/4/AeEiNI2wEXAbu3ksWXB/jLLHEKIGo0gHLu4PrAJcEn8/EeE0OrH4o9mKDAifvYBQrdgr0p/GO1dPWpU3hVst8andwOjCc3a722fF0fXA2xfACDpZTJabmv7beCcOKIeSDjQ60FJmxMmZwwjnIq8FaHGnhNtuJQgkI3jZ38EWv+lgPbLXAzMioOqF4HfAwsJA0SAacCZwG8d+ruDgesknUSIoi0DXrS9QlJvVzEPIfUxy8T2Ctu/sD2e0IxDaE5/AyBpX8I/8+UalPu2Q1/wVkIo9XTg/wgzh0YRzlJaHAc1WxH6qucRmt7+0b5KggMrbV9uewdC7P2kOCAcQphzMEZhxeoE4FXbjxDi/xsBu0tatyBKSftKmlDp907C7AIOAxYIYcTL4/P9gMdYXbvUotwbbe8CfMv2fxN+COMIIoUQav0NwWH+JKEmPSfe29UAwdxYkz4OfAe4jFA7jiQ029fGWnwzwoG2A4Gbo88UQrRr/UrLTe6iKonN3iDg34F7bT+9lspVLPdiwij+JWAHgrP+qwTxXFJUc1U1KaWo3E0J54lOAKbY/rSk4wjutSttN0u6nNCdGE3YHeVQF0WTynE1pRqzSmKz97bti4G1tt7dgSW2v0yYSbQYuCMKYCTweHEfLwtRxnzmOfht/0KItY8g+HXXiaIcRGjWXyPMQbjZ9jJJm0WfLZ2JElKN2a0ohA4J8wD6EmrOla5h1EbSAILLbCLQTBgAvQFcB1xCaEl2IHg1PkQY8R9l+68l803C7H7E0fLhhCl876zFMo8B7rH9qKQrgVmEUO7ZBK0dGEOrHy7qm7dLchd1Q+II/rI6lHkegMJ59YMJ7qZhwBJggaTfE/y9M2K6Dvu9SZiJzLH9JmESDAoz+EUIq55FcGE90FkeqSlP1JzoxzyWEHs/yfYLnd6ThJlYW8TJLvNctN6ow7RJmIk8kvyYiVyShJnIJUmYiVyShJnIJUmYiVyShJnIJUmYiVzy/4GVTMts6MZpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 129.6x100.8 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(1.8, 1.4) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [ np.mean(scores[4]), np.mean(scores[-1]), np.mean(scores[1] + scores[3]) ]\n",
"vardata = [ np.std(scores[4])/np.sqrt(len(scores[4])), \n",
" np.std(scores[-1])/np.sqrt(len(scores[-1])), \n",
" np.std(scores[1] + scores[3])/np.sqrt(len(scores[1] + scores[3])) ]\n",
"\n",
"colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.5,1.0,0.5)]\n",
"pl.bar( [0,1,2], data, 0.7, yerr=vardata, color=colors_list, \n",
" edgecolor=[(0.2,0.2,0.2)]*3, lw=1.3, capsize=8 )\n",
"\n",
"pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color=colors_list[2:], hatch=\"//\",\n",
" edgecolor=[(0.1,0.4,1.0)]*3, lw=1.5, capsize=8 )\n",
"pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color='none',\n",
" edgecolor=[(0.0,0.0,0.0)]*3, lw=1.5, capsize=8 )\n",
"\n",
"\n",
"ax.set_ylim(0,41)\n",
"ax.set_ylabel('Altmetric \\n readership')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xlim(-0.5,2.5)\n",
"ax.set_xticks([0,1,2])\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible'], \n",
" rotation=-25, fontsize=9)\n",
"\n",
"ax.text(1.1, 40, '*', fontsize=12)\n",
"ax.plot([2,2,0.5,0.5], [38,40,40,38], color='k', lw=1.5)\n",
"ax.plot([1,0], [38,38], color='k', lw=1.5)\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('1e.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-1.00000000e+00 2.07209979e-01 1.16657681e-06]\n",
" [ 2.07209979e-01 -1.00000000e+00 8.42073375e-15]\n",
" [ 1.16657681e-06 8.42073375e-15 -1.00000000e+00]]\n"
]
}
],
"source": [
"lists = scores[-1]+scores[4]+scores[1]+scores[3]\n",
"categories = len(scores[-1])*[-1]+len(scores[4])*[4]+len(scores[1]+scores[3])*[1]\n",
"\n",
"print(sph.posthoc_dunn([scores[-1],scores[4],(scores[1]+scores[3])], p_adjust='fdr_bh'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Altmetric citation"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"scores = {}\n",
"for status in [1,3,-1,4]:\n",
" scores[status] = []\n",
"\n",
"for index, row in uniqueLinks.iterrows():\n",
" if row.id in dictAltmetric.keys():\n",
" numYears = (2018 - year)\n",
" scores[ row.status ].append( dictAltmetric[row.id]['cited'] / numYears )"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 252x108 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(3.5, 1.5) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [scores[4], scores[-1], scores[1], scores[3]]\n",
"\n",
"parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False, \n",
" showextrema=False, widths=0.7, points=500 )\n",
"\n",
"j = 0\n",
"for pc in parts['bodies']:\n",
" pc.set_facecolor(colors_list[j])\n",
" pc.set_edgecolor( (0.2,0.2,0.2) )\n",
" pc.set_edgecolors( (0.2,0.2,0.2) )\n",
" pc.set_alpha(1)\n",
" j += 1\n",
" \n",
"\n",
"def buttpos(x): return [x-0.05,x+0.05]\n",
"for j in range(4):\n",
" pl.plot(buttpos(j), [np.mean(data[j])]*2, color=\"k\", \n",
" linewidth=6, solid_capstyle=\"butt\", zorder=4)\n",
"\n",
"# for j in range(4):\n",
"# ax.text( j-0.4, -2, 'n='+str(len(data[j])), fontsize=7 )\n",
"\n",
"ax.set_ylim(0,9)\n",
"ax.set_ylabel('Social media \\n citations per year')\n",
"ax.set_yticks([0,2,4,6,8])\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xticks([0,1,2,3])\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'], \n",
" rotation=-25, fontsize=8 )\n",
"\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('Altmetric Cited vs status.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 129.6x100.8 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ax = pl.subplots( 1, 1, figsize=(1.8, 1.4) )\n",
"\n",
"# let's make sure the order is maintained:\n",
"data = [ np.mean(scores[4]), np.mean(scores[-1]), np.mean(scores[1] + scores[3]) ]\n",
"vardata = [ np.std(scores[4])/np.sqrt(len(scores[4])), \n",
" np.std(scores[-1])/np.sqrt(len(scores[-1])), \n",
" np.std(scores[1] + scores[3])/np.sqrt(len(scores[1] + scores[3])) ]\n",
"\n",
"colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.5,1.0,0.5)]\n",
"pl.bar( [0,1,2], data, 0.7, yerr=vardata, color=colors_list, \n",
" edgecolor=[(0.2,0.2,0.2)]*3, lw=1.3, capsize=8 )\n",
"\n",
"pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color=colors_list[2:], hatch=\"//\",\n",
" edgecolor=[(0.1,0.4,1.0)]*3, lw=1.5, capsize=8 )\n",
"pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color='none',\n",
" edgecolor=[(0.0,0.0,0.0)]*3, lw=1.5, capsize=8 )\n",
"\n",
"\n",
"ax.set_ylim(0,15)\n",
"ax.set_ylabel('Social media \\n citations per year')\n",
"\n",
"ax.set_xlabel('')\n",
"ax.set_xlim(-0.5,2.5)\n",
"ax.set_xticks([0,1,2])\n",
"ax.set_xticklabels( ['Broken','Timeout','Accessible'], \n",
" rotation=-25, fontsize=9)\n",
"\n",
"\n",
"# ax.text(1.3, 4.7, '*', fontsize=12)\n",
"# ax.plot([2,2,0.5,0.5], [4,4.7,4.7,4], color='k', lw=1.5)\n",
"# ax.plot([0,1], [4,4], color='k', lw=1.5)\n",
"simpleaxis(ax)\n",
"\n",
"pl.savefig('1d.pdf',bbox_inches = \"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KruskalResult(statistic=746.4778493513235, pvalue=1.7515548139742244e-161)\n",
"[[-1.00000000e+000 1.22907386e-007 9.65803244e-103]\n",
" [ 1.22907386e-007 -1.00000000e+000 3.37058804e-076]\n",
" [ 9.65803244e-103 3.37058804e-076 -1.00000000e+000]]\n"
]
}
],
"source": [
"lists = scores[-1]+scores[4]+scores[1]+scores[3]\n",
"categories = len(scores[-1])*[-1]+len(scores[4])*[4]+len(scores[1]+scores[3])*[1]\n",
"\n",
"from scipy.stats.mstats import kruskalwallis\n",
"print(kruskalwallis(scores[-1],scores[4],scores[1],scores[3]))\n",
"\n",
"print(sph.posthoc_dunn([scores[-1],scores[4],(scores[1]+scores[3])], p_adjust='fdr_bh'))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}