Historical_birth_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Examining a reported link between solar phase at birth and human health from Norwegian Historical Church records\n",
    "\n",
    "Analysis of [Skjaervo et al. (2014)](http://dx.doi.org/10.1098/rspb.2014.2032), who concluded that Solar activity at birth predicted infant survival and women’s fertility in historical Norway.\n",
    "\n",
    "Solar data taken from  [Usoskin, et al. (2002), A physical reconstruction of cosmic ray intensity since 1610. J. Geophys. Res., 107 (A11), 1374](http://cc.oulu.fi/~usoskin/personal/2002ja009343.pdf). [Access original data here](ftp://ftp.ncdc.noaa.gov/pub/data/paleo/climate_forcing/solar_variability/usoskin-cosmic-ray.txt)\n",
    "\n",
    "All data has been converted to CSV format and placed on a publicly accessible server. Reading the data from the web is automatic using the pandas module below.\n",
    "\n",
    "Code by Benjamin A. Laken: e-mail blaken@geo.uio.no   web: [benlaken.com](http://www.benlaken.com)\n",
    "\n",
    "###Notes on the Original study:\n",
    "\n",
    "Abstract of Skjaervo et al. (2014):\n",
    "\n",
    ">*Ultraviolet radiation (UVR) can suppress essential molecular and cellular mech- anisms during early development in living organisms and variations in solar activity during early development may thus influence their health and repro- duction. Although the ultimate consequences of UVR on aquatic organisms in early life are well known, similar studies on terrestrial vertebrates, including humans, have remained limited. Using data on temporal variation in sunspot numbers and individual-based demographic data (N1⁄48662 births) from Norway between 1676 and 1878, while controlling for maternal effects, socioe- conomic status, cohort and ecology, we show that solar activity (total solar irradiance) at birth decreased the probability of survival to adulthood for both men and women. On average, the lifespans of individuals born in a solar maximum period were 5.2 years shorter than those born in a solar mini- mum period. In addition, fertility and lifetime reproductive success (LRS) were reduced among low-status women born in years with high solar activity. The proximate explanation for the relationship between solar activity and infant mortality may be an effect of folate degradation during pregnancy caused by UVR. Our results suggest that solar activity at birth may have consequences for human lifetime performance both within and between generations.*\n",
    "\n",
    "**Notes on the data**:\n",
    "\n",
    "An individual-based dataset from two human populations in Norway that spans 160 years. The data include information regarding natural fertility and mortality.\n",
    "\n",
    "Norwegian church records of two different populations in mid-Norway that included 9062 individuals born between 1676 and 1878. The two populations were located at similar latitudes (638N) and experienced similar climatic conditions.\n",
    "Smøla Group: a coastal island population at low altitude (0 to 70 m.a.s.l). \n",
    "Soknedal Group: an inland population at higher altitude (200 to 600 m.a.s.l). \n",
    "Both populations had  two distinct socioeconomic groups, a low (poor) or high (wealthy) status group.\n",
    "\n",
    "The chief demographic characteristics of both populations during the study period can be described as strictly monogamous, where both men and women married late and experienced moderate infant mortality and natural fertility.\n",
    "\n",
    "Solar activity, measured as the number of sunspots (SS) observed on the solar surface, varies in an 11-year cycle, with 8 years of low activity (solar minimum—SSmin) followed by 3 years of high activity (solar maximum—SSmax). The mean annual numbers of sunspots were downloaded from a [NOAA website](http://solarscience.msfc.nasa.gov/SunspotCycle.shtml).\n",
    "\n",
    "**Hypothesis under examination:** High solar activity during early development affects human life traits (survival, fertility and reproductive sucess) with diffrences between gender and age. \n",
    "\n",
    "Specifically, testing if solar activity at birth is: \n",
    "1. Related to the probability of survival from birth to the age of 20 years. \n",
    "2. Related to fertility. \n",
    "3. Related to reproductive success (LRS). \n",
    "\n",
    "*Male/Females analysed separately as UV radition has been found to affect men and women differently (and the hypothesised mechanisim linking solar activity to human health relates to UV radiation).*\n",
    "\n",
    "Two different measures were used to capture the variation in fitness: fertility (number of children born) and LRS (number of children surviving to the age of 20 years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function,division,generators\n",
    "from IPython.display import Image, display, display_png, HTML\n",
    "%pylab inline\n",
    "import pandas as pd\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch the data from web source using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Adults = pd.read_csv('http://www.files.benlaken.com/documents/Adults.csv')    # Very nice!\n",
    "Kids = pd.read_csv('http://www.files.benlaken.com/documents/Kids.csv')\n",
    "Survival =pd.read_csv('http://www.files.benlaken.com/documents/Survival.csv')\n",
    "DeathAge =pd.read_csv('http://www.files.benlaken.com/documents/age_death.csv')\n",
    "Solar = pd.read_csv('http://www.files.benlaken.com/documents/Usoskin.csv')     # Read solar dataset also\n",
    "Solar.index = Solar.Year                    # Index the solar data by year\n",
    "#Adults.index = Adults.Born                 # Index the data by the year of birth\n",
    "#Adults                                     # Preview the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple calls to the database to generate summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Construct some summary data of number of people born per year\n",
    "\n",
    "trange=arange(min(Adults.Born),max(Adults.Born))  # Generate a list of all years of data\n",
    "\n",
    "BirthsPerYr = [len(Adults.Born[Adults.Born == year]) for year in trange] # How many adults born each year?\n",
    "\n",
    "BirthsPerYr_GA = [len(Adults.Born[(Adults.Born == year) & (Adults.Group == 'A')])\n",
    "                  for year in trange]                      \n",
    "BirthsPerYr_GB = [len(Adults.Born[(Adults.Born == year) & (Adults.Group == 'B')])\n",
    "                  for year in trange] \n",
    "\n",
    "BirthsPerYr = pd.Series(data=BirthsPerYr,index=trange)   # Make these pd series objects\n",
    "BirthsPeYr_GA = pd.Series(data=BirthsPerYr_GB,index=trange)\n",
    "BirthsPerYr_GB = pd.Series(data=BirthsPerYr_GB,index=trange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple data viz. with Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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7UgeHmXsf6jdlBu5euADrgP3D478CPgo8YppllC2EE28qKirNFvBV4GeAr8qa\nHmrp0n5Zoa6rZ9kG8LXjrw3LPC9vmTnvWQW+dsZtrGy5PTm2uXVN76vR4/S+quJ4qOS3W6ZdyPbw\n97HAVuAE4EttboBK4f0333YdVOIsRWIj9WW+JtYfoHb2XXf2S15dV9qGst8bXdovfdiGNuqq35RC\n+8iz5u895QmyX4W/JwDvcvfzzOx1Uy5DRDrGk+75m1lKvlWPJ7q1X/LqWsc2dGm/5OnSNnSprjJ9\njtdVZvZO4A+ALWa2b4llSAtc1+MlR5HYUNJyti7tl7y6rrQNZb83urRf8nRpG9qoq35TZjDlabM7\nAr8P3C9MHwIc1+YpOxUVlXpLl/JdtF+mrutMOV5d3y992IYu1XVoJa/d0nrFZt0AlcL7b77tOqjE\nWbJio0vJt1UmVVe03lPrrE+Z7S1b10mxkf2edo5HH2KqTP3a+nzCyafEvI9iKJU0vEjGaTyRpLvp\nq0N5VZsboFJ4/823XQeVOEvOj2tn/ouOra5116fJ7e16bPS1xHAM4PAT2q5D7CWv3VLozvUjZvZp\nkvt8fIWlRHvc/W8LL6Qk3blepFldGn4ltrrWXZ+2t7ft9UscxyCGOsRspiGDUgu51N0fUmnNiq9b\nDS+RhnVp+JXY6lp3fdre3rbXL3EcgxjqEKuZhgxK+Xcze1hFdZIGmdl823WQOOXFhnp1xVufpra3\nD7HRV20fAzObb7sOnVXwOuX2UC4DbgGuSM37WpvXSlUK77/5tuugMukY1Z/Qm72Ow08YX0cMOSRT\nbFNUdS1Tn2mOvXK8VJqNgbzYfPWpioNJ+w7PnF/wzWtCWZ16vDivzQ1QUelLaeLLtOg6mmgEVrhN\nUdW1TH2mOfZtb2/b61dp9hisEJvrFAeT9h2eNX/aHK83uPvJk+bVQTleMgRNJKsqITZOOi4SK8Vm\nOVUl11/s7g8fm7fd3R9aQR0nrVsNrxmY2bzrTsOd0ESyanodYGsUG3GILVFZ3xsyMh6bio3JZkqu\nN7M/NbPtwAPMbHuq7AS+VnFdRQariWTV8XXA4Xeqeh0yPSUqS6wUm9UqdMbLzA4A7gy8G3guMGrB\n3eTuP6qvesvqoDNe0mup0/kbPBn0dtl0V9Yh09NxkVgpNsub6YyXu99Icorxru5+pbvvDKWRRpfI\nQMyR+jILfzeE+V1aR2lmrB3/b9qMVWasbatORVRQ76iPS1ldPZ59Unds6hhPr/B9vDw5NfYVMzuq\nxvpITXTd/EjwAAAcvElEQVQfr/i5s2X8P0h3druzpc51gB1Z5TpmtABsHH2Rp/67Xmi1VpPNVO8m\njn0ZFXxvdPV49kktsQn20yqWP0TT3kD10cAXzOw7qTwv5XiJSCVS/01vDMm8nbik0dV61037pX11\nHwMd4+lN26txTdZ8d99ZTXVWXLdyvEQGIrbefUV1td51035pX9+HsIpRJUMGpXK7lpXKaikig9fV\nHlRdrXfdtF/a15chrPqi6O0kFsLfn5jZTWPlx/VWUaqgHC/JE1NsjPWY2snSJYyov8i7Wu9JZo2N\nvu6XLqnrGIxiQ8d4ekV7Nc6Fv3dy9/3cfT/g3sAB7r5/nRUUkUGJsndfgZ5bU9e7rt5g1S735EeP\n3ptOnh5NF1hmlMdzYOo+BqWWP+jekAXHG3oMsBX4Z+DhwKXANcB1wO+0OeaRioqKSt2ljnE06xqb\ns8rlpt67OuevBkRWKVWaHOi7vW3Es+YXvYHqV4BXAAcA7wKOd/cvmtnhwIfd/ciK2oEr1cFdyfUi\n0pI6xqurawy8KpebWtY7gHOAE4HnV1VXGa664j8WM43VaGaXjBpXZvYNd39g6rk9xm+sgxpes9G4\nWpJHsVFcHT236uoNVsVyR7GRWtbRwLZZlin9UNX3Rp97Q87aqzHdOvt5yQrc08wuNLOvm9mlZvbi\nMP8gM7vAzK4ws/PNTAl5IhKdOnpu1dUbrMrlppZ1BPD28Fc912Rmg+0NWfA65a+Am0L5ZerxTcAv\nCy7jYODI8PhOwH8BDwQ2AS8P808GXj/NtVIVFZXZCvja8byKkG+xton3x7QtKyxXOV4t53jFFGcq\nlRzPweZ4tVmhjwGPBy4H7hbmHQxcPs0GqKiozFZm/QKM6Qu0xsZM5T/6NTYSK1vuaFnpZaanm2z0\nxBRnKpUcz943pPPaLVPdub4q4Q74nwMeAnzX3Q8M8w3YNZoee4+7crxKUx6P5Enux+OXMEOSa0xJ\nsjHVpeti+97QsY1HbLERo7x2y94tVOROwD8B/9vdb0raWgl3dzPLbQma2ZmwmHy3G7hkdOBHN3PT\ndPY0cKSZRVMfTcc1DXYkHLsNzt8BHAZ2pFlz769y2p3dZsdtg1fugPnDkum49ndXpkdiqY8nif6b\nYesOOO3p7ufvbrs+A54+kuQ2U7HUp/XpYB5YwwoaPeNlZvsA5wGfdPfTw7zLgXl3v8bMDgEudPfD\nM97rrjNeIrWY9UxCTGciYqqLVEvHVrokr90y1ViNM1bAgPcAl40aXcHHgWeHx88myf0SkYbMOuRH\nTEOGxFQXqZaOrfRFYw0vkuEDngn8lpldHMrxwOuBY83sCuCYMC0VG790MBRlh6UY1nAWp7yAcOYg\ntX2LQ36ktzs9VExq/xxHkrOJGWu93WFhFocvmbQtZXQtLmatb2TfG4WHpunaceqiMrGh4xJ4BJn/\nRUpS1fbr0dVCcjm39Xo0v93lekKVfV8XSzo2Jm13arr12wvUdez7FBez1rer3xtdO05dLGViY2jH\nJa/d0nrFZt0AFZVJJfXhXjPdj06593W9TNru1PMPA98e/ka5f+o4hl2Li67VV9vd7zKk45LXbmnl\ndhJlKLleZlF2WIo+D2exkknb3aUhZLo0zE9dulbfqgx1u2M3lOPSenK9tCuyXI1GlR2WYijDWex5\n64CVt7tLQ8h0aZifusxS3y5/b3TtOHVN2djQcUGXGodS6GiuxnTbmHkn5NXg58WQ45VTv9bv1Ey1\nOV7rYtnGMseQ5XdoH92dfbH+08RT2eNNhXeLnzWOu/q9UcfnV2X22Bjacclrt7ResVk3QEVlVHI+\n1OeBr854XaEfv2nfV6J+UX3pTNruAo2CdbFsY5ljmK5vKO8KZTRdOJ7KHu8CjdvC+7KOOO5CGep2\nx16Gdlzy2i3K8ZJeif0Gi7HXrwpd38ax+m8IszfS4I1lU+97B3AOcCLw/GnXLyLtyWu3qOE1EGbD\nGVcr9sTN2OpXR2zEto3TStc/zCq9LRV07GitA8OQvjdkOoqNyZRcL4MQe+Jm7PWrQte3caz+G0Ip\ntS0VdOyIvgODiExHDa+BGMJ/JrEPKRJr/aqMjVi3sah0/WHZJb3RXdJrH0op9b5NJJcXTwh/N02z\n/ioM4XtDylFslKdLjQIkQzkAC+n8kfAFP+fOlvZqVlzs29BW/Zpcb+zHIM+o3iTDzyx4MuTQuvD0\n+YT6T7Mt6X2RWj6TlpVTl1Wj6aLrj01XY0Oa07cYyW23tJ31P2vvAJXC+29+5efj73GnUvbYT7pF\nRDdvGdDkPop9+fXtl+pio6v7QKX+2FhaZr9iJK/dokuNAoD7skspa1i6RKIeVB2nYztZ3ftIx0D7\nQCYbSozoUqMs0/XeaJJPx3ayuveRjoH2gUzWlxhRr0aZqOu90SSfju1kde8jHQPtA5lsCDGihtdA\nTBpXq+u90STfpGPb5fH4qlJ3/Hf181VlbHR1H0i2Or43hhIjanjJyBypa+mpa+1zsyzUjLXjHxoz\nVoXeK40uM6a61F2vMbUc20lG25XePjPWhbK4fdNsa5X7amxZc4S71JuxtoZ91MoxiIz2geyh4c9h\nHNrO+p+1d4BK3KWOXioVjIHXel3qrlcMJWfMwXeBnzU2/uEUgzZXt6/6ut9VVLpU+vw5zGu3tF6x\nWTdAJf6S+iCtqeoDVXaZMdWl7nrFUFLb9TDw7eHvaODpUtta5b7q635XUelS6evnMK/dol6NA9H2\nuFp19FIpu8yY6lJ3vYqtt97YSG3X4piD4anGxz+se1l90/b3hsSr6tjo4+dQvRqlNXX0Uim7zJjq\nUne9YpDarvSYg62Mf1j3skSknMF9Dts+FTfrKTuVuEv6ej342lS+T/p6/tqyy8yarvp9NdVlbcZ7\nVo/m1326Pb3+sW2Z6lgU3Dd5OV7risTDCvvq1Fniqa3ckib2vYpKV0pbn8Nmtg3PnN92xWbdAJW4\nSx0NjLI/XHX84M1Ql8obpFPWu/Yvu9QxTsfAulBWhb8T42GFfbW6yPubjIdY9r2KSldKn/8RUcNr\n4IVIxuNL/cisGfqPTdv7Ymn9xz6trWNRdB/kva7tfdjVY1+8nnF8b6jEVxQbhfaRZ81Xcv1AxJQk\n28ckyrLa3hfJ+rfugPnWjkXRfZD3urb3YVldqHdM3xsSF8XGZEquH7hYPiCDS6JcQdv7Ymn9860d\ni6L7IO91be/DsrpS71i+NyQ+io0ZtH0qbtZTdirdKcptiWdftL3+aeqwwuuy8uKij6cY9r2Kikr9\nJa/dokuNAxHDaeEwrMuChyFDwrxVwJw7W9qrWb1ytnsdgDvnpuYV3hez7sv0+0ex0fSxKLoNK7zu\nJcDpXYunLn0OYvjekDgpNibLa7eo4TUQ+pC0x5YP/Lp7fLrtZSo2JI9iQ/IoNiZTw0ukRamG0WaS\n3J7Sja46lykiItVQw0ukZbEOVSQiItVTr8aBM7P5tuswZLEOVZQsR7Eh2RQbkkexUZ4aXiIFmbE2\ndRuDtWasCmVtmLdulDSfes+qMG+Uf7WTZJzCjVOML7l2/LVmrAbOzltmznsW6xqTLtVVmqXYkD5S\nw2sglARZiQWWGjcLJLlVm4GFMO9Y4Nixe0xtDO9dzL8KfzcAcyXWO1ruGcCLVlhm1ns2hvnLRBAb\nhesqzVJsSJ4IYqOzlOMlMoWxhPYNYfZGQnJ7arrShPcyifRdSr7vUl2lWYoN6Sol1w+cuv5WJ53Q\nHmYtS26vK+G9zHKLvCeW2FBHgfgoNiRPLLERMyXXi1RgLKF9QyiLye11DQVTZrldGZYGulVXaZZi\nQ/pGZ7xECkrfpDTM2hz+rh+frupGqePrLbrcOm7aWpcu1VWapdiQLtMZLylsmp5Es/Y6KtBTcMVl\nlV1/yffNsfSFP0fS4FpPMtTLbuAC4IIZkuhXXC8wZ8aq9HJXqHO6rlXWpQ5dqqs0S7Ehi3rTy7Xt\nQSRnHWxSpfD+my/+2uKD+M464G/69aG8K5RVRZZVdv1dHKi4rjpPExsqwyqKDZW80kZsdO17O6/d\nojNesgdf+q9yY0hqzT21P81rJ60Llv0nU+iSQtn1z1rvNnSxziIiVenLd6ByvCTXND2JZu11NKmn\nYJV1reJ9bepinUVEqtKV70DleMlUpulJNGuvo0k9Bausa5X1bkMX6ywiUpVefAe2fQ101mulKoX3\n33zx1yrHK8aiHK+Z9t3asL/Wju2/xflt1zHGMoTYUClXmoqNjM/sGeCrU5/daL+389otOuMlWabp\nSTRrr6NJPQUnLavs+rvYW6qLdY7FAkk+yKUk+SGr09NoCBqRWKWHjZoDNgEvBxa6+h2oHC8RGYTU\nPaDeAZwDnAg8nw4m54oMSVeHjdKQQSIyeKmk3KOBbUSenCsiia4k1KdFkVxvZu81s2vNbHtq3kFm\ndoGZXWFm55tZ9xLlOsDM5tuug8RpKLGRSso9Anh7+NvN5NyGDCU2ZHpNxkYvEupTms7xeh9w/Ni8\nU4AL3P3+wGfCtIhIZVKXKjaRXF48IfzdxFL+iIhEZmyYqJ0s3cers5/ZRhte7r4NuGFs9pOAs8Lj\ns4CnNFmnWdQ1XM2sz2fx1CjydQy7UNdQDm0PEdHwkEQz1XFs+KVV6fkrvT8dGz02GnbpISRf4Fem\np+lYcm5TBhIbUkKDsdG/TkUtdK9cA2xPTd+Qemzp6SLdMtvtKlrPrQxmna6r3k0vs87ltn2Ma6rj\n6py/UXa1VlFRUelzyWu3tFGR3IZXmN41zQa0XVI/emtKNn4y3zfr8xn7b76KetexL9pabtvHuKY6\nPgx8e/hbaJ3jsaGiMiqKDZW8otgotI88c34LFRlveF0OHBweHwJcnrcBwJnAqaG8JH3ggfm2ppMf\n1gsdjn3adO8/9mngnry/jueXTWfsr6neX2g6NDI8WXaV+/vYpyX72Ne0c7zLrb++/bFiPDx2mnhc\nqmscnydNxzM9mhdLfTQd1fRLIqtP69OhnErSVjmTiBtem4CTw+NTgNfnvC9zA9ouXTnjVVW9m15m\nnctt+xjXVMepz3ipqKioqFRfomh4AR8CfgD8Avge8FzgIOD/AVcA5wOZPxQxNrxSP3bK8VKOV2v1\nTi17dc5fNb5UVFRUGi5RNLzq2IDZlrk0BlRqXuFx28q+P+d968DXpZ9PL2vs8dTrXX5atPj7i752\n1n1ZZF9N2i81LX/xuFRwjGuJrVS9S41DmI6NmEpdMaUyzTGIMzZU2i91x8akz38Xvh/U8Mo+sK2e\nTWmyLmU/JH3fR+llhDLVIN1d2taqY6NP+0ClW7Gh0n5poOFV65WfhvaRZ85vu2KzbkCFB7eV/KFY\n6xJrveqoy9gyRw2vXm5r14r2gYrKcMukz3/s3w957RaN1UhcY0DFVJe0mOpVR13SywyzerutXaN9\nIDJckz7/MX8/RDFWY4xiGgOqzrrMMq5W3/fR2DI3hNLLbc1eT7zj8cUUe0MUc2xIu5qIjUmf/85+\nP7R9Km7WU3YVncZMXyM+D3x1xusqSxLPWm6B69eFEw1ZSqpOJewffgIZydaz1qvZGChWl0nblLdM\nlOMVVYkp9oZaYo0NlfZL3bFR4Dcx+u+HvHZL6xWbdQNmPLBZP9CrQ+Or0dsiVNAwy2pAnBX+ribn\n9gKz1qvZGCjcw7LwBzKnwVpZr8m6t7XPRftARWW4pcBvYvTfD3ntFuV4Za5rcTT0zSSnMRcH6Gxz\nuZPeP/b8hjD7LcA5wInA87PWWdf2tqmP2yQiIt2R125Rwyt3ffUk7M263GkSDcOsHcDRsHUbzOeu\nM+YExbL6uE11MLN5d9/adj0kPooNyaPYmEzJ9VOoK2Fv1uVOmWg4ShI/Ang7vPN5eevsbILiCvq4\nTSIi0n0647XHehYvUW1wZ/f4dFvLnfT+9HR4y2bgtiTDM/018HKScTFfnl5nXdvbpj5uk4iIdIsu\nNRZeD2uBhYzcqTl3trS13EnvTz8/egwcB+DOuaPXhvmL6yxTr2nqUub5SftikrqXLyIiMokaXgNX\n5fX4ac6+lZmuoo5SnHI1JI9iQ/IoNiZTjpdUJjSONgAbQwL7skbTrM+LiIj0lc54SWmzDuWgXoci\nItJXOuMllZp1KAf1OhQRkSFSwyswY21G42CdGevG5q0KydtF3p/52qpMs06zU06ZpX7pdaVysjYB\nD2bpsuH48xvCmaypnpdmaTw+yaPYkDwxxkYbv8NlqOG1ZIE9GwfHAsdmNBgWCr4/77V11jlnnf9y\n6Yz1S69rjqVbUyykcrbmwmvnyM7pKvq8iIjItNr4HZ6acryWrWPPYWbCU4WGnsl6f90J49Osc9b6\ntbF9IiIiRcX0O6XbSRRez54J39MkgbeRMN5k/ZQQLyIiMYvld0rJ9QVkJXxPkwTeRsJ40XWa2fys\n9VNCfD/FmKshcVBsSJ5YY6MTv1Pu3omSVLXO5fsq8DPAV6Wm3xXKqqzXFHh/5mtrrnNO/Q4/YZb6\ntbF9Ks0UYL7tOqjEWRQbKnklxtiI7Xcqr93Su0uNOcPFrIPFoXNGw+nA0hA2q4CXAKfnvS81L3Po\nmUnrnfT+MqYZGmfWYXSaHIZHQ/6IiEhRqd/1OZaGzttjmLymf1sGk+OVMxzN5vD0+vB3fLqWO6fn\n1EV3aZ9A+01ERIoq+pvR9G/LYHK8PHs4mvWhbIRl13tr3ek5dWml8RDr9fgsMe23IehSbEizFBuS\nJ6bYKPqbEctvy95NrqwpoSW7maVeDbsB0vPCS5c932RdZGXabyIiUlTR34wYflt6d8YLCvVO3BBK\n7b0eYulh4R0bRT6W/TYEXYsNaY5iQ/LEFhvFe/i3/9vSu4ZX+potyXA2m0hyujaHeQ7cM7w8fdpx\nxZ0/GorAxobOSc9fqS5eYmgc68jwB1Wbdb+JiMhw5PxmnG3G6rHXrQbOpuXflt41vFg+HM0CYVgb\n4ILw/BnA80lavHOpa76ThqtZIDmwo6F3VqenyR6SYNahcSob/iCm6/EFaEihBnUsNqRBig3JE1ls\nZP1mvAg4Y+z38wzgRW3/tvSuV+Oe76tu+IDUst4BnAOcSNKIqy05r6r6m9l8bKeGJQ6KDcmj2JA8\nXYiNKn//y61/ILeTyH5vdcMHpJZ1NLCtimVOsU4N0yMiIlJQm7+fg7mdxLgqE+lSyzoCeHv4O4jk\nfBERkS6J9fez1w2vKpO0U8vaRHJ58YTwd1PZZU6xzgrqH9X1eImIYkPyKDYkT+yxEXMnrc42vAr2\n7qsySXsuvPchYZlXpqdHy6y4J6KSzEVERKa3+PuZ+v1N/1avGw3rN9LUXQM6leMFfmDsQ8houBsR\nEZF4TBpKsK7f6l4k14O/jZZ6J0yj7Z4UIiIisiTrdzk8VdtvdV8aXofRkd59sfVE7ELXX2mHYkPy\nKDYkTxdjI+t3uc7f6r70aoyud0KWWHtSiIiIDFGBoQQb+63u2hkv5XiJiIhIYcrxKml8A8JOmnNn\nS4vV2kPoEbGQPnCx1lVERKTvcn6X1wG4c+7o+fDUnDtbqvjd7l3DS6bTxevx0gzFhuRRbEiePsVG\nXVeq+pLjJSIiIlKZ1D0yN4Zk+1rTg3TGS0RERAav6h6OOuMlIiIikqHJHo6da3jl3Oa/tVv/z6Li\n4YUmrCvucbWkPYoNyaPYkDx9io2ccR3PNmN1eH7t6PYTo9/ndLsj5/nchlunGl5hQ44Fjh1t1Arz\nNrLUSyFWC6QG7exQvUVERPoia1zkFwFnhN/lBZLbT2wGFjLaHVnPb8xbWadyvMKQQZm3+c+a14X7\nZml4IRERkfiM/T6n2xlZ7Y6M5+2Gzt9OAnzF2/zHNkxPUV2tt4iISJ+lf5/DrNx2x/jz0SfXm9nx\nZna5mX3TzE7OeVnubf67OkxPU/Xu0/V4qZZiQ/IoNiTPEGJj7Pd5Qyh57Y49ns9bbhQNLzPbC3gr\ncDzwIODpZvbAjJduYOk6ajoJLmvextgbXzkJfXXV+8galin9oNiQPIoNydPr2Ej/PsOy9J/RPb8W\n2x05z+fmeEXR8AKOAr7l7jvd/Rbgw8CTx18Ucp8uAC4Id5cd9f5Lz1tFSJQLf2OWldBXV72jboRK\nqxQbkkexIXl6FxtjdxoYtSMAXkJyZmt9eAypdgdwXJhOPz967x5iaXgdCnwvNf39MG8P7pzrzrlh\ncoGkVXl+GG9psVegO7tjHxvRnS3jifRdqLeIiEgPLd5pIPU7vBE4Pfw27wZOZ892x+PCdPr5XLE0\nvEpl+Dd9m/+OW9N2BSRaa9qugERrTdsVkGitabsCVSvSppj0mvTzeeuJolejmT0aONXdjw/TrwBu\ndfc3pF7TfkVFRERECor2dhJmtjfwX8BvAz8ALgKe7u7faLViIiIiIhXau+0KALj7L83sz4BPA3sB\n71GjS0RERPomijNeIiIiIkMQS3K9lGBm7zWza81se2reh83s4lB2mNnFqedeEW5Qe7mZHZea/+tm\ntj089+amt0OqNU1cmNkaM/tZ6rm3pd6juOiZnNg4yswuCsf/P8zskann9J0xENPEhr43ZuTuKh0t\nwNHAw4HtOc+/EfjL8PhBwCXAPiS9Ub7F0hnPi4CjwuNPAMe3vW0qjcXFmhVep7joWcmKDWAr8ITw\n+HeAC8NjfWcMqEwZG/remKHojFeHufs24Ias58zMgHXAh8KsJwMfcvdb3H0nyZfoo8zsEGA/d78o\nvO79wFNqrbjUasq4yKS46Kec2LgaOCA8XgVcFR7rO2NApoyNTIqNYqJIrpdaHA1c6+7fDtN3B76Y\nen50k9pbwuORq8i5ea30wnhcABwWLj3eSHIm7PMkMaC4GIZTgM+b2RtJ0k8eE+brO0PyYgP0vVGa\nznj119OBD7ZdCYnOeFz8ALinuz8ceCnwQTPbr5WaSVveA7zY3e8F/B/gvS3XR+KRFxv63piBznj1\nULgv2u8Bj0jNvgq4Z2r6HiT/mVwVHqfnr3g6WbopKy7c/RfAL8Ljr5rZt4H7obgYkqPc/fHh8T8C\n7w6P9Z0hmbGh743Z6IxXPz0e+Ia7/yA17+PA08zstmZ2GMmH5CJ3vwb4sZk9KuT/PAv4WPNVlgbs\nERdmdhcz2ys8vjdJXHzH3a9GcTEU3zKzx4XHxwBXhMf6zpDM2ND3xmx0xqvDzOxDJINz3tnMvge8\nyt3fB/wBY8nT7n6ZmZ0LXAb8Enihh24nwAuBM4HbA59w9081tAlSg2niAvgfwGvN7BbgVuD57j4a\nl0xx0TOp2LjLKDaAPwHOMLPbAT8L0/rOGJhpYgN9b8xEN1AVERERaYguNYqIiIg0RA0vERERkYao\n4SUiIiLSEDW8RERERBqihpeIiIhIQ9TwEhEREWmIGl4i0juW2GZmx6fmPdXMPtlmvUREdB8vEekl\nM3sw8BHg4cA+wFeBJ7j7jhLL2tvdf1lxFUVkgNTwEpHeMrM3ADcDdwR+AqwGHkLSEDvV3T9uZmuA\n94fXAPyZu3/BzOaB1wG7gMPd/QHN1l5E+kgNLxHpLTO7A8mZrl8A5wFfd/dzzGwV8CWSs2EO3Oru\n/21m9wM+6O6PDA2v84AHu/uV7WyBiPSNxmoUkd5y95vN7B9IznatA37XzF4Wnr4dcE/gGuCtZnYE\n8CuSAX9HLlKjS0SqpIaXiPTdraEY8D/d/ZvpJ83sVOBqd3+Wme0F/Dz19E8bq6WIDIJ6NYrIUHwa\nePFowsweHh7uT3LWC+AkYK+G6yUiA6KGl4gMgZMkyu9jZl8zs0uB14Tn3gY828wuAR5Aclky/T4R\nkcoouV5ERESkITrjJSIiItIQNbxEREREGqKGl4iIiEhD1PASERERaYgaXiIiIiINUcNLREREpCFq\neImIiIg0RA0vERERkYb8f1Q97wStOVtQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104527910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "fig1.set_size_inches(10,5) \n",
    "ax1 = fig1.add_subplot(111) \n",
    "ax1.plot(trange,BirthsPerYr,'xb')\n",
    "ax1.set_xlim(min(trange),max(trange))\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel(r'Births Yr$^{-1}$')\n",
    "ax1.set_title(r'Recorded Historical births by Year in Adult dataset')\n",
    "plt.grid(True)\n",
    "plt.show(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1045355d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure()\n",
    "fig2.set_size_inches(10,5) \n",
    "ax1 = fig2.add_subplot(111) \n",
    "\n",
    "width = 1.0       # the width of the bars: can also be len(x) sequence\n",
    "ax1.bar(trange,BirthsPerYr_GA,   width, color='r',label='GroupA')\n",
    "ax1.bar(trange,BirthsPerYr_GB, width, color='y',\n",
    "             bottom=BirthsPerYr_GA,label='Group B')\n",
    "ax1.set_xlim(min(trange),max(trange))\n",
    "ax1.set_ylabel(r'Births Yr$^{-1}$')\n",
    "ax1.set_title('Births per year by Group')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.legend()\n",
    "plt.show(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also check a Solar Activity dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rXwKuiwv+qMqrwNPAttVWzzCMBhwATAwtiHkymyp9aIpdgdsLlvUksHnBew0j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\n9ej/AR2wOMy10fWYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104527710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Old fashioned plot\n",
    "fig3 = plt.figure()\n",
    "fig3.set_size_inches(10,5) \n",
    "ax1 = fig3.add_subplot(111) \n",
    "ax1.plot(Solar.index[0:36],Solar.Counts[0:36],'-b') # Jump in data (not handeld well by plot)\n",
    "ax1.plot(Solar.index[36:],Solar.Counts[36:],'-b')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_xlim(1675,1880)\n",
    "ax1.set_ylabel(r'Neutron Counts $\\times10^{-3} hr^{-1}$')\n",
    "ax1.set_title(r'Reconstructed Solar Activity from Usoskin et al.(2002) over study period')\n",
    "plt.grid(True)\n",
    "plt.show(fig1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nb. *Data suggests some long term variability in solar cycles during the study period. It is not the case that a solar max or solar min over the 11-year cycle are necessarily above/below long term average.*\n",
    "\n",
    "Indeed, this is partially taken into account as in the original paper the authours write \"...we categorized all children born between 1800 and 1825 as part of the solar minimum period as the sunspot peaks during this period were most of the time below (mean: 20.1, range: 0 – 47.5) the mean number of sunspots for the entire study period.\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats of solar max and solar min group and Fertility ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.files.benlaken.com/documents/Norway_solar_fig2.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load an enhanced copy of Figure 2 from Skjaervo et al. (2014)\n",
    "# The significant fertility result in low-status (poor) females will be tested first.\n",
    "Image(url='http://www.files.benlaken.com/documents/Norway_solar_fig2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstructing data of figure 2: \n",
    ">Fertility (mean + s.e.) in relation to solar activity for men and for women in relation to socioeconomic status. *p , 0.05. Dark grey bars denote SSmax, light grey bars denote SSmin.\n",
    "\n",
    "Notes: Fertility = Total Number of children per person. Data from both communities (Groups), data divided by gender, and, for women, social status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adult Men with >0 Kids: 1629\n",
      "Adult Women with >0 Kids: 1738\n"
     ]
    }
   ],
   "source": [
    "print('Adult Men with >0 Kids:',len(Adults.NumKids[(Adults.Gender == 'M')&(Adults.NumKids > 0)]))\n",
    "print('Adult Women with >0 Kids:',len(Adults.NumKids[(Adults.Gender == 'F')&(Adults.NumKids > 0)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is strange; already I have a diffrent result to the original paper. They report:\n",
    "\n",
    ">*Third, we analysed potential effects of solar activity during early life on adult survival and fitness by restricting our sample to individuals who survived to adult- hood, got married and sired at least one child. Consequently, we excluded individuals who were either unmarried (925 indi- viduals) or emigrated (1422 individuals). We also excluded individuals with incomplete data (1375 individuals), thus leaving full-life-history information on **1498 women** and **1623 men**.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def GenFertilityStats(Gender,Sphase):\n",
    "    '''Function to convienently generate a dictionary object of statistics.\n",
    "    Gender can be either M or F\n",
    "    Sphase can be either MAX or MIN\n",
    "    This will return a dictionary of mean, count, std, and s.e. statistics for Fertility.\n",
    "    '''\n",
    "    tmp={}\n",
    "    stats = ['count','mean','std','se']\n",
    "    sep='_'\n",
    "    for metric in stats:\n",
    "        if metric == 'mean':\n",
    "            tmp[metric]=(mean(Adults.NumKids[(Adults.Gender == Gender)&(Adults.SolarPhase == Sphase)]))\n",
    "        if metric == 'count':\n",
    "            tmp[metric]=(len(Adults.NumKids[(Adults.Gender == Gender)&(Adults.SolarPhase == Sphase)]))\n",
    "        if metric == 'std':\n",
    "            tmp[metric]=(std(Adults.NumKids[(Adults.Gender == Gender)&(Adults.SolarPhase == Sphase)]))\n",
    "        if metric == 'se':\n",
    "            tmp[metric]=(tmp['std']/np.sqrt(tmp['count']-1))\n",
    "    return tmp\n",
    "\n",
    "\n",
    "def GenFertilityStats_byClass(Gender,Sphase,Class):\n",
    "    '''Function to convienently generate a dictionary object of statistics.\n",
    "    Gender can be either M or F\n",
    "    Sphase can be either MAX or MIN\n",
    "    Class can be either High or Low (SocialStatus)\n",
    "    This will return a dictionary of mean, count, std, and s.e. statistics for Fertility.\n",
    "    '''\n",
    "    tmp={}\n",
    "    stats = ['count','mean','std','se']\n",
    "    sep='_'\n",
    "    for metric in stats:\n",
    "        if metric == 'mean':\n",
    "            tmp[metric]=(mean(Adults.NumKids[(Adults.Gender == Gender)&(Adults.SolarPhase == Sphase)\n",
    "                                                                        &(Adults.SocialStatus == Class)]))\n",
    "        if metric == 'count':\n",
    "            tmp[metric]=(len(Adults.NumKids[(Adults.Gender == Gender)&(Adults.SolarPhase == Sphase)\n",
    "                                                                       &(Adults.SocialStatus == Class)]))\n",
    "        if metric == 'std':\n",
    "            tmp[metric]=(std(Adults.NumKids[(Adults.Gender == Gender)&(Adults.SolarPhase == Sphase)\n",
    "                                                                       &(Adults.SocialStatus == Class)]))\n",
    "        if metric == 'se':\n",
    "            tmp[metric]=(tmp['std']/np.sqrt(tmp['count']-1))\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fertility Summary statistics\n",
      "M_SMAX_fertility {'count': 349, 'std': 3.0312775807236139, 'se': 0.16249353454332857, 'mean': 4.8567335243553007}\n",
      "M_SMIN_fertility {'count': 1369, 'std': 2.9004457734989377, 'se': 0.078419072548285243, 'mean': 4.8816654492330169}\n",
      "F_SMAX_lowclass {'count': 125, 'std': 2.7438250673102322, 'se': 0.24640276496144514, 'mean': 3.6320000000000001}\n",
      "F_SMIN_lowclass {'count': 496, 'std': 2.6852893649894209, 'se': 0.12069478136999162, 'mean': 4.094758064516129}\n",
      "F_SMAX_highclass {'count': 239, 'std': 2.9655444489348946, 'se': 0.19222769541590501, 'mean': 5.1464435146443517}\n",
      "F_SMIN_highclass {'count': 983, 'std': 2.8655714739282714, 'se': 0.091444060294410295, 'mean': 5.1749745676500511}\n"
     ]
    }
   ],
   "source": [
    "print('Fertility Summary statistics')\n",
    "M_SMAX_fertility = GenFertilityStats(Gender='M',Sphase='MAX')\n",
    "M_SMIN_fertility = GenFertilityStats(Gender='M',Sphase='MIN')\n",
    "F_SMAX_lowclass = GenFertilityStats_byClass(Gender='F',Sphase='MAX',Class='Low')\n",
    "F_SMIN_lowclass = GenFertilityStats_byClass(Gender='F',Sphase='MIN',Class='Low')\n",
    "F_SMAX_highclass = GenFertilityStats_byClass(Gender='F',Sphase='MAX',Class='High')\n",
    "F_SMIN_highclass = GenFertilityStats_byClass(Gender='F',Sphase='MIN',Class='High')\n",
    "\n",
    "print('M_SMAX_fertility',M_SMAX_fertility)\n",
    "print('M_SMIN_fertility',M_SMIN_fertility)\n",
    "print('F_SMAX_lowclass',F_SMAX_lowclass)\n",
    "print('F_SMIN_lowclass',F_SMIN_lowclass)\n",
    "print('F_SMAX_highclass',F_SMAX_highclass)\n",
    "print('F_SMIN_highclass',F_SMIN_highclass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Quite unequal samples: Male Solar max population is 349 individuals, but Solar min is 1369)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+/4YIHZpPVACKLi25nJK+nOqlf7lnRGjG8EcEAM3J9hRlrIhwRNQ1mNdszF5X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\nAAAKjKIOAAAAAAqMog4AAAAACuz/A4fPPPj5cAg+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c12a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [\n",
    "    Adults.NumKids[(Adults.Gender == 'M')&(Adults.SolarPhase == 'MAX')],\n",
    "    Adults.NumKids[(Adults.Gender == 'M')&(Adults.SolarPhase == 'MIN')],\n",
    "    Adults.NumKids[(Adults.Gender == 'F')&(Adults.SolarPhase == 'MAX')&(Adults.SocialStatus == 'High')],\n",
    "    Adults.NumKids[(Adults.Gender == 'F')&(Adults.SolarPhase == 'MIN')&(Adults.SocialStatus == 'High')],\n",
    "    Adults.NumKids[(Adults.Gender == 'F')&(Adults.SolarPhase == 'MAX')&(Adults.SocialStatus == 'Low')],\n",
    "    Adults.NumKids[(Adults.Gender == 'F')&(Adults.SolarPhase == 'MIN')&(Adults.SocialStatus == 'Low')],\n",
    "        ]\n",
    "\n",
    "labels = list(['Males \\n n=349',\n",
    "               'Males \\n n=1369',\n",
    "               'Rich Females \\n n=239',\n",
    "               'Rich Females \\n n=983',\n",
    "               'Poor Females \\n n=125',\n",
    "               'Poor Females \\n n=496'])\n",
    "\n",
    "fig5 = plt.figure()\n",
    "fig5.set_size_inches(15,8) \n",
    "ax1 = fig5.add_subplot(111) \n",
    "box = ax1.boxplot(data,notch=True,sym='b+',vert=True,bootstrap=10000,patch_artist=True)\n",
    "\n",
    "colors = ['#FFBBA9', '#33D6FF', '#FFBBA9', '#33D6FF', '#FFBBA9','#33D6FF']\n",
    "for patch, color in zip(box['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "\n",
    "ax1.set_ylim(-1,16)\n",
    "ax1.set_title('Num of Children born i.e. Fertility (Red = Solar Max, Blue = Solar Min)  \\n Bootstrapped 95% asymetric C.I.')\n",
    "ax1.set_xlabel('Catagory')\n",
    "ax1.set_ylabel(r'Fertility (N$^{o}$ Children)')\n",
    "ax1.set_xticklabels(labels)\n",
    "plt.show(fig5)\n",
    "#plt.savefig(\"boxplots.png\",dpi=100,transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*n.b. Bootstrapped confidence intervals around the median of notched boxplots (95% confidence level).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Children of Men: 1718\n",
      "Total Children of Women: 1843\n",
      "Unclaimed Children: 125\n"
     ]
    }
   ],
   "source": [
    "# Small sanity check: Number of children born for Men and Women should be approx equal as they should be the same children\n",
    "print('Total Children of Men:',len(Adults.NumKids[(Adults.Gender == 'M')]))\n",
    "print('Total Children of Women:',len(Adults.NumKids[(Adults.Gender == 'F')]))\n",
    "print('Unclaimed Children:',len(Adults.NumKids[(Adults.Gender == 'F')]) - len(Adults.NumKids[(Adults.Gender == 'M')]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###So, it seems there is a potential sample diffrence for Poor Females here. As Skjaervo et al. (2014) reported. Time for a further test of this with a Monte Carlo to see if this diffrence is really *p*<0.05###\n",
    "\n",
    "Approach to the Monte Carlo test: Test the hypothesis that the Median diffrence between the Fertility (Number of Children born) to Poor Females was statstically significantly diffrent between the Solar Max and Solar Min Samples from random cases (i.e. in the absence of the proposed causal factor).\n",
    "\n",
    "To do this I will generate two large populations of Median values basd on the entirety of the Poor Female dataset. Where the medians are calculated from randomly selected samples of n = 125, and n = 496 individuals respectivley. These will be used to determine a cumulative density function (CDF) of the null hypothesis cases, against which to evaluate the Smax and Smin samples respectivley. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "NumKids_poor_females = Adults.NumKids[(Adults.Gender == 'F')&(Adults.SocialStatus == 'Low')]\n",
    "\n",
    "MC_size = 100000\n",
    "pop_n125 = []\n",
    "pop_n496 = []\n",
    "pop_n125=[mean(random.choice(NumKids_poor_females,size=125)) for n in range(MC_size)] # Yep, it's just that easy.\n",
    "pop_n496=[mean(random.choice(NumKids_poor_females,size=496)) for n in range(MC_size)] # A one line MC. Hooray Python!\n",
    "\n",
    "pop_125_hist, bins = np.histogram(pop_n125, bins=arange(3.0, 5.0, 0.025))  # Bin the MC data to generate a PDF\n",
    "pop_496_hist, bins = np.histogram(pop_n496, bins=arange(3.0, 5.0, 0.025))  # will do this manually for more flexibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- Calculate the PDFs for the two MC samples and create a step plot with Gaussian fit---\n",
    "density_125 = pop_125_hist/sum(pop_125_hist)       # Normalize the population to 1.0\n",
    "step = (bins[1] - bins[0]) *0.5                    # need to center the bins:\n",
    "bcenter = [ bpos + step for bpos in bins[:-1]]     \n",
    "width = bins[1] - bins[0]\n",
    "xx = np.ravel(zip(bins[0:-1], bins[0:-1] + width))\n",
    "yy = np.ravel(zip(density_125, density_125))\n",
    "\n",
    "density_496 = pop_496_hist/sum(pop_496_hist)\n",
    "yy2 = np.ravel(zip(density_496, density_496))\n",
    "\n",
    "# Create ideal Gaussian distributions \n",
    "mu, sigma = np.mean(pop_n125), np.std(pop_n125)        # Idealized Gaussian...\n",
    "ygauss = normpdf(bins, mu, sigma)                      # ...a np function to generate a Gaussian pdf\n",
    "ynormgauss = ygauss/sum(ygauss)                        #...a normalized guassian function\n",
    "\n",
    "mu2, sigma2 = np.mean(pop_n496), np.std(pop_n496)      # More clunky boiler plate code (I should function this)\n",
    "ygauss2 = normpdf(bins, mu2, sigma2)                      \n",
    "ynormgauss2 = ygauss2/sum(ygauss2)                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability Density Functions of 100k samples from poor female data\n"
     ]
    },
    {
     "data": {
      "image/png": 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bmCxVXFzMXXfdxX//939XvxYNXlOnTuXhhx/mtddeq163fv16/vKXv9S7r3fe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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf22a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Probability Density Functions of 100k samples from poor female data')\n",
    "#-----Plotting-----\n",
    "my_hist1 = plt.figure()\n",
    "my_hist1.set_size_inches(12,5)            # Specify the output size\n",
    "ax1 = my_hist1.add_subplot(121)              # Add an axis frame object to the plot (i.e. a pannel)\n",
    "ax2 = my_hist1.add_subplot(122)              # Add an axis frame object to the plot (i.e. a pannel)\n",
    "\n",
    "# first pannel\n",
    "ax1.bar(bins[0:-1], density_125, width=width, facecolor='k',linewidth=0.0,alpha=0.2)  # Filled bars\n",
    "ax1.plot(bins,ynormgauss,'r-',linewidth=1.5)                                          # Ideal Gaussian line\n",
    "ax1.vlines(F_SMAX_lowclass['mean'], 0.0, 0.1,colors='b')                              # Marker line of Mean\n",
    "ax1.grid(True)\n",
    "ax1.set_ylabel(r'Density',fontsize=11)\n",
    "ax1.set_xlabel('Mean fertility in random samples of n=125',fontsize=11)\n",
    "leg1=ax1.legend(['Gaussian fit','Solar Max sample','MC population',],prop={'size':11},\n",
    "                numpoints=1,markerscale=5.,frameon=True,fancybox=True)\n",
    "leg1.get_frame().set_alpha(1.0)                # Make the ledgend semi-transparent\n",
    "ax1.plot(xx, yy, 'k',alpha=0.8)                # Edges for the step plot\n",
    "ax1.vlines(F_SMAX_lowclass['mean']+F_SMAX_lowclass['se'], 0.0, 0.1,colors='b',linestyle='dashed')\n",
    "ax1.vlines(F_SMAX_lowclass['mean']-F_SMAX_lowclass['se'], 0.0, 0.1,colors='b',linestyle='dashed')\n",
    "ax1.set_ylim(0.00,0.09)\n",
    "\n",
    "# -- second pannel\n",
    "ax2.bar(bins[0:-1], density_496, width=width, facecolor='k',linewidth=0.0,alpha=0.2)  # Filled bars\n",
    "ax2.plot(bins,ynormgauss2,'r-',linewidth=1.5) # Ideal Gaussian line\n",
    "ax2.vlines(F_SMIN_lowclass['mean'], 0.0, 0.1,colors='b')  \n",
    "ax2.grid(True)\n",
    "ax2.set_ylabel(r'Density',fontsize=11)\n",
    "ax2.set_xlabel('Mean fertility in random samples of n=496',fontsize=11)\n",
    "leg2=ax2.legend(['Gaussian fit','Solar Min sample','MC population',],prop={'size':11},\n",
    "                numpoints=1,markerscale=5.,frameon=True,fancybox=True)\n",
    "leg2.get_frame().set_alpha(1.0)                # Make the ledgend semi-transparent\n",
    "ax2.plot(xx, yy2, 'k',alpha=0.8)                # Edges for the step plot\n",
    "ax2.vlines(F_SMIN_lowclass['mean']+F_SMIN_lowclass['se'], 0.0, 0.1,colors='b',linestyle='dashed')\n",
    "ax2.vlines(F_SMIN_lowclass['mean']-F_SMIN_lowclass['se'], 0.0, 0.1,colors='b',linestyle='dashed')\n",
    "ax2.set_ylim(0.00,0.09)\n",
    "#plt.show(my_hist1)\n",
    "plt.savefig(\"Solar_norway_distrib.png\",dpi=100,transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming a one tailed test (i.e. a strong increase in fertility during solar maximum would have been considerd a null result), therefore all alpha should be in the lower tail. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means ±S.E. of Poor Female sample during Solar Maximum (n=125)\n",
      "F_SMAX_lowclass: 3.632 ± 0.246402764961\n",
      "Percentile rank compared to random (null) MC-cases:\n",
      "Percentile of mean: 6.0515\n",
      "Percentile of lower CI: 30.229\n",
      "Percentile of upper CI: 0.434\n"
     ]
    }
   ],
   "source": [
    "# Use a percentile rank function to find the rank of a given value:\n",
    "print('Means ±S.E. of Poor Female sample during Solar Maximum (n=125)')\n",
    "print('F_SMAX_lowclass:',F_SMAX_lowclass['mean'],'±',F_SMAX_lowclass['se'])\n",
    "print('Percentile rank compared to random (null) MC-cases:')\n",
    "print('Percentile of mean:',stats.percentileofscore(pop_n125, F_SMAX_lowclass['mean'])) \n",
    "print('Percentile of lower CI:',stats.percentileofscore(pop_n125, F_SMAX_lowclass['mean']+F_SMAX_lowclass['se'])) \n",
    "print('Percentile of upper CI:',stats.percentileofscore(pop_n125, F_SMAX_lowclass['mean']-F_SMAX_lowclass['se'])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means ±S.E. of Poor Female sample during Solar Minimum (n=496)\n",
      "F_SMAX_lowclass: 4.09475806452 ± 0.12069478137\n",
      "Percentile rank compared to random (null) MC-cases:\n",
      "Percentile of mean: 77.817\n",
      "Percentile of lower CI: 95.965\n",
      "Percentile of upper CI: 41.221\n"
     ]
    }
   ],
   "source": [
    "# Use a percentile rank function to find the rank of a given value:\n",
    "print('Means ±S.E. of Poor Female sample during Solar Minimum (n=496)')\n",
    "print('F_SMAX_lowclass:',F_SMIN_lowclass['mean'],'±',F_SMIN_lowclass['se'])\n",
    "print('Percentile rank compared to random (null) MC-cases:')\n",
    "print('Percentile of mean:',stats.percentileofscore(pop_n496, F_SMIN_lowclass['mean'])) \n",
    "print('Percentile of lower CI:',stats.percentileofscore(pop_n496, F_SMIN_lowclass['mean']+F_SMIN_lowclass['se'])) \n",
    "print('Percentile of upper CI:',stats.percentileofscore(pop_n496, F_SMIN_lowclass['mean']-F_SMIN_lowclass['se'])) "
   ]
  }
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