Module 1, Lesson 3 - Probability, randomness, and the risk of de-anonymization.ipynb

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    "# 3. Probability, randomness, and the risk of de-anonymization\n",
    "\n",
    "> Research outcomes must be better than guessing\n",
    "\n",
    "Data and research are powerful tools but they can be easily discredited. Not everyone can replicate published research and, even if they could, the time invested would be enormous.\n",
    "\n",
    "In most cases, we need to trust that the assumptions informing a research question are valid. Where that trust is broken, it doesn't only undermine that body of research but risks eroding public trust in all research. If the scientific process is no more than guessing, then anyone's opinion is as valid.\n",
    "\n",
    "The objective of this lesson is to gain confidence in probability and statistical measures in evaluating causality.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Research question:</b>\n",
    "<i>The predictive value of diagnostic tools depends on test sensitivity to accurately measure what it is supposed to, and its specificity to measure only what it is supposed to. Using only core probabilistic techniques, evaluate two papers \\cite{greco_axillary_2001, rafferty_assessing_2013} and assess whether the tests they suggest are sufficiently accurate to use as general diagnostic tools. Which, if any, would you trust, and under what circumstances?</i>\n",
    "</div>\n",
    "\n",
    "The two papers we will refer to are these:\n",
    "\n",
    "> _Greco Marco, Crippa Flavio, Agresti Roberto et al., ``Axillary Lymph Node Staging in Breast Cancer by 2-Fluoro-2-deoxy-d-glucose–Positron Emission Tomography: Clinical Evaluation and Alternative Management'', JNCI: Journal of the National Cancer Institute, vol. 93, number 8, pp. 630--635, April 2001. [10.1001/jama.283.20.2701](https://jamanetwork.com/journals/jama/fullarticle/192740)_\n",
    "> \n",
    "> _Rafferty Elizabeth A., Park Jeong Mi, Philpotts Liane E. et al., ``Assessing Radiologist Performance Using Combined Digital Mammography and Breast Tomosynthesis Compared with Digital Mammography Alone: Results of a Multicenter, Multireader Trial'', Radiology, vol. 266, number 1, pp. 104--113, January 2013. [doi:10.1148/radiol.12120674](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410947/)_\n",
    "\n",
    "Don't worry if you don't understand them on first read. Our objective is to learn how to evaluate research even when you are not immersed in the topic."
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    "## 3.1 Ethics: fallacies, randomness, and treating intuition with skepticism\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Determine the implications in the collection, mining and recombination of open- and digital data.</i>\n",
    "</div>\n",
    "\n",
    "According to [Tyler Vigen](https://tylervigen.com/spurious-correlations) the rate of divorce in Maine, the northernmost US state, is almost entirely correlated with their per capita consumption of margarine:\n",
    "\n",
    "![Divorce vs margarine consumption](images/module-1-lesson-3-spurious-correlations.jpg)\n",
    "\n",
    "Perhaps you have strong feelings about margarine and this confirms your concerns about its capacity to undermine relationships? Perhaps you think this is just random chance?\n",
    "\n",
    "Vigen runs the website [Spurious Correlations](https://tylervigen.com/spurious-correlations) where he satirises the very human obsession with forming patterns where none exist. There you will find that the age of Miss America is related to steam-related deaths, or that swimming pool drownings relates to nuclear power production.\n",
    "\n",
    "[Apophenia](https://en.wikipedia.org/wiki/Apophenia) is the tendency to mistakenly see connections and meaning between unrelated things. All of Vigen's charts are spurious correlations. Patterns of numbers that happen to coincide but don't have any broader meaning, no different from two people walking side-by-side down a street together who don't know each other and will shortly go in different directions to different destinations.\n",
    "\n",
    "An objective of the scientific method is to expose mistaken tendencies and ensure they are excluded from any research process. Bad reasoning is challenging since it requires that the people responsible for defining a research question, and carrying out an investigation, admit and accept that their assumptions must be open to challenge.\n",
    "\n",
    "### 3.1.1 Fooled by fallacies\n",
    "\n",
    "An argument, or hypothesis, that does not work is a __fallacy__ \\cite{baggini_ethics_2007}. There are an enormous [list of fallacies](https://en.wikipedia.org/wiki/List_of_fallacies), and researchers should be aware of how they arise and how to mitigate them in a research process. As examples, an _appeal to probability_ assumes that something may be taken for granted because it's likely to be true. Another is _affirming the consequent_, which has the following structure where two premises lead to a conclusion:\n",
    "\n",
    " 1. If _p_, then _q_\n",
    " 2. _q_\n",
    " 3. Therefore, _p_\n",
    " \n",
    "You can have a set of premises - each of which could well be true - but the assumption underlying the connection of causality between the premises does not support the conclusion. As example:\n",
    "\n",
    " 1. People with cancer and undergoing chemotherapy lose their hair.\n",
    " 2. This person has no hair.\n",
    " 3. Therefore, this person has cancer.\n",
    "\n",
    "Or, a common approach to this fallacy:\n",
    "\n",
    " 1. Untrustworthy people have pointy eyebrows.\n",
    " 2. This person has pointy eyebrows.\n",
    " 3. Therefore, this person is untrustworthy.\n",
    " \n",
    "Fallacies often arise from claims to __intuition__. A person may state that they have a _feeling_ that a particular set of facts are causally related even despite an absence of evidence. If they have sufficient __authority__ - power to assert their will over others regardless of their desires - then they may impose these fallacies on society.\n",
    "\n",
    "In 1948, the National Party was elected to power in South Africa and immediately imposed its policy of [_Apartheid_](https://en.wikipedia.org/wiki/Apartheid) on the nation. This was a system of institutionalised racial segregation where society was stratified according to race; \"white\" people accorded highest status and \"black\" people placed at the bottom where they were stripped of their citizenship, right to own property, vote, or even receive an education.\n",
    "\n",
    "Since racial classification was crucial to such a system, in 1950 the [Population Registration Act](https://en.wikipedia.org/wiki/Population_Registration_Act,_1950) was introduced along with a suite of tools designed to provide rapid racial classification tests.\n",
    "\n",
    "One of these involved a [pencil](https://en.wikipedia.org/wiki/Pencil_test_(South_Africa%29).\n",
    "\n",
    "If a government official felt unable to make a racial classification by observation, they would insert a pencil into a subject's hair. If it fell out, that person was classified as \"white\". If it remained in place, that person was classified as \"coloured\", a social status above \"black\" but below \"white\".\n",
    "\n",
    "The impact of these classifications could tear families apart. \n",
    "\n",
    "One story illustrates the effect on millions of South Africans. When [Sandra Laing](https://en.wikipedia.org/wiki/Sandra_Laing) was 10 years old, despite having \"white\" parents, she was classified as \"coloured\". She could no longer attend the same school as her brothers and was eventually ostracised by her family. Even after Apartheid ended in 1994, her brothers continued to refuse to associate with her.\n",
    "\n",
    "If you are a researcher working on racial classification, you may improve on the accuracy of the pencil test but the foundation of that work is based on two fallacies: that human race is a biological rather than political construct; and that racial classification is a determinant of human worth.\n",
    "\n",
    "A tool developed for the purpose of implementing intentional unethical outcomes is not a neutral object. The people developing that tool are moral agents and are responsible for the consequences of the use of that tool.\n",
    "\n",
    "### 3.1.2 Individual autonomy and moral luck\n",
    "\n",
    "Data scientists have powerful tools at their disposal capable of finding patterns and correlations in what can often - for individual humans - be overwhelming and indecipherable datasets. We are also living in an era with ready availability of vast and diverse data from public repositories. \n",
    "\n",
    "It is tempting to assume that data are truthful and unbiased, and then to attempt to answer all sorts of controversial and challenging questions and let a machine \"sort it out\".\n",
    "\n",
    "What differentiates the skills and techniques of a researcher producing a better racial classifier from one producing a better fruit sorter? Few would have any ethical concerns with a machine designed to ensure that poor quality fruit are kept out of produce sent to market. Many would take issue with a machine designed to ensure a specific subset of society receives greater social status by virtue of superficial characteristics derived from an accident of birth.\n",
    "\n",
    "The skills of the people working on each are no different, yet those who enabled Apartheid were charged with crimes against humanity. Where you end up working, and on what types of projects, is a matter of chance, of __moral luck__. \n",
    "\n",
    "Your technical skills cannot help you with an ethical decision. Neither are these ethical dilemmas a matter only for historians.\n",
    "\n",
    "In 2016, research sought to infer criminality from a sample of facial images \\cite{wu_automated_2016}. The researchers applied probabilistic classification techniques to a sample of 1,856 people, half of whom were convicted criminals, to answer the question as to whether there is \"any diagnostic merit of the face-induced inferences on an individual's social attributes?\"\n",
    "\n",
    "However, computers can't answer a question of \"who looks more criminal\", so the researchers instead created a proxy, measuring specific facial features, like head shape, eye corners, and mouth corners. The computer did not choose these, the researchers did. \n",
    "\n",
    "Is there some fallacy at work in these choices? Could this fallacy be present also in the faces of the convicted criminals?\n",
    "\n",
    "The authors caveat their work with claims that they are investigating whether a computer can model human bias, however, their work does not occur separate from the concerns of their country's politics. \n",
    "\n",
    "The researchers work at Shanghai Jiao Tong University in China. The ruling Chinese Communist Party has introduced a [Social Credit System](https://en.wikipedia.org/wiki/Social_Credit_System) based on facial recognition to track and evaluate the population of China in terms of their trustworthiness. People who are classified as \"untrustworthy\" may be denied social services, including the ability to use public transport.\n",
    "\n",
    "It is seductive to attempt to infer human behaviour from human appearance, and such research isn't always produced in service to brutal states. In \"Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings\", French researchers state that they have designed \"an algorithm to automatically generate trustworthiness evaluations for the facial action units (smile, eye brows, etc.) of European portraits in large historical databases.\" They claim their results \"show that trustworthiness in portraits increased over the period 1500–2000 paralleling the decline of interpersonal violence and the rise of democratic values observed in Western Europe.\" \\cite{safra_tracking_2020}\n",
    "\n",
    "Classifying people based on fallacies takes away __individual autonomy__ and imposes an incredible ethical choice on data scientists who enable such systems to function. A researcher may spend an entire career inside a system that supports unethical choices and never requires them to account for their consequences, or they may find that systems change and ushers in an era of [transparency and accountability](https://en.wikipedia.org/wiki/Truth_and_Reconciliation_Commission_(South_Africa%29).\n",
    "\n",
    "A facial recognition system that can instantly classify people as \"untrustworthy\" based on markers of the distance of eye corners apart, the angle of eyebrows, or the degree of facial roundness, can not be said to be answering a question of \"will this person commit a crime\" unless there is specific research demonstrating beyond any doubt that such characteristics do indicate criminal behaviour. \n",
    "\n",
    "> A process, system or tool which makes a specific prediction based on input data requires that its predictions are based on answerable questions, and that it be provably accurate at such high-speed diagnostics.\n",
    "\n",
    "### 3.1.3 Causation and probability\n",
    "\n",
    "[__Causality__](https://en.wikipedia.org/wiki/Causality) - the influence by which an event, process or state (a _cause_) contributes to the production of another event, process or state (an _effect_) - is critical and challenging for research. \n",
    "\n",
    "> We cannot usually say with absolute certainty that a specific cause leads to a specific effect. \n",
    "\n",
    "Smoking cigarettes leads to an increased risk of lung cancer, and lung cancer leads to an increased risk of death, but smoking in and of itself does not cause death. Tobacco companies used this lack of immediate causation to claim that any research findings that linked smoking to ill-health were [purely a coincidence](https://sciencebasedmedicine.org/evidence-in-medicine-correlation-and-causation/). That _correlation is not causation_.\n",
    "\n",
    "Bad-faith arguments of this nature have been used to validate fallacies about anti-vaccination, flat-Earthism, and bigotry of all varieties.\n",
    "\n",
    "Researchers prefer to talk about [__probabilistic causation__](https://en.wikipedia.org/wiki/Probabilistic_causation). That, all else being equal, there is a greater probability of a cause contributing to an effect than any other alternatives.\n",
    "\n",
    "The __probability__ of an outcome is the proportion of times the outcome would occur if the random phenomenon could be observed an infinite number of times. For some phenomenon, the proportion is known exactly (e.g. the number of faces on a dice, or the probability of inheriting a gene-specific disease from one of your parents). For others, we don't know how the phenomenon occurs and estimate its likelihood by observation (e.g. an obese person developing heart disease, or a woman in her 40's developing breast cancer). \\cite{dietz_openintro_2015}\n",
    "\n",
    "Unusually for a section on ethics, we're going to dive directly into probability in terms of mathematical definitions and code.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "Ethics grapples with considerations that are inherently inexact or which have ambiguous outcomes. It is essential that the words and concepts we use, and the way they are used, are well-defined, unambiguous and exact. Axiomatic probability gives us a language to explore ambiguous outcomes.\n",
    "</div>\n",
    "\n",
    "Probability lends itself to simulation and a classic example is to roll a six-sided die multiple times. For this simulation, let $\\hat{p}_n$ be the proportion of outcomes that are $1$ after the first $n$ rolls:"
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N+CDLUYiIiKiOnByEC5vb0tOk3wfwOSllzohKCHGdEGKdEGLdgQMHKrW+ujt5zgQsmhzCHWt2QcrGblcYiWtYu/Mwdh4aKvtcuVoUBrwuuAQz4URERFRfTg7CuwDMsHw9HUBP2jHLAdwlhNgJ4B0AfiyEeEv6iaSUt0opl0spl3d2dlZpubUnhMA1p8zG5j39WLuzt97LKcsrewcR1yUGwuUHx7nKUYQQaPF5MMiacCIiIqojJwfhawEsEELMEUL4AFwO4H7rAVLKOVLK2VLK2QDuBvBvUsp7a77SOnrL0mloDXhwx5qd9V5KWTb09AEABisShGfPhANGr/BhlqMQERFRHTk2CJdSxgF8FEbXk80Afi+l3CiEuF4IcX19V+ccTT43LjtpBh7csBd7+8L1Xk7JNnQbQXh/BYLwXC0KAWNq5iDLUYiIiKiOHBuEA4CU8gEp5UIp5Twp5TcSt90ipczYiCmlvFZKeXftV1l/71k5G7qU+O2zjduucENPPwBgMBIr+1y5hvUAQIvPg2GWoxAREVEdOToIp8LMbG/GOUdNxG+fe90MQBtJTNOxeU8/3C6BcExHTNPLOl+usfUA0OJ3c1gPERER1RWD8FHimlNm4+BgFH97eW+9l1K01w4MIhrXcey0NgAoe3Nmru4oALgxk4iIiOqOQfgocdr8DsztaMHtq3fWeylF29BtlKKsmtcOoPzNmZG4Dp/bBZfLrsulURPOFoVERERUTwzCRwmXS+DqVbPwwu4jeHH3kXovpygbuvvQ7HPjuEQmvD9cXl14JKZnzYIDRjkKh/UQERFRPTEIH0Xevmw6WnzuhmtXuLGnD4untKKtyQsAZZeKhOOa7bRMpcXHTDgRERHVF4PwUSQU8OLty6bjLy/uwaHBSL2XUxBdl9jY048l09oQDBgj5cutCTcy4fabMgGg2e/BcFSDrjf2lFEiIiJqXAzCR5mrV81CVNNx19rd9V5KQXYcGsJwVMMxU1sRCqhMeJnlKHky4UG/EaAPx1iSQkRERPXBIHyUmT8xhNPmd+A3z+xCvMxWf7WghvQsmdaGoL8ymfBwvky4z3ieIXZIISIiojphED4KXb1qFvb0hfGPTfvqvZS8Nvb0w+dxYf7EIEKVKkeJa1kH9QAwg30G4URERFQvDMJHoXOPnoRp45oaYoPmhu4+HD05BK/bBb/HBa9bVCAIz90dpdlnZMk5sIeIiIjqhUH4KOR2Cbxn1Sw8s/0wtuztr/dyspJSYkN3H45JtCYUQiAU8JZfEx7TcpajmJlwdkghIiKiOmEQPkpdtnwG/B4XfrVmV72XklVX7wj6w3Esmdpm3hb0e6qfCWc5ChEREdUZg/BRanyLD5cunYo/Pd+NvuHyMsvVktyU2WreFgpUJggPeHNlwhPlKFGWoxAREVF9MAgfxa5eNRsjMQ1/WO/MdoUbevrgcQkcNTlk3hYKeMoeWx+OaXlqwpkJJyIiovpiED6KLZnWhuWzxuPXz+xy5GCaDd39WDgplFK/HfR7yx9bH9dzT8xkOQoRERHVGYPwUe6aU2Zj16FhPPHKgXovJYXalGktRQGA1oCn7LH1kZiGQI6NmS3sjkJERER1xiB8lLtwyWRMDPlx++qd9V5Kin39ERwaimLJtLaU24MVqAkP58mEexLtEIfZHYWIiIjqhEH4KOd1u3Dlill44pUD2HFwqN7LMalNmcdMTQ3CQ4lMuJSllc/ENR2aLnO2KASMkpRyM+5EREREpWIQPga8e8UMeN0Cv3LQ8J4NPX1wCeDoKaGU24N+LzRdYiRWWqlIJK4DQM6JmQDQ4ndjmN1RiIiIqE4YhI8BE0MBXHzsFNy9rssxmxE3dPdjXmfQ7FSiqNH1pXZICSeC97yZcB8z4URERFQ/DMLHiKtXzcZAJI57/tVd76UAADb29GXUgwPJILy/xCBcZcJztSgEjHIU1oQTERFRvTAIHyNOnDkOx05rw69W7yy53rpSDg5GsKcvjGOmtmbcp4LwgRLbFCbLUXJnwpt9bgyyOwoRERHVCYPwMUIIgatXzcKr+wex5rVDdV1LclKmXSbcCwAll4oky1Fy/2gH/R4MsxyFiIiI6oRB+BjypuOnYnyzF3fUeYPmxp5+AMBim0x40K8y4WWWo+TZmNns8zimPp6IiIjGHgbhY0jA68blJ8/EPzbtQ1fvcN3WsaG7D7Pbm9GayHpblbsxM5LIhOca1gMAQb8bQ+yOQkRERHXCIHyMuWrlLADAnc++Xrc1bOjpwzE2pSgAEPIbgXmpo+vDhWbC/UYmvN718URERDQ2MQgfY6aNa8L5iyfhrudeN+una6lvOIbdh0ewZKp9EB5UmfASS0UiBbYoDPo9iOsSUU0v6XmIiIiIysEgfAy65pTZ6B2O4c8v9tT8uTf2qE2ZmfXgAOB2CTT73GXXhOcb1tPsM4L0IXZIISIiojpgED4GrZrbjoWTgrhjTe3bFW7osR9XbxUKeKo/rCexAZSbM4mIiKgeGISPQUIIXHvKHGzo7sfT22rbrnBDdz+mjWvChBZf1mOCfg8GIuX1Cc87rCcxqXOIA3uIiIioDhiEj1FvXzYNU9oCuOmRV2qaDd/Q02c7pMcqFPBWoEVhvkw4y1GIiIiofhiEj1F+jxsfPmse1u7srdnwnsFIHDsODtkO6bEKBTwlB+GFDuthOQoRERHVE4PwMexdy2dgUqsf33/k1Zo83+Y9/ZAy+6ZMxQjCa1OOMsxyFCIiIqoDBuFjWMDrxofPnIfndhyuSTbcHFefY1MmYPQKL7lFYVyD3+OCECLncaocZZDlKERERFQHDMLHuMtPnomJIT9ueuSVqj/Xhu5+dIb8mNgayHlcsIxylEhMz5sFB5LlKMyE57b9wCD2D4TrvQwiIqJRh0H4GBfwuvGhM+fhme2H8ez26mbDN/b0YUmeTZmAUY4yHNWg6cVvGI3EtbybMoFkOUqpGfex4t/ufB7feXBrvZdBREQ06jAIJ1y5YiY6gn7cVMXa8HBMw6v7B/NuygSMFoUASuoVHonpeQf1AMYwH5fgxsx8BiNx9I2UVp9PRERE2TEIJwS8blx/5lysfu0Q1u48XJXn2LJ3AJoucw7pUVoDXgAoqVd4OK7lHdQDGL3SW3wetijMQ9clwonNrkRERFQ5DMIJAHDlilnoCPpw08PVyYabmzLzdEYBjJpwACXVhRdaEw4YdeHMhOcW1yUiMV6oEBERVRqDcAIANPncuO6MuXhq20Gs31X5bPjGnj6Ma/Zi2rimvMeGAqXXa0fihQfhzX43hqMMMHPRJTPhRERE1cAgnExXrZyF9hYfbnpkW8XPvaG7H0umtuVtHQgYEzMBlNQrPBLXEChgYyZg1J5zY2ZuzIQTERFVB4NwMjX7PPjgGXPxz1cO4F+v91bsvNG4jq17B3BMAaUoQHJjZinlKOEiylGafW62KMxD0yWizIQTERFVHINwSvGelbMwocVX0U4pr+4fQFTT8w7pUVrLqQkvcGMmoDLhzPLmousSYWbCiYiIKs7RQbgQ4kIhxFYhxDYhxA02918qhHhJCPGCEGKdEOK0eqxzNGnxe/CB0+fg8a0H8MLuIxU558bufgAoqD0hUObGzHhhLQoBI/PPTHhucV0iwkw4ERFRxTk2CBdCuAHcDOAiAIsBvFsIsTjtsEcAHC+lXArgfQB+XtNFjlJXr5qNcc1e/KBC2fANPX0I+j2YNaG5oOObvG64XQKDpbQojBWeCWd3lPx0yUw4ERFRNTg2CAdwMoBtUsrtUsoogLsAXGo9QEo5KKVUYxVbABQ/YpEyBP0efPD0uXh0y3681HWk7PNt6O7D4qmtcLnyb8oEjB7eQX9po+sjcR3+AjPhLT43+4TnwUw4ERFRdVQlCBdCnCmEWC+EeF0I8agQ4ntCiKuFEMcJITwFnmYagN2Wr7sSt6U/11uFEFsA/BVGNpwq4OpVs9DWVH42XNMlNu3pL7geXAkFPGVMzCw8Ez4S06DpvHazo+sSUhqBeFxjIE5ERFRJ1cqE3wYjgP5PAP8EMA/AfwN4AcBggeewS5tmREtSyj9JKRcBeAuAr9ueSIjrEjXj6w4cOFDg049toYAX7z9tDh7evN8ctFOK7QcGEY7pBQ3psQr6PegvMgiXUiYmZhY6rMcI1lkXbk+TyV83ZsOJiIgqq1pB+GQAn5RS/kZK+VUp5aVSypkAOgBcUuA5ugDMsHw9HUBPtoOllP8EME8I0WFz361SyuVSyuWdnZ2Fv4ox7tpTZ6M14CmrU8qGHjUps7hMeGvAW3RNeEwzMrfFTMwE0FAlKTFNR+9QtCbPZf2EgEE4ERFRZVUrCH8SwOz0G6WUh6WUjxR4jrUAFggh5gghfAAuB3C/9QAhxHyRmP4ihDgRgA/AoXIWTkmtAS/ed9oc/GPTPmzsKS0bvqG7HwGvC3M7Wop6XChQfE14JG4E0wWXo/gSQXgDZcJ//uQOnPmdx0oaZFQsaxDOzZlERESVVa0g/CcAviSEKDntLKWMA/gogIcAbAbweynlRiHE9UKI6xOHvR3ABiHECzA6qVxm2ahJFfDeU+cgFPCUXBu+obsPR09phcdd3I9aMFD8NMtwzMjWFp8Jb5wg/OXuI+gPx/HXl/ZU/blYjkJERFQ91QrC7wVwJoBXhBC/EUJ8RAhxihCisB51CVLKB6SUC6WU86SU30jcdouU8pbEn78tpTxGSrlUSrlKSvlUxV/JGNfW5MV7T52Dhzbuw+Y9/UU9VtclNvUUvykTKC8TXnCLQp9xXCOVo2w/MAQA+MP6rqo/l6ZZg/DG+R5Z6brE23+yGn/fuLfeSyEiIkpRrSB8BoA3AfgOjBKRj8HYoNknhNhUpeekKnn/qXMQ8nvww0eLy4a/fngYA5F40ZsyASDo92IgHEMxH2yobG3BLQobLBOu6xI7Dw0h5Pdg/a5ebD9Q6B7n0lgz4epThkYT03Ws39WLzXsG6r0UIiKiFFUJwqWU3VLKv0opvymlfJeU8igArQDOAHBTNZ6Tqqet2YtrT52NB17ei617Cw9m1KbMY0rMhMe04npUq7rlYob1AI1TE763P4xwTMf7TpsDt0vg7ipnw1M2ZjZoTbie+PHR9Ma8iCAiotGr7CBcCDFdCPE/Qoi/CCEeEkL8TAjxjsRmSpOUclhKuUZK+dNyn5Nq7/2nzUHQ78EPisiGb+juh9ctsHBSqOjnC5Uwur74THhjlaPsOGiUoqyYOwFnLuzEPc93V7XHecrGzAatCVfZfI1bRYiIyGEKilaEELOEEI/Z3H4WgK0APgXgYgDnA3g/gN/BqAe/NP0x1JjGNftwzSmz8MDLe/DqvsKy4Rt7+nDU5BB8BW6UtFJBeDGbMyOJkolAkZnwRukTrspP5nYE8Y5l07G3P4wnX61e3/vRkAlXryHOgUxEROQweaMjIcQHAbwMYJfN3d8G0ATgRwCOh9HL+7TE7S0A7hFCfKZiq6W6ev9pc9HkdeMHj27Le6yUEhu6+0ralAkYNeEAimrFF1YbMwvMhDcnWhkW24WlXrYfHEKzz41JrX6ce/REjGv2VnWD5mjIhOuJ16AzCCciIofJGa0IIT4Bo4b7U1LKa20OOQ7AP6SUH5dSviyl7JFSrpZS/ieAhQD+AeBGIcS5FV431cGEFh+uXjUbf3mpB9v2586G9/SF0TscwzFFDulRzEx4MeUoRbYo9LhdCHhdGI42RpZ3x8EhzOlogRACfo8bb1k6Df/YuA99w9XpGZ7SorBRM+GSmXAiInKmfNGKSPyX7V/gCIB/2d0hpeyF0cd7D4DPlrpAcpYPnj4HAY8bP8yTDVej7pdMLb4zCpAMwosZXV/ssB7AGNjTKJlwFYQrZx7ViaimY9uB6nT+GA0TM5kJJyIip8oZhEsp/x+ATwD4vhDi5zaHvATgqByPHwJwH4CVZayRHKQ96MfVq2bhzy/24LUcLfI2dvfB7RI4ekqJQXiiHKWUmvBCM+GAURc+3ABBeDSuY/fh4ZTJo51BPwDg4GB1xtiPhomZ3JhJREROlTdaSXQzWQpggc3dPwZwiRBiVY5TRADwX8BR5INnzIXf48aPcmTDN/T0Y8HEYFFZaatkd5TCSy2KHdYDAM0+NwYboDvK64eHoUtgTmcyCO8wg/BIVZ5zNGTC1WuoZhcZIiKiUhSUMpRSbgdwls1dKwFsA/CAEOKK9DsTEzLfDODpMtZIDtMR9OOqlTNx3wvdWQfGbOjuK6k/uBIso0VhoMCNmQAQ9HsaojuK+j7P6Qiat01oMbqAHqpBJrxRa8KTfcIZhBMRkbMUHK1I+9GFHwOwCEAbgF8LIXYKIX4qhPi8EOLbMLqqiMRxNIpcd8Y8+Dwu/OixzGz4/v4w9g9ESpqUqXgTmyaLKUcpdlgPADT7PQ0xMVP1CLfWhPs8LrQ1eXGoSpnw+GjIhHNjJhEROVS5w3pOBfDvAH4Joz58CoAPAvgGgE8DmAOgD8AnhRAfEEIsSx/iQ42pM+THlStm4b4XerAzESAqalLmkhI7oyhqdH2hInEdQgBetyjiOdwYaoDuKDsODqEj6ENbkzfl9vagDweHqpMJ12VjBOF3rN6JU2981PY+jRsziYjIoTzlPFhKuQbAGvV1IsA+FsCJif+WAVgCo6YcMGrDNSHEZinl8eU8N9Xfh86ci988sws/emwb/vedybdzQ3c/hEDJmzKV1oCn6HKUgMcNIQoPwpt9jZEJ357WGUXpaPFXLxOuNcbGzK/cvzHrfToz4URE5FBlj623klJGpZTrpZQ/k1J+WEp5MoAQjCD8fQBuBrAWwNxKPi/Vx8RQAFesmIk//asbrx8aNm/f0N2HOR0tCPrLusZDsMggPBzTCh7UYz5HA5Wj2AXh7UFf1bqjNEomXLHLdpuZcHZHISIih6loEG5HSqlJKV+SUt4upfyYlPJUAOWlSMkxrj9zHtwugZstteEbe/pLnpRpFQoU18M7EtOLak8IGN1RhqIa7Lc8OMNAOIYDA5GUTZlKe9BXk5pwJ2fCFbtstzm2XnPu+0tERGNT1YNwO1k2eVIDmtQawLtPmoE/Pt+F3YeHcXgoiu4jI2VtylRCRdeEa0VtygSMPuGaLh2d6bXblKm0t/jROxxDXKv8+vUG25hp1wFFZcCZCSciIqepSxBOo8v1Z82DSwj8+PFt2Kg2ZVYgEx4MeIoaWx+O6UW1JwSAFp8RtDu5JEUF4fM6bWrCQ0av8MPDlS9JUZllv8fVEJnwmJ55oWBmwlkTTkREDlNe0S4RgCltTbjspBn4v+deh9tlbIosp0e4Eip6Y2ZpmXAAGI5qaC/qkbWz/cAQhABmtjdn3Ndh6RU+MRSo6POqALbF72mMTLhNyYnKgLNPOBEROQ0z4VQRHz5rHoQAfvPM65gxoQltzd78D8oj5PdgMBovuL1cJF58TbgKwoupPa+1HQeHMH18k+0FRnsVp2aqALbZ5zankTqZfU248X+WoxARkdMwCKeKmDquCe9aPgNAZUpRACAU8EJKYKjAiZbhmIaAt7RMuNPLUew2ZQLGxkygtKmZmi6xeU9/1vtVUNvscyMcKy4T/uq+gZpkn63bS+yejxsziYjIqRiEU8X829nzEfC6sHz2hIqcr9jR9aVkwlUbxWLKXmrt9cPDmDUhsxQFMPqEA6Vlwh/fuh8X3fQkVr920PZ+3QzCPUVlwvcPhHHB9/+Jf2zaV/SaimW9OIjZbE7lxkwiInIqBuFUMdPGNeGpz52Da1bNqsj5QoHiSkUicb3oPuGdiXKOAwPVafNXLl2X6A/HMD5LeU9rkwdet8ChEqZm7us3XvMvntphe3/crAkvLhPePxKHLoEjVdgsms7aPSdnJrzArPwtT7yGv7zUU5nFERER5cCNmVRRHYmgthKSWerC2hSGYxoCRW7MnNhqrHf/QLi4xdXIYDQOKYHWJvsgXAiB9hKnZqrv6yNb9mPXoSHMak/tvpKSCS+iO0o80aXELjNdaf2Wnw3bmnCVCS8wCL/xb1sAAG88bmoFVkdERJQdM+HkWKGAEXgWVY5SZCY84HUjFPBgv0Mz4eq1q08F7JQ6NXMgHIcQgFsI3LF6V8b9Zibc5y6qO0osbjwuVoM67H7Lz4Ztn3C2KCQiIodiEE6O1VpsTXis+BaFADAx5Mf+fqcG4UamV12Q2GkPlp4JD/k9uOS4KfjDut0ZZT8qi9ycaFFY6Iwt1a87btO3u9KsPxt2mXcVmGfbJLr78DC6eoersziH+cEjr2LlNx+p9zKIiCiBQTg5VrDImvBwCZlwAJgYCji2HKV/xHjtrTmC8I6W0jLh/eE4Wpu8uPaU2RiIxPG3l/ek3K8lglo10KjQbHgsrspRqp99zlcTnq9P+On/8xhO+/Zj1Vmcw3zvH69gb78zf86JiMYiBuHkWMlylPw14VJKRON6aZnwVr+Dy1FUJjx3OcqhoUjBmWrruUMBL5ZMM1pKdh8ZSblfxdDNPuO5IwVuzozXsC2gNRNuV3KibtPYHYWIiByGQTg5VrPXDSFQ0Oh6laUttkUhkChHGSg+iK0FtfEw28ZMwNgMG47pGI4WN1CnPxxHKOCB1+3C+GZvRptDLVFO0uJXmfDCzh/ValeOErZsGI2XUI7SCH6/djfW7jxc72UQEVGFMQgnx3K5BIJ+T8rmu2xUlrbYYT2AUY4Sjetm6YeTFLYxs7Re4QPhuFl3b9SVp5a0qJi2SWXCiyxHidagO4o1uC6lHKURfPaPL+Gdt6yp9zKIiKjCGISTo4X8noI2ZqosbUmZcAe3KewfKawcBUDRdeH9IzGz1ry9xWcThKfWhIcLbFNYy3IUa3Cdc2x9Awfh2UgpR+XrIiIaKxiEk6OFAl4MRvLXhJdXjhIAAEfWhQ+E4/B7XDlr3dXUzGI7pBg14UZw3xHy25SjGP9vLjYTrspRapAJj+fLhI/iFoUX/+ApLPji38o6x8aePuznZk0iorpgEE6OFgwUlwkvqRzFyZnwxObJXFQmPNfUTCkl/ri+C8PRuPn1YCRuntvosGJfE95UZCZcdUWJ1SDwtWaCbVsUjuKx9Zv39JddZnPJD57Cmd95vDILIiKiojAIJ0cLBTwFtShUY9VL3ZgJwJG9wo02grkH205oSQThOTLhOw4O4VN/eBF/ecloQzgU1aDLZJlLe9CP/nAcUUu2W5MSbpdAIPE9rWcmfF9/GMd99SFs2dufcru160klxtY72WNb91flvCNFTEMlIqLKYRBOjhYstia8hEx40O9Bk9ft2HKUfJlwNfUzV034kURt+YHEaxxI67rSkdjceWgo+T3QdBhBuLfImnCt/D7hj2zeh1XfesR8zq7eYfSH49h1KHWwTr6a8EbemHnvv7qx/L//YX69oauvIud1YhcgIqKxiEE4OVoo4C0sCFfdUUrIhAshHNsr3Ng8mTsTDhhBdK7uKOp7qIJw1QkmmQlX2fRkIK/pOtxCmAOQCs2ER1U5ShmZ8O0HhrCnL2x+CqKeO32zZ3p3lA/csQ6/fHpHxv2FBOG5NjnutaylVr7wp5dTLqwqFTo34PUIEdGolP9fd6I6ag14ChrWEy4jEw4YJSn7HLhBbSAcw7RxTXmPs+tukn4eIFk3nhwClJoJtwbymg54XAIBT3F9wpPlKKVHe+kdVlSZTHrvcS2tJvzhzfvw8OZ9eO+pc1LuV//feXAIE1v95mZTu+e0s/Jbj2B2ezMe/8zZpb4k06U/egoA8L7T5qAz6Me08U3oDGWuqVo19UYmXFTl3EREVDgG4eRoQb8HkbiOaFyHL0eWO1JGTThgdEjZtKc//4E1pgbq5NMe9GHHwaGs96tM+EGzHCU1E95h0+ZQ03W4XMlMeLjQiZkVGNaTLGlJ9BxPBOHpGe1i+oRLKXHW/z6O0+Z34DcfWJH12Gx2ppXClOrFRFnJx+96wbzt5DkT8PsPrUo5rpxPEnJhJpyIyBlYjkKOpoLEfKUAqlyhlO4oANAZ8juyVdtAOJZzWqZiN2wn/TxAMtNtTuJUfcKDmW0ONSlTM+EF1oQny1HKz4SbQbgZ2KcF4ZbAOVefcE1KM0h/attB2+esZN34H9d3YfYNfzW70eTz3I7MiZjVKt2udqeYrt5hR3YaIiJyGgbh5GjBRJCYb3S92sBXcia81Y+hqIahGtf95hKN6wjHdIT8BdSEt/hweDiatSOJynyrchQ1hVTVm7f43Ah4XSltDjVdpmbCC6wJr0gmXE8NuqOWmvCBcAyfuOtfODIcLTgTLmXuiwIpZUU7qPzosW0AgD19hQejX/jTyznvb5T9lKd9+zGc/I1HsPo1+4udQryw+0hN+swTEdUTg3ByNJUJ789TF17OsB7AmQN70juY5NIR8kNKoHfY/vukgvDeRKCeXhMuhEB7i98sVwGMoNbjEuagoEiB5Sgqex2LVzATbpaj6NjU0497X+jBi1190HQJn9uVcqyVNTCP5riIiGmVnT4pEiXXxQTOdz77et5jNL38ddaqZ/oVP3u2pMe93NWHt9z8NL7/8KsVXhERkbMwCCdHU1ngfB1SyhnWA1h7hTvnY/T0uu1c2lsyN1ZaqYsYKYHDQ1EMhONGqYk3+VdAR9CHg5ZMeFyXcAkBt0vA6xZFbMxUw3pyB+2aLtGX5aJBbciMp3VaievSLEHRdB2aLs0LL/tylORtav3CZk9iXNcrmgl3JZ6kku0AJSTm/ecDeNtPVpd1nkq9TCkre+GiqA3Smx24R4OIqJIYhJOjqUxtvprwcob1AMCkVudlwtPrtnNRgXq2chrrRcyBwYg5sl5YItKOoD+lJlzXjWE9AOD3uAvemFlod5RbnngNx//X32270qSXtFhbFGpmltz4s9lC0WZ91qyvOod6xanTNmXBGeLXDw1j/a7MGm6rxLetopsg1fJe2H2krPNUKhN+0U1PYsEX/2Z+LaXEgxv2phyzp2+kIs9FRDQaMQgnRwsGVCY8XzmKBrdLwOMutRxFja53ThBeTCZcXXxkC5QHwjF43UZkeGgwiv6ReEaZS3swdXR9PFGOAgABr6voFoX5unuobjQ/efy1jPvilkAbSN2Yqe7TEllxVY4StlmffSbceE0pmzo1PevGzPRs7xnfeQxv/8manK9NZcJLCXillLjtqR35DyxROTH4tv0DWLvTuADZsncg5Xv25KsHcf1v1qccf953nyh+faUvj4iooTg6CBdCXCiE2CqE2CaEuMHm/iuFEC8l/lsthDi+Huuk6im4O0pMLzkLDgDjmr3wuV2O6urQP5Jat51LvqmWA+E4ZkxoBmCUrKhMuJXqsKJKKHRpbMwEisuEm6UklgDt8/e8jG8+sDnlOHXh89vnXs/4vqeXo1hrwjXL+TVdwmtegNgE4ZaIU61fZanTp21mC8K1MqLWUoLwzXsG8PW/bCr5OfMpp0TmvO/9E++8xf4CxG7NQ9HCLtzycdKGaSKiSnFsEC6EcAO4GcBFABYDeLcQYnHaYTsAnCmlPA7A1wHcWttVUrWFAoXVhIfjWsn14ICRHe0M+XGg33mZ8Nam/JlwMwjPkq3uD8cwt6MFgArC4wj5U4P7jqAfcV2a0zTjWjIT7vcUngk3s9aWTPjL3UewoTt17LoKmqNxHa/sHUy5z8yE66kbM2OaNRNuZK9VzbrdRYKekglX5Sgi5TmM82bPhJfSujBZE559PdmU01WmEGoJlR5f/+r+wfwHFSC9ZH/dzsM45isP4bEt+yty/mwODUbw5KsHMm7ftn8Qtz9dvU8miGjscmwQDuBkANuklNullFEAdwG41HqAlHK1lLI38eUzAKbXeI1UZX6PGz63K//GzDIz4UCiV7iDylH6w8VkwrPXRQNGQD+lrQk+jwuHBo2NmemZcDWw50CiJEWX0gwm/V53wWPr4zZ9wmNxmVGeYg2a0+9TgWgsnjmsJ70m3OMyNo/aXSRYT2verzLhlvXFNZk1411SEJ74UUzPhH/qDy8WfS7lpkfydwv5xF3/wu/WZnZZsW44tg4wsoppetn15tXwjkTmfc32Q1V9nqtuew7vue25jJ/Ft978NL76500Vv2ghInJyED4NwG7L112J27J5P4C/2d0hhLhOCLFOCLHuwIHMTAc5W6iA0fWRePlB+MSQ31nlKOE4hEBBfcJzZcKllGbQ3Rn048BgBP02Q4BUhxW1OTOuS3jcyUx4tlKXdHY14TFdz2gROGIpVUgP8M2x9apPeEpNeGpA7hICHper6I2Z1oxzXM+eCS+la4q6eEl/7J/+1Z33saKMkfL3vtCDz/0xs9/4yd98xPyz+pZYl/bghr248W9b8Jabn8Yr+wZKfv5ifOOvm3D9r9dn3F5qqKvpEjc/ti2jdOWpVw9i9g1/xcaeviyPNLyWyOSrnxldl/jKfRswkDgfY3AiqjQnB+F2/xLZ/jUohDgbRhD+Obv7pZS3SimXSymXd3Z2VnCJVAvBgKeA7ijllaMAxsAeJ2XCB8IxBH0esy47l1wbM0diGjRdIhTwGm0Is2XCQ0YmXA3sUQEuoDZmFtgdJS2ABoyAPJrWLSUc19Dic5v3W1nH1m/o7rMM60kGy3Hd6GjiTmTCrRcJmp6Z7Y2YNeECH/3t8/jwnc9b1pejJrzAIPz+F3vMi0Vz82eRAfyBgQje9KOninrM7U/vwF3P5e8xrkgp8fzrveZAIQD4/sOvmOVC2dpclur6X6+3/ZTiZ0/uwIMb99o8wiBE6vcvXyb6Ly/14DsPbcV3HtqacvvDm/cBAH782Gu5J5im9XbfsncAd6zZlXz+nM9eefe90O2opAARVV7+FFv9dAGYYfl6OoCe9IOEEMcB+DmAi6SU1f28kurCyITnH1tffiY8gCPDMUTimjmgpp7sOphkk2tjprW2vD3oR8+REQxG4hllLumZcF1aa8LdODIcRSFi8cxMeFyTiKYFYiNRDaGAF0NRzSYIN0KeZ3ccxsfvegGLJoeM2y3dUeKabnZw8bpFyqcAkbiGZp8npcTE2if8Ly/tyXg+u/7hxnPaX3xIKSGEwKNb9qHF58HH/u9fuPjYyfjxlcvMzZ/52jSmy9f60M5X/2xsiLxoyZSCjtcl8LYfp/YaL+RioZSyHAB4cONe/Pix13D0lBAuzLHGe//VjRbLpz4Pb96Pef/5gPm1NQYfCMfg87hSfk/Vz356JvyZRBnLX1823vObrzwx5f4N3X1o8rnNrE+2WN+4CCj9U4pi9A3H8PG7XsDRU1rx1hOm4syFE3FU4ncg+9qQ0nK0knYcHMKOg4M4Z9GkqpyfaKxyciZ8LYAFQog5QggfgMsB3G89QAgxE8A9AN4jpXylDmukGgj6PXnH1lcicFbdOg44JBtu18EkG3UBYpettk7H7Aj68PrhYQDJkfXKhBYfhAAODBrBdlxLdkexZsK3HxjEKd96BHuzjGS36xMe0/SMsfHhuG5uOk0vVVGBtnovuo8Y/aatNeFmOYqqCbd8CqD+bL8x02bNOcpRst0e0yR6h6J43+3r8J5fPJdYp/E9cZWYCe85Unrm87pfryvoOLuOLcNRzcz0rtvZm5J1vu+Fbnzlvg0pAXF6/+9c00gBo579+t88n7Mk5BO/ewEf/FVhr+HYr/4dR33xQTy+1dis+Yd1u/HsDvsLmC17k+U1dgOA3vjDp3Dud59IvmdmQJt6XDHvZN9IDN/9+1bsODiErt7hIh5pUO/Rnr4RfPOBLXk/Hbns1mcw5/MP5DymHGf/7+N43+2FvTdEVDjHBuFSyjiAjwJ4CMBmAL+XUm4UQlwvhLg+cdiXAbQD+LEQ4gUhBP+WGIVCAW/esfXhmG4ObSnVxFYjCN/nkA4p/UUE4UIIo4OJTSa839JvvCPox3CiFjt9CJDbJdDW5DUz3umZcJVpfHX/IHr6wth+wL4bhipHSakJ12w2ZkY1cw3ZNmaq8gGVzbfWbqu2gu5ETXhqJjxZN55+mxDC7C1uPl+OjZnZstlxXTdr1VUQqjLgKn7LNzU0XTnDbbIFoensXk33kRE8l3j89/7xCu5/Mfmh48fveiGlLAMANnQng9lwTMMjiZKPfH76xPaKbnC89pdr0Tccw2fufgn3PJ+/3h4C2H142PYCUgXda14zMufpFyu6lFi/qxezb/greo7kfp++/pdN+OGj23D2/z6O0779GAbCMXz5vg0p+yByLlMNe1J7IvJc5DxX4HtPRM7i2CAcAKSUD0gpF0op50kpv5G47RYp5S2JP39ASjleSrk08d/y+q6YqiHkL7QcpdxMuDE184BD6jAHwvGCpmUqAa87dzlKIghX7AJ8v8eVrL9OmZiZzISbH/tnCShilsergCumZW7MDMc1s9wmY2NmIvAdimgZt2uWchTVotDtEikBjlpjSjlKLFmO4k6rs881rCdXJvzhtOBTndXMqhZZjvKzJyvbCk/TJf6eVnddSJtEuymmVnes3mn+edGXHsS2AtsT3v9iDx7KUQcOIGs2PNuqh2zqvEeiGr583wbbDd2n/89jWPmtRzJuV++dev6bHk7tRiMl8PMntwMAnt52MMtqEmtKK4m55YnX8Ks1u/CrNTtx21M7sHrbQWzo7sv6vfjwb4z9Cv05/t4LxzT0h2M497uPZz3m9+t2o28kdwKDiOrHyTXhRACMYDHvsJ64Vn4m3GFTM/vDMSyYGCz4eKODSe5ylPZEG0L1dTqfJQi3jq0PWFoUZqu9VVK7jkh43cKoCXeltyjUzJKY9FIVVY6S/hyatSY8sTHT63bB4xYpgXwknr0cxeimkrrmmC7hKrI7yqHBCL7wpw0pt6maXJXJLKWzSiEKzSb/8ukd+O+/pg5JKuShL3dnlm1YPZUWhG7I03nE6vrfPI8tX7/Q/Hr7gUFzP0Iutz21A1964+KM1273cu58dhd+tWYXmnypF+a5KqZdafUnj21N7Uu+6EsPmn/OlxRIv3BTS35k8348tzM1a/2Xfz8N8ycGzX0dmi5t2zF+6d4N+Ppbltiux86G7j589u6X8Ojm/bjlPcsy7t/bF8Y3H9iM/3nHcWVvaiei0jg6E04EGMHiYCSeM/CIxHQEysyEtwf9cAlgv0PKUQbChW/MBBKZcJsuFAOWcpTOPJlwr9uFiKUdoFskM+Eq+FYZZ7sMJJAaUMc1IxsetcmEj0STmfBsNeHDadl2owQlWWqisvUeV+qwHrUJ0xrbJ8tRkBGN5Rxbn+XnLn1tgE0mvEpB+POv9+Y9Zvfh4YwAHIA5dj6XP7+YsQc+p0c2FzdI58OW8fbnfPcJXPLDJwt63CnfegQf/b9/pdwWTytl2nV4GD/95/bEfYV//wfSLvhybXJUGfFs0n9m1M+DXVb6jT98KuVizlp7b/XrZ3bh9+t2F7xBWv2+HsjS7ebrf92E+1/swW1PcRARUb0wCCfHCwY80HSJkRx9qiuRCXe7BDqCzugVbu3tXaiA175XdsrGzFAyCLcL8H1ul1lOojY9Gue2ZMIT/8+WCU/vD54crpNejqKj2eeBS2RvUZge6Md1PTUTbpajpNbDh203ZibKUWA3qCZ7i8JsgZzdJlgVt6lhPdZPBdLLQsrx9p/Yj463Ov1/HrO9vZyBQdkUm/F/bGvqvIau3sJq4Xv6wvhrWmebM7/zeMrXz+04bG7o/WXapMvXDgyZf/7OQ1sw+4a/Zn2uXJ1Be/rC+MO65BiL/f1h/PzJ7bj4pifRNxzL+FlS7SC3ZunB/lLXEdz13Ov4wB1rsz8pgM/e/RI+8tvnM16XsnlPP6Q09l/0Dhu/9+t39eL87z2BcEzDJT94Eo9t3Y/Vrx00pwN/56GtKeVH+/vDeWvQs3mp60jemQ52onGdZTM0JrEchRzPOrq+2Wf/I1uJiZmAc3qFD0eTvb0LlS0T3j8Sh0sALT53YTXhWjIIt46t13TjH/dkOUqWmvC01oQxV2L4iTSCa4/bOFc0rqPJ64bP48raonDYriZcS21RaGzMzGxRqF6DeVssuTEzPbCOaTo0af/zk70mPHugogbuWJ/nOpvBNFRdua4Nbn7staz3Lf7yg7alXVafufsl9BwJ4+PnLcA7f7oGuw4ZXVC+94+tGRcZ+by6fxA33JM5ZMnO09sO4elt9t14L7rJ/hOFV/cPYtOefmzs6cd7f5kZ6J/27Ufxz8+ejfYWP07+5iNmW9g3LJ6EW69O3WoV13Q8/doh9A5FIQSwbNZ4DEc1zG5vwZt/9DRWzJmAX1x7Eo75ykP4zAVH4SNnz095/GAkjq7eYfz22dfxqzW7sPPGS/D+O9biyVcPYueNlwAwkhCHh6JoD/rx6r4BLJiUvT1jIaJxHR6XKGjmAlEtMQgnxwv6k0H4pFb7Y8Lx8of1AMbmzGyt92opuZmyiCDck21jZgxBvwdCCIxr8sLtEokA374cRQWXmkxmwtWnDJG4bn4ikT0TLuESRgAU03Rz6qa6z+NOBskBr8sogckoR0l0HUkLdNNrwtXGTI9bpJTBqIA7tU94soNJeu/vuK5n3USZrU+4bSYcAvf+q9usma5WOUotvOe2Z7F4apZfuFHOrtTIzv97+BUcNTloBuAAMjrJOEV6b3irmCax6luP4ppVswAk//75+6Z9+O7fk8OPcn1ycMNFiwAYXXqO+cpDAIws+0fOno/XDgzi3O8+Yfu4U2981GxBqtyxeie++udN+M+LF+GbD2wBAJx39EQ8vHk/vn/ZUjT73Dh+xjh89LfPY+3OXmz7xkXwWDoe7e0LQ5cSU8c14fGt+3Ft4sJj49cuSOlFX4q+4Rj8Xhfr6KkiGIST46lANNvHnEaGVlYmEx7y46WuwjeZVUu/WUJS+K+o3+uy3TBmlLUY30OXS2BCiw99IzHbbjLWjZnWTLj6BycS08wAN707ykhUQ5PPjZhmlJkMRuJGEB5PBuFRTUcT3GZdecDrNkpgMloUZsk+p/UJVxMzPWkZLpUVtytHkTIzQxrL0aIwWyBt225OGP2u872OavvNM9kDwfHNXrNUIZcnXz2IJ1/N3QWEjI2mo4XdBcQPH91mc2SmG/+2xfb2Y7/6UM6NrNYA/Iz/eQznHj0Rtye676gAHDCGNwGpv1/Kad9+DHsTJTWffsNC/O/fk2ND5na0mH8eCMdx7wvdCMd0zJzQDAA4f7ExgOjF3UdweDiKs4+aCMD4u+PO517HO5dNTwm4j/+vvwMAtn/z4pIy66/sG8DcjhbzoiGm6XAJkdGxicYGBuHkeMFEIJqtQ4oKGisx5XJiyI9DQxGzbKJe1AVHMRsz/R43DsYzN231p9WWdwT9WTtkeN0uM7hWPbiNcxvfi3BcT27MtLwf2/YP4oLv/xMPfOx0xDTd3Ewb1yTi7uSTRdPqylU5SsbGzCxZac1SEx7Tkhsz0/8By5UJtwu245rM2rovWyBtN4o9vV/zzkNDOOs7j+E3H1hhe45q+eK9G7Let2zWhIzWikTVkq+TjNXrh4fxy6d3Fv0cey017dYAHAC2H0zuA7BrTXnq/HbM7Qji11kuXL907wa8/NU34PGtB/DG45ITX+f+5wN47gvn4sv3bsSDif0eD3zsdOztH8GuQ8MY3+zD1n0DOHfRRMzpaMH7bl+LFxMJnnMWTcQ333osJrcFsOALfwMA/OTKE3HO0RPxwMt7sGzmBEQ1DVLCLMX584s9aG/x4ZT5HTm/F7oucXAwggktPrhdompTVKkyGIST41lrwu2oEoxAmRszAaCzNQApgUNDUUxqDZR9vlL1jyQ7mhTK2JhpX45iLWvpCPpsjwMyM+Fum0y4yjIPWzZNdh8ZgaZL7D48jLgm0ZQ4Pq7riMat5SjGuVUg70+Uo2Qb1pMurkmz84Sm6+bGTI8r9b23DusRwsh+m4G5TYAfjWuI6/bfa2sm3BqoFzJ45fbVOxGN67jn+e6UXuv1ZFeyRDRW5aqxV479qpH9/ve0zjwnfyM1qL/4B5k1+T95PHPvwaNb9mdcEHz4zsI/UfnSGxfD53HhS5aL7evOmItPnr/QtnXlJ85bgLvXd6GrdwSXnzQDMU3izKM6sXrbQew4OISjJofw+NYD+PBZ83DxsVPQ4nNDkxLrdvbixJnjsWb7QUwd1wS/x41P/+FF/ODdJ2DauKaU5xgIxxDXJMa3+DKeP52UEgOR4uZgjFYMwsnxkjXh9h+hRyqcCQeMNoV1DcJVJrxCw3qmtCVfy/tOm4PDg/ZtznxuF6KWTY3WYT2A0XVEBZ/WTyast8V1ieZEf+aYJuF22WTCE+tMbsxMDYyzdirRpZklj6sWhUKk1J0DySy96iMejeuWtoU2Y9tjGsZnm5hpWYu1Rr2QYFa93oDXhSaf2xFBeHqPbyJqLF//y6aM227953bc+k/71pnftwyeumut0dXnj893mbepabufv+dlfL6ADcKn3vhoUevN5o73nYyHNu7Fxu4+vPfUOWap0ftOnYNfJDoA/dtZ8/Cm46fC6xYY1+zDDX98GQ9v3ocfvvsEXLRkMp7cdhA/enQbzlzYiWOnt2HWhGbM7QxiMBKHpkn8Yf1uXL1qNnwVKFetBgbh5HghsybcPhOugqtK1YQDSLQpbCv7fKXqt0y5LJTf4zLLPKwGIjEsDCSH/qiaRzvWgFjVWwOAX2XC45r5HNbNa+o9UD2M1ZAUozuKdXx9ahAe8LpTNmb+8ukd2NsXzgjKFWufcFVCkl4T7nO7cOezu/CeVbOg6RJ+MwjPHGWvDEe0rCUw1uy3NfAeydM9wyoS09HsdeMI2IaNiAgArvnFc+afrbX+v7C04Pzx46/hxzafJqR/KrF+V/bZCWpeguq+4yRjLwiXGhCu/8Y7KlxQSoQwjMjgEdv3Ljo4iBCGEcRw2e/tJH8UIQyj9/BBIFy/THhk8DBCGEYII0C4sJaJbWIEnthAxvdAjvShw9tc0PemFcPwJs4R0AbRLIeAcB+a9SGEMIzY0BGIcD9CGIYMwzxnfOgIQhjG8EAvQhhGuyeAEIYRHzkCd8yFEIzuEbHhI0BYR2yoL/GeDWGcawSeWBwI92HNph3Y2zeCgBaBjsxMsz/uNs/njQ0goA+iWTYhKqPmc9xw/lG48cGtWLd1F/zxIYx3jwCIwR31IoRheKSAL23OYnzkCFxRzTxHyvcq8XoBIDzYa/5ZH+lLHp/HQN8htHvDGCjw+GpRnXGIiMaant4hTB3fkv/AGhKFjj8eLZZPdct11xU+CpyIiIiIGtvWdz+Do446uubPK4RYL6Vcbnff2MuEt00DLvhcvVdBRfr+w69iXmcL3nT81Iz7Xj88jDtW78QVK2ZiXmf5F1j/+/etOHpKKy45dkr+g/N4Zvsh/GPTPly1chbmdBR+Bf7Xl/dgy55+fOoNRxX8mCdfPYDHtx7Af158tFlGEtV0fPtvW3DOook4Nc+uegB4aONevLj7CD574SLc+LctOHHmOLzhmMnY2xfGz57cjncun4HHtuzHwcEI/F4XPnuB0Rt49WsH8cjm/Th2Whte7u7DMVPbsLGnD1etnAW3S+CORMuxq1fNwqz2Fmzs6cM9z3fj+rPm4cENe6FpEteeOhs/efw1jMQ0DEc12CUIOoJ+TB3XhJe6jmDhpCB2947gmKmtCMd0bOg2stcfOH0ufv7kdrzpuKlY/3ovhqNxHBmOob3Fh0NDmbXwQgCLprRidnsL/vayMY3xS29cbNZdvu3Eabjn+e6Mxy2fPR7rduYfHw8ACyeF0DscNSc5ZrNocghb9tpPVSQiotK9zeu8uQdjLwhvmQis+ki9V0FF+suaJ7AwFMSbVi3LuG/Xqwdw25PP4cLjVmHe7AllP9c/nvkntvibcckq2wvXojx4YCNu13biuBlLMWfptIIf99DOf+GlviP41KqzC37Mlth23LZpMz6x7A1mHX1vfxi3/eURzF24BKeumJX3HM8f2Yxf7tyJz666CL/8698gp83GG1YdjcH9g7jt8Sdw3Jyl+O2rW7FbG4FLBz6z8mIIIfDs4Cu4bcOrOLupE49pB3DZpBn43e7dOH3hSfB5XLjtyWcBAGcftQKzFnRg47rduG3tS7h22dl4pGsDDg9Fce2q03DHY49gSItjIG5f/z/H24KlE8bhT7u6cWawE88f7sXbJ03HUCSOP7xubDS69uSzcdvjj2HatMW470A3hlxxvDYwhGmuJnRrmePRxzV7cULzOJw9ayJue2EjAOBLqy7BbfcZg0mOm7cUt619IeNx/eOm4w9aV8btds4KdaIrPoIdcihnOcilnVNxX3dPQeckIqLC/ce0SfVeQgZnbhclSqPGKNtRrecqsTETMEbXHxiozNTM3YeNGuB9/cWdrz8cK2pkPZCcamkdt90/oob+FHYuf6JdoJQypU94wDoxM2qcX5fJzjSqbeGRxPOlbMy0bHhUGzMjaRszVReRwUgcQ9HsfYVjWrJPuKYbGzM9LpEyBU/1Vh+OxjEUiZv32fX1BowONEPR1I2Z1smA2TZs2m2CzUa1VnTn6dkbyNLh58SZ4wp+LiKisaTNMk/j+Olt+K9Lj8HpCzrQEUy2S/z7f5xhdlpzEuetiMhG0J8jCK9gi0LAGF2/bf9gRc61u9cIwvf3F7a5UhkIx9HaVNyvpwrgrMGm6rJSaL9xn8cFKRNdSKzdUTzWiZkaPC6BuC4xGIkj4HWbF0J9iUmMyRaFOqxxpxm0J45v8qkWhTq0xPmy8Xtc0KzdUXTdXKPf0iO+2eeG1y0wGNHQfWQE5x49CS919ZlrTNfa5MFwNG72H09X1MTMLKKaDikBlwuw2W9qUhcv6biXkohK5RLGpmyVELFOzX3bCdNw3ZlzsamnHzsPDuH/1u42y+ZOm9+BBZOC+OXTO3Hy7Am46NjJ6O4dwSnz2/GXl/agxefB6Qs68Lu1u/G9y5YiEtdw5zOvY9Oefnz83AU4OBjB6Qs6AQBD0ThCfk/W4UF7+kYwocVXsX/Hr141uyLnqTYG4dQQWgNe9BzJLCUAKjusB1CZ8Ah0XZY0lliRUmL3YWPN+/PUAqfrH4mhM1hcfbtdJtycvFlgEO51JzPeUsLSojB5eziuoT3ow77+CIYjGhBMBqS9iRaFZhCuSwgts0XhiHrPPC6jN7mmZ82Aq2E7zT53okVhok+4ZvzZ5RJo9iVfnyfxdVfvMMIxHbMS46mz9egO+b3Y2x/O2Zvczv4iPi1RkzSNTEz2DLq/Qj/DRFRdE0N+27/Xr141Cx88fS4+e/dLWLP9ECa0+HDYshflwmMm49Et+xHVdNxy1TL88fku/GPTPmz6rwuwpy+MFp8Ht6/eidPmd+C0BR14aONe9A3HcHAogmtWzcbmPf1YODlkO0Pik79/Afc8342NX7sAvcNRCCFShuqYf3fqOq782bM49+hJ+PBZ8wAAiyYb9dKftNmH9JU3HZNx2zmLkqUdbzhmcuJPXvzH+Qttv1/5Zl5MaWvKef9oxSCcGkLQ78maJa18JtyPuC7ROxxFe9Bf8nkODUXNYLPYcpSBtFHzhVBTLa29rAfMTHhh5ShqoIEKqs1ylMT3diiiIaZJTGjxY19/xHxPVDlKX6IcRa0lrukQlqo367Aer1vA43bB6xaIxSUGs3zS0dbkxZHhGJp9RsY6+Q+JEYR7XAItlgyyEAJBvwev7DM2OM5MBOFRzT74DXhdGEoMGbKj2UzvdAngpa7i22HO62zBi119+OqbFqN3OIabHnk15f5s5ShMhBMlfezcBXh620Gs39WLr75pMQ4PRfGDR7flfdz6L56HZf/9MADg+5ctxdRxTXjXT9eY919y7BTcfOWJOO3bj6KrNzXps2BiEJedNAMfOH2uedu3/rYZm3r6cctVy+ASAgGvy8z0/t91KxGOaXC7hJncUKSU2HFwCHM7g7jgmEnQEwkP1VjghosWmcdeYAa4huU59j3d+Lbj8MnzF6LF70kp0VNUUsXtcuPuD5+S/RtFNcMgnBpCzprwCg7rAYxyFMDIXpcThKu/xJt97rxdMdL1h2NmbXOhAt7McpSBIstR1D8WKpB3JyZRet0CQgBHRoyMjqq1U6Pr1fEqjlWZaaOe2j4TrgJOn8fIhGd7fxdMDOLF3X2Y09GCF7uOmMFyXNehS8AlRMY/OM0+N3YcHAIAzGxvtj3vqfPb8fS2Q5jcFsDanb04lGWK6O/XGZsvb77iRHzkt8Zo6bOOmohHt+y3PT6X+RND+Pk1J6Ej6MOnfv9ixv3qPcxQhVayfo/LERM8S/Gu5dPN9+WBj51uOy6c6uu8oyfi4c35f0eOmhRCZ8hvTnL96XuW4UO/Xg8hgLs+uBKX3fqMeeziKa1oa/Lik+cvxCfTMq5dR0awbf8g5nS04Ma3HYfBSBxrth/C41v2455/dcPncaE96MfqG85BR9BvJhxe/PIb4Pe6Un73nvrcOQW9xs9flLvdXbbfZyEE5iYCbiEE3KV/4JrC53Fh+nj7v+/ImRiEU0MIBbwYjmqIazo8aVkFVX5RqY/yJ7aqqZkRHF1Gl0K1KXPpjHFFZU3jmo7hqFZ0Jtw6Wl5R5ShFZ8JjqZlwIQQCHrdZ893eYgThKhOePj2yyWecJ6brsO7/jmrJmvBAInvtdbsQi+sYjNhPk5zXGcTvP7QK33xgM55/vdfMhKusevrGTABo9nvM+keVCU/37+cswJ0fWInv/X0rhqJxdPXaD9J5OdH6cHJb8oLMuuGnGC4BdCamsnbZlFdlK6mqRiY8Ww280xw1KYSt+1LbNn7h4sVmEL54av62Y99627EFjeMerU6cOQ7HzxiHXz69s+RzXLFiJn777Ospt122fAZ+t263+fUXLj4abU1ejMQ0LJocsg3C77puJaaPb8KOg0P4xF0v4L8uPQYz25vx5fs24vuXLUWL35My2VCNMH/z8VPxg3efkHV933vX0pSvm3xuvPn4qXjz8VNx49uPM2+fOi617KGtubhkB1ElsQCRGkIwEZAORTJ3tSUz4ZUrRwGA/UWWkKRTmzKXzRpvdP3IsenQSgW2+Wro0mXLhLsEUso1cvG508pRLDXxfq/L7H6iPiFQo+utJTAA0ORNZsKtZSDWchQVcKpMeH+WTLjPY3zE63a5ENel2a1EZXGNmvDU1xf0uxP/96C9xf7TDDXqvtnvgZTAawdyb8ZVn5BYX3+xXJZNSYdtepY3ZcmcVSNezlYDf97RE83vTTUtntKa8b7Zsasx9bgFrj1lNo6eUljf32L3i/zkyhOLOr4YFx4zGatvOAdLZ4zLesw7lk3HVStnZtz+npWpbUZPmDkOW75+Ia5ZNQsr5iTLFGZOaDYvlAGjNOKLlyw2v37jccnsgvVx6vaLlhglEL//0CrcctUyfO7CRfjmW4/NWM+333Ectn3jIjz/pfOx88ZL8MEz5uJdJ83ANafMTtlM/NsPrMDbTpgGIYCVc9sxfXwzTl/QifVfOh8r5rZjSlsTfnb1ctsSis9ccBTesHgSPmcp0SiWz+MyEwxETsKfSmoIKivcH87MlkbiOrxukRIwlsNajlKO3YeN3d6z21uKOl//SHElJEogy8bMYI4d6ekyMuGW72nA48aRxMbL9mBqJjw9CLd2R4mnbMyU5vEq4FQbM9PLUdSS1YWB1y0QT3RRAZKtKd0ukdF6SpXDTBvXlPXnQn2ioh6785B9JtzrFvjU+QtTNjiNLzF75rL8jXv+4syetdk+vj5jYf5BS8XK1nFFCIFt37zY9j6fu3L/ZOhS4tn/PDfvcXZvn9sl8NU3H4O/ffx0AMB1Z8zNPMhixZz2otYWDHiwbNb4rPfffEXpQfot71mWkY21eu+ps/G/7zwe1585z7xtbmcLPnj6HHz9LUtSssR/+rdTEfC68bVLl+B3H1qFNZ8/Bx86cy4e//RZWP+l882fMZcw/n584jNn4RfXLk+5yBIC+OqbFqMj6MM5iyYCMC58dt54CU6eMwEXLplsbt6z43G7MKEl85Mhmfj8ZuXcCThlfge+d9lS7PjWJRnH5dPkc+PWq5en/P4RjRYMwqkhhBKBkt3mzHBMq1gWHDD+0g/5PUXXcafr6h3G9PFNyfKWAjPr/UWWkCiqxjp9Y2Yx5zEz4YlzeNIy4WrjZUciuzycNwhP7ROuMuEjMc0MOH1uoy2iOrdiBumJCwO3S0CXyZIW9X9PWncUIJn5nzY+RxCeuL0jkdXWdGmbiV4xpx3/fu6ClE452TLW+Vgvhj5t04XAmrFVWclls8bjjcdlToo9bnpbSWvIJ9e17CvfuKhizyNlYT/jLpsLyPSNbukXL2u/cB5uuSoZKOcKeu0ICNv3R5E2BUKhEnsQ//YDK1Iy0zJtXwUAPPSJM/AFSyb75Cyb86a0NeHzFx1t/qz+8N0n4InPnGV+v2a1t6R0tQCM13rtqXOw7ovn420nTseGr12AhZNCJb0WK/U6BKr/qQpRo2IQTg1B/WNtt3kvEtcr1p5Q6Wz1F9WCzk5X7whmjG/GpNbiMusqCC+6T7jZHcUyrKfILiteVVeeKDOxBp5+TzIIV5nwIbMcJb0mPNkdJaZnbswMW4Jw9ZyH0zZGpgfhKmhWgbwa+GNszEwNwtTH2tPGNWUNKj2J3VCdoWQWb8aEzGBtnE3Wu9SLPuta7C4Ogn4vHvv0WXjys2fj7SdOB2B8ImIXiFYyKw0Ab1lqBPrVDJo+evZ8889LphV2EWH3IU76ty79kAktPly4ZAouWz4DP7u6uMm3HpfAsdPasGpeO85Y2GnefuWKZHmIXXnQ/EmZLUXnT8zeZnRy4u+F8S0+/OiKE/GVNy1OnDv15OOavRkXHXd+cAU2/dcFeV9LwOvGrMQncVanzk9+sjK7I3XPRCEDTW64aFHev1fUy3AxyiDKihszqSGomnC7zXuRmF7RTDiQ6AFb5IAdK12X6O4dwRuOmWTWmBfaplBdaBRbE642ZqbWhMeKOk+uTHjA68aRYXWB4IXHJcw695GYZpaVqGMBo0+4Knuw3t87FDO7lqgA4/BQ6vc7kJEJT319qibcbbMx0wzCxzcl6slFRg10eiYcAKaPb8Yr+5K14afOb8cXLsnsgFDMJuBp45rQndiEaRdMWwUDHszpMIIm68AouwuJQmtc33biNNzzfDcA4DvvOA6fufsl2+POXzwZ977QU1bQ5HEJvP3E6Smb9aw+fcFRuHt9F/b2h3OWOIxr9po/a+kBKICM8qr0b6v6fn37Hcchn1Vz2/G1S4/Btv2DmNDiw8q5ydIVa0C8eGorjp3Whpe7+3CSTSbaLjD/8ZUn4jN/eBEv2mzM/p93HocLlkwy69rVS1CnGdfkxaVLp9oOHfG6Xbbfl0K9Y9l0nL1oIl7cfSQlIM/nzg+swN6+MN6+bHpKuYyd42a0YUKLD584z75vNBExCKcGobIu9plwrWLtCZWJoQBe2H2k5MfvGwgjqumYMb4ZbU1e+Dyugstb1Kj5UjdmWrPSg5G4mXErhM9jhAIjliyz4ve4zPaATV43mn1uMwgPx4wBPnv6jAsNn9tlTNXUdEiX0d4w4DXG00sp0X1kBKvmtSee03jvDqVtVDQ3blpqwoFk8K3W4nYJtPjSa8IT5SiJMgS3ENCQHoQb57UG4TPGp2bCb7vmpJRSh/s+ciqGIvGsm0jtTGz1Fx6EZ8lC2tX0W7sEze1owfZES8Z0y2aNN4PwE2eNx3ffeTw+9YfM9oiqW0q2/QPvPnlGzrVfe8psfPINC9Ea8GYNwgFASzxPeib/+5ctxW+e2YXPXHAU/u1OoxXk9WfOwzEFdD9R11cTWnzQpcy5B0K1wFMmtfqxcFLItgQjPbD+zftXYPvBQUxuC2DnjZdg9g1/Tbn/tx9cgenjmjF9fBM0KeF1u3DfR0/DV+/fiEhcw4fOSAaurQEv3nrCdPNr9alTMoMscNPl2buBlEMIgY6gH+cenbkvIZdiAvbWgBfPf+n8YpdGNKbwgyJqCKre0i4ID8d0+Eus0c1mUqIcJf2j4UKpSZkzJjRDCJF1upqdYnt7K8kWhak14cEizuNzG99H1R3F407NhFv/HPR7zHKUSEw3S1QAI1PncRuj7aOaDq/bldIFZTASNwNkX+I50ruFqJIWv6UmHMicfOl2iYxyJBXMTk8E1XalH+q1tfg9aPK64XEJTGpLvWBJDxSPnzEOp8zvKDgTfuPbjjXbPAL2pRVW1smm1rpj20x4Yv0nzR6PRz99VtZzaro0v9cuIfD2ZdNxzqKJ+NEVqQGeCsKzXSjYZX+tls4YV9CFo56ImFXG/StvWoybLl+Kt5wwDXd/+BSssGSiP3D6nIzH/4dNZlWVOb3v1Nl44ctvyPn8b1g8CZcunYr/fssSNPvceP9p2Td1LkgrMWlr9uKEmckNm9ZuJRLAKfM6MLO9Ga60AS1fffMx+NbbjsPsjszSEEV18VGfnBHR6McgnBpC7prw6mTCw7HsbfPyUT3CVWZ1YshfdDlKsUG4yyXg87jM6ZWAkQkvpMZT8SYy4eEsmXAl4HWh2e/BUMSYYBnV9JRWgF63EYRE4zrimoTXJeBL9APvTgwxmpb43qhM+OGhaErXEVUTroIZVT6SvgnU7RIZmc/p45vR5HWbpR22Qbil7qIj5MPktkBGaz5XloLyQn/eLj95Zko/bncB5SjpBOyz0+r7lq+GO2L5ZEQd+YtrT0rZ7PncF85NZmATB33y/IW45apl5jHZuqkodpsV7ahMuPr+v/fUObh06bSCHnvRksm2nVDOO9ro6nHWURPznkMII8N81cpZ2PRfF+LYHBtcP3/R0ZjbmT1w/vpbluDJz54NAFieo5tKIS4+djJ+dMUJOct0iGh0YTkKNYSA1yhvGMjSorDiQXiio8mBgTDaipxcCSR7hKuuDBNDAWzL04da6Q/H0OxzZwwlKoTf40oJugbL7o6SXIO17r7J60ZLIhOugmJrJtyTqFk1ploKeD0ueD0uxDQdPYnSDPW9UUH2oaEo2oN+9CZqgdNrwtX3IyMTbhOgXnDMJDz7hXPNzGyuTDgAzBjfDK/blbdcRMk62dKGNXjNFtQr2bquWB+2am471mw/VHBNsHWPQLbXNzEUyMiEf+zcBWbWGkitj57V3oxdWVo65hNLvH+5atqlzZ8ntPjwE8tFgdWyWRNSWvdVis/jwoo57dh+wL7UBzA+7Xr4k2eYrUhLJYSw7YJDRKMXM+HUEIQQCAY8ti0KI5ZOG5XSaQ7sKW1zZlfvCCa1+s11TWz1F9yicCAcKzoLrgS8bjPoCsc0RDW9qHOZfcKjatNj8j5rCUbA60ZLoiZcBeHW2upkTbjRotDjMoLyqKab9dHT0oLw3qFoyiTKzI2Zqd1RFLsAWwiRUhphnwlP3vb/LluK77zjuIL7qRdz0WcNXnOd/uPnLkh5/nmJsdZvOGZySvCcraY6G+segVzPry6yrD8v1uPVy3jxy2/Agx8/A4AxRTH9fqsf2wy9URdRhXQ0Eln+XIqg34Pjq9TWcf7EUEkXzUQ0tjETTg0jFPBkbVFYjXIUoPSBPbsPD2PG+GTrr0mtAfSH4ymt+bIZjBSXvbYKeF1m0KUuWIoKwtMy4anlKKk14S1+Dw4PDZvHWif0eRLlKDFNQggJn9soR4nGJXqOjMDncZnHqyA7rsuUQN46zAdA1imOhQxpss+EJ39mVBvJQuc9lZwJT4uCH/7kGTjve/8EkDkZclZ7Czb91wVo8rrNCxcgOenSXH/amr/ypsXY1NOPP6w3xrrHtNSLlmwuXDIZn73wKFxj6cZhvShQmXI15nvrf18Ir8uVMcrc6uJjp2BcsxdxTZqb9NSG2kCOjkY/vvJE/PSJ1zCu2YfBxO98rnrqQmz4Wv6WfnY+cd4C7OsP483HM0tNRJXFIJwaRtDvzR6EVzgTbg7YKbFXeFfvCE62jIO2ZtZVa75sBsLF1XFbBTxuMzOtvlfFnMuXtrkzdWNmsluJ0ZHEjaFo3Az6raPcPS6R2JiZGKjjNspRopqOriMjmNoWMEszrBndqeOajJIaS+/39Ex4OnX7uYsmZt0waVeyYhfUF5ptLSoTnmOD5fyJIXzjrUuwdsdh28eqgS3W4F1NIM0Sg+O9p87BPc93mUH4h8+ahz+/2GMcm+MFul0C/3bW/Kz3p9eEF9oWdN0XzgOQvGj43XUrcf+LPTlLc1bObTdbBbY1e3HbNctxomVDZC1Nag3gF9eeVJfnJqLRjUE4NQwjE55ZEx6OaQhUOBMe8nsQ8LpKKkeJaTr29I2ktLtTHQ/2D4QLCsLLKUdRAfSgucGz8Ky6Kg0ZjhqPtcuEq0C3xe/BcCRZEx4KeOB1CwgYGyU9LoGYpkNAwOsW8Cc2ZvYcGTE3ZQKptcFT2wJmEJ4+rCdbDbQKwm/LESjlK0dR8tVs2605H2t2364m+8oVs3DlilkZt6esyxqEq9aMidvsAmt126VLp2Jcc+ZI8VIUuvEyXXqZxoq57SkdUApRbCs9IqJGwCI2ahghf5aa8Lhe1PCUQhhtBQMllaP0HBmBLoHpE1LLUYDCylvKqQlXAaw6D1BaJnwkpkbCp3ZEAZJlIsGAB/3hmFmOYvQO95jZc1WOoloUej3CqAnvHcHUtmQQbg2up41vNks9AllaFKbL13HE+ljrObLVkhciXyu+ZbPG4+FPGnXT/+9dS81PQgo9f+a6kn9W5SjqXKo7yj8/czYe+NjpKbdVglr7uKbcwXyJ3TyJiMYsBuHUMLLWhMe0ik/MBBJTM0soR+lKtOCbbpMJL6RN4WAkjpC/1JpwSzlKCTXhnsRgneTY+uR96nusguTOoB8xTZqvSfUOV0G11+1CXNMRV33C3S4MReLYPxBJzYSnlKMEzAuqZE2421ybdZ1KITXh6nhrGYldQFxMTXiubhwr507A/InG8JfxLT5ccfLMrMcWwrpUdUGX/rpntjdjcWKwjWqrtzzR21vFx6VcBKy+4RzcdPlSXHzs5IIfs3zW+Jwj24mIiOUo1ECydUcJW+qHK2liqx9b9g4U/bhkj/BkJnx8sw8elygwE17cgB2rgNeFw0OJjZkl9BsXwthQad+iMDU4VhnS1xOvV03RVN1Z1LAeKZMbNdWxqj0hkJzSCRgdU9SGvfRyFGtZg9/jQjxxoVBIEK7KTAJeN4ajWtbjKpVBTp++qNZY6vAnVY4S8nvMgDrX6z5u+jg89bmzzQ40Simvzut2FdTH2/rK7v7wKSU8ExHR2MIgnBpGKODFQDgGaRlLHdd0aLqsUiY8gCdfOVj043b3DsPtEphimb7ociWmZuapMdd0ieGoVno5itdtDusppRwFAPyWINxazmuWiSQueFQHGTUdNOB1ocVSMuR1GX3BpUSiHMVlBsDTx9mXo7Q1ec1M+DmLJqI/HDMzqtbsd8DrNqd1FtQdJfHzkm/vQKGZ8Fwe/MTpOCotCFfn1csMwiEALbHZ1ZWjJhwwBhYREZFzMQinhhEKeBDTZKJzhhEQqvrnSrcoBIxM70AkjpGoZo5QL8TuwyOYOi6QsSGtszWQt7xlsISOJlYBj9sc1qOC4WKz6l6Pyxxb77bJhAfSMuFdicFEqhzlsGop6BaIxnXoUqLJ54Y/rQuK4ksrEVGZ8I6gH5+5YJF5nzXYtr7fhQzYUY9VXXSyD8UpPwpfNLk14zZ10Vhgt8AM1osDVROubitkydli/5suX4ojw5mbnUtx0ZLCy1WIiIhBODWQUCIwHQjHzUBQ1T9XelgPYN1MGcasIqbh7e5N7RGuTAz58XqeKYMDESMgyrfxLxu/12WWgwyE4/B5XEV/SuBzJ89h3fSYzITbl6MYvcPd8CY2ZnoSNeASiUy4JQifMi75KUF61xOVCbe2RwRSM+HWlpTpx9lxp9WET7Z8SmFVTgzeEfTj4KD9Jx3llqNYy2RUEK7OWUwJTfrrK3RcfCFaSrxwJCIaq/i3JjUM1WpvIBwzA8BqZsKTbQUjRQXhXb0jOPuozozbJ7X6sW6nfT9oxeztXWpNuMdt9u0eiMTRWsJ5vB6B/sHMUo/0mvDWgAd+jwvdvclylA+cPhcHE3XvPrdATJPQpTQ2ZiYe3xnyp1wYpLf7U5lwaz06kFoTbt3MWUz2WgXv6r1NV2r3EgD4+3+ckbWcpdxyFEUAuPaUOVi7sxfvWTkLrx8exifThvzYKbW9IBERVQ+7o1DDUCUa1s2ZZhBehY2ZMxItBm9fvbPgqYPhmIYDA5EsmfAAeodjZpbZTilTLq2MiZnJPuGllLX4UmrCrdlnVY5i/F8Igc6QPzkB0evGSbMn4KJjpwAwgui4riOuS3gTGzOB1FIUIBncX3LclJTnSc9we92ZazGeJ3/grILf4cT3N1smvNia8D9aNiAG/Z6sPbldZZajqCBaCIFLjpuCnTdegomtAdx0+QlFXSBWsnUhERGVh5lwahgqMLW2KTTLUaqwMXNORws+d+EifPvBLRiOxPHjK5flrQ1X9dEzJtiXowDAgYFI1k1zpW6mVAJeN+K6RFzTMRCOlZRR93ncZg2x25VZjmL9HnSG/OjqHYHHJTLKSjxugbgmoSUy4d5EF5TpGUG4G09+9myz/CeZCU8NGNtbktlr6/tdyMZMNe3x0FAUADC5NVsQXlyQumxWcopjrosBdd5SM+HqYaUm6u9438n4v2dfx6RW+08AiIio9pgJp4YRtAnCq5kJB4yR399867F4/JUDuPoXz6JvJPcmNtUpZMaEpoz7JrYmy1uyGShhyqWVylJH4nrJ/cZ9loyzXTmKtZREXVjY1eQH/R70h+OIaxIel8vcmDl1XGYAPGNCs1mW4ve64HaJjNKQ9mAyy2x9vwsJnFUt9lUrZuLU+e14/+lzbI8rpyY817RNdVepNeGhgAczJzTjm289tqTHL5rciq9duqSschsiIqosRwfhQogLhRBbhRDbhBA32Ny/SAixRggREUJ8uh5rpNpptdSEK5FEJrwaLQqVK1bMxI/efSJe2H0El/10Tc4OJ7t7M3uEK6qlX642hQMl9Pa2Ut+HcEwrud+4tUbbbmNmeibcep9VZ8iPw0MRhGMafJ5kpjy9d7Xda7DLbqe3MlQK2ZipMtCT2gK48wMrzfciXbWCVBWgayUG4R63C//87Nm4OFHqQ0REjc+x5ShCCDeAmwGcD6ALwFohxP1Syk2Www4D+BiAt9R+hVRrdjXh4SpuzLS65LgpCAU8+NCv1+Odt6zBb96/wrbkZPfhYfg9LjM4tUpmwrMH8WZbwZLLUYzvQziuYyAcLymYtwa7bndmJtxaCtIZDKQ8r9XEkB+6BA4PR+FxJTdmpteEpzttfoftUCYAGNfsxZHhGFbObUf3kRH86/UjBb1GVY6SXjKTrhJ9wu0IsxylOuevp7/8+2nYcXCo3ssgImo4Ts6Enwxgm5Ryu5QyCuAuAJdaD5BS7pdSrgVQmUa35Gi25ShVbFGY7oyFnbjzgytwZDiGt/9kNbbs7c84pqt3BNPGN9lmVNtb/HCJfJnwGFwCaC6iL7mVtXWjUY5SuUy4yrI3+ZL3qwuLbJlwAMlhPSoTPj53EH7e4kn433ceb3vfhMTGR5/HhT9efwoe//RZmNKW+3xAMhPuzZM1t25cvOWqZXnPCxQWuJdbjuJkS6a14U3HT633MoiIGo6Tg/BpAHZbvu5K3FY0IcR1Qoh1Qoh1Bw4cqMjiqPa8bheavG777ihVzoQrJ84cj99/aBWEAN7xkzV4fOv+lPuz9QgHjPrqzpA/dyY80dGk1LIIFSiPRBNBeAm15db2f9aykHHNXsxqb8YCyzTIzqARaNsNv+kMWXuBCyyYFMS0cU2YXUQ3j3TjW4wg3OMScLkEZncUdi4V+6a3PUyngvVLjp2CCwscPvPIp87CT648Mecxqm59FMbgRERUIicH4XZRSEn/hEkpb5VSLpdSLu/szOzfTI0jGPCk1ISHa1ATnu6oySHc+5FTMXNCM953+1rcsXqned/uwyO2mzKViaEA9uWpCS91UyaQLAvpG4lB02VJNeFej30QHvC68cRnzsbZR000b0vWhNuXo5jndLtw+oJOPH3DOWUNdRmfyIQX0hHFKpkJLywIL+YaaE5Hi9mWMRu1XG001qMQEVFJnByEdwGYYfl6OoCeOq2FHCIUMDpuKCoTbhcEVtOUtib84fpVOGfRJHzl/o348n0b0DsURd9ILGsmHDAC05zdUSKl1XEr6mJETW4spbbcnyUTbqeQchSgsM2ThZjQYlygDEez91q3U2g5ilLpDZquUVwTTkREpXFyEL4WwAIhxBwhhA/A5QDur/OaqM5Cfg8G7VoU1jATrrT4Pfjpe5bhg6fPwa/W7MIVP38WgH2PcGViawAHcpSjDIRjZQXh6mLkQCLQL3tjZp4gXPXutgvCA163+fz5MtCFUuUohxP9vgulJ4bk5FuH2Y+76JXllixHYRROREQGx3ZHkVLGhRAfBfAQADeAX0gpNwohrk/cf4sQYjKAdQBaAehCiE8AWCylzNwxR6NCKOC1L0epcSZccbsEvnDJYsztDOJL924AAEzPsfFwYsiPQ0NRxDTdNiAcjMTNOutSqGD4wGDpQbh1Y2a+aZQ+jwvjm71ZN8ZODPkxEI4XnIHOpyMR9Pfn6deeTgW/+TLyKmNe6S4pqhS91BaFREQ0+jg2CAcAKeUDAB5Iu+0Wy5/3wihToTEi6PekbGys9cbMbN598kzMmtCMv768B4smt2Y9bmKrH1Ia5SJ2XT0GwnHM7QiWvA4VDB8cMDLFpdSXWy8OChmEc/2Z8zB/ov2aO0N+vHZgqGKZ8CtWzMSWvQO47oy5RT2u0BaF6rhiJ2fmw3IUIiJK5+ggnChdKOBJm5ipwedxOWIS4CnzO3DK/I6cx1gH9tgF4YMlDthR1MVIOTXhxWTCAeBDZ87Lep96vZ4KBeEtfg+++y779oW5FLoxU1apHqXcsfVERDT6OLkmnChDMJBWEx7T654FL8akPKPrSx2wo5iZ8AoF4blGsRdCbc70VagcpVQq9M13USGrnAlnTTgRESmNE70QwSivGIzGoSc+14/EtZoM6qkUlRne15+5OTMS1xDV9JIG7CjpGzNbS+oTbgSMhWTB81FtCvP15642Ffz68lywSVSnJvzEWeMAAJedNLOyJyYioobFchRqKCG/B1ICQ1Gjn3ajZcI7gj4IYZ8JV2U2ZfUJT3SJOZToHtLiL/4CRQWq5WbBgWQLQ2+d3yPdHNaTb2Om8f9KZ8KntDVh542XVPScRETU2BoneiFCstuHCljDca2hgnCP24X2Fr9tm0JVZlNKCYnicgn43C5oukSzz11SLbaqm3ZXIBDtDBqZf2+lU8tFUkNyqjGsh4iIqBSNE70QIZklVqPrIzG9ocpRAKNEw25qZjITXt4HVOqipNRgXmXCK1GOsmhKCAsmBrFwcij/wVVU+MZM9SdG4UREVF0sR6GGEjQz4Uaf6Ei8scpRAGNzpl1N+EDEeE3ldEcBAL/XXdbkTRWoVqIcpSPoxz8+eWbZ5ymbKkfJs0FUVqlPOBERUbrGil5ozFOBpRpdH45pdZmWWY7JbQHbIFyVo5SymdJKbc4MlngefwUz4U5RaCa8WjXhRERE6RiEU0NRnUNUwBqJ62bQ2SgmtQZwcDCKaGLQkDJQgZpwINmmsNQuK74KZsKdotCNmZI14UREVCONFb3QmKdqwgfMILwBM+GtiYE9aZszVZ17uTXh6qKk3HKU0ZQJXzpjHID85Shq6BMz4UREVG2sCaeGouqlBxP10+GYDn+jZcLbkr3Cp49vNm9Xde5l14QnLkrK3Zg5mgLRW69ehu0HhvJesF120gxs3TeA/zhvYY1WRkREYxWDcGooLT43hEjNhAcaNBO+ty+1Q8pAJA6fx1V2Zj+ZCS+tJtzMhNd5ymUlhQJeHJ/IhucS8LrxzbceW/0FERHRmNdYKUQa84QQCPo9liC88TLhZhCetjlzIBwva1qmoi5KSs2oq0x4JfqEExERkb3Gil6IYHQPGUjpjtJYP8bjmr3weVwZHVIGw6W3FbRSGzNbSzyX+n66R1FNOBERkdM0VvRCBGPD4WAkBillojtKY5WjCCEwpS2APX3pmfBY2fXgAMxPBkqtCTcnZjIIJyIiqhoG4dRwVDlKTJOQEg2XCQeMNoX7+jK7o4T85fUIBywbM8stR2EQTkREVDWNF73QmBcKGEF4JK4BQMO1KASMunC7mvBKZMLL35hpBN8MwomIiKqHQTg1nGDAi8FIHOGYMeym0Yb1AMbUzL39YXM4DJDYmFnBmnC2KCQiInKuxoteaMwzMuGxhs6ET2oNIBrXcWQ4Zt42EI5VtDtKqRszfaNwWA8REZHTMAinhhPyq3IUIxPeaC0Kgcw2hVJKoya8xBISK3NjZpk14aNpbD0REZHTNF70QmNeKOBBJK6bbQobcWPm5DY/gGQQPhzVoMvyp2UCwJKpbThmaivaW/wlPZ6ZcCIiourjxExqOKrW+dCgMXHS32AtCgGjHAUA9iY6pAxGjAuKStSEn7agA39dcHrJj3e7BITgxkwiIqJqarwUIo15qmTjoArCGzATPjEUgBDJIHwgbNSGl7qZspKEEPC5XQzCiYiIqqjxohca81S2+OBgFEBjbsz0eVxob/GbUzNVaU1rBWrCK8HndnFsPRERURUxCKeGo+qmDwwYmfBGbFEIGHXhe9OC8ErUhFeCz8NMOBERUTU1ZvRCY5rKFh8aatxMOJAY2FOFmvBK8LIchYiIqKoYhFPDUXXTBwcatyYcSIyu73deTThgZMLZopCIiKh6nPEvPlERVLb4wKAqR2ncTHjvcAzhmGaWo1SiT3glXH/mPEwb31TvZRAREY1aDMKp4ai66WSLwgbNhLcZbQr39YeTNeEOyYRfsWJmvZdAREQ0qjVm9EJjmt/jhs/jQm9i5HujlqNMaUv2Ch+MxNHic7MOm4iIaIxozOiFxrxQImMsRHLCY6Oxjq4fCMcc0xmFiIiIqq8xoxca81RduN/jgmjQftbWcpTBSNwx9eBERERUfQzCqSGpgLVR2xMCRja/2efG3r4IBsJxx9SDExERUfUxCKeGpALWRh3UAxjj4Scn2hQOhOOO6RFORERE1de4EQyNaclylMbNhANGr3BVE84gnIiIaOxgEE4NKWipCW9kk9sCZneUkJ814URERGMFU2/UkNTo+kYd1KOoqZlet4vdUYiIiMYQ/qtPDUnVhDd6JnxKWwBxXSKuayxHISIiGkMaO4KhMcusCW/gjZmAkQlX2B2FiIho7GjsCIbGLFW6EWjwjZmT25JBeCv7hBMREY0ZDMKpIZl9whs8Ez7ZmglnOQoREdGY0dgRDI1Zo6VFYUfQB1di4CdrwomIiMYOBuHUkEKjYFgPAHjcLnSG/ABYE05ERDSWODqCEUJcKITYKoTYJoS4weZ+IYT4QeL+l4QQJ9ZjnVR7o2FsvaJKUkKsCSciIhozHBuECyHcAG4GcBGAxQDeLYRYnHbYRQAWJP67DsBParpIqpvRMqwHSHZIYTkKERHR2OHkCOZkANuklNullFEAdwG4NO2YSwH8ShqeATBOCDGl1gul2ku2KGz8TPiUNgbhREREY42Tg/BpAHZbvu5K3FbsMRBCXCeEWCeEWHfgwIGKL5RqL+jz4PQFHThx5rh6L6Vspy/oxBkLO9E0Ci4oiIiIqDBOTr0Jm9tkCcdASnkrgFsBYPny5Rn3U+NxuQR+/f4V9V5GRZy3eBLOWzyp3ssgIiKiGnJyJrwLwAzL19MB9JRwDBERERGRozg5CF8LYIEQYo4QwgfgcgD3px1zP4CrE11SVgLok1LuqfVCiYiIiIiK4dhyFCllXAjxUQAPAXAD+IWUcqMQ4vrE/bcAeADAxQC2ARgG8N56rZeIiIiIqFCODcIBQEr5AIxA23rbLZY/SwAfqfW6iIiIiIjK4eRyFCIiIiKiUYlBOBERERFRjTEIJyIiIiKqMQbhREREREQ1xiCciIiIiKjGGIQTEREREdUYg3AiIiIiohpjEE5EREREVGMMwomIiIiIakwYQyfHDiHEAQC7LDe1AejL8ZBc92e7L9vtHQAOFrDMWsn32utx3mIfW8jx5R5TzPvstPcYqM77XO45i3l8ocdW43c52318nyv/2Hq+z43ydzbA97mQ+xv9fea/zYUd0yj/Ns+SUnba3iOlHNP/Abi11Puz3Zfj9nX1fr3FvPZ6nLfYxxZyfLnHFPM+O+09rtb7XO45i3l8ocdW43eZ77Ozfper9T43yt/ZfJ/HxvvMf5sr/z477T1W/7EcBfhzGfdnuy/fOZ2iWuss57zFPraQ48s9hu9z5c9ZzOMLPbYav8vFPH+9Oe19rsbvciHHjebfZYDvcyH3N/r7zH+bCzum0d/nsVeOUk9CiHVSyuX1XgdVD9/jsYHv89jA93ls4Ps8+jn1PWYmvLZurfcCqOr4Ho8NfJ/HBr7PYwPf59HPke8xM+FERERERDXGTDgRERERUY0xCCciIiIiqjEG4URERERENcYgvE6EEC1CiDuEED8TQlxZ7/VQdQgh5gohbhNC3F3vtVD1CCHekvhdvk8I8YZ6r4cqTwhxtBDiFiHE3UKID9d7PVQ9iX+f1wsh3ljvtVB1CCHOEkI8mfidPqte62AQXkFCiF8IIfYLITak3X6hEGKrEGKbEOKGxM1vA3C3lPKDAN5c88VSyYp5n6WU26WU76/PSqkcRb7P9yZ+l68FcFkdlkslKPI93iylvB7AuwA4rtUZZVfkv80A8DkAv6/tKqlcRb7PEsAggACArlqvVWEQXlm3A7jQeoMQwg3gZgAXAVgM4N1CiMUApgPYnThMq+EaqXy3o/D3mRrX7Sj+ff5i4n5qDLejiPdYCPFmAE8BeKS2y6Qy3Y4C32chxHkANgHYV+tFUtluR+G/z09KKS+CccH1tRqv08QgvIKklP8EcDjt5pMBbEtkRKMA7gJwKYwrr+mJY/g+NJAi32dqUMW8z8LwbQB/k1I+X+u1UmmK/V2WUt4vpTwFAEsIG0iR7/PZAFYCuALAB4UQ/Pe5QRTzPksp9cT9vQD8NVxmCk+9nngMmYZkxhswgu8VAH4A4EdCiEvQQCNWKSvb91kI0Q7gGwBOEEJ8Xkr5rbqsjiol2+/zvwM4D0CbEGK+lPKWeiyOKiLb7/JZMMoI/QAeqP2yqMJs32cp5UcBQAhxLYCDlmCNGlO23+e3AbgAwDgAP6rDugAwCK8FYXOblFIOAXhvrRdDVZPtfT4E4PpaL4aqJtv7/AMYF9bU+LK9x48DeLy2S6Eqsn2fzT9IeXvtlkJVlO33+R4A99R6Men4MUv1dQGYYfl6OoCeOq2Fqofv89jA93n043s8NvB9Hhsc/T4zCK++tQAWCCHmCCF8AC4HcH+d10SVx/d5bOD7PPrxPR4b+D6PDY5+nxmEV5AQ4v8ArAFwlBCiSwjxfillHMBHATwEYDOA30spN9ZznVQevs9jA9/n0Y/v8djA93lsaMT3WUgp8x9FREREREQVw0w4EREREVGNMQgnIiIiIqoxBuFERERERDXGIJyIiIiIqMYYhBMRERER1RiDcCIiIiKiGmMQTkRERERUYwzCiYiIiIhqjEE4EREREVGNMQgnIiIiIqoxBuFERJRBCHG+EEIKIb4lhFgihPitEGKfEGJQCLFaCLGi3mskImpkDMKJiMjOCYn/LwSwFkAQwB0AngCwCsD9QohQndZGRNTwPPVeABEROdKJif+fDuBUKeXz6g4hxB8BvA3AUgBP1n5pRESNj5lwIiKyozLh77UG4AmbE/8P1HA9RESjCoNwIiJKIYQIApgP4HUAD9gcMjfx/9dqtigiolGGQTgREaVbCuPfh79LKaXN/ScC6AOwo5aLIiIaTRiEExFROlUPvj79jsRmzAUA/pUlQCciogIwCCcionSqHnxdlvtcsAnQiYiocAzCiYgo3YkAogBesrlvWeL/6Zs1iYioCAzCiYjIJITwAzgawAYpZdTmEBWEMxNORFQGBuFERGS1BIAX9qUogBGEDwJ4tWYrIiIahQT31RARERER1RYz4URERERENcYgnIiIiIioxhiEExERERHVGINwIiIiIqIaYxBORERERFRjDMKJiIiIiGqMQTgRERERUY0xCCciIiIiqjEG4URERERENcYgnIiIiIioxv4/KF+REowL5n8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "x = []\n",
    "die_sides = 6\n",
    "die_value = 1\n",
    "for n in range(1, 100001):\n",
    "    # Loop and increase the number of die rolls by 1, upto 100,001 rolls\n",
    "    # Generate the die value from each roll, count the number of 1s\n",
    "    r = np.random.randint(die_sides, size=n) + 1 # since Python is 0-indexed\n",
    "    # Calculate the proportion of rolls which are 1 out of n rolls\n",
    "    x.append(np.count_nonzero(r == die_value)/n)\n",
    "# Plot as a log-based line chart\n",
    "plt.figure(figsize=(12,6))\n",
    "# Expected result, i.e. 1/6\n",
    "e = np.full((len(x)), die_value/die_sides)\n",
    "plt.ylabel(r\"$\\hat{p}_n$\", fontsize=20)\n",
    "plt.xlabel(r\"$n$\", fontsize=20)\n",
    "plt.plot(x)\n",
    "plt.plot(e)\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that the proportion of 1-rolls diverges a great deal from the expected value ($\\frac16$) for low values of $n$ (i.e. low numbers of rolls), but gradually converges on the expected count. From a distribution perspective, the ideal would be an equal distribution for all die rolls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16,4))\n",
    "distributions = [[10, r\"$n = 10$\"], [10000, r\"$n = 10,000$\"]]\n",
    "for i, [n, lbl] in enumerate(distributions):\n",
    "    r = np.random.randint(die_sides, size=n) + 1\n",
    "    x = np.count_nonzero(r)\n",
    "    axs[i].hist(r, density=True, histtype=\"stepfilled\", alpha=0.2, label=lbl)\n",
    "    axs[i].legend(loc=\"best\", frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disproportionate outcomes and outliers are a characteristic of low sample sizes. The more observations we record, the more likely, or probable, that the rate of occurrences will converge on its true probability.\n",
    "\n",
    "The right-most chart, where that convergence has happened, represents a __probability distribution__. \n",
    "\n",
    "> __Law of small numbers__: A tendency for an initial segment of data to have an unusual distribution or be dominated by rare outliers causing any small sample group to run the risk of failing to sufficiently represent an entire population.\n",
    ">\n",
    "> __Law of large numbers__: As more observations are collected, the proportion $\\hat{p}_n$ of occurrences with a particular outcome converges to the probability $p$ of that outcome.\n",
    "\n",
    "A probability is defined as a proportion and takes a value from 0 to 1. The notation we use, the probability $P$ of an outcome $A$ is:\n",
    "\n",
    "$$P(A)$$\n",
    "\n",
    "<br>\n",
    "<div class=\"well\">\n",
    "    <p><b>There will be maths</b>: Any data-driven research process leads to computation for analysis. Formal mathematical notation, axioms and formulae are a concise language which can be converted into programmatic code. Understanding these derivations will not only improve your coding ability, but also permit you to more easily interrogate and review the research and work of others.</p>\n",
    "</div>\n",
    "\n",
    "#### Axioms of probability\n",
    "\n",
    "There are a number of axioms that form a foundation for probability and interpretation of these can aid in understanding experimental design, randomisation, outliers and statistical analysis \\cite{pishro-nik_introduction_2014}:\n",
    "\n",
    "> 1. Axiom 1: For any event $A$, $P(A) \\ge 0$\n",
    "> 2. Axiom 2: Probability of sample space $S$ is $P(S) = 1$\n",
    "> 3. Axiom 3: If $A_1, A_2, A_3, ...$ are disjoint events, then $P(A_1 \\cup A_2 \\cup A_3 ...) = P(A_1) + P(A_2) + P(A_3)  + ...$\n",
    "\n",
    "Let's be clear on what these mean. The probability of any event cannot be less than zero. It may not happen at all, but it can't happen a negative number of times. The probability of any event within a sample space - all the possible outcomes for our experiment - is one. If you roll a six-sided die, the outcome will always be one of the faces, not something else.\n",
    "\n",
    "The final axiom says that, for events that are __disjoint__ (i.e. no overlap between them), then the probability of their union is equal to the sum of their probabilities.\n",
    "\n",
    "A way to understand this is to plot intersections of potential outcomes as a Venn diagram, which shows all possible logical relations between a finite collection of different sets. Install [Matplotlib-venn](https://github.com/konstantint/matplotlib-venn) to draw these charts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib_venn import venn3, venn2\n",
    "\n",
    "# Draw three disjoint sets\n",
    "# https://github.com/konstantint/matplotlib-venn\n",
    "\n",
    "v = venn3(subsets=(1,1,0,1,0,0,0))\n",
    "v.get_label_by_id('100').set_text(\"A\")\n",
    "v.get_label_by_id('010').set_text(\"B\")\n",
    "v.get_label_by_id('001').set_text(\"C\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These three sets have no overlap and are independent of each other. If $P(A) = 1$ then the probabilities of B and C will be zero. You can also think of $\\cup$ as representing a __union__ or an `or` boolean operator.\n",
    "\n",
    "As an example, in a competition with only one winner, then the events $\\text{{A wins}}$, $\\text{{B wins}}$ or $\\text{{C wins}}$ are disjoint since only one can occur at a time. If A has a 20% chance of winning and B has 40%, then what is the probability of A or B winning?\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "\\text{P(A wins or B wins)} & = P(\\text{{A wins}} \\cup \\text{{B wins}}) \\\\\n",
    "& = P(\\text{{A wins}}) + P(\\text{{B wins}}) \\\\\n",
    "& = 0.2 + 0.4 \\\\\n",
    "& = 0.6\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "Besides disjoint sets, we can also have sets that are __intersections__, that is where events can be simultaneously true, as a boolean `and` operator. The intersection of A and B is represented as $P(A \\cap B)$ or $P(A,B)$ or $P(AB)$.\n",
    "\n",
    "That notation is defined as:\n",
    "\n",
    "$$P(A \\cap B) = P(A,B) = P(AB)$$\n",
    "$$P(A \\cup B) = P(A or B)$$\n",
    "\n",
    "Consider a Venn diagram with an intersection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assume two sets, ABC and CDE\n",
    "v = venn2([set([\"X\", \"Y\"]), set([\"Y\", \"Z\"])])\n",
    "v.get_label_by_id(\"100\").set_text(\"X\")\n",
    "v.get_label_by_id(\"110\").set_text(\"Y\")\n",
    "v.get_label_by_id(\"010\").set_text(\"Z\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the notation we have so far, it should be clear that the probability that any event in A and B occur at the same time (the subset of events Y) is equivalent to $P(A \\cap B)$, while the probability of events $P(A \\cup B)$ can be defined as the __General Addition Rule__:\n",
    "\n",
    "$$ P(A \\cup B) = P(A) + P(B) - P(A \\cap B) $$\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "P(A \\cup B \\cup C) = & P(A) + P(B) + P(C) - \\\\\n",
    "& P(A \\cap B) - P(A \\cap C) - P(B \\cap C) + P(A \\cap B \\cap C)\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "This can be extended indefinitely into an _inclusion-exclusion principle_ for $n$ events $A_1, A_2, ... , A_n$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "P\\left(\\bigcup_{i=1}^{n}A_i\\right) = & \\sum_{i=1}^{n}P(A_i) - \\sum_{i<j}P(A_i \\cap A_j) \\\\\n",
    "& + \\sum_{i<j<k}P(A_i \\cap A_j \\cap A_k) - ... + (-1)^{n-1}P\\left(\\bigcap_{i=1}^{n}A_i\\right)\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "We can also define the __complement__ of an event. If the probability that an event, A, will occur is $P(A)$, then the probability that the event does __not__ occur is its complement, defined as $P(A^c)$.\n",
    "\n",
    "$$ P(A) + P(A^c) = 1 \\text{  or  } P(A) = 1 - P(A^c) $$ \n",
    "\n",
    "#### Discrete and continuous probability distributions\n",
    "\n",
    "Dice rolls have a finite range of outcomes and are referred to as __discrete probability distributions__. If a sample space $S$ is _countable_ then we can list all the elements in $S$:\n",
    "\n",
    "$$ S = {\\{s_1, s_2, s_3, ...\\}} $$\n",
    "\n",
    "The important thing here is that these options are finite. There are also infinite sample spaces which are _uncountable_:\n",
    "\n",
    "$$ S = [0, 1] $$\n",
    "\n",
    "Where the sample space $[0,1]$ means any floating-point number from 0 to 1. A square bracket $[$ or $]$ indicates that the distribution includes the value at the bracket, a rounded bracket $($ or $)$ means the distribution does not include the value at the bracket.\n",
    "\n",
    "A practical example of a continuous sample space could be any group of people's specific heights. This is known as a __continuous probability distribution__.\n",
    "\n",
    "In general, when we run experiments on populations, we create subsets of those populations. A subset $A$ of $S$ is defined as $A \\subset S$ such that if $k \\in A$ then $k \\in S$ (where $\\in$ is an _element_).\n",
    "\n",
    "Research spans both discrete and continuous sample spaces, and sometimes at the same time. Consider [haemophilia](https://en.wikipedia.org/wiki/Haemophilia), an inherited genetic disorder impairing the body's ability to produce blood clots, which can lead to excessive bleeding.\n",
    "\n",
    "![X-linked recessive inheritance scenarios for either the mother being a carrier or the father being affected](images/haemophilia-X-linked_recessive.png)\n",
    "\n",
    "The gene is carried on the X chromosome in which a clotting factor is nonfunctional. A genetic test can indicate one of a discrete set of outcomes, as indicated in the image, but the expression of that nonfunctional gene will vary. Therefore the expression of the illness is on a continuous probability distribution (from mild to severe), while diagnosis of having the genetic mutation is discrete.\n",
    "\n",
    "#### Independent and conditional probability\n",
    "\n",
    "__Independent__ processes are such that knowing the outcome of one provides no information on the others. Knowing a person's height doesn't imply anything about their eye colour. Flipping a coin doesn't tell you what the result of the next coin toss will be.\n",
    "\n",
    "For independent processes, the probability of a specific sequence of outcomes - such as rolling one on a die five times in a row - is the product of their separate probabilities. This is the __multiplication rule for independent processes__:\n",
    "\n",
    "$$ P(A \\cap B) = P(A) \\times P(B) $$\n",
    "\n",
    "More generally:\n",
    "\n",
    "$$ P\\left(\\bigcap_{i=1}^{n}A_i\\right) = \\prod_{i=1}^{n}P(A_i) $$\n",
    "\n",
    "__Conditional__ probabilities have some degree of dependency.\n",
    "\n",
    "__Conditional__ probabilities describe how the probability of an outcome varies with the knowledge of another factor or condition, and is closely related to the concepts of __marginal and joint probabilities__. \n",
    "\n",
    "There are rare forms of haemophilia where the disease is acquired as a result of cancer or an autoimmune disorder but, largely, expressing the disease is conditional on having a nonfunctional clotting gene. What this means is, if you test any member of the public for nonfunctional clotting factor genes, and they do not have such a disorder, their risk of having haemophilia is close to zero. However, if they do have such a gene, their probability of having haemophilia rises considerably.\n",
    "\n",
    "A _marginal probability_ is a probability only related to a single event or process, such as $P(A)$. A _joint probability_ is the probability that two or more events or processes occur jointly, such as $P(A \\cap B)$.\n",
    "\n",
    "When considering the probability of having a disease we don't work in certainties. You don't have your 44th birthday and promptly develop diabetes. Age increases the risk but is not like rolling a 100-sided dice and, if your age comes up, you are ill. A proportion of a population which has a particular illness can be thought of as a probability, and the proportion of an age range which has the disease can reflect a conditional probability:\n",
    "\n",
    "_Table summarising diabetes status as a proportion of the total population, categorised by age group for the US population_\n",
    "\n",
    "|                      | Diabetes | No Diabetes | Sum    |\n",
    "| :--------------------|---------:|------------:|-------:|\n",
    "|Less than 20 years    | 0.001    |       0.277 |   0.277|\n",
    "|20 to 44 years        | 0.014    |       0.315 |   0.329|\n",
    "|45 to 64 years        | 0.043    |       0.219 |   0.261|\n",
    "|Greater than 64 years | 0.036    |       0.097 |   0.132|\n",
    "|Sum                   | 0.093    |       0.907 |   1.000|\n",
    "\n",
    "If you selected a person from this population at random, and they were 30 years old, their probability of having diabetes is 1.4%. If that person was 20 years older, their probability will have increased to 4.3%. These are _joint_ probabilities since $P(\\text{Diabetes} \\cap \\text{45 to 64 years} ) = 0.043$. Whereas, the _marginal_ probability of anyone in the general population having diabetes is $P(\\text{Diabetes}) = 0.093$. \n",
    "\n",
    "#### Chain rule and tree diagrams\n",
    "\n",
    "Conditional probabilities can be defined as:\n",
    "\n",
    "$$ P(A|B) = \\frac{P(A \\cap B)}{P(B)} \\text{, when } P(B) > 0  $$\n",
    "\n",
    "The general multiplication rule can then be updated:\n",
    "\n",
    "$$ P(A \\cap B) = P(A) \\times P(B|A) = P(B) \\times P(A|B) $$\n",
    "\n",
    "You can think of $P(A|B)$ as indicating our interest in the probability of A given the conditional probability of B (i.e. if B then what is A?), and vice versa. You can then draw a __tree diagram__ representing the various probabilities:\n",
    "\n",
    "                                             x P(B|A)\n",
    "                     x P(A)            ┌───────────────────── P(A ∩ B)\n",
    "              ┌──────────────────P(A)──┤     x P(Bᶜ|A)\n",
    "              │                        └───────────────────── P(A ∩ Bᶜ)\n",
    "              │                        \n",
    "       1 ─────┤\n",
    "              │                              x P(B|Aᶜ)\n",
    "              │      x P(Aᶜ)           ┌───────────────────── P(Aᶜ ∩ B)\n",
    "              └──────────────────P(Aᶜ)─┤     x P(Bᶜ|Aᶜ)\n",
    "                                       └───────────────────── P(Aᶜ ∩ Bᶜ)\n",
    "\n",
    "An indefinite number of probabilities can be chained:\n",
    "\n",
    "$$ P(A_1 \\cap A_2 \\cap \\dots \\cap A_n) = P(A_1)P(A_2|A_1)P(A_3|A_2,A_1) \\dots P(A_n|A_{n-1}A_{n-2} \\dots A_1) $$\n",
    "\n",
    "We can apply this to an example from \\cite{vu_introductory_2020}:\n",
    "\n",
    "> _In Canada, about 0.35% of women over 40 will develop breast cancer in any given year. A common screening test for cancer is the mammogram, but it is not perfect. In about 11% of patients with breast cancer, the test gives a false negative: it indicates a woman does not have breast cancer when she does have breast cancer. Similarly, the test gives a false positive in 7% of patients who do not have breast cancer: it indicates these patients have breast cancer when they actually do not. If a randomly selected woman over 40 is tested for breast cancer using a mammogram and the test is positive – that is, the test suggests the woman has cancer – what is the probability she has breast cancer?_\n",
    "\n",
    "This is not this lesson's research question, but you can see how our foundation in formal probability will give us the language and tools to answer it. For this example, we can define a tree diagram to answer this question:\n",
    "\n",
    "                   Cancer Status            Mammogram Status\n",
    "\n",
    "                                         P(positive) = 0.89\n",
    "                P(cancer) = 0.0035     ┌───────────────────── 0.0035*0.89 = 0.00312\n",
    "              ┌────────────────────────┤ P(negative) = 0.11\n",
    "              │                        └───────────────────── 0.0035*0.11 = 0.00038\n",
    "              │                        \n",
    "       1 ─────┤\n",
    "              │                          P(positive) = 0.07\n",
    "              │ P(no cancer) = 0.9965  ┌───────────────────── 0.9965*0.07 = 0.06976\n",
    "              └────────────────────────┤ P(negative) = 0.93\n",
    "                                       └───────────────────── 0.9965*0.93 = 0.92675\n",
    "                                       \n",
    "If you know 7% of patients get a false positive, then the complement of that is $1 - 0.07 = 0.93$ and so you can see how the tree diagram is constructed. For this example, a randomly selected 40-year-old woman who tests positive and has breast cancer has the following probability:\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "P(\\text{Breast cancer} \\cap \\text{Mammogram}^+) &= P(\\text{Mammogram}^+|\\text{Breast cancer}) \\times P(\\text{Breast cancer}) \\\\\n",
    "&= (0.89) \\times (0.0035) = 0.00312\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "You can see that in the tree diagram there are two outcomes where our patient tests positive, in one of which she actually doesn't have cancer. The probability of a positive test result - irrespective of cancer status - is the sum of these two amounts: 0.06976 + 0.00312 = 0.07288. The probability, given a positive test result, that a randomly-selected patient actually has breast cancer is:\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "P(\\text{Breast cancer}^+ | \\text{Mammogram}^+) &= \\frac{P(\\text{Breast cancer}^+ \\cap \\text{Mammogram}^+)}{P(\\text{Mammogram}^+)} \\\\\n",
    "&= \\frac{0.00312}{0.07288} \\approx 0.0428\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "Even with a positive test result, our patient only has a 4% chance of actually having breast cancer. Ordinarily, we would only test patients presenting with symptoms, which would change the sample space dramatically, but you can see how a test - something which instinctively you may believe resolves a problem of an unknown situation - can raise ethical concerns.\n",
    "\n",
    "#### Baye's Theorem and the Law of Total Probability\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "These definitions - the language of probability - are intended to support analysis and communication. When we use words with precise meanings, we're able to effectively and rapidly communicate ideas where complexity may obscure our thoughts.\n",
    "</div>\n",
    "\n",
    "Tree diagrams and step-by-step axiomatic derivations are useful while you are learning, but eventually you will work faster with less explanation. It is critical to remember these axioms, though, as researchers are often called upon to explain their work to non-specialists.\n",
    "\n",
    "The above example can be reduced to a single equation, and redefined where $D = \\text{[has disease]}$, $D^c = \\text{[does not have disease]}$, $T^+ = \\text{[positive test result]}$, $T^- = \\text{[negative test result]}$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{split}\n",
    "P(D|T^+) &= \\frac{P(D \\cap T^+)}{P(T^+)} \\\\\n",
    "&= \\frac{P(T^+|D) \\times P(D)}{[P(T^+|D) \\times P(D)] + [P(T^+|D^c) \\times P(D^c)]}\n",
    "\\end{split}\n",
    "\\end{equation}\n",
    "\n",
    "The formula in the denominator (below the divisor) is a sum of probabilities, or the total probability of an event. In words, we are saying: \"Given the probability of an event (a positive test), what is the total probability of that event in the entire sample space (both having the disease and not having the disease)?\"\n",
    "\n",
    "This is the __Law of Total Probability__:\n",
    "\n",
    "$$ P(A) = \\sum_{i}P(A \\cap B_i) = \\sum_{i}P(A|B_i)P(B_i) $$\n",
    "\n",
    "Allowing us to derive one of the most useful formulae in conditional probability, __Baye's Theorem__ or __Baye's Rule__:\n",
    "\n",
    "$$ P(B_j|A) = \\frac{P(A|B_j) \\times P(B_j)}{\\sum_{i}P(A|B_i)P(B_i)} $$\n",
    "\n",
    "Where the sample space $S = {\\{B_1, B_2, B_3, \\dots\\}}$, and $A$ is any event with $P(A) \\neq 0$.\n",
    "\n",
    "### 3.1.4 Intuition, prevalence and skepticism\n",
    "\n",
    "The UK government operates more than 50,000 closed-circuit television cameras to monitor people moving around the country. In 2017 British Metropolitan police ran a pilot project, connecting these cameras to databases of faces and using facial recognition software to identify people on a government watch list attending the [Notting Hill Carnival](https://www.theguardian.com/uk-news/2018/may/15/uk-police-use-of-facial-recognition-technology-failure).\n",
    "\n",
    "The Notting Hill Carnival celebrates Caribbean culture in the UK and about 150,000 people attend every year. The police pilot software flagged 104 people as potential suspects. 102 of them turned out to be innocent bystanders.\n",
    "\n",
    "The software was wrong 98% of the time.\n",
    "\n",
    "Despite this, the UK government continues to fund pilot projects using facial identity, and these systems continue to perform exceptionally badly.\n",
    "\n",
    "__Intuition__ is the capacity to know or comprehend something directly, without any need for justification or rational argument to support that belief. There are certain concepts where intuition is all we have, like explaining to someone what the colour yellow looks like.\n",
    "\n",
    "Our intuition is rarely a good foundation on which to make predictions. Our minds are easily fooled by emergent patterns in random data, and differences in the inferences individuals draw in apprehending the same random patterns mean that there can be little agreement as to meaning.\n",
    "\n",
    "The same failing in the facial identification system causes the low diagnostic power of the mammography test system, and it arises from both the laws of small and large numbers. \n",
    "\n",
    "A rare event in a large population is a rare event. When we wish to test for such an event within a sample space, we must consider the __sensitivity__ and __specificity__ of that test. \n",
    "\n",
    "- __Sensitivity__ is the probability that a test will positively identify the exact event we are testing for. \n",
    "- __Specificity__ is the probability that a test will return a negative result for every non-matching event within the sample space.\n",
    "- The probability of a specific event in a sample space is its __prevalence__.\n",
    "- __Positive Predictive Value__ (PPV) is the probability that a specific event occurred when a test result is positive.\n",
    "- __Negative Predictive Value__ (NPV) is the probability that a specific event did not occur when a test result is negative.\n",
    "\n",
    "The tree diagram presented earlier is a chart of the various outcomes from such a test, presenting four outcomes:\n",
    "\n",
    "- __True positive__ (TP), a specific event is correctly identified as matching a specific event\n",
    "- __False positive__ (FP), a random and unspecified event is incorrectly identified as matching a specific event\n",
    "- __True negative__ (TN), a random and unspecified event is correctly identified as not matching a specific event\n",
    "- __False negative__ (FN), a random and unspecified event is incorrectly identified as matching a specific event\n",
    "\n",
    "When evaluating test sensitivity and specificity, we can simplify the tree diagram down to a __confusion matrix__ presenting only these data.\n",
    "\n",
    "Where intuition lets you down is in not considering the __prevalence__ of a specified event within your sample space. You may assume that people presenting themselves for a cancer test have already undergone some selection process and so a test with 93% specificity is incredibly accurate.  However, if only 1% of the general population ever suffer breast cancer and you are testing a very large sample space, then a test with 100% sensitivity will quickly be overwhelmed by an insufficiently acute specificity ... too many false positives are returned, drowning out the information you were actually looking for.\n",
    "\n",
    "This is compounded the greater level of specificity required. Facial recognition requires recognising a single specific individual from amongst a total population. If you wish to track that specific person across multiple locations, then your matching algorithm needs to have a positive predictive value of 100% or it will be regularly matching incorrectly.\n",
    "\n",
    "Consider a requirement to match one person from a crowd of 100,000 people. If your test had 100% sensitivity and 99.9% specificity, it would still identify 101 people as that specified individual every time. Its positive predictive value would only be 1%.\n",
    "\n",
    "The positive predictive value of the Metropolitan police facial recognition system is 2% (NPV is 98%), on 150,000 carnival-goers, so the specificity is about 99.93%.\n",
    "\n",
    "If you were told that a test is 100% sensitive and 99.93% specific, you may well feel an intuitive trust for that test. The law of large numbers says that, given a large enough sample space, outliers are common and any flaws in methodology will be exposed. The law of small numbers says that the model may originally been tested on a biased sample with an over-abundance - too great a prevalence - of target events relative to the general population.\n",
    "\n",
    "Given that the UK government is conducting a public study of their facial recognition system, it is clear they intend for it to work as claimed. They want the general public to trust it and are using the best technology at their disposal to achieve that end.\n",
    "\n",
    "What of the Chinese government's social credit facial identity system? This is not aimed at a specific controlled event at a specific time, but at the population in total, and at all times and places. How often do you think people are wrongly identified? And, given that _social credit_ is a nebulous term, how easy is it for wrongly identified individuals to protest errors? China is run as a police state, with its population subject to arbitrary arrest. An automated system that can render arbitrary justice is an extension of that form of governance. False negatives are a feature, not a failure, of such a system.\n",
    "\n",
    "You may choose to work to support such a system, or choose not to. You should always be clear on what the systems and tools you build are capable of, and how they fail.\n",
    "\n",
    "__Skepticism__ is critical for any review of any claim. It is essential that a data scientist be capable of self-reflection and review of their work and the work of others. If your predictions are based on fallacies, perhaps your predictions can be tremendously accurate but that doesn't mean the total predictive value is meaningful. And you may be lucky that the fallacy corresponds with reality, but that doesn't make your work of any more value than a person who wasn't lucky.\n",
    "\n",
    "A fallacy may lead to bias, but is a class on its own. There are criminals, but deciding that an accident of birth - be it face shape or skin colour - is an indicator of criminality is a fallacy. Using that information to predict future criminality is bias.\n",
    "\n",
    "Using an intuition based on a fallacy and making a claim to authority to impose that bias on people takes away their autonomy.\n",
    "\n",
    "An experiment should test an intuition to establish whether it is meaningful. Effectively, what is the probability of a hypothesis being true, and is that hypothesis better than chance?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Curation: software, interoperability and risks from data reuse\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Employ methods for presenting data for synthesis and usage, and employing methods for data maintenance.</i>\n",
    "</div>\n",
    "\n",
    "There are multiple challenges in preparing and presenting data for reuse, irrespective of whether those data are intended for the general public or inside an organisation only. \\cite{chait_technical_2014}\n",
    "\n",
    "If data publication systems are to encourage publication and data reuse then they must integrate well with in-house data process management systems. There are three generalised systems used to manage online and public versions of research reports, content and research data:\n",
    "\n",
    "1. __Content Management Systems__ (CMS): permit publishing, editing and modifying of qualitative content, as well as providing mechanisms to manage workflows and individual users in a collaborative environment;\n",
    "2.\t__Data Discovery Systems__ (DDS): are similar to CMS but provide mechanisms to manage the semi-structured quantitative and qualitative data in documents and spreadsheets and offer methods for data publication, discovery and reuse;\n",
    "3.\t__Business Intelligence Systems__ (BIS): provide a platform for engaging with structured quantitative data to produce custom slices of that data, charts, tables and geospatial representations;\n",
    "\n",
    "While it is certainly possible for a single, integrated, software system to serve all of these requirements, this is not a common use-case. Data managers usually operate a number of different systems which often require manual data restructuring for availability in each system.\n",
    "\n",
    "A visual representation of the various data and software components is presented as follows:\n",
    "\n",
    "![Overview of the technical components and processes in a data publication platform](images/module-1-lesson-3-data-software-components.png)\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <b>Self-study:</b> If you have not yet done so, complete the companion self-study course on <a href=\"https://github.com/whythawk/data-wrangling-and-validation\">Data Wrangling and Validation for Open Data</a> to ensure you have the technical skills to work with the \"wild\" data we'll be using during this training series. This constitutes the Extract, Transform and Load (ETL) component indicated in the diagram.\n",
    "</div>\n",
    "\n",
    "### 3.2.1 Microdata and macrodata\n",
    "\n",
    "Data are often presented as a ubiquitous mass. Statistical data encompasses a range of forms, from questionnaires and individual – personally-identifying – responses, through to aggregated tables of numerical values, analysis and text-driven reports.\n",
    "\n",
    "We can differentiate between:\n",
    "\n",
    "1.\t__Microdata__: information at the level of individual respondents, households and businesses, typically through surveys; for example, a national census may collect age, address, education, employment status, etc. from individuals; \n",
    "2.\t__Macrodata__: aggregations derived from microdata; for example, the total number of people of a particular education category; \n",
    "\n",
    "Dissemination of microdata is an issue of particular concern for personally-identifying information. There is often the need and sometimes a legal obligation, to protect confidentiality and privacy for the providers of microdata. In some cases, provenance of microdata may lie with external partners (such as academic institutions) with their own policies or restrictions on dissemination.\n",
    "\n",
    "NSOs have used a variety of approaches to making microdata publicly available1. Some are quite consistent with open data principles; however, many policies may place restrictions on who may access the data and how the data may be used. \n",
    "\n",
    "There are already platforms that are well suited for distributing microdata online. \n",
    "\n",
    "For example, the Integrated Public Use Microdata Series ([IPUMS](https://ipums.org/))  requires researchers to implement security measures, avoid redistribution of microdata, use microdata only for non-commercial research/education purposes, and not make any attempt to identify the individuals recorded.\n",
    "\n",
    "The International Household Survey Network ([IHSN](https://www.ihsn.org/)) has developed tools and guidelines to help interested statistical agencies improve their microdata management practices, including a Microdata Cataloging Tool ([NADA](https://nada.ihsn.org/)). NADA allows administrators to specify an access policy for each dataset. Policies can range from \"Open access\" (similar to \"open data\") to \"Data not available\" (metadata only) for each microdata file. \n",
    "\n",
    "Additionally, there are an incredible number of knowledge management and data publishing platforms available for general use.\n",
    "\n",
    "It is common for publishers to have a separate set of dissemination policies - and separate dissemination platforms - for microdata that are particularly sensitive than for their other data products. This dual approach may be beneficial for users, since it highlights that certain microdata are provided with additional conditions with respect to data access.\n",
    "\n",
    "### 3.2.2 Proprietary file formats\n",
    "\n",
    "Data __interoperability__ is a mechanism by which data from different sources can be easily imported and reused in alternative applications. Whether that be multiple hospitals participating in a single clinical trial, or multiple research institutions collaborating on a shared research project, without agreed formats and standards for interoperability these data will either be unusable, or require a major commitment in time and resources for conversion.\n",
    "\n",
    "One of the criteria for reuse is that data be machine-readable, non-proprietary electronic data files for data distribution. These formats reduce technical barriers to data access to an absolute minimum for broad categories of users. Open formats include CSV, XML, and text files, and some  proprietary formats used in data analysis software products include SAS, STATA, and SPSS. \n",
    "\n",
    "The latter are legitimate even in an open data initiative since these are the software systems used by many professional data users. However, since these formats are not interoperable, the potential for data re-use is limited unless open formats are also supported. Data dissemination in proprietary formats does not preclude dissemination in open formats and vice versa. \n",
    "\n",
    "### 3.2.3 Application Programming Interfaces (APIs) are public\n",
    "\n",
    "Interoperability requires an __Application Programming Interface__ ([API](https://en.wikipedia.org/wiki/API)) through which standardised commands are available to an external system and used to query the data, metadata and other attributes in a database.\n",
    "\n",
    "Such interoperability permits a range of actions by other software systems, for example:\n",
    "\n",
    "- Import data into another application (such as Python, Jupyter Notebooks, Tableau, R, or Excel) for analysis and merging with other data sources;\n",
    "- Development of free-standing applications, such as transport apps on mobile phones;\n",
    "- Automate repetitive processes, such as setting a routine to regularly download data released monthly;\n",
    "\n",
    "APIs can also be used by site administrators to automate data harvesting, uploading or similar bulk processes. Such APIs are often connected to ETL systems for data transformation prior to loading.\n",
    "\n",
    "The most common approach to implementing an API is via a Representational State Transfer ([REST](https://en.wikipedia.org/wiki/Representational_state_transfer)) system. The standard commands generally used in REST for creating, reading, updating, or deleting data are POST, GET, PUT, DELETE. \n",
    "\n",
    "The more sophisticated software platforms often have a query-builder utility which permits users to experiment live on the server and see the results of different API queries. Such interfaces should permit unique URLs so that particular queries can be bookmarked and shared.\n",
    "\n",
    "### 3.2.4 Datasets are reachable via persistent URI\n",
    "\n",
    "Permanent and persistent availability means not just that data are available, but also that they are always in the same place.\n",
    "\n",
    "For online systems, this implies a requirement for __Uniform Resource Identifiers__ (URIs) which ensure that resources, content or data are always located through one discrete address for any and all users or software-driven applications. The most familiar of these are the __Uniform Resource Locators__ (URLs) that you see as links to websites. These can also be described as endpoints.\n",
    "\n",
    "It is essential that these endpoints never change and that their behaviour is predictable. A person who bookmarks a link, or who includes such a link in an article, assumes that it will still be there when they need this.\n",
    "\n",
    "More generally, a URI can also define a persistent link to a point within a dataset. This permits the interlinking of different datasets, the creation of more complex data aggregations and better insight into that data.\n",
    "\n",
    "Software should be capable of providing a clear and easy to find permanent URLs for every dataset being served by the platform. Some systems are capable of providing URIs to subsets of data, or points, within datasets.\n",
    "\n",
    "### 3.2.5 Data structure, linked data and endpoints\n",
    "\n",
    "Data are usually stored in a relational database. Structural metadata permits data contained in relational databases to be aligned and joined. This, however, can be slow and inefficient for large and complex datasets.\n",
    "\n",
    "The World Wide Web Consortium (W3C) develops web standards. Their approach for data interchange on the web is known as the Resource Description Framework ([RDF](http://www.w3.org/RDF/)). It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax formats.\n",
    "\n",
    "RDF breaks away from the standard relational database and can be thought of as a graph of entity-relationships of the form: subject, predicate and object. The subject (e.g. John) is linked to the object (e.g. Carol) by a predicate (e.g. 'is a friend') and gives rise to the terms __Triple Store__ (the three entities being the 'triple') or __Linked Data__. \n",
    "\n",
    "![RDF subject, predicate, object model (A) combining to form an RDF graph](images/module-1-lesson-3-rdf.jpg)\n",
    "\n",
    "This image from \\cite{anwar_francisella_2009}. The subject and object are also known as __nodes__, while the predicate is an __edge__. A network of nodes linked by edges is called a __graph__.\n",
    "\n",
    "Numerous implementations of this have resulted in interoperable structured, and machine-readable, metadata systems. There are also, however, numerous legacy approaches to categorising data which have arisen in individual research institutions across the world.\n",
    "\n",
    "Structured data __endpoints__ return data in predictable ways.  These can be as simple as a known type of serialisation format while more complex implementations permit the data to be queried, filtering or refining the dataset prior to download.\n",
    "\n",
    "What they have in common is that there is a fixed URL to reach the end-point.\n",
    "\n",
    "A number of commonly-used endpoints are:\n",
    "\n",
    "1. JavaScript Object Notation ([JSON](https://www.json.org/)): an open standard presenting data as a set of key-value pairs; as an end-point, it is accessible via RESTful APIs;\n",
    "2. [OData](http://www.odata.org/): While RDF has achieved a high degree of traction for linking diverse data together, it is still not that straightforward to connect it back to the tools researchers use most frequently to work with data. OData offers a standardised protocol for creating and consuming data. The format is extremely popular, and software as diverse as Tableau (for analysis and visualisation), Drupal (for content management), and Microsoft’s Excel are all able to accept OData as an input.\n",
    "3. Extensible Markup Language ([XML](https://www.w3.org/XML/)): a markup language, more difficult for humans to read, found in diverse uses such as encoding Microsoft Office documents, websites and for data interchange; it too can be presented via RESTful APIs;\n",
    "4. SPARQL Protocol and RDF Query Language ([SPARQL](http://www.w3.org/TR/sparql11-overview/)): an RDF database query language able to retrieve and manipulate RDF data, returning it as RDF or – with appropriate interpreters – conversion for use by SQL or other query languages;\n",
    "\n",
    "### 3.2.6 Risks from deanonymisation and reuse\n",
    "\n",
    "Data released to the public in general, or specifically to other researchers, will not be used in isolation. It will be stripped of the original context in which it was used and combined with other data to answer a new research question the original publisher may never have imagined.\n",
    "\n",
    "There are consequences to this.\n",
    "\n",
    "In 2014, New York City released data on 173 million individual tax journeys. Driver's licence numbers were obscured using a hashing algorithm. These produce a specific pseudo-random text string from a specific input. Researchers found it relatively straightforward to regenerate the original identifiers from the hash. What followed was [a case-study in the risks of deanonymisation](https://arstechnica.com/tech-policy/2014/06/poorly-anonymized-logs-reveal-nyc-cab-drivers-detailed-whereabouts/) as researchers shared methods for using these data to compile detailed analysis of individual driver's earnings, and linking up trips to known locations of celebrities and public figures at known times.\n",
    "\n",
    "There are certainly [legitimate reasons](https://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/) to release such data, but redaction needs to be carefullly done. Even so, when a database of photographs used in a psychology study is repurposed to train an image classifier to identify potential criminals, then the data is being asked to perform tasks which the publisher may not want, or may not believe is even possible from their data.\n",
    "\n",
    "As more organisations which manage data release their data under API access, there are more cases of deanonymisation from recombination. Whether it be Strava, which produces a fitness app logging their user's exercise routines, [accidentally revealing locations of secret US army bases](https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location-of-secret-us-army-bases) or a future risk of individual genomes released to the public, these are issues that become more likely the more data that are released.\n",
    "\n",
    "And that is before we consider the risks from arbitrary data collated in bulk. [Clearview AI](https://www.wired.com/story/clearview-ai-scraping-web/), a secretive US-based surveillance company, scraped billions of publicly available photographs from social media and have used it to create a facial recognition system used by law enforcement agencies.\n",
    "\n",
    "Data may be anonymous in isolation, but once recombined with other seemingly anonymous data, patterns emerge that can reveal information of value to the public, but potentially of harm to uninvolved individuals. Probabilistic systems, research fallacies, and social bias mean that some proportion of the people targeted by these systems will experience significant harm.\n",
    "\n",
    "Appropriate licences can ensure that legitimate users only reuse data in ways that are acceptable to a research publisher, but cannot prevent inadvertant or deliberate disclosure, or outright malfeasance. As we get deeper into this course, you will learn mechanisms for deanonymisation, and gain more skills on reducing this risk. What we cannot do is prevent it entirely, unless we choose to stop research publication.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Analysis: predictive value of synthetic data\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Perform techniques in randomness and probability to understand distribution and prevalence.</i>\n",
    "</div>\n",
    "\n",
    "### 3.3.1 Improving our synthetic population modeller\n",
    "\n",
    "In the last lesson we created a synthetic sample profile to generate random individuals. We can do the same here, this time simulating a population of women of all ages and their risk for developing breast cancer.\n",
    "\n",
    "According to the [World Cancer Research Fund](https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics) the age-standardised breast cancer rate per 100,000 in Lebanon is about 100. Purely for testing a null hypothesis, we'll also generate their foot size. Women's feet in Europe average 245 mm in length, with a standard deviation of 10 mm (i.e. 70% of women have feet from 235 mm to 255 mm long). \\cite{jurca_analysis_2019}\n",
    "\n",
    "The Jurca foot analysis study is worth a read if only to see a paper that uses much of the theory and tools taught in this course (including `pandas` and `seaborn`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries we're going to use. If any of these are not installed, you can run\n",
    "# `pip install` to import them into your development environment\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import trapz\n",
    "import random, uuid, math, json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Let's improve the synthetic data algorithm developed in the last lesson to make it \n",
    "easier to create any profile. We'll start by defining a schema for specifying any\n",
    "characteristic.\n",
    "\n",
    "We also need to handle conditional characteristics, such as if a smoker has a greater\n",
    "risk of lung cancer, or if age > 40 and female has a greater risk of breast cancer.\n",
    "\n",
    "Technically, since the function won't generate the random distribution, we can provide\n",
    "any list of values. As long as the length of the array is the same as the number \n",
    "of individuals in our synthetic population, the function will work.\n",
    "\"\"\"\n",
    "\n",
    "def create_synthetic_population(**kwargs):\n",
    "    \"\"\"\n",
    "    Return a population for a group of patients defined by a population characteristics\n",
    "    schema:\n",
    "    \n",
    "        [{\n",
    "            \"name\": \"name\",\n",
    "            \"values\": [],\n",
    "            \"conditional\": [\n",
    "                {\n",
    "                    \"name\": \"name\",\n",
    "                    \"values\": [],\n",
    "                    \"range\": (from, to),\n",
    "                    \"conditional\": []\n",
    "                }        \n",
    "            ]\n",
    "        }]\n",
    "\n",
    "    This is relatively simple, and we may need to improve it in future, but it will\n",
    "    work for now. The schema can be deeply nested and so relies on recursion.\n",
    "    \n",
    "    This function will add the following to each population member:\n",
    "    \n",
    "        id: a unique anonymous string\n",
    "        coordinates: random coordinates for plotting on a scatter plot, not meaningful\n",
    "        \n",
    "    Args:\n",
    "        total: int, default 10000\n",
    "        groups: int, default 4\n",
    "        profile: list of dicts which each conforms to schema\n",
    "    \n",
    "    Returns:\n",
    "        list of dicts\n",
    "    \"\"\"\n",
    "    # Get arguments\n",
    "    population_total = kwargs.get(\"total\", 10000)\n",
    "    colour_list = [F\"C{n}\" for n in range(kwargs.get(\"groups\", 4))]\n",
    "    population_profile = kwargs.get(\"profile\", [])\n",
    "    # Generate the colour coordinates to plot on a grid\n",
    "    cc = int(len(colour_list)/2)\n",
    "    # if the count is uneven\n",
    "    if len(colour_list)%2: cc = int((len(colour_list)-1)/2)\n",
    "    cc_coords = [i+1 for i in range(cc)]*2\n",
    "    # And add in the extra group\n",
    "    if len(colour_list)%2: cc_coords.append(cc+1)\n",
    "    cc_coords.sort()\n",
    "    colour_coordinates = {\n",
    "        F\"C{n}\": ((20*c, 20) if n%2 else (20*c, 40)) for n, c in enumerate(cc_coords)\n",
    "    }\n",
    "    distance = 20\n",
    "    # Create the population\n",
    "    population = {\n",
    "        \"id\": np.array([str(uuid.uuid4()) for i in range(population_total)]),\n",
    "        \"color\": np.random.choice(colour_list, population_total),\n",
    "        \"coordinates\": []\n",
    "    }\n",
    "    for c in population[\"color\"]:\n",
    "        population[\"coordinates\"].append(\n",
    "            (\n",
    "                np.random.uniform(colour_coordinates[c][0], colour_coordinates[c][0] + distance),\n",
    "                np.random.uniform(colour_coordinates[c][1], colour_coordinates[c][1] + distance)\n",
    "            )        \n",
    "        )\n",
    "    # Get the individual characteristics\n",
    "    # https://stackoverflow.com/a/26853961\n",
    "    for pp in population_profile:\n",
    "        # Iterate over the list of profiles\n",
    "        population = {**population, **add_profile_field(pp, population_total)}\n",
    "    # Convert the dictionary of arrays into an array of dictionaries\n",
    "    # where each dict item represents a single person\n",
    "    # https://stackoverflow.com/a/33046935 (we could use pandas as well)\n",
    "    population = [dict(zip(population, v)) for v in zip(*population.values())]\n",
    "    # Shuffle the ordered list to create a random population\n",
    "    np.random.shuffle(population)\n",
    "    return population\n",
    "\n",
    "def add_profile_field(field, size):\n",
    "    \"\"\"\n",
    "    Return a list of profile fields as part of an individual in a population.\n",
    "    \n",
    "    Recursive function - it calls itself until an endpoint is reached - so as\n",
    "    to traverse conditional fields and ensure those dependencies are met.\n",
    "    \n",
    "        {\n",
    "            \"name\": \"name\",\n",
    "            \"values\": [],\n",
    "            \"conditional\": [\n",
    "                {\n",
    "                    \"name\": \"name\",\n",
    "                    \"values\": [],\n",
    "                    \"range\": (from, to),\n",
    "                    \"conditional\": []\n",
    "                }        \n",
    "            ]\n",
    "        } \n",
    "        \n",
    "    Args:\n",
    "        field: dict of specific terms\n",
    "        size: int of total population to be created\n",
    "        \n",
    "    Returns:\n",
    "        dict of fields with value lists\n",
    "    \"\"\"\n",
    "    response = {}\n",
    "    sample = np.random.choice(field[\"values\"], size=size, replace=False)\n",
    "    response[field[\"name\"]] = []\n",
    "    for c_field in field.get(\"conditional\", []):\n",
    "        if len(c_field[\"range\"]) == 2:\n",
    "            # Filter the sample so that we get the \n",
    "            c_sample = sample[(sample >= c_field[\"range\"][0]) &\n",
    "                              (sample < c_field[\"range\"][1])]\n",
    "        else:\n",
    "            # only 1 value in range, so is open-ended to end\n",
    "            c_sample = sample[sample >= c_field[\"range\"][0]]\n",
    "        # Populate the conditional fields\n",
    "        response[field[\"name\"]].extend(c_sample)\n",
    "        c_response = add_profile_field(c_field, len(c_sample))\n",
    "        for k, v in c_response.items():\n",
    "            if k not in response:\n",
    "                response[k] = []\n",
    "            response[k].extend(v)\n",
    "    # If there were no conditionals, then ensure response has a sample value\n",
    "    if not response[field[\"name\"]]:\n",
    "        response[field[\"name\"]] = sample\n",
    "    # This should result in a list of fields all with arrays of the same length\n",
    "    return response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions will permit us to create fairly diverse random sample populations to demonstrate various concepts. As this develops, by the end of the course it will be a sophisticated synthetic population generator.\n",
    "\n",
    "For the purposes of our analysis, let's create a population that reflects the distribution of 100,000 women in terms of their risk from breast-cancer at specific ages, and their foot size.\n",
    "\n",
    "According to the US [National Cancer Institute](https://www.cancer.gov/types/breast/risk-fact-sheet), the age-associated risk for diagnosis with breast cancer is as follows:\n",
    "\n",
    "| Range  | Risk  |\n",
    "|--------|-------|\n",
    "| Age 30 | 0.48% |\n",
    "| Age 40 | 1.53% |\n",
    "| Age 50 | 2.38% |\n",
    "| Age 60 | 3.54% |\n",
    "| Age 70 | 4.07% |\n",
    "\n",
    "We can generate a list of uniformly-distributed random floating-point numbers from 0 to 1. If a person is 30 years old, and their random number is less than 0.48, then they have breast cancer. This is our conditional probability.\n",
    "\n",
    "We also need to create a skewed age distribution; one that reflects an older population. These normally have a distribution charted [like this](https://commons.wikimedia.org/wiki/File:Population_pyramid_for_Wales_using_2011_census_data.png):\n",
    "![Population pyramid for Wales using 2011 census data](images/Population_pyramid_for_Wales_using_2011_census_data.png)\n",
    "\n",
    "We're often going to need age-distributed populations like this, so this is a good time to create this. We're going to use the `scipy` library's [trapezoidal distribution function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.trapz.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_age_distribution(mean=45, low=5, high=60, total=100, maxAge=100):\n",
    "    \"\"\"\n",
    "    Generate a list of ages of population size 'total', and distributed\n",
    "    in a trapezoidal weighting with 'mean', 'low' and 'high'.\n",
    "    \n",
    "    https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html\n",
    "    https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.trapz.html\n",
    "    \n",
    "    Args:\n",
    "        mean: int, average age for population\n",
    "        low: int, lower-edge of trapezoid weighting distribution\n",
    "        high: int, upper-edge of trapezoid weighting distribution\n",
    "        total: int, total number of values to be returned\n",
    "        maxAge: int, maximum age, default is 100\n",
    "    \n",
    "    Returns:\n",
    "        list of ages    \n",
    "    \"\"\"\n",
    "    # All input values are a ratio of 1\n",
    "    d = 100\n",
    "    mean, low, high = mean/d, low/d, high/d\n",
    "    mean, var, skew, kurt = trapz.stats(low, high, moments='mvsk')\n",
    "    ages = np.linspace(trapz.ppf(0.01, low, high),\n",
    "                       trapz.ppf(0.99, low, high), 100)\n",
    "    probability_distribution = trapz(low, high).pdf(ages)\n",
    "    probability_distribution = probability_distribution/sum(probability_distribution)\n",
    "    distribution = np.random.choice(ages, total, p=probability_distribution) * 100\n",
    "    return distribution.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting this for a population of 100,000 people produces this histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "total_population = 100000\n",
    "\n",
    "age_distribution = generate_age_distribution(total=total_population)\n",
    "plt.hist(age_distribution, density=True, histtype=\"stepfilled\", alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not a perfect reflection of a population age distribution, but it isn't bad for a few lines of code generating random numbers. We can now go on to producing the rest of our population profile and creating a random list of people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total sample size\n",
    "population_size = 100000\n",
    "# Breast cancer stats\n",
    "age_distribution = generate_age_distribution(total=population_size)\n",
    "# Foot size stats\n",
    "mu, sigma = 245, 10 # mean and standard deviation\n",
    "length_distribution = np.random.normal(mu, sigma, population_size).astype(int)\n",
    "# Profile schema\n",
    "profile = [\n",
    "    {\n",
    "        \"name\": \"age\",\n",
    "        \"values\": age_distribution,\n",
    "        \"conditional\": [\n",
    "            {\n",
    "                \"name\": \"breast_cancer\",\n",
    "                # Generate a set of random numbers from 0 to 1. If <= n, then set True\n",
    "                \"values\": np.random.uniform(size=population_size)<=0.0,\n",
    "                \"range\": [0, 30]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"breast_cancer\",\n",
    "                \"values\": np.random.uniform(size=population_size)<=0.0048,\n",
    "                \"range\": [30, 40]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"breast_cancer\",\n",
    "                \"values\": np.random.uniform(size=population_size)<=0.0153,\n",
    "                \"range\": [40, 50]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"breast_cancer\",\n",
    "                \"values\": np.random.uniform(size=population_size)<=0.0238,\n",
    "                \"range\": [50, 60]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"breast_cancer\",\n",
    "                \"values\": np.random.uniform(size=population_size)<=0.0354,\n",
    "                \"range\": [60, 70]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"breast_cancer\",\n",
    "                \"values\": np.random.uniform(size=population_size)<=0.0407,\n",
    "                \"range\": [70]\n",
    "            },\n",
    "        ]\n",
    "    },\n",
    "    {  \n",
    "        \"name\": \"foot_length\",\n",
    "        \"values\": length_distribution,\n",
    "        \"conditional\": [\n",
    "            {\n",
    "                \"name\": \"foot_size\",\n",
    "                \"values\": np.array([\"small\"]*population_size),\n",
    "                \"range\": [0, 235]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"foot_size\",\n",
    "                \"values\": np.array([\"medium\"]*population_size),\n",
    "                \"range\": [235,255]\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"foot_size\",\n",
    "                \"values\": np.array([\"large\"]*population_size),\n",
    "                \"range\": [255]\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "kwargs = {\n",
    "    \"total\": population_size,\n",
    "    \"groups\": 4,\n",
    "    \"profile\": profile\n",
    "}\n",
    "synthetic_population = create_synthetic_population(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need a universal test with a specified sensitivity and specificity. Given a random patient and a set of criteria, it should return whether that person has, or not, the target event we're testing for. \n",
    "\n",
    "Since ours is a \"magic\" universal test, it can also tell us whether or not it is returning a true or false positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_universal_test_results(sample, sensitivity=0.5, specificity=0.5):\n",
    "    \"\"\"\n",
    "    Given an array of True and False corresponding to a sample population's actual status,\n",
    "    return the universal test measurement result with an accuracy corresponding to the\n",
    "    test sensitivity and specificity.\n",
    "    \n",
    "    Prints out a report on the test result accuracy\n",
    "    \n",
    "    Args:\n",
    "        sample: array of True, False\n",
    "        sensitivity: float, corresponding to test sensitivity, default 0.5\n",
    "        specificity: float, corresponding to test specificity, default 0.5\n",
    "        \n",
    "    Returns:\n",
    "        numpy array of test results\n",
    "    \"\"\"\n",
    "    # https://numpy.org/doc/stable/reference/generated/numpy.where.html\n",
    "    response = np.where(sample,\n",
    "                        # If actually true, the probability that the test returns true\n",
    "                        sample * (np.random.random_sample((len(sample), )) < sensitivity),\n",
    "                        # If actually false, the probability that the test returns false\n",
    "                        ~sample * (np.random.random_sample((len(sample), )) > specificity))\n",
    "    s = len(sample) # sample size\n",
    "    tr = len(response[response==True]) # true test response\n",
    "    fr = s - tr # false test response\n",
    "    ap = len(sample[sample==True]) # actual positive\n",
    "    an = s - ap # actual negative\n",
    "    tp = len(sample[(sample == response) & (sample == True)]) # true positive of response\n",
    "    tn = len(sample[(sample == response) & (sample == False)]) # true negative of response\n",
    "    fn = ap - tp # false negative of response\n",
    "    fp = an - tn # false positive of response\n",
    "    # https://en.wikipedia.org/wiki/Sensitivity_and_specificity#Prevalence_Threshold\n",
    "    if sensitivity + specificity == 1:\n",
    "        prevalence_threshold = 0.5\n",
    "    else:\n",
    "        prevalence_threshold = ((math.sqrt(sensitivity*(-specificity + 1)) - 1 + specificity)/\n",
    "                                (sensitivity + specificity - 1))\n",
    "    print(\"Universal Test Results\")\n",
    "    print(\"----------------------\")\n",
    "    print(F\"{tr:,} positive test results for sample (n = {s:,}), with {ap/s*100:.1f}% sample prevalence.\")\n",
    "    print(\"----------------------\")\n",
    "    # '\\t' is the special 'tab' character\n",
    "    print(F\"Actual positive  = {ap:,}  \\t|\\t Actual negative  = {an:,}\")\n",
    "    print(F\"True positive  = {tp:,}   \\t|\\t True negative  = {tn:,}\")\n",
    "    print(F\"False positive = {fp:,}  \\t|\\t False negative = {fn:,}\")\n",
    "    print(\"----------------------\")\n",
    "    print(F\"Positive predictive value (PPV) = {tp/(tp+fp)*100:.1f}%\")\n",
    "    print(F\"Prevalence threshold = {prevalence_threshold*100:.1f}% \\t|\\t Actual prevalence = {ap/s*100:.1f}%\")\n",
    "    print(F\"Accuracy = {(tp+tn)/s*100:.1f}%\")\n",
    "    return response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could, at this point, sample our population in some way, and test them and see how things go. Let's assume, however, that a government has decided that all women with a \"large\" foot size need to be identified as they have decided that such women are likely to be witches.\n",
    "\n",
    "They have set up a camera at the entrance to a large shopping mall and this is connected to a machine intelligence system that can measure foot size as people pass below, and flag which women need to be stopped and arrested as potential witches.\n",
    "\n",
    "Except the magic test isn't really a test. It's a person flipping a coin. Heads they're a witch, tails they're not.\n",
    "\n",
    "How does it do?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "50,250 positive test results for sample (n = 100,000), with 13.5% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 13,508  \t|\t Actual negative  = 86,492\n",
      "True positive  = 6,718   \t|\t True negative  = 42,960\n",
      "False positive = 43,532  \t|\t False negative = 6,790\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 13.4%\n",
      "Prevalence threshold = 50.0% \t|\t Actual prevalence = 13.5%\n",
      "Accuracy = 49.7%\n"
     ]
    }
   ],
   "source": [
    "# We're going to convert our population from a list of dicts to a dict of lists:\n",
    "test_population = {k: np.array([d[k] for d in synthetic_population]) for k in synthetic_population[0]}\n",
    "\n",
    "# We can use the 'foot_size' or 'foot_length' fields, depending on how we feel\n",
    "response = get_universal_test_results(test_population[\"foot_length\"] > 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It wrongly identified about 50,000 women, and missed more than 6,000. Which - since our test is a literal coin-toss - is as expected. What happens, though, if we improve the sensitivity and specificity dramatically? How well does it work then?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "17,154 positive test results for sample (n = 100,000), with 13.5% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 13,508  \t|\t Actual negative  = 86,492\n",
      "True positive  = 12,809   \t|\t True negative  = 82,147\n",
      "False positive = 4,345  \t|\t False negative = 699\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 74.7%\n",
      "Prevalence threshold = 18.7% \t|\t Actual prevalence = 13.5%\n",
      "Accuracy = 95.0%\n"
     ]
    }
   ],
   "source": [
    "response = get_universal_test_results(test_population[\"foot_length\"] > 255, sensitivity=0.95, specificity=0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our more accurate test results in only 4,000 wrongly-identified women. Women with a foot length larger than 255 mm only appear in 13% of the population. The prevalence threshold of 18.7% indicates that positive predictive value drops when the actual prevalence is beneath this.\n",
    "\n",
    "The prevalence threshold equation is independent of the population size, type of test or question being asked:\n",
    "\n",
    "$$ \\theta_e = \\frac{\\sqrt{\\text{sensitivity} \\times (1 - \\text{specificity})} - 1 + \\text{specificity}}{(\\text{sensitivity} + \\text{specificity} - 1)}$$\n",
    "\n",
    "If you have a test that is right 95% of the time, you are subjecting 5% of a population to the risk of being found positive when they are not. The scale of your error depends very much on the prevalence of the characteristic you're testing for. You need to decide whether that risk is worth it.\n",
    "\n",
    "Our test isn't answering the question as to whether or not a randomly-selected woman is a witch. The question it answers is whether her foot length is greater than 255 mm. \n",
    "\n",
    "Perhaps you think this is silly, but how more ridiculous is it to treat face shape, eye-brow curvature or skin colour as having predictive power for criminality?\n",
    "\n",
    "### 3.3.2 Assessing the predictive value for screening tests\n",
    "\n",
    "Let's use our synthetic population to answer our research question. If a woman actually has breast cancer, how well can a test tell us if she does have cancer?\n",
    "\n",
    "> These tests are more appropriately called _screening tests_ or even _medical surveillance_, being a test or procedure performed on subjects of a defined asymptomatic population to assess the likelihood of their members having a particular disease or target feature. \\cite{maxim_screening_2014}\n",
    "\n",
    "Screening tests do not diagnose an illness, but identify those needing further diagnostic tests or procedures. For example, a pap smear for cervical cancer, or cholesterol levels for heart disease. The strength of a screening test is that it is looking for characteristics that research has identified as being correlated with a specific illness.\n",
    "\n",
    "There are multiple risks from using a derivative test, but the most important are that it must actually serve to identify the target being sought (i.e. _it should ask the right question_), and it must have a high positive predictive value.\n",
    "\n",
    "The logical possibilities for a true target state and screening test outcome:\n",
    "\n",
    "| Test result | Subject has target    | Subject target free        | Subtotal                    |\n",
    "|-------------|-----------------------|----------------------------|-----------------------------|\n",
    "| Positive    | Correct result        | False positive             | Total positive test results |\n",
    "| Negative    | False negative        | Correct result             | Total negative test results |\n",
    "| Subtotal    | Total target subjects | Total target-free subjects | Total subjects              |\n",
    "\n",
    "Unless absolutely obvious, or there is no alternative, a screening test is benchmarked against a __gold standard__ test \\cite{maxim_screening_2014}. This is a diagnostic test regarded as definitive. It provides 100% positive predictive value under all circumstances; it has 100% sensitivity and 100% specificity.\n",
    "\n",
    "> _\"As science increases its hold on the practice of medicine we become more aware of the limitations of the clinical method. Unfortunately, we also become more aware of the limitations of various diagnostic tests. Nevertheless, at any given time there may well be a consensus that a given test in a given situation is the best available test. It therefore serves as the gold standard against which newer tests can be compared. When enough data have accumulated to make that gold standard untenable, it can perfectly reasonably be replaced by another. This can then preside until it too is toppled.\"_ \\cite{versi_gold_1992}\n",
    "\n",
    "The problem is, some questions need answers even in the absence of a perfect gold standard. We may know how to treat an illness, but only if we know that a patient is suffering from it. Stretching the metaphors, imperfect tests are called __alloyed gold standards__.\n",
    "\n",
    "There is a constant process of seeking to identify tests which trade-off cost versus efficacy and so being able to read and evaluate research which presents new approaches, procedures or tests is a critical part of a data scientist's professional development.\n",
    "\n",
    "Common sources of bias in study design include \\cite{maxim_screening_2014}:\n",
    "\n",
    "| Type of bias | Description |\n",
    "|:--|:--|\n",
    "| Verification bias | Non-random selection for definitive assessment for disease with the old standard reference test |\n",
    "| Errors in the reference | True disease status is subject to misclassification because the gold standard is imperfect |\n",
    "| Spectrum bias | Types of cases and controls included are not representative of the population |\n",
    "| Test interpretation bias | Information is available that can distort the diagnostic test |\n",
    "| Unsatisfactory tests | Tests that are uninterpretable or incomplete do not yield a test result |\n",
    "| Extrapolation bias | The conditions or characteristics of populations in the study are different from those in which the test will be applied |\n",
    "| Lead time bias | Earlier detection by screening may erroneously appear to indicate beneficial effects on the outcome of a progressive disease |\n",
    "| Length bias | Slowly progressing disease is over-represented in screened subjects relative to all cases of disease that arise in the population |\n",
    "| Overdiagnosis bias | Subclinical disease may regress and never become a clinical problem in the absence of screening, but is detected by screening |\n",
    "\n",
    "It is sometimes easy to imagine, besides some inconvenience, a false positive or false negative has little consequence. Numerous studies \\cite{maxim_screening_2014} have demonstrated the contrary. The consequences to individuals and to society at large are enormous. From polygraph tests (\"lie\" detectors) falsely accusing the innocent, to invasive biopsies - with all the consequent costs - performed on healthy people identified as having cancer.\n",
    "\n",
    "Breast cancer is relatively common - with [2 million cases identified internationally in 2018](https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics) - but population-wide mammography screening has been associated with an [estimated $4 billion in annual costs in the US](https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2014.1087) as a result of diagnostic tests and treatment for false positives.\n",
    "\n",
    "\"It is estimated that over a 10-year period, approximately one third to one half of all women who participate in breast cancer screening will receive at least one abnormal result requiring additional workup.\" \\cite{lam_imaging-based_2014} Long-term mental health issues are [reported in women experiencing false-positive breast cancer diagnoses](https://www.annfammed.org/content/11/2/106.short). \n",
    "\n",
    "There are further consequences. Radiographic imaging (CT scans used for mammography, colonography) carry a risk of radiation-induced carcinogenesis. And a too-perfect test can be so sensitive that it identifies disease at such an early stage that it isn't clear whether it will ever result in real concern.\n",
    "\n",
    "Your immune system can, and does, combat cancer. Some cancers are never dangerous. Identifying them causes anxiety and potentially unnecessary medical interventions.\n",
    "\n",
    "Any test we assess must serve to limit these negative consequences.\n",
    "\n",
    "Which leads us to our two papers, and the screening tests they examine:\n",
    "\n",
    " - Combined tomosynthesis and digital mammography, \\cite{rafferty_assessing_2013}\n",
    " - Positron emission tomography (PET) with 2-[18F]fluoro-2-deoxy-d-glucose (FDG), \\cite{greco_axillary_2001}\n",
    " \n",
    "Summarising the study results against the existing mammography standard looks as follows:\n",
    " \n",
    "| Test | Sample size | Actual positive | Actual negative | Sensitivity (%) | Specificity (%) | PPV (%) | Sample prevalence (%) |\n",
    "|---|---|---|---|---|---|---|---|\n",
    "| Mammography | | | | 65.5 | 84.1 | 42.9 | |\n",
    "| Tomosynthesis | 622 | 99 | 523 | 76.2 | 89.2 | 56.2 | 15.9 |\n",
    "| PET | 167 | 72 | 95 | 94.4 | 86.3 | 84.0 | 43.1 |\n",
    "\n",
    "The sample chosen, and the prevalence of a target within a sample population, have a significant impact on the predictive power identified in a study.\n",
    "\n",
    "![The relationship of sensitivity, specificity, and prevalence to the overall accuracy of a screening or diagnostic test.](images/module-1-lesson-3-sensitivity-specificity-prevalence.jpg)\n",
    "\n",
    "The relationship of sensitivity, specificity, and prevalence to the overall accuracy of a screening or diagnostic test are important. The less prevalent the disease, the greater the weight applied to specificity in calculating overall accuracy; conversely, the more prevalent the disease, the greater the weight applied to sensitivity. Second, extreme differences in sensitivity and specificity under circumstances where disease prevalence is very low or very high lead to overall accuracy deviating considerably from sensitivity or specificity, respectively \\cite{alberg_use_2004}.\n",
    "\n",
    "The claimed sensitivity and specificity are the result of the population characteristics. This means you can't just look at the results in isolation. Even a \"good\" test needs to be considered in terms of the study design.\n",
    "\n",
    "Many countries around the world invite women for regular breast-cancer screening tests. Usually women between the ages of 50 and 70 are invited for these tests. That population prevalence for breast cancer looks quite different to the sample above.\n",
    "\n",
    "Let's filter our population of 100,000 women for only those between the ages of 50 and 70, and run each of these tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "16,576 positive test results for sample (n = 100,000), with 1.5% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 1,544  \t|\t Actual negative  = 98,456\n",
      "True positive  = 1,020   \t|\t True negative  = 82,900\n",
      "False positive = 15,556  \t|\t False negative = 524\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 6.2%\n",
      "Prevalence threshold = 33.0% \t|\t Actual prevalence = 1.5%\n",
      "Accuracy = 83.9%\n"
     ]
    }
   ],
   "source": [
    "# First, filter our synthetic sample population\n",
    "test_population = [p for p in synthetic_population if (p[\"age\"] >= 50) and (p[\"age\"] <= 70)]\n",
    "# Convert to dict of lists\n",
    "test_population = {k: np.array([d[k] for d in synthetic_population]) for k in synthetic_population[0]}\n",
    "\n",
    "# Run the standard mammography\n",
    "response = get_universal_test_results(test_population[\"breast_cancer\"], sensitivity=0.655, specificity=0.841)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual population prevalence, at 1.5%, is far lower than in either of the studies. The test missed about 500 women, and wrongly identified a further 15,000 women for further testing, which could include the _gold standard_ of an invasive surgical biopsy.\n",
    "\n",
    "Do either of the studies do better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "11,830 positive test results for sample (n = 100,000), with 1.5% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 1,544  \t|\t Actual negative  = 98,456\n",
      "True positive  = 1,158   \t|\t True negative  = 87,784\n",
      "False positive = 10,672  \t|\t False negative = 386\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 9.8%\n",
      "Prevalence threshold = 27.4% \t|\t Actual prevalence = 1.5%\n",
      "Accuracy = 88.9%\n"
     ]
    }
   ],
   "source": [
    "# Combined tomosynthesis and digital mammography\n",
    "response = get_universal_test_results(test_population[\"breast_cancer\"], sensitivity=0.762, specificity=0.892)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The addition of tomosynthesis has the impact of increasing accuracy by 5%, and removing 5,000 false positives from the results, however, it still missed 350 actual breast cancer incidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "14,834 positive test results for sample (n = 100,000), with 1.5% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 1,544  \t|\t Actual negative  = 98,456\n",
      "True positive  = 1,449   \t|\t True negative  = 85,071\n",
      "False positive = 13,385  \t|\t False negative = 95\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 9.8%\n",
      "Prevalence threshold = 27.6% \t|\t Actual prevalence = 1.5%\n",
      "Accuracy = 86.5%\n"
     ]
    }
   ],
   "source": [
    "# Positron emission tomography (PET) with 2-[18F]fluoro-2-deoxy-d-glucose (FDG)\n",
    "response = get_universal_test_results(test_population[\"breast_cancer\"], sensitivity=0.944, specificity=0.863)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PET test has a much lower false negative, missing only 100 patients, but with the trade-off of higher false positives. It is still lower than mammography, but by 2,000.\n",
    "\n",
    "But we still come back to a fundamental problem. The prevalence of breast cancer in the general population is only 1.5%. Any test with that prevalence is vulnerable to weakness in the specificity.\n",
    "\n",
    "Both tests have a lower prevalence threshold required, about 27% in comparison to mammography at 33%. Both almost double the positive predictive from 6% to 10% of those tested, which still indicates that 90% of those receiving a positive result are actually negative.  What that requires is that we need to send women to be tested only if we have a reasonable suspicion they need to __be__ tested in the first place. If 1 in 4 women sent for testing have some reasonable reason to suspect the need for a test, then we would achieve far greater accuracy.\n",
    "\n",
    "What's interesting in the tomosynthesis study is that its sample population reflects a real test population much more accurately (prevalence of 15.9%) compared to the PET study (prevalence of 43.1%). The PET study has a similar specificity to mammography but the research study flatters overall predictive value with a sample that dramatically overstates actual prevalence.\n",
    "\n",
    "The reality is that neither test is useful for mass screening (even if better than mammography on its own), and that there is a genuine trade-off between false positives and false negatives which must be considered. If the sample prevalence is closer to 16%, though, then a test with lower false positives is likely to be more useful.\n",
    "\n",
    "That doesn't mean the tests aren't useful. They're clearly better than mammography alone, but context is everything. If this is the best non-invasive screening test at present, then there should be no mass-screening program. Or, the test itself should be treated as a two-step process, in which it is communicated that all positive tests automatically result in a second screening test. This would increase prevalence to 10% and help improve things.\n",
    "\n",
    "That's a political decision, rather than technical one, and brings us back to ethics.\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Presentation: communication and visualisation\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Apply appropriate plots to communicate probability to a lay public.</i>\n",
    "</div>\n",
    "\n",
    "There are two groups of people to whom we may wish to communicate our research findings. \n",
    "\n",
    "The first are fellow research professionals. We can expect them to have a grounding in scientific methodology, mathematics, probability and statistics, and even experience and direct knowledge of the research subject area. Histograms, scatter plots and box plots are familiar, and well-presented tables and charts are aids to rapid review and analysis.\n",
    "\n",
    "If something is unclear, or our methods and analysis don't support our conclusions, our peers won't accept what we say because we're \"data scientists\". They'll push back, ask questions and - if we're lucky - help us correct and improve our work.\n",
    "\n",
    "Members of the lay public are far more diverse. Some have no technical or research skills at all. Some are highly technical, but their experience offers them no context for evaluating our specific research area. Nevertheless, they will have strong opinions, and - more commonly than we would prefer - will take from your research what they will.\n",
    "\n",
    "Most researchers communicate primarily to a peer audience first, and are left struggling to catch up should their work end up causing some controversy in the public domain.\n",
    "\n",
    "### 3.4.1 Positive tests in a low prevalence environment\n",
    "\n",
    "As in earlier lessons, what follows are a set of building-blocks you can think about and integrate into your work. They are not a set of rules, and are not universally appropriate.\n",
    "\n",
    "[Dimensions](https://www.dimensions.com/) provides high-quality vector graphics (SVGs) for use as architectural reference drawings. [Vikas](https://stackoverflow.com/a/58552620) has described how to convert such SVGs for use as markers in `matplotlib` charts using the [`svgpath2mpl`](https://github.com/nvictus/svgpath2mpl) library. You can install this as you would any Python library, and we will use the vectors saved as a JSON file in the `images` directory for this course.\n",
    "\n",
    "`Matplotlib` draws vector markers very slowly, so expect these charts to take some time to render.\n",
    "\n",
    "We'll start by creating a sample space of 100 individuals which reflects the US population of convicted felons. A 2017 study by [Shannon, et al.](https://link.springer.com/article/10.1007/s13524-017-0611-1) estimates that 8% of the total US adult population, but 33% of the African American adult male population, has a felony conviction. The [US is a substantial outlier](https://en.wikipedia.org/wiki/Incarceration_in_the_United_States) when it comes to convicting and imprisoning its population, accounting for about 700 people jailed per 100,000 and the largest prison population on Earth.\n",
    "\n",
    "We will create a synthetic population of 100 people. 15 will be African American, of which 5 will be convicted criminals, and the balance will be non-African Americans, of which 3 will be convicted criminals, making up our total population.\n",
    "\n",
    "We can create and draw this population as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we create arrays for the African-American population\n",
    "aa_total = 15\n",
    "aa_fel_total = 5\n",
    "pop_total = 100\n",
    "pop_fel_total = 8\n",
    "aa_pop = [True]*aa_total\n",
    "aa_fel = [True]*aa_fel_total + [False]*(aa_total - aa_fel_total)\n",
    "# Then for the total population\n",
    "na_pop = [False] * (pop_total - aa_total)\n",
    "na_fel = [True] * (pop_fel_total - aa_fel_total) + [False] * (pop_total - aa_total - pop_fel_total + aa_fel_total)\n",
    "population = {\n",
    "    \"population\": np.array(na_pop + aa_pop),\n",
    "    \"felons\": np.array(na_fel + aa_fel)\n",
    "}\n",
    "\n",
    "# Convert to dict of lists and shuffle our population\n",
    "population = [dict(zip(population, v)) for v in zip(*population.values())]\n",
    "np.random.shuffle(population)\n",
    "# And reconvert to list of dicts\n",
    "population = {k: np.array([d[k] for d in population]) for k in population[0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from svgpath2mpl import parse_path\n",
    "\n",
    "# Prepare a chart using our people silhouettes as markers\n",
    "def parse_svg(s):\n",
    "    \"\"\"\n",
    "    Process a vector graphic into a format that can be used by matplotlib.\n",
    "    https://stackoverflow.com/a/58552620\n",
    "    \n",
    "    An SVG is a set of vector points, so we can normalise the vector (remove\n",
    "    random white-space placing the image at an arbitrary point) by centering\n",
    "    the average at 0. We can similarly reoriante image if it is upside down.\n",
    "    \n",
    "    Args:\n",
    "        s: SVG path\n",
    "    \n",
    "    Returns:\n",
    "        Matplotlib Marker Path\n",
    "    \"\"\"\n",
    "    s = parse_path(s)\n",
    "    s.vertices -= s.vertices.mean(axis=0)\n",
    "    # Do this if they're upside down\n",
    "    #s.vertices[:,1] *= -1\n",
    "    return s\n",
    "\n",
    "def load_json(source):\n",
    "    \"\"\"\n",
    "    Load and return a JSON file, if it exists.\n",
    "\n",
    "    Args:\n",
    "        source: the filename & path to open\n",
    "\n",
    "    Raises:\n",
    "        JSONDecoderError if not a valid json file\n",
    "        FileNotFoundError if not a valid source\n",
    "\n",
    "    Returns:\n",
    "        dict\n",
    "    \"\"\"\n",
    "    with open(source, \"r\") as f:\n",
    "        try:\n",
    "            return json.load(f)\n",
    "        except json.decoder.JSONDecodeError:\n",
    "            e = \"File at `{}` not valid json.\".format(source)\n",
    "            raise json.decoder.JSONDecodeError(e)\n",
    "\n",
    "mini_people = load_json(\"images/svg-people.json\")\n",
    "mini_men = [parse_svg(m) for m in mini_people[\"male-silhouette\"]]\n",
    "\n",
    "total_population = 100\n",
    "cols = 20\n",
    "rows = 5\n",
    "\n",
    "# Create a matrix of x and y points in a neat grid\n",
    "x = np.tile(np.arange(cols), rows)\n",
    "y = np.repeat(np.arange(rows), cols)\n",
    "# Generate random markers for our population\n",
    "marker = np.random.choice(mini_men, total_population)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next chart draws only the convicted felons in the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=\"w\")\n",
    "ax.axis(\"off\")\n",
    "\n",
    "for i in range(cols*rows):\n",
    "    c = \"C1\"\n",
    "    a = 0.1\n",
    "    if population[\"felons\"][i]:\n",
    "        a = 0.8\n",
    "    ax.scatter(x[i], y[i], c=c, alpha=a, marker=marker[i], s=3000, clip_on=False)\n",
    "    \n",
    "plt.margins(x=0.09, y=0.09)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart draws only the African American's in the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=\"w\")\n",
    "ax.axis(\"off\")\n",
    "\n",
    "for i in range(cols*rows):\n",
    "    c = \"C1\"\n",
    "    a = 0.1\n",
    "    if population[\"population\"][i]:\n",
    "        a = 0.8\n",
    "    ax.scatter(x[i], y[i], c=c, alpha=a, marker=marker[i], s=3000, clip_on=False)\n",
    "    \n",
    "plt.margins(x=0.09, y=0.09)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now simulate a typical criminality facial recognition system used by law-enforcement. Let's assume 95% sensitivity and 95% specificity. Our system can't identify criminality directly, but it assumes that race is a proxy for criminality in line with [common US prejudice](https://link.springer.com/article/10.1007/s13524-017-0611-1). \n",
    "\n",
    "This chart draws the population at large with _blue_ for those who were identified as potential criminals who are also convicted felons, and those wrongly identified as potential criminals as _olive green_. Unidentified convicted felons are marked in _dark orange_. It does not indicate race."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "18 positive test results for sample (n = 100), with 15.0% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 15  \t|\t Actual negative  = 85\n",
      "True positive  = 14   \t|\t True negative  = 81\n",
      "False positive = 4  \t|\t False negative = 1\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 77.8%\n",
      "Prevalence threshold = 18.7% \t|\t Actual prevalence = 15.0%\n",
      "Accuracy = 95.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response = get_universal_test_results(population[\"population\"], sensitivity=0.95, specificity=0.95)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=\"w\")\n",
    "ax.axis(\"off\")\n",
    "\n",
    "for i in range(cols*rows):\n",
    "    c = \"C1\"\n",
    "    a = 0.1\n",
    "    if population[\"felons\"][i]:\n",
    "        # Undiagnosed\n",
    "        a = 0.8\n",
    "    if response[i]:\n",
    "        a = 0.8\n",
    "        # False positive\n",
    "        c = \"C8\"\n",
    "        if population[\"felons\"][i]:\n",
    "            # True positive\n",
    "            c = \"C0\"\n",
    "    ax.scatter(x[i], y[i], c=c, alpha=a, marker=marker[i], s=3000, clip_on=False)\n",
    "    \n",
    "plt.margins(x=0.09, y=0.09)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the problem with this, though. That's not the answer to the question we asked. The textual output is at odds with the graphical presentation.\n",
    "\n",
    "Why?\n",
    "\n",
    "You wanted to know who was a potential criminal, but your system has identified 14 out of 15 African Americans instead. All those people in _olive_ were falsely categorised as being untrustworthy and suffered the consequences. Let's draw that chart in a way that better reflects the question that was asked. Who are the African Americans?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor=\"w\")\n",
    "ax.axis(\"off\")\n",
    "\n",
    "for i in range(cols*rows):\n",
    "    c = \"C1\"\n",
    "    a = 0.1\n",
    "    if population[\"population\"][i]:\n",
    "        # Undiagnosed\n",
    "        a = 0.8\n",
    "    if response[i]:\n",
    "        a = 0.8\n",
    "        # False positive\n",
    "        c = \"C8\"\n",
    "        if population[\"population\"][i]:\n",
    "            # True positive\n",
    "            c = \"C0\"\n",
    "    ax.scatter(x[i], y[i], c=c, alpha=a, marker=marker[i], s=3000, clip_on=False)\n",
    "    \n",
    "plt.margins(x=0.09, y=0.09)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 8 convicted felons in this chart, and the algorithm has identified about 20 of them. In reality, it missed what it was intended to look for almost entirely. This is not a fault of the algorithm, but of a human research-driven decision to treat a test for ethnicity as if it is a test for trustworthiness.\n",
    "\n",
    "This is a useful illustration of fallacies in action, but even when research is on your side, prevalence can let you down. Let's demonstrate this with our synthetic population and our screening test for breast cancer. This time _green_ for true positive, _red_ for false positive, and _orange_ for a false negative.\n",
    "\n",
    "There are 100,000 people in this sample. We'll draw only the first 400 results from the random population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Test Results\n",
      "----------------------\n",
      "11,845 positive test results for sample (n = 100,000), with 1.5% sample prevalence.\n",
      "----------------------\n",
      "Actual positive  = 1,544  \t|\t Actual negative  = 98,456\n",
      "True positive  = 1,199   \t|\t True negative  = 87,810\n",
      "False positive = 10,646  \t|\t False negative = 345\n",
      "----------------------\n",
      "Positive predictive value (PPV) = 10.1%\n",
      "Prevalence threshold = 27.4% \t|\t Actual prevalence = 1.5%\n",
      "Accuracy = 89.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mini_women = [parse_svg(m) for m in mini_people[\"female-silhouette\"]]\n",
    "\n",
    "cols = 40\n",
    "rows = 10\n",
    "\n",
    "x = np.tile(np.arange(cols), rows)\n",
    "y = np.repeat(np.arange(rows), cols)\n",
    "marker = np.random.choice(mini_women, population_size)\n",
    "response = get_universal_test_results(test_population[\"breast_cancer\"], sensitivity=0.762, specificity=0.892)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30, 15), facecolor=\"w\")\n",
    "ax.axis(\"off\")\n",
    "\n",
    "for i in range(cols*rows):\n",
    "    c = \"C1\"\n",
    "    a = 0.1\n",
    "    if test_population[\"breast_cancer\"][i]:\n",
    "        # Undiagnosed\n",
    "        a = 0.8\n",
    "    if response[i]:\n",
    "        a = 0.8\n",
    "        # False positive\n",
    "        c = \"C3\"\n",
    "        if test_population[\"breast_cancer\"][i]:\n",
    "            # True positive\n",
    "            c = \"C2\"\n",
    "    ax.scatter(x[i], y[i], c=c, alpha=a, marker=marker[i], s=6000, clip_on=False)\n",
    "    \n",
    "plt.margins(x=0.09, y=0.09)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4.2 Intimidated by randomess\n",
    "\n",
    "Mastery in research does not come from skills with software or algorithms, but with experience and ability in navigating ambiguity in randomness, and the pursuit of high probable causation.\n",
    "\n",
    "The objective of presentation is appropriate communication. In thinking carefully about what would best convey the realities of your research, and the prevalence of a target event within a population, to a non-specialist audience. \n",
    "\n",
    "Probability is especially challenging for a lay public to understand, being the intersection of maths, randomness, and prevalence.\n",
    "\n",
    "> Good communication includes both the words we write to explain what we did and what we found, as well as the charts we use to summarise and explain what we looked at and what we discovered.\n",
    "\n",
    "Here are some of the things we need to communicate:\n",
    "\n",
    "- _Randomness_ means that experiencing, or not experiencing, a research event can be a lot like winning the lottery - it doesn't always conform to social conventions of fairness. A low probability does not mean __no__ probability, and a high probability is not certainty.\n",
    "- _Prevalence_ means that some research events are extremely rare, or extremely common, and the way in which experimental sampling reflects actual prevalence may have a huge impact on the results of any experiment.\n",
    "- _Small sample size_ can increase the prevalence of dominance by outliers or rare events, making them seem to be more common than they really are. \n",
    "- _Small sample sizes_ may exclude rare - but likely - events entirely leading to a false sense of security.\n",
    "- _High prevalence_ of target in sample relative to actual prevalence in a population risks over-confidence in test accuracy.\n",
    "\n",
    "There's no reason to over-simplify or dumb-down research findings when talking to those outside your research field. However, what is required is a greater awareness of a gap between what you know and what they know. Some ramp or path into your findings is required.\n",
    "\n",
    "The simplest way to do this is to use the same charts as you would normally, but change the structure and emphasis. `Matplotlib` and other charting libraries permit you to use custom markers on plots. That does not mean always drawing lots of pictures of little silhouetted people, but a little extra effort can convert a scatter-plot of probabilities into something more relateable.\n",
    "\n",
    "While you can certainly hope for a good-faith response to what you present, you should not assume that any lack of comprehension is due to stupidity. The responsibility lies with a researcher to appropriately and clearly present their findings in a way that resonates with others.\n",
    "\n",
    "---"
   ]
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    "## 3.5 Lesson summary\n",
    "\n",
    "[Ethics](#3.1-Ethics:-fallacies,-randomness,-and-treating-intuition-with-skepticism): An argument, or hypothesis, that does not work is a fallacy. A process, system or tool which makes a specific prediction based on input data requires that its predictions are based on answerable questions, and that it be provably accurate at such high-speed diagnostics. \n",
    "\n",
    "> __Law of small numbers__: A tendency for an initial segment of data to have an unusual distribution or be dominated by rare outliers causing any small sample group to run the risk of failing to sufficiently represent an entire population.\n",
    ">\n",
    "> __Law of large numbers__: As more observations are collected, the proportion $\\hat{p}_n$ of occurrences with a particular outcome converges to the probability $p$ of that outcome.\n",
    "\n",
    "This is the __Law of Total Probability__:\n",
    "\n",
    "$$ P(A) = \\sum_{i}P(A \\cap B_i) = \\sum_{i}P(A|B_i)P(B_i) $$\n",
    "\n",
    "Leading to derivation of one of the most useful formulae in conditional probability, __Baye's Theorem__ or __Baye's Rule__:\n",
    "\n",
    "$$ P(B_j|A) = \\frac{P(A|B_j) \\times P(B_j)}{\\sum_{i}P(A|B_i)P(B_i)} $$\n",
    "\n",
    "When we wish to test for a target event within a sample space, we must consider the __sensitivity__ and __specificity__ of that test.\n",
    "\n",
    "Ethics grapples with considerations that are inherently inexact or which have ambiguous outcomes. It is essential that the words and concepts we use, and they way they are used, are well-defined, unambiguous and exact. Axiomatic probability gives us a language to explore ambiguous outcomes.\n",
    "\n",
    "[Curation](#3.2-Curation:-software,-interoperability-and-risks-from-data-reuse): Statistical data encompasses a range of forms, from questionnaires and individual – personally-identifying – responses, through to aggregated tables of numerical values, analysis and text-driven reports.\n",
    "\n",
    "Data released to the public in general, or specifically to other researchers, will not be used in isolation. It will be stripped of the original context in which it was used and combined with other data to answer a new research question the original publisher may never have imagined.\n",
    "\n",
    "[Analysis](#3.3-Analysis:-predictive-value-of-synthetic-data): There is a constant process of seeking to identify tests which trade-off cost versus efficacy and so being able to read and evaluate research which presents new approaches, procedures or tests is a critical part of a data scientist's professional development.\n",
    "\n",
    "The relationship of sensitivity, specificity, and prevalence to the overall accuracy of a screening or diagnostic test are important. The less prevalent the disease, the greater the weight applied to specificity in calculating overall accuracy; conversely, the more prevalent the disease, the greater the weight applied to sensitivity. Second, extreme differences in sensitivity and specificity under circumstances where disease prevalence is very low or very high lead to overall accuracy deviating considerably from sensitivity or specificity, respectively.\n",
    "\n",
    "[Presentation](#3.4-Presentation:-communication-and-visualisation): The objective of presentation is appropriate communication. In thinking carefully about what would best convey the realities of your research, and the prevalence of a target event within a population, to a non-specialist audience. Good communication includes both the words we write to explain what we did and what we found, as well as the charts we use to summarise and explain what we looked at and what we discovered. The responsibility lies with a researcher to appropriately and clearly present their findings in a way that resonates with others.\n",
    "\n",
    "---"
   ]
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    "## 3.6 Exercises\n",
    "\n",
    "Conduct your own peer review on any of the publications listed here:\n",
    "\n",
    "- _Tong MJ, Blatt LM, Kao VW. Surveillance for hepatocellular carcinoma in patients with chronic viral hepatitis in the United States of America. J Gastroenterol Hepatol. 2001 May; 16(5):553-9. [doi: 10.1046/j.1440-1746.2001.02470.x. PMID: 11350553](https://pubmed.ncbi.nlm.nih.gov/11350553/)_\n",
    "- _Ogawa K, Oida A, Sugimura H, Kaneko N, Nogi N, Hasumi M, Numao T, Nagao I, Mori S. Clinical significance of blood brain natriuretic peptide level measurement in the detection of heart disease in untreated outpatients: comparison of electrocardiography, chest radiography and echocardiography. Circ J. 2002 Feb;66(2):122-6. [doi: 10.1253/circj.66.122. PMID: 11999635.](https://pubmed.ncbi.nlm.nih.gov/11999635/)_\n",
    "\n",
    "\"The Use of 'Overall Accuracy' to Evaluate the Validity of Screening or Diagnostic Tests\" \\cite{alberg_use_2004} also has links to other papers you can review. As your skills develop, you will gain confidence in ever-deeper review but, for now, focus on prevalence, specificity and sensitivity.\n",
    "\n",
    "You are also welcome to conduct your own review. There are a host of publication repositories which support redistribution of published research. Here are a few and you can explore them to run your own reviews:\n",
    "\n",
    "- [Pubmed](https://www.ncbi.nlm.nih.gov/pmc/) US National Library of Medicine National Institutes of Health\n",
    "- [PubPeer](https://pubpeer.com/) for post-publication peer review\n",
    "- [SciHub](https://en.wikipedia.org/wiki/Sci-Hub) for open access research publication\n",
    "\n",
    "---"
   ]
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    "# References\n",
    "\n",
    "(<a id=\"cit-greco_axillary_2001\" href=\"#call-greco_axillary_2001\">Greco, Crippa <em>et al.</em>, 2001</a>) Greco Marco, Crippa Flavio, Agresti Roberto <em>et al.</em>, ``_Axillary Lymph Node Staging in Breast Cancer by 2-Fluoro-2-deoxy-d-glucose–Positron Emission Tomography: Clinical Evaluation and Alternative Management_'', JNCI: Journal of the National Cancer Institute, vol. 93, number 8, pp. 630--635, April 2001.  [online](https://academic.oup.com/jnci/article/93/8/630/2906548)\n",
    "\n",
    "(<a id=\"cit-rafferty_assessing_2013\" href=\"#call-rafferty_assessing_2013\">Rafferty, Park <em>et al.</em>, 2013</a>) Rafferty Elizabeth A., Park Jeong Mi, Philpotts Liane E. <em>et al.</em>, ``_Assessing Radiologist Performance Using Combined Digital Mammography and Breast Tomosynthesis Compared with Digital Mammography Alone: Results of a Multicenter, Multireader Trial_'', Radiology, vol. 266, number 1, pp. 104--113, January 2013.  [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410947/)\n",
    "\n",
    "(<a id=\"cit-baggini_ethics_2007\" href=\"#call-baggini_ethics_2007\">Baggini and Fosl, 2007</a>) Julian Baggini and Peter Fosl, ``_The Ethics Toolkit_'',  2007.  [online](http://www.blackwellpublishing.com/)\n",
    "\n",
    "(<a id=\"cit-wu_automated_2016\" href=\"#call-wu_automated_2016\">Wu and Zhang, 2016</a>) Wu Xiaolin and Zhang Xi, ``_Automated Inference on Criminality using Face Images_'', arXiv:1611.04135 [cs], vol. , number , pp. , November 2016.  [online](http://arxiv.org/abs/1611.04135)\n",
    "\n",
    "(<a id=\"cit-safra_tracking_2020\" href=\"#call-safra_tracking_2020\">Safra, Chevallier <em>et al.</em>, 2020</a>) Safra Lou, Chevallier Coralie, Grèzes Julie <em>et al.</em>, ``_Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings_'', Nature Communications, vol. 11, number 1, pp. 4728, September 2020.  [online](https://www.nature.com/articles/s41467-020-18566-7)\n",
    "\n",
    "(<a id=\"cit-dietz_openintro_2015\" href=\"#call-dietz_openintro_2015\">Dietz, Barr <em>et al.</em>, 2015</a>) David M Dietz, Christopher D Barr and Mine Çetinkaya-Rundel, ``_OpenIntro Statistics_'',  2015.  [online](https://www.openintro.org/)\n",
    "\n",
    "(<a id=\"cit-pishro-nik_introduction_2014\" href=\"#call-pishro-nik_introduction_2014\">Pishro-Nik, 2014</a>) Hossein Pishro-Nik, ``_Introduction to probability, statistics, and random processes_'',  2014.  [online](https://www.probabilitycourse.com/)\n",
    "\n",
    "(<a id=\"cit-vu_introductory_2020\" href=\"#call-vu_introductory_2020\">Vu and Harrington, 2020</a>) Julie Vu and David Harrington, ``_Introductory Statistics for the Life and Biomedical Sciences_'', July 2020.  [online](https://www.openintro.org/book/biostat/)\n",
    "\n",
    "(<a id=\"cit-chait_technical_2014\" href=\"#call-chait_technical_2014\">Chait, 2014</a>) Gavin Chait, ``Technical assessment of open data platforms for national statistical organisations'', World Bank Group, number: ,  December 2014.  [online](https://openknowledge.worldbank.org/handle/10986/21111)\n",
    "\n",
    "(<a id=\"cit-anwar_francisella_2009\" href=\"#call-anwar_francisella_2009\">Anwar and Hunt, 2009</a>) Anwar Nadia and Hunt Ela, ``_Francisella tularensis novicida proteomic and transcriptomic data integration and annotation based on semantic web technologies_'', BMC Bioinformatics, vol. 10, number 10, pp. S3, October 2009.  [online](https://doi.org/10.1186/1471-2105-10-S10-S3)\n",
    "\n",
    "(<a id=\"cit-jurca_analysis_2019\" href=\"#call-jurca_analysis_2019\">Jurca, Žabkar <em>et al.</em>, 2019</a>) Jurca Ales, Žabkar Jure and Džeroski Sašo, ``_Analysis of 1.2 million foot scans from North America, Europe and Asia_'', Scientific Reports, vol. 9, number 1, pp. 19155, December 2019.  [online](https://www.nature.com/articles/s41598-019-55432-z)\n",
    "\n",
    "(<a id=\"cit-maxim_screening_2014\" href=\"#call-maxim_screening_2014\">Maxim, Niebo <em>et al.</em>, 2014</a>) Maxim L. Daniel, Niebo Ron and Utell Mark J., ``_Screening tests: a review with examples_'', Inhalation Toxicology, vol. 26, number 13, pp. 811--828, November 2014.  [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4389712/)\n",
    "\n",
    "(<a id=\"cit-versi_gold_1992\" href=\"#call-versi_gold_1992\">Versi, 1992</a>) Versi E., ``_\"Gold standard\" is an appropriate term._'', BMJ : British Medical Journal, vol. 305, number 6846, pp. 187, July 1992.  [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1883235/)\n",
    "\n",
    "(<a id=\"cit-lam_imaging-based_2014\" href=\"#call-lam_imaging-based_2014\">Lam, Pandharipande <em>et al.</em>, 2014</a>) Lam Diana L., Pandharipande Pari V., Lee Janie M. <em>et al.</em>, ``_Imaging-Based Screening: Understanding the Controversies_'', AJR. American journal of roentgenology, vol. 203, number 5, pp. 952--956, November 2014.  [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4220242/)\n",
    "\n",
    "(<a id=\"cit-alberg_use_2004\" href=\"#call-alberg_use_2004\">Alberg, Park <em>et al.</em>, 2004</a>) Alberg Anthony J, Park Ji Wan, Hager Brant W <em>et al.</em>, ``_The Use of “Overall Accuracy” to Evaluate the Validity of Screening or Diagnostic Tests_'', Journal of General Internal Medicine, vol. 19, number 5 Pt 1, pp. 460--465, May 2004.  [online](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1492250/)\n",
    "\n"
   ]
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